ANIMALCHANGE SEVENTH FRAMEWORK PROGRAMME THEME 2: FOOD, AGRICULTURE AND FISHERIES, AND BIOTECHNOLOGIES

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1 ANIMALCHANGE SEVENTH FRAMEWORK PROGRAMME THEME 2: FOOD, AGRICULTURE AND FISHERIES, AND BIOTECHNOLOGIES Grant agreement number: FP DELIVERABLE 10.2 Deliverable title: Compiled database on LCA (Life Cycle Assessment) coefficients for including pre-chain emissions in LCA of animal products Abstract: One aim of task 10.2 is to analyse the effect of mitigation and adaptation options on carbon footprint of animal products in a life cycle perspective. To understand off-farm implication on carbon footprint and GHG emissions, GHG emissions at farm level calculated by the simple farm model, FarmAC (developed in WP9), will be adapted to account for off-farm GHG emissions by life cycle assessment (LCA) that take into account also the pre-chain GHG emissions (LCA values for pre-chain GHG emissions adapted to local situations). This paper gives an overview on the feasibility of the LCA approach. Due date of deliverable: M24 Start date of the project: March 1 st, 2011 Actual submission date: M35 Duration: 48 months Organisation name of lead contractor: AU Authors: Lisbeth Mogensen, Jan Peter Lesschen, Katja Klumpp, Nick Hutchings, Marcia Stienezen, Vera Eory, Philippe Lecomte, Jørgen Olesen Revision: V1 Dissemination level: PU 1

2 Table of Contents 1. INTRODUCTION 4 2. IDENTIFICATION OF LCA VALUES (GHG EMISSIONS IN THE PRE-CHAIN) NEEDED TO EXTEND MODEL RESULTS BEYOND FARM SCALE AND THE FUNCTIONAL UNIT (FU) OF THE LCA STUDY PRE-CHAIN GHG EMISSIONS FUNCTIONAL UNIT (FU) AND ALLOCATION CONTRIBUTIONS TO FARM GHG - FROM CHANGES IN SOIL C AND LAND USE CHANGE (LUC) 9 3. PRE-CHAIN INPUTS AND OUTPUTS - DATA GIVEN BY FARMAC MODEL (WP9) AND FINANCIAL INPUT DATA (WP11) FEED IMPORT FERTILIZER (N, P, K) MANURE IMPORT PESTICIDES SEED DIESEL INCLUDING THAT USED BY MACHINE POOL ELECTRICITY HEAT IMPORT OF ANIMALS OUTPUT OF DEAD ANIMALS AMOUNT OF OUTPUTS EMISSIONS RELATED TO CHANGES IN SOIL CARBON ON FARM AND OFF FARM EMISSIONS RELATED TO LUC ON FARM AND OFF FARM LIST OF LCA VALUES TO ACCOUNT FOR THE OFF-FARM EMISSIONS FEED IMPORT 13 2

3 4.2. FERTILIZER (N, P, K) MANURE IMPORT PESTICIDES SEED DIESEL, ELECTRICITY AND HEAT GHG EMISSIONS RELATED TO CHANGES IN SOIL CARBON ON FARM AND OFF FARM GHG EMISSIONS RELATED TO LAND USE CHANGE (LUC) ON FARM AND OFF FARM CONCLUDING REMARKS LIST OF FARM TYPES INCLUDED IN THE STUDY REFERENCES 25 3

4 1. Introduction AnimalChange will provide scientific guidance on the integration of adaptation and mitigation objectives and design sustainable development pathways for livestock production in different parts of the world. An important part of AnimalChange focuses on the farm level (WP9, WP10 and WP11, together Component 3). Figure 1 provides an overview of information flows within Component 3. Figure 1. Structure and information flows regarding Component 3 of AnimalChange. The current deliverable D10.2 is part of WP10, task WP10 of AnimalChange will investigate, test and demonstrate the effect of single and combined mitigation and adaptation options at farm level using both model farms and real farms (showcase farms). The objective of WP10 is to describe livestock systems, identify and use case study farms, integrate adaptation and mitigation at farm scale and extend the spatial scale to include further issues (e.g. animal mobility) that are relevant for the regional scale. This deliverable (D10.2) is a step in this process. Results on GHG emissions at farm level calculated by the simple farm model, FARMAC (developed in WP9), will be adapted to account for off-farm GHG emissions by life cycle assessment (LCA) that take into account also the pre-chain GHG emissions (LCA values for pre-chain GHG emissions adapted to local situations). 4

5 The current deliverable (D10.2) includes: Identification of which LCA values are needed to account for pre-chain GHG emissions (Chapter 2) Description of how flows in the pre-chain can be quantified through data that are available from the simple farm model FarmAC (WP9) and from the financial data collected for financial modelling of GHG adaptation and mitigation options (WP11) (Chapter 3) List of LCA values for pre-chain GHG emissions per unit of input (Chapter 4) List of farm types included in the study in component 3 (Chapter 6) 5

6 2. Identification of LCA values (GHG emissions in the pre-chain) needed to extend model results beyond farm scale and the functional unit (FU) of the LCA study In task 10.2, the model results of GHG emissions at farm level calculated by the simple farm model (FarmAC, WP9) will be extended to take into account the whole life cycle of the agricultural products until farm gate, i.e. GHG emission related to pre-chain will be included. These emissions will be included by using typical Life Cycle Assessment (LCA) values adapted to the local situations for each farm type in the major agro-climate zones (Europe, Sub-Saharan Africa, and Brazil). Furthermore, the simple farm model, FarmAC estimates GHG emission as a total number per farm. In task 10.2 the GHG emission from on-farm production inclusive pre-chain is expressed per product unit. Thereby, total GHG emissions per farm inclusive pre-chain need to be allocated between the different products from the farm: milk, meat, crop for sale, manure etc Pre-chain GHG emissions Figure 2 illustrates that some GHG emissions are caused by the on-farm activities, for example CH 4 from enteric fermentation, N 2 O related to application of manure to fields etc. These on farm GHG emissions will be calculated by the simple farm model, FarmAC. Whereas, other GHG emissions are related to the pre-chain, e.g. production and transport of inputs like fertilizer (N,P,K), feed, diesel, electricity, heat. To calculate the GHG emissions related to these inputs (pre-chain) we need to know the amount of different inputs (estimation hereof will be discussed in chapter 3) and the LCA value for the GHG emission per unit of input (estimation hereof will be discussed in chapter 4). 2.2 Functional unit (FU) and allocation In the simple farm model used in WP9, the functional unit (FU) is total GHG emissions per farm from one year of production i.e. the results are given as for example a total GHG emission of 2.3 million kg CO 2 -eq. from a North European dairy farm with 192 cows. In task 10.2, the functional unit is GHG emission per products, e.g. kg CO 2 -eq./kg milk, kg CO 2 -eq./kg meat, kg CO 2 -eq./kg barley etc. To estimate these carbon footprints, the total GHG emissions (from on-farm production and from the pre-chain) need to be allocated between the different products from the farm. In the example with a dairy farm shown in Figure 2, the farm products include 1726 ton milk, 69.8 ton meat (live weight), crops for sale (390 ton cereals and 700 ton carrots) and 692 ton cattle slurry for sale. In the literature, different methods have been used for allocation between these types of products. The animal products, meat and milk are the main products from the types of livestock farms involved in WP10 in the AnimalChange project (list of farms given in chapter 6), whereas crops for sale and manure are seen as by-products. When total GHG emissions per farm per year has been calculated, the emissions related to amount of manure and crops produced are deducted from this total GHG emission by using standard LCA values for GHG emission per kg N, P and K in the manure collected and per kg of different crops produced. The remaining GHG emission is then divided between the amount of meat produced or allocated between meat and milk, if both products exist. 6

7 Figure 2. Illustration of pre-chain input, on farm emissions and different products (output) for e.g. a dairy farm Crops for sale Total GHG emissions related to output of crops for sale are calculated by using standard LCA values for emission per kg of different crops produced. In section 4.1, these LCA values are given. For example, in table 2 for crops grown under conventional European condition. Manure for sale Recently, the view on manure has changed from being a waste product to be considered as a co-product from the livestock production (Dalgaard and Halberg, 2007; EU, 2013). According to this approach, the livestock farm pays all environmental costs related to emissions from manure in livestock housing and storage and if emissions from spreading of manure exceed emissions from spreading the same amount of fertilizer. However, the livestock farm also gets credit for the reduction in emissions resulting from the reduction in the use of fertilizer, due to the use of manure as an alternative source of nutrients i.e. the environmental costs saved as a result of not having to produce the saved amount of fertilizer. The saved amount of N fertilizer can be calculated as the total N content in the manure after losses multiplied the percentage of N that is supposed to be available for crops (Anonymous, 2010). Extra emissions related to transport of manure compared to that of fertilizer need to be taken into account. 7

8 In task 10.2, amount of manure for sale will be defined as amount of collected manure (slurry, deep litter etc.) not used on own fields. Table 1. Saved GHG emission from 100 kg N ex-animal Manure system Fertilizer value of manure N, kg 1) P, kg 1) K, kg 1) Deposited atslurry pasture Deep litter GHG from avoid fertilizer prod., kg CO 2 -eq - N 2) P 3) K 4) GWP from avoid fertilizer prod., kg CO 2 -eq ) Anonymous, ) 7.95 kg CO 2/kg N from calcium ammonium nitrate (EU, 2006) from Table 6 3) 0.8 kg CO 2/kg P from triple superphosphate (EU, 2006) from Table 6 4) 0.6 kg CO 2/kg K from muriate of potash (EU, 2006) from Table 6 Allocation between meat and milk on dairy farms If a farm produces milk, meat, crops and manure for sale, the first step is to calculate the total GHG emissions per year of the farm. Then the emissions related to output of manure and crops for sale are deducted from this total GHG emission by using standard LCA values (see above). The remaining GHG emission is then divided between the amount of meat and milk by allocation between meat and milk. According to Kristensen et al. (2011), the choice of method used to divide total farm GHG emissions into meat and milk has significant impact on the estimated emission per kg product. Kristensen et al. (2011) compared four different methods; three based on attributional LCA and one on consequential LCA. The four methods are: 1) Allocation according to the proportions of milk and meat proteins produced, this method is recommended by FAO (2010) 2) Biological allocation based on a standard marginal net energy requirement to produce the actual amount of milk (0.4 SFU/kg ECM) and meat in the shape of kg live weight gain (4.0 SFU/kg LW gain). This is done without adjustment for maintenance requirement or type of animal behind one kg live weight. 3) Economic allocation based on the amount of milk and meat produced on each farm at standard unit price (Under North European conditions: milk 0.13 EUR per kg ECM and meat 0.99 EUR per kg live weight, Anonymous, 2009). 4) System expansion where the emission related to the meat production is subtracted from the total emission, based on the assumption that the produced meat in the dairy system will replace 50:50 pig meat and beef meat as argued by Nielsen et al. (2003). Emission from pork (3.6 kg CO 2 -eq. per kg carcass, from Dalgaard et al. (2007)) and from beef meat (21.7 kg CO 2 -eq. per kg carcass calculated as the average of EU suckler cows and intensive steer 8

9 production, from Nguyen et al. (2010a)), giving CO 2 -eq. per kg carcass weight, which is equal to 6.33 kg CO 2 -eq. per kg live weight. Kristensen et al. (2011) found, that allocation method had a larger impact on the range in emission per kg meat (here kg live weight (LW)) than per kg ECM, leading to a variation in emission per kg meat from 3.4 kg CO 2 /kg LW (economic allocation), over 5.05 kg CO 2 /kg LW (protein mass allocation), 6.35 kg CO2/kg LW (system expansion) up to 6.9 kg CO 2 /kg LW (biological allocation). The variation in emission per kg ECM only ranged from 0.91 kg CO 2 /kg (biological allocation), over 0.94 kg CO 2 /kg (system expansion), 0.99 kg CO 2 /kg (protein mass allocation) up to 1.06 kg CO 2 /kg ECM (economic allocation) (Kristensen et al., 2011). In task 10.2, the method 1 and 2 (allocation according to the proportions of milk and meat proteins produced, and biological allocation based on the standard marginal net energy required to produce milk and meat) will be used as these methods don t require extra information like prices for economic allocation and a decision on which meat is replaced by the produced meat from the farm Contributions to farm GHG - from changes in soil C and land use change (LUC) In task 10.2, the LCA result will be presented both with and without contribution from changes in soil carbon (C) and land use change (LUC). Regarding GHG contribution from changes in soil C, FarmAC will calculate emissions related to changes in soil carbon on farm. However, emissions from changes in soil C related to the area used to grow imported feed is not included in these calculations. Standard LCA values are needed for that contribution (see section 4.7 for further details). These values have not been identified yet, and will as a starting point be based on the work in AnimalChange in work package WP3. Regarding GHG contribution from LUC, two different methods will be used in Task 10.2 to include LUC. The first method follows the guidelines from PAS2050 (BSI, 2008). According to PAS2050, it is only feeds that are grown on new area after deforestation that contribute to LUC. Whereas, according to the second method, LUC are included for all feeds based on the view that the global food system is highly connected. Therefore, increased demand for feed will increase pressure on land and thereby land-use change somewhere in the world. The burden of deforestation caused by agricultural production is therefore shared by all crop production. According to the second method (Audsley et al., 2009), a single emission factor for agricultural land is used where LUC cause emission of 1.43 t CO 2 -eq/hectare of agricultural land used or 143 g CO 2 /m 2 (see section 4.8 for further details). 9

10 3. Pre-chain inputs and outputs - data given by FarmAC model (WP9) and financial input data (WP11) In AnimalChange task 10.1, 24 model farms were selected for use in component 3. These farms were defined by farm types and region (Stienezen et al., 2013). The list of farms is given in chapter 6. These farms will be used for calculations of on-farm GHG emissions by the simple FarmAC model developed in WP 9. In task 10.2 we will extend the results of GHG emissions per farm per year to take into account also the pre-chain GHG emissions. To calculate the GHG emissions related to the prechain we need to know the amount of different input (chapter 3) and the LCA value for the GHG emission per unit of input (chapter 4). In this chapter are mentioned which data related to the amount of pre-chain input that are available from the simple farm model FarmAC (WP9) and from the financial data collected for financial modelling of GHG adaptation and mitigation options costs in WP11 and for which further data collection is needed for task Data for nine types of pre-chain input are described; feed, fertilizer, manure, pesticides, diesel, electricity, heat, animals (section ). Besides that, the amounts of different outputs need to be quantified to make it possible to calculate carbon footprint per unit of different products (3.10). Finally, the LCA results from task 10.2 will be given both with and without GHG contribution from soil C and LUC ( ) Feed import The FarmAC model provides data on amount of feed import per farm per year. In the sheet Balance the total amount of purchased and sold feed ingredients are calculated per feed item as kg dry matter. The list of different feed ingredients that can be chosen in the sheets Ruminants and Non ruminants where the feeding of the farm animals is defined, are quite comprehensive. Some feed ingredients like synthetic amino acids and phytase are not currently included but will be so in the future. In the LCA calculations (task 10.2), we need to take into account the GHG contribution from transport of imported feed ingredients, therefore data on place/country of origin needed to be known. These data are not given. This information would also make it possible (at least theoretically) to take into account the actual productivity in the place of production Fertilizer (N, P, K) The FarmAC model gives in the sheet Manure data on the amount of (kg) N fertilizer used per crop per year. For the LCA calculations in task 10.2, total amount of different types of N fertilizer imported needed to be calculated. In the FarmAC model, the type of N fertilizer is chosen from a quite comprehensive list. The FarmAC model does not give any information about amount of P and K fertilizer used and thereby imported. 10

11 In the financial data in the sheet Arable, amount of imported P and K fertilizer are included as other fertilizer cost. From these economic data an estimate of the amount could be made assuming an average cost per kg P and K. Otherwise further data are needed for the LCA calculations Manure import In the FarmAC model in the sheet Farm the total amount of kg N per year from purchased manure is given. The type of manure is given as well and is quite detailed as it is chosen from a list that is imported by the user. Thereby, the composition of the manure becomes representative for the manure used locally. For the LCA calculation in task 10.2, we need to calculate the corresponding amount of P and K that are imported together with N in the imported manure as a farm that import manure pays GHG emissions corresponding to GHG emission related to production of same amount of plant available N, P and K from fertilizer Pesticides In the FarmAC model no information is given on the amount of pesticides used. In the financial data in the sheet Arable, cost for plant protection is given as Euro/ha/year for the different crops. From that a very rough estimate for the amount of pesticides used could be made for the LCA calculations Seed In the FarmAC model no information is given on the amount of seed used. In financial data in the sheet Arable, costs for seed are given as Euro/ha/year for the different crops. From that a rough estimate for the amount of seed used could be made for the LCA calculations. Alternative typical numbers for amount of seed for different crops could be used Diesel including that used by machine pool In FarmAC model no information is given on the amount of diesel used. In financial data on the sheet Arable, cost for machinery mighty includes that for diesel. Data are given as Euro/ha/year for the different crops. From that a rough estimate for the amount of diesel used could be made for the LCA calculations Electricity Electricity can be used in the livestock housing, for example heating, cooling, housing, milking and in the fields for example for irrigation and drying of cereals at harvest. Neither the FarmAC model nor financial data provide data of on farm use of electricity. 11

12 Therefore, the LCA calculations need to use standard values according to the different farming systems in different regions for the used amount of electricity Heat Heat can be used in the livestock housing and for drying of cereals at harvest. Neither the FarmAC model nor financial data provide data of on farm use of heat. Therefore, the LCA calculations need to use standard values according to the different farming systems in different regions for the used amount of heat Import of animals output of dead animals During one year of production, some animals are purchased and others are sold for slaughter or as live animals or discarded as dead. The FarmAC model doesn t provide data of input and output of animals to the farm, whereas, financial data includes data on mortality rate Amount of outputs For the LCA calculations in task 10.2, the amounts of different farm products (outputs) need to be quantified to make it possible to calculate carbon footprint per unit of different products. From the FarmAC model, data are needed on output of: Meat (as kg live weight of live animal + dead animal), milk (also the amount used in the household), crops for sale, manure for sale etc Emissions related to changes in soil carbon on farm and off farm The FarmAC model includes the calculation of GHG emissions related to soil carbon changes on farm. However, emissions from changes in soil C related to area used to grow imported feed is not included in these calculations and need to be added in the LCA calculation (see section 4.7 for further details about soil C) Emissions related to LUC on farm and off farm The FarmAC model will not include emissions related to land use change (LUC) (see section 4.8 for further details about LUC). 12

13 4. List of LCA values to account for the off-farm emissions In AnimalChange task 10.2 we will include GHG emissions related to the pre-chain. Therefore, we need to know the amount of the different inputs per farm (the method for quantifying these inputs is given in chapter 3) and a LCA value for the GHG emission per unit of input (chapter 4). These LCA value for the GHG emissions per unit of input needs to be adapted to the local situations for each farm type in the major agro-climate zones (Europe, Africa, Brazil) included in the AnimalChange project. The list of included farm types and regions is given in chapter 6. Regarding LCA values for GHG emissions from fertilizer and imported feeds, these are based on the numbers used in WP3 in the AnimalChange project. Whereas, LCA values for other off-farm emissions originate from other sources in the literature among others from two newly published reports from FAO (2013a, 2013b) working on a global scale on GHG emissions from pig and chicken, and ruminant supply chains as well as the data used in the MID- AIR project for modelling GHG emissions from European dairy farms (Olesen et al., 2012) Feed import When the simple farm model, FarmAC calculate on-farm GHG emissions, the emissions related to imported feed is not taken into account. Depending on the degree of selfsufficiency with feed on the actual farm, contribution to GHG from imported feed can be a quite big contribution to overall GHG emissions. For example for monogastric animals, 64% of GHG was found to be caused by feed production (Nguyen al., 2010b) and 43% of GHG from milk production was related to feed production (Kristensen et al., 2011). GHG from feed production comes from both the primary stage of crop production primarily as N 2 O and fossil energy related to fertilizer production, and from use of fossil energy in the processing of the crop into animal feed. The magnitude of the contribution of transport to GHG from feed can be slightly over 50% of the total GWP (Knudsen et al., 2010). As GHG from imported feed can be a potential hotspot, LCA values for GHG of feed need to be adapted to the local situations for each farm type in the major agro-climate zones (Europe, Africa, and Latin America) included in the AnimalChange project. From WP3 in AnimalChange, GHG emissions from crop products (Lesschen et al., 2011 and 2013) based on MITERRA Global calculations are given in Table 2, 3 and 4 for EU-27, Africa, and Latin America respectively. These LCA values are expressed on DM basis. The value included the N 2 O soil emissions (both direct and indirect), GHG emissions from fertilizer production, fuel use, pesticide use and methane emissions from rice cultivation. The emissions are till farm gate at the crop production farm, thus processing and transport emissions are not included. 13

14 Table 2. GHG emissions from production of feed crops in EU-27 until farm gate at an arable farm, g CO 2 -eq/kg DM product. Feed crop Total GHG emission Contribution from N 2 O soil Fertilizer Pesticide Fuel use CH 4 (rice) Barley Beans Grass Maize Millet Oats Other Cereals Other Pulses Other Root Crops Potatoes Rapeseed Rice Rye Sorghum Soybeans Sugar beet Sugarcane Sunflower Sweet Potatoes Vegetables Wheat

15 Table 3. GHG emissions from production of feed crops in Africa until farm gate at an arable farm, g CO 2 -eq/kg DM product. Feed crop Total GHG Contribution from emission N 2 O soil Fertilizer Pesticide Fuel use CH 4 (rice) Bananas Barley Beans Cassava Grass Maize Millet Oats Other Cereals Other Pulses Other Root Crops Potatoes Rapeseed Rice Rye Sorghum Soybeans Sugar beet Sugarcane Sunflower Sweet Potatoes Vegetables Wheat

16 Table 4. GHG emissions from production of feed crops in Latin America until farm gate at an arable farm, g CO 2 -eq/kg DM product. Feed crop Total GHG Contribution from emission N 2 O soil Fertilizer Pesticide Fuel use CH 4 (rice) Bananas Barley Beans Cassava Grass Maize Millet Oats Other Cereals Other Pulses Other Root Crops Potatoes Rapeseed Rice Rye Sorghum Soybeans Sugar beet Sugarcane Sunflower Sweet Potatoes Vegetables Wheat The FAO report (2013a) gives some emission factors for non-crop feed, these are presented in Table 5. Table 5. Emission from production of non-crop feed (FAO, 2013a). Emission Source Fish meal 1.4 kg CO 2 /kg DM Berglund et al., 2009 Synthetic amino acids 3.6 kg CO 2 /kg DM Berglund et al., 2009 Lime kg CO 2 /kg DM FEEDPRINT* 4.2. Fertilizer (N, P, K) GHG emissions from production and transport of fertilizer imported and used on the farm is one of the hotspots in GHG emissions from production of home-grown feed. In AnimalChange Task 10.2, we will use the same numbers as they have used in Animal- Change WP3 for GHG emissions from fertilizer production. They have chosen to use the same numbers, the EU_2006 values from Brentrup & Palliere (2008) for fertilizer used in EU, Africa and Brazil. These numbers are given in Table 6 both as average for each type of fertilizer used in Europe and as BAT values, i.e. the GHG value if the best available technology was used for the fertilizer production. 16

17 Table 6. GHG emissions from production of different types of fertilizer used in Europe (Brentrup & Palliere, 2008) (the same values will also be used for Africa and Brazil), kg CO 2 - eq/kg fertilizer. Type of fertilizer Contribution from CO 2 N 2 O Other GHG Total GHG Nutrient Content 1, % N, P, K Urea EU, , 0, 0 BAT Urea ammonium nitrate EU , 0, 0 BAT Calcium ammonium EU , 0, 0 nitrate BAT Ammonium nitrate EU ,0, 0 BAT Calcium nitrate EU , 0, 0 BAT Ammonium phosphate EU, , 21, 0 BAT 0, NPK_ EU, , 15, 15 BAT Triple superphosphate EU, , 44, 0 BAT Muriate of potash EU, , 0, 49.6 BAT ) Based on FAOSTAT data 2) Best Available Technique (BAT) In the FAO report (2013a), they have used the following general emission factors for N fertilizer. Table 7. Emission Factors (EF) for fertilizer (FAO. 2013a). EF Source Ammonium nitrate 6.8 kg CO 2 /kg N Jenssen & Kongshaug Olesen et al. (2012) used the following GHG emissions for fertilizers representative for the conditions in Germany (transportation distances. energy mix etc.) based on Patyk & Reinhardt (1997). 17

18 Table 8. Primary energy use and associated GHG emissions for the supply of fertilizers in Germany (Patyk & Reinhardt, 1997, cf Olesen et al., 2012). Fertilizer Relation Emissions [g kg -1 fertilizer] (related to P, K and Ca) CO 2 CH 4 N 2 O NH 3 NO X N 1 kg N P 1 kg P 2 O (2558) 2.07 (4.74) 0.04 (0.09) (0.0275) 8.58 (19.65) K 1 kg K 2 O (746.6) CaO 1 kg CaO 112 (156.8) (1.67) (0.239) (0.061) (0.0003) (0.002) (0.0003) (1.39) 0.52 (0.73) 4.3. Manure import In AnimalChange task 10.2 we will use the approach described by Dalgaard and Halberg (2007) to account for the environmental burden of using manure, considering it as a coproduct from the livestock production. A new publication from EU (2013) supports that approach. According to this approach, GHG emission from imported manure corresponds to the saved environmental costs from producing the saved amount of fertilizer. The saved amount of mineral fertilizer can be calculated as the total N content in the manure multiplied by the percentage that is supposed to be available for crops (see section 2.2). The extra emissions related to transport of manure compared to that of fertilizer are allocated to the livestock farm. Contrary to this approach, the European study by Olsen et al. (2012) did not see manure as a co-product but as a waste product Pesticides GHG contribution from production and transport of pesticides is not one of the hotspots in on farm GHG emissions, and has often been ignored in LCA calculation. In AnimalChange task 10.2 we will include this contribution, though data on amount of input of pesticides are weak. Olesen et al. (2012) reported GHG emissions of average pesticide production according to Kaltschmitt and Reinhardt (1997)(Table 9). The GHG values based on data from the French GESTIM database is shown in Table 10. Table 9. GHG emissions from production of an average pesticide, (g/kg) (Kaltschmitt and Reinhardt (1997). CO 2 CH 4 N 2 O NH 3 Average pesticide

19 Table 10. GHG emissions from production of pesticide, (g/kg active material) data from the French GESTIM database. Kg CO 2 / kg active Energy, MJ / kg Type of pesticide material active material Source Adjuvant Default assumption Fungicide GES'TIM (2006) Insecticide GES'TIM (2006) Herbicide GES'TIM (2006) Regulators and other substances (average product) GES'TIM 4.5. Seed GHG contribution from production and transport of seed is not one of the hotspots in on farm GHG emissions, and has often been ignored in LCA calculation. In AnimalChange task 10.2 we will include this contribution, though data on amount of input are weak. In table 11 is presented GHG emissions from production of seed based on Kaltschmitt & Reinhardt (1997) and FAL (2000). Table 11. GHG emissions for the supply of seeds and seedlings of different crops according to Kaltschmitt & Reinhardt (1997) and FAL (2000) (cf. Olesen et al., 2012). Crop Emissions [g kg -1 ] CO 2 CH 4 N 2 O NH 3 Rape 1) Wheat 1) Rye 1) Barley 1) Peas 2) Lupines 2) (Alfalfa, Lucerne) Field beans 2) Maize 1) Clover-grass 2) Potatoes 1) Sugar beets 1) Oat 3) ) Data according Kaltschmitt & Reinhardt (1997) 2) Data according to FAL (2000) 3) Average of wheat, rye and barley 4.6. Diesel, electricity and heat The importance of GHG contribution from fossil energy on total farm GHG emissions will depend on farm type and region. For example on dairy farms in Northern Europe, contribution from fossil energy was found to be 12% of total GHG emission per kg milk produced (Kristensen et al., 2011). Diesel is typically used for field operation, electricity in the livestock housing and for irrigation and drying of crops at harvest, and heat can be used in the livestock housing and for drying. In table 12 and 13 is shown general factors for GHG emission from production and use of fuel and average regional specific emissions per MJ of electricity and heat. 19

20 Table 12. A general factor for GHG emission from production and use of fuel (FAO, 2013a). EF Source Diesel 3.2 kg CO 2 /l diesel Berglund et al., 2009 Oil 5.7 kg CO 2 /kg oil De Boer, 2009 Coal 17.8 kg CO 2 /kg coal De Boer, 2009 Gas 7.6 kg CO 2 /m 3 gas De Boer, 2009 Table 13. Regional specific GHG emissions from production and use of one MJ electricity and heat generation. (FAO, 2013a). GHG emission CO 2 g/mj Europe North America 142 Australia 254 New Zealand 84 Latin America 54 Asia 202 China 216 Africa 175 Pacific 139 In Europe, country specific values for the GHG emissions from production and use of electricity will be considered, since the energy mix for electricity production varies considerably across countries. In the report by Olesen et al (2012) they use the following numbers for GHG emissions for the supply of fossil fuels in Central Europe according to Fritsche et al. (1997), Kaltschmitt & Reinhardt (1997), Habersatter et al. (1998), FAL (2000) and Moerschner (2000). Table 14. GHG emissions for the supply of fossil fuels in Central Europe (cf. Olesen et al., 2012). Energy source Heating density Emissions (g kg -1,m -3 or l -1 ) value [kg l -1 ] CO 2 CH 4 N 2 O NH 3 Diesel MJ kg g l g l g l g l -1 Heavy fuel oil MJ kg g l g l g l g l -1 Fuel oil MJ kg g l g l g l g l -1 Hard coal 29.4 MJ kg g kg g kg g kg g kg -1 Brown coal 8.1 MJ kg g kg g kg g kg g kg -1 Natural gas 33.8 MJ m g m g m g m g m -3 Propane 46.4 MJ m g m g m g m g m -3 Electricity mg MJ mg MJ mg MJ mg MJ -1 20

21 4.7. GHG emissions related to changes in soil carbon on farm and off farm Crop production influences soil carbon balance depending on crop type and management (IPCC, 2006). Typically, grasslands are supposed to act as carbon sinks, whereas croplands are releasing carbon (e.g. Vleeshouwers & Verhagen, 2002; Vellinga et al., 2004). However, so far, very few life cycle assessments (LCA) include soil C sequestration in the overall GHG estimations, mainly due to methodological limitations. In those studies that include soil C sequestration, the time horizon used is often less than the 100 years typically used for other emissions in a LCA (Gabrielle and Gagnaire, 2008). The simple farm model, FarmAC is expected to estimate the GHG emissions related to changes in soil carbon on the farm. However, emissions from changes in soil C related to the area used to grow imported feed is not included in these calculations. Eventually, the LCA methodology developed in work packages WP3 in AnimalChange can be used for estimating these emissions. As a sensitivity analysis, the effect of changes in C in soil could be included in a very simple way as suggested by Vleeshouwers & Verhagen (2002). According to them, changes in C in soil are assumed to depend only on type of crop grown. Carbon sequestration in grassland correspond to 0.52 Mg C/ha/year (191 g CO 2 /m 2 /year), whereas growing other crops is assumed to cause that C is released from soil corresponding to 0.84 Mg C/ha/year (308 g CO 2 /m 2 /year) (Vleeshouwers & Verhagen, 2002). The only needed information to do this calculation for the imported feed is crop yield per ha. However, if possible these values will be discriminated between temperate Europe and humid tropic, where soil C changes can be much higher GHG emissions related to land use change (LUC) on farm and off farm Around 12.2% of worlds total GHG emissions comes from land use change (LUC) (Herzog, 2009). Even though this estimate of the contribution from LUC was latest downgraded (Herzog, 2009), it still represents a huge impact. As livestock is the world s largest user of land resources for feed production, LUC may contribute significantly to the GWP of animal product. The question is how to account for this contribution. So far, very few life cycle assessments (LCA) include LUC in the overall GHG estimations, mainly due to methodological limitations. Basically, there are now two quite opposite approaches: a product-based and a landbased approach (Cederberg et al., 2013). According to the product based approach, LUC is considered to be associated with the feeds grown in the regions where deforestation takes place (BSI, 2011). Whereas, according to the land-based approach, LUC is assigned to all feeds based on the assumption that all use of land for crop production is assumed to increase pressure on land use and thereby cause LUC somewhere in the world. (e.g. Audsley et al., 2009). In AnimalChange task 10.2, the LCA result for GHG emissions per kg livestock product or per farm will be presented both with and without including the contribution from GHG emissions from LUC and as a sensitivity results from both of the above mentioned approached will be presented. Calculation of LUC according to the product based approach follow the guidelines from PAS2050 (BSI, 2011) for feed that are grown on new area after deforestation. That result in a contribution from LUC of 930 g CO 2 eq /kg soybean meal produced in Argentina, where only 22% of new area with soybean originate from rainforest (FAO, 2010) and a LUC factor of 7690 g CO 2 eq /kg soybean meal from Brazil, where 100% of new area with soybean origi- 21

22 nate from rainforest (FAO, 2010). At the same time, deforestation in Brazil causes a release of kg CO 2 /ha/year whereas the numbers for Argentina is CO 2 /ha/year (BSI, 2011). Thereby, using this product based approach it matter from which country the soybean meal is imported. In table 15 is presented regional average LUC factors based on the weighted share of net imports from Brazil and Argentina (FAO, 2013a). Table 15. Regional product based LUC factors for GHG emissions from soybeans and cake (FAO, 2013a). Region for feed used LUC factor kg CO 2 /kg product Soybean cake Soybeans Africa Americas Asia Europe Oceania With the other approach, the land-based one, GHG contribution from LUC is assigned to all feeds based on the assumption that all use of land for crop production is assumed to increase pressure on land use and thereby cause LUC somewhere in the world. The burden of deforestation caused by food production is therefore shared by all feed production (Audsley et al., 2009). Audsley et al., 2009 have calculated a single LUC emissions factor for agricultural land of 1.43 t CO 2 e/hectare of agricultural land used or 143 g CO 2 /m 2. 22

23 5. Concluding remarks In this paper it was illustrated, how the model results of GHG emissions at farm level calculated by the simple farm model, FarmAC can be extended to take into account the whole life cycle of the agricultural products until farm gate, i.e. GHG emission related to pre-chain will be included. This will be done by using the typical Life Cycle Assessment (LCA) values presented in Chapter 4 and if possible adapted to the local situations for the farm types included in this study. The list of farm types is given in Chapter 6. These GHG emission from on-farm production inclusive pre-chain will be expressed per product unit. Thereby, total GHG emissions per farm inclusive pre-chain need to be allocated between the different products from the farm: milk, meat, crop for sale, manure etc. 23

24 6. List of farm types included in the study In table 16 is given an updated list (November, 2013) of the model farms included in the modelling with the simple farm model FarmAC. Stienezen and van der Pol (2012) give further details about the different farm types. Table 16. Model farms include in modelling by the simple farm model, FarmAC (Stienezen, 2013). Region Farm type Study region in AnimalChange Europe Maritime mixed dairy Dutch common dairy - Netherland Europe Maritime grassland beef Average national beef farm Ireland Europe Maritime grassland dairy (2) Average national dairy farm and Greenfield Ireland Europe Maritime mixed beef Fattened oxen Haute Normandie - France Europe Continental mixed dairy Dairy farm Lorraine - France Europe Continental mixed beef Fattened young bulls Lorraine - France Europe Continental grassland beef Fattened calves Bourgogne - France Europe Mountain grassland beef Grazing calves Auvergne - France Europe Mountain grassland sheep Auvergne France Europe Mediterranean grassland sheep (2) SP01 and SP05 Canjuers paca - France Europe Mediterranean mixed dairy Mediterranean mixed dairy - Italian Europe Northern European pig Denmark Europe Southern European pig Cataluña typical farm sows - Spain Brazil Industrial dairy Cerrado region Brazil Industrial beef Cerrado region Brazil Sub-humid / Humid Campos (South Brazil) Dairy cattle on sown pasture relatively intensive integrated systems Brazil Sub-humid / Humid Campos (South Brazil) Beef cattle on sown pasture relatively intensive integrated systems Brazil Sub-humid / Humid Campos (South Brazil) Beef cattle on extensive native rangeland systems Brazil Sub-humid / Humid Dairy cattle on pasture, 400 Ha Pastures after deforestation in Eastern (Belem) Brazilian Amazon Brazil Sub-humid / Humid Beef cattle on pasture, 2000 Ha Pastures after deforestation in Eastern (Belem) Brazilian Amazon Africa Semi-arid grassland Peanut belt - Senegal Africa Semi-arid grassland Burkina Faso Cotton/Maize crop livestock model farm Africa Sub-humid Burkina Faso model farm combining crop-livestock and pastoral (a moving herd) Africa Africa Semi-arid grassland Semi-arid grassland South Africa Kenya 24

25 7. References 1. Anonym, 2009b. Figures on Danish cattle Anonymous, Vejledning om gødskning og harmoniregler. Ministeriet for Fødevarer. Landbrug og Fiskeri. 118 pp. Online at: 3. Audsley, E., Brander, M., Chatterton, J., Murphy-Bokern, D., Webster, C., and Williams, A., How low can we go? An assessment of greenhouse gas emissions from the UK food system and the scope for to reduction them by Published by Food Climate Research Network (FCRN) and WWF-UK. 80 pp. 4. BSI, 2011, PAS2050. Specification for the assessment of life cycle greenhouse gas emissions of goods and services. Published by British Standards Institution. 37 pp. 5. Cederberg, C., Henriksson, M., Berglund, M An LCA researcher s wish list data and emission models needed to improve LCA studies of animal production. Animal. 7 (S2) BSI PAS2050. Specification for the assessment of the life cycle greenhouse gas emissions of goods and services. 7. Brentrup, F. and C. Pallière GHG Emissions and Energy Efficiency in European Nitrogen Fertiliser Production and Use. Proceedings 639, International Fertiliser Society, York, UK. 8. Dalgaard, R., Halberg, N., How to account for emissions from manure? Who bears the burden? Proceedings from the 5th International Conference 'LCA in foods', April 2007, Gothenburg, Sweden. 9. Dalgaard, R., Halberg, N., Hermansen, J.E., Danish pork production. An environmental assessment. University of Aarhus, Faculty of Agricultural Sciences, DJF Animal Science 82, ISBN: , 34 pp. 10. Ecoinvent Centre, 2010., Ecoinveny Data v2.2. Swiss Cebtre for Life Cycle Inventories, Dübendorf, Switzerland. Online at: Elsgaard, L., GHG emission from cultivation of winter wheat and winter rapeseed for biofuels. Report requested by the Danish Ministry of Food, Agriculture and Fisheries. The Faculty of Agricultural Sciences, Aarhus University. 34 pp. Online at: EU Annex II: product environmental footprint (PEF) guide. Draft. 13. FAO Gerber, P., Vellinga, T., Opio., C., Henderson, B., Steinfeld, H. Greenhouse gas emissions from the dairy sector. A life cycle assessment. Food and Agriculture Organization of the United Nations (FAO), Rome. 98 pp. 14. FAO, 2013a. MacLeod, M., Gerber, P., Mottet, A., Tempio, G., Falcucci, A., Opio, C., Vellinga, T., Henderson, B. & Steinfeld, H. Greenhouse gas emissions from pig and chicken supply chains A global life cycle assessment. Food and Agriculture Organization of the United Nations (FAO), Rome. 196 pp. 15. FAO, 2013b. Opio C., Gerber P., Mottet A., Falcucci A., Tempio G., MacLeod M., Vellinga T., Henderson B. & Steinfeld, H. Greenhouse gas emissions from ruminant supply chains A global life cycle assessment. FAO, Rome. 16. Gabrielle, B. & Gagnaire, N Life-cycle assessment of straw use in bio-ethanol production: a case-study based on deterministic modelling. Biomass and Bioenergy, 32, Herzog, T., World Green House Gas Emissions in Working paper by World Resources Institute. July pp. Online at: IPCC, IPCC Guidelines for national greenhouse gas inventories. Online at: 25

26 19. Knudsen, M. T., Yu Hui, Q., Yan, L., Halberg, N., Environmental assessment of organic soybean (Glycine max.) imported from China to Denmark: a case study. Journal of Cleaner Production, 18 (14), Kristensen, T, Mogensen, L., Knudsen, M.T., Hermansen, J.E., Effect of production system and farming strategy on greenhouse gas emissions from commercial dairy farms in a life cycle approach. Livestock Sci. 140, Lesschen, J.P., I. Staritsky and O. Oenema Improvements in MITERRA framework/tool and for the extension to Africa and Latin America. AnimalChange Deliverable Lesschen, J.P., van den Berg, M., Westhoek, H.J., Witzke, H.P., Oenema, O., Greenhouse gas emission profiles of European livestock sectors. Animal Feed Science and Technology , Nguyen, T.L.T., Hermansen, J.E., Mogensen, L., 2010a. Environmental consequences of different beef production systems in the EU. J. Cleaner Prod., 18, Nguyen, T.L.T., Hermansen, J.E., Mogensen, L., 2010b. Fossil enrgy and GHG saving potentials of pig farming in the EU. Energy Policy. 38, Nielsen, P.H., Nielsen, A.M., Weidema, B.P., Dalgaard, R., Halberg, N LCA Food data Base. Online at: Olesen, J.E., Weiske, A., W.A. Asman, M.R. Weisbjerg, J. Djurhuus & K. Schelde FarmGHG - A model for estimating greenhouse gas emissions from livestock farms. Documentation. Danish Institute of Agricultural Sciences. 44 pp. 27. Stienezen, M Updated list of model farm types that are expected to be included in the modelling with the simple farm model FarmAC. May Stienezen, M., van der Pol, A Deliverable Compiled database on characteristic livestock farms for use in task AnimalChange. 29. Vellinga, T.V., van den Pol-van Dasselaar, A., Kuikman, P.J The impact of grassland ploughing on CO 2 and N 2 O emissions in the Netherlands. Nutr. Cycl. Agroecosyst. 70, Vleeshouwers, L.M., Verhagen, A., Carbon emission and sequestration by agricultural land use: a model study for Europe. Global Change Biologi. 8,