domain: shs sdv info

text:
Claire Mosnier
email: cmosnier@clermont.inra.fr

Will French beef and dairy farms reduce their GHG emissions by 2035?

Keywords: Greenhouse gas emissions, cattle farms, bioeconomic model, prospective, intensification

The agricultural sector is called upon to reduce its greenhouse gases emissions. A scenario approach has been developed to explore the plausible futures of the French bovine sector and their impacts on climate change. These scenarios encompass a trend scenario (S1) and alternative contrasted scenarios: further intensification and export of bovine production (S2), development of grassland based organic farming (S3) and committed policy to reduce GHG emissions (S4). These scenarios have been evaluated both at national and farm levels. This paper focuses on the farm level approach. The bio-economic model Orfee has been created and used to assess the impacts of the main drivers of these scenarios on the evolution (production, economics, GHG) of typical French beef and dairy farms. These drivers encompass technological progresses (higher milk yield, younger first calving, legume fodders, higher efficiency of fertilizer), increase in labor efficiency, organic farming with low concentrates and tax on GHG emissions. For the trend scenario, this study shows that technological progresses foster milk production and raise profit and GHG emissions of dairy farms but GHG emissions per milk unit are improved.

Under 2010 prices and without coupled public supports, beef production would decrease in suckler cow farms that are hardly profitable. GHG emission efficiency would be improved, thanks namely to younger age at first calving. Alternative scenarios underline that further production intensification doesn't necessary improve GHG emissions per output unit and that in some cases organic farming with low concentrate feed reduces emissions per unit of product and per farm but with lower production levels.

A tax on GHG emission decreases emissions and livestock production, it would be particularly detrimental to suckler cow production.

Introduction

Paris Agreement (COP21, 2015) acknowledges the need to limit the temperature increase to 2 degrees Celsius to avoid the worst climate impacts. 188 countries have committed to reducing their greenhouse gas (GHG) emissions and have set out a roadmap. The French low carbon national strategy targets a reduction of 12% of agricultural emission in 2028 relative to 2013 and of 50% between 1990 and 2050 1 .

The agricultural sector contributes to 19% to national emissions [START_REF] Citepa | Rapport national d'inventaire pour la France au titre de la convention cadre des nations unies sur les changements climatiques et du protocole de Kyoto[END_REF]. With a population of 19 million of bovine, beef and dairy cattle productions are the main contributor to agricultural sector GHG emissions (60%). Evolution of the bovine sector in the next 20 years would be crucial to meet the GHG emissions target. This evolution would depend on numerous factors including technology, production organizations and markets, human population growth and consumer demand, climate change and policy.

The Gesebov project has investigated the joint evolution of the dairy and beef cattle sectors in horizon 2035 and its associated level of GHG emissions at farm and national levels. The scenario approach is a widely used method to explore a highly uncertain future for agriculture [START_REF] Abildtrup | Socio-economic scenario development for the assessment of climate change impacts on agricultural land use: a pairwise comparison approach[END_REF][START_REF] Audsley | What can scenario modelling tell us about future European scale agricultural land use, and what not?[END_REF][START_REF] Mandryk | Scenarios of long-term farm structural change for application in climate change impact assessment[END_REF]) by describing coherent and plausible future states of the world. Since emissions of GHG by the bovine sector are first explained by the bovine inventory (Casey & Holden 2006b) and second by the way meat and milk are produced [START_REF] Monteny | Greenhouse gas abatement strategies for animal husbandry[END_REF][START_REF] Johnson | Agricultural opportunities to mitigate greenhouse gas emissions[END_REF]), Gesebov scenarios have been specifically elaborated to be contrasted in terms of volume and technology of bovine production. The impacts of those scenarios on climate change were assessed at national level and at farm level. National level analysis provide estimates of beef, meat and GHG produced in France.

Farm level analysis provide information regarding the potential evolution of heterogeneous farming systems (technical, economic and environmental). This paper focuses on the farm scale. Farm scale models enables to study relationships between production and GHG emissions per unit of product [START_REF] Schils | A review of farm level modelling approaches for mitigating greenhouse gas emissions from ruminant livestock systems[END_REF][START_REF] Crosson | A review of whole farm systems models of greenhouse gas emissions from beef and dairy cattle production systems[END_REF]. Bio-economic farm models can simulate impacts of new technologies or changes in the socio-economic environment on farming systems [START_REF] Lien | Whole-farm planning under uncertainty: impacts of subsidy scheme and utility function on portfolio choice in Norwegian agriculture[END_REF][START_REF] Louhichi | A dynamic model to analyse the bio-technical and socioeconomic interactions in dairy farming systems on the Reunion Island[END_REF][START_REF] Janssen | Assessing farm innovations and responses to policies: A review of bio-economic farm models[END_REF][START_REF] Lengers | Comparison of GHG-Emission Indicators for Dairy Farms with Respect to Induced Abatement Costs, Accuracy, and Feasibility[END_REF][START_REF] Kanellopoulos | Assessing climate change and associated socio-economic scenarios for arable farming in the Netherlands: An application of benchmarking and bio-economic farm modelling[END_REF].

The objectives of this paper are to simulate which technologies would be adopted by some typical suckler cow and dairy farms according to scenarios, and to assess whether evolution of GHG emissions are compatible with climate change mitigation objectives. Technologies encompass increased milk potential, younger age at first calving for beef and dairy cows, fodder legumes, cropping activities with practices ranging from organic to intensive and higher fertilization efficiency. Simulations are run for typical French suckler cow and dairy farms with the bio-economic model Orfee developed for that purpose.

1 Décret n° 2015-1491, http://www.gouvernement.fr/conseil-des-ministres/2015-11-18/l-adoption-de-lastrategie-nationale-bas-carbone-pour-le-cli

Material and methods

Model description

The bio-economic model Orfee aims at simulating a large range of farms producing beef, milk, annual crops and/or grasslands. It provides indicators of production, economic performance and GHG emissions. Farm functioning is modeled for an average year, at a steady state, with a monthly level of disaggregation. This model is run in Gams (GAMS development Corporation, 1217 Potomac Street W;Washington, DC 20007, USA) and resolved by the linear solver CPLEX. A short description is provided in the following subsections, but a detailed documentation is available in the supplementary material.

Focus has been made on technology that could potentially affect GHG emissions such as productivity per animal (age at first calving, type of animal product, milk yield, breed, calving period..), protein selfsufficiency with the possibility to introduce alfalfa or a mixture of cereal and protein crops in the foraging systems and in animal diets, animal diet composition and fertilizer consumption (various production intensity from organic to intensive farming). Decisions that could be optimised to maximize net profit concern crop and grassland production, animal production and animal diets, buildings and materials.

Cattle module

Animal categories are defined by three sets: breed, type of animal and calving period (or period of birth).

The most widespread cattle productions in the studied regions are included: calves, weanlings, heifers and young bulls, culled cows, steers and milk production. Heifer for reproduction could calve at 24month-old, 30-month-old or 36-month-old. Breed modifies animal characteristics: live weight growth and carcass weight, intake capacity, reproduction performance, milk production etc... Breed proposed in the model encompass the one predominantly present in France Charolais, Limousin and Salers for beef breeds, Holstein, Montbéliarde and Normande for dairy ones. Different calving periods are possible (autumn, winter spring, summer) in order to control calves mortality or to better match feed or labour requirements with farm resources.

Feed requirements are calculated on a monthly basis for each animal category to cover animal needs for maintenance and gestation, milk production and growth. Intake capacity, net energy and protein requirements are calculated thanks to the Inra methodology [START_REF] Inra | Alimentation des bovins, ovins et caprins: Besoins des animaux-Valeurs des aliments[END_REF]. It provides flexibility to adapt diet composition to production contexts. Optimisation constraints impose that 1) energy and protein content of each animal diet (averaged monthly) meet animal needs, 2) fill value of animal diets equals their intake capacity (except at pasture where fill value could be 30% below intake capacity), 3) the concentrate feed doesn't exceed a maximum value2 and 4) feed could be available. Demographic constraints between animal categories enable to ensure that herd composition is balanced and respect the reproduction and ageing process.

Four types of effluents are defined according to their straw and water content: compact manure, soft manure, diluted effluent and liquid manure. The quantity of manure produced depend on the type of building, of animals and of the length of the indoor period. Operations (feed distribution, milking, reproduction monitoring etc.) and housing needs are specified too and impact directly on labour needs.

Crop module

Crop rotation is considered to be a « cornerstone of 'integrated farming', (Leteinturier et al., 2006). In this model, production intensity, inputs and outputs are explicitly linked with crop rotations. Crop activities are the combination of three sets. The first one corresponds to the combination between previous crop-current crop family. Objective of these crop families is to group crops that could have the same agronomic behaviour regarding crop successions. The second one specifies the end use or precise crop specie. To cover main current crop productions as well as crops that could reduce fertilisation and plant protection and improve animal feed self-sufficiency, 11 crops (wheat, barley, triticale, corn, rapeseed, sunflower, peas, mix of protein and cereals, alfalfa, temporary and permanent grass) are introduced with various end uses (silage, grain, number of grass cuts..). Eventually, the third set indicates the crop intensity. The conventional crop intensity corresponds to average observation in the studied areas. Definitions of intensive and integrated farming are based on the terminology used in the "Ecophyto" project3 . The intensive level targets the yield potential and use phytosanitary treatments without limitation (roughly +30% of treatments, +4% yield compared to conventional level). Integrated level aims at reducing the use of inputs (-30% of phytosanitary products in average) while accepting lower yield (-6% in average). Organic is defined upon the standard of this label, without phytosanitary treatment and mineral fertilisation. Nitrogen requirements are estimated thanks to the nitrogen (N) balance approach, they depend on previous crops, crop intensity and soil quality. Crop operations (tillage, seeding, spreading, cutting, harvesting, etc.) are defined on a monthly basis, based namely on Arvalis data (Boigneville experimental farm).

Optimization constraints concern non tillable lands that should be allocated to permanent grasslands, maximum share of a crop activity in tillable area according to crop intensity, equilibrium between previous crop-current crop activities, satisfaction of fertilizer and crop operation requirements.

Stable and machinery

Crop operations could be implemented thanks to different types of machines. Labour, fuel consumption, gas emissions and machinery cost vary according to the type of machine4 . The type of milking and feed distribution materials is parameterized by the operator. They affect labour required for the different herd operations, feeding system (no grazing with milking robot), machine costs and fuel consumption.

Optimisation constraints ensure that there is enough place in a suitable barn to house animals that should be kept indoors or milked, enough manure storage capacity and enough material to realize crop operation. Machine costs for crop operations are proportional to their use. Building costs are proportional to their area or capacity and to their characteristics (free stalls, cubicle and manure pit type).

Labor module

The quantity of labor required encompasses the time to monitor calvings and calves during their first days. It is proportional to the number of calvings. Time to milk dairy cow is proportional to the number of dairy cows producing milk a given month. Time to clean and renew litter is proportional to the number of animal present in a barn each month. Feeding time is calculated upon animal diets (proportional to the quantity of feed distributed). Additional time requiring handling animals (vaccinations and other seasonal operations) is fixed per livestock unit (LSU). Labor associated to crop activities is proportional to the time calculated to carry out the different operations (tillage, transport, conditioning, etc.). Data comes from descriptions of some farm types [START_REF] Charroin | Le système d'information des Réseaux d'Elevage français sur l'approche globale de l'exploitation. Intérêt et enjeux dans une perspective de prise en compte de la durabilité[END_REF], surveys on dairy and beef cattle farms [START_REF] Cournut | Référentiel travail dans 7 filières animales. Synthèse de 640 Bilans Travail bovins viande et lait, ovins viande et lait, caprins, porcs, volailles[END_REF][START_REF] Fagon | Référentiel travail en élevages bovins lait[END_REF][START_REF] Kentzel | Référentiel travail en élevages bovins viande[END_REF]) and from a survey of the regional extension service of Bourgogne5 . Optimization constraints related to labor specify that labor needs per month and per year mustn't exceed the allowed workload per worker unit.

GHG emissions

Thanks to a Life Cycle Assessment (LCA) approach, climate change impact associated with all the stages of an agricultural product's life from cradle to farm exit gate are assessed. Three gases contributes to global warming: methane (CH4), nitrous oxide (N2O) and carbon dioxide (CO2) Methane emissions come from enteric fermentation and excreta of animals and are estimated with IPCC Tier2 or Tier 3 approach [START_REF] Dong | Emissions from livestock and manure management[END_REF]. Enteric methane emission factor (EF) is calculated according to Sauvant [START_REF] Sauvant | Influences des régimes et de leur fermentation dans le rumen sur la production de méthane par les ruminants[END_REF]) using the equation 9 6 where EF is expressed in g CH4/kg DOM. DOM is the amount of Digestible Organic Matter ingested by the animal, calculated by the product of the amount of organic matter ingested (OM, kg) by OM digestibility (dOM) of the diet. This latter is equal to the average dMO of diet with three corrective parameters7 to take into account digestive interactions [START_REF] Sauvant | Quantification of the main digestive processes in ruminants: the equations involved in the renewed energy and protein feed evaluation systems[END_REF]: the quantity of dry matter intake per unit of live weight (DMI%LW), the amount of concentrate feed (CO) and the rumen protein balance (RPB). EF is a second degree polynomial function involving DMI%LW and CO in animal diet. Globally, this emission factor decreases when the quantity of dry matter intake per kg of live weight increases and when the amount of concentrate feeds exceeds 30%. To estimate methane from dejections, we use IPCC equation 10.23 [START_REF] Dong | Emissions from livestock and manure management[END_REF]. Following [START_REF] Eugène | Projet « MONDFERENT » -Emissions de méthane par les bovins en France Rapport N° Programme 154 Action 14 Sous action 12[END_REF], the daily volatile solid excreted is estimated by the non-digestible organic matter ingested by animals which is the difference between total organic matters ingested and the digestible organic matter ingested. Urine components of volatile solid were assumed negligible.

Nitrous emissions are divided into direct emissions from manure management and managed soils and indirect N2O emissions that arise from volatilization of fertilizers, and nitrogen (N) lost via runoff and leaching from agricultural soils. N2O emissions from manure management systems, calculated according to IPCC Tier2-Tier 3 [START_REF] Dong | Emissions from livestock and manure management[END_REF], are proportional to the quantity of N excreted by animals. N excretion is calculated for each animal activity and month by the difference between N ingested via conserved feed or fresh grass and N fixed by meat and milk (equation 10.33). N excretion is then allocated to the different manure management systems according to the time spent in a given barn or paddock. Direct emissions of N2O from managed soils are computed according to IPCC Tier 1 [START_REF] De Klein | N2O emissions from managed soils, and CO2 emissions from lime and urea application. IPCC Guidelines for National Greenhouse Gas Inventories[END_REF]. They take into account manure spreading and inorganic N fertilization, annual amount of N in crop residues and from pasture renewal and the annual amount of urine and dung N deposited by grazing animals on pasture. Indirect N2O emissions has been estimated based on the nitrogen balance calculated on a farm scale [START_REF] Simon | Le bilan apparent de l'azote al'echelle de l'exploitation agricole: methodologie, exemples de resultats[END_REF].

Indirect CO2 emissions of inputs purchased are estimated thanks to Dia'terre methodology (ADEME 2010). CO2 may be emitted from or sequestered into agricultural soils. Assumptions made here rely on [START_REF] Fourrages Soussana | Mitigating the greenhouse gas balance of ruminant production systems through carbon sequestration in grasslands[END_REF]. We suppose that permanent grasslands store 570 kg C/ha/yea, annual crops destock 160 kg C/an/year, temporary grasslands store 570 kg C/ha/an and then destock 950 kg/year the two years following grassland destruction. Note that CO2 emissions from liming and urea fertilization have not been accounted for since these operations have not been introduced in the set of crop operations. Emissions are computed at farm level and by unit of animal product i.e. kg of milk and kg of live weight (kglw), without consideration of their quality (fat nor protein content). A biophysical allocation, as applied in the French AGRIBALYSE® program [START_REF] Koch | AGRIBALYSE®: METHODOLOGY, Version 1.1[END_REF] is used here to share the environmental burden of the systems between milk and meat. A ratio of energy requirement for lactation and maintenance on total energy requirements of dairy cows is used to allocate impacts to milk production; the rest of the impact is allocated to meat production from dairy cows.

Objective function

Net profit is defined as total revenues including sales from animal (milk, carcass, lean animals) and crops, and compensatory payments (decoupled payments, suckler cow payments, grass payments, least favoured area payment etc.) minus total costs encompassing herd and crop variable costs, machinery (fuel, depreciation and maintenance cost or enterprise cost) and buildings (depreciation and maintenance) costs.

Case studies

Four farm types have been selected within the Inosys-Réseaux d'élevage referential 8 [START_REF] Charroin | Le système d'information des Réseaux d'Elevage français sur l'approche globale de l'exploitation. Intérêt et enjeux dans une perspective de prise en compte de la durabilité[END_REF] to cross cattle production orientation with land characteristics: a dairy farm with permanent grassland only (DC_Grass) in Normandy (oceanic climate north-west of France), a dairy farm with temporary grasslands and annual crops (DC_Crops) in Pays de la Loire (west of France), a suckler cow farm with permanent grasslands only (SC_grass) in the mountains of Cantal (south Massif Central centre of France), and a suckler cow farm fattening young bulls with grasslands and annual crops (SC_Crops) in the North of Massif Central (table 1). Further details are provided in Appendix 1. Note: LSU = livestock unit (equivalent to 1 dairy cow over a whole year)

Scenarios

Two The main technological progresses and socio-economic drivers of the scenarios were selected to simulate their effects at farm level (table 3). In order to facilitate the interpretation of results, a voluntary limited number of factors have been modified between scenarios.

A set of new technologies is proposed for all 2035 scenarios (S1-S4). These technologies can be adopted

or not according to farm type and scenarios (decision is endogenous). Holstein Friesian dairy cows with higher milk potential (10000L/cow max.) are proposed in addition to the actual Holstein Friesian (8000 L/ cow) and Normande breed (6000 L/cow). Milk yield is indeed expected to rise but genetic potential improvement would slow down in the future (+25%) compared to past trends (+50% in average between 1990 and 2010). 10 000 L-milk-yield cows are supposed to be fed indoors only because of the high energy content of their diet. They are also slightly heavier (+3.5%) in order to have an intake capacity compatible with their requirements. Although carcasses have enlarged over the past (+13% between 1990 and 2013 [START_REF] Veysset | Productivity and technical efficiency of suckler beef production systems: trends for the period 1990 to 2012[END_REF]), according to experts heavy carcasses don't meet anymore the market demand. Consequently, we assume constant carcass weight for beef breeds. Given practices currently observed in other countries, first calving would be possible three months younger for beef and dairy breeds in 2035. The fertilization efficiency has progressed a lot during the past decades in the studied systems (at least -20% of mineral nitrogen fertilization between 1990 and 2010) and would continue to progress but at a smaller rate (10% less of nitrogen is required for the same average yield in 2035). Eventually, legume fodder and mixture of cereal and protein crops are supposed to be accessible everywhere land is tillable. Regarding prices, milk, beef and cereal prices are set at baseline for scenarios S1 to S4 (average over the period [2008][2009][2010][2011][2012][2013]. A sensitivity analysis of the results to beef and dairy prices are nonetheless provided in appendix 2 given uncertainties related to milk and beef prices. Similar to [START_REF] Kanellopoulos | Assessing climate change and associated socio-economic scenarios for arable farming in the Netherlands: An application of benchmarking and bio-economic farm modelling[END_REF], fuel and fertilizers prices are assumed to increase by 40%. Regarding policy, simulations are made without subsidies. We know that the current CAP policy will be reformed but we don't make hypothesis on the issues of future negotiations.

Differences between scenarios arise from the doubling of labor productivity in S2 (more than +100%

were observed between 1990 and 2010 [START_REF] Veysset | Productivity and technical efficiency of suckler beef production systems: trends for the period 1990 to 2012[END_REF]). This implies both a reduction of production cost and an increase of the production capacity since labor is a limiting resource on the farm.

In S3, we test the obligation to produce organic products with not more than 10% of concentrate feed in the total dry matter intakes. Similar to the current French situation, organic prices are 20% higher of milk and 15% higher for animals ready to be slaughtered. Eventually, a tax on carbon net emissions, equal to 40€/t CO2e, is enforced in S4. This value is in line with assumptions used in several studies (IPCC 2007;[START_REF] De Cara | Marginal abatement costs of greenhouse gas emissions from European agriculture, cost effectiveness, and the EU non-ETS burden sharing agreement[END_REF].

Farms could adapt to the different scenarios by modifying building and machinery capacities, crop activities, herd size, production per animal, animal feeding and variable inputs. Nonetheless, the type of animal product selected can be modified only at the margin. The farm that fattens animals (SC_crops) could produce steers instead of young bulls but couldn't produce weanlings; similarly the farms producing only weanlings (SC_grass) can sell weanlings at different ages and steers but not finished young bulls. Dairy farms could opt for a different dairy breeds (Holstein Friesian or the dual-purpose breed Normande) and produce steers. Farm structure (worker unit and arable area) is considered a constant. Tax on net carbon emission (40€/ t)

Results

Global GHG emission and production at farm level

Evolution of total gross emissions of GHG at farm level follows roughly variations of cattle production (figure 1 and 2) even if the technology of production impact also on total GHG emissions: production and GHG are globally higher in 2035 scenarios than in the Baseline for dairy farms and lower for suckler cow farms. Nonetheless variations between scenarios are important.

In the trend scenario S1, milk production is multiplied by 1.5 in the dairy cow farm with annual crops (DC_Crops) (figure 1). It increases even more in DC_Grass (x2.5) which specializes in milk production and switches dual-purpose Normande cows for more productive Holstein Friesian ones. Because of the highest increase in milk yield, meat production augments in smaller proportion in DC_Grass (+12%) than in DC_Crops (+23%). Regarding suckler cow farms, meat production decreases (-8% in SC_Grass and -35% in SC_Crops). Temporary grasslands are replaced by cash crops in SC_Crops (figure 3). In the case of DC_Crops, oilseeds, alfalfa and the mixture of cereals and protein crops expand at the expense of temporary grasslands. Total gross GHG emissions are multiplied by 1.6, 1.3, 1.1 and 1 in DC_Grass, DC_Crops, SC_Grass and SC_Crops, respectively. Change in net emissions are particularly important in DC_Grass since sequestration compensates 41% of gross emissions in the baseline but only 26% in S1 (stocking rate increases).

In scenario S2, the gain in labor efficiency reduces production costs and allows more cows or cash crops per worker unit. Cattle production increases slightly in suckler cow farms (+7% for DC_Grass; +4% for SC_Crops relative to S1) and strongly in dairy farms (+33% for DC_grass; +57% for DC_Crops). All crops grown on DC_crops are used to feed animals while SC_Crops maintains the maximum area with cash crops. Total gross GHG emissions are multiplied respectively by 1.3, 1.6, 1 and 1.1 relative to S1 in DC_Grass, DC_Crops, SC_Grass and SC_Crops.

The scenario 3 imposes organic farming with less than 10% of concentrate feed. Grasslands and alfalfa expand on tillable lands. Beef produced in suckler cow farms and milk are close to their baseline level thanks to organic prices which are more attractive than conventional ones. Total gross GHG emissions are multiplied in average by 0.8 relative to B0. Net emissions are reduced by up to 35% thanks to a reduction of animal stocking rate.

In scenario S4, taxes on GHG induce a reduction of beef production in suckler cow farms by half. Dairy farms produce quantities of beef and milk comparable to the trend scenario (S1), alfalfa partly replaces annual fodder crops because it is assumed to store more carbon. Total gross GHG emissions are multiplied respectively by 1.4, 1.2 and 0.7 and 0.1 in DC_Grass, DC_Crops, SC_Grass and SC_Crops.

Net emissions are negative in the case of SC_Grass (carbone storage in grasslands exceeds GHG emissions).

GHG emission efficiency and production technology

For suckler cow farms, gross GHG emissions per kg of live meat decreases between 4% and 10% in S1 relative to B0. This benefit could be first attributed to younger first calving (≈-0.5 kg CO2e /kglw9 ).

Early calving raises meat production per LSU (between +3% and +5% relative to B0) with counterpart a slight increase in organic matter ingested to meet higher feed requirements of female heifers. A higher share of liquid manure in total manure production in SC_Crops (25% instead of 0% in B0) decreases N2O and CH4 emissions linked to manure management (cows are housed in cubicles with a liquid manure system). Spring calving instead of winter calving for SC_Crops (winter calving is retained in SC_Grass) and a better optimization of the system explain also these gains. GHG emissions in S2 are rather similar to S1 since the production system is little impacted by the increase in labor efficiency. In S3, animal diets are significantly modified (figure 4). The introduction of grass silage reduces slightly CH4 emissions for SC_Grass; the partial substitution of concentrate feed by green fodder and alfalfa increases CH4 emissions for SC_Crops. Nonetheless, the reduction of the consumption of concentrate feeds and purchased fertilizer enable to reduce GHG emissions per kg of meat up to 5% relative to S1

and up to 14% relative to B0. In S4, beef production per hectare becomes very low in SC_Grass (0.5 LSU/ha), probably too low to prevent the emergence of shrubs. Gross emissions per kg of meat deteriorates because of weanlings sold younger. Net emissions become negative because carbon storage more than offsets GHG emissions.

For dairy farms, emissions per kg of milk decrease significantly in S1 for DC_Grass (-16% in S1 compared to B0) and to a lesser extent for DC_Crops (-10% in S1 compared to B0). These gains are first obtained through an increase in milk yield. DC_Grass switches Normande for Holstein Friesian cows (average milk yield=8970 L/ cow, +61% relative to B0) and DC_Crops benefits from the increase in milk potential of Holstein Friesian (average milk yield= 9735 L/cow, +24%). It reduces enteric fermentation per milk unit (enteric CH4: -30% for DC_Grass, -15% for DC_Crops) and N excretion per milk unit. The calving period chosen is spring. Younger first calving which is always chosen whatever the farm and scenario and the heaviest carcass of Holstein Friesian in the case of dairy farms (+3.5%) raises meat production and reduces GHG emissions per kglw (table 4). Because of the intensification of animal production per hectare in DC_Crops (from 1.4 to 2.2 LSU/ha), the share of grass based fodder shrinks while concentrate feed (30% of total DM intake) and crop fodders (corn, mix cereal-protein and alfalfa) take more importance in animal diets (figure 4). In S2, the gain in animal productivity is little and production intensifies per hectare. The consumption of feed concentrate is more important for all animal categories. GHG emissions allocated to beef increase significantly. In S3, milk yield strongly decreases (average milk yield: 6.5 tons of milk per cow in DC_Grass; 6 tons/cow in DC_Crops) because of the organic constraint and above all the limitation of feed concentrate consumption (< 10% of total DM intake). Stocking rates decrease and diets are based principally on grassland products and alfalfa.

The increase in methane emission per liter of milk is partly offset by a reduction of CO2 emissions for DC_Crops and more than offset for DC_Grass. Net emissions are reduced per liter of milk and meat. In S4, production systems are rather comparable to the trend scenario except that, in DC_Crops purchased concentrates decrease and alfalfa increases at the expense of corn silage. Emissions are lower than in B0.

Economic results

Economic results (figure 5) are analyzed without public support (except for the tax on net GHG) and after salaries (included family worker salaries). Dairy cow farms have negative revenue for the baseline, but the increase in milk yield and the introduction of legume productions enable to raise net profit to positive level in S1 (+26 k€ for DC_Crops and +35k€ for DC_Grass). In spite of an increase in net income compared to S0 (+6 k€), the suckler cow farms located in mountainous areas couldn't stay in business in any 2035 scenarios without public supports, higher beef prices, new technologies or other sources of income. SC_Crops net income raises by 14k€ in S1 because of the higher share of cash crops.

The scenario S2 is the most favorable to producers' net income thanks to higher production levels and lower labor costs. Net income are close between scenarios S3 and S4 but lower than the trend scenario (except for SC_Grass). S3 reduces farm sales while S4 taxes profit (15k€ per farm in average).

Discussion

The main objectives of this study was to simulate how beef and milk production would be produced in the future and to assess how far the simulated scenarios were compatible with climate change mitigation objectives. Main results are that 1) in the future dairy farms are likely to increase their production per hectare while suckler cow farms would reduce it, 2) gross GHG emissions per unit of milk and meat would be reduced thanks namely to an increase in milk yield, younger age at first calving, spring calving and legumes fodders and 3) the most favorable scenarios for the reduction of GHG emissions at farm level involve the development of organic farming for suckler cow farms and grass based dairy farm and the introduction of a tax on GHG emissions for dairy farms with annual crops. In this section, we discuss the validity and limitations of 2035 projections and the GHG mitigation strategies.

Validity of the farm model simulations

The reliability of this bio-economic model could be assessed by its ability to reproduce production processes, estimate GHG emissions and calculate economic results in an appropriate level of details and to predict farmers' decisions in various context.

• Simulation of the production processes and farmers' decisions Some calibrating method, such as Positive Mathematical Programming are often used to reproduce exactly the observed production decisions in adjusting automatically the production costs of the different farm activities [START_REF] Heckelei | Models based on positive mathematical programming: state of the art and further extensions[END_REF][START_REF] Kanellopoulos | Assessing the Forecasting Performance of a Generic Bio-Economic Farm Model Calibrated With Two Different PMP Variants[END_REF]. Nonetheless this method gets very complicated when activities are highly embedded, especially with animal production, and is not relevant for long term simulation in which technology could change drastically [START_REF] Kanellopoulos | Assessing climate change and associated socio-economic scenarios for arable farming in the Netherlands: An application of benchmarking and bio-economic farm modelling[END_REF]. We opted instead for a calibration/ validation in two steps: 1) animal diets and crop operations were optimized for fixed levels of animal products and crop activities; 2) Crop allocation and herd size were also optimized for the average economic situation of 2008-2013, considering that building investments have been previously decided (short term optimization) and that milk quotas are still enforced. Technical and economic model outputs were compared to farm type referential from Inosys Réseaux-d'élevage [START_REF] Charroin | Le système d'information des Réseaux d'Elevage français sur l'approche globale de l'exploitation. Intérêt et enjeux dans une perspective de prise en compte de la durabilité[END_REF]. When crop and animal activities were fixed (Appendix 1), we observe a good match for feed, fuel and fertilizer consumption and close economic results.

When these activities are also optimized10 , results show that 1) suckler cow production in mountainous area slightly decreases, 2) suckler cow production sharply decreases in SC_Crops where cash crops can be expanded. Nonetheless, the feeding systems are comparable, 3) the dairy cow production system based on grasslands (DC_Grass) maintains its level of milk production but reduces the number of heifers sold. Grassland production becomes more extensive while concentrate feed increases slightly, 4) milk production is maintained in DC_Crops but with a higher stocking rate so that cash crop production could increase. The model doesn't reproduce exactly the decisions observed for the period 2008-2013.

Nonetheless, these decisions appear sensible since this period was more favorable to cash crop production than beef production. To reproduce more accurately current famers' decisions, it would be necessary to take into account the dynamic of past investments [START_REF] Lengers | Comparison of GHG-Emission Indicators for Dairy Farms with Respect to Induced Abatement Costs, Accuracy, and Feasibility[END_REF]) and farmers' expectation regarding the future [START_REF] Nerlove | Expectations, information and dynamics[END_REF]. Nonetheless, we assume that the current investment situation of these farms would have little impact on their 2035 farming systems.

Results of the 2035 simulations show that younger age at first calving and in some cases legume based fodders become very attractive. The question is why these technologies would be more attractive in the future than now since these options already exist but are underdeveloped? Regarding age at first calving, the main bottleneck seems to be labor organization. Farmers prefer to delay the age at first calving in order to group primiparous cows calving with the highest chance of success. We can imagine that in the future, genetic selection of earlier sexual maturity (above all for beef breeds) and electronic developments or subcontracting of heifer breeding would help to monitor more carefully herd heterogeneity. Regarding alfalfa and the mixture of cereals and proteins, these crops are attracting increasing attention (7 th Research Framework Programme of the European Union, ANR funded project Legitimes, etc.) and we could expect that when more information is available and analysis of feed quality more systematic, these crops will expand.

• Validity of GHG emissions indicators

There is a wide range of values in the literature regarding GHG emitted by bovine systems (Crosson et al. 2011). These differences stem from the productions systems themselves but also from the methods of calculation of GHG emissions (parameters or equations more or less detailed) and of emission allocation to the different farm products (Nguyen et al. 2013b). Regarding beef production from suckler cow farms, [START_REF] Crosson | A review of whole farm systems models of greenhouse gas emissions from beef and dairy cattle production systems[END_REF] reported values around 30 kg CO2e/kg of carcass beef (≈15 kg CO2e/ kg of liveweight) which matches with the values estimated in this study. Regarding dairy production, [START_REF] Crosson | A review of whole farm systems models of greenhouse gas emissions from beef and dairy cattle production systems[END_REF]) reported values ranging between 0.5 and 1.5 kg CO2. Values simulated for the baseline scenarios lie between 0.7 and 0.9. These values could appear rather low. This could be explained first by the allocation method which only attributes emissions from dairy cows (and not heifers) to milk production and excludes the fraction of emissions linked to pregancy and weight gain (AGRIBALYSE® program, Koch & Salou 2014). Second, the calculation of methane production [START_REF] Sauvant | Influences des régimes et de leur fermentation dans le rumen sur la production de méthane par les ruminants[END_REF] provides slighlty lower values than IPCC Tier2 for animals with a high level of ingestion per unit of liveweight and/or concentrate feed in the diet, this is typically the case of dairy cows with a high milk yield (appendix 2). Nontheless, conclusions of the studies would have been the same with the IPCC Tier2 methodology for enteric fermentation.

Are possible evolutions of cattle farms compatible with climate change mitigation objectives?

• Impacts of scenarios on production technology and on GHG emissions per unit of product Higher milk yield and younger age at first calving were chosen in almost all scenarios and farm types (except for milk yield in scenario S3). It enabled to reduce significantly methane emissions. As enteric methane is a leading source of GHG emissions, a strategy often put forward is indeed to increase the ratio of livestock 'production' to 'maintenance' thanks to faster growth, higher milk yields or shorter dry periods lactating cows [START_REF] Monteny | Greenhouse gas abatement strategies for animal husbandry[END_REF]. Feed intake increases usually with energy expenditures. Nonetheless, the rumen activity is modified by larger diets and by diets with higher share of concentrate feeds, leading to a reduction of CH4 emissions per unit of product [START_REF] Monteny | Greenhouse gas abatement strategies for animal husbandry[END_REF].

The indirect CO2 emissions linked to the consumption of concentrate feeds partly offset the reduction of methane emissions. It explains why organic farming associated to a low level of feed concentrate (scenario 3) presents the lowest emission per output unit for suckler cow systems and for dairy production based on grasslands. We also show that legume fodders can limit the increase of indirect CO2 emisions induced by an intensification of the forage area. Taking into account carbon sequestration in grasslands, scenarios 3 and 4 (tax on GHG emissions) brings the best results in terms of net GHG emission efficiency.

In the literature, interests of intensification of animal production to improve GHG emissions efficiency is controversial. Regarding beef production, [START_REF] Cardoso | Impact of the intensification of beef production in Brazil on greenhouse gas emissions and land use[END_REF] demonstrate for instance that intensification of pasture production from degraded pasture to fertilized grassland and supplementary feeding during the finishing period lead to significant improvement of GHG emission efficiency. At the opposite, suckler cow grass based systems with low supplementary feeding in France [START_REF] Veysset | Energy consumption, greenhouse gas emissions and economic performance assessments in French Charolais suckler cattle farms: Model-based analysis and forecasts[END_REF], moderate intensification [START_REF] Foley | Whole-farm systems modelling of greenhouse gas emissions from pastoral suckler beef cow production systems[END_REF] or organic farming systems in Ireland (Casey & Holden 2006a) appear as a valuable alternative to reduce GHG emissions per unit of meat. In dairy production, Nguyen et al. ( 2013b) estimate that more intensive French dairy production systems emit less GHG per milk unit while New-Zealand studies point that, for a given level of milk production per animal, milk production intensification per hectare reduces GHG emission efficiency [START_REF] Basset-Mens | Eco-efficiency of intensification scenarios for milk production in New Zealand[END_REF][START_REF] Adler | Managing greenhouse gas emissions in two major dairy regions of New Zealand: A system-level evaluation[END_REF]. Gains obtained thanks to the intensification of the production systems are not linear and beyond a certain level of intensification, an increase in fertiliser use and concentrate feed consumptions offset the reduction of methane emissions. Some mitigations options have not been introduced in this study (simplified cropping practices, techniques of fertilizer applications, use of lipids or nitrates in animal diets, manure storage coverage, biogas production, etc.). Beside the fact that reduction of GHG emissions could have been greater than estimated here, it could also affect scenarios comparison. It could be indeed less costly for large intensive (scenario 2) farm to invest in some mitigation options [START_REF] Lengers | What Drives Marginal Abatement Costs of Greenhouse Gases on Dairy Farms? A Meta-modelling Approach[END_REF]. Nonetheless, investments can also be shared by several smaller farms to reduce their cost per unit of farm output.

Technologies chosen for each simulation are sensitive to many parameters, namely outputs prices. It was difficult for experts to estimate prices for each 2035 scenarios. Consequently, they were set at their baseline values. Appendix 2 shows that a variation of +/-15% of beef or milk prices would affect the volume of production but not the main conclusions, except that cattle production would be eliminated in some farms if prices decrease by 15% and a 40€ tax on GHG emissions is introduced. Production and market risks are also assumed to increase in the future because of climate change and trade globalization (especially in scenario 2). This would reduce incentive to invest and to intensify production per hectare and animal, except if farms are well insured [START_REF] Mosnier | A dynamic bio-economic model to simulate optimal adjustments of suckler cow farm management to production and market shocks in France[END_REF][START_REF] Mosnier | Self-insurance and multi-peril grassland crop insurance: the case of French suckler cow farms[END_REF]. Introducing this dimension in this modeling framework may have resulted in smaller technological differences between scenarios.

• Impacts of scenarios on production level, total GHG emissions and economic results

We simulated that GHG emissions, milk and beef production from dairy farms are likely to increase in all scenarios, except S3, and that GHG emissions and beef production from suckler cow farms are likely to decrease, especially if a tax on GHG emissions is introduced. Sensitivity analysis also emphasizes that cattle productions from farms with tillable lands are more sensitive to prices since they have more room to intensify their cattle production if the prices are good or otherwise to switch to annual cash crop production. This findings are globally in line with assumptions made at France level. At France level, the trend scenario projects an increase of beef and above all milk productions from dairy farms, mainly in north-west France and a reduction of beef production from suckler cow systems that would be maintained primarily on permanent grasslands. Milk and beef are interlinked. [START_REF] Zehetmeier | Does increasing milk yield per cow reduce greenhouse gas emissions? A system approach[END_REF] demonstrate that if we need more suckler cow to compensate the reduction of the number of dairy cows, this may downgrade the GHG performance of the global bovine sector. Nonetheless it was assumed that milk surplus would be exported, thus the global performance of the bovine sector is maintained.

Nonetheless, according to export-import scenarios, more or less milk and beef would be produced elsewhere, modifying the carbon footprint at global level. This underlines the necessity to analyse the milk and beef carbon footprint worldwide [START_REF] Garnett | Livestock-related greenhouse gas emissions: impacts and options for policy makers[END_REF]Nguyen et al. 2013a;[START_REF] Cohn | Cattle ranching intensification in Brazil can reduce global greenhouse gas emissions by sparing land from deforestation[END_REF]). In addition, our scenarios assume changes in per capita food consumptions. Scenarios 3 and above all 4 hypothesise a reduction in beef and milk consumption. According to [START_REF] Dong | Emissions from livestock and manure management[END_REF], a reduction of the consumption of animal products may induce an increase of vegetable food that would in fine have negative impact on global GHG emissions. The assessment of scenarios should then be extended to other agricultural products (cereal, vegetables, etc.). Other dimensions should also be accounted for since these scenarios could have major impacts on water and air quality (manure surplus, dust), biodiversity (namely in grasslands), rural development (employment, equal development between territories) or resilience.

Conclusion

In order to explore possible future changes for the beef and milk French sectors and for their related impact on climate change, a scenario approach has been developed. The objectives of this paper were to simulate, thanks to the bioeconomic model Orfee, which technologies would be adopted by typical suckler cow and dairy farms and to assess whether evolution of GHG emissions per unit of product and per farm were compatible with climate change mitigation objectives.

Originalities of this farm level study lies first in its systemic approach emphasizing the relationships between crop and herd management, economic and environmental results. Second, the detailed calculation of GHG emissions, particularly for enteric fermentation which takes into account digestive interactions, enable to test effects of production intensification per animal and per hectare on GHG emission efficiency. Eventually the same modelling framework is used for contrasted beef and dairy cow farms which enable to compare effects of drivers in a large range of situation. Main results are that 1) in the future dairy farms are likely to increase their production per hectare while suckler cow farms would reduce it, 2) gross GHG emissions per unit of milk and meat would be reduced through an increase in milk yield, younger age at first calving, spring calving and legumes fodders and 3) the most favorable scenarios for the reduction of GHG net emissions involve the development of organic farming or the introduction of a tax on GHG emissions but they are also associated to lower beef and milk production per hectare.

Nonetheless, impacts of these scenarios have been analyzed only partially. Appendix 2: Sensitivity analysis of production and GHG emission efficiency to beef and milk price

  The three gases are aggregated by their potential of global warming into a single indicator expressed in CO2 equivalent (CO2e). Values are those proposed by IPCC ([START_REF] Forster | Changes in atmospheric constituents and in radiative forcing[END_REF]), p212, 100-year time horizon): CO2 =1; CH4 = 25; N2O = 298. Emissions are computed at farm level and by unit of animal product. To estimate net impact of livestock production on climate change the quantity of carbon stored (net CO2e) in soils have been deducted from total emissions.

  kinds of scenarios have been developed. The trend scenario (S1) is considered as the most probable from the 2014 perspective. It has been elaborated considering past trends and the most likely evolution of technology and markets[START_REF] Idele | Quelle production française de viande bovine à l'horizon 2020 ?[END_REF]. Alternatives scenarios have been constructed by expert groups gathering people working in the beef and dairy sectors and researchers, to explore other plausible futures (table2). For the trend scenario S1, it is assumed that at national level dairy production would increase taking advantage of new opportunities to export while suckler beef production (-11% of suckler cows) would shrink because of a reduction in outlets for exports and an increase of beef produced by the dairy herd. Scenario S2 assumes an expansion of beef and above all dairy production to meet the raising global world demand. Farm enlargement would be accompanied by a high increase in labor efficiency and a high level of mechanization (without additional costs thanks to the high level of diffusion of technological progress). The scenario S3 considers the opposite situation with the development of a local environmentally friendly production based on grasslands with double purpose breeds (Normande, Montbeliarde). It is associated to a decrease in the quantity of beef and milk produced and consumed.Eventually, the scenario S4, depicts a situation where the reduction of GHG emission is a priority and is enforced by a proactive policy and by a growing vegetarian population. These scenarios are compared to the baseline scenario B0 which reproduces the farming systems for the average economic situation of2008-2013.

Figure 1 :Figure 2 :

 12 Figure 1 : Evolution of beef and milk production per farm

Figure 4 :

 4 Figure 4: Quantity of feed in animal diets

Figure 5 :

 5 Figure 5: Net income without subsidies (except GHG tax) and after wages

a

  First, only a limited number of mitigation options have been introduced in this study, focusing on production intensification or extensification. Second, other dimensions -including air and water quality, biodiversity, energy consumption, rural development and employment and resilience of the farming systems-should be taken into account at farm and territorial levels to assess which scenario may be suitable. Eventually, this study should be complemented by an analysis at global scale, including other agricultural sectors and taking into account the evolution of human diets. Réseaux d'élevage Auvergne-lozère, farm type BV10, 2010 : http://idele.fr/rss/publication/idelesolr/recommends/systemes-de-production-bovins-viande-du-bassin-rustiquesud-massif-central.html b Réseaux d'élevage charolais, Farm type 31060, 2011: http://idele.fr/rss/publication/idelesolr/recommends/systemes-bv-du-bassin-charolais-actualisation-2011.html c Réseaux d'élevage Normandie (vivre du lait en Normandie), Farm type 2, 2012: http://idele.fr/filieres/bovin-lait/publication/idelesolr/recommends/vivre-du-lait-en-normandie-2012.html d Réseaux d'élevage Pays de la Loire, farm type 2B, 2011 B0 : Animal diets and crop operations are optimized for fixed levels of animal products and crop activities. B': Crop allocation and herd size are optimized too for the average economic situation of 2008-2013, considering that building investments have been previously decided and that milk quotas are still enforced.

Table 1 : Main characteristics of the farm types selected

 1

		SC_GRASS	SC_CROPS	DC_GRASS	DC_CROPS
	WORKER UNIT	1.5	2	1.5	1.7
	HERD SIZE (LSU)	86	251	63	73
	MEAT PRODUCTION (LIVE KG /LSU)	309	384	na	na
	BREED AND ANIMAL PRODUCTS	Salers and	Charolais	Normande	Holstein
		crossbred	cows,	Cow, heifers,	Friesian, Cow,
		Cow,	heifers,	newborn	newborn
		weanlings	young bulls	calves	calves
	MILK QUOTA (TONS)			200	390
	MILK YIELD (1000 L/COW)			5,7	7,8

8 Inosys-Réseaux d'élevage builds description of typical farm types per region thanks to a large network of commercial farms and expert knowledge

Table 2 : Main characteristics of scenarios at national level

 2

		S1 «Trend »	S2 «Production +» S3 « Grass+ »	S4 « GHG »
	Context	Low economic growth	Increase of	Fold on an	Large drop in
		and demand in	production to	internal demand	consumption,
		France.	answer a high	which goes	high level of
			global demand,	upmarket	constraints for
			export ++		GHG emissions
	National	milk : +36% ; ↑export	milk : + 60%	milk : +7%	milk : -21%
	production	beef : + 6%; ↓export	beef : +16%	beef : -14%	beef : -32%
	Production	Concentration +	Concentration ++	Concentration --	Concentration -
	systems	Enlargement +	Enlargement ++	Enlargement --	Enlargement =
		mechanisation +	mechanisation ++	mechanisation --	mechanisation +

Table 3 : Summary of assumptions in the simulated scenarios

 3

	B0	Baseline price (average 2008-2013)
	S1-S4	-Prices =B0 (standard milk = 335€/ton, charolais culled cow = 3.5 €/kg carc, wheat
		=187€/t), fuel and fertilizers: B0 x1.4
		-First calving three month younger possible
		-Same breeds as S0 + Holstein Friesian 2035 : milk yield +30%, liveweight+3.5%, fed
		indoors
		-Free calving periods
		-Mixture of cereal_protein crops, alfalfa
		-Increase of fertilisation efficiency (+10%)
	S2	Labor productivity x2

S3

Organic farming with 10% max. of concentrate feed (organic milk price x 1.2, beef carcass price x1.15, lean animals 1.1 , crop price ≈ x 2 ) S4

Table 4 : GHG emissions of suckler cow farms in kg CO2e/kg of liveweight

 4

			B0	S1	S2	S3	S4
		CO2	1.29	1.05	1.14	0.71	1.10
		CH4	9.86	9.71	9.63	9.58	9.81
		N2O	3.63	3.43	3.46	3.13	4.28
	SC_Grass	Gross CO2e	14.8	14.2	14.2	13.4	15.2
		Net CO2e	7.28	5.69	6.48	5.34	-3.23
		CO2	1.70	1.34	1.42	0.84	1.12
		CH4	8.83	8.37	8.37	8.60	8.55
		N2O	4.51	3.67	3.79	3.39	3.57
		Gross CO2e	15.0	13.4	13.6	12.9	13.2
	SC_Crops	Net CO2e	12.2	10.1	10.4	9.1	8.8

Table 5 : GHG emissions in dairy farms (in kg CO2e/kg of liveweight or per kg of milk )

 5

		kg CO2e/kg liveweight			kg CO2e /kg Milk
	B0	S1	S2	S3	S4	B0	S1	S2	S3 S4

.73 0.66 0.65 0.73 0.65 Net CO2e 8.52 8.34 8.50 7.20 7.48 0.68 0.65 0.64 0.65 0.60

30% except for dairy cow: 70% and during fattening periods: 50%

http://institut.inra.fr/Missions/Eclairer-les-decisions/Etudes/Toutes-les-actualites/Ecophyto-R-D, p8

http://www.loiret.chambagri.fr/fileadmin/documents/Machinisme/Bareme_VITI_ARBO_Edition_2013.pdf

http://www.bourgogne.chambagri.fr/uploads/media/plaquette_Le_travail_en_%C3%A9levage_laitier_01.pdf

Also reported in[START_REF] Sauvant | Quantification of the main digestive processes in ruminants: the equations involved in the renewed energy and protein feed evaluation systems[END_REF] eq 48

equation 24

Additional simulations have been run to isolate effects of each 'technological progress'

Note that in this optimization, the number of animals is optimized but the type of animal produced (weanlings, finished animals, milk) can be modified only at the margin (weanlings sold of different ages for instance)

Acknowledgments:

The authors would like to thanks Ademe for co-financing this project, M Eugène for checking methane emissions, experts who took part to this projects, Inosys Réseaux d'Elevage and Inra-Umrh for their production of technical and economic references on livestock farms, P. Veysset and C. Lebecq for their proofreading.

a Enteric fermentation calculated upon gross energy intake [START_REF] Dong | Emissions from livestock and manure management[END_REF] b Enteric fermentation calculated upon the digestible organic matter, taking into account digestive interactions [START_REF] Sauvant | Influences des régimes et de leur fermentation dans le rumen sur la production de méthane par les ruminants[END_REF]


text:
Environmental footprint of milk and meat from the French cattle sector: progresses since 1990 and future trends to 2035

Keywords: climate change, energy, livestock, national level, prospective

French cattle farming is committing to take part to national and European mitigation targets. This study combine macroeconomics and life cycle assessment to estimate the contributions of the different methods of producing milk and meat at national level and to propose improvement strategies for the future. Analyses of passed production systems, livestock population and agricultural practices allowed us to specify GHG emissions, energy consumption and corresponding footprints, from 1990 to 2010. Various coherent, plausible and contrasted economic scenarios has been chosen to explore the future possible for the horizon 2035. From 1990 to 2010, the cattle sector reduced its emissions and energy consumptions (respectively -10.6% and -22%). This reduction is mainly the result of the decrease in cattle population: improvements in dairy productivity have been followed by a decrease in the number of dairy cows, only partially balanced by an increase of the suckler cows population. Farmers' progress in fertilization management and energy saving also contributed to the overall reduction. This is translated in a reduction of 20% of the carbon footprint (CF) of milk but an increase of 5% in the CF of meat, due to changes in animal products (less dairy cows, more animals from suckler herd, with impact allocation). On the basis of an underlying projection of milk and meat productions for 2035, the future trend would be a stabilization in GHG emissions (+0.5%) and a decrease of energy consumption (-13%) from 2010 to 2035. The CF of milk would reach 0.94 kg CO2eq/kg FPCM and CF of beef 14.9 kg CO2eq/kg LW. Adoption of additional mitigation techniques would lead to improve both CF of milk and meat by -6% and -13%. Other scenarios explore contrasted situations, on the level of production, as well as on the ways to produce milk and meat. The results should help the sector to make the future stakes their own, in a context of questionings and strong expectations for livestock regarding climate change.

Introduction

With a contribution of 12.6% to the French greenhouse gas (GHG) balance [START_REF] Dollé | Elevage de ruminants et changement climatique[END_REF], cattle farming is committing to take part to national and European mitigation objectives. This observation is related to the importance of this sector at national level: 1 st producer of beef and 2 nd producer of milk in Europe, cattle farms use directly about 40% of the French agricultural area [START_REF] Perrot | L'élevage d'herbivores au Recensement agricole 2010. Cheptels, Exploitations, Productions[END_REF] and provide 433 000 direct and indirect employments [START_REF] Lang | Les emplois liés à l'élevage français[END_REF].

The French low carbon national strategy launched by the government at the end of 2015, targets a reduction of 12% of the agricultural sector emissions of GHG in 2028 in comparison with 2013 and a reduction of 50% in 2050 in comparison with 1990. In this context, a recent analysis of different scenarios combining GHG mitigation options showed that agriculture and forestry could reduce their GHG by 20% in 2035 [START_REF] Martin | Analyse comparative de scenarios de lutte contre le changement climatique pour l'agriculture à l'horizon 2035[END_REF]. The scenario approach is a widely used method to explore a highly uncertain future for agriculture [START_REF] Abildtrup | Socio-economic scenario development for the assessment of climate change impacts on agricultural land use: a pairwise comparison approach[END_REF][START_REF] Audsley | What can scenario modelling tell us about future European scale agricultural land use, and what not?[END_REF][START_REF] Mandryk | Scenarios of long-term farm structural change for application in climate change impact assessment[END_REF]) by describing coherent and plausible future states of the world. Concerning the bovine sector, its in the next 20 years will depend on numerous factors including not only the use of GHG mitigation technics, but also other technological improvements, production organizations, population growth and consumer behavior and policy.

The Gesebov project investigated the joint evolution of the dairy and beef cattle sectors in horizon 2035, through contrasted prospective scenarios, and its associated level of GHG emissions and energy consumption in a life cycle perspective, both at farm and national levels. Since emissions of GHG of the bovine sector are first explained by the bovine inventory (Casey & Holden 2006b) and second by the way meat and milk are produced [START_REF] Monteny | Greenhouse gas abatement strategies for animal husbandry[END_REF][START_REF] Johnson | Agricultural opportunities to mitigate greenhouse gas emissions[END_REF]), Gesebov scenarios have been specifically elaborated to be contrasted in terms of volume of milk and beef produced and technology of bovine production.

This paper focuses on the national level, providing the state of national impacts from 1990 to 2035 and evolution of environmental efficiency at product level. The aim is to imagine how beef and milk would be produced in the future, in different contexts, and to assess how far the simulated scenarios are compatible with climate change mitigation objectives and consumers expectations in terms of environment performance.

Methods

This study combines economics, including prospective, and a life cycle assessment approach. Various coherent, plausible and contrasted economic scenarios has been chosen to explore the future possible for the horizon 2035. The trend scenario (S1) is considered as the most probable (from the 2014 perspective). It has been elaborated considering past trends and the most likely evolution of technology and markets. Assumptions are in lines with the previous prospective for beef and dairy production in 2020 [START_REF] Idele | Quelle production française de viande bovine à l'horizon 2020 ? Dossier Economie de l'Elevage n°450[END_REF]. Alternative scenarios (S2 to S4) have been constructed to explore other plausible futures, considering that new driving forces could be reinforced in future (as a strong growth of world food market, or changes in European social demand for food quality and environmental respect). Those scenarios have been built by expert groups gathering people working in the beef and dairy sectors and researchers. In addition, environmental scenarios have been built, as a declension of S1 and S2, called S1Bis and S2Bis. Those two ones include high investments in GHG and energy mitigation technics, most of them selected in [START_REF] Pellerin | Quelle contribution de l'agriculture française à la réduction des émissions de gaz à effet de serre ? Potentiel d'atténuation et coût de dix actions techniques[END_REF]. Changes concern herd management (age at first calving, mortality), feeding (use of lipids or nitrates, total crude protein intake management), crop and grassland management (legume fodder, mineral fertilization, simplified cropping practices), manure management especially biogas production, energy consumption reduction, hedges and agroforestry development to enhance carbon storage.

Finally, 6 scenarios are available:

-S1: Trend evolution (raise of milk production, due to increasing global demand, stabilization of beef production)

-S1Bis: Trend evolution and strengthened environmental strategy (idem and improvement of practices, including GHG mitigation options)

-S2: Answer to a high global demand (high increase of milk and beef production to satisfy a highly raising global demand)

-S2Bis: Answer to a high global demand and strengthened environmental strategy (idem and improvement of practices, including GHG mitigation options)

-S3: Fold on an internal demand which goes upmarket (the production reaches first the demand of national consumers, wanting for products from French and grass based origin, and secondly a decreasing global demand but still for "French quality" products)

-S4: Large drop in consumption and strengthened environmental strategy (decreasing French cattle production because of pressure from citizen and policy makers to reach the GHG mitigation objectives). The scenarios are mainly driven by the amount and nature of the demand for milk and beef (Table 1): consumption of milk and beef per inhabitant, imports and exports, determine the volumes to be produced, and then the number of animals needed; concerns of consumers and policy makers also influence the type of production systems and practices (degree of efficiency and of intensification at animal and area, use of inputs, etc.).

Description of the bovine French farm in 1990 and 2010 is based on national census data and on technical references from Inosys (national network of about 2000 breeders). The number of dairy cows is distributed through 8 categories described by [START_REF] Ballot | Observatoire de l'Alimentation des vaches laitières[END_REF] in terms of forage systems, diet and production level; dairy heifers are distinguished in 3 classes, depending on age at first calving; suckler cows are distributed in 3 forage systems (with a gradient in the place of grass and maize); for the 12 other classes of animals dedicated to produce meat (weaners, young bulls, etc.), an average diet is determined.

For each class of animals and each year, or scenario, the diet and indicators such as milk productivity, weight, mortality, duration of fattening, etc. are described. From the diets, we assessed the need of area of grass, maize silage, grain, etc. with a coherence control in 1990 and 2010 to correspond to census data about areas in cattle farms. The need of bought concentrates is also then established. The time spent in grazing pasture and the type of building allowed to determine the manure management system (slurry / farmyard manure) and the amount available for organic fertilization. Data from national census and UNIFA (national union of fertilizing producers) gave the amount of mineral fertilizers used in 1990 and 2010. For 2035, they are determined from plant needs, and nitrogen inputs by legumes and effluents. In parallel, simulations at farm scale with the Orfee model (Mosnier et al.,205) allowed to validate these assumptions and trajectories. The ClimAgri® tool has been used to assess the GHG emissions and energy consumption of the French bovine farm. It is the national tool to make climate and energy diagnosis for the agriculture and forestry sectors at territory level, taking into account all inputs and direct environmental fluxes, in a life cycle perspective [START_REF] Doublet | CLIMAGRI : Guide méthodologique et guide des facteurs d'émissions et références utilisées[END_REF]. This tool also provide other helpful indicators such as variation in direct soil organic carbon (under permanent grasslands and hedges) and its potential to compensate climate change (in % of the GHG emission), direct ammonia emissions (kg N-NH3/ha of agricultural area, AA) and need of external AA to produce bought feed (in ha imported AA/ ha AA of the French farm).

For the Gesebov project, ClimAgri® was refined. For the animal classes mentioned above, considering their diet and productivity, their enteric emissions and nitrogen excretion were specifically calculated.

The LCA impacts assessed correspond to climate change expressed in kg CO2eq, with IPCC 2013, and energy consumption, as Non renewable, fossil expressed in MJ, with the Cumulative Energy Demand 1.8 method. For climate change, emissions of GHG and carbon storage are accounted separately, according to ISO 14067:2013.

Functional units to express impacts on climate change (only GHG emissions) and on energy resource depletion are here one kg of FPCM (fat and protein corrected milk) produced and one kg of LW (live weight) of beef produced by the French bovine farm.

A biophysical allocation, similar to the one applied in the French AGRIBALYSE® program [START_REF] Koch | AGRIBALYSE®: METHODOLOGY, Version 1.2[END_REF], is here used to share the environmental impacts between milk and meat: the burdens associated to dairy cows are calculated in separate ClimAgri® files and attributed to milk production, while the burdens of all the other bovine animals (including dairy heifers) are attributed to meat production.

Results

From 1990 to 2010, the cattle sector already reduced its GHG emissions and energy consumptions, respectively of -10.6% and -22% (Table 2). Those reductions are mainly the result of the change in cattle population: improvements in dairy productivity have been followed by a decrease in the number of dairy cows, only partially balanced by an increase of the suckler cows population. Farmers' progress in fertilization management and energy saving also contributed to the overall reduction. This is translated in a reduction of carbon footprint (CF) of milk from 1.44 to 1.15 kg CO2eq./ kg FPCM and an increase in CF of meat from 13.96 to 14.7 kg CO2eq./ kg LW (Table 4), due to changes in animal products (less meat from the dairy herd, more from the suckler herd, with longer cycles of production). The intensification of bovine production (increase of productivity and stocking rate, Table 1) also led to decrease the areas of permanent grasslands and the subsequent carbon storage (-6%, Table 3).

The future trend for 2035 would be a total stabilization in GHG emissions and an extra decrease of energy consumption comparing to 2010 (respectively +0.5% and -13%). Gains in productivity, efficiency and improvements of practices are still possible. The CF of milk would reach a decrease (-19% since 2010) and CF of meat would be stabilized (+1%). Adoption of additional mitigation techniques in the scenario S1Bis would lead to improve both CF of milk and meat, as well as the national GHG and energy balance of the cattle sector. However, in those scenarios, attention must be paid on the risk of continuing decreasing of carbon storage (-20% and -13% for S1 and S1Bis). At the same time, the sector would become less self-sufficient in concentrate feed.

In the scenario S2, the high increase in production induces more GHG for the French cattle farm, but a similar energy consumption, thanks to gains in productivity, also allowing to reduce footprints. The S2Bis leads to a higher environmental efficiency, almost in the same proportion as between S1 and S1bis. The loss of carbon storage, due to conversion of grasslands to crops, is the weak point of this scenario (Table 3).

Decrease of production and more extensive grass-based systems in S3, would lead to significantly decrease GHG and energy consumption. From the products point of view, footprints are still improved. However, for milk, reductions in footprints are here the weakest. Nevertheless, this does not consider the higher carbon storage potential (Table 3). Ammonia emissions are here significantly decreased, due to a decrease of the stocking rate (Table 1) and a higher use of pasture (less manure in buildings). Feed self-sufficiency is significantly improved.

In S4, the higher decrease in national GHG emissions and fossil energy use is directly linked to the fall of milk and beef production. Gains in footprints are quite high (similar to S1Bis or S2 for milk and to S3 for meat), thanks to a mix of extensive grass-based systems and of very efficient herd management, both in beef and dairy production. It allows ammonia reduction and feed selfsufficiency. Grasslands, with their capacity to compensate GHG emissions, are here preserved (compensation increases by 27%).

Discussion

The prospective Gesebov scenarios are voluntarily contrasted to help the cattle sector to think about the different possible futures and to decide the actions they want to invest in. One have to keep in mind that the reality will be somewhere in between all of them.

The environmental impacts obtained for 2010 are comparable to those from the AGRIBALYSE® French LCI database (www.ademe.fr/agribalyse) for the average French milk (0.89 kg CO2eq/ kg FPCM, 2.17 MJ/ kg FPCM) and the average French beef cattle (11.93 kg CO2eq/ kg LW, 19.60 MJ/ kg LW) at farm gate. For AGRIBALYSE®, optimized systems, described in case studies, were used [START_REF] Koch | AGRIBALYSE®: METHODOLOGY, Version 1.2[END_REF], while here the data used allowed to draw a more realistic picture of the French cattle farm. That can explain higher footprints. The detailed use of ClimAgri® (instead of using default values per head for enteric methane or nitrogen excretion) gave a valuable precision, needed when considering a sector with a high contribution to the national GHG emissions. It also allowed sensitivity of the results through scenarios. One important point of the ClimAgri® that should be improved in the future, is the accounting for soil carbon dynamics. Even if the method provide accounting for carbon below permanent grasslands and hedges, improvements are needed to also consider temporary grasslands and crops. It would help considering the whole picture of the contribution of the cattle sector to climate change and to identify strength and weakness, especially between scenarios with different use of soils (grains and forages vs grass). Ongoing programs, focusing on how to account for soil carbon in LCA, should also help to improve this point in the future.

Considering the national targets for GHG mitigation in the future, the scenarios S3 and S1Bis would met the objectives at mid term (-12% between 2013 and 2028). For the objective at long term (-50% between 1990 and 2050) only the scenario S4 would be able to reach it. This should make become aware of possible split-over effects. What are the other environmental impacts of the sector (such as effects on biodiversity, water quality, etc.)? If cattle activity declines, what would become of the liberated areas (agricultural production or not, which practices, which loss of carbon)? If the global demand is raising but French activity is declining, reducing its export, the production should be transferred in other countries in the world. Then, we should question on the new carbon footprint of the milk and beef done elsewhere (which environmental efficiency?). And last, but not least, social and economic impacts of those scenarios, especially in terms of employment, were not here considered. This lets open a wide and interesting field of study, which should help considering futures for the cattle sector, not only from a carbon footprint point of view.

Conclusions

Analyses of French cattle production systems, livestock population and agricultural practices allowed us to specify GHG emissions, energy consumption and corresponding footprints, from 1990 to 2035. Results show that the sector already enhanced its environmental efficiency and that improvement strategies are still available for the future. Producing milk and beef is compatible with taking care of climate and energy resources, as long as the mitigation strategies are integrated in the systems.

One have to keep in mind that scenarios are prospective and not predictive of the future. The results should now help the sector to make the future stakes their own, in a context of questionings and strong expectations for livestock regarding climate change.

Table :

 : Main caracteristics of the French bovine production in 1990, 2010 and 2035, through the

	Gesebov scenarios
		1990 2010	S1	S1Bis	S2	S2Bis	S3	S4
	Milk consumption (kg/hab)	351.7 311.8 268.2 268.2 295.0 295.0 241.4 214.6
	Total milk consumption(Mt)	19.9	19.6	18.4	18.4	20.2	20.2	16.5	14.7
	Milk import (Mt)	2.19	5.18	5.00	5.00	6.50	6.50	2.00	1.00
	Milk export (Mt)	-6.46 -9.13 -18.67 -18.67 -24.00 -24.00 -7.50	-5.0
	Milk production (Mt)	24.19 23.60 32.04 32.04 37.71 37.71 22.03 18.70
	Evolution since 1990		1.1% 37.2% 37.2% 61.5% 61.5% -5.6% -19.9%
	Milk yield (kg / dairy cow)	4 676 6 956 9 093 9 093 9 725 9 725 7 164 8 591
	Dairy cows (1000 heads)	5 303 3 716 3 635 3 635 4 020 4 020 3 227 2 290
	Beef consumption (kg cwe/hab)	29.8	26.0 21.92 21.92	24.1	24.1	19.7 17.54
	Total beef consumption (Mt cwe)			1501.	1501.	1651.	1651.	1351.
		1 500 1 641	6	6	7	7	4 1201.2
	Beef import (Mt cwe)	459.3 411.0 400.0 400.0 500.0 500.0 230.0 200.0
	Beef export (Mt cwe)	-844.7 -541.1 -550.0 -550.0 -900.0 -900.0 -390.0 -200.0
	Beef production (Mt cwe)	1 903 1 764 1 652 1 652 2 052 2 052 1 511 1 201
	Evolution since 1990		-7.3% -13.2% -13.2% 7.8% 7.8% -20.6% -36.9%
	Suckler cows (1000 heads)	3 708 4 179 3 747 3 177 4 640 3 970 2 810 2 510
	Stocking rate (heads/ha AA)	1.86	1.91	1.92	1.95	1.89	1.91	1.82	1.80
	cwe: carcass weight equivalent

Table 2 :

 2 Total GHG emissions and energy consumptions (direct + indirect) of the French cattle sector from 1990 to 2035 through the Gesebov scenarios.

		1990	2010	S1	S1Bis	S2	S2Bis	S3	S4
	GHG (Mt CO2 eq.)	83.15	74.33	74.67	66.75	87.89	77.97	60.94 47.71
	Energy (GJ)	193	150	131	105	149	115 914 90 150 71 127
		846	575	194	080	561

Table 3 :

 3 Additional direct environmental indicators of the French cattle sector from 1990 to 2035 through the Gesebov scenarios.

		1990	2010	S1	S1Bis	S2	S2Bis	S3	S4
	C compensation (%)	16%	15%	12%	13%	10%	12%	16%	19%
	kg N-NH3/ha	35	33	34	32	35	32	28	27
	Imported /French AA	32% 31%	33%	34%	26%	27%	24%	24%
	(%)
	Table 4: Environmental impact of milk and beef in France from 1990 to 2035 through the Gesebov
	scenarios
		1990	2010	S1	S1Bis	S2	S2Bis	S3	S4
	kg CO2 eq. / kg FPCM	1.44	1.15	0.94	0.88	0.89	0.82	0.98	0.89
	MJ / kg FPCM	5.25	3.66	2.17	1.94	1.92	1.65	2.16	1.99