Estimating emission inventories of French farms using the Farm Accountancy Data Network (FADN)

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1 Estimating emission inventories of French farms using the Farm Accountancy Data Network (FADN) M.S. Corson, J.F. Ruas, F. Levert, C. Guerrier, P. Dupraz, H.M.G. van der Werf SAS and SMART Research Units, Rennes, France

2 Introduction

3 Source: Google Earth

4

5 Source: Google Earth

6 Source: Google Earth

7 Source:

8 Introduction Using pre-existing farm databases Amazing farm database Goal and scope definition Inventory analysis Impact assessment LCA Framework Interpretation Direct applications: - Product development and improvement - Public policy development - Others... Source: ISO 14040, 2006

9 Introduction Farm Accountancy Data Network (FADN) Annual economic database Begun by EU in 1965 EU, national, regional scales ca. 60,000 farms in EU Data regarding: income and expenses farm type and area crop area by type livestock numbers by type Source: European Commission

10 Introduction Previous uses of FADN in LCA Dalgaard et al. (2006) 2138 farms in Denmark 31 farm types 2 soil types Thomassen et al. (2009) 119 farms in the Netherlands specialized conventional dairy

11 Introduction Objectives Estimate farm-level inputs (of material and energy) and emissions on French farms using FADN data as the base Knowing that the French FADN would not contain all the data necessary, identify the most important questions to add to it for future sample years

12 The French FADN (RICA)

13 French FADN French FADN characteristics Approximately 600 economic and technical variables Representation of all production systems, from field crops to mixed crop-livestock systems to specialized livestock farms Careful sampling allows extrapolation to the national level Data collection began in 1968

14 French FADN Sampling strategy Commercial farms only Annually ca of profit and 0.75 work units Represents 95% of farm production, 67% of farms Extrapolation coefficients Sampling by quota method Stratified by region / farm type / farm size criteria Represents 527,350 farms in France

15 Based on the relative gross profit a farm received from crop and animal production For example, dairy farms can fall into 8 types: 411: Milk 412: Milk & cattle rearing 431: Dairying with rearing & fattening 432: Rearing & fattening with dairying 711: Mixed livestock - mainly dairying 721: Mixed livestock - granivores & dairying 811: Field crops & dairying 812: Dairying & field crops French FADN Farm type specification Source: USDA-ARS

16 Expenses for inputs Fertilizer, feed, forage, energy carriers, etc. Value and quantity of outputs (production) Crops: surface area (ares), production (quintals), sales (, quintals) Animals: livestock numbers, sales (, head) Accounting data French FADN Types of data Farm-management finances (other revenue, debts, capital assets, subsidies, etc.)

17 Methods

18 Methods Sample size and data sources Developed with year 2000 FADN 7758 French farms classified into 68 farm types, then grouped into 17 farm classes Additional statistical data: 2000 French Agricultural Census (AC) 2000 French Annual Agricultural Statistics (AAS) French Union of Fertilizer Industries (UNIFA) French Union of Animal Nutrition Industries (SNIA)

19 Analysis focused on the 12 most common animal types: 3 types of cows (calves, dairy cows, other cows) Sheep and goats 3 types of pigs (piglets, sows, other pigs) 3 types of chickens (broilers, layers, other chickens) Rabbits Equines Methods Animal types These data used to estimate inorganic fertilizer and feed inputs

20 Methods Estimating inorganic fertilizer input Challenge: convert /farm to kg N-P-K/farm Nonlinear regression model developed at French department level (AAS and UNIFA data): fertilizer N or P or K = f(n-p-k conc., area by crop, LU by type) Model applied to each FADN farm with addition of a coefficient for farm-level fertilizer expenses Farm-level estimates extrapolated to the national level and then adjusted to agree with national data for amount of N, P, and K in fertilizer purchased (UNIFA)

21 Methods Estimating concentrated feed input Challenge: convert /farm to kg N-P-K/farm Nonlinear regression model developed at farm level (FADN): % feed by animal type = f(lu by type, area by crop, crop autoconsumption, forage stocks, milk & egg production) Model re-applied to each FADN farm, then by type adjusted to equal total farm feed expenses The estimated by type was divided by mean feed cost (year 2000) and multiplied by N-P-K contents in average feed for each animal type

22 Methods Estimating energy use Direct energy use estimated from farm-level electricity, fuel, and natural-gas expenses (FADN), multiplied by their per-unit prices and energy contents Indirect energy use estimated from farm-level expenses for these energy carriers (electricity, fuel, and natural gas) as well as those for inorganic fertilizer and feed (FADN), multiplied by their per-unit prices and energy required for production

23 Methods Estimating NPK output in products Based on quantity of 173 agricultural products sold (FADN), multiplied by mean N-P-K contents in each product (multiple data sources)

24 Methods Estimating emissions NH 3, N 2 O, and NO emissions from manure (as a function of location) and inorganic fertilizer based on French emissions factors (Gac et al., 2006) CH 4 emission from enteric fermentation estimated from expert opinion (kg CH 4 = 0.92 L milk LU; P. Faverdin)

25 Methods Summary: fluxes estimated N-P-K input from fertilizer, feed, and forage N-P-K output from agricultural products NH 3, N 2 O, and NO emissions from manure and inorganic fertilizer Farm-gate N-P-K balances CH 4 emissions from enteric fermentation Direct and indirect energy use

26 Preliminary Results

27 Preliminary results Nitrogen input Fertilizer N input (kg/km 2 ) Feed N input (kg/km 2 )

28 Preliminary results Farm-gate nitrogen balance (kg/km 2 )

29 Preliminary results Annual variability and trends Example: NH 3 emissions by farm type (kg / ha UAA) Field crops Vegetables Horticulture Vinyards Fruits, other permanent crops Dairy and meat cows Other ruminants Pigs and poultry Mixed crops Mixed crops and livestock Year All types

30 Preliminary results Evaluating estimates Example: total energy use in 2006 (Mton of oil equivalent) Field crops Vegetables Horticulture Vinyards Fruits, other permanent crops Dairy and meat cows Other ruminants Pigs and poultry Mixed crops Mixed crops and livestock Site TOTAL indirect direct Direction Générale de l Énergie et des Matières Premières / Observatoire de l Énergie MEEDAD

31 Discussion

32 Discussion Estimates Estimates, in particular those of nitrogen dynamics, qualitatively represent regional trends Direct energy-use estimates close to national total Evaluation and calibration of emission estimates to continue

33 Approach still needs to include several processes to extend and improve emissions estimates and estimate potential impacts: N fixation and atmospheric deposition N 2 emissions Discussion Keyword: preliminary NO 3 and PO 4 emissions (eutrophication) CO 2 and CH 4 manure emissions (climate change) SO 2 and NO x emissions (acidification) Heavy-metal emissions (terrestrial toxicity)

34 Discussion Remarks Identification of data useful for inclusion in French FADN: Quantity of fertilizer purchased Quantity of feed imported (directly purchased or not!) Some of these data have existed in FADN of other countries (DE, NL) for years Request for inclusion of these data to be made to French Ministry of Agriculture

35

36

37 Methods Forage and pesticide inputs Forage expenses (FADN) divided by mean price for hay and multiplied by hay N-P-K content Pesticide expenses (FADN) multiplied by a mean price per kg of active ingredient

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