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# Evaluating net energy and protein feed conversion efficiency for dairy ruminant systems in France

B. Rouillé, J. Jost, B. Bluet, M.P. Jacqueroud, J. Seegers, T Charroin,
Yannick Le Cozler

► To cite this version:

B. Rouillé, J. Jost, B. Bluet, M.P. Jacqueroud, J. Seegers, et al.. Evaluating net energy and protein feed conversion efficiency for dairy ruminant systems in France. Livestock Science, 2023, 269, pp.105170. 10.1016/j.livsci.2023.105170. hal-04047303

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https://hal.inrae.fr/hal-04047303v1

Submitted on 31 Mar 2025

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1 Evaluating net energy and protein feed conversion efficiency for dairy ruminant systems in France

4 Rouillé B¹., Jost J¹., Fança B¹., Bluet B²., Jacqueroud M.P¹., Seegers J³., Charroin

5 T⁴., Le Cozler Y⁵.

7 ¹ IDELE, Service Productions laitières, 75595 Paris, France

8 ² Chambre d'agriculture de l'Indre, 36022 Chateauroux, France

9 ³ IDELE, Coordination nationale INOSYS Réseaux d'élevage, 31321 Castanet

10 Tolosan, France

11 ⁴ IDELE, Service Méthodes et outils pour les références et le conseil, 42272 Saint

12 Priest en Jarez, France

13 ⁵ PEGASE, INRAE, Institut Agro, 35590 Saint Gilles, France

15 **Corresponding author:**

16 Yannick Le Cozler

17 Institut Agro Rennes-Angers

18 65 rue de Saint-Brieuc, 35042 Rennes cedex, France

19 Tel.: 33 (0) 2 23 48 59 18

20 E-mail: yannick.lecozler@agrocampus-ouest.fr

22 **Abstract**

23 Modern systems of animal production are facing increasing challenges on issues

24 such as animal welfare, product quality, and environmental impact. Ruminants in

25 particular are often regarded as being inefficient with respect to feed conversion for
both protein and energy. Efficiency is defined as the ratio between resources and production; when applied to dairy production systems, it corresponds to the total amount of feed (kg of dry matter of roughages and concentrates) consumed per kg of milk and meat produced. This ratio underlines the biological ability of dairy cows, ewes, and goats to convert feed into products that are edible by humans while maintaining other physiological functions. Feeds consumed by animals can be "human-edible", i.e. directly usable for human nutrition (cereals, legumes, maize silage), or not (grasslands, wild grass areas). Calculations of livestock efficiency are strongly influenced by whether or not they consider, and distinguish between, the consumption of human-edible or -inedible plant resources (roughages and concentrates) as well as the production of edible and/or inedible animal products (e.g., milk, meat, wool, leather). An improved understanding of the inedible fraction of both energy and protein in animal feed will be invaluable in analyses of feed-food competition, especially in ruminants. In this study, we investigated the feed conversion efficiency (FCE) of dairy ruminant systems in France (cow, goat, ewe) with respect to both energy and protein. We hypothesized that FCE would be influenced by the composition of the diet and its grass content, as well as, by animal type. Data from a French database (Diapason) provided information on land, labor, herds, and production from 2012 to 2016 for 274 dairy goat farms, 108 dairy ewe farms, and 498 dairy cow farms. These herds consumed feeds that were human-edible (cereals, legumes, maize silage) as well as -inedible (grasslands, wild grass areas). We estimated that French dairy systems are, on average, net consumers of energy and net producers of protein, with higher efficiencies for grass-based systems compared to maize-based systems. At the national scale, average net protein efficiencies were 1.16, 1.12, and 1.88 for ewes, goats, and cows, respectively. These
results on the net FCE in dairy systems will promote a more objective quantification
of their contribution to food security, which should be taken into account in
assessments of feed-food competition and its impact on human nutrition. When
combined with other factors such as greenhouse gas emissions and land and
resource use, such considerations will help to inform discussions of the future of
livestock production.

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58 **Keywords:** Feed-food competition; feed conversion efficiency; dairy production

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60 **Highlights**

61 - French dairy cow, sheep, and goat systems are net producers of protein and
net consumers of energy.

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63 - Feed-food competition is lower for grass-based systems.

64 - The use of concentrates or by-products that are poorly digested by humans,
65 e.g., unusable in human nutrition, has a positive impact on edible feed
66 conversion efficiency.

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67 - Land use and protein digestibility should be addressed when evaluating feed-food competition.
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69

70 **Funding**

71 The authors thank the French Ministry of Agriculture, Food Industry, and Forestry for
72 funding the project (#5710, special CAS DAR grant on "Rural and Agricultural
73 Development").

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73

74

75 **1. Introduction**
Ruminant livestock have the ability to convert resources that are not edible by humans, such as grass, into high-quality animal products that can make important contributions to human nutrition and food security. Indeed, animal products may be invaluable in meeting the increasing demand for human food (Mottet et al., 2017). Despite the potential benefits, animal production faces increasing challenges regarding issues such as animal welfare, product quality, and environmental impact (Steinfeld et al., 2006). For ruminant production systems in particular, two main targets are to reduce the amount of human-edible food that is fed to ruminants, and to lower enteric methane production, which tends to be higher from forage-based diets (Coppa et al., 2021). Ruminants are regarded as inefficient in feed (protein and energy) conversion, and may be in direct competition for the use of arable lands where crops can be produced for the human food supply. Peyraud and Peeters (2016) calculated that more than 3 kg of plant protein in dry matter (DM) are needed to produce 1 kg of milk protein, and 5 to 10 kg are required to produce 1 kg of bovine meat protein. However, these figures do not differentiate between livestock feeds that are human-edible and those that are not (e.g., grass, by-products of food processing and biofuels industries). CAST (1999) was the first to propose an approach for characterizing the edible portion of feedstuffs, and, more recently, Wilkinson (2011) provided results using this approach for specific systems. At a global scale, Mottet et al. (2017) estimated that 86% of global livestock feed intake, in terms of dry matter, consists of feed materials that are not suitable for human nutrition. Moreover, compared to plant-based products, ruminant products offer important benefits to human nutrition with respect to protein digestibility and fat composition (CAST, 1999; FAO, 2011). Indeed, the Digestible Indispensable Amino Acid Score (DIAAS score), which is used to compare the protein quality of foodstuffs, is slightly higher for animal
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products than for the underlying ruminant diets. Animal products, especially dairy
milk, are rich in unsaturated fatty acids which are beneficial for human health; this is
particularly true of grass-fed ruminant milk.

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One important way to gain insight into the contributions of livestock to human food
production is to understand the amount of edible protein and energy produced by
dairy systems (milk and meat) per kg of human-edible plant protein and energy in
livestock feeds (roughages, concentrates, and by-products); this is referred to as "net
efficiency". This approach is more informative for addressing feed-food competition
than one based on the total amount of energy or protein used to produce 1 kg of
animal protein (Wilkinson, 2011; Ertl et al., 2016a). Wilkinson (2011) proposed a re-
definition of the efficiency of feed use by livestock and was the first to distinguish
between the human-edible and -inedible fractions of both energy and protein in
animal feeds. His work focused on characterizing feed-food competition using both
total and net feed conversion efficiency for ruminants, pigs, and poultry systems, with
a maximum of two systems for each type of production as examples. Ertl et al.
(2016a) used the same approach to estimate feed conversion efficiency, both total
and net, for 31 dairy farms split into 6 groups. The conclusions of these two
publications were nearly the same: ruminants do not necessarily compete with
humans for food. They have the potential to turn roughages and inedible resources
into high-quality animal products with important benefits for human diets. Overall,
though, there is a dearth of such results, especially for small dairy ruminants.

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The limited studies that exist to date clearly indicate that better consideration of the
inedible fraction of both energy and protein in animal feed is essential in accurately
characterizing feed-food competition, especially in ruminants. It is possible that feed
conversion efficiency is influenced by the composition of ruminant diets, especially

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their grass content, and may also differ among different types of animals. The aim of this work was thus to evaluate and compare the net feed conversion efficiency with respect to both protein and energy among different dairy goat, sheep, and cow systems in France. For this, we utilized a database containing detailed data on a diversity of dairy systems, especially dairy ewe and dairy goat systems. We then used this information to discuss the land use associated with different systems, as well as the protein and energy quality of dairy products (milk and meat).

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## 2. Materials and methods

### 2.1. Dairy farm data

Data were collected from the INOSYS network "Diapason" (Couzy, 2012), a French reference network (Inosys-Réseaux d'élevage, Idele, Chambres d'agriculture). Its goal is to compile a wide range of detailed data on selected farms over at least five years, to better understand their performance and use them as references for all French farms (https://idele.fr/inosys-reseau-elevage/publications). Data are available separately for each year. In this study, we used data from 2012 to 2016 that originated from 274 dairy goat farms (847 farm-years), 108 dairy sheep farms (343 farm-years), and 498 dairy cattle farms (1382 farm-years). Briefly, this information system gathers the main data of interest to the global concerns of breeding farms (Charroin et al., 2005). The results provide information and assist in decision making for individuals, businesses/organizations, and public officials. The database is organized by the means of production, the global structure of the farm, the performance/production levels of the animals, and economic results. It contains information on the labor force present, the land, constraints related to land use, materials and equipment, and the buildings present. Along with these main
quantitative elements, there are also qualitative data on the history of the farm, features of the workforce, and the goals of the breeder, for example. The different types of production are described with respect to composition of the livestock herd (number of animals throughout the year in different categories), land use for different crops, and mode of use (grazing, harvesting). The main elements of crop management are also specified (yields, fertilization), as well as animal feed (grazing, forage stocks distributed, nature and quantities of concentrated feed used). The database provides calculations of milk and meat production per year, milk production per head, meat production per livestock unit for beef, and numerical productivity per ewe, among others. The criteria for analysing reproductive performance are, depending on the species, the gestation rate, the prolificacy rate, the mortality rate of young animals, or numerical productivity. For the economic considerations, products are allocated to the different animal and plant activities of the farm. Although this database provides a detailed picture of the various activities on these farms, it only covers a limited number of farms within different sectors (e.g., dairy cow, beef, sheep, and goat). These data can be complemented by information from the agricultural census and the accounting information network to better enable characterization of each of the production systems at a regional or national scale.

Table 1 presents some characteristics of the main production systems of dairy goat, sheep, and cattle in the INOSYS network. The categories are professional definitions applied to the different sectors in France, and are based on two main factors: the location (lowland versus mountain) and the proportion of maize silage and grass in the fodder. The latter is a technical criterion that is used as a simple means of qualifying the dominant feeding strategy on a farm, and it provides a good
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representation of the diversity of ruminant feeding systems in France. In all these
systems, young animals are taken into account: their consumption of roughages and
concentrates, as well as that of edible feedstuffs, has been characterized, and there
are data on the balance between the number of young animals bought and those that
are sold outside the farm for meat production.

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Data on dairy cows were classified into five groups: lowland farms with >30% maize in the forage area (C-L30+), lowland farms with 10-30% maize in the forage area (C-L1030), lowland farms with <10% maize in the forage area (C-L10-), mountain farms with >10% maize in the forage area (C-M10+), and mountain farms with <10% maize in the forage area (C-M10-). As a whole, these groups provide a good representation of the diversity of dairy cow systems in France with respect to location (lowland and mountain) and the proportion of maize silage in the diet.

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Dairy sheep data were also divided into five groups based on feeding strategy and location: selling milk without processing in Corsica (S-DC), no transhumance in Pyrénées-Atlantiques (S-NPA), transhumance in Pyrénées-Atlantiques (S-TPA), natural grasslands in Roquefort area (S-GR), and mountains in Roquefort area (S-MR). Transhumance refers to the periodic migration of sheep from the plains to the mountains for the summer period. These regions, all in the South of France, are the main ones producing sheep milk.

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Dairy goat data were separated into nine groups, based on the major type of forage
used: cut-and-carry (G-CC), baled grass (G-BG), maize silage (G-MS), mixed
legume-grass hay (G-MH), legume hay (G-LH), extensive grass production and in-
barn feeding G-EB), low-inputs and pasture (G-EP), mainly pasture (G-PM), and dry
diet (G-DD). It is important to note that, while the INOSYS database contains detailed
information on the different types of dairy goat, sheep, and cattle systems in France,

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it does not accurately represent the proportions of each system in the country as a
whole. To address this, we used official data from national statistics (agricultural
census and accounting records) in order to better estimate the correct proportions of
each group at the national level. Our ultimate goal was to calculate FCE at the
national level for dairy cow, dairy goat, and dairy ewe systems.

For dairy cow systems, the average farm comprised 116 ha with 69 dairy cows,
producing 7382 kg of milk/head/year, on a 365-day basis. The main type of roughage
was grass (53%) and maize silage represented 28% of the total dry matter intake
(DMI).

The average dairy sheep farm had an area of 116 ha with 435 dairy ewes, producing
208 kg of milk/head/year, on a 365-day basis. Grass was the main roughage,
accounting for 76% of the total DMI.

The average goat farm had an area of 82 ha with 155 dairy goats, producing 701 kg
of milk/head/year. Here again, grass was the main roughage, accounting for 65% of
the total DMI.

## 2.2. Edible feedstuffs and animal products

Some of the ingredients/feedstuffs used for goat, sheep, and/or cattle nutrition can
also be used for human nutrition, and there is thus competition between humans and
animals for their use. In the present paper, ingredients subject to such competition
are referred to as "edible" feed. For other feedstuffs, such as roughages (grass in
particular), this competition is low or non-existent. Maize silage is considered partly
edible, because the grains can be used for human consumption. Thus, in this paper,
we also considered scenarios in which maize grains could be 100% used for human
consumption, although this is dependent on whether the grains are allowed to ripen
sufficiently to be harvested for human consumption. Such distinctions become especially important in discussions of the conflict between land sharing and land sparing. Concentrates, such as grains and/or by-products from industrial crop processing, form another part of the ruminant diet. In France, the major concentrate sources used are cereals (mainly from wheat), soybean and rapeseed meals, sugar beet pulps, and spent grains from distilling (RESEDA, 2018). For example, the compound feed 40% CP is a mix of gluten feed, soya meal, rapeseed meal, sunflower meal, decorticated sunflower meal, and urea. The proportion of each ingredient in the feed varies over time depending on economic trends. Information regarding the formulation of concentrates was given by the manufacturer, except for "on-farm" concentrates, which were provided by farmers.

Two types of concentrates were identified based on the content of human-edible energy and protein: highly edible and poorly edible. Cereals, protein crops, compound feeds with less than 30% crude protein (CP), and by-products with less than 15% CP were categorized as "highly edible" concentrates. Dried roughages such as alfalfa, compound feeds with more than 30% CP, by-products with more than 15% CP, and minerals were categorized as "poorly edible" concentrates. Levels of 15% and 30% are commonly used by farmers to enable the distribution of concentrates to animals across a range from high energy/low protein content to low energy/high protein content. "Very edible" concentrates are lightly processed by-products with a high degree of edible ingredients, mainly due to their cereal content for instance. The proportion of each feed in the overall diet was then multiplied by its own proportion of edible energy and protein to estimate the share of non-human edible energy and protein in the whole diet.
In this study, feed consumption was determined for the whole system, comprising all categories of animals (including young stock) at the scale of the entire farm over a year. For all feed sources (roughages and concentrates), metabolizable energy (ME) and crude protein (CP) contents in the dry matter (DM) were used to estimate energy and protein content, which were determined by taking into account the ways in which grains were used by the food industry for human nutrition in France in 2020. We carried out this analysis for almost 200 different feedstuffs used in animal nutrition, as proposed by Laisse et al. (2018). Table 2 presents some examples. Most types of roughage are not human-edible, i.e. they have 0% edible energy and 0% edible protein. The estimated proportion of human-edible protein in wheat is presented in Figure 2; this represents the current estimate used in human food processing that 66% of wheat proteins are human-edible. In total, 34% of the feeds are used only for animal nutrition. For maize silage, 32% of energy and 10% of protein are human-edible.

For animal products, the human-edible output included all edible protein from the current production system. All of the milk produced and sold by dairy cattle farms is human-edible. For "meat", the edible fraction of dairy cow, dairy goat, and dairy sheep (meat plus edible offal and by-products, as shown in Figure 3) currently consumed in France contains 86 g, 75 g, and 81 g of protein/kg of live weight, respectively (Laisse et al., 2016). Edible energy values are 3337 kJ, 2068 kJ, and 2998 kJ, respectively, of the total energy/kg of live weight. These figures vary among dairy species due to differences among types of animals (adult/young, male/female), the baseline protein and energy content of the meat, and the proportion of human-edible fractions in the animals' diets.
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## 2.3. Feed conversion efficiencies

Efficiency is defined as the ratio between resources and production (Faverdin and Van Milgen, 2019) and is a key factor in performance evaluation. Applied to dairy production systems, feed conversion efficiency (FCE) is defined as the ratio between the total amount of feed (kg of DM of roughages and concentrates) consumed per kg of milk and meat produced. In other words, it represents the biological ability of dairy cows, ewes, and goats to convert feeds into products that are edible by humans, while maintaining other physiological functions such as reproduction, growth, and maintenance (Phocas et al., 2014).

The FCE method used in this paper was defined by the collaborative group "GIS Elevages Demain", which was led by INRAE. This methodology was used in earlier work by Laisse et al. (2018), in which it was applied to multiple systems of animal production in France (pork, poultry, and ruminant cattle). All calculations are made at the level of the overall production system, taking into account the total amount of animal production and feed consumption over a year. In its simplest form, FCE consists of a ratio between the production of the farm animals in the system and their consumption of feed resources. The net feed conversion efficiency is then the ratio between the production of edible animal products (milk and meat) and the consumption of human-edible plant resources (roughages and concentrates), calculated for both energy (eeFCE) and protein (epFCE). Four efficiency ratios were calculated, as follows:

- eFCE = energy in edible animal products / energy in total intake of plant resources

- eeFCE = energy in edible animal products / energy in total intake of human-edible plant resources
300 - pFCE = protein in edible animal products / protein in total intake of plant resources

301 - epFCE = protein in edible animal products / protein in total intake of human-edible plant resources

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304 If the FCE is higher than 1, the animal production system is a net producer of protein
305 or energy. If the FCE is lower than 1, the animal production system is a net consumer
306 and thus not efficient.

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308 3. Results

309 On average in France, dairy ruminant production systems are net producers of
310 protein (FCE > 1) but net consumers of energy for human nutrition (FCE < 1; Table
311 3). These results are mainly influenced by animals' consumption of non-human-
312 edible energy and protein in roughages and by-products. Because FCE is a ratio, the
313 production of energy and protein is of critical importance, especially considerations of
314 milk production level. This is why FCE values vary among farms and systems,
315 especially when using the "net" approach. When we examined the FCE values of
316 individual farms, we found that most are net producers of protein for human nutrition:
317 76% of cow farms, 50% of goat farms, and 52% of sheep farms.

318 3.1. General considerations

319 In France, dairy cows, sheep, and goats mostly consume feeds that are not edible by
320 humans, meaning that 89%, 89%, and 86%, respectively, of protein in the average
321 diet is not in competition with human nutrition. For energy, these figures are 86%,
322 88%, and 83%, respectively. Although dairy cows eat more than small ruminants,
323 they also produce more milk, with a correspondingly higher production of energy and
324 protein. Thus, eFCE and pFCE are higher for dairy cow systems, with no significant
difference among groups compared to small dairy ruminants (figure 4 and figure 5).

For small dairy ruminants, eFCE and pFCE are very consistent among groups.

Taking into account, this edible approach leads to estimate epFCE and eeFCE.

When we took into account only the edible portion of feeds (eeFCE and epFCE), we
found that, on average, dairy cow, sheep, and goat systems are net producers of
protein, with the highest efficiency for dairy cattle (1.88 +/- 1.45, 1.16 +/- 0.54, and
1.12 +/- 0.67, respectively). Dairy sheep and goat systems are net consumers of
energy (0.63 +/- 0.18 and 0.54 +/- 0.18, respectively), while dairy cattle systems are
well balanced, with an eeFCE of 1.00 +/- 0.63.

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3.2. Dairy cattle systems

On average, the pFCE for dairy cattle systems is low (0.20 +/- 0.03). This means that
a dairy cow system consumes 5 kg of plant protein to produce 1 kg of animal protein
(1/0.20). Values of pFCE varied from 0.17 for C-L10- to 0.22 for C-L30+, with a very
low standard deviation, meaning that there is not much difference among the five
groups of dairy cattle production systems in France. However, the edible protein
fraction of the average diet is only 11% (89% inedible proteins), which leads to a
mean epFCE value of 1.88 (+/- 1.45) in France. The systems with the largest
fractions of maize silage in cow diets (C-M10+, C-L30+) have the lowest values of
epFCE, but even in these cases values are still above 1. In general, the more grass
in the diet, the higher the epFCE value. This is particularly the case for the C-L10-
group, whose diet contains 86% grass. For edible protein efficiency, the standard
deviation is higher than the global one, ranging from 0.55 for C-M10+ up to 6.08 for
C-L10-. The results in this latter group can be explained by the relative importance of
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edible concentrates compared to non-edible ones and the fact that this group has the lowest milk production totals of all five groups.

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The mean eFCE is low (0.14 +/- 0.02) for dairy cow systems, meaning that a dairy cow system consumes 29.7 kJ of energy to produce only 4.184 kJ of energy in animal products (1/0.14). Values of eFCE range from 0.11 for C-L10- to 0.15 for C-L30+, but have a very low standard deviation, showing that eFCE is quite consistent among systems in France. However, the edible energy fraction of the average diet is only 14% (86% non-edible energy) and leads to a mean eeFCE value of 1.00 (+/- 0.63). The systems with the largest fraction of maize silage in the cow diets (C-M10+, C-L30+) have the lowest eeFCE values: 0.74 (+/- 0.18) and 0.67 (+/- 0.16), respectively. As with protein, the more grass there is in the diet, the higher the eeFCE. This is particularly the case for C-L10-, which has an eeFCE of 2.67. The standard deviation for edible energy efficiency is higher than the global one, ranging from 0.16 for C-L30+ to 5.61 for C-L10-. As for protein, this indicates that the proportion of edible energy in the diet is of crucial importance and should be taken into consideration by the farmer in choosing by-products and/or concentrates.

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### 3.3. Dairy sheep systems

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The mean pFCE is low (0.13 +/- 0.02) for dairy sheep systems, meaning that an average dairy sheep system consumes 7.7 kg of total plant protein to produce a single kg of animal protein (1/0.13). Values of pFCE range from 0.10 for S-TPA and S-DC to 0.15 for S-MR, with a very low standard deviation, indicating a high degree of homogeneity among groups. Even though overall values of pFCE are low, the edible protein fraction of the average diet is only 11% (89% non-edible protein), leading to a mean epFCE value of 1.16 (+/- 0.54). Although the S-DC group

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produces the least amount of milk of the five groups, it has the highest epFCE value (1.38 +/- 0.85), because it also has the lowest proportion of edible protein in the average diet (9%) and the highest proportion of grass (82%). The epFCE values for the S-TPA and S-NPA groups are nearly identical, at 1.28 +/- 0.71 and 1.28 +/- 0.54, respectively, while S-GR and S-MR have epFCE values just above 1: 1.02 for both with +/- 0.29 and +/- 0.47, respectively. As was noted above for dairy cows, the standard deviation of edible energy efficiency is higher than the global one, ranging from 0.29 for S-GR to 0.85 for S-DC. Here as well, then, the proportion of edible protein in the average diet is of crucial importance and must be taken into consideration.

The mean eFCE is very low (0.07 +/- 0.01) for dairy sheep systems; this means that a dairy sheep system consumes 59.83 kJ of energy in feed to produce only 4.184 kJ of energy in animal products (1/0.07). There was little difference (low standard deviation) among groups, with values ranging from 0.06 (+/- 0.01) for S-TPA and S-DC to 0.09 (+/- 0.01) for S-MR. Here again, though, the edible energy fraction of the average diet is only 12% (88% non-edible energy) so that the mean eeFCE value is much higher, 0.63 (+/- 0.18). Values of eeFCE are similar among sheep systems, ranging from 0.59 (+/- 0.19) for S-NPA to 0.67 (+/- 0.16) for S-MR, with a low standard deviation among groups.

### 3.4. Dairy goat systems

Similar to what was observed with sheep, the mean pFCE is low (0.15 +/- 0.04) for dairy goat systems. This means that a dairy goat system consumes 6.7 kg of total plant protein to produce 1 kg of animal protein (1/0.15). Values of pFCE are quite homogeneous among the different groups of dairy goat systems in France, ranging
from 0.11 for G-EP to 0.18 for G-MS, with a very low standard deviation for the system as a whole. Again, though, the edible protein fraction of the average diet is only 14% (86% non-edible proteins) and leads to a mean epFCE value of 1.12 (+/- 0.67). The system with the lowest epFCE is G-DD (0.66 +/- 0.14), which uses a high proportion of concentrates in the diet. In this system, 26% of the protein intake of the goats is edible by humans, the highest value obtained in the entire study. Furthermore, the production level does not offset the negative impact of the diet, leading to an overall negative contribution. The groups G-MS, G-LH, and G-BG are also net consumers of protein, with epFCE values of 0.74 (+/- 0.42), 0.93 (+/-0.43), and 0.96 (+/- 0.65), respectively. The epFCE values for the five other systems are all above 1; G-EP has the highest value (1.57 +/- 1.35), with only 7% edible protein in the average diet. Despite the high degree of variability among the different systems, it is still clear that the share of edible protein in the diet plays a key role in determining the impacts of a production system.

The mean eFCE is low (0.09 +/- 0.02) for dairy goat systems, meaning that an average dairy goat system consumes 46.4 kJ of energy in feed to produce only 4.19 kJ of energy in animal products (1/0.09). Values of eFCE were universally low among the different groups of dairy goat systems in France, ranging from 0.06 (+/- 0.03) for G-EP to 0.11 (+/- 0.03) for G-MS, G-CC, and G-DD, with a low standard deviation across systems. Because the edible energy fraction of the average diet is only 17% (83% non-edible energy), though, the average eeFCE value in France is 0.54 (+/- 0.18). The G-DD system presents the lowest eeFCE (0.39 +/- 0.10) and once again, the high proportion of edible energy in the diet (29%), especially in concentrates, is the main reason. As with the other ruminant systems, the more grass in the diet, the
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higher the epFCE value. This effect was particularly notable for the G-EB group, which has a mean eeFCE of 0.73 (+/- 0.29). Slightly more variation was observed in edible energy efficiency than the global FCE, with a range from 0.10 for G-DD to 0.24 for G-EP.

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To our knowledge, these results represent the first time that feed-food competition has been quantified in France using field data and a consistent methodology for the three major dairy species (cattle, sheep, and goat). The low values obtained for total protein (pFCE) and energy (eFCE) feed conversion efficiency suggest that dairy ruminant systems are not very efficient. However, as we highlight here, most livestock feed is not edible by humans and therefore can make little to no direct contribution to the protein or energy needs of the human population. A better way to address the competition between animal feed and human food is thus to use the net approach (epFCE and eeFCE) based on the human-edible fraction of livestock feed, which is a more realistic and relevant way to consider the "feed-food" debate. The values of eeFCE and epFCE obtained here for the main French dairy systems are in line with those recently presented by several authors (Ert et al., 2015a; Laisse et al., 2016; Wilkinson and Lee, 2017; Laisse et al., 2018). We observed a clear effect of the proportion of fresh and/or harvested grass in the diets, but also of the proportion of other feedstuffs that are not human-edible, such as by-products used as concentrates. Although data on the use of concentrates were gathered from the Diapason database for the years 2012-2016, their average compositions were obtained directly from animal feed companies in 2020 in order to provide as precise an estimate as possible for the current French context. These figures show that
ruminants are efficient in converting feeds that humans are unable to eat into human edible animal products of high nutritive value. However, it is important to underline the significant variability we observed among farms within the different systems. This variability means that many French dairy systems have opportunities to increase their FCE with respect to both energy and protein. For instance, within a given system of production, variability in FCE can arise from differences in the diet and, more precisely, in the proportions of human-edible energy and protein in the diet. Looking at it another way, a given diet may result in differences in milk production depending on the variability in animal efficiency or the quality of roughages. Regarding roughages, the amount of grass in the diet directly correlates with FCE, as grass is not edible by humans. However, this only applies as long as the milk production level does not decrease, meaning that the grass in the diet must be of high quality. Regarding concentrates, there are two potential strategies for increasing efficiency: reducing the amount of concentrates per kg of milk or replacing common feeds with an increasing proportion of by-products from industrial food processing that are less human-edible. As argued by Oltjen and Beckett (1996), Wilkinson (2011), and Ertl et al. (2016a; 2016b), there is great potential for increasing the FCE for both energy and protein in French dairy systems through re-examinations of animal efficiency, the quality of roughages, and the choice of non-human-edible feeds such as grass and by-products.

The estimation of the percentage of edible protein and energy in feeds is a critical point of the methodology used here. In this study, these values were estimated with the goal of reflecting the actual use of raw materials (such as grains) by the food industry in France. This point was addressed by Baber et al. (2018) for beef
production, but in a simpler way, with only three categories (edible, partially edible, inedible). This type of classification, inspired by Wilkinson (2011) and Ertl et al. (2016b), enables an approach that is more straightforward but less precise than the method used here. Wilkinson (2011) also proposed a "potential" scenario that anticipated improvements in the use of raw materials in human food. In such a scenario, ruminant systems would face increased competition for plant resources, as concluded also by Laisse et al. (2018). Even in this case, though, a large proportion of grass in the ruminant diet would help to better maintain the efficiency of the system. Future trends in the use of protein from soybean or rapeseed meal for human nutrition are difficult to predict, since this will depend on the ability of the food industry to better extract protein directly for human consumption. There is likely more promise in the use of by-products of industrial food processing, which have been estimated at 12 million tons of dry matter in France (RESEDA, 2018). As of 2017, 76% of these by-products are already destined for use in animal nutrition. Ruminant systems, including dairy, can therefore play a central role in the creation of a circular bio-economy by transforming the by-products of the food industry into animal products with high nutritional quality. Although there is increasing competition between the use of by-products in animal feed and their use in the production of energy, such as biogas, animal nutrition will probably remain an important means of extracting value from by-products, especially given the quantities involved and the ability to produce valuable human-edible foods.

Although dairy ruminant systems are efficient in converting non-edible resources into high-quality animal products, they may sometimes be in direct competition for land occupation. Globally, livestock consume 32% of the grain and 40% of the arable land
available, and 700 million hectares of potentially arable grassland (Mottet et al., 2017). In France, this issue mainly concerns lowland dairy cattle systems, where arable areas are used for silage maize, legume silage, or temporary pastures. Mountain systems mostly use permanent grasslands, and are thus not in direct competition (or at least to a much lesser extent) for land use. The extensive availability of biomass from grasslands in France allows dairy ruminant systems to be defined as efficient. However, future market conditions and policy decisions may result in changes to the availability and economic viability of certain types of land use. Currently, agriculture in France utilizes an area of 28 million hectares, with about 18 million hectares used for animal feed (64%) (Agreste, 2013). Of these 18 million hectares, 14 million are occupied by fodder crops, of which 9.8 million are on non-arable land. If the fodder crops were replaced by those dedicated solely for human food, the same amount of calories for human consumption could be grown in only 8.2 million hectares. However, such a shift would be accompanied by important decisions regarding the management of these lands, rotations for human consumption, yield variations, and the intensification of animal production in non-arable areas. The Land Use Ratio (LUR) approach proposed by Van Zanten et al. (2016) will be of great help in discussing these issues. This indicator focuses on the production of digestible protein for human nutrition. The goal is to compare the current production of animal protein from a farm with the potential production of plant protein if 100% of the arable land were to be converted to plant production. In their study, Van Zanten et al. (2016) gave several examples from different types of production that highlighted the importance of organic fertilization and soil quality in such a shift. However, there might be challenges in generalizing this approach throughout France due to the high degree of diversity in soil quality across the country. A recent effort to examine the
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effect of beef production on land use in Europe (Mosnier et al., 2021) found that grass-based systems use more land area per kg of carcass, but mostly non-tilled land that is not in competition with human nutrition. Some trade-offs were identified between meat production costs and feed-food competition, which could open avenues for improving ruminant sustainability. This type of approach to land use might be interesting to apply to dairy production in France.

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The last issue that remains to be better understood is variation in the quality of animal protein for human consumption. To this end, FAO (2011) proposed a Digestible Indispensable Amino Acid Score (DIAAS score) for use in assessing the protein quality of human-edible feedstuffs in ruminant diets and of human-edible animal products from ruminants. Using the DIAAS of animal products and of the ruminant diet, it is possible to calculate the Protein Quality Ratio (PQR), which highlights a slight gain for animal products versus ruminant diets. Values of PQR vary from 1.7 to 2.4 for dairy ruminants (Laisse et al., 2018; Ertl et al., 2016a & 2016b), and from 2.11 to 3.16 for different beef production systems (Baber et al., 2018). This type of analysis will be important to take into account for future ruminant systems as well as for human nutrition.

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## 5. Conclusion

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The three main dairy ruminant systems in France—goat, sheep, and cow—are, on average, net producers of protein and net consumers of energy for human consumption. The only exception was observed for dairy cow systems, which demonstrate balanced net energy efficiency. These results add needed context to debates in which dairy systems are characterized as inefficient, and more generally,

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to discussions of feed-food competition. They also make an important contribution to considerations of the future of livestock production, which should not be reduced to some of its negative environmental impacts (greenhouse gas or nitrogen emissions, for example). Due to their ability to digest fiber, and thus produce high-quality animal products from feedstuffs that are inedible to humans, ruminants can play a key role in human nutrition. This is particularly true for grass-based dairy systems, which, although potentially less productive, largely avoid the issue of feed-food competition.

The efficient use of grasslands, from both an agronomic and an animal point of view, can also create the potential for increased biodiversity. Another means of improving efficiency in dairy systems is the use of food industry by-products, through which dairy ruminants may play an important role in a virtuous bio-economic circle. In the approach used here, the main output variable of the system is milk production per head, while the main input variables are the types of roughages and concentrates used. The more roughages and grass in the diet, the more efficient the systems will be in converting both energy and protein. This approach could be used all over the world to estimate the net contribution to human consumption for both energy and protein, with an appropriate edible fraction calculated for each country. It is also an interesting way to perform objective comparisons with other production systems, such as pork and poultry.

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648 Table 1: Main characteristics of dairy production systems

649 Table 2: Edible energy and protein in selected feed sources for dairy animals and in
650 selected animal products

651 Table 3: Total and edible feed conversion efficiencies for energy and protein for
652 different dairy ruminant systems in France

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654 Figure 1: Feed conversion efficiencies in dairy goat, cow and sheep in France

655 Figure 2: Edible fraction of wheat (in % of protein content)

656 Figure 3: Edible fraction of a dairy cow (in % of live weight)

657 Figure 4: Comparison of pFCE and epFCE for ruminant systems in France

658 Figure 5: Comparison of eFCE and eeFCE for ruminant systems in France
<table><caption>Figure 1</caption><thead><tr><th></th><th></th><th>847 data<br>from 274 farms</th><th>1382 data<br>from 398 farms</th><th>343 data<br>from 108 farms</th></tr></thead><tbody><tr><td colspan="5">5 years, from 2012 to 2016</td></tr><tr><td>E</td><td>eFCE = energy in edible animal products<br>/ energy in vegetal sources</td><td>0.07</td><td>0.14</td><td>0.09</td></tr><tr><td>N</td><td>eeFCE = energy in edible animal products<br>/ energy in EDIBLE vegetal sources</td><td>0.63</td><td>1.00</td><td>0.54</td></tr><tr><td>P</td><td>pFCE = protein in edible animal products<br>/ protein in vegetal sources</td><td>0.13</td><td>0.20</td><td>0.15</td></tr><tr><td>R</td><td>epFCE = protein in edible animal products<br>/ protein in EDIBLE vegetal sources</td><td>1.16</td><td>1.88</td><td>1.12</td></tr></tbody></table>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

If FCE is above 1, the animal production system is producer. If FCE is under 1, the animal production system is consumer.

Dairy ruminant systems are net producers of proteins and net consumers of energy in France
<figure><img src="image_6.png" /><figcaption>Figure 2</figcaption></figure>
<figure><img src="image_7.png" /><figcaption>Figure 3</figcaption></figure>
<figure><img src="image_8.png" /><figcaption>Figure 4</figcaption></figure>

Signification: C: cow, S: sheep, G: Goat. Dairy cow systems were classified according to feeding systems and location, as follow: C-L30+: lowland farms with >30% maize in the forage area; C-L1030: lowland farms with 10-30% maize in the forage area; C-L10-: lowland farms with <10% maize in the forage area; C-M10+: mountain farms with >10% maize in the forage area; C-M10-: farms with <10% maize in the forage area; For dairy sheep, classification was based on feeding strategy and location, as follow: S-DC: selling milk without processing in Corsica; S-NPA: no transhumance in Pyrénées-Atlantiques; S-TPA: transhumance in Pyrénées-Atlantiques; S-GR: natural grasslands in Roquefort area; S-MR: mountains in Roquefort area; For dairy goat farms, data were based on the major type of forage used, as follow: G-CC: cut-and-carry; G-BG: baled grass; G-MS: maize silage; G-MH: mixed legume-grass hay; G-MH: legume hay; G-EB: extensive grass production and in-barn feeding; G-EP: low-inputs and pasture; G-PM: mainly pasture; G-DD: dry diet. FCE: feed conversion efficiency; pFCE = protein in edible animal products / protein in total intake of plant resources; epFCE = protein in edible animal products / protein in total intake of human-edible plant resources.
<figure><img src="image_9.png" /><figcaption>Figure 5</figcaption></figure>

Signification: C: cow, S: sheep, G: Goat. Dairy cow systems were classified according to feeding systems and location, as follow: C-L30+: lowland farms with >30% maize in the forage area; C-L1030: lowland farms with 10-30% maize in the forage area; C-L10-: lowland farms with <10% maize in the forage area; C-M10+: mountain farms with >10% maize in the forage area; C-M10-: farms with <10% maize in the forage area; For dairy sheep, classification was based on feeding strategy and location, as follow: S-DC: selling milk without processing in Corsica; S-NPA: no transhumance in Pyrénées-Atlantiques; S-TPA: transhumance in Pyrénées-Atlantiques; S-GR: natural grasslands in Roquefort area; S-MR: mountains in Roquefort area; For dairy goat farms, data were based on the major type of forage used, as follow: G-CC: cut-and-carry; G-BG: baled grass; G-MS: maize silage; G-MH: mixed legume-grass hay; G-MH: legume hay; G-EB: extensive grass production and in-barn feeding; G-EP: low-inputs and pasture; G-PM: mainly pasture; G-DD: dry diet. FCE: feed conversion efficiency; eFCE = energy in edible animal products / energy in total intake of plant resources; eeFCE = energy in edible animal products / energy in total intake of human-edible plant resources.
<table><caption>Table 1</caption><thead><tr><th>Dairy systems</th><th>Farm size (ha)</th><th>Lactating females (n)</th><th>Milk production (kg/cow/ye ar)</th><th>% maize silage</th><th>% of grass</th><th>% of other roughages</th><th>% of "high edible" concentrates</th><th>% of "low edible" concentrates</th><th>% of non-edible proteins in the diet</th><th>% of non-edible energy in the diet</th><th>Ponderation</th><th>Source of ponderation</th></tr></thead><tbody><tr><td>C-M10-</td><td>97</td><td>52</td><td>6056</td><td>2%</td><td>82%</td><td>1%</td><td>12%</td><td>3%</td><td>92%</td><td>91%</td><td>24,2%</td><td></td></tr><tr><td>C-M10+</td><td>89</td><td>63</td><td>7764</td><td>30%</td><td>48%</td><td>1%</td><td>10%</td><td>10%</td><td>83%</td><td>81%</td><td>8,5%</td><td></td></tr><tr><td>C-L10-</td><td>113</td><td>64</td><td>5434</td><td>2%</td><td>86%</td><td>1%</td><td>8%</td><td>2%</td><td>96%</td><td>96%</td><td>6,5%</td><td rowspan="3">Idele and Cniel, 2015</td></tr><tr><td>C-L30+</td><td>132</td><td>81</td><td>8379</td><td>46%</td><td>32%</td><td>4%</td><td>7%</td><td>12%</td><td>85%</td><td>78%</td><td>42,7%</td></tr><tr><td>C-L1030</td><td>116</td><td>65</td><td>7328</td><td>30%</td><td>53%</td><td>2%</td><td>7%</td><td>8%</td><td>90%</td><td>94%</td><td>18,1%</td></tr><tr><td><strong>Ponderated average</strong></td><td><strong>116</strong></td><td><strong>69</strong></td><td><strong>7382</strong></td><td><strong>28%</strong></td><td><strong>53%</strong></td><td><strong>2%</strong></td><td><strong>9%</strong></td><td><strong>8%</strong></td><td><strong>89%</strong></td><td><strong>86%</strong></td><td><strong>100%</strong></td><td></td></tr><tr><td>S-TPA</td><td>42</td><td>332</td><td>140</td><td>3%</td><td>76%</td><td>0%</td><td>0%</td><td>21%</td><td>92%</td><td>90%</td><td>35%</td><td></td></tr><tr><td>S-GR</td><td>265</td><td>529</td><td>252</td><td>0%</td><td>77%</td><td>0%</td><td>0%</td><td>23%</td><td>86%</td><td>88%</td><td>24%</td><td></td></tr><tr><td>S-MR</td><td>91</td><td>491</td><td>288</td><td>2%</td><td>75%</td><td>0%</td><td>0%</td><td>23%</td><td>85%</td><td>87%</td><td>24%</td><td rowspan="3">Bossis and Jost, 2016</td></tr><tr><td>S-NPA</td><td>61</td><td>399</td><td>207</td><td>4%</td><td>70%</td><td>0%</td><td>0%</td><td>26%</td><td>89%</td><td>86%</td><td>9%</td></tr><tr><td>S-DC</td><td>124</td><td>467</td><td>129</td><td>0%</td><td>82%</td><td>0%</td><td>0%</td><td>18%</td><td>92%</td><td>91%</td><td>8%</td></tr><tr><td><strong>Ponderated average</strong></td><td><strong>116</strong></td><td><strong>435</strong></td><td><strong>208</strong></td><td><strong>2%</strong></td><td><strong>76%</strong></td><td><strong>0%</strong></td><td><strong>0%</strong></td><td><strong>22%</strong></td><td><strong>89%</strong></td><td><strong>88%</strong></td><td><strong>100%</strong></td><td></td></tr><tr><td>G-MS</td><td>107</td><td>320</td><td>867</td><td>26%</td><td>36%</td><td>3%</td><td>25%</td><td>10%</td><td>76%</td><td>74%</td><td>10%</td><td></td></tr><tr><td>G-LH</td><td>88</td><td>191</td><td>787</td><td>0%</td><td>64%</td><td>1%</td><td>30%</td><td>5%</td><td>83%</td><td>80%</td><td>19%</td><td></td></tr><tr><td>G-WG</td><td>103</td><td>203</td><td>814</td><td>0%</td><td>63%</td><td>1%</td><td>31%</td><td>5%</td><td>83%</td><td>79%</td><td>5%</td><td></td></tr><tr><td>G-PM</td><td>61</td><td>101</td><td>626</td><td>0%</td><td>71%</td><td>0%</td><td>23%</td><td>5%</td><td>86%</td><td>84%</td><td>18%</td><td></td></tr><tr><td>G-CC</td><td>84</td><td>271</td><td>831</td><td>2%</td><td>49%</td><td>3%</td><td>30%</td><td>16%</td><td>88%</td><td>78%</td><td>6%</td><td rowspan="5">Agreste RA, 2010</td></tr><tr><td>G-EB</td><td>84</td><td>62</td><td>590</td><td>0%</td><td>79%</td><td>1%</td><td>18%</td><td>2%</td><td>90%</td><td>88%</td><td>10%</td></tr><tr><td>G-MH</td><td>34</td><td>109</td><td>733</td><td>0%</td><td>70%</td><td>0%</td><td>24%</td><td>6%</td><td>87%</td><td>84%</td><td>19%</td></tr><tr><td>G-DD</td><td>81</td><td>265</td><td>918</td><td>1%</td><td>44%</td><td>2%</td><td>49%</td><td>5%</td><td>74%</td><td>71%</td><td>3%</td></tr><tr><td>G-EP</td><td>161</td><td>74</td><td>355</td><td>0%</td><td>82%</td><td>1%</td><td>16%</td><td>2%</td><td>93%</td><td>89%</td><td>10%</td></tr><tr><td><strong>Ponderated average</strong></td><td><strong>82</strong></td><td><strong>155</strong></td><td><strong>701</strong></td><td><strong>3%</strong></td><td><strong>65%</strong></td><td><strong>1%</strong></td><td><strong>25%</strong></td><td><strong>6%</strong></td><td><strong>86%</strong></td><td><strong>83%</strong></td><td><strong>100%</strong></td><td></td></tr></tbody></table>

Signification: C: cow, S: sheep; G: Goat. Dairy cow systems were classified according to feeding systems and location, as follow: C-L30+: lowland farms with >30% maize in the forage area; C-L1030: lowland farms with 10–30% maize in the forage area; C-L10-: lowland farms with <10% maize in the forage area; C-M10+: mountain farms with >10% maize in the forage area; C-M10-: farms with <10% maize in the forage area; For dairy sheep, classification was based on feeding strategy and location, as follow: S-DC: selling milk without processing in Corsica; S-NPA: no transhumance in Pyrénées-Atlantiques; S-TPA:
transhumance in Pyrénées-Atlantiques; S-GR: natural grasslands in Roquefort area; S-MR: mountains in Roquefort area; For dairy goat farms, data were based on the major type of forage used, as follow: G-CC: cut-and-carry; G-BG: baled grass ; G-MS: maize silage; G-MH: mixed legume-grass hay; G-MH; legume hay; G-EB: extensive grass production and in-barn feeding; G-EP: low-inputs and pasture; G-PM: mainly pasture; G-DD: dry diet.
<table><caption>Table 2</caption><thead><tr><th></th><th></th><th>Edible energy</th><th>Edible protein</th></tr></thead><tbody><tr><td rowspan="4">Roughages</td><td>Pasture, %</td><td>0</td><td>0</td></tr><tr><td>Maize silage, %</td><td>32</td><td>10</td></tr><tr><td>Harvested grass (silage, hay), %</td><td>0</td><td>0</td></tr><tr><td>Cereals and protein crops silage, %</td><td>70</td><td>70</td></tr><tr><td rowspan="6">Concentrates</td><td>Faba bean, %</td><td>83</td><td>92</td></tr><tr><td>Wheat grain, %</td><td>68</td><td>66</td></tr><tr><td>Soja meal, %</td><td>38</td><td>60</td></tr><tr><td>Rapeseed meal, %</td><td>0</td><td>0</td></tr><tr><td>Compound feed 40% of CP, %</td><td>24</td><td>32</td></tr><tr><td>Beet pulps, %</td><td>0</td><td>0</td></tr><tr><td rowspan="3">Milk</td><td>Ewe, %</td><td>100</td><td>100</td></tr><tr><td>Goat, %</td><td>100</td><td>100</td></tr><tr><td>Cow, %</td><td>100</td><td>100</td></tr><tr><td rowspan="3">Meat</td><td>Lamb, kJ or g of protein/kg of live weight</td><td>2998</td><td>81</td></tr><tr><td>Goat, kJ or g of protein/kg of live weight</td><td>2068</td><td>75</td></tr><tr><td>Cow, in kJ or g of protein/kg of live weight</td><td>3337</td><td>86</td></tr></tbody></table>
<table><caption>Table 3</caption><thead><tr><th>System</th><th>pFCE</th><th>pFCE SD</th><th>epFCE</th><th>epFCE SD</th><th>eFCE</th><th>eFCE SD</th><th>eeFCE</th><th>eeFCE SD</th><th>Ponderation</th></tr></thead><tbody><tr><td>C-M10-</td><td>0.18</td><td>0.03</td><td>2.17</td><td>1.00</td><td>0.12</td><td>0.02</td><td>1.31</td><td>0.49</td><td>24.2%</td></tr><tr><td>C-M10+</td><td>0.20</td><td>0.03</td><td>1.18</td><td>0.55</td><td>0.14</td><td>0.02</td><td>0.74</td><td>0.18</td><td>8.5%</td></tr><tr><td>C-L10-</td><td>0.17</td><td>0.03</td><td>4.23</td><td>6.08</td><td>0.11</td><td>0.02</td><td>2.67</td><td>5.61</td><td>6.5%</td></tr><tr><td>C-L30+</td><td>0.22</td><td>0.03</td><td>1.41</td><td>1.12</td><td>0.15</td><td>0.02</td><td>0.67</td><td>0.16</td><td>42.7%</td></tr><tr><td>C-L1030</td><td>0.21</td><td>0.04</td><td>2.08</td><td>1.58</td><td>0.14</td><td>0.02</td><td>0.88</td><td>0.31</td><td>18.1%</td></tr><tr><td>National average for dairy cows</td><td><strong>0.20</strong></td><td><strong>0.03</strong></td><td><strong>1.88</strong></td><td><strong>1.45</strong></td><td><strong>0.14</strong></td><td><strong>0.02</strong></td><td><strong>1.00</strong></td><td><strong>0.63</strong></td><td><strong>100%</strong></td></tr><tr><td>S-TPA</td><td>0.10</td><td>0.02</td><td>1.28</td><td>0.71</td><td>0.06</td><td>0.01</td><td>0.60</td><td>0.20</td><td>34.7%</td></tr><tr><td>S-GR</td><td>0.14</td><td>0.02</td><td>1.02</td><td>0.29</td><td>0.08</td><td>0.01</td><td>0.65</td><td>0.14</td><td>24.3%</td></tr><tr><td>S-MR</td><td>0.15</td><td>0.03</td><td>1.02</td><td>0.47</td><td>0.09</td><td>0.01</td><td>0.67</td><td>0.16</td><td>24.0%</td></tr><tr><td>S-NPA</td><td>0.14</td><td>0.02</td><td>1.28</td><td>0.54</td><td>0.08</td><td>0.01</td><td>0.59</td><td>0.19</td><td>9.3%</td></tr><tr><td>S-DC</td><td>0.10</td><td>0.02</td><td>1.38</td><td>0.85</td><td>0.06</td><td>0.01</td><td>0.61</td><td>0.32</td><td>7.7%</td></tr><tr><td>National average for dairy ewes</td><td><strong>0.13</strong></td><td><strong>0.02</strong></td><td><strong>1.16</strong></td><td><strong>0.54</strong></td><td><strong>0.07</strong></td><td><strong>0.01</strong></td><td><strong>0.63</strong></td><td><strong>0.18</strong></td><td><strong>100%</strong></td></tr><tr><td>G-MS</td><td>0.18</td><td>0.04</td><td>0.74</td><td>0.42</td><td>0.11</td><td>0.03</td><td>0.41</td><td>0.13</td><td>10%</td></tr><tr><td>G-LH</td><td>0.16</td><td>0.03</td><td>0.93</td><td>0.43</td><td>0.09</td><td>0.02</td><td>0.47</td><td>0.11</td><td>19%</td></tr><tr><td>G-WG</td><td>0.16</td><td>0.04</td><td>0.96</td><td>0.65</td><td>0.10</td><td>0.02</td><td>0.48</td><td>0.11</td><td>5%</td></tr><tr><td>G-PM</td><td>0.15</td><td>0.04</td><td>1.08</td><td>0.59</td><td>0.09</td><td>0.02</td><td>0.56</td><td>0.22</td><td>18%</td></tr><tr><td>G-CC</td><td>0.17</td><td>0.05</td><td>1.44</td><td>1.79</td><td>0.11</td><td>0.03</td><td>0.51</td><td>0.12</td><td>6%</td></tr><tr><td>G-EB</td><td>0.15</td><td>0.04</td><td>1.48</td><td>0.66</td><td>0.09</td><td>0.03</td><td>0.73</td><td>0.29</td><td>10%</td></tr><tr><td>G-MH</td><td>0.15</td><td>0.04</td><td>1.13</td><td>0.53</td><td>0.09</td><td>0.03</td><td>0.58</td><td>0.17</td><td>19%</td></tr><tr><td>G-DD</td><td>0.17</td><td>0.04</td><td>0.66</td><td>0.14</td><td>0.11</td><td>0.03</td><td>0.39</td><td>0.10</td><td>3%</td></tr><tr><td>G-EP</td><td>0.11</td><td>0.05</td><td>1.57</td><td>1.35</td><td>0.06</td><td>0.03</td><td>0.59</td><td>0.24</td><td>10%</td></tr><tr><td>National average for dairy goats</td><td><strong>0.15</strong></td><td><strong>0.04</strong></td><td><strong>1.12</strong></td><td><strong>0.67</strong></td><td><strong>0.09</strong></td><td><strong>0.02</strong></td><td><strong>0.54</strong></td><td><strong>0.18</strong></td><td><strong>100%</strong></td></tr></tbody></table>

Signification: C: cow, S: sheep; G: Goat. Dairy cow systems were classified according to feeding systems and location, as follow: C-L30+: lowland farms with >30% maize in the forage area; C-L1030: lowland farms with 10–30% maize in the forage area; C-L10-: lowland farms with <10% maize in the forage area; C-M10+: mountain farms with >10% maize in the forage area; C-M10-: farms with <10% maize in the forage area; For dairy sheep, classification was based on feeding strategy and location, as follow: S-DC: selling milk without processing in Corsica; S-NPA: no transhumance in Pyrénées-Atlantiques; S-TPA: transhumance in Pyrénées-Atlantiques; S-GR: natural grasslands in Roquefort area; S-MR: mountains in Roquefort area; For dairy goat farms, data were based on the major type of forage used, as follow: G-CC: cut-and-carry; G-BG: baled grass; G-MS: maize silage; G-MH: mixed legume-grass hay; G-MH; legume hay;
G-EB: extensive grass production and in-barn feeding; G-EP: low-inputs and pasture; G-PM: mainly pasture; G-DD: dry diet. FCE: feed conversion efficiency; pFCE = protein in edible animal products / protein in total intake of plant resources; pFCE SD = pFCE standard deviation; epFCE = protein in edible animal products / protein in total intake of human-edible plant resources; epFCE SD = epFCE standard deviation; eFCE = energy in edible animal products / energy in total intake of plant resources; eFCE SD = eFCE standard deviation; eeFCE = energy in edible animal products / energy in total intake of human-edible plant resources; eeFCE SD = eeFCE standard deviation;
<table><caption>Figure 1</caption><thead><tr><th></th><th>847 data<br>from 274 farms</th><th>1382 data<br>from 398 farms</th><th>343 data<br>from 108 farms</th></tr></thead><tbody><tr><td>5 years, from 2012 to 2016</td><td></td><td></td><td></td></tr><tr><td>E<br>N<br>E<br>R<br>G<br>Y</td><td>eFCE = energy in edible animal products<br>/ energy in vegetal sources</td><td>0.07</td><td>0.14</td><td>0.09</td></tr><tr><td></td><td>eeFCE = energy in edible animal products<br>/ energy in EDIBLE vegetal sources</td><td>0.63</td><td>1.00</td><td>0.54</td></tr><tr><td>P<br>R<br>O<br>T<br>E<br>I<br>N</td><td>pFCE = protein in edible animal products<br>/ protein in vegetal sources</td><td>0.13</td><td>0.20</td><td>0.15</td></tr><tr><td></td><td>epFCE = protein in edible animal products<br>/ protein in EDIBLE vegetal sources</td><td>1.16</td><td>1.88</td><td>1.12</td></tr></tbody><tfoot><tr><td colspan="4">If FCE is above 1, the animal production system is producer. If FCE is under 1, the animal production system is consumer.</td></tr></tfoot></table>

Dairy ruminant systems are net producers of proteins and net consumers of energy in France