# What prospective scenarios for 2035 will be compatible with reduced impact of French beef and dairy farm on climate change?

Claire Mosnier, Anne Duclos, Jacques Agabriel, Armelle Gac

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Claire Mosnier, Anne Duclos, Jacques Agabriel, Armelle Gac. What prospective scenarios for 2035 will be compatible with reduced impact of French beef and dairy farm on climate change?. Agricultural Systems, 2017, 157, pp.193-201. 10.1016/j.agsy.2017.07.006. hal-01607068

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# What prospective scenarios for 2035 will be compatible with reduced impact of French beef and dairy farm on climate change?

MOSNIER Claire (1), DUCLOS Anne (1), AGABRIEL Jacques (1), GAC Armelle (2).

(1) INRA, UMR 1213 Herbivore, 63122 Saint-Genès-Champanelle, cmosnier@clermont.inra.fr

(2) Institut de l'Élevage, Monvoisin, 35652 Le Rheu cedex

## Abstract

The agricultural sector is being called upon to reduce its greenhouse gas emissions (GHG). A scenario approach was developed to explore the plausible futures of the French bovine sector and their impact on climate change. These scenarios encompass a Business As Usual scenario (S1-BAU) and alternative contrasting scenarios: (S2) cattle production increase to meet a high global demand under a liberal policy, (S3) refocus on internal demand within France, with an upmarket move to 'green' products, (S4) committed public policy to reduce GHG emissions. This paper analyses how key drivers of these scenarios (e.g. subsidies on investment, reduction of market risks, carbon tax, limitation of concentrate feed in animal diets) affect the evolution of production, economics, and environmental impact on climate change of typical French suckler cow and dairy farms, by means of simulations performed with a bio-economic model. To adapt their farming systems to the scenarios, farms can opt for variably intensive/integrated practices per animal and per unit land area. Some technological progress in animal production, crop production, and farm equipment is also modeled. Results show that in S1-BAU, milk production, net income and impact on climate change of dairy farms rise. Beef production and impact on climate change decrease slightly in suckler cow farms. Impact on climate change per unit of product decreases owing to higher productivity per animal and to a more integrated management of crop production. Alternative scenarios underline that reorienting public support toward farm investment would further intensify dairy farms and increase their income, but would reduce production and income of suckler cow farms and favor crop production (S2). Climate change impact per unit of product is more strongly reduced in S3 (organic farming with low feed concentrate) than in S2, but with a reduced production, particularly for milk. A carbon tax decreases emissions, but to the detriment of cattle production, especially suckler cow farms.

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## Highlights

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* Impact of contrasting socio-economic scenarios on the evolution of typical suckler cow and dairy farms has been assessed, taking into account technological progress
* GHG emissions per unit of output would be reduced

* In 2035, dairy farms are likely to increase and intensify their production, for the Business as usual scenario and above all the liberal scenario.

* The most effective scenario to reduce impact on climate change involve the development of organic farming with low feed concentrate, or a carbon tax, but with markedly reduced milk and above all beef productions.

**Key words:** Greenhouse gas emissions, cattle farms, bio-economic model, intensification, prospective

# 1 Introduction

The Paris Agreement (COP21, 2015) acknowledges the need to limit the global temperature increase to two degrees Celsius to avoid the worst climate impact. In all, 188 countries have committed to reducing their greenhouse gas (GHG) emissions, and have drawn up a roadmap. The French low carbon national strategy targets a reduction of 12% in agricultural emissions by 2028 relative to 2013 and of 50% between 1990 and 2050¹. The agricultural sector makes up 19% of national emissions<fnref n="2" /> (Citepa 2015). With a population of 19 million cattle, beef and dairy production are the main contributors to agricultural sector GHG emissions (60% without carbon sequestration). The evolution of the bovine sector in the next 20 years will be crucial to meeting the GHG emissions target.

Over the last decade, many studies have analyzed the mitigation potential of specific technologies at animal level (Monteny et al. 2006; Doreau et al. 2011), farm level (Crosson et al. 2011; Nguyen et al. 2013) or at larger scales (Neufeldt & Schäfer 2008; Smith et al. 2008; Havlík et al. 2012; Pellerin et al. 2013). However, the adoption of new technologies and levels of production are tightly intertwined with numerous factors such as the evolution of consumer demand, markets, policy, production organization and climate change. The prospective scenario approach combines different dimensions of the future: it is used to explore a highly uncertain future (Abildtrup et al. 2006; Audsley et al. 2006; Moss et al. 2010) by describing coherent, plausible future states of the world. The Intergovernmental Panel on Climate Change first developed the Millennium Ecosystem Assessment scenarios (IPCC 2000; Carpenter & Pingali 2005) to study climate change. Several studies have since used prospective scenarios to estimate the evolution of agriculture and its impact on climate change (Westhoek et al. 2006; Havlík et al. 2012; Grundy et al. 2016). In France, the Center for Studies and Outlook of the Ministry of Agriculture has developed four contrasting scenarios for the agricultural sector (Vert & Portet 2010); several scenarios were also designed to reduce impact on climate change (De Perthuis et al. 2011; Vidalenc et al. 2013;

<footnote n="1">¹ Law No. 2015-1491, http://www.gouvernement.fr/conseil-des-ministres/2015-11-18/l-adoption-de-la-strategie-nationale-bas-carbone-pour-le-cli</footnote>

<footnote n="2">² UNFCC: http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/8812.ph</footnote>
Couturier et al. 2016). However, results for the cattle sector are aggregated. The Gesebov project(Gac et al. 2015)investigates the joint evolution of the dairy and beef cattle sectors for 2035 at farm and national levels, putting technology of production at the heart of the project. This paper focuses on the farm scale. It first simulates how some typical suckler cow and dairy farms would adapt their production to contrasting socio-economic contexts, taking into account some technological progress (higher milk yield, younger first calving, legume fodders, higher efficiency of fertilizer, milking robots, etc.). Secondly it assesses their impact on climate change. The simulations are run with the bio-economic model Orfee.

## 2 Materials and methods

### 1.1. Model overview

The bio-economic model Orfee (Optimization of Ruminant Farm for Economic and Environmental assessment) simulates French farms producing beef, milk, grass and annual crops (see Mosnier et al., XX, companion paper). It represents an annual farm production at equilibrium with a monthly level of disaggregation. Livestock and crop production, buildings and machinery are optimized under economic risks to maximize average net profit balances with standard deviation, with a weight of 0.5 (Figure 1). These decisions are made under constraints related to animal feeding and herd demography; crop rotation, farmland; fertilizer needs; crop operations carried out with appropriate machinery; housing, milking and manure storage requirements; labor availability, and compliance with policy rules for entitlement to various public subsidies.

<figure><img src="image_2.png" /><figcaption>Figure 1. Definition of the objective function within the Orfee model</figcaption></figure>
A large gradient of intensification and integration of beef and dairy farming systems is possible. Intensification per animal can be obtained by modifying the breed and type of animal (age at first calving, average daily gain, milk production) and its diet. Intake capacity, net energy and protein requirements are calculated on a monthly basis for each animal category using the Inra method (INRA 2007). Different cropping intensities are defined for crops, from organic to intensive. Integrated cropping systems consist of reducing target yield simultaneously with favorable crop rotation in order to reduce input consumption. Fertilizer requirements take into account yield target, crop rotation and soil type using N mass balance equations. Integration between livestock and crop production can be obtained (i) by optimizing parturition periods and animal types to match animal needs with forage availability, (ii) by introducing grasslands and legume crops into crop rotation, (iii) by using manure to fertilize crops, and (iv) by feeding animals with on-farm forages and concentrates. In addition, machinery, buildings and labor are optimized, and include some trade-offs between level of equipment, workload, management possibilities and energy consumption. The number of worker units required is estimated considering an annual workload of 1900 h, and the 2010 minimum wage rate of 8.9€/h.

Two indicators of livestock production impact on climate change are calculated using a life cycle assessment approach to agricultural products from cradle to farm exit gate, with (CC-LULUC) and without (CC) carbon sequestration in grasslands. These indicators are computed at farm level and allocated to milk and meat production. A biophysical allocation, as applied in the French AGRIBALYSE® program (Koch & Salou 2014) is used here to share the environmental burden of the systems between milk and meat. Three greenhouse gases contributing to global warming ($CH_4$, $N_2O$, $CO_2$) are aggregated by their global warming potential, and expressed in $CO_2$-equivalent ($CO_2e$). Values are those proposed by IPCC ((Forster et al. 2007), p212, 100-year time horizon): $CO_2$ = 1; $CH_4$ = 25; $N_2O$ = 298. Methane ($CH_4$) emissions come from animal excreta and enteric fermentation estimated using a method that takes into account diet composition (Sauvant & Nozière 2016). Nitrous oxide ($N_2O$) emissions are divided into direct emissions from manure management and managed soils, and indirect $N_2O$ emissions that arise from volatilization of fertilizers and nitrogen lost via runoff and leached from agricultural soils. Carbon dioxide ($CO_2$) includes indirect input emissions and carbon sequestration.

## 2.1 Case studies

Four farm types were selected in the Inosys-Réseaux d'Élevage reference, which builds descriptions of typical farm types per region through a large network of commercial farms and expert knowledge (Charroin et al. 2005). The four types 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) (Table 1). Further details are provided in Tables 4 and 5 and in Appendices 2 and 3.

<caption>Table 1. Main characteristics of the four farm types selected</caption>
<table><thead><tr><th></th><th>DC_GRASS</th><th>DC_CROPS</th><th>SC_GRASS</th><th>SC_CROPS</th></tr></thead><tbody><tr><td>Herd size (Isu)</td><td>63</td><td>73</td><td>86</td><td>251</td></tr><tr><td>Breed and animal products</td><td>Normande cows (5700L/cow), heifers, newborn calves</td><td>Holstein-Friesian cows (7800L/cow), newborn calves</td><td>Salers and crossbred cows. weanlings</td><td>Charolais cows, heifers, young bulls</td></tr><tr><td>Stocking rate (Isu/forage area)</td><td>1.1</td><td>1.5</td><td>1.0</td><td>1.4</td></tr><tr><td>Arable Farmed land (ha)</td><td>55</td><td>61</td><td>87</td><td>280</td></tr><tr><td>% Of permanent grasslands</td><td>100</td><td>0</td><td>100</td><td>45</td></tr></tbody></table>

Note: Isu = livestock unit (equivalent to 1 dairy cow over a whole year)

## 1.2. Scenarios

The scenarios simulated are based on the global scenarios defined for the project by joint expert groups of French researchers and persons working in the beef and dairy sectors. In these global scenarios, assumptions were made on the evolution of the general context, policy, volumes of beef and milk and on the evolution of production systems (Table 2). The simulations run at farm levels test the assumptions concerning the effects of key elements of context and policy on the evolutions of farming systems, quantify the resulting evolutions, and estimate their impact on climate change.

<table><caption>Table 2. Main characteristics of global scenarios defined by groups of experts</caption><thead><tr><th></th><th>S1-BAU</th><th>S2</th><th>S3</th><th>S4</th></tr></thead><tbody><tr><td>Context</td><td>Low economic growth and demand in France.</td><td>Increased production to meet a high global demand</td><td>Refocus on an internal 'green' demand for cattle products. which moves upmarket</td><td>Large drop in beef and milk consumption</td></tr><tr><td>Policy</td><td>Application of CAP reform 2015-2020</td><td>Subsidies for investments Risk management tool</td><td>Less decoupled payments. more environmental payments</td><td>No decoupled payments. strong policy against climate change</td></tr><tr><td>National production</td><td>milk: +36%<br>beef: + 6%</td><td>milk: + 60%<br>beef: +16%</td><td>milk: +7%<br>beef: -14%</td><td>milk: -21%<br>beef: -32%</td></tr><tr><td>Production systems</td><td>Concentration +<br>Enlargement +<br>Mechanization +<br>Intensification per animal +</td><td>Concentration ++<br>Enlargement ++<br>Mechanization ++<br>Intensification per animal ++</td><td>Concentration --<br>Enlargement --<br>Mechanization -<br>Intensification per animal --</td><td>Concentration-<br>Enlargement =<br>Mechanization +<br>Intensification per animal: -/+</td></tr></tbody></table>

Key technological progress, policies and market evolutions were selected to depict 2035 scenarios (Table 3). These scenarios are compared with the baseline situation B0, which refers to 2010. The scenarios for 2035 differ first in the policy implemented. The CAP reform voted for 2015–2020 is applied to the Business As Usual (S1-BAU) scenario. In S2, the policy is assumed to be liberal and to encourage investments through tax deduction, low-interest loans or direct subsidies. Depreciation costs
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are halved to simulate this reduction of investment costs. Risk aversion is also halved, since the
government will support a part of the market risks. None of the coupled and decoupled 2015–2020
instruments are maintained in this scenario. In S3, the obligation to produce organic products with no
more than 10% of concentrate feed in the total dry matter intakes is tested. This scenario assumes that
consumer demand is for “green” products, based mainly on grasslands and with feed not competing with
human food. Environmental, organic and coupled subsidies are maintained at their 2015 level.
Decoupled payments are reduced, since environmental commitments are required for grant entitlement.

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Lastly a tax on greenhouse gas emissions and a subsidy for carbon storage are introduced in S4. They
are set at 40 €/t CO2e. This value is in line with assumptions used in several studies (IPCC 2007; De
Cara & Jayet 2011) but is well below the value planned to be enforced in France by 2030 (100€)<fnref n="3" />.

However, at 100€/ton most farm abandon beef cattle production. Moreover, such a value would induce
important price changes whereas prices do not vary between scenarios. Conservative assumptions are
made for 2035 agricultural price commodities. According to the FAO report (OECD-FAO 2015), world
prices in 2024 are not expected to be very different from those of 2010. 2035 prices for agricultural
products have been set to their values observed in 2005–2015 (Appendix 1). Organic product prices are
20% higher than standard milk and 15% higher for animals ready for slaughter, and about double for
crop products, in line with current observations. Organic prices for lean animals are taken to be 10%
higher assuming the organic market for weanlings will develop (prices are currently similar to
conventional ones, since weanlings are exported to similar markets). Similarly to Kanellopoulos et al.
(2014), fuel and fertilizer prices are assumed to increase by 40%.

<footnote n="3">³ http://www.developpement-durable.gouv.fr/Le-prix-du-carbone.html</footnote>
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<table><caption>Table 3. Policy and prices defined per scenario</caption><thead><tr><th></th><th>B0</th><th>S1-BAU</th><th>S2</th><th>S3</th><th>S4</th></tr></thead><tbody><tr><td><strong>Policy</strong></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Single farm payments <sup>a</sup></td><td>✓</td><td>90%</td><td></td><td>50%</td><td></td></tr><tr><td>Coupled cow premium<sup>b</sup></td><td>✓</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Environmental payments<sup>c</sup></td><td>✓</td><td>✓</td><td></td><td>✓</td><td>✓</td></tr><tr><td>Organic payments<sup>d</sup></td><td></td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Limitation of concentrate feed up to 10%</td><td></td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Subsidies on investments<sup>e</sup></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Risk management tool<sup>f</sup></td><td></td><td></td><td>✓</td><td></td><td></td></tr><tr><td>Carbon price (emission and storage) (40€/ton of CO<sub>2</sub>e)</td><td></td><td></td><td></td><td></td><td>✓</td></tr><tr><td><strong>Prices (appendix 1)</strong></td><td>Time series [2005–2015]</td><td>Time series [2005-2015].</td><td></td><td></td><td></td></tr></tbody></table>

Note: <sup>a</sup> the percentages indicate the amount of B0 SFP maintained; <sup>b</sup> Suckler Cow premium (≈230 €/SC in B0. ≈150 €/SC since 2015 with several thresholds) and Dairy Cow premium since 2015 (30 €/dairy cow for max.30 DC); <sup>c</sup> less-favored area payments are granted per unit of forage area provided the stocking rate is below a threshold (1.4 lsu/ha) and according to farm location (≈3000 €/farm in B0 and 6000 €/farm thereafter) protein crops (≈200 €/ha); grassland payments (in B0 and S3: max 7600 € provided the share of grassland exceeds 75% of the total area and stocking rate is below 1.2 lsu/ha); <sup>d</sup> organic payment to maintain forage and grass crops (90€/ha) and cash crops (160/ha); <sup>e</sup> depreciation costs are halved; <sup>f</sup> risk aversion is halved.

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Regarding production technology, the same technologies are proposed for all the 2035 scenarios (S1-BAU-S4), but the technology that will be adopted depends on farm type and scenario, since production decisions are endogenous (Table 4). They encompass technologies that were available for farmers in 2010, plus some new ones. Holstein Friesian dairy cows with higher milk potential (max 11000 L/cow) are proposed for dairy farms in addition to the current Holstein Friesian (8000 L/ cow) and Normande breeds (6000 L/cow). Milk yield is expected to rise, but according to experts, genetic potential improvement should slow down in the future (+25%) compared with past trends (+50% between 1990 and 2010) because other criteria are now taken into account in genetic selection schemes, namely cow fertility. 11 000 L-milk-yield cows are assumed to be fed indoors because of the high energy content of their diet. In the western part of France, Normande and Holstein-Friesian breeds are kept indoors between November and February inclusive, and alternate during the remainder of the year's between grazing period on pasture and indoors for resting, feeding and milking. 2035 Holstein Friesian breeds are also slightly heavier (+3.5%) than current ones, so that intake capacity is compatible with their requirements. For suckler cows, although carcasses have grown larger over the last couple of decades (+13% between 1990 and 2013 (Veysset et al. 2015)), according to experts, heavy carcasses no longer meet the market requirements. Consequently, we assume constant carcass weight for beef breeds. Given the references for practices observed in other countries (for dairy breeds), first calving would be possible three months earlier for beef and dairy breeds in all 2035 scenarios. Genetic selection of earlier sexual maturity (above all for beef breeds) and electronic developments are likely to help monitor herd heterogeneity more carefully in the future. Crop fertilization efficiency has greatly progressed in recent decades in the systems studied (at least -20% of mineral nitrogen fertilization between 1990 and 2010) thanks to a better estimation of crop needs and fertilizer value, more accurate equipment, higher fertilizer
prices and nitrate directive enforcement. It is assumed that this will continue, but at a slower rate (10% less nitrogen is required for the same average yield in 2035). Eventually, legume fodder and mixtures of cereal and protein crops are expected to be accessible wherever land is tillable. Legume crops are attracting increasing attention (e.g. 7th Research Framework Programme of the European Union) and we can expect that when more information becomes available and analysis of feed quality is more systematic, these crops will expand.

In B0, animal diets and crop management are optimized, but animal products, crop areas and barn capacity are fixed so that the baseline is as close as possible to 2010 farm descriptions in Inosys (Charroin et al. 2005). When these decisions were also optimized, they matched those described, but with wider deviations and an intensification of dairy production (see companion paper, Mosnier et al.). For the 2035 scenarios, farms can adapt their production systems by modifying crop activities, herd size, production per animal, animal feeding and variable inputs, building and machinery, without labor constraints (but cost is included). However, farm production orientation (suckler cow farms with weanlings, suckler cow farms with young bulls, dairy farms), the minimum of permanent grassland and the total arable area available are maintained.

<table><caption>Table 4. Production possibilities according to farm type for the 2035 scenarios</caption><thead><tr><th></th><th>DC_Grass</th><th>DC_Crops</th><th>SC_Grass</th><th>SC_Crops</th></tr></thead><tbody><tr><td><strong>BREED</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Charolais</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Salers and Crossbred (Sa. x Ch).</td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Normande</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>Prim'Holstein</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>Prim'Holstein 2035</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td><strong>TYPE of animal sold</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>New-born calf</td><td>✓</td><td>✓</td><td></td><td></td></tr><tr><td>Weanlings 8, 10, 11, 12 or 14 m.o.</td><td></td><td></td><td>✓</td><td></td></tr><tr><td>Young bulls 16, 18, or 22 m.o.</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Fattened heifer</td><td></td><td></td><td></td><td>✓</td></tr><tr><td>Fattened culled cows</td><td></td><td></td><td>✓</td><td>✓</td></tr><tr><td>Culled cows without fattening period</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td><strong>Calving periods</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>January, March, July, September, November</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td><strong>Crops</strong></td><td></td><td></td><td></td><td></td></tr><tr><td>Permanent grasslands</td><td>✓</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Temporary grassland, wrapped alfalfa, Winter cereals, Corn, Oilseeds, Mixed protein-cereals</td><td></td><td>✓</td><td></td><td>✓</td></tr></tbody></table>
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# 3 Results

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## 3.1 Scenario S1-BAU

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In the trend scenario S1-BAU, milk production is multiplied by 1.5 in DC_Crops and by 2.2 in DC_Grass reflecting an increase in both cows and milk yield (Table 5). DC_Crops has two third of highly productive 2035 Holstein-Friesian. DC_Grass specializes in milk production and switches from dual-purpose Normande cows to more productive Holstein-Friesian ones. In DC_Crops, corn, alfalfa and the mixture of cereals and protein crops expand at the expense of temporary grasslands and wheat (Figure 2), and more concentrate feed is consumed by animals (2100 kg /lsu, Figure 3). In the case of DC_Grass, the rise in milk production is achieved by additional purchase of concentrate feed (1700 kg/lsu) and more intensive management of grasslands (2.8 tons of grass harvested/lsu; increase in fertilizer and fuel consumed per hectare). The increase in the number of worker units roughly follows herd size evolution. Net income (profit as defined in Figure 1 but before salaries) per worker unit is almost doubled in DC_Grass and increases for DC_Crops (+25%). Impact on climate change is greater by 45% at farm level but are reduced by 13% per kg of milk through higher milk yield (Table 5) and by up to 25% per kg of live weight through earlier age at first calving. Carbon sequestration per animal product decreases, particularly for DC_Grass.

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In suckler cow farms, animal production slightly decreases (-2% of lsu in SC_Grass and -5% in SC_Crops). In SC_Grass, the males are sold younger, the culling rate decreases to its minimum allowed and younger age at first calving is not chosen. Animal intensification is not cost-efficient in this farm context. Impact on climate change with carbon sequestration is slightly reduced per unit of live weight (-9%) and at farm level (-12%) but without carbon sequestration impact is similar to B0. The reduction of public support (less suckler cow payments and single farm payments) is not offset by reduced production costs. Consequently net income per worker unit decreases significantly in SC_Grass.

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In SC_Crops, crops are all managed in an integrated way, associated with a lower consumption of fertilizer and fuel (table 3). This reduction of inputs cumulated with an increase in meat production per lsu owing to younger age at first calving lead to 12% less impact on climate change (with and without carbon sequestration) per kg of live weight. This reduction is even greater at farm level (≈ -20%). Income is stable relative to B0.

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## 3.2 Scenario 2

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In scenario S2, investments are subsidized and market risks reduced. The number of dairy cows and milk yield increase even more than in S1-BAU. Animal diets are rich in energy, with a high level of concentrate feed (2100 kg /lsu for DC_Grass, 2600 kg /lsu for DC_Crops), green fodder and, in the case of DC_Crops, a large amount of corn silage. The usable area of DC_Crops is managed intensively with 3/5 of corn (figure2). Farms have better equipped barns and milking robots; labor efficiency thus increases. In S2, dairy farms obtain the highest income of all the scenarios. The higher milk yield reduces

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emissions per kg of milk relative to B0. In the case of DC_Grass, this reduction is even greater than in S1-BAU, but not in the case of DC_Crops. In addition, emissions are less fully offset by carbon storage. At farm level, the evolution of greenhouse gas emissions follows the marked increase in milk production.

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This scenario is less favorable to suckler cow production, which is less capital-intensive. SC_Grass has a similar production system as in B0 but with the males sold older. The subsidies are far below B0, and so net income per worker unit is negative. In SC_Crops, the cash crop area expands with a higher cropping intensity at the expense of animal production, which decreases by 40%. Net income per worker unit is halved. Impact on climate change is reduced by 30% at farm level relative to B0 but only by 7% per kglw (kg of live weight).

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## 3.3 Scenario 3

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Scenario 3 involves organic farming with less than 10% of concentrate feed. Consequences for dairy farms are a marked reduction in herd size and animal stocking rate (0.7 lsu/ha in DC_Grass, 1 lsu/ha in DC_Crops). In DC_Crops, dairy production is reduced in favor of cash crops. Animal productivity decreases with first calving at 36 months. Milk yield decreases, the Normande breed is adopted in addition to the Holstein-Friesian breed in DC_Crops. Holstein-Friesian cows are chosen in DC_Grass. In DC_Grass, diets consist mainly of fresh grass, grazed or distributed green. In DC_Crops, silages of alfalfa, corn and cereals-protein crops make up a larger part of animal diets. Fuel and fertilizer decrease. Organic fertilizers are nonetheless purchased to partly replace mineral fertilizers. All animal receipts, production costs and worker units decrease. Net income per worker unit falls. Total greenhouse gas emissions of dairy farms are halved (Table 5) owing to the reduction in herd size and in input consumption. This latter leads to a reduction of emissions per kg of milk (Figure 3. Table 5), but the older age at first calving increase emissions per kg of meat. Impact on climate change when accounting for carbon sequestration is low, particularly for DC_Grass (less than half of B0).

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In suckler cow farms, herd size is reduced by 11% for SC_Grass and 25% for SC_Crops relative to B0. Productivity per animal and per hectare declines. Weanlings are sold younger, with no pre-fattening phase in SC_Grass. SC_Crops adds some Salers cows in addition to Charolais cows, and opts for a less intensive but longer fattening phase for young bulls. Fuel consumption increases in SC_Grass because grasslands are partially harvested to make silage and green fodder instead of hay only. Net income per worker unit is reduced by 28% in SC_Grass, but remains stable in SC_Crops, which benefits from higher beef price for finished animals (+15%) and higher subsidies for organic cash crops (160 €/ha). Impact on climate change of SC_Crops is reduced by 45% relative to B0 owing to herd size and input reductions. When carbon storage is accounted for the reductions reach 55%. Emissions per kg of meat decrease by 13% without carbon sequestration and by 36% with sequestration. Variations for SC_Grass are smaller.

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## 3.4 Scenario 4

In scenario S4, the carbon price of 40 € per ton (emitted and stored) strongly impacts suckler cow production but is less detrimental to dairy production. The level of milk production in DC_Grass increases relative to B0 and is reduced by 20% in DC_Crops. Production systems are moderately intensive per animal and per unit of land. DC_Crops production is organic and its impact on climate change without carbon sequestration, per kglw and per kg of milk is the lowest among all scenarios. DC_Grass impact on climate change per kg of product is close to the trend scenario.

SC_Grass abandons animal production. Animal production is halved for SC_Crops with an intermediate productivity per animal compared to the other scenarios. Production is organic. Net income per worker unit is close to zero. Impact on climate change without carbon sequestration, per kglw, is similar to S3. Carbon sequestration per kglw is very high.

<figure><img src="image_3.png" /><figcaption>Figure 2. Share of crop activities (in % of the total area)</figcaption></figure>

<figure><img src="image_4.png" /></figure>
<table><caption>Table 5. Evolution of animal production, input consumption economic results and climate change impact according to farm and scenario</caption><thead><tr><th rowspan="2"></th><th colspan="5">DC_GRASS</th><th colspan="5">DC_CROPS</th><th colspan="5">SC_GRASS</th><th colspan="5">SC_CROPS</th></tr><tr><th>B0</th><th>S1-BAU</th><th>S2</th><th>S3</th><th>S4</th><th>B0</th><th>S1-BAU</th><th>S2</th><th>S3</th><th>S4</th><th>B0</th><th>S1-BAU</th><th>S2</th><th>S3</th><th>S4</th><th>B0</th><th>S1-BAU</th><th>S2</th><th>S3</th><th>S4</th></tr></thead><tbody><tr><td colspan="17"><strong>PRODUCTION</strong></td></tr><tr><td>Herd size (Isu)</td><td>63</td><td>80</td><td>80</td><td>37</td><td>65</td><td>73</td><td>93</td><td>134</td><td>47</td><td>57</td><td>85</td><td>81</td><td>86</td><td>76</td><td>0</td><td>248</td><td>217</td><td>159</td><td>185</td><td>121</td></tr><tr><td>Milk produced (1000l)</td><td>207</td><td>450</td><td>542</td><td>187</td><td>368</td><td>394</td><td>596</td><td>995</td><td>211</td><td>319</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Milk yield (1000l/cow)</td><td>5.3</td><td>7.4</td><td>8.6</td><td>7.2</td><td>7.4</td><td>7.3</td><td>9.1</td><td>9.8</td><td>6.6</td><td>7.4</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Beef sold (kg/Isu)</td><td>200</td><td>188</td><td>191</td><td>161</td><td>188</td><td>181</td><td>190</td><td>195</td><td>155</td><td>188</td><td>301</td><td>301</td><td>302</td><td>287</td><td>/</td><td>376</td><td>397</td><td>397</td><td>366</td><td>374</td></tr><tr><td>Animals sold</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Males: av. Age (month)</td><td>0.2</td><td>0.2</td><td>0.2</td><td>0.2</td><td>0.2</td><td>0.2</td><td>0.2</td><td>0.2</td><td>0.2</td><td>0.2</td><td>11</td><td>9</td><td>11</td><td>8</td><td>/</td><td>18</td><td>18</td><td>18</td><td>22</td><td>22</td></tr><tr><td>Males: av. live weight kg</td><td>48</td><td>45</td><td>46</td><td>45</td><td>45</td><td>45</td><td>46</td><td>47</td><td>48</td><td>45</td><td>346</td><td>320</td><td>341</td><td>268</td><td>/</td><td>728</td><td>728</td><td>728</td><td>726</td><td>731</td></tr><tr><td>Cows: av. live weight kg</td><td>733</td><td>645</td><td>655</td><td>645</td><td>645</td><td>645</td><td>654</td><td>666</td><td>657</td><td>645</td><td>677</td><td>684</td><td>679</td><td>685</td><td>/</td><td>810</td><td>819</td><td>819</td><td>718</td><td>718</td></tr><tr><td>Av. Age at 1<sup>st</sup> calving</td><td>36</td><td>27</td><td>27</td><td>36</td><td>27</td><td>30</td><td>27</td><td>27</td><td>36</td><td>27</td><td>36</td><td>36</td><td>36</td><td>36</td><td>/</td><td>36</td><td>33</td><td>33</td><td>36</td><td>33</td></tr><tr><td>Culling rate (%)</td><td>41</td><td>35</td><td>35</td><td>35</td><td>35</td><td>35</td><td>35</td><td>35</td><td>32</td><td>35</td><td>19</td><td>14</td><td>14</td><td>14</td><td>/</td><td>29</td><td>23</td><td>23</td><td>25</td><td>23</td></tr><tr><td>N purchased kg/ha</td><td>44</td><td>59</td><td>62</td><td>13</td><td>33</td><td>78</td><td>33</td><td>50</td><td>24</td><td>10</td><td>17</td><td>15</td><td>15</td><td>0</td><td></td><td>64</td><td>48</td><td>71</td><td>1</td><td>1</td></tr><tr><td>Fuel l/ha</td><td>40</td><td>63</td><td>79</td><td>20</td><td>58</td><td>65</td><td>92</td><td>131</td><td>21</td><td>63</td><td>61</td><td>55</td><td>58</td><td>71</td><td>-</td><td>55</td><td>47</td><td>47</td><td>26</td><td>18</td></tr><tr><td colspan="17"><strong>ECONOMICS</strong></td></tr><tr><td>Animal sales (k€)</td><td>23</td><td>21</td><td>12</td><td>9</td><td>17</td><td>144</td><td>206</td><td>324</td><td>78</td><td>118</td><td>58</td><td>55</td><td>59</td><td>55</td><td></td><td>186</td><td>173</td><td>127</td><td>152</td><td>103</td></tr><tr><td>Crop sales (k€)</td><td></td><td></td><td></td><td></td><td></td><td>14</td><td>12</td><td>1</td><td>18</td><td>15</td><td></td><td></td><td></td><td></td><td></td><td></td><td>115</td><td>117</td><td>164</td><td>69</td><td>74</td></tr><tr><td>Subsidies (k€)</td><td>13</td><td>9</td><td>23</td><td>13</td><td>-7</td><td>19</td><td>19</td><td>38</td><td>16</td><td>-3</td><td>44</td><td>37</td><td>9</td><td>39</td><td></td><td>105</td><td>86</td><td>23</td><td>82</td><td>24</td></tr><tr><td>Costs<sup>a</sup> (k€)</td><td>53</td><td>73</td><td>86</td><td>44</td><td>64</td><td>137</td><td>184</td><td>285</td><td>103</td><td>117</td><td>55</td><td>55</td><td>56</td><td>56</td><td>37</td><td>187</td><td>179</td><td>168</td><td>150</td><td>136</td></tr><tr><td>Net income<sup>b</sup> Mean (k€)</td><td>18</td><td>41</td><td>50</td><td>5</td><td>18</td><td>40</td><td>54</td><td>79</td><td>10</td><td>13</td><td>26</td><td>17</td><td>-8</td><td>17</td><td>-26</td><td>96</td><td>97</td><td>45</td><td>74</td><td>3</td></tr><tr><td>Sd (k€)</td><td>3</td><td>6</td><td>7</td><td>2</td><td>5</td><td>6</td><td>7</td><td>13</td><td>4</td><td>4</td><td>4</td><td>3</td><td>3</td><td>3</td><td>1</td><td>13</td><td>16</td><td>23</td><td>11</td><td>11</td></tr><tr><td>LABOR (in WU)</td><td>1.2</td><td>1.5</td><td>1.2</td><td>0.7</td><td>1.3</td><td>1.6</td><td>2.4</td><td>2.2</td><td>1.0</td><td>0.9</td><td>1.0</td><td>1.0</td><td>1.0</td><td>0.9</td><td>0</td><td>3.0</td><td>3.1</td><td>2.6</td><td>2.3</td><td>1.7</td></tr><tr><td colspan="17"><strong>IMPACT ON CLIMATE CHANGE</strong></td></tr><tr><td>Climate change (tco<sub>2</sub>e)</td><td>308</td><td>445</td><td>479</td><td>165</td><td>353</td><td>404</td><td>594</td><td>863</td><td>191</td><td>161</td><td>367</td><td>345</td><td>362</td><td>287</td><td>45</td><td>1283</td><td>1053</td><td>897</td><td>720</td><td>489</td></tr><tr><td>Cc_luluc (tco<sub>2</sub>e)</td><td>192</td><td>330</td><td>364</td><td>50</td><td>238</td><td>374</td><td>579</td><td>859</td><td>165</td><td>141</td><td>185</td><td>163</td><td>180</td><td>105</td><td>-136</td><td>1046</td><td>850</td><td>724</td><td>437</td><td>206</td></tr><tr><td>Cc (co<sub>2</sub>e/kglw)</td><td>10.8</td><td>8.7</td><td>9.0</td><td>11.2</td><td>8.4</td><td>9.2</td><td>8.4</td><td>9.2</td><td>10.7</td><td>7.7</td><td>16.9</td><td>16.6</td><td>16.4</td><td>15.7</td><td>/</td><td>14.3</td><td>12.7</td><td>13.3</td><td>12.4</td><td>12.4</td></tr><tr><td>Cc-luluc (co<sub>2</sub>e/kglw)</td><td>6.4</td><td>6.3</td><td>6.7</td><td>4.7</td><td>5.4</td><td>8.8</td><td>8.4</td><td>9.3</td><td>9.7</td><td>7.2</td><td>8.5</td><td>7.8</td><td>8.2</td><td>5.8</td><td>/</td><td>11.7</td><td>10.3</td><td>10.7</td><td>7.5</td><td>5.2</td></tr><tr><td>Cc (co<sub>2</sub>e/kg milk)</td><td>0.88</td><td>0.80</td><td>0.74</td><td>0.66</td><td>0.77</td><td>0.84</td><td>0.71</td><td>0.74</td><td>0.69</td><td>0.67</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Cc-luluc (co<sub>2</sub>e/kg milk)</td><td>0.52</td><td>0.58</td><td>0.56</td><td>0.28</td><td>0.50</td><td>0.80</td><td>0.71</td><td>0.75</td><td>0.63</td><td>0.62</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr></tbody></table>

Superscripts:ª. Operational, structural, depreciation and financial costs ᵇ profit before salaries,
## 4 Discussion

291 • Impact on climate change at farm and global levels

292 The impact on climate change is first explained by the bovine inventory as underlined by (Casey &
293 Holden 2006b) and second by the way meat, milk and forage are produced (Monteny et al. 2006;
294 Johnson et al. 2007). We found that in S1-BAU, milk and beef production of dairy farms are likely to
295 increase and beef production from suckler cows will decrease slightly. S2 appears very favorable to
296 dairy farms that take advantage of low investment prices to increase even more their production. For
297 suckler cow farms, this subsidy on investment is more beneficial to crop production. S3 induces a
298 reduction of cattle production in all farms. Dairy farms are particularly affected. Suckler cow production
299 is sharply reduced in S4, dairy farms maintain a high level of production. However, the extent of changes
300 would depend namely on market prices that have not been modeled. Scenarios 3 and 4 appear more
301 compatible with a reduced impact on climate change in 2035 owing to changes in bovine inventory.
302 Zehetmeier et al. (2012) demonstrate that if we need more suckler cow to compensate the reduction of
303 the number of dairy cows such as in S3, this may downgrade the GHG performance of the global bovine
304 sector. However, these two scenarios assume that beef and milk consumption per capita will be reduced,
305 consequently they also have the lowest impact on climate change at national level (CC-LULUC resp. -
306 23% and -42% relative to BAU-S1; (Gac et al. 2015; Gac et al. 2016)). According to Esnouf et al.
307 (2011), an important reduction in the consumption of animal products may induce an increase in
308 vegetable food that may *in fine* have negative impact on climate change. Scenario 2 has the largest
309 footprint at farm level and national level (+20% relative to BAU-S1, Gac et al. 2015). Nonetheless, this
310 scenario assumes ranging exports, which would impact the beef and milk produced elsewhere,
311 modifying the carbon footprint at a global level (Garnett 2009; Nguyen et al. 2013; Cohn et al. 2014).
312 An analysis at a global scale, including other agricultural sectors, and taking into account the evolution
313 of human diets would provide further information to rank these scenarios.

314 • Impact on climate change per unit of product

315 These scenarios induce changes in the way meat, milk and forage are produced that modifies emissions
316 of greenhouse gas per unit of milk or meat. These emissions would be reduced in most of our 2035
317 scenarios and farm types relative to the baseline. Without accounting for carbon sequestration, this
318 reduction reaches up to 14% per kglw (kg of live weight) for suckler cow farms in S3 and S4, 12% per
319 kg of milk in S1-BAU and up to 20% in S3 and S4. Higher milk yield potential and younger age at first
320 calving lead to a significant reduction of impact on climate change. Gains obtained by increased
321 productivity per animal and per unit of land are not linear, and beyond a certain level of intensification,
322 an increase in fertilizer use and concentrate feed consumption offsets the reduction of methane
323 emissions. This explains why scenarios S3 and S4 present lower impact on climate change per kg of
324 milk and live weight than S2, which is more intensive per animal and per hectare. This is in line with
other studies such as in Martin and Willaume (2016), who demonstrate that the dairy farms that have evolved in the past toward agroecology have decreased their GHG emissions per kg of milk the most, while those that specialized and intensified their farming system have increased their GHG emissions per kg of milk. Similarly, (Gerber et al. 2011) underline that milk productivity gain has the greatest mitigation potential per milk unit for the lower range of genetic merit of cattle (e.g. 1000-4000 L milk/year cow) but gains are less clear at the level that European system already operate (>4000 L/year cow). In Ireland, organic farming appears to be a valuable alternative to reduce GHG emissions per unit of meat (Casey & Holden 2006a). Note that the high variability and uncertainty regarding forage quality (grass-based ones above all) are not taken into account here, and could affect emissions from rumination of scenarios favoring grass-based diets.

### • Limits on the scenario assumptions

Impact on climate change could has been further improved if we had proposed ways of reducing GHG emissions such as changes in the manure management system (Chadwick et al. 2011), manure storage coverage (Külling et al. 2003), biogas plants (Massé et al. 2011), techniques of fertilizer application (Lengers et al. 2014) or small amount of lipids in animal diets (Martin et al. 2008). Nonetheless, most of them were considered at national level (Gac et al. 2010). Climate change was not considered while the evolution of temperature, precipitation and C:N ratios (Hungate et al. 2003) will modify crop yields, animal behavior and the processes leading to methane and nitrous oxide emissions and to carbon sequestration (Davidson & Janssens 2006). Average impact of climate change is planned first to be favorable in France in 2035 but unfavorable thereafter, and, above all, the 2035 between year variability (not taken into account in the model) will increase (Ruget et al. 2012) that impact production decisions (Mosnier 2015) and farm resilience (Mosnier et al. 2009). We consider that including only partially the effect of climate change could lead to misleading messages.

## 5 Conclusion

A scenario approach was developed to explore possible future evolutions of suckler cow and dairy farms and their related impact on climate change. The objectives were to simulate how some typical suckler cow and dairy farms would adapt their production to contrasting scenarios, and to discuss whether these evolutions would be compatible with climate change mitigation objectives. Results show that in the business as usual scenario, milk production, net income and impact on climate change of dairy farms rise. Beef production and impact on climate change fall slightly in suckler cow farms. Impact on climate change per unit of product decreases owing to higher productivity per animal and to the introduction of legume fodder. Alternative scenarios underline that reorienting public support toward farm investment would further intensify dairy farms and increase their incomes, but would reduce production and income of suckler cow farms (S2). On the contrary, organic farming with low feed concentrate would affect
production and income of dairy farms more strongly (S3). Climate change impact per unit of product is more strongly improved in S2 than in S3. A 40 € carbon tax decreases emissions, but would be highly detrimental to suckler cow production. Scenarios 3 and 4 are more compatible with reduced impact on climate change in 2035 owing to the reduction in herd size and in input consumption. However, further development of this model will be necessary to introduce other mitigation options, to include other dimensions of sustainability, including air and water quality, biodiversity, rural development and resilience in deciding what scenarios at farm and territorial levels are to be assessed and to account for interactions with other agricultural products and with production in other countries.

**Acknowledgments:** The authors thank Ademe for co-financing this project, M Eugène for checking methane emissions, all the experts who took part, and Inosys-Réseaux d'Élevage and Inra-Umrh for their production of technical and economic references on livestock farms. They also thank the anonymous reviewers for their constructive comments.

<table><caption>**Appendix 1. Prices 2005-2015 of main agricultural products**</caption><thead><tr><th></th><th>Average<sup>a</sup></th><th>Coefficient of variation<sup>b</sup></th></tr></thead><tbody><tr><td><strong>Male weanlings</strong></td><td></td><td></td></tr><tr><td>-Charolais</td><td>2.5€/kglw</td><td>7%</td></tr><tr><td>-Salers</td><td>2.3€/kglw</td><td></td></tr><tr><td><strong>Young bulls</strong></td><td>3.4€/kgc</td><td>10%</td></tr><tr><td><strong>Fattened Cows</strong></td><td></td><td></td></tr><tr><td>-Charolais</td><td>3.6€/kgc</td><td>14%</td></tr><tr><td>- Salers</td><td>4.0€/kgc</td><td></td></tr><tr><td>-Normande</td><td>3.4€/kgc</td><td></td></tr><tr><td><strong>Cow (not fattened)</strong></td><td></td><td></td></tr><tr><td>-Charolais</td><td>1.7€/kglw</td><td>14%</td></tr><tr><td>-Salers</td><td>1.3€/kglw</td><td></td></tr><tr><td>-Holstein</td><td>2.9€/kgc</td><td></td></tr><tr><td>-Normande</td><td>3.0€/kgc</td><td></td></tr><tr><td><strong>Milk</strong></td><td></td><td></td></tr><tr><td>-Holstein</td><td>316€/ton</td><td>10%</td></tr><tr><td>-Normande</td><td>323€/ton</td><td></td></tr><tr><td><strong>Wheat</strong></td><td>170€/ton</td><td>26%</td></tr><tr><td><strong>Sunflower</strong></td><td>349€/ton</td><td>21%</td></tr><tr><td><strong>Rapeseed</strong></td><td>346€/ton</td><td>24%</td></tr><tr><td><strong>Fuel</strong></td><td>1.0€/L</td><td>17%</td></tr><tr><td><strong>N Mineral</strong></td><td>0.84€/kgN</td><td>20%</td></tr></tbody></table>

Source: <sup>a</sup>Inosys (Charroin et al. 2005) and Inra farm network (Veysset et al. 2015); <sup>b</sup>Ippap and Ipampa price indices, provided by Insee, the French national statistics office
Kglw: kg of live weight, kgc: kg of carcass
380 Appendix 2: Animal production parameters used for the simulations

<table><thead><tr><th></th><th>Charolais</th><th>Salers - crossbred</th><th>Holstein Frisian</th><th>Normande</th><th>Holstein Frisian 2035</th></tr></thead><tbody><tr><td colspan="6"><strong>Cows</strong></td></tr><tr><td>Av. age at 1<sup>st</sup> calving in 2010 and → in 2035</td><td>36 → 33 m.o.</td><td>36 → 33 m.o.</td><td>30 → 27 m.o.</td><td>36 → 33 m.o.</td><td>30 → 27 m.o.</td></tr><tr><td>minimum culling rate calf mortality&lt; 3 m.o.*</td><td>23% [7-9%]*</td><td>14% [4-5%]*</td><td>35% [9-11%]*</td><td>25% [9-11%]*</td><td>35% [9-11%]*</td></tr><tr><td>Cow carcass weight (without - with fattening)</td><td>350 - 434</td><td>300-363</td><td>303</td><td>325-380</td><td>313</td></tr><tr><td>Milk yield (1000 L/cow)</td><td></td><td></td><td>[5.2-8.0]</td><td>[3.6-5.6]</td><td>[7.2-11]</td></tr><tr><td colspan="6"><strong>Male Weanlings (in kg live weight)</strong></td></tr><tr><td>8 month old</td><td></td><td>273-289</td><td></td><td></td><td></td></tr><tr><td>10 month old</td><td></td><td>353 - 373</td><td></td><td></td><td></td></tr><tr><td>11 month old</td><td></td><td>369 - 390</td><td></td><td></td><td></td></tr><tr><td>12 month old</td><td></td><td>327 - 345</td><td></td><td></td><td></td></tr><tr><td>14 month old</td><td></td><td>399 - 422</td><td></td><td></td><td></td></tr><tr><td colspan="6"><strong>Young bulls (kg carcass)</strong></td></tr><tr><td>16 month old</td><td>415</td><td></td><td></td><td></td><td></td></tr><tr><td>18 month old</td><td>440</td><td>387-409</td><td></td><td></td><td></td></tr><tr><td>22 month old</td><td>440</td><td>387-409</td><td></td><td></td><td></td></tr></tbody></table>

382 Appendix 3: Grassland and crop yields according to farm type and cropping intensity *

<table><thead><tr><th></th><th></th><th>SC_Grass</th><th>SC_Crops</th><th>DC_Grass and DC_Crops</th></tr></thead><tbody><tr><td colspan="5">GRASSLANDS (t of dry matter / ha)</td></tr><tr><td>Grazing only</td><td>Grass</td><td>[5.6-7]</td><td>[6-7.9]</td><td>[6.5-8.7]</td></tr><tr><td rowspan="2">Grazing + 1 cut hay + grazing</td><td>Hay</td><td>[2.4-3]</td><td>[3-4]</td><td>[3-3.9]</td></tr><tr><td>Grass</td><td>[1.8-2.2]</td><td>[2.4-3.2]</td><td>[3.3-4.4]</td></tr><tr><td rowspan="2">Grazing + 1 cut hay</td><td>Hay</td><td>[3-3.8]</td><td>[3.5-4.6]</td><td>[3.6-4.7]</td></tr><tr><td>Grass</td><td>[0.9-1.2]</td><td>[1.5-2]</td><td>[2.4-3.3]</td></tr><tr><td rowspan="2">Grazing +2 cuts hay</td><td>Hay</td><td>[4.7-5.9]</td><td>[4.7-6.2]</td><td>[5.6-7.4]</td></tr><tr><td>Grass</td><td>[0.5-0.6]</td><td>[0.5-0.7]</td><td>[1-1.4]</td></tr><tr><td rowspan="2">Grazing + 1 cut wrapping</td><td>Wrapping</td><td>[2.5-3.2]</td><td>[3.5-4.6]</td><td>[3.2-4.2]</td></tr><tr><td>Grass</td><td>[1.7-2.1]</td><td>[1.5-2]</td><td>[3.3-4.4]</td></tr><tr><td rowspan="2">Grazing + 2 cuts wrapping</td><td>Wrapping</td><td>[4.1-5.2]</td><td>[4.7-6.2]</td><td>[4.7-6.2]</td></tr><tr><td>Grass</td><td>[0-0]</td><td>[0.5-0.7]</td><td>[2.1-2.8]</td></tr><tr><td rowspan="2">Grazing + 1 cut silage</td><td>Silage</td><td>[3.1-3.9]</td><td>[2.7-3.6]</td><td>[3.2-4.2]</td></tr><tr><td>Grass</td><td>[1.7-2.1]</td><td>[2-2.7]</td><td>[3.3-4.4]</td></tr><tr><td rowspan="2">1C_ens_T</td><td>Late silage patur</td><td></td><td>[3.2-4.3]</td><td>[3.6-4.7]</td></tr><tr><td>Silage</td><td></td><td>[1.9-2.5]</td><td>[2.4-3.3]</td></tr><tr><td rowspan="2">Grazing + 2 cuts silage</td><td>Silage</td><td></td><td>[4.3-5.7]</td><td>[4.7-6.2]</td></tr><tr><td>Grass</td><td></td><td>[1-1.4]</td><td>[1.9-2.5]</td></tr></tbody></table>
<table><tbody><tr><td>Grazing + 1 cut</td><td>Wrapping</td><td>[2.5-3.2]</td><td>[3.5-4.6]</td><td>[3.2-4.2]</td></tr><tr><td rowspan="2">wrapping + 1cut hay</td><td>Hay</td><td>[2-2.5]</td><td>[1.8-2.4]</td><td>[2.4-3.1]</td></tr><tr><td>Grass</td><td>[0-0]</td><td>[1-1.4]</td><td>[1.9-2.5]</td></tr><tr><td rowspan="3">1 cut silage+ 1cut hay</td><td>Silage</td><td>[2.3-2.9]</td><td>[1.8-2.4]</td><td>[2.8-3.7]</td></tr><tr><td>Hay</td><td>[3.1-3.9]</td><td>[2.7-3.6]</td><td>[3.2-4.2]</td></tr><tr><td>Grass</td><td>[0-0]</td><td>[1-1.4]</td><td>[1.9-2.5]</td></tr><tr><td>wrapped alfalfa</td><td></td><td></td><td>[7.7-8.5]</td><td>[8.8-9.7]</td></tr><tr><td>CROPS</td><td></td><td></td><td></td><td></td></tr><tr><td>Barley (t grain/ha)</td><td></td><td></td><td>[3-6.2]</td><td>[3.2-6.8]</td></tr><tr><td>Winter wheat (t grain /ha)</td><td></td><td></td><td>[3-6.2]</td><td>[3.4-7.2]</td></tr><tr><td>Triticale (t grain /ha)</td><td></td><td></td><td>[2.9-6.1]</td><td>[3.2-6.8]</td></tr><tr><td>corn silage (t dry matter/ha)</td><td></td><td></td><td>[6.9-9]</td><td>[8.8-11.4]</td></tr><tr><td>Corn grain (t grain /ha)</td><td></td><td></td><td>[4.7-9.8]</td><td>[4.4-9.2]</td></tr><tr><td>mix cereal-protein (dry matter /ha)</td><td></td><td></td><td>[7.5-11.2]</td><td>[8.2-12.2]</td></tr><tr><td>sunflower (t grain/ha)</td><td></td><td></td><td>[1.3-4.6]</td><td>[1.5-5.2]</td></tr><tr><td>rapeseed (t grain /ha)</td><td></td><td></td><td>[1.1-2.2]</td><td>[1.3-2.8]</td></tr></tbody></table>

Note: *The effect of the cropping intensity is represented by the yield range: in general, the lowest value refers to organic production and the uppers to conventional or intensive production. Weather variability is not accounted for.; Source: most of the estimates come from Inosys Réseau d'Elevage. For standing biomass, estimations come from expert knowledge from the French Livestock Institute; wasting at pasture (≈30%) is not deducted.

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