JQ2022FRA

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JFSQ2022 Country Replies France

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Guidelines

Dear Correspondent, Thank you for contributing to the Joint Forest Sector Questionnaire (JFSQ). Before filling in the worksheets, please read these guidelines. Please use only this questionnaire to report your data. Use this questionnaire also to revise any historical data - fill in the correct year and your name on the cover page. The worksheet 'LAM & CHIPS' covers data on Glulam and X-lam. This sheet has a yellow drop-down cell where you can choose your unit of measurement. The total number of sheets to be filled in is eight core sheets (green tabs - to be validated by Eurostat) plus three for ITTO (brown tabs - not validated by Eurostat). Four sheets containing cross-references are included at the end. The flat file is for Eurostat for validation purposes, please do not change any cells here. Also, please do not add / delete rows in any of the sheets, because this will affect the functioning of the flatfile. Put all your data into one Excel file. If you send some data in later, give your file a new version number and date (see A.1. below) and notify us of the changes with respect to the previous version. Only send us completely filled-in sheets, highlighting the changes in yellow. Do not delete worksheets. Each sheet has a working area for your input. Most sheets have checking cells and tables. Each working area has white cells and shaded green cells. Eurostat has highlighted the variables it considers most important for its publications - please fill those in as a priority. When you submit a revision, please highlight changes in yellow and explain them in the appropriate 'Note' column. Please use flags and notes (see A.6 below). This information is important for Eurostat. A. General recommendations A.1 Please use eDAMIS to send your questionnaire to Eurostat. Choose the correct domain ("FOREST_A_A") and the correct reference year (for this data collection: 2021). A.2 Fill in the JFSQ quality report if you haven't done so yet. If you already sent the quality report to Eurostat before, please fill it in only if major changes had happened to its content. A.3 The cover page is for your contact details, which are automatically copied to the other worksheets • Check your country code • If necessary change the reference year [red cell] as appropriate - the previous year will appear automatically If you distribute worksheets to various experts, they can each put their contact details into the sheets. It will then be your job to put all the information together again and to verify the checking tables, since some of them will not work as designed in isolation. A.4 Look at the unit of measurement to be used for each item and report in this unit if possible, using the conversion factors on the last page of the JFSQ definitions. Please report the monetary values in the same unit for both reporting years. Only report data or modify cells in the working areas. Please do not delete checking areas or checking sheets. • Look at the checking areas and make the necessary corrections to your data to remove all warnings (see the specific recommendations) before sending in your data. Fill in real zeros '0' in the worksheets if there is no production or trade. Empty cells will be interpreted as 'Data not available'. • There are counters at the bottom of the tables to indicate the number of cells left to be filled in and the number of cells filled with text. Report all data with at least three decimals. Do not use a separator for thousands; for the decimal point, please use the one set up by default. A.5 Report numbers only. If data are confidential, please provide them if possible, appropriately flagged (see A.6). • Eurostat has a right to all confidential data necessary for its work. It has an obligation to use such data only in aggregates and to respect all the legal obligations. • If you cannot provide confidential data, a good option is to send in your own estimate flagged as a national estimate '9'. • As a last resort, leave the cell empty, flag it and write a note indicating data sources and links. Checking tables contain formulae to sum up the totals for sub-items. A.6 Flag cells and write notes as appropriate. Flags should be entered in the 'Flag' columns and notes in the 'Note' columns for the appropriate year and item. The flags to use are: • 5 for repeating the data of a previous year • 6 for confidential data • 7 for provisional data • 9 for national estimate B Specific recommendations B.1 Sheet 'Removals over bark' is for volumes of wood products measured over bark. General over bark/under bark conversion factors are calculated automatically. • Should you use different conversion factor(s) please delete the ones provided and insert your own • If you only have under bark data, please leave this worksheet empty, but revise the table with the conversion factors. • Unchanged conversion factors will be considered revised. A checking table verifies that sums of sub-items agree with the totals. B.2 Checking tables on worksheets improve data quality, verifying that: • The sum of the sub-items equals the total. • The sum of 'of which' items is not larger than the total. All cells in a checking table should be zero or empty. If this is not the case, please check your numbers for the sub-items and totals. The checking table indicates the difference, so if you see a negative value, you will have to decide which number should be increased by that amount. The only exception is when no data are entered due to confidentiality. B.3 Worksheets 'JQ2' and 'EU1' contain a checking table for apparent consumption and for unit values. Apparent consumption = Production + Imports – Exports. It should be positive or nil. If this is not the case, the cell will change colour and indicate the difference. • Please correct the data in the sheets until checking results are positive or nil. One solution is to increase production. • If the data are correct but apparent consumption is still negative, please explain why in the 'Note' column provided in the apparent consumption checking table. B.4 Sheets 'JQ2', 'ECE-EU Species' and 'EU1' on trade have checking tables to verify data consistency. Both quantity and value must be present. When something is missing, messages or coloured cells appear in the checking tables. Please correct your data until all warnings disappear. The meaning of the messages is: • 0: both value and quantity are zero – all is well, there is no trade • ZERO Q: value is reported, quantity is zero - please correct • ZERO V: quantity is reported, value is zero - please correct • REPORT: both quantity and value are blank - please fill in • NO Q: blank cell for quantity – please fill in • NO V: blank cell for value – please fill in Please enter even very small numbers to resolve problems, using as many decimal places as necessary. If there is no way to correct the problem, please write an explanation in the 'Note' column. If there is no trade for a product, please enter 0 for both quantity and value. Thank you for collecting data for the JFSQ, Eurostat's Forestry Team

JFSQ quality report

Joint Forest Sector Questionnaire Quality Report
		Quality information	Country reply
1	Contact
	Country name	Country name
	Contact organisation	Contact organisation
	Contact name	Contact name
	Contact email address	Contact email address
2	Changes to previous year
	Necessity of update	Are there any changes to the quality report of the last data collection?	NO
		If yes, please provide details below.
3	Statistical processing
	Overview of the source data	Please provide an overview of the sources used to produce JFSQ data.
		Do you use a dedicated survey (of the industry, of households, of forest owners, etc.)?	YES
		If yes, please provide details (e.g., who are the respondents, what is its frequency?).
		Do you use forestry statistics?	NO
		If yes, please provide details.
		Do you use national forest inventory?	NO
		If yes, please provide details.
		Do you use national PRODCOM data compiled according to the CPA classification?	NO
		If yes, please provide details (which products, units, etc.).	Our data for sawn are used for PRODCOM data
		Do you use any other national production statistics?	YES
		If yes, please provide details.	To estimate removals production for energie by citizen (owners)
		Do you use data collected by associations of industry?	YES
		If yes, please provide details.	for wood charcoal, pellets, veneer shits, wood based panels, wood pulp and papers
		Do you collect data from direct contacts with manufacturing companies?	YES
		If yes, please provide details.	sometimes for removals and sawmill if they don't answer the survey
		Do you use estimates of roundwood use (in manufacturing)?	NO
		If yes, please provide details.
		Do you use national trade data?	YES
		If yes, please provide details.	custom data
		Do you use felling reports?	NO
		If yes, please provide details.
		Do you use forestry companies' accounting network?	NO
		If yes, please provide details.
		Do you use administrative data (e.g. tax records, business registers)?	NO
		If yes, please provide details.
		Do you use data from national accounts?	NO
		If yes, please provide details (e.g. for which data, from which account tables?).
		Do you use SBS (Structural business statistics)?	NO
		If yes, please provide details (e.g. for which data?).
		Do you use other environmental accounts?	NO
		If yes, please provide details.
		Do you use other statistics (e.g. agriculture statistics)?	NO
		If yes, please specify them.
		Do you use any other sources?	NO
		If yes, please specify them.
	Methodological issues	Are there any pending classification or measurement issues?	YES
		If yes, please specify them.	the year 2020 is a break in the series of data on wood harvests and sawn timber due to a change in the method of treating non-response
	Data validation	Do you check the quality of the data collected to compile JFSQ?	NO
		If yes, please explain the quality assurance procedure.
		Do you compare JFSQ data with different data sources or do you perform other cross-checks?	YES
		If yes, please explain your approach.	compare with forest inventory statistics for removals
		Do you have validation rules and other plausibility checks for the outputs of your JFSQ data compilation process?	NO
		If yes, please briefly describe them.
4	Relevance
	User needs	Please provide references to the relevance of JFSQ at national level e.g. main users, national indicator sets, quantitative policy targets etc.
5	Coherence and comparability
	Coherence - cross domain	Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?	NO
		It not, please explain.
		Do you compare the JFSQ results with business, energy and agricultural and foreign trade statistics?	NO
		It not, please explain.
		Do you cross-check the JFSQ data with the results of European Forest Accounts?	YES
		If yes, please indicate for which reporting items, and comments on the discrepancies observed, if any. It not, please explain.	our data for removals are used for EFA
	Coherence - internal	Are there any other consistency issues related to your JFSQ data?	YES
		If yes, please explain them.	Computer's programs consistency
6	Accessibility and clarity
	Publications	Do you disseminate JFSQ data nationally (e.g. in news releases or other documents)?	NO
		If yes, please provide URLs and/or the reference to the relevant publications.
	Online database	Do you publish your JFSQ accounts in an online data base?	NO
		If yes, please provide URLs.
	Documentation on methodology	Did you prepare a description of your national JFSQ methodology or metadata?	NO
		If yes, please provide URLs.
	Quality documentation	Do you have national quality documentation?	NO
		If yes, please provide URLs.
7	Other comments
	Other comments	Please provide any further feedback you might have on the quality of the reported data, sources and methods used and/or Eurostat's validation and quality report templates.

Cover

Joint Forest Sector Questionnaire
			2021
	DATA INPUT FILE
		Correspondent country:			FR
		Reference year:			2021	Fill in the year
Name of person responsible for reply:

Official address (in full):				Ministère de l'Agriculture et de la souveraineté alimentaire

	Telephone:
	Fax:
	E-mail:

Removals over bark

Country:

Date:

Name of Official responsible for reply:

Check Table

Official Address (in full):

EU JQ1 OB

Ministère de l'Agriculture et de la souveraineté alimentaire

FOREST SECTOR QUESTIONNAIRE

Telephone:

Discrepancies

Removals

E-mail:

Please verify, if there's an error!

Year 1

Year 2

Flag

Note

Product

Unit

2020

2021

2020

2021

2020

2021

Product

Unit

2020

2021

Code

Quantity

Code

Quantity

ROUNDWOOD REMOVALS OVERBARK

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ob

52861

59263

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ob

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ob

24376

28284

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ob

1.1.C

Coniferous

1000 m3ob

2438

2828

1.1.C

Coniferous

1000 m3ob

1.1.NC

Non-Coniferous

1000 m3ob

21938

25456

1.1.NC

Non-Coniferous

1000 m3ob

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ob

28485

30979

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ob

1.2.C

Coniferous

1000 m3ob

19630

21927

1.2.C

Coniferous

1000 m3ob

1.2.NC

Non-Coniferous

1000 m3ob

8855

9052

1.2.NC

Non-Coniferous

1000 m3ob

1.2.NC.T

of which: Tropical

1000 m3ob

1.2.NC.T

of which: Tropical

1000 m3ob

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ob

18463

20854

1.2.1

SAWLOGS AND VENEER LOGS

1000 m3ob

1.2.1.C

Coniferous

1000 m3ob

13712

15839

1.2.1.C

Coniferous

1000 m3ob

1.2.1.NC

Non-Coniferous

1000 m3ob

4751

5015

1.2.1.NC

Non-Coniferous

1000 m3ob

1.2.2

PULPWOOD, ROUND AND SPLIT

1000 m3ob

9476

9469

1.2.2

PULPWOOD, ROUND AND SPLIT

1000 m3ob

1.2.2.C

Coniferous

1000 m3ob

5680

5745

1.2.2.C

Coniferous

1000 m3ob

1.2.2.NC

Non-Coniferous

1000 m3ob

3796

3724

1.2.2.NC

Non-Coniferous

1000 m3ob

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ob

546

656

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

1000 m3ob

1.2.3.C

Coniferous

1000 m3ob

238

343

1.2.3.C

Coniferous

1000 m3ob

1.2.3.NC

Non-Coniferous

1000 m3ob

308

313

1.2.3.NC

Non-Coniferous

1000 m3ob

To fill:

Product

Unit

2020

2021

Code

OVERBARK/UNDERBARK CONVERSION FACTORS

ROUNDWOOD (WOOD IN THE ROUGH)

m3/m3

1.115

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

m3/m3

1.045

1.049

1.1.C

Coniferous

m3/m3

1.045

1.049

1.1.NC

Non-Coniferous

m3/m3

1.045

1.050

1.2

INDUSTRIAL ROUNDWOOD

m3/m3

1.100

1.2.C

Coniferous

m3/m3

1.200

1.2.NC

Non-Coniferous

m3/m3

1.144

1.143

1.2.NC.T

of which: Tropical

m3/m3

1.360

1.2.1

SAWLOGS AND VENEER LOGS

m3/m3

1.164

1.165

1.2.1.C

Coniferous

m3/m3

1.178

1.2.1.NC

Non-Coniferous

m3/m3

1.100

1.2.2

PULPWOOD, ROUND AND SPLIT

m3/m3

1.200

1.2.2.C

Coniferous

m3/m3

1.270

1.2.2.NC

Non-Coniferous

m3/m3

1.100

1.2.3

OTHER INDUSTRIAL ROUNDWOOD

m3/m3

1.200

1.2.3.C

Coniferous

m3/m3

1.163

1.2.3.NC

Non-Coniferous

m3/m3

1.100

JQ1 Production

		Country:	FR	Date:
		Name of Official responsible for reply:		0

		Official Address (in full):
FOREST SECTOR QUESTIONNAIRE JQ1		Ministère de l'Agriculture et de la souveraineté alimentaire
														Industrial Roundwood Balance
PRIMARY PRODUCTS		Telephone:	0	0						This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!		Discrepancies
Removals and Production		E-mail:	0											test for good numbers, missing number, bad number, negative number
			Year 1	Year 2	Flag	Flag	Note	Note
Product	Product	Unit	2020	2021	2020	2021	2020	2021	Product	Product	Unit	2020	2021			2020	2021	% change		Conversion factors
Code			Quantity	Quantity					Code			Quantity	Quantity	Roundwood	Industrial roundwood availability McCusker 14/6/07: McCusker 14/6/07: minus 1.2.3 (other ind. RW) production	20,955	-196,405	-1037%		m3 of wood in m3 or t of product
ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)										ALL REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)					Recovered wood used in particle board	910	910	0%		Solid wood equivalent
1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	47388	53139					1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	OK	OK	Solid Wood Demand	agglomerate production	1,760	1,930	10%		2.4
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	23324	26950					1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	OK	OK		Sawnwood production	7,575	8,581	13%		1
1.1.C	Coniferous	1000 m3ub	2332	2695					1.1.C	Coniferous	1000 m3ub				veneer production	157	157	0%		1
1.1.NC	Non-Coniferous	1000 m3ub	20991	24255					1.1.NC	Non-Coniferous	1000 m3ub				plywood production	234	270	15%		1
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	24,064	26,189					1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	OK	OK		particle board production (incl OSB)	2,600	2,600	0%		1.58
1.2.C	Coniferous	1000 m3ub	16,323	18,271					1.2.C	Coniferous	1000 m3ub	OK	OK		fibreboard production	900	900	0%		1.8
1.2.NC	Non-Coniferous	1000 m3ub	7,741	7,918					1.2.NC	Non-Coniferous	1000 m3ub	OK	OK		mechanical/semi-chemical pulp production	264	300	14%		2.5
1.2.NC.T	of which: Tropical	1000 m3ub	57	60					1.2.NC.T	of which: Tropical	1000 m3ub	OK	OK		chemical pulp production	1,351	1,310	-3%		4.9
1.2.1	SAWLOGS AND VENEER LOGS	1000 m3ub	15857	17897					1.2.1	SAWLOGS AND VENEER LOGS	1000 m3ub	OK	OK		dissolving pulp production	0	0	missing data		5.7
1.2.1.C	Coniferous	1000 m3ub	11645	13451					1.2.1.C	Coniferous	1000 m3ub			Availability	Solid Wood Demand	25,198	26,537	5%
1.2.1.NC	Non-Coniferous	1000 m3ub	4212	4446					1.2.1.NC	Non-Coniferous	1000 m3ub				Difference (roundwood-demand)	-24,288	-25,627	6%	positive = surplus
1.2.2	PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)	1000 m3ub	7738	7727					1.2.2	PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)	1000 m3ub	OK	OK		gap (demand/availability)	-20%	114%		Negative number means not enough roundwood available
1.2.2.C	Coniferous	1000 m3ub	4474	4525					1.2.2.C	Coniferous	1000 m3ub								Positive number means more roundwood available than demanded
1.2.2.NC	Non-Coniferous	1000 m3ub	3265	3203					1.2.2.NC	Non-Coniferous	1000 m3ub
1.2.3	OTHER INDUSTRIAL ROUNDWOOD	1000 m3ub	470	564					1.2.3	OTHER INDUSTRIAL ROUNDWOOD	1000 m3ub	OK	OK
1.2.3.C	Coniferous	1000 m3ub	205	295					1.2.3.C	Coniferous	1000 m3ub									% of particle board that is from recovered wood	35%
1.2.3.NC	Non-Coniferous	1000 m3ub	265	269					1.2.3.NC	Non-Coniferous	1000 m3ub									share of agglomerates produced from industrial roundwood residues	100%
PRODUCTION										PRODUCTION										usable industrial roundwood - amount of roundwood that is used, remainder leaves industry	98.5%
2	WOOD CHARCOAL	1000 t	50	50					2	WOOD CHARCOAL	1000 t
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	13404	16366					3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	OK	OK
3.1	WOOD CHIPS AND PARTICLES	1000 m3	5752	6941					3.1	WOOD CHIPS AND PARTICLES	1000 m3
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	7652	9425					3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3
4	RECOVERED POST-CONSUMER WOOD	1000 t	6382	6382					4	RECOVERED POST-CONSUMER WOOD	1000 t
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	1760	1930					5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	OK	OK
5.1	WOOD PELLETS	1000 t	1700	1850					5.1	WOOD PELLETS	1000 t
5.2	OTHER AGGLOMERATES	1000 t	60	80					5.2	OTHER AGGLOMERATES	1000 t
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	7575	8581					6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	OK	OK
6.C	Coniferous	1000 m3	6442	7268					6.C	Coniferous	1000 m3
6.NC	Non-Coniferous	1000 m3	1133	1313					6.NC	Non-Coniferous	1000 m3
6.NC.T	of which: Tropical	1000 m3	10	25					6.NC.T	of which: Tropical	1000 m3	OK	OK
7	VENEER SHEETS	1000 m3	157	157					7	VENEER SHEETS	1000 m3	OK	OK
7.C	Coniferous	1000 m3	2	2					7.C	Coniferous	1000 m3
7.NC	Non-Coniferous	1000 m3	155	155					7.NC	Non-Coniferous	1000 m3
7.NC.T	of which: Tropical	1000 m3							7.NC.T	of which: Tropical	1000 m3	OK	OK
8	WOOD-BASED PANELS	1000 m3	3734	3770					8	WOOD-BASED PANELS	1000 m3	OK	OK
8.1	PLYWOOD	1000 m3	234	270					8.1	PLYWOOD	1000 m3	OK	OK
8.1.C	Coniferous	1000 m3	94	120					8.1.C	Coniferous	1000 m3
8.1.NC	Non-Coniferous	1000 m3	140	150					8.1.NC	Non-Coniferous	1000 m3
8.1.NC.T	of which: Tropical	1000 m3	105	105					8.1.NC.T	of which: Tropical	1000 m3	OK	OK
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	2600	2600					8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3							8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	OK	OK
8.3	FIBREBOARD	1000 m3	900	900					8.3	FIBREBOARD	1000 m3	OK	OK
8.3.1	HARDBOARD	1000 m3							8.3.1	HARDBOARD	1000 m3
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	751	751					8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3
8.3.3	OTHER FIBREBOARD	1000 m3							8.3.3	OTHER FIBREBOARD	1000 m3
9	WOOD PULP	1000 t	1,620	1,615					9	WOOD PULP	1000 t	OK	OK
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	264	300					9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t
9.2	CHEMICAL WOOD PULP	1000 t	1,351	1,310					9.2	CHEMICAL WOOD PULP	1000 t	OK	OK
9.2.1	SULPHATE PULP	1000 t	1,237	1,199					9.2.1	SULPHATE PULP	1000 t
9.2.1.1	of which: BLEACHED	1000 t	0	0					9.2.1.1	of which: BLEACHED	1000 t	OK	OK
9.2.2	SULPHITE PULP	1000 t	114	111					9.2.2	SULPHITE PULP	1000 t
9.3	DISSOLVING GRADES	1000 t	0	0					9.3	DISSOLVING GRADES	1000 t
10	OTHER PULP	1000 t	4,305	4,573					10	OTHER PULP	1000 t	OK	OK
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	5	6					10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t
10.2	RECOVERED FIBRE PULP	1000 t	4,300	4,567					10.2	RECOVERED FIBRE PULP	1000 t
11	RECOVERED PAPER	1000 t	6,317	6,885					11	RECOVERED PAPER	1000 t
12	PAPER AND PAPERBOARD	1000 t	6,873	7,359					12	PAPER AND PAPERBOARD	1000 t	OK	OK
12.1	GRAPHIC PAPERS	1000 t	1,198	1,314					12.1	GRAPHIC PAPERS	1000 t	OK	OK
12.1.1	NEWSPRINT	1000 t	479	508					12.1.1	NEWSPRINT	1000 t
12.1.2	UNCOATED MECHANICAL	1000 t	42	43					12.1.2	UNCOATED MECHANICAL	1000 t
12.1.3	UNCOATED WOODFREE	1000 t	506	552					12.1.3	UNCOATED WOODFREE	1000 t
12.1.4	COATED PAPERS	1000 t	171	211					12.1.4	COATED PAPERS	1000 t
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	832	817					12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t
12.3	PACKAGING MATERIALS	1000 t	4,422	4,840					12.3	PACKAGING MATERIALS	1000 t	OK	OK
12.3.1	CASE MATERIALS	1000 t	3,576	3,933					12.3.1	CASE MATERIALS	1000 t
12.3.2	CARTONBOARD	1000 t	647	682					12.3.2	CARTONBOARD	1000 t
12.3.3	WRAPPING PAPERS	1000 t	199	225					12.3.3	WRAPPING PAPERS	1000 t
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	0	0					12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	421	388					12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t
		To fill:	4	4
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)
	m3 = cubic metres solid volume
	t = metric tonnes

JQ2 Trade

			61	62	61	62	91	92	91	92
		FOREST SECTOR QUESTIONNAIRE JQ2				Country:	FR		Date:																																							0	both VALUE and quantity reported ZERO
						Name of Official responsible for reply:		0																																								ZERO Q	quantity ZERO when VALUE is reported									INTRA-EU	The difference might be caused by Intra-EU trade
		PRIMARY PRODUCTS				Official Address (in full):		Ministère de l'Agriculture et de la souveraineté alimentaire																														This table highlights discrepancies between production and trade. For any negative number, indicating greater net exports than production, please verify your data!										ZERO V	Value ZERO when quantity is reported									CHECK
		Trade				Telephone:	0		Fax:	0																		This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!																		ZERO CHECK 1 - if no value please CHECK		NO Q	no quantity reported						ZERO CHECK 2 - if no value in Zero Check 1
	Value must always be in 1000 NAC (national currency)					E-mail:	0																																		Country:	FR						NO V	no value reported			Treshold:	2		verifies whether the JQ2 figures refers only to intra-EU trade
	Specify Currency and Unit of Value (e.g.:1000 USD):			1000 NAC							Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note		Trade		Discrepancies																		REPORT	no figures reported
Product		Unit of	I M P O R T				E X P O R T				Import				Export				Import				Export				Product			I M P O R T				E X P O R T				Product			Apparent Consumption		Related Notes		Product		Value per	I M P O R T		E X P O R T		Column1	Column2	Product		Value per	I M P O R T		E X P O R T
code	Product	quantity	2020		2021		2020		2021		2020		2021		2020		2021		2020		2021		2020		2021		code			2020		2021		2020		2021		code			2020	2021	2020	2021	code	Product	unit	2020	2021	2020	2021	IMPORT	EXPORT	code	Product	unit	2020	2021	2020	2021
			Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value				Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	1125.67135	144711.575	1182.850775	174216.045	4032.24893	369012.92	4546.654125	488442.5																	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1	ROUNDWOOD (WOOD IN THE ROUGH)	1000 m3ub	44,481	-171,163			1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3	129	147	92	107	ACCEPT	ACCEPT	1	ROUNDWOOD (WOOD IN THE ROUGH)	NAC/m3
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	182.19013	18992.55	197.68982	17393.93	449.48352	21264.3	435.13554	24033.4																	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	1000 m3ub	23,056	24,678			1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3	104	88	47	55	ACCEPT	ACCEPT	1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	NAC/m3
1.1.C	Coniferous	1000 m3ub	158.9655	14529	177.7995	13876.5	353.751	16228.5	299.9145	16665																	1.1.C	Coniferous	1000 m3ub									1.1.C	Coniferous	1000 m3ub	2,138	996			1.1.C	Coniferous	NAC/m3	91	78	46	56	ACCEPT	ACCEPT	1.1.C	Coniferous	NAC/m3
1.1.NC	Non-Coniferous	1000 m3ub	23.22463	4463.55	19.89032	3517.43	95.73252	5035.8	135.22104	7368.4																	1.1.NC	Non-Coniferous	1000 m3ub									1.1.NC	Non-Coniferous	1000 m3ub	20,919	23,683			1.1.NC	Non-Coniferous	NAC/m3	192	177	53	54	ACCEPT	ACCEPT	1.1.NC	Non-Coniferous	NAC/m3
1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	943.48122	125719.025	985.160955	156822.115	3582.76541	347748.62	4111.518585	464409.1																	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	21,425	-195,841			1.2	INDUSTRIAL ROUNDWOOD	NAC/m3	133	159	97	113	ACCEPT	ACCEPT	1.2	INDUSTRIAL ROUNDWOOD	NAC/m3
1.2.C	Coniferous	1000 m3ub	718.7168	83022.4	794.4816	108792	1436.4016	109099.2	1617.3616	163259.2																	1.2.C	Coniferous	1000 m3ub									1.2.C	Coniferous	1000 m3ub	15,605	-7,806			1.2.C	Coniferous	NAC/m3	116	137	76	101	ACCEPT	ACCEPT	1.2.C	Coniferous	NAC/m3
1.2.NC	Non-Coniferous	1000 m3ub	224.76442	42696.625	190.679355	48030.115	2146.36381	238649.42	2494.156985	301149.9																	1.2.NC	Non-Coniferous	1000 m3ub									1.2.NC	Non-Coniferous	1000 m3ub	5,820	-188,035			1.2.NC	Non-Coniferous	NAC/mt	190	252	111	121	ACCEPT	ACCEPT	1.2.NC	Non-Coniferous	NAC/mt
1.2.NC.T	of which: Tropical	1000 m3ub	36.08472	17342.425	46.396055	24048.815	3.06231	614.72	1.913785	560.1																	1.2.NC.T	of which: Tropical	1000 m3ub	OK	OK	OK	OK	OK	OK	OK	OK	1.2.NC.T	of which: Tropical	1000 m3ub	90	16,788			1.2.NC.T	of which: Tropical	1000 m3	481	518	201	293	ACCEPT	ACCEPT	1.2.NC.T	of which: Tropical	1000 m3
2	WOOD CHARCOAL	1000 t	106.029	61977	80.945	54658	9.192	7796	6.912	6952																	2	WOOD CHARCOAL	1000 t									2	WOOD CHARCOAL	1000 t	147	54,231			2	WOOD CHARCOAL	1000 m3	585	675	848	1006	ACCEPT	ACCEPT	2	WOOD CHARCOAL	1000 m3
3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	2509.72788	143600.7	2185.52254	117701.72	699.2563	58678.04	631.02238	36015.1																	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	15,215	101,289			3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3	57	54	84	57	ACCEPT	ACCEPT	3	WOOD CHIPS, PARTICLES AND RESIDUES	1000 m3
3.1	WOOD CHIPS AND PARTICLES	1000 m3	652.81428	48736.9	513.55254	33751.12	451.2223	42730.24	311.45418	16724.5																	3.1	WOOD CHIPS AND PARTICLES	1000 m3									3.1	WOOD CHIPS AND PARTICLES	1000 m3	5,954	12,948			3.1	WOOD CHIPS AND PARTICLES	1000 mt	75	66	95	54	ACCEPT	ACCEPT	3.1	WOOD CHIPS AND PARTICLES	1000 mt
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	1856.9136	94863.8	1671.97	83950.6	248.034	15947.8	319.5682	19290.6																	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3									3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 m3	9,261	88,341			3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 mt	51	50	64	60	ACCEPT	ACCEPT	3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	1000 mt
4	RECOVERED POST-CONSUMER WOOD	1000 t																									4	RECOVERED POST-CONSUMER WOOD	1000 t									4	RECOVERED POST-CONSUMER WOOD	1000 t	6,382	6,382			4	RECOVERED POST-CONSUMER WOOD	1000 mt	REPORT	REPORT	REPORT	REPORT	CHECK	CHECK	4	RECOVERED POST-CONSUMER WOOD	1000 mt	CHECK	CHECK	CHECK	CHECK
5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	548.344	99126	807.856	142805	286.105	25530	342.339	34392																	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	5	WOOD PELLETS AND OTHER AGGLOMERATES	1000 t	2,022	75,526			5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC/m3	181	177	89	100	ACCEPT	ACCEPT	5	WOOD PELLETS AND OTHER AGGLOMERATES	NAC/m3
5.1	WOOD PELLETS	1000 t	412.381	86007	660.949	127444	96.539	20703	121.64	25701																	5.1	WOOD PELLETS	1000 t									5.1	WOOD PELLETS	1000 t	2,016	67,154			5.1	WOOD PELLETS	NAC/m3	209	193	214	211	ACCEPT	ACCEPT	5.1	WOOD PELLETS	NAC/m3
5.2	OTHER AGGLOMERATES	1000 t	135.963	13119	146.907	15361	189.566	4827	220.699	8691																	5.2	OTHER AGGLOMERATES	1000 t									5.2	OTHER AGGLOMERATES	1000 t	6	8,372			5.2	OTHER AGGLOMERATES	NAC/m3	96	105	25	39	ACCEPT	ACCEPT	5.2	OTHER AGGLOMERATES	NAC/m3
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	2714.582	1395705.1	3132.4063	2218546	1517.7849	594802.6	1646.4064	807830.7																	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	8,772	809,484			6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3	514	708	392	491	ACCEPT	ACCEPT	6	SAWNWOOD (INCLUDING SLEEPERS)	NAC/m3
6.C	Coniferous	1000 m3	2437.9542	1129098.6	2846.07	1915698.6	1047.8826	322027.2	1055.3076	438489																	6.C	Coniferous	1000 m3									6.C	Coniferous	1000 m3	7,832	814,339			6.C	Coniferous	NAC/m3	463	673	307	416	ACCEPT	ACCEPT	6.C	Coniferous	NAC/m3
6.NC	Non-Coniferous	1000 m3	276.6278	266606.5	286.3363	302847.4	469.9023	272775.4	591.0988	369341.7																	6.NC	Non-Coniferous	1000 m3									6.NC	Non-Coniferous	1000 m3	940	-4,856			6.NC	Non-Coniferous	NAC/m3	964	1058	580	625	ACCEPT	ACCEPT	6.NC	Non-Coniferous	NAC/m3
6.NC.T	of which: Tropical	1000 m3	129.1808	120276.8	135.128	137781	2.933	3927	4.9252	6273.4																	6.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	6.NC.T	of which: Tropical	1000 m3	136	116,375			6.NC.T	of which: Tropical	NAC/m3	931	1020	1339	1274	ACCEPT	ACCEPT	6.NC.T	of which: Tropical	NAC/m3
7	VENEER SHEETS	1000 m3	311.22133	296963.73	308.50281	360703.98	68.28353	128978.08	71.28401	141373.68																	7	VENEER SHEETS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	7	VENEER SHEETS	1000 m3	400	168,143			7	VENEER SHEETS	NAC/m3	954	1169	1889	1983	ACCEPT	ACCEPT	7	VENEER SHEETS	NAC/m3
7.C	Coniferous	1000 m3	17.48551	13907.81	14.67256	13269.41	0.25802	603.82	0.5586	809.97																	7.C	Coniferous	1000 m3									7.C	Coniferous	1000 m3	19	13,306			7.C	Coniferous	NAC/m3	795	904	2340	1450	ACCEPT	ACCEPT	7.C	Coniferous	NAC/m3
7.NC	Non-Coniferous	1000 m3	293.73582	283055.92	293.83025	347434.57	68.02551	128374.26	70.72541	140563.71																	7.NC	Non-Coniferous	1000 m3									7.NC	Non-Coniferous	1000 m3	381	154,837			7.NC	Non-Coniferous	NAC/m3	964	1182	1887	1987	ACCEPT	ACCEPT	7.NC	Non-Coniferous	NAC/m3
7.NC.T	of which: Tropical	1000 m3	78.65354	92392.44	86.10154	105934.5	53.54181	105910.56	54.0778	110759.74																	7.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	7.NC.T	of which: Tropical	1000 m3	25	-13,518			7.NC.T	of which: Tropical	NAC/m3	1175	1230	1978	2048	ACCEPT	ACCEPT	7.NC.T	of which: Tropical	NAC/m3
8	WOOD-BASED PANELS	1000 m3	1350.29964	944721.2	1298.93177	1237733.81	1048.06319	547327.36	1056.5873	559393.1																	8	WOOD-BASED PANELS	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8	WOOD-BASED PANELS	1000 m3	4,036	401,164			8	WOOD-BASED PANELS	NAC/m3	700	953	522	529	ACCEPT	ACCEPT	8	WOOD-BASED PANELS	NAC/m3
8.1	PLYWOOD	1000 m3	443.77102	442465.1	469.58142	563693.9	158.7894	230145.3	154.9394	215553.8																	8.1	PLYWOOD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.1	PLYWOOD	1000 m3	519	212,590			8.1	PLYWOOD	NAC/m3	997	1200	1449	1391	ACCEPT	ACCEPT	8.1	PLYWOOD	NAC/m3
8.1.C	Coniferous	1000 m3	103.65586	114716.14	129.35692	161401.24	80.02302	81501.42	73.04682	52795.82																	8.1.C	Coniferous	1000 m3									8.1.C	Coniferous	1000 m3	118	33,335			8.1.C	Coniferous	NAC/m3	1107	1248	1018	723	ACCEPT	ACCEPT	8.1.C	Coniferous	NAC/m3
8.1.NC	Non-Coniferous	1000 m3	340.11516	327748.96	340.2245	402292.66	78.76638	148643.88	81.89258	162757.98																	8.1.NC	Non-Coniferous	1000 m3									8.1.NC	Non-Coniferous	1000 m3	401	179,255			8.1.NC	Non-Coniferous	NAC/m3	964	1182	1887	1987	ACCEPT	ACCEPT	8.1.NC	Non-Coniferous	NAC/m3
8.1.NC.T	of which: Tropical	1000 m3	91.07252	106980.72	99.69652	122661	61.99578	122633.28	62.6164	128248.12																	8.1.NC.T	of which: Tropical	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.1.NC.T	of which: Tropical	1000 m3	134	-15,548			8.1.NC.T	of which: Tropical	NAC/m3	1175	1230	1978	2048	ACCEPT	ACCEPT	8.1.NC.T	of which: Tropical	NAC/m3
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	118.05192	58113.82	148.05814	129798.82	34.85956	14713.42	28.81442	17880.54																	8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3									8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) AND SIMILAR BOARD	1000 m3	2,683	46,000			8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3	492	877	422	621	ACCEPT	ACCEPT	8.2	PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD	NAC/m3
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3																									8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.2.1	of which: ORIENTED STRAND BOARD (OSB)	1000 m3	0	0			8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3	REPORT	REPORT	REPORT	REPORT	CHECK	CHECK	8.2.1	of which: ORIENTED STRANDBOARD (OSB)	NAC/m3	CHECK	CHECK	CHECK	CHECK
8.3	FIBREBOARD	1000 m3	788.4767	444142.28	681.29221	544241.09	854.41423	302468.64	872.83348	325958.76																	8.3	FIBREBOARD	1000 m3	OK	OK	OK	OK	OK	OK	OK	OK	8.3	FIBREBOARD	1000 m3	834	142,574			8.3	FIBREBOARD	NAC/m3	563	799	354	373	ACCEPT	ACCEPT	8.3	FIBREBOARD	NAC/m3
8.3.1	HARDBOARD	1000 m3	235.48761	107675.55	211.98669	144493.14	449.40348	139046.37	443.30625	131170.92																	8.3.1	HARDBOARD	1000 m3									8.3.1	HARDBOARD	1000 m3	-214	-31,371			8.3.1	HARDBOARD	NAC/mt	457	682	309	296	ACCEPT	ACCEPT	8.3.1	HARDBOARD	NAC/mt
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	481.14736	276418.54	388.1669	321553.54	312.43096	108678	344.64614	144664.46																	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3									8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	1000 m3	920	168,492			8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/mt	574	828	348	420	ACCEPT	ACCEPT	8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	NAC/mt
8.3.3	OTHER FIBREBOARD	1000 m3	71.84173	60048.19	81.13862	78194.41	92.57979	54744.27	84.88109	50123.38																	8.3.3	OTHER FIBREBOARD	1000 m3									8.3.3	OTHER FIBREBOARD	1000 m3	-21	5,304			8.3.3	OTHER FIBREBOARD	NAC/mt	836	964	591	591	ACCEPT	ACCEPT	8.3.3	OTHER FIBREBOARD	NAC/mt
9	WOOD PULP	1000 t	1757.856	796995	1603.663	951410	447.092	203655	412.901	227432																	9	WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9	WOOD PULP	1000 t	2,931	594,955			9	WOOD PULP	NAC/mt	453	593	456	551	ACCEPT	ACCEPT	9	WOOD PULP	NAC/mt
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	75.74	33763	82.514	42563	4.643	1253	32.302	6479																	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t									9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	1000 t	335	32,810			9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/mt	446	516	270	201	ACCEPT	ACCEPT	9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	NAC/mt
9.2	CHEMICAL WOOD PULP	1000 t	1668.806	763232	1521.149	908847	442.449	202402	380.599	220953																	9.2	CHEMICAL WOOD PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9.2	CHEMICAL WOOD PULP	1000 t	2,577	562,140			9.2	CHEMICAL WOOD PULP	NAC/mt	457	597	457	581	ACCEPT	ACCEPT	9.2	CHEMICAL WOOD PULP	NAC/mt
9.2.1	SULPHATE PULP	1000 t	1645.881	745962	1510.177	896474	436.7	199023	379.414	219787																	9.2.1	SULPHATE PULP	1000 t									9.2.1	SULPHATE PULP	1000 t	2,446	548,138			9.2.1	SULPHATE PULP	NAC/mt	453	594	456	579	ACCEPT	ACCEPT	9.2.1	SULPHATE PULP	NAC/mt
9.2.1.1	of which: BLEACHED	1000 t	1645.881	745962	1510.177	896474	436.7	199023	379.414	219787																	9.2.1.1	of which: BLEACHED	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	9.2.1.1	of which: BLEACHED	1000 t	1,209	546,939			9.2.1.1	of which: BLEACHED	NAC/mt	453	594	456	579	ACCEPT	ACCEPT	9.2.1.1	of which: BLEACHED	NAC/mt
9.2.2	SULPHITE PULP	1000 t	22.925	17270	10.972	12373	5.749	3379	1.185	1166																	9.2.2	SULPHITE PULP	1000 t									9.2.2	SULPHITE PULP	1000 t	131	14,002			9.2.2	SULPHITE PULP	NAC/mt	753	1128	588	984	ACCEPT	ACCEPT	9.2.2	SULPHITE PULP	NAC/mt
9.3	DISSOLVING GRADES	1000 t	13.31	0	0	0	0	0	0	0																	9.3	DISSOLVING GRADES	1000 t									9.3	DISSOLVING GRADES	1000 t	13	0			9.3	DISSOLVING GRADES	NAC/mt	ZERO V	0	0	0	CHECK	ACCEPT	9.3	DISSOLVING GRADES	NAC/mt	CHECK
10	OTHER PULP	1000 t	23.131	30360	23.497	32037	70.485	44499	83.456	56220																	10	OTHER PULP	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	10	OTHER PULP	1000 t	4,258	-9,566			10	OTHER PULP	NAC/mt	1313	1363	631	674	ACCEPT	ACCEPT	10	OTHER PULP	NAC/mt
10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	16.325	26045	19.152	29028	2.343	4471	2.01	4738																	10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t									10.1	PULP FROM FIBRES OTHER THAN WOOD	1000 t	19	21,580			10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/mt	1595	1516	1908	2357	ACCEPT	ACCEPT	10.1	PULP FROM FIBRES OTHER THAN WOOD	NAC/mt
10.2	RECOVERED FIBRE PULP	1000 t	6.806	4315	4.345	3009	68.142	40028	81.446	51482																	10.2	RECOVERED FIBRE PULP	1000 t									10.2	RECOVERED FIBRE PULP	1000 t	4,239	-31,146			10.2	RECOVERED FIBRE PULP	NAC/mt	634	693	587	632	ACCEPT	ACCEPT	10.2	RECOVERED FIBRE PULP	NAC/mt
11	RECOVERED PAPER	1000 t	1859.992	3716226	1895.719	4027706	921.811	2100841	955.364	2280836																	11	RECOVERED PAPER	1000 t									11	RECOVERED PAPER	1000 t	7,255	1,622,270			11	RECOVERED PAPER	NAC/mt	1998	2125	2279	2387	ACCEPT	ACCEPT	11	RECOVERED PAPER	NAC/mt
12	PAPER AND PAPERBOARD	1000 t	4502.25	3386847	4740.495	3838835	3464.735	2649898	3806.804	3433153																	12	PAPER AND PAPERBOARD	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12	PAPER AND PAPERBOARD	1000 t	7,911	744,308			12	PAPER AND PAPERBOARD	NAC/mt	752	810	765	902	ACCEPT	ACCEPT	12	PAPER AND PAPERBOARD	NAC/mt
12.1	GRAPHIC PAPERS	1000 t	2131.837	1618320	2126.105	1643723	895.994	715201	1008.647	834097																	12.1	GRAPHIC PAPERS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12.1	GRAPHIC PAPERS	1000 t	2,434	904,433			12.1	GRAPHIC PAPERS	NAC/mt	759	773	798	827	ACCEPT	ACCEPT	12.1	GRAPHIC PAPERS	NAC/mt
12.1.1	NEWSPRINT	1000 t	0	0	0	0	0	0	0	0																	12.1.1	NEWSPRINT	1000 t									12.1.1	NEWSPRINT	1000 t	479	508			12.1.1	NEWSPRINT	NAC/mt	0	0	0	0	ACCEPT	ACCEPT	12.1.1	NEWSPRINT	NAC/mt
12.1.2	UNCOATED MECHANICAL	1000 t	0	0	0	0	0	0	0	0																	12.1.2	UNCOATED MECHANICAL	1000 t									12.1.2	UNCOATED MECHANICAL	1000 t	42	43			12.1.2	UNCOATED MECHANICAL	NAC/mt	0	0	0	0	ACCEPT	ACCEPT	12.1.2	UNCOATED MECHANICAL	NAC/mt
12.1.3	UNCOATED WOODFREE	1000 t	0	0	0	0	0	0	0	0																	12.1.3	UNCOATED WOODFREE	1000 t									12.1.3	UNCOATED WOODFREE	1000 t	506	552			12.1.3	UNCOATED WOODFREE	NAC/mt	0	0	0	0	ACCEPT	ACCEPT	12.1.3	UNCOATED WOODFREE	NAC/mt
12.1.4	COATED PAPERS	1000 t	0	0	0	0	0	0	0	0																	12.1.4	COATED PAPERS	1000 t									12.1.4	COATED PAPERS	1000 t	171	211			12.1.4	COATED PAPERS	NAC/mt	0	0	0	0	ACCEPT	ACCEPT	12.1.4	COATED PAPERS	NAC/mt
12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	90.586	108249	89.624	105394	79.583	92484	67.563	91041																	12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t									12.2	HOUSEHOLD AND SANITARY PAPERS	1000 t	843	16,582			12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/mt	1195	1176	1162	1347	ACCEPT	ACCEPT	12.2	HOUSEHOLD AND SANITARY PAPERS	NAC/mt
12.3	PACKAGING MATERIALS	1000 t	2249.687	1587681	2483.641	2007051	2478.484	1698501	2672.381	2194104																	12.3	PACKAGING MATERIALS	1000 t	OK	OK	OK	OK	OK	OK	OK	OK	12.3	PACKAGING MATERIALS	1000 t	4,193	-105,980			12.3	PACKAGING MATERIALS	NAC/mt	706	808	685	821	ACCEPT	ACCEPT	12.3	PACKAGING MATERIALS	NAC/mt
12.3.1	CASE MATERIALS	1000 t	0	0	0	0	0	0	0	0																	12.3.1	CASE MATERIALS	1000 t									12.3.1	CASE MATERIALS	1000 t	3,576	3,933			12.3.1	CASE MATERIALS	NAC/mt	0	0	0	0	ACCEPT	ACCEPT	12.3.1	CASE MATERIALS	NAC/mt
12.3.2	CARTONBOARD	1000 t	0	0	0	0	0	0	0	0																	12.3.2	CARTONBOARD	1000 t									12.3.2	CARTONBOARD	1000 t	647	682			12.3.2	CARTONBOARD	NAC/mt	0	0	0	0	ACCEPT	ACCEPT	12.3.2	CARTONBOARD	NAC/mt
12.3.3	WRAPPING PAPERS	1000 t	0	0	0	0	0	0	0	0																	12.3.3	WRAPPING PAPERS	1000 t									12.3.3	WRAPPING PAPERS	1000 t	199	225			12.3.3	WRAPPING PAPERS	NAC/mt	0	0	0	0	ACCEPT	ACCEPT	12.3.3	WRAPPING PAPERS	NAC/mt
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	0	0	0	0	0	0	0	0																	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t									12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	1000 t	0	0			12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/mt	0	0	0	0	ACCEPT	ACCEPT	12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	NAC/mt
12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	30.14	72597	41.125	82667	10.674	143712	58.213	313911																	12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t									12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	1000 t	440	-70,727			12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	NAC/mt	2409	2010	13464	5392	ACCEPT	CHECK	12.4	OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)	NAC/mt
		To fill:	2	2	2	2	2	2	2	2
	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)
	t = metric tonnes

JQ3 Secondary PP Trade

			62	91	91
			Country:	FR	Date:
			Name of Official responsible for reply:		0											Specify Currency and Unit of Value (e.g.:1000 US $):	_____________________
			Official Address (in full):
	FOREST SECTOR QUESTIONNAIRE JQ3		Ministère de l'Agriculture et de la souveraineté alimentaire
	SECONDARY PROCESSED PRODUCTS		Telephone/Fax:	0	0
	Trade		E-mail:	0
															This table highlights discrepancies between items and sub-items. Please verify your data if there's an error!
	Value must always be in 1000 NAC (national currency)															Discrepancies
	Eurozone countries may use the old national currency, but only in both years					Flag	Flag	Flag	Flag	Note	Note	Note	Note
Product	Product	I M P O R T V A L U E		E X P O R T V A L U E		Import		Export		Import		Export		Product	Product	I M P O R T V A L U E		E X P O R T V A L U E
code		2020	2021	2020	2021	2020	2021	2020	2021	2020	2021	2020	2021	Code		2020	2021	2020	2021
13	SECONDARY WOOD PRODUCTS	5143356	6555724	1591779	1897502									13	SECONDARY WOOD PRODUCTS	OK	OK	OK	OK
13.1	FURTHER PROCESSED SAWNWOOD	241153	296641	40530	48500									13.1	FURTHER PROCESSED SAWNWOOD	OK	OK	OK	OK
13.1.C	Coniferous	87166	114718	23814	31666									13.1.C	Coniferous
13.1.NC	Non-coniferous	153987	181923	16716	16834									13.1.NC	Non-coniferous
13.1.NC.T	of which: Tropical	77809	91925	1597	1608									13.1.NC.T	of which: Tropical	OK	OK	OK	OK
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	260380	377370	492085	532513									13.2	WOODEN WRAPPING AND PACKAGING MATERIAL
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	152042	206172	52987	68432									13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD	502348	685375	96924	138891									13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD
13.5	WOODEN FURNITURE	3480344	4370059	815749	990390									13.5	WOODEN FURNITURE
13.6	PREFABRICATED BUILDINGS OF WOOD	87771	94449	8252	12903									13.6	PREFABRICATED BUILDINGS OF WOOD
13.7	OTHER MANUFACTURED WOOD PRODUCTS	419318	525658	85252	105873									13.7	OTHER MANUFACTURED WOOD PRODUCTS
14	SECONDARY PAPER PRODUCTS	3716226	4027706	2100841	2280836									14	SECONDARY PAPER PRODUCTS	OK	OK	OK	OK
14.1	COMPOSITE PAPER AND PAPERBOARD													14.1	COMPOSITE PAPER AND PAPERBOARD
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS													14.2	SPECIAL COATED PAPER AND PULP PRODUCTS
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE													14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE
14.4	PACKAGING CARTONS, BOXES ETC.													14.4	PACKAGING CARTONS, BOXES ETC.
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE													14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	OK	OK	OK	OK
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE													14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP													14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE													14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE

	To fill:	8	8	8	8

LAM & CHIPS

Unit of quantity:

1000 m3

Year

Product

Flow

Year

App. Cons.

Unit price

Production

Total Export

Total Import

Extra-EU Export

Extra-EU Import

TOT EXP

TOT IMP

X-EU EXP

X-EU IMP

1000 m3

1000 NAC

1000 m3

1000 NAC

1000 m3

1000 NAC

1000 m3

1000 NAC

1000 m3

2020

Glulam

200

2020

200

ERROR:#DIV/0!

2021

200

2021

200

ERROR:#DIV/0!

2020

X-lam

100

2020

100

ERROR:#DIV/0!

2021

100

2021

100

ERROR:#DIV/0!

Definitions: Glulam: Builders' carpentry also includes glue-laminated timber (glulam), which is a structural timber product obtained by gluing together a number of wood laminations having their grain essentially parallel. Laminations of curved members are arranged so that the plane of each lamination is at 90 degrees to the plane of the applied load; thus, laminations of a straight gluman beam are laid flat. [from HS 4418, Builders' joinery and carpentry of wood, including cellular wood panels, assembled flooring panels, shingles and shakes] X-lam: Panels consisting of laths of roughly sawn wood, assembled with glue in order to facilitate transport or later working. [from HS4421, Other articles of wood]

ECE-EU Species


							Country:	FR		Date:
							Name of Official responsible for reply:			0																			DISCREPANCIES

			FOREST SECTOR QUESTIONNAIRE ECE/EU Species Trade				Official Address (in full):																						Checks								Check Table
							Ministère de l'Agriculture et de la souveraineté alimentaire																																		0	both VALUE and quantity reported ZERO
			Trade in Roundwood and Sawnwood by species				Telephone:	0		Fax:	0																		- checks whether the sum of subitems is bigger than the total												ZERO Q	quantity ZERO when VALUE is reported
							E-mail:	0																																	ZERO V	Value ZERO when quantity is reported
																																							Zero check - if no value please CHECK		NO Q	no quantity reported
		Value must always be in 1000 NAC ( national currency)																																							NO V	no value reported			Treshold:	2
		Eurozone countries may use the old national currency, but only in both years			1000NAC								Flag	Flag	Flag	Flag	Flag	Flag	Flag	Flag	Note	Note	Note	Note	Note	Note	Note	Note													REPORT	no figures reported
					I M P O R T				E X P O R T				Import				Export				Import				Export				I M P O R T				E X P O R T							Value per	I M P O R T		E X P O R T		Unit price check
Product	Classification	Classification		Unit of	2020		2021		2020		2021		2020		2021		2020		2021		2020		2021		2020		2021		2020		2021		2020		2021		Classification	Classification		unit	2020	2021	2020	2021	IMPORT	EXPORT
Code	HS2017	CN2017	Product	Quantity	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value	HS2007	CN2007	Product
1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood, Coniferous	1000 m3ub	719	83022	794	108792	1436	109099	1617	163259																	OK	OK	OK	OK	OK	OK	OK	OK	4403.11/21/22/23/24/25/26		Industrial Roundwood, Coniferous	NAC/m3	116	137	76	101	ACCEPT	ACCEPT							PRODUCTION		I M P O R T				E X P O R T
	4403.23/24		Fir/Spruce (Abies spp., Picea spp.)	1000 m3ub	246	20699	272	27123	697	57489	785	86029																	OK	OK	OK	OK	OK	OK	OK	OK	4403.23/24		Fir/Spruce (Abies spp., Picea spp.)	NAC/m3	84	100	82	110	ACCEPT	ACCEPT	Product	Classification	Classification		Unit of		2020	2021	2020		2021		2020		2021
		4403 23 10	sawlogs and veneer logs	1000 m3ub	75	6236	83	8171	394	27661	444	41392																										4403 23 10	sawlogs and veneer logs (Abies alba, Picea abies)	NAC/m3	84	99	70	93	ACCEPT	ACCEPT	Code	HS2007	CN2007	Product	Quantity		Quantity	Quantity	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
		4403 23 90 4403 24 00	pulpwood and other industrial roundwood	1000 m3ub	171	14463	189	18952	303	29829	341	44637																										4403 23 90 4403 24 00	pulpwood and other industrial roundwood (Abies alba, Picea abies)	NAC/m3	85	100	98	131	ACCEPT	ACCEPT	1	4401.11/12 44.03		Roundwood production	1000 m3	JQ1	47,388	53,139
	4403.21/22		Pine (Pinus spp.)	1000 m3ub	171	25336	190	33200	306	17332	345	25936																	OK	OK	OK	OK	OK	OK	OK	OK	4403.21/22		Pine (Pinus spp.)	NAC/m3	148	175	57	75	ACCEPT	ACCEPT						EU2	47387	52915
		4403 21 10	sawlogs and veneer logs	1000 m3ub	148	17371	164	22763	278	14791	313	22134																										4403 21 10	sawlogs and veneer logs (Pinus sylvestris)	NAC/m3	117	139	53	71	ACCEPT	ACCEPT						dif	1	224
		4403 21 90 4403 22 00	pulpwood and other industrial roundwood	1000 m3ub	23	7965	26	10437	29	2540	32	3802																										4403 21 90 4403 22 00	pulpwood and other industrial roundwood (Pinus sylvestris)	NAC/m3	344	408	89	118	ACCEPT	ACCEPT	1.2.C	4403.11/21/22/23/24/25/26		Industrial Roundwood (wood in the rough), Coniferous	1000 m3	JQ2			719	83,022	794	108,792	1,436	109,099	1,617	163,259
1.2.NC	4403.12/41/49/91/93/94 4403.95/96/97/98/99		Industrial Roundwood, Non-Coniferous	1000 m3ub	225	42697	191	48030	2146	238649	2494	301150																	OK	OK	OK	OK	OK	OK	OK	OK	4403.12/41/49/91/93/94 4403.95/96/97/98/99		Industrial Roundwood, Non-Coniferous	NAC/m3	190	252	111	121	ACCEPT	ACCEPT						ECE/EU			719	83,022	794	108,792	1,436	109,099	1,617	163,259
	4403.91		of which: Oak (Quercus spp.)	1000 m3ub	45	13982	44	14689	514	104014	605	142711																									4403.91		of which: Oak (Quercus spp.)	NAC/m3	307	337	202	236	ACCEPT	ACCEPT						dif			0	0	0	0	0	0	0	0
	4403.93/94		of which: Beech (Fagus spp.)	1000 m3ub	44	2934	35	2576	235	18110	260	24840																									4403.93/94		of which: Beech (Fagus spp.)	NAC/m3	67	73	77	96	ACCEPT	ACCEPT	1.2.NC	4403.12/41/49/91/93/94/95/96/97/98/99		Industrial Roundwood (wood in the rough), Non-Coniferous	1000 m3	JQ2			225	42,697	191	48,030	2,146	238,649	2,494	301,150
	4403.95/96		of which: Birch (Betula spp.)	1000 m3ub	48	1749	46	2315	276	25193	299	29720																	OK	OK	OK	OK	OK	OK	OK	OK	4403.95/96		of which: Birch (Betula spp.)	NAC/m3	37	51	91	99	ACCEPT	ACCEPT						ECE/EU			225	42,697	191	48,030	2,146	238,649	2,494	301,150
		4403 95 10	sawlogs and veneer logs	1000 m3ub	6	192	6	192	8	827	10	1099																										4403 95 10	sawlogs and veneer logs	NAC/m3	31	30	104	111	ACCEPT	ACCEPT						dif			0	0	0	0	0	0	0	0
		4403 95 90 4403 96 00	pulpwood and other industrial roundwood	1000 m3ub	42	1557	39	2123	269	24366	290	28621																										4403 95 90 4403 96 00	pulpwood and other industrial roundwood	NAC/m3	38	54	91	99	ACCEPT	ACCEPT	6.C	4406.11/91 4407.11/12/19		Sawnwood, Coniferous	1000 m3	JQ2			2,438	1,129,099	2,846	1,915,699	1,048	322,027	1,055	438,489
	4403.97		of which: Poplar/Aspen (Populus spp.)	1000 m3ub	42	1557	39	2123	269	24366	290	28621																									4403.97		of which: Poplar/Aspen (Populus spp.)	NAC/m3	38	54	91	99	ACCEPT	ACCEPT						ECE/EU			2,438	1,129,099	2,846	1,915,699	1,048	322,027	1,055	438,489
	4403.98		of which: Eucalyptus (Eucalyptus spp.)	1000 m3ub	20	1257	3	342	0	0	0	66																									4403.98		of which: Eucalyptus (Eucalyptus spp.)	NAC/m3	64	101	0	9400	ACCEPT	CHECK						dif			0	0	0	0	0	0	0	0
6.C	4406.11/91 4407.11/12/19		Sawnwood, Coniferous	1000 m3	2438	1129099	2846	1915699	1048	322027	1055	438489																	OK	OK	OK	OK	OK	OK	OK	OK	4406.11/91 4407.11/12/19		Sawnwood, Coniferous	NAC/m3	463	673	307	416	ACCEPT	ACCEPT	6.NC	4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	1000 m3	JQ2			277	266,607	286	302,847	470	272,775	591	369,342
	4407.12		of which: Fir/Spruce (Abies spp., Picea spp.)	1000 m3	1582	689184	1703	1157708	421	129010	420	187526																									4407.12		of which: Fir/Spruce (Abies spp., Picea spp.)	NAC/m3	436	680	307	447	ACCEPT	ACCEPT						ECE/EU			277	266,607	286	302,847	470	272,775	591	369,342
	4407.11		of which: Pine (Pinus spp.)	1000 m3	708	323141	827	548260	206	43446	207	59159																									4407.11		of which: Pine (Pinus spp.)	NAC/m3	456	663	211	285	ACCEPT	ACCEPT						dif			0	0	0	0	0	0	0	0
6.NC	4406.12/92 4407.21/22/25/26/27/28/29/ 91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	1000 m3	277	266607	286	302847	470	272775	591	369342																	OK	OK	OK	OK	OK	OK	OK	OK	4406.12/92 4407.21/22/25/26/27/28/29/ 91/92/93/94/95/96/97/99		Sawnwood, Non-coniferous	NAC/m3	964	1058	580	625	ACCEPT	ACCEPT
	4407.91		of which: Oak (Quercus spp.)	1000 m3	63	80048	58	78080	262	200109	356	278727																									4407.91		of which: Oak (Quercus spp.)	NAC/m3	1267	1350	763	782	ACCEPT	ACCEPT
	4407.92		of which: Beech (Fagus spp.)	1000 m3	31	30677	38	40589	132	44037	153	53187																									4407.92		of which: Beech (Fagus spp.)	NAC/m3	1000	1062	335	348	ACCEPT	ACCEPT							WRONG	WRONG
	4407.93		of which: Maple (Acer spp.)	1000 m3	0	384	0	454	2	546	3	1078																									4407.93		of which: Maple (Acer spp.)	NAC/m3	1034	1620	336	364	ACCEPT	ACCEPT									OK	OK	OK	OK	OK	OK	OK	OK
	4407.94		of which: Cherry (Prunus spp.)	1000 m3	0	27	0	43	0	13	0	6																									4407.94		of which: Cherry (Prunus spp.)	NAC/m3	2375	2583	474	267	ACCEPT	ACCEPT									OK	OK	OK	OK	OK	OK	OK	OK
	4407.95		of which: Ash (Fraxinus spp.)	1000 m3	1	1379	2	2436	21	7990	31	10983																									4407.95		of which: Ash (Fraxinus spp.)	NAC/m3	992	1157	379	358	ACCEPT	ACCEPT									OK	OK	OK	OK	OK	OK	OK	OK
	4407.97		of which: Poplar/Aspen (Populus spp.)	1000 m3	0	283	1	317	4	1069	4	1220																									4407.97		of which: Poplar/Aspen (Populus spp.)	NAC/m3	616	564	277	323	ACCEPT	ACCEPT									OK	OK	OK	OK	OK	OK	OK	OK
	4407.96		of which: Birch (Betula spp.)	1000 m3	0	204	0	315	0	148	0	165																									4407.96		of which: Birch (Betula spp.)	NAC/m3	1570	1278	1860	2034	ACCEPT	ACCEPT
Light blue cells are requested only for EU members using the Combined Nomenclature to fill in - other countries are welcome to do so if their trade classification nomenclature permits
Please note that information on tropical species trade is requested in questionnaire ITTO2 for ITTO member countries
"ex" codes indicate that only part of that trade classication code is used				To fill:	0	0	0	0	0	0	0	0
m3ub = cubic metres underbark (i.e. excluding bark)
Please complete each cell if possible with
data (numerical value)
or "…" for not available
or "0" for zero data

EU1 ExtraEU Trade

EU1

Country:

Date:

JQ2/EU1 comparison

both VALUE and quantity reported ZERO

Name of Official responsible for reply:

ZERO Q

quantity ZERO when VALUE is reported

FOREST SECTOR QUESTIONNAIRE

Official Address (in full):

Ministère de l'Agriculture et de la souveraineté alimentaire

JQ2>=EU1

ZERO V

Value ZERO when quantity is reported

Trade with countries outside EU

Telephone:

Fax:

Please verify if there's an error!

Zero check - if no value please CHECK

NO Q

no quantity reported

Value must always be in 1000 NAC (national currency)

E-mail:

NO V

no value reported

Treshold:

Eurozone countries may use the old national currency, but only in both years

1000NAC

Flag

Note

Trade

Discrepancies

REPORT

no figures reported

Product

Unit of

I M P O R T

E X P O R T

Import

Export

Import

Export

I M P O R T

E X P O R T

Product

I M P O R T

E X P O R T

Product

Value per

I M P O R T

E X P O R T

Column1

Column2

code

Product

quantity

2020

2021

2020

2021

2020

2021

2020

2021

2020

2021

2020

2021

2020

2021

2020

2021

code

2020

2021

2020

2021

code

Product

unit

2020

2021

2020

2021

IMPORT

EXPORT

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

Quantity

Value

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

1126

145

1183

174

4032

369

4547

488

ROUNDWOOD (WOOD IN THE ROUGH)

1000 m3ub

ROUNDWOOD (WOOD IN THE ROUGH)

NAC/m3

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

182

198

449

435

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

1000 m3ub

1.1

WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)

NAC/m3

1.1.C

Coniferous

1000 m3ub

159

178

354

300

1.1.C

Coniferous

1000 m3ub

1.1.C

Coniferous

NAC/m3

1.1.NC

Non-Coniferous

1000 m3ub

135

1.1.NC

Non-Coniferous

1000 m3ub

1.1.NC

Non-Coniferous

NAC/m3

CHECK

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

943

126

985

157

3583

348

4112

464

1.2

INDUSTRIAL ROUNDWOOD

1000 m3ub

1.2

INDUSTRIAL ROUNDWOOD

NAC/m3

1.2.C

Coniferous

1000 m3ub

719

794

109

1436

109

1617

163

1.2.C

Coniferous

1000 m3ub

1.2.C

Coniferous

NAC/m3

1.2.NC

Non-Coniferous

1000 m3ub

225

191

2146

239

2494

301

1.2.NC

Non-Coniferous

1000 m3ub

1.2.NC

Non-Coniferous

NAC/m3

CHECK

1.2.NC.T

of which: Tropical

1000 m3ub

1.2.NC.T

of which: Tropical

1000 m3ub

1.2.NC.T

of which: Tropical

NAC/m3

CHECK

WOOD CHARCOAL

1000 t

106

WOOD CHARCOAL

1000 t

WOOD CHARCOAL

NAC/t

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

2510

144

2186

118

699

631

WOOD CHIPS, PARTICLES AND RESIDUES

1000 m3

WOOD CHIPS, PARTICLES AND RESIDUES

NAC/m3

3.1

WOOD CHIPS AND PARTICLES

1000 m3

653

514

451

311

3.1

WOOD CHIPS AND PARTICLES

1000 m3

3.1

WOOD CHIPS AND PARTICLES

NAC/m3

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

1857

1672

248

320

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

1000 m3

3.2

WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)

NAC/m3

RECOVERED POST-CONSUMER WOOD

1000 t

RECOVERED POST-CONSUMER WOOD

1000 t

RECOVERED POST-CONSUMER WOOD

NAC/t

REPORT

CHECK

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

548

808

143

286

342

WOOD PELLETS AND OTHER AGGLOMERATES

1000 t

WOOD PELLETS AND OTHER AGGLOMERATES

NAC/t

5.1

WOOD PELLETS

1000 t

412

661

127

122

5.1

WOOD PELLETS

1000 t

5.1

WOOD PELLETS

NAC/t

5.2

OTHER AGGLOMERATES

1000 t

136

147

190

221

5.2

OTHER AGGLOMERATES

1000 t

5.2

OTHER AGGLOMERATES

NAC/t

CHECK

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

2715

1396

3132

2219

1518

595

1646

808

SAWNWOOD (INCLUDING SLEEPERS)

1000 m3

SAWNWOOD (INCLUDING SLEEPERS)

NAC/m3

6.C

Coniferous

1000 m3

2438

1129

2846

1916

1048

322

1055

438

6.C

Coniferous

1000 m3

6.C

Coniferous

NAC/m3

CHECK

6.NC

Non-Coniferous

1000 m3

277

267

286

303

470

273

591

369

6.NC

Non-Coniferous

1000 m3

6.NC

Non-Coniferous

NAC/m3

6.NC.T

of which: Tropical

1000 m3

129

120

135

138

6.NC.T

of which: Tropical

1000 m3

6.NC.T

of which: Tropical

NAC/m3

VENEER SHEETS

1000 m3

311

297

309

361

129

141

VENEER SHEETS

1000 m3

VENEER SHEETS

NAC/m3

7.C

Coniferous

1000 m3

7.C

Coniferous

1000 m3

7.C

Coniferous

NAC/m3

CHECK

7.NC

Non-Coniferous

1000 m3

294

283

294

347

128

141

7.NC

Non-Coniferous

1000 m3

7.NC

Non-Coniferous

NAC/m3

7.NC.T

of which: Tropical

1000 m3

106

111

7.NC.T

of which: Tropical

1000 m3

7.NC.T

of which: Tropical

NAC/m3

CHECK

WOOD-BASED PANELS

1000 m3

1232

887

1151

1108

1013

533

1028

542

WOOD-BASED PANELS

1000 m3

WOOD-BASED PANELS

NAC/m3

8.1

PLYWOOD

1000 m3

444

442

470

564

159

230

155

216

8.1

PLYWOOD

1000 m3

8.1

PLYWOOD

NAC/m3

8.1.C

Coniferous

1000 m3

104

115

129

161

8.1.C

Coniferous

1000 m3

8.1.C

Coniferous

NAC/m3

8.1.NC

Non-Coniferous

1000 m3

340

328

340

402

149

163

8.1.NC

Non-Coniferous

1000 m3

8.1.NC

Non-Coniferous

NAC/m3

8.1.NC.T

of which: Tropical

1000 m3

107

100

123

128

8.1.NC.T

of which: Tropical

1000 m3

8.1.NC.T

of which: Tropical

NAC/m3

CHECK

8.2

PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD

1000 m3

8.2

PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD

1000 m3

8.2

PARTICLE BOARD, ORIENTED STRANDBOARD (OSB) AND SIMILAR BOARD

NAC/m3

REPORT

CHECK

8.2.1

of which: ORIENTED STRANDBOARD (OSB)

1000 m3

118

148

130

WRONG

8.2.1

of which: ORIENTED STRANDBOARD (OSB)

1000 m3

Error

8.2.1

of which: ORIENTED STRANDBOARD (OSB)

NAC/m3

CHECK

8.3

FIBREBOARD

1000 m3

788

444

681

544

854

302

873

326

8.3

FIBREBOARD

1000 m3

8.3

FIBREBOARD

NAC/m3

CHECK

8.3.1

HARDBOARD

1000 m3

235

108

212

144

449

139

443

131

8.3.1

HARDBOARD

1000 m3

8.3.1

HARDBOARD

NAC/m3

CHECK

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

481

276

388

322

312

109

345

145

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

1000 m3

8.3.2

MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)

NAC/m3

CHECK

8.3.3

OTHER FIBREBOARD

1000 m3

8.3.3

OTHER FIBREBOARD

1000 m3

8.3.3

OTHER FIBREBOARD

NAC/m3

WOOD PULP

1000 t

112

WOOD PULP

1000 t

WOOD PULP

NAC/t

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

1000 t

9.1

MECHANICAL AND SEMI-CHEMICAL WOOD PULP

NAC/t

9.2

CHEMICAL WOOD PULP

1000 t

9.2

CHEMICAL WOOD PULP

1000 t

9.2

CHEMICAL WOOD PULP

NAC/t

9.2.1

SULPHATE PULP

1000 t

9.2.1

SULPHATE PULP

1000 t

9.2.1

SULPHATE PULP

NAC/t

9.2.1.1

of which: BLEACHED

1000 t

1646

746

1510

896

437

199

379

220

9.2.1.1

of which: BLEACHED

1000 t

Error

9.2.1.1

of which: BLEACHED

NAC/t

9.2.2

SULPHITE PULP

1000 t

9.2.2

SULPHITE PULP

1000 t

9.2.2

SULPHITE PULP

NAC/t

9.3

DISSOLVING GRADES

1000 t

9.3

DISSOLVING GRADES

1000 t

9.3

DISSOLVING GRADES

NAC/t

ZERO V

OTHER PULP

1000 t

OTHER PULP

1000 t

OTHER PULP

NAC/t

CHECK

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

10.1

PULP FROM FIBRES OTHER THAN WOOD

1000 t

10.1

PULP FROM FIBRES OTHER THAN WOOD

NAC/t

CHECK

10.2

RECOVERED FIBRE PULP

1000 t

10.2

RECOVERED FIBRE PULP

1000 t

10.2

RECOVERED FIBRE PULP

NAC/t

RECOVERED PAPER

1000 t

1860

3716

1896

4028

922

2101

955

2281

RECOVERED PAPER

1000 t

RECOVERED PAPER

NAC/t

CHECK

PAPER AND PAPERBOARD

1000 t

4502

3387

4740

3839

3465

2650

3807

3433

PAPER AND PAPERBOARD

1000 t

PAPER AND PAPERBOARD

NAC/t

12.1

GRAPHIC PAPERS

1000 t

2132

1618

2126

1644

896

715

1009

834

12.1

GRAPHIC PAPERS

1000 t

12.1

GRAPHIC PAPERS

NAC/t

12.1.1

NEWSPRINT

1000 t

12.1.1

NEWSPRINT

1000 t

12.1.1

NEWSPRINT

NAC/t

12.1.2

UNCOATED MECHANICAL

1000 t

12.1.2

UNCOATED MECHANICAL

1000 t

12.1.2

UNCOATED MECHANICAL

NAC/t

12.1.3

UNCOATED WOODFREE

1000 t

12.1.3

UNCOATED WOODFREE

1000 t

12.1.3

UNCOATED WOODFREE

NAC/t

12.1.4

COATED PAPERS

1000 t

12.1.4

COATED PAPERS

1000 t

12.1.4

COATED PAPERS

NAC/t

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

108

105

12.2

HOUSEHOLD AND SANITARY PAPERS

1000 t

12.2

HOUSEHOLD AND SANITARY PAPERS

NAC/t

12.3

PACKAGING MATERIALS

1000 t

2250

1588

2484

2007

2478

1699

2672

2194

12.3

PACKAGING MATERIALS

1000 t

12.3

PACKAGING MATERIALS

NAC/t

12.3.1

CASE MATERIALS

1000 t

12.3.1

CASE MATERIALS

1000 t

12.3.1

CASE MATERIALS

NAC/t

12.3.2

CARTONBOARD

1000 t

12.3.2

CARTONBOARD

1000 t

12.3.2

CARTONBOARD

NAC/t

12.3.3

WRAPPING PAPERS

1000 t

12.3.3

WRAPPING PAPERS

1000 t

12.3.3

WRAPPING PAPERS

NAC/t

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

1000 t

12.3.4

OTHER PAPERS MAINLY FOR PACKAGING

NAC/t

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

144

314

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

1000 t

12.4

OTHER PAPER AND PAPERBOARD N.E.S. (NOT ELSEWHERE SPECIFIED)

NAC/t

CHECK

To fill:

EU2 Removals

Country:

Date:

Name of Official responsible for reply:

Official Address (in full):

Check Table

Ministère de l'Agriculture et de la souveraineté alimentaire

Phone/Fax:

E-mail:

EU2

Please verify if there's an error!

FOREST SECTOR QUESTIONNAIRE Removals by type of ownership

Discrepancies

Product code

Ownership

Flag

Note

Product code

Ownership

Unit

2020

2021

2020

2021

2020

2021

Unit

2020

2021

Quantity

ROUNDWOOD REMOVALS (under bark)

ROUNDWOOD REMOVALS

ROUNDWOOD

1000 m3

47387

52915

ROUNDWOOD

1000 m3

1.C

Coniferous

1000 m3

18655

20944

1.C

Coniferous

1000 m3

1.NC

Non-coniferous

1000 m3

28732

31971

1.NC

Non-coniferous

1000 m3

State forests

1000 m3

4244

4557

State forests

1000 m3

Coniferous

1000 m3

2224

2271

Coniferous

1000 m3

Non-coniferous

1000 m3

2020

2286

Non-coniferous

1000 m3

Other publicly owned forests

1000 m3

5907

7983

Other publicly owned forests

1000 m3

Coniferous

1000 m3

2908

4129

Coniferous

1000 m3

Non-coniferous

1000 m3

2998

3854

Non-coniferous

1000 m3

Private forest

1000 m3

37236

40375

Private forest

1000 m3

Coniferous

1000 m3

13522

14544

Coniferous

1000 m3

Non-coniferous

1000 m3

23714

25831

Non-coniferous

1000 m3

To fill:

Note:

–

Ownership categories correspond to those of the TBFRA.

State forests: Forests owned by national, state and regional governments, or government-owned corporations; Crown forests.

Other publicly owned forests: Forests belonging to cities, municipalities, villages and communes.

Private forests: Forests owned by individuals, co-operatives, enterprises and industries and other private institutions.

–

The unit should be solid cubic metres, under bark.

ITTO1-Estimates

				Country:	FR	Date:
				Name of Official responsible for reply:		0

				Official Address (in full):		Ministère de l'Agriculture et de la souveraineté alimentaire
	ITTO1
				Telephone:	0	Fax:	0
	FOREST SECTOR QUESTIONNAIRE			E-mail:		0
	Production and Trade Estimates for	2022
	Value must always be in 1000 NAC (national currency)			1000 NAC

Product		Unit of	Production	Imports		Exports
Code	Product	quantity	Quantity	Quantity	Value	Quantity	Value

1.2	INDUSTRIAL ROUNDWOOD	1000 m3ub	26,189	985	156,822	4,112	464,409
1.2.C	Coniferous	1000 m3ub	18,271	794	108,792	1,617	163,259
1.2.NC	Non-Coniferous	1000 m3ub	7,918	191	48,030	2,494	301,150
1.2.NC.T	of which: Tropical	1000 m3ub	60	46	24,049	2	560
6	SAWNWOOD (INCLUDING SLEEPERS)	1000 m3	8,581	3,132	2,218,546	1,646	807,831
6.C	Coniferous	1000 m3	7,268	2,846	1,915,699	1,055	438,489
6.NC	Non-Coniferous	1000 m3	1,313	286	302,847	591	369,342
6.NC.T	of which: Tropical	1000 m3	25	135	137,781	5	6,273
7	VENEER SHEETS	1000 m3	157	309	360,704	71	141,374
7.C	Coniferous	1000 m3	2	15	13,269	1	810
7.NC	Non-Coniferous	1000 m3	155	294	347,435	71	140,564
7.NC.T	of which: Tropical	1000 m3		86	105,935	54	110,760
8.1	PLYWOOD	1000 m3	270	470	563,694	155	215,554
8.1.C	Coniferous	1000 m3	120	129	161,401	73	52,796
8.1.NC	Non-Coniferous	1000 m3	150	340	402,293	82	162,758
8.1.NC.T	of which: Tropical	1000 m3	105	100	122,661	63	128,248
	m3 = cubic metres solid volume
	m3ub = cubic metres solid volume underbark (i.e. excluding bark)

ITTO2-Species


						Country:	FR	Date:
		ITTO2				Name of Official responsible for reply:		0

						Official Address (in full):		Ministère de l'Agriculture et de la souveraineté alimentaire
		FOREST SECTOR QUESTIONNAIRE
		Trade in Tropical Species				Telephone:	0	Fax:	0
						E-mail:		0
	Value must always be in 1000 NAC (national currency)			1000 NAC

				I M P O R T				E X P O R T
Product	Classifications			2020		2021		2020		2021
	HS2007/HS2002/HS96	Scientific Name	Local/Trade Name	Quantity	Value	Quantity	Value	Quantity	Value	Quantity	Value
				(1000 m3)		(1000 m3)		(1000 m3)		(1000 m3)
1.2.NC.T	HS2017:			36	17,342	46	24,049	3	615	2	560
Industrial Roundwood, Tropical	ex4403.12 4403.41/49		Dark red meranti, light red meranti, meranti bakau	0	13	0	0	0	0	0	0
	HS2012/2007:		Sapelli, acajou d'Afrique, iroko	7	3,574	13	6,603	0	61	0	26
	ex4403.10 4403.41/49 ex4403.99		Okoumé et sipo	12	6,735	13	7,428	1	280	0	316
			Autres tropicaux	17	7,021	21	10,019	2	274	2	218
6.NC.T	HS2017:			129	120,277	135	137,781	3	3,927	5	6,273
Sawnwood, Tropical	ex4406.12/92 4407.21/22/25/26/27/28/29		Sciages de mahogany	129	120,277	135	137,781	3	3,927	5	6,273
	HS2012/2007:
	ex4406.10/90 4407.21/22/25/26/27/28/30
7.NC.T	HS2017:			79	92,392	86	105,935	54	105,911	54	110,760
Veneer Sheets, Tropical	4408.31/39		Dark red meranti, light red meranti, meranti bakau	79	92,392	86	105,935	54	105,911	54	110,760
	HS2012/2007:
	4408.31/39 ex4408.90
8.1.NC.T	HS2017:			91	106,981	100	122,661	62	122,633	63	128,248
Plywood, Tropical	4412.31 ex4412.94/99		All species	91	106,981	100	122,661	62	122,633	63	128,248
	HS2012/2007:
	4412.31 ex4412.32/94/99
Note: List the major species traded in each category. Use additional sheet if more species are to be explicitly reported. For tropical plywood, identify by face veneer if composed of more than one species.

ITTO3-Miscellaneous

Country:

Date:

Name of Official responsible for reply:

Official Address (in full):

FOREST SECTOR QUESTIONNAIRE ITTO3

Miscellaneous Items

Telephone:

Fax:

(use additional paper if necessary)

E-mail:

Please enter current import tariff rates applied to tropical and non-tropical timber products. If available, please provide tariffs by the relevant customs classification category. If tariff levels have been reported in previous years, enter changes only. (Logs = JQ code 1.2, Sawn = JQ code 6, Veneer = JQ code 7, and Plywood = JQ code 8.1)

Current import tariff

Logs

Tropical:

Sawn

Tropical:

Veneer

Tropical:

Plywood

Tropical:

Non-Tropical:

Comments (if any):

Please comment on any quotas, incentives, disincentives, tariff/non-tariff barriers or other related factors which now or in future will significantly affect your production and trade of tropical timber products.

Please elaborate on any short or medium term plans for expanding capacity for (further) processing of tropical timber products in your country.

Please indicate any trends or changes expected in the species composition of your trade. How important are lesser-used tropical timber species and/or minor tropical forest products?

Please indicate trends in domestic building activity, housing starts, mortgage/interest rates, substitution of non-tropical wood and/or non-wood products for tropical timbers, and any other domestic factors having a significant impact on tropical timber consumption in your country.

Please indicate the extent of foreign involvement in your timber sector (e.g. number and nationalities of concessionaires/mill (joint) owners, area of forest allocated, scale of investment, etc.).

Please provide details of any relevant forest law enforcement activities (e.g. legislation, fines, arrests, etc.) in your country in the past year.

Please indicate the current extent of forest plantations in your country (ha), annual establishment rate (ha/yr) and proportion of industrial roundwood production from plantations.

TS-OB

Min:

80%

Max:

120%

Notes

JQ1

Country

Flow

Unit

Product

2016

2017

2018

2019

2020

2021

16/17

17/18

18/19

19/20

20/20

20/21

2016

2017

2018

2019

P.OB

1000 m3

ERROR:#N/A

52860.723

59263.000

112.11%

P.OB

1000 m3

1_C

ERROR:#N/A

24375.723

28284.000

116.03%

P.OB

1000 m3

1_NC

ERROR:#N/A

2437.572

2828.000

116.02%

P.OB

1000 m3

1_1

ERROR:#N/A

21938.151

25456.000

116.04%

P.OB

1000 m3

1_1_C

ERROR:#N/A

28485.000

30979.000

108.76%

P.OB

1000 m3

1_1_NC

ERROR:#N/A

19630.000

21927.000

111.70%

P.OB

1000 m3

1_2

ERROR:#N/A

8855.000

9052.000

102.22%

P.OB

1000 m3

1_2_C

ERROR:#N/A

78.000

81.900

105.00%

P.OB

1000 m3

1_2_NC

ERROR:#N/A

18463.000

20854.000

112.95%

P.OB

1000 m3

1_2_1

ERROR:#N/A

13712.000

15839.000

115.51%

P.OB

1000 m3

1_2_1_C

ERROR:#N/A

4751.000

5015.000

105.56%

P.OB

1000 m3

1_2_1_NC

ERROR:#N/A

9476.000

9469.000

99.93%

P.OB

1000 m3

1_2_2

ERROR:#N/A

5680.000

5745.000

101.14%

P.OB

1000 m3

1_2_2_C

ERROR:#N/A

3796.000

3724.000

98.10%

P.OB

1000 m3

1_2_2_NC

ERROR:#N/A

546.000

656.000

120.15%

P.OB

1000 m3

1_2_3

ERROR:#N/A

238.000

343.000

144.12%

P.OB

1000 m3

1_2_3_C

ERROR:#N/A

308.000

313.000

101.62%

P.OB

1000 m3

1_2_3_NC

ERROR:#N/A

ERROR:#REF!

TS-JQ1

Min:

80%

Max:

120%

Notes

JQ1

Country

Flow

Unit

Product

2016

2017

2018

2019

2020

2021

16/17

17/18

18/19

19/20

20/20

20/21

2016

2017

2018

2019

1000 m3

ERROR:#N/A

47388.045

53138.525

112.13%

1000 m3

1_C

ERROR:#N/A

23323.765

26950.000

115.55%

1000 m3

1_NC

ERROR:#N/A

2332.376

2695.000

115.55%

1000 m3

1_1

ERROR:#N/A

20991.388

24255.000

115.55%

1000 m3

1_1_C

ERROR:#N/A

24064.280

26188.525

108.83%

1000 m3

1_1_NC

ERROR:#N/A

16323.088

18270.917

111.93%

1000 m3

1_2

ERROR:#N/A

7741.193

7917.608

102.28%

1000 m3

1_2_C

ERROR:#N/A

57.353

60.221

105.00%

1000 m3

1_2_NC

ERROR:#N/A

15856.540

17896.911

112.87%

1000 m3

1_2_1

ERROR:#N/A

11644.787

13451.122

115.51%

1000 m3

1_2_1_C

ERROR:#N/A

4211.753

4445.788

105.56%

1000 m3

1_2_1_NC

ERROR:#N/A

7738.180

7727.455

99.86%

1000 m3

1_2_2

ERROR:#N/A

4473.620

4524.815

101.14%

1000 m3

1_2_2_C

ERROR:#N/A

3264.560

3202.640

98.10%

1000 m3

1_2_2_NC

ERROR:#N/A

469.560

564.160

120.15%

1000 m3

1_2_3

ERROR:#N/A

204.680

294.980

144.12%

1000 m3

1_2_3_C

ERROR:#N/A

264.880

269.180

101.62%

1000 m3

1_2_3_NC

ERROR:#N/A

0.000

1000 mt

ERROR:#N/A

13404.115

16366.217

122.10%

1000 m3

ERROR:#N/A

5752.055

6941.291

120.67%

1000 m3

3_1

ERROR:#N/A

7652.060

9424.926

123.17%

1000 m3

3_2

ERROR:#N/A

6382.000

100.00%

1000 mt

ERROR:#N/A

1760.000

1930.000

109.66%

1000 mt

4_1

ERROR:#N/A

1700.000

1850.000

108.82%

1000 mt

4_2

ERROR:#N/A

60.000

80.000

133.33%

1000 m3

ERROR:#N/A

7575.000

8581.000

113.28%

1000 m3

5_C

ERROR:#N/A

6442.000

7268.000

112.82%

1000 m3

5_NC

ERROR:#N/A

1133.000

1313.000

115.89%

1000 m3

5_NC_T

ERROR:#N/A

10.000

25.000

250.00%

1000 m3

ERROR:#N/A

157.000

100.00%

1000 m3

6_1

ERROR:#N/A

2.000

100.00%

1000 m3

6_1_C

ERROR:#N/A

155.000

100.00%

1000 m3

6_1_NC

ERROR:#N/A

0.000

1000 m3

6_1_NC_T

ERROR:#N/A

3734.000

3770.000

100.96%

1000 m3

6_2

ERROR:#N/A

234.000

270.000

115.38%

1000 m3

6_2_C

ERROR:#N/A

94.000

120.000

127.66%

1000 m3

6_2_NC

ERROR:#N/A

140.000

150.000

107.14%

1000 m3

6_2_NC_T

ERROR:#N/A

105.000

100.00%

1000 m3

6_3

ERROR:#N/A

2600.000

100.00%

1000 m3

6_3_1

ERROR:#N/A

0.000

1000 m3

6_4

ERROR:#N/A

900.000

100.00%

1000 m3

6_4_1

ERROR:#N/A

0.000

1000 m3

6_4_2

ERROR:#N/A

751.000

100.00%

1000 m3

6_4_3

ERROR:#N/A

0.000

1000 mt

ERROR:#N/A

1620.000

1615.000

99.69%

1000 mt

7_1

ERROR:#N/A

264.000

300.000

113.64%

1000 mt

7_2

ERROR:#N/A

1351.000

1310.000

96.97%

1000 mt

7_3

ERROR:#N/A

1237.000

1199.000

96.93%

1000 mt

7_3_1

ERROR:#N/A

0.000

1000 mt

7_3_2

ERROR:#N/A

114.000

111.000

97.37%

1000 mt

7_3_3

ERROR:#N/A

0.000

1000 mt

7_3_4

ERROR:#N/A

4305.000

4573.000

106.23%

1000 mt

7_4

ERROR:#N/A

5.000

6.000

120.00%

1000 mt

ERROR:#N/A

4300.000

4567.000

106.21%

1000 mt

8_1

ERROR:#N/A

6317.000

6885.000

108.99%

1000 mt

8_2

ERROR:#N/A

6873.000

7359.000

107.07%

1000 mt

ERROR:#N/A

1198.000

1314.000

109.68%

1000 mt

ERROR:#N/A

479.000

508.000

106.05%

1000 mt

10_1

ERROR:#N/A

42.000

43.000

102.38%

1000 mt

10_1_1

ERROR:#N/A

506.000

552.000

109.09%

1000 mt

10_1_2

ERROR:#N/A

171.000

211.000

123.39%

1000 mt

10_1_3

ERROR:#N/A

832.000

817.000

98.20%

1000 mt

10_1_4

ERROR:#N/A

4422.000

4840.000

109.45%

1000 mt

10_2

ERROR:#N/A

3576.000

3933.000

109.98%

1000 mt

10_3

ERROR:#N/A

647.000

682.000

105.41%

1000 mt

10_3_1

ERROR:#N/A

199.000

225.000

113.07%

1000 mt

10_3_2

ERROR:#N/A

0.000

1000 mt

10_3_3

ERROR:#N/A

421.000

388.000

92.16%

1000 mt

10_3_4

ERROR:#N/A

0.000

1000 mt

10_4

ERROR:#N/A

ERROR:#REF!

TS-JQ2

Min:

80%

Max:

120%

Notes

JQ2

Country

Flow

Unit

Product

2016

2017

2018

2019

2020

2021

16/17

17/18

18/19

19/20

20/20

20/21

2016

2017

2018

2019

1000 m3

ERROR:#N/A

1125.671

1182.851

105.08%

€

1000 NAC

ERROR:#N/A

144711.575

174216.045

120.39%

1000 m3

ERROR:#N/A

128.556

147.285

114.57%

1000 m3

ERROR:#N/A

4032.249

4546.654

112.76%

€

1000 NAC

ERROR:#N/A

369012.920

488442.500

132.36%

1000 m3

ERROR:#N/A

91.515

107.429

117.39%

1000 m3

1_1

ERROR:#N/A

182.190

197.690

108.51%

€

1000 NAC

1_1

ERROR:#N/A

18992.550

17393.930

91.58%

1000 m3

1_1

ERROR:#N/A

104.246

87.986

84.40%

1000 m3

1_1

ERROR:#N/A

449.484

435.136

96.81%

€

1000 NAC

1_1

ERROR:#N/A

21264.300

24033.400

113.02%

1000 m3

1_1

ERROR:#N/A

47.308

55.232

116.75%

1000 m3

1_2

ERROR:#N/A

158.966

177.800

111.85%

€

1000 NAC

1_2

ERROR:#N/A

14529.000

13876.500

95.51%

1000 m3

1_2

ERROR:#N/A

91.397

78.046

85.39%

1000 m3

1_2

ERROR:#N/A

353.751

299.915

84.78%

€

1000 NAC

1_2

ERROR:#N/A

16228.500

16665.000

102.69%

1000 m3

1_2

ERROR:#N/A

45.875

55.566

121.12%

1000 m3

1_2_C

ERROR:#N/A

23.225

19.890

85.64%

€

1000 NAC

1_2_C

ERROR:#N/A

4463.550

3517.430

78.80%

1000 m3

1_2_C

ERROR:#N/A

192.190

176.841

92.01%

1000 m3

1_2_C

ERROR:#N/A

95.733

135.221

141.25%

€

1000 NAC

1_2_C

ERROR:#N/A

5035.800

7368.400

146.32%

1000 m3

1_2_C

ERROR:#N/A

52.603

54.492

103.59%

1000 m3

1_2_NC

ERROR:#N/A

943.481

985.161

104.42%

€

1000 NAC

1_2_NC

ERROR:#N/A

125719.025

156822.115

124.74%

1000 m3

1_2_NC

ERROR:#N/A

133.250

159.184

119.46%

1000 m3

1_2_NC

ERROR:#N/A

3582.765

4111.519

114.76%

€

1000 NAC

1_2_NC

ERROR:#N/A

347748.620

464409.100

133.55%

1000 m3

1_2_NC

ERROR:#N/A

97.062

112.953

116.37%

1000 m3

1_2_NC_T

ERROR:#N/A

718.717

794.482

110.54%

€

1000 NAC

1_2_NC_T

ERROR:#N/A

83022.400

108792.000

131.04%

1000 m3

1_2_NC_T

ERROR:#N/A

115.515

136.935

118.54%

1000 m3

1_2_NC_T

ERROR:#N/A

1436.402

1617.362

112.60%

€

1000 NAC

1_2_NC_T

ERROR:#N/A

109099.200

163259.200

149.64%

1000 m3

1_2_NC_T

ERROR:#N/A

75.953

100.942

132.90%

1000 mt

ERROR:#N/A

224.764

190.679

84.84%

€

1000 NAC

ERROR:#N/A

42696.625

48030.115

112.49%

1000 mt

ERROR:#N/A

189.962

251.889

132.60%

1000 mt

ERROR:#N/A

2146.364

2494.157

116.20%

€

1000 NAC

ERROR:#N/A

238649.420

301149.900

126.19%

1000 mt

ERROR:#N/A

111.188

120.742

108.59%

1000 m3

ERROR:#N/A

36.085

46.396

128.58%

€

1000 NAC

ERROR:#N/A

17342.425

24048.815

138.67%

1000 m3

ERROR:#N/A

480.603

518.337

107.85%

1000 m3

ERROR:#N/A

3.062

1.914

62.49%

€

1000 NAC

ERROR:#N/A

614.720

560.100

91.11%

1000 m3

ERROR:#N/A

200.737

292.666

145.80%

1000 m3

3_1

ERROR:#N/A

106.029

80.945

76.34%

€

1000 NAC

3_1

ERROR:#N/A

61977.000

54658.000

88.19%

1000 m3

3_1

ERROR:#N/A

584.529

675.249

115.52%

1000 m3

3_1

ERROR:#N/A

9.192

6.912

75.20%

€

1000 NAC

3_1

ERROR:#N/A

7796.000

6952.000

89.17%

1000 m3

3_1

ERROR:#N/A

848.129

1005.787

118.59%

1000 m3

3_2

ERROR:#N/A

2509.728

2185.523

87.08%

€

1000 NAC

3_2

ERROR:#N/A

143600.700

117701.720

81.96%

1000 m3

3_2

ERROR:#N/A

57.218

53.855

94.12%

1000 m3

3_2

ERROR:#N/A

699.256

631.022

90.24%

€

1000 NAC

3_2

ERROR:#N/A

58678.040

36015.100

61.38%

1000 m3

3_2

ERROR:#N/A

83.915

57.074

68.01%

1000 mt

ERROR:#N/A

652.814

513.553

78.67%

€

1000 NAC

ERROR:#N/A

48736.900

33751.120

69.25%

1000 mt

ERROR:#N/A

74.657

65.721

88.03%

1000 mt

ERROR:#N/A

451.222

311.454

69.02%

€

1000 NAC

ERROR:#N/A

42730.240

16724.500

39.14%

1000 mt

ERROR:#N/A

94.699

53.698

56.70%

1000 mt

4_1

ERROR:#N/A

1856.914

1671.970

90.04%

€

1000 NAC

4_1

ERROR:#N/A

94863.800

83950.600

88.50%

1000 mt

4_1

ERROR:#N/A

51.087

50.211

98.28%

1000 mt

4_1

ERROR:#N/A

248.034

319.568

128.84%

€

1000 NAC

4_1

ERROR:#N/A

15947.800

19290.600

120.96%

1000 mt

4_1

ERROR:#N/A

64.297

60.365

93.88%

1000 mt

4_2

ERROR:#N/A

0.000

€

1000 NAC

4_2

ERROR:#N/A

0.000

1000 mt

4_2

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

4_2

ERROR:#N/A

0.000

€

1000 NAC

4_2

ERROR:#N/A

0.000

1000 mt

4_2

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ERROR:#N/A

548.344

807.856

147.33%

€

1000 NAC

ERROR:#N/A

99126.000

142805.000

144.06%

1000 m3

ERROR:#N/A

180.773

176.770

97.79%

1000 m3

ERROR:#N/A

286.105

342.339

119.66%

€

1000 NAC

ERROR:#N/A

25530.000

34392.000

134.71%

1000 m3

ERROR:#N/A

89.233

100.462

112.58%

1000 m3

5_C

ERROR:#N/A

412.381

660.949

160.28%

€

1000 NAC

5_C

ERROR:#N/A

86007.000

127444.000

148.18%

1000 m3

5_C

ERROR:#N/A

208.562

192.820

92.45%

1000 m3

5_C

ERROR:#N/A

96.539

121.640

126.00%

€

1000 NAC

5_C

ERROR:#N/A

20703.000

25701.000

124.14%

1000 m3

5_C

ERROR:#N/A

214.452

211.287

98.52%

1000 m3

5_NC

ERROR:#N/A

135.963

146.907

108.05%

€

1000 NAC

5_NC

ERROR:#N/A

13119.000

15361.000

117.09%

1000 m3

5_NC

ERROR:#N/A

96.489

104.563

108.37%

1000 m3

5_NC

ERROR:#N/A

189.566

220.699

116.42%

€

1000 NAC

5_NC

ERROR:#N/A

4827.000

8691.000

180.05%

1000 m3

5_NC

ERROR:#N/A

25.463

39.379

154.65%

1000 m3

5_NC_T

ERROR:#N/A

2714.582

3132.406

115.39%

€

1000 NAC

5_NC_T

ERROR:#N/A

1395705.100

2218546.000

158.96%

1000 m3

5_NC_T

ERROR:#N/A

514.151

708.256

137.75%

1000 m3

5_NC_T

ERROR:#N/A

1517.785

1646.406

108.47%

€

1000 NAC

5_NC_T

ERROR:#N/A

594802.600

807830.700

135.81%

1000 m3

5_NC_T

ERROR:#N/A

391.889

490.663

125.20%

1000 m3

ERROR:#N/A

2437.954

2846.070

116.74%

€

1000 NAC

ERROR:#N/A

1129098.600

1915698.600

169.67%

1000 m3

ERROR:#N/A

463.134

673.103

145.34%

1000 m3

ERROR:#N/A

1047.883

1055.308

100.71%

€

1000 NAC

ERROR:#N/A

322027.200

438489.000

136.17%

1000 m3

ERROR:#N/A

307.312

415.508

135.21%

1000 m3

6_1

ERROR:#N/A

276.628

286.336

103.51%

€

1000 NAC

6_1

ERROR:#N/A

266606.500

302847.400

113.59%

1000 m3

6_1

ERROR:#N/A

963.773

1057.663

109.74%

1000 m3

6_1

ERROR:#N/A

469.902

591.099

125.79%

€

1000 NAC

6_1

ERROR:#N/A

272775.400

369341.700

135.40%

1000 m3

6_1

ERROR:#N/A

580.494

624.839

107.64%

1000 m3

6_1_C

ERROR:#N/A

129.181

135.128

104.60%

€

1000 NAC

6_1_C

ERROR:#N/A

120276.800

137781.000

114.55%

1000 m3

6_1_C

ERROR:#N/A

931.073

1019.633

109.51%

1000 m3

6_1_C

ERROR:#N/A

2.933

4.925

167.92%

€

1000 NAC

6_1_C

ERROR:#N/A

3927.000

6273.400

159.75%

1000 m3

6_1_C

ERROR:#N/A

1338.902

1273.735

95.13%

1000 m3

6_1_NC

ERROR:#N/A

311.221

308.503

99.13%

€

1000 NAC

6_1_NC

ERROR:#N/A

296963.730

360703.980

121.46%

1000 m3

6_1_NC

ERROR:#N/A

954.188

1169.208

122.53%

1000 m3

6_1_NC

ERROR:#N/A

68.284

71.284

104.39%

€

1000 NAC

6_1_NC

ERROR:#N/A

128978.080

141373.680

109.61%

1000 m3

6_1_NC

ERROR:#N/A

1888.861

1983.245

105.00%

1000 m3

6_1_NC_T

ERROR:#N/A

17.486

14.673

83.91%

€

1000 NAC

6_1_NC_T

ERROR:#N/A

13907.810

13269.410

95.41%

1000 m3

6_1_NC_T

ERROR:#N/A

795.391

904.369

113.70%

1000 m3

6_1_NC_T

ERROR:#N/A

0.258

0.559

216.49%

€

1000 NAC

6_1_NC_T

ERROR:#N/A

603.820

809.970

134.14%

1000 m3

6_1_NC_T

ERROR:#N/A

2340.206

1450.000

61.96%

1000 m3

6_2

ERROR:#N/A

293.736

293.830

100.03%

€

1000 NAC

6_2

ERROR:#N/A

283055.920

347434.570

122.74%

1000 m3

6_2

ERROR:#N/A

963.641

1182.433

122.70%

1000 m3

6_2

ERROR:#N/A

68.026

70.725

103.97%

€

1000 NAC

6_2

ERROR:#N/A

128374.260

140563.710

109.50%

1000 m3

6_2

ERROR:#N/A

1887.149

1987.457

105.32%

1000 m3

6_2_C

ERROR:#N/A

78.654

86.102

109.47%

€

1000 NAC

6_2_C

ERROR:#N/A

92392.440

105934.500

114.66%

1000 m3

6_2_C

ERROR:#N/A

1174.676

1230.344

104.74%

1000 m3

6_2_C

ERROR:#N/A

53.542

54.078

101.00%

€

1000 NAC

6_2_C

ERROR:#N/A

105910.560

110759.740

104.58%

1000 m3

6_2_C

ERROR:#N/A

1978.091

2048.155

103.54%

1000 m3

6_2_NC

ERROR:#N/A

1350.300

1298.932

96.20%

€

1000 NAC

6_2_NC

ERROR:#N/A

944721.200

1237733.810

131.02%

1000 m3

6_2_NC

ERROR:#N/A

699.638

952.886

136.20%

1000 m3

6_2_NC

ERROR:#N/A

1048.063

1056.587

100.81%

€

1000 NAC

6_2_NC

ERROR:#N/A

547327.360

559393.100

102.20%

1000 m3

6_2_NC

ERROR:#N/A

522.227

529.434

101.38%

1000 m3

6_2_NC_T

ERROR:#N/A

443.771

469.581

105.82%

€

1000 NAC

6_2_NC_T

ERROR:#N/A

442465.100

563693.900

127.40%

1000 m3

6_2_NC_T

ERROR:#N/A

997.057

1200.418

120.40%

1000 m3

6_2_NC_T

ERROR:#N/A

158.789

154.939

97.58%

€

1000 NAC

6_2_NC_T

ERROR:#N/A

230145.300

215553.800

93.66%

1000 m3

6_2_NC_T

ERROR:#N/A

1449.374

1391.214

95.99%

1000 m3

6_3

ERROR:#N/A

103.656

129.357

124.79%

€

1000 NAC

6_3

ERROR:#N/A

114716.140

161401.240

140.70%

1000 m3

6_3

ERROR:#N/A

1106.702

1247.720

112.74%

1000 m3

6_3

ERROR:#N/A

80.023

73.047

91.28%

€

1000 NAC

6_3

ERROR:#N/A

81501.420

52795.820

64.78%

1000 m3

6_3

ERROR:#N/A

1018.475

722.767

70.97%

1000 m3

6_3_1

ERROR:#N/A

340.115

340.225

100.03%

€

1000 NAC

6_3_1

ERROR:#N/A

327748.960

402292.660

122.74%

1000 m3

6_3_1

ERROR:#N/A

963.641

1182.433

122.70%

1000 m3

6_3_1

ERROR:#N/A

78.766

81.893

103.97%

€

1000 NAC

6_3_1

ERROR:#N/A

148643.880

162757.980

109.50%

1000 m3

6_3_1

ERROR:#N/A

1887.149

1987.457

105.32%

1000 m3

6_4

ERROR:#N/A

91.073

99.697

109.47%

€

1000 NAC

6_4

ERROR:#N/A

106980.720

122661.000

114.66%

1000 m3

6_4

ERROR:#N/A

1174.676

1230.344

104.74%

1000 m3

6_4

ERROR:#N/A

61.996

62.616

101.00%

€

1000 NAC

6_4

ERROR:#N/A

122633.280

128248.120

104.58%

1000 m3

6_4

ERROR:#N/A

1978.091

2048.155

103.54%

1000 m3

6_4_1

ERROR:#N/A

118.052

148.058

125.42%

€

1000 NAC

6_4_1

ERROR:#N/A

58113.820

129798.820

223.35%

1000 m3

6_4_1

ERROR:#N/A

492.273

876.675

178.09%

1000 m3

6_4_1

ERROR:#N/A

34.860

28.814

82.66%

€

1000 NAC

6_4_1

ERROR:#N/A

14713.420

17880.540

121.53%

1000 m3

6_4_1

ERROR:#N/A

422.077

620.541

147.02%

1000 m3

6_4_2

ERROR:#N/A

0.000

€

1000 NAC

6_4_2

ERROR:#N/A

0.000

1000 m3

6_4_2

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

6_4_2

ERROR:#N/A

0.000

€

1000 NAC

6_4_2

ERROR:#N/A

0.000

1000 m3

6_4_2

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

6_4_3

ERROR:#N/A

788.477

681.292

86.41%

€

1000 NAC

6_4_3

ERROR:#N/A

444142.280

544241.090

122.54%

1000 m3

6_4_3

ERROR:#N/A

563.292

798.837

141.82%

1000 m3

6_4_3

ERROR:#N/A

854.414

872.833

102.16%

€

1000 NAC

6_4_3

ERROR:#N/A

302468.640

325958.760

107.77%

1000 m3

6_4_3

ERROR:#N/A

354.007

373.449

105.49%

1000 mt

ERROR:#N/A

235.488

211.987

90.02%

€

1000 NAC

ERROR:#N/A

107675.550

144493.140

134.19%

1000 mt

ERROR:#N/A

457.245

681.614

149.07%

1000 mt

ERROR:#N/A

449.403

443.306

98.64%

€

1000 NAC

ERROR:#N/A

139046.370

131170.920

94.34%

1000 mt

ERROR:#N/A

309.402

295.892

95.63%

1000 mt

7_1

ERROR:#N/A

481.147

388.167

80.68%

€

1000 NAC

7_1

ERROR:#N/A

276418.540

321553.540

116.33%

1000 mt

7_1

ERROR:#N/A

574.499

828.390

144.19%

1000 mt

7_1

ERROR:#N/A

312.431

344.646

110.31%

€

1000 NAC

7_1

ERROR:#N/A

108678.000

144664.460

133.11%

1000 mt

7_1

ERROR:#N/A

347.846

419.748

120.67%

1000 mt

7_2

ERROR:#N/A

71.842

81.139

112.94%

€

1000 NAC

7_2

ERROR:#N/A

60048.190

78194.410

130.22%

1000 mt

7_2

ERROR:#N/A

835.840

963.714

115.30%

1000 mt

7_2

ERROR:#N/A

92.580

84.881

91.68%

€

1000 NAC

7_2

ERROR:#N/A

54744.270

50123.380

91.56%

1000 mt

7_2

ERROR:#N/A

591.320

590.513

99.86%

1000 mt

7_3

ERROR:#N/A

1757.856

1603.663

91.23%

€

1000 NAC

7_3

ERROR:#N/A

796995.000

951410.000

119.37%

1000 mt

7_3

ERROR:#N/A

453.390

593.273

130.85%

1000 mt

7_3

ERROR:#N/A

447.092

412.901

92.35%

€

1000 NAC

7_3

ERROR:#N/A

203655.000

227432.000

111.68%

1000 mt

7_3

ERROR:#N/A

455.510

550.815

120.92%

1000 mt

7_3_1

ERROR:#N/A

75.740

82.514

108.94%

€

1000 NAC

7_3_1

ERROR:#N/A

33763.000

42563.000

126.06%

1000 mt

7_3_1

ERROR:#N/A

445.775

515.828

115.71%

1000 mt

7_3_1

ERROR:#N/A

4.643

32.302

695.71%

€

1000 NAC

7_3_1

ERROR:#N/A

1253.000

6479.000

517.08%

1000 mt

7_3_1

ERROR:#N/A

269.869

200.576

74.32%

1000 mt

7_3_2

ERROR:#N/A

1668.806

1521.149

91.15%

€

1000 NAC

7_3_2

ERROR:#N/A

763232.000

908847.000

119.08%

1000 mt

7_3_2

ERROR:#N/A

457.352

597.474

130.64%

1000 mt

7_3_2

ERROR:#N/A

442.449

380.599

86.02%

€

1000 NAC

7_3_2

ERROR:#N/A

202402.000

220953.000

109.17%

1000 mt

7_3_2

ERROR:#N/A

457.458

580.540

126.91%

1000 mt

7_3_3

ERROR:#N/A

1645.881

1510.177

91.75%

€

1000 NAC

7_3_3

ERROR:#N/A

745962.000

896474.000

120.18%

1000 mt

7_3_3

ERROR:#N/A

453.230

593.622

130.98%

1000 mt

7_3_3

ERROR:#N/A

436.700

379.414

86.88%

€

1000 NAC

7_3_3

ERROR:#N/A

199023.000

219787.000

110.43%

1000 mt

7_3_3

ERROR:#N/A

455.743

579.280

127.11%

1000 mt

7_3_4

ERROR:#N/A

1645.881

1510.177

91.75%

€

1000 NAC

7_3_4

ERROR:#N/A

745962.000

896474.000

120.18%

1000 mt

7_3_4

ERROR:#N/A

453.230

593.622

130.98%

1000 mt

7_3_4

ERROR:#N/A

436.700

379.414

86.88%

€

1000 NAC

7_3_4

ERROR:#N/A

199023.000

219787.000

110.43%

1000 mt

7_3_4

ERROR:#N/A

455.743

579.280

127.11%

1000 mt

7_4

ERROR:#N/A

22.925

10.972

47.86%

€

1000 NAC

7_4

ERROR:#N/A

17270.000

12373.000

71.64%

1000 mt

7_4

ERROR:#N/A

753.326

1127.689

149.69%

1000 mt

7_4

ERROR:#N/A

5.749

1.185

20.61%

€

1000 NAC

7_4

ERROR:#N/A

3379.000

1166.000

34.51%

1000 mt

7_4

ERROR:#N/A

587.754

983.966

167.41%

1000 mt

ERROR:#N/A

13.310

0.000

0.00%

€

1000 NAC

ERROR:#N/A

0.000

1000 mt

ERROR:#N/A

0.000

ERROR:#DIV/0!

1000 mt

ERROR:#N/A

0.000

€

1000 NAC

ERROR:#N/A

0.000

1000 mt

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

8_1

ERROR:#N/A

23.131

23.497

101.58%

€

1000 NAC

8_1

ERROR:#N/A

30360.000

32037.000

105.52%

1000 mt

8_1

ERROR:#N/A

1312.524

1363.451

103.88%

1000 mt

8_1

ERROR:#N/A

70.485

83.456

118.40%

€

1000 NAC

8_1

ERROR:#N/A

44499.000

56220.000

126.34%

1000 mt

8_1

ERROR:#N/A

631.326

673.648

106.70%

1000 mt

8_2

ERROR:#N/A

16.325

19.152

117.32%

€

1000 NAC

8_2

ERROR:#N/A

26045.000

29028.000

111.45%

1000 mt

8_2

ERROR:#N/A

1595.406

1515.664

95.00%

1000 mt

8_2

ERROR:#N/A

2.343

2.010

85.79%

€

1000 NAC

8_2

ERROR:#N/A

4471.000

4738.000

105.97%

1000 mt

8_2

ERROR:#N/A

1908.237

2357.214

123.53%

1000 mt

ERROR:#N/A

6.806

4.345

63.84%

€

1000 NAC

ERROR:#N/A

4315.000

3009.000

69.73%

1000 mt

ERROR:#N/A

633.999

692.520

109.23%

1000 mt

ERROR:#N/A

68.142

81.446

119.52%

€

1000 NAC

ERROR:#N/A

40028.000

51482.000

128.61%

1000 mt

ERROR:#N/A

587.420

632.100

107.61%

1000 mt

ERROR:#N/A

1859.992

1895.719

101.92%

€

1000 NAC

ERROR:#N/A

3716226.000

4027706.000

108.38%

1000 mt

ERROR:#N/A

1997.980

2124.632

106.34%

1000 mt

ERROR:#N/A

921.811

955.364

103.64%

€

1000 NAC

ERROR:#N/A

2100841.000

2280836.000

108.57%

1000 mt

ERROR:#N/A

2279.037

2387.400

104.75%

1000 mt

10_1

ERROR:#N/A

4502.250

4740.495

105.29%

€

1000 NAC

10_1

ERROR:#N/A

3386847.000

3838835.000

113.35%

1000 mt

10_1

ERROR:#N/A

752.257

809.796

107.65%

1000 mt

10_1

ERROR:#N/A

3464.735

3806.804

109.87%

€

1000 NAC

10_1

ERROR:#N/A

2649898.000

3433153.000

129.56%

1000 mt

10_1

ERROR:#N/A

764.820

901.847

117.92%

1000 mt

10_1_1

ERROR:#N/A

2131.837

2126.105

99.73%

€

1000 NAC

10_1_1

ERROR:#N/A

1618320.000

1643723.000

101.57%

1000 mt

10_1_1

ERROR:#N/A

759.120

773.115

101.84%

1000 mt

10_1_1

ERROR:#N/A

895.994

1008.647

112.57%

€

1000 NAC

10_1_1

ERROR:#N/A

715201.000

834097.000

116.62%

1000 mt

10_1_1

ERROR:#N/A

798.221

826.946

103.60%

1000 mt

10_1_2

ERROR:#N/A

0.000

€

1000 NAC

10_1_2

ERROR:#N/A

0.000

1000 mt

10_1_2

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_1_2

ERROR:#N/A

0.000

€

1000 NAC

10_1_2

ERROR:#N/A

0.000

1000 mt

10_1_2

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_1_3

ERROR:#N/A

0.000

€

1000 NAC

10_1_3

ERROR:#N/A

0.000

1000 mt

10_1_3

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_1_3

ERROR:#N/A

0.000

€

1000 NAC

10_1_3

ERROR:#N/A

0.000

1000 mt

10_1_3

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_1_4

ERROR:#N/A

0.000

€

1000 NAC

10_1_4

ERROR:#N/A

0.000

1000 mt

10_1_4

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_1_4

ERROR:#N/A

0.000

€

1000 NAC

10_1_4

ERROR:#N/A

0.000

1000 mt

10_1_4

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_2

ERROR:#N/A

0.000

€

1000 NAC

10_2

ERROR:#N/A

0.000

1000 mt

10_2

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_2

ERROR:#N/A

0.000

€

1000 NAC

10_2

ERROR:#N/A

0.000

1000 mt

10_2

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_3

ERROR:#N/A

90.586

89.624

98.94%

€

1000 NAC

10_3

ERROR:#N/A

108249.000

105394.000

97.36%

1000 mt

10_3

ERROR:#N/A

1194.986

1175.957

98.41%

1000 mt

10_3

ERROR:#N/A

79.583

67.563

84.90%

€

1000 NAC

10_3

ERROR:#N/A

92484.000

91041.000

98.44%

1000 mt

10_3

ERROR:#N/A

1162.107

1347.498

115.95%

1000 mt

10_3_1

ERROR:#N/A

2249.687

2483.641

110.40%

€

1000 NAC

10_3_1

ERROR:#N/A

1587681.000

2007051.000

126.41%

1000 mt

10_3_1

ERROR:#N/A

705.734

808.108

114.51%

1000 mt

10_3_1

ERROR:#N/A

2478.484

2672.381

107.82%

€

1000 NAC

10_3_1

ERROR:#N/A

1698501.000

2194104.000

129.18%

1000 mt

10_3_1

ERROR:#N/A

685.298

821.030

119.81%

1000 mt

10_3_2

ERROR:#N/A

0.000

€

1000 NAC

10_3_2

ERROR:#N/A

0.000

1000 mt

10_3_2

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_3_2

ERROR:#N/A

0.000

€

1000 NAC

10_3_2

ERROR:#N/A

0.000

1000 mt

10_3_2

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_3_3

ERROR:#N/A

0.000

€

1000 NAC

10_3_3

ERROR:#N/A

0.000

1000 mt

10_3_3

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_3_3

ERROR:#N/A

0.000

€

1000 NAC

10_3_3

ERROR:#N/A

0.000

1000 mt

10_3_3

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_3_4

ERROR:#N/A

0.000

€

1000 NAC

10_3_4

ERROR:#N/A

0.000

1000 mt

10_3_4

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_3_4

ERROR:#N/A

0.000

€

1000 NAC

10_3_4

ERROR:#N/A

0.000

1000 mt

10_3_4

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_4

ERROR:#N/A

0.000

€

1000 NAC

10_4

ERROR:#N/A

0.000

1000 mt

10_4

ERROR:#N/A

ERROR:#DIV/0!

1000 mt

10_4

ERROR:#N/A

0.000

€

1000 NAC

10_4

ERROR:#N/A

0.000

1000 mt

10_4

ERROR:#N/A

ERROR:#DIV/0!

TS-JQ3

Min:

80%

Max:

120%

Notes

JQ3

Country

Flow

Unit

Product

2016

2017

2018

2019

2020

2021

16/17

17/18

18/19

19/20

20/20

20/21

2016

2017

2018

2019

1000 NAC

11_1

ERROR:#N/A

1000 NAC

11_1_C

ERROR:#N/A

1000 NAC

11_1_NC

ERROR:#N/A

1000 NAC

11_1_NC_T

ERROR:#N/A

1000 NAC

11_2

ERROR:#N/A

1000 NAC

11_3

ERROR:#N/A

1000 NAC

11_4

ERROR:#N/A

1000 NAC

11_5

ERROR:#N/A

1000 NAC

11_6

ERROR:#N/A

1000 NAC

11_7

ERROR:#N/A

1000 NAC

11_7_1

ERROR:#N/A

1000 NAC

12_1

ERROR:#N/A

1000 NAC

12_2

ERROR:#N/A

1000 NAC

12_3

ERROR:#N/A

1000 NAC

12_4

ERROR:#N/A

1000 NAC

12_5

ERROR:#N/A

1000 NAC

12_6

ERROR:#N/A

1000 NAC

12_6_1

ERROR:#N/A

1000 NAC

12_6_2

ERROR:#N/A

1000 NAC

12_6_3

ERROR:#N/A

1000 NAC

12_7

ERROR:#N/A

1000 NAC

12_7_1

ERROR:#N/A

1000 NAC

12_7_2

ERROR:#N/A

1000 NAC

12_7_3

ERROR:#N/A

1000 NAC

11_1

ERROR:#N/A

1000 NAC

11_1_C

ERROR:#N/A

1000 NAC

11_1_NC

ERROR:#N/A

1000 NAC

11_1_NC_T

ERROR:#N/A

1000 NAC

11_2

ERROR:#N/A

1000 NAC

11_3

ERROR:#N/A

1000 NAC

11_4

ERROR:#N/A

1000 NAC

11_5

ERROR:#N/A

1000 NAC

11_6

ERROR:#N/A

1000 NAC

11_7

ERROR:#N/A

1000 NAC

11_7_1

ERROR:#N/A

1000 NAC

12_1

ERROR:#N/A

1000 NAC

12_2

ERROR:#N/A

1000 NAC

12_3

ERROR:#N/A

1000 NAC

12_4

ERROR:#N/A

1000 NAC

12_5

ERROR:#N/A

1000 NAC

12_6

ERROR:#N/A

1000 NAC

12_6_1

ERROR:#N/A

1000 NAC

12_6_2

ERROR:#N/A

1000 NAC

12_6_3

ERROR:#N/A

TS-ECEEU

Min:

80%

Max:

120%

Notes

ECEEU

Country

Flow

Unit

Product

2016

2017

2018

2019

2020

2021

16/17

17/18

18/19

19/20

20/20

20/21

2016

2017

2018

2019

1000 m3

ST_1_2_C

ERROR:#N/A

718.717

794.482

110.54%

€

1000 NAC

ST_1_2_C

ERROR:#N/A

83022.400

108792.000

131.04%

1000 m3

ST_1_2_C

ERROR:#N/A

115.515

136.935

118.54%

1000 m3

ST_1_2_C

ERROR:#N/A

1436.402

1617.362

112.60%

€

1000 NAC

ST_1_2_C

ERROR:#N/A

109099.200

163259.200

149.64%

1000 m3

ST_1_2_C

ERROR:#N/A

75.953

100.942

132.90%

1000 m3

ST_1_2_C_1

ERROR:#N/A

245.675

271.573

110.54%

€

1000 NAC

ST_1_2_C_1

ERROR:#N/A

20698.518

27123.200

131.04%

1000 m3

ST_1_2_C_1

ERROR:#N/A

84.252

99.875

118.54%

1000 m3

ST_1_2_C_1

ERROR:#N/A

697.248

785.088

112.60%

€

1000 NAC

ST_1_2_C_1

ERROR:#N/A

57489.399

86028.800

149.64%

1000 m3

ST_1_2_C_1

ERROR:#N/A

82.452

109.579

132.90%

1000 m3

ST_1_2_C_1_1

ERROR:#N/A

74.643

82.512

110.54%

€

1000 NAC

ST_1_2_C_1_1

ERROR:#N/A

6235.685

8171.200

131.04%

1000 m3

ST_1_2_C_1_1

ERROR:#N/A

83.540

99.030

118.54%

1000 m3

ST_1_2_C_1_1

ERROR:#N/A

394.405

444.093

112.60%

€

1000 NAC

ST_1_2_C_1_1

ERROR:#N/A

27660.518

41392.000

149.64%

1000 m3

ST_1_2_C_1_1

ERROR:#N/A

70.132

93.206

132.90%

1000 m3

ST_1_2_C_1_2

ERROR:#N/A

148.328

163.965

110.54%

€

1000 NAC

ST_1_2_C_1_2

ERROR:#N/A

17371.273

22763.200

131.04%

1000 m3

ST_1_2_C_1_2

ERROR:#N/A

117.114

138.830

118.54%

1000 m3

ST_1_2_C_1_2

ERROR:#N/A

277.930

312.944

112.60%

€

1000 NAC

ST_1_2_C_1_2

ERROR:#N/A

14791.481

22134.400

149.64%

1000 m3

ST_1_2_C_1_2

ERROR:#N/A

53.220

70.730

132.90%

1000 m3

ST_1_2_C_1_3

ERROR:#N/A

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#N/A

ERROR:#REF!

€

1000 NAC

ST_1_2_C_1_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_1_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_2

ERROR:#N/A

171.470

189.546

110.54%

€

1000 NAC

ST_1_2_C_2

ERROR:#N/A

25335.904

33200.000

131.04%

1000 m3

ST_1_2_C_2

ERROR:#N/A

147.757

175.156

118.54%

1000 m3

ST_1_2_C_2

ERROR:#N/A

306.442

345.048

112.60%

€

1000 NAC

ST_1_2_C_2

ERROR:#N/A

17331.929

25936.000

149.64%

1000 m3

ST_1_2_C_2

ERROR:#N/A

56.559

75.166

132.90%

1000 m3

ST_1_2_C_2_1

ERROR:#N/A

171.031

189.061

110.54%

€

1000 NAC

ST_1_2_C_2_1

ERROR:#N/A

14462.833

18952.000

131.04%

1000 m3

ST_1_2_C_2_1

ERROR:#N/A

84.563

100.243

118.54%

1000 m3

ST_1_2_C_2_1

ERROR:#N/A

302.843

340.995

112.60%

€

1000 NAC

ST_1_2_C_2_1

ERROR:#N/A

29828.881

44636.800

149.64%

1000 m3

ST_1_2_C_2_1

ERROR:#N/A

98.496

130.902

132.90%

1000 m3

ST_1_2_C_2_2

ERROR:#N/A

23.141

25.581

110.54%

€

1000 NAC

ST_1_2_C_2_2

ERROR:#N/A

7964.631

10436.800

131.04%

1000 m3

ST_1_2_C_2_2

ERROR:#N/A

344.174

407.993

118.54%

1000 m3

ST_1_2_C_2_2

ERROR:#N/A

28.512

32.104

112.60%

€

1000 NAC

ST_1_2_C_2_2

ERROR:#N/A

2540.448

3801.600

149.64%

1000 m3

ST_1_2_C_2_2

ERROR:#N/A

89.101

118.415

132.90%

1000 m3

ST_1_2_C_2_3

ERROR:#N/A

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#N/A

ERROR:#REF!

€

1000 NAC

ST_1_2_C_2_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_2_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#N/A

ERROR:#REF!

€

1000 NAC

ST_1_2_C_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#N/A

ERROR:#REF!

€

1000 NAC

ST_1_2_C_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_C_3

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_1_2_NC

ERROR:#N/A

224.764

190.679

84.84%

€

1000 NAC

ST_1_2_NC

ERROR:#N/A

42696.625

48030.115

112.49%

1000 m3

ST_1_2_NC

ERROR:#N/A

189.962

251.889

132.60%

1000 m3

ST_1_2_NC

ERROR:#N/A

2146.364

2494.157

116.20%

€

1000 NAC

ST_1_2_NC

ERROR:#N/A

238649.420

301149.900

126.19%

1000 m3

ST_1_2_NC

ERROR:#N/A

111.188

120.742

108.59%

1000 m3

ST_1_2_NC_1

ERROR:#N/A

45.480

43.528

95.71%

€

1000 NAC

ST_1_2_NC_1

ERROR:#N/A

13982.400

14689.200

105.05%

1000 m3

ST_1_2_NC_1

ERROR:#N/A

307.441

337.469

109.77%

1000 m3

ST_1_2_NC_1

ERROR:#N/A

513.890

604.981

117.73%

€

1000 NAC

ST_1_2_NC_1

ERROR:#N/A

104013.600

142711.200

137.20%

1000 m3

ST_1_2_NC_1

ERROR:#N/A

202.404

235.894

116.55%

1000 m3

ST_1_2_NC_1_1

ERROR:#N/A

43.656

35.112

80.43%

€

1000 NAC

ST_1_2_NC_1_1

ERROR:#N/A

2934.000

2576.000

87.80%

1000 m3

ST_1_2_NC_1_1

ERROR:#N/A

67.207

73.365

109.16%

1000 m3

ST_1_2_NC_1_1

ERROR:#N/A

235.097

260.070

110.62%

€

1000 NAC

ST_1_2_NC_1_1

ERROR:#N/A

18110.000

24840.000

137.16%

1000 m3

ST_1_2_NC_1_1

ERROR:#N/A

77.032

95.513

123.99%

1000 m3

ST_1_2_NC_1_2

ERROR:#N/A

41.510

39.225

94.49%

€

1000 NAC

ST_1_2_NC_1_2

ERROR:#N/A

1557.400

2122.900

136.31%

1000 m3

ST_1_2_NC_1_2

ERROR:#N/A

37.518

54.121

144.25%

1000 m3

ST_1_2_NC_1_2

ERROR:#N/A

268.501

289.583

107.85%

€

1000 NAC

ST_1_2_NC_1_2

ERROR:#N/A

24365.900

28620.800

117.46%

1000 m3

ST_1_2_NC_1_2

ERROR:#N/A

90.748

98.835

108.91%

1000 m3

ST_1_2_NC_1_3

ERROR:#N/A

2437.954

2846.070

116.74%

€

1000 NAC

ST_1_2_NC_1_3

ERROR:#N/A

1129098.600

1915698.600

169.67%

1000 m3

ST_1_2_NC_1_3

ERROR:#N/A

463.134

673.103

145.34%

1000 m3

ST_1_2_NC_1_3

ERROR:#N/A

1047.883

1055.308

100.71%

€

1000 NAC

ST_1_2_NC_1_3

ERROR:#N/A

322027.200

438489.000

136.17%

1000 m3

ST_1_2_NC_1_3

ERROR:#N/A

307.312

415.508

135.21%

1000 m3

ST_1_2_NC_2

ERROR:#N/A

6.103

6.360

104.21%

€

1000 NAC

ST_1_2_NC_2

ERROR:#N/A

192.000

100.00%

1000 m3

ST_1_2_NC_2

ERROR:#N/A

31.460

30.189

95.96%

1000 m3

ST_1_2_NC_2

ERROR:#N/A

7.927

9.881

124.65%

€

1000 NAC

ST_1_2_NC_2

ERROR:#N/A

827.000

1099.000

132.89%

1000 m3

ST_1_2_NC_2

ERROR:#N/A

104.327

111.224

106.61%

1000 m3

ST_1_2_NC_2_1

ERROR:#N/A

47.613

45.585

95.74%

€

1000 NAC

ST_1_2_NC_2_1

ERROR:#N/A

1749.400

2314.900

132.33%

1000 m3

ST_1_2_NC_2_1

ERROR:#N/A

36.742

50.782

138.21%

1000 m3

ST_1_2_NC_2_1

ERROR:#N/A

276.428

299.464

108.33%

€

1000 NAC

ST_1_2_NC_2_1

ERROR:#N/A

25192.900

29719.800

117.97%

1000 m3

ST_1_2_NC_2_1

ERROR:#N/A

91.137

99.243

108.89%

1000 m3

ST_1_2_NC_2_2

ERROR:#N/A

41.510

39.225

94.49%

€

1000 NAC

ST_1_2_NC_2_2

ERROR:#N/A

1557.400

2122.900

136.31%

1000 m3

ST_1_2_NC_2_2

ERROR:#N/A

37.518

54.121

144.25%

1000 m3

ST_1_2_NC_2_2

ERROR:#N/A

268.501

289.583

107.85%

€

1000 NAC

ST_1_2_NC_2_2

ERROR:#N/A

24365.900

28620.800

117.46%

1000 m3

ST_1_2_NC_2_2

ERROR:#N/A

90.748

98.835

108.91%

1000 m3

ST_1_2_NC_2_3

ERROR:#N/A

1581.590

1702.782

107.66%

€

1000 NAC

ST_1_2_NC_2_3

ERROR:#N/A

689184.000

1157707.800

167.98%

1000 m3

ST_1_2_NC_2_3

ERROR:#N/A

435.754

679.892

156.03%

1000 m3

ST_1_2_NC_2_3

ERROR:#N/A

420.563

419.737

99.80%

€

1000 NAC

ST_1_2_NC_2_3

ERROR:#N/A

129009.600

187525.800

145.36%

1000 m3

ST_1_2_NC_2_3

ERROR:#N/A

306.755

446.770

145.64%

1000 m3

ST_1_2_NC_3

ERROR:#N/A

19.530

3.377

17.29%

€

1000 NAC

ST_1_2_NC_3

ERROR:#N/A

1257.200

341.600

27.17%

1000 m3

ST_1_2_NC_3

ERROR:#N/A

64.373

101.161

157.15%

1000 m3

ST_1_2_NC_3

ERROR:#N/A

0.000

0.007

€

1000 NAC

ST_1_2_NC_3

ERROR:#N/A

0.000

65.800

1000 m3

ST_1_2_NC_3

ERROR:#N/A

ERROR:#DIV/0!

9400.000

1000 m3

ST_1_2_NC_4

ERROR:#N/A

707.988

826.506

116.74%

€

1000 NAC

ST_1_2_NC_4

ERROR:#N/A

323140.511

548260.200

169.67%

1000 m3

ST_1_2_NC_4

ERROR:#N/A

456.421

663.347

145.34%

1000 m3

ST_1_2_NC_4

ERROR:#N/A

206.015

207.475

100.71%

€

1000 NAC

ST_1_2_NC_4

ERROR:#N/A

43446.341

59158.800

136.17%

1000 m3

ST_1_2_NC_4

ERROR:#N/A

210.889

285.137

135.21%

1000 m3

ST_1_2_NC_5

ERROR:#N/A

276.628

286.336

103.51%

€

1000 NAC

ST_1_2_NC_5

ERROR:#N/A

266606.500

302847.400

113.59%

1000 m3

ST_1_2_NC_5

ERROR:#N/A

963.773

1057.663

109.74%

1000 m3

ST_1_2_NC_5

ERROR:#N/A

469.902

591.099

125.79%

€

1000 NAC

ST_1_2_NC_5

ERROR:#N/A

272775.400

369341.700

135.40%

1000 m3

ST_1_2_NC_5

ERROR:#N/A

580.494

624.839

107.64%

1000 m3

ST_5_C

ERROR:#N/A

63.163

57.834

91.56%

€

1000 NAC

ST_5_C

ERROR:#N/A

80048.300

78079.800

97.54%

1000 m3

ST_5_C

ERROR:#N/A

1267.329

1350.074

106.53%

1000 m3

ST_5_C

ERROR:#N/A

262.341

356.342

135.83%

€

1000 NAC

ST_5_C

ERROR:#N/A

200108.800

278727.100

139.29%

1000 m3

ST_5_C

ERROR:#N/A

762.783

782.191

102.54%

1000 m3

ST_5_C_1

ERROR:#N/A

30.663

38.237

124.70%

€

1000 NAC

ST_5_C_1

ERROR:#N/A

30676.800

40588.800

132.31%

1000 m3

ST_5_C_1

ERROR:#N/A

1000.457

1061.511

106.10%

1000 m3

ST_5_C_1

ERROR:#N/A

131.590

152.870

116.17%

€

1000 NAC

ST_5_C_1

ERROR:#N/A

44037.000

53187.400

120.78%

1000 m3

ST_5_C_1

ERROR:#N/A

334.653

347.925

103.97%

1000 m3

ST_5_C_2

ERROR:#N/A

0.371

0.280

75.47%

€

1000 NAC

ST_5_C_2

ERROR:#N/A

383.600

453.600

118.25%

1000 m3

ST_5_C_2

ERROR:#N/A

1033.962

1620.000

156.68%

1000 m3

ST_5_C_2

ERROR:#N/A

1.625

2.961

182.17%

€

1000 NAC

ST_5_C_2

ERROR:#N/A

546.000

1078.000

197.44%

1000 m3

ST_5_C_2

ERROR:#N/A

335.917

364.066

108.38%

1000 m3

ST_5_NC

ERROR:#N/A

0.011

0.017

150.00%

€

1000 NAC

ST_5_NC

ERROR:#N/A

26.600

43.400

163.16%

1000 m3

ST_5_NC

ERROR:#N/A

2375.000

2583.333

108.77%

1000 m3

ST_5_NC

ERROR:#N/A

0.027

0.021

78.95%

€

1000 NAC

ST_5_NC

ERROR:#N/A

12.600

5.600

44.44%

1000 m3

ST_5_NC

ERROR:#N/A

473.684

266.667

56.30%

1000 m3

ST_5_NC_1

ERROR:#N/A

1.390

2.106

151.46%

€

1000 NAC

ST_5_NC_1

ERROR:#N/A

1379.000

2436.000

176.65%

1000 m3

ST_5_NC_1

ERROR:#N/A

991.944

1156.915

116.63%

1000 m3

ST_5_NC_1

ERROR:#N/A

21.059

30.654

145.57%

€

1000 NAC

ST_5_NC_1

ERROR:#N/A

7989.800

10983.000

137.46%

1000 m3

ST_5_NC_1

ERROR:#N/A

379.404

358.285

94.43%

1000 m3

ST_5_NC_2

ERROR:#N/A

0.459

0.562

122.35%

€

1000 NAC

ST_5_NC_2

ERROR:#N/A

282.600

316.800

112.10%

1000 m3

ST_5_NC_2

ERROR:#N/A

615.686

564.103

91.62%

1000 m3

ST_5_NC_2

ERROR:#N/A

3.865

3.778

97.76%

€

1000 NAC

ST_5_NC_2

ERROR:#N/A

1069.200

1220.400

114.14%

1000 m3

ST_5_NC_2

ERROR:#N/A

276.665

323.011

116.75%

1000 m3

ST_5_NC_3

ERROR:#N/A

0.130

0.246

189.25%

€

1000 NAC

ST_5_NC_3

ERROR:#N/A

204.400

315.000

154.11%

1000 m3

ST_5_NC_3

ERROR:#N/A

1569.892

1278.409

81.43%

1000 m3

ST_5_NC_3

ERROR:#N/A

0.080

0.081

101.75%

€

1000 NAC

ST_5_NC_3

ERROR:#N/A

148.400

165.200

111.32%

1000 m3

ST_5_NC_3

ERROR:#N/A

1859.649

2034.483

109.40%

1000 m3

ST_5_NC_4

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_4

ERROR:#N/A

0.000

1000 m3

ST_5_NC_4

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_4

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_4

ERROR:#N/A

0.000

1000 m3

ST_5_NC_4

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_5

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_5

ERROR:#N/A

0.000

1000 m3

ST_5_NC_5

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_5

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_5

ERROR:#N/A

0.000

1000 m3

ST_5_NC_5

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_6

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_6

ERROR:#N/A

0.000

1000 m3

ST_5_NC_6

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_6

ERROR:#N/A

0.000

€

1000 NAC

ST_5_NC_6

ERROR:#N/A

0.000

1000 m3

ST_5_NC_6

ERROR:#N/A

ERROR:#DIV/0!

1000 m3

ST_5_NC_7

ERROR:#N/A

ERROR:#REF!

€

1000 NAC

ST_5_NC_7

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#N/A

ERROR:#REF!

€

1000 NAC

ST_5_NC_7

ERROR:#N/A

ERROR:#REF!

1000 m3

ST_5_NC_7

ERROR:#N/A

ERROR:#REF!

TS-EU1

Min:

80%

Max:

120%

Notes

EU1

Country

Flow

Unit

Product

2016

2017

2018

2019

2020

2021

16/17

17/18

18/19

19/20

20/20

20/21

2016

2017

2018

2019

EX_M

1000 m3

ERROR:#N/A

1125.671

1182.851

105.08%

€

EX_M

1000 NAC

ERROR:#N/A

144.712

174.216

120.39%

EX_M

1000 m3

ERROR:#N/A

0.129

0.147

114.57%

EX_X

1000 m3

ERROR:#N/A

4032.249

4546.654

112.76%

€

EX_X

1000 NAC

ERROR:#N/A

369.013

488.443

132.36%

EX_X

1000 m3

ERROR:#N/A

0.092

0.107

117.39%

EX_M

1000 m3

1_1

ERROR:#N/A

182.190

197.690

108.51%

€

EX_M

1000 NAC

1_1

ERROR:#N/A

18.993

17.394

91.58%

EX_M

1000 m3

1_1

ERROR:#N/A

0.104

0.088

84.40%

EX_X

1000 m3

1_1

ERROR:#N/A

449.484

435.136

96.81%

€

EX_X

1000 NAC

1_1

ERROR:#N/A

21.264

24.033

113.02%

EX_X

1000 m3

1_1

ERROR:#N/A

0.047

0.055

116.75%

EX_M

1000 m3

1_2

ERROR:#N/A

158.966

177.800

111.85%

€

EX_M

1000 NAC

1_2

ERROR:#N/A

14.529

13.877

95.51%

EX_M

1000 m3

1_2

ERROR:#N/A

0.091

0.078

85.39%

EX_X

1000 m3

1_2

ERROR:#N/A

353.751

299.915

84.78%

€

EX_X

1000 NAC

1_2

ERROR:#N/A

16.229

16.665

102.69%

EX_X

1000 m3

1_2

ERROR:#N/A

0.046

0.056

121.12%

EX_M

1000 m3

1_2_C

ERROR:#N/A

23.225

19.890

85.64%

€

EX_M

1000 NAC

1_2_C

ERROR:#N/A

4.464

3.517

78.80%

EX_M

1000 m3

1_2_C

ERROR:#N/A

0.192

0.177

92.01%

EX_X

1000 m3

1_2_C

ERROR:#N/A

95.733

135.221

141.25%

€

EX_X

1000 NAC

1_2_C

ERROR:#N/A

5.036

7.368

146.32%

EX_X

1000 m3

1_2_C

ERROR:#N/A

0.053

0.054

103.59%

EX_M

1000 m3

1_2_NC

ERROR:#N/A

943.481

985.161

104.42%

€

EX_M

1000 NAC

1_2_NC

ERROR:#N/A

125.719

156.822

124.74%

EX_M

1000 m3

1_2_NC

ERROR:#N/A

0.133

0.159

119.46%

EX_X

1000 m3

1_2_NC

ERROR:#N/A

3582.765

4111.519

114.76%

€

EX_X

1000 NAC

1_2_NC

ERROR:#N/A

347.749

464.409

133.55%

EX_X

1000 m3

1_2_NC

ERROR:#N/A

0.097

0.113

116.37%

EX_M

1000 m3

1_2_NC_T

ERROR:#N/A

718.717

794.482

110.54%

€

EX_M

1000 NAC

1_2_NC_T

ERROR:#N/A

83.022

108.792

131.04%

EX_M

1000 m3

1_2_NC_T

ERROR:#N/A

0.116

0.137

118.54%

EX_X

1000 m3

1_2_NC_T

ERROR:#N/A

1436.402

1617.362

112.60%

€

EX_X

1000 NAC

1_2_NC_T

ERROR:#N/A

109.099

163.259

149.64%

EX_X

1000 m3

1_2_NC_T

ERROR:#N/A

0.076

0.101

132.90%

EX_M

1000 mt

ERROR:#N/A

224.764

190.679

84.84%

€

EX_M

1000 NAC

ERROR:#N/A

42.697

48.030

112.49%

EX_M

1000 mt

ERROR:#N/A

0.190

0.252

132.60%

EX_X

1000 mt

ERROR:#N/A

2146.364

2494.157

116.20%

€

EX_X

1000 NAC

ERROR:#N/A

238.649

301.150

126.19%

EX_X

1000 mt

ERROR:#N/A

0.111

0.121

108.59%

EX_M

1000 m3

ERROR:#N/A

36.085

46.396

128.58%

€

EX_M

1000 NAC

ERROR:#N/A

17.342

24.049

138.67%

EX_M

1000 m3

ERROR:#N/A

0.481

0.518

107.85%

EX_X

1000 m3

ERROR:#N/A

3.062

1.914

62.49%

€

EX_X

1000 NAC

ERROR:#N/A

0.615

0.560

91.11%

EX_X

1000 m3

ERROR:#N/A

0.201

0.293

145.80%

EX_M

1000 m3

3_1

ERROR:#N/A

106.029

80.945

76.34%

€

EX_M

1000 NAC

3_1

ERROR:#N/A

61.977

54.658

88.19%

EX_M

1000 m3

3_1

ERROR:#N/A

0.585

0.675

115.52%

EX_X

1000 m3

3_1

ERROR:#N/A

9.192

6.912

75.20%

€

EX_X

1000 NAC

3_1

ERROR:#N/A

7.796

6.952

89.17%

EX_X

1000 m3

3_1

ERROR:#N/A

0.848

1.006

118.59%

EX_M

1000 m3

3_2

ERROR:#N/A

2509.728

2185.523

87.08%

€

EX_M

1000 NAC

3_2

ERROR:#N/A

143.601

117.702

81.96%

EX_M

1000 m3

3_2

ERROR:#N/A

0.057

0.054

94.12%

EX_X

1000 m3

3_2

ERROR:#N/A

699.256

631.022

90.24%

€

EX_X

1000 NAC

3_2

ERROR:#N/A

58.678

36.015

61.38%

EX_X

1000 m3

3_2

ERROR:#N/A

0.084

0.057

68.01%

EX_M

1000 mt

ERROR:#N/A

652.814

513.553

78.67%

€

EX_M

1000 NAC

ERROR:#N/A

48.737

33.751

69.25%

EX_M

1000 mt

ERROR:#N/A

0.075

0.066

88.03%

EX_X

1000 mt

ERROR:#N/A

451.222

311.454

69.02%

€

EX_X

1000 NAC

ERROR:#N/A

42.730

16.725

39.14%

EX_X

1000 mt

ERROR:#N/A

0.095

0.054

56.70%

EX_M

1000 mt

4_1

ERROR:#N/A

1856.914

1671.970

90.04%

€

EX_M

1000 NAC

4_1

ERROR:#N/A

94.864

83.951

88.50%

EX_M

1000 mt

4_1

ERROR:#N/A

0.051

0.050

98.28%

EX_X

1000 mt

4_1

ERROR:#N/A

248.034

319.568

128.84%

€

EX_X

1000 NAC

4_1

ERROR:#N/A

15.948

19.291

120.96%

EX_X

1000 mt

4_1

ERROR:#N/A

0.064

0.060

93.88%

EX_M

1000 mt

4_2

ERROR:#N/A

0.000

€

EX_M

1000 NAC

4_2

ERROR:#N/A

0.000

EX_M

1000 mt

4_2

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 mt

4_2

ERROR:#N/A

0.000

€

EX_X

1000 NAC

4_2

ERROR:#N/A

0.000

EX_X

1000 mt

4_2

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 m3

ERROR:#N/A

548.344

807.856

147.33%

€

EX_M

1000 NAC

ERROR:#N/A

99.126

142.805

144.06%

EX_M

1000 m3

ERROR:#N/A

0.181

0.177

97.79%

EX_X

1000 m3

ERROR:#N/A

286.105

342.339

119.66%

€

EX_X

1000 NAC

ERROR:#N/A

25.530

34.392

134.71%

EX_X

1000 m3

ERROR:#N/A

0.089

0.100

112.58%

EX_M

1000 m3

5_C

ERROR:#N/A

412.381

660.949

160.28%

€

EX_M

1000 NAC

5_C

ERROR:#N/A

86.007

127.444

148.18%

EX_M

1000 m3

5_C

ERROR:#N/A

0.209

0.193

92.45%

EX_X

1000 m3

5_C

ERROR:#N/A

96.539

121.640

126.00%

€

EX_X

1000 NAC

5_C

ERROR:#N/A

20.703

25.701

124.14%

EX_X

1000 m3

5_C

ERROR:#N/A

0.214

0.211

98.52%

EX_M

1000 m3

5_NC

ERROR:#N/A

135.963

146.907

108.05%

€

EX_M

1000 NAC

5_NC

ERROR:#N/A

13.119

15.361

117.09%

EX_M

1000 m3

5_NC

ERROR:#N/A

0.096

0.105

108.37%

EX_X

1000 m3

5_NC

ERROR:#N/A

189.566

220.699

116.42%

€

EX_X

1000 NAC

5_NC

ERROR:#N/A

4.827

8.691

180.05%

EX_X

1000 m3

5_NC

ERROR:#N/A

0.025

0.039

154.65%

EX_M

1000 m3

5_NC_T

ERROR:#N/A

2714.582

3132.406

115.39%

€

EX_M

1000 NAC

5_NC_T

ERROR:#N/A

1395.705

2218.546

158.96%

EX_M

1000 m3

5_NC_T

ERROR:#N/A

0.514

0.708

137.75%

EX_X

1000 m3

5_NC_T

ERROR:#N/A

1517.785

1646.406

108.47%

€

EX_X

1000 NAC

5_NC_T

ERROR:#N/A

594.803

807.831

135.81%

EX_X

1000 m3

5_NC_T

ERROR:#N/A

0.392

0.491

125.20%

EX_M

1000 m3

ERROR:#N/A

2437.954

2846.070

116.74%

€

EX_M

1000 NAC

ERROR:#N/A

1129.099

1915.699

169.67%

EX_M

1000 m3

ERROR:#N/A

0.463

0.673

145.34%

EX_X

1000 m3

ERROR:#N/A

1047.883

1055.308

100.71%

€

EX_X

1000 NAC

ERROR:#N/A

322.027

438.489

136.17%

EX_X

1000 m3

ERROR:#N/A

0.307

0.416

135.21%

EX_M

1000 m3

6_1

ERROR:#N/A

276.628

286.336

103.51%

€

EX_M

1000 NAC

6_1

ERROR:#N/A

266.607

302.847

113.59%

EX_M

1000 m3

6_1

ERROR:#N/A

0.964

1.058

109.74%

EX_X

1000 m3

6_1

ERROR:#N/A

469.902

591.099

125.79%

€

EX_X

1000 NAC

6_1

ERROR:#N/A

272.775

369.342

135.40%

EX_X

1000 m3

6_1

ERROR:#N/A

0.580

0.625

107.64%

EX_M

1000 m3

6_1_C

ERROR:#N/A

129.181

135.128

104.60%

€

EX_M

1000 NAC

6_1_C

ERROR:#N/A

120.277

137.781

114.55%

EX_M

1000 m3

6_1_C

ERROR:#N/A

0.931

1.020

109.51%

EX_X

1000 m3

6_1_C

ERROR:#N/A

2.933

4.925

167.92%

€

EX_X

1000 NAC

6_1_C

ERROR:#N/A

3.927

6.273

159.75%

EX_X

1000 m3

6_1_C

ERROR:#N/A

1.339

1.274

95.13%

EX_M

1000 m3

6_1_NC

ERROR:#N/A

311.221

308.503

99.13%

€

EX_M

1000 NAC

6_1_NC

ERROR:#N/A

296.964

360.704

121.46%

EX_M

1000 m3

6_1_NC

ERROR:#N/A

0.954

1.169

122.53%

EX_X

1000 m3

6_1_NC

ERROR:#N/A

68.284

71.284

104.39%

€

EX_X

1000 NAC

6_1_NC

ERROR:#N/A

128.978

141.374

109.61%

EX_X

1000 m3

6_1_NC

ERROR:#N/A

1.889

1.983

105.00%

EX_M

1000 m3

6_1_NC_T

ERROR:#N/A

17.486

14.673

83.91%

€

EX_M

1000 NAC

6_1_NC_T

ERROR:#N/A

13.908

13.269

95.41%

EX_M

1000 m3

6_1_NC_T

ERROR:#N/A

0.795

0.904

113.70%

EX_X

1000 m3

6_1_NC_T

ERROR:#N/A

0.258

0.559

216.49%

€

EX_X

1000 NAC

6_1_NC_T

ERROR:#N/A

0.604

0.810

134.14%

EX_X

1000 m3

6_1_NC_T

ERROR:#N/A

2.340

1.450

61.96%

EX_M

1000 m3

6_2

ERROR:#N/A

293.736

293.830

100.03%

€

EX_M

1000 NAC

6_2

ERROR:#N/A

283.056

347.435

122.74%

EX_M

1000 m3

6_2

ERROR:#N/A

0.964

1.182

122.70%

EX_X

1000 m3

6_2

ERROR:#N/A

68.026

70.725

103.97%

€

EX_X

1000 NAC

6_2

ERROR:#N/A

128.374

140.564

109.50%

EX_X

1000 m3

6_2

ERROR:#N/A

1.887

1.987

105.32%

EX_M

1000 m3

6_2_C

ERROR:#N/A

78.654

86.102

109.47%

€

EX_M

1000 NAC

6_2_C

ERROR:#N/A

92.392

105.935

114.66%

EX_M

1000 m3

6_2_C

ERROR:#N/A

1.175

1.230

104.74%

EX_X

1000 m3

6_2_C

ERROR:#N/A

53.542

54.078

101.00%

€

EX_X

1000 NAC

6_2_C

ERROR:#N/A

105.911

110.760

104.58%

EX_X

1000 m3

6_2_C

ERROR:#N/A

1.978

2.048

103.54%

EX_M

1000 m3

6_2_NC

ERROR:#N/A

1232.248

1150.874

93.40%

€

EX_M

1000 NAC

6_2_NC

ERROR:#N/A

886.607

1107.935

124.96%

EX_M

1000 m3

6_2_NC

ERROR:#N/A

0.720

0.963

133.80%

EX_X

1000 m3

6_2_NC

ERROR:#N/A

1013.204

1027.773

101.44%

€

EX_X

1000 NAC

6_2_NC

ERROR:#N/A

532.614

541.513

101.67%

EX_X

1000 m3

6_2_NC

ERROR:#N/A

0.526

0.527

100.23%

EX_M

1000 m3

6_2_NC_T

ERROR:#N/A

443.771

469.581

105.82%

€

EX_M

1000 NAC

6_2_NC_T

ERROR:#N/A

442.465

563.694

127.40%

EX_M

1000 m3

6_2_NC_T

ERROR:#N/A

0.997

1.200

120.40%

EX_X

1000 m3

6_2_NC_T

ERROR:#N/A

158.789

154.939

97.58%

€

EX_X

1000 NAC

6_2_NC_T

ERROR:#N/A

230.145

215.554

93.66%

EX_X

1000 m3

6_2_NC_T

ERROR:#N/A

1.449

1.391

95.99%

EX_M

1000 m3

6_3

ERROR:#N/A

103.656

129.357

124.79%

€

EX_M

1000 NAC

6_3

ERROR:#N/A

114.716

161.401

140.70%

EX_M

1000 m3

6_3

ERROR:#N/A

1.107

1.248

112.74%

EX_X

1000 m3

6_3

ERROR:#N/A

80.023

73.047

91.28%

€

EX_X

1000 NAC

6_3

ERROR:#N/A

81.501

52.796

64.78%

EX_X

1000 m3

6_3

ERROR:#N/A

1.018

0.723

70.97%

EX_M

1000 m3

6_3_1

ERROR:#N/A

340.115

340.225

100.03%

€

EX_M

1000 NAC

6_3_1

ERROR:#N/A

327.749

402.293

122.74%

EX_M

1000 m3

6_3_1

ERROR:#N/A

0.964

1.182

122.70%

EX_X

1000 m3

6_3_1

ERROR:#N/A

78.766

81.893

103.97%

€

EX_X

1000 NAC

6_3_1

ERROR:#N/A

148.644

162.758

109.50%

EX_X

1000 m3

6_3_1

ERROR:#N/A

1.887

1.987

105.32%

EX_M

1000 m3

6_4

ERROR:#N/A

91.073

99.697

109.47%

€

EX_M

1000 NAC

6_4

ERROR:#N/A

106.981

122.661

114.66%

EX_M

1000 m3

6_4

ERROR:#N/A

1.175

1.230

104.74%

EX_X

1000 m3

6_4

ERROR:#N/A

61.996

62.616

101.00%

€

EX_X

1000 NAC

6_4

ERROR:#N/A

122.633

128.248

104.58%

EX_X

1000 m3

6_4

ERROR:#N/A

1.978

2.048

103.54%

EX_M

1000 m3

6_4_1

ERROR:#N/A

0.000

€

EX_M

1000 NAC

6_4_1

ERROR:#N/A

0.000

EX_M

1000 m3

6_4_1

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 m3

6_4_1

ERROR:#N/A

0.000

€

EX_X

1000 NAC

6_4_1

ERROR:#N/A

0.000

EX_X

1000 m3

6_4_1

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 m3

6_4_2

ERROR:#N/A

118.052

148.058

125.42%

€

EX_M

1000 NAC

6_4_2

ERROR:#N/A

58.114

129.799

223.35%

EX_M

1000 m3

6_4_2

ERROR:#N/A

0.492

0.877

178.09%

EX_X

1000 m3

6_4_2

ERROR:#N/A

34.860

28.814

82.66%

€

EX_X

1000 NAC

6_4_2

ERROR:#N/A

14.713

17.881

121.53%

EX_X

1000 m3

6_4_2

ERROR:#N/A

0.422

0.621

147.02%

EX_M

1000 m3

6_4_3

ERROR:#N/A

788.477

681.292

86.41%

€

EX_M

1000 NAC

6_4_3

ERROR:#N/A

444.142

544.241

122.54%

EX_M

1000 m3

6_4_3

ERROR:#N/A

0.563

0.799

141.82%

EX_X

1000 m3

6_4_3

ERROR:#N/A

854.414

872.833

102.16%

€

EX_X

1000 NAC

6_4_3

ERROR:#N/A

302.469

325.959

107.77%

EX_X

1000 m3

6_4_3

ERROR:#N/A

0.354

0.373

105.49%

EX_M

1000 mt

ERROR:#N/A

235.488

211.987

90.02%

€

EX_M

1000 NAC

ERROR:#N/A

107.676

144.493

134.19%

EX_M

1000 mt

ERROR:#N/A

0.457

0.682

149.07%

EX_X

1000 mt

ERROR:#N/A

449.403

443.306

98.64%

€

EX_X

1000 NAC

ERROR:#N/A

139.046

131.171

94.34%

EX_X

1000 mt

ERROR:#N/A

0.309

0.296

95.63%

EX_M

1000 mt

7_1

ERROR:#N/A

481.147

388.167

80.68%

€

EX_M

1000 NAC

7_1

ERROR:#N/A

276.419

321.554

116.33%

EX_M

1000 mt

7_1

ERROR:#N/A

0.574

0.828

144.19%

EX_X

1000 mt

7_1

ERROR:#N/A

312.431

344.646

110.31%

€

EX_X

1000 NAC

7_1

ERROR:#N/A

108.678

144.664

133.11%

EX_X

1000 mt

7_1

ERROR:#N/A

0.348

0.420

120.67%

EX_M

1000 mt

7_2

ERROR:#N/A

71.842

81.139

112.94%

€

EX_M

1000 NAC

7_2

ERROR:#N/A

60.048

78.194

130.22%

EX_M

1000 mt

7_2

ERROR:#N/A

0.836

0.964

115.30%

EX_X

1000 mt

7_2

ERROR:#N/A

92.580

84.881

91.68%

€

EX_X

1000 NAC

7_2

ERROR:#N/A

54.744

50.123

91.56%

EX_X

1000 mt

7_2

ERROR:#N/A

0.591

99.86%

EX_M

1000 mt

7_3

ERROR:#N/A

111.975

93.486

83.49%

€

EX_M

1000 NAC

7_3

ERROR:#N/A

51.033

54.936

107.65%

EX_M

1000 mt

7_3

ERROR:#N/A

0.456

0.588

128.94%

EX_X

1000 mt

7_3

ERROR:#N/A

10.392

33.487

322.24%

€

EX_X

1000 NAC

7_3

ERROR:#N/A

4.632

7.645

165.05%

EX_X

1000 mt

7_3

ERROR:#N/A

0.446

0.228

51.22%

EX_M

1000 mt

7_3_1

ERROR:#N/A

75.740

82.514

108.94%

€

EX_M

1000 NAC

7_3_1

ERROR:#N/A

33.763

42.563

126.06%

EX_M

1000 mt

7_3_1

ERROR:#N/A

0.446

0.516

115.71%

EX_X

1000 mt

7_3_1

ERROR:#N/A

4.643

32.302

695.71%

€

EX_X

1000 NAC

7_3_1

ERROR:#N/A

1.253

6.479

517.08%

EX_X

1000 mt

7_3_1

ERROR:#N/A

0.270

0.201

74.32%

EX_M

1000 mt

7_3_2

ERROR:#N/A

22.925

10.972

47.86%

€

EX_M

1000 NAC

7_3_2

ERROR:#N/A

17.270

12.373

71.64%

EX_M

1000 mt

7_3_2

ERROR:#N/A

0.753

1.128

149.69%

EX_X

1000 mt

7_3_2

ERROR:#N/A

5.749

1.185

20.61%

€

EX_X

1000 NAC

7_3_2

ERROR:#N/A

3.379

1.166

34.51%

EX_X

1000 mt

7_3_2

ERROR:#N/A

0.588

0.984

167.41%

EX_M

1000 mt

7_3_3

ERROR:#N/A

0.000

€

EX_M

1000 NAC

7_3_3

ERROR:#N/A

0.000

EX_M

1000 mt

7_3_3

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 mt

7_3_3

ERROR:#N/A

0.000

€

EX_X

1000 NAC

7_3_3

ERROR:#N/A

0.000

EX_X

1000 mt

7_3_3

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 mt

7_3_4

ERROR:#N/A

1645.881

1510.177

91.75%

€

EX_M

1000 NAC

7_3_4

ERROR:#N/A

745.962

896.474

120.18%

EX_M

1000 mt

7_3_4

ERROR:#N/A

0.453

0.594

130.98%

EX_X

1000 mt

7_3_4

ERROR:#N/A

436.700

379.414

86.88%

€

EX_X

1000 NAC

7_3_4

ERROR:#N/A

199.023

219.787

110.43%

EX_X

1000 mt

7_3_4

ERROR:#N/A

0.456

0.579

127.11%

EX_M

1000 mt

7_4

ERROR:#N/A

22.925

10.972

47.86%

€

EX_M

1000 NAC

7_4

ERROR:#N/A

17.270

12.373

71.64%

EX_M

1000 mt

7_4

ERROR:#N/A

0.753

1.128

149.69%

EX_X

1000 mt

7_4

ERROR:#N/A

5.749

1.185

20.61%

€

EX_X

1000 NAC

7_4

ERROR:#N/A

3.379

1.166

34.51%

EX_X

1000 mt

7_4

ERROR:#N/A

0.588

0.984

167.41%

EX_M

1000 mt

ERROR:#N/A

13.310

0.000

0.00%

€

EX_M

1000 NAC

ERROR:#N/A

0.000

EX_M

1000 mt

ERROR:#N/A

0.000

ERROR:#DIV/0!

EX_X

1000 mt

ERROR:#N/A

0.000

€

EX_X

1000 NAC

ERROR:#N/A

0.000

EX_X

1000 mt

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 mt

8_1

ERROR:#N/A

23.131

23.497

101.58%

€

EX_M

1000 NAC

8_1

ERROR:#N/A

30.360

32.037

105.52%

EX_M

1000 mt

8_1

ERROR:#N/A

1.313

1.363

103.88%

EX_X

1000 mt

8_1

ERROR:#N/A

70.485

83.456

118.40%

€

EX_X

1000 NAC

8_1

ERROR:#N/A

44.499

56.220

126.34%

EX_X

1000 mt

8_1

ERROR:#N/A

0.631

0.674

106.70%

EX_M

1000 mt

8_2

ERROR:#N/A

16.325

19.152

117.32%

€

EX_M

1000 NAC

8_2

ERROR:#N/A

26.045

29.028

111.45%

EX_M

1000 mt

8_2

ERROR:#N/A

1.595

1.516

95.00%

EX_X

1000 mt

8_2

ERROR:#N/A

2.343

2.010

85.79%

€

EX_X

1000 NAC

8_2

ERROR:#N/A

4.471

4.738

105.97%

EX_X

1000 mt

8_2

ERROR:#N/A

1.908

2.357

123.53%

EX_M

1000 mt

ERROR:#N/A

6.806

4.345

63.84%

€

EX_M

1000 NAC

ERROR:#N/A

4.315

3.009

69.73%

EX_M

1000 mt

ERROR:#N/A

0.634

0.693

109.23%

EX_X

1000 mt

ERROR:#N/A

68.142

81.446

119.52%

€

EX_X

1000 NAC

ERROR:#N/A

40.028

51.482

128.61%

EX_X

1000 mt

ERROR:#N/A

0.587

0.632

107.61%

EX_M

1000 mt

ERROR:#N/A

1859.992

1895.719

101.92%

€

EX_M

1000 NAC

ERROR:#N/A

3716.226

4027.706

108.38%

EX_M

1000 mt

ERROR:#N/A

1.998

2.125

106.34%

EX_X

1000 mt

ERROR:#N/A

921.811

955.364

103.64%

€

EX_X

1000 NAC

ERROR:#N/A

2100.841

2280.836

108.57%

EX_X

1000 mt

ERROR:#N/A

2.279

2.387

104.75%

EX_M

1000 mt

10_1

ERROR:#N/A

4502.250

4740.495

105.29%

€

EX_M

1000 NAC

10_1

ERROR:#N/A

3386.847

3838.835

113.35%

EX_M

1000 mt

10_1

ERROR:#N/A

0.752

0.810

107.65%

EX_X

1000 mt

10_1

ERROR:#N/A

3464.735

3806.804

109.87%

€

EX_X

1000 NAC

10_1

ERROR:#N/A

2649.898

3433.153

129.56%

EX_X

1000 mt

10_1

ERROR:#N/A

0.765

0.902

117.92%

EX_M

1000 mt

10_1_1

ERROR:#N/A

2131.837

2126.105

99.73%

€

EX_M

1000 NAC

10_1_1

ERROR:#N/A

1618.320

1643.723

101.57%

EX_M

1000 mt

10_1_1

ERROR:#N/A

0.759

0.773

101.84%

EX_X

1000 mt

10_1_1

ERROR:#N/A

895.994

1008.647

112.57%

€

EX_X

1000 NAC

10_1_1

ERROR:#N/A

715.201

834.097

116.62%

EX_X

1000 mt

10_1_1

ERROR:#N/A

0.798

0.827

103.60%

EX_M

1000 mt

10_1_2

ERROR:#N/A

0.000

€

EX_M

1000 NAC

10_1_2

ERROR:#N/A

0.000

EX_M

1000 mt

10_1_2

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 mt

10_1_2

ERROR:#N/A

0.000

€

EX_X

1000 NAC

10_1_2

ERROR:#N/A

0.000

EX_X

1000 mt

10_1_2

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 mt

10_1_3

ERROR:#N/A

0.000

€

EX_M

1000 NAC

10_1_3

ERROR:#N/A

0.000

EX_M

1000 mt

10_1_3

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 mt

10_1_3

ERROR:#N/A

0.000

€

EX_X

1000 NAC

10_1_3

ERROR:#N/A

0.000

EX_X

1000 mt

10_1_3

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 mt

10_1_4

ERROR:#N/A

0.000

€

EX_M

1000 NAC

10_1_4

ERROR:#N/A

0.000

EX_M

1000 mt

10_1_4

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 mt

10_1_4

ERROR:#N/A

0.000

€

EX_X

1000 NAC

10_1_4

ERROR:#N/A

0.000

EX_X

1000 mt

10_1_4

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 mt

10_2

ERROR:#N/A

0.000

€

EX_M

1000 NAC

10_2

ERROR:#N/A

0.000

EX_M

1000 mt

10_2

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 mt

10_2

ERROR:#N/A

0.000

€

EX_X

1000 NAC

10_2

ERROR:#N/A

0.000

EX_X

1000 mt

10_2

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 mt

10_3

ERROR:#N/A

90.586

89.624

98.94%

€

EX_M

1000 NAC

10_3

ERROR:#N/A

108.249

105.394

97.36%

EX_M

1000 mt

10_3

ERROR:#N/A

1.195

1.176

98.41%

EX_X

1000 mt

10_3

ERROR:#N/A

79.583

67.563

84.90%

€

EX_X

1000 NAC

10_3

ERROR:#N/A

92.484

91.041

98.44%

EX_X

1000 mt

10_3

ERROR:#N/A

1.162

1.347

115.95%

EX_M

1000 mt

10_3_1

ERROR:#N/A

2249.687

2483.641

110.40%

€

EX_M

1000 NAC

10_3_1

ERROR:#N/A

1587.681

2007.051

126.41%

EX_M

1000 mt

10_3_1

ERROR:#N/A

0.706

0.808

114.51%

EX_X

1000 mt

10_3_1

ERROR:#N/A

2478.484

2672.381

107.82%

€

EX_X

1000 NAC

10_3_1

ERROR:#N/A

1698.501

2194.104

129.18%

EX_X

1000 mt

10_3_1

ERROR:#N/A

0.685

0.821

119.81%

EX_M

1000 mt

10_3_2

ERROR:#N/A

0.000

€

EX_M

1000 NAC

10_3_2

ERROR:#N/A

0.000

EX_M

1000 mt

10_3_2

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 mt

10_3_2

ERROR:#N/A

0.000

€

EX_X

1000 NAC

10_3_2

ERROR:#N/A

0.000

EX_X

1000 mt

10_3_2

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 mt

10_3_3

ERROR:#N/A

0.000

€

EX_M

1000 NAC

10_3_3

ERROR:#N/A

0.000

EX_M

1000 mt

10_3_3

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 mt

10_3_3

ERROR:#N/A

0.000

€

EX_X

1000 NAC

10_3_3

ERROR:#N/A

0.000

EX_X

1000 mt

10_3_3

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 mt

10_3_4

ERROR:#N/A

0.000

€

EX_M

1000 NAC

10_3_4

ERROR:#N/A

0.000

EX_M

1000 mt

10_3_4

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 mt

10_3_4

ERROR:#N/A

0.000

€

EX_X

1000 NAC

10_3_4

ERROR:#N/A

0.000

EX_X

1000 mt

10_3_4

ERROR:#N/A

ERROR:#DIV/0!

EX_M

1000 mt

10_4

ERROR:#N/A

0.000

€

EX_M

1000 NAC

10_4

ERROR:#N/A

0.000

EX_M

1000 mt

10_4

ERROR:#N/A

ERROR:#DIV/0!

EX_X

1000 mt

10_4

ERROR:#N/A

0.000

€

EX_X

1000 NAC

10_4

ERROR:#N/A

0.000

EX_X

1000 mt

10_4

ERROR:#N/A

ERROR:#DIV/0!

TS-EU2

Min:

80%

Max:

120%

Notes

EU2

Country

Flow

Unit

Product

2016

2017

2018

2019

2020

2021

16/17

17/18

18/19

19/20

20/20

20/21

2016

2017

2018

2019

1000 m3

EU2_1

ERROR:#N/A

47387

52915

111.67%

1000 m3

EU2_1_C

ERROR:#N/A

18655

20944

112.27%

1000 m3

EU2_1_NC

ERROR:#N/A

28732

31971

111.27%

1000 m3

EU2_1_1

ERROR:#N/A

4244.4802825341

4557.123304733

107.37%

1000 m3

EU2_1_1_C

ERROR:#N/A

2224.3297372312

2271.126956543

102.10%

1000 m3

EU2_1_1_NC

ERROR:#N/A

2020.1505453029

2285.9963481899

113.16%

1000 m3

EU2_1_2

ERROR:#N/A

5906.5827642783

7982.77189

135.15%

1000 m3

EU2_1_2_C

ERROR:#N/A

2908.3862908533

4128.624044745

141.96%

1000 m3

EU2_1_2_NC

ERROR:#N/A

2998.196473425

3854.147845255

128.55%

1000 m3

EU2_1_3

ERROR:#N/A

37235.9369531876

40375.104805267

108.43%

1000 m3

EU2_1_3_C

ERROR:#N/A

13522.2839719155

14544.2489987119

107.56%

1000 m3

EU2_1_3_NC

ERROR:#N/A

23713.652981272

25830.8558065551

108.93%

Annex1 | JQ1-Corres.




		FOREST SECTOR QUESTIONNAIRE JQ1 (Supp. 1)
		PRIMARY PRODUCTS
		Removals and Production
		CORRESPONDENCES to CPC Ver.2.1
		Central Product Classification Version 2.1 (CPC Ver. 2.1)
Product	Product
Code

REMOVALS OF ROUNDWOOD (WOOD IN THE ROUGH)
1	ROUNDWOOD (WOOD IN THE ROUGH)	031
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	0313
1.1.C	Coniferous	03131
1.1.NC	Non-Coniferous	03132
1.2	INDUSTRIAL ROUNDWOOD	0311 0312
1.2.C	Coniferous	0311
1.2.NC	Non-Coniferous	0312
1.2.NC.T	of which: Tropical	ex0312
1.2.1	SAWLOGS AND VENEER LOGS	ex03110 ex03120
1.2.1.C	Coniferous	ex03110
1.2.1.NC	Non-Coniferous	ex03120
1.2.2	PULPWOOD, ROUND AND SPLIT (INCLUDING WOOD FOR PARTICLE BOARD, OSB AND FIBREBOARD)	ex03110 ex03120
1.2.2.C	Coniferous	ex03110
1.2.2.NC	Non-Coniferous	ex03120
1.2.3	OTHER INDUSTRIAL ROUNDWOOD	ex03110 ex03120
1.2.3.C	Coniferous	ex03110
1.2.3.NC	Non-Coniferous	ex03120
PRODUCTION
2	WOOD CHARCOAL	ex34510
3	WOOD CHIPS, PARTICLES AND RESIDUES	ex31230 ex39283
3.1	WOOD CHIPS AND PARTICLES	ex31230
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	ex39283
4	RECOVERED POST-CONSUMER WOOD	ex39283
5	WOOD PELLETS AND OTHER AGGLOMERATES	39281 39282
5.1	WOOD PELLETS	39281
5.2	OTHER AGGLOMERATES	39282
6	SAWNWOOD (INCLUDING SLEEPERS)	311 3132
6.C	Coniferous	31101 ex31109 ex3132
6.NC	Non-Coniferous	31102 ex31109 ex3132
6.NC.T	of which: Tropical	ex31102 ex31109 ex3132
7	VENEER SHEETS	3151
7.C	Coniferous	31511
7.NC	Non-Coniferous	31512
7.NC.T	of which: Tropical	ex31512
8	WOOD-BASED PANELS	3141 3142 3143 3144
8.1	PLYWOOD	3141 3142
8.1.C	Coniferous	31411 31421
8.1.NC	Non-Coniferous	31412 31422
8.1.NC.T	of which: Tropical	ex31412 ex31422
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD	3143
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	31432
8.3	FIBREBOARD	3144
8.3.1	HARDBOARD	31442
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	31441
8.3.3	OTHER FIBREBOARD	31449
9	WOOD PULP	32111 32112 ex32113
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	ex32113
9.2	CHEMICAL WOOD PULP	32112
9.2.1	SULPHATE PULP	ex32112
9.2.1.1	of which: BLEACHED	ex32112
9.2.2	SULPHITE PULP	ex32112
9.3	DISSOLVING GRADES	32111
10	OTHER PULP	ex32113
10.1	PULP FROM FIBRES OTHER THAN WOOD	ex32113
10.2	RECOVERED FIBRE PULP	ex32113
11	RECOVERED PAPER	3924
12	PAPER AND PAPERBOARD	3212 3213 32142 32143 ex32149 32151 32198 ex32199
12.1	GRAPHIC PAPERS	3212 ex32143 ex32149
12.1.1	NEWSPRINT	32121
12.1.2	UNCOATED MECHANICAL	ex32122 ex32129
12.1.3	UNCOATED WOODFREE	32122 ex32129
12.1.4	COATED PAPERS	ex32143 ex32149
12.2	HOUSEHOLD AND SANITARY PAPERS	32131
12.3	PACKAGING MATERIALS	32132 ex32133 32134 32135 ex32136 ex32137 32142 32151 ex32143 ex32149
12.3.1	CASE MATERIALS	32132 32134 32135 ex32136
12.3.2	CARTONBOARD	ex32133 ex32136 ex32143 ex32149
12.3.3	WRAPPING PAPERS	ex32133 ex32136 ex32137 32142 32151
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	ex32136
12.4	OTHER PAPER AND PAPERBOARD N.E.S.	ex32149 ex32133 ex32136 ex32137 32198 ex32199
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the CPC Ver.2.1 code is applicable.
For instance "ex31512" under product 7.NC.T means that only a part of CPC Ver.2.1 code 31512 refers to non-coniferous tropical veneer sheets.
In CPC, if only 3 or 4 digits are shown, then all sub-codes at lower degrees of aggregation are included (for example, 0313 includes 03131 and 03132).

Annex2 | JQ2-Corres.




		FOREST SECTOR QUESTIONNAIRE JQ2 (Supp. 1)
		PRIMARY PRODUCTS
		Trade
		CORRESPONDENCES to HS2017, HS2012 and SITC Rev.4
		C l a s s i f i c a t i o n s
Product	Product
Code		HS2017	HS2012	SITC Rev.4

1	ROUNDWOOD (WOOD IN THE ROUGH)	4401.11/12 44.03	4401.10 44.03	245.01 247
1.1	WOOD FUEL (INCLUDING WOOD FOR CHARCOAL)	4401.11/12	4401.10	245.01
1.1.C	Coniferous	4401.11	ex4401.10	ex245.01
1.1.NC	Non-Coniferous	4401.12	ex4401.10	ex245.01
1.2	INDUSTRIAL ROUNDWOOD	44.03	44.03	247
1.2.C	Coniferous	4403.11/21/22/23/24/25/26	ex4403.10 4403.20	ex247.3 247.4
1.2.NC	Non-Coniferous	4403.12/41/49/91/93/94/95/96/97/98/99	ex4403.10 4403.41/49/91/92/99	ex247.3 247.5 247.9
1.2.NC.T	of which: Tropical	ex4403.12 4403.41/49	ex4403.10 4403.41/49 ex4403.99	ex247.3 247.5 ex247.9
2	WOOD CHARCOAL	4402.90	4402.90	ex245.02
3	WOOD CHIPS, PARTICLES AND RESIDUES	4401.21/22 ex4401.40	4401.21/22 ex4401.39	246.1 ex246.2
3.1	WOOD CHIPS AND PARTICLES	4401.21/22	4401.21/22	246.1
3.2	WOOD RESIDUES (INCLUDING WOOD FOR AGGLOMERATES)	ex4401.40	ex4401.39	ex246.2
4	RECOVERED POST-CONSUMER WOOD	ex4401.40	ex4401.39	ex246.2
5	WOOD PELLETS AND OTHER AGGLOMERATES	4401.31/39	4401.31 ex4401.39	ex246.2
5.1	WOOD PELLETS	4401.31	4401.31	ex246.2
5.2	OTHER AGGLOMERATES	4401.39	ex4401.39	ex246.2
6	SAWNWOOD (INCLUDING SLEEPERS)	44.06 44.07	44.06 44.07	248.1 248.2 248.4
6.C	Coniferous	4406.11/91 4407.11/12/19	ex4406.10/90 4407.10	ex248.11 ex248.19 248.2
6.NC	Non-Coniferous	4406.12/92 4407.21/22/25/26/27/28/29/91/92/93/94/95/96/97/99	ex4406.10/90 4407.21/22/25/26/27/28/29/91/92/93/94/95/99	ex248.11 ex248.19 248.4
6.NC.T	of which: Tropical	ex4406.12/92 4407.21/22/25/26/27/28/29	ex4406.10/90 4407.21/22/25/26/27/28/29 ex4407.99	ex248.11 ex248.19 ex248.4
7	VENEER SHEETS	44.08	44.08	634.1
7.C	Coniferous	4408.10	4408.10	634.11
7.NC	Non-Coniferous	4408.31/39/90	4408.31/39/90	634.12
7.NC.T	of which: Tropical	4408.31/39	4408.31/39 ex4408.90	ex634.12
8	WOOD-BASED PANELS	44.10 44.11 4412.31/33/34/39/94/99	44.10 44.11 4412.31/32/39/94/99	634.22/23/31/33/39 634.5
8.1	PLYWOOD	4412.31/33/34/39/94/99	4412.31/32/39/94/99	634.31/33/39
8.1.C	Coniferous	4412.39 ex4412.94 ex4412.99	4412.39 ex4412.94 ex.4412.99	ex634.31 ex634.33 ex634.39
8.1.NC	Non-Coniferous	4412.31/33/34 ex4412.94 ex4412.99	4412.31/32 ex4412.94 ex4412.99	ex634.31 ex634.33 ex634.39
8.1.NC.T	of which: Tropical	4412.31 ex4412.94 ex4412.99	4412.31 ex4412.32 ex4412.94 ex4412.99	ex634.31 ex634.33 ex634.39
8.2	PARTICLE BOARD, ORIENTED STRAND BOARD (OSB) and SIMILAR BOARD	44.10	44.10	634.22/23
8.2.1	of which: ORIENTED STRAND BOARD (OSB)	4410.12	4410.12	ex634.22
8.3	FIBREBOARD	44.11	44.11	634.5
8.3.1	HARDBOARD	4411.92	4411.92	ex634.54 ex634.55
8.3.2	MEDIUM/HIGH DENSITY FIBREBOARD (MDF/HDF)	4411.12/13 ex4411.14*	4411.12/13 ex4411.14*	ex634.54 ex634.55
8.3.3	OTHER FIBREBOARD	ex4411.14 4411.93/94	ex4411.14 4411.93/94	ex634.54 ex634.55
9	WOOD PULP	47.01/02/03/04/05	47.01/02/03/04/05	251.2 251.3 251.4 251.5 251.6 251.91
9.1	MECHANICAL AND SEMI-CHEMICAL WOOD PULP	47.01 47.05	47.01 47.05	251.2 251.91
9.2	CHEMICAL WOOD PULP	47.03 47.04	47.03 47.04	251.4 251.5 251.6
9.2.1	SULPHATE PULP	47.03	47.03	251.4 251.5
9.2.1.1	of which: BLEACHED	4703.21/29	4703.21/29	251.5
9.2.2	SULPHITE PULP	47.04	47.04	251.6
9.3	DISSOLVING GRADES	47.02	47.02	251.3
10	OTHER PULP	47.06	47.06	251.92
10.1	PULP FROM FIBRES OTHER THAN WOOD	4706.10/30/91/92/93	4706.10/30/91/92/93	ex251.92
10.2	RECOVERED FIBRE PULP	4706.20	4706.20	ex251.92
11	RECOVERED PAPER	47.07	47.07	251.1
12	PAPER AND PAPERBOARD	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	48.01 48.02 48.03 48.04 48.05 48.06 48.08 48.09 48.10 4811.51/59 48.12 48.13	641.1 641.2 641.3 641.4 641.5 641.62/63/64/69/71/72/74/75/76/77/93 642.41
12.1	GRAPHIC PAPERS	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	48.01 4802.10/20/54/55/56/57/58/61/62/69 48.09 4810.13/14/19/22/29	641.1 641.21/22/26/29 641.3
12.1.1	NEWSPRINT	48.01	48.01	641.1
12.1.2	UNCOATED MECHANICAL	4802.61/62/69	4802.61/62/69	641.29
12.1.3	UNCOATED WOODFREE	4802.10/20/54/55/56/57/58	4802.10/20/54/55/56/57/58	641.21/22/26
12.1.4	COATED PAPERS	48.09 4810.13/14/19/22/29	48.09 4810.13/14/19/22/29	641.3
12.2	HOUSEHOLD AND SANITARY PAPERS	48.03	48.03	641.63
12.3	PACKAGING MATERIALS	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	4804.11/19/21/29/31/39/42/49/51/52/59 4805.11/12/19/24/25/30/91/92/93 4806.10/20/40 48.08 4810.31/32/39/92/99 4811.51/59	641.41/42/46 ex641.47 641.48/51/52 ex641.53 641.54/59/62/64/69/71/72/74/75/76/77
12.3.1	CASE MATERIALS	4804.11/19 4805.11/12/19/24/25/91	4804.11/19 4805.11/12/19/24/25/91	641.41/51/54 ex641.59
12.3.2	CARTONBOARD	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	4804.42/49/51/52/59 4805.92 4810.32/39/92 4811.51/59	ex641.47 641.48 ex641.59 641.75/76 ex641.77 641.71/72
12.3.3	WRAPPING PAPERS	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	4804.21/29/31/39 4805.30 4806.10/20/40 48.08 4810.31/99	641.42/46/52 ex641.53 641.62/64/69/74 ex641.77
12.3.4	OTHER PAPERS MAINLY FOR PACKAGING	4805.93	4805.93	ex641.59
12.4	OTHER PAPER AND PAPERBOARD N.E.S.	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	4802.40 4804.41 4805.40/50 4806.30 48.12 48.13	641.24 ex641.47 641.56 ex641.53 641.55/93 642.41
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017 or SITC Rev.4 code is applicable.
For instance "ex4401.40" under product 3.2 means that only a part of HS2017 code 4401.40 refers to wood residues coming from wood processing (the other part coded under 4401.40 is recovered post-consumer wood).
In SITC Rev.4, if only 4 digits are shown, then all sub-headings at lower degrees of aggregation are included (for example, 634.1 includes 634.11 and 634.12).
* - Please assign the trade data for HS code 4411.14 to product 8.3.2 (MDF/HDF) and 8.3.3 (other fibreboard) if it is possible to do this in national statistics. If not, please assign all the trade data to item 8.3.2 as in most cases MDF/HDF will represent the large majority of trade.

Annex3 | JQ3-Corres.



		FOREST SECTOR QUESTIONNAIRE JQ3 (Supp. 1)
		SECONDARY PROCESSED PRODUCTS
		Trade
		CORRESPONDENCES to HS2017, HS2012 and SITC Rev.4
		C l a s s i f i c a t i o n s
Product	Product
Code		HS2017	HS2012	SITC Rev.4

13	SECONDARY WOOD PRODUCTS
13.1	FURTHER PROCESSED SAWNWOOD	4409.10/22/29	4409.10/29	248.3 248.5
13.1.C	Coniferous	4409.10	4409.10	248.3
13.1.NC	Non-coniferous	4409.22/29	4409.29	248.5
13.1.NC.T	of which: Tropical	4409.22	ex4409.29	ex248.5
13.2	WOODEN WRAPPING AND PACKAGING MATERIAL	44.15/16	44.15/16	635.1 635.2
13.3	WOOD PRODUCTS FOR DOMESTIC/DECORATIVE USE	44.14 4419.90 44.20	44.14 ex4419.00 44.20	635.41 ex635.42 635.49
13.4	BUILDER’S JOINERY AND CARPENTRY OF WOOD	4418.10/20/40/50/60/74/75/79/99	4418.10/20/40/50/60 ex4418.71 ex4418.72 ex4418.79 ex4418.90	635.31/32/33 ex635.34 ex635.39
13.5	WOODEN FURNITURE	9401.61/69 ex9401.90 9403.30/40/50/60 ex9403.90	9401.61/69 ex9401.90 9403.30/40/50/60 ex9403.90	821.16 ex821.19 821.51/53/55/59 ex821.8
13.6	PREFABRICATED BUILDINGS OF WOOD	9406.10	ex94.06	ex811.0
13.7	OTHER MANUFACTURED WOOD PRODUCTS	44.04/05/13/17 4421.10/99	44.04/05/13/17 4421.10 ex4421.90	634.21/91/93 635.91 ex635.99
14	SECONDARY PAPER PRODUCTS
14.1	COMPOSITE PAPER AND PAPERBOARD	48.07	48.07	641.92
14.2	SPECIAL COATED PAPER AND PULP PRODUCTS	4811.10/41/49/60/90	4811.10/41/49/60/90	641.73/78/79
14.3	HOUSEHOLD AND SANITARY PAPER, READY FOR USE	48.18	48.18	642.43/94
14.4	PACKAGING CARTONS, BOXES ETC.	48.19	48.19	642.1
14.5	OTHER ARTICLES OF PAPER AND PAPERBOARD, READY FOR USE	48.14/16/17/20/21/22/23	48.14/16/17/20/21/22/23	641.94 642.2 642.3 642.42/45/91/93/99 892.81
14.5.1	of which: PRINTING AND WRITING PAPER, READY FOR USE	ex4823.90	ex4823.90	ex642.99
14.5.2	of which: ARTICLES, MOULDED OR PRESSED FROM PULP	4823.70	4823.70	ex642.99
14.5.3	of which: FILTER PAPER AND PAPERBOARD, READY FOR USE	4823.20	4823.20	642.45
Notes:
The term "ex" means that there is not a complete correlation between the two codes and that only a part of the HS2012/HS2017 or SITC Rev.4 code is applicable.
For instance "ex811.00" under "Prefabricated buildings of wood" means that only a part of SITC code 811.00 refers to buildings prefabricated from wood, as that code does not distinguish between the materials buildings were prefabricated from.
In SITC Rev.4, if only 4 digits are shown, then all subheadings at lower degrees of aggregation are included (for example, 892.2 includes 892.21 and 892.29).

SentData

Country	Flow	Year	Unit	Product	Conc	Data value
FR	P	2020	1000 m3	1	FR_P_2020_1000 m3_1	47388.0450260872	JQ1
FR	P	2020	1000 m3	1_C	FR_P_2020_1000 m3_1_C	23323.76455
FR	P	2020	1000 m3	1_NC	FR_P_2020_1000 m3_1_NC	2332.376455
FR	P	2020	1000 m3	1_1	FR_P_2020_1000 m3_1_1	20991.388095
FR	P	2020	1000 m3	1_1_C	FR_P_2020_1000 m3_1_1_C	24064.2804760872
FR	P	2020	1000 m3	1_1_NC	FR_P_2020_1000 m3_1_1_NC	16323.0876374444
FR	P	2020	1000 m3	1_2	FR_P_2020_1000 m3_1_2	7741.1928386428
FR	P	2020	1000 m3	1_2_C	FR_P_2020_1000 m3_1_2_C	57.3529411765
FR	P	2020	1000 m3	1_2_NC	FR_P_2020_1000 m3_1_2_NC	15856.5402671983
FR	P	2020	1000 m3	1_2_1	FR_P_2020_1000 m3_1_2_1	11644.7874285555
FR	P	2020	1000 m3	1_2_1_C	FR_P_2020_1000 m3_1_2_1_C	4211.7528386429
FR	P	2020	1000 m3	1_2_1_NC	FR_P_2020_1000 m3_1_2_1_NC	7738.1802088889
FR	P	2020	1000 m3	1_2_2	FR_P_2020_1000 m3_1_2_2	4473.6202088889
FR	P	2020	1000 m3	1_2_2_C	FR_P_2020_1000 m3_1_2_2_C	3264.56
FR	P	2020	1000 m3	1_2_2_NC	FR_P_2020_1000 m3_1_2_2_NC	469.56
FR	P	2020	1000 m3	1_2_3	FR_P_2020_1000 m3_1_2_3	204.68
FR	P	2020	1000 m3	1_2_3_C	FR_P_2020_1000 m3_1_2_3_C	264.88
FR	P	2020	1000 m3	1_2_3_NC	FR_P_2020_1000 m3_1_2_3_NC	0
FR	P	2020	1000 mt	2	FR_P_2020_1000 mt_2	13404.1152270974
FR	P	2020	1000 m3	3	FR_P_2020_1000 m3_3	5752.0552168567
FR	P	2020	1000 m3	3_1	FR_P_2020_1000 m3_3_1	7652.0600102407
FR	P	2020	1000 m3	3_2	FR_P_2020_1000 m3_3_2	6382
FR	P	2020	1000 mt	4	FR_P_2020_1000 mt_4	1760
FR	P	2020	1000 mt	4_1	FR_P_2020_1000 mt_4_1	1700
FR	P	2020	1000 mt	4_2	FR_P_2020_1000 mt_4_2	60
FR	P	2020	1000 m3	5	FR_P_2020_1000 m3_5	7575
FR	P	2020	1000 m3	5_C	FR_P_2020_1000 m3_5_C	6442
FR	P	2020	1000 m3	5_NC	FR_P_2020_1000 m3_5_NC	1133
FR	P	2020	1000 m3	5_NC_T	FR_P_2020_1000 m3_5_NC_T	10
FR	P	2020	1000 m3	6	FR_P_2020_1000 m3_6	157
FR	P	2020	1000 m3	6_1	FR_P_2020_1000 m3_6_1	2
FR	P	2020	1000 m3	6_1_C	FR_P_2020_1000 m3_6_1_C	155
FR	P	2020	1000 m3	6_1_NC	FR_P_2020_1000 m3_6_1_NC	0
FR	P	2020	1000 m3	6_1_NC_T	FR_P_2020_1000 m3_6_1_NC_T	3734
FR	P	2020	1000 m3	6_2	FR_P_2020_1000 m3_6_2	234
FR	P	2020	1000 m3	6_2_C	FR_P_2020_1000 m3_6_2_C	94
FR	P	2020	1000 m3	6_2_NC	FR_P_2020_1000 m3_6_2_NC	140
FR	P	2020	1000 m3	6_2_NC_T	FR_P_2020_1000 m3_6_2_NC_T	105
FR	P	2020	1000 m3	6_3	FR_P_2020_1000 m3_6_3	2600
FR	P	2020	1000 m3	6_3_1	FR_P_2020_1000 m3_6_3_1	0
FR	P	2020	1000 m3	6_4	FR_P_2020_1000 m3_6_4	900
FR	P	2020	1000 m3	6_4_1	FR_P_2020_1000 m3_6_4_1	0
FR	P	2020	1000 m3	6_4_2	FR_P_2020_1000 m3_6_4_2	751
FR	P	2020	1000 m3	6_4_3	FR_P_2020_1000 m3_6_4_3	0
FR	P	2020	1000 mt	7	FR_P_2020_1000 mt_7	1620
FR	P	2020	1000 mt	7_1	FR_P_2020_1000 mt_7_1	264
FR	P	2020	1000 mt	7_2	FR_P_2020_1000 mt_7_2	1351
FR	P	2020	1000 mt	7_3	FR_P_2020_1000 mt_7_3	1237
FR	P	2020	1000 mt	7_3_1	FR_P_2020_1000 mt_7_3_1	0
FR	P	2020	1000 mt	7_3_2	FR_P_2020_1000 mt_7_3_2	114
FR	P	2020	1000 mt	7_3_3	FR_P_2020_1000 mt_7_3_3	0
FR	P	2020	1000 mt	7_3_4	FR_P_2020_1000 mt_7_3_4	4305
FR	P	2020	1000 mt	7_4	FR_P_2020_1000 mt_7_4	5
FR	P	2020	1000 mt	8	FR_P_2020_1000 mt_8	4300
FR	P	2020	1000 mt	8_1	FR_P_2020_1000 mt_8_1	6317
FR	P	2020	1000 mt	8_2	FR_P_2020_1000 mt_8_2	6873
FR	P	2020	1000 mt	9	FR_P_2020_1000 mt_9	1198
FR	P	2020	1000 mt	10	FR_P_2020_1000 mt_10	479
FR	P	2020	1000 mt	10_1	FR_P_2020_1000 mt_10_1	42
FR	P	2020	1000 mt	10_1_1	FR_P_2020_1000 mt_10_1_1	506
FR	P	2020	1000 mt	10_1_2	FR_P_2020_1000 mt_10_1_2	171
FR	P	2020	1000 mt	10_1_3	FR_P_2020_1000 mt_10_1_3	832
FR	P	2020	1000 mt	10_1_4	FR_P_2020_1000 mt_10_1_4	4422
FR	P	2020	1000 mt	10_2	FR_P_2020_1000 mt_10_2	3576
FR	P	2020	1000 mt	10_3	FR_P_2020_1000 mt_10_3	647
FR	P	2020	1000 mt	10_3_1	FR_P_2020_1000 mt_10_3_1	199
FR	P	2020	1000 mt	10_3_2	FR_P_2020_1000 mt_10_3_2	0
FR	P	2020	1000 mt	10_3_3	FR_P_2020_1000 mt_10_3_3	421
FR	P	2020	1000 mt	10_3_4	FR_P_2020_1000 mt_10_3_4	0
FR	P	2020	1000 mt	10_4	FR_P_2020_1000 mt_10_4	ERROR:#REF!
FR	P	2021	1000 m3	1	FR_P_2021_1000 m3_1	53138.5253983089
FR	P	2021	1000 m3	1_C	FR_P_2021_1000 m3_1_C	26950
FR	P	2021	1000 m3	1_NC	FR_P_2021_1000 m3_1_NC	2695
FR	P	2021	1000 m3	1_1	FR_P_2021_1000 m3_1_1	24255
FR	P	2021	1000 m3	1_1_C	FR_P_2021_1000 m3_1_1_C	26188.5253983089
FR	P	2021	1000 m3	1_1_NC	FR_P_2021_1000 m3_1_1_NC	18270.9170409538
FR	P	2021	1000 m3	1_2	FR_P_2021_1000 m3_1_2	7917.6083573551
FR	P	2021	1000 m3	1_2_C	FR_P_2021_1000 m3_1_2_C	60.2205882353
FR	P	2021	1000 m3	1_2_NC	FR_P_2021_1000 m3_1_2_NC	17896.9105919591
FR	P	2021	1000 m3	1_2_1	FR_P_2021_1000 m3_1_2_1	13451.122234604
FR	P	2021	1000 m3	1_2_1_C	FR_P_2021_1000 m3_1_2_1_C	4445.7883573551
FR	P	2021	1000 m3	1_2_1_NC	FR_P_2021_1000 m3_1_2_1_NC	7727.4548063498
FR	P	2021	1000 m3	1_2_2	FR_P_2021_1000 m3_1_2_2	4524.8148063498
FR	P	2021	1000 m3	1_2_2_C	FR_P_2021_1000 m3_1_2_2_C	3202.64
FR	P	2021	1000 m3	1_2_2_NC	FR_P_2021_1000 m3_1_2_2_NC	564.16
FR	P	2021	1000 m3	1_2_3	FR_P_2021_1000 m3_1_2_3	294.98
FR	P	2021	1000 m3	1_2_3_C	FR_P_2021_1000 m3_1_2_3_C	269.18
FR	P	2021	1000 m3	1_2_3_NC	FR_P_2021_1000 m3_1_2_3_NC	0
FR	P	2021	1000 mt	2	FR_P_2021_1000 mt_2	16366.2173858924
FR	P	2021	1000 m3	3	FR_P_2021_1000 m3_3	6941.2914386318
FR	P	2021	1000 m3	3_1	FR_P_2021_1000 m3_3_1	9424.9259472606
FR	P	2021	1000 m3	3_2	FR_P_2021_1000 m3_3_2	6382
FR	P	2021	1000 mt	4	FR_P_2021_1000 mt_4	1930
FR	P	2021	1000 mt	4_1	FR_P_2021_1000 mt_4_1	1850
FR	P	2021	1000 mt	4_2	FR_P_2021_1000 mt_4_2	80
FR	P	2021	1000 m3	5	FR_P_2021_1000 m3_5	8581
FR	P	2021	1000 m3	5_C	FR_P_2021_1000 m3_5_C	7268
FR	P	2021	1000 m3	5_NC	FR_P_2021_1000 m3_5_NC	1313
FR	P	2021	1000 m3	5_NC_T	FR_P_2021_1000 m3_5_NC_T	25
FR	P	2021	1000 m3	6	FR_P_2021_1000 m3_6	157
FR	P	2021	1000 m3	6_1	FR_P_2021_1000 m3_6_1	2
FR	P	2021	1000 m3	6_1_C	FR_P_2021_1000 m3_6_1_C	155
FR	P	2021	1000 m3	6_1_NC	FR_P_2021_1000 m3_6_1_NC	0
FR	P	2021	1000 m3	6_1_NC_T	FR_P_2021_1000 m3_6_1_NC_T	3770
FR	P	2021	1000 m3	6_2	FR_P_2021_1000 m3_6_2	270
FR	P	2021	1000 m3	6_2_C	FR_P_2021_1000 m3_6_2_C	120
FR	P	2021	1000 m3	6_2_NC	FR_P_2021_1000 m3_6_2_NC	150
FR	P	2021	1000 m3	6_2_NC_T	FR_P_2021_1000 m3_6_2_NC_T	105
FR	P	2021	1000 m3	6_3	FR_P_2021_1000 m3_6_3	2600
FR	P	2021	1000 m3	6_3_1	FR_P_2021_1000 m3_6_3_1	0
FR	P	2021	1000 m3	6_4	FR_P_2021_1000 m3_6_4	900
FR	P	2021	1000 m3	6_4_1	FR_P_2021_1000 m3_6_4_1	0
FR	P	2021	1000 m3	6_4_2	FR_P_2021_1000 m3_6_4_2	751
FR	P	2021	1000 m3	6_4_3	FR_P_2021_1000 m3_6_4_3	0
FR	P	2021	1000 mt	7	FR_P_2021_1000 mt_7	1615
FR	P	2021	1000 mt	7_1	FR_P_2021_1000 mt_7_1	300
FR	P	2021	1000 mt	7_2	FR_P_2021_1000 mt_7_2	1310
FR	P	2021	1000 mt	7_3	FR_P_2021_1000 mt_7_3	1199
FR	P	2021	1000 mt	7_3_1	FR_P_2021_1000 mt_7_3_1	0
FR	P	2021	1000 mt	7_3_2	FR_P_2021_1000 mt_7_3_2	111
FR	P	2021	1000 mt	7_3_3	FR_P_2021_1000 mt_7_3_3	0
FR	P	2021	1000 mt	7_3_4	FR_P_2021_1000 mt_7_3_4	4573
FR	P	2021	1000 mt	7_4	FR_P_2021_1000 mt_7_4	6
FR	P	2021	1000 mt	8	FR_P_2021_1000 mt_8	4567
FR	P	2021	1000 mt	8_1	FR_P_2021_1000 mt_8_1	6885
FR	P	2021	1000 mt	8_2	FR_P_2021_1000 mt_8_2	7359
FR	P	2021	1000 mt	9	FR_P_2021_1000 mt_9	1314
FR	P	2021	1000 mt	10	FR_P_2021_1000 mt_10	508
FR	P	2021	1000 mt	10_1	FR_P_2021_1000 mt_10_1	43
FR	P	2021	1000 mt	10_1_1	FR_P_2021_1000 mt_10_1_1	552
FR	P	2021	1000 mt	10_1_2	FR_P_2021_1000 mt_10_1_2	211
FR	P	2021	1000 mt	10_1_3	FR_P_2021_1000 mt_10_1_3	817
FR	P	2021	1000 mt	10_1_4	FR_P_2021_1000 mt_10_1_4	4840
FR	P	2021	1000 mt	10_2	FR_P_2021_1000 mt_10_2	3933
FR	P	2021	1000 mt	10_3	FR_P_2021_1000 mt_10_3	682
FR	P	2021	1000 mt	10_3_1	FR_P_2021_1000 mt_10_3_1	225
FR	P	2021	1000 mt	10_3_2	FR_P_2021_1000 mt_10_3_2	0
FR	P	2021	1000 mt	10_3_3	FR_P_2021_1000 mt_10_3_3	388
FR	P	2021	1000 mt	10_3_4	FR_P_2021_1000 mt_10_3_4	0
FR	P	2021	1000 mt	10_4	FR_P_2021_1000 mt_10_4	ERROR:#REF!
FR	M	2020	1000 m3	1	FR_M_2020_1000 m3_1	1125.67135	JQ2
FR	M	2020	1000 m3	1_1	FR_M_2020_1000 m3_1_1	182.19013
FR	M	2020	1000 m3	1_2	FR_M_2020_1000 m3_1_2	158.9655
FR	M	2020	1000 m3	1_2_C	FR_M_2020_1000 m3_1_2_C	23.22463
FR	M	2020	1000 m3	1_2_NC	FR_M_2020_1000 m3_1_2_NC	943.48122
FR	M	2020	1000 m3	1_2_NC_T	FR_M_2020_1000 m3_1_2_NC_T	718.7168
FR	M	2020	1000 mt	2	FR_M_2020_1000 mt_2	224.76442
FR	M	2020	1000 m3	3	FR_M_2020_1000 m3_3	36.08472
FR	M	2020	1000 m3	3_1	FR_M_2020_1000 m3_3_1	106.029
FR	M	2020	1000 m3	3_2	FR_M_2020_1000 m3_3_2	2509.72788
FR	M	2020	1000 mt	4	FR_M_2020_1000 mt_4	652.81428
FR	M	2020	1000 mt	4_1	FR_M_2020_1000 mt_4_1	1856.9136
FR	M	2020	1000 mt	4_2	FR_M_2020_1000 mt_4_2	0
FR	M	2020	1000 m3	5	FR_M_2020_1000 m3_5	548.344
FR	M	2020	1000 m3	5_C	FR_M_2020_1000 m3_5_C	412.381
FR	M	2020	1000 m3	5_NC	FR_M_2020_1000 m3_5_NC	135.963
FR	M	2020	1000 m3	5_NC_T	FR_M_2020_1000 m3_5_NC_T	2714.582
FR	M	2020	1000 m3	6	FR_M_2020_1000 m3_6	2437.9542
FR	M	2020	1000 m3	6_1	FR_M_2020_1000 m3_6_1	276.6278
FR	M	2020	1000 m3	6_1_C	FR_M_2020_1000 m3_6_1_C	129.1808
FR	M	2020	1000 m3	6_1_NC	FR_M_2020_1000 m3_6_1_NC	311.22133
FR	M	2020	1000 m3	6_1_NC_T	FR_M_2020_1000 m3_6_1_NC_T	17.48551
FR	M	2020	1000 m3	6_2	FR_M_2020_1000 m3_6_2	293.73582
FR	M	2020	1000 m3	6_2_C	FR_M_2020_1000 m3_6_2_C	78.65354
FR	M	2020	1000 m3	6_2_NC	FR_M_2020_1000 m3_6_2_NC	1350.29964
FR	M	2020	1000 m3	6_2_NC_T	FR_M_2020_1000 m3_6_2_NC_T	443.77102
FR	M	2020	1000 m3	6_3	FR_M_2020_1000 m3_6_3	103.65586
FR	M	2020	1000 m3	6_3_1	FR_M_2020_1000 m3_6_3_1	340.11516
FR	M	2020	1000 m3	6_4	FR_M_2020_1000 m3_6_4	91.07252
FR	M	2020	1000 m3	6_4_1	FR_M_2020_1000 m3_6_4_1	118.05192
FR	M	2020	1000 m3	6_4_2	FR_M_2020_1000 m3_6_4_2	0
FR	M	2020	1000 m3	6_4_3	FR_M_2020_1000 m3_6_4_3	788.4767
FR	M	2020	1000 mt	7	FR_M_2020_1000 mt_7	235.48761
FR	M	2020	1000 mt	7_1	FR_M_2020_1000 mt_7_1	481.14736
FR	M	2020	1000 mt	7_2	FR_M_2020_1000 mt_7_2	71.84173
FR	M	2020	1000 mt	7_3	FR_M_2020_1000 mt_7_3	1757.856
FR	M	2020	1000 mt	7_3_1	FR_M_2020_1000 mt_7_3_1	75.74
FR	M	2020	1000 mt	7_3_2	FR_M_2020_1000 mt_7_3_2	1668.806
FR	M	2020	1000 mt	7_3_3	FR_M_2020_1000 mt_7_3_3	1645.881
FR	M	2020	1000 mt	7_3_4	FR_M_2020_1000 mt_7_3_4	1645.881
FR	M	2020	1000 mt	7_4	FR_M_2020_1000 mt_7_4	22.925
FR	M	2020	1000 mt	8	FR_M_2020_1000 mt_8	13.31
FR	M	2020	1000 mt	8_1	FR_M_2020_1000 mt_8_1	23.131
FR	M	2020	1000 mt	8_2	FR_M_2020_1000 mt_8_2	16.325
FR	M	2020	1000 mt	9	FR_M_2020_1000 mt_9	6.806
FR	M	2020	1000 mt	10	FR_M_2020_1000 mt_10	1859.992
FR	M	2020	1000 mt	10_1	FR_M_2020_1000 mt_10_1	4502.25
FR	M	2020	1000 mt	10_1_1	FR_M_2020_1000 mt_10_1_1	2131.837
FR	M	2020	1000 mt	10_1_2	FR_M_2020_1000 mt_10_1_2	0
FR	M	2020	1000 mt	10_1_3	FR_M_2020_1000 mt_10_1_3	0
FR	M	2020	1000 mt	10_1_4	FR_M_2020_1000 mt_10_1_4	0
FR	M	2020	1000 mt	10_2	FR_M_2020_1000 mt_10_2	0
FR	M	2020	1000 mt	10_3	FR_M_2020_1000 mt_10_3	90.586
FR	M	2020	1000 mt	10_3_1	FR_M_2020_1000 mt_10_3_1	2249.687
FR	M	2020	1000 mt	10_3_2	FR_M_2020_1000 mt_10_3_2	0
FR	M	2020	1000 mt	10_3_3	FR_M_2020_1000 mt_10_3_3	0
FR	M	2020	1000 mt	10_3_4	FR_M_2020_1000 mt_10_3_4	0
FR	M	2020	1000 mt	10_4	FR_M_2020_1000 mt_10_4	0
FR	M	2020	1000 NAC	1	FR_M_2020_1000 NAC_1	144711.575
FR	M	2020	1000 NAC	1_1	FR_M_2020_1000 NAC_1_1	18992.55
FR	M	2020	1000 NAC	1_2	FR_M_2020_1000 NAC_1_2	14529
FR	M	2020	1000 NAC	1_2_C	FR_M_2020_1000 NAC_1_2_C	4463.55
FR	M	2020	1000 NAC	1_2_NC	FR_M_2020_1000 NAC_1_2_NC	125719.025
FR	M	2020	1000 NAC	1_2_NC_T	FR_M_2020_1000 NAC_1_2_NC_T	83022.4
FR	M	2020	1000 NAC	2	FR_M_2020_1000 NAC_2	42696.625
FR	M	2020	1000 NAC	3	FR_M_2020_1000 NAC_3	17342.425
FR	M	2020	1000 NAC	3_1	FR_M_2020_1000 NAC_3_1	61977
FR	M	2020	1000 NAC	3_2	FR_M_2020_1000 NAC_3_2	143600.7
FR	M	2020	1000 NAC	4	FR_M_2020_1000 NAC_4	48736.9
FR	M	2020	1000 NAC	4_1	FR_M_2020_1000 NAC_4_1	94863.8
FR	M	2020	1000 NAC	4_2	FR_M_2020_1000 NAC_4_2	0
FR	M	2020	1000 NAC	5	FR_M_2020_1000 NAC_5	99126
FR	M	2020	1000 NAC	5_C	FR_M_2020_1000 NAC_5_C	86007
FR	M	2020	1000 NAC	5_NC	FR_M_2020_1000 NAC_5_NC	13119
FR	M	2020	1000 NAC	5_NC_T	FR_M_2020_1000 NAC_5_NC_T	1395705.1
FR	M	2020	1000 NAC	6	FR_M_2020_1000 NAC_6	1129098.6
FR	M	2020	1000 NAC	6_1	FR_M_2020_1000 NAC_6_1	266606.5
FR	M	2020	1000 NAC	6_1_C	FR_M_2020_1000 NAC_6_1_C	120276.8
FR	M	2020	1000 NAC	6_1_NC	FR_M_2020_1000 NAC_6_1_NC	296963.73
FR	M	2020	1000 NAC	6_1_NC_T	FR_M_2020_1000 NAC_6_1_NC_T	13907.81
FR	M	2020	1000 NAC	6_2	FR_M_2020_1000 NAC_6_2	283055.92
FR	M	2020	1000 NAC	6_2_C	FR_M_2020_1000 NAC_6_2_C	92392.44
FR	M	2020	1000 NAC	6_2_NC	FR_M_2020_1000 NAC_6_2_NC	944721.2
FR	M	2020	1000 NAC	6_2_NC_T	FR_M_2020_1000 NAC_6_2_NC_T	442465.1
FR	M	2020	1000 NAC	6_3	FR_M_2020_1000 NAC_6_3	114716.14
FR	M	2020	1000 NAC	6_3_1	FR_M_2020_1000 NAC_6_3_1	327748.96
FR	M	2020	1000 NAC	6_4	FR_M_2020_1000 NAC_6_4	106980.72
FR	M	2020	1000 NAC	6_4_1	FR_M_2020_1000 NAC_6_4_1	58113.82
FR	M	2020	1000 NAC	6_4_2	FR_M_2020_1000 NAC_6_4_2	0
FR	M	2020	1000 NAC	6_4_3	FR_M_2020_1000 NAC_6_4_3	444142.28
FR	M	2020	1000 NAC	7	FR_M_2020_1000 NAC_7	107675.55
FR	M	2020	1000 NAC	7_1	FR_M_2020_1000 NAC_7_1	276418.54
FR	M	2020	1000 NAC	7_2	FR_M_2020_1000 NAC_7_2	60048.19
FR	M	2020	1000 NAC	7_3	FR_M_2020_1000 NAC_7_3	796995
FR	M	2020	1000 NAC	7_3_1	FR_M_2020_1000 NAC_7_3_1	33763
FR	M	2020	1000 NAC	7_3_2	FR_M_2020_1000 NAC_7_3_2	763232
FR	M	2020	1000 NAC	7_3_3	FR_M_2020_1000 NAC_7_3_3	745962
FR	M	2020	1000 NAC	7_3_4	FR_M_2020_1000 NAC_7_3_4	745962
FR	M	2020	1000 NAC	7_4	FR_M_2020_1000 NAC_7_4	17270
FR	M	2020	1000 NAC	8	FR_M_2020_1000 NAC_8	0
FR	M	2020	1000 NAC	8_1	FR_M_2020_1000 NAC_8_1	30360
FR	M	2020	1000 NAC	8_2	FR_M_2020_1000 NAC_8_2	26045
FR	M	2020	1000 NAC	9	FR_M_2020_1000 NAC_9	4315
FR	M	2020	1000 NAC	10	FR_M_2020_1000 NAC_10	3716226
FR	M	2020	1000 NAC	10_1	FR_M_2020_1000 NAC_10_1	3386847
FR	M	2020	1000 NAC	10_1_1	FR_M_2020_1000 NAC_10_1_1	1618320
FR	M	2020	1000 NAC	10_1_2	FR_M_2020_1000 NAC_10_1_2	0
FR	M	2020	1000 NAC	10_1_3	FR_M_2020_1000 NAC_10_1_3	0
FR	M	2020	1000 NAC	10_1_4	FR_M_2020_1000 NAC_10_1_4	0
FR	M	2020	1000 NAC	10_2	FR_M_2020_1000 NAC_10_2	0
FR	M	2020	1000 NAC	10_3	FR_M_2020_1000 NAC_10_3	108249
FR	M	2020	1000 NAC	10_3_1	FR_M_2020_1000 NAC_10_3_1	1587681
FR	M	2020	1000 NAC	10_3_2	FR_M_2020_1000 NAC_10_3_2	0
FR	M	2020	1000 NAC	10_3_3	FR_M_2020_1000 NAC_10_3_3	0
FR	M	2020	1000 NAC	10_3_4	FR_M_2020_1000 NAC_10_3_4	0
FR	M	2020	1000 NAC	10_4	FR_M_2020_1000 NAC_10_4	0
FR	M	2021	1000 m3	1	FR_M_2021_1000 m3_1	1182.850775
FR	M	2021	1000 m3	1_1	FR_M_2021_1000 m3_1_1	197.68982
FR	M	2021	1000 m3	1_2	FR_M_2021_1000 m3_1_2	177.7995
FR	M	2021	1000 m3	1_2_C	FR_M_2021_1000 m3_1_2_C	19.89032
FR	M	2021	1000 m3	1_2_NC	FR_M_2021_1000 m3_1_2_NC	985.160955
FR	M	2021	1000 m3	1_2_NC_T	FR_M_2021_1000 m3_1_2_NC_T	794.4816
FR	M	2021	1000 mt	2	FR_M_2021_1000 mt_2	190.679355
FR	M	2021	1000 m3	3	FR_M_2021_1000 m3_3	46.396055
FR	M	2021	1000 m3	3_1	FR_M_2021_1000 m3_3_1	80.945
FR	M	2021	1000 m3	3_2	FR_M_2021_1000 m3_3_2	2185.52254
FR	M	2021	1000 mt	4	FR_M_2021_1000 mt_4	513.55254
FR	M	2021	1000 mt	4_1	FR_M_2021_1000 mt_4_1	1671.97
FR	M	2021	1000 mt	4_2	FR_M_2021_1000 mt_4_2	0
FR	M	2021	1000 m3	5	FR_M_2021_1000 m3_5	807.856
FR	M	2021	1000 m3	5_C	FR_M_2021_1000 m3_5_C	660.949
FR	M	2021	1000 m3	5_NC	FR_M_2021_1000 m3_5_NC	146.907
FR	M	2021	1000 m3	5_NC_T	FR_M_2021_1000 m3_5_NC_T	3132.4063
FR	M	2021	1000 m3	6	FR_M_2021_1000 m3_6	2846.07
FR	M	2021	1000 m3	6_1	FR_M_2021_1000 m3_6_1	286.3363
FR	M	2021	1000 m3	6_1_C	FR_M_2021_1000 m3_6_1_C	135.128
FR	M	2021	1000 m3	6_1_NC	FR_M_2021_1000 m3_6_1_NC	308.50281
FR	M	2021	1000 m3	6_1_NC_T	FR_M_2021_1000 m3_6_1_NC_T	14.67256
FR	M	2021	1000 m3	6_2	FR_M_2021_1000 m3_6_2	293.83025
FR	M	2021	1000 m3	6_2_C	FR_M_2021_1000 m3_6_2_C	86.10154
FR	M	2021	1000 m3	6_2_NC	FR_M_2021_1000 m3_6_2_NC	1298.93177
FR	M	2021	1000 m3	6_2_NC_T	FR_M_2021_1000 m3_6_2_NC_T	469.58142
FR	M	2021	1000 m3	6_3	FR_M_2021_1000 m3_6_3	129.35692
FR	M	2021	1000 m3	6_3_1	FR_M_2021_1000 m3_6_3_1	340.2245
FR	M	2021	1000 m3	6_4	FR_M_2021_1000 m3_6_4	99.69652
FR	M	2021	1000 m3	6_4_1	FR_M_2021_1000 m3_6_4_1	148.05814
FR	M	2021	1000 m3	6_4_2	FR_M_2021_1000 m3_6_4_2	0
FR	M	2021	1000 m3	6_4_3	FR_M_2021_1000 m3_6_4_3	681.29221
FR	M	2021	1000 mt	7	FR_M_2021_1000 mt_7	211.98669
FR	M	2021	1000 mt	7_1	FR_M_2021_1000 mt_7_1	388.1669
FR	M	2021	1000 mt	7_2	FR_M_2021_1000 mt_7_2	81.13862
FR	M	2021	1000 mt	7_3	FR_M_2021_1000 mt_7_3	1603.663
FR	M	2021	1000 mt	7_3_1	FR_M_2021_1000 mt_7_3_1	82.514
FR	M	2021	1000 mt	7_3_2	FR_M_2021_1000 mt_7_3_2	1521.149
FR	M	2021	1000 mt	7_3_3	FR_M_2021_1000 mt_7_3_3	1510.177
FR	M	2021	1000 mt	7_3_4	FR_M_2021_1000 mt_7_3_4	1510.177
FR	M	2021	1000 mt	7_4	FR_M_2021_1000 mt_7_4	10.972
FR	M	2021	1000 mt	8	FR_M_2021_1000 mt_8	0
FR	M	2021	1000 mt	8_1	FR_M_2021_1000 mt_8_1	23.497
FR	M	2021	1000 mt	8_2	FR_M_2021_1000 mt_8_2	19.152
FR	M	2021	1000 mt	9	FR_M_2021_1000 mt_9	4.345
FR	M	2021	1000 mt	10	FR_M_2021_1000 mt_10	1895.719
FR	M	2021	1000 mt	10_1	FR_M_2021_1000 mt_10_1	4740.495
FR	M	2021	1000 mt	10_1_1	FR_M_2021_1000 mt_10_1_1	2126.105
FR	M	2021	1000 mt	10_1_2	FR_M_2021_1000 mt_10_1_2	0
FR	M	2021	1000 mt	10_1_3	FR_M_2021_1000 mt_10_1_3	0
FR	M	2021	1000 mt	10_1_4	FR_M_2021_1000 mt_10_1_4	0
FR	M	2021	1000 mt	10_2	FR_M_2021_1000 mt_10_2	0
FR	M	2021	1000 mt	10_3	FR_M_2021_1000 mt_10_3	89.624
FR	M	2021	1000 mt	10_3_1	FR_M_2021_1000 mt_10_3_1	2483.641
FR	M	2021	1000 mt	10_3_2	FR_M_2021_1000 mt_10_3_2	0
FR	M	2021	1000 mt	10_3_3	FR_M_2021_1000 mt_10_3_3	0
FR	M	2021	1000 mt	10_3_4	FR_M_2021_1000 mt_10_3_4	0
FR	M	2021	1000 mt	10_4	FR_M_2021_1000 mt_10_4	0
FR	M	2021	1000 NAC	1	FR_M_2021_1000 NAC_1	174216.045
FR	M	2021	1000 NAC	1_1	FR_M_2021_1000 NAC_1_1	17393.93
FR	M	2021	1000 NAC	1_2	FR_M_2021_1000 NAC_1_2	13876.5
FR	M	2021	1000 NAC	1_2_C	FR_M_2021_1000 NAC_1_2_C	3517.43
FR	M	2021	1000 NAC	1_2_NC	FR_M_2021_1000 NAC_1_2_NC	156822.115
FR	M	2021	1000 NAC	1_2_NC_T	FR_M_2021_1000 NAC_1_2_NC_T	108792
FR	M	2021	1000 NAC	2	FR_M_2021_1000 NAC_2	48030.115
FR	M	2021	1000 NAC	3	FR_M_2021_1000 NAC_3	24048.815
FR	M	2021	1000 NAC	3_1	FR_M_2021_1000 NAC_3_1	54658
FR	M	2021	1000 NAC	3_2	FR_M_2021_1000 NAC_3_2	117701.72
FR	M	2021	1000 NAC	4	FR_M_2021_1000 NAC_4	33751.12
FR	M	2021	1000 NAC	4_1	FR_M_2021_1000 NAC_4_1	83950.6
FR	M	2021	1000 NAC	4_2	FR_M_2021_1000 NAC_4_2	0
FR	M	2021	1000 NAC	5	FR_M_2021_1000 NAC_5	142805
FR	M	2021	1000 NAC	5_C	FR_M_2021_1000 NAC_5_C	127444
FR	M	2021	1000 NAC	5_NC	FR_M_2021_1000 NAC_5_NC	15361
FR	M	2021	1000 NAC	5_NC_T	FR_M_2021_1000 NAC_5_NC_T	2218546
FR	M	2021	1000 NAC	6	FR_M_2021_1000 NAC_6	1915698.6
FR	M	2021	1000 NAC	6_1	FR_M_2021_1000 NAC_6_1	302847.4
FR	M	2021	1000 NAC	6_1_C	FR_M_2021_1000 NAC_6_1_C	137781
FR	M	2021	1000 NAC	6_1_NC	FR_M_2021_1000 NAC_6_1_NC	360703.98
FR	M	2021	1000 NAC	6_1_NC_T	FR_M_2021_1000 NAC_6_1_NC_T	13269.41
FR	M	2021	1000 NAC	6_2	FR_M_2021_1000 NAC_6_2	347434.57
FR	M	2021	1000 NAC	6_2_C	FR_M_2021_1000 NAC_6_2_C	105934.5
FR	M	2021	1000 NAC	6_2_NC	FR_M_2021_1000 NAC_6_2_NC	1237733.81
FR	M	2021	1000 NAC	6_2_NC_T	FR_M_2021_1000 NAC_6_2_NC_T	563693.9
FR	M	2021	1000 NAC	6_3	FR_M_2021_1000 NAC_6_3	161401.24
FR	M	2021	1000 NAC	6_3_1	FR_M_2021_1000 NAC_6_3_1	402292.66
FR	M	2021	1000 NAC	6_4	FR_M_2021_1000 NAC_6_4	122661
FR	M	2021	1000 NAC	6_4_1	FR_M_2021_1000 NAC_6_4_1	129798.82
FR	M	2021	1000 NAC	6_4_2	FR_M_2021_1000 NAC_6_4_2	0
FR	M	2021	1000 NAC	6_4_3	FR_M_2021_1000 NAC_6_4_3	544241.09
FR	M	2021	1000 NAC	7	FR_M_2021_1000 NAC_7	144493.14
FR	M	2021	1000 NAC	7_1	FR_M_2021_1000 NAC_7_1	321553.54
FR	M	2021	1000 NAC	7_2	FR_M_2021_1000 NAC_7_2	78194.41
FR	M	2021	1000 NAC	7_3	FR_M_2021_1000 NAC_7_3	951410
FR	M	2021	1000 NAC	7_3_1	FR_M_2021_1000 NAC_7_3_1	42563
FR	M	2021	1000 NAC	7_3_2	FR_M_2021_1000 NAC_7_3_2	908847
FR	M	2021	1000 NAC	7_3_3	FR_M_2021_1000 NAC_7_3_3	896474
FR	M	2021	1000 NAC	7_3_4	FR_M_2021_1000 NAC_7_3_4	896474
FR	M	2021	1000 NAC	7_4	FR_M_2021_1000 NAC_7_4	12373
FR	M	2021	1000 NAC	8	FR_M_2021_1000 NAC_8	0
FR	M	2021	1000 NAC	8_1	FR_M_2021_1000 NAC_8_1	32037
FR	M	2021	1000 NAC	8_2	FR_M_2021_1000 NAC_8_2	29028
FR	M	2021	1000 NAC	9	FR_M_2021_1000 NAC_9	3009
FR	M	2021	1000 NAC	10	FR_M_2021_1000 NAC_10	4027706
FR	M	2021	1000 NAC	10_1	FR_M_2021_1000 NAC_10_1	3838835
FR	M	2021	1000 NAC	10_1_1	FR_M_2021_1000 NAC_10_1_1	1643723
FR	M	2021	1000 NAC	10_1_2	FR_M_2021_1000 NAC_10_1_2	0
FR	M	2021	1000 NAC	10_1_3	FR_M_2021_1000 NAC_10_1_3	0
FR	M	2021	1000 NAC	10_1_4	FR_M_2021_1000 NAC_10_1_4	0
FR	M	2021	1000 NAC	10_2	FR_M_2021_1000 NAC_10_2	0
FR	M	2021	1000 NAC	10_3	FR_M_2021_1000 NAC_10_3	105394
FR	M	2021	1000 NAC	10_3_1	FR_M_2021_1000 NAC_10_3_1	2007051
FR	M	2021	1000 NAC	10_3_2	FR_M_2021_1000 NAC_10_3_2	0
FR	M	2021	1000 NAC	10_3_3	FR_M_2021_1000 NAC_10_3_3	0
FR	M	2021	1000 NAC	10_3_4	FR_M_2021_1000 NAC_10_3_4	0
FR	M	2021	1000 NAC	10_4	FR_M_2021_1000 NAC_10_4	0
FR	X	2020	1000 m3	1	FR_X_2020_1000 m3_1	4032.24893
FR	X	2020	1000 m3	1_1	FR_X_2020_1000 m3_1_1	449.48352
FR	X	2020	1000 m3	1_2	FR_X_2020_1000 m3_1_2	353.751
FR	X	2020	1000 m3	1_2_C	FR_X_2020_1000 m3_1_2_C	95.73252
FR	X	2020	1000 m3	1_2_NC	FR_X_2020_1000 m3_1_2_NC	3582.76541
FR	X	2020	1000 m3	1_2_NC_T	FR_X_2020_1000 m3_1_2_NC_T	1436.4016
FR	X	2020	1000 mt	2	FR_X_2020_1000 mt_2	2146.36381
FR	X	2020	1000 m3	3	FR_X_2020_1000 m3_3	3.06231
FR	X	2020	1000 m3	3_1	FR_X_2020_1000 m3_3_1	9.192
FR	X	2020	1000 m3	3_2	FR_X_2020_1000 m3_3_2	699.2563
FR	X	2020	1000 mt	4	FR_X_2020_1000 mt_4	451.2223
FR	X	2020	1000 mt	4_1	FR_X_2020_1000 mt_4_1	248.034
FR	X	2020	1000 mt	4_2	FR_X_2020_1000 mt_4_2	0
FR	X	2020	1000 m3	5	FR_X_2020_1000 m3_5	286.105
FR	X	2020	1000 m3	5_C	FR_X_2020_1000 m3_5_C	96.539
FR	X	2020	1000 m3	5_NC	FR_X_2020_1000 m3_5_NC	189.566
FR	X	2020	1000 m3	5_NC_T	FR_X_2020_1000 m3_5_NC_T	1517.7849
FR	X	2020	1000 m3	6	FR_X_2020_1000 m3_6	1047.8826
FR	X	2020	1000 m3	6_1	FR_X_2020_1000 m3_6_1	469.9023
FR	X	2020	1000 m3	6_1_C	FR_X_2020_1000 m3_6_1_C	2.933
FR	X	2020	1000 m3	6_1_NC	FR_X_2020_1000 m3_6_1_NC	68.28353
FR	X	2020	1000 m3	6_1_NC_T	FR_X_2020_1000 m3_6_1_NC_T	0.25802
FR	X	2020	1000 m3	6_2	FR_X_2020_1000 m3_6_2	68.02551
FR	X	2020	1000 m3	6_2_C	FR_X_2020_1000 m3_6_2_C	53.54181
FR	X	2020	1000 m3	6_2_NC	FR_X_2020_1000 m3_6_2_NC	1048.06319
FR	X	2020	1000 m3	6_2_NC_T	FR_X_2020_1000 m3_6_2_NC_T	158.7894
FR	X	2020	1000 m3	6_3	FR_X_2020_1000 m3_6_3	80.02302
FR	X	2020	1000 m3	6_3_1	FR_X_2020_1000 m3_6_3_1	78.76638
FR	X	2020	1000 m3	6_4	FR_X_2020_1000 m3_6_4	61.99578
FR	X	2020	1000 m3	6_4_1	FR_X_2020_1000 m3_6_4_1	34.85956
FR	X	2020	1000 m3	6_4_2	FR_X_2020_1000 m3_6_4_2	0
FR	X	2020	1000 m3	6_4_3	FR_X_2020_1000 m3_6_4_3	854.41423
FR	X	2020	1000 mt	7	FR_X_2020_1000 mt_7	449.40348
FR	X	2020	1000 mt	7_1	FR_X_2020_1000 mt_7_1	312.43096
FR	X	2020	1000 mt	7_2	FR_X_2020_1000 mt_7_2	92.57979
FR	X	2020	1000 mt	7_3	FR_X_2020_1000 mt_7_3	447.092
FR	X	2020	1000 mt	7_3_1	FR_X_2020_1000 mt_7_3_1	4.643
FR	X	2020	1000 mt	7_3_2	FR_X_2020_1000 mt_7_3_2	442.449
FR	X	2020	1000 mt	7_3_3	FR_X_2020_1000 mt_7_3_3	436.7
FR	X	2020	1000 mt	7_3_4	FR_X_2020_1000 mt_7_3_4	436.7
FR	X	2020	1000 mt	7_4	FR_X_2020_1000 mt_7_4	5.749
FR	X	2020	1000 mt	8	FR_X_2020_1000 mt_8	0
FR	X	2020	1000 mt	8_1	FR_X_2020_1000 mt_8_1	70.485
FR	X	2020	1000 mt	8_2	FR_X_2020_1000 mt_8_2	2.343
FR	X	2020	1000 mt	9	FR_X_2020_1000 mt_9	68.142
FR	X	2020	1000 mt	10	FR_X_2020_1000 mt_10	921.811
FR	X	2020	1000 mt	10_1	FR_X_2020_1000 mt_10_1	3464.735
FR	X	2020	1000 mt	10_1_1	FR_X_2020_1000 mt_10_1_1	895.994
FR	X	2020	1000 mt	10_1_2	FR_X_2020_1000 mt_10_1_2	0
FR	X	2020	1000 mt	10_1_3	FR_X_2020_1000 mt_10_1_3	0
FR	X	2020	1000 mt	10_1_4	FR_X_2020_1000 mt_10_1_4	0
FR	X	2020	1000 mt	10_2	FR_X_2020_1000 mt_10_2	0
FR	X	2020	1000 mt	10_3	FR_X_2020_1000 mt_10_3	79.583
FR	X	2020	1000 mt	10_3_1	FR_X_2020_1000 mt_10_3_1	2478.484
FR	X	2020	1000 mt	10_3_2	FR_X_2020_1000 mt_10_3_2	0
FR	X	2020	1000 mt	10_3_3	FR_X_2020_1000 mt_10_3_3	0
FR	X	2020	1000 mt	10_3_4	FR_X_2020_1000 mt_10_3_4	0
FR	X	2020	1000 mt	10_4	FR_X_2020_1000 mt_10_4	0
FR	X	2020	1000 NAC	1	FR_X_2020_1000 NAC_1	369012.92
FR	X	2020	1000 NAC	1_1	FR_X_2020_1000 NAC_1_1	21264.3
FR	X	2020	1000 NAC	1_2	FR_X_2020_1000 NAC_1_2	16228.5
FR	X	2020	1000 NAC	1_2_C	FR_X_2020_1000 NAC_1_2_C	5035.8
FR	X	2020	1000 NAC	1_2_NC	FR_X_2020_1000 NAC_1_2_NC	347748.62
FR	X	2020	1000 NAC	1_2_NC_T	FR_X_2020_1000 NAC_1_2_NC_T	109099.2
FR	X	2020	1000 NAC	2	FR_X_2020_1000 NAC_2	238649.42
FR	X	2020	1000 NAC	3	FR_X_2020_1000 NAC_3	614.72
FR	X	2020	1000 NAC	3_1	FR_X_2020_1000 NAC_3_1	7796
FR	X	2020	1000 NAC	3_2	FR_X_2020_1000 NAC_3_2	58678.04
FR	X	2020	1000 NAC	4	FR_X_2020_1000 NAC_4	42730.24
FR	X	2020	1000 NAC	4_1	FR_X_2020_1000 NAC_4_1	15947.8
FR	X	2020	1000 NAC	4_2	FR_X_2020_1000 NAC_4_2	0
FR	X	2020	1000 NAC	5	FR_X_2020_1000 NAC_5	25530
FR	X	2020	1000 NAC	5_C	FR_X_2020_1000 NAC_5_C	20703
FR	X	2020	1000 NAC	5_NC	FR_X_2020_1000 NAC_5_NC	4827
FR	X	2020	1000 NAC	5_NC_T	FR_X_2020_1000 NAC_5_NC_T	594802.6
FR	X	2020	1000 NAC	6	FR_X_2020_1000 NAC_6	322027.2
FR	X	2020	1000 NAC	6_1	FR_X_2020_1000 NAC_6_1	272775.4
FR	X	2020	1000 NAC	6_1_C	FR_X_2020_1000 NAC_6_1_C	3927
FR	X	2020	1000 NAC	6_1_NC	FR_X_2020_1000 NAC_6_1_NC	128978.08
FR	X	2020	1000 NAC	6_1_NC_T	FR_X_2020_1000 NAC_6_1_NC_T	603.82
FR	X	2020	1000 NAC	6_2	FR_X_2020_1000 NAC_6_2	128374.26
FR	X	2020	1000 NAC	6_2_C	FR_X_2020_1000 NAC_6_2_C	105910.56
FR	X	2020	1000 NAC	6_2_NC	FR_X_2020_1000 NAC_6_2_NC	547327.36
FR	X	2020	1000 NAC	6_2_NC_T	FR_X_2020_1000 NAC_6_2_NC_T	230145.3
FR	X	2020	1000 NAC	6_3	FR_X_2020_1000 NAC_6_3	81501.42
FR	X	2020	1000 NAC	6_3_1	FR_X_2020_1000 NAC_6_3_1	148643.88
FR	X	2020	1000 NAC	6_4	FR_X_2020_1000 NAC_6_4	122633.28
FR	X	2020	1000 NAC	6_4_1	FR_X_2020_1000 NAC_6_4_1	14713.42
FR	X	2020	1000 NAC	6_4_2	FR_X_2020_1000 NAC_6_4_2	0
FR	X	2020	1000 NAC	6_4_3	FR_X_2020_1000 NAC_6_4_3	302468.64
FR	X	2020	1000 NAC	7	FR_X_2020_1000 NAC_7	139046.37
FR	X	2020	1000 NAC	7_1	FR_X_2020_1000 NAC_7_1	108678
FR	X	2020	1000 NAC	7_2	FR_X_2020_1000 NAC_7_2	54744.27
FR	X	2020	1000 NAC	7_3	FR_X_2020_1000 NAC_7_3	203655
FR	X	2020	1000 NAC	7_3_1	FR_X_2020_1000 NAC_7_3_1	1253
FR	X	2020	1000 NAC	7_3_2	FR_X_2020_1000 NAC_7_3_2	202402
FR	X	2020	1000 NAC	7_3_3	FR_X_2020_1000 NAC_7_3_3	199023
FR	X	2020	1000 NAC	7_3_4	FR_X_2020_1000 NAC_7_3_4	199023
FR	X	2020	1000 NAC	7_4	FR_X_2020_1000 NAC_7_4	3379
FR	X	2020	1000 NAC	8	FR_X_2020_1000 NAC_8	0
FR	X	2020	1000 NAC	8_1	FR_X_2020_1000 NAC_8_1	44499
FR	X	2020	1000 NAC	8_2	FR_X_2020_1000 NAC_8_2	4471
FR	X	2020	1000 NAC	9	FR_X_2020_1000 NAC_9	40028
FR	X	2020	1000 NAC	10	FR_X_2020_1000 NAC_10	2100841
FR	X	2020	1000 NAC	10_1	FR_X_2020_1000 NAC_10_1	2649898
FR	X	2020	1000 NAC	10_1_1	FR_X_2020_1000 NAC_10_1_1	715201
FR	X	2020	1000 NAC	10_1_2	FR_X_2020_1000 NAC_10_1_2	0
FR	X	2020	1000 NAC	10_1_3	FR_X_2020_1000 NAC_10_1_3	0
FR	X	2020	1000 NAC	10_1_4	FR_X_2020_1000 NAC_10_1_4	0
FR	X	2020	1000 NAC	10_2	FR_X_2020_1000 NAC_10_2	0
FR	X	2020	1000 NAC	10_3	FR_X_2020_1000 NAC_10_3	92484
FR	X	2020	1000 NAC	10_3_1	FR_X_2020_1000 NAC_10_3_1	1698501
FR	X	2020	1000 NAC	10_3_2	FR_X_2020_1000 NAC_10_3_2	0
FR	X	2020	1000 NAC	10_3_3	FR_X_2020_1000 NAC_10_3_3	0
FR	X	2020	1000 NAC	10_3_4	FR_X_2020_1000 NAC_10_3_4	0
FR	X	2020	1000 NAC	10_4	FR_X_2020_1000 NAC_10_4	0
FR	X	2021	1000 m3	1	FR_X_2021_1000 m3_1	4546.654125
FR	X	2021	1000 m3	1_1	FR_X_2021_1000 m3_1_1	435.13554
FR	X	2021	1000 m3	1_2	FR_X_2021_1000 m3_1_2	299.9145
FR	X	2021	1000 m3	1_2_C	FR_X_2021_1000 m3_1_2_C	135.22104
FR	X	2021	1000 m3	1_2_NC	FR_X_2021_1000 m3_1_2_NC	4111.518585
FR	X	2021	1000 m3	1_2_NC_T	FR_X_2021_1000 m3_1_2_NC_T	1617.3616
FR	X	2021	1000 mt	2	FR_X_2021_1000 mt_2	2494.156985
FR	X	2021	1000 m3	3	FR_X_2021_1000 m3_3	1.913785
FR	X	2021	1000 m3	3_1	FR_X_2021_1000 m3_3_1	6.912
FR	X	2021	1000 m3	3_2	FR_X_2021_1000 m3_3_2	631.02238
FR	X	2021	1000 mt	4	FR_X_2021_1000 mt_4	311.45418
FR	X	2021	1000 mt	4_1	FR_X_2021_1000 mt_4_1	319.5682
FR	X	2021	1000 mt	4_2	FR_X_2021_1000 mt_4_2	0
FR	X	2021	1000 m3	5	FR_X_2021_1000 m3_5	342.339
FR	X	2021	1000 m3	5_C	FR_X_2021_1000 m3_5_C	121.64
FR	X	2021	1000 m3	5_NC	FR_X_2021_1000 m3_5_NC	220.699
FR	X	2021	1000 m3	5_NC_T	FR_X_2021_1000 m3_5_NC_T	1646.4064
FR	X	2021	1000 m3	6	FR_X_2021_1000 m3_6	1055.3076
FR	X	2021	1000 m3	6_1	FR_X_2021_1000 m3_6_1	591.0988
FR	X	2021	1000 m3	6_1_C	FR_X_2021_1000 m3_6_1_C	4.9252
FR	X	2021	1000 m3	6_1_NC	FR_X_2021_1000 m3_6_1_NC	71.28401
FR	X	2021	1000 m3	6_1_NC_T	FR_X_2021_1000 m3_6_1_NC_T	0.5586
FR	X	2021	1000 m3	6_2	FR_X_2021_1000 m3_6_2	70.72541
FR	X	2021	1000 m3	6_2_C	FR_X_2021_1000 m3_6_2_C	54.0778
FR	X	2021	1000 m3	6_2_NC	FR_X_2021_1000 m3_6_2_NC	1056.5873
FR	X	2021	1000 m3	6_2_NC_T	FR_X_2021_1000 m3_6_2_NC_T	154.9394
FR	X	2021	1000 m3	6_3	FR_X_2021_1000 m3_6_3	73.04682
FR	X	2021	1000 m3	6_3_1	FR_X_2021_1000 m3_6_3_1	81.89258
FR	X	2021	1000 m3	6_4	FR_X_2021_1000 m3_6_4	62.6164
FR	X	2021	1000 m3	6_4_1	FR_X_2021_1000 m3_6_4_1	28.81442
FR	X	2021	1000 m3	6_4_2	FR_X_2021_1000 m3_6_4_2	0
FR	X	2021	1000 m3	6_4_3	FR_X_2021_1000 m3_6_4_3	872.83348
FR	X	2021	1000 mt	7	FR_X_2021_1000 mt_7	443.30625
FR	X	2021	1000 mt	7_1	FR_X_2021_1000 mt_7_1	344.64614
FR	X	2021	1000 mt	7_2	FR_X_2021_1000 mt_7_2	84.88109
FR	X	2021	1000 mt	7_3	FR_X_2021_1000 mt_7_3	412.901
FR	X	2021	1000 mt	7_3_1	FR_X_2021_1000 mt_7_3_1	32.302
FR	X	2021	1000 mt	7_3_2	FR_X_2021_1000 mt_7_3_2	380.599
FR	X	2021	1000 mt	7_3_3	FR_X_2021_1000 mt_7_3_3	379.414
FR	X	2021	1000 mt	7_3_4	FR_X_2021_1000 mt_7_3_4	379.414
FR	X	2021	1000 mt	7_4	FR_X_2021_1000 mt_7_4	1.185
FR	X	2021	1000 mt	8	FR_X_2021_1000 mt_8	0
FR	X	2021	1000 mt	8_1	FR_X_2021_1000 mt_8_1	83.456
FR	X	2021	1000 mt	8_2	FR_X_2021_1000 mt_8_2	2.01
FR	X	2021	1000 mt	9	FR_X_2021_1000 mt_9	81.446
FR	X	2021	1000 mt	10	FR_X_2021_1000 mt_10	955.364
FR	X	2021	1000 mt	10_1	FR_X_2021_1000 mt_10_1	3806.804
FR	X	2021	1000 mt	10_1_1	FR_X_2021_1000 mt_10_1_1	1008.647
FR	X	2021	1000 mt	10_1_2	FR_X_2021_1000 mt_10_1_2	0
FR	X	2021	1000 mt	10_1_3	FR_X_2021_1000 mt_10_1_3	0
FR	X	2021	1000 mt	10_1_4	FR_X_2021_1000 mt_10_1_4	0
FR	X	2021	1000 mt	10_2	FR_X_2021_1000 mt_10_2	0
FR	X	2021	1000 mt	10_3	FR_X_2021_1000 mt_10_3	67.563
FR	X	2021	1000 mt	10_3_1	FR_X_2021_1000 mt_10_3_1	2672.381
FR	X	2021	1000 mt	10_3_2	FR_X_2021_1000 mt_10_3_2	0
FR	X	2021	1000 mt	10_3_3	FR_X_2021_1000 mt_10_3_3	0
FR	X	2021	1000 mt	10_3_4	FR_X_2021_1000 mt_10_3_4	0
FR	X	2021	1000 mt	10_4	FR_X_2021_1000 mt_10_4	0
FR	X	2021	1000 NAC	1	FR_X_2021_1000 NAC_1	488442.5
FR	X	2021	1000 NAC	1_1	FR_X_2021_1000 NAC_1_1	24033.4
FR	X	2021	1000 NAC	1_2	FR_X_2021_1000 NAC_1_2	16665
FR	X	2021	1000 NAC	1_2_C	FR_X_2021_1000 NAC_1_2_C	7368.4
FR	X	2021	1000 NAC	1_2_NC	FR_X_2021_1000 NAC_1_2_NC	464409.1
FR	X	2021	1000 NAC	1_2_NC_T	FR_X_2021_1000 NAC_1_2_NC_T	163259.2
FR	X	2021	1000 NAC	2	FR_X_2021_1000 NAC_2	301149.9
FR	X	2021	1000 NAC	3	FR_X_2021_1000 NAC_3	560.1
FR	X	2021	1000 NAC	3_1	FR_X_2021_1000 NAC_3_1	6952
FR	X	2021	1000 NAC	3_2	FR_X_2021_1000 NAC_3_2	36015.1
FR	X	2021	1000 NAC	4	FR_X_2021_1000 NAC_4	16724.5
FR	X	2021	1000 NAC	4_1	FR_X_2021_1000 NAC_4_1	19290.6
FR	X	2021	1000 NAC	4_2	FR_X_2021_1000 NAC_4_2	0
FR	X	2021	1000 NAC	5	FR_X_2021_1000 NAC_5	34392
FR	X	2021	1000 NAC	5_C	FR_X_2021_1000 NAC_5_C	25701
FR	X	2021	1000 NAC	5_NC	FR_X_2021_1000 NAC_5_NC	8691
FR	X	2021	1000 NAC	5_NC_T	FR_X_2021_1000 NAC_5_NC_T	807830.7
FR	X	2021	1000 NAC	6	FR_X_2021_1000 NAC_6	438489
FR	X	2021	1000 NAC	6_1	FR_X_2021_1000 NAC_6_1	369341.7
FR	X	2021	1000 NAC	6_1_C	FR_X_2021_1000 NAC_6_1_C	6273.4
FR	X	2021	1000 NAC	6_1_NC	FR_X_2021_1000 NAC_6_1_NC	141373.68
FR	X	2021	1000 NAC	6_1_NC_T	FR_X_2021_1000 NAC_6_1_NC_T	809.97
FR	X	2021	1000 NAC	6_2	FR_X_2021_1000 NAC_6_2	140563.71
FR	X	2021	1000 NAC	6_2_C	FR_X_2021_1000 NAC_6_2_C	110759.74
FR	X	2021	1000 NAC	6_2_NC	FR_X_2021_1000 NAC_6_2_NC	559393.1
FR	X	2021	1000 NAC	6_2_NC_T	FR_X_2021_1000 NAC_6_2_NC_T	215553.8
FR	X	2021	1000 NAC	6_3	FR_X_2021_1000 NAC_6_3	52795.82
FR	X	2021	1000 NAC	6_3_1	FR_X_2021_1000 NAC_6_3_1	162757.98
FR	X	2021	1000 NAC	6_4	FR_X_2021_1000 NAC_6_4	128248.12
FR	X	2021	1000 NAC	6_4_1	FR_X_2021_1000 NAC_6_4_1	17880.54
FR	X	2021	1000 NAC	6_4_2	FR_X_2021_1000 NAC_6_4_2	0
FR	X	2021	1000 NAC	6_4_3	FR_X_2021_1000 NAC_6_4_3	325958.76
FR	X	2021	1000 NAC	7	FR_X_2021_1000 NAC_7	131170.92
FR	X	2021	1000 NAC	7_1	FR_X_2021_1000 NAC_7_1	144664.46
FR	X	2021	1000 NAC	7_2	FR_X_2021_1000 NAC_7_2	50123.38
FR	X	2021	1000 NAC	7_3	FR_X_2021_1000 NAC_7_3	227432
FR	X	2021	1000 NAC	7_3_1	FR_X_2021_1000 NAC_7_3_1	6479
FR	X	2021	1000 NAC	7_3_2	FR_X_2021_1000 NAC_7_3_2	220953
FR	X	2021	1000 NAC	7_3_3	FR_X_2021_1000 NAC_7_3_3	219787
FR	X	2021	1000 NAC	7_3_4	FR_X_2021_1000 NAC_7_3_4	219787
FR	X	2021	1000 NAC	7_4	FR_X_2021_1000 NAC_7_4	1166
FR	X	2021	1000 NAC	8	FR_X_2021_1000 NAC_8	0
FR	X	2021	1000 NAC	8_1	FR_X_2021_1000 NAC_8_1	56220
FR	X	2021	1000 NAC	8_2	FR_X_2021_1000 NAC_8_2	4738
FR	X	2021	1000 NAC	9	FR_X_2021_1000 NAC_9	51482
FR	X	2021	1000 NAC	10	FR_X_2021_1000 NAC_10	2280836
FR	X	2021	1000 NAC	10_1	FR_X_2021_1000 NAC_10_1	3433153
FR	X	2021	1000 NAC	10_1_1	FR_X_2021_1000 NAC_10_1_1	834097
FR	X	2021	1000 NAC	10_1_2	FR_X_2021_1000 NAC_10_1_2	0
FR	X	2021	1000 NAC	10_1_3	FR_X_2021_1000 NAC_10_1_3	0
FR	X	2021	1000 NAC	10_1_4	FR_X_2021_1000 NAC_10_1_4	0
FR	X	2021	1000 NAC	10_2	FR_X_2021_1000 NAC_10_2	0
FR	X	2021	1000 NAC	10_3	FR_X_2021_1000 NAC_10_3	91041
FR	X	2021	1000 NAC	10_3_1	FR_X_2021_1000 NAC_10_3_1	2194104
FR	X	2021	1000 NAC	10_3_2	FR_X_2021_1000 NAC_10_3_2	0
FR	X	2021	1000 NAC	10_3_3	FR_X_2021_1000 NAC_10_3_3	0
FR	X	2021	1000 NAC	10_3_4	FR_X_2021_1000 NAC_10_3_4	0
FR	X	2021	1000 NAC	10_4	FR_X_2021_1000 NAC_10_4	0
FR	M	5143356	1000 NAC	11_1	FR_M_5143356_1000 NAC_11_1	87166	JQ3
FR	M	5143356	1000 NAC	11_1_C	FR_M_5143356_1000 NAC_11_1_C	153987
FR	M	5143356	1000 NAC	11_1_NC	FR_M_5143356_1000 NAC_11_1_NC	77809.0454477993
FR	M	5143356	1000 NAC	11_1_NC_T	FR_M_5143356_1000 NAC_11_1_NC_T	260380
FR	M	5143356	1000 NAC	11_2	FR_M_5143356_1000 NAC_11_2	152042
FR	M	5143356	1000 NAC	11_3	FR_M_5143356_1000 NAC_11_3	502348
FR	M	5143356	1000 NAC	11_4	FR_M_5143356_1000 NAC_11_4	3480344
FR	M	5143356	1000 NAC	11_5	FR_M_5143356_1000 NAC_11_5	87771
FR	M	5143356	1000 NAC	11_6	FR_M_5143356_1000 NAC_11_6	419318
FR	M	5143356	1000 NAC	11_7	FR_M_5143356_1000 NAC_11_7	3716226
FR	M	5143356	1000 NAC	11_7_1	FR_M_5143356_1000 NAC_11_7_1	0
FR	M	5143356	1000 NAC	12_1	FR_M_5143356_1000 NAC_12_1	0
FR	M	5143356	1000 NAC	12_2	FR_M_5143356_1000 NAC_12_2	0
FR	M	5143356	1000 NAC	12_3	FR_M_5143356_1000 NAC_12_3	0
FR	M	5143356	1000 NAC	12_4	FR_M_5143356_1000 NAC_12_4	0
FR	M	5143356	1000 NAC	12_5	FR_M_5143356_1000 NAC_12_5	0
FR	M	5143356	1000 NAC	12_6	FR_M_5143356_1000 NAC_12_6	0
FR	M	5143356	1000 NAC	12_6_1	FR_M_5143356_1000 NAC_12_6_1	0
FR	M	5143356	1000 NAC	12_6_2	FR_M_5143356_1000 NAC_12_6_2	8
FR	M	5143356	1000 NAC	12_6_3	FR_M_5143356_1000 NAC_12_6_3	ERROR:#REF!
FR	M	6555724	1000 NAC	11_1	FR_M_6555724_1000 NAC_11_1	114718
FR	M	6555724	1000 NAC	11_1_C	FR_M_6555724_1000 NAC_11_1_C	181923
FR	M	6555724	1000 NAC	11_1_NC	FR_M_6555724_1000 NAC_11_1_NC	91925
FR	M	6555724	1000 NAC	11_1_NC_T	FR_M_6555724_1000 NAC_11_1_NC_T	377370
FR	M	6555724	1000 NAC	11_2	FR_M_6555724_1000 NAC_11_2	206172
FR	M	6555724	1000 NAC	11_3	FR_M_6555724_1000 NAC_11_3	685375
FR	M	6555724	1000 NAC	11_4	FR_M_6555724_1000 NAC_11_4	4370059
FR	M	6555724	1000 NAC	11_5	FR_M_6555724_1000 NAC_11_5	94449
FR	M	6555724	1000 NAC	11_6	FR_M_6555724_1000 NAC_11_6	525658
FR	M	6555724	1000 NAC	11_7	FR_M_6555724_1000 NAC_11_7	4027706
FR	M	6555724	1000 NAC	11_7_1	FR_M_6555724_1000 NAC_11_7_1	0
FR	M	6555724	1000 NAC	12_1	FR_M_6555724_1000 NAC_12_1	0
FR	M	6555724	1000 NAC	12_2	FR_M_6555724_1000 NAC_12_2	0
FR	M	6555724	1000 NAC	12_3	FR_M_6555724_1000 NAC_12_3	0
FR	M	6555724	1000 NAC	12_4	FR_M_6555724_1000 NAC_12_4	0
FR	M	6555724	1000 NAC	12_5	FR_M_6555724_1000 NAC_12_5	0
FR	M	6555724	1000 NAC	12_6	FR_M_6555724_1000 NAC_12_6	0
FR	M	6555724	1000 NAC	12_6_1	FR_M_6555724_1000 NAC_12_6_1	0
FR	M	6555724	1000 NAC	12_6_2	FR_M_6555724_1000 NAC_12_6_2	8
FR	M	6555724	1000 NAC	12_6_3	FR_M_6555724_1000 NAC_12_6_3	ERROR:#REF!
FR	X	5143356	1000 NAC	11_1	FR_X_5143356_1000 NAC_11_1	23814
FR	X	5143356	1000 NAC	11_1_C	FR_X_5143356_1000 NAC_11_1_C	16716
FR	X	5143356	1000 NAC	11_1_NC	FR_X_5143356_1000 NAC_11_1_NC	1596.7285256029
FR	X	5143356	1000 NAC	11_1_NC_T	FR_X_5143356_1000 NAC_11_1_NC_T	492085
FR	X	5143356	1000 NAC	11_2	FR_X_5143356_1000 NAC_11_2	52987
FR	X	5143356	1000 NAC	11_3	FR_X_5143356_1000 NAC_11_3	96924
FR	X	5143356	1000 NAC	11_4	FR_X_5143356_1000 NAC_11_4	815749
FR	X	5143356	1000 NAC	11_5	FR_X_5143356_1000 NAC_11_5	8252
FR	X	5143356	1000 NAC	11_6	FR_X_5143356_1000 NAC_11_6	85252
FR	X	5143356	1000 NAC	11_7	FR_X_5143356_1000 NAC_11_7	2100841
FR	X	5143356	1000 NAC	11_7_1	FR_X_5143356_1000 NAC_11_7_1	0
FR	X	5143356	1000 NAC	12_1	FR_X_5143356_1000 NAC_12_1	0
FR	X	5143356	1000 NAC	12_2	FR_X_5143356_1000 NAC_12_2	0
FR	X	5143356	1000 NAC	12_3	FR_X_5143356_1000 NAC_12_3	0
FR	X	5143356	1000 NAC	12_4	FR_X_5143356_1000 NAC_12_4	0
FR	X	5143356	1000 NAC	12_5	FR_X_5143356_1000 NAC_12_5	0
FR	X	5143356	1000 NAC	12_6	FR_X_5143356_1000 NAC_12_6	0
FR	X	5143356	1000 NAC	12_6_1	FR_X_5143356_1000 NAC_12_6_1	0
FR	X	5143356	1000 NAC	12_6_2	FR_X_5143356_1000 NAC_12_6_2	8
FR	X	5143356	1000 NAC	12_6_3	FR_X_5143356_1000 NAC_12_6_3	ERROR:#REF!
FR	X	6555724	1000 NAC	11_1	FR_X_6555724_1000 NAC_11_1	31666
FR	X	6555724	1000 NAC	11_1_C	FR_X_6555724_1000 NAC_11_1_C	16834
FR	X	6555724	1000 NAC	11_1_NC	FR_X_6555724_1000 NAC_11_1_NC	1608
FR	X	6555724	1000 NAC	11_1_NC_T	FR_X_6555724_1000 NAC_11_1_NC_T	532513
FR	X	6555724	1000 NAC	11_2	FR_X_6555724_1000 NAC_11_2	68432
FR	X	6555724	1000 NAC	11_3	FR_X_6555724_1000 NAC_11_3	138891
FR	X	6555724	1000 NAC	11_4	FR_X_6555724_1000 NAC_11_4	990390
FR	X	6555724	1000 NAC	11_5	FR_X_6555724_1000 NAC_11_5	12903
FR	X	6555724	1000 NAC	11_6	FR_X_6555724_1000 NAC_11_6	105873
FR	X	6555724	1000 NAC	11_7	FR_X_6555724_1000 NAC_11_7	2280836
FR	X	6555724	1000 NAC	11_7_1	FR_X_6555724_1000 NAC_11_7_1	0
FR	X	6555724	1000 NAC	12_1	FR_X_6555724_1000 NAC_12_1	0
FR	X	6555724	1000 NAC	12_2	FR_X_6555724_1000 NAC_12_2	0
FR	X	6555724	1000 NAC	12_3	FR_X_6555724_1000 NAC_12_3	0
FR	X	6555724	1000 NAC	12_4	FR_X_6555724_1000 NAC_12_4	0
FR	X	6555724	1000 NAC	12_5	FR_X_6555724_1000 NAC_12_5	0
FR	X	6555724	1000 NAC	12_6	FR_X_6555724_1000 NAC_12_6	0
FR	X	6555724	1000 NAC	12_6_1	FR_X_6555724_1000 NAC_12_6_1	0
FR	X	6555724	1000 NAC	12_6_2	FR_X_6555724_1000 NAC_12_6_2	8
FR	X	6555724	1000 NAC	12_6_3	FR_X_6555724_1000 NAC_12_6_3	ERROR:#REF!
FR	M	2020	1000 m3	ST_1_2_C	FR_M_2020_1000 m3_ST_1_2_C	718.7168	ECEEU
FR	M	2020	1000 m3	ST_1_2_C_1	FR_M_2020_1000 m3_ST_1_2_C_1	245.674580485
FR	M	2020	1000 m3	ST_1_2_C_1_1	FR_M_2020_1000 m3_ST_1_2_C_1_1	74.6433405149
FR	M	2020	1000 m3	ST_1_2_C_2_1	FR_M_2020_1000 m3_ST_1_2_C_2_1	171.0312399701
FR	M	2020	1000 m3	ST_1_2_C_2	FR_M_2020_1000 m3_ST_1_2_C_2	171.4698075904
FR	M	2020	1000 m3	ST_1_2_C_1_2	FR_M_2020_1000 m3_ST_1_2_C_1_2	148.3284903875
FR	M	2020	1000 m3	ST_1_2_C_2_2	FR_M_2020_1000 m3_ST_1_2_C_2_2	23.1413172029
FR	M	2020	1000 m3	ST_1_2_C_3	FR_M_2020_1000 m3_ST_1_2_C_3	ERROR:#REF!
FR	M	2020	1000 m3	ST_1_2_C_1_3	FR_M_2020_1000 m3_ST_1_2_C_1_3	ERROR:#REF!
FR	M	2020	1000 m3	ST_1_2_C_2_3	FR_M_2020_1000 m3_ST_1_2_C_2_3	ERROR:#REF!
FR	M	2020	1000 m3	ST_1_2_NC	FR_M_2020_1000 m3_ST_1_2_NC	224.76442
FR	M	2020	1000 m3	ST_1_2_NC_1	FR_M_2020_1000 m3_ST_1_2_NC_1	45.48
FR	M	2020	1000 m3	ST_1_2_NC_1_1	FR_M_2020_1000 m3_ST_1_2_NC_1_1	43.656
FR	M	2020	1000 m3	ST_1_2_NC_2_1	FR_M_2020_1000 m3_ST_1_2_NC_2_1	47.6133
FR	M	2020	1000 m3	ST_1_2_NC_2	FR_M_2020_1000 m3_ST_1_2_NC_2	6.103
FR	M	2020	1000 m3	ST_1_2_NC_1_2	FR_M_2020_1000 m3_ST_1_2_NC_1_2	41.5103
FR	M	2020	1000 m3	ST_1_2_NC_2_2	FR_M_2020_1000 m3_ST_1_2_NC_2_2	41.5103
FR	M	2020	1000 m3	ST_1_2_NC_3	FR_M_2020_1000 m3_ST_1_2_NC_3	19.53
FR	M	2020	1000 m3	ST_1_2_NC_1_3	FR_M_2020_1000 m3_ST_1_2_NC_1_3	2437.9542
FR	M	2020	1000 m3	ST_1_2_NC_2_3	FR_M_2020_1000 m3_ST_1_2_NC_2_3	1581.5898
FR	M	2020	1000 m3	ST_1_2_NC_4	FR_M_2020_1000 m3_ST_1_2_NC_4	707.9881289024
FR	M	2020	1000 m3	ST_1_2_NC_5	FR_M_2020_1000 m3_ST_1_2_NC_5	276.6278
FR	M	2020	1000 m3	ST_5_C	FR_M_2020_1000 m3_ST_5_C	63.163
FR	M	2020	1000 m3	ST_5_C_1	FR_M_2020_1000 m3_ST_5_C_1	30.6628
FR	M	2020	1000 m3	ST_5_C_2	FR_M_2020_1000 m3_ST_5_C_2	0.371
FR	M	2020	1000 m3	ST_5_NC	FR_M_2020_1000 m3_ST_5_NC	0.0112
FR	M	2020	1000 m3	ST_5_NC_1	FR_M_2020_1000 m3_ST_5_NC_1	1.3902
FR	M	2020	1000 m3	ST_5_NC_2	FR_M_2020_1000 m3_ST_5_NC_2	0.459
FR	M	2020	1000 m3	ST_5_NC_3	FR_M_2020_1000 m3_ST_5_NC_3	0.1302
FR	M	2020	1000 m3	ST_5_NC_4	FR_M_2020_1000 m3_ST_5_NC_4	0
FR	M	2020	1000 m3	ST_5_NC_5	FR_M_2020_1000 m3_ST_5_NC_5	0
FR	M	2020	1000 m3	ST_5_NC_6	FR_M_2020_1000 m3_ST_5_NC_6	0
FR	M	2020	1000 m3	ST_5_NC_7	FR_M_2020_1000 m3_ST_5_NC_7	ERROR:#REF!
FR	M	2020	1000 NAC	ST_1_2_C	FR_M_2020_1000 NAC_ST_1_2_C	83022.4
FR	M	2020	1000 NAC	ST_1_2_C_1	FR_M_2020_1000 NAC_ST_1_2_C_1	20698.517902787
FR	M	2020	1000 NAC	ST_1_2_C_1_1	FR_M_2020_1000 NAC_ST_1_2_C_1_1	6235.6849297743
FR	M	2020	1000 NAC	ST_1_2_C_2_1	FR_M_2020_1000 NAC_ST_1_2_C_2_1	14462.8329730127
FR	M	2020	1000 NAC	ST_1_2_C_2	FR_M_2020_1000 NAC_ST_1_2_C_2	25335.9041105964
FR	M	2020	1000 NAC	ST_1_2_C_1_2	FR_M_2020_1000 NAC_ST_1_2_C_1_2	17371.2726641665
FR	M	2020	1000 NAC	ST_1_2_C_2_2	FR_M_2020_1000 NAC_ST_1_2_C_2_2	7964.6314464299
FR	M	2020	1000 NAC	ST_1_2_C_3	FR_M_2020_1000 NAC_ST_1_2_C_3	ERROR:#REF!
FR	M	2020	1000 NAC	ST_1_2_C_1_3	FR_M_2020_1000 NAC_ST_1_2_C_1_3	ERROR:#REF!
FR	M	2020	1000 NAC	ST_1_2_C_2_3	FR_M_2020_1000 NAC_ST_1_2_C_2_3	ERROR:#REF!
FR	M	2020	1000 NAC	ST_1_2_NC	FR_M_2020_1000 NAC_ST_1_2_NC	42696.625
FR	M	2020	1000 NAC	ST_1_2_NC_1	FR_M_2020_1000 NAC_ST_1_2_NC_1	13982.4
FR	M	2020	1000 NAC	ST_1_2_NC_1_1	FR_M_2020_1000 NAC_ST_1_2_NC_1_1	2934
FR	M	2020	1000 NAC	ST_1_2_NC_2_1	FR_M_2020_1000 NAC_ST_1_2_NC_2_1	1749.4
FR	M	2020	1000 NAC	ST_1_2_NC_2	FR_M_2020_1000 NAC_ST_1_2_NC_2	192
FR	M	2020	1000 NAC	ST_1_2_NC_1_2	FR_M_2020_1000 NAC_ST_1_2_NC_1_2	1557.4
FR	M	2020	1000 NAC	ST_1_2_NC_2_2	FR_M_2020_1000 NAC_ST_1_2_NC_2_2	1557.4
FR	M	2020	1000 NAC	ST_1_2_NC_3	FR_M_2020_1000 NAC_ST_1_2_NC_3	1257.2
FR	M	2020	1000 NAC	ST_1_2_NC_1_3	FR_M_2020_1000 NAC_ST_1_2_NC_1_3	1129098.6
FR	M	2020	1000 NAC	ST_1_2_NC_2_3	FR_M_2020_1000 NAC_ST_1_2_NC_2_3	689184
FR	M	2020	1000 NAC	ST_1_2_NC_4	FR_M_2020_1000 NAC_ST_1_2_NC_4	323140.510858921
FR	M	2020	1000 NAC	ST_1_2_NC_5	FR_M_2020_1000 NAC_ST_1_2_NC_5	266606.5
FR	M	2020	1000 NAC	ST_5_C	FR_M_2020_1000 NAC_ST_5_C	80048.3
FR	M	2020	1000 NAC	ST_5_C_1	FR_M_2020_1000 NAC_ST_5_C_1	30676.8
FR	M	2020	1000 NAC	ST_5_C_2	FR_M_2020_1000 NAC_ST_5_C_2	383.6
FR	M	2020	1000 NAC	ST_5_NC	FR_M_2020_1000 NAC_ST_5_NC	26.6
FR	M	2020	1000 NAC	ST_5_NC_1	FR_M_2020_1000 NAC_ST_5_NC_1	1379
FR	M	2020	1000 NAC	ST_5_NC_2	FR_M_2020_1000 NAC_ST_5_NC_2	282.6
FR	M	2020	1000 NAC	ST_5_NC_3	FR_M_2020_1000 NAC_ST_5_NC_3	204.4
FR	M	2020	1000 NAC	ST_5_NC_4	FR_M_2020_1000 NAC_ST_5_NC_4	0
FR	M	2020	1000 NAC	ST_5_NC_5	FR_M_2020_1000 NAC_ST_5_NC_5	0
FR	M	2020	1000 NAC	ST_5_NC_6	FR_M_2020_1000 NAC_ST_5_NC_6	0
FR	M	2020	1000 NAC	ST_5_NC_7	FR_M_2020_1000 NAC_ST_5_NC_7	ERROR:#REF!
FR	M	2021	1000 m3	ST_1_2_C	FR_M_2021_1000 m3_ST_1_2_C	794.4816
FR	M	2021	1000 m3	ST_1_2_C_1	FR_M_2021_1000 m3_ST_1_2_C_1	271.5728
FR	M	2021	1000 m3	ST_1_2_C_1_1	FR_M_2021_1000 m3_ST_1_2_C_1_1	82.512
FR	M	2021	1000 m3	ST_1_2_C_2_1	FR_M_2021_1000 m3_ST_1_2_C_2_1	189.0608
FR	M	2021	1000 m3	ST_1_2_C_2	FR_M_2021_1000 m3_ST_1_2_C_2	189.5456
FR	M	2021	1000 m3	ST_1_2_C_1_2	FR_M_2021_1000 m3_ST_1_2_C_1_2	163.9648
FR	M	2021	1000 m3	ST_1_2_C_2_2	FR_M_2021_1000 m3_ST_1_2_C_2_2	25.5808
FR	M	2021	1000 m3	ST_1_2_C_3	FR_M_2021_1000 m3_ST_1_2_C_3	ERROR:#REF!
FR	M	2021	1000 m3	ST_1_2_C_1_3	FR_M_2021_1000 m3_ST_1_2_C_1_3	ERROR:#REF!
FR	M	2021	1000 m3	ST_1_2_C_2_3	FR_M_2021_1000 m3_ST_1_2_C_2_3	ERROR:#REF!
FR	M	2021	1000 m3	ST_1_2_NC	FR_M_2021_1000 m3_ST_1_2_NC	190.679355
FR	M	2021	1000 m3	ST_1_2_NC_1	FR_M_2021_1000 m3_ST_1_2_NC_1	43.5276
FR	M	2021	1000 m3	ST_1_2_NC_1_1	FR_M_2021_1000 m3_ST_1_2_NC_1_1	35.112
FR	M	2021	1000 m3	ST_1_2_NC_2_1	FR_M_2021_1000 m3_ST_1_2_NC_2_1	45.5849
FR	M	2021	1000 m3	ST_1_2_NC_2	FR_M_2021_1000 m3_ST_1_2_NC_2	6.36
FR	M	2021	1000 m3	ST_1_2_NC_1_2	FR_M_2021_1000 m3_ST_1_2_NC_1_2	39.2249
FR	M	2021	1000 m3	ST_1_2_NC_2_2	FR_M_2021_1000 m3_ST_1_2_NC_2_2	39.2249
FR	M	2021	1000 m3	ST_1_2_NC_3	FR_M_2021_1000 m3_ST_1_2_NC_3	3.3768
FR	M	2021	1000 m3	ST_1_2_NC_1_3	FR_M_2021_1000 m3_ST_1_2_NC_1_3	2846.07
FR	M	2021	1000 m3	ST_1_2_NC_2_3	FR_M_2021_1000 m3_ST_1_2_NC_2_3	1702.782
FR	M	2021	1000 m3	ST_1_2_NC_4	FR_M_2021_1000 m3_ST_1_2_NC_4	826.506
FR	M	2021	1000 m3	ST_1_2_NC_5	FR_M_2021_1000 m3_ST_1_2_NC_5	286.3363
FR	M	2021	1000 m3	ST_5_C	FR_M_2021_1000 m3_ST_5_C	57.8337
FR	M	2021	1000 m3	ST_5_C_1	FR_M_2021_1000 m3_ST_5_C_1	38.2368
FR	M	2021	1000 m3	ST_5_C_2	FR_M_2021_1000 m3_ST_5_C_2	0.28
FR	M	2021	1000 m3	ST_5_NC	FR_M_2021_1000 m3_ST_5_NC	0.0168
FR	M	2021	1000 m3	ST_5_NC_1	FR_M_2021_1000 m3_ST_5_NC_1	2.1056
FR	M	2021	1000 m3	ST_5_NC_2	FR_M_2021_1000 m3_ST_5_NC_2	0.5616
FR	M	2021	1000 m3	ST_5_NC_3	FR_M_2021_1000 m3_ST_5_NC_3	0.2464
FR	M	2021	1000 m3	ST_5_NC_4	FR_M_2021_1000 m3_ST_5_NC_4	0
FR	M	2021	1000 m3	ST_5_NC_5	FR_M_2021_1000 m3_ST_5_NC_5	0
FR	M	2021	1000 m3	ST_5_NC_6	FR_M_2021_1000 m3_ST_5_NC_6	0
FR	M	2021	1000 m3	ST_5_NC_7	FR_M_2021_1000 m3_ST_5_NC_7	ERROR:#REF!
FR	M	2021	1000 NAC	ST_1_2_C	FR_M_2021_1000 NAC_ST_1_2_C	108792
FR	M	2021	1000 NAC	ST_1_2_C_1	FR_M_2021_1000 NAC_ST_1_2_C_1	27123.2
FR	M	2021	1000 NAC	ST_1_2_C_1_1	FR_M_2021_1000 NAC_ST_1_2_C_1_1	8171.2
FR	M	2021	1000 NAC	ST_1_2_C_2_1	FR_M_2021_1000 NAC_ST_1_2_C_2_1	18952
FR	M	2021	1000 NAC	ST_1_2_C_2	FR_M_2021_1000 NAC_ST_1_2_C_2	33200
FR	M	2021	1000 NAC	ST_1_2_C_1_2	FR_M_2021_1000 NAC_ST_1_2_C_1_2	22763.2
FR	M	2021	1000 NAC	ST_1_2_C_2_2	FR_M_2021_1000 NAC_ST_1_2_C_2_2	10436.8
FR	M	2021	1000 NAC	ST_1_2_C_3	FR_M_2021_1000 NAC_ST_1_2_C_3	ERROR:#REF!
FR	M	2021	1000 NAC	ST_1_2_C_1_3	FR_M_2021_1000 NAC_ST_1_2_C_1_3	ERROR:#REF!
FR	M	2021	1000 NAC	ST_1_2_C_2_3	FR_M_2021_1000 NAC_ST_1_2_C_2_3	ERROR:#REF!
FR	M	2021	1000 NAC	ST_1_2_NC	FR_M_2021_1000 NAC_ST_1_2_NC	48030.115
FR	M	2021	1000 NAC	ST_1_2_NC_1	FR_M_2021_1000 NAC_ST_1_2_NC_1	14689.2
FR	M	2021	1000 NAC	ST_1_2_NC_1_1	FR_M_2021_1000 NAC_ST_1_2_NC_1_1	2576
FR	M	2021	1000 NAC	ST_1_2_NC_2_1	FR_M_2021_1000 NAC_ST_1_2_NC_2_1	2314.9
FR	M	2021	1000 NAC	ST_1_2_NC_2	FR_M_2021_1000 NAC_ST_1_2_NC_2	192
FR	M	2021	1000 NAC	ST_1_2_NC_1_2	FR_M_2021_1000 NAC_ST_1_2_NC_1_2	2122.9
FR	M	2021	1000 NAC	ST_1_2_NC_2_2	FR_M_2021_1000 NAC_ST_1_2_NC_2_2	2122.9
FR	M	2021	1000 NAC	ST_1_2_NC_3	FR_M_2021_1000 NAC_ST_1_2_NC_3	341.6
FR	M	2021	1000 NAC	ST_1_2_NC_1_3	FR_M_2021_1000 NAC_ST_1_2_NC_1_3	1915698.6
FR	M	2021	1000 NAC	ST_1_2_NC_2_3	FR_M_2021_1000 NAC_ST_1_2_NC_2_3	1157707.8
FR	M	2021	1000 NAC	ST_1_2_NC_4	FR_M_2021_1000 NAC_ST_1_2_NC_4	548260.2
FR	M	2021	1000 NAC	ST_1_2_NC_5	FR_M_2021_1000 NAC_ST_1_2_NC_5	302847.4
FR	M	2021	1000 NAC	ST_5_C	FR_M_2021_1000 NAC_ST_5_C	78079.8
FR	M	2021	1000 NAC	ST_5_C_1	FR_M_2021_1000 NAC_ST_5_C_1	40588.8
FR	M	2021	1000 NAC	ST_5_C_2	FR_M_2021_1000 NAC_ST_5_C_2	453.6
FR	M	2021	1000 NAC	ST_5_NC	FR_M_2021_1000 NAC_ST_5_NC	43.4
FR	M	2021	1000 NAC	ST_5_NC_1	FR_M_2021_1000 NAC_ST_5_NC_1	2436
FR	M	2021	1000 NAC	ST_5_NC_2	FR_M_2021_1000 NAC_ST_5_NC_2	316.8
FR	M	2021	1000 NAC	ST_5_NC_3	FR_M_2021_1000 NAC_ST_5_NC_3	315
FR	M	2021	1000 NAC	ST_5_NC_4	FR_M_2021_1000 NAC_ST_5_NC_4	0
FR	M	2021	1000 NAC	ST_5_NC_5	FR_M_2021_1000 NAC_ST_5_NC_5	0
FR	M	2021	1000 NAC	ST_5_NC_6	FR_M_2021_1000 NAC_ST_5_NC_6	0
FR	M	2021	1000 NAC	ST_5_NC_7	FR_M_2021_1000 NAC_ST_5_NC_7	ERROR:#REF!
FR	X	2020	1000 m3	ST_1_2_C	FR_X_2020_1000 m3_ST_1_2_C	1436.4016
FR	X	2020	1000 m3	ST_1_2_C_1	FR_X_2020_1000 m3_ST_1_2_C_1	697.2477022707
FR	X	2020	1000 m3	ST_1_2_C_1_1	FR_X_2020_1000 m3_ST_1_2_C_1_1	394.4050659225
FR	X	2020	1000 m3	ST_1_2_C_2_1	FR_X_2020_1000 m3_ST_1_2_C_2_1	302.8426363482
FR	X	2020	1000 m3	ST_1_2_C_2	FR_X_2020_1000 m3_ST_1_2_C_2	306.441985068
FR	X	2020	1000 m3	ST_1_2_C_1_2	FR_X_2020_1000 m3_ST_1_2_C_1_2	277.9299708305
FR	X	2020	1000 m3	ST_1_2_C_2_2	FR_X_2020_1000 m3_ST_1_2_C_2_2	28.5120142375
FR	X	2020	1000 m3	ST_1_2_C_3	FR_X_2020_1000 m3_ST_1_2_C_3	ERROR:#REF!
FR	X	2020	1000 m3	ST_1_2_C_1_3	FR_X_2020_1000 m3_ST_1_2_C_1_3	ERROR:#REF!
FR	X	2020	1000 m3	ST_1_2_C_2_3	FR_X_2020_1000 m3_ST_1_2_C_2_3	ERROR:#REF!
FR	X	2020	1000 m3	ST_1_2_NC	FR_X_2020_1000 m3_ST_1_2_NC	2146.36381
FR	X	2020	1000 m3	ST_1_2_NC_1	FR_X_2020_1000 m3_ST_1_2_NC_1	513.8904
FR	X	2020	1000 m3	ST_1_2_NC_1_1	FR_X_2020_1000 m3_ST_1_2_NC_1_1	235.097
FR	X	2020	1000 m3	ST_1_2_NC_2_1	FR_X_2020_1000 m3_ST_1_2_NC_2_1	276.4277
FR	X	2020	1000 m3	ST_1_2_NC_2	FR_X_2020_1000 m3_ST_1_2_NC_2	7.927
FR	X	2020	1000 m3	ST_1_2_NC_1_2	FR_X_2020_1000 m3_ST_1_2_NC_1_2	268.5007
FR	X	2020	1000 m3	ST_1_2_NC_2_2	FR_X_2020_1000 m3_ST_1_2_NC_2_2	268.5007
FR	X	2020	1000 m3	ST_1_2_NC_3	FR_X_2020_1000 m3_ST_1_2_NC_3	0
FR	X	2020	1000 m3	ST_1_2_NC_1_3	FR_X_2020_1000 m3_ST_1_2_NC_1_3	1047.8826
FR	X	2020	1000 m3	ST_1_2_NC_2_3	FR_X_2020_1000 m3_ST_1_2_NC_2_3	420.5628
FR	X	2020	1000 m3	ST_1_2_NC_4	FR_X_2020_1000 m3_ST_1_2_NC_4	206.0154328572
FR	X	2020	1000 m3	ST_1_2_NC_5	FR_X_2020_1000 m3_ST_1_2_NC_5	469.9023
FR	X	2020	1000 m3	ST_5_C	FR_X_2020_1000 m3_ST_5_C	262.3405
FR	X	2020	1000 m3	ST_5_C_1	FR_X_2020_1000 m3_ST_5_C_1	131.5902
FR	X	2020	1000 m3	ST_5_C_2	FR_X_2020_1000 m3_ST_5_C_2	1.6254
FR	X	2020	1000 m3	ST_5_NC	FR_X_2020_1000 m3_ST_5_NC	0.0266
FR	X	2020	1000 m3	ST_5_NC_1	FR_X_2020_1000 m3_ST_5_NC_1	21.0588
FR	X	2020	1000 m3	ST_5_NC_2	FR_X_2020_1000 m3_ST_5_NC_2	3.8646
FR	X	2020	1000 m3	ST_5_NC_3	FR_X_2020_1000 m3_ST_5_NC_3	0.0798
FR	X	2020	1000 m3	ST_5_NC_4	FR_X_2020_1000 m3_ST_5_NC_4	0
FR	X	2020	1000 m3	ST_5_NC_5	FR_X_2020_1000 m3_ST_5_NC_5	0
FR	X	2020	1000 m3	ST_5_NC_6	FR_X_2020_1000 m3_ST_5_NC_6	0
FR	X	2020	1000 m3	ST_5_NC_7	FR_X_2020_1000 m3_ST_5_NC_7	ERROR:#REF!
FR	X	2020	1000 NAC	ST_1_2_C	FR_X_2020_1000 NAC_ST_1_2_C	109099.2
FR	X	2020	1000 NAC	ST_1_2_C_1	FR_X_2020_1000 NAC_ST_1_2_C_1	57489.3988023952
FR	X	2020	1000 NAC	ST_1_2_C_1_1	FR_X_2020_1000 NAC_ST_1_2_C_1_1	27660.5182825838
FR	X	2020	1000 NAC	ST_1_2_C_2_1	FR_X_2020_1000 NAC_ST_1_2_C_2_1	29828.8805198114
FR	X	2020	1000 NAC	ST_1_2_C_2	FR_X_2020_1000 NAC_ST_1_2_C_2	17331.9289277419
FR	X	2020	1000 NAC	ST_1_2_C_1_2	FR_X_2020_1000 NAC_ST_1_2_C_1_2	14791.4808628243
FR	X	2020	1000 NAC	ST_1_2_C_2_2	FR_X_2020_1000 NAC_ST_1_2_C_2_2	2540.4480649176
FR	X	2020	1000 NAC	ST_1_2_C_3	FR_X_2020_1000 NAC_ST_1_2_C_3	ERROR:#REF!
FR	X	2020	1000 NAC	ST_1_2_C_1_3	FR_X_2020_1000 NAC_ST_1_2_C_1_3	ERROR:#REF!
FR	X	2020	1000 NAC	ST_1_2_C_2_3	FR_X_2020_1000 NAC_ST_1_2_C_2_3	ERROR:#REF!
FR	X	2020	1000 NAC	ST_1_2_NC	FR_X_2020_1000 NAC_ST_1_2_NC	238649.42
FR	X	2020	1000 NAC	ST_1_2_NC_1	FR_X_2020_1000 NAC_ST_1_2_NC_1	104013.6
FR	X	2020	1000 NAC	ST_1_2_NC_1_1	FR_X_2020_1000 NAC_ST_1_2_NC_1_1	18110
FR	X	2020	1000 NAC	ST_1_2_NC_2_1	FR_X_2020_1000 NAC_ST_1_2_NC_2_1	25192.9
FR	X	2020	1000 NAC	ST_1_2_NC_2	FR_X_2020_1000 NAC_ST_1_2_NC_2	827
FR	X	2020	1000 NAC	ST_1_2_NC_1_2	FR_X_2020_1000 NAC_ST_1_2_NC_1_2	24365.9
FR	X	2020	1000 NAC	ST_1_2_NC_2_2	FR_X_2020_1000 NAC_ST_1_2_NC_2_2	24365.9
FR	X	2020	1000 NAC	ST_1_2_NC_3	FR_X_2020_1000 NAC_ST_1_2_NC_3	0
FR	X	2020	1000 NAC	ST_1_2_NC_1_3	FR_X_2020_1000 NAC_ST_1_2_NC_1_3	322027.2
FR	X	2020	1000 NAC	ST_1_2_NC_2_3	FR_X_2020_1000 NAC_ST_1_2_NC_2_3	129009.6
FR	X	2020	1000 NAC	ST_1_2_NC_4	FR_X_2020_1000 NAC_ST_1_2_NC_4	43446.3412294493
FR	X	2020	1000 NAC	ST_1_2_NC_5	FR_X_2020_1000 NAC_ST_1_2_NC_5	272775.4
FR	X	2020	1000 NAC	ST_5_C	FR_X_2020_1000 NAC_ST_5_C	200108.8
FR	X	2020	1000 NAC	ST_5_C_1	FR_X_2020_1000 NAC_ST_5_C_1	44037
FR	X	2020	1000 NAC	ST_5_C_2	FR_X_2020_1000 NAC_ST_5_C_2	546
FR	X	2020	1000 NAC	ST_5_NC	FR_X_2020_1000 NAC_ST_5_NC	12.6
FR	X	2020	1000 NAC	ST_5_NC_1	FR_X_2020_1000 NAC_ST_5_NC_1	7989.8
FR	X	2020	1000 NAC	ST_5_NC_2	FR_X_2020_1000 NAC_ST_5_NC_2	1069.2
FR	X	2020	1000 NAC	ST_5_NC_3	FR_X_2020_1000 NAC_ST_5_NC_3	148.4
FR	X	2020	1000 NAC	ST_5_NC_4	FR_X_2020_1000 NAC_ST_5_NC_4	0
FR	X	2020	1000 NAC	ST_5_NC_5	FR_X_2020_1000 NAC_ST_5_NC_5	0
FR	X	2020	1000 NAC	ST_5_NC_6	FR_X_2020_1000 NAC_ST_5_NC_6	0
FR	X	2020	1000 NAC	ST_5_NC_7	FR_X_2020_1000 NAC_ST_5_NC_7	ERROR:#REF!
FR	X	2021	1000 m3	ST_1_2_C	FR_X_2021_1000 m3_ST_1_2_C	1617.3616
FR	X	2021	1000 m3	ST_1_2_C_1	FR_X_2021_1000 m3_ST_1_2_C_1	785.088
FR	X	2021	1000 m3	ST_1_2_C_1_1	FR_X_2021_1000 m3_ST_1_2_C_1_1	444.0928
FR	X	2021	1000 m3	ST_1_2_C_2_1	FR_X_2021_1000 m3_ST_1_2_C_2_1	340.9952
FR	X	2021	1000 m3	ST_1_2_C_2	FR_X_2021_1000 m3_ST_1_2_C_2	345.048
FR	X	2021	1000 m3	ST_1_2_C_1_2	FR_X_2021_1000 m3_ST_1_2_C_1_2	312.944
FR	X	2021	1000 m3	ST_1_2_C_2_2	FR_X_2021_1000 m3_ST_1_2_C_2_2	32.104
FR	X	2021	1000 m3	ST_1_2_C_3	FR_X_2021_1000 m3_ST_1_2_C_3	ERROR:#REF!
FR	X	2021	1000 m3	ST_1_2_C_1_3	FR_X_2021_1000 m3_ST_1_2_C_1_3	ERROR:#REF!
FR	X	2021	1000 m3	ST_1_2_C_2_3	FR_X_2021_1000 m3_ST_1_2_C_2_3	ERROR:#REF!
FR	X	2021	1000 m3	ST_1_2_NC	FR_X_2021_1000 m3_ST_1_2_NC	2494.156985
FR	X	2021	1000 m3	ST_1_2_NC_1	FR_X_2021_1000 m3_ST_1_2_NC_1	604.9812
FR	X	2021	1000 m3	ST_1_2_NC_1_1	FR_X_2021_1000 m3_ST_1_2_NC_1_1	260.07
FR	X	2021	1000 m3	ST_1_2_NC_2_1	FR_X_2021_1000 m3_ST_1_2_NC_2_1	299.4638
FR	X	2021	1000 m3	ST_1_2_NC_2	FR_X_2021_1000 m3_ST_1_2_NC_2	9.881
FR	X	2021	1000 m3	ST_1_2_NC_1_2	FR_X_2021_1000 m3_ST_1_2_NC_1_2	289.5828
FR	X	2021	1000 m3	ST_1_2_NC_2_2	FR_X_2021_1000 m3_ST_1_2_NC_2_2	289.5828
FR	X	2021	1000 m3	ST_1_2_NC_3	FR_X_2021_1000 m3_ST_1_2_NC_3	0.007
FR	X	2021	1000 m3	ST_1_2_NC_1_3	FR_X_2021_1000 m3_ST_1_2_NC_1_3	1055.3076
FR	X	2021	1000 m3	ST_1_2_NC_2_3	FR_X_2021_1000 m3_ST_1_2_NC_2_3	419.7366
FR	X	2021	1000 m3	ST_1_2_NC_4	FR_X_2021_1000 m3_ST_1_2_NC_4	207.4752
FR	X	2021	1000 m3	ST_1_2_NC_5	FR_X_2021_1000 m3_ST_1_2_NC_5	591.0988
FR	X	2021	1000 m3	ST_5_C	FR_X_2021_1000 m3_ST_5_C	356.3416
FR	X	2021	1000 m3	ST_5_C_1	FR_X_2021_1000 m3_ST_5_C_1	152.8702
FR	X	2021	1000 m3	ST_5_C_2	FR_X_2021_1000 m3_ST_5_C_2	2.961
FR	X	2021	1000 m3	ST_5_NC	FR_X_2021_1000 m3_ST_5_NC	0.021
FR	X	2021	1000 m3	ST_5_NC_1	FR_X_2021_1000 m3_ST_5_NC_1	30.6544
FR	X	2021	1000 m3	ST_5_NC_2	FR_X_2021_1000 m3_ST_5_NC_2	3.7782
FR	X	2021	1000 m3	ST_5_NC_3	FR_X_2021_1000 m3_ST_5_NC_3	0.0812
FR	X	2021	1000 m3	ST_5_NC_4	FR_X_2021_1000 m3_ST_5_NC_4	0
FR	X	2021	1000 m3	ST_5_NC_5	FR_X_2021_1000 m3_ST_5_NC_5	0
FR	X	2021	1000 m3	ST_5_NC_6	FR_X_2021_1000 m3_ST_5_NC_6	0
FR	X	2021	1000 m3	ST_5_NC_7	FR_X_2021_1000 m3_ST_5_NC_7	ERROR:#REF!
FR	X	2021	1000 NAC	ST_1_2_C	FR_X_2021_1000 NAC_ST_1_2_C	163259.2
FR	X	2021	1000 NAC	ST_1_2_C_1	FR_X_2021_1000 NAC_ST_1_2_C_1	86028.8
FR	X	2021	1000 NAC	ST_1_2_C_1_1	FR_X_2021_1000 NAC_ST_1_2_C_1_1	41392
FR	X	2021	1000 NAC	ST_1_2_C_2_1	FR_X_2021_1000 NAC_ST_1_2_C_2_1	44636.8
FR	X	2021	1000 NAC	ST_1_2_C_2	FR_X_2021_1000 NAC_ST_1_2_C_2	25936
FR	X	2021	1000 NAC	ST_1_2_C_1_2	FR_X_2021_1000 NAC_ST_1_2_C_1_2	22134.4
FR	X	2021	1000 NAC	ST_1_2_C_2_2	FR_X_2021_1000 NAC_ST_1_2_C_2_2	3801.6
FR	X	2021	1000 NAC	ST_1_2_C_3	FR_X_2021_1000 NAC_ST_1_2_C_3	ERROR:#REF!
FR	X	2021	1000 NAC	ST_1_2_C_1_3	FR_X_2021_1000 NAC_ST_1_2_C_1_3	ERROR:#REF!
FR	X	2021	1000 NAC	ST_1_2_C_2_3	FR_X_2021_1000 NAC_ST_1_2_C_2_3	ERROR:#REF!
FR	X	2021	1000 NAC	ST_1_2_NC	FR_X_2021_1000 NAC_ST_1_2_NC	301149.9
FR	X	2021	1000 NAC	ST_1_2_NC_1	FR_X_2021_1000 NAC_ST_1_2_NC_1	142711.2
FR	X	2021	1000 NAC	ST_1_2_NC_1_1	FR_X_2021_1000 NAC_ST_1_2_NC_1_1	24840
FR	X	2021	1000 NAC	ST_1_2_NC_2_1	FR_X_2021_1000 NAC_ST_1_2_NC_2_1	29719.8
FR	X	2021	1000 NAC	ST_1_2_NC_2	FR_X_2021_1000 NAC_ST_1_2_NC_2	1099
FR	X	2021	1000 NAC	ST_1_2_NC_1_2	FR_X_2021_1000 NAC_ST_1_2_NC_1_2	28620.8
FR	X	2021	1000 NAC	ST_1_2_NC_2_2	FR_X_2021_1000 NAC_ST_1_2_NC_2_2	28620.8
FR	X	2021	1000 NAC	ST_1_2_NC_3	FR_X_2021_1000 NAC_ST_1_2_NC_3	65.8
FR	X	2021	1000 NAC	ST_1_2_NC_1_3	FR_X_2021_1000 NAC_ST_1_2_NC_1_3	438489
FR	X	2021	1000 NAC	ST_1_2_NC_2_3	FR_X_2021_1000 NAC_ST_1_2_NC_2_3	187525.8
FR	X	2021	1000 NAC	ST_1_2_NC_4	FR_X_2021_1000 NAC_ST_1_2_NC_4	59158.8
FR	X	2021	1000 NAC	ST_1_2_NC_5	FR_X_2021_1000 NAC_ST_1_2_NC_5	369341.7
FR	X	2021	1000 NAC	ST_5_C	FR_X_2021_1000 NAC_ST_5_C	278727.1
FR	X	2021	1000 NAC	ST_5_C_1	FR_X_2021_1000 NAC_ST_5_C_1	53187.4
FR	X	2021	1000 NAC	ST_5_C_2	FR_X_2021_1000 NAC_ST_5_C_2	1078
FR	X	2021	1000 NAC	ST_5_NC	FR_X_2021_1000 NAC_ST_5_NC	5.6
FR	X	2021	1000 NAC	ST_5_NC_1	FR_X_2021_1000 NAC_ST_5_NC_1	10983
FR	X	2021	1000 NAC	ST_5_NC_2	FR_X_2021_1000 NAC_ST_5_NC_2	1220.4
FR	X	2021	1000 NAC	ST_5_NC_3	FR_X_2021_1000 NAC_ST_5_NC_3	165.2
FR	X	2021	1000 NAC	ST_5_NC_4	FR_X_2021_1000 NAC_ST_5_NC_4	0
FR	X	2021	1000 NAC	ST_5_NC_5	FR_X_2021_1000 NAC_ST_5_NC_5	0
FR	X	2021	1000 NAC	ST_5_NC_6	FR_X_2021_1000 NAC_ST_5_NC_6	0
FR	X	2021	1000 NAC	ST_5_NC_7	FR_X_2021_1000 NAC_ST_5_NC_7	ERROR:#REF!
FR	EX_M	2020	1000 m3	1	FR_EX_M_2020_1000 m3_1	1125.67135	EU1
FR	EX_M	2020	1000 m3	1_1	FR_EX_M_2020_1000 m3_1_1	182.19013
FR	EX_M	2020	1000 m3	1_2	FR_EX_M_2020_1000 m3_1_2	158.9655
FR	EX_M	2020	1000 m3	1_2_C	FR_EX_M_2020_1000 m3_1_2_C	23.22463
FR	EX_M	2020	1000 m3	1_2_NC	FR_EX_M_2020_1000 m3_1_2_NC	943.48122
FR	EX_M	2020	1000 m3	1_2_NC_T	FR_EX_M_2020_1000 m3_1_2_NC_T	718.7168
FR	EX_M	2020	1000 mt	2	FR_EX_M_2020_1000 mt_2	224.76442
FR	EX_M	2020	1000 m3	3	FR_EX_M_2020_1000 m3_3	36.08472
FR	EX_M	2020	1000 m3	3_1	FR_EX_M_2020_1000 m3_3_1	106.029
FR	EX_M	2020	1000 m3	3_2	FR_EX_M_2020_1000 m3_3_2	2509.72788
FR	EX_M	2020	1000 mt	4	FR_EX_M_2020_1000 mt_4	652.81428
FR	EX_M	2020	1000 mt	4_1	FR_EX_M_2020_1000 mt_4_1	1856.9136
FR	EX_M	2020	1000 mt	4_2	FR_EX_M_2020_1000 mt_4_2	0
FR	EX_M	2020	1000 m3	5	FR_EX_M_2020_1000 m3_5	548.344
FR	EX_M	2020	1000 m3	5_C	FR_EX_M_2020_1000 m3_5_C	412.381
FR	EX_M	2020	1000 m3	5_NC	FR_EX_M_2020_1000 m3_5_NC	135.963
FR	EX_M	2020	1000 m3	5_NC_T	FR_EX_M_2020_1000 m3_5_NC_T	2714.582
FR	EX_M	2020	1000 m3	6	FR_EX_M_2020_1000 m3_6	2437.9542
FR	EX_M	2020	1000 m3	6_1	FR_EX_M_2020_1000 m3_6_1	276.6278
FR	EX_M	2020	1000 m3	6_1_C	FR_EX_M_2020_1000 m3_6_1_C	129.1808
FR	EX_M	2020	1000 m3	6_1_NC	FR_EX_M_2020_1000 m3_6_1_NC	311.22133
FR	EX_M	2020	1000 m3	6_1_NC_T	FR_EX_M_2020_1000 m3_6_1_NC_T	17.48551
FR	EX_M	2020	1000 m3	6_2	FR_EX_M_2020_1000 m3_6_2	293.73582
FR	EX_M	2020	1000 m3	6_2_C	FR_EX_M_2020_1000 m3_6_2_C	78.65354
FR	EX_M	2020	1000 m3	6_2_NC	FR_EX_M_2020_1000 m3_6_2_NC	1232.24772
FR	EX_M	2020	1000 m3	6_2_NC_T	FR_EX_M_2020_1000 m3_6_2_NC_T	443.77102
FR	EX_M	2020	1000 m3	6_3	FR_EX_M_2020_1000 m3_6_3	103.65586
FR	EX_M	2020	1000 m3	6_3_1	FR_EX_M_2020_1000 m3_6_3_1	340.11516
FR	EX_M	2020	1000 m3	6_4	FR_EX_M_2020_1000 m3_6_4	91.07252
FR	EX_M	2020	1000 m3	6_4_1	FR_EX_M_2020_1000 m3_6_4_1	0
FR	EX_M	2020	1000 m3	6_4_2	FR_EX_M_2020_1000 m3_6_4_2	118.05192
FR	EX_M	2020	1000 m3	6_4_3	FR_EX_M_2020_1000 m3_6_4_3	788.4767
FR	EX_M	2020	1000 mt	7	FR_EX_M_2020_1000 mt_7	235.48761
FR	EX_M	2020	1000 mt	7_1	FR_EX_M_2020_1000 mt_7_1	481.14736
FR	EX_M	2020	1000 mt	7_2	FR_EX_M_2020_1000 mt_7_2	71.84173
FR	EX_M	2020	1000 mt	7_3	FR_EX_M_2020_1000 mt_7_3	111.975
FR	EX_M	2020	1000 mt	7_3_1	FR_EX_M_2020_1000 mt_7_3_1	75.74
FR	EX_M	2020	1000 mt	7_3_2	FR_EX_M_2020_1000 mt_7_3_2	22.925
FR	EX_M	2020	1000 mt	7_3_3	FR_EX_M_2020_1000 mt_7_3_3	0
FR	EX_M	2020	1000 mt	7_3_4	FR_EX_M_2020_1000 mt_7_3_4	1645.881
FR	EX_M	2020	1000 mt	7_4	FR_EX_M_2020_1000 mt_7_4	22.925
FR	EX_M	2020	1000 mt	8	FR_EX_M_2020_1000 mt_8	13.31
FR	EX_M	2020	1000 mt	8_1	FR_EX_M_2020_1000 mt_8_1	23.131
FR	EX_M	2020	1000 mt	8_2	FR_EX_M_2020_1000 mt_8_2	16.325
FR	EX_M	2020	1000 mt	9	FR_EX_M_2020_1000 mt_9	6.806
FR	EX_M	2020	1000 mt	10	FR_EX_M_2020_1000 mt_10	1859.992
FR	EX_M	2020	1000 mt	10_1	FR_EX_M_2020_1000 mt_10_1	4502.25
FR	EX_M	2020	1000 mt	10_1_1	FR_EX_M_2020_1000 mt_10_1_1	2131.837
FR	EX_M	2020	1000 mt	10_1_2	FR_EX_M_2020_1000 mt_10_1_2	0
FR	EX_M	2020	1000 mt	10_1_3	FR_EX_M_2020_1000 mt_10_1_3	0
FR	EX_M	2020	1000 mt	10_1_4	FR_EX_M_2020_1000 mt_10_1_4	0
FR	EX_M	2020	1000 mt	10_2	FR_EX_M_2020_1000 mt_10_2	0
FR	EX_M	2020	1000 mt	10_3	FR_EX_M_2020_1000 mt_10_3	90.586
FR	EX_M	2020	1000 mt	10_3_1	FR_EX_M_2020_1000 mt_10_3_1	2249.687
FR	EX_M	2020	1000 mt	10_3_2	FR_EX_M_2020_1000 mt_10_3_2	0
FR	EX_M	2020	1000 mt	10_3_3	FR_EX_M_2020_1000 mt_10_3_3	0
FR	EX_M	2020	1000 mt	10_3_4	FR_EX_M_2020_1000 mt_10_3_4	0
FR	EX_M	2020	1000 mt	10_4	FR_EX_M_2020_1000 mt_10_4	0
FR	EX_M	2020	1000 NAC	1	FR_EX_M_2020_1000 NAC_1	144.711575
FR	EX_M	2020	1000 NAC	1_1	FR_EX_M_2020_1000 NAC_1_1	18.99255
FR	EX_M	2020	1000 NAC	1_2	FR_EX_M_2020_1000 NAC_1_2	14.529
FR	EX_M	2020	1000 NAC	1_2_C	FR_EX_M_2020_1000 NAC_1_2_C	4.46355
FR	EX_M	2020	1000 NAC	1_2_NC	FR_EX_M_2020_1000 NAC_1_2_NC	125.719025
FR	EX_M	2020	1000 NAC	1_2_NC_T	FR_EX_M_2020_1000 NAC_1_2_NC_T	83.0224
FR	EX_M	2020	1000 NAC	2	FR_EX_M_2020_1000 NAC_2	42.696625
FR	EX_M	2020	1000 NAC	3	FR_EX_M_2020_1000 NAC_3	17.342425
FR	EX_M	2020	1000 NAC	3_1	FR_EX_M_2020_1000 NAC_3_1	61.977
FR	EX_M	2020	1000 NAC	3_2	FR_EX_M_2020_1000 NAC_3_2	143.6007
FR	EX_M	2020	1000 NAC	4	FR_EX_M_2020_1000 NAC_4	48.7369
FR	EX_M	2020	1000 NAC	4_1	FR_EX_M_2020_1000 NAC_4_1	94.8638
FR	EX_M	2020	1000 NAC	4_2	FR_EX_M_2020_1000 NAC_4_2	0
FR	EX_M	2020	1000 NAC	5	FR_EX_M_2020_1000 NAC_5	99.126
FR	EX_M	2020	1000 NAC	5_C	FR_EX_M_2020_1000 NAC_5_C	86.007
FR	EX_M	2020	1000 NAC	5_NC	FR_EX_M_2020_1000 NAC_5_NC	13.119
FR	EX_M	2020	1000 NAC	5_NC_T	FR_EX_M_2020_1000 NAC_5_NC_T	1395.7051
FR	EX_M	2020	1000 NAC	6	FR_EX_M_2020_1000 NAC_6	1129.0986
FR	EX_M	2020	1000 NAC	6_1	FR_EX_M_2020_1000 NAC_6_1	266.6065
FR	EX_M	2020	1000 NAC	6_1_C	FR_EX_M_2020_1000 NAC_6_1_C	120.2768
FR	EX_M	2020	1000 NAC	6_1_NC	FR_EX_M_2020_1000 NAC_6_1_NC	296.96373
FR	EX_M	2020	1000 NAC	6_1_NC_T	FR_EX_M_2020_1000 NAC_6_1_NC_T	13.90781
FR	EX_M	2020	1000 NAC	6_2	FR_EX_M_2020_1000 NAC_6_2	283.05592
FR	EX_M	2020	1000 NAC	6_2_C	FR_EX_M_2020_1000 NAC_6_2_C	92.39244
FR	EX_M	2020	1000 NAC	6_2_NC	FR_EX_M_2020_1000 NAC_6_2_NC	886.60738
FR	EX_M	2020	1000 NAC	6_2_NC_T	FR_EX_M_2020_1000 NAC_6_2_NC_T	442.4651
FR	EX_M	2020	1000 NAC	6_3	FR_EX_M_2020_1000 NAC_6_3	114.71614
FR	EX_M	2020	1000 NAC	6_3_1	FR_EX_M_2020_1000 NAC_6_3_1	327.74896
FR	EX_M	2020	1000 NAC	6_4	FR_EX_M_2020_1000 NAC_6_4	106.98072
FR	EX_M	2020	1000 NAC	6_4_1	FR_EX_M_2020_1000 NAC_6_4_1	0
FR	EX_M	2020	1000 NAC	6_4_2	FR_EX_M_2020_1000 NAC_6_4_2	58.11382
FR	EX_M	2020	1000 NAC	6_4_3	FR_EX_M_2020_1000 NAC_6_4_3	444.14228
FR	EX_M	2020	1000 NAC	7	FR_EX_M_2020_1000 NAC_7	107.67555
FR	EX_M	2020	1000 NAC	7_1	FR_EX_M_2020_1000 NAC_7_1	276.41854
FR	EX_M	2020	1000 NAC	7_2	FR_EX_M_2020_1000 NAC_7_2	60.04819
FR	EX_M	2020	1000 NAC	7_3	FR_EX_M_2020_1000 NAC_7_3	51.033
FR	EX_M	2020	1000 NAC	7_3_1	FR_EX_M_2020_1000 NAC_7_3_1	33.763
FR	EX_M	2020	1000 NAC	7_3_2	FR_EX_M_2020_1000 NAC_7_3_2	17.27
FR	EX_M	2020	1000 NAC	7_3_3	FR_EX_M_2020_1000 NAC_7_3_3	0
FR	EX_M	2020	1000 NAC	7_3_4	FR_EX_M_2020_1000 NAC_7_3_4	745.962
FR	EX_M	2020	1000 NAC	7_4	FR_EX_M_2020_1000 NAC_7_4	17.27
FR	EX_M	2020	1000 NAC	8	FR_EX_M_2020_1000 NAC_8	0
FR	EX_M	2020	1000 NAC	8_1	FR_EX_M_2020_1000 NAC_8_1	30.36
FR	EX_M	2020	1000 NAC	8_2	FR_EX_M_2020_1000 NAC_8_2	26.045
FR	EX_M	2020	1000 NAC	9	FR_EX_M_2020_1000 NAC_9	4.315
FR	EX_M	2020	1000 NAC	10	FR_EX_M_2020_1000 NAC_10	3716.226
FR	EX_M	2020	1000 NAC	10_1	FR_EX_M_2020_1000 NAC_10_1	3386.847
FR	EX_M	2020	1000 NAC	10_1_1	FR_EX_M_2020_1000 NAC_10_1_1	1618.32
FR	EX_M	2020	1000 NAC	10_1_2	FR_EX_M_2020_1000 NAC_10_1_2	0
FR	EX_M	2020	1000 NAC	10_1_3	FR_EX_M_2020_1000 NAC_10_1_3	0
FR	EX_M	2020	1000 NAC	10_1_4	FR_EX_M_2020_1000 NAC_10_1_4	0
FR	EX_M	2020	1000 NAC	10_2	FR_EX_M_2020_1000 NAC_10_2	0
FR	EX_M	2020	1000 NAC	10_3	FR_EX_M_2020_1000 NAC_10_3	108.249
FR	EX_M	2020	1000 NAC	10_3_1	FR_EX_M_2020_1000 NAC_10_3_1	1587.681
FR	EX_M	2020	1000 NAC	10_3_2	FR_EX_M_2020_1000 NAC_10_3_2	0
FR	EX_M	2020	1000 NAC	10_3_3	FR_EX_M_2020_1000 NAC_10_3_3	0
FR	EX_M	2020	1000 NAC	10_3_4	FR_EX_M_2020_1000 NAC_10_3_4	0
FR	EX_M	2020	1000 NAC	10_4	FR_EX_M_2020_1000 NAC_10_4	0
FR	EX_M	2021	1000 m3	1	FR_EX_M_2021_1000 m3_1	1182.850775
FR	EX_M	2021	1000 m3	1_1	FR_EX_M_2021_1000 m3_1_1	197.68982
FR	EX_M	2021	1000 m3	1_2	FR_EX_M_2021_1000 m3_1_2	177.7995
FR	EX_M	2021	1000 m3	1_2_C	FR_EX_M_2021_1000 m3_1_2_C	19.89032
FR	EX_M	2021	1000 m3	1_2_NC	FR_EX_M_2021_1000 m3_1_2_NC	985.160955
FR	EX_M	2021	1000 m3	1_2_NC_T	FR_EX_M_2021_1000 m3_1_2_NC_T	794.4816
FR	EX_M	2021	1000 mt	2	FR_EX_M_2021_1000 mt_2	190.679355
FR	EX_M	2021	1000 m3	3	FR_EX_M_2021_1000 m3_3	46.396055
FR	EX_M	2021	1000 m3	3_1	FR_EX_M_2021_1000 m3_3_1	80.945
FR	EX_M	2021	1000 m3	3_2	FR_EX_M_2021_1000 m3_3_2	2185.52254
FR	EX_M	2021	1000 mt	4	FR_EX_M_2021_1000 mt_4	513.55254
FR	EX_M	2021	1000 mt	4_1	FR_EX_M_2021_1000 mt_4_1	1671.97
FR	EX_M	2021	1000 mt	4_2	FR_EX_M_2021_1000 mt_4_2	0
FR	EX_M	2021	1000 m3	5	FR_EX_M_2021_1000 m3_5	807.856
FR	EX_M	2021	1000 m3	5_C	FR_EX_M_2021_1000 m3_5_C	660.949
FR	EX_M	2021	1000 m3	5_NC	FR_EX_M_2021_1000 m3_5_NC	146.907
FR	EX_M	2021	1000 m3	5_NC_T	FR_EX_M_2021_1000 m3_5_NC_T	3132.4063
FR	EX_M	2021	1000 m3	6	FR_EX_M_2021_1000 m3_6	2846.07
FR	EX_M	2021	1000 m3	6_1	FR_EX_M_2021_1000 m3_6_1	286.3363
FR	EX_M	2021	1000 m3	6_1_C	FR_EX_M_2021_1000 m3_6_1_C	135.128
FR	EX_M	2021	1000 m3	6_1_NC	FR_EX_M_2021_1000 m3_6_1_NC	308.50281
FR	EX_M	2021	1000 m3	6_1_NC_T	FR_EX_M_2021_1000 m3_6_1_NC_T	14.67256
FR	EX_M	2021	1000 m3	6_2	FR_EX_M_2021_1000 m3_6_2	293.83025
FR	EX_M	2021	1000 m3	6_2_C	FR_EX_M_2021_1000 m3_6_2_C	86.10154
FR	EX_M	2021	1000 m3	6_2_NC	FR_EX_M_2021_1000 m3_6_2_NC	1150.87363
FR	EX_M	2021	1000 m3	6_2_NC_T	FR_EX_M_2021_1000 m3_6_2_NC_T	469.58142
FR	EX_M	2021	1000 m3	6_3	FR_EX_M_2021_1000 m3_6_3	129.35692
FR	EX_M	2021	1000 m3	6_3_1	FR_EX_M_2021_1000 m3_6_3_1	340.2245
FR	EX_M	2021	1000 m3	6_4	FR_EX_M_2021_1000 m3_6_4	99.69652
FR	EX_M	2021	1000 m3	6_4_1	FR_EX_M_2021_1000 m3_6_4_1	0
FR	EX_M	2021	1000 m3	6_4_2	FR_EX_M_2021_1000 m3_6_4_2	148.05814
FR	EX_M	2021	1000 m3	6_4_3	FR_EX_M_2021_1000 m3_6_4_3	681.29221
FR	EX_M	2021	1000 mt	7	FR_EX_M_2021_1000 mt_7	211.98669
FR	EX_M	2021	1000 mt	7_1	FR_EX_M_2021_1000 mt_7_1	388.1669
FR	EX_M	2021	1000 mt	7_2	FR_EX_M_2021_1000 mt_7_2	81.13862
FR	EX_M	2021	1000 mt	7_3	FR_EX_M_2021_1000 mt_7_3	93.486
FR	EX_M	2021	1000 mt	7_3_1	FR_EX_M_2021_1000 mt_7_3_1	82.514
FR	EX_M	2021	1000 mt	7_3_2	FR_EX_M_2021_1000 mt_7_3_2	10.972
FR	EX_M	2021	1000 mt	7_3_3	FR_EX_M_2021_1000 mt_7_3_3	0
FR	EX_M	2021	1000 mt	7_3_4	FR_EX_M_2021_1000 mt_7_3_4	1510.177
FR	EX_M	2021	1000 mt	7_4	FR_EX_M_2021_1000 mt_7_4	10.972
FR	EX_M	2021	1000 mt	8	FR_EX_M_2021_1000 mt_8	0
FR	EX_M	2021	1000 mt	8_1	FR_EX_M_2021_1000 mt_8_1	23.497
FR	EX_M	2021	1000 mt	8_2	FR_EX_M_2021_1000 mt_8_2	19.152
FR	EX_M	2021	1000 mt	9	FR_EX_M_2021_1000 mt_9	4.345
FR	EX_M	2021	1000 mt	10	FR_EX_M_2021_1000 mt_10	1895.719
FR	EX_M	2021	1000 mt	10_1	FR_EX_M_2021_1000 mt_10_1	4740.495
FR	EX_M	2021	1000 mt	10_1_1	FR_EX_M_2021_1000 mt_10_1_1	2126.105
FR	EX_M	2021	1000 mt	10_1_2	FR_EX_M_2021_1000 mt_10_1_2	0
FR	EX_M	2021	1000 mt	10_1_3	FR_EX_M_2021_1000 mt_10_1_3	0
FR	EX_M	2021	1000 mt	10_1_4	FR_EX_M_2021_1000 mt_10_1_4	0
FR	EX_M	2021	1000 mt	10_2	FR_EX_M_2021_1000 mt_10_2	0
FR	EX_M	2021	1000 mt	10_3	FR_EX_M_2021_1000 mt_10_3	89.624
FR	EX_M	2021	1000 mt	10_3_1	FR_EX_M_2021_1000 mt_10_3_1	2483.641
FR	EX_M	2021	1000 mt	10_3_2	FR_EX_M_2021_1000 mt_10_3_2	0
FR	EX_M	2021	1000 mt	10_3_3	FR_EX_M_2021_1000 mt_10_3_3	0
FR	EX_M	2021	1000 mt	10_3_4	FR_EX_M_2021_1000 mt_10_3_4	0
FR	EX_M	2021	1000 mt	10_4	FR_EX_M_2021_1000 mt_10_4	0
FR	EX_M	2021	1000 NAC	1	FR_EX_M_2021_1000 NAC_1	174.216045
FR	EX_M	2021	1000 NAC	1_1	FR_EX_M_2021_1000 NAC_1_1	17.39393
FR	EX_M	2021	1000 NAC	1_2	FR_EX_M_2021_1000 NAC_1_2	13.8765
FR	EX_M	2021	1000 NAC	1_2_C	FR_EX_M_2021_1000 NAC_1_2_C	3.51743
FR	EX_M	2021	1000 NAC	1_2_NC	FR_EX_M_2021_1000 NAC_1_2_NC	156.822115
FR	EX_M	2021	1000 NAC	1_2_NC_T	FR_EX_M_2021_1000 NAC_1_2_NC_T	108.792
FR	EX_M	2021	1000 NAC	2	FR_EX_M_2021_1000 NAC_2	48.030115
FR	EX_M	2021	1000 NAC	3	FR_EX_M_2021_1000 NAC_3	24.048815
FR	EX_M	2021	1000 NAC	3_1	FR_EX_M_2021_1000 NAC_3_1	54.658
FR	EX_M	2021	1000 NAC	3_2	FR_EX_M_2021_1000 NAC_3_2	117.70172
FR	EX_M	2021	1000 NAC	4	FR_EX_M_2021_1000 NAC_4	33.75112
FR	EX_M	2021	1000 NAC	4_1	FR_EX_M_2021_1000 NAC_4_1	83.9506
FR	EX_M	2021	1000 NAC	4_2	FR_EX_M_2021_1000 NAC_4_2	0
FR	EX_M	2021	1000 NAC	5	FR_EX_M_2021_1000 NAC_5	142.805
FR	EX_M	2021	1000 NAC	5_C	FR_EX_M_2021_1000 NAC_5_C	127.444
FR	EX_M	2021	1000 NAC	5_NC	FR_EX_M_2021_1000 NAC_5_NC	15.361
FR	EX_M	2021	1000 NAC	5_NC_T	FR_EX_M_2021_1000 NAC_5_NC_T	2218.546
FR	EX_M	2021	1000 NAC	6	FR_EX_M_2021_1000 NAC_6	1915.6986
FR	EX_M	2021	1000 NAC	6_1	FR_EX_M_2021_1000 NAC_6_1	302.8474
FR	EX_M	2021	1000 NAC	6_1_C	FR_EX_M_2021_1000 NAC_6_1_C	137.781
FR	EX_M	2021	1000 NAC	6_1_NC	FR_EX_M_2021_1000 NAC_6_1_NC	360.70398
FR	EX_M	2021	1000 NAC	6_1_NC_T	FR_EX_M_2021_1000 NAC_6_1_NC_T	13.26941
FR	EX_M	2021	1000 NAC	6_2	FR_EX_M_2021_1000 NAC_6_2	347.43457
FR	EX_M	2021	1000 NAC	6_2_C	FR_EX_M_2021_1000 NAC_6_2_C	105.9345
FR	EX_M	2021	1000 NAC	6_2_NC	FR_EX_M_2021_1000 NAC_6_2_NC	1107.93499
FR	EX_M	2021	1000 NAC	6_2_NC_T	FR_EX_M_2021_1000 NAC_6_2_NC_T	563.6939
FR	EX_M	2021	1000 NAC	6_3	FR_EX_M_2021_1000 NAC_6_3	161.40124
FR	EX_M	2021	1000 NAC	6_3_1	FR_EX_M_2021_1000 NAC_6_3_1	402.29266
FR	EX_M	2021	1000 NAC	6_4	FR_EX_M_2021_1000 NAC_6_4	122.661
FR	EX_M	2021	1000 NAC	6_4_1	FR_EX_M_2021_1000 NAC_6_4_1	0
FR	EX_M	2021	1000 NAC	6_4_2	FR_EX_M_2021_1000 NAC_6_4_2	129.79882
FR	EX_M	2021	1000 NAC	6_4_3	FR_EX_M_2021_1000 NAC_6_4_3	544.24109
FR	EX_M	2021	1000 NAC	7	FR_EX_M_2021_1000 NAC_7	144.49314
FR	EX_M	2021	1000 NAC	7_1	FR_EX_M_2021_1000 NAC_7_1	321.55354
FR	EX_M	2021	1000 NAC	7_2	FR_EX_M_2021_1000 NAC_7_2	78.19441
FR	EX_M	2021	1000 NAC	7_3	FR_EX_M_2021_1000 NAC_7_3	54.936
FR	EX_M	2021	1000 NAC	7_3_1	FR_EX_M_2021_1000 NAC_7_3_1	42.563
FR	EX_M	2021	1000 NAC	7_3_2	FR_EX_M_2021_1000 NAC_7_3_2	12.373
FR	EX_M	2021	1000 NAC	7_3_3	FR_EX_M_2021_1000 NAC_7_3_3	0
FR	EX_M	2021	1000 NAC	7_3_4	FR_EX_M_2021_1000 NAC_7_3_4	896.474
FR	EX_M	2021	1000 NAC	7_4	FR_EX_M_2021_1000 NAC_7_4	12.373
FR	EX_M	2021	1000 NAC	8	FR_EX_M_2021_1000 NAC_8	0
FR	EX_M	2021	1000 NAC	8_1	FR_EX_M_2021_1000 NAC_8_1	32.037
FR	EX_M	2021	1000 NAC	8_2	FR_EX_M_2021_1000 NAC_8_2	29.028
FR	EX_M	2021	1000 NAC	9	FR_EX_M_2021_1000 NAC_9	3.009
FR	EX_M	2021	1000 NAC	10	FR_EX_M_2021_1000 NAC_10	4027.706
FR	EX_M	2021	1000 NAC	10_1	FR_EX_M_2021_1000 NAC_10_1	3838.835
FR	EX_M	2021	1000 NAC	10_1_1	FR_EX_M_2021_1000 NAC_10_1_1	1643.723
FR	EX_M	2021	1000 NAC	10_1_2	FR_EX_M_2021_1000 NAC_10_1_2	0
FR	EX_M	2021	1000 NAC	10_1_3	FR_EX_M_2021_1000 NAC_10_1_3	0
FR	EX_M	2021	1000 NAC	10_1_4	FR_EX_M_2021_1000 NAC_10_1_4	0
FR	EX_M	2021	1000 NAC	10_2	FR_EX_M_2021_1000 NAC_10_2	0
FR	EX_M	2021	1000 NAC	10_3	FR_EX_M_2021_1000 NAC_10_3	105.394
FR	EX_M	2021	1000 NAC	10_3_1	FR_EX_M_2021_1000 NAC_10_3_1	2007.051
FR	EX_M	2021	1000 NAC	10_3_2	FR_EX_M_2021_1000 NAC_10_3_2	0
FR	EX_M	2021	1000 NAC	10_3_3	FR_EX_M_2021_1000 NAC_10_3_3	0
FR	EX_M	2021	1000 NAC	10_3_4	FR_EX_M_2021_1000 NAC_10_3_4	0
FR	EX_M	2021	1000 NAC	10_4	FR_EX_M_2021_1000 NAC_10_4	0
FR	EX_X	2020	1000 m3	1	FR_EX_X_2020_1000 m3_1	4032.24893
FR	EX_X	2020	1000 m3	1_1	FR_EX_X_2020_1000 m3_1_1	449.48352
FR	EX_X	2020	1000 m3	1_2	FR_EX_X_2020_1000 m3_1_2	353.751
FR	EX_X	2020	1000 m3	1_2_C	FR_EX_X_2020_1000 m3_1_2_C	95.73252
FR	EX_X	2020	1000 m3	1_2_NC	FR_EX_X_2020_1000 m3_1_2_NC	3582.76541
FR	EX_X	2020	1000 m3	1_2_NC_T	FR_EX_X_2020_1000 m3_1_2_NC_T	1436.4016
FR	EX_X	2020	1000 mt	2	FR_EX_X_2020_1000 mt_2	2146.36381
FR	EX_X	2020	1000 m3	3	FR_EX_X_2020_1000 m3_3	3.06231
FR	EX_X	2020	1000 m3	3_1	FR_EX_X_2020_1000 m3_3_1	9.192
FR	EX_X	2020	1000 m3	3_2	FR_EX_X_2020_1000 m3_3_2	699.2563
FR	EX_X	2020	1000 mt	4	FR_EX_X_2020_1000 mt_4	451.2223
FR	EX_X	2020	1000 mt	4_1	FR_EX_X_2020_1000 mt_4_1	248.034
FR	EX_X	2020	1000 mt	4_2	FR_EX_X_2020_1000 mt_4_2	0
FR	EX_X	2020	1000 m3	5	FR_EX_X_2020_1000 m3_5	286.105
FR	EX_X	2020	1000 m3	5_C	FR_EX_X_2020_1000 m3_5_C	96.539
FR	EX_X	2020	1000 m3	5_NC	FR_EX_X_2020_1000 m3_5_NC	189.566
FR	EX_X	2020	1000 m3	5_NC_T	FR_EX_X_2020_1000 m3_5_NC_T	1517.7849
FR	EX_X	2020	1000 m3	6	FR_EX_X_2020_1000 m3_6	1047.8826
FR	EX_X	2020	1000 m3	6_1	FR_EX_X_2020_1000 m3_6_1	469.9023
FR	EX_X	2020	1000 m3	6_1_C	FR_EX_X_2020_1000 m3_6_1_C	2.933
FR	EX_X	2020	1000 m3	6_1_NC	FR_EX_X_2020_1000 m3_6_1_NC	68.28353
FR	EX_X	2020	1000 m3	6_1_NC_T	FR_EX_X_2020_1000 m3_6_1_NC_T	0.25802
FR	EX_X	2020	1000 m3	6_2	FR_EX_X_2020_1000 m3_6_2	68.02551
FR	EX_X	2020	1000 m3	6_2_C	FR_EX_X_2020_1000 m3_6_2_C	53.54181
FR	EX_X	2020	1000 m3	6_2_NC	FR_EX_X_2020_1000 m3_6_2_NC	1013.20363
FR	EX_X	2020	1000 m3	6_2_NC_T	FR_EX_X_2020_1000 m3_6_2_NC_T	158.7894
FR	EX_X	2020	1000 m3	6_3	FR_EX_X_2020_1000 m3_6_3	80.02302
FR	EX_X	2020	1000 m3	6_3_1	FR_EX_X_2020_1000 m3_6_3_1	78.76638
FR	EX_X	2020	1000 m3	6_4	FR_EX_X_2020_1000 m3_6_4	61.99578
FR	EX_X	2020	1000 m3	6_4_1	FR_EX_X_2020_1000 m3_6_4_1	0
FR	EX_X	2020	1000 m3	6_4_2	FR_EX_X_2020_1000 m3_6_4_2	34.85956
FR	EX_X	2020	1000 m3	6_4_3	FR_EX_X_2020_1000 m3_6_4_3	854.41423
FR	EX_X	2020	1000 mt	7	FR_EX_X_2020_1000 mt_7	449.40348
FR	EX_X	2020	1000 mt	7_1	FR_EX_X_2020_1000 mt_7_1	312.43096
FR	EX_X	2020	1000 mt	7_2	FR_EX_X_2020_1000 mt_7_2	92.57979
FR	EX_X	2020	1000 mt	7_3	FR_EX_X_2020_1000 mt_7_3	10.392
FR	EX_X	2020	1000 mt	7_3_1	FR_EX_X_2020_1000 mt_7_3_1	4.643
FR	EX_X	2020	1000 mt	7_3_2	FR_EX_X_2020_1000 mt_7_3_2	5.749
FR	EX_X	2020	1000 mt	7_3_3	FR_EX_X_2020_1000 mt_7_3_3	0
FR	EX_X	2020	1000 mt	7_3_4	FR_EX_X_2020_1000 mt_7_3_4	436.7
FR	EX_X	2020	1000 mt	7_4	FR_EX_X_2020_1000 mt_7_4	5.749
FR	EX_X	2020	1000 mt	8	FR_EX_X_2020_1000 mt_8	0
FR	EX_X	2020	1000 mt	8_1	FR_EX_X_2020_1000 mt_8_1	70.485
FR	EX_X	2020	1000 mt	8_2	FR_EX_X_2020_1000 mt_8_2	2.343
FR	EX_X	2020	1000 mt	9	FR_EX_X_2020_1000 mt_9	68.142
FR	EX_X	2020	1000 mt	10	FR_EX_X_2020_1000 mt_10	921.811
FR	EX_X	2020	1000 mt	10_1	FR_EX_X_2020_1000 mt_10_1	3464.735
FR	EX_X	2020	1000 mt	10_1_1	FR_EX_X_2020_1000 mt_10_1_1	895.994
FR	EX_X	2020	1000 mt	10_1_2	FR_EX_X_2020_1000 mt_10_1_2	0
FR	EX_X	2020	1000 mt	10_1_3	FR_EX_X_2020_1000 mt_10_1_3	0
FR	EX_X	2020	1000 mt	10_1_4	FR_EX_X_2020_1000 mt_10_1_4	0
FR	EX_X	2020	1000 mt	10_2	FR_EX_X_2020_1000 mt_10_2	0
FR	EX_X	2020	1000 mt	10_3	FR_EX_X_2020_1000 mt_10_3	79.583
FR	EX_X	2020	1000 mt	10_3_1	FR_EX_X_2020_1000 mt_10_3_1	2478.484
FR	EX_X	2020	1000 mt	10_3_2	FR_EX_X_2020_1000 mt_10_3_2	0
FR	EX_X	2020	1000 mt	10_3_3	FR_EX_X_2020_1000 mt_10_3_3	0
FR	EX_X	2020	1000 mt	10_3_4	FR_EX_X_2020_1000 mt_10_3_4	0
FR	EX_X	2020	1000 mt	10_4	FR_EX_X_2020_1000 mt_10_4	0
FR	EX_X	2020	1000 NAC	1	FR_EX_X_2020_1000 NAC_1	369.01292
FR	EX_X	2020	1000 NAC	1_1	FR_EX_X_2020_1000 NAC_1_1	21.2643
FR	EX_X	2020	1000 NAC	1_2	FR_EX_X_2020_1000 NAC_1_2	16.2285
FR	EX_X	2020	1000 NAC	1_2_C	FR_EX_X_2020_1000 NAC_1_2_C	5.0358
FR	EX_X	2020	1000 NAC	1_2_NC	FR_EX_X_2020_1000 NAC_1_2_NC	347.74862
FR	EX_X	2020	1000 NAC	1_2_NC_T	FR_EX_X_2020_1000 NAC_1_2_NC_T	109.0992
FR	EX_X	2020	1000 NAC	2	FR_EX_X_2020_1000 NAC_2	238.64942
FR	EX_X	2020	1000 NAC	3	FR_EX_X_2020_1000 NAC_3	0.61472
FR	EX_X	2020	1000 NAC	3_1	FR_EX_X_2020_1000 NAC_3_1	7.796
FR	EX_X	2020	1000 NAC	3_2	FR_EX_X_2020_1000 NAC_3_2	58.67804
FR	EX_X	2020	1000 NAC	4	FR_EX_X_2020_1000 NAC_4	42.73024
FR	EX_X	2020	1000 NAC	4_1	FR_EX_X_2020_1000 NAC_4_1	15.9478
FR	EX_X	2020	1000 NAC	4_2	FR_EX_X_2020_1000 NAC_4_2	0
FR	EX_X	2020	1000 NAC	5	FR_EX_X_2020_1000 NAC_5	25.53
FR	EX_X	2020	1000 NAC	5_C	FR_EX_X_2020_1000 NAC_5_C	20.703
FR	EX_X	2020	1000 NAC	5_NC	FR_EX_X_2020_1000 NAC_5_NC	4.827
FR	EX_X	2020	1000 NAC	5_NC_T	FR_EX_X_2020_1000 NAC_5_NC_T	594.8026
FR	EX_X	2020	1000 NAC	6	FR_EX_X_2020_1000 NAC_6	322.0272
FR	EX_X	2020	1000 NAC	6_1	FR_EX_X_2020_1000 NAC_6_1	272.7754
FR	EX_X	2020	1000 NAC	6_1_C	FR_EX_X_2020_1000 NAC_6_1_C	3.927
FR	EX_X	2020	1000 NAC	6_1_NC	FR_EX_X_2020_1000 NAC_6_1_NC	128.97808
FR	EX_X	2020	1000 NAC	6_1_NC_T	FR_EX_X_2020_1000 NAC_6_1_NC_T	0.60382
FR	EX_X	2020	1000 NAC	6_2	FR_EX_X_2020_1000 NAC_6_2	128.37426
FR	EX_X	2020	1000 NAC	6_2_C	FR_EX_X_2020_1000 NAC_6_2_C	105.91056
FR	EX_X	2020	1000 NAC	6_2_NC	FR_EX_X_2020_1000 NAC_6_2_NC	532.61394
FR	EX_X	2020	1000 NAC	6_2_NC_T	FR_EX_X_2020_1000 NAC_6_2_NC_T	230.1453
FR	EX_X	2020	1000 NAC	6_3	FR_EX_X_2020_1000 NAC_6_3	81.50142
FR	EX_X	2020	1000 NAC	6_3_1	FR_EX_X_2020_1000 NAC_6_3_1	148.64388
FR	EX_X	2020	1000 NAC	6_4	FR_EX_X_2020_1000 NAC_6_4	122.63328
FR	EX_X	2020	1000 NAC	6_4_1	FR_EX_X_2020_1000 NAC_6_4_1	0
FR	EX_X	2020	1000 NAC	6_4_2	FR_EX_X_2020_1000 NAC_6_4_2	14.71342
FR	EX_X	2020	1000 NAC	6_4_3	FR_EX_X_2020_1000 NAC_6_4_3	302.46864
FR	EX_X	2020	1000 NAC	7	FR_EX_X_2020_1000 NAC_7	139.04637
FR	EX_X	2020	1000 NAC	7_1	FR_EX_X_2020_1000 NAC_7_1	108.678
FR	EX_X	2020	1000 NAC	7_2	FR_EX_X_2020_1000 NAC_7_2	54.74427
FR	EX_X	2020	1000 NAC	7_3	FR_EX_X_2020_1000 NAC_7_3	4.632
FR	EX_X	2020	1000 NAC	7_3_1	FR_EX_X_2020_1000 NAC_7_3_1	1.253
FR	EX_X	2020	1000 NAC	7_3_2	FR_EX_X_2020_1000 NAC_7_3_2	3.379
FR	EX_X	2020	1000 NAC	7_3_3	FR_EX_X_2020_1000 NAC_7_3_3	0
FR	EX_X	2020	1000 NAC	7_3_4	FR_EX_X_2020_1000 NAC_7_3_4	199.023
FR	EX_X	2020	1000 NAC	7_4	FR_EX_X_2020_1000 NAC_7_4	3.379
FR	EX_X	2020	1000 NAC	8	FR_EX_X_2020_1000 NAC_8	0
FR	EX_X	2020	1000 NAC	8_1	FR_EX_X_2020_1000 NAC_8_1	44.499
FR	EX_X	2020	1000 NAC	8_2	FR_EX_X_2020_1000 NAC_8_2	4.471
FR	EX_X	2020	1000 NAC	9	FR_EX_X_2020_1000 NAC_9	40.028
FR	EX_X	2020	1000 NAC	10	FR_EX_X_2020_1000 NAC_10	2100.841
FR	EX_X	2020	1000 NAC	10_1	FR_EX_X_2020_1000 NAC_10_1	2649.898
FR	EX_X	2020	1000 NAC	10_1_1	FR_EX_X_2020_1000 NAC_10_1_1	715.201
FR	EX_X	2020	1000 NAC	10_1_2	FR_EX_X_2020_1000 NAC_10_1_2	0
FR	EX_X	2020	1000 NAC	10_1_3	FR_EX_X_2020_1000 NAC_10_1_3	0
FR	EX_X	2020	1000 NAC	10_1_4	FR_EX_X_2020_1000 NAC_10_1_4	0
FR	EX_X	2020	1000 NAC	10_2	FR_EX_X_2020_1000 NAC_10_2	0
FR	EX_X	2020	1000 NAC	10_3	FR_EX_X_2020_1000 NAC_10_3	92.484
FR	EX_X	2020	1000 NAC	10_3_1	FR_EX_X_2020_1000 NAC_10_3_1	1698.501
FR	EX_X	2020	1000 NAC	10_3_2	FR_EX_X_2020_1000 NAC_10_3_2	0
FR	EX_X	2020	1000 NAC	10_3_3	FR_EX_X_2020_1000 NAC_10_3_3	0
FR	EX_X	2020	1000 NAC	10_3_4	FR_EX_X_2020_1000 NAC_10_3_4	0
FR	EX_X	2020	1000 NAC	10_4	FR_EX_X_2020_1000 NAC_10_4	0
FR	EX_X	2021	1000 m3	1	FR_EX_X_2021_1000 m3_1	4546.654125
FR	EX_X	2021	1000 m3	1_1	FR_EX_X_2021_1000 m3_1_1	435.13554
FR	EX_X	2021	1000 m3	1_2	FR_EX_X_2021_1000 m3_1_2	299.9145
FR	EX_X	2021	1000 m3	1_2_C	FR_EX_X_2021_1000 m3_1_2_C	135.22104
FR	EX_X	2021	1000 m3	1_2_NC	FR_EX_X_2021_1000 m3_1_2_NC	4111.518585
FR	EX_X	2021	1000 m3	1_2_NC_T	FR_EX_X_2021_1000 m3_1_2_NC_T	1617.3616
FR	EX_X	2021	1000 mt	2	FR_EX_X_2021_1000 mt_2	2494.156985
FR	EX_X	2021	1000 m3	3	FR_EX_X_2021_1000 m3_3	1.913785
FR	EX_X	2021	1000 m3	3_1	FR_EX_X_2021_1000 m3_3_1	6.912
FR	EX_X	2021	1000 m3	3_2	FR_EX_X_2021_1000 m3_3_2	631.02238
FR	EX_X	2021	1000 mt	4	FR_EX_X_2021_1000 mt_4	311.45418
FR	EX_X	2021	1000 mt	4_1	FR_EX_X_2021_1000 mt_4_1	319.5682
FR	EX_X	2021	1000 mt	4_2	FR_EX_X_2021_1000 mt_4_2	0
FR	EX_X	2021	1000 m3	5	FR_EX_X_2021_1000 m3_5	342.339
FR	EX_X	2021	1000 m3	5_C	FR_EX_X_2021_1000 m3_5_C	121.64
FR	EX_X	2021	1000 m3	5_NC	FR_EX_X_2021_1000 m3_5_NC	220.699
FR	EX_X	2021	1000 m3	5_NC_T	FR_EX_X_2021_1000 m3_5_NC_T	1646.4064
FR	EX_X	2021	1000 m3	6	FR_EX_X_2021_1000 m3_6	1055.3076
FR	EX_X	2021	1000 m3	6_1	FR_EX_X_2021_1000 m3_6_1	591.0988
FR	EX_X	2021	1000 m3	6_1_C	FR_EX_X_2021_1000 m3_6_1_C	4.9252
FR	EX_X	2021	1000 m3	6_1_NC	FR_EX_X_2021_1000 m3_6_1_NC	71.28401
FR	EX_X	2021	1000 m3	6_1_NC_T	FR_EX_X_2021_1000 m3_6_1_NC_T	0.5586
FR	EX_X	2021	1000 m3	6_2	FR_EX_X_2021_1000 m3_6_2	70.72541
FR	EX_X	2021	1000 m3	6_2_C	FR_EX_X_2021_1000 m3_6_2_C	54.0778
FR	EX_X	2021	1000 m3	6_2_NC	FR_EX_X_2021_1000 m3_6_2_NC	1027.77288
FR	EX_X	2021	1000 m3	6_2_NC_T	FR_EX_X_2021_1000 m3_6_2_NC_T	154.9394
FR	EX_X	2021	1000 m3	6_3	FR_EX_X_2021_1000 m3_6_3	73.04682
FR	EX_X	2021	1000 m3	6_3_1	FR_EX_X_2021_1000 m3_6_3_1	81.89258
FR	EX_X	2021	1000 m3	6_4	FR_EX_X_2021_1000 m3_6_4	62.6164
FR	EX_X	2021	1000 m3	6_4_1	FR_EX_X_2021_1000 m3_6_4_1	0
FR	EX_X	2021	1000 m3	6_4_2	FR_EX_X_2021_1000 m3_6_4_2	28.81442
FR	EX_X	2021	1000 m3	6_4_3	FR_EX_X_2021_1000 m3_6_4_3	872.83348
FR	EX_X	2021	1000 mt	7	FR_EX_X_2021_1000 mt_7	443.30625
FR	EX_X	2021	1000 mt	7_1	FR_EX_X_2021_1000 mt_7_1	344.64614
FR	EX_X	2021	1000 mt	7_2	FR_EX_X_2021_1000 mt_7_2	84.88109
FR	EX_X	2021	1000 mt	7_3	FR_EX_X_2021_1000 mt_7_3	33.487
FR	EX_X	2021	1000 mt	7_3_1	FR_EX_X_2021_1000 mt_7_3_1	32.302
FR	EX_X	2021	1000 mt	7_3_2	FR_EX_X_2021_1000 mt_7_3_2	1.185
FR	EX_X	2021	1000 mt	7_3_3	FR_EX_X_2021_1000 mt_7_3_3	0
FR	EX_X	2021	1000 mt	7_3_4	FR_EX_X_2021_1000 mt_7_3_4	379.414
FR	EX_X	2021	1000 mt	7_4	FR_EX_X_2021_1000 mt_7_4	1.185
FR	EX_X	2021	1000 mt	8	FR_EX_X_2021_1000 mt_8	0
FR	EX_X	2021	1000 mt	8_1	FR_EX_X_2021_1000 mt_8_1	83.456
FR	EX_X	2021	1000 mt	8_2	FR_EX_X_2021_1000 mt_8_2	2.01
FR	EX_X	2021	1000 mt	9	FR_EX_X_2021_1000 mt_9	81.446
FR	EX_X	2021	1000 mt	10	FR_EX_X_2021_1000 mt_10	955.364
FR	EX_X	2021	1000 mt	10_1	FR_EX_X_2021_1000 mt_10_1	3806.804
FR	EX_X	2021	1000 mt	10_1_1	FR_EX_X_2021_1000 mt_10_1_1	1008.647
FR	EX_X	2021	1000 mt	10_1_2	FR_EX_X_2021_1000 mt_10_1_2	0
FR	EX_X	2021	1000 mt	10_1_3	FR_EX_X_2021_1000 mt_10_1_3	0
FR	EX_X	2021	1000 mt	10_1_4	FR_EX_X_2021_1000 mt_10_1_4	0
FR	EX_X	2021	1000 mt	10_2	FR_EX_X_2021_1000 mt_10_2	0
FR	EX_X	2021	1000 mt	10_3	FR_EX_X_2021_1000 mt_10_3	67.563
FR	EX_X	2021	1000 mt	10_3_1	FR_EX_X_2021_1000 mt_10_3_1	2672.381
FR	EX_X	2021	1000 mt	10_3_2	FR_EX_X_2021_1000 mt_10_3_2	0
FR	EX_X	2021	1000 mt	10_3_3	FR_EX_X_2021_1000 mt_10_3_3	0
FR	EX_X	2021	1000 mt	10_3_4	FR_EX_X_2021_1000 mt_10_3_4	0
FR	EX_X	2021	1000 mt	10_4	FR_EX_X_2021_1000 mt_10_4	0
FR	EX_X	2021	1000 NAC	1	FR_EX_X_2021_1000 NAC_1	488.4425
FR	EX_X	2021	1000 NAC	1_1	FR_EX_X_2021_1000 NAC_1_1	24.0334
FR	EX_X	2021	1000 NAC	1_2	FR_EX_X_2021_1000 NAC_1_2	16.665
FR	EX_X	2021	1000 NAC	1_2_C	FR_EX_X_2021_1000 NAC_1_2_C	7.3684
FR	EX_X	2021	1000 NAC	1_2_NC	FR_EX_X_2021_1000 NAC_1_2_NC	464.4091
FR	EX_X	2021	1000 NAC	1_2_NC_T	FR_EX_X_2021_1000 NAC_1_2_NC_T	163.2592
FR	EX_X	2021	1000 NAC	2	FR_EX_X_2021_1000 NAC_2	301.1499
FR	EX_X	2021	1000 NAC	3	FR_EX_X_2021_1000 NAC_3	0.5601
FR	EX_X	2021	1000 NAC	3_1	FR_EX_X_2021_1000 NAC_3_1	6.952
FR	EX_X	2021	1000 NAC	3_2	FR_EX_X_2021_1000 NAC_3_2	36.0151
FR	EX_X	2021	1000 NAC	4	FR_EX_X_2021_1000 NAC_4	16.7245
FR	EX_X	2021	1000 NAC	4_1	FR_EX_X_2021_1000 NAC_4_1	19.2906
FR	EX_X	2021	1000 NAC	4_2	FR_EX_X_2021_1000 NAC_4_2	0
FR	EX_X	2021	1000 NAC	5	FR_EX_X_2021_1000 NAC_5	34.392
FR	EX_X	2021	1000 NAC	5_C	FR_EX_X_2021_1000 NAC_5_C	25.701
FR	EX_X	2021	1000 NAC	5_NC	FR_EX_X_2021_1000 NAC_5_NC	8.691
FR	EX_X	2021	1000 NAC	5_NC_T	FR_EX_X_2021_1000 NAC_5_NC_T	807.8307
FR	EX_X	2021	1000 NAC	6	FR_EX_X_2021_1000 NAC_6	438.489
FR	EX_X	2021	1000 NAC	6_1	FR_EX_X_2021_1000 NAC_6_1	369.3417
FR	EX_X	2021	1000 NAC	6_1_C	FR_EX_X_2021_1000 NAC_6_1_C	6.2734
FR	EX_X	2021	1000 NAC	6_1_NC	FR_EX_X_2021_1000 NAC_6_1_NC	141.37368
FR	EX_X	2021	1000 NAC	6_1_NC_T	FR_EX_X_2021_1000 NAC_6_1_NC_T	0.80997
FR	EX_X	2021	1000 NAC	6_2	FR_EX_X_2021_1000 NAC_6_2	140.56371
FR	EX_X	2021	1000 NAC	6_2_C	FR_EX_X_2021_1000 NAC_6_2_C	110.75974
FR	EX_X	2021	1000 NAC	6_2_NC	FR_EX_X_2021_1000 NAC_6_2_NC	541.51256
FR	EX_X	2021	1000 NAC	6_2_NC_T	FR_EX_X_2021_1000 NAC_6_2_NC_T	215.5538
FR	EX_X	2021	1000 NAC	6_3	FR_EX_X_2021_1000 NAC_6_3	52.79582
FR	EX_X	2021	1000 NAC	6_3_1	FR_EX_X_2021_1000 NAC_6_3_1	162.75798
FR	EX_X	2021	1000 NAC	6_4	FR_EX_X_2021_1000 NAC_6_4	128.24812
FR	EX_X	2021	1000 NAC	6_4_1	FR_EX_X_2021_1000 NAC_6_4_1	0
FR	EX_X	2021	1000 NAC	6_4_2	FR_EX_X_2021_1000 NAC_6_4_2	17.88054
FR	EX_X	2021	1000 NAC	6_4_3	FR_EX_X_2021_1000 NAC_6_4_3	325.95876
FR	EX_X	2021	1000 NAC	7	FR_EX_X_2021_1000 NAC_7	131.17092
FR	EX_X	2021	1000 NAC	7_1	FR_EX_X_2021_1000 NAC_7_1	144.66446
FR	EX_X	2021	1000 NAC	7_2	FR_EX_X_2021_1000 NAC_7_2	50.12338
FR	EX_X	2021	1000 NAC	7_3	FR_EX_X_2021_1000 NAC_7_3	7.645
FR	EX_X	2021	1000 NAC	7_3_1	FR_EX_X_2021_1000 NAC_7_3_1	6.479
FR	EX_X	2021	1000 NAC	7_3_2	FR_EX_X_2021_1000 NAC_7_3_2	1.166
FR	EX_X	2021	1000 NAC	7_3_3	FR_EX_X_2021_1000 NAC_7_3_3	0
FR	EX_X	2021	1000 NAC	7_3_4	FR_EX_X_2021_1000 NAC_7_3_4	219.787
FR	EX_X	2021	1000 NAC	7_4	FR_EX_X_2021_1000 NAC_7_4	1.166
FR	EX_X	2021	1000 NAC	8	FR_EX_X_2021_1000 NAC_8	0
FR	EX_X	2021	1000 NAC	8_1	FR_EX_X_2021_1000 NAC_8_1	56.22
FR	EX_X	2021	1000 NAC	8_2	FR_EX_X_2021_1000 NAC_8_2	4.738
FR	EX_X	2021	1000 NAC	9	FR_EX_X_2021_1000 NAC_9	51.482
FR	EX_X	2021	1000 NAC	10	FR_EX_X_2021_1000 NAC_10	2280.836
FR	EX_X	2021	1000 NAC	10_1	FR_EX_X_2021_1000 NAC_10_1	3433.153
FR	EX_X	2021	1000 NAC	10_1_1	FR_EX_X_2021_1000 NAC_10_1_1	834.097
FR	EX_X	2021	1000 NAC	10_1_2	FR_EX_X_2021_1000 NAC_10_1_2	0
FR	EX_X	2021	1000 NAC	10_1_3	FR_EX_X_2021_1000 NAC_10_1_3	0
FR	EX_X	2021	1000 NAC	10_1_4	FR_EX_X_2021_1000 NAC_10_1_4	0
FR	EX_X	2021	1000 NAC	10_2	FR_EX_X_2021_1000 NAC_10_2	0
FR	EX_X	2021	1000 NAC	10_3	FR_EX_X_2021_1000 NAC_10_3	91.041
FR	EX_X	2021	1000 NAC	10_3_1	FR_EX_X_2021_1000 NAC_10_3_1	2194.104
FR	EX_X	2021	1000 NAC	10_3_2	FR_EX_X_2021_1000 NAC_10_3_2	0
FR	EX_X	2021	1000 NAC	10_3_3	FR_EX_X_2021_1000 NAC_10_3_3	0
FR	EX_X	2021	1000 NAC	10_3_4	FR_EX_X_2021_1000 NAC_10_3_4	0
FR	EX_X	2021	1000 NAC	10_4	FR_EX_X_2021_1000 NAC_10_4	0
FR	P	2020	1000 m3	EU2_1	FR_P_2020_1000 m3_EU2_1	47387	EU2
FR	P	2020	1000 m3	EU2_1_C	FR_P_2020_1000 m3_EU2_1_C	18655
FR	P	2020	1000 m3	EU2_1_NC	FR_P_2020_1000 m3_EU2_1_NC	28732
FR	P	2020	1000 m3	EU2_1_1	FR_P_2020_1000 m3_EU2_1_1	4244.4802825341
FR	P	2020	1000 m3	EU2_1_1_C	FR_P_2020_1000 m3_EU2_1_1_C	2224.3297372312
FR	P	2020	1000 m3	EU2_1_1_NC	FR_P_2020_1000 m3_EU2_1_1_NC	2020.1505453029
FR	P	2020	1000 m3	EU2_1_2	FR_P_2020_1000 m3_EU2_1_2	5906.5827642783
FR	P	2020	1000 m3	EU2_1_2_C	FR_P_2020_1000 m3_EU2_1_2_C	2908.3862908533
FR	P	2020	1000 m3	EU2_1_2_NC	FR_P_2020_1000 m3_EU2_1_2_NC	2998.196473425
FR	P	2020	1000 m3	EU2_1_3	FR_P_2020_1000 m3_EU2_1_3	37235.9369531876
FR	P	2020	1000 m3	EU2_1_3_C	FR_P_2020_1000 m3_EU2_1_3_C	13522.2839719155
FR	P	2020	1000 m3	EU2_1_3_NC	FR_P_2020_1000 m3_EU2_1_3_NC	23713.652981272
FR	P	2021	1000 m3	EU2_1	FR_P_2021_1000 m3_EU2_1	52915
FR	P	2021	1000 m3	EU2_1_C	FR_P_2021_1000 m3_EU2_1_C	20944
FR	P	2021	1000 m3	EU2_1_NC	FR_P_2021_1000 m3_EU2_1_NC	31971
FR	P	2021	1000 m3	EU2_1_1	FR_P_2021_1000 m3_EU2_1_1	4557.123304733
FR	P	2021	1000 m3	EU2_1_1_C	FR_P_2021_1000 m3_EU2_1_1_C	2271.126956543
FR	P	2021	1000 m3	EU2_1_1_NC	FR_P_2021_1000 m3_EU2_1_1_NC	2285.9963481899
FR	P	2021	1000 m3	EU2_1_2	FR_P_2021_1000 m3_EU2_1_2	7982.77189
FR	P	2021	1000 m3	EU2_1_2_C	FR_P_2021_1000 m3_EU2_1_2_C	4128.624044745
FR	P	2021	1000 m3	EU2_1_2_NC	FR_P_2021_1000 m3_EU2_1_2_NC	3854.147845255
FR	P	2021	1000 m3	EU2_1_3	FR_P_2021_1000 m3_EU2_1_3	40375.104805267
FR	P	2021	1000 m3	EU2_1_3_C	FR_P_2021_1000 m3_EU2_1_3_C	14544.2489987119
FR	P	2021	1000 m3	EU2_1_3_NC	FR_P_2021_1000 m3_EU2_1_3_NC	25830.8558065551
FR	P.OB	2020	1000 m3	1	FR_P.OB_2020_1000 m3_1	52860.723	OB
FR	P.OB	2020	1000 m3	1_C	FR_P.OB_2020_1000 m3_1_C	24375.723
FR	P.OB	2020	1000 m3	1_NC	FR_P.OB_2020_1000 m3_1_NC	2437.5723
FR	P.OB	2020	1000 m3	1_1	FR_P.OB_2020_1000 m3_1_1	21938.1507
FR	P.OB	2020	1000 m3	1_1_C	FR_P.OB_2020_1000 m3_1_1_C	28485
FR	P.OB	2020	1000 m3	1_1_NC	FR_P.OB_2020_1000 m3_1_1_NC	19630
FR	P.OB	2020	1000 m3	1_2	FR_P.OB_2020_1000 m3_1_2	8855
FR	P.OB	2020	1000 m3	1_2_C	FR_P.OB_2020_1000 m3_1_2_C	78
FR	P.OB	2020	1000 m3	1_2_NC	FR_P.OB_2020_1000 m3_1_2_NC	18463
FR	P.OB	2020	1000 m3	1_2_1	FR_P.OB_2020_1000 m3_1_2_1	13712
FR	P.OB	2020	1000 m3	1_2_1_C	FR_P.OB_2020_1000 m3_1_2_1_C	4751
FR	P.OB	2020	1000 m3	1_2_1_NC	FR_P.OB_2020_1000 m3_1_2_1_NC	9476
FR	P.OB	2020	1000 m3	1_2_2	FR_P.OB_2020_1000 m3_1_2_2	5680
FR	P.OB	2020	1000 m3	1_2_2_C	FR_P.OB_2020_1000 m3_1_2_2_C	3796
FR	P.OB	2020	1000 m3	1_2_2_NC	FR_P.OB_2020_1000 m3_1_2_2_NC	546
FR	P.OB	2020	1000 m3	1_2_3	FR_P.OB_2020_1000 m3_1_2_3	238
FR	P.OB	2020	1000 m3	1_2_3_C	FR_P.OB_2020_1000 m3_1_2_3_C	308
FR	P.OB	2020	1000 m3	1_2_3_NC	FR_P.OB_2020_1000 m3_1_2_3_NC	ERROR:#REF!
FR	P.OB	2021	1000 m3	1	FR_P.OB_2021_1000 m3_1	59263
FR	P.OB	2021	1000 m3	1_C	FR_P.OB_2021_1000 m3_1_C	28284
FR	P.OB	2021	1000 m3	1_NC	FR_P.OB_2021_1000 m3_1_NC	2828
FR	P.OB	2021	1000 m3	1_1	FR_P.OB_2021_1000 m3_1_1	25456
FR	P.OB	2021	1000 m3	1_1_C	FR_P.OB_2021_1000 m3_1_1_C	30979
FR	P.OB	2021	1000 m3	1_1_NC	FR_P.OB_2021_1000 m3_1_1_NC	21927
FR	P.OB	2021	1000 m3	1_2	FR_P.OB_2021_1000 m3_1_2	9052
FR	P.OB	2021	1000 m3	1_2_C	FR_P.OB_2021_1000 m3_1_2_C	81.9
FR	P.OB	2021	1000 m3	1_2_NC	FR_P.OB_2021_1000 m3_1_2_NC	20854
FR	P.OB	2021	1000 m3	1_2_1	FR_P.OB_2021_1000 m3_1_2_1	15839
FR	P.OB	2021	1000 m3	1_2_1_C	FR_P.OB_2021_1000 m3_1_2_1_C	5015
FR	P.OB	2021	1000 m3	1_2_1_NC	FR_P.OB_2021_1000 m3_1_2_1_NC	9469
FR	P.OB	2021	1000 m3	1_2_2	FR_P.OB_2021_1000 m3_1_2_2	5745
FR	P.OB	2021	1000 m3	1_2_2_C	FR_P.OB_2021_1000 m3_1_2_2_C	3724
FR	P.OB	2021	1000 m3	1_2_2_NC	FR_P.OB_2021_1000 m3_1_2_2_NC	656
FR	P.OB	2021	1000 m3	1_2_3	FR_P.OB_2021_1000 m3_1_2_3	343
FR	P.OB	2021	1000 m3	1_2_3_C	FR_P.OB_2021_1000 m3_1_2_3_C	313
FR	P.OB	2021	1000 m3	1_2_3_NC	FR_P.OB_2021_1000 m3_1_2_3_NC	ERROR:#REF!

Database

Country

Flow

Year

Unit

Product

conc

2015

1000 m3

FR_P_2015_1000 m3_1

2015

1000 m3

1_C

FR_P_2015_1000 m3_1_C

2015

1000 m3

1_NC

FR_P_2015_1000 m3_1_NC

2015

1000 m3

1_1

FR_P_2015_1000 m3_1_1

2015

1000 m3

1_1_C

FR_P_2015_1000 m3_1_1_C

2015

1000 m3

1_1_NC

FR_P_2015_1000 m3_1_1_NC

2015

1000 m3

1_2

FR_P_2015_1000 m3_1_2

2015

1000 m3

1_2_C

FR_P_2015_1000 m3_1_2_C

2015

1000 m3

1_2_NC

FR_P_2015_1000 m3_1_2_NC

2015

1000 m3

1_2_1

FR_P_2015_1000 m3_1_2_1

2015

1000 m3

1_2_1_C

FR_P_2015_1000 m3_1_2_1_C

2015

1000 m3

1_2_1_NC

FR_P_2015_1000 m3_1_2_1_NC

2015

1000 m3

1_2_2

FR_P_2015_1000 m3_1_2_2

2015

1000 m3

1_2_2_C

FR_P_2015_1000 m3_1_2_2_C

2015

1000 m3

1_2_2_NC

FR_P_2015_1000 m3_1_2_2_NC

2015

1000 m3

1_2_3

FR_P_2015_1000 m3_1_2_3

2015

1000 m3

1_2_3_C

FR_P_2015_1000 m3_1_2_3_C

2015

1000 m3

1_2_3_NC

FR_P_2015_1000 m3_1_2_3_NC

2015

1000 mt

FR_P_2015_1000 mt_2

2015

1000 m3

FR_P_2015_1000 m3_3

2015

1000 m3

3_1

FR_P_2015_1000 m3_3_1

2015

1000 m3

3_2

FR_P_2015_1000 m3_3_2

2015

1000 mt

FR_P_2015_1000 mt_4

2015

1000 mt

4_1

FR_P_2015_1000 mt_4_1

2015

1000 mt

4_2

FR_P_2015_1000 mt_4_2

2015

1000 m3

FR_P_2015_1000 m3_5

2015

1000 m3

5_C

FR_P_2015_1000 m3_5_C

2015

1000 m3

5_NC

FR_P_2015_1000 m3_5_NC

2015

1000 m3

5_NC_T

FR_P_2015_1000 m3_5_NC_T

2015

1000 m3

FR_P_2015_1000 m3_6

2015

1000 m3

6_1

FR_P_2015_1000 m3_6_1

2015

1000 m3

6_1_C

FR_P_2015_1000 m3_6_1_C

2015

1000 m3

6_1_NC

FR_P_2015_1000 m3_6_1_NC

2015

1000 m3

6_1_NC_T

FR_P_2015_1000 m3_6_1_NC_T

2015

1000 m3

6_2

FR_P_2015_1000 m3_6_2

2015

1000 m3

6_2_C

FR_P_2015_1000 m3_6_2_C

2015

1000 m3

6_2_NC

FR_P_2015_1000 m3_6_2_NC

2015

1000 m3

6_2_NC_T

FR_P_2015_1000 m3_6_2_NC_T

2015

1000 m3

6_3

FR_P_2015_1000 m3_6_3

2015

1000 m3

6_3_1

FR_P_2015_1000 m3_6_3_1

2015

1000 m3

6_4

FR_P_2015_1000 m3_6_4

2015

1000 m3

6_4_1

FR_P_2015_1000 m3_6_4_1

2015

1000 m3

6_4_2

FR_P_2015_1000 m3_6_4_2

2015

1000 m3

6_4_3

FR_P_2015_1000 m3_6_4_3

2015

1000 mt

FR_P_2015_1000 mt_7

2015

1000 mt

7_1

FR_P_2015_1000 mt_7_1

2015

1000 mt

7_2

FR_P_2015_1000 mt_7_2

2015

1000 mt

7_3

FR_P_2015_1000 mt_7_3

2015

1000 mt

7_3_1

FR_P_2015_1000 mt_7_3_1

2015

1000 mt

7_3_2

FR_P_2015_1000 mt_7_3_2

2015

1000 mt

7_3_3

FR_P_2015_1000 mt_7_3_3

2015

1000 mt

7_3_4

FR_P_2015_1000 mt_7_3_4

2015

1000 mt

7_4

FR_P_2015_1000 mt_7_4

2015

1000 mt

FR_P_2015_1000 mt_8

2015

1000 mt

8_1

FR_P_2015_1000 mt_8_1

2015

1000 mt

8_2

FR_P_2015_1000 mt_8_2

2015

1000 mt

FR_P_2015_1000 mt_9

2015

1000 mt

FR_P_2015_1000 mt_10

2015

1000 mt

10_1

FR_P_2015_1000 mt_10_1

2015

1000 mt

10_1_1

FR_P_2015_1000 mt_10_1_1

2015

1000 mt

10_1_2

FR_P_2015_1000 mt_10_1_2

2015

1000 mt

10_1_3

FR_P_2015_1000 mt_10_1_3

2015

1000 mt

10_1_4

FR_P_2015_1000 mt_10_1_4

2015

1000 mt

10_2

FR_P_2015_1000 mt_10_2

2015

1000 mt

10_3

FR_P_2015_1000 mt_10_3

2015

1000 mt

10_3_1

FR_P_2015_1000 mt_10_3_1

2015

1000 mt

10_3_2

FR_P_2015_1000 mt_10_3_2

2015

1000 mt

10_3_3

FR_P_2015_1000 mt_10_3_3

2015

1000 mt

10_3_4

FR_P_2015_1000 mt_10_3_4

2015

1000 mt

10_4

FR_P_2015_1000 mt_10_4

2015

1000 m3

FR_M_2015_1000 m3_1

2015

1000 m3

1_1

FR_M_2015_1000 m3_1_1

2015

1000 m3

1_2

FR_M_2015_1000 m3_1_2

2015

1000 m3

1_2_C

FR_M_2015_1000 m3_1_2_C

2015

1000 m3

1_2_NC

FR_M_2015_1000 m3_1_2_NC

2015

1000 m3

1_2_NC_T

FR_M_2015_1000 m3_1_2_NC_T

2015

1000 mt

FR_M_2015_1000 mt_2

2015

1000 m3

FR_M_2015_1000 m3_3

2015

1000 m3

3_1

FR_M_2015_1000 m3_3_1

2015

1000 m3

3_2

FR_M_2015_1000 m3_3_2

2015

1000 mt

FR_M_2015_1000 mt_4

2015

1000 mt

4_1

FR_M_2015_1000 mt_4_1

2015

1000 mt

4_2

FR_M_2015_1000 mt_4_2

2015

1000 m3

FR_M_2015_1000 m3_5

2015

1000 m3

5_C

FR_M_2015_1000 m3_5_C

2015

1000 m3

5_NC

FR_M_2015_1000 m3_5_NC

2015

1000 m3

5_NC_T

FR_M_2015_1000 m3_5_NC_T

2015

1000 m3

FR_M_2015_1000 m3_6

2015

1000 m3

6_1

FR_M_2015_1000 m3_6_1

2015

1000 m3

6_1_C

FR_M_2015_1000 m3_6_1_C

2015

1000 m3

6_1_NC

FR_M_2015_1000 m3_6_1_NC

2015

1000 m3

6_1_NC_T

FR_M_2015_1000 m3_6_1_NC_T

2015

1000 m3

6_2

FR_M_2015_1000 m3_6_2

2015

1000 m3

6_2_C

FR_M_2015_1000 m3_6_2_C

2015

1000 m3

6_2_NC

FR_M_2015_1000 m3_6_2_NC

2015

1000 m3

6_2_NC_T

FR_M_2015_1000 m3_6_2_NC_T

2015

1000 m3

6_3

FR_M_2015_1000 m3_6_3

2015

1000 m3

6_3_1

FR_M_2015_1000 m3_6_3_1

2015

1000 m3

6_4

FR_M_2015_1000 m3_6_4

2015

1000 m3

6_4_1

FR_M_2015_1000 m3_6_4_1

2015

1000 m3

6_4_2

FR_M_2015_1000 m3_6_4_2

2015

1000 m3

6_4_3

FR_M_2015_1000 m3_6_4_3

2015

1000 mt

FR_M_2015_1000 mt_7

2015

1000 mt

7_1

FR_M_2015_1000 mt_7_1

2015

1000 mt

7_2

FR_M_2015_1000 mt_7_2

2015

1000 mt

7_3

FR_M_2015_1000 mt_7_3

2015

1000 mt

7_3_1

FR_M_2015_1000 mt_7_3_1

2015

1000 mt

7_3_2

FR_M_2015_1000 mt_7_3_2

2015

1000 mt

7_3_3

FR_M_2015_1000 mt_7_3_3

2015

1000 mt

7_3_4

FR_M_2015_1000 mt_7_3_4

2015

1000 mt

7_4

FR_M_2015_1000 mt_7_4

2015

1000 mt

FR_M_2015_1000 mt_8

2015

1000 mt

8_1

FR_M_2015_1000 mt_8_1

2015

1000 mt

8_2

FR_M_2015_1000 mt_8_2

2015

1000 mt

FR_M_2015_1000 mt_9

2015

1000 mt

FR_M_2015_1000 mt_10

2015

1000 mt

10_1

FR_M_2015_1000 mt_10_1

2015

1000 mt

10_1_1

FR_M_2015_1000 mt_10_1_1

2015

1000 mt

10_1_2

FR_M_2015_1000 mt_10_1_2

2015

1000 mt

10_1_3

FR_M_2015_1000 mt_10_1_3

2015

1000 mt

10_1_4

FR_M_2015_1000 mt_10_1_4

2015

1000 mt

10_2

FR_M_2015_1000 mt_10_2

2015

1000 mt

10_3

FR_M_2015_1000 mt_10_3

2015

1000 mt

10_3_1

FR_M_2015_1000 mt_10_3_1

2015

1000 mt

10_3_2

FR_M_2015_1000 mt_10_3_2

2015

1000 mt

10_3_3

FR_M_2015_1000 mt_10_3_3

2015

1000 mt

10_3_4

FR_M_2015_1000 mt_10_3_4

2015

1000 mt

10_4

FR_M_2015_1000 mt_10_4

2015

1000 NAC

FR_M_2015_1000 NAC_1

2015

1000 NAC

1_1

FR_M_2015_1000 NAC_1_1

2015

1000 NAC

1_2

FR_M_2015_1000 NAC_1_2

2015

1000 NAC

1_2_C

FR_M_2015_1000 NAC_1_2_C

2015

1000 NAC

1_2_NC

FR_M_2015_1000 NAC_1_2_NC

2015

1000 NAC

1_2_NC_T

FR_M_2015_1000 NAC_1_2_NC_T

2015

1000 NAC

FR_M_2015_1000 NAC_2

2015

1000 NAC

FR_M_2015_1000 NAC_3

2015

1000 NAC

3_1

FR_M_2015_1000 NAC_3_1

2015

1000 NAC

3_2

FR_M_2015_1000 NAC_3_2

2015

1000 NAC

FR_M_2015_1000 NAC_4

2015

1000 NAC

4_1

FR_M_2015_1000 NAC_4_1

2015

1000 NAC

4_2

FR_M_2015_1000 NAC_4_2

2015

1000 NAC

FR_M_2015_1000 NAC_5

2015

1000 NAC

5_C

FR_M_2015_1000 NAC_5_C

2015

1000 NAC

5_NC

FR_M_2015_1000 NAC_5_NC

2015

1000 NAC

5_NC_T

FR_M_2015_1000 NAC_5_NC_T

2015

1000 NAC

FR_M_2015_1000 NAC_6

2015

1000 NAC

6_1

FR_M_2015_1000 NAC_6_1

2015

1000 NAC

6_1_C

FR_M_2015_1000 NAC_6_1_C

2015

1000 NAC

6_1_NC

FR_M_2015_1000 NAC_6_1_NC

2015

1000 NAC

6_1_NC_T

FR_M_2015_1000 NAC_6_1_NC_T

2015

1000 NAC

6_2

FR_M_2015_1000 NAC_6_2

2015

1000 NAC

6_2_C

FR_M_2015_1000 NAC_6_2_C

2015

1000 NAC

6_2_NC

FR_M_2015_1000 NAC_6_2_NC

2015

1000 NAC

6_2_NC_T

FR_M_2015_1000 NAC_6_2_NC_T

2015

1000 NAC

6_3

FR_M_2015_1000 NAC_6_3

2015

1000 NAC

6_3_1

FR_M_2015_1000 NAC_6_3_1

2015

1000 NAC

6_4

FR_M_2015_1000 NAC_6_4

2015

1000 NAC

6_4_1

FR_M_2015_1000 NAC_6_4_1

2015

1000 NAC

6_4_2

FR_M_2015_1000 NAC_6_4_2

2015

1000 NAC

6_4_3

FR_M_2015_1000 NAC_6_4_3

2015

1000 NAC

FR_M_2015_1000 NAC_7

2015

1000 NAC

7_1

FR_M_2015_1000 NAC_7_1

2015

1000 NAC

7_2

FR_M_2015_1000 NAC_7_2

2015

1000 NAC

7_3

FR_M_2015_1000 NAC_7_3

2015

1000 NAC

7_3_1

FR_M_2015_1000 NAC_7_3_1

2015

1000 NAC

7_3_2

FR_M_2015_1000 NAC_7_3_2

2015

1000 NAC

7_3_3

FR_M_2015_1000 NAC_7_3_3

2015

1000 NAC

7_3_4

FR_M_2015_1000 NAC_7_3_4

2015

1000 NAC

7_4

FR_M_2015_1000 NAC_7_4

2015

1000 NAC

FR_M_2015_1000 NAC_8

2015

1000 NAC

8_1

FR_M_2015_1000 NAC_8_1

2015

1000 NAC

8_2

FR_M_2015_1000 NAC_8_2

2015

1000 NAC

FR_M_2015_1000 NAC_9

2015

1000 NAC

FR_M_2015_1000 NAC_10

2015

1000 NAC

10_1

FR_M_2015_1000 NAC_10_1

2015

1000 NAC

10_1_1

FR_M_2015_1000 NAC_10_1_1

2015

1000 NAC

10_1_2

FR_M_2015_1000 NAC_10_1_2

2015

1000 NAC

10_1_3

FR_M_2015_1000 NAC_10_1_3

2015

1000 NAC

10_1_4

FR_M_2015_1000 NAC_10_1_4

2015

1000 NAC

10_2

FR_M_2015_1000 NAC_10_2

2015

1000 NAC

10_3

FR_M_2015_1000 NAC_10_3

2015

1000 NAC

10_3_1

FR_M_2015_1000 NAC_10_3_1

2015

1000 NAC

10_3_2

FR_M_2015_1000 NAC_10_3_2

2015

1000 NAC

10_3_3

FR_M_2015_1000 NAC_10_3_3

2015

1000 NAC

10_3_4

FR_M_2015_1000 NAC_10_3_4

2015

1000 NAC

10_4

FR_M_2015_1000 NAC_10_4

2015

1000 m3

FR_X_2015_1000 m3_1

2015

1000 m3

1_1

FR_X_2015_1000 m3_1_1

2015

1000 m3

1_2

FR_X_2015_1000 m3_1_2

2015

1000 m3

1_2_C

FR_X_2015_1000 m3_1_2_C

2015

1000 m3

1_2_NC

FR_X_2015_1000 m3_1_2_NC

2015

1000 m3

1_2_NC_T

FR_X_2015_1000 m3_1_2_NC_T

2015

1000 mt

FR_X_2015_1000 mt_2

2015

1000 m3

FR_X_2015_1000 m3_3

2015

1000 m3

3_1

FR_X_2015_1000 m3_3_1

2015

1000 m3

3_2

FR_X_2015_1000 m3_3_2

2015

1000 mt

FR_X_2015_1000 mt_4

2015

1000 mt

4_1

FR_X_2015_1000 mt_4_1

2015

1000 mt

4_2

FR_X_2015_1000 mt_4_2

2015

1000 m3

FR_X_2015_1000 m3_5

2015

1000 m3

5_C

FR_X_2015_1000 m3_5_C

2015

1000 m3

5_NC

FR_X_2015_1000 m3_5_NC

2015

1000 m3

5_NC_T

FR_X_2015_1000 m3_5_NC_T

2015

1000 m3

FR_X_2015_1000 m3_6

2015

1000 m3

6_1

FR_X_2015_1000 m3_6_1

2015

1000 m3

6_1_C

FR_X_2015_1000 m3_6_1_C

2015

1000 m3

6_1_NC

FR_X_2015_1000 m3_6_1_NC

2015

1000 m3

6_1_NC_T

FR_X_2015_1000 m3_6_1_NC_T

2015

1000 m3

6_2

FR_X_2015_1000 m3_6_2

2015

1000 m3

6_2_C

FR_X_2015_1000 m3_6_2_C

2015

1000 m3

6_2_NC

FR_X_2015_1000 m3_6_2_NC

2015

1000 m3

6_2_NC_T

FR_X_2015_1000 m3_6_2_NC_T

2015

1000 m3

6_3

FR_X_2015_1000 m3_6_3

2015

1000 m3

6_3_1

FR_X_2015_1000 m3_6_3_1

2015

1000 m3

6_4

FR_X_2015_1000 m3_6_4

2015

1000 m3

6_4_1

FR_X_2015_1000 m3_6_4_1

2015

1000 m3

6_4_2

FR_X_2015_1000 m3_6_4_2

2015

1000 m3

6_4_3

FR_X_2015_1000 m3_6_4_3

2015

1000 mt

FR_X_2015_1000 mt_7

2015

1000 mt

7_1

FR_X_2015_1000 mt_7_1

2015

1000 mt

7_2

FR_X_2015_1000 mt_7_2

2015

1000 mt

7_3

FR_X_2015_1000 mt_7_3

2015

1000 mt

7_3_1

FR_X_2015_1000 mt_7_3_1

2015

1000 mt

7_3_2

FR_X_2015_1000 mt_7_3_2

2015

1000 mt

7_3_3

FR_X_2015_1000 mt_7_3_3

2015

1000 mt

7_3_4

FR_X_2015_1000 mt_7_3_4

2015

1000 mt

7_4

FR_X_2015_1000 mt_7_4

2015

1000 mt

FR_X_2015_1000 mt_8

2015

1000 mt

8_1

FR_X_2015_1000 mt_8_1

2015

1000 mt

8_2

FR_X_2015_1000 mt_8_2

2015

1000 mt

FR_X_2015_1000 mt_9

2015

1000 mt

FR_X_2015_1000 mt_10

2015

1000 mt

10_1

FR_X_2015_1000 mt_10_1

2015

1000 mt

10_1_1

FR_X_2015_1000 mt_10_1_1

2015

1000 mt

10_1_2

FR_X_2015_1000 mt_10_1_2

2015

1000 mt

10_1_3

FR_X_2015_1000 mt_10_1_3

2015

1000 mt

10_1_4

FR_X_2015_1000 mt_10_1_4

2015

1000 mt

10_2

FR_X_2015_1000 mt_10_2

2015

1000 mt

10_3

FR_X_2015_1000 mt_10_3

2015

1000 mt

10_3_1

FR_X_2015_1000 mt_10_3_1

2015

1000 mt

10_3_2

FR_X_2015_1000 mt_10_3_2

2015

1000 mt

10_3_3

FR_X_2015_1000 mt_10_3_3

2015

1000 mt

10_3_4

FR_X_2015_1000 mt_10_3_4

2015

1000 mt

10_4

FR_X_2015_1000 mt_10_4

2015

1000 NAC

FR_X_2015_1000 NAC_1

2015

1000 NAC

1_1

FR_X_2015_1000 NAC_1_1

2015

1000 NAC

1_2

FR_X_2015_1000 NAC_1_2

2015

1000 NAC

1_2_C

FR_X_2015_1000 NAC_1_2_C

2015

1000 NAC

1_2_NC

FR_X_2015_1000 NAC_1_2_NC

2015

1000 NAC

1_2_NC_T

FR_X_2015_1000 NAC_1_2_NC_T

2015

1000 NAC

FR_X_2015_1000 NAC_2

2015

1000 NAC

FR_X_2015_1000 NAC_3

2015

1000 NAC

3_1

FR_X_2015_1000 NAC_3_1

2015

1000 NAC

3_2

FR_X_2015_1000 NAC_3_2

2015

1000 NAC

FR_X_2015_1000 NAC_4

2015

1000 NAC

4_1

FR_X_2015_1000 NAC_4_1

2015

1000 NAC

4_2

FR_X_2015_1000 NAC_4_2

2015

1000 NAC

FR_X_2015_1000 NAC_5

2015

1000 NAC

5_C

FR_X_2015_1000 NAC_5_C

2015

1000 NAC

5_NC

FR_X_2015_1000 NAC_5_NC

2015

1000 NAC

5_NC_T

FR_X_2015_1000 NAC_5_NC_T

2015

1000 NAC

FR_X_2015_1000 NAC_6

2015

1000 NAC

6_1

FR_X_2015_1000 NAC_6_1

2015

1000 NAC

6_1_C

FR_X_2015_1000 NAC_6_1_C

2015

1000 NAC

6_1_NC

FR_X_2015_1000 NAC_6_1_NC

2015

1000 NAC

6_1_NC_T

FR_X_2015_1000 NAC_6_1_NC_T

2015

1000 NAC

6_2

FR_X_2015_1000 NAC_6_2

2015

1000 NAC

6_2_C

FR_X_2015_1000 NAC_6_2_C

2015

1000 NAC

6_2_NC

FR_X_2015_1000 NAC_6_2_NC

2015

1000 NAC

6_2_NC_T

FR_X_2015_1000 NAC_6_2_NC_T

2015

1000 NAC

6_3

FR_X_2015_1000 NAC_6_3

2015

1000 NAC

6_3_1

FR_X_2015_1000 NAC_6_3_1

2015

1000 NAC

6_4

FR_X_2015_1000 NAC_6_4

2015

1000 NAC

6_4_1

FR_X_2015_1000 NAC_6_4_1

2015

1000 NAC

6_4_2

FR_X_2015_1000 NAC_6_4_2

2015

1000 NAC

6_4_3

FR_X_2015_1000 NAC_6_4_3

2015

1000 NAC

FR_X_2015_1000 NAC_7

2015

1000 NAC

7_1

FR_X_2015_1000 NAC_7_1

2015

1000 NAC

7_2

FR_X_2015_1000 NAC_7_2

2015

1000 NAC

7_3

FR_X_2015_1000 NAC_7_3

2015

1000 NAC

7_3_1

FR_X_2015_1000 NAC_7_3_1

2015

1000 NAC

7_3_2

FR_X_2015_1000 NAC_7_3_2

2015

1000 NAC

7_3_3

FR_X_2015_1000 NAC_7_3_3

2015

1000 NAC

7_3_4

FR_X_2015_1000 NAC_7_3_4

2015

1000 NAC

7_4

FR_X_2015_1000 NAC_7_4

2015

1000 NAC

FR_X_2015_1000 NAC_8

2015

1000 NAC

8_1

FR_X_2015_1000 NAC_8_1

2015

1000 NAC

8_2

FR_X_2015_1000 NAC_8_2

2015

1000 NAC

FR_X_2015_1000 NAC_9

2015

1000 NAC

FR_X_2015_1000 NAC_10

2015

1000 NAC

10_1

FR_X_2015_1000 NAC_10_1

2015

1000 NAC

10_1_1

FR_X_2015_1000 NAC_10_1_1

2015

1000 NAC

10_1_2

FR_X_2015_1000 NAC_10_1_2

2015

1000 NAC

10_1_3

FR_X_2015_1000 NAC_10_1_3

2015

1000 NAC

10_1_4

FR_X_2015_1000 NAC_10_1_4

2015

1000 NAC

10_2

FR_X_2015_1000 NAC_10_2

2015

1000 NAC

10_3

FR_X_2015_1000 NAC_10_3

2015

1000 NAC

10_3_1

FR_X_2015_1000 NAC_10_3_1

2015

1000 NAC

10_3_2

FR_X_2015_1000 NAC_10_3_2

2015

1000 NAC

10_3_3

FR_X_2015_1000 NAC_10_3_3

2015

1000 NAC

10_3_4

FR_X_2015_1000 NAC_10_3_4

2015

1000 NAC

10_4

FR_X_2015_1000 NAC_10_4

2015

1000 NAC

11_1

FR_M_2015_1000 NAC_11_1

2015

1000 NAC

11_1_C

FR_M_2015_1000 NAC_11_1_C

2015

1000 NAC

11_1_NC

FR_M_2015_1000 NAC_11_1_NC

2015

1000 NAC

11_1_NC_T

FR_M_2015_1000 NAC_11_1_NC_T

2015

1000 NAC

11_2

FR_M_2015_1000 NAC_11_2

2015

1000 NAC

11_3

FR_M_2015_1000 NAC_11_3

2015

1000 NAC

11_4

FR_M_2015_1000 NAC_11_4

2015

1000 NAC

11_5

FR_M_2015_1000 NAC_11_5

2015

1000 NAC

11_6

FR_M_2015_1000 NAC_11_6

2015

1000 NAC

11_7

FR_M_2015_1000 NAC_11_7

2015

1000 NAC

11_7_1

FR_M_2015_1000 NAC_11_7_1

2015

1000 NAC

12_1

FR_M_2015_1000 NAC_12_1

2015

1000 NAC

12_2

FR_M_2015_1000 NAC_12_2

2015

1000 NAC

12_3

FR_M_2015_1000 NAC_12_3

2015

1000 NAC

12_4

FR_M_2015_1000 NAC_12_4

2015

1000 NAC

12_5

FR_M_2015_1000 NAC_12_5

2015

1000 NAC

12_6

FR_M_2015_1000 NAC_12_6

2015

1000 NAC

12_6_1

FR_M_2015_1000 NAC_12_6_1

2015

1000 NAC

12_6_2

FR_M_2015_1000 NAC_12_6_2

2015

1000 NAC

12_6_3

FR_M_2015_1000 NAC_12_6_3

2015

1000 NAC

12_7

FR_M_2015_1000 NAC_12_7

2015

1000 NAC

12_7_1

FR_M_2015_1000 NAC_12_7_1

2015

1000 NAC

12_7_2

FR_M_2015_1000 NAC_12_7_2

2015

1000 NAC

12_7_3

FR_M_2015_1000 NAC_12_7_3

2015

1000 NAC

11_1

FR_X_2015_1000 NAC_11_1

2015

1000 NAC

11_1_C

FR_X_2015_1000 NAC_11_1_C

2015

1000 NAC

11_1_NC

FR_X_2015_1000 NAC_11_1_NC

2015

1000 NAC

11_1_NC_T

FR_X_2015_1000 NAC_11_1_NC_T

2015

1000 NAC

11_2

FR_X_2015_1000 NAC_11_2

2015

1000 NAC

11_3

FR_X_2015_1000 NAC_11_3

2015

1000 NAC

11_4

FR_X_2015_1000 NAC_11_4

2015

1000 NAC

11_5

FR_X_2015_1000 NAC_11_5

2015

1000 NAC

11_6

FR_X_2015_1000 NAC_11_6

2015

1000 NAC

11_7

FR_X_2015_1000 NAC_11_7

2015

1000 NAC

11_7_1

FR_X_2015_1000 NAC_11_7_1

2015

1000 NAC

12_1

FR_X_2015_1000 NAC_12_1

2015

1000 NAC

12_2

FR_X_2015_1000 NAC_12_2

2015

1000 NAC

12_3

FR_X_2015_1000 NAC_12_3

2015

1000 NAC

12_4

FR_X_2015_1000 NAC_12_4

2015

1000 NAC

12_5

FR_X_2015_1000 NAC_12_5

2015

1000 NAC

12_6

FR_X_2015_1000 NAC_12_6

2015

1000 NAC

12_6_1

FR_X_2015_1000 NAC_12_6_1

2015

1000 NAC

12_6_2

FR_X_2015_1000 NAC_12_6_2

2015

1000 NAC

12_6_3

FR_X_2015_1000 NAC_12_6_3

2015

1000 NAC

12_7

FR_X_2015_1000 NAC_12_7

2015

1000 NAC

12_7_1

FR_X_2015_1000 NAC_12_7_1

2015

1000 NAC

12_7_2

FR_X_2015_1000 NAC_12_7_2

2015

1000 NAC

12_7_3

FR_X_2015_1000 NAC_12_7_3

2015

1000 m3

ST_1_2_C

FR_M_2015_1000 m3_ST_1_2_C

2015

1000 m3

ST_1_2_C_1

FR_M_2015_1000 m3_ST_1_2_C_1

2015

1000 m3

ST_1_2_C_1_1

FR_M_2015_1000 m3_ST_1_2_C_1_1

2015

1000 m3

ST_1_2_C_2_1

FR_M_2015_1000 m3_ST_1_2_C_2_1

2015

1000 m3

ST_1_2_C_2

FR_M_2015_1000 m3_ST_1_2_C_2

2015

1000 m3

ST_1_2_C_1_2

FR_M_2015_1000 m3_ST_1_2_C_1_2

2015

1000 m3

ST_1_2_C_2_2

FR_M_2015_1000 m3_ST_1_2_C_2_2

2015

1000 m3

ST_1_2_C_3

FR_M_2015_1000 m3_ST_1_2_C_3

2015

1000 m3

ST_1_2_C_1_3

FR_M_2015_1000 m3_ST_1_2_C_1_3

2015

1000 m3

ST_1_2_C_2_3

FR_M_2015_1000 m3_ST_1_2_C_2_3

2015

1000 m3

ST_1_2_NC

FR_M_2015_1000 m3_ST_1_2_NC

2015

1000 m3

ST_1_2_NC_1

FR_M_2015_1000 m3_ST_1_2_NC_1

2015

1000 m3

ST_1_2_NC_1_1

FR_M_2015_1000 m3_ST_1_2_NC_1_1

2015

1000 m3

ST_1_2_NC_2_1

FR_M_2015_1000 m3_ST_1_2_NC_2_1

2015

1000 m3

ST_1_2_NC_2

FR_M_2015_1000 m3_ST_1_2_NC_2

2015

1000 m3

ST_1_2_NC_1_2

FR_M_2015_1000 m3_ST_1_2_NC_1_2

2015

1000 m3

ST_1_2_NC_2_2

FR_M_2015_1000 m3_ST_1_2_NC_2_2

2015

1000 m3

ST_1_2_NC_3

FR_M_2015_1000 m3_ST_1_2_NC_3

2015

1000 m3

ST_1_2_NC_1_3

FR_M_2015_1000 m3_ST_1_2_NC_1_3

2015

1000 m3

ST_1_2_NC_2_3

FR_M_2015_1000 m3_ST_1_2_NC_2_3

2015

1000 m3

ST_1_2_NC_4

FR_M_2015_1000 m3_ST_1_2_NC_4

2015

1000 m3

ST_1_2_NC_5

FR_M_2015_1000 m3_ST_1_2_NC_5

2015

1000 m3

ST_5_C

FR_M_2015_1000 m3_ST_5_C

2015

1000 m3

ST_5_C_1

FR_M_2015_1000 m3_ST_5_C_1

2015

1000 m3

ST_5_C_2

FR_M_2015_1000 m3_ST_5_C_2

2015

1000 m3

ST_5_NC

FR_M_2015_1000 m3_ST_5_NC

2015

1000 m3

ST_5_NC_1

FR_M_2015_1000 m3_ST_5_NC_1

2015

1000 m3

ST_5_NC_2

FR_M_2015_1000 m3_ST_5_NC_2

2015

1000 m3

ST_5_NC_3

FR_M_2015_1000 m3_ST_5_NC_3

2015

1000 m3

ST_5_NC_4

FR_M_2015_1000 m3_ST_5_NC_4

2015

1000 m3

ST_5_NC_5

FR_M_2015_1000 m3_ST_5_NC_5

2015

1000 m3

ST_5_NC_6

FR_M_2015_1000 m3_ST_5_NC_6

2015

1000 m3

ST_5_NC_7

FR_M_2015_1000 m3_ST_5_NC_7

2015

1000 NAC

ST_1_2_C

FR_M_2015_1000 NAC_ST_1_2_C

2015

1000 NAC

ST_1_2_C_1

FR_M_2015_1000 NAC_ST_1_2_C_1

2015

1000 NAC

ST_1_2_C_1_1

FR_M_2015_1000 NAC_ST_1_2_C_1_1

2015

1000 NAC

ST_1_2_C_2_1

FR_M_2015_1000 NAC_ST_1_2_C_2_1

2015

1000 NAC

ST_1_2_C_2

FR_M_2015_1000 NAC_ST_1_2_C_2

2015

1000 NAC

ST_1_2_C_1_2

FR_M_2015_1000 NAC_ST_1_2_C_1_2

2015

1000 NAC

ST_1_2_C_2_2

FR_M_2015_1000 NAC_ST_1_2_C_2_2

2015

1000 NAC

ST_1_2_C_3

FR_M_2015_1000 NAC_ST_1_2_C_3

2015

1000 NAC

ST_1_2_C_1_3

FR_M_2015_1000 NAC_ST_1_2_C_1_3

2015

1000 NAC

ST_1_2_C_2_3

FR_M_2015_1000 NAC_ST_1_2_C_2_3

2015

1000 NAC

ST_1_2_NC

FR_M_2015_1000 NAC_ST_1_2_NC

2015

1000 NAC

ST_1_2_NC_1

FR_M_2015_1000 NAC_ST_1_2_NC_1

2015

1000 NAC

ST_1_2_NC_1_1

FR_M_2015_1000 NAC_ST_1_2_NC_1_1

2015

1000 NAC

ST_1_2_NC_2_1

FR_M_2015_1000 NAC_ST_1_2_NC_2_1

2015

1000 NAC

ST_1_2_NC_2

FR_M_2015_1000 NAC_ST_1_2_NC_2

2015

1000 NAC

ST_1_2_NC_1_2

FR_M_2015_1000 NAC_ST_1_2_NC_1_2

2015

1000 NAC

ST_1_2_NC_2_2

FR_M_2015_1000 NAC_ST_1_2_NC_2_2

2015

1000 NAC

ST_1_2_NC_3

FR_M_2015_1000 NAC_ST_1_2_NC_3

2015

1000 NAC

ST_1_2_NC_1_3

FR_M_2015_1000 NAC_ST_1_2_NC_1_3

2015

1000 NAC

ST_1_2_NC_2_3

FR_M_2015_1000 NAC_ST_1_2_NC_2_3

2015

1000 NAC

ST_1_2_NC_4

FR_M_2015_1000 NAC_ST_1_2_NC_4

2015

1000 NAC

ST_1_2_NC_5

FR_M_2015_1000 NAC_ST_1_2_NC_5

2015

1000 NAC

ST_5_C

FR_M_2015_1000 NAC_ST_5_C

2015

1000 NAC

ST_5_C_1

FR_M_2015_1000 NAC_ST_5_C_1

2015

1000 NAC

ST_5_C_2

FR_M_2015_1000 NAC_ST_5_C_2

2015

1000 NAC

ST_5_NC

FR_M_2015_1000 NAC_ST_5_NC

2015

1000 NAC

ST_5_NC_1

FR_M_2015_1000 NAC_ST_5_NC_1

2015

1000 NAC

ST_5_NC_2

FR_M_2015_1000 NAC_ST_5_NC_2

2015

1000 NAC

ST_5_NC_3

FR_M_2015_1000 NAC_ST_5_NC_3

2015

1000 NAC

ST_5_NC_4

FR_M_2015_1000 NAC_ST_5_NC_4

2015

1000 NAC

ST_5_NC_5

FR_M_2015_1000 NAC_ST_5_NC_5

2015

1000 NAC

ST_5_NC_6

FR_M_2015_1000 NAC_ST_5_NC_6

2015

1000 NAC

ST_5_NC_7

FR_M_2015_1000 NAC_ST_5_NC_7

2015

1000 m3

ST_1_2_C

FR_X_2015_1000 m3_ST_1_2_C

2015

1000 m3

ST_1_2_C_1

FR_X_2015_1000 m3_ST_1_2_C_1

2015

1000 m3

ST_1_2_C_1_1

FR_X_2015_1000 m3_ST_1_2_C_1_1

2015

1000 m3

ST_1_2_C_2_1

FR_X_2015_1000 m3_ST_1_2_C_2_1

2015

1000 m3

ST_1_2_C_2

FR_X_2015_1000 m3_ST_1_2_C_2

2015

1000 m3

ST_1_2_C_1_2

FR_X_2015_1000 m3_ST_1_2_C_1_2

2015

1000 m3

ST_1_2_C_2_2

FR_X_2015_1000 m3_ST_1_2_C_2_2

2015

1000 m3

ST_1_2_C_3

FR_X_2015_1000 m3_ST_1_2_C_3

2015

1000 m3

ST_1_2_C_1_3

FR_X_2015_1000 m3_ST_1_2_C_1_3

2015

1000 m3

ST_1_2_C_2_3

FR_X_2015_1000 m3_ST_1_2_C_2_3

2015

1000 m3

ST_1_2_NC

FR_X_2015_1000 m3_ST_1_2_NC

2015

1000 m3

ST_1_2_NC_1

FR_X_2015_1000 m3_ST_1_2_NC_1

2015

1000 m3

ST_1_2_NC_1_1

FR_X_2015_1000 m3_ST_1_2_NC_1_1

2015

1000 m3

ST_1_2_NC_2_1

FR_X_2015_1000 m3_ST_1_2_NC_2_1

2015

1000 m3

ST_1_2_NC_2

FR_X_2015_1000 m3_ST_1_2_NC_2

2015

1000 m3

ST_1_2_NC_1_2

FR_X_2015_1000 m3_ST_1_2_NC_1_2

2015

1000 m3

ST_1_2_NC_2_2

FR_X_2015_1000 m3_ST_1_2_NC_2_2

2015

1000 m3

ST_1_2_NC_3

FR_X_2015_1000 m3_ST_1_2_NC_3

2015

1000 m3

ST_1_2_NC_1_3

FR_X_2015_1000 m3_ST_1_2_NC_1_3

2015

1000 m3

ST_1_2_NC_2_3

FR_X_2015_1000 m3_ST_1_2_NC_2_3

2015

1000 m3

ST_1_2_NC_4

FR_X_2015_1000 m3_ST_1_2_NC_4

2015

1000 m3

ST_1_2_NC_5

FR_X_2015_1000 m3_ST_1_2_NC_5

2015

1000 m3

ST_5_C

FR_X_2015_1000 m3_ST_5_C

2015

1000 m3

ST_5_C_1

FR_X_2015_1000 m3_ST_5_C_1

2015

1000 m3

ST_5_C_2

FR_X_2015_1000 m3_ST_5_C_2

2015

1000 m3

ST_5_NC

FR_X_2015_1000 m3_ST_5_NC

2015

1000 m3

ST_5_NC_1

FR_X_2015_1000 m3_ST_5_NC_1

2015

1000 m3

ST_5_NC_2

FR_X_2015_1000 m3_ST_5_NC_2

2015

1000 m3

ST_5_NC_3

FR_X_2015_1000 m3_ST_5_NC_3

2015

1000 m3

ST_5_NC_4

FR_X_2015_1000 m3_ST_5_NC_4

2015

1000 m3

ST_5_NC_5

FR_X_2015_1000 m3_ST_5_NC_5

2015

1000 m3

ST_5_NC_6

FR_X_2015_1000 m3_ST_5_NC_6

2015

1000 m3

ST_5_NC_7

FR_X_2015_1000 m3_ST_5_NC_7

2015

1000 NAC

ST_1_2_C

FR_X_2015_1000 NAC_ST_1_2_C

2015

1000 NAC

ST_1_2_C_1

FR_X_2015_1000 NAC_ST_1_2_C_1

2015

1000 NAC

ST_1_2_C_1_1

FR_X_2015_1000 NAC_ST_1_2_C_1_1

2015

1000 NAC

ST_1_2_C_2_1

FR_X_2015_1000 NAC_ST_1_2_C_2_1

2015

1000 NAC

ST_1_2_C_2

FR_X_2015_1000 NAC_ST_1_2_C_2

2015

1000 NAC

ST_1_2_C_1_2

FR_X_2015_1000 NAC_ST_1_2_C_1_2

2015

1000 NAC

ST_1_2_C_2_2

FR_X_2015_1000 NAC_ST_1_2_C_2_2

2015

1000 NAC

ST_1_2_C_3

FR_X_2015_1000 NAC_ST_1_2_C_3

2015

1000 NAC

ST_1_2_C_1_3

FR_X_2015_1000 NAC_ST_1_2_C_1_3

2015

1000 NAC

ST_1_2_C_2_3

FR_X_2015_1000 NAC_ST_1_2_C_2_3

2015

1000 NAC

ST_1_2_NC

FR_X_2015_1000 NAC_ST_1_2_NC

2015

1000 NAC

ST_1_2_NC_1

FR_X_2015_1000 NAC_ST_1_2_NC_1

2015

1000 NAC

ST_1_2_NC_1_1

FR_X_2015_1000 NAC_ST_1_2_NC_1_1

2015

1000 NAC

ST_1_2_NC_2_1

FR_X_2015_1000 NAC_ST_1_2_NC_2_1

2015

1000 NAC

ST_1_2_NC_2

FR_X_2015_1000 NAC_ST_1_2_NC_2

2015

1000 NAC

ST_1_2_NC_1_2

FR_X_2015_1000 NAC_ST_1_2_NC_1_2

2015

1000 NAC

ST_1_2_NC_2_2

FR_X_2015_1000 NAC_ST_1_2_NC_2_2

2015

1000 NAC

ST_1_2_NC_3

FR_X_2015_1000 NAC_ST_1_2_NC_3

2015

1000 NAC

ST_1_2_NC_1_3

FR_X_2015_1000 NAC_ST_1_2_NC_1_3

2015

1000 NAC

ST_1_2_NC_2_3

FR_X_2015_1000 NAC_ST_1_2_NC_2_3

2015

1000 NAC

ST_1_2_NC_4

FR_X_2015_1000 NAC_ST_1_2_NC_4

2015

1000 NAC

ST_1_2_NC_5

FR_X_2015_1000 NAC_ST_1_2_NC_5

2015

1000 NAC

ST_5_C

FR_X_2015_1000 NAC_ST_5_C

2015

1000 NAC

ST_5_C_1

FR_X_2015_1000 NAC_ST_5_C_1

2015

1000 NAC

ST_5_C_2

FR_X_2015_1000 NAC_ST_5_C_2

2015

1000 NAC

ST_5_NC

FR_X_2015_1000 NAC_ST_5_NC

2015

1000 NAC

ST_5_NC_1

FR_X_2015_1000 NAC_ST_5_NC_1

2015

1000 NAC

ST_5_NC_2

FR_X_2015_1000 NAC_ST_5_NC_2

2015

1000 NAC

ST_5_NC_3

FR_X_2015_1000 NAC_ST_5_NC_3

2015

1000 NAC

ST_5_NC_4

FR_X_2015_1000 NAC_ST_5_NC_4

2015

1000 NAC

ST_5_NC_5

FR_X_2015_1000 NAC_ST_5_NC_5

2015

1000 NAC

ST_5_NC_6

FR_X_2015_1000 NAC_ST_5_NC_6

2015

1000 NAC

ST_5_NC_7

FR_X_2015_1000 NAC_ST_5_NC_7

EX_M

2015

1000 m3

FR_EX_M_2015_1000 m3_1

EX_M

2015

1000 m3

1_1

FR_EX_M_2015_1000 m3_1_1

EX_M

2015

1000 m3

1_2

FR_EX_M_2015_1000 m3_1_2

EX_M

2015

1000 m3

1_2_C

FR_EX_M_2015_1000 m3_1_2_C

EX_M

2015

1000 m3

1_2_NC

FR_EX_M_2015_1000 m3_1_2_NC

EX_M

2015

1000 m3

1_2_NC_T

FR_EX_M_2015_1000 m3_1_2_NC_T

EX_M

2015

1000 mt

FR_EX_M_2015_1000 mt_2

EX_M

2015

1000 m3

FR_EX_M_2015_1000 m3_3

EX_M

2015

1000 m3

3_1

FR_EX_M_2015_1000 m3_3_1

EX_M

2015

1000 m3

3_2

FR_EX_M_2015_1000 m3_3_2

EX_M

2015

1000 mt

FR_EX_M_2015_1000 mt_4

EX_M

2015

1000 mt

4_1

FR_EX_M_2015_1000 mt_4_1

EX_M

2015

1000 mt

4_2

FR_EX_M_2015_1000 mt_4_2

EX_M

2015

1000 m3

FR_EX_M_2015_1000 m3_5

EX_M

2015

1000 m3

5_C

FR_EX_M_2015_1000 m3_5_C

EX_M

2015

1000 m3

5_NC

FR_EX_M_2015_1000 m3_5_NC

EX_M

2015

1000 m3

5_NC_T

FR_EX_M_2015_1000 m3_5_NC_T

EX_M

2015

1000 m3

FR_EX_M_2015_1000 m3_6

EX_M

2015

1000 m3

6_1

FR_EX_M_2015_1000 m3_6_1

EX_M

2015

1000 m3

6_1_C

FR_EX_M_2015_1000 m3_6_1_C

EX_M

2015

1000 m3

6_1_NC

FR_EX_M_2015_1000 m3_6_1_NC

EX_M

2015

1000 m3

6_1_NC_T

FR_EX_M_2015_1000 m3_6_1_NC_T

EX_M

2015

1000 m3

6_2

FR_EX_M_2015_1000 m3_6_2

EX_M

2015

1000 m3

6_2_C

FR_EX_M_2015_1000 m3_6_2_C

EX_M

2015

1000 m3

6_2_NC

FR_EX_M_2015_1000 m3_6_2_NC

EX_M

2015

1000 m3

6_2_NC_T

FR_EX_M_2015_1000 m3_6_2_NC_T

EX_M

2015

1000 m3

6_3

FR_EX_M_2015_1000 m3_6_3

EX_M

2015

1000 m3

6_3_1

FR_EX_M_2015_1000 m3_6_3_1

EX_M

2015

1000 m3

6_4

FR_EX_M_2015_1000 m3_6_4

EX_M

2015

1000 m3

6_4_1

FR_EX_M_2015_1000 m3_6_4_1

EX_M

2015

1000 m3

6_4_2

FR_EX_M_2015_1000 m3_6_4_2

EX_M

2015

1000 m3

6_4_3

FR_EX_M_2015_1000 m3_6_4_3

EX_M

2015

1000 mt

FR_EX_M_2015_1000 mt_7

EX_M

2015

1000 mt

7_1

FR_EX_M_2015_1000 mt_7_1

EX_M

2015

1000 mt

7_2

FR_EX_M_2015_1000 mt_7_2

EX_M

2015

1000 mt

7_3

FR_EX_M_2015_1000 mt_7_3

EX_M

2015

1000 mt

7_3_1

FR_EX_M_2015_1000 mt_7_3_1

EX_M

2015

1000 mt

7_3_2

FR_EX_M_2015_1000 mt_7_3_2

EX_M

2015

1000 mt

7_3_3

FR_EX_M_2015_1000 mt_7_3_3

EX_M

2015

1000 mt

7_3_4

FR_EX_M_2015_1000 mt_7_3_4

EX_M

2015

1000 mt

7_4

FR_EX_M_2015_1000 mt_7_4

EX_M

2015

1000 mt

FR_EX_M_2015_1000 mt_8

EX_M

2015

1000 mt

8_1

FR_EX_M_2015_1000 mt_8_1

EX_M

2015

1000 mt

8_2

FR_EX_M_2015_1000 mt_8_2

EX_M

2015

1000 mt

FR_EX_M_2015_1000 mt_9

EX_M

2015

1000 mt

FR_EX_M_2015_1000 mt_10

EX_M

2015

1000 mt

10_1

FR_EX_M_2015_1000 mt_10_1

EX_M

2015

1000 mt

10_1_1

FR_EX_M_2015_1000 mt_10_1_1

EX_M

2015

1000 mt

10_1_2

FR_EX_M_2015_1000 mt_10_1_2

EX_M

2015

1000 mt

10_1_3

FR_EX_M_2015_1000 mt_10_1_3

EX_M

2015

1000 mt

10_1_4

FR_EX_M_2015_1000 mt_10_1_4

EX_M

2015

1000 mt

10_2

FR_EX_M_2015_1000 mt_10_2

EX_M

2015

1000 mt

10_3

FR_EX_M_2015_1000 mt_10_3

EX_M

2015

1000 mt

10_3_1

FR_EX_M_2015_1000 mt_10_3_1

EX_M

2015

1000 mt

10_3_2

FR_EX_M_2015_1000 mt_10_3_2

EX_M

2015

1000 mt

10_3_3

FR_EX_M_2015_1000 mt_10_3_3

EX_M

2015

1000 mt

10_3_4

FR_EX_M_2015_1000 mt_10_3_4

EX_M

2015

1000 mt

10_4

FR_EX_M_2015_1000 mt_10_4

EX_M

2015

1000 NAC

FR_EX_M_2015_1000 NAC_1

EX_M

2015

1000 NAC

1_1

FR_EX_M_2015_1000 NAC_1_1

EX_M

2015

1000 NAC

1_2

FR_EX_M_2015_1000 NAC_1_2

EX_M

2015

1000 NAC

1_2_C

FR_EX_M_2015_1000 NAC_1_2_C

EX_M

2015

1000 NAC

1_2_NC

FR_EX_M_2015_1000 NAC_1_2_NC

EX_M

2015

1000 NAC

1_2_NC_T

FR_EX_M_2015_1000 NAC_1_2_NC_T

EX_M

2015

1000 NAC

FR_EX_M_2015_1000 NAC_2

EX_M

2015

1000 NAC

FR_EX_M_2015_1000 NAC_3

EX_M

2015

1000 NAC

3_1

FR_EX_M_2015_1000 NAC_3_1

EX_M

2015

1000 NAC

3_2

FR_EX_M_2015_1000 NAC_3_2

EX_M

2015

1000 NAC

FR_EX_M_2015_1000 NAC_4

EX_M

2015

1000 NAC

4_1

FR_EX_M_2015_1000 NAC_4_1

EX_M

2015

1000 NAC

4_2

FR_EX_M_2015_1000 NAC_4_2

EX_M

2015

1000 NAC

FR_EX_M_2015_1000 NAC_5

EX_M

2015

1000 NAC

5_C

FR_EX_M_2015_1000 NAC_5_C

EX_M

2015

1000 NAC

5_NC

FR_EX_M_2015_1000 NAC_5_NC

EX_M

2015

1000 NAC

5_NC_T

FR_EX_M_2015_1000 NAC_5_NC_T

EX_M

2015

1000 NAC

FR_EX_M_2015_1000 NAC_6

EX_M

2015

1000 NAC

6_1

FR_EX_M_2015_1000 NAC_6_1

EX_M

2015

1000 NAC

6_1_C

FR_EX_M_2015_1000 NAC_6_1_C

EX_M

2015

1000 NAC

6_1_NC

FR_EX_M_2015_1000 NAC_6_1_NC

EX_M

2015

1000 NAC

6_1_NC_T

FR_EX_M_2015_1000 NAC_6_1_NC_T

EX_M

2015

1000 NAC

6_2

FR_EX_M_2015_1000 NAC_6_2

EX_M

2015

1000 NAC

6_2_C

FR_EX_M_2015_1000 NAC_6_2_C

EX_M

2015

1000 NAC

6_2_NC

FR_EX_M_2015_1000 NAC_6_2_NC

EX_M

2015

1000 NAC

6_2_NC_T

FR_EX_M_2015_1000 NAC_6_2_NC_T

EX_M

2015

1000 NAC

6_3

FR_EX_M_2015_1000 NAC_6_3

EX_M

2015

1000 NAC

6_3_1

FR_EX_M_2015_1000 NAC_6_3_1

EX_M

2015

1000 NAC

6_4

FR_EX_M_2015_1000 NAC_6_4

EX_M

2015

1000 NAC

6_4_1

FR_EX_M_2015_1000 NAC_6_4_1

EX_M

2015

1000 NAC

6_4_2

FR_EX_M_2015_1000 NAC_6_4_2

EX_M

2015

1000 NAC

6_4_3

FR_EX_M_2015_1000 NAC_6_4_3

EX_M

2015

1000 NAC

FR_EX_M_2015_1000 NAC_7

EX_M

2015

1000 NAC

7_1

FR_EX_M_2015_1000 NAC_7_1

EX_M

2015

1000 NAC

7_2

FR_EX_M_2015_1000 NAC_7_2

EX_M

2015

1000 NAC

7_3

FR_EX_M_2015_1000 NAC_7_3

EX_M

2015

1000 NAC

7_3_1

FR_EX_M_2015_1000 NAC_7_3_1

EX_M

2015

1000 NAC

7_3_2

FR_EX_M_2015_1000 NAC_7_3_2

EX_M

2015

1000 NAC

7_3_3

FR_EX_M_2015_1000 NAC_7_3_3

EX_M

2015

1000 NAC

7_3_4

FR_EX_M_2015_1000 NAC_7_3_4

EX_M

2015

1000 NAC

7_4

FR_EX_M_2015_1000 NAC_7_4

EX_M

2015

1000 NAC

FR_EX_M_2015_1000 NAC_8

EX_M

2015

1000 NAC

8_1

FR_EX_M_2015_1000 NAC_8_1

EX_M

2015

1000 NAC

8_2

FR_EX_M_2015_1000 NAC_8_2

EX_M

2015

1000 NAC

FR_EX_M_2015_1000 NAC_9

EX_M

2015

1000 NAC

FR_EX_M_2015_1000 NAC_10

EX_M

2015

1000 NAC

10_1

FR_EX_M_2015_1000 NAC_10_1

EX_M

2015

1000 NAC

10_1_1

FR_EX_M_2015_1000 NAC_10_1_1

EX_M

2015

1000 NAC

10_1_2

FR_EX_M_2015_1000 NAC_10_1_2

EX_M

2015

1000 NAC

10_1_3

FR_EX_M_2015_1000 NAC_10_1_3

EX_M

2015

1000 NAC

10_1_4

FR_EX_M_2015_1000 NAC_10_1_4

EX_M

2015

1000 NAC

10_2

FR_EX_M_2015_1000 NAC_10_2

EX_M

2015

1000 NAC

10_3

FR_EX_M_2015_1000 NAC_10_3

EX_M

2015

1000 NAC

10_3_1

FR_EX_M_2015_1000 NAC_10_3_1

EX_M

2015

1000 NAC

10_3_2

FR_EX_M_2015_1000 NAC_10_3_2

EX_M

2015

1000 NAC

10_3_3

FR_EX_M_2015_1000 NAC_10_3_3

EX_M

2015

1000 NAC

10_3_4

FR_EX_M_2015_1000 NAC_10_3_4

EX_M

2015

1000 NAC

10_4

FR_EX_M_2015_1000 NAC_10_4

EX_X

2015

1000 m3

FR_EX_X_2015_1000 m3_1

EX_X

2015

1000 m3

1_1

FR_EX_X_2015_1000 m3_1_1

EX_X

2015

1000 m3

1_2

FR_EX_X_2015_1000 m3_1_2

EX_X

2015

1000 m3

1_2_C

FR_EX_X_2015_1000 m3_1_2_C

EX_X

2015

1000 m3

1_2_NC

FR_EX_X_2015_1000 m3_1_2_NC

EX_X

2015

1000 m3

1_2_NC_T

FR_EX_X_2015_1000 m3_1_2_NC_T

EX_X

2015

1000 mt

FR_EX_X_2015_1000 mt_2

EX_X

2015

1000 m3

FR_EX_X_2015_1000 m3_3

EX_X

2015

1000 m3

3_1

FR_EX_X_2015_1000 m3_3_1

EX_X

2015

1000 m3

3_2

FR_EX_X_2015_1000 m3_3_2

EX_X

2015

1000 mt

FR_EX_X_2015_1000 mt_4

EX_X

2015

1000 mt

4_1

FR_EX_X_2015_1000 mt_4_1

EX_X

2015

1000 mt

4_2

FR_EX_X_2015_1000 mt_4_2

EX_X

2015

1000 m3

FR_EX_X_2015_1000 m3_5

EX_X

2015

1000 m3

5_C

FR_EX_X_2015_1000 m3_5_C

EX_X

2015

1000 m3

5_NC

FR_EX_X_2015_1000 m3_5_NC

EX_X

2015

1000 m3

5_NC_T

FR_EX_X_2015_1000 m3_5_NC_T

EX_X

2015

1000 m3

FR_EX_X_2015_1000 m3_6

EX_X

2015

1000 m3

6_1

FR_EX_X_2015_1000 m3_6_1

EX_X

2015

1000 m3

6_1_C

FR_EX_X_2015_1000 m3_6_1_C

EX_X

2015

1000 m3

6_1_NC

FR_EX_X_2015_1000 m3_6_1_NC

EX_X

2015

1000 m3

6_1_NC_T

FR_EX_X_2015_1000 m3_6_1_NC_T

EX_X

2015

1000 m3

6_2

FR_EX_X_2015_1000 m3_6_2

EX_X

2015

1000 m3

6_2_C

FR_EX_X_2015_1000 m3_6_2_C

EX_X

2015

1000 m3

6_2_NC

FR_EX_X_2015_1000 m3_6_2_NC

EX_X

2015

1000 m3

6_2_NC_T

FR_EX_X_2015_1000 m3_6_2_NC_T

EX_X

2015

1000 m3

6_3

FR_EX_X_2015_1000 m3_6_3

EX_X

2015

1000 m3

6_3_1

FR_EX_X_2015_1000 m3_6_3_1

EX_X

2015

1000 m3

6_4

FR_EX_X_2015_1000 m3_6_4

EX_X

2015

1000 m3

6_4_1

FR_EX_X_2015_1000 m3_6_4_1

EX_X

2015

1000 m3

6_4_2

FR_EX_X_2015_1000 m3_6_4_2

EX_X

2015

1000 m3

6_4_3

FR_EX_X_2015_1000 m3_6_4_3

EX_X

2015

1000 mt

FR_EX_X_2015_1000 mt_7

EX_X

2015

1000 mt

7_1

FR_EX_X_2015_1000 mt_7_1

EX_X

2015

1000 mt

7_2

FR_EX_X_2015_1000 mt_7_2

EX_X

2015

1000 mt

7_3

FR_EX_X_2015_1000 mt_7_3

EX_X

2015

1000 mt

7_3_1

FR_EX_X_2015_1000 mt_7_3_1

EX_X

2015

1000 mt

7_3_2

FR_EX_X_2015_1000 mt_7_3_2

EX_X

2015

1000 mt

7_3_3

FR_EX_X_2015_1000 mt_7_3_3

EX_X

2015

1000 mt

7_3_4

FR_EX_X_2015_1000 mt_7_3_4

EX_X

2015

1000 mt

7_4

FR_EX_X_2015_1000 mt_7_4

EX_X

2015

1000 mt

FR_EX_X_2015_1000 mt_8

EX_X

2015

1000 mt

8_1

FR_EX_X_2015_1000 mt_8_1

EX_X

2015

1000 mt

8_2

FR_EX_X_2015_1000 mt_8_2

EX_X

2015

1000 mt

FR_EX_X_2015_1000 mt_9

EX_X

2015

1000 mt

FR_EX_X_2015_1000 mt_10

EX_X

2015

1000 mt

10_1

FR_EX_X_2015_1000 mt_10_1

EX_X

2015

1000 mt

10_1_1

FR_EX_X_2015_1000 mt_10_1_1

EX_X

2015

1000 mt

10_1_2

FR_EX_X_2015_1000 mt_10_1_2

EX_X

2015

1000 mt

10_1_3

FR_EX_X_2015_1000 mt_10_1_3

EX_X

2015

1000 mt

10_1_4

FR_EX_X_2015_1000 mt_10_1_4

EX_X

2015

1000 mt

10_2

FR_EX_X_2015_1000 mt_10_2

EX_X

2015

1000 mt

10_3

FR_EX_X_2015_1000 mt_10_3

EX_X

2015

1000 mt

10_3_1

FR_EX_X_2015_1000 mt_10_3_1

EX_X

2015

1000 mt

10_3_2

FR_EX_X_2015_1000 mt_10_3_2

EX_X

2015

1000 mt

10_3_3

FR_EX_X_2015_1000 mt_10_3_3

EX_X

2015

1000 mt

10_3_4

FR_EX_X_2015_1000 mt_10_3_4

EX_X

2015

1000 mt

10_4

FR_EX_X_2015_1000 mt_10_4

EX_X

2015

1000 NAC

FR_EX_X_2015_1000 NAC_1

EX_X

2015

1000 NAC

1_1

FR_EX_X_2015_1000 NAC_1_1

EX_X

2015

1000 NAC

1_2

FR_EX_X_2015_1000 NAC_1_2

EX_X

2015

1000 NAC

1_2_C

FR_EX_X_2015_1000 NAC_1_2_C

EX_X

2015

1000 NAC

1_2_NC

FR_EX_X_2015_1000 NAC_1_2_NC

EX_X

2015

1000 NAC

1_2_NC_T

FR_EX_X_2015_1000 NAC_1_2_NC_T

EX_X

2015

1000 NAC

FR_EX_X_2015_1000 NAC_2

EX_X

2015

1000 NAC

FR_EX_X_2015_1000 NAC_3

EX_X

2015

1000 NAC

3_1

FR_EX_X_2015_1000 NAC_3_1

EX_X

2015

1000 NAC

3_2

FR_EX_X_2015_1000 NAC_3_2

EX_X

2015

1000 NAC

FR_EX_X_2015_1000 NAC_4

EX_X

2015

1000 NAC

4_1

FR_EX_X_2015_1000 NAC_4_1

EX_X

2015

1000 NAC

4_2

FR_EX_X_2015_1000 NAC_4_2

EX_X

2015

1000 NAC

FR_EX_X_2015_1000 NAC_5

EX_X

2015

1000 NAC

5_C

FR_EX_X_2015_1000 NAC_5_C

EX_X

2015

1000 NAC

5_NC

FR_EX_X_2015_1000 NAC_5_NC

EX_X

2015

1000 NAC

5_NC_T

FR_EX_X_2015_1000 NAC_5_NC_T

EX_X

2015

1000 NAC

FR_EX_X_2015_1000 NAC_6

EX_X

2015

1000 NAC

6_1

FR_EX_X_2015_1000 NAC_6_1

EX_X

2015

1000 NAC

6_1_C

FR_EX_X_2015_1000 NAC_6_1_C

EX_X

2015

1000 NAC

6_1_NC

FR_EX_X_2015_1000 NAC_6_1_NC

EX_X

2015

1000 NAC

6_1_NC_T

FR_EX_X_2015_1000 NAC_6_1_NC_T

EX_X

2015

1000 NAC

6_2

FR_EX_X_2015_1000 NAC_6_2

EX_X

2015

1000 NAC

6_2_C

FR_EX_X_2015_1000 NAC_6_2_C

EX_X

2015

1000 NAC

6_2_NC

FR_EX_X_2015_1000 NAC_6_2_NC

EX_X

2015

1000 NAC

6_2_NC_T

FR_EX_X_2015_1000 NAC_6_2_NC_T

EX_X

2015

1000 NAC

6_3

FR_EX_X_2015_1000 NAC_6_3

EX_X

2015

1000 NAC

6_3_1

FR_EX_X_2015_1000 NAC_6_3_1

EX_X

2015

1000 NAC

6_4

FR_EX_X_2015_1000 NAC_6_4

EX_X

2015

1000 NAC

6_4_1

FR_EX_X_2015_1000 NAC_6_4_1

EX_X

2015

1000 NAC

6_4_2

FR_EX_X_2015_1000 NAC_6_4_2

EX_X

2015

1000 NAC

6_4_3

FR_EX_X_2015_1000 NAC_6_4_3

EX_X

2015

1000 NAC

FR_EX_X_2015_1000 NAC_7

EX_X

2015

1000 NAC

7_1

FR_EX_X_2015_1000 NAC_7_1

EX_X

2015

1000 NAC

7_2

FR_EX_X_2015_1000 NAC_7_2

EX_X

2015

1000 NAC

7_3

FR_EX_X_2015_1000 NAC_7_3

EX_X

2015

1000 NAC

7_3_1

FR_EX_X_2015_1000 NAC_7_3_1

EX_X

2015

1000 NAC

7_3_2

FR_EX_X_2015_1000 NAC_7_3_2

EX_X

2015

1000 NAC

7_3_3

FR_EX_X_2015_1000 NAC_7_3_3

EX_X

2015

1000 NAC

7_3_4

FR_EX_X_2015_1000 NAC_7_3_4

EX_X

2015

1000 NAC

7_4

FR_EX_X_2015_1000 NAC_7_4

EX_X

2015

1000 NAC

FR_EX_X_2015_1000 NAC_8

EX_X

2015

1000 NAC

8_1

FR_EX_X_2015_1000 NAC_8_1

EX_X

2015

1000 NAC

8_2

FR_EX_X_2015_1000 NAC_8_2

EX_X

2015

1000 NAC

FR_EX_X_2015_1000 NAC_9

EX_X

2015

1000 NAC

FR_EX_X_2015_1000 NAC_10

EX_X

2015

1000 NAC

10_1

FR_EX_X_2015_1000 NAC_10_1

EX_X

2015

1000 NAC

10_1_1

FR_EX_X_2015_1000 NAC_10_1_1

EX_X

2015

1000 NAC

10_1_2

FR_EX_X_2015_1000 NAC_10_1_2

EX_X

2015

1000 NAC

10_1_3

FR_EX_X_2015_1000 NAC_10_1_3

EX_X

2015

1000 NAC

10_1_4

FR_EX_X_2015_1000 NAC_10_1_4

EX_X

2015

1000 NAC

10_2

FR_EX_X_2015_1000 NAC_10_2

EX_X

2015

1000 NAC

10_3

FR_EX_X_2015_1000 NAC_10_3

EX_X

2015

1000 NAC

10_3_1

FR_EX_X_2015_1000 NAC_10_3_1

EX_X

2015

1000 NAC

10_3_2

FR_EX_X_2015_1000 NAC_10_3_2

EX_X

2015

1000 NAC

10_3_3

FR_EX_X_2015_1000 NAC_10_3_3

EX_X

2015

1000 NAC

10_3_4

FR_EX_X_2015_1000 NAC_10_3_4

EX_X

2015

1000 NAC

10_4

FR_EX_X_2015_1000 NAC_10_4

2015

1000 m3

EU2_1

FR_P_2015_1000 m3_EU2_1

2015

1000 m3

EU2_1_C

FR_P_2015_1000 m3_EU2_1_C

2015

1000 m3

EU2_1_NC

FR_P_2015_1000 m3_EU2_1_NC

2015

1000 m3

EU2_1_1

FR_P_2015_1000 m3_EU2_1_1

2015

1000 m3

EU2_1_1_C

FR_P_2015_1000 m3_EU2_1_1_C

2015

1000 m3

EU2_1_1_NC

FR_P_2015_1000 m3_EU2_1_1_NC

2015

1000 m3

EU2_1_2

FR_P_2015_1000 m3_EU2_1_2

2015

1000 m3

EU2_1_2_C

FR_P_2015_1000 m3_EU2_1_2_C

2015

1000 m3

EU2_1_2_NC

FR_P_2015_1000 m3_EU2_1_2_NC

2015

1000 m3

EU2_1_3

FR_P_2015_1000 m3_EU2_1_3

2015

1000 m3

EU2_1_3_C

FR_P_2015_1000 m3_EU2_1_3_C

2015

1000 m3

EU2_1_3_NC

FR_P_2015_1000 m3_EU2_1_3_NC

P.OB

2015

1000 m3

FR_P.OB_2015_1000 m3_1

P.OB

2015

1000 m3

1_C

FR_P.OB_2015_1000 m3_1_C

P.OB

2015

1000 m3

1_NC

FR_P.OB_2015_1000 m3_1_NC

P.OB

2015

1000 m3

1_1

FR_P.OB_2015_1000 m3_1_1

P.OB

2015

1000 m3

1_1_C

FR_P.OB_2015_1000 m3_1_1_C

P.OB

2015

1000 m3

1_1_NC

FR_P.OB_2015_1000 m3_1_1_NC

P.OB

2015

1000 m3

1_2

FR_P.OB_2015_1000 m3_1_2

P.OB

2015

1000 m3

1_2_C

FR_P.OB_2015_1000 m3_1_2_C

P.OB

2015

1000 m3

1_2_NC

FR_P.OB_2015_1000 m3_1_2_NC

P.OB

2015

1000 m3

1_2_1

FR_P.OB_2015_1000 m3_1_2_1

P.OB

2015

1000 m3

1_2_1_C

FR_P.OB_2015_1000 m3_1_2_1_C

P.OB

2015

1000 m3

1_2_1_NC

FR_P.OB_2015_1000 m3_1_2_1_NC

P.OB

2015

1000 m3

1_2_2

FR_P.OB_2015_1000 m3_1_2_2

P.OB

2015

1000 m3

1_2_2_C

FR_P.OB_2015_1000 m3_1_2_2_C

P.OB

2015

1000 m3

1_2_2_NC

FR_P.OB_2015_1000 m3_1_2_2_NC

P.OB

2015

1000 m3

1_2_3

FR_P.OB_2015_1000 m3_1_2_3

P.OB

2015

1000 m3

1_2_3_C

FR_P.OB_2015_1000 m3_1_2_3_C

P.OB

2015

1000 m3

1_2_3_NC

FR_P.OB_2015_1000 m3_1_2_3_NC

2014

1000 m3

FR_P_2014_1000 m3_1

2014

1000 m3

1_C

FR_P_2014_1000 m3_1_C

2014

1000 m3

1_NC

FR_P_2014_1000 m3_1_NC

2014

1000 m3

1_1

FR_P_2014_1000 m3_1_1

2014

1000 m3

1_1_C

FR_P_2014_1000 m3_1_1_C

2014

1000 m3

1_1_NC

FR_P_2014_1000 m3_1_1_NC

2014

1000 m3

1_2

FR_P_2014_1000 m3_1_2

2014

1000 m3

1_2_C

FR_P_2014_1000 m3_1_2_C

2014

1000 m3

1_2_NC

FR_P_2014_1000 m3_1_2_NC

2014

1000 m3

1_2_1

FR_P_2014_1000 m3_1_2_1

2014

1000 m3

1_2_1_C

FR_P_2014_1000 m3_1_2_1_C

2014

1000 m3

1_2_1_NC

FR_P_2014_1000 m3_1_2_1_NC

2014

1000 m3

1_2_2

FR_P_2014_1000 m3_1_2_2

2014

1000 m3

1_2_2_C

FR_P_2014_1000 m3_1_2_2_C

2014

1000 m3

1_2_2_NC

FR_P_2014_1000 m3_1_2_2_NC

2014

1000 m3

1_2_3

FR_P_2014_1000 m3_1_2_3

2014

1000 m3

1_2_3_C

FR_P_2014_1000 m3_1_2_3_C

2014

1000 m3

1_2_3_NC

FR_P_2014_1000 m3_1_2_3_NC

2014

1000 mt

FR_P_2014_1000 mt_2

2014

1000 m3

FR_P_2014_1000 m3_3

2014

1000 m3

3_1

FR_P_2014_1000 m3_3_1

2014

1000 m3

3_2

FR_P_2014_1000 m3_3_2

2014

1000 mt

FR_P_2014_1000 mt_4

2014

1000 mt

4_1

FR_P_2014_1000 mt_4_1

2014

1000 mt

4_2

FR_P_2014_1000 mt_4_2

2014

1000 m3

FR_P_2014_1000 m3_5

2014

1000 m3

5_C

FR_P_2014_1000 m3_5_C

2014

1000 m3

5_NC

FR_P_2014_1000 m3_5_NC

2014

1000 m3

5_NC_T

FR_P_2014_1000 m3_5_NC_T

2014

1000 m3

FR_P_2014_1000 m3_6

2014

1000 m3

6_1

FR_P_2014_1000 m3_6_1

2014

1000 m3

6_1_C

FR_P_2014_1000 m3_6_1_C

2014

1000 m3

6_1_NC

FR_P_2014_1000 m3_6_1_NC

2014

1000 m3

6_1_NC_T

FR_P_2014_1000 m3_6_1_NC_T

2014

1000 m3

6_2

FR_P_2014_1000 m3_6_2

2014

1000 m3

6_2_C

FR_P_2014_1000 m3_6_2_C

2014

1000 m3

6_2_NC

FR_P_2014_1000 m3_6_2_NC

2014

1000 m3

6_2_NC_T

FR_P_2014_1000 m3_6_2_NC_T

2014

1000 m3

6_3

FR_P_2014_1000 m3_6_3

2014

1000 m3

6_3_1

FR_P_2014_1000 m3_6_3_1

2014

1000 m3

6_4

FR_P_2014_1000 m3_6_4

2014

1000 m3

6_4_1

FR_P_2014_1000 m3_6_4_1

2014

1000 m3

6_4_2

FR_P_2014_1000 m3_6_4_2

2014

1000 m3

6_4_3

FR_P_2014_1000 m3_6_4_3

2014

1000 mt

FR_P_2014_1000 mt_7

2014

1000 mt

7_1

FR_P_2014_1000 mt_7_1

2014

1000 mt

7_2

FR_P_2014_1000 mt_7_2

2014

1000 mt

7_3

FR_P_2014_1000 mt_7_3

2014

1000 mt

7_3_1

FR_P_2014_1000 mt_7_3_1

2014

1000 mt

7_3_2

FR_P_2014_1000 mt_7_3_2

2014

1000 mt

7_3_3

FR_P_2014_1000 mt_7_3_3

2014

1000 mt

7_3_4

FR_P_2014_1000 mt_7_3_4

2014

1000 mt

7_4

FR_P_2014_1000 mt_7_4

2014

1000 mt

FR_P_2014_1000 mt_8

2014

1000 mt

8_1

FR_P_2014_1000 mt_8_1

2014

1000 mt

8_2

FR_P_2014_1000 mt_8_2

2014

1000 mt

FR_P_2014_1000 mt_9

2014

1000 mt

FR_P_2014_1000 mt_10

2014

1000 mt

10_1

FR_P_2014_1000 mt_10_1

2014

1000 mt

10_1_1

FR_P_2014_1000 mt_10_1_1

2014

1000 mt

10_1_2

FR_P_2014_1000 mt_10_1_2

2014

1000 mt

10_1_3

FR_P_2014_1000 mt_10_1_3

2014

1000 mt

10_1_4

FR_P_2014_1000 mt_10_1_4

2014

1000 mt

10_2

FR_P_2014_1000 mt_10_2

2014

1000 mt

10_3

FR_P_2014_1000 mt_10_3

2014

1000 mt

10_3_1

FR_P_2014_1000 mt_10_3_1

2014

1000 mt

10_3_2

FR_P_2014_1000 mt_10_3_2

2014

1000 mt

10_3_3

FR_P_2014_1000 mt_10_3_3

2014

1000 mt

10_3_4

FR_P_2014_1000 mt_10_3_4

2014

1000 mt

10_4

FR_P_2014_1000 mt_10_4

2014

1000 m3

FR_M_2014_1000 m3_1

2014

1000 m3

1_1

FR_M_2014_1000 m3_1_1

2014

1000 m3

1_2

FR_M_2014_1000 m3_1_2

2014

1000 m3

1_2_C

FR_M_2014_1000 m3_1_2_C

2014

1000 m3

1_2_NC

FR_M_2014_1000 m3_1_2_NC

2014

1000 m3

1_2_NC_T

FR_M_2014_1000 m3_1_2_NC_T

2014

1000 mt

FR_M_2014_1000 mt_2

2014

1000 m3

FR_M_2014_1000 m3_3

2014

1000 m3

3_1

FR_M_2014_1000 m3_3_1

2014

1000 m3

3_2

FR_M_2014_1000 m3_3_2

2014

1000 mt

FR_M_2014_1000 mt_4

2014

1000 mt

4_1

FR_M_2014_1000 mt_4_1

2014

1000 mt

4_2

FR_M_2014_1000 mt_4_2

2014

1000 m3

FR_M_2014_1000 m3_5

2014

1000 m3

5_C

FR_M_2014_1000 m3_5_C

2014

1000 m3

5_NC

FR_M_2014_1000 m3_5_NC

2014

1000 m3

5_NC_T

FR_M_2014_1000 m3_5_NC_T

2014

1000 m3

FR_M_2014_1000 m3_6

2014

1000 m3

6_1

FR_M_2014_1000 m3_6_1

2014

1000 m3

6_1_C

FR_M_2014_1000 m3_6_1_C

2014

1000 m3

6_1_NC

FR_M_2014_1000 m3_6_1_NC

2014

1000 m3

6_1_NC_T

FR_M_2014_1000 m3_6_1_NC_T

2014

1000 m3

6_2

FR_M_2014_1000 m3_6_2

2014

1000 m3

6_2_C

FR_M_2014_1000 m3_6_2_C

2014

1000 m3

6_2_NC

FR_M_2014_1000 m3_6_2_NC

2014

1000 m3

6_2_NC_T

FR_M_2014_1000 m3_6_2_NC_T

2014

1000 m3

6_3

FR_M_2014_1000 m3_6_3

2014

1000 m3

6_3_1

FR_M_2014_1000 m3_6_3_1

2014

1000 m3

6_4

FR_M_2014_1000 m3_6_4

2014

1000 m3

6_4_1

FR_M_2014_1000 m3_6_4_1

2014

1000 m3

6_4_2

FR_M_2014_1000 m3_6_4_2

2014

1000 m3

6_4_3

FR_M_2014_1000 m3_6_4_3

2014

1000 mt

FR_M_2014_1000 mt_7

2014

1000 mt

7_1

FR_M_2014_1000 mt_7_1

2014

1000 mt

7_2

FR_M_2014_1000 mt_7_2

2014

1000 mt

7_3

FR_M_2014_1000 mt_7_3

2014

1000 mt

7_3_1

FR_M_2014_1000 mt_7_3_1

2014

1000 mt

7_3_2

FR_M_2014_1000 mt_7_3_2

2014

1000 mt

7_3_3

FR_M_2014_1000 mt_7_3_3

2014

1000 mt

7_3_4

FR_M_2014_1000 mt_7_3_4

2014

1000 mt

7_4

FR_M_2014_1000 mt_7_4

2014

1000 mt

FR_M_2014_1000 mt_8

2014

1000 mt

8_1

FR_M_2014_1000 mt_8_1

2014

1000 mt

8_2

FR_M_2014_1000 mt_8_2

2014

1000 mt

FR_M_2014_1000 mt_9

2014

1000 mt

FR_M_2014_1000 mt_10

2014

1000 mt

10_1

FR_M_2014_1000 mt_10_1

2014

1000 mt

10_1_1

FR_M_2014_1000 mt_10_1_1

2014

1000 mt

10_1_2

FR_M_2014_1000 mt_10_1_2

2014

1000 mt

10_1_3

FR_M_2014_1000 mt_10_1_3

2014

1000 mt

10_1_4

FR_M_2014_1000 mt_10_1_4

2014

1000 mt

10_2

FR_M_2014_1000 mt_10_2

2014

1000 mt

10_3

FR_M_2014_1000 mt_10_3

2014

1000 mt

10_3_1

FR_M_2014_1000 mt_10_3_1

2014

1000 mt

10_3_2

FR_M_2014_1000 mt_10_3_2

2014

1000 mt

10_3_3

FR_M_2014_1000 mt_10_3_3

2014

1000 mt

10_3_4

FR_M_2014_1000 mt_10_3_4

2014

1000 mt

10_4

FR_M_2014_1000 mt_10_4

2014

1000 NAC

FR_M_2014_1000 NAC_1

2014

1000 NAC

1_1

FR_M_2014_1000 NAC_1_1

2014

1000 NAC

1_2

FR_M_2014_1000 NAC_1_2

2014

1000 NAC

1_2_C

FR_M_2014_1000 NAC_1_2_C

2014

1000 NAC

1_2_NC

FR_M_2014_1000 NAC_1_2_NC

2014

1000 NAC

1_2_NC_T

FR_M_2014_1000 NAC_1_2_NC_T

2014

1000 NAC

FR_M_2014_1000 NAC_2

2014

1000 NAC

FR_M_2014_1000 NAC_3

2014

1000 NAC

3_1

FR_M_2014_1000 NAC_3_1

2014

1000 NAC

3_2

FR_M_2014_1000 NAC_3_2

2014

1000 NAC

FR_M_2014_1000 NAC_4

2014

1000 NAC

4_1

FR_M_2014_1000 NAC_4_1

2014

1000 NAC

4_2

FR_M_2014_1000 NAC_4_2

2014

1000 NAC

FR_M_2014_1000 NAC_5

2014

1000 NAC

5_C

FR_M_2014_1000 NAC_5_C

2014

1000 NAC

5_NC

FR_M_2014_1000 NAC_5_NC

2014

1000 NAC

5_NC_T

FR_M_2014_1000 NAC_5_NC_T

2014

1000 NAC

FR_M_2014_1000 NAC_6

2014

1000 NAC

6_1

FR_M_2014_1000 NAC_6_1

2014

1000 NAC

6_1_C

FR_M_2014_1000 NAC_6_1_C

2014

1000 NAC

6_1_NC

FR_M_2014_1000 NAC_6_1_NC

2014

1000 NAC

6_1_NC_T

FR_M_2014_1000 NAC_6_1_NC_T

2014

1000 NAC

6_2

FR_M_2014_1000 NAC_6_2

2014

1000 NAC

6_2_C

FR_M_2014_1000 NAC_6_2_C

2014

1000 NAC

6_2_NC

FR_M_2014_1000 NAC_6_2_NC

2014

1000 NAC

6_2_NC_T

FR_M_2014_1000 NAC_6_2_NC_T

2014

1000 NAC

6_3

FR_M_2014_1000 NAC_6_3

2014

1000 NAC

6_3_1

FR_M_2014_1000 NAC_6_3_1

2014

1000 NAC

6_4

FR_M_2014_1000 NAC_6_4

2014

1000 NAC

6_4_1

FR_M_2014_1000 NAC_6_4_1

2014

1000 NAC

6_4_2

FR_M_2014_1000 NAC_6_4_2

2014

1000 NAC

6_4_3

FR_M_2014_1000 NAC_6_4_3

2014

1000 NAC

FR_M_2014_1000 NAC_7

2014

1000 NAC

7_1

FR_M_2014_1000 NAC_7_1

2014

1000 NAC

7_2

FR_M_2014_1000 NAC_7_2

2014

1000 NAC

7_3

FR_M_2014_1000 NAC_7_3

2014

1000 NAC

7_3_1

FR_M_2014_1000 NAC_7_3_1

2014

1000 NAC

7_3_2

FR_M_2014_1000 NAC_7_3_2

2014

1000 NAC

7_3_3

FR_M_2014_1000 NAC_7_3_3

2014

1000 NAC

7_3_4

FR_M_2014_1000 NAC_7_3_4

2014

1000 NAC

7_4

FR_M_2014_1000 NAC_7_4

2014

1000 NAC

FR_M_2014_1000 NAC_8

2014

1000 NAC

8_1

FR_M_2014_1000 NAC_8_1

2014

1000 NAC

8_2

FR_M_2014_1000 NAC_8_2

2014

1000 NAC

FR_M_2014_1000 NAC_9

2014

1000 NAC

FR_M_2014_1000 NAC_10

2014

1000 NAC

10_1

FR_M_2014_1000 NAC_10_1

2014

1000 NAC

10_1_1

FR_M_2014_1000 NAC_10_1_1

2014

1000 NAC

10_1_2

FR_M_2014_1000 NAC_10_1_2

2014

1000 NAC

10_1_3

FR_M_2014_1000 NAC_10_1_3

2014

1000 NAC

10_1_4

FR_M_2014_1000 NAC_10_1_4

2014

1000 NAC

10_2

FR_M_2014_1000 NAC_10_2

2014

1000 NAC

10_3

FR_M_2014_1000 NAC_10_3

2014

1000 NAC

10_3_1

FR_M_2014_1000 NAC_10_3_1

2014

1000 NAC

10_3_2

FR_M_2014_1000 NAC_10_3_2

2014

1000 NAC

10_3_3

FR_M_2014_1000 NAC_10_3_3

2014

1000 NAC

10_3_4

FR_M_2014_1000 NAC_10_3_4

2014

1000 NAC

10_4

FR_M_2014_1000 NAC_10_4

2014

1000 m3

FR_X_2014_1000 m3_1

2014

1000 m3

1_1

FR_X_2014_1000 m3_1_1

2014

1000 m3

1_2

FR_X_2014_1000 m3_1_2

2014

1000 m3

1_2_C

FR_X_2014_1000 m3_1_2_C

2014

1000 m3

1_2_NC

FR_X_2014_1000 m3_1_2_NC

2014

1000 m3

1_2_NC_T

FR_X_2014_1000 m3_1_2_NC_T

2014

1000 mt

FR_X_2014_1000 mt_2

2014

1000 m3

FR_X_2014_1000 m3_3

2014

1000 m3

3_1

FR_X_2014_1000 m3_3_1

2014

1000 m3

3_2

FR_X_2014_1000 m3_3_2

2014

1000 mt

FR_X_2014_1000 mt_4

2014

1000 mt

4_1

FR_X_2014_1000 mt_4_1

2014

1000 mt

4_2

FR_X_2014_1000 mt_4_2

2014

1000 m3

FR_X_2014_1000 m3_5

2014

1000 m3

5_C

FR_X_2014_1000 m3_5_C

2014

1000 m3

5_NC

FR_X_2014_1000 m3_5_NC

2014

1000 m3

5_NC_T

FR_X_2014_1000 m3_5_NC_T

2014

1000 m3

FR_X_2014_1000 m3_6

2014

1000 m3

6_1

FR_X_2014_1000 m3_6_1

2014

1000 m3

6_1_C

FR_X_2014_1000 m3_6_1_C

2014

1000 m3

6_1_NC

FR_X_2014_1000 m3_6_1_NC

2014

1000 m3

6_1_NC_T

FR_X_2014_1000 m3_6_1_NC_T

2014

1000 m3

6_2

FR_X_2014_1000 m3_6_2

2014

1000 m3

6_2_C

FR_X_2014_1000 m3_6_2_C

2014

1000 m3

6_2_NC

FR_X_2014_1000 m3_6_2_NC

2014

1000 m3

6_2_NC_T

FR_X_2014_1000 m3_6_2_NC_T

2014

1000 m3

6_3

FR_X_2014_1000 m3_6_3

2014

1000 m3

6_3_1

FR_X_2014_1000 m3_6_3_1

2014

1000 m3

6_4

FR_X_2014_1000 m3_6_4

2014

1000 m3

6_4_1

FR_X_2014_1000 m3_6_4_1

2014

1000 m3

6_4_2

FR_X_2014_1000 m3_6_4_2

2014

1000 m3

6_4_3

FR_X_2014_1000 m3_6_4_3

2014

1000 mt

FR_X_2014_1000 mt_7

2014

1000 mt

7_1

FR_X_2014_1000 mt_7_1

2014

1000 mt

7_2

FR_X_2014_1000 mt_7_2

2014

1000 mt

7_3

FR_X_2014_1000 mt_7_3

2014

1000 mt

7_3_1

FR_X_2014_1000 mt_7_3_1

2014

1000 mt

7_3_2

FR_X_2014_1000 mt_7_3_2

2014

1000 mt

7_3_3

FR_X_2014_1000 mt_7_3_3

2014

1000 mt

7_3_4

FR_X_2014_1000 mt_7_3_4

2014

1000 mt

7_4

FR_X_2014_1000 mt_7_4

2014

1000 mt

FR_X_2014_1000 mt_8

2014

1000 mt

8_1

FR_X_2014_1000 mt_8_1

2014

1000 mt

8_2

FR_X_2014_1000 mt_8_2

2014

1000 mt

FR_X_2014_1000 mt_9

2014

1000 mt

FR_X_2014_1000 mt_10

2014

1000 mt

10_1

FR_X_2014_1000 mt_10_1

2014

1000 mt

10_1_1

FR_X_2014_1000 mt_10_1_1

2014

1000 mt

10_1_2

FR_X_2014_1000 mt_10_1_2

2014

1000 mt

10_1_3

FR_X_2014_1000 mt_10_1_3

2014

1000 mt

10_1_4

FR_X_2014_1000 mt_10_1_4

2014

1000 mt

10_2

FR_X_2014_1000 mt_10_2

2014

1000 mt

10_3

FR_X_2014_1000 mt_10_3

2014

1000 mt

10_3_1

FR_X_2014_1000 mt_10_3_1

2014

1000 mt

10_3_2

FR_X_2014_1000 mt_10_3_2

2014

1000 mt

10_3_3

FR_X_2014_1000 mt_10_3_3

2014

1000 mt

10_3_4

FR_X_2014_1000 mt_10_3_4

2014

1000 mt

10_4

FR_X_2014_1000 mt_10_4

2014

1000 NAC

FR_X_2014_1000 NAC_1

2014

1000 NAC

1_1

FR_X_2014_1000 NAC_1_1

2014

1000 NAC

1_2

FR_X_2014_1000 NAC_1_2

2014

1000 NAC

1_2_C

FR_X_2014_1000 NAC_1_2_C

2014

1000 NAC

1_2_NC

FR_X_2014_1000 NAC_1_2_NC

2014

1000 NAC

1_2_NC_T

FR_X_2014_1000 NAC_1_2_NC_T

2014

1000 NAC

FR_X_2014_1000 NAC_2

2014

1000 NAC

FR_X_2014_1000 NAC_3

2014

1000 NAC

3_1

FR_X_2014_1000 NAC_3_1

2014

1000 NAC

3_2

FR_X_2014_1000 NAC_3_2

2014

1000 NAC

FR_X_2014_1000 NAC_4

2014

1000 NAC

4_1

FR_X_2014_1000 NAC_4_1

2014

1000 NAC

4_2

FR_X_2014_1000 NAC_4_2

2014

1000 NAC

FR_X_2014_1000 NAC_5

2014

1000 NAC

5_C

FR_X_2014_1000 NAC_5_C

2014

1000 NAC

5_NC

FR_X_2014_1000 NAC_5_NC

2014

1000 NAC

5_NC_T

FR_X_2014_1000 NAC_5_NC_T

2014

1000 NAC

FR_X_2014_1000 NAC_6

2014

1000 NAC

6_1

FR_X_2014_1000 NAC_6_1

2014

1000 NAC

6_1_C

FR_X_2014_1000 NAC_6_1_C

2014

1000 NAC

6_1_NC

FR_X_2014_1000 NAC_6_1_NC

2014

1000 NAC

6_1_NC_T

FR_X_2014_1000 NAC_6_1_NC_T

2014

1000 NAC

6_2

FR_X_2014_1000 NAC_6_2

2014

1000 NAC

6_2_C

FR_X_2014_1000 NAC_6_2_C

2014

1000 NAC

6_2_NC

FR_X_2014_1000 NAC_6_2_NC

2014

1000 NAC

6_2_NC_T

FR_X_2014_1000 NAC_6_2_NC_T

2014

1000 NAC

6_3

FR_X_2014_1000 NAC_6_3

2014

1000 NAC

6_3_1

FR_X_2014_1000 NAC_6_3_1

2014

1000 NAC

6_4

FR_X_2014_1000 NAC_6_4

2014

1000 NAC

6_4_1

FR_X_2014_1000 NAC_6_4_1

2014

1000 NAC

6_4_2

FR_X_2014_1000 NAC_6_4_2

2014

1000 NAC

6_4_3

FR_X_2014_1000 NAC_6_4_3

2014

1000 NAC

FR_X_2014_1000 NAC_7

2014

1000 NAC

7_1

FR_X_2014_1000 NAC_7_1

2014

1000 NAC

7_2

FR_X_2014_1000 NAC_7_2

2014

1000 NAC

7_3

FR_X_2014_1000 NAC_7_3

2014

1000 NAC

7_3_1

FR_X_2014_1000 NAC_7_3_1

2014

1000 NAC

7_3_2

FR_X_2014_1000 NAC_7_3_2

2014

1000 NAC

7_3_3

FR_X_2014_1000 NAC_7_3_3

2014

1000 NAC

7_3_4

FR_X_2014_1000 NAC_7_3_4

2014

1000 NAC

7_4

FR_X_2014_1000 NAC_7_4

2014

1000 NAC

FR_X_2014_1000 NAC_8

2014

1000 NAC

8_1

FR_X_2014_1000 NAC_8_1

2014

1000 NAC

8_2

FR_X_2014_1000 NAC_8_2

2014

1000 NAC

FR_X_2014_1000 NAC_9

2014

1000 NAC

FR_X_2014_1000 NAC_10

2014

1000 NAC

10_1

FR_X_2014_1000 NAC_10_1

2014

1000 NAC

10_1_1

FR_X_2014_1000 NAC_10_1_1

2014

1000 NAC

10_1_2

FR_X_2014_1000 NAC_10_1_2

2014

1000 NAC

10_1_3

FR_X_2014_1000 NAC_10_1_3

2014

1000 NAC

10_1_4

FR_X_2014_1000 NAC_10_1_4

2014

1000 NAC

10_2

FR_X_2014_1000 NAC_10_2

2014

1000 NAC

10_3

FR_X_2014_1000 NAC_10_3

2014

1000 NAC

10_3_1

FR_X_2014_1000 NAC_10_3_1

2014

1000 NAC

10_3_2

FR_X_2014_1000 NAC_10_3_2

2014

1000 NAC

10_3_3

FR_X_2014_1000 NAC_10_3_3

2014

1000 NAC

10_3_4

FR_X_2014_1000 NAC_10_3_4

2014

1000 NAC

10_4

FR_X_2014_1000 NAC_10_4

2014

1000 NAC

11_1

FR_M_2014_1000 NAC_11_1

2014

1000 NAC

11_1_C

FR_M_2014_1000 NAC_11_1_C

2014

1000 NAC

11_1_NC

FR_M_2014_1000 NAC_11_1_NC

2014

1000 NAC

11_1_NC_T

FR_M_2014_1000 NAC_11_1_NC_T

2014

1000 NAC

11_2

FR_M_2014_1000 NAC_11_2

2014

1000 NAC

11_3

FR_M_2014_1000 NAC_11_3

2014

1000 NAC

11_4

FR_M_2014_1000 NAC_11_4

2014

1000 NAC

11_5

FR_M_2014_1000 NAC_11_5

2014

1000 NAC

11_6

FR_M_2014_1000 NAC_11_6

2014

1000 NAC

11_7

FR_M_2014_1000 NAC_11_7

2014

1000 NAC

11_7_1

FR_M_2014_1000 NAC_11_7_1

2014

1000 NAC

12_1

FR_M_2014_1000 NAC_12_1

2014

1000 NAC

12_2

FR_M_2014_1000 NAC_12_2

2014

1000 NAC

12_3

FR_M_2014_1000 NAC_12_3

2014

1000 NAC

12_4

FR_M_2014_1000 NAC_12_4

2014

1000 NAC

12_5

FR_M_2014_1000 NAC_12_5

2014

1000 NAC

12_6

FR_M_2014_1000 NAC_12_6

2014

1000 NAC

12_6_1

FR_M_2014_1000 NAC_12_6_1

2014

1000 NAC

12_6_2

FR_M_2014_1000 NAC_12_6_2

2014

1000 NAC

12_6_3

FR_M_2014_1000 NAC_12_6_3

2014

1000 NAC

12_7

FR_M_2014_1000 NAC_12_7

2014

1000 NAC

12_7_1

FR_M_2014_1000 NAC_12_7_1

2014

1000 NAC

12_7_2

FR_M_2014_1000 NAC_12_7_2

2014

1000 NAC

12_7_3

FR_M_2014_1000 NAC_12_7_3

2014

1000 NAC

11_1

FR_X_2014_1000 NAC_11_1

2014

1000 NAC

11_1_C

FR_X_2014_1000 NAC_11_1_C

2014

1000 NAC

11_1_NC

FR_X_2014_1000 NAC_11_1_NC

2014

1000 NAC

11_1_NC_T

FR_X_2014_1000 NAC_11_1_NC_T

2014

1000 NAC

11_2

FR_X_2014_1000 NAC_11_2

2014

1000 NAC

11_3

FR_X_2014_1000 NAC_11_3

2014

1000 NAC

11_4

FR_X_2014_1000 NAC_11_4

2014

1000 NAC

11_5

FR_X_2014_1000 NAC_11_5

2014

1000 NAC

11_6

FR_X_2014_1000 NAC_11_6

2014

1000 NAC

11_7

FR_X_2014_1000 NAC_11_7

2014

1000 NAC

11_7_1

FR_X_2014_1000 NAC_11_7_1

2014

1000 NAC

12_1

FR_X_2014_1000 NAC_12_1

2014

1000 NAC

12_2

FR_X_2014_1000 NAC_12_2

2014

1000 NAC

12_3

FR_X_2014_1000 NAC_12_3

2014

1000 NAC

12_4

FR_X_2014_1000 NAC_12_4

2014

1000 NAC

12_5

FR_X_2014_1000 NAC_12_5

2014

1000 NAC

12_6

FR_X_2014_1000 NAC_12_6

2014

1000 NAC

12_6_1

FR_X_2014_1000 NAC_12_6_1

2014

1000 NAC

12_6_2

FR_X_2014_1000 NAC_12_6_2

2014

1000 NAC

12_6_3

FR_X_2014_1000 NAC_12_6_3

2014

1000 NAC

12_7

FR_X_2014_1000 NAC_12_7

2014

1000 NAC

12_7_1

FR_X_2014_1000 NAC_12_7_1

2014

1000 NAC

12_7_2

FR_X_2014_1000 NAC_12_7_2

2014

1000 NAC

12_7_3

FR_X_2014_1000 NAC_12_7_3

2014

1000 m3

ST_1_2_C

FR_M_2014_1000 m3_ST_1_2_C

2014

1000 m3

ST_1_2_C_1

FR_M_2014_1000 m3_ST_1_2_C_1

2014

1000 m3

ST_1_2_C_1_1

FR_M_2014_1000 m3_ST_1_2_C_1_1

2014

1000 m3

ST_1_2_C_2_1

FR_M_2014_1000 m3_ST_1_2_C_2_1

2014

1000 m3

ST_1_2_C_2

FR_M_2014_1000 m3_ST_1_2_C_2

2014

1000 m3

ST_1_2_C_1_2

FR_M_2014_1000 m3_ST_1_2_C_1_2

2014

1000 m3

ST_1_2_C_2_2

FR_M_2014_1000 m3_ST_1_2_C_2_2

2014

1000 m3

ST_1_2_C_3

FR_M_2014_1000 m3_ST_1_2_C_3

2014

1000 m3

ST_1_2_C_1_3

FR_M_2014_1000 m3_ST_1_2_C_1_3

2014

1000 m3

ST_1_2_C_2_3

FR_M_2014_1000 m3_ST_1_2_C_2_3

2014

1000 m3

ST_1_2_NC

FR_M_2014_1000 m3_ST_1_2_NC

2014

1000 m3

ST_1_2_NC_1

FR_M_2014_1000 m3_ST_1_2_NC_1

2014

1000 m3

ST_1_2_NC_1_1

FR_M_2014_1000 m3_ST_1_2_NC_1_1

2014

1000 m3

ST_1_2_NC_2_1

FR_M_2014_1000 m3_ST_1_2_NC_2_1

2014

1000 m3

ST_1_2_NC_2

FR_M_2014_1000 m3_ST_1_2_NC_2

2014

1000 m3

ST_1_2_NC_1_2

FR_M_2014_1000 m3_ST_1_2_NC_1_2

2014

1000 m3

ST_1_2_NC_2_2

FR_M_2014_1000 m3_ST_1_2_NC_2_2

2014

1000 m3

ST_1_2_NC_3

FR_M_2014_1000 m3_ST_1_2_NC_3

2014

1000 m3

ST_1_2_NC_1_3

FR_M_2014_1000 m3_ST_1_2_NC_1_3

2014

1000 m3

ST_1_2_NC_2_3

FR_M_2014_1000 m3_ST_1_2_NC_2_3

2014

1000 m3

ST_1_2_NC_4

FR_M_2014_1000 m3_ST_1_2_NC_4

2014

1000 m3

ST_1_2_NC_5

FR_M_2014_1000 m3_ST_1_2_NC_5

2014

1000 m3

ST_5_C

FR_M_2014_1000 m3_ST_5_C

2014

1000 m3

ST_5_C_1

FR_M_2014_1000 m3_ST_5_C_1

2014

1000 m3

ST_5_C_2

FR_M_2014_1000 m3_ST_5_C_2

2014

1000 m3

ST_5_NC

FR_M_2014_1000 m3_ST_5_NC

2014

1000 m3

ST_5_NC_1

FR_M_2014_1000 m3_ST_5_NC_1

2014

1000 m3

ST_5_NC_2

FR_M_2014_1000 m3_ST_5_NC_2

2014

1000 m3

ST_5_NC_3

FR_M_2014_1000 m3_ST_5_NC_3

2014

1000 m3

ST_5_NC_4

FR_M_2014_1000 m3_ST_5_NC_4

2014

1000 m3

ST_5_NC_5

FR_M_2014_1000 m3_ST_5_NC_5

2014

1000 m3

ST_5_NC_6

FR_M_2014_1000 m3_ST_5_NC_6

2014

1000 m3

ST_5_NC_7

FR_M_2014_1000 m3_ST_5_NC_7

2014

1000 NAC

ST_1_2_C

FR_M_2014_1000 NAC_ST_1_2_C

2014

1000 NAC

ST_1_2_C_1

FR_M_2014_1000 NAC_ST_1_2_C_1

2014

1000 NAC

ST_1_2_C_1_1

FR_M_2014_1000 NAC_ST_1_2_C_1_1

2014

1000 NAC

ST_1_2_C_2_1

FR_M_2014_1000 NAC_ST_1_2_C_2_1

2014

1000 NAC

ST_1_2_C_2

FR_M_2014_1000 NAC_ST_1_2_C_2

2014

1000 NAC

ST_1_2_C_1_2

FR_M_2014_1000 NAC_ST_1_2_C_1_2

2014

1000 NAC

ST_1_2_C_2_2

FR_M_2014_1000 NAC_ST_1_2_C_2_2

2014

1000 NAC

ST_1_2_C_3

FR_M_2014_1000 NAC_ST_1_2_C_3

2014

1000 NAC

ST_1_2_C_1_3

FR_M_2014_1000 NAC_ST_1_2_C_1_3

2014

1000 NAC

ST_1_2_C_2_3

FR_M_2014_1000 NAC_ST_1_2_C_2_3

2014

1000 NAC

ST_1_2_NC

FR_M_2014_1000 NAC_ST_1_2_NC

2014

1000 NAC

ST_1_2_NC_1

FR_M_2014_1000 NAC_ST_1_2_NC_1

2014

1000 NAC

ST_1_2_NC_1_1

FR_M_2014_1000 NAC_ST_1_2_NC_1_1

2014

1000 NAC

ST_1_2_NC_2_1

FR_M_2014_1000 NAC_ST_1_2_NC_2_1

2014

1000 NAC

ST_1_2_NC_2

FR_M_2014_1000 NAC_ST_1_2_NC_2

2014

1000 NAC

ST_1_2_NC_1_2

FR_M_2014_1000 NAC_ST_1_2_NC_1_2

2014

1000 NAC

ST_1_2_NC_2_2

FR_M_2014_1000 NAC_ST_1_2_NC_2_2

2014

1000 NAC

ST_1_2_NC_3

FR_M_2014_1000 NAC_ST_1_2_NC_3

2014

1000 NAC

ST_1_2_NC_1_3

FR_M_2014_1000 NAC_ST_1_2_NC_1_3

2014

1000 NAC

ST_1_2_NC_2_3

FR_M_2014_1000 NAC_ST_1_2_NC_2_3

2014

1000 NAC

ST_1_2_NC_4

FR_M_2014_1000 NAC_ST_1_2_NC_4

2014

1000 NAC

ST_1_2_NC_5

FR_M_2014_1000 NAC_ST_1_2_NC_5

2014

1000 NAC

ST_5_C

FR_M_2014_1000 NAC_ST_5_C

2014

1000 NAC

ST_5_C_1

FR_M_2014_1000 NAC_ST_5_C_1

2014

1000 NAC

ST_5_C_2

FR_M_2014_1000 NAC_ST_5_C_2

2014

1000 NAC

ST_5_NC

FR_M_2014_1000 NAC_ST_5_NC

2014

1000 NAC

ST_5_NC_1

FR_M_2014_1000 NAC_ST_5_NC_1

2014

1000 NAC

ST_5_NC_2

FR_M_2014_1000 NAC_ST_5_NC_2

2014

1000 NAC

ST_5_NC_3

FR_M_2014_1000 NAC_ST_5_NC_3

2014

1000 NAC

ST_5_NC_4

FR_M_2014_1000 NAC_ST_5_NC_4

2014

1000 NAC

ST_5_NC_5

FR_M_2014_1000 NAC_ST_5_NC_5

2014

1000 NAC

ST_5_NC_6

FR_M_2014_1000 NAC_ST_5_NC_6

2014

1000 NAC

ST_5_NC_7

FR_M_2014_1000 NAC_ST_5_NC_7

2014

1000 m3

ST_1_2_C

FR_X_2014_1000 m3_ST_1_2_C

2014

1000 m3

ST_1_2_C_1

FR_X_2014_1000 m3_ST_1_2_C_1

2014

1000 m3

ST_1_2_C_1_1

FR_X_2014_1000 m3_ST_1_2_C_1_1

2014

1000 m3

ST_1_2_C_2_1

FR_X_2014_1000 m3_ST_1_2_C_2_1

2014

1000 m3

ST_1_2_C_2

FR_X_2014_1000 m3_ST_1_2_C_2

2014

1000 m3

ST_1_2_C_1_2

FR_X_2014_1000 m3_ST_1_2_C_1_2

2014

1000 m3

ST_1_2_C_2_2

FR_X_2014_1000 m3_ST_1_2_C_2_2

2014

1000 m3

ST_1_2_C_3

FR_X_2014_1000 m3_ST_1_2_C_3

2014

1000 m3

ST_1_2_C_1_3

FR_X_2014_1000 m3_ST_1_2_C_1_3

2014

1000 m3

ST_1_2_C_2_3

FR_X_2014_1000 m3_ST_1_2_C_2_3

2014

1000 m3

ST_1_2_NC

FR_X_2014_1000 m3_ST_1_2_NC

2014

1000 m3

ST_1_2_NC_1

FR_X_2014_1000 m3_ST_1_2_NC_1

2014

1000 m3

ST_1_2_NC_1_1

FR_X_2014_1000 m3_ST_1_2_NC_1_1

2014

1000 m3

ST_1_2_NC_2_1

FR_X_2014_1000 m3_ST_1_2_NC_2_1

2014

1000 m3

ST_1_2_NC_2

FR_X_2014_1000 m3_ST_1_2_NC_2

2014

1000 m3

ST_1_2_NC_1_2

FR_X_2014_1000 m3_ST_1_2_NC_1_2

2014

1000 m3

ST_1_2_NC_2_2

FR_X_2014_1000 m3_ST_1_2_NC_2_2

2014

1000 m3

ST_1_2_NC_3

FR_X_2014_1000 m3_ST_1_2_NC_3

2014

1000 m3

ST_1_2_NC_1_3

FR_X_2014_1000 m3_ST_1_2_NC_1_3

2014

1000 m3

ST_1_2_NC_2_3

FR_X_2014_1000 m3_ST_1_2_NC_2_3

2014

1000 m3

ST_1_2_NC_4

FR_X_2014_1000 m3_ST_1_2_NC_4

2014

1000 m3

ST_1_2_NC_5

FR_X_2014_1000 m3_ST_1_2_NC_5

2014

1000 m3

ST_5_C

FR_X_2014_1000 m3_ST_5_C

2014

1000 m3

ST_5_C_1

FR_X_2014_1000 m3_ST_5_C_1

2014

1000 m3

ST_5_C_2

FR_X_2014_1000 m3_ST_5_C_2

2014

1000 m3

ST_5_NC

FR_X_2014_1000 m3_ST_5_NC

2014

1000 m3

ST_5_NC_1

FR_X_2014_1000 m3_ST_5_NC_1

2014

1000 m3

ST_5_NC_2

FR_X_2014_1000 m3_ST_5_NC_2

2014

1000 m3

ST_5_NC_3

FR_X_2014_1000 m3_ST_5_NC_3

2014

1000 m3

ST_5_NC_4

FR_X_2014_1000 m3_ST_5_NC_4

2014

1000 m3

ST_5_NC_5

FR_X_2014_1000 m3_ST_5_NC_5

2014

1000 m3

ST_5_NC_6

FR_X_2014_1000 m3_ST_5_NC_6

2014

1000 m3

ST_5_NC_7

FR_X_2014_1000 m3_ST_5_NC_7

2014

1000 NAC

ST_1_2_C

FR_X_2014_1000 NAC_ST_1_2_C

2014

1000 NAC

ST_1_2_C_1

FR_X_2014_1000 NAC_ST_1_2_C_1

2014

1000 NAC

ST_1_2_C_1_1

FR_X_2014_1000 NAC_ST_1_2_C_1_1

2014

1000 NAC

ST_1_2_C_2_1

FR_X_2014_1000 NAC_ST_1_2_C_2_1

2014

1000 NAC

ST_1_2_C_2

FR_X_2014_1000 NAC_ST_1_2_C_2

2014

1000 NAC

ST_1_2_C_1_2

FR_X_2014_1000 NAC_ST_1_2_C_1_2

2014

1000 NAC

ST_1_2_C_2_2

FR_X_2014_1000 NAC_ST_1_2_C_2_2

2014

1000 NAC

ST_1_2_C_3

FR_X_2014_1000 NAC_ST_1_2_C_3

2014

1000 NAC

ST_1_2_C_1_3

FR_X_2014_1000 NAC_ST_1_2_C_1_3

2014

1000 NAC

ST_1_2_C_2_3

FR_X_2014_1000 NAC_ST_1_2_C_2_3

2014

1000 NAC

ST_1_2_NC

FR_X_2014_1000 NAC_ST_1_2_NC

2014

1000 NAC

ST_1_2_NC_1

FR_X_2014_1000 NAC_ST_1_2_NC_1

2014

1000 NAC

ST_1_2_NC_1_1

FR_X_2014_1000 NAC_ST_1_2_NC_1_1

2014

1000 NAC

ST_1_2_NC_2_1

FR_X_2014_1000 NAC_ST_1_2_NC_2_1

2014

1000 NAC

ST_1_2_NC_2

FR_X_2014_1000 NAC_ST_1_2_NC_2

2014

1000 NAC

ST_1_2_NC_1_2

FR_X_2014_1000 NAC_ST_1_2_NC_1_2

2014

1000 NAC

ST_1_2_NC_2_2

FR_X_2014_1000 NAC_ST_1_2_NC_2_2

2014

1000 NAC

ST_1_2_NC_3

FR_X_2014_1000 NAC_ST_1_2_NC_3

2014

1000 NAC

ST_1_2_NC_1_3

FR_X_2014_1000 NAC_ST_1_2_NC_1_3

2014

1000 NAC

ST_1_2_NC_2_3

FR_X_2014_1000 NAC_ST_1_2_NC_2_3

2014

1000 NAC

ST_1_2_NC_4

FR_X_2014_1000 NAC_ST_1_2_NC_4

2014

1000 NAC

ST_1_2_NC_5

FR_X_2014_1000 NAC_ST_1_2_NC_5

2014

1000 NAC

ST_5_C

FR_X_2014_1000 NAC_ST_5_C

2014

1000 NAC

ST_5_C_1

FR_X_2014_1000 NAC_ST_5_C_1

2014

1000 NAC

ST_5_C_2

FR_X_2014_1000 NAC_ST_5_C_2

2014

1000 NAC

ST_5_NC

FR_X_2014_1000 NAC_ST_5_NC

2014

1000 NAC

ST_5_NC_1

FR_X_2014_1000 NAC_ST_5_NC_1

2014

1000 NAC

ST_5_NC_2

FR_X_2014_1000 NAC_ST_5_NC_2

2014

1000 NAC

ST_5_NC_3

FR_X_2014_1000 NAC_ST_5_NC_3

2014

1000 NAC

ST_5_NC_4

FR_X_2014_1000 NAC_ST_5_NC_4

2014

1000 NAC

ST_5_NC_5

FR_X_2014_1000 NAC_ST_5_NC_5

2014

1000 NAC

ST_5_NC_6

FR_X_2014_1000 NAC_ST_5_NC_6

2014

1000 NAC

ST_5_NC_7

FR_X_2014_1000 NAC_ST_5_NC_7

EX_M

2014

1000 m3

FR_EX_M_2014_1000 m3_1

EX_M

2014

1000 m3

1_1

FR_EX_M_2014_1000 m3_1_1

EX_M

2014

1000 m3

1_2

FR_EX_M_2014_1000 m3_1_2

EX_M

2014

1000 m3

1_2_C

FR_EX_M_2014_1000 m3_1_2_C

EX_M

2014

1000 m3

1_2_NC

FR_EX_M_2014_1000 m3_1_2_NC

EX_M

2014

1000 m3

1_2_NC_T

FR_EX_M_2014_1000 m3_1_2_NC_T

EX_M

2014

1000 mt

FR_EX_M_2014_1000 mt_2

EX_M

2014

1000 m3

FR_EX_M_2014_1000 m3_3

EX_M

2014

1000 m3

3_1

FR_EX_M_2014_1000 m3_3_1

EX_M

2014

1000 m3

3_2

FR_EX_M_2014_1000 m3_3_2

EX_M

2014

1000 mt

FR_EX_M_2014_1000 mt_4

EX_M

2014

1000 mt

4_1

FR_EX_M_2014_1000 mt_4_1

EX_M

2014

1000 mt

4_2

FR_EX_M_2014_1000 mt_4_2

EX_M

2014

1000 m3

FR_EX_M_2014_1000 m3_5

EX_M

2014

1000 m3

5_C

FR_EX_M_2014_1000 m3_5_C

EX_M

2014

1000 m3

5_NC

FR_EX_M_2014_1000 m3_5_NC

EX_M

2014

1000 m3

5_NC_T

FR_EX_M_2014_1000 m3_5_NC_T

EX_M

2014

1000 m3

FR_EX_M_2014_1000 m3_6

EX_M

2014

1000 m3

6_1

FR_EX_M_2014_1000 m3_6_1

EX_M

2014

1000 m3

6_1_C

FR_EX_M_2014_1000 m3_6_1_C

EX_M

2014

1000 m3

6_1_NC

FR_EX_M_2014_1000 m3_6_1_NC

EX_M

2014

1000 m3

6_1_NC_T

FR_EX_M_2014_1000 m3_6_1_NC_T

EX_M

2014

1000 m3

6_2

FR_EX_M_2014_1000 m3_6_2

EX_M

2014

1000 m3

6_2_C

FR_EX_M_2014_1000 m3_6_2_C

EX_M

2014

1000 m3

6_2_NC

FR_EX_M_2014_1000 m3_6_2_NC

EX_M

2014

1000 m3

6_2_NC_T

FR_EX_M_2014_1000 m3_6_2_NC_T

EX_M

2014

1000 m3

6_3

FR_EX_M_2014_1000 m3_6_3

EX_M

2014

1000 m3

6_3_1

FR_EX_M_2014_1000 m3_6_3_1

EX_M

2014

1000 m3

6_4

FR_EX_M_2014_1000 m3_6_4

EX_M

2014

1000 m3

6_4_1

FR_EX_M_2014_1000 m3_6_4_1

EX_M

2014

1000 m3

6_4_2

FR_EX_M_2014_1000 m3_6_4_2

EX_M

2014

1000 m3

6_4_3

FR_EX_M_2014_1000 m3_6_4_3

EX_M

2014

1000 mt

FR_EX_M_2014_1000 mt_7

EX_M

2014

1000 mt

7_1

FR_EX_M_2014_1000 mt_7_1

EX_M

2014

1000 mt

7_2

FR_EX_M_2014_1000 mt_7_2

EX_M

2014

1000 mt

7_3

FR_EX_M_2014_1000 mt_7_3

EX_M

2014

1000 mt

7_3_1

FR_EX_M_2014_1000 mt_7_3_1

EX_M

2014

1000 mt

7_3_2

FR_EX_M_2014_1000 mt_7_3_2

EX_M

2014

1000 mt

7_3_3

FR_EX_M_2014_1000 mt_7_3_3

EX_M

2014

1000 mt

7_3_4

FR_EX_M_2014_1000 mt_7_3_4

EX_M

2014

1000 mt

7_4

FR_EX_M_2014_1000 mt_7_4

EX_M

2014

1000 mt

FR_EX_M_2014_1000 mt_8

EX_M

2014

1000 mt

8_1

FR_EX_M_2014_1000 mt_8_1

EX_M

2014

1000 mt

8_2

FR_EX_M_2014_1000 mt_8_2

EX_M

2014

1000 mt

FR_EX_M_2014_1000 mt_9

EX_M

2014

1000 mt

FR_EX_M_2014_1000 mt_10

EX_M

2014

1000 mt

10_1

FR_EX_M_2014_1000 mt_10_1

EX_M

2014

1000 mt

10_1_1

FR_EX_M_2014_1000 mt_10_1_1

EX_M

2014

1000 mt

10_1_2

FR_EX_M_2014_1000 mt_10_1_2

EX_M

2014

1000 mt

10_1_3

FR_EX_M_2014_1000 mt_10_1_3

EX_M

2014

1000 mt

10_1_4

FR_EX_M_2014_1000 mt_10_1_4

EX_M

2014

1000 mt

10_2

FR_EX_M_2014_1000 mt_10_2

EX_M

2014

1000 mt

10_3

FR_EX_M_2014_1000 mt_10_3

EX_M

2014

1000 mt

10_3_1

FR_EX_M_2014_1000 mt_10_3_1

EX_M

2014

1000 mt

10_3_2

FR_EX_M_2014_1000 mt_10_3_2

EX_M

2014

1000 mt

10_3_3

FR_EX_M_2014_1000 mt_10_3_3

EX_M

2014

1000 mt

10_3_4

FR_EX_M_2014_1000 mt_10_3_4

EX_M

2014

1000 mt

10_4

FR_EX_M_2014_1000 mt_10_4

EX_M

2014

1000 NAC

FR_EX_M_2014_1000 NAC_1

EX_M

2014

1000 NAC

1_1

FR_EX_M_2014_1000 NAC_1_1

EX_M

2014

1000 NAC

1_2

FR_EX_M_2014_1000 NAC_1_2

EX_M

2014

1000 NAC

1_2_C

FR_EX_M_2014_1000 NAC_1_2_C

EX_M

2014

1000 NAC

1_2_NC

FR_EX_M_2014_1000 NAC_1_2_NC

EX_M

2014

1000 NAC

1_2_NC_T

FR_EX_M_2014_1000 NAC_1_2_NC_T

EX_M

2014

1000 NAC

FR_EX_M_2014_1000 NAC_2

EX_M

2014

1000 NAC

FR_EX_M_2014_1000 NAC_3

EX_M

2014

1000 NAC

3_1

FR_EX_M_2014_1000 NAC_3_1

EX_M

2014

1000 NAC

3_2

FR_EX_M_2014_1000 NAC_3_2

EX_M

2014

1000 NAC

FR_EX_M_2014_1000 NAC_4

EX_M

2014

1000 NAC

4_1

FR_EX_M_2014_1000 NAC_4_1

EX_M

2014

1000 NAC

4_2

FR_EX_M_2014_1000 NAC_4_2

EX_M

2014

1000 NAC

FR_EX_M_2014_1000 NAC_5

EX_M

2014

1000 NAC

5_C

FR_EX_M_2014_1000 NAC_5_C

EX_M

2014

1000 NAC

5_NC

FR_EX_M_2014_1000 NAC_5_NC

EX_M

2014

1000 NAC

5_NC_T

FR_EX_M_2014_1000 NAC_5_NC_T

EX_M

2014

1000 NAC

FR_EX_M_2014_1000 NAC_6

EX_M

2014

1000 NAC

6_1

FR_EX_M_2014_1000 NAC_6_1

EX_M

2014

1000 NAC

6_1_C

FR_EX_M_2014_1000 NAC_6_1_C

EX_M

2014

1000 NAC

6_1_NC

FR_EX_M_2014_1000 NAC_6_1_NC

EX_M

2014

1000 NAC

6_1_NC_T

FR_EX_M_2014_1000 NAC_6_1_NC_T

EX_M

2014

1000 NAC

6_2

FR_EX_M_2014_1000 NAC_6_2

EX_M

2014

1000 NAC

6_2_C

FR_EX_M_2014_1000 NAC_6_2_C

EX_M

2014

1000 NAC

6_2_NC

FR_EX_M_2014_1000 NAC_6_2_NC

EX_M

2014

1000 NAC

6_2_NC_T

FR_EX_M_2014_1000 NAC_6_2_NC_T

EX_M

2014

1000 NAC

6_3

FR_EX_M_2014_1000 NAC_6_3

EX_M

2014

1000 NAC

6_3_1

FR_EX_M_2014_1000 NAC_6_3_1

EX_M

2014

1000 NAC

6_4

FR_EX_M_2014_1000 NAC_6_4

EX_M

2014

1000 NAC

6_4_1

FR_EX_M_2014_1000 NAC_6_4_1

EX_M

2014

1000 NAC

6_4_2

FR_EX_M_2014_1000 NAC_6_4_2

EX_M

2014

1000 NAC

6_4_3

FR_EX_M_2014_1000 NAC_6_4_3

EX_M

2014

1000 NAC

FR_EX_M_2014_1000 NAC_7

EX_M

2014

1000 NAC

7_1

FR_EX_M_2014_1000 NAC_7_1

EX_M

2014

1000 NAC

7_2

FR_EX_M_2014_1000 NAC_7_2

EX_M

2014

1000 NAC

7_3

FR_EX_M_2014_1000 NAC_7_3

EX_M

2014

1000 NAC

7_3_1

FR_EX_M_2014_1000 NAC_7_3_1

EX_M

2014

1000 NAC

7_3_2

FR_EX_M_2014_1000 NAC_7_3_2

EX_M

2014

1000 NAC

7_3_3

FR_EX_M_2014_1000 NAC_7_3_3

EX_M

2014

1000 NAC

7_3_4

FR_EX_M_2014_1000 NAC_7_3_4

EX_M

2014

1000 NAC

7_4

FR_EX_M_2014_1000 NAC_7_4

EX_M

2014

1000 NAC

FR_EX_M_2014_1000 NAC_8

EX_M

2014

1000 NAC

8_1

FR_EX_M_2014_1000 NAC_8_1

EX_M

2014

1000 NAC

8_2

FR_EX_M_2014_1000 NAC_8_2

EX_M

2014

1000 NAC

FR_EX_M_2014_1000 NAC_9

EX_M

2014

1000 NAC

FR_EX_M_2014_1000 NAC_10

EX_M

2014

1000 NAC

10_1

FR_EX_M_2014_1000 NAC_10_1

EX_M

2014

1000 NAC

10_1_1

FR_EX_M_2014_1000 NAC_10_1_1

EX_M

2014

1000 NAC

10_1_2

FR_EX_M_2014_1000 NAC_10_1_2

EX_M

2014

1000 NAC

10_1_3

FR_EX_M_2014_1000 NAC_10_1_3

EX_M

2014

1000 NAC

10_1_4

FR_EX_M_2014_1000 NAC_10_1_4

EX_M

2014

1000 NAC

10_2

FR_EX_M_2014_1000 NAC_10_2

EX_M

2014

1000 NAC

10_3

FR_EX_M_2014_1000 NAC_10_3

EX_M

2014

1000 NAC

10_3_1

FR_EX_M_2014_1000 NAC_10_3_1

EX_M

2014

1000 NAC

10_3_2

FR_EX_M_2014_1000 NAC_10_3_2

EX_M

2014

1000 NAC

10_3_3

FR_EX_M_2014_1000 NAC_10_3_3

EX_M

2014

1000 NAC

10_3_4

FR_EX_M_2014_1000 NAC_10_3_4

EX_M

2014

1000 NAC

10_4

FR_EX_M_2014_1000 NAC_10_4

EX_X

2014

1000 m3

FR_EX_X_2014_1000 m3_1

EX_X

2014

1000 m3

1_1

FR_EX_X_2014_1000 m3_1_1

EX_X

2014

1000 m3

1_2

FR_EX_X_2014_1000 m3_1_2

EX_X

2014

1000 m3

1_2_C

FR_EX_X_2014_1000 m3_1_2_C

EX_X

2014

1000 m3

1_2_NC

FR_EX_X_2014_1000 m3_1_2_NC

EX_X

2014

1000 m3

1_2_NC_T

FR_EX_X_2014_1000 m3_1_2_NC_T

EX_X

2014

1000 mt

FR_EX_X_2014_1000 mt_2

EX_X

2014

1000 m3

FR_EX_X_2014_1000 m3_3

EX_X

2014

1000 m3

3_1

FR_EX_X_2014_1000 m3_3_1

EX_X

2014

1000 m3

3_2

FR_EX_X_2014_1000 m3_3_2

EX_X

2014

1000 mt

FR_EX_X_2014_1000 mt_4

EX_X

2014

1000 mt

4_1

FR_EX_X_2014_1000 mt_4_1

EX_X

2014

1000 mt

4_2

FR_EX_X_2014_1000 mt_4_2

EX_X

2014

1000 m3

FR_EX_X_2014_1000 m3_5

EX_X

2014

1000 m3

5_C

FR_EX_X_2014_1000 m3_5_C

EX_X

2014

1000 m3

5_NC

FR_EX_X_2014_1000 m3_5_NC

EX_X

2014

1000 m3

5_NC_T

FR_EX_X_2014_1000 m3_5_NC_T

EX_X

2014

1000 m3

FR_EX_X_2014_1000 m3_6

EX_X

2014

1000 m3

6_1

FR_EX_X_2014_1000 m3_6_1

EX_X

2014

1000 m3

6_1_C

FR_EX_X_2014_1000 m3_6_1_C

EX_X

2014

1000 m3

6_1_NC

FR_EX_X_2014_1000 m3_6_1_NC

EX_X

2014

1000 m3

6_1_NC_T

FR_EX_X_2014_1000 m3_6_1_NC_T

EX_X

2014

1000 m3

6_2

FR_EX_X_2014_1000 m3_6_2

EX_X

2014

1000 m3

6_2_C

FR_EX_X_2014_1000 m3_6_2_C

EX_X

2014

1000 m3

6_2_NC

FR_EX_X_2014_1000 m3_6_2_NC

EX_X

2014

1000 m3

6_2_NC_T

FR_EX_X_2014_1000 m3_6_2_NC_T

EX_X

2014

1000 m3

6_3

FR_EX_X_2014_1000 m3_6_3

EX_X

2014

1000 m3

6_3_1

FR_EX_X_2014_1000 m3_6_3_1

EX_X

2014

1000 m3

6_4

FR_EX_X_2014_1000 m3_6_4

EX_X

2014

1000 m3

6_4_1

FR_EX_X_2014_1000 m3_6_4_1

EX_X

2014

1000 m3

6_4_2

FR_EX_X_2014_1000 m3_6_4_2

EX_X

2014

1000 m3

6_4_3

FR_EX_X_2014_1000 m3_6_4_3

EX_X

2014

1000 mt

FR_EX_X_2014_1000 mt_7

EX_X

2014

1000 mt

7_1

FR_EX_X_2014_1000 mt_7_1

EX_X

2014

1000 mt

7_2

FR_EX_X_2014_1000 mt_7_2

EX_X

2014

1000 mt

7_3

FR_EX_X_2014_1000 mt_7_3

EX_X

2014

1000 mt

7_3_1

FR_EX_X_2014_1000 mt_7_3_1

EX_X

2014

1000 mt

7_3_2

FR_EX_X_2014_1000 mt_7_3_2

EX_X

2014

1000 mt

7_3_3

FR_EX_X_2014_1000 mt_7_3_3

EX_X

2014

1000 mt

7_3_4

FR_EX_X_2014_1000 mt_7_3_4

EX_X

2014

1000 mt

7_4

FR_EX_X_2014_1000 mt_7_4

EX_X

2014

1000 mt

FR_EX_X_2014_1000 mt_8

EX_X

2014

1000 mt

8_1

FR_EX_X_2014_1000 mt_8_1

EX_X

2014

1000 mt

8_2

FR_EX_X_2014_1000 mt_8_2

EX_X

2014

1000 mt

FR_EX_X_2014_1000 mt_9

EX_X

2014

1000 mt

FR_EX_X_2014_1000 mt_10

EX_X

2014

1000 mt

10_1

FR_EX_X_2014_1000 mt_10_1

EX_X

2014

1000 mt

10_1_1

FR_EX_X_2014_1000 mt_10_1_1

EX_X

2014

1000 mt

10_1_2

FR_EX_X_2014_1000 mt_10_1_2

EX_X

2014

1000 mt

10_1_3

FR_EX_X_2014_1000 mt_10_1_3

EX_X

2014

1000 mt

10_1_4

FR_EX_X_2014_1000 mt_10_1_4

EX_X

2014

1000 mt

10_2

FR_EX_X_2014_1000 mt_10_2

EX_X

2014

1000 mt

10_3

FR_EX_X_2014_1000 mt_10_3

EX_X

2014

1000 mt

10_3_1

FR_EX_X_2014_1000 mt_10_3_1

EX_X

2014

1000 mt

10_3_2

FR_EX_X_2014_1000 mt_10_3_2

EX_X

2014

1000 mt

10_3_3

FR_EX_X_2014_1000 mt_10_3_3

EX_X

2014

1000 mt

10_3_4

FR_EX_X_2014_1000 mt_10_3_4

EX_X

2014

1000 mt

10_4

FR_EX_X_2014_1000 mt_10_4

EX_X

2014

1000 NAC

FR_EX_X_2014_1000 NAC_1

EX_X

2014

1000 NAC

1_1

FR_EX_X_2014_1000 NAC_1_1

EX_X

2014

1000 NAC

1_2

FR_EX_X_2014_1000 NAC_1_2

EX_X

2014

1000 NAC

1_2_C

FR_EX_X_2014_1000 NAC_1_2_C

EX_X

2014

1000 NAC

1_2_NC

FR_EX_X_2014_1000 NAC_1_2_NC

EX_X

2014

1000 NAC

1_2_NC_T

FR_EX_X_2014_1000 NAC_1_2_NC_T

EX_X

2014

1000 NAC

FR_EX_X_2014_1000 NAC_2

EX_X

2014

1000 NAC

FR_EX_X_2014_1000 NAC_3

EX_X

2014

1000 NAC

3_1

FR_EX_X_2014_1000 NAC_3_1

EX_X

2014

1000 NAC

3_2

FR_EX_X_2014_1000 NAC_3_2

EX_X

2014

1000 NAC

FR_EX_X_2014_1000 NAC_4

EX_X

2014

1000 NAC

4_1

FR_EX_X_2014_1000 NAC_4_1

EX_X

2014

1000 NAC

4_2

FR_EX_X_2014_1000 NAC_4_2

EX_X

2014

1000 NAC

FR_EX_X_2014_1000 NAC_5

EX_X

2014

1000 NAC

5_C

FR_EX_X_2014_1000 NAC_5_C

EX_X

2014

1000 NAC

5_NC

FR_EX_X_2014_1000 NAC_5_NC

EX_X

2014

1000 NAC

5_NC_T

FR_EX_X_2014_1000 NAC_5_NC_T

EX_X

2014

1000 NAC

FR_EX_X_2014_1000 NAC_6

EX_X

2014

1000 NAC

6_1

FR_EX_X_2014_1000 NAC_6_1

EX_X

2014

1000 NAC

6_1_C

FR_EX_X_2014_1000 NAC_6_1_C

EX_X

2014

1000 NAC

6_1_NC

FR_EX_X_2014_1000 NAC_6_1_NC

EX_X

2014

1000 NAC

6_1_NC_T

FR_EX_X_2014_1000 NAC_6_1_NC_T

EX_X

2014

1000 NAC

6_2

FR_EX_X_2014_1000 NAC_6_2

EX_X

2014

1000 NAC

6_2_C

FR_EX_X_2014_1000 NAC_6_2_C

EX_X

2014

1000 NAC

6_2_NC

FR_EX_X_2014_1000 NAC_6_2_NC

EX_X

2014

1000 NAC

6_2_NC_T

FR_EX_X_2014_1000 NAC_6_2_NC_T

EX_X

2014

1000 NAC

6_3

FR_EX_X_2014_1000 NAC_6_3

EX_X

2014

1000 NAC

6_3_1

FR_EX_X_2014_1000 NAC_6_3_1

EX_X

2014

1000 NAC

6_4

FR_EX_X_2014_1000 NAC_6_4

EX_X

2014

1000 NAC

6_4_1

FR_EX_X_2014_1000 NAC_6_4_1

EX_X

2014

1000 NAC

6_4_2

FR_EX_X_2014_1000 NAC_6_4_2

EX_X

2014

1000 NAC

6_4_3

FR_EX_X_2014_1000 NAC_6_4_3

EX_X

2014

1000 NAC

FR_EX_X_2014_1000 NAC_7

EX_X

2014

1000 NAC

7_1

FR_EX_X_2014_1000 NAC_7_1

EX_X

2014

1000 NAC

7_2

FR_EX_X_2014_1000 NAC_7_2

EX_X

2014

1000 NAC

7_3

FR_EX_X_2014_1000 NAC_7_3

EX_X

2014

1000 NAC

7_3_1

FR_EX_X_2014_1000 NAC_7_3_1

EX_X

2014

1000 NAC

7_3_2

FR_EX_X_2014_1000 NAC_7_3_2

EX_X

2014

1000 NAC

7_3_3

FR_EX_X_2014_1000 NAC_7_3_3

EX_X

2014

1000 NAC

7_3_4

FR_EX_X_2014_1000 NAC_7_3_4

EX_X

2014

1000 NAC

7_4

FR_EX_X_2014_1000 NAC_7_4

EX_X

2014

1000 NAC

FR_EX_X_2014_1000 NAC_8

EX_X

2014

1000 NAC

8_1

FR_EX_X_2014_1000 NAC_8_1

EX_X

2014

1000 NAC

8_2

FR_EX_X_2014_1000 NAC_8_2

EX_X

2014

1000 NAC

FR_EX_X_2014_1000 NAC_9

EX_X

2014

1000 NAC

FR_EX_X_2014_1000 NAC_10

EX_X

2014

1000 NAC

10_1

FR_EX_X_2014_1000 NAC_10_1

EX_X

2014

1000 NAC

10_1_1

FR_EX_X_2014_1000 NAC_10_1_1

EX_X

2014

1000 NAC

10_1_2

FR_EX_X_2014_1000 NAC_10_1_2

EX_X

2014

1000 NAC

10_1_3

FR_EX_X_2014_1000 NAC_10_1_3

EX_X

2014

1000 NAC

10_1_4

FR_EX_X_2014_1000 NAC_10_1_4

EX_X

2014

1000 NAC

10_2

FR_EX_X_2014_1000 NAC_10_2

EX_X

2014

1000 NAC

10_3

FR_EX_X_2014_1000 NAC_10_3

EX_X

2014

1000 NAC

10_3_1

FR_EX_X_2014_1000 NAC_10_3_1

EX_X

2014

1000 NAC

10_3_2

FR_EX_X_2014_1000 NAC_10_3_2

EX_X

2014

1000 NAC

10_3_3

FR_EX_X_2014_1000 NAC_10_3_3

EX_X

2014

1000 NAC

10_3_4

FR_EX_X_2014_1000 NAC_10_3_4

EX_X

2014

1000 NAC

10_4

FR_EX_X_2014_1000 NAC_10_4

2014

1000 m3

EU2_1

FR_P_2014_1000 m3_EU2_1

2014

1000 m3

EU2_1_C

FR_P_2014_1000 m3_EU2_1_C

2014

1000 m3

EU2_1_NC

FR_P_2014_1000 m3_EU2_1_NC

2014

1000 m3

EU2_1_1

FR_P_2014_1000 m3_EU2_1_1

2014

1000 m3

EU2_1_1_C

FR_P_2014_1000 m3_EU2_1_1_C

2014

1000 m3

EU2_1_1_NC

FR_P_2014_1000 m3_EU2_1_1_NC

2014

1000 m3

EU2_1_2

FR_P_2014_1000 m3_EU2_1_2

2014

1000 m3

EU2_1_2_C

FR_P_2014_1000 m3_EU2_1_2_C

2014

1000 m3

EU2_1_2_NC

FR_P_2014_1000 m3_EU2_1_2_NC

2014

1000 m3

EU2_1_3

FR_P_2014_1000 m3_EU2_1_3

2014

1000 m3

EU2_1_3_C

FR_P_2014_1000 m3_EU2_1_3_C

2014

1000 m3

EU2_1_3_NC

FR_P_2014_1000 m3_EU2_1_3_NC

P.OB

2014

1000 m3

FR_P.OB_2014_1000 m3_1

P.OB

2014

1000 m3

1_C

FR_P.OB_2014_1000 m3_1_C

P.OB

2014

1000 m3

1_NC

FR_P.OB_2014_1000 m3_1_NC

P.OB

2014

1000 m3

1_1

FR_P.OB_2014_1000 m3_1_1

P.OB

2014

1000 m3

1_1_C

FR_P.OB_2014_1000 m3_1_1_C

P.OB

2014

1000 m3

1_1_NC

FR_P.OB_2014_1000 m3_1_1_NC

P.OB

2014

1000 m3

1_2

FR_P.OB_2014_1000 m3_1_2

P.OB

2014

1000 m3

1_2_C

FR_P.OB_2014_1000 m3_1_2_C

P.OB

2014

1000 m3

1_2_NC

FR_P.OB_2014_1000 m3_1_2_NC

P.OB

2014

1000 m3

1_2_1

FR_P.OB_2014_1000 m3_1_2_1

P.OB

2014

1000 m3

1_2_1_C

FR_P.OB_2014_1000 m3_1_2_1_C

P.OB

2014

1000 m3

1_2_1_NC

FR_P.OB_2014_1000 m3_1_2_1_NC

P.OB

2014

1000 m3

1_2_2

FR_P.OB_2014_1000 m3_1_2_2

P.OB

2014

1000 m3

1_2_2_C

FR_P.OB_2014_1000 m3_1_2_2_C

P.OB

2014

1000 m3

1_2_2_NC

FR_P.OB_2014_1000 m3_1_2_2_NC

P.OB

2014

1000 m3

1_2_3

FR_P.OB_2014_1000 m3_1_2_3

P.OB

2014

1000 m3

1_2_3_C

FR_P.OB_2014_1000 m3_1_2_3_C

P.OB

2014

1000 m3

1_2_3_NC

FR_P.OB_2014_1000 m3_1_2_3_NC

2013

1000 m3

FR_P_2013_1000 m3_1

2013

1000 m3

1_C

FR_P_2013_1000 m3_1_C

2013

1000 m3

1_NC

FR_P_2013_1000 m3_1_NC

2013

1000 m3

1_1

FR_P_2013_1000 m3_1_1

2013

1000 m3

1_1_C

FR_P_2013_1000 m3_1_1_C

2013

1000 m3

1_1_NC

FR_P_2013_1000 m3_1_1_NC

2013

1000 m3

1_2

FR_P_2013_1000 m3_1_2

2013

1000 m3

1_2_C

FR_P_2013_1000 m3_1_2_C

2013

1000 m3

1_2_NC

FR_P_2013_1000 m3_1_2_NC

2013

1000 m3

1_2_1

FR_P_2013_1000 m3_1_2_1

2013

1000 m3

1_2_1_C

FR_P_2013_1000 m3_1_2_1_C

2013

1000 m3

1_2_1_NC

FR_P_2013_1000 m3_1_2_1_NC

2013

1000 m3

1_2_2

FR_P_2013_1000 m3_1_2_2

2013

1000 m3

1_2_2_C

FR_P_2013_1000 m3_1_2_2_C

2013

1000 m3

1_2_2_NC

FR_P_2013_1000 m3_1_2_2_NC

2013

1000 m3

1_2_3

FR_P_2013_1000 m3_1_2_3

2013

1000 m3

1_2_3_C

FR_P_2013_1000 m3_1_2_3_C

2013

1000 m3

1_2_3_NC

FR_P_2013_1000 m3_1_2_3_NC

2013

1000 mt

FR_P_2013_1000 mt_2

2013

1000 m3

FR_P_2013_1000 m3_3

2013

1000 m3

3_1

FR_P_2013_1000 m3_3_1

2013

1000 m3

3_2

FR_P_2013_1000 m3_3_2

2013

1000 mt

FR_P_2013_1000 mt_4

2013

1000 mt

4_1

FR_P_2013_1000 mt_4_1

2013

1000 mt

4_2

FR_P_2013_1000 mt_4_2

2013

1000 m3

FR_P_2013_1000 m3_5

2013

1000 m3

5_C

FR_P_2013_1000 m3_5_C

2013

1000 m3

5_NC

FR_P_2013_1000 m3_5_NC

2013

1000 m3

5_NC_T

FR_P_2013_1000 m3_5_NC_T

2013

1000 m3

FR_P_2013_1000 m3_6

2013

1000 m3

6_1

FR_P_2013_1000 m3_6_1

2013

1000 m3

6_1_C

FR_P_2013_1000 m3_6_1_C

2013

1000 m3

6_1_NC

FR_P_2013_1000 m3_6_1_NC

2013

1000 m3

6_1_NC_T

FR_P_2013_1000 m3_6_1_NC_T

2013

1000 m3

6_2

FR_P_2013_1000 m3_6_2

2013

1000 m3

6_2_C

FR_P_2013_1000 m3_6_2_C

2013

1000 m3

6_2_NC

FR_P_2013_1000 m3_6_2_NC

2013

1000 m3

6_2_NC_T

FR_P_2013_1000 m3_6_2_NC_T

2013

1000 m3

6_3

FR_P_2013_1000 m3_6_3

2013

1000 m3

6_3_1

FR_P_2013_1000 m3_6_3_1

2013

1000 m3

6_4

FR_P_2013_1000 m3_6_4

2013

1000 m3

6_4_1

FR_P_2013_1000 m3_6_4_1

2013

1000 m3

6_4_2

FR_P_2013_1000 m3_6_4_2

2013

1000 m3

6_4_3

FR_P_2013_1000 m3_6_4_3

2013

1000 mt

FR_P_2013_1000 mt_7

2013

1000 mt

7_1

FR_P_2013_1000 mt_7_1

2013

1000 mt

7_2

FR_P_2013_1000 mt_7_2

2013

1000 mt

7_3

FR_P_2013_1000 mt_7_3

2013

1000 mt

7_3_1

FR_P_2013_1000 mt_7_3_1

2013

1000 mt

7_3_2

FR_P_2013_1000 mt_7_3_2

2013

1000 mt

7_3_3

FR_P_2013_1000 mt_7_3_3

2013

1000 mt

7_3_4

FR_P_2013_1000 mt_7_3_4

2013

1000 mt

7_4

FR_P_2013_1000 mt_7_4

2013

1000 mt

FR_P_2013_1000 mt_8

2013

1000 mt

8_1

FR_P_2013_1000 mt_8_1

2013

1000 mt

8_2

FR_P_2013_1000 mt_8_2

2013

1000 mt

FR_P_2013_1000 mt_9

2013

1000 mt

FR_P_2013_1000 mt_10

2013

1000 mt

10_1

FR_P_2013_1000 mt_10_1

2013

1000 mt

10_1_1

FR_P_2013_1000 mt_10_1_1

2013

1000 mt

10_1_2

FR_P_2013_1000 mt_10_1_2

2013

1000 mt

10_1_3

FR_P_2013_1000 mt_10_1_3

2013

1000 mt

10_1_4

FR_P_2013_1000 mt_10_1_4

2013

1000 mt

10_2

FR_P_2013_1000 mt_10_2

2013

1000 mt

10_3

FR_P_2013_1000 mt_10_3

2013

1000 mt

10_3_1

FR_P_2013_1000 mt_10_3_1

2013

1000 mt

10_3_2

FR_P_2013_1000 mt_10_3_2

2013

1000 mt

10_3_3

FR_P_2013_1000 mt_10_3_3

2013

1000 mt

10_3_4

FR_P_2013_1000 mt_10_3_4

2013

1000 mt

10_4

FR_P_2013_1000 mt_10_4

2013

1000 m3

FR_M_2013_1000 m3_1

2013

1000 m3

1_1

FR_M_2013_1000 m3_1_1

2013

1000 m3

1_2

FR_M_2013_1000 m3_1_2

2013

1000 m3

1_2_C

FR_M_2013_1000 m3_1_2_C

2013

1000 m3

1_2_NC

FR_M_2013_1000 m3_1_2_NC

2013

1000 m3

1_2_NC_T

FR_M_2013_1000 m3_1_2_NC_T

2013

1000 mt

FR_M_2013_1000 mt_2

2013

1000 m3

FR_M_2013_1000 m3_3

2013

1000 m3

3_1

FR_M_2013_1000 m3_3_1

2013

1000 m3

3_2

FR_M_2013_1000 m3_3_2

2013

1000 mt

FR_M_2013_1000 mt_4

2013

1000 mt

4_1

FR_M_2013_1000 mt_4_1

2013

1000 mt

4_2

FR_M_2013_1000 mt_4_2

2013

1000 m3

FR_M_2013_1000 m3_5

2013

1000 m3

5_C

FR_M_2013_1000 m3_5_C

2013

1000 m3

5_NC

FR_M_2013_1000 m3_5_NC

2013

1000 m3

5_NC_T

FR_M_2013_1000 m3_5_NC_T

2013

1000 m3

FR_M_2013_1000 m3_6

2013

1000 m3

6_1

FR_M_2013_1000 m3_6_1

2013

1000 m3

6_1_C

FR_M_2013_1000 m3_6_1_C

2013

1000 m3

6_1_NC

FR_M_2013_1000 m3_6_1_NC

2013

1000 m3

6_1_NC_T

FR_M_2013_1000 m3_6_1_NC_T

2013

1000 m3

6_2

FR_M_2013_1000 m3_6_2

2013

1000 m3

6_2_C

FR_M_2013_1000 m3_6_2_C

2013

1000 m3

6_2_NC

FR_M_2013_1000 m3_6_2_NC

2013

1000 m3

6_2_NC_T

FR_M_2013_1000 m3_6_2_NC_T

2013

1000 m3

6_3

FR_M_2013_1000 m3_6_3

2013

1000 m3

6_3_1

FR_M_2013_1000 m3_6_3_1

2013

1000 m3

6_4

FR_M_2013_1000 m3_6_4

2013

1000 m3

6_4_1

FR_M_2013_1000 m3_6_4_1

2013

1000 m3

6_4_2

FR_M_2013_1000 m3_6_4_2

2013

1000 m3

6_4_3

FR_M_2013_1000 m3_6_4_3

2013

1000 mt

FR_M_2013_1000 mt_7

2013

1000 mt

7_1

FR_M_2013_1000 mt_7_1

2013

1000 mt

7_2

FR_M_2013_1000 mt_7_2

2013

1000 mt

7_3

FR_M_2013_1000 mt_7_3

2013

1000 mt

7_3_1

FR_M_2013_1000 mt_7_3_1

2013

1000 mt

7_3_2

FR_M_2013_1000 mt_7_3_2

2013

1000 mt

7_3_3

FR_M_2013_1000 mt_7_3_3

2013

1000 mt

7_3_4

FR_M_2013_1000 mt_7_3_4

2013

1000 mt

7_4

FR_M_2013_1000 mt_7_4

2013

1000 mt

FR_M_2013_1000 mt_8

2013

1000 mt

8_1

FR_M_2013_1000 mt_8_1

2013

1000 mt

8_2

FR_M_2013_1000 mt_8_2

2013

1000 mt

FR_M_2013_1000 mt_9

2013

1000 mt

FR_M_2013_1000 mt_10

2013

1000 mt

10_1

FR_M_2013_1000 mt_10_1

2013

1000 mt

10_1_1

FR_M_2013_1000 mt_10_1_1

2013

1000 mt

10_1_2

FR_M_2013_1000 mt_10_1_2

2013

1000 mt

10_1_3

FR_M_2013_1000 mt_10_1_3

2013

1000 mt

10_1_4

FR_M_2013_1000 mt_10_1_4

2013

1000 mt

10_2

FR_M_2013_1000 mt_10_2

2013

1000 mt

10_3

FR_M_2013_1000 mt_10_3

2013

1000 mt

10_3_1

FR_M_2013_1000 mt_10_3_1

2013

1000 mt

10_3_2

FR_M_2013_1000 mt_10_3_2

2013

1000 mt

10_3_3

FR_M_2013_1000 mt_10_3_3

2013

1000 mt

10_3_4

FR_M_2013_1000 mt_10_3_4

2013

1000 mt

10_4

FR_M_2013_1000 mt_10_4

2013

1000 NAC

FR_M_2013_1000 NAC_1

2013

1000 NAC

1_1

FR_M_2013_1000 NAC_1_1

2013

1000 NAC

1_2

FR_M_2013_1000 NAC_1_2

2013

1000 NAC

1_2_C

FR_M_2013_1000 NAC_1_2_C

2013

1000 NAC

1_2_NC

FR_M_2013_1000 NAC_1_2_NC

2013

1000 NAC

1_2_NC_T

FR_M_2013_1000 NAC_1_2_NC_T

2013

1000 NAC

FR_M_2013_1000 NAC_2

2013

1000 NAC

FR_M_2013_1000 NAC_3

2013

1000 NAC

3_1

FR_M_2013_1000 NAC_3_1

2013

1000 NAC

3_2

FR_M_2013_1000 NAC_3_2

2013

1000 NAC

FR_M_2013_1000 NAC_4

2013

1000 NAC

4_1

FR_M_2013_1000 NAC_4_1

2013

1000 NAC

4_2

FR_M_2013_1000 NAC_4_2

2013

1000 NAC

FR_M_2013_1000 NAC_5

2013

1000 NAC

5_C

FR_M_2013_1000 NAC_5_C

2013

1000 NAC

5_NC

FR_M_2013_1000 NAC_5_NC

2013

1000 NAC

5_NC_T

FR_M_2013_1000 NAC_5_NC_T

2013

1000 NAC

FR_M_2013_1000 NAC_6

2013

1000 NAC

6_1

FR_M_2013_1000 NAC_6_1

2013

1000 NAC

6_1_C

FR_M_2013_1000 NAC_6_1_C

2013

1000 NAC

6_1_NC

FR_M_2013_1000 NAC_6_1_NC

2013

1000 NAC

6_1_NC_T

FR_M_2013_1000 NAC_6_1_NC_T

2013

1000 NAC

6_2

FR_M_2013_1000 NAC_6_2

2013

1000 NAC

6_2_C

FR_M_2013_1000 NAC_6_2_C

2013

1000 NAC

6_2_NC

FR_M_2013_1000 NAC_6_2_NC

2013

1000 NAC

6_2_NC_T

FR_M_2013_1000 NAC_6_2_NC_T

2013

1000 NAC

6_3

FR_M_2013_1000 NAC_6_3

2013

1000 NAC

6_3_1

FR_M_2013_1000 NAC_6_3_1

2013

1000 NAC

6_4

FR_M_2013_1000 NAC_6_4

2013

1000 NAC

6_4_1

FR_M_2013_1000 NAC_6_4_1

2013

1000 NAC

6_4_2

FR_M_2013_1000 NAC_6_4_2

2013

1000 NAC

6_4_3

FR_M_2013_1000 NAC_6_4_3

2013

1000 NAC

FR_M_2013_1000 NAC_7

2013

1000 NAC

7_1

FR_M_2013_1000 NAC_7_1

2013

1000 NAC

7_2

FR_M_2013_1000 NAC_7_2

2013

1000 NAC

7_3

FR_M_2013_1000 NAC_7_3

2013

1000 NAC

7_3_1

FR_M_2013_1000 NAC_7_3_1

2013

1000 NAC

7_3_2

FR_M_2013_1000 NAC_7_3_2

2013

1000 NAC

7_3_3

FR_M_2013_1000 NAC_7_3_3

2013

1000 NAC

7_3_4

FR_M_2013_1000 NAC_7_3_4

2013

1000 NAC

7_4

FR_M_2013_1000 NAC_7_4

2013

1000 NAC

FR_M_2013_1000 NAC_8

2013

1000 NAC

8_1

FR_M_2013_1000 NAC_8_1

2013

1000 NAC

8_2

FR_M_2013_1000 NAC_8_2

2013

1000 NAC

FR_M_2013_1000 NAC_9

2013

1000 NAC

FR_M_2013_1000 NAC_10

2013

1000 NAC

10_1

FR_M_2013_1000 NAC_10_1

2013

1000 NAC

10_1_1

FR_M_2013_1000 NAC_10_1_1

2013

1000 NAC

10_1_2

FR_M_2013_1000 NAC_10_1_2

2013

1000 NAC

10_1_3

FR_M_2013_1000 NAC_10_1_3

2013

1000 NAC

10_1_4

FR_M_2013_1000 NAC_10_1_4

2013

1000 NAC

10_2

FR_M_2013_1000 NAC_10_2

2013

1000 NAC

10_3

FR_M_2013_1000 NAC_10_3

2013

1000 NAC

10_3_1

FR_M_2013_1000 NAC_10_3_1

2013

1000 NAC

10_3_2

FR_M_2013_1000 NAC_10_3_2

2013

1000 NAC

10_3_3

FR_M_2013_1000 NAC_10_3_3

2013

1000 NAC

10_3_4

FR_M_2013_1000 NAC_10_3_4

2013

1000 NAC

10_4

FR_M_2013_1000 NAC_10_4

2013

1000 m3

FR_X_2013_1000 m3_1

2013

1000 m3

1_1

FR_X_2013_1000 m3_1_1

2013

1000 m3

1_2

FR_X_2013_1000 m3_1_2

2013

1000 m3

1_2_C

FR_X_2013_1000 m3_1_2_C

2013

1000 m3

1_2_NC

FR_X_2013_1000 m3_1_2_NC

2013

1000 m3

1_2_NC_T

FR_X_2013_1000 m3_1_2_NC_T

2013

1000 mt

FR_X_2013_1000 mt_2

2013

1000 m3

FR_X_2013_1000 m3_3

2013

1000 m3

3_1

FR_X_2013_1000 m3_3_1

2013

1000 m3

3_2

FR_X_2013_1000 m3_3_2

2013

1000 mt

FR_X_2013_1000 mt_4

2013

1000 mt

4_1

FR_X_2013_1000 mt_4_1

2013

1000 mt

4_2

FR_X_2013_1000 mt_4_2

2013

1000 m3

FR_X_2013_1000 m3_5

2013

1000 m3

5_C

FR_X_2013_1000 m3_5_C

2013

1000 m3

5_NC

FR_X_2013_1000 m3_5_NC

2013

1000 m3

5_NC_T

FR_X_2013_1000 m3_5_NC_T

2013

1000 m3

FR_X_2013_1000 m3_6

2013

1000 m3

6_1

FR_X_2013_1000 m3_6_1

2013

1000 m3

6_1_C

FR_X_2013_1000 m3_6_1_C

2013

1000 m3

6_1_NC

FR_X_2013_1000 m3_6_1_NC

2013

1000 m3

6_1_NC_T

FR_X_2013_1000 m3_6_1_NC_T

2013

1000 m3

6_2

FR_X_2013_1000 m3_6_2

2013

1000 m3

6_2_C

FR_X_2013_1000 m3_6_2_C

2013

1000 m3

6_2_NC

FR_X_2013_1000 m3_6_2_NC

2013

1000 m3

6_2_NC_T

FR_X_2013_1000 m3_6_2_NC_T

2013

1000 m3

6_3

FR_X_2013_1000 m3_6_3

2013

1000 m3

6_3_1

FR_X_2013_1000 m3_6_3_1

2013

1000 m3

6_4

FR_X_2013_1000 m3_6_4

2013

1000 m3

6_4_1

FR_X_2013_1000 m3_6_4_1

2013

1000 m3

6_4_2

FR_X_2013_1000 m3_6_4_2

2013

1000 m3

6_4_3

FR_X_2013_1000 m3_6_4_3

2013

1000 mt

FR_X_2013_1000 mt_7

2013

1000 mt

7_1

FR_X_2013_1000 mt_7_1

2013

1000 mt

7_2

FR_X_2013_1000 mt_7_2

2013

1000 mt

7_3

FR_X_2013_1000 mt_7_3

2013

1000 mt

7_3_1

FR_X_2013_1000 mt_7_3_1

2013

1000 mt

7_3_2

FR_X_2013_1000 mt_7_3_2

2013

1000 mt

7_3_3

FR_X_2013_1000 mt_7_3_3

2013

1000 mt

7_3_4

FR_X_2013_1000 mt_7_3_4

2013

1000 mt

7_4

FR_X_2013_1000 mt_7_4

2013

1000 mt

FR_X_2013_1000 mt_8

2013

1000 mt

8_1

FR_X_2013_1000 mt_8_1

2013

1000 mt

8_2

FR_X_2013_1000 mt_8_2

2013

1000 mt

FR_X_2013_1000 mt_9

2013

1000 mt

FR_X_2013_1000 mt_10

2013

1000 mt

10_1

FR_X_2013_1000 mt_10_1

2013

1000 mt

10_1_1

FR_X_2013_1000 mt_10_1_1

2013

1000 mt

10_1_2

FR_X_2013_1000 mt_10_1_2

2013

1000 mt

10_1_3

FR_X_2013_1000 mt_10_1_3

2013

1000 mt

10_1_4

FR_X_2013_1000 mt_10_1_4

2013

1000 mt

10_2

FR_X_2013_1000 mt_10_2

2013

1000 mt

10_3

FR_X_2013_1000 mt_10_3

2013

1000 mt

10_3_1

FR_X_2013_1000 mt_10_3_1

2013

1000 mt

10_3_2

FR_X_2013_1000 mt_10_3_2

2013

1000 mt

10_3_3

FR_X_2013_1000 mt_10_3_3

2013

1000 mt

10_3_4

FR_X_2013_1000 mt_10_3_4

2013

1000 mt

10_4

FR_X_2013_1000 mt_10_4

2013

1000 NAC

FR_X_2013_1000 NAC_1

2013

1000 NAC

1_1

FR_X_2013_1000 NAC_1_1

2013

1000 NAC

1_2

FR_X_2013_1000 NAC_1_2

2013

1000 NAC

1_2_C

FR_X_2013_1000 NAC_1_2_C

2013

1000 NAC

1_2_NC

FR_X_2013_1000 NAC_1_2_NC

2013

1000 NAC

1_2_NC_T

FR_X_2013_1000 NAC_1_2_NC_T

2013

1000 NAC

FR_X_2013_1000 NAC_2

2013

1000 NAC

FR_X_2013_1000 NAC_3

2013

1000 NAC

3_1

FR_X_2013_1000 NAC_3_1

2013

1000 NAC

3_2

FR_X_2013_1000 NAC_3_2

2013

1000 NAC

FR_X_2013_1000 NAC_4

2013

1000 NAC

4_1

FR_X_2013_1000 NAC_4_1

2013

1000 NAC

4_2

FR_X_2013_1000 NAC_4_2

2013

1000 NAC

FR_X_2013_1000 NAC_5

2013

1000 NAC

5_C

FR_X_2013_1000 NAC_5_C

2013

1000 NAC

5_NC

FR_X_2013_1000 NAC_5_NC

2013

1000 NAC

5_NC_T

FR_X_2013_1000 NAC_5_NC_T

2013

1000 NAC

FR_X_2013_1000 NAC_6

2013

1000 NAC

6_1

FR_X_2013_1000 NAC_6_1

2013

1000 NAC

6_1_C

FR_X_2013_1000 NAC_6_1_C

2013

1000 NAC

6_1_NC

FR_X_2013_1000 NAC_6_1_NC

2013

1000 NAC

6_1_NC_T

FR_X_2013_1000 NAC_6_1_NC_T

2013

1000 NAC

6_2

FR_X_2013_1000 NAC_6_2

2013

1000 NAC

6_2_C

FR_X_2013_1000 NAC_6_2_C

2013

1000 NAC

6_2_NC

FR_X_2013_1000 NAC_6_2_NC

2013

1000 NAC

6_2_NC_T

FR_X_2013_1000 NAC_6_2_NC_T

2013

1000 NAC

6_3

FR_X_2013_1000 NAC_6_3

2013

1000 NAC

6_3_1

FR_X_2013_1000 NAC_6_3_1

2013

1000 NAC

6_4

FR_X_2013_1000 NAC_6_4

2013

1000 NAC

6_4_1

FR_X_2013_1000 NAC_6_4_1

2013

1000 NAC

6_4_2

FR_X_2013_1000 NAC_6_4_2

2013

1000 NAC

6_4_3

FR_X_2013_1000 NAC_6_4_3

2013

1000 NAC

FR_X_2013_1000 NAC_7

2013

1000 NAC

7_1

FR_X_2013_1000 NAC_7_1

2013

1000 NAC

7_2

FR_X_2013_1000 NAC_7_2

2013

1000 NAC

7_3

FR_X_2013_1000 NAC_7_3

2013

1000 NAC

7_3_1

FR_X_2013_1000 NAC_7_3_1

2013

1000 NAC

7_3_2

FR_X_2013_1000 NAC_7_3_2

2013

1000 NAC

7_3_3

FR_X_2013_1000 NAC_7_3_3

2013

1000 NAC

7_3_4

FR_X_2013_1000 NAC_7_3_4

2013

1000 NAC

7_4

FR_X_2013_1000 NAC_7_4

2013

1000 NAC

FR_X_2013_1000 NAC_8

2013

1000 NAC

8_1

FR_X_2013_1000 NAC_8_1

2013

1000 NAC

8_2

FR_X_2013_1000 NAC_8_2

2013

1000 NAC

FR_X_2013_1000 NAC_9

2013

1000 NAC

FR_X_2013_1000 NAC_10

2013

1000 NAC

10_1

FR_X_2013_1000 NAC_10_1

2013

1000 NAC

10_1_1

FR_X_2013_1000 NAC_10_1_1

2013

1000 NAC

10_1_2

FR_X_2013_1000 NAC_10_1_2

2013

1000 NAC

10_1_3

FR_X_2013_1000 NAC_10_1_3

2013

1000 NAC

10_1_4

FR_X_2013_1000 NAC_10_1_4

2013

1000 NAC

10_2

FR_X_2013_1000 NAC_10_2

2013

1000 NAC

10_3

FR_X_2013_1000 NAC_10_3

2013

1000 NAC

10_3_1

FR_X_2013_1000 NAC_10_3_1

2013

1000 NAC

10_3_2

FR_X_2013_1000 NAC_10_3_2

2013

1000 NAC

10_3_3

FR_X_2013_1000 NAC_10_3_3

2013

1000 NAC

10_3_4

FR_X_2013_1000 NAC_10_3_4

2013

1000 NAC

10_4

FR_X_2013_1000 NAC_10_4

2013

1000 NAC

11_1

FR_M_2013_1000 NAC_11_1

2013

1000 NAC

11_1_C

FR_M_2013_1000 NAC_11_1_C

2013

1000 NAC

11_1_NC

FR_M_2013_1000 NAC_11_1_NC

2013

1000 NAC

11_1_NC_T

FR_M_2013_1000 NAC_11_1_NC_T

2013

1000 NAC

11_2

FR_M_2013_1000 NAC_11_2

2013

1000 NAC

11_3

FR_M_2013_1000 NAC_11_3

2013

1000 NAC

11_4

FR_M_2013_1000 NAC_11_4

2013

1000 NAC

11_5

FR_M_2013_1000 NAC_11_5

2013

1000 NAC

11_6

FR_M_2013_1000 NAC_11_6

2013

1000 NAC

11_7

FR_M_2013_1000 NAC_11_7

2013

1000 NAC

11_7_1

FR_M_2013_1000 NAC_11_7_1

2013

1000 NAC

12_1

FR_M_2013_1000 NAC_12_1

2013

1000 NAC

12_2

FR_M_2013_1000 NAC_12_2

2013

1000 NAC

12_3

FR_M_2013_1000 NAC_12_3

2013

1000 NAC

12_4

FR_M_2013_1000 NAC_12_4

2013

1000 NAC

12_5

FR_M_2013_1000 NAC_12_5

2013

1000 NAC

12_6

FR_M_2013_1000 NAC_12_6

2013

1000 NAC

12_6_1

FR_M_2013_1000 NAC_12_6_1

2013

1000 NAC

12_6_2

FR_M_2013_1000 NAC_12_6_2

2013

1000 NAC

12_6_3

FR_M_2013_1000 NAC_12_6_3

2013

1000 NAC

12_7

FR_M_2013_1000 NAC_12_7

2013

1000 NAC

12_7_1

FR_M_2013_1000 NAC_12_7_1

2013

1000 NAC

12_7_2

FR_M_2013_1000 NAC_12_7_2

2013

1000 NAC

12_7_3

FR_M_2013_1000 NAC_12_7_3

2013

1000 NAC

11_1

FR_X_2013_1000 NAC_11_1

2013

1000 NAC

11_1_C

FR_X_2013_1000 NAC_11_1_C

2013

1000 NAC

11_1_NC

FR_X_2013_1000 NAC_11_1_NC

2013

1000 NAC

11_1_NC_T

FR_X_2013_1000 NAC_11_1_NC_T

2013

1000 NAC

11_2

FR_X_2013_1000 NAC_11_2

2013

1000 NAC

11_3

FR_X_2013_1000 NAC_11_3

2013

1000 NAC

11_4

FR_X_2013_1000 NAC_11_4

2013

1000 NAC

11_5

FR_X_2013_1000 NAC_11_5

2013

1000 NAC

11_6

FR_X_2013_1000 NAC_11_6

2013

1000 NAC

11_7

FR_X_2013_1000 NAC_11_7

2013

1000 NAC

11_7_1

FR_X_2013_1000 NAC_11_7_1

2013

1000 NAC

12_1

FR_X_2013_1000 NAC_12_1

2013

1000 NAC

12_2

FR_X_2013_1000 NAC_12_2

2013

1000 NAC

12_3

FR_X_2013_1000 NAC_12_3

2013

1000 NAC

12_4

FR_X_2013_1000 NAC_12_4

2013

1000 NAC

12_5

FR_X_2013_1000 NAC_12_5

2013

1000 NAC

12_6

FR_X_2013_1000 NAC_12_6

2013

1000 NAC

12_6_1

FR_X_2013_1000 NAC_12_6_1

2013

1000 NAC

12_6_2

FR_X_2013_1000 NAC_12_6_2

2013

1000 NAC

12_6_3

FR_X_2013_1000 NAC_12_6_3

2013

1000 NAC

12_7

FR_X_2013_1000 NAC_12_7

2013

1000 NAC

12_7_1

FR_X_2013_1000 NAC_12_7_1

2013

1000 NAC

12_7_2

FR_X_2013_1000 NAC_12_7_2

2013

1000 NAC

12_7_3

FR_X_2013_1000 NAC_12_7_3

2013

1000 m3

ST_1_2_C

FR_M_2013_1000 m3_ST_1_2_C

2013

1000 m3

ST_1_2_C_1

FR_M_2013_1000 m3_ST_1_2_C_1

2013

1000 m3

ST_1_2_C_1_1

FR_M_2013_1000 m3_ST_1_2_C_1_1

2013

1000 m3

ST_1_2_C_2_1

FR_M_2013_1000 m3_ST_1_2_C_2_1

2013

1000 m3

ST_1_2_C_2

FR_M_2013_1000 m3_ST_1_2_C_2

2013

1000 m3

ST_1_2_C_1_2

FR_M_2013_1000 m3_ST_1_2_C_1_2

2013

1000 m3

ST_1_2_C_2_2

FR_M_2013_1000 m3_ST_1_2_C_2_2

2013

1000 m3

ST_1_2_C_3

FR_M_2013_1000 m3_ST_1_2_C_3

2013

1000 m3

ST_1_2_C_1_3

FR_M_2013_1000 m3_ST_1_2_C_1_3

2013

1000 m3

ST_1_2_C_2_3

FR_M_2013_1000 m3_ST_1_2_C_2_3

2013

1000 m3

ST_1_2_NC

FR_M_2013_1000 m3_ST_1_2_NC

2013

1000 m3

ST_1_2_NC_1

FR_M_2013_1000 m3_ST_1_2_NC_1

2013

1000 m3

ST_1_2_NC_1_1

FR_M_2013_1000 m3_ST_1_2_NC_1_1

2013

1000 m3

ST_1_2_NC_2_1

FR_M_2013_1000 m3_ST_1_2_NC_2_1

2013

1000 m3

ST_1_2_NC_2

FR_M_2013_1000 m3_ST_1_2_NC_2

2013

1000 m3

ST_1_2_NC_1_2

FR_M_2013_1000 m3_ST_1_2_NC_1_2

2013

1000 m3

ST_1_2_NC_2_2

FR_M_2013_1000 m3_ST_1_2_NC_2_2

2013

1000 m3

ST_1_2_NC_3

FR_M_2013_1000 m3_ST_1_2_NC_3

2013

1000 m3

ST_1_2_NC_1_3

FR_M_2013_1000 m3_ST_1_2_NC_1_3

2013

1000 m3

ST_1_2_NC_2_3

FR_M_2013_1000 m3_ST_1_2_NC_2_3

2013

1000 m3

ST_1_2_NC_4

FR_M_2013_1000 m3_ST_1_2_NC_4

2013

1000 m3

ST_1_2_NC_5

FR_M_2013_1000 m3_ST_1_2_NC_5

2013

1000 m3

ST_5_C

FR_M_2013_1000 m3_ST_5_C

2013

1000 m3

ST_5_C_1

FR_M_2013_1000 m3_ST_5_C_1

2013

1000 m3

ST_5_C_2

FR_M_2013_1000 m3_ST_5_C_2

2013

1000 m3

ST_5_NC

FR_M_2013_1000 m3_ST_5_NC

2013

1000 m3

ST_5_NC_1

FR_M_2013_1000 m3_ST_5_NC_1

2013

1000 m3

ST_5_NC_2

FR_M_2013_1000 m3_ST_5_NC_2

2013

1000 m3

ST_5_NC_3

FR_M_2013_1000 m3_ST_5_NC_3

2013

1000 m3

ST_5_NC_4

FR_M_2013_1000 m3_ST_5_NC_4

2013

1000 m3

ST_5_NC_5

FR_M_2013_1000 m3_ST_5_NC_5

2013

1000 m3

ST_5_NC_6

FR_M_2013_1000 m3_ST_5_NC_6

2013

1000 m3

ST_5_NC_7

FR_M_2013_1000 m3_ST_5_NC_7

2013

1000 NAC

ST_1_2_C

FR_M_2013_1000 NAC_ST_1_2_C

2013

1000 NAC

ST_1_2_C_1

FR_M_2013_1000 NAC_ST_1_2_C_1

2013

1000 NAC

ST_1_2_C_1_1

FR_M_2013_1000 NAC_ST_1_2_C_1_1

2013

1000 NAC

ST_1_2_C_2_1

FR_M_2013_1000 NAC_ST_1_2_C_2_1

2013

1000 NAC

ST_1_2_C_2

FR_M_2013_1000 NAC_ST_1_2_C_2

2013

1000 NAC

ST_1_2_C_1_2

FR_M_2013_1000 NAC_ST_1_2_C_1_2

2013

1000 NAC

ST_1_2_C_2_2

FR_M_2013_1000 NAC_ST_1_2_C_2_2

2013

1000 NAC

ST_1_2_C_3

FR_M_2013_1000 NAC_ST_1_2_C_3

2013

1000 NAC

ST_1_2_C_1_3

FR_M_2013_1000 NAC_ST_1_2_C_1_3

2013

1000 NAC

ST_1_2_C_2_3

FR_M_2013_1000 NAC_ST_1_2_C_2_3

2013

1000 NAC

ST_1_2_NC

FR_M_2013_1000 NAC_ST_1_2_NC

2013

1000 NAC

ST_1_2_NC_1

FR_M_2013_1000 NAC_ST_1_2_NC_1

2013

1000 NAC

ST_1_2_NC_1_1

FR_M_2013_1000 NAC_ST_1_2_NC_1_1

2013

1000 NAC

ST_1_2_NC_2_1

FR_M_2013_1000 NAC_ST_1_2_NC_2_1

2013

1000 NAC

ST_1_2_NC_2

FR_M_2013_1000 NAC_ST_1_2_NC_2

2013

1000 NAC

ST_1_2_NC_1_2

FR_M_2013_1000 NAC_ST_1_2_NC_1_2

2013

1000 NAC

ST_1_2_NC_2_2

FR_M_2013_1000 NAC_ST_1_2_NC_2_2

2013

1000 NAC

ST_1_2_NC_3

FR_M_2013_1000 NAC_ST_1_2_NC_3

2013

1000 NAC

ST_1_2_NC_1_3

FR_M_2013_1000 NAC_ST_1_2_NC_1_3

2013

1000 NAC

ST_1_2_NC_2_3

FR_M_2013_1000 NAC_ST_1_2_NC_2_3

2013

1000 NAC

ST_1_2_NC_4

FR_M_2013_1000 NAC_ST_1_2_NC_4

2013

1000 NAC

ST_1_2_NC_5

FR_M_2013_1000 NAC_ST_1_2_NC_5

2013

1000 NAC

ST_5_C

FR_M_2013_1000 NAC_ST_5_C

2013

1000 NAC

ST_5_C_1

FR_M_2013_1000 NAC_ST_5_C_1

2013

1000 NAC

ST_5_C_2

FR_M_2013_1000 NAC_ST_5_C_2

2013

1000 NAC

ST_5_NC

FR_M_2013_1000 NAC_ST_5_NC

2013

1000 NAC

ST_5_NC_1

FR_M_2013_1000 NAC_ST_5_NC_1

2013

1000 NAC

ST_5_NC_2

FR_M_2013_1000 NAC_ST_5_NC_2

2013

1000 NAC

ST_5_NC_3

FR_M_2013_1000 NAC_ST_5_NC_3

2013

1000 NAC

ST_5_NC_4

FR_M_2013_1000 NAC_ST_5_NC_4

2013

1000 NAC

ST_5_NC_5

FR_M_2013_1000 NAC_ST_5_NC_5

2013

1000 NAC

ST_5_NC_6

FR_M_2013_1000 NAC_ST_5_NC_6

2013

1000 NAC

ST_5_NC_7

FR_M_2013_1000 NAC_ST_5_NC_7

2013

1000 m3

ST_1_2_C

FR_X_2013_1000 m3_ST_1_2_C

2013

1000 m3

ST_1_2_C_1

FR_X_2013_1000 m3_ST_1_2_C_1

2013

1000 m3

ST_1_2_C_1_1

FR_X_2013_1000 m3_ST_1_2_C_1_1

2013

1000 m3

ST_1_2_C_2_1

FR_X_2013_1000 m3_ST_1_2_C_2_1

2013

1000 m3

ST_1_2_C_2

FR_X_2013_1000 m3_ST_1_2_C_2

2013

1000 m3

ST_1_2_C_1_2

FR_X_2013_1000 m3_ST_1_2_C_1_2

2013

1000 m3

ST_1_2_C_2_2

FR_X_2013_1000 m3_ST_1_2_C_2_2

2013

1000 m3

ST_1_2_C_3

FR_X_2013_1000 m3_ST_1_2_C_3

2013

1000 m3

ST_1_2_C_1_3

FR_X_2013_1000 m3_ST_1_2_C_1_3

2013

1000 m3

ST_1_2_C_2_3

FR_X_2013_1000 m3_ST_1_2_C_2_3

2013

1000 m3

ST_1_2_NC

FR_X_2013_1000 m3_ST_1_2_NC

2013

1000 m3

ST_1_2_NC_1

FR_X_2013_1000 m3_ST_1_2_NC_1

2013

1000 m3

ST_1_2_NC_1_1

FR_X_2013_1000 m3_ST_1_2_NC_1_1

2013

1000 m3

ST_1_2_NC_2_1

FR_X_2013_1000 m3_ST_1_2_NC_2_1

2013

1000 m3

ST_1_2_NC_2

FR_X_2013_1000 m3_ST_1_2_NC_2

2013

1000 m3

ST_1_2_NC_1_2

FR_X_2013_1000 m3_ST_1_2_NC_1_2

2013

1000 m3

ST_1_2_NC_2_2

FR_X_2013_1000 m3_ST_1_2_NC_2_2

2013

1000 m3

ST_1_2_NC_3

FR_X_2013_1000 m3_ST_1_2_NC_3

2013

1000 m3

ST_1_2_NC_1_3

FR_X_2013_1000 m3_ST_1_2_NC_1_3

2013

1000 m3

ST_1_2_NC_2_3

FR_X_2013_1000 m3_ST_1_2_NC_2_3

2013

1000 m3

ST_1_2_NC_4

FR_X_2013_1000 m3_ST_1_2_NC_4

2013

1000 m3

ST_1_2_NC_5

FR_X_2013_1000 m3_ST_1_2_NC_5

2013

1000 m3

ST_5_C

FR_X_2013_1000 m3_ST_5_C

2013

1000 m3

ST_5_C_1

FR_X_2013_1000 m3_ST_5_C_1

2013

1000 m3

ST_5_C_2

FR_X_2013_1000 m3_ST_5_C_2

2013

1000 m3

ST_5_NC

FR_X_2013_1000 m3_ST_5_NC

2013

1000 m3

ST_5_NC_1

FR_X_2013_1000 m3_ST_5_NC_1

2013

1000 m3

ST_5_NC_2

FR_X_2013_1000 m3_ST_5_NC_2

2013

1000 m3

ST_5_NC_3

FR_X_2013_1000 m3_ST_5_NC_3

2013

1000 m3

ST_5_NC_4

FR_X_2013_1000 m3_ST_5_NC_4

2013

1000 m3

ST_5_NC_5

FR_X_2013_1000 m3_ST_5_NC_5

2013

1000 m3

ST_5_NC_6

FR_X_2013_1000 m3_ST_5_NC_6

2013

1000 m3

ST_5_NC_7

FR_X_2013_1000 m3_ST_5_NC_7

2013

1000 NAC

ST_1_2_C

FR_X_2013_1000 NAC_ST_1_2_C

2013

1000 NAC

ST_1_2_C_1

FR_X_2013_1000 NAC_ST_1_2_C_1

2013

1000 NAC

ST_1_2_C_1_1

FR_X_2013_1000 NAC_ST_1_2_C_1_1

2013

1000 NAC

ST_1_2_C_2_1

FR_X_2013_1000 NAC_ST_1_2_C_2_1

2013

1000 NAC

ST_1_2_C_2

FR_X_2013_1000 NAC_ST_1_2_C_2

2013

1000 NAC

ST_1_2_C_1_2

FR_X_2013_1000 NAC_ST_1_2_C_1_2

2013

1000 NAC

ST_1_2_C_2_2

FR_X_2013_1000 NAC_ST_1_2_C_2_2

2013

1000 NAC

ST_1_2_C_3

FR_X_2013_1000 NAC_ST_1_2_C_3

2013

1000 NAC

ST_1_2_C_1_3

FR_X_2013_1000 NAC_ST_1_2_C_1_3

2013

1000 NAC

ST_1_2_C_2_3

FR_X_2013_1000 NAC_ST_1_2_C_2_3

2013

1000 NAC

ST_1_2_NC

FR_X_2013_1000 NAC_ST_1_2_NC

2013

1000 NAC

ST_1_2_NC_1

FR_X_2013_1000 NAC_ST_1_2_NC_1

2013

1000 NAC

ST_1_2_NC_1_1

FR_X_2013_1000 NAC_ST_1_2_NC_1_1

2013

1000 NAC

ST_1_2_NC_2_1

FR_X_2013_1000 NAC_ST_1_2_NC_2_1

2013

1000 NAC

ST_1_2_NC_2

FR_X_2013_1000 NAC_ST_1_2_NC_2

2013

1000 NAC

ST_1_2_NC_1_2

FR_X_2013_1000 NAC_ST_1_2_NC_1_2

2013

1000 NAC

ST_1_2_NC_2_2

FR_X_2013_1000 NAC_ST_1_2_NC_2_2

2013

1000 NAC

ST_1_2_NC_3

FR_X_2013_1000 NAC_ST_1_2_NC_3

2013

1000 NAC

ST_1_2_NC_1_3

FR_X_2013_1000 NAC_ST_1_2_NC_1_3

2013

1000 NAC

ST_1_2_NC_2_3

FR_X_2013_1000 NAC_ST_1_2_NC_2_3

2013

1000 NAC

ST_1_2_NC_4

FR_X_2013_1000 NAC_ST_1_2_NC_4

2013

1000 NAC

ST_1_2_NC_5

FR_X_2013_1000 NAC_ST_1_2_NC_5

2013

1000 NAC

ST_5_C

FR_X_2013_1000 NAC_ST_5_C

2013

1000 NAC

ST_5_C_1

FR_X_2013_1000 NAC_ST_5_C_1

2013

1000 NAC

ST_5_C_2

FR_X_2013_1000 NAC_ST_5_C_2

2013

1000 NAC

ST_5_NC

FR_X_2013_1000 NAC_ST_5_NC

2013

1000 NAC

ST_5_NC_1

FR_X_2013_1000 NAC_ST_5_NC_1

2013

1000 NAC

ST_5_NC_2

FR_X_2013_1000 NAC_ST_5_NC_2

2013

1000 NAC

ST_5_NC_3

FR_X_2013_1000 NAC_ST_5_NC_3

2013

1000 NAC

ST_5_NC_4

FR_X_2013_1000 NAC_ST_5_NC_4

2013

1000 NAC

ST_5_NC_5

FR_X_2013_1000 NAC_ST_5_NC_5

2013

1000 NAC

ST_5_NC_6

FR_X_2013_1000 NAC_ST_5_NC_6

2013

1000 NAC

ST_5_NC_7

FR_X_2013_1000 NAC_ST_5_NC_7

EX_M

2013

1000 m3

FR_EX_M_2013_1000 m3_1

EX_M

2013

1000 m3

1_1

FR_EX_M_2013_1000 m3_1_1

EX_M

2013

1000 m3

1_2

FR_EX_M_2013_1000 m3_1_2

EX_M

2013

1000 m3

1_2_C

FR_EX_M_2013_1000 m3_1_2_C

EX_M

2013

1000 m3

1_2_NC

FR_EX_M_2013_1000 m3_1_2_NC

EX_M

2013

1000 m3

1_2_NC_T

FR_EX_M_2013_1000 m3_1_2_NC_T

EX_M

2013

1000 mt

FR_EX_M_2013_1000 mt_2

EX_M

2013

1000 m3

FR_EX_M_2013_1000 m3_3

EX_M

2013

1000 m3

3_1

FR_EX_M_2013_1000 m3_3_1

EX_M

2013

1000 m3

3_2

FR_EX_M_2013_1000 m3_3_2

EX_M

2013

1000 mt

FR_EX_M_2013_1000 mt_4

EX_M

2013

1000 mt

4_1

FR_EX_M_2013_1000 mt_4_1

EX_M

2013

1000 mt

4_2

FR_EX_M_2013_1000 mt_4_2

EX_M

2013

1000 m3

FR_EX_M_2013_1000 m3_5

EX_M

2013

1000 m3

5_C

FR_EX_M_2013_1000 m3_5_C

EX_M

2013

1000 m3

5_NC

FR_EX_M_2013_1000 m3_5_NC

EX_M

2013

1000 m3

5_NC_T

FR_EX_M_2013_1000 m3_5_NC_T

EX_M

2013

1000 m3

FR_EX_M_2013_1000 m3_6

EX_M

2013

1000 m3

6_1

FR_EX_M_2013_1000 m3_6_1

EX_M

2013

1000 m3

6_1_C

FR_EX_M_2013_1000 m3_6_1_C

EX_M

2013

1000 m3

6_1_NC

FR_EX_M_2013_1000 m3_6_1_NC

EX_M

2013

1000 m3

6_1_NC_T

FR_EX_M_2013_1000 m3_6_1_NC_T

EX_M

2013

1000 m3

6_2

FR_EX_M_2013_1000 m3_6_2

EX_M

2013

1000 m3

6_2_C

FR_EX_M_2013_1000 m3_6_2_C

EX_M

2013

1000 m3

6_2_NC

FR_EX_M_2013_1000 m3_6_2_NC

EX_M

2013

1000 m3

6_2_NC_T

FR_EX_M_2013_1000 m3_6_2_NC_T

EX_M

2013

1000 m3

6_3

FR_EX_M_2013_1000 m3_6_3

EX_M

2013

1000 m3

6_3_1

FR_EX_M_2013_1000 m3_6_3_1

EX_M

2013

1000 m3

6_4

FR_EX_M_2013_1000 m3_6_4

EX_M

2013

1000 m3

6_4_1

FR_EX_M_2013_1000 m3_6_4_1

EX_M

2013

1000 m3

6_4_2

FR_EX_M_2013_1000 m3_6_4_2

EX_M

2013

1000 m3

6_4_3

FR_EX_M_2013_1000 m3_6_4_3

EX_M

2013

1000 mt

FR_EX_M_2013_1000 mt_7

EX_M

2013

1000 mt

7_1

FR_EX_M_2013_1000 mt_7_1

EX_M

2013

1000 mt

7_2

FR_EX_M_2013_1000 mt_7_2

EX_M

2013

1000 mt

7_3

FR_EX_M_2013_1000 mt_7_3

EX_M

2013

1000 mt

7_3_1

FR_EX_M_2013_1000 mt_7_3_1

EX_M

2013

1000 mt

7_3_2

FR_EX_M_2013_1000 mt_7_3_2

EX_M

2013

1000 mt

7_3_3

FR_EX_M_2013_1000 mt_7_3_3

EX_M

2013

1000 mt

7_3_4

FR_EX_M_2013_1000 mt_7_3_4

EX_M

2013

1000 mt

7_4

FR_EX_M_2013_1000 mt_7_4

EX_M

2013

1000 mt

FR_EX_M_2013_1000 mt_8

EX_M

2013

1000 mt

8_1

FR_EX_M_2013_1000 mt_8_1

EX_M

2013

1000 mt

8_2

FR_EX_M_2013_1000 mt_8_2

EX_M

2013

1000 mt

FR_EX_M_2013_1000 mt_9

EX_M

2013

1000 mt

FR_EX_M_2013_1000 mt_10

EX_M

2013

1000 mt

10_1

FR_EX_M_2013_1000 mt_10_1

EX_M

2013

1000 mt

10_1_1

FR_EX_M_2013_1000 mt_10_1_1

EX_M

2013

1000 mt

10_1_2

FR_EX_M_2013_1000 mt_10_1_2

EX_M

2013

1000 mt

10_1_3

FR_EX_M_2013_1000 mt_10_1_3

EX_M

2013

1000 mt

10_1_4

FR_EX_M_2013_1000 mt_10_1_4

EX_M

2013

1000 mt

10_2

FR_EX_M_2013_1000 mt_10_2

EX_M

2013

1000 mt

10_3

FR_EX_M_2013_1000 mt_10_3

EX_M

2013

1000 mt

10_3_1

FR_EX_M_2013_1000 mt_10_3_1

EX_M

2013

1000 mt

10_3_2

FR_EX_M_2013_1000 mt_10_3_2

EX_M

2013

1000 mt

10_3_3

FR_EX_M_2013_1000 mt_10_3_3

EX_M

2013

1000 mt

10_3_4

FR_EX_M_2013_1000 mt_10_3_4

EX_M

2013

1000 mt

10_4

FR_EX_M_2013_1000 mt_10_4

EX_M

2013

1000 NAC

FR_EX_M_2013_1000 NAC_1

EX_M

2013

1000 NAC

1_1

FR_EX_M_2013_1000 NAC_1_1

EX_M

2013

1000 NAC

1_2

FR_EX_M_2013_1000 NAC_1_2

EX_M

2013

1000 NAC

1_2_C

FR_EX_M_2013_1000 NAC_1_2_C

EX_M

2013

1000 NAC

1_2_NC

FR_EX_M_2013_1000 NAC_1_2_NC

EX_M

2013

1000 NAC

1_2_NC_T

FR_EX_M_2013_1000 NAC_1_2_NC_T

EX_M

2013

1000 NAC

FR_EX_M_2013_1000 NAC_2

EX_M

2013

1000 NAC

FR_EX_M_2013_1000 NAC_3

EX_M

2013

1000 NAC

3_1

FR_EX_M_2013_1000 NAC_3_1

EX_M

2013

1000 NAC

3_2

FR_EX_M_2013_1000 NAC_3_2

EX_M

2013

1000 NAC

FR_EX_M_2013_1000 NAC_4

EX_M

2013

1000 NAC

4_1

FR_EX_M_2013_1000 NAC_4_1

EX_M

2013

1000 NAC

4_2

FR_EX_M_2013_1000 NAC_4_2

EX_M

2013

1000 NAC

FR_EX_M_2013_1000 NAC_5

EX_M

2013

1000 NAC

5_C

FR_EX_M_2013_1000 NAC_5_C

EX_M

2013

1000 NAC

5_NC

FR_EX_M_2013_1000 NAC_5_NC

EX_M

2013

1000 NAC

5_NC_T

FR_EX_M_2013_1000 NAC_5_NC_T

EX_M

2013

1000 NAC

FR_EX_M_2013_1000 NAC_6

EX_M

2013

1000 NAC

6_1

FR_EX_M_2013_1000 NAC_6_1

EX_M

2013

1000 NAC

6_1_C

FR_EX_M_2013_1000 NAC_6_1_C

EX_M

2013

1000 NAC

6_1_NC

FR_EX_M_2013_1000 NAC_6_1_NC

EX_M

2013

1000 NAC

6_1_NC_T

FR_EX_M_2013_1000 NAC_6_1_NC_T

EX_M

2013

1000 NAC

6_2

FR_EX_M_2013_1000 NAC_6_2

EX_M

2013

1000 NAC

6_2_C

FR_EX_M_2013_1000 NAC_6_2_C

EX_M

2013

1000 NAC

6_2_NC

FR_EX_M_2013_1000 NAC_6_2_NC

EX_M

2013

1000 NAC

6_2_NC_T

FR_EX_M_2013_1000 NAC_6_2_NC_T

EX_M

2013

1000 NAC

6_3

FR_EX_M_2013_1000 NAC_6_3

EX_M

2013

1000 NAC

6_3_1

FR_EX_M_2013_1000 NAC_6_3_1

EX_M

2013

1000 NAC

6_4

FR_EX_M_2013_1000 NAC_6_4

EX_M

2013

1000 NAC

6_4_1

FR_EX_M_2013_1000 NAC_6_4_1

EX_M

2013

1000 NAC

6_4_2

FR_EX_M_2013_1000 NAC_6_4_2

EX_M

2013

1000 NAC

6_4_3

FR_EX_M_2013_1000 NAC_6_4_3

EX_M

2013

1000 NAC

FR_EX_M_2013_1000 NAC_7

EX_M

2013

1000 NAC

7_1

FR_EX_M_2013_1000 NAC_7_1

EX_M

2013

1000 NAC

7_2

FR_EX_M_2013_1000 NAC_7_2

EX_M

2013

1000 NAC

7_3

FR_EX_M_2013_1000 NAC_7_3

EX_M

2013

1000 NAC

7_3_1

FR_EX_M_2013_1000 NAC_7_3_1

EX_M

2013

1000 NAC

7_3_2

FR_EX_M_2013_1000 NAC_7_3_2

EX_M

2013

1000 NAC

7_3_3

FR_EX_M_2013_1000 NAC_7_3_3

EX_M

2013

1000 NAC

7_3_4

FR_EX_M_2013_1000 NAC_7_3_4

EX_M

2013

1000 NAC

7_4

FR_EX_M_2013_1000 NAC_7_4

EX_M

2013

1000 NAC

FR_EX_M_2013_1000 NAC_8

EX_M

2013

1000 NAC

8_1

FR_EX_M_2013_1000 NAC_8_1

EX_M

2013

1000 NAC

8_2

FR_EX_M_2013_1000 NAC_8_2

EX_M

2013

1000 NAC

FR_EX_M_2013_1000 NAC_9

EX_M

2013

1000 NAC

FR_EX_M_2013_1000 NAC_10

EX_M

2013

1000 NAC

10_1

FR_EX_M_2013_1000 NAC_10_1

EX_M

2013

1000 NAC

10_1_1

FR_EX_M_2013_1000 NAC_10_1_1

EX_M

2013

1000 NAC

10_1_2

FR_EX_M_2013_1000 NAC_10_1_2

EX_M

2013

1000 NAC

10_1_3

FR_EX_M_2013_1000 NAC_10_1_3

EX_M

2013

1000 NAC

10_1_4

FR_EX_M_2013_1000 NAC_10_1_4

EX_M

2013

1000 NAC

10_2

FR_EX_M_2013_1000 NAC_10_2

EX_M

2013

1000 NAC

10_3

FR_EX_M_2013_1000 NAC_10_3

EX_M

2013

1000 NAC

10_3_1

FR_EX_M_2013_1000 NAC_10_3_1

EX_M

2013

1000 NAC

10_3_2

FR_EX_M_2013_1000 NAC_10_3_2

EX_M

2013

1000 NAC

10_3_3

FR_EX_M_2013_1000 NAC_10_3_3

EX_M

2013

1000 NAC

10_3_4

FR_EX_M_2013_1000 NAC_10_3_4

EX_M

2013

1000 NAC

10_4

FR_EX_M_2013_1000 NAC_10_4

EX_X

2013

1000 m3

FR_EX_X_2013_1000 m3_1

EX_X

2013

1000 m3

1_1

FR_EX_X_2013_1000 m3_1_1

EX_X

2013

1000 m3

1_2

FR_EX_X_2013_1000 m3_1_2

EX_X

2013

1000 m3

1_2_C

FR_EX_X_2013_1000 m3_1_2_C

EX_X

2013

1000 m3

1_2_NC

FR_EX_X_2013_1000 m3_1_2_NC

EX_X

2013

1000 m3

1_2_NC_T

FR_EX_X_2013_1000 m3_1_2_NC_T

EX_X

2013

1000 mt

FR_EX_X_2013_1000 mt_2

EX_X

2013

1000 m3

FR_EX_X_2013_1000 m3_3

EX_X

2013

1000 m3

3_1

FR_EX_X_2013_1000 m3_3_1

EX_X

2013

1000 m3

3_2

FR_EX_X_2013_1000 m3_3_2

EX_X

2013

1000 mt

FR_EX_X_2013_1000 mt_4

EX_X

2013

1000 mt

4_1

FR_EX_X_2013_1000 mt_4_1

EX_X

2013

1000 mt

4_2

FR_EX_X_2013_1000 mt_4_2

EX_X

2013

1000 m3

FR_EX_X_2013_1000 m3_5

EX_X

2013

1000 m3

5_C

FR_EX_X_2013_1000 m3_5_C

EX_X

2013

1000 m3

5_NC

FR_EX_X_2013_1000 m3_5_NC

EX_X

2013

1000 m3

5_NC_T

FR_EX_X_2013_1000 m3_5_NC_T

EX_X

2013

1000 m3

FR_EX_X_2013_1000 m3_6

EX_X

2013

1000 m3

6_1

FR_EX_X_2013_1000 m3_6_1

EX_X

2013

1000 m3

6_1_C

FR_EX_X_2013_1000 m3_6_1_C

EX_X

2013

1000 m3

6_1_NC

FR_EX_X_2013_1000 m3_6_1_NC

EX_X

2013

1000 m3

6_1_NC_T

FR_EX_X_2013_1000 m3_6_1_NC_T

EX_X

2013

1000 m3

6_2

FR_EX_X_2013_1000 m3_6_2

EX_X

2013

1000 m3

6_2_C

FR_EX_X_2013_1000 m3_6_2_C

EX_X

2013

1000 m3

6_2_NC

FR_EX_X_2013_1000 m3_6_2_NC

EX_X

2013

1000 m3

6_2_NC_T

FR_EX_X_2013_1000 m3_6_2_NC_T

EX_X

2013

1000 m3

6_3

FR_EX_X_2013_1000 m3_6_3

EX_X

2013

1000 m3

6_3_1

FR_EX_X_2013_1000 m3_6_3_1

EX_X

2013

1000 m3

6_4

FR_EX_X_2013_1000 m3_6_4

EX_X

2013

1000 m3

6_4_1

FR_EX_X_2013_1000 m3_6_4_1

EX_X

2013

1000 m3

6_4_2

FR_EX_X_2013_1000 m3_6_4_2

EX_X

2013

1000 m3

6_4_3

FR_EX_X_2013_1000 m3_6_4_3

EX_X

2013

1000 mt

FR_EX_X_2013_1000 mt_7

EX_X

2013

1000 mt

7_1

FR_EX_X_2013_1000 mt_7_1

EX_X

2013

1000 mt

7_2

FR_EX_X_2013_1000 mt_7_2

EX_X

2013

1000 mt

7_3

FR_EX_X_2013_1000 mt_7_3

EX_X

2013

1000 mt

7_3_1

FR_EX_X_2013_1000 mt_7_3_1

EX_X

2013

1000 mt

7_3_2

FR_EX_X_2013_1000 mt_7_3_2

EX_X

2013

1000 mt

7_3_3

FR_EX_X_2013_1000 mt_7_3_3

EX_X

2013

1000 mt

7_3_4

FR_EX_X_2013_1000 mt_7_3_4

EX_X

2013

1000 mt

7_4

FR_EX_X_2013_1000 mt_7_4

EX_X

2013

1000 mt

FR_EX_X_2013_1000 mt_8

EX_X

2013

1000 mt

8_1

FR_EX_X_2013_1000 mt_8_1

EX_X

2013

1000 mt

8_2

FR_EX_X_2013_1000 mt_8_2

EX_X

2013

1000 mt

FR_EX_X_2013_1000 mt_9

EX_X

2013

1000 mt

FR_EX_X_2013_1000 mt_10

EX_X

2013

1000 mt

10_1

FR_EX_X_2013_1000 mt_10_1

EX_X

2013

1000 mt

10_1_1

FR_EX_X_2013_1000 mt_10_1_1

EX_X

2013

1000 mt

10_1_2

FR_EX_X_2013_1000 mt_10_1_2

EX_X

2013

1000 mt

10_1_3

FR_EX_X_2013_1000 mt_10_1_3

EX_X

2013

1000 mt

10_1_4

FR_EX_X_2013_1000 mt_10_1_4

EX_X

2013

1000 mt

10_2

FR_EX_X_2013_1000 mt_10_2

EX_X

2013

1000 mt

10_3

FR_EX_X_2013_1000 mt_10_3

EX_X

2013

1000 mt

10_3_1

FR_EX_X_2013_1000 mt_10_3_1

EX_X

2013

1000 mt

10_3_2

FR_EX_X_2013_1000 mt_10_3_2

EX_X

2013

1000 mt

10_3_3

FR_EX_X_2013_1000 mt_10_3_3

EX_X

2013

1000 mt

10_3_4

FR_EX_X_2013_1000 mt_10_3_4

EX_X

2013

1000 mt

10_4

FR_EX_X_2013_1000 mt_10_4

EX_X

2013

1000 NAC

FR_EX_X_2013_1000 NAC_1

EX_X

2013

1000 NAC

1_1

FR_EX_X_2013_1000 NAC_1_1

EX_X

2013

1000 NAC

1_2

FR_EX_X_2013_1000 NAC_1_2

EX_X

2013

1000 NAC

1_2_C

FR_EX_X_2013_1000 NAC_1_2_C

EX_X

2013

1000 NAC

1_2_NC

FR_EX_X_2013_1000 NAC_1_2_NC

EX_X

2013

1000 NAC

1_2_NC_T

FR_EX_X_2013_1000 NAC_1_2_NC_T

EX_X

2013

1000 NAC

FR_EX_X_2013_1000 NAC_2

EX_X

2013

1000 NAC

FR_EX_X_2013_1000 NAC_3

EX_X

2013

1000 NAC

3_1

FR_EX_X_2013_1000 NAC_3_1

EX_X

2013

1000 NAC

3_2

FR_EX_X_2013_1000 NAC_3_2

EX_X

2013

1000 NAC

FR_EX_X_2013_1000 NAC_4

EX_X

2013

1000 NAC

4_1

FR_EX_X_2013_1000 NAC_4_1

EX_X

2013

1000 NAC

4_2

FR_EX_X_2013_1000 NAC_4_2

EX_X

2013

1000 NAC

FR_EX_X_2013_1000 NAC_5

EX_X

2013

1000 NAC

5_C

FR_EX_X_2013_1000 NAC_5_C

EX_X

2013

1000 NAC

5_NC

FR_EX_X_2013_1000 NAC_5_NC

EX_X

2013

1000 NAC

5_NC_T

FR_EX_X_2013_1000 NAC_5_NC_T

EX_X

2013

1000 NAC

FR_EX_X_2013_1000 NAC_6

EX_X

2013

1000 NAC

6_1

FR_EX_X_2013_1000 NAC_6_1

EX_X

2013

1000 NAC

6_1_C

FR_EX_X_2013_1000 NAC_6_1_C

EX_X

2013

1000 NAC

6_1_NC

FR_EX_X_2013_1000 NAC_6_1_NC

EX_X

2013

1000 NAC

6_1_NC_T

FR_EX_X_2013_1000 NAC_6_1_NC_T

EX_X

2013

1000 NAC

6_2

FR_EX_X_2013_1000 NAC_6_2

EX_X

2013

1000 NAC

6_2_C

FR_EX_X_2013_1000 NAC_6_2_C

EX_X

2013

1000 NAC

6_2_NC

FR_EX_X_2013_1000 NAC_6_2_NC

EX_X

2013

1000 NAC

6_2_NC_T

FR_EX_X_2013_1000 NAC_6_2_NC_T

EX_X

2013

1000 NAC

6_3

FR_EX_X_2013_1000 NAC_6_3

EX_X

2013

1000 NAC

6_3_1

FR_EX_X_2013_1000 NAC_6_3_1

EX_X

2013

1000 NAC

6_4

FR_EX_X_2013_1000 NAC_6_4

EX_X

2013

1000 NAC

6_4_1

FR_EX_X_2013_1000 NAC_6_4_1

EX_X

2013

1000 NAC

6_4_2

FR_EX_X_2013_1000 NAC_6_4_2

EX_X

2013

1000 NAC

6_4_3

FR_EX_X_2013_1000 NAC_6_4_3

EX_X

2013

1000 NAC

FR_EX_X_2013_1000 NAC_7

EX_X

2013

1000 NAC

7_1

FR_EX_X_2013_1000 NAC_7_1

EX_X

2013

1000 NAC

7_2

FR_EX_X_2013_1000 NAC_7_2

EX_X

2013

1000 NAC

7_3

FR_EX_X_2013_1000 NAC_7_3

EX_X

2013

1000 NAC

7_3_1

FR_EX_X_2013_1000 NAC_7_3_1

EX_X

2013

1000 NAC

7_3_2

FR_EX_X_2013_1000 NAC_7_3_2

EX_X

2013

1000 NAC

7_3_3

FR_EX_X_2013_1000 NAC_7_3_3

EX_X

2013

1000 NAC

7_3_4

FR_EX_X_2013_1000 NAC_7_3_4

EX_X

2013

1000 NAC

7_4

FR_EX_X_2013_1000 NAC_7_4

EX_X

2013

1000 NAC

FR_EX_X_2013_1000 NAC_8

EX_X

2013

1000 NAC

8_1

FR_EX_X_2013_1000 NAC_8_1

EX_X

2013

1000 NAC

8_2

FR_EX_X_2013_1000 NAC_8_2

EX_X

2013

1000 NAC

FR_EX_X_2013_1000 NAC_9

EX_X

2013

1000 NAC

FR_EX_X_2013_1000 NAC_10

EX_X

2013

1000 NAC

10_1

FR_EX_X_2013_1000 NAC_10_1

EX_X

2013

1000 NAC

10_1_1

FR_EX_X_2013_1000 NAC_10_1_1

EX_X

2013

1000 NAC

10_1_2

FR_EX_X_2013_1000 NAC_10_1_2

EX_X

2013

1000 NAC

10_1_3

FR_EX_X_2013_1000 NAC_10_1_3

EX_X

2013

1000 NAC

10_1_4

FR_EX_X_2013_1000 NAC_10_1_4

EX_X

2013

1000 NAC

10_2

FR_EX_X_2013_1000 NAC_10_2

EX_X

2013

1000 NAC

10_3

FR_EX_X_2013_1000 NAC_10_3

EX_X

2013

1000 NAC

10_3_1

FR_EX_X_2013_1000 NAC_10_3_1

EX_X

2013

1000 NAC

10_3_2

FR_EX_X_2013_1000 NAC_10_3_2

EX_X

2013

1000 NAC

10_3_3

FR_EX_X_2013_1000 NAC_10_3_3

EX_X

2013

1000 NAC

10_3_4

FR_EX_X_2013_1000 NAC_10_3_4

EX_X

2013

1000 NAC

10_4

FR_EX_X_2013_1000 NAC_10_4

2013

1000 m3

EU2_1

FR_P_2013_1000 m3_EU2_1

2013

1000 m3

EU2_1_C

FR_P_2013_1000 m3_EU2_1_C

2013

1000 m3

EU2_1_NC

FR_P_2013_1000 m3_EU2_1_NC

2013

1000 m3

EU2_1_1

FR_P_2013_1000 m3_EU2_1_1

2013

1000 m3

EU2_1_1_C

FR_P_2013_1000 m3_EU2_1_1_C

2013

1000 m3

EU2_1_1_NC

FR_P_2013_1000 m3_EU2_1_1_NC

2013

1000 m3

EU2_1_2

FR_P_2013_1000 m3_EU2_1_2

2013

1000 m3

EU2_1_2_C

FR_P_2013_1000 m3_EU2_1_2_C

2013

1000 m3

EU2_1_2_NC

FR_P_2013_1000 m3_EU2_1_2_NC

2013

1000 m3

EU2_1_3

FR_P_2013_1000 m3_EU2_1_3

2013

1000 m3

EU2_1_3_C

FR_P_2013_1000 m3_EU2_1_3_C

2013

1000 m3

EU2_1_3_NC

FR_P_2013_1000 m3_EU2_1_3_NC

P.OB

2013

1000 m3

FR_P.OB_2013_1000 m3_1

P.OB

2013

1000 m3

1_C

FR_P.OB_2013_1000 m3_1_C

P.OB

2013

1000 m3

1_NC

FR_P.OB_2013_1000 m3_1_NC

P.OB

2013

1000 m3

1_1

FR_P.OB_2013_1000 m3_1_1

P.OB

2013

1000 m3

1_1_C

FR_P.OB_2013_1000 m3_1_1_C

P.OB

2013

1000 m3

1_1_NC

FR_P.OB_2013_1000 m3_1_1_NC

P.OB

2013

1000 m3

1_2

FR_P.OB_2013_1000 m3_1_2

P.OB

2013

1000 m3

1_2_C

FR_P.OB_2013_1000 m3_1_2_C

P.OB

2013

1000 m3

1_2_NC

FR_P.OB_2013_1000 m3_1_2_NC

P.OB

2013

1000 m3

1_2_1

FR_P.OB_2013_1000 m3_1_2_1

P.OB

2013

1000 m3

1_2_1_C

FR_P.OB_2013_1000 m3_1_2_1_C

P.OB

2013

1000 m3

1_2_1_NC

FR_P.OB_2013_1000 m3_1_2_1_NC

P.OB

2013

1000 m3

1_2_2

FR_P.OB_2013_1000 m3_1_2_2

P.OB

2013

1000 m3

1_2_2_C

FR_P.OB_2013_1000 m3_1_2_2_C

P.OB

2013

1000 m3

1_2_2_NC

FR_P.OB_2013_1000 m3_1_2_2_NC

P.OB

2013

1000 m3

1_2_3

FR_P.OB_2013_1000 m3_1_2_3

P.OB

2013

1000 m3

1_2_3_C

FR_P.OB_2013_1000 m3_1_2_3_C

P.OB

2013

1000 m3

1_2_3_NC

FR_P.OB_2013_1000 m3_1_2_3_NC

2012

1000 m3

FR_P_2012_1000 m3_1

2012

1000 m3

1_C

FR_P_2012_1000 m3_1_C

2012

1000 m3

1_NC

FR_P_2012_1000 m3_1_NC

2012

1000 m3

1_1

FR_P_2012_1000 m3_1_1

2012

1000 m3

1_1_C

FR_P_2012_1000 m3_1_1_C

2012

1000 m3

1_1_NC

FR_P_2012_1000 m3_1_1_NC

2012

1000 m3

1_2

FR_P_2012_1000 m3_1_2

2012

1000 m3

1_2_C

FR_P_2012_1000 m3_1_2_C

2012

1000 m3

1_2_NC

FR_P_2012_1000 m3_1_2_NC

2012

1000 m3

1_2_1

FR_P_2012_1000 m3_1_2_1

2012

1000 m3

1_2_1_C

FR_P_2012_1000 m3_1_2_1_C

2012

1000 m3

1_2_1_NC

FR_P_2012_1000 m3_1_2_1_NC

2012

1000 m3

1_2_2

FR_P_2012_1000 m3_1_2_2

2012

1000 m3

1_2_2_C

FR_P_2012_1000 m3_1_2_2_C

2012

1000 m3

1_2_2_NC

FR_P_2012_1000 m3_1_2_2_NC

2012

1000 m3

1_2_3

FR_P_2012_1000 m3_1_2_3

2012

1000 m3

1_2_3_C

FR_P_2012_1000 m3_1_2_3_C

2012

1000 m3

1_2_3_NC

FR_P_2012_1000 m3_1_2_3_NC

2012

1000 mt

FR_P_2012_1000 mt_2

2012

1000 m3

FR_P_2012_1000 m3_3

2012

1000 m3

3_1

FR_P_2012_1000 m3_3_1

2012

1000 m3

3_2

FR_P_2012_1000 m3_3_2

2012

1000 mt

FR_P_2012_1000 mt_4

2012

1000 mt

4_1

FR_P_2012_1000 mt_4_1

2012

1000 mt

4_2

FR_P_2012_1000 mt_4_2

2012

1000 m3

FR_P_2012_1000 m3_5

2012

1000 m3

5_C

FR_P_2012_1000 m3_5_C

2012

1000 m3

5_NC

FR_P_2012_1000 m3_5_NC

2012

1000 m3

5_NC_T

FR_P_2012_1000 m3_5_NC_T

2012

1000 m3

FR_P_2012_1000 m3_6

2012

1000 m3

6_1

FR_P_2012_1000 m3_6_1

2012

1000 m3

6_1_C

FR_P_2012_1000 m3_6_1_C

2012

1000 m3

6_1_NC

FR_P_2012_1000 m3_6_1_NC

2012

1000 m3

6_1_NC_T

FR_P_2012_1000 m3_6_1_NC_T

2012

1000 m3

6_2

FR_P_2012_1000 m3_6_2

2012

1000 m3

6_2_C

FR_P_2012_1000 m3_6_2_C

2012

1000 m3

6_2_NC

FR_P_2012_1000 m3_6_2_NC

2012

1000 m3

6_2_NC_T

FR_P_2012_1000 m3_6_2_NC_T

2012

1000 m3

6_3

FR_P_2012_1000 m3_6_3

2012

1000 m3

6_3_1

FR_P_2012_1000 m3_6_3_1

2012

1000 m3

6_4

FR_P_2012_1000 m3_6_4

2012

1000 m3

6_4_1

FR_P_2012_1000 m3_6_4_1

2012

1000 m3

6_4_2

FR_P_2012_1000 m3_6_4_2

2012

1000 m3

6_4_3

FR_P_2012_1000 m3_6_4_3

2012

1000 mt

FR_P_2012_1000 mt_7

2012

1000 mt

7_1

FR_P_2012_1000 mt_7_1

2012

1000 mt

7_2

FR_P_2012_1000 mt_7_2

2012

1000 mt

7_3

FR_P_2012_1000 mt_7_3

2012

1000 mt

7_3_1

FR_P_2012_1000 mt_7_3_1

2012

1000 mt

7_3_2

FR_P_2012_1000 mt_7_3_2

2012

1000 mt

7_3_3

FR_P_2012_1000 mt_7_3_3

2012

1000 mt

7_3_4

FR_P_2012_1000 mt_7_3_4

2012

1000 mt

7_4

FR_P_2012_1000 mt_7_4

2012

1000 mt

FR_P_2012_1000 mt_8

2012

1000 mt

8_1

FR_P_2012_1000 mt_8_1

2012

1000 mt

8_2

FR_P_2012_1000 mt_8_2

2012

1000 mt

FR_P_2012_1000 mt_9

2012

1000 mt

FR_P_2012_1000 mt_10

2012

1000 mt

10_1

FR_P_2012_1000 mt_10_1

2012

1000 mt

10_1_1

FR_P_2012_1000 mt_10_1_1

2012

1000 mt

10_1_2

FR_P_2012_1000 mt_10_1_2

2012

1000 mt

10_1_3

FR_P_2012_1000 mt_10_1_3

2012

1000 mt

10_1_4

FR_P_2012_1000 mt_10_1_4

2012

1000 mt

10_2

FR_P_2012_1000 mt_10_2

2012

1000 mt

10_3

FR_P_2012_1000 mt_10_3

2012

1000 mt

10_3_1

FR_P_2012_1000 mt_10_3_1

2012

1000 mt

10_3_2

FR_P_2012_1000 mt_10_3_2

2012

1000 mt

10_3_3

FR_P_2012_1000 mt_10_3_3

2012

1000 mt

10_3_4

FR_P_2012_1000 mt_10_3_4

2012

1000 mt

10_4

FR_P_2012_1000 mt_10_4

2012

1000 m3

FR_M_2012_1000 m3_1

2012

1000 m3

1_1

FR_M_2012_1000 m3_1_1

2012

1000 m3

1_2

FR_M_2012_1000 m3_1_2

2012

1000 m3

1_2_C

FR_M_2012_1000 m3_1_2_C

2012

1000 m3

1_2_NC

FR_M_2012_1000 m3_1_2_NC

2012

1000 m3

1_2_NC_T

FR_M_2012_1000 m3_1_2_NC_T

2012

1000 mt

FR_M_2012_1000 mt_2

2012

1000 m3

FR_M_2012_1000 m3_3

2012

1000 m3

3_1

FR_M_2012_1000 m3_3_1

2012

1000 m3

3_2

FR_M_2012_1000 m3_3_2

2012

1000 mt

FR_M_2012_1000 mt_4

2012

1000 mt

4_1

FR_M_2012_1000 mt_4_1

2012

1000 mt

4_2

FR_M_2012_1000 mt_4_2

2012

1000 m3

FR_M_2012_1000 m3_5

2012

1000 m3

5_C

FR_M_2012_1000 m3_5_C

2012

1000 m3

5_NC

FR_M_2012_1000 m3_5_NC

2012

1000 m3

5_NC_T

FR_M_2012_1000 m3_5_NC_T

2012

1000 m3

FR_M_2012_1000 m3_6

2012

1000 m3

6_1

FR_M_2012_1000 m3_6_1

2012

1000 m3

6_1_C

FR_M_2012_1000 m3_6_1_C

2012

1000 m3

6_1_NC

FR_M_2012_1000 m3_6_1_NC

2012

1000 m3

6_1_NC_T

FR_M_2012_1000 m3_6_1_NC_T

2012

1000 m3

6_2

FR_M_2012_1000 m3_6_2

2012

1000 m3

6_2_C

FR_M_2012_1000 m3_6_2_C

2012

1000 m3

6_2_NC

FR_M_2012_1000 m3_6_2_NC

2012

1000 m3

6_2_NC_T

FR_M_2012_1000 m3_6_2_NC_T

2012

1000 m3

6_3

FR_M_2012_1000 m3_6_3

2012

1000 m3

6_3_1

FR_M_2012_1000 m3_6_3_1

2012

1000 m3

6_4

FR_M_2012_1000 m3_6_4

2012

1000 m3

6_4_1

FR_M_2012_1000 m3_6_4_1

2012

1000 m3

6_4_2

FR_M_2012_1000 m3_6_4_2

2012

1000 m3

6_4_3

FR_M_2012_1000 m3_6_4_3

2012

1000 mt

FR_M_2012_1000 mt_7

2012

1000 mt

7_1

FR_M_2012_1000 mt_7_1

2012

1000 mt

7_2

FR_M_2012_1000 mt_7_2

2012

1000 mt

7_3

FR_M_2012_1000 mt_7_3

2012

1000 mt

7_3_1

FR_M_2012_1000 mt_7_3_1

2012

1000 mt

7_3_2

FR_M_2012_1000 mt_7_3_2

2012

1000 mt

7_3_3

FR_M_2012_1000 mt_7_3_3

2012

1000 mt

7_3_4

FR_M_2012_1000 mt_7_3_4

2012

1000 mt

7_4

FR_M_2012_1000 mt_7_4

2012

1000 mt

FR_M_2012_1000 mt_8

2012

1000 mt

8_1

FR_M_2012_1000 mt_8_1

2012

1000 mt

8_2

FR_M_2012_1000 mt_8_2

2012

1000 mt

FR_M_2012_1000 mt_9

2012

1000 mt

FR_M_2012_1000 mt_10

2012

1000 mt

10_1

FR_M_2012_1000 mt_10_1

2012

1000 mt

10_1_1

FR_M_2012_1000 mt_10_1_1

2012

1000 mt

10_1_2

FR_M_2012_1000 mt_10_1_2

2012

1000 mt

10_1_3

FR_M_2012_1000 mt_10_1_3

2012

1000 mt

10_1_4

FR_M_2012_1000 mt_10_1_4

2012

1000 mt

10_2

FR_M_2012_1000 mt_10_2

2012

1000 mt

10_3

FR_M_2012_1000 mt_10_3

2012

1000 mt

10_3_1

FR_M_2012_1000 mt_10_3_1

2012

1000 mt

10_3_2

FR_M_2012_1000 mt_10_3_2

2012

1000 mt

10_3_3

FR_M_2012_1000 mt_10_3_3

2012

1000 mt

10_3_4

FR_M_2012_1000 mt_10_3_4

2012

1000 mt

10_4

FR_M_2012_1000 mt_10_4

2012

1000 NAC

FR_M_2012_1000 NAC_1

2012

1000 NAC

1_1

FR_M_2012_1000 NAC_1_1

2012

1000 NAC

1_2

FR_M_2012_1000 NAC_1_2

2012

1000 NAC

1_2_C

FR_M_2012_1000 NAC_1_2_C

2012

1000 NAC

1_2_NC

FR_M_2012_1000 NAC_1_2_NC

2012

1000 NAC

1_2_NC_T

FR_M_2012_1000 NAC_1_2_NC_T

2012

1000 NAC

FR_M_2012_1000 NAC_2

2012

1000 NAC

FR_M_2012_1000 NAC_3

2012

1000 NAC

3_1

FR_M_2012_1000 NAC_3_1

2012

1000 NAC

3_2

FR_M_2012_1000 NAC_3_2

2012

1000 NAC

FR_M_2012_1000 NAC_4

2012

1000 NAC

4_1

FR_M_2012_1000 NAC_4_1

2012

1000 NAC

4_2

FR_M_2012_1000 NAC_4_2

2012

1000 NAC

FR_M_2012_1000 NAC_5

2012

1000 NAC

5_C

FR_M_2012_1000 NAC_5_C

2012

1000 NAC

5_NC

FR_M_2012_1000 NAC_5_NC

2012

1000 NAC

5_NC_T

FR_M_2012_1000 NAC_5_NC_T

2012

1000 NAC

FR_M_2012_1000 NAC_6

2012

1000 NAC

6_1

FR_M_2012_1000 NAC_6_1

2012

1000 NAC

6_1_C

FR_M_2012_1000 NAC_6_1_C

2012

1000 NAC

6_1_NC

FR_M_2012_1000 NAC_6_1_NC

2012

1000 NAC

6_1_NC_T

FR_M_2012_1000 NAC_6_1_NC_T

2012

1000 NAC

6_2

FR_M_2012_1000 NAC_6_2

2012

1000 NAC

6_2_C

FR_M_2012_1000 NAC_6_2_C

2012

1000 NAC

6_2_NC

FR_M_2012_1000 NAC_6_2_NC

2012

1000 NAC

6_2_NC_T

FR_M_2012_1000 NAC_6_2_NC_T

2012

1000 NAC

6_3

FR_M_2012_1000 NAC_6_3

2012

1000 NAC

6_3_1

FR_M_2012_1000 NAC_6_3_1

2012

1000 NAC

6_4

FR_M_2012_1000 NAC_6_4

2012

1000 NAC

6_4_1

FR_M_2012_1000 NAC_6_4_1

2012

1000 NAC

6_4_2

FR_M_2012_1000 NAC_6_4_2

2012

1000 NAC

6_4_3

FR_M_2012_1000 NAC_6_4_3

2012

1000 NAC

FR_M_2012_1000 NAC_7

2012

1000 NAC

7_1

FR_M_2012_1000 NAC_7_1

2012

1000 NAC

7_2

FR_M_2012_1000 NAC_7_2

2012

1000 NAC

7_3

FR_M_2012_1000 NAC_7_3

2012

1000 NAC

7_3_1

FR_M_2012_1000 NAC_7_3_1

2012

1000 NAC

7_3_2

FR_M_2012_1000 NAC_7_3_2

2012

1000 NAC

7_3_3

FR_M_2012_1000 NAC_7_3_3

2012

1000 NAC

7_3_4

FR_M_2012_1000 NAC_7_3_4

2012

1000 NAC

7_4

FR_M_2012_1000 NAC_7_4

2012

1000 NAC

FR_M_2012_1000 NAC_8

2012

1000 NAC

8_1

FR_M_2012_1000 NAC_8_1

2012

1000 NAC

8_2

FR_M_2012_1000 NAC_8_2

2012

1000 NAC

FR_M_2012_1000 NAC_9

2012

1000 NAC

FR_M_2012_1000 NAC_10

2012

1000 NAC

10_1

FR_M_2012_1000 NAC_10_1

2012

1000 NAC

10_1_1

FR_M_2012_1000 NAC_10_1_1

2012

1000 NAC

10_1_2

FR_M_2012_1000 NAC_10_1_2

2012

1000 NAC

10_1_3

FR_M_2012_1000 NAC_10_1_3

2012

1000 NAC

10_1_4

FR_M_2012_1000 NAC_10_1_4

2012

1000 NAC

10_2

FR_M_2012_1000 NAC_10_2

2012

1000 NAC

10_3

FR_M_2012_1000 NAC_10_3

2012

1000 NAC

10_3_1

FR_M_2012_1000 NAC_10_3_1

2012

1000 NAC

10_3_2

FR_M_2012_1000 NAC_10_3_2

2012

1000 NAC

10_3_3

FR_M_2012_1000 NAC_10_3_3

2012

1000 NAC

10_3_4

FR_M_2012_1000 NAC_10_3_4

2012

1000 NAC

10_4

FR_M_2012_1000 NAC_10_4

2012

1000 m3

FR_X_2012_1000 m3_1

2012

1000 m3

1_1

FR_X_2012_1000 m3_1_1

2012

1000 m3

1_2

FR_X_2012_1000 m3_1_2

2012

1000 m3

1_2_C

FR_X_2012_1000 m3_1_2_C

2012

1000 m3

1_2_NC

FR_X_2012_1000 m3_1_2_NC

2012

1000 m3

1_2_NC_T

FR_X_2012_1000 m3_1_2_NC_T

2012

1000 mt

FR_X_2012_1000 mt_2

2012

1000 m3

FR_X_2012_1000 m3_3

2012

1000 m3

3_1

FR_X_2012_1000 m3_3_1

2012

1000 m3

3_2

FR_X_2012_1000 m3_3_2

2012

1000 mt

FR_X_2012_1000 mt_4

2012

1000 mt

4_1

FR_X_2012_1000 mt_4_1

2012

1000 mt

4_2

FR_X_2012_1000 mt_4_2

2012

1000 m3

FR_X_2012_1000 m3_5

2012

1000 m3

5_C

FR_X_2012_1000 m3_5_C

2012

1000 m3

5_NC

FR_X_2012_1000 m3_5_NC

2012

1000 m3

5_NC_T

FR_X_2012_1000 m3_5_NC_T

2012

1000 m3

FR_X_2012_1000 m3_6

2012

1000 m3

6_1

FR_X_2012_1000 m3_6_1

2012

1000 m3

6_1_C

FR_X_2012_1000 m3_6_1_C

2012

1000 m3

6_1_NC

FR_X_2012_1000 m3_6_1_NC

2012

1000 m3

6_1_NC_T

FR_X_2012_1000 m3_6_1_NC_T

2012

1000 m3

6_2

FR_X_2012_1000 m3_6_2

2012

1000 m3

6_2_C

FR_X_2012_1000 m3_6_2_C

2012

1000 m3

6_2_NC

FR_X_2012_1000 m3_6_2_NC

2012

1000 m3

6_2_NC_T

FR_X_2012_1000 m3_6_2_NC_T

2012

1000 m3

6_3

FR_X_2012_1000 m3_6_3

2012

1000 m3

6_3_1

FR_X_2012_1000 m3_6_3_1

2012

1000 m3

6_4

FR_X_2012_1000 m3_6_4

2012

1000 m3

6_4_1

FR_X_2012_1000 m3_6_4_1

2012

1000 m3

6_4_2

FR_X_2012_1000 m3_6_4_2

2012

1000 m3

6_4_3

FR_X_2012_1000 m3_6_4_3

2012

1000 mt

FR_X_2012_1000 mt_7

2012

1000 mt

7_1

FR_X_2012_1000 mt_7_1

2012

1000 mt

7_2

FR_X_2012_1000 mt_7_2

2012

1000 mt

7_3

FR_X_2012_1000 mt_7_3

2012

1000 mt

7_3_1

FR_X_2012_1000 mt_7_3_1

2012

1000 mt

7_3_2

FR_X_2012_1000 mt_7_3_2

2012

1000 mt

7_3_3

FR_X_2012_1000 mt_7_3_3

2012

1000 mt

7_3_4

FR_X_2012_1000 mt_7_3_4

2012

1000 mt

7_4

FR_X_2012_1000 mt_7_4

2012

1000 mt

FR_X_2012_1000 mt_8

2012

1000 mt

8_1

FR_X_2012_1000 mt_8_1

2012

1000 mt

8_2

FR_X_2012_1000 mt_8_2

2012

1000 mt

FR_X_2012_1000 mt_9

2012

1000 mt

FR_X_2012_1000 mt_10

2012

1000 mt

10_1

FR_X_2012_1000 mt_10_1

2012

1000 mt

10_1_1

FR_X_2012_1000 mt_10_1_1

2012

1000 mt

10_1_2

FR_X_2012_1000 mt_10_1_2

2012

1000 mt

10_1_3

FR_X_2012_1000 mt_10_1_3

2012

1000 mt

10_1_4

FR_X_2012_1000 mt_10_1_4

2012

1000 mt

10_2

FR_X_2012_1000 mt_10_2

2012

1000 mt

10_3

FR_X_2012_1000 mt_10_3

2012

1000 mt

10_3_1

FR_X_2012_1000 mt_10_3_1

2012

1000 mt

10_3_2

FR_X_2012_1000 mt_10_3_2

2012

1000 mt

10_3_3

FR_X_2012_1000 mt_10_3_3

2012

1000 mt

10_3_4

FR_X_2012_1000 mt_10_3_4

2012

1000 mt

10_4

FR_X_2012_1000 mt_10_4

2012

1000 NAC

FR_X_2012_1000 NAC_1

2012

1000 NAC

1_1

FR_X_2012_1000 NAC_1_1

2012

1000 NAC

1_2

FR_X_2012_1000 NAC_1_2

2012

1000 NAC

1_2_C

FR_X_2012_1000 NAC_1_2_C

2012

1000 NAC

1_2_NC

FR_X_2012_1000 NAC_1_2_NC

2012

1000 NAC

1_2_NC_T

FR_X_2012_1000 NAC_1_2_NC_T

2012

1000 NAC

FR_X_2012_1000 NAC_2

2012

1000 NAC

FR_X_2012_1000 NAC_3

2012

1000 NAC

3_1

FR_X_2012_1000 NAC_3_1

2012

1000 NAC

3_2

FR_X_2012_1000 NAC_3_2

2012

1000 NAC

FR_X_2012_1000 NAC_4

2012

1000 NAC

4_1

FR_X_2012_1000 NAC_4_1

2012

1000 NAC

4_2

FR_X_2012_1000 NAC_4_2

2012

1000 NAC

FR_X_2012_1000 NAC_5

2012

1000 NAC

5_C

FR_X_2012_1000 NAC_5_C

2012

1000 NAC

5_NC

FR_X_2012_1000 NAC_5_NC

2012

1000 NAC

5_NC_T

FR_X_2012_1000 NAC_5_NC_T

2012

1000 NAC

FR_X_2012_1000 NAC_6

2012

1000 NAC

6_1

FR_X_2012_1000 NAC_6_1

2012

1000 NAC

6_1_C

FR_X_2012_1000 NAC_6_1_C

2012

1000 NAC

6_1_NC

FR_X_2012_1000 NAC_6_1_NC

2012

1000 NAC

6_1_NC_T

FR_X_2012_1000 NAC_6_1_NC_T

2012

1000 NAC

6_2

FR_X_2012_1000 NAC_6_2

2012

1000 NAC

6_2_C

FR_X_2012_1000 NAC_6_2_C

2012

1000 NAC

6_2_NC

FR_X_2012_1000 NAC_6_2_NC

2012

1000 NAC

6_2_NC_T

FR_X_2012_1000 NAC_6_2_NC_T

2012

1000 NAC

6_3

FR_X_2012_1000 NAC_6_3

2012

1000 NAC

6_3_1

FR_X_2012_1000 NAC_6_3_1

2012

1000 NAC

6_4

FR_X_2012_1000 NAC_6_4

2012

1000 NAC

6_4_1

FR_X_2012_1000 NAC_6_4_1

2012

1000 NAC

6_4_2

FR_X_2012_1000 NAC_6_4_2

2012

1000 NAC

6_4_3

FR_X_2012_1000 NAC_6_4_3

2012

1000 NAC

FR_X_2012_1000 NAC_7

2012

1000 NAC

7_1

FR_X_2012_1000 NAC_7_1

2012

1000 NAC

7_2

FR_X_2012_1000 NAC_7_2

2012

1000 NAC

7_3

FR_X_2012_1000 NAC_7_3

2012

1000 NAC

7_3_1

FR_X_2012_1000 NAC_7_3_1

2012

1000 NAC

7_3_2

FR_X_2012_1000 NAC_7_3_2

2012

1000 NAC

7_3_3

FR_X_2012_1000 NAC_7_3_3

2012

1000 NAC

7_3_4

FR_X_2012_1000 NAC_7_3_4

2012

1000 NAC

7_4

FR_X_2012_1000 NAC_7_4

2012

1000 NAC

FR_X_2012_1000 NAC_8

2012

1000 NAC

8_1

FR_X_2012_1000 NAC_8_1

2012

1000 NAC

8_2

FR_X_2012_1000 NAC_8_2

2012

1000 NAC

FR_X_2012_1000 NAC_9

2012

1000 NAC

FR_X_2012_1000 NAC_10

2012

1000 NAC

10_1

FR_X_2012_1000 NAC_10_1

2012

1000 NAC

10_1_1

FR_X_2012_1000 NAC_10_1_1

2012

1000 NAC

10_1_2

FR_X_2012_1000 NAC_10_1_2

2012

1000 NAC

10_1_3

FR_X_2012_1000 NAC_10_1_3

2012

1000 NAC

10_1_4

FR_X_2012_1000 NAC_10_1_4

2012

1000 NAC

10_2

FR_X_2012_1000 NAC_10_2

2012

1000 NAC

10_3

FR_X_2012_1000 NAC_10_3

2012

1000 NAC

10_3_1

FR_X_2012_1000 NAC_10_3_1

2012

1000 NAC

10_3_2

FR_X_2012_1000 NAC_10_3_2

2012

1000 NAC

10_3_3

FR_X_2012_1000 NAC_10_3_3

2012

1000 NAC

10_3_4

FR_X_2012_1000 NAC_10_3_4

2012

1000 NAC

10_4

FR_X_2012_1000 NAC_10_4

2012

1000 NAC

11_1

FR_M_2012_1000 NAC_11_1

2012

1000 NAC

11_1_C

FR_M_2012_1000 NAC_11_1_C

2012

1000 NAC

11_1_NC

FR_M_2012_1000 NAC_11_1_NC

2012

1000 NAC

11_1_NC_T

FR_M_2012_1000 NAC_11_1_NC_T

2012

1000 NAC

11_2

FR_M_2012_1000 NAC_11_2

2012

1000 NAC

11_3

FR_M_2012_1000 NAC_11_3

2012

1000 NAC

11_4

FR_M_2012_1000 NAC_11_4

2012

1000 NAC

11_5

FR_M_2012_1000 NAC_11_5

2012

1000 NAC

11_6

FR_M_2012_1000 NAC_11_6

2012

1000 NAC

11_7

FR_M_2012_1000 NAC_11_7

2012

1000 NAC

11_7_1

FR_M_2012_1000 NAC_11_7_1

2012

1000 NAC

12_1

FR_M_2012_1000 NAC_12_1

2012

1000 NAC

12_2

FR_M_2012_1000 NAC_12_2

2012

1000 NAC

12_3

FR_M_2012_1000 NAC_12_3

2012

1000 NAC

12_4

FR_M_2012_1000 NAC_12_4

2012

1000 NAC

12_5

FR_M_2012_1000 NAC_12_5

2012

1000 NAC

12_6

FR_M_2012_1000 NAC_12_6

2012

1000 NAC

12_6_1

FR_M_2012_1000 NAC_12_6_1

2012

1000 NAC

12_6_2

FR_M_2012_1000 NAC_12_6_2

2012

1000 NAC

12_6_3

FR_M_2012_1000 NAC_12_6_3

2012

1000 NAC

12_7

FR_M_2012_1000 NAC_12_7

2012

1000 NAC

12_7_1

FR_M_2012_1000 NAC_12_7_1

2012

1000 NAC

12_7_2

FR_M_2012_1000 NAC_12_7_2

2012

1000 NAC

12_7_3

FR_M_2012_1000 NAC_12_7_3

2012

1000 NAC

11_1

FR_X_2012_1000 NAC_11_1

2012

1000 NAC

11_1_C

FR_X_2012_1000 NAC_11_1_C

2012

1000 NAC

11_1_NC

FR_X_2012_1000 NAC_11_1_NC

2012

1000 NAC

11_1_NC_T

FR_X_2012_1000 NAC_11_1_NC_T

2012

1000 NAC

11_2

FR_X_2012_1000 NAC_11_2

2012

1000 NAC

11_3

FR_X_2012_1000 NAC_11_3

2012

1000 NAC

11_4

FR_X_2012_1000 NAC_11_4

2012

1000 NAC

11_5

FR_X_2012_1000 NAC_11_5

2012

1000 NAC

11_6

FR_X_2012_1000 NAC_11_6

2012

1000 NAC

11_7

FR_X_2012_1000 NAC_11_7

2012

1000 NAC

11_7_1

FR_X_2012_1000 NAC_11_7_1

2012

1000 NAC

12_1

FR_X_2012_1000 NAC_12_1

2012

1000 NAC

12_2

FR_X_2012_1000 NAC_12_2

2012

1000 NAC

12_3

FR_X_2012_1000 NAC_12_3

2012

1000 NAC

12_4

FR_X_2012_1000 NAC_12_4

2012

1000 NAC

12_5

FR_X_2012_1000 NAC_12_5

2012

1000 NAC

12_6

FR_X_2012_1000 NAC_12_6

2012

1000 NAC

12_6_1

FR_X_2012_1000 NAC_12_6_1

2012

1000 NAC

12_6_2

FR_X_2012_1000 NAC_12_6_2

2012

1000 NAC

12_6_3

FR_X_2012_1000 NAC_12_6_3

2012

1000 NAC

12_7

FR_X_2012_1000 NAC_12_7

2012

1000 NAC

12_7_1

FR_X_2012_1000 NAC_12_7_1

2012

1000 NAC

12_7_2

FR_X_2012_1000 NAC_12_7_2

2012

1000 NAC

12_7_3

FR_X_2012_1000 NAC_12_7_3

2012

1000 m3

ST_1_2_C

FR_M_2012_1000 m3_ST_1_2_C

2012

1000 m3

ST_1_2_C_1

FR_M_2012_1000 m3_ST_1_2_C_1

2012

1000 m3

ST_1_2_C_1_1

FR_M_2012_1000 m3_ST_1_2_C_1_1

2012

1000 m3

ST_1_2_C_2_1

FR_M_2012_1000 m3_ST_1_2_C_2_1

2012

1000 m3

ST_1_2_C_2

FR_M_2012_1000 m3_ST_1_2_C_2

2012

1000 m3

ST_1_2_C_1_2

FR_M_2012_1000 m3_ST_1_2_C_1_2

2012

1000 m3

ST_1_2_C_2_2

FR_M_2012_1000 m3_ST_1_2_C_2_2

2012

1000 m3

ST_1_2_C_3

FR_M_2012_1000 m3_ST_1_2_C_3

2012

1000 m3

ST_1_2_C_1_3

FR_M_2012_1000 m3_ST_1_2_C_1_3

2012

1000 m3

ST_1_2_C_2_3

FR_M_2012_1000 m3_ST_1_2_C_2_3

2012

1000 m3

ST_1_2_NC

FR_M_2012_1000 m3_ST_1_2_NC

2012

1000 m3

ST_1_2_NC_1

FR_M_2012_1000 m3_ST_1_2_NC_1

2012

1000 m3

ST_1_2_NC_1_1

FR_M_2012_1000 m3_ST_1_2_NC_1_1

2012

1000 m3

ST_1_2_NC_2_1

FR_M_2012_1000 m3_ST_1_2_NC_2_1

2012

1000 m3

ST_1_2_NC_2

FR_M_2012_1000 m3_ST_1_2_NC_2

2012

1000 m3

ST_1_2_NC_1_2

FR_M_2012_1000 m3_ST_1_2_NC_1_2

2012

1000 m3

ST_1_2_NC_2_2

FR_M_2012_1000 m3_ST_1_2_NC_2_2

2012

1000 m3

ST_1_2_NC_3

FR_M_2012_1000 m3_ST_1_2_NC_3

2012

1000 m3

ST_1_2_NC_1_3

FR_M_2012_1000 m3_ST_1_2_NC_1_3

2012

1000 m3

ST_1_2_NC_2_3

FR_M_2012_1000 m3_ST_1_2_NC_2_3

2012

1000 m3

ST_1_2_NC_4

FR_M_2012_1000 m3_ST_1_2_NC_4

2012

1000 m3

ST_1_2_NC_5

FR_M_2012_1000 m3_ST_1_2_NC_5

2012

1000 m3

ST_5_C

FR_M_2012_1000 m3_ST_5_C

2012

1000 m3

ST_5_C_1

FR_M_2012_1000 m3_ST_5_C_1

2012

1000 m3

ST_5_C_2

FR_M_2012_1000 m3_ST_5_C_2

2012

1000 m3

ST_5_NC

FR_M_2012_1000 m3_ST_5_NC

2012

1000 m3

ST_5_NC_1

FR_M_2012_1000 m3_ST_5_NC_1

2012

1000 m3

ST_5_NC_2

FR_M_2012_1000 m3_ST_5_NC_2

2012

1000 m3

ST_5_NC_3

FR_M_2012_1000 m3_ST_5_NC_3

2012

1000 m3

ST_5_NC_4

FR_M_2012_1000 m3_ST_5_NC_4

2012

1000 m3

ST_5_NC_5

FR_M_2012_1000 m3_ST_5_NC_5

2012

1000 m3

ST_5_NC_6

FR_M_2012_1000 m3_ST_5_NC_6

2012

1000 m3

ST_5_NC_7

FR_M_2012_1000 m3_ST_5_NC_7

2012

1000 NAC

ST_1_2_C

FR_M_2012_1000 NAC_ST_1_2_C

2012

1000 NAC

ST_1_2_C_1

FR_M_2012_1000 NAC_ST_1_2_C_1

2012

1000 NAC

ST_1_2_C_1_1

FR_M_2012_1000 NAC_ST_1_2_C_1_1

2012

1000 NAC

ST_1_2_C_2_1

FR_M_2012_1000 NAC_ST_1_2_C_2_1

2012

1000 NAC

ST_1_2_C_2

FR_M_2012_1000 NAC_ST_1_2_C_2

2012

1000 NAC

ST_1_2_C_1_2

FR_M_2012_1000 NAC_ST_1_2_C_1_2

2012

1000 NAC

ST_1_2_C_2_2

FR_M_2012_1000 NAC_ST_1_2_C_2_2

2012

1000 NAC

ST_1_2_C_3

FR_M_2012_1000 NAC_ST_1_2_C_3

2012

1000 NAC

ST_1_2_C_1_3

FR_M_2012_1000 NAC_ST_1_2_C_1_3

2012

1000 NAC

ST_1_2_C_2_3

FR_M_2012_1000 NAC_ST_1_2_C_2_3

2012

1000 NAC

ST_1_2_NC

FR_M_2012_1000 NAC_ST_1_2_NC

2012

1000 NAC

ST_1_2_NC_1

FR_M_2012_1000 NAC_ST_1_2_NC_1

2012

1000 NAC

ST_1_2_NC_1_1

FR_M_2012_1000 NAC_ST_1_2_NC_1_1

2012

1000 NAC

ST_1_2_NC_2_1

FR_M_2012_1000 NAC_ST_1_2_NC_2_1

2012

1000 NAC

ST_1_2_NC_2

FR_M_2012_1000 NAC_ST_1_2_NC_2

2012

1000 NAC

ST_1_2_NC_1_2

FR_M_2012_1000 NAC_ST_1_2_NC_1_2

2012

1000 NAC

ST_1_2_NC_2_2

FR_M_2012_1000 NAC_ST_1_2_NC_2_2

2012

1000 NAC

ST_1_2_NC_3

FR_M_2012_1000 NAC_ST_1_2_NC_3

2012

1000 NAC

ST_1_2_NC_1_3

FR_M_2012_1000 NAC_ST_1_2_NC_1_3

2012

1000 NAC

ST_1_2_NC_2_3

FR_M_2012_1000 NAC_ST_1_2_NC_2_3

2012

1000 NAC

ST_1_2_NC_4

FR_M_2012_1000 NAC_ST_1_2_NC_4

2012

1000 NAC

ST_1_2_NC_5

FR_M_2012_1000 NAC_ST_1_2_NC_5

2012

1000 NAC

ST_5_C

FR_M_2012_1000 NAC_ST_5_C

2012

1000 NAC

ST_5_C_1

FR_M_2012_1000 NAC_ST_5_C_1

2012

1000 NAC

ST_5_C_2

FR_M_2012_1000 NAC_ST_5_C_2

2012

1000 NAC

ST_5_NC

FR_M_2012_1000 NAC_ST_5_NC

2012

1000 NAC

ST_5_NC_1

FR_M_2012_1000 NAC_ST_5_NC_1

2012

1000 NAC

ST_5_NC_2

FR_M_2012_1000 NAC_ST_5_NC_2

2012

1000 NAC

ST_5_NC_3

FR_M_2012_1000 NAC_ST_5_NC_3

2012

1000 NAC

ST_5_NC_4

FR_M_2012_1000 NAC_ST_5_NC_4

2012

1000 NAC

ST_5_NC_5

FR_M_2012_1000 NAC_ST_5_NC_5

2012

1000 NAC

ST_5_NC_6

FR_M_2012_1000 NAC_ST_5_NC_6

2012

1000 NAC

ST_5_NC_7

FR_M_2012_1000 NAC_ST_5_NC_7

2012

1000 m3

ST_1_2_C

FR_X_2012_1000 m3_ST_1_2_C

2012

1000 m3

ST_1_2_C_1

FR_X_2012_1000 m3_ST_1_2_C_1

2012

1000 m3

ST_1_2_C_1_1

FR_X_2012_1000 m3_ST_1_2_C_1_1

2012

1000 m3

ST_1_2_C_2_1

FR_X_2012_1000 m3_ST_1_2_C_2_1

2012

1000 m3

ST_1_2_C_2

FR_X_2012_1000 m3_ST_1_2_C_2

2012

1000 m3

ST_1_2_C_1_2

FR_X_2012_1000 m3_ST_1_2_C_1_2

2012

1000 m3

ST_1_2_C_2_2

FR_X_2012_1000 m3_ST_1_2_C_2_2

2012

1000 m3

ST_1_2_C_3

FR_X_2012_1000 m3_ST_1_2_C_3

2012

1000 m3

ST_1_2_C_1_3

FR_X_2012_1000 m3_ST_1_2_C_1_3

2012

1000 m3

ST_1_2_C_2_3

FR_X_2012_1000 m3_ST_1_2_C_2_3

2012

1000 m3

ST_1_2_NC

FR_X_2012_1000 m3_ST_1_2_NC

2012

1000 m3

ST_1_2_NC_1

FR_X_2012_1000 m3_ST_1_2_NC_1

2012

1000 m3

ST_1_2_NC_1_1

FR_X_2012_1000 m3_ST_1_2_NC_1_1

2012

1000 m3

ST_1_2_NC_2_1

FR_X_2012_1000 m3_ST_1_2_NC_2_1

2012

1000 m3

ST_1_2_NC_2

FR_X_2012_1000 m3_ST_1_2_NC_2

2012

1000 m3

ST_1_2_NC_1_2

FR_X_2012_1000 m3_ST_1_2_NC_1_2

2012

1000 m3

ST_1_2_NC_2_2

FR_X_2012_1000 m3_ST_1_2_NC_2_2

2012

1000 m3

ST_1_2_NC_3

FR_X_2012_1000 m3_ST_1_2_NC_3

2012

1000 m3

ST_1_2_NC_1_3

FR_X_2012_1000 m3_ST_1_2_NC_1_3

2012

1000 m3

ST_1_2_NC_2_3

FR_X_2012_1000 m3_ST_1_2_NC_2_3

2012

1000 m3

ST_1_2_NC_4

FR_X_2012_1000 m3_ST_1_2_NC_4

2012

1000 m3

ST_1_2_NC_5

FR_X_2012_1000 m3_ST_1_2_NC_5

2012

1000 m3

ST_5_C

FR_X_2012_1000 m3_ST_5_C

2012

1000 m3

ST_5_C_1

FR_X_2012_1000 m3_ST_5_C_1

2012

1000 m3

ST_5_C_2

FR_X_2012_1000 m3_ST_5_C_2

2012

1000 m3

ST_5_NC

FR_X_2012_1000 m3_ST_5_NC

2012

1000 m3

ST_5_NC_1

FR_X_2012_1000 m3_ST_5_NC_1

2012

1000 m3

ST_5_NC_2

FR_X_2012_1000 m3_ST_5_NC_2

2012

1000 m3

ST_5_NC_3

FR_X_2012_1000 m3_ST_5_NC_3

2012

1000 m3

ST_5_NC_4

FR_X_2012_1000 m3_ST_5_NC_4

2012

1000 m3

ST_5_NC_5

FR_X_2012_1000 m3_ST_5_NC_5

2012

1000 m3

ST_5_NC_6

FR_X_2012_1000 m3_ST_5_NC_6

2012

1000 m3

ST_5_NC_7

FR_X_2012_1000 m3_ST_5_NC_7

2012

1000 NAC

ST_1_2_C

FR_X_2012_1000 NAC_ST_1_2_C

2012

1000 NAC

ST_1_2_C_1

FR_X_2012_1000 NAC_ST_1_2_C_1

2012

1000 NAC

ST_1_2_C_1_1

FR_X_2012_1000 NAC_ST_1_2_C_1_1

2012

1000 NAC

ST_1_2_C_2_1

FR_X_2012_1000 NAC_ST_1_2_C_2_1

2012

1000 NAC

ST_1_2_C_2

FR_X_2012_1000 NAC_ST_1_2_C_2

2012

1000 NAC

ST_1_2_C_1_2

FR_X_2012_1000 NAC_ST_1_2_C_1_2

2012

1000 NAC

ST_1_2_C_2_2

FR_X_2012_1000 NAC_ST_1_2_C_2_2

2012

1000 NAC

ST_1_2_C_3

FR_X_2012_1000 NAC_ST_1_2_C_3

2012

1000 NAC

ST_1_2_C_1_3

FR_X_2012_1000 NAC_ST_1_2_C_1_3

2012

1000 NAC

ST_1_2_C_2_3

FR_X_2012_1000 NAC_ST_1_2_C_2_3

2012

1000 NAC

ST_1_2_NC

FR_X_2012_1000 NAC_ST_1_2_NC

2012

1000 NAC

ST_1_2_NC_1

FR_X_2012_1000 NAC_ST_1_2_NC_1

2012

1000 NAC

ST_1_2_NC_1_1

FR_X_2012_1000 NAC_ST_1_2_NC_1_1

2012

1000 NAC

ST_1_2_NC_2_1

FR_X_2012_1000 NAC_ST_1_2_NC_2_1

2012

1000 NAC

ST_1_2_NC_2

FR_X_2012_1000 NAC_ST_1_2_NC_2

2012

1000 NAC

ST_1_2_NC_1_2

FR_X_2012_1000 NAC_ST_1_2_NC_1_2

2012

1000 NAC

ST_1_2_NC_2_2

FR_X_2012_1000 NAC_ST_1_2_NC_2_2

2012

1000 NAC

ST_1_2_NC_3

FR_X_2012_1000 NAC_ST_1_2_NC_3

2012

1000 NAC

ST_1_2_NC_1_3

FR_X_2012_1000 NAC_ST_1_2_NC_1_3

2012

1000 NAC

ST_1_2_NC_2_3

FR_X_2012_1000 NAC_ST_1_2_NC_2_3

2012

1000 NAC

ST_1_2_NC_4

FR_X_2012_1000 NAC_ST_1_2_NC_4

2012

1000 NAC

ST_1_2_NC_5

FR_X_2012_1000 NAC_ST_1_2_NC_5

2012

1000 NAC

ST_5_C

FR_X_2012_1000 NAC_ST_5_C

2012

1000 NAC

ST_5_C_1

FR_X_2012_1000 NAC_ST_5_C_1

2012

1000 NAC

ST_5_C_2

FR_X_2012_1000 NAC_ST_5_C_2

2012

1000 NAC

ST_5_NC

FR_X_2012_1000 NAC_ST_5_NC

2012

1000 NAC

ST_5_NC_1

FR_X_2012_1000 NAC_ST_5_NC_1

2012

1000 NAC

ST_5_NC_2

FR_X_2012_1000 NAC_ST_5_NC_2

2012

1000 NAC

ST_5_NC_3

FR_X_2012_1000 NAC_ST_5_NC_3

2012

1000 NAC

ST_5_NC_4

FR_X_2012_1000 NAC_ST_5_NC_4

2012

1000 NAC

ST_5_NC_5

FR_X_2012_1000 NAC_ST_5_NC_5

2012

1000 NAC

ST_5_NC_6

FR_X_2012_1000 NAC_ST_5_NC_6

2012

1000 NAC

ST_5_NC_7

FR_X_2012_1000 NAC_ST_5_NC_7

EX_M

2012

1000 m3

FR_EX_M_2012_1000 m3_1

EX_M

2012

1000 m3

1_1

FR_EX_M_2012_1000 m3_1_1

EX_M

2012

1000 m3

1_2

FR_EX_M_2012_1000 m3_1_2

EX_M

2012

1000 m3

1_2_C

FR_EX_M_2012_1000 m3_1_2_C

EX_M

2012

1000 m3

1_2_NC

FR_EX_M_2012_1000 m3_1_2_NC

EX_M

2012

1000 m3

1_2_NC_T

FR_EX_M_2012_1000 m3_1_2_NC_T

EX_M

2012

1000 mt

FR_EX_M_2012_1000 mt_2

EX_M

2012

1000 m3

FR_EX_M_2012_1000 m3_3

EX_M

2012

1000 m3

3_1

FR_EX_M_2012_1000 m3_3_1

EX_M

2012

1000 m3

3_2

FR_EX_M_2012_1000 m3_3_2

EX_M

2012

1000 mt

FR_EX_M_2012_1000 mt_4

EX_M

2012

1000 mt

4_1

FR_EX_M_2012_1000 mt_4_1

EX_M

2012

1000 mt

4_2

FR_EX_M_2012_1000 mt_4_2

EX_M

2012

1000 m3

FR_EX_M_2012_1000 m3_5

EX_M

2012

1000 m3

5_C

FR_EX_M_2012_1000 m3_5_C

EX_M

2012

1000 m3

5_NC

FR_EX_M_2012_1000 m3_5_NC

EX_M

2012

1000 m3

5_NC_T

FR_EX_M_2012_1000 m3_5_NC_T

EX_M

2012

1000 m3

FR_EX_M_2012_1000 m3_6

EX_M

2012

1000 m3

6_1

FR_EX_M_2012_1000 m3_6_1

EX_M

2012

1000 m3

6_1_C

FR_EX_M_2012_1000 m3_6_1_C

EX_M

2012

1000 m3

6_1_NC

FR_EX_M_2012_1000 m3_6_1_NC

EX_M

2012

1000 m3

6_1_NC_T

FR_EX_M_2012_1000 m3_6_1_NC_T

EX_M

2012

1000 m3

6_2

FR_EX_M_2012_1000 m3_6_2

EX_M

2012

1000 m3

6_2_C

FR_EX_M_2012_1000 m3_6_2_C

EX_M

2012

1000 m3

6_2_NC

FR_EX_M_2012_1000 m3_6_2_NC

EX_M

2012

1000 m3

6_2_NC_T

FR_EX_M_2012_1000 m3_6_2_NC_T

EX_M

2012

1000 m3

6_3

FR_EX_M_2012_1000 m3_6_3

EX_M

2012

1000 m3

6_3_1

FR_EX_M_2012_1000 m3_6_3_1

EX_M

2012

1000 m3

6_4

FR_EX_M_2012_1000 m3_6_4

EX_M

2012

1000 m3

6_4_1

FR_EX_M_2012_1000 m3_6_4_1

EX_M

2012

1000 m3

6_4_2

FR_EX_M_2012_1000 m3_6_4_2

EX_M

2012

1000 m3

6_4_3

FR_EX_M_2012_1000 m3_6_4_3

EX_M

2012

1000 mt

FR_EX_M_2012_1000 mt_7

EX_M

2012

1000 mt

7_1

FR_EX_M_2012_1000 mt_7_1

EX_M

2012

1000 mt

7_2

FR_EX_M_2012_1000 mt_7_2

EX_M

2012

1000 mt

7_3

FR_EX_M_2012_1000 mt_7_3

EX_M

2012

1000 mt

7_3_1

FR_EX_M_2012_1000 mt_7_3_1

EX_M

2012

1000 mt

7_3_2

FR_EX_M_2012_1000 mt_7_3_2

EX_M

2012

1000 mt

7_3_3

FR_EX_M_2012_1000 mt_7_3_3

EX_M

2012

1000 mt

7_3_4

FR_EX_M_2012_1000 mt_7_3_4

EX_M

2012

1000 mt

7_4

FR_EX_M_2012_1000 mt_7_4

EX_M

201

Presentation, Thomas Lellouch, INSEE, (France)

Read more about Presentation, Thomas Lellouch, INSEE, (France)

Better understanding and measurement of extreme poverty in France, Thomas Lellouch, INSEE, France

Languages and translations

English

Group of Experts on Measuring Poverty and Inequality – UNECE

Better understanding and measurement of extreme poverty in France

8/11/2023

1MULTIDIMENSIONAL POVERTY

2A PROJECT WITH ATD AND SECOURS CATHOLIQUE

MULTIDIMENSIONAL POVERTY01

MONETARY AND NON-MONETARY DIMENSIONS

− Monetary approach unavoidable…

⚫ Traditional indicators has many advantages :

− Based on objective criteria, statistically observable

− Allowing for international comparisons

− … but probably incomplete

⚫ Coverage problems of the indicators

− Indicators based on general population surverys

⚫ Other dimensions exist

− The Hidden dimensions of poverty (ATD, Oxford University…)

− … remain very complex to measure

A PROJECT WITH ATD AND SECOURS CATHOLIQUE02

THE PROJECT

PARTNERSHIP : INSEE, ATD, SECOURS CATHOLIQUE

− Objectives : compare the tools used by Insee to measure poverty

and the experiences of people that are actually living poverty

− Methodology : « croisement des savoirs »

⚫ Locals groups of people experiencing extreme proverty

⚫ Animation techniques by the associations

− Two phases :

⚫ Phase 1 : build a base of knowledge and common benchmarks to define

poverty

⚫ Phase 2 : specific work on two dimensions : “social isolation” and

“institutional mistreatment”

A COMPLEX AND MULTIPLE PHENONEMON

− Poverty, a multitude of representations

− Experiences and feelings

⚫ Important of non monetary determinants

− Entries into and exits from great poverty

⚫ Negative and positive spirals

⚫ Importance of being part of a social network

− Systemic approach, with very close links and interactions between the

different dimensions of poverty

CONCLUSIONS AND FOLLOW-UP

− On conception of Insee questionnaires :

⚫ Questionnaires are :

− relevant on many issues…

− … but does not cover exactly all the elements identified by the local groups

⚫ Some avenues for reflection to develop the module « Administrative

Difficulties » of French EU-SILC

− In the long run, avenues for resarch work :

⚫ How to operationalise statistically non monetary dimensions ?

⚫ Partnership with a research Lab in France

MORE DETAILS AVAILABLE...

… in French

⚫ Document de Travail de l’Insee n°2023-21, Octobre 2023,

« Rapport du Groupe de travail sur le thème “Mieux comprendre

et mesurer la grande pauvreté », ATD Quart Monde, Secours

Catholique - Caritas France, Insee

THANK YOU FOR YOUR ATTENTION !

insee.fr

Retrouvez-nous sur

Thomas Lellouch

Directeur de Projet Statistiques de la Grande Pauvreté

Département des ressources et des conditions de vie des ménages

01 87 69 63 86

[email protected]

8/11/2023

mailto:[email protected]

ANNEXE 1 : SOCIAL ISOLATION

− Exclusion mechanisms

⚫ Self-exclusion

⚫ From others

− Reject and judgment

− Words and looks

− Characteristics

⚫ Losses and ruptures of social ties

⚫ Lack of networks

⚫ Rupture of trust

− Adaptation strategies

⚫ Violence to « survive »

⚫ Addictions

ANNEXE 2 : INSTITUTIONNAL ABUSE

− Strong link with all the other dimensions

− Lack of recognition of competencies

⚫ Volunteering and other competencies acquired in daily life

− Judgment and disregard

− Dependancy to other people’s decisions

− Difficulty of access

⚫ Digitalization

Slide 1: Group of Experts on Measuring Poverty and Inequality – UNECE
Slide 2
Slide 3: Multidimensional poverty
Slide 4: Monetary and non-monetary dimensions
Slide 5: a project with atd and secours catholique
Slide 6: The project
Slide 7: A complex and multiple phenonemon
Slide 8: Conclusions and follow-up
Slide 9: More details available...
Slide 10: Retrouvez-nous sur
Slide 11: Annexe 1 : social isolation
Slide 12: Annexe 2 : institutionnal abuse

Russian

Группа экспертов по измерению бедности и неравенства - ЕЭК

ООН

Более глубокое понимание и измерение крайней бедности во Франции

8/11/2023

1МНОГОМЕРНАЯ БЕДНОСТЬ

2ПРОЕКТ СОВМЕСТНО С КОМПАНИЯМИ ATD И

SECOURS CATHOLIQUE

МНОГОМЕРНАЯ БЕДНОСТЬ01

ДЕНЕЖНЫЕ И НЕДЕНЕЖНЫЕ ИЗМЕРЕНИЯ

− Монетарный подход неизбежен…

⚫ Традиционные индикаторы имеют много преимуществ:

− На основе объективных критериев, статистически наблюдаемых

− Возможность проведения международных сравнений

− … но, вероятно, неполный

⚫ Проблемы охвата индикаторов

− Показатели, основанные на обследованиях населения

⚫ Существуют и другие измерения

− Скрытые аспекты бедности (ATD, Оксфордский университет…)

− … остаются весьма сложными для измерения

ПРОЕКТ СОВМЕСТНО С ATD И SECOURS CATHOLIQUE02

ПРОЕКТ

ПАРТНЕРСТВО: INSEE, ATD, SECOURS CATHOLIQUE

− Задачи: сравнить инструменты, используемые Insee для измерения бедности, и опыт людей, которые действительно живут в бедности

− Методология: „взаимообогащение знаний”

⚫ Местные группы людей, испытывающих крайнюю бедность

⚫ Анимационные приемы по ассоциациям

− Два этапа:

⚫ Этап 1: создание базы знаний и общих ориентиров для определения бедности

⚫ Этап 2: конкретная работа по двум измерениям: "социальная изоляция" и "жестокое обращение в учреждениях"

СЛОЖНЫЙ И МНОЖЕСТВЕННЫЙ ФЕНОМЕН

− Бедность, множество представлений

− Переживания и чувства

⚫ Важность неденежных детерминант

− Вход в большую бедность и выход из нее

⚫ Негативные и позитивные спирали

⚫ Важность принадлежности к социальной сети

− Системный подход, предусматривающий очень тесную связь и взаимодействие между различными аспектами бедности

ВЫВОДЫ И ПОСЛЕДУЮЩИЕ ДЕЙСТВИЯ

− О концепции анкет Insee:

⚫ Анкеты являются:

− актуальны по многим вопросам …

− … но не охватывают в точности все элементы, определенные местными группами

⚫ Некоторые направления анализа для развития модуля "Административные трудности" французской программы EU-SILC

− В долгосрочной перспективе направления исследовательской работы:

⚫ Как операционализировать статистические неденежные измерения?

⚫ Партнерство с исследовательской лабораторией во Франции

БОЛЕЕ ПОДРОБНАЯ ИНФОРМАЦИЯ ДОСТУПНА...

… на французском языке

⚫ Document de Travail de l’Insee n°2023-21, Octobre 2023,

« Rapport du Groupe de travail sur le thème “Mieux comprendre

et mesurer la grande pauvreté », ATD Quart Monde, Secours

Catholique - Caritas France, Insee

СПАСИБО ЗА ВНИМАНИЕ!

insee.fr

Retrouvez-nous sur

Thomas Lellouch

Directeur de Projet Statistiques de la Grande Pauvreté

Département des ressources et des conditions de vie des ménages

01 87 69 63 86

[email protected]

8/11/2023

mailto:[email protected]

ПРИЛОЖЕНИЕ 1 : СОЦИАЛЬНАЯ ИЗОЛЯЦИЯ

− Механизмы исключения

⚫ Самоисключение

⚫ От других

− Отвержение и осуждение

− Слова и взгляды

− Характеристики

⚫ Утрата и разрыв социальных связей

⚫ Отсутствие контактов

⚫ Разрыв доверия

− Стратегии адаптации

⚫ Насилие ради "выживания"

⚫ Зависимости

ПРИЛОЖЕНИЕ 2: ИНСТИТУЦИОНАЛЬНЫЕ ЗЛОУПОТРЕБЛЕНИЯ

− Сильная связь со всеми другими измерениями

− Недостаточное признание компетенций

⚫ Волонтерство и другие компетенции, приобретенные в

повседневной жизни

− Осуждение и пренебрежение

− Зависимость от решений других людей

− Сложность доступа

⚫ Цифровизация

Slide 1: Группа экспертов по измерению бедности и неравенства - ЕЭК ООН
Slide 2
Slide 3: Многомерная бедность
Slide 4: Денежные и неденежные измерения
Slide 5: проект совместно с atd и secours catholique
Slide 6: Проект
Slide 7: Сложный и множественный феномен
Slide 8: Выводы и последующие действия
Slide 9: Более подробная информация доступна...
Slide 10: Retrouvez-nous sur
Slide 11: Приложение 1 : социальная изоляция
Slide 12: Приложение 2: Институциональные злоупотребления

Paper (INSEE, France)

Read more about Paper (INSEE, France)

The project is the result of a partnership between ATD-Quart-Monde (ATD), the Secours Catholique (SCCF) and the French Statistical Office (Insee), conducted in 2022, with a view to better understand and measure great poverty and more specifically some hidden dimensions. The project is based on the active participation of people having an experience of poverty; it aims at identifying how the tools used by INSEE to measure poverty are consistent with the experiences of people that are actually living poverty day by day.

Languages and translations

English

Economic Commission for Europe

Conference of European Statisticians Group of Experts on Measuring Poverty and Inequality Geneva, Switzerland, 28-29 November 2023

Agenda item: Disaggregation for 2030 Agenda for Sustainable Development: Going beyond averages

To better understand and measure great poverty

Note by Insee, France

Abstract

The starting point of the project is the program carried out as part of participatory

international research on « the hidden dimensions of poverty », published in 2019

and conducted by the ATD Fourth World Movement and the University of Oxford,

jointly in 6 countries1.

1 https://www.atd-quartmonde.org/wp-content/uploads/2019/12/Hidden-Dimensions-of-Poverty-20-11-2019.pdf

Working paper 1

Distr.: General

14 November 2023

English

https://www.atd-quartmonde.org/wp-content/uploads/2019/12/Hidden-Dimensions-of-Poverty-20-11-2019.pdf

Project objectives and methodological approach

The project is the result of a partnership between ATD-Quart-Monde (ATD), the Secours

Catholique (SCCF) and the French Statistical Office (Insee), conducted in 2022, with a view to

better understand and measure great poverty and more specifically some hidden dimensions.

The project is based on the active participation of people having an experience of poverty ; it aims

at identifying how the tools used by Insee to measure poverty are consistent with the experiences

of people that are actually living poverty day by day. Some local groups of people experiencing

poverty have been formed, coordinated by animators from the associations with an approach that

promotes trust and active participation of people. The project is punctuated by regular meetings of

local groups and three plenary meetings with everyone.

The project is divided in two phases:

• phase 1: build a base of knowledge and common benchmarks to define poverty. Starting

from people's experiences to look at poverty, and discovering the research on the dimensions of

poverty, always linking them to the experiences of the participants. This appropriation phase is

necessary to create a common culture and shared knowledge before entering into a constructive

and substantiated dialogue with Insee representatives.

• phase 2: specific work on two dimensions: “social isolation” and “institutional

mistreatment”, with a view to identify common points and divergences between Insee tools and

people’s experience

Phase 1: The different dimensions of poverty and its relationships

On 5th February 2022, a plenary meeting has been organised to launch the project. The objectives

was to gather all participants to the project, to begin to get to know each other, and to have a first

approach of the dimensions of poverty.

A first exercise was to ask each people to note on a post-it one word that represents for each person

the word « poverty ». Three groups of words have emerged: (i) system and domination; (ii)

privations ; (iii) ignorance and misunderstanding. Other concepts positive (solidarity, muddling

through), negative (isolation, misery) or ambiguous (surviving, struggle, fight) were also

mentioned. Then, the group watched a short video presenting the resultats of the international

study on the hidden dimensions of poverty. Finally, the representatives of Insee presented the role

and the main missions of the national statistical office.

Then, local groups came back home and worked within their respective groups on deepening their

understanding and analysis of poverty. Specific animation techniques were used, such as building

an analysis from a lived experience, identifying the characteristics of povery through a person’s

silhouette, or working on a spiral analysis to understand what brings people into or out of poverty.

Very rich discussions took place among the local groups, and a restitution was made at a plenary

meeting on 1st April 2022. In particular, mainly non-monetary determinants of poverty were

reported by the local groups : social isolation, fear, pain, the gaze of others, trust in ourselves or in

other people, institutional abuse, dependency to other’s people decision, fight, violence, fatigue,

mental and physical health, impossibility of planning. The importance of being part of a social

network has been many times identified as a key element of the positive spiral. Another important

element was the systemic approach, with very close links and interactions between the different

dimensions of poverty.

At the plenary meeting, the representatives of Insee also presented to the group the formal

indicators of the poverty, such as the monetary poverty rate, or the poverty rate in living conditions.

Phase 2: Deepening two dimensions: social isolation and institutional abuse

During the second phase, local groups have worked on two specific dimensions: social isolation

and institutional abuse. Several working sessions have taken place at the local level, with the

following objectives: (i) identifying characteristics of poverty in terms of social isolation or

institutional abuse ; (ii) comparing these results with the questions of Insee’s surveys on those

thematic, so as to identify convergences but also differences and missing points.

• The characteristics of social isolation identified by local groups were mostly about the lack

of relationships with family (in particular difficult experiences during childhood) and with friends,

the existence of social network with associations, the rejection of loved ones, the defense

mechanisms which lead to auto-exclusion, the ambiguous effects of addictions, the violence of the

exclusion.

• For institutional abuse, local groups considered there is a strong link with the other

dimensions, with a formation of a vicious circle. Characteristics of institutional abuse are mainly

the lack of recognition of the competencies (in particular experiencing of volunteering), the

judgment and disregard, the dependency to other people’s decisions, the difficulty of access

(digitalization), or the interference with private life.

Overall, the questionnaires of Insee have been considered as relevant on many issues, but not

sufficiently complete to cover all characteristics mentioned above.

The project has been concluded on a final day of plenary meeting on 24 June 2022 in the premises

of Insee, with all the participants of local groups, the representatives of the associations, 3 workers

from Credoc (https://www.credoc.fr/), and around ten Insee agents from the Department of Living

Conditions of Households, including the Head of Department.

During this day, Insee has presented two recent studies on social isolation2 and administrative

procedures3, and the group of people experiencing poverty have presented the results of their work

on those two dimensions. Then, mixed workshops between Insee agents, associations and people

experiencing poverty took place to try to pool together the two approaches.

As a conclusion, some work avenues have then been identified in terms of conception of Insee

questionnaires and promising further research.

Russian

Economic Commission for Europe

Conference of European Statisticians Group of Experts on Measuring Poverty and Inequality Geneva, Switzerland, 28-29 November 2023

Agenda item: Disaggregation for 2030 Agenda for Sustainable Development: Going beyond averages

Для лучшего понимания и измерения крайней бедности

Национального института статистики и экономических

исследований (НИСЭИ), Франция

Резюме

Отправной точкой проекта является программа, осуществляемая в рамках

совместного международного исследования «скрытые измерения бедности»,

опубликованного в 2019 году и проведенного Движением за оказание помощи

бедствующим группам населения «Четвертый мир» (ATD Fourth World

Movement) и Оксфордским университетом совместно в 6 странах1.

Цели проекта и методологический подход

1 https://www.atd-quartmonde.org/wp-content/uploads/2019/12/Hidden-Dimensions-of-Poverty-20-11-2019.pdf

Working paper 1

Distr.: General

17 November 2023

English

https://www.atd-quartmonde.org/wp-content/uploads/2019/12/Hidden-Dimensions-of-Poverty-20-11-2019.pdf

Проект является результатом партнерства между ATD-Quart-Monde (ATD), Secours

Catholique (SCCF) и Французским статистическим управлением (INSEE), проведенного в

2022 году, с целью лучшего понимания и измерения крайней нищеты и, более конкретно,

некоторых скрытых аспектов.

Проект основан на активном участии людей, имеющих опыт бедности; его цель -

определить, насколько инструменты, используемые INSEE для измерения бедности,

соответствуют опыту людей, которые на самом деле живут в бедности изо дня в день. Были

созданы некоторые местные группы людей, испытывающих бедность, координируемые

аниматорами из ассоциаций с подходом, способствующим доверию и активному участию

людей. В рамках проекта проводятся регулярные встречи локальных групп и три пленарных

заседания со всеми желающими.

Проект реализуется в два этапа:

• этап 1: создание базы знаний и общих ориентиров для определения бедности.

При рассмотрении проблемы бедности следует отталкиваться от опыта людей и

знакомиться с исследованиями, посвященными измерениям бедности, всегда увязывая их с

опытом участников. Эта фаза присвоения необходима для создания общей культуры и

обмена знаниями, прежде чем вступать в конструктивный и обоснованный диалог с

представителями НИСЭИ.

• этап 2: конкретная работа по двум измерениям: «социальная изоляция» и

«жестокое обращение в учреждениях» с целью выявления общих точек и расхождений

между инструментами НИСЭИ и опытом людей.

Этап 1: Различные аспекты бедности и их взаимосвязь

5 февраля 2022 года было организовано пленарное заседание, посвященное началу

реализации проекта. Задача состояла в том, чтобы собрать всех участников проекта, начать

знакомство друг с другом и получить первое представление об измерениях бедности.

Первое упражнение состояло в том, чтобы попросить каждого участника написать на

стикере одно слово, которое для каждого человека означает слово «бедность». Появились

три группы слов: (i) система и господство; (ii) лишения; (iii) невежество и непонимание.

Также упоминались другие понятия: позитивные (солидарность, преодоление трудностей),

негативные (изоляция, страдания) или неоднозначные (выживание, противостояние,

преодоление). Затем группа посмотрела короткое видео, в котором представлены

результаты международного исследования скрытых аспектов бедности. Наконец,

представители НИСЭИ рассказали о роли и основных задачах национального

статистического управления.

Затем местные группы вернулись домой и работали в рамках своих соответствующих групп

над углублением понимания и анализа проблемы бедности. Использовались специфические

приемы анимации, такие как построение анализа на основе жизненного опыта, выявление

характеристик бедности по силуэту человека или работа по спиральному анализу, чтобы

понять, что приводит людей в бедность или выводит из нее.

Между местными группами состоялись очень насыщенные дискуссии, и на пленарном

заседании 1 апреля 2022 года было принято решение о реституции. В частности, местные

группы сообщали в основном о неденежных детерминантах бедности: социальной

изоляции, страхе, боли, взгляде других, доверии к себе или другим людям,

институциональном насилии, зависимости от решения других людей, борьбе, насилии,

усталости, психическом и физическом здоровье, невозможности планирования. Важность

принадлежности к социальной сети неоднократно указывалась в качестве ключевого

элемента позитивной спирали. Другим важным элементом является системный подход,

предусматривающий очень тесную связь и взаимодействие между различными аспектами

бедности.

На пленарном заседании представители НИСЭИ также представили группе официальные

показатели бедности, такие как уровень денежной бедности или уровень бедности по

условиям жизни.

Этап 2: Углубление двух аспектов: социальная изоляция и институциональное

насилие

На втором этапе локальные группы работали над двумя конкретными аспектами:

социальной изоляцией и институциональным насилием. На местном уровне было

проведено несколько рабочих сессий со следующими целями: (i) определение

характеристик бедности с точки зрения социальной изоляции или институционального

насилия; (ii) сравнение этих результатов с вопросами обследований НИСЭИ по этим темам,

с целью выявления совпадений, а также различий и недостающих моментов.

• Характеристики социальной изоляции, выявленные локальными группами, в

основном касались отсутствия отношений с семьей (в частности, с трудными

переживаниями в детстве) и с друзьями, наличия социальной сети с ассоциациями,

отвержения близких, защитных механизмов, приводящих к самоисключению,

неоднозначных последствий зависимостей, жестокости исключения.

• Что касается институционального насилия, по мнению локальных групп, существует

тесная связь с другими измерениями, что приводит к образованию замкнутого круга.

Характерными чертами институционального насилия являются, прежде всего, отсутствие

признания компетенций (в частности, опыт волонтерства), осуждение и пренебрежение,

зависимость от решений других людей, затрудненный доступ (цифровизация) или

вмешательство в личную жизнь.

В целом анкеты НИСЭИ были признаны релевантными по многим вопросам, но

недостаточно полными, чтобы охватить все характеристики, упомянутые выше.

Проект был завершен на заключительном пленарном заседании 24 июня 2022 года в

помещении НИСЭИ, со всеми участниками локальных групп, представителями

ассоциаций, тремя работниками Credoc (https://www.credoc.fr/) и примерно десятью

представителями НИСЭИ из Департамента условий жизни домохозяйств, включая главу

департамента.

В течение этого дня НИСЭИ представила два последних исследования по социальной

изоляции2 и административным процедурам3, а группа людей, испытывающих бедность,

представила результаты своей работы по этим двум направлениям. Затем были проведены

смешанные семинары с участием представителей НИСЭИ, ассоциаций и и людей,

сталкивающихся с бедностью, чтобы попытаться объединить два подхода.

В заключение были определены направления работы над концепцией опросников

INSEE и перспективные направления дальнейших исследований.

2 Insee Focus n° 265, avril 2022, «Pendant les périodes de confinement, un tiers des personnes de 18 ans ou plus, ont échangé

avec leur famille. », Мари Клерк, Амандин Нугаре

3 Insee Focus n° 267, май 2022, «Un tiers des adultes ont renoncé à effectuer une démarche administrative en ligne en 2021»,

Франсуа Глез, Амандин Нугаре, Анна Пла, Луиза Виар-Гийо

https://www.credoc.fr/

Better understanding and measurement of extreme poverty in France, Thomas Lellouch (INSEE, France)

Read more about Better understanding and measurement of extreme poverty in France, Thomas Lellouch (INSEE, France)

Languages and translations

English

United Nations Economic Commission for Europe

Palais des Nations, 1211 Geneva 10, Switzerland

UNITED NATIONS

ECONOMIC COMMISSION FOR EUROPE

CONFERENCE OF EUROPEAN STATISTICIANS

Group of Experts on Measuring Poverty and Inequality

28-29 November 2023

Workshop on Harmonization of Poverty Statistics to

Measure SDG 1 and 10

27 November 2023

Title of contribution To better Understand and Measure Great Poverty – a participative approach

Author Name(s) Thomas LELLLOUCH

Presenter Name Thomas LELLLOUCH

Presenter Organization INSEE

Presenter’s email [email protected]

Topic Social policies, social transfers and data

The project is the result of a partnership between ATD-Quart-Monde (ATD), the Secours Catholique (SCCF) and

the French Statistical Office (Insee), conducted in 2022, with a view to better understand and measure great

poverty and more specifically some hidden dimensions.

The project is based on the active participation of people having an experience of poverty; it aims at identifying

how the tools used by INSEE to measure poverty are consistent with the experiences of people that are actually

living poverty day by day. Some local groups of people experiencing poverty have been formed, coordinated by

animators from the associations with an approach that promotes trust and active participation of people. The

project is punctuated by regular meetings of local groups and three plenary meetings with everyone.

The project is divided in two phases :

• phase 1 : build a base of knowledge and common benchmarks to define poverty.

• phase 2 : specific work on two dimensions : “social isolation” and “institutional mistreatment”,

with a view to identify common points and divergences between Insee tools and people’s experience

The characteristics of social isolation identified by local groups were mostly about the lack of relationships with

family (in particular difficult experiences during childhood) and with friends, the existence of social network

with associations, the rejection of loved ones, the defense mechanisms which lead to auto-exclusion, the

ambiguous effects of addictions, the violence of the exclusion.

For institutional abuse, local groups considered there is a strong link with the other dimensions, with a formation

of a vicious circle. Characteristics of institutional abuse are mainly the lack of recognition of the competencies

(in particular experiencing of volunteering), the judgment and disregard, the dependency to other people’s

decisions, the difficulty of access (digitalization), or the interference with private life.

Overall, the questionnaires of Insee have been considered as relevant on many issues, but not sufficiently

complete to cover all characteristics mentioned above.

Please select your preferred contribution (you may select both options):

X Presentation

X Paper (to be submitted by 20 October)

(France, UK) Proposal for a new supplement to UN Regulation No. 155

Read more about (France, UK) Proposal for a new supplement to UN Regulation No. 155

Languages and translations

English

Proposal for a new supplement to UN Regulation No. 155

The text below was prepared by the experts from France and the United Kingdom of Great Britain and Northern Ireland. The modifications to the existing text of the Regulation are marked in bold for new or ~~strikethrough~~ for deleted characters.

I. Proposal

Paragraph 1.1., amend to read:

“1.1. This Regulation applies to vehicles, with regard to cyber security, of ~~the~~ Categories L, M ~~and~~ , N, O, R, S and T, if fitted with at least one electronic control unit.

~~This Regulation also applies to vehicles of Category O if fitted with at least one electronic control unit~~.”

Paragraph 1.2., shall be deleted:

“1.2. This Regulation also applies to vehicles of the Categories L6 and L7 if equipped with automated driving functionalities from level 3 onwards, as defined in the reference document with definitions of Automated Driving under WP.29 and the General Principles for developing a UN Regulation on automated vehicles (ECE/TRANS/WP.29/1140).”

Paragraphs 1.3. (former) and 1.4., renumber as paragraphs 1.2. and 1.3.

Paragraph 7.3.1., amend to read:

“7.3.1. The manufacturer shall have a valid Certificate of Compliance for the Cyber

Security Management System relevant to the vehicle type being approved. However, for type approvals of vehicles of Categories M, N and O first issued before 1 July 2024, and for type approvals of vehicles of Categories L, R, S and T first issued before 1 July 2027, and for each extension thereof, if the vehicle manufacturer can demonstrate that the vehicle type could not be developed in compliance with the CSMS, then the vehicle manufacturer shall demonstrate that cyber security was adequately considered during the development phase of the vehicle type concerned.”

Paragraph 7.3.4., amend to read:

“7.3.4. The vehicle manufacturer shall protect the vehicle type against risks identified in the vehicle manufacturer’s risk assessment. Proportionate mitigations shall be implemented to protect the vehicle type. The mitigations implemented shall include all mitigations referred to in Annex 5, Part B and C which are relevant for the risks identified. However, if a mitigation referred to in Annex 5, Part B or C, is not relevant or not sufficient for the risk identified, the vehicle manufacturer shall ensure that another appropriate mitigation is implemented. In particular, for type approvals of vehicles of Categories M, N and O first issued before 1 July 2024, and for type approvals of vehicles of Categories L, R, S and T first issued before 1 July 2027, and for each extension thereof, the vehicle manufacturer shall ensure that another appropriate mitigation is implemented if a mitigation measure referred to in Annex 5, Part B or C is technically not feasible. The respective assessment of the technical feasibility shall be provided by the manufacturer to the approval authority.”

II. Justification

1. At the 16th session of GRVA in May 2023, the subsidiary Working Party accepted the Chair’s proposal to finalise the discussion of the inclusion of all categories of vehicles in UN Regulation No. 155 at its 17th session in September.

2. The purpose of UN Regulation No. 155 is to offer an international framework for the homologation of road vehicles with regard to cyber security. Therefore, GRVA should strive to offer the broadest scope possible to its Contracting Parties, and to allow manufacturers of vehicles of any relevant category to apply for a type approval.

3. During the previous sessions of GRVA and of its informal working group on cyber security and software updates, no technical argument was put forward to justify the exclusion of vehicles of Categories L, R, S and T from the scope of the Regulation. Not including these categories thus forces Contracting Parties and regional organisations to use national or regional laws on cyber security for these categories of vehicles. This could lead to unique requirements and a level of divergence that could be onerous on the industry.

4. The scope of UN Regulation No. 156 already includes all categories of vehicles: this current discrepancy between the two Regulations is an implicit statement that some vehicles, while able to receive over-the-air software updates, should not be type approved with regard to cyber security. Aligning the scope of UN Regulation No. 155 with that of UN Regulation No. 156 is a logical step towards a comprehensive regulatory environment for connected vehicles.

5. Similarly to what was granted to Categories M and N in the original version of the Regulation (paragraphs 7.3.1. and 7.3.4.), an adequate lead time is necessary for manufacturers of vehicles of the categories introduced in this proposal to demonstrate adequate cybersecurity measures for the approval of vehicle types whose development phase started prior to the implementation of the manufacturer’s Cyber Security Management System. Category L vehicles that were already in scope of the Regulation have been included in this lead time to simplify the drafting and remove reference to SAE levels of automation. As the provisions still require demonstration that cyber security was adequately addressed and any alternative mitigations are appropriate, there should be no issues in allowing additional time in this case.

Submitted by the experts from France and the United Kingdom of Great Britain and Northern Ireland

Informal document GRVA-17-13 17th GRVA, 25-29 September 2023 Provisional agenda item 5(a)

Proposal for a new supplement to UN Regulation No. 155

The text below was prepared by the experts from France and the United Kingdom of Great Britain and Northern Ireland. The modifications to the existing text of the Regulation are marked in bold for new or strikethrough for deleted characters.

I. Proposal

Paragraph 1.1., amend to read:

“1.1. This Regulation applies to vehicles, with regard to cyber security, of the Categories L, M and, N, O, R, S and T, if fitted with at least one electronic control unit.

This Regulation also applies to vehicles of Category O if fitted with at least one electronic control unit.”

Paragraph 1.2., shall be deleted:

Paragraphs 1.3. (former) and 1.4., renumber as paragraphs 1.2. and 1.3.

Paragraph 7.3.1., amend to read:

“7.3.1. The manufacturer shall have a valid Certificate of Compliance for the Cyber

Security Management System relevant to the vehicle type being approved. However, for type approvals of vehicles of Categories M, N and O first issued before 1 July 2024, and for type approvals of vehicles of Categories L, R, S and T first issued before 1 July 2027, and for each extension thereof, if the vehicle manufacturer can demonstrate that the vehicle type could not be developed in compliance with the CSMS, then the vehicle manufacturer shall demonstrate that cyber security was adequately considered during the development phase of the vehicle type concerned.”

Paragraph 7.3.4., amend to read:

“7.3.4. The vehicle manufacturer shall protect the vehicle type against risks identified in the vehicle manufacturer’s risk assessment. Proportionate mitigations shall be implemented to protect the vehicle type. The mitigations implemented shall include all mitigations referred to in Annex 5, Part B and C which are relevant for the risks identified. However, if a mitigation referred to in Annex 5, Part B or C, is not relevant or not sufficient for the risk identified, the vehicle manufacturer shall ensure that another appropriate mitigation is implemented. In particular, for type approvals of vehicles of Categories M, N and O first issued before 1 July 2024, and for type approvals of vehicles of Categories L, R, S and T first issued before 1 July 2027, and for each extension thereof, the vehicle manufacturer shall ensure that another appropriate mitigation is implemented if a mitigation measure referred to in Annex 5, Part B or C is technically not feasible. The respective assessment of the technical feasibility shall be provided by the manufacturer to the approval authority.”

II. Justification

Exploring methodologies to integrate new scanner data in the French CPI: Making use of multilateral methods

Read more about Exploring methodologies to integrate new scanner data in the French CPI: Making use of multilateral methods

Languages and translations

English

MEETING OF THE GROUP OF EXPERTS ON CPI 7 JUNE 2023

Exploring methodologies to integrate new scanner data in the French CPI:

Making use of multilateral methods

MEETING OF THE GROUP OF EXPERTS ON CPI 2

1 CONTEXT AND GOALS 2 THEORY 3 RESULTS : BY VARIETY (COICOP 7 DIGITS) 4 RESULTS : BY COICOP 6 DIGITS(MAKE UP) 5 RESULTS : CONTRIBUTIONS

MEETING OF THE GROUP OF EXPERTS ON CPI 3

INTRODUCTION01

MEETING OF THE GROUP OF EXPERTS ON CPI 4

CONTEXT

– We are starting to receive data from 2 hard discounters. – We already have and use in production (since Jan 2020) scanner data from other retailers – Our current methodology with scanner data requires an external referential allowing us from GTIN/EAN to

have ● Additional characteristics (volume, unit, label, color ...) ● Nomenclature

– With classification rules and using the characteristics we classify at the variety level (level 7 of COICOP, French specificity).

● We are able to group EAN into equivalence classes to follow products better, avoid basket churn and catch the relaunches.

● We compute a Geometric Laspeyres, the methodology is similar than with the field collected data and the quality adjustment is slightly different since we can use the price history for the replacement product.

– Hard discount data has for now a low match rate with the referential (17 % of expenditure share according to 1 test file for one retailer and 39% for the other)

– We will experiment multilateral methods mainly to check what we could do without the referential and with the constraints of avoiding chain drift and basket churn.

MEETING OF THE GROUP OF EXPERTS ON CPI 5

TEST PROTOCOL/STRATEGY

– We will use our already possess scanner data (not enough history with hard discounters)

– Our product definition will vary between using GTIN/EAN or a article grouping methods (extended article number)

– We will compute micro indexes at the outlet level.

– We follow the average price of each product per month.

MEETING OF THE GROUP OF EXPERTS ON CPI 6

DATA OF THE EXPERIMENT

– Scanner data from January 2020 to December 2022, from 6 retailers (without hard discount because we don’t have background data).

– 3 varieties & their corresponding 6 digits COICOP level ● Whole milk & whole milk=> few replacements ● Foie gras & canned meat=> a high seasonality and 85% of

replacement during the year ● Lipstick & make up and care products => a lot of distinct

GTIN/EAN.

MEETING OF THE GROUP OF EXPERTS ON CPI 7

MULTILATERAL METHODS02

MEETING OF THE GROUP OF EXPERTS ON CPI 8

MULTILATERAL INDEXES TESTED

– We focus on GEKS-Törnqvist

where and

– The sample S can be a COICOP 6 digit level or a variety

– The product i can be the GTIN/EAN or an Extended article number ● Choice of the window size and splicing:

– Rolling window of size 13 and mean splice – Rolling window of size 25 and half splice

– Using R and IndexNumR package

IGEKS 0 , t =∏l=0

T ( I

0 , l

I t ,l ) 1 T +1=∏l=0

T ( I 0 ,l∗I l , t)

1 T +1

I T 0 ,t=∏i∈S

( pi t

p i 0 ) si 0+ si

2 si t=

pi tq i t

∑ j∈S p j t q j

MEETING OF THE GROUP OF EXPERTS ON CPI 9

RESULTS : VARIETY03

MEETING OF THE GROUP OF EXPERTS ON CPI 10

PRESENCE RATE PER VARIETY

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

22 0

0,2

0,4

0,6

0,8

1,2

Proportion of EAN x Outlet present in January 2020 and at the month m

foie gras presence rate

whole milk presence rate

lipstick/gloss presence rate

The presence rate is computed as

|N i∩N1| |N1|

Where are the products (EAN X Outlet in our case) sold in period i.

N i

– The presence rate is low for foie gras and lipstick

– There is a seasonality for foie gras

MEETING OF THE GROUP OF EXPERTS ON CPI 11

USING GTIN OR EXPENDED ARTICLE GROUP

100

105

110

115

120

-1 -0,7 -0,4 -0,1 0,2 0,5 0,8

Price indices for the variety whole milk between Jan 2020 and Dec 2022

(GEKS - GEKS by EAN) GEKS 25 months GEKS by ean 25 months

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

22 90

100

105

110

115

-1

-0,5

0,5

Price indices for the variety foie gras between January 2020 and December

2022

(GEKS - GEKS by EAN) GEKS 25 by extended article GEKS 25 by EAN

88 90 92 94 96 98 100 102

-3 -2,2 -1,4 -0,6 0,2

1 1,8 2,6

Price indices for the variety lipstick/gloss between Jan 2020 and Dec 2022

(GEKS - GEKS by EAN) GEKS by EAN 25 HASP GEKS 25 HASP with extend article number

Indexes using EAN or extended article number are very close for milk and foie gras.

There is more volatility for lipstick.

MEETING OF THE GROUP OF EXPERTS ON CPI 12

GEKS VS CURRENT CPI

jan vie

r-2 0

av ril-

jui lle

t-2 0

oc to

br e-

jan vie

r-2 1

av ril-

jui lle

t-2 1

oc to

br e-

jan vie

r-2 2

av ril-

jui lle

t-2 2

oc to

br e-

22 95

100 105 110 115 120 125

-2 -1,3 -0,6 0,1 0,8 1,5

Price indices for the variety whole milk between Jan 2020 and Dec 2022

CPI - GEKS by EAN CPI base 100 janv 20 GEKS by EAN half spliced 25 mois

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

22 90

100

105

110

115

120

-4 -3,1 -2,2 -1,3 -0,4 0,5 1,4 2,3 3,2

Price indices for the variety foie gras between January 2020 and December 2022

CPI - GEKS by ean CPI, base 100 = Jan 2020 GEKS by ean

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

22 80 85 90 95

100 105

-1 0,5 2 3,5 5

Price indices for the variety lipstick/gloss between Jan 2020 and Dec 2022

(CPI - GEKS ) CPI Base 100 janv 20 GEKS by EAN 25 HASP

There is more difference between GEKS and CPI than between two GEKS.

A highest volatility in some period (June 2020 for lipstick for instance)

MEETING OF THE GROUP OF EXPERTS ON CPI 13

RESULTS: COICOP 6 DIGITS (MAKE UP)04

MEETING OF THE GROUP OF EXPERTS ON CPI 14

MAKE UP AND CARE PRODUCT PRESENCE RATES

The match rate is computed as

|N i∩N j| |N i∪N j|

Where are the products (EAN X Outlet in our case) sold in period I.

There might be lockdowns effects in some periods.

Even for two consecutive periods, the match rate is quite low.

N i

MEETING OF THE GROUP OF EXPERTS ON CPI 15

UNCLASSIFIED DATA AT THE COICOP 6 DIGITS LEVEL

– In some COICOP 6 digits level we have a high proportion of unclassified data they can be

● Linked to field varieties (we do not have yet a corresponding scanner data variety) : nail make up for instance

● Do not correspond to the classification rules (a canned meat with honey flavour for instance)

They aren’t followed taken into account our current CPI.

– What would be the impact of keeping them ?

Expenditure share by variety for the poste make up and care product between Jan 2020 and Dec 2022

MEETING OF THE GROUP OF EXPERTS ON CPI 16

YEAR-ON-YEAR INFLATION PER VARIETY

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

-20

-15

-10

-5

Year-on-year inflation (GEKS 25 half spliced) for varieties of the poste make up and care products

between Jan 2021 and Dec 2022

face cleanser

mascara

Lipstick/gloss

face powder

women face cream

body care cream

unclassified

– The index for unclassified data is more volatile

● Beginning of 2021 ● Summer 2022

MEETING OF THE GROUP OF EXPERTS ON CPI 17

UNCLASSIFIED DATA AT THE POSTE LEVEL

1 4 7 10 13 16 19 22 25 28 31 34 70,00 %

75,00 %

80,00 %

85,00 %

90,00 %

95,00 %

100,00 %

105,00 %

110,00 %

115,00 %

120,00 %

Price indexes for the poste make up and care products

GEKS 25 HASP with unclassi- fied

GEKS 25 HASP without un- classified

GEKS 25 HASP by variety with CPI annual weights

CPI (Scanner data varieties only) aggregated

The trend is kept with unclassified data but there is a high volatility

MEETING OF THE GROUP OF EXPERTS ON CPI 18

RESULTS : CONTRIBUTIONS05

MEETING OF THE GROUP OF EXPERTS ON CPI 19

CONTRIBUTIONS BETWEEN TWO PERIODS

IGEKS−TQ t1 , t2 =∏i∈N

( pi t 2)wi

∗ , t 2

( pi t 1)w i

∗ , t 1∏t∈W ( pi

t) w i t , t 1−w i

t , t 2

cardW IGEKS−TQ t1 , t2 =∏i∈N

contributioni t1 ,t 2

ln (IGEKS−TQ t1 , t2 )=∑i∈N

ln (contributioni t1 ,t 2)

with unspliced indexes we can use the transitivity : IGEKS−TQ 12,13 =

IGEKS−TQ 1,13

IGEKS−TQ 1,12

– Goal : understand and explain the index variation from the observations

– We could also make sub groups (retailer, geo) to sum log(contrib)

MEETING OF THE GROUP OF EXPERTS ON CPI 20

CONTRIBUTIONS FOR LIPSTICK

17 18 19 20 21 22 23 24 25 26 27 28 29 92

100

101

102

GEKS-Tq ean X out- let 13 Mean splice

GEKS-Tq by EAN wi- thout splicing (period 17 as reference)

The decrease of the index between period 28 (June 2022) and 29 is carried by few products

Using R and GEKSDecomp

package

MEETING OF THE GROUP OF EXPERTS ON CPI 21

CONCLUSION

– Learnings ● At a really fine scale (GTIN/EAN), the GEKS indexes behave

quite closely to our current methodology ● At a more aggregate scale, there is more volatility and we have

to progress in our understanding and tools including classification issues.

– Future works ● Classification tools ● Theoretical understanding of the link with micro-economic

theory

7 JUNE 2023 MEETING OF THE GROUP OF EXPERTS ON CPI

insee.fr

Join us on

Adrien Montbroussous & Martin Monziols Methodologist & Head of the methodology unit Consumer Prices Division [email protected] [email protected]

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Exploring methodologies to integrate new scanner data in the French CPI: Making use of multilateral methods

Read more about Exploring methodologies to integrate new scanner data in the French CPI: Making use of multilateral methods

Languages and translations

English

MEETING OF THE GROUP OF EXPERTS ON CPI 7 JUNE 2023

Exploring methodologies to integrate new scanner data in the French CPI:

Making use of multilateral methods

MEETING OF THE GROUP OF EXPERTS ON CPI

1 CONTEXT AND GOALS 2 THEORY 3 RESULTS : BY VARIETY (COICOP 7 DIGITS) 4 RESULTS : BY COICOP 6 DIGITS(MAKE UP) 5 RESULTS : CONTRIBUTIONS

MEETING OF THE GROUP OF EXPERTS ON CPI

INTRODUCTION01

MEETING OF THE GROUP OF EXPERTS ON CPI

CONTEXT

have ● Additional characteristics (volume, unit, label, color ...) ● Nomenclature

– With classification rules and using the characteristics we classify at the variety level (level 7 of COICOP, French specificity).

● We are able to group EAN into equivalence classes to follow products better, avoid basket churn and catch the relaunches.

– Hard discount data has for now a low match rate with the referential (17 % of expenditure share according to 1 test file for one retailer and 39% for the other)

– We will experiment multilateral methods mainly to check what we could do without the referential and with the constraints of avoiding chain drift and basket churn.

MEETING OF THE GROUP OF EXPERTS ON CPI

TEST PROTOCOL/STRATEGY

– We will use our already possess scanner data (not enough history with hard discounters)

– Our product definition will vary between using GTIN/EAN or a article grouping methods (extended article number)

– We will compute micro indexes at the outlet level.

– We follow the average price of each product per month.

MEETING OF THE GROUP OF EXPERTS ON CPI

DATA OF THE EXPERIMENT

– Scanner data from January 2020 to December 2022, from 6 retailers (without hard discount because we don’t have background data).

– 3 varieties & their corresponding 6 digits COICOP level ● Whole milk & whole milk=> few replacements ● Foie gras & canned meat=> a high seasonality and 85% of

replacement during the year ● Lipstick & make up and care products => a lot of distinct

GTIN/EAN.

MEETING OF THE GROUP OF EXPERTS ON CPI

MULTILATERAL METHODS02

MEETING OF THE GROUP OF EXPERTS ON CPI

MULTILATERAL INDEXES TESTED

– We focus on GEKS-Törnqvist

where and

– The sample S can be a COICOP 6 digit level or a variety

– The product i can be the GTIN/EAN or an Extended article number ● Choice of the window size and splicing:

– Rolling window of size 13 and mean splice – Rolling window of size 25 and half splice

– Using R and IndexNumR package

IGEKS 0 , t =∏l=0

T ( I

0 , l

I t ,l ) 1 T +1=∏l=0

T ( I 0 ,l∗I l , t)

1 T +1

I T 0 ,t=∏i∈S

( pi t

p i 0 ) si 0+ si

2 si t=

pi tq i t

∑ j∈S p j t q j

MEETING OF THE GROUP OF EXPERTS ON CPI

RESULTS : VARIETY03

MEETING OF THE GROUP OF EXPERTS ON CPI

PRESENCE RATE PER VARIETY

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

22 0

0,2

0,4

0,6

0,8

1,2

Proportion of EAN x Outlet present in January 2020 and at the month m

foie gras presence rate

whole milk presence rate

lipstick/gloss presence rate

The presence rate is computed as

|N i∩N1| |N1|

Where are the products (EAN X Outlet in our case) sold in period i.

N i

– The presence rate is low for foie gras and lipstick

– There is a seasonality for foie gras

MEETING OF THE GROUP OF EXPERTS ON CPI

USING GTIN OR EXPENDED ARTICLE GROUP

100

105

110

115

120

-1 -0,7 -0,4 -0,1 0,2 0,5 0,8

Price indices for the variety whole milk between Jan 2020 and Dec 2022

(GEKS - GEKS by EAN) GEKS 25 months GEKS by ean 25 months

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

22 90

100

105

110

115

-1

-0,5

0,5

Price indices for the variety foie gras between January 2020 and December

2022

(GEKS - GEKS by EAN) GEKS 25 by extended article GEKS 25 by EAN

88 90 92 94 96 98 100 102

-3 -2,2 -1,4 -0,6 0,2

1 1,8 2,6

Price indices for the variety lipstick/gloss between Jan 2020 and Dec 2022

(GEKS - GEKS by EAN) GEKS by EAN 25 HASP GEKS 25 HASP with extend article number

Indexes using EAN or extended article number are very close for milk and foie gras.

There is more volatility for lipstick.

MEETING OF THE GROUP OF EXPERTS ON CPI

GEKS VS CURRENT CPI

jan vie

r-2 0

av ril-

jui lle

t-2 0

oc to

br e-

jan vie

r-2 1

av ril-

jui lle

t-2 1

oc to

br e-

jan vie

r-2 2

av ril-

jui lle

t-2 2

oc to

br e-

22 95

100 105 110 115 120 125

-2 -1,3 -0,6 0,1 0,8 1,5

Price indices for the variety whole milk between Jan 2020 and Dec 2022

CPI - GEKS by EAN CPI base 100 janv 20 GEKS by EAN half spliced 25 mois

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

01 /0

1/ 20

01 /0

3/ 20

01 /0

5/ 20

01 /0

7/ 20

01 /0

9/ 20

01 /1

1/ 20

22 90

100

105

110

115

120

-4 -3,1 -2,2 -1,3 -0,4 0,5 1,4 2,3 3,2

Price indices for the variety foie gras between January 2020 and December 2022

CPI - GEKS by ean CPI, base 100 = Jan 2020 GEKS by ean

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

01 /0

1/ 20

01 /0

4/ 20

01 /0

7/ 20

01 /1

0/ 20

22 80 85 90 95

100 105

-1 0,5 2 3,5 5

Price indices for the variety lipstick/gloss between Jan 2020 and Dec 2022

(CPI - GEKS ) CPI Base 100 janv 20 GEKS by EAN 25 HASP

There is more difference between GEKS and CPI than between two GEKS.

A highest volatility in some period (June 2020 for lipstick for instance)

MEETING OF THE GROUP OF EXPERTS ON CPI

RESULTS: COICOP 6 DIGITS (MAKE UP)04

MEETING OF THE GROUP OF EXPERTS ON CPI

MAKE UP AND CARE PRODUCT PRESENCE RATES

The match rate is computed as

|N i∩N j| |N i∪N j|

Where are the products (EAN X Outlet in our case) sold in period I.

There might be lockdowns effects in some periods.

Even for two consecutive periods, the match rate is quite low.

N i

MEETING OF THE GROUP OF EXPERTS ON CPI

UNCLASSIFIED DATA AT THE COICOP 6 DIGITS LEVEL

– In some COICOP 6 digits level we have a high proportion of unclassified data they can be

● Linked to field varieties (we do not have yet a corresponding scanner data variety) : nail make up for instance

● Do not correspond to the classification rules (a canned meat with honey flavour for instance)

They aren’t followed taken into account our current CPI.

– What would be the impact of keeping them ?

Expenditure share by variety for the poste make up and care product between Jan 2020 and Dec 2022

MEETING OF THE GROUP OF EXPERTS ON CPI

YEAR-ON-YEAR INFLATION PER VARIETY

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

-20

-15

-10

-5

Year-on-year inflation (GEKS 25 half spliced) for varieties of the poste make up and care products

between Jan 2021 and Dec 2022

face cleanser

mascara

Lipstick/gloss

face powder

women face cream

body care cream

unclassified

– The index for unclassified data is more volatile

● Beginning of 2021 ● Summer 2022

MEETING OF THE GROUP OF EXPERTS ON CPI

UNCLASSIFIED DATA AT THE POSTE LEVEL

1 4 7 10 13 16 19 22 25 28 31 34 70,00 %

75,00 %

80,00 %

85,00 %

90,00 %

95,00 %

100,00 %

105,00 %

110,00 %

115,00 %

120,00 %

Price indexes for the poste make up and care products

GEKS 25 HASP with unclassi- fied

GEKS 25 HASP without un- classified

GEKS 25 HASP by variety with CPI annual weights

CPI (Scanner data varieties only) aggregated

The trend is kept with unclassified data but there is a high volatility

MEETING OF THE GROUP OF EXPERTS ON CPI

RESULTS : CONTRIBUTIONS05

MEETING OF THE GROUP OF EXPERTS ON CPI

CONTRIBUTIONS BETWEEN TWO PERIODS

IGEKS−TQ t1 , t2 =∏i∈N

( pi t 2)wi

∗ , t 2

( pi t 1)w i

∗ , t 1∏t∈W ( pi

t) w i t , t 1−w i

t , t 2

cardW IGEKS−TQ t1 , t2 =∏i∈N

contributioni t1 ,t 2

ln (IGEKS−TQ t1 , t2 )=∑i∈N

ln (contributioni t1 ,t 2)

with unspliced indexes we can use the transitivity : IGEKS−TQ 12,13 =

IGEKS−TQ 1,13

IGEKS−TQ 1,12

– Goal : understand and explain the index variation from the observations

– We could also make sub groups (retailer, geo) to sum log(contrib)

MEETING OF THE GROUP OF EXPERTS ON CPI

CONTRIBUTIONS FOR LIPSTICK

17 18 19 20 21 22 23 24 25 26 27 28 29 92

100

101

102

GEKS-Tq ean X out- let 13 Mean splice

GEKS-Tq by EAN wi- thout splicing (period 17 as reference)

The decrease of the index between period 28 (June 2022) and 29 is carried by few products

Using R and GEKSDecomp

package

MEETING OF THE GROUP OF EXPERTS ON CPI

CONCLUSION

– Learnings ● At a really fine scale (GTIN/EAN), the GEKS indexes behave

quite closely to our current methodology ● At a more aggregate scale, there is more volatility and we have

to progress in our understanding and tools including classification issues.

– Future works ● Classification tools ● Theoretical understanding of the link with micro-economic

theory

7 JUNE 2023 MEETING OF THE GROUP OF EXPERTS ON CPI

insee.fr

Join us on

Adrien Montbroussous & Martin Monziols Methodologist & Head of the methodology unit Consumer Prices Division [email protected] [email protected]

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Exploring methodologies to integrate new scanner data in the French CPI: making use of multilateral methods

Read more about Exploring methodologies to integrate new scanner data in the French CPI: making use of multilateral methods

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English

Exploring methodologies to integrate new scanner data in the French CPI: making use of multilateral

methods

Unece Conference – Geneva

June 2023

Authors: Adrien Montbroussous, Martin Monziols (Insee, France)

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Abstract:

Scanner data has been used in production to compute the HICP and the CPI for France since January 2020, for most of French retailers. The current methodology with this data uses a product referential bought from an external provider giving us detailed characteristics for each article. These characteristics allow us to match articles in our data with the COICOP and to create homogeneous groups of articles. Thanks to this information, we can compute a unit price value for each group of articles and each month. The following steps in the methodology are very similar to the process with field-collected data: we select a sample of observations that will be used to compute price evolutions and aggregate them using a geometric Laspeyres formula at the lowest level. And as with field data, replacements are made for unavailable products. This choice is quite specific to France whereas the use of multilateral methods is more widespread in other countries. But recently, new retailers (hard discounters) have started to implement a data flux to provide Insee with their scanner data. The specificity of their data is that most of their articles aren’t covered by the product referential, which makes the current methodology hard to apply at first sight.

In this study, the goal is to be able to use these new scanner data in the following years. We will address two questions to do so. First, relying on the other INS experiences, we will test the generalization of multilateral methods on our already received and used scanner data to document on a large scale the behaviours of such methods in the French context. This experience is an opportunity to gain practical and theoretical skills with known data. Second, we will present a strategy to make use of these new scanner data with these methods. Given the raw data of these retailers, the process to be developed goes from classifying the products to integrating the computed indexes in our main process that produces our French CPI.

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Contents I) Context of this study : scanner data in France now and future.........................................................4

1) Usual data....................................................................................................................................4 2) Scanner data : current methodology............................................................................................5 3) Information technology infrastructure.........................................................................................5 4) New data and data not yet used...................................................................................................6

a) Hard discounters.....................................................................................................................6 b) Overseas scanner data.............................................................................................................6 c) Other sectors not yet used.......................................................................................................6

II) Theory and strategy of our experimentation...................................................................................7 1) Multilateral methods and milestones of the process....................................................................7

a) Individual product specification ............................................................................................7 b) Multilateral Index...................................................................................................................8 c) Time windows & splicing.......................................................................................................9 d) Aggregation structure...........................................................................................................10

i. There is no way of having a decomposition of the multilateral indexes...........................10 ii. Choosing a level and aggregating these indexes.............................................................10

2) Test protocol..............................................................................................................................11 III) Results..........................................................................................................................................11

1 ) Presence of references across time...........................................................................................11 a) Milk.......................................................................................................................................13 b) Foie gras................................................................................................................................14 c) Lipstick.................................................................................................................................14 d) Canned meat.........................................................................................................................15 e) Make up and care products...................................................................................................15

2) Indexes at the « variety » level..................................................................................................16 a) Whole milk ...........................................................................................................................16 b) Foie gras................................................................................................................................17 c) Lipstick / gloss :....................................................................................................................19

3) Indexes at the « poste » level.....................................................................................................20 a) Whole Milk...........................................................................................................................20 b) Canned meat.........................................................................................................................23 c) Make-up and care products...................................................................................................27

4) Contributions behind GEKS-Tq variation.................................................................................31 a) Theory...................................................................................................................................31 b) Experiment............................................................................................................................32

IV) Next steps ? Our « research » agenda..........................................................................................34 1) Link between those multilateral indexes and microeconomic theory.......................................34 2) Explore the outlet dimension.....................................................................................................34 3) Going further with classification methods................................................................................34 4) Strategy to include those indexes inside our current methodology...........................................35

V) Conclusion.....................................................................................................................................35 References..........................................................................................................................................36 Appendix............................................................................................................................................37

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I) Context of this study : scanner data in France now and future

After almost 10 years of experimentation, scanner data have been introduced in the French CPI and HICP in January 2020. During these experimentations, first, some retailers were collaborating, then a law has been issued to frame this operation and regulates the transmission of data. Almost all the field of retailers was transmitting data except hard discounters and some retailers in oversea departments.

Recently new providers (hard-discounters) started to send their data as expected by the law. As it will be detailed below, these data are challenging in order to use them in our CPI and HICP given our current methodology.

1) Usual data In France, scanner data is used in production to compute our CPI since January 2020. Data was until now provided by all the Super and Hypermarket, hard discounter excluded. We are getting data from retailers, thanks to an article of law originally published in 2017, and modified in 20211, making mandatory for retailers to provide us data for any day and shop, each day. These are used for prices’ statistics and turnover indicators. The data requested is the following:

• EAN (European Article Numbering)

• Outlet id

• Date of the sale

• At least two variables among the 3 following: number of article sold, the whole expenditure and the unit price of the article.

• A label, which can be relatively short and rarely exceeds 25 characters (space included)

• The intern nomenclature code given by the retailer

The law as it is written at the moment implicitly supposes that there is a « referential » that we can use to our purpose of describing and classifying products into a nomenclature. Because, as it is written, the only descriptive information is the label. These data are indeed used in our process with a referential (bought from an external society, CIRCANA previously known as IRI) allowing us to get more information on the data : characteristics of the products and a « family number ». With these data, we are able to classify at a granular level our article (variety, which is even finer than COICOP on 6 positions – a specific level of France) using rules made for each variety to select observations. Lastly, according to the law, we keep 3 years of archives and the data for the current year.

The “SKU” (Stock Keeping Unit) is mentioned in the « guide on Multilateral Methods ». It’s a code associated with article that retailers keep to group EAN representing products of the same nature in order to have a better stock and supply process. Unfortunately, we do not possess that kind of information because not specified in the law.

1 https://www.legifrance.gouv.fr/loda/id/LEGITEXT000034540407

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2) Scanner data : current methodology

To compute our CPI and HICP indexes with scanner data, we use sampling and a geometric Laspeyres. Thanks to the product referential, we are able to define an « expanded article number » which allow us to keep tract of similar articles, even if they do not keep the same EAN (European Article Number) – it could be interpreted as a SKU code, but for statistical purposes. For instance, if a glue stick changes packaging (with a “Halloween theme” for example), with our extended article number we will be able to keep following the price whereas with only EAN it will be considered as a different product. We are then able to classify these expanded article number into ECOICOP at the variety level. For each group of articles, classification rules of the variety predicted are checked, and if verified the product is linked to the variety. Otherwise it is stashed in the corresponding “poste” (the French specific 6-positions level of COICOP) special category « unclassified ». Each year, operations are made in order to update the basket and the classification rules when it is needed.

There is some sampling because we only follow varieties representing at least 1% of their “poste” (which is an applicative constraint) so that if EANs represent a product not bought enough we do not follow it. In the end, nevertheless, we follow more than 80 millions of EANs. These varieties are updated and modified if necessary each year when we update the basket and the associated weights. These weights are computed with the previous year expenditures as the rest of the CPI basket. Micro-indexes are computed at the outlet x variety scale and then aggregated at the variety level, which is in turn included in the CPI “poste” calculation.

The quality adjustment for replacement is slightly different than the bridged overlap method : since we are able to have an history for the replacement product, we do not need to impute prices of previous periods for the replacement product. The process is automatized for scanner data : we have classification rules that classifies article in homogeneous group and outlay candidates for replacement. Then, among the potential candidates for replacement, we proceed with a sampling with replacement (a product can replace more than one product). Products are replaced if absent for two months in a row. Also, we anticipate some replacements : if a product has both quantity and price declining for two consecutive months, it is replaced the next month.

In each 6 digits COICOP level (“poste”) we have varieties using scanner data and varieties using field collected data. We then aggregate the micro indexes to have an higher level index. For field data, we have micro-indexes computed by geographical areas, for scanner data index we compute the index at the outlet level then aggregate it at the whole France level.

The scope of scanner data used in production is hyper and supermarkets in Metropolitan France, for sales of processed food products, cleaning products and hygiene and beauty products and also, some durable goods. The scanner data expenditure share in the CPI weights is around 10 % of the whole basket. We do not use a larger consumption scope because we do not possess informations in our referential about these products. Hence, we cannot classify these products, we cannot control for their units, etc.

This current methodology was chosen because it was very close to what we do on the field so that our methodology is homogeneous.

3) Information technology infrastructure These huge datasets (approx. 9 Go per day of received data) is managed with a specific big data infrastructure, divided into:

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• A Postgre database containing some information about metadata, referential, nomenclatures, indicators, composition of our scanner data baskets, etc.

• A NoSQL infrastructure for the detailed scanner data, accessible through HUE (Hadoop user experience) on which we can make HiveQL requests. For experimentation and in order to interfere at the least with production processes, we extract subset of the data to explore them with R and dedicated packages. This process is a bit long and laborious since the platform is not designed for such work.

We are on the verge of putting in place a new infrastructure, which will be more flexible and at the state of

art, allowing us to foster our experimentation work.

4) New data and data not yet used

a) Hard discounters

We are just in the process of getting data from 2 hard-discounters. Using these data will allow us to have a better coverage of hard-discount and also a better geographic coverage because they are more present in a specific area (north-east). The match rate of these data with the referential is relatively low: for one Saturday of sales, 16,5 % of EANs of one hard discounter are present in our referential, representing approximately 45 % of the data and 40 % of total expenditure. We were not able to compute such statistics with the other retailer since we only have a test file at this stage. Since the data is compliant with what is required by the law, we only have few descriptive information and item labels for each EAN. It has to be noted that the labels seem to be richer than for other retailers, with a lot of products with a 25 character label.

With this situation, we are facing two questions:

• how to classify the product in the COICOP

• how to calculate an index without detailed information about a product (volumes, weight, etc.)

Work will be done on these two subjects, this paper focuses mainly on the later question.

b) Overseas scanner data As for the hard discounters, we receive and are making new contacts with some retailers to have overseas’ scanner data. A large proportion of products are specifically sold in this region of the globe, hence not all the scope of these data is covered by the referential. Then, the same two questions need to be answered: are we able to classify these products with the sole label and is it possible to calculate a good quality price index.

So far we received data for some retailers at La Reunion and are making progress to receive some data from Guadeloupe. It has a relatively low impact on the whole French index but could be source of efficiency and

precision for the CPI of these territories2.

c) Other sectors not yet used Among the already received scanner data, we restrict our scope to specific products:

• Food

• Hygiene and makeup products

• House cleaning products

2 France does not publish geographic CPI except for the overseas’ departments.

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• Some durable goods such as pregnancy test, highlighters…

But, for instance, we have data on clothes that we do not use so far. The reason is that this remaining data is not covered by the referential so that products are not classified.

We expect that our work on multilateral methods for the hard discounters will have some positive externalities on existing scanner data not used yet.

II) Theory and strategy of our experimentation We describe in this section our strategy of experimentation with some reminders regarding multilateral indexes. This work builds on previous experiments with these methods by previous colleagues. They used in their experiment homogeneous product groups using an « expanded article number » created thanks to the referential, on whole milk and foie gras, products that we kept in our study.

In parallel of this experiment, we will start a process to classify hard discounter data in COICOP nomenclature or even granularly (French nomenclature). As for multilateral methods, it will be continuing some previous work done on overseas products.

1) Multilateral methods and milestones of the process Since our data from hard discounters has only been provided since recently, we decided to start our experiment on the data of the other retailers we already possess. The idea is to calculate indexes based on two assumptions :

• we can classify products at a certain level (COICOP 6 positions for instance) • we do not possess any detailed information about the product except this classification

In our experiment, we always work at a fixed outlet dimension (at the outlet scale), we will discuss the results according several choices on the product dimension. Since we are dealing in our experiment only with goods, we assume that for the consumer the good has the same utility for each day of the month. We will follow the average price of products by month.

The benefit we expect to have from using a multilateral method is to bypass the classification issues, prevent a basket churn and avoid chain drifts problem caused by bilateral indexes. Instead of comparing only two periods, we will use all the available data within a window of time to compute an index.

Thanks to the multilateral guide produced by Eurostat, we highlighted 4 steps in which we had to analyse several methods/choices, presented below.

a) Individual product specification

In our experiments, we test the following specifications for products and outlets :

• Products : as explained earlier, the only identifier that we have in the raw data is the EAN. So the goal here is to document how these multilateral methods behave when we consider the EAN as the identifier of a product or the « expanded article number » which gathers several products that are very similar. These expanded article numbers have been developed in order to capture commercial relaunches and are currently used in our current method. What we want to see is whether we can do without such an « expanded article number » or not. An option not yet explored, and that we will consider if this test is not conclusive, is to build such « expanded article number » based on the available information in the raw data through clustering methods.

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• Outlets : this dimension has not been fully explored yet. As in the current methodology, we considered the outlets as outlets. We did not aggregate them in any manner, except in the subsection dedicated to contributions below.

At the end, in our data, the way we identify a price and a quantity is at the couple (product x outlet), where product is either EAN or the expanded article number.

b) Multilateral Index There are several family of multilateral methods:

• Geary Khamis (GK) is a quality adjusted value index. It is an additive method, the index is obtained by solving the following system of equations:

I GK 0 ,t =

∑i∈N t

pi t q i

t/∑i∈N0

pi 0q i

∑i∈N t

vi q i t /∑i∈N0

v iq i 0

where vi inside the window W is vi=∑z∈W

qi z

∑s∈W q i

pi z

I GK 0 , z

• Weighted time-product dummy method consisting in an econometric model including dummies for each time period and characteristics. In our context, since we cannot revise our indexes, it is not the more appropriate.

• The last one consists on making transitive these bilateral indexes by averaging across all the possible paths between two dates, inside a time window. It is the GEKS ( Gini-Eltetö-Köves-Szulc) , which consists of a geometric mean of couple of bilateral indexes:

I GEKS 0 ,t =∏l=0

T ( I 0 ,l

It ,l ) 1

T +1=∏l=0

T (I 0 ,l∗I l ,t)

1 T +1 where T represents the size of the window.

◦ GEKS Törnqvist is also called CCD. 3

In the continuation of this paper, we will focus on GEKS indexes.

• GEKS method is based on bilateral Indexes that are reversible. In our experiments, we consider two of these bilateral indexes :

• Törnqvist : Index that is frequent in the literature. It is based on the micro-indexes of products and their relative shares in the expenditures at the two periods of time considered.

IT 0 , t=∏i∈S

( pi

pi 0 )

si 0+si

where the expenditure share of product i in the sample S is si t=

p i t qi

∑ j∈S p j

t q j t . S is the

intersection of the basket at time 0 and the basket at time t, i.e. all the products present at both periods.

3 Caves, D. W., L. R. Christensen, and W. E. Diewert. 1982. "Multilateral Comparisons of Output, Input, and Productivity Using Superlative Index Numbers." The Economic Journal 92, no. 365: 73-86.

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• Fisher : a common index with good property. It is the geometric mean of a Laspeyres index and a Paasche index. One for the structure of consumption at the first period of time, the other for the one at the other period of time.

I F 0 , t=√∑i∈S

p i t qi

pi 0 qi

0 ∑ i∈S

pi t q i

pi 0 qi

c) Time windows & splicing The GEKS method has some parameters :

• The nature of the window for which we consider the mean of the bilateral indexes

◦ Rolling window (each month, the time window is shifted forward by 1 month) with the sub- question of the length of this window : 13 months and 25 months have proved to be useful. The latter has the drawback of needing 25 months before starting to publish indexes but can handle seasonality better.

◦ Expansive window (Each month, the time window is extended by 1 month). This method allows to start the production without any background data.

An index between period 0 and a period t can be computed within several windows and hence lead to several results. In order to avoid revising previous indexes, we apply splicing technique to link the index of the latest period with the previous ones. Two choices are possible: using as link the previously published indexes or the recalculated with the new window indexes.

Technically, the splicing is operated via (a) link month(s), that can be

◦ Mean splicing : all overlap periods between the two windows are used in order to link the indexes by computing a geometric average of the pairs of corresponding indexes.

▪ Linking with previously (with previous windows) calculated series version

I pub 0 , t =I pub

0 ,t−1∗∏k=t−T +1

t−1 ( I [t−T ,t−1 ]

t−1 , k ∗I[t−T +1, t ] k, t )

1 T−1

▪ Linking to published series version

I pub 0 , t =I pub

0 ,t−1∗∏k=t−T +1

t−1 ( I [ pub]

t−1 , k∗I[ t−T +1 ,t ] k ,t )

1 T−1

◦ Half splicing : period t – ((T+1)/2)+1

▪ Linking with previously (with previous windows) calculated series:

• I pub 0 , t =I pub

0 ,t−1∗I[ t−T , t−1]

t−1 ,t−(T + 1 2 ) ∗I [t−T +1 ,t ]

t−(T +1 2 )+1 ,t

▪ Linking to published series:

• I pub 0 , t =I pub

0 ,t−1∗I pub

t−1 ,t−(T + 1 2 ) ∗I [t−T +1 ,t ]

t−(T +1 2 )+1 ,t

In our study we will focus on mean and half splicing, as implemented in the R package IndexNumR4.

4 https://rdrr.io/cran/IndexNumR/

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d) Aggregation structure.

i. There is no way of having a decomposition of the multilateral indexes

We demonstrate here that there is no way to decompose a multilateral index in a sum or product of multilateral indexes. Lets consider a set of product N, which can be decomposed in two subsets N1 and N2.

I Tornqvist , N 0 ,t =∏i∈N

( pi

p i 0 )

s̄i

where s̄i= 1 2 (

pi 0 qi

∑i ∈N p i

0q i 0 +

p i t qi

∑i∈N pi

t qi t )

Working on s̄i :

s̄i=

1 2 ∑k ∈(1,2)

I {i∈Nk }( p i

0 qi 0×∑i∈Nk

p i 0 qi

∑i∈Nk pi

0 q i 0×∑i∈N

pi 0 q i

0 + p i

t qi t×∑i∈Nk

pi t q i

∑i∈Nk pi

t q i t×∑i∈N

pi t q i

t )

s̄i= 1 2 ∑k ∈(1,2)

I {i∈Nk }( p i

0 qi 0

∑i∈Nk pi

0 q i 0 pNk

0 + pi

t q i t

∑i∈Nk p i

t qi t pNk

t )

with pNk t the share of expenditures of the subset Nk of products in the total set N. As we can see,

as long as pNk t ≠pNk

0 we can’t have something like ∀ i , s̄ i=a I {i∈N 1}s̄i N 1+b I {i∈N 2 }s̄i

N 2 which

would have given this decomposition of the bilateral Törnqvist index :

ITornqvist , N 0 , t =∏i∈N

( pi

pi 0 )

s̄i

=∏i∈N 1 (

pi t

pi 0 )

s̄i

∏i∈N 2 (

p i t

p i 0 )

s̄i

I Tornqvist , N 0 ,t =∏i∈N 1

( pi

pi 0 )

a s̄i N 1

∏i∈N 2 (

p i t

pi 0 )

b s̄i N 2

=(∏i∈N 1 (

p i t

p i 0 )

s̄i N 1

) a

(∏i∈N 2 (

pi t

pi 0 )

s̄i N 2

) b

I Tornqvist , N 0 ,t =(I Tornqvist ,N 1

0 ,t )a(I Tornqvist , N 2 0 ,t )b

Hence, a GEKS index cannot be decomposed as the sum or product of GEKS indexes. We may have approximate decomposition (that could be useful for analysis) but we have to choose a level at which we would compute the index that we will publish.

ii. Choosing a level and aggregating these indexes

There are advantages and drawbacks of choosing a high or low level of aggregation. The lower we compute our micro-indexes with multilateral formula and dynamic weight, the harder it is to include new products or outlets during the year, we also apply dynamic weights only at a low level and may not catch well changes of expenditure. However, it introduce stability in the index which makes easier the interpretation and the consistency of it.

In practice, there will be not that much choices for the level at which computing the multilateral index. It will depend on the performance of our classification tool.

As recommended by the Eurostat guide on multilateral methods5, we would use fixed weights at the subclass level at least. In the French context, in which we publish indexes at a more dis-aggregated level, ECOICOP on 6 positions (postes).

5Guide on Multilateral Methods in the Harmonised Index of Consumer Prices, Chapter 6, 2022 edition, Eurostat

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2) Test protocol Given these elements about our current methodology and the multilateral index (GEKS), we aim at testing some elements :

• Is the multilateral index far from the one we publish on a comparable field ?

• Do the results differ when considering the EAN or the « expanded article number » ?

• What does this new method give at the « poste » level ?

In order to proceed we have the following steps :

• Extracting data : given our data infrastructure, we have to construct and extract our data in order to use them with our usual statistical tools rather than coding the index in HiveQL. To limit the time spent in doing so, we choose 3 products. The level of aggregation should allow us to try several methods, we need to extract data at the EAN level. We keep the following information :

◦ The unit price (price per unity of volume)

◦ The sales (price per article X number of articles sold)

◦ The total volume (number of articles sold X volume of each article)

◦ The number of articles sold

◦ EAN

◦ Extended article number

• Choosing products :

◦ Milk, because it has a low replacement rate

◦ Foie gras, because it has a high replacement rate and high seasonality

◦ Lipstick, because it has a high number of EAN by « expanded article number »

◦ Then, we generalise at the whole poste to which they belong.

▪ Milk : 2 varieties + unclassified. ▪ Canned meat : 7 varieties + unclassified. ▪ Make-up and care : 6 varieties + unclassified.

III) Results

1 ) Presence of references across time Before computing indexes, we looked at the disappearance rate inside each group in order to get some sense of how data behave. This is some useful information to know to understand how indexes will behave on the one hand. On the other hand, this is the kind of side informations that will be useful to index producers in practice.

One measure has a bilateral approach, it is to follow the products sold in January 2020 and check if they are still available the following months.

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Figure 1: Source: scanner data. Scope: Metropolitan France. Reading note : in January 2021, 13,5 % of the lipstick’s EAN sold in January 2020 are still sold in the same outlet

We can see that the product (at the EAN x outlet scale) available in January 2020 disappear rapidly from the market. It makes clear that chain drift is a risk with these methods. When product disappearing is followed with commercial relaunched, EAN change, even if the products are very closed with substantial price rise.

Interestingly, we can see that some products are appearing and disappearing with seasonal patterns. According the variety considered, we can identify a “stock” of products present on the market for many months : approximatively 80-85% for the milk, 10% for the lipstick and foie-gras.

We keep the dates in order to see the clear impact of the Covid-19 crisis, and the lockdown in France.

Figure 2: Source: scanner data. Scope: Metropolitan France. Reading note : in January 2021, 61,9 % of the canned meat EAN sold in an outlet January 2020 are still sold in the same outlet

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Presence rate by poste

canned meat presence rate

make up and care products presence rate

whole milk presence rate

With the same analysis with one level of aggregation, we can see that the trend for canned meat is quite different than the one for foie gras. It is due to the seasonality of the sales that is specific to this variety.

To catch better the matching process that is used in multilateral indexes, we also produced heat maps by comparing the presence of EAN x outlet between each couples of periods within the windows.

a) Milk

Figure 3: Source: scanner data. Scope: Metropolitan France. Reading note : the quantity represented in the heat map is the EAN match rate computed as (number of EAN X outlet present in both period)/(number of EAN X outlet present in at least one period). Example, 51 % of the EAN X outlet sold in period 1 or 5 are sold in both periods .

The match rate of period 5 (May 2020) is the lowest comparing with other periods. It is more likely related to the lockdown in France following the Covid-19 pandemic.

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b) Foie gras

Figure 4: Source: scanner data. Scope: Metropolitan France. Reading note : the quantity represented in the heat map is the EAN match rate computed as (number of EAN X outlet present in both period)/(number of EAN X outlet present in at least one period). Example, between 10 and 20 % of the EAN X outlet sold in period 1 or 5 are sold in both periods .

There is a specificity in the December months (periods 12, 24 and 36) : they have a lower match rate with other months of the year. Indeed, during the winter holidays new foie gras products are introduced into the markets.

c) Lipstick

Figure 5: Source: scanner data. Scope: Metropolitan France. Reading note : the quantity represented in the heat map is the EAN x outlet match rate computed as (number of EAN X outlet present in both period)/(number of EAN X outlet present in at least one period). Example: between 20 and 30 % of the EAN X outlet sold in period 1 or 2 are sold in both periods

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Period 4 (April 2020) has the lowest match rate with other periods, it is most likely, as for the milk, related to the lockdown.

d) Canned meat

Figure 6: Source: scanner data. Scope: Metropolitan France. Unclassified data are included. Reading note : between 30 % and 40 % of the EAN sold in outlets in period 1 or 25 are present in both periods in the same outlet.

The match rate are higher at the canned meat poste level than for the variety foie gras. An explanation is that most of the varieties does not have the seasonality that foie gras has in the sales.

e) Make up and care products

Figure 7: Source: scanner data. Scope: Metropolitan France. Unclassified data are not included. Reading note : between 20 % and 30 % of the EAN sold in outlets in period 1 or 25 are present in both periods in the same outlet.

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For the poste make up and care product, the match rate are computed without the unclassified data for reasons of performance and duration of computation.

It seems that there is heterogeneity among products regarding their presence over time. A larger study is needed to have an idea of the scope of possible values of presence over time.

2) Indexes at the « variety » level For the first comparisons, we only computed the GEKS Indexes within windows of 25 months. The idea is to firstly analyse the difference between a multilateral index and our current index and then the differences between using only the EAN and using groups of article (extended article number in our experiment). We used the half splicing method.

a) Whole milk

Figure 8: Source : Scanner data Scope : Metropolitan France .Reading note : In December 2022, the price index computed using the GEKS method on article sold grouped by EAN in outlet is 117 it is 1.0 point less than the index computed with a use of « expanded article number ».

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jan vie

r-2 0

m ar

s- 20

m ai-

jui lle

t-2 0

se pt

em br

e- 20

no ve

m br

e- 20

jan vie

r-2 1

m ar

s- 21

m ai-

jui lle

t-2 1

se pt

em br

e- 21

no ve

m br

e- 21

jan vie

r-2 2

m ar

s- 22

m ai-

jui lle

t-2 2

se pt

em br

e- 22

no ve

m br

e- 22

100

105

110

115

120

125

-2 -1,5 -1 -0,5 0 0,5 1 1,5 2

Price indices for the variety whole milk between Jan 2020 and Dec 2022

CPI - GEKS by EAN CPI base 100 janv 20 GEKS by EAN half spliced 25 mois

Figure 9: Source : Scanner data Scope : Metropolitan France Reading note : In June 2021, the price index computed using the GEKS method on article sold grouped by EAN is 100.0, it is 0.3 point less than the index computed with a use of « expanded article number ».

Both graphs exhibit very similar price trajectories: between grouping articles by EAN or by « expanded article number » and between a GEKS and the current CPI. This is working well because whole milk has stable products, few products disappear and the relative shares of the sub-products are relatively stable across time. The price trajectories are the same across all sub- products.

b) Foie gras During the year 2020, 85% of the products present in our CPI basket in December 2019 were replaced for the variety foie gras.

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115

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-1 -0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6 0,8 1

Price indices for the variety whole milk between Jan 2020 and Dec 2022

(GEKS - GEKS by EAN) GEKS 25 months GEKS by ean 25 months

Figure 10: Source : Scanner data Scope : Metropolitan France Reading note : In December 2021, the price index computed using the GEKS method on article sold grouped by « expanded article number » and outlet is 98.48 , it is 0.74 point less than the index computed with EAN .

There is a small difference between grouping the expenditure by EAN or by expanded article number in the case of foie gras. CPI and GEKS index using EAN are relatively comparable, the trend is the same but there is up to 3 points of difference, in a stronger inflation context.

These small differences are even smaller if considered with year-to-year inflation.

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Figure 11: Source : Scanner data and French CPI. Scope : Metropolitan France Reading note : In February 2022, the French CPI (rebased in January 2020) is 104.25 it is 2.78 points more than a GEKS index computed with a use of EAN.

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-4 -3,1 -2,2 -1,3 -0,4 0,5 1,4 2,3 3,2

Price indices for the variety foie gras between January 2020 and December 2022

CPI - GEKS by ean CPI, base 100 = Jan 2020 GEKS by ean

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-0,8

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Price indices for the variety foie gras between January 2020 and December 2022

(GEKS - GEKS by EAN) GEKS 25 by extended article

GEKS 25 by EAN

c) Lipstick / gloss :

For lipstick and gloss, each expanded article number gathers a high number of EAN: 775 « expanded article » representing 5564 EAN.

363534333231302928272625242322212019181716151413121110987654321 88

100

102

-3 -2,5

-2 -1,5

-1 -0,5

0 0,5

1 1,5

2 2,5

Price indices for the variety lipstick/gloss between Jan 2020 and Dec 2022

(GEKS - GEKS by EAN) GEKS by EAN 25 HASP

GEKS 25 HASP with extend article number

Figure 12: Source : Scanner data. Scope : Metropolitan France. Reading note : In June 2020, the price index computed using the GEKS method on article sold grouped by EAN with a window of size 25 and the half splicing method is 89.6, is it 2.3 point less than the index computed with a use of « expanded article number ».

This first comparison gives similar results with a bit more volatility with the index constructed at the EAN level.

We analysed at the expanded article level the price dynamic and expenditure share to understand better the dynamic, graphics are available in appendix, figures 29 and 30.

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102

-1

Price indices for the variety lipstick/gloss between Jan 2020 and Dec 2022

(CPI - GEKS ) CPI Base 100 janv 20 GEKS by EAN 25 HASP

Figure 13: Source : Scanner data and French CPI. Scope : Metropolitan France. Reading note : In February 2022, the French CPI (rebased in January 2020) is 104.25 it is 2.78 points more than a GEKS index computed with a use of EAN x outlet.

The index here again are giving globally the same trends but larger differences than for the 2 other examples. The GEKS is more subject to volatility: each drop is a bit stronger.

The largest difference is July 2020, where some COVID-19 consequences are probably at stake.

3) Indexes at the « poste » level In our current methodology, in “poste level”, there are varieties using scanner data and varieties using field collected data. They are the aggregated together using an arithmetic Laspeyres.

a) Whole Milk This table presents the weight distribution among all the varieties regarding whole milk – from scanner data and field collected data, the one from scanner data are prefixed by “DC”.

YEAR Label WEIGHT 2020 WHOLE MILK PASTEURISED 9 2020 Whole milk UHT 17 2020 DC_Whole Milk 60 2020 DC_Fresh pasteurised whole milk 14

The scanner data weight 74 % in 2020 in our poste index. In our raw data, we have 2 varieties that are included in the index compilation and some unclassified data, not used. The data size of 3 years, aggregated by EAN X Outlet X Month represents approximatively 4,3*10⁶ lines.

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Expenditure share of varieties inside the whole milk poste between Jan 2020 and Dec 2022.

The variety whole milk represent the large majority of the scanner data varieties in the poste whole milk. The unclassified products, are almost negligible.

The thing with these unclassified data is that we won’t be able with our classifying tool to have such non stable and excluded data. We will maybe have some unclassified observations because our tool won’t be able to classify them with enough confidence but with no guaranty that it will be same kind of products.

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Figure 14 Source : Scanner data. Scope : Metropolitan France. The dotted lines represent annual expenditure shares and the continuous one monthly shares.

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Indexes for the whole milk poste between Jan 2020 and Dec 2022

GEKS per variety weighted (annual weights)

aggregation of scanner data CPI

GEKS poste 25 HASP with unclassified

GEKS poste 25 HASP without unclassified

Figure 15: Scanner data Scope : Metropolitan France. Reading note : In July 2021, the GEKS index computed with a window of 25 months grouping by EAN x outlet, half splicing method and including the unclassified data is 101.5. The GEKS indexes are computed with a Törnqvist index formula, the splicing method is mean for the window size 13 and half for the window size 25.

1 4 7 10 13 16 19 22 25 28 31 34 90,00 %

95,00 %

100,00 %

105,00 %

110,00 %

115,00 %

120,00 %

GEKS indexes by varieties inside the whole milk poste between Jan 2020 and Dec 2020

Whole milk

Fresh pasteurised whole milk

Unclassified

Figure 16: Source: Scanner data. Scope : Metropolitan France. Reading note : In period 28 (April 2022), for the variety fresh pasteurised whole milk the GEKS index computed with a window of 25 months grouping by EAN x outlet and half splicing method is 103.75.

The unclassified products have a more erratic price variation, but they weight very lightly in this poste. It explains the fact that the several GEKS indexes lead to very close results at the whole milk poste level.

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b) Canned meat Scanner data weights 65% in 2020 in our poste index, it represents 7 varieties that are included in our index and unclassified data, which weight more in this “poste” than for whole milk.

The data size of 3 years aggregated by EAN X Outlet X Month is approximatively 22,5*10⁶ lines

In order to understand what weights more in the indexes variation, we firstly looked at the monthly and annual expenditure shares of each varieties within the poste canned meat.

Year Label WEIGHT 2020 Canned charcuterie 35 2020 DC_Canned rillettes 4 2020 DC_Canned duck confit 20 2020 DC_Canned country style pâté 19 2020 DC_Canned liver pâté 4 2020 DC_Canned poultry pâté 3 2020 DC_Canned full foie gras 9 2020 DC_Canned bloc of foie gras 6

Expenditure share of varieties inside the canned meat poste between Jan 2020 and Dec 2022.

We can see the seasonality in the sale of some varieties : • Foie gras are more sold during the end of the years (December principally). • Country style pâté & unclassified are less sold in December.

Unclassified data has the most important weight in all periods (approx 35% annually), it is really different than for milk.

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Figure 17 Source: Scanner data. The dotted lines represent annual expenditure shares and the continuous one monthly shares.

We wanted to investigate more these unclassified data, to do so we used the nomenclature we have from Circana which provide us with the referential of products.

Figure 18: Source: scanner data in 2020 and 2021. The dotted lines represent annual expenditure shares and the continuous one monthly shares. Reading note: in January 2020, the Circana family tinned foie gras represented 8.6% of the expenditure of unclassified data in the poste canned meat.

The EAN represented are part of 4 different “Ciracana families” (a specific nomenclature). Among these families, one could be linked to a field collected variety: “Corned beef and ham”. It weights less than 10% of the products in most periods, including this data in our computation could induce “double counts” with the field variety and lead to an overestimation of the weight of the variety canned charcuterie.

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GEKS indexes by Circana family in 2020 for unclassified data at the canned meat poste level

Figure 19: Source: scanner data. Scope: Metropolitan France. Reading note: in December 2020, GEKS index for the Circana (previously IRI) family “canned pâtés and tinned rillettes” grouping by EAN x outlet with a window of 25 month and half splicing was 105.2.

Figure 20: Source: scanner data. Scope: Metropolitan France. Reading note: in December 2020, GEKS index for the unclassified data among the poste canned meat grouping by EAN x outlet with a window of 25 month and half splicing was 106.5.

There is an increase of the index for unclassified data in December 2020. Thanks to the Figure 18, we can see that it is most likely due to unclassified pate, rillettes and confits.

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 95,00 %

100,00 %

105,00 %

110,00 %

115,00 %

120,00 %

125,00 %

130,00 %

135,00 %

140,00 %

GEKS indexes by varieties inside the canned meat poste between Jan 2020 and Dec 2020

Canned duck confit

Canned country style pâté

Canned liver pâté

Canned Poultry pâté

Canned rillettes

Canned full foie gras

Canned Block of foie gras

Unclassified

The increase in the index including unclassified data in December 2020 is still present. All the indexes are relatively close and have the same trend, except for this month.

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22 95

100

105

110

115

120

GEKS price indices for the poste canned meat between Jan 2020 and Dec 2022

GEKS-Fisher half 25 with unclassified

GEKS-Törnqvist 25 half with unclassified

Figure 22 Source: scanner data. Scope: Metropolitan France. Reading note: in December 2020, GEKS index for the poste canned meat including the unclassified data grouping by EAN x outlet with the Fisher Index is 106.1. The GEKS indexes are computed with a window size of 25 and half splicing method.

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Figure 21: Source: scanner data and French CPI. Scope: Metropolitan France. Reading note: in period 12 (December 2020), GEKS index for the poste canned meat including the unclassified data grouping by EAN x outlet with a window of 13 month and mean splicing was 106.2. The GEKS indexes are computed with a Törnqvist index formula, the splicing method is mean for the window size 13 and half for the window size 25.

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 90

100

105

110

115

120

125

Price indexes at the canned meat poste level :

GEKS 25 by variety aggregated (annual weights)

CPI scanner data aggregated

GEKS 25 with unclassified

GEKS 25 without unclassified

GEKS 13 with unclassified

GEKS 13 without unclassified

CPI poste

We also compared the results between a GEKS-Törnqvist and a GEKS-Fisher. It gives relatively similar results, Fisher index seems to lead to higher values.

c) Make-up and care products Year Label Weight 2020 Lipstick 2 2020 Face powder 5 2020 Nail polish 4 2020 Sun products 6 2020 Cleansing milk 10 2020 Care cream 16 2020 MASCARA 5 2020 Depilatory products 5 2020 Body moisturising milk 7 2020 DC_Face women care cream 17 2020 DC_Face cleanser 6 2020 DC_Body care cream/milk 5 2020 DC_Mascara 5 2020 DC_Face powder 4 2020 DC_Lipstick and gloss 3

Scanner data weight 40% in 2020 in our “Make-up and care products“ poste index, it is composed of 6 varieties contributing to the publish CPI and also some unclassified data. The data size of 3 years aggregated by EAN X Outlet X Month is approximatively 148,1*10⁶ lines.

Expenditure share by variety for the poste make up and care product between Jan 2020 and Dec 2022

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Figure 23 Source: scanner data. Scope: Metropolitan France. Reading note: in December 2020, the expenditure share of unclassified data among the poste make-up and care products is 53.1%

Here also, unclassified data weight a lot, with a strong seasonality. Given that, we can anticipate that at the poste level, with this unclassified data, we could have something quite different from our published index: it weights a lot and has some seasonal pattern.

The unclassified has a high weight for several reasons. First, it is not always easy to make homogeneous class of products. Second, there is an applicative constraint which is that a variety has to be at least 1% of a “poste” so that homogeneous class of products have to gather enough expenditure shares. Third, with time available, the most promising unclassified are prioritise. Hence, some are not studied.

Figure 24: Figure 24 Source: scanner data. Scope: Metropolitan France. Reading note: in December 2020, the GEKS index using a window size of 25, half splicing and EAN x outlet level for unclassified data among the poste make-up and care products is 118.1.

Here we have the multilateral indexes at the “variety” level. The unclassified exhibits some weird behaviour. There are probably some micro-trajectories very steep that have some macro-impact. This case is of interest: we have to develop tools to elucidate that kind of observations: either to understand what it is going on or to cancel these observations if not reliable.

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1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 85,00 %

90,00 %

95,00 %

100,00 %

105,00 %

110,00 %

115,00 %

120,00 %

GEKS HALF SPLICE window 25 by variety for the poste make up and care products

body care cream

face cleanser

face powder

mascara

Lipstick/gloss

women face cream

unclassified

Figure 25: Figure 25: Source: scanner data. Scope: Metropolitan France. Reading note : Between February 2020 and February 2021, the price level for unclassified canned meat products has

decreased of 10,0 %. Year-on-year inflation is computed as 100∗( I m, y

Im , y−1

−1)%

Figure 26 Source: scanner data and French CPI. Scope: Metropolitan France. Reading note: in period 12 (December 2020), GEKS index for the poste make up and care product including the unclassified data grouping by EAN x outlet with a window of 13 month and mean splicing was 105.9. The GEKS indexes are computed with a Törnqvist index formula, the splicing method is mean for the window size 13 and half for the window size 25. As we can see just above, the unclassified data have a strong impact. Also, we can see that the window length has no real impact on the multilateral indexes if unclassified are excluded. But, regardless of this

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1 4 7 10 13 16 19 22 25 28 31 34 70,00 %

75,00 %

80,00 %

85,00 %

90,00 %

95,00 %

100,00 %

105,00 %

110,00 %

115,00 %

120,00 %

Price indexes for the poste make up and care products

GEKS MEAN 13 without unclassified

GEKS MEAN 13 with unclassified

GEKS 25 HASP with un- classified

GEKS 25 HASP without unclassified

CPI poste

13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36

-20

-15

-10

-5

Year-on-year inflation (GEKS 25 half spliced) for varieties of the poste make up and care products

face cleanser

mascara

Lipstick/gloss

face powder

women face cream

body care cream

unclassified

length, the multilateral indexes are quite far from the current one. This current index shows an increase of prices when the multilateral ones demonstrate more stability.

GEKS indexes by Circana family in 2020 for unclassified data

at the make up and care poste level

Figure 28: Source: scanner data in 2020. Scope : Metropolitan France. Reading note: in November 2020, the Circana (previously IRI) family “eyes make-up” represented 23.1% of the expenditure of unclassified make up and care products. Only families representing more than 1% of the expenditure are represented.

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Figure 27: Source: scanner data in 2020. Scope : Metropolitan France. Reading note: in November 2020, GEKS index for the Circana (previously IRI) family “eyes make-up” grouping by EAN x outlet with a window of 25 month and half splicing was 97.5. Only the four family with the highest expenditure share are represented

Among unclassified data, some are linked to Circana Families representing field collected varieties, some others don’t respect the precise specifications of varieties : for instance eyes make-up isn’t in our scanner data varieties currently.

4) Contributions behind GEKS-Tq variation

a) Theory To understand better the variation of the indexes computed on the previous section of this paper, we wanted to take a look into the contributions of individual products to the index variation6.

A contribution is defined between two periods

To do so, we have to start by looking at the contribution in the bilateral index. If the product is present at both periods, its expenditure share in the corresponding bilateral index is :

w i t 1 ,t 2=0,5∗(

pi t 1q i

t 1

∑ j∈N t 1∩N t2

p j t 1q j

t 1 + pi

t 2 qi t 2

∑j∈N t1∩N t 2

p j t 2 q j

t 2 )

were pi t is the price of product i at period t and q i

t is the number of product i sold at period t. It is the

weight it has in the Törnqvist index.

We are then able to compute the average bilateral share of this product from a period t with all the other periods in the window in the multilateral index:

w i ∗ , t= 1

card W ∑r∈W w i

r ,t

To look at the contribution of a product in the index variation between period t1 and t2, we have to apply these weights to the product of price variation in each period: which give the formula:

I GEKS−TQ t 1 ,t2 =∏i∈N

(p i t 2)wi

∗ , t2

(p i t 1)wi

∗ , t1 ∏t∈W (p i

t) w i

t, t 1−w i t, t 2

cardW

and so we have a decomposition

I GEKS−TQ t 1 ,t2 =∏i∈N

contributioni t 1 ,t 2

With this formula, the index is represented as the product of the contribution of each product. In order to facilitate the interpretation by having a summability between contributions, we looked at the log of the index and the log of the contributions.

ln( I GEKS−TQ t 1, t 2 )=∑i∈N

ln (contributioni t 1 , t 2)

ln ( IGEKS−TQ t 1, t 2 )=∑i∈N

ln ( ( pi

t 2)w i ∗ , t 2

( pi t 1)w i

∗ , t 1 ∏t∈W ( pi

t) w i

t, t 1−w i t , t2

|W| )

Our product definition here, is still an EAN x Outlet.

6 Thanks to the paper, Decomposing Multilateral Price Indexes into the Contributions of Individual Commodities, and the guide on Multilateral Methods (Chapter 8) guide we looked into this direction.

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Due to expensive computation costs, these contribution are computed only at a level EAN x Outlet. We had to explore ways to reduce the number of observation and time of computation.

We used this R package: https://github.com/MjStansfi/GEKSdecomp/. It seems to be compatible only with comparing the two last periods of the window. We would have to adapt this tool to have more flexible options to analyse contributions to evolution. Also, this is limited at analysis “inside” a given window. But for longer evolution, splicing has probably to be taken into account.

b) Experiment From our previous results, several periods for each varieties/poste where interesting to look at in order understand better the indexes variation (between period 4 and 5 for lipstick and 5 and 6 28,29 et 29,30 between 1 and 2 for unclassified make up & car products, between period 1 and 12 for unclassified canned meat, between period 3 and 4 and 4 and 5 for unclassified canned meat).

For practical reasons, we chose to calculate contributions for lipstick between period 28 and 29, with a window size of 13, without separating outlets. We studied the contributions for each EAN.

Figure 29: Source: scanner data. Reading note: Each bar represent the log contribution of an EAN in the price evolution of lipstick/gloss between period 28 (April 2022) and period 29 (May 2022), measured inside a window of 13 months. A log contribution superior than 0 means that the EAN contribute positively (price increase) and a negative one negatively.

By transitivity of the GEKS index, we can compare contributions between period 28 and 29 to the ratio of

I GEKS−TQ 17,29

I GEKS−TQ 17,28 . We cannot theoretically compare the ratio of spliced indexes.

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https://github.com/MjStansfi/GEKSdecomp/

GEKS using EAN and window size of 13 for lipstick:

Time Period

GEKS-Tq EAN 13

Mean splice

GEKS-T ean X outlet 13 Mean splice

GEKS by EAN without splicing (period 17 as reference)

17 102,70 98,90 100 18 104,01 99,05 100,88 19 100,56 97,05 96,88 20 104,60 98,74 99,94 21 104,06 97,30 98,89 22 104,25 98,00 99,55 23 103,39 98,90 100,8 24 103,31 98,33 99,66 25 94,12 96,73 95,42 26 98,87 98,22 96,77 27 105,15 96,91 97,31 28 105,90 98,90 99,38 29 104,57 95,55 96,97

I GEKS−TQ 17,29

I GEKS−TQ 17,28 =

96,97 99,38

= 0,9756 = 97,5%

With the contributions computed with the R package GEKSdecomp we have the following results:

e ∑i∈EANs

log(contribi) = 0.9691 = 96,9% We are also able to find the EAN with the contribution the furthest from 1. It has a log(contribution) of - 0.00353 and a contribution of 0.9965

Figure 30: Source: scanner data. Scope: Metropolitan France. Reading note: in period 20 (July 2021), the average price for the EAN studied is 13.21€. The average price is computed as an average weighted by the expenditure share.

33/37

IV) Next steps ? Our « research » agenda

This work is the beginning of a longer project about multilateral methods and their interest given our context. If this work shows some interesting leads, a lot has still to be done.

1) Link between those multilateral indexes and microeconomic theory First, while we kept some very close methodology for our scanner data, we were able to use the same explanation for our methodology. This is a fixed basket representing the mean consumption of French households optimising their utility. As some links exist between Laspeyres, Paasche and Fisher indexes on one hand and micro-economic theory one the other hand: there is some theoretical grounds to our current method.

At this stage, we need to better understand the economical approach on which are based multilateral methods and how to communicate and interpret results with these methods. This is of interest to make this index understandable by anyone in society.

2) Explore the outlet dimension In our current CPI methodology for field collected varieties, we use sampling and define targets

among the outlet according to their classification (supermarket, hypermarket, specialized shop). For scanner data varieties, they represent only two kind of outlet: supermarket and hypermarket. In this experiment, we are producing micro indexes at the outlet index, which means that we consider for the customer there is no substitution between buying in a shop or another. The latter point can be discussed, because for instance we could consider that outlets of the same size, from the same retailer and in a close geographic area could be considered equivalent. Following ean into a group of shop could improve the quality of the index because it could improve the match rate between periods.

3) Going further with classification methods As explained above, this work requires a classifying tool. Without this, we cannot classify data into the COICOP and consequently, we cannot compute relevant indexes. This task will be tackle in the following months by making progress with the existing tools we have.

We use a fasttext algorithm which is a neural network tool specialized in dealing with characters strings. By extracting labels of products and their corresponding expenditures we will optimize the classifying function. Our goal is to have a good performance at the “poste” level – going further seems to be unreasonable given the information we have.

4) Strategy to include those indexes inside our current methodology Before hoping to use these indexes in production, we have to deepen our look into the contributions, the interpretability/decomposition of an index evolution. We presented some first contribution computation but we will need to conceive more practical routines for understanding such index evolutions.

34/37

And when we will be able to classify product, to compute multilateral index with enough understanding of it (from both statistical and theoretical approaches), we will have to have a reflection on how it will be possible to use this kind of methods with the rest of the basket we follow and to see how to adapt this with our current methodology (whether to change everything or to have some cohabitation).

V) Conclusion

This first real experimentation of multilateral index with our scanner data gives us some first learnings : • at a really fine scale, this index behaves quite closely to our current methodology and consequently

seems possible to work at the EAN scale – it sill remain to be confirmed at a larger scale • working at the EAN scale seems to be acceptable but it has to be confirmed with a larger scale

experiment • at a more aggregate scale (our “poste level”), there is more volatility and we have to progress in our

understanding and tools for this

This first work emphasises two major elements that we need to work on : • Classification tool to classify the products in the COICOP nomenclature • Theoretical understanding of the links with micro-economic theory

35/37

References

• Guide on Multilateral Methods in the Harmonised Index of Consumer Prices, Eurostat, 2022.

• MARS: A method for defining products and linking barcodes of item relaunches, Antonio G. Chessa, Statistics Netherlands.

• “Chain drift” in the Chained Consumer Price Index: 1999–2017, Monthly Labor Review, BLS December 2021.

• Évaluation des méthodes multilatérales de calcul de l'indice, STATBEL, Ken Van Loon et Dorien Roels, 07/2019

• Eliminating Chain Drift in Price Indexes Based on Scanner Data, Jan de Haana and Heymerik van der Grient, Statistics Netherlands,2 April 2009

• FMI, CPI Manual, 2020.

• From GEKS to cycle method , 11/2017, Leon Willenborg

• A Closer Look at the Rolling Window GEKS Index with a Movement Splice, Jan De Haan, 16 October 2017

• Extension of multilateral index series over time: Analysis and comparison of methods, Antonio G. Chessa, 7 May 2021

• Transitivity of price indexes, Leon Willenborg , May 2018

• Comparing Price indexes of Clothing and Footwear for Scanner Data and Web Scraped Data

• Antonio G. Chessa* and Robert Griffioen**, Statistics Netherlands, Team CPI ,1 st

April 2019

• Leclair (2019), « Utiliser les données de caisses pour le calcul de l’indice des prix à la consommation », Le Courrier des statistiques, n°3

• Decomposing Multilateral Price Indexes into the Contributions of Individual Commodities, Michaël Webster and Rory C. Tarnow-Mordy , 2019

• Introducing multilateral index methods into consumer price statistics, Liam Greenhough , ONS, 28 November 2022

• The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity, Caves, Christensen and Diewert, 1982

• The use of weighted GEKS for the calculation of consumer price indexes: an experimental application to Italian scanner data Alessandro Brunetti (Istat), Stefania Fatello (Istat), Tiziana Laureti (Università della Tuscia), Federico Polidoro (Istat) 17th Ottawa Group Meeting, Rome, 7 – 10 June 2022

36/37

Appendix Average price evolution of Circana families among unclassified make up and care products between January

2020 and December 2022

Figure 31: Source: Scanner data. Scope: Metropolitan France. Reading note : The evolution is computed as the ratio of average price at period m/ average price at period 0

Expenditure share of Circana families among unclassified make up and care products between January 2020 and December 2022

Figure 31: Source: Scanner data. Scope: Metropolitan France. The dotted lines represent annual expenditure shares and the continuous one monthly shares. Results are presented for extended group representing more than 1 % of the expenditure in 2020

37/37

I) Context of this study : scanner data in France now and future

1) Usual data
2) Scanner data : current methodology
3) Information technology infrastructure
4) New data and data not yet used

a) Hard discounters
b) Overseas scanner data
c) Other sectors not yet used

II) Theory and strategy of our experimentation

1) Multilateral methods and milestones of the process

a) Individual product specification
b) Multilateral Index
c) Time windows & splicing
d) Aggregation structure.

i. There is no way of having a decomposition of the multilateral indexes
ii. Choosing a level and aggregating these indexes

2) Test protocol

III) Results

1 ) Presence of references across time

a) Milk
b) Foie gras
c) Lipstick
d) Canned meat
e) Make up and care products

2) Indexes at the « variety » level

a) Whole milk
b) Foie gras
c) Lipstick / gloss :

3) Indexes at the « poste » level

a) Whole Milk
b) Canned meat
c) Make-up and care products

4) Contributions behind GEKS-Tq variation

a) Theory
b) Experiment

IV) Next steps ? Our « research » agenda

1) Link between those multilateral indexes and microeconomic theory
2) Explore the outlet dimension
3) Going further with classification methods
4) Strategy to include those indexes inside our current methodology

V) Conclusion
References
Appendix

Presentation

Read more about Presentation

Languages and translations

English

Measurement of gender inequalities

in the French labour market

using efficiency measures

Poleth VEGA RUALES

Audrey DUMAS

CDEDYS - University of Perpignan

Meeting of the UNECE Group of Experts on Gender Statistics

Geneva, Switzerland, 10 May 2023

Content

• Motivation

• Literature

• Methodology

• Data

• Results

• Conclusion

Motivation

The existence of wage gaps between men and women could be

justified because women:

- work fewer hours;

- have less work experience;

- are engaged in low paying job (sectoral and occupational

segregation).

However, when isolating these factors, wage gaps does not disappear

(Carillo et al., 2014).

Labour market discrimination

Motivation Literature Methodology Data Results Conclusion

Two related but distinct concepts in the realm of gender workplace

inequality:

➢Glass ceiling: prevents women from advancing to senior leadership

positions, despite their qualifications and achievements.

→Systemic bias

➢Sticky floor: women stuck in low-paying, low-status jobs, with limited

opportunities for advancement.

→Lack of access to training and education, and social expectations

about gender roles

Labour market discrimination

Motivation

Motivation Literature Methodology Data Results Conclusion

Methods of measurement

Unadjusted GPG

Adjusted

various factors (occupation, education,

experience, etc.)

Blinder-Oaxaca (BO) decomposition

(1973)

Efficiency approaches:

DEA-based Malmquist Index

(Amado et al., 2018)

Motivation Methods of measuring the gender pay gap

Motivation Literature Methodology Data Results Conclusion

1. Better understand the differences between the traditional measures

of GPG and the Malmquist Index (MI).

2. Measure “glass ceilings” and “sticky floors” using a new

methodology based on MIs.

3. Contribute to the empirical work by measuring the GPG in the

French labour market with an efficiency approach.

4. Show “glass ceilings” and “sticky floors” by economic activity and

occupation to propose recommendations.

Motivation Literature Methodology Data Results Conclusion

Motivation Research objectives

Methods of measurement

Unadjusted GPG: 𝑦𝐻−𝑦𝐹

𝑦𝐻

Adjusted

various factors (occupation, education,

experience, etc.)

Blinder-Oaxaca (BO) decomposition

(1973)

Efficiency approaches:

DEA-based Malmquist Index

(Amado et al., 2018)

Motivation Literature Methodology Data Results Conclusion

Literature Methods of measuring the gender pay gap

a woman F

a man M y

(ex: wage)

Mean predicted y given x for male: 𝐸(𝑌𝑀/𝑋𝑀) estimated with linear regression

Mean predicted y given x for female: 𝐸(𝑌𝐹/𝑋𝐹) estimated with linear regression

Fx Mx

unadjusted GPG

My adjusted

GPG

Blinder-Oaxaca Decomposition

one input

one output

x (ex: tenure)

Motivation Literature Methodology Data Results Conclusion

Malmquist Index Male frontier: Highest y given x for male computed with DEA method

Female frontier: Highest y given x for female computed with DEA method

Average distance for male-to-male frontier

Average distance for female to male frontierAdjusted GPG

Distance to the frontier : efficiency score computed with DEA method

Motivation Literature Methodology Data Results Conclusion

Blinder-Oaxaca Descomposition Malmquist Index

Definition of “average” point Average characteristics (x) Average efficiency score

Definition of the “reference”

situation Average predicted wage given x Max wage given x

Computation of the “line” or

“frontier” Assumption on the functional forms

No assumption on functional forms

 non-parametric method

Number of outputs “y” One Multi-output

Input

Output

Female frontier

Male frontier

• To measure a MI

- an output-oriented MI

- an input-oriented MI

Output-oriented distance: “Glass ceiling”

Input-oriented distance: “Sticky floor”

Methodology

Motivation Literature Methodology Data Results Conclusion

Input

Output

y 𝑀𝐼𝑂 = 𝐷𝑂 𝑀 𝑋𝑂

𝐹 , 𝑌𝑂 𝐹

𝐷𝑂 𝑀 𝑋𝑂

𝑀, 𝑌𝑂 𝑀 ∙

𝐷𝑂 𝐹 𝑋𝑂

𝐹 , 𝑌𝑂 𝐹

𝐷𝑂 𝐹 𝑋𝑂

𝑀, 𝑌𝑂 𝑀

Τ1 2

𝑀𝐼𝐼 = 𝐷𝐼 𝑀 𝑋𝐼

𝐹 , 𝑌𝐼 𝐹

𝐷𝐼 𝑀 𝑋𝐼

𝑀, 𝑌𝐼 𝑀 ∙

𝐷𝐼 𝐹 𝑋𝐼

𝐹 , 𝑌𝐼 𝐹

𝐷𝐼 𝐹 𝑋𝐼

𝑀, 𝑌𝐼 𝑀

Τ1 2

𝑝𝑠𝑒𝑢𝑑𝑜 𝐻𝑀𝐼 = 𝑀𝐼𝑂

𝑀𝐼𝐼

1/2

 adjusted GPG

Motivation Literature Methodology Data Results Conclusion

Methodology

• 2019 Labour Force Survey (LFS) from France (before Covid): Cross-sectional survey

of over 50,000 households

• Sample: working age individuals (15 years and older) employed in formal economy, only

one job.

• Input variables: Measures of human capital investment:

- Theoretical number of years of education of the highest diploma obtained

- Number of work seniority

• Output variables:

- Hourly earnings in euros

• Final sample: 40,978 workers; 19,294 men and 21,684 women

• Disaggregation: 18 economic activities and 9 occupations

Data

Motivation Literature Methodology Data Results Conclusion

Descriptive statistics

Motivation Literature Methodology Data Results Conclusion

Mean no.

of years of

education

Mean no.

of years of

work

seniority

Mean

hourly

earnings

(€)

Mean no.

of years of

education

Mean no.

of years of

work

seniority

Mean

hourly

earnings

(€)

Mean 12.8 11.9 14.3 13.2 12.6 13.0

Variation 0.2 0.9 0.4 0.2 0.9 0.4

Min 5 0 2.2 5 0 2.4

Max 20 48 58.8 20 47 65.6

Unadjusted gender pay gap : 13.4%

Male Female

Summary statistics of the inputs and outputs (LFS, 2019)

Results

Output-oriented MI Input-oriented MI

Glass ceilings Sticky-floors

Agriculture, forestry and fishing 0.9 3.1 0.8 1.9

Manufacturing 10.1 10.5 6.4 8.5

Electricity, gas, steam and air conditioning supply 14.1 12.3 2.7 7.4

Water supply; sewerage, waste management 8.0 14.0 16.5 15.3

Construction 2.0 9.9 13.0 11.4

Wholesale and retail trade; repair of motor vehicles 7.7 7.7 4.5 6.1

Transportation and storage 1.6 11.0 9.5 10.3

Accommodation and food service activities 4.6 6.1 6.6 6.4

Information and communication 10.3 12.2 4.2 8.1

Financial and insurance activities 11.6 11.9 2.6 7.2

Real state activities 4.8 5.0 5.5 5.3

Professional, scientific and technical activities 9.3 10.0 3.8 6.8

Administrative and support service activities 6.3 5.7 4.9 5.3

Public administration and defence; social security 6.1 9.5 6.7 8.1

Education 15.3 14.3 4.5 9.2

Human health and social work activities 8.5 9.0 1.1 5.0

Arts, entertainment and recreation 7.1 10.6 6.2 8.4

Other service activities 11.2 10.3 1.6 5.9

Pseudo HMIEconomic Activity

Values of the Malmquist index by economic activity (in percentage)

Unadjusted GPG

Motivation Literature Methodology Data Results Conclusion

Results

Output-oriented MI Input-oriented MI

Glass ceilings Sticky-floors

Managers 10.4 13.2 2.5 7.7

Professionals 7.8 13.0 5.1 9.0

Technicians and associate professionals 8.2 9.6 7.4 8.5

Clerical support workers 5.9 7.5 4.5 6.0

Service and sales workers 11.4 14.0 0.8 7.2

Skilled agricultural, forestry and fishery workers 14.5 11.0 8.6 9.8

Craft and related trades workers 11.3 10.6 2.0 6.2

Plant and machine operators, and assemblers 10.7 11.9 4.3 8.0

Elementary occupations 11.9 10.0 -1.8 3.9

Unadjusted GPG Pseudo HMIOccupation

Values of the Malmquist index by occupation (in percentage)

Motivation Literature Methodology Data Results Conclusion

Conclusion

• Original methodology based on efficiency approach that bring complementary

results of traditional measures of the GPG.

• Limitations:

The MI is computed on the “average” distance of male and female to the frontier.

The “average” is still not always representative of the sample.

• Future research:

New measure based on the Hicks-Moorsteen Index.

Motivation Literature Methodology Data Results Conclusion

Thank you for your attention

Poleth VEGA RUALES

Audrey DUMAS

CDEDYS - University of Perpignan

Meeting of the UNECE Group of Experts on Gender Statistics

Geneva, Switzerland, 10 May 2023

Input

Output

Motivation Literature Methodology Data Results Conclusion

(France) Review of WP.29 UN Regulations and GTRs on their fitness for ADS

Read more about (France) Review of WP.29 UN Regulations and GTRs on their fitness for ADS

Languages and translations

English

Review of UN Regulations and GTRs on their fitness for ADS

Transmitted by the representative of France on behalf of the chairs of the WP29 screening taskforces

Informal document WP.29-189-20 189th WP29, 7–9 March 2023 Agenda item 8.6.2.

At its 186th session in March 2022, WP.29 requested all its subsidiary working parties to perform a screening of the UN Regulations and Global Technical Regulations (GTR) of relevance regarding their fitness for Automated Driving Systems (ADS) until March 2023.

At its 14th session in September 2022, GRVA gave additional guidance as to what kind of automated vehicles to consider, etc. (informal document GRVA-14-54r1)

Pending request for deadline extension until June 2023 to give additional time to GRs with staggered session schedules

Mandate from WP.29

Common work method across GRs

The taskforces of the different GRs have agreed to work on the same deliverables:

High-level summaries for each Regulation

Comprehensive files for the detailed screening

A “whitebook” for handling automated driving when drafting new Regulations

The review does not include definitive solutions for changing Regulations, but taskforces may offer suggestions (amendments, new Regulations, new vehicle categories…)

This template can both serve as a preliminary analysis of a Regulation before its detailed review, and as a final report to give a summary of the detected issues and possible options

High-level assessment

Detailed screening (1/2)

Points of interest:

Use cases: full automation, dual-mode, no occupants, etc.

Possible approaches: amending Regulations, drafting specific Regulations for automated vehicles, creating new vehicle categories, etc.

Consider both explicit and implicit concepts

Online collaborative environment and example of a detailed review for a GRVA Regulation

Detailed screening (2/2)

As of early March 2023:

GRBP: Detailed screening ready to start

GRE: Detailed screening and deliverables done for R48

GRPE: First meeting to take place soon

GRSG: Detailed screening in progress (55% done)

GRSP: Detailed screening in progress (80% done)

GRVA: Detailed screening in progress (75% done)

Status of the screening taskforces

GRSG GRVA GRSP

GRBP

GRPE

GRE

Not all taskforces might be able to get validation from their GR before the June WP29 session

Detailed screening

Drafting deliverables

High level assessment

R89	Not yet screened
R30	Applicable (or mostly applicable)
R12	Not applicable (or not applicable except in dual mode)
R13	Work needed on the Regulation / new Regulation needed
R158	Not applicable or mostly not applicable AND amendments or new Regulation needed
R48	Applicable or mostly applicable AND amendments or new Regulation needed

GRBP

R28

R30

R41

R51

R54

R59

R63

R64

R75

R92

R106

R108

R109

R117

R124

R138

R141

R142

R164

R165

GTR16

GRE

R10

R37

R48

R53

R74

R86

R99

R128

R148

R149

R150

GRPE

R24

R40

R47

R49

R68

R83

R84

R85

R96

R101

R103

R115

R120

R132

R133

R143

R154

GTR2

GTR4

GTR5

GTR10

GTR11

GTR15

GTR17

GTR18

GTR19

GTR21

GTR22

GTR23

GRSG

R18

R26

R34

R35

R36

R39

R43

R46

R52

R55

R58

R60

R61

R62

R66

R67

R71

R73

R81

R93

R97

R102

R105

R107

R110

R116

R118

R121

R122

R125

R144

R147

R151

R158

R159

R160

R161

R162

R163

R166

R167

GTR6

GTR12

GRSP

R11

R12

R14

R16

R17

R21

R22

R25

R29

R32

R33

R42

R44

R80

R94

R95

R100

R114

R127

R129

R134

R135

R136

R137

R145

R146

R153

GTR1

GTR7

GTR9

GTR13

GTR14

GTR20

GRVA

R13

R13H

R78

R79

R89

R90

R130

R131

R139

R140

R152

R155

R156

R157

GTR3

GTR8

Status of all WP29 Regulations and GTRs

Work in progress, to be validated after completion of screening for all Regulations and GTRs

Examples of high level issues

Vehicle categories: current categories do not reflect the diversity of use cases for automated driving:

Dual mode vs fully automated

Carrying occupants vs freight only

Supervision inside vehicle vs remote supervision

Telltales & warning signals: a standardised way to share information is necessary: what information is relevant to whom (passenger, occupant in driver seat, remote supervisor…)

Test mode: might be necessary for certain Regulations

GRBP (SIG AVRS): Jan Sybren BOERSMA (NL) [email protected]

GRE (TF AVSR): Karl MANZ (DE) [email protected]

GRPE (TBD) - Point of contect:Niels DEN OUDEN (NL) [email protected]

GRSG (TF AVRS): Hans LAMMERS (NL) [email protected]

GRSP (TF AVRS): Rudolf GERLACH (DE) [email protected]

GRVA (TF FADS): Romain PESSIA (FR) and Linlin ZHANG (CN)

[email protected] [email protected]

Contact information

The GRVA TF can be used as a point of contact for questions related to automated driving (definitions, use cases etc.)

Review of UN Regulations and GTRs on their fitness for ADS

Transmitted by the representative of France on behalf of the chairs of the WP29 screening taskforces

Informal document WP.29-189-20 189th WP29, 7–9 March 2023 Agenda item 8.6.2.

• At its 186th session in March 2022, WP.29 requested all its subsidiary working parties to perform a screening of the UN Regulations and Global Technical Regulations (GTR) of relevance regarding their fitness for Automated Driving Systems (ADS) until March 2023.

• At its 14th session in September 2022, GRVA gave additional guidance as to what kind of automated vehicles to consider, etc. (informal document GRVA-14-54r1)

Pending request for deadline extension until June 2023 to give additional time to GRs with staggered session schedules

Mandate from WP.29

Common work method across GRs

The taskforces of the different GRs have agreed to work on the same deliverables:

• High-level summaries for each Regulation

• Comprehensive files for the detailed screening

• A “whitebook” for handling automated driving when drafting new Regulations

3 The review does not include definitive solutions for changing Regulations, but taskforces may offer suggestions (amendments, new Regulations, new vehicle categories…)

This template can both serve as a preliminary analysis of a Regulation before its detailed review, and as a final report to give a summary of the detected issues and possible options

High-level assessment

Detailed screening (1/2)

• Points of interest: • Use cases: full automation, dual-mode, no occupants, etc. • Possible approaches: amending Regulations, drafting specific

Regulations for automated vehicles, creating new vehicle categories, etc. • Consider both explicit and implicit concepts

Online collaborative environment and example of a detailed review for a GRVA Regulation

Detailed screening (2/2)

As of early March 2023: GRBP: Detailed screening ready to start GRE: Detailed screening and deliverables done for R48 GRPE: First meeting to take place soon GRSG: Detailed screening in progress (55% done) GRSP: Detailed screening in progress (80% done) GRVA: Detailed screening in progress (75% done)

Status of the screening taskforces

High level assessment

Detailed screening

Drafting deliverables

GRSG GRVA GRSPGRBPGRPE GRE

Not all taskforces might be able to get validation from their GR before the June WP29 session

R89 Not yet screened

R30 Applicable (or mostly applicable)

R12 Not applicable (or not applicable except in dual mode)

R13 Work needed on the Regulation / new Regulation needed

R158 Not applicable or mostly not applicable AND amendments or new Regulation needed

R48 Applicable or mostly applicable AND amendments or new Regulation needed

GRBP

R 9

R 2 8

R 3 0

R 4 1

R 5 1

R 5 4

R 5 9

R 6 3

R 6 4

R 7 5

R 9 2

R 1 0 6

R 1 0 8

R 1 0 9

R 1 1 7

R 1 2 4

R 1 3 8

R 1 4 1

R 1 4 2

R 1 6 4

R 1 6 5

G T R 1 6

GRE R 1 0

R 3 7

R 4 8

R 5 3

R 7 4

R 8 6

R 9 9

R 1 2 8

R 1 4 8

R 1 4 9

R 1 5 0

GRPE

R 2 4

R 4 0

R 4 7

R 4 9

R 6 8

R 8 3

R 8 4

R 8 5

R 9 6

R 1 0 1

R 1 0 3

R 1 1 5

R 1 2 0

R 1 3 2

R 1 3 3

R 1 4 3

R 1 5 4

G T R 2

G T R 4

G T R 5

G T R 1 0

G T R 1 1

G T R 1 5

G T R 1 7

G T R 1 8

G T R 1 9

G T R 2 1

G T R 2 2

G T R 2 3

GRSG

R 1 8

R 2 6

R 3 4

R 3 5

R 3 6

R 3 9

R 4 3

R 4 6

R 5 2

R 5 5

R 5 8

R 6 0

R 6 1

R 6 2

R 6 6

R 6 7

R 7 1

R 7 3

R 8 1

R 9 3

R 9 7

R 1 0 2

R 1 0 5

R 1 0 7

R 1 1 0

R 1 1 6

R 1 1 8

R 1 2 1

R 1 2 2

R 1 2 5

R 1 4 4

R 1 4 7

R 1 5 1

R 1 5 8

R 1 5 9

R 1 6 0

R 1 6 1

R 1 6 2

R 1 6 3

R 1 6 6

R 1 6 7

G T R 6

G T R 1 2

GRSP

R 1 1

R 1 2

R 1 4

R 1 6

R 1 7

R 2 1

R 2 2

R 2 5

R 2 9

R 3 2

R 3 3

R 4 2

R 4 4

R 8 0

R 9 4

R 9 5

R 1 0 0

R 1 1 4

R 1 2 7

R 1 2 9

R 1 3 4

R 1 3 5

R 1 3 6

R 1 3 7

R 1 4 5

R 1 4 6

R 1 5 3

G T R 1

G T R 7

G T R 9

G T R 1 3

G T R 1 4

G T R 2 0

GRVA R 1 3

R 1 3 H

R 7 8

R 7 9

R 8 9

R 9 0

R 1 3 0

R 1 3 1

R 1 3 9

R 1 4 0

R 1 5 2

R 1 5 5

R 1 5 6

R 1 5 7

G T R 3

G T R 8

Status of all WP29 Regulations and GTRs

Work in progress, to be validated after completion of screening for all Regulations and GTRs

Examples of high level issues

• Vehicle categories: current categories do not reflect the diversity of use cases for automated driving:

• Dual mode vs fully automated • Carrying occupants vs freight only • Supervision inside vehicle vs remote supervision

• Telltales & warning signals: a standardised way to share information is necessary: what information is relevant to whom (passenger, occupant in driver seat, remote supervisor…)

• Test mode: might be necessary for certain Regulations

GRBP (SIG AVRS): Jan Sybren BOERSMA (NL) [email protected]

GRE (TF AVSR): Karl MANZ (DE) [email protected]

GRPE (TBD) - Point of contect:Niels DEN OUDEN (NL) [email protected]

GRSG (TF AVRS): Hans LAMMERS (NL) [email protected]

GRSP (TF AVRS): Rudolf GERLACH (DE) [email protected]

GRVA (TF FADS): Romain PESSIA (FR) and Linlin ZHANG (CN) [email protected] [email protected]

Contact information

mailto:[email protected]

Review of UN Regulations and GTRs on their fitness for ADS
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Slide Number 8
Examples of high level issues
Slide Number 10

France

Guidelines

JFSQ quality report

Cover

Removals over bark

JQ1 Production

JQ2 Trade

JQ3 Secondary PP Trade

LAM & CHIPS

ECE-EU Species

EU1 ExtraEU Trade

EU2 Removals

ITTO1-Estimates

ITTO2-Species

ITTO3-Miscellaneous

TS-OB

TS-JQ1

TS-JQ2

TS-JQ3

TS-ECEEU

TS-EU1

TS-EU2

Annex1 | JQ1-Corres.

Annex2 | JQ2-Corres.

Annex3 | JQ3-Corres.

SentData

Database

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