**Abstract**

**Background:** Selection for feed efficiency is one of the best ways to decrease poultry production costs and environmental impacts. While literature on its genetic determinism is abundant, it is limited to one or a few periodic values over the animals' lifespans. With the development of new phenotyping tools, kinetics of growth and feed intake are now available, providing access to daily data on feed efficiency. In this study, over the course of 6 weeks, we described the kinetics of body weight (BW), average daily weight gain (ADG), feed intake (FI), and daily cumulative feed conversion ratio (DCFCR) using electronic feed stations. We then estimated the genetic parameters of daily data in two fast growing lines of chicken divergently selected for breast meat ultimate pH (heritability and genetic correlations with breast meat yield and pH).

**Results:** Birds from the line selected to have a more acidic meat (pHu-), were more efficient than those selected to have a less acidic meat (pHu+), with a 4.3% higher BW from d7 to d29 and 5.0% lower feed efficiency from d12 to slaughter. The line effect for ADG and DCFCR appeared to be significant as early as d5, which is consistent with the early age at which metabolic differences between the two lines appear. Genetic parameters estimated within each line revealed different genetic determinisms of growth and feed efficiency, with a higher impact of maternal effects on BW during the growing phase (d10 to d20) in the pHu+ line and much higher heritability values of DCFCR during the finishing phase (d26-d42) in the pHu- line.

**Conclusion:** Genetic profiles of daily performance highlighted the difference between both lines. Their behavior during dietary transitions reinforced the already known impact of these periods in the animals' lives. Based on the profiles of genetic parameters within each line, it seems feasible to identify early criteria for selecting feed efficiency, but they must be defined for each line, as the genetic determinism of these traits is line-dependent.

**Keywords:** Kinetics, growth, feed efficiency, genetic determinism, broiler, alternative feedstuff, meat quality

**Background**

Feed efficiency has been one of the most important criteria of selection in poultry for decades because of its economic importance and benefits for the environmental impact of poultry production [1]. In the US context, with an annual production of 19.5 10⁹ tons of poultry meat, Abasht et al. [2] estimated that a 1% reduction in feed conversion ratio would result in a savings of $ 294 10⁶ on broiler feed and saving reduction of 0.53 10⁶ tons of fresh manure produced. Improving feed conversion ratio in a context of diversifying feedstuffs is also a way to reduce the social impact of poultry production. It can make it possible to increase the
proportion of local or less concentrated feedstuffs and agricultural by-products in poultry diets, reducing importations of soybean into Europe [3] and competition between human and animals for lands and cereals [4-6].

Literature on the genetic determinism of feed efficiency is abundant. Estimates of heritability vary largely depending on the periods studied and the strains. For example, heritability estimates for feed conversion ratio at 42 d vary from 0.13 to 0.41 depending on the strains and on the duration of data collection [4, 7-10]. Estimates are generally lower in commercial lines than in random-bred or crossbred lines. Data from the literature also indicate that heritability of feed conversion ratio tends to decrease with age [2]. Despite this obvious effect of age, most of the literature on the genetics of feed efficiency has been concentrated on the last part of the production cycle, and has relied on one or a few data of feed efficiency calculated over periods of one to 6 weeks.

Thanks to recent developments in the phenotyping of feed consumption and animal weight, it is now possible to have access to the kinetics of animal growth and feed efficiency. Thus, using the BIRD-e electronic feeder that provides daily data of feed intake and body weight on individuals raised on the floor [11-12], Berger et al. [4] described the response of slow- or fast-growing chickens to conventional or alternative diets. Their work highlighted several interesting points such as the importance of transition periods around diet changes and the impact of early growth or feed efficiency parameters on the final performance of birds, including body composition and meat quality.

These new phenotyping tools make it possible to revisit the genetic study of feed efficiency in chickens and to optimize the search for selection criteria by considering the kinetics of daily data. The objective of this study was thus to describe the evolution profile of genetic parameters of body weight, feed intake and feed conversion ratio in fast-growing chickens, in a context feedstuffs diversification. Animals were thus fed with a diet containing the same amount of energy and proteins than usual diets, but including alternative feedstuffs to soybean, produced locally and less in competition with human food. Feed efficiency depends on many factors such as basal metabolism, protein turnover, body composition, and digestive efficiency [2]. A link with meat quality traits such as wooden breast defect was also recently reported [13]. In the current study, we relied on two lines of fast-growing broilers divergently selected for breast meat ultimate pH [14], in order to study the genetic control of the kinetics of body weight and feed efficiency in birds with different metabolic profiles and meat quality.
<caption>Figure 4.2. Kinetics of BW (2a), ADG (2b), DFI (2c), and DCFCR (2d) in both chicken lines.</caption>

<figure><img src="image_41.png" /><figcaption>4.2.b</figcaption></figure>

<figure><img src="image_42.png" /><figcaption>4.2.c</figcaption></figure>

<figure><img src="image_43.png" /><figcaption>4.2.d</figcaption></figure>

<figure><img src="image_44.png" /></figure>
# Results

## Line effect on phenotypic performance

As expected from the selection performed on these divergent lines, breast meat ultimate pH at 42 d was much lower in the pHu- line than in the pHu+ line (5.58±0.12 vs 6.21±0.14, P= 0.001). Breast meat yield (BMY) was also 3.5% lower in the pHu- line than in the pHu+ line (18.83±1.54 vs 19.51±1.82, P=0.001).

The line effect on daily performance is shown in Figure 4.2 and detailed statistics can be found in Supplementary Tables 4.1 and 4.2. Line effect was significant from 8 to 36 days for BW, from 5 to 20 days for ADG, from 5 to 10 days and from 23 to 42 days for DFI, and from 6 to 42 days for DCFCR. During these periods, pHu- birds showed better performance than those from the pHu+ line (7.3% higher ADG, 4.3% higher BW, and 4.2% lower DCFCR). The ranking of both lines changed for DFI between the first period (5-10 d), during which pHu+ birds had a 9.1% lower feed intake, and the second period (23-42 d) during which pHu- birds ate 4.3% less than pHu+ birds.

For all daily traits, pHu+ performance was more variable than pHu- performance, as shown by larger coefficients of variation reported in Figure 4.2. For growth traits, the performance variability of both lines was constant throughout the entire period (with CV comprised between 11 and 25%), while the variability of feed intake and feed efficiency was much higher before the first change of diet (CV from 15.7 to 38.8%) than after (CV from 5.3 to 21%).

## Heritability estimates for daily performance and slaughter traits

Heritability of pHu and BMY were quite significant and close in the two lines (pHu: 0.55±0.03 in pHu- and 0.58±0.03 in pHu+, BMY: 0.59±0.03 in pHu+ and 0.54±0.03 in pHu-).

Heritability estimates of daily performance are presented in Figure 4.3 and Supplementary table 5.3. Heritability of DCFCR (Figure 4.3d) increased with age for both lines, until 26 days for pHu+ and 30 days for pHu-. Then it remained stable around 0.57 for the pHu- line and slightly decreased to stabilize around 0.30-0.35 for the pHu+ line.

Heritability of ADG and DFI showed comparable kinetics (Figure 4.3.b, c). Before 26 d, heritability tended to increase in the pHu+ line whereas it was stable in the pHu- line. After 26 d, heritability estimates decreased in both lines for ADG. For DFI, it decreased to 0.11 for pHu+ whereas it remained stable around 0.25 for the heritability of DFI for pHu-. The largest difference in heritability between the two lines for both traits occurred on the 8 days after the second change of diet.
<figure><img src="image_45.png" /><figcaption>Figure 4.3. Heritability estimates (+ 1 standard error) of (3.a) BW, (3.b) ADG, (3.c) DFI, and (3.d) DCFCR.</figcaption></figure>
Finally, for BW, heritability increased until 14 d for pHu- and 20 d for the pHu+ line, estimates being 14 to 146% higher for pHu- (Figure 4.3a). After this first age, heritability estimates from both lines decreased until 42 d and remained close between the two lines, with slightly higher values after 35 days in the pHu+ line (9 to 13%).

## Maternal effects on daily performance

Maternal effects did not exceed 0.07 for both lines for DCFCR and DFI and nearly disappeared after the first diet change for DCFCR and the second diet change for DFI (Figure 4.4c, d). In contrast, maternal effects reached higher values and remained present later for BW and ADG (Figure 4.4.a, b), especially in the pHu+ line. For both traits, maximum values were reached for pHu+ animals between the two diet changes (0.10 for ADG and 0.13 for BW). Maternal effects on BW in pHu- were lower (<0.08), but remained significant until 38 d.

## Genetic correlations

### *Correlations between final cumulative feed conversion ratio and daily performance*

Genetic correlations between final cumulative feed conversion ratio ($DCFCR_{42}$) and daily traits are reported in Figure 4.5 and Supplementary table 5.4. As expected, the genetic correlation between daily and final cumulative FCR increased with age. It reached strong values before the first diet change in pHu- (>0.8) and just after in pHu+ (>0.7), suggesting that feed efficiency recorded around the first diet change is an early predictor of final feed efficiency. In contrast, correlations between $DCFCR_{42}$ and body weight were low throughout the whole period in both lines (0.17 in pHu+ and -0.02 in pHu- on average).

Profiles of correlations between DFI and $DCFCR_{42}$ varied more between the two lines. Two periods of stability occurred for pHu+. The first one occurred between day 5 and day 13 and showed values around 0.62. The second one was between day 15 and slaughter, and showed values around 0.4. The correlation between DFI and $DCFCR_{42}$ in pHu- was more variable, increasing to 0.7 before the first change of diet and decreasing to 0.2 the day after. After this change, the correlation remained stable until 22 days. Then it increased strongly to 0.72 at 29 days and finally decreased to 0.2 in the last days. The line difference could also be seen in the correlations between $DCFCR_{42}$ and ADG. Thus, the correlation within the pHu+ line remained stable and low during the whole life of the chickens (0.10 on average), whereas this correlation showed a decrease after 27 days in the pHu- line. Average correlation values were equal to 0.04 before 27 d and -0.32 after 27 d, indicating that in pHu- a lower efficiency was associated with a decreased growth rate at later ages.
<figure><img src="image_46.png" /><figcaption>Figure 4.4.Maternal permanent environment effect ($c^2$) on (4.a) BW, (4.b) ADG, (4.c) DFI, and (4.d) DCFR.</figcaption></figure>
<table><caption>Table 4.1. Body composition and meat characteristics of label rouge (LR) and Cobb500 (STD) genotypes fed with either the alternative diet or the classical diet.</caption><thead><tr><th rowspan="2">Trait<sup>1</sup></th><th rowspan="2">Genotype</th><th colspan="2">LS Means</th><th rowspan="2">P-value<br>of diet<br>effect</th></tr><tr><th colspan="2">Diet<sup>2</sup></th></tr><tr><th></th><th></th><th>AD</th><th>CD</th><th></th></tr></thead><tbody><tr><td rowspan="2">Slaughter<br>weight (g)</td><td>LR</td><td>3010</td><td>2951</td><td>0.371</td></tr><tr><td>STD</td><td>2334</td><td>2355</td><td>0.720</td></tr><tr><td rowspan="2">AFY (%)</td><td>LR</td><td>3.53</td><td>3.95</td><td>0.080</td></tr><tr><td>STD</td><td>1.57</td><td>1.83</td><td>0.001</td></tr><tr><td rowspan="2">BMY (%)</td><td>LR</td><td>14.56</td><td>14.40</td><td>0.550</td></tr><tr><td>STD</td><td>20.44</td><td>20.40</td><td>0.970</td></tr><tr><td rowspan="2">TY (%)</td><td>LR</td><td>25.64</td><td>25.16</td><td>0.030</td></tr><tr><td>STD</td><td>22.58</td><td>22.94</td><td>0.100</td></tr><tr><td rowspan="2">L*</td><td>LR</td><td>48.76</td><td>49.14</td><td>0.520</td></tr><tr><td>STD</td><td>47.99</td><td>47.38</td><td>0.250</td></tr><tr><td rowspan="2">a*</td><td>LR</td><td>-1.06</td><td>-1.09</td><td>0.860</td></tr><tr><td>STD</td><td>-0.51</td><td>-0.72</td><td>0.100</td></tr><tr><td rowspan="2">b*</td><td>LR</td><td>9.82</td><td>9.48</td><td>0.230</td></tr><tr><td>STD</td><td>8.02</td><td>8.89</td><td>0.001</td></tr><tr><td rowspan="2">pHu</td><td>LR</td><td>5.74</td><td>5.72</td><td>0.350</td></tr><tr><td>STD</td><td>5.89</td><td>5.79</td><td>0.001</td></tr></tbody></table>

<footnote n="1">¹ AFY: abdominal fat yield, BMY: breast muscle yield, TY: thigh yield, L*: breast meat luminance, a*: breast meat redness, b*: breast meat yellowness, pHu: breast meat pH 24 h after slaughter</footnote>

<footnote n="2">² AD: alternative diet; CD: control diet</footnote>
<caption>Figure 4.5 .Kinetics of genetic correlations between (DCFCR42) and daily performance for pHu+ (5.4.a) and pHu- (5.4.b). DCFCR42 is for final cumulative feed conversion ratio.</caption>

<figure><img src="image_47.png" /><figcaption>4.5.b</figcaption></figure>

<figure><img src="image_48.png" /></figure>
<figure><img src="image_49.png" /><figcaption>Figure 4.6. Kinetics of genetic correlations between pHu and daily performance for pHu+ (4.6.a) and pHu- (4.6.b).</figcaption></figure>
## Correlation between pHu and daily performance

Genetic correlations between the criteria of selection, breast meat ultimate pH, and daily traits are reported in Figure 4.6 and Supplementary table 5.5. Since the lines had been divergently selected for pHu, different patterns of correlations between pHu and growth or feed efficiency traits could be expected between the two lines. Indeed, for most correlations, the signs of correlations were different in the two lines. For example, correlations between BW or DFI and pHu were low and negative in pHu+ (μ=-0.23 and -0.37, respectively), but low and positive in pHu- (μ=+0.11 and +0.33, respectively). Regarding the correlation between ADG and pHu, the difference of pattern between lines was marked after the second diet change, as this correlation increased from -0.24 to +0.58 in pHu+, but decreased from +0.37 to -0.56 in pHu-.

The correlation between DCFCR and pHu increased in both lines from hatch to the first diet change (to +0.38 for pHu+ and +0.80 for pHu-). After this event, the correlation for pHu+ decreased down to -0.6 at 13 days and remained stable until the second diet change. Finally, the correlation increased slowly up to -0.15 at 28 days. From 29 d until the end, it decreased to reach -0.89. The correlation for pHu- was more stable, decreasing after the first change of diet and being stable from 15 to 30 days, at around +0.53. After this day, it increased and reached +0.71 at 42 days.

## Correlation between BMY and DCFCR

Figure 4.7 and Supplementary table 5.6 contain the genetic correlations between DCFCR and BMY. For the pHu- birds, this correlation first increased from 0 to 0.38 until 12 d and then decreased until 42 d, being close to 0 at the end. In comparison, the correlation for pHu+ showed 3 phases: from 0 to 16 d around 0.2-0.4, from 17 to 28 d between 0.4 and 0.6, and from 29 d to slaughter a decrease until 0.

<figure><img src="image_50.png" /><figcaption>Figure 4.7. Kinetics of genetic correlations between BMY and the DCFCR for both lines.</figcaption></figure>
Discussion

This study made it possible to give a detailed description of the kinetics of growth and feed efficiency in the two divergent lines for breast meat ultimate pH. While the weight of chicks was higher at hatching in the pHu+ line (40.9 vs 38.4, P<0.001), their daily feed intake was lower in the first days of life so that BW became similar between the two lines at 5 d. As ultimate pH and muscle glycogen stores are highly genetically correlated at slaughter age (-0.97) [15], selection applied to pHu modified glycogen levels between pHu+ and pHu- broiler chickens [14, 16]. This difference was found to be already present at hatch, with a higher glycogen content in the breast and pipping muscles of pHu- chicks [17]. These early metabolic differences are accompanied by a lower quality of pHu+ chicks [18] estimated according to the Tona's grid [19]. In poor quality chicks, the starting phase seems more difficult with an altered feed intake in the first days of life and a lower average daily gain continuing up to 3 weeks of age, as evidenced in the current study. As shown by transcriptomic and metabolomic analyses conducted at 6 weeks [20, 21], growing chickens from the two lines rely on different metabolic pathways to produce energy, with an intensive use of carbohydrate metabolism in the pHu- line and of muscle proteolysis and amino acid catabolism as well as lipid oxidation in the pHu+ line. In the current study, we showed that although the pHu+ birds consumed a higher amount of feed from 23 days onwards, they were characterized by a lower BW and 4% higher FCR, in consistency with their lower metabolic efficiency. At the same time, pHu+ birds had a higher breast yield at slaughter (19.5 vs 18.8, P<0.001). Although a favorable relationship is usually observed between breast development and feed efficiency [1], this was not recovered in the pHu lines for which the higher protein deposition in the pHu+ line seems to be made at a higher energetic cost. This absence of correlation and the lower DCFCR heritability in pHu+ birds after the second change of diet may underline a difficulty for these animals to express fully their genetic potential. This could be due to the fact that the energetic needs are fulfilled by the diet for the pHu- birds but not for the less efficient pHu+ birds. For example, the Hubbard nutritional recommendations during the finishing phase vary between 2850 and 3200 kcal/kg [22]. Our diet is formulated at 2950 kcal/kg, which may be sufficient in pHu- but not in pHu+, whose needs may be closer to the upper values recommended. During this period, this lack of energy is compensated by an increased feed intake, leading to an identical growth in the two lines but to a higher DCFCR in pHu+ line. We can thus hypothesize that a more energetic diet would be more appropriate in the pHu+ line.

The current study also provided original results on the genetic determinism of the kinetics
of growth and feed efficiency in chickens. While heritability of BW evolved in a similar way in
the two lines from 20 days onwards, decreasing from high values of 0.7 to moderate values of
0.35-0.40, genetic controls of BW differed between the two lines in the first part of life. In
particular, BW heritability was much lower between 10 d and 20 d in the pHu+ line. This may
partly be due to maternal effects that greatly influenced BW variability during this period in the
pHu+ line, while they remained low in the pHu- line. In the current standardized conditions of
poultry production, maternal effects are mainly mediated by egg characteristics. Factors such as
egg size and nutrient content (vitamins, carotenoids, etc.) or maternal hormones can impact the
development and the phenotypes linked to the fitness of the offspring [23]. The higher egg weight
observed in the pHu+ line [17] might contribute to the higher maternal effects observed in this
line, but additional studies are needed to evaluate if variations in nutrient and hormone contents
in the eggs could also exist between the two lines.

In contrast to BW, the genetic difference between the two lines for feed efficiency was
mostly present after 25 days, when heritability stabilized at a high value of 0.5-0.6 in the pHu-
line and at a moderate value of 0.30 in the pHu+ line. A difference in the profiles of heritability
of feed efficiency around diet transitions was also evidenced. Indeed, pHu+ heritability was quite
stable around these periods, whereas pHu- showed a strong increase in heritability around the
first transition and a sharp decrease before the second transition. The profiles of genetic
correlations showed that, in both lines, maintenance costs were not decisive for feed efficiency,
but that feed intake was the trait contributing the most to feed efficiency. However, after 30 d in
the pHu- line, the influence of FI decreased in favor of weight gain. The difference in energetic
metabolism between lines may contribute to these differences in genetic profiles. At the first
transition, feed intake and body weight both increased linearly and needs may be fulfilled thanks
to the increase of the ratio of energy to protein between the first and the second diet. In contrast,
between 15 and 20 days, feed intake increase was still linear whereas growth was exponential.
For pHu- birds, the ratio of energy to protein in the second diet may thus be limiting to fulfill
animal needs until the last diet transition, after which heritability increased again and reached a
higher value than in the pHu+ line.

Access to daily data can offer new opportunities to improve selection strategies for feed
efficiency. At first, the profiles of genetic parameters can lead to identifying earlier predictors of
the final feed efficiency. By combining information on heritability and genetic correlations, we
compared expected response to a direct selection for the cumulative feed conversion ratio
measured at slaughter age (i.e. DCFCR42) to the indirect selection for the same indicator
measured earlier. Given our experimental results, selection for DCFCR could be envisaged as
soon as 20 d in pHu+ birds and 29 d in pHu- birds with an expected response amounting to 80%
of the maximum response that would be obtained by selecting for DCFCR42. Whatever the
scenario, the expected response would be higher in the pHu- line than in the pHu+ line (0.42±0.14
and 0.21±0.09, respectively for pHu- and pHu+). Moreover, given the profiles of correlations
with BMY and meat pHu, improved FE would lead to an increased pH value and a decreased
breast yield in the pHu+ line, but a more acidic meat without any correlated response on BMY
in the pHu- line. This illustrated again the different genetic determinism of FE in the two lines.
Selection on feed conversion ratio with an alternative diet would improve the efficiency of the system by two ways 1) the improvement of FCR performance with a high economic impact or 2) at the same FCR, the spare of resources that could be used in human food. In a former study, we already showed that rapid growing chickens could reach similar performances with a classical corn-soybean diet than with an alternative diet [1]. We considered the proportion of energy and protein of each feedstuff that could be used for human food [24], the composition of our alternative diet and of the classical diet as used in Berger et al. [1], and the mean feed intake of starter, grower and finisher diets we got in the current study to calculate the quantity of human-edible energy and proteins that could be spared by using an alternative diet instead of a classical diet. For each 2 kg chicken fed with the alternative diet and with the same FCR, depending on the line, we would use 63.8-65.2 g less human-edible proteins and 153.5-158.0 g less human-edible energy than with the classical diet. This represents a decrease of 1.4% on protein and 1.8 to 3.5% on energy. This saving of resources by changing diet composition without any FCR improvement is roughly equivalent to the gain expected by a genetic improvement on the alternative diet of 0.10 of FCR for human-edible proteins (77.8-80.2 g) and by a decrease of 0.15 of FCR for human-edible energy (155.4-159.7 g).

## Conclusion

Our study highlighted differences in the kinetics of feed intake, growth rate, and feed efficiency between the two broiler lines diverging for breast meat ultimate pH. The genetic determinism of body weight and feed efficiency varied to some extent between the two lines, which exhibited early metabolic differences. Having access to daily FCR data allowed for the identification of early relevant predictors, and opens the way to new strategies or methodologies for selecting feed efficiency.

## Materials and methods

The present study was performed in agreement with the French National Regulation for human care and use of animals for research purposes and received the authorization number 2019071715406895_V2-21517. Animals were reared at the PEAT INRAE poultry experimental facility (2018, https://doi.org/10.15454/1.5572326250887292E12) registered by the French Ministry of Agriculture under license number C-37-175-1 for animal experimentation (INRAE, Centre Val de Loire, Nouzilly, France).
<table><caption>Table 4.2. Composition and age of distribution of diets.</caption><thead><tr><th>Ingredient (%)</th><th>1-8 d</th><th>9-20 d</th><th>21-42 d</th></tr></thead><tbody><tr><td>Corn</td><td>20.42</td><td>19.89</td><td>23.50</td></tr><tr><td>Wheat</td><td>30.10</td><td>30.10</td><td>30.10</td></tr><tr><td>Fava bean</td><td>-</td><td>12.00</td><td>13.00</td></tr><tr><td>Soybean meal</td><td>24.22</td><td>11.61</td><td>7.13</td></tr><tr><td>Rapeseed meal</td><td>5.00</td><td>5.00</td><td>8.00</td></tr><tr><td>Wheat DDGS</td><td>3.00</td><td>5.00</td><td>5.00</td></tr><tr><td>High fiber sunflower meal</td><td>8.12</td><td>7.73</td><td>5.19</td></tr><tr><td>Soybean oil</td><td>5.00</td><td>5.00</td><td>5.00</td></tr><tr><td>Corn gluten</td><td></td><td></td><td></td></tr><tr><td>Calcium carbonate</td><td>0.655</td><td>0.142</td><td>0.00</td></tr><tr><td>Bicalcic phosphate</td><td>2.05</td><td>1.73</td><td>1.40</td></tr><tr><td>Salt</td><td>0.192</td><td>0.15</td><td>0.158</td></tr><tr><td>Vitamins and minerals</td><td>0.40</td><td>0.40</td><td>0.40</td></tr><tr><td>Sodium carbonate</td><td>0.196</td><td>0.262</td><td>0.250</td></tr><tr><td>DL-Methionine</td><td>0.234</td><td>0.285</td><td>0.234</td></tr><tr><td>HCL Lysine</td><td>0.176</td><td>0.264</td><td>0.250</td></tr><tr><td>Threonine</td><td>0.088</td><td>0.157</td><td>0.135</td></tr><tr><td>Valine</td><td>0.038</td><td>0.130</td><td>0.106</td></tr><tr><td>Tryptophane</td><td>-</td><td>-</td><td>0.005</td></tr><tr><td colspan="4"><strong>Calculated composition</strong></td></tr><tr><td>AMEn, kcal/kg</td><td>2850</td><td>2900</td><td>2950</td></tr><tr><td>CP, g/kg</td><td>215</td><td>194</td><td>181</td></tr><tr><td>Lys, g/kg</td><td>11.2</td><td>10.9</td><td>10</td></tr><tr><td>Met + Cys, g/kg</td><td>8.4</td><td>8.17</td><td>7.5</td></tr><tr><td>Trp, g/kg</td><td>2.28</td><td>1.84</td><td>1.7</td></tr></tbody></table>
## Birds and housing

In this study, we used animals from two experimental broiler lines divergently selected for a high (pHu+) or a low (pHu-) ultimate pH of the *Pectoralis major* muscle during fourteen generations. These lines came from a fast-growing grandparental female broiler line selected for both growth and reproduction traits. A complete description of the breeding scheme and performance of these lines can be found in Alnahhas et al. [14]. Two batches were reared in parallel for 42 days for the current study, the second batch starting one week later than the first one. The first and second batches respectively included 183 and 156 pHu- and 144 and 167 pHu+ of both sexes.

The temperature was set at 32°C at hatch and decreased progressively to reach 20-23°C from 29 d on. The light program was 24D:0N at d0, 23D:1N at 1 d and 18D:6N from 3 to 42 d. At hatch, the animals were identified with a wing band and an electronic Radio frequency identification device (RFID) chip, then weighed and placed in one pen on a floor covered with wooden chips. The RFID chip was placed at the base of the neck and secured with a plastic string passing under the skin.

The animals were fed *ad libitum* with a diet containing a high proportion of sunflower, rapeseed, and fava bean in order to reduce the soybean meal proportion in the diet. The detailed composition is shown in Table 4.2. A starter diet was given from hatch to 8 d (2850 kcal.kg⁻¹ DM; 21.5 % CP), a grower diet from 9 to 19 d (2900 kcal.kg⁻¹ DM; 20.0 % CP), and a finisher diet from 20 to 42 d (2950 kcal.kg⁻¹ DM; 18.5 % CP). Diet was formulated using the PORFAL software v.4.0.17 (ITP-INRA, Paris) in order to follow Hubbard broiler nutrition recommendations [22]. Potential interesting feedstuffs, such as Faba bean, sunflower meal, rapeseed and DDGS were included in the diet based on the results of a former project dedicated to the test of alternative feedstuffs for poultry diets [25].

Body weight and feed intake were individually and continuously recorded throughout the experiment thanks to electronic feed stations [11] and calculated following the procedure presented in Berger et al. [4].

## Body weight, feed intake, weight gain, and feed conversion ratio

Body weight (BW), daily feed intake (DFI), average daily gain (ADG), and daily cumulative feed conversion ratio (DCFCR) were calculated as in Berger et al. [1]. Briefly, for each animal i at day j, average daily gain (ADG<sub>ij</sub>) and daily cumulative feed conversion ratio (DCFCR<sub>ij</sub>) were calculated as:

$$ADG_{ij} = \frac{BW_{i(j+2)} - BW_{i(j-2)}}{5}$$

$$DCFCR_{ij} = \frac{\sum_{k=3}^{k=j} DFI_{ik}}{BW_{ij} - BW_{i3}}$$
where DFI<sub>ij</sub> and BW<sub>ij</sub> are the daily feed intake and body weight of animal i for day j, respectively. DCFCR<sub>42</sub> stands for the cumulative feed conversion ratio over the whole rearing period.

Meat ultimate pH

At 42 d, the animals were weighed after 8 hours of feed withdrawal and transferred to the slaughterhouse of the PEAT INRAE poultry experimental facility (2018, https://doi.org/10.15454/1.5572326250887292E12).

After 24 hours of chilling, the right *Pectoralis major* and *Pectoralis minor* were cut and weighed. Breast meat yield (BMY) was calculated as:

$$BMY = 100 \times \frac{2 \times (Pectoralis major + Pectoralis minor)}{BW_{42}}$$

The ultimate pH of the *Pectoralis major* (pHu) was measured with a portable pH meter (model 506, Crison Instruments SA, Alella, Barcelona, Spain).

Phenotypic analyses

All phenotypes were first analyzed using the PROC GLM procedure of SAS 9.4 [26] with the following model:

$$y_{ijkl} = L_i + S_j + H_k + e_{ijkl}$$

with $y_{ijkl}$ being the trait for animal l of line i, sex j and hatch k and $e_{ijkl}$ the residual for animal l.

Genetic analyses

Heritability and genetic correlations between traits were estimated using VCE6.0 software [27, 28]. In addition to data collected during this experiment, data of pHu collected from previous generations of selection were included in the genetic analyses, leading to a variable number of data depending on the trait: 578 for BW, ADG, DFI, DCFCR and 9,541 for pHu and BMY. The pedigree file contained all animals from the beginning of the selection experiment, i.e. 23,319 animals.

Genetic analyses were conducted separately for each line. The animal model included the fixed effects of the sex (N=2) and batch (N=31) and the direct genetic effect of animal (N=23,319). When the maternal permanent environment effect (N=1,961) was not null, it was also included in the model of analysis.

Abbreviations

ADG: Average Daily Gain (g.d<sup>-1</sup>); BMY: Breast Meat Yield (%); CP: Crude Protein (%); CV: Coefficient of Variation (%); DCFCR: Daily Cumulative Feed Conversion Ratio (g.g<sup>-1</sup>);
DCFCR<sub>42</sub>: Final Cumulative Feed Conversion Ratio at 42 days (g.g<sup>-1</sup>); DFI: Daily Feed Intake (g.d<sup>-1</sup>); FCR: Feed Conversion Ratio (g.g<sup>-1</sup>); FE: Feed Efficiency; pHu: Ultimate pH of breast meat; pHu-: Broiler lines divergently selected for a low pHu; pHu+: Broiler lines divergently selected for a high pHu; RFID: Radio Frequency Identification Device

**Authors' contributions**

QB: data analysis; EG, QB, SG: feed station development; QB, JB, PG, MC: data acquisition.
ELBD, SG, QB: project conception, interpretation of results, manuscript drafting; SG, ELBD: project supervision. All authors read and approved the manuscript.

**Funding**

This study was supported by Feed-a-Gene, a project that has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 633531.

**Availability of data and materials**

The datasets supporting the conclusions of this article are available in the Data INRAE repository, under the unique persistent identifier https://doi.org/10.15454/QBNHAD.

**Ethics approval and consent to participate**

Animals were reared at the PEAT INRAE poultry experimental facility (2018, https://doi.org/10.15454/1.5572326250887292E12) registered by the French Ministry of Agriculture under license number C-37-175-1 for animal experimentation (INRAE, Centre Val de Loire, and Nouzilly, France).

**Competing interests**

The authors declare that they have no competing interests

**References**

1. Méda B, Dusart L, Lessire M, Hallouis JM, Bordeau T, Souchet C, et al. Replacing soybean meal by alternative protein sources: multicriteria assessment of a “Label Rouge” chicken production system. Proceedings of 11èmes Journées de la Recherche Avicole et Palmipèdes à Foie Gras, Tours, France, 25-26/03/2015. 2015;1082-87.

2. Abasht B, Mignon-Grasteau S, Bottje W, Lake J. Genetics and genomics of feed utilization efficiency in poultry species. In: Aggrey SE, Zhou H, Tixier-Boichard M, Rhoads DD,
editors. Advances in poultry genetics and genomics. Cambridge: Burleigh Dodds Series in Agricultural Science; 2020. p. 183–220. https://doi:10.19103/AS.2020.0065.11.

3. Lathuillière MJ, Miranda EJ, Bulle C, Couto EG, Johnson MS. Land occupation and transformation impacts of soybean production in Southern Amazonia, Brazil J Clean Prod. 2017;149:680–9. https://doi:10.1016/J.JCLEPRO.2017.02.120.

4. Berger Q, Guettier E, Urvoix S, Bernard J, Ganier P, Chahnamian M, et al. The kinetics of growth, feed intake, and feed efficiency reveal a good capacity of adaptation of slow and rapid growing broilers to alternative diets. Poult Sci. 2021;100:101010. https://doi:10.1016/j.psj.2021.01.032.

5. Foltyn M, Rada V, Lichovnikova M, Dračková E. Effect of corn DDGS on broilers performance and meat quality. Acta Univ Agric Silvic Mendelianae Brun. 2013; 61:59–64. https://doi:10.11118/actaun201361010059.

6. Gopinger E, Xavier EG, Elias MC, Catalan AAS, Castro MLS, Nunes AP, et al. The effect of different dietary levels of canola meal on growth performance, nutrient digestibility, and gut morphology of broiler chickens. Poult Sci. 2014; 93:1130–6. https://doi:10.3382/ps.2013-03426.

7. Aggrey SE, Karnuah AB, Sebastian B, Anthony NB. Genetic properties of feed efficiency parameters in meat-type chickens. Genet Sel Evol. 2010; 42:25. https://doi.org/10.1186/1297-9686-42-25.

8. Aggrey S, González-Cerón F, Rekaya R. Association of SNPs with components of residual feed intake parameters in a meat-type chicken population. Proc 10th World Congress on Genetics Applied to Livestock Production, Vancouver, BC, Canada, 17-22/08/2014. 2014.

9. Beaumont C, Guillaumin S, Geraert PA, Mignon-Grasteau S, Leclercq B. Genetic parameters of body weight of broiler chickens measured at 22°C or 32°C. Br Poult Sci. 1998;39:488–91. https://doi:10.1080/00071669888647.

10. Zhang X, Tsuruta S, Andonov S, Lourenco DAL, Sapp RL, Wang C, et al. Relationships among mortality, performance, and disorder traits in broiler chickens: a genetic and genomic approach. Poult Sci. 2018;97:1511–8. https://doi:10.3382/ps/pex431.

11. Guettier E, Urvoix S, Servant P, Mika A, Berger Q, Mignon-Grasteau S. Mise au point d'un automate de consommation alimentaire pour volailles nommé BIRD-e: Bird Individual Ration Dispenser-electronic. Cahier des Techniques de l'INRA. 2020;99:12 p.

12. INRAE. Bird-e. Automate de consommation alimentaire pour volailles. 2020. https://www feed-a-gene eu/media/bird-e-automate-de-consommation-alimentaire-pour-volailles.

13. Abasht B, Zhou N, Lee WR, Zhuo Z, Peripolli E. The metabolic characteristics of susceptibility to wooden breast disease in chickens with high feed efficiency. Poult Sci. 2019;98:3246-56. https://doi: 10.3382/ps/pez183.
14. Alnahhas N, Berri C, Boulay M, Baéza E, Jégo Y, Baumard Y, et al. Selecting broiler chickens for ultimate pH of breast muscle: Analysis of divergent selection experiment and phenotypic consequences on meat quality, growth, and body composition traits. J Anim Sci. 2014;92:3816-24. https://doi: 10.2527/jas.2014-7597.

15. Le Bihan-Duval E, Debut M, Berri CM, Sellier N, Santé-Lhoutellier V, Jégo Y, et al. Chicken meat quality: genetic variability and relationship with growth and muscle characteristics. BMC Genet. 2008;9:53. https://doi:10.1186/1471-2156-9-53.

16. Alnahhas N, Le Bihan-Duval E, Baéza E, Chabault M, Chartrin P, Bordeau T, et al. Impact of divergent selection for ultimate pH of Pectoralis major muscle on biochemical, histological, and sensorial attributes of broiler meat. J Anim Sci. 2015; 93:4524-31. https://doi: 10.2527/jas.2015-9100.

17. Métayer-Coustard S, Tesseraud S, Praud C, Royer D, Bordeau T, Coudert E, et al. Early growth and protein-energy metabolism in chicken lines divergently selected on ultimate pH. Front Physiol. 2021;12. https://doi:10.3389/fphys.2021.643580.

18. Metayer-Coustard S, Franzoni A, Réhault-Godbert S, Collin A, Travel A, Raynaud E, et al. Variation in energy stores: impact on reproduction and egg characteristics in meat-type strains. Proceedings of 13èmes Journées de la Recherche Avicole et Palmipèdes à Foie Gras, Tours, France, 20-21/03/2019. 2019; 206-9.

19. Tona K, Bamelis F, De Ketelaere B, Bruggeman V, Moraes VMB, Buyse J, et al. Effects of egg storage time on spread of hatch, chick quality, and chick juvenile growth. Poult Sci. 2003;82:736-41. https://doi: 10.1093/ps/82.5.736.

20. Beauclercq S, Nadal-Desbarats L, Hennequet-Antier C, Collin A, Tesseraud S, Bourin M, et al. Serum and muscle metabolomics for the prediction of ultimate pH, a key factor for chicken-meat quality. J Proteome Res. 2016;15:1168-78. https://doi: 10.1021/acs.jproteome.5b01050.

21. Beauclercq S, Hennequet-Antier C, Praud C, Godet E, Collin A, Tesseraud S, et al. Muscle transcriptome analysis reveals molecular pathways and biomarkers involved in extreme ultimate pH and meat defect occurrence in chicken. Sci Rep. 2017;7:6447. https://doi:10.1038/s41598-017-06511-6.

22. Hubbard. Poulet de chair : Guide et recommandations nutritionnelles. 2020. https://www.hubbardbreeders.com/media/hep_guide_broiler_fr.pdf.

23. Reed WL, Clark ME. Beyond maternal effects in birds: responses of the embryo to the environment. Integr Comp Biol. 2011;51:73-80. https://doi:10.1093/icb/icr032.

24. Laisse S, Baumont R, Dusart L, Gaudré D, Rouillé B, Benoit M, et al. L'efficience nette de conversion des aliments par les animaux d'élevage : une nouvelle approche pour évaluer la contribution de l'élevage à l'alimentation humaine. INRAE Prod Anim. 2018;31:269-88. https://10.20870/productions-animales.2018.31.3.2355.
25. Pampouille E, Dusart L, Bonnouvrier A, Danel J, Dauguet S, Désolé M, et al. VOCALIM - Mieux valoriser des matières premières métropolitaines dans l'alimentation des poulets de chair pour améliorer l'autonomie protéique française. Innov Agron. 2021;82:425-40.

26. SAS Institute Inc. SAS/STAT® 13.1 User's Guide. Cary, NC: SAS Institute Inc. 2013.

27. Groeneveld LF, Lenstra JA, Eding H, Toro MA, Scherf B, Pilling D, et al. Genetic diversity in farm animals – a review. Anim Genet. 2010; 41:6-31. https://doi: 10.1111/j.1365-2052.2010.02038.x.

28. Neumaier A, Groeneveld E. Restricted maximum likelihood estimation of covariances in sparse linear models. Genet Sel Evol. 1998;30:3. https://doi:10.1186/1297-9686-30-1-3.

## Résumé des principaux résultats et conclusions

**Différences entre lignées pHu+ et pHu-** - Par rapport à la lignée pHu-, la lignée pHu+ présente une moindre efficacité alimentaire liée à une réduction de consommation alimentaire et de croissance en début d'élevage puis à une augmentation de consommation à croissance équivalente en fin d'élevage. Les performances des animaux pHu+ sont également plus variables que celles des pHu-.

**Profils d'héritabilité** – Les profils d'héritabilité des caractères varient fortement avec l'âge et la lignée. Cette étude génétique confirme l'importance des périodes de transitions alimentaires, déjà détectée dans l'étude phénotypique. Ainsi, l'héritabilité de l'efficacité alimentaire est plus élevée chez les pHu- sur toute la période d'élevage hormis les 5-6 jours autour de la seconde transition, pendant laquelle la différence d'héritabilité entre lignées disparaît.

**Profils de corrélations génétiques** - Les corrélations génétiques obtenues entre efficacité alimentaire finale, pHu et performances journalières, ont permis de confirmer l'existence d'une différence de métabolisme entre les deux lignées, avec une opposition des corrélations entre le pHu et les autres caractères entre les deux lignées. Dans les deux cas néanmoins, les profils de corrélations indiquent que l'élément déterminant de l'efficacité alimentaire est le niveau de consommation alimentaire.

**Identification d'indicateurs précoces pour la sélection de l'efficacité alimentaire** – Comptementu des valeurs d'héritabilité des variables quotidiennes et de leurs corrélations avec les performances finales, l'efficacité alimentaire précoce serait un critère de sélection pertinent de l'efficacité finale des animaux, mais l'âge auquel la mesurer diffèrerait entre les deux lignées
# CHAPITRE 5 : Evaluation de différentes stratégies de sélection de l'efficacité alimentaire du poulet de chair dans un contexte alimentaire alternatif au modèle mais-soja

Dans le chapitre précédent, nous avons montré qu'il était possible de trouver des indicateurs précoces de l'efficacité alimentaire qui pouvaient être utilisés pour la sélection de l'efficacité alimentaire finale. L'objectif de cette dernière partie expérimentale était donc d'établir différentes stratégies de sélection de l'efficacité alimentaire et d'en comparer les impacts en termes d'efficacité alimentaire, mais également de qualité des produits. Ces stratégies ont été élaborées à partir des données collectées lors de la seconde expérience sur les lignées pHu+ et pHu- (chapitre 4).

Les différentes stratégies qui ont été comparées sont les suivantes :

* sélection directe sur l'efficacité alimentaire cumulée à 42 jours, qui est l'objectif final

* sélection indirecte sur le rendement en filet à 42 jours. En effet, les animaux les plus efficaces sont en général ceux qui déposent le plus de protéines et ont un filet plus développé. Ce caractère est donc régulièrement utilisé comme critère de sélection indirect de l'efficacité alimentaire

* sélection indirecte sur la valeur d'efficacité alimentaire cumulée à un âge particulier :

* soit à l'âge le plus précoce possible. D'après les résultats obtenus dans le chapitre précédent, nous avons retenu l'âge de 17 jours, qui correspond à un pic d'héritabilité dans la lignée pHu- tout en étant une valeur intéressante d'héritabilité dans la lignée pHu+

* soit à un âge présentant la combinaison la plus intéressante possible d'héritabilité et de corrélation génétique avec l'efficacité finale (26 jours chez les pHu+ et 30 jours chez les pHu-).

* sélection sur un profil d'évolution de l'efficacité alimentaire. Cela permettrait d'être moins dépendant des évolutions d'un jour à l'autre des valeurs d'efficacité en prenant en compte l'ensemble de la croissance, tout en distinguant des animaux d'efficacité alimentaire finale équivalente, mais y parvenant par des chemins différents. La Figure 5.1
illustre par exemple, pour des animaux d'efficacité finale proche, les différences observables au long de leur vie quant à l'évolution de leur efficacité alimentaire.

<figure><img src="image_51.png" /><figcaption>Figure 5.1.Profil d'efficacité de différents animaux présentant le même DCFCF42.</figcaption></figure>

Nous avons donc comparé les réponses attendues à la sélection de ces différentes stratégies sur :

* l'indice de consommation cumulé à 42 jours, ainsi que sur la consommation alimentaire et le poids à l'abattage, qui en sont les composants, afin de comprendre quel paramètre du ratio allait le plus évoluer

* le rendement en filet et le pH ultime de la viande du filet, deux paramètres clés de rentabilité et de qualité pour la filière de production du poulet de chair.

Les résultats de cette troisième partie font l'objet d'une publication qui sera prochainement soumise dans Journal of Animal Science (Berger et al., 2022).
Comparison of different strategies of selection of feed efficiency in broilers

Quentin Berger<fnref n="1" />, Elodie Guettier<fnref n="1" />, Isabelle Bouvarel<fnref n="2" />, Bertrand Méda<fnref n="1" />, Jérémy Bernard<fnref n="3" />, Patrice Ganier<fnref n="3" />, Marine Chahnamian<fnref n="3" />, Elisabeth Le Bihan-Duval<fnref n="1" />, Sandrine Mignon-Grasteau*

<footnote n="1">¹ INRAE, Université de Tours, BOA, 37380 Nouzilly, France</footnote>

<footnote n="2">² ITAVI, 37380 Nouzilly, France</footnote>

<footnote n="3">³ INRAE, PEAT, 37380 Nouzilly, France</footnote>

<footnote>*Corresponding author: Sandrine Mignon-Grasteau</footnote>

<footnote>Email: [sandrine.grasteau@inrae.fr](mailto:sandrine.grasteau@inrae.fr)</footnote>

<footnote>Address: INRAE, Université de Tours, BOA, 37380 Nouzilly, France</footnote>

<footnote>Phone Number: +33247427691</footnote>
**Abstract**

The recent development of feed stations allows to re-examine the potential strategies of selection of feed efficiency in chickens in real conditions of production, i.e. on floor and in group whereas selection was previously done on data collected in individual cages. Moreover, by giving access to daily data and not only periodic data, these tools also give access to new traits such as daily feed efficiency and permit to model kinetics of feed efficiency throughout animals’ life.

Using electronic feed stations, we thus collected during 42 days daily data of body weight, feed intake and cumulated feed conversion ratio (DCFCR) on 578 broilers from two lines divergently selected on high (pHu+) or low (pHu-) breast meat pH. At 6 weeks, we measured breast yield and breast meat pH. Based on their daily data of feed intake and body weight from hatch to 42 days, animals were categorized by hierarchical clustering into 5 clusters per line. Considering the estimated heritability of daily feed efficiency traits and their genetic correlations with feed conversion ratio at 42 days (DCFCR<sub>42</sub>), we identified one early stage (17 d) and one late stage (26 d in pHu+, 30 d in pHu-) as potential criteria of selection of feed efficiency. We then compared expected responses of DCFCR<sub>42</sub> either to direct selection of DCFCR<sub>42</sub> or to indirect selection on body weight at 42 d, feed conversion ratio at early or late age and cluster number.

Clustering showed that the most efficient animals at 42 d were either those with a low feed conversion ratio at the first diet change or those with a decreasing FCR between the first and the second diet changes. The comparison of the strategies of improvement of DCFCR<sub>42</sub> showed that the best strategy differs between lines. In the pHu+ line, selection on DCFCR<sub>42</sub> or on cluster is equivalent and is associated mostly to a lower feed intake, but with a negative impact on breast yield. In the pHu- line, progress on profile is mostly obtained by a higher body weight at constant feed intake, while progress on DCFCR<sub>42</sub> is due to a higher body weight for a lower feed intake.

**Keywords:** selection, feed efficiency, kinetics, broiler, cluster

**Introduction**

Avian selection on feed efficiency requires data on individual feed intake and body weight. Therefore, as individual data collection of feed intake is mostly done on animals reared in cages, they are obtained in conditions that are far from real production conditions. As animals reared on floor have a higher physical activity (Ozhan et al., 2016), this is leading to a bias in feed efficiency measurement and selection based on cage data may thus not be adapted for the animals used in production. Moreover, in order to limit animal handling, animals are weighed
less than once per week, and feed conversion ratio can thus be calculated only over one or few periods of 1 week up to the whole rearing period (Berger et al., 2021).

The alternative is to use electronic feeders to collect individual feed intake on group- and floor-reared animals. These feeders are highly democratized in the pig industry (Lecce, 1969; Roller and Teague, 1970; Randall et al., 1976) but are still rare in poultry. Different prototypes have been developed, either on duck (Bley and Bessei, 2008; Basso et al., 2014) or on chicken (Yan et al., 2019; Li et al., 2021), giving access to individual feed intake, but some drawbacks are nevertheless present in existing feed stations. Firstly, most of designs rely on systems of isolation of animals. This is preventing double visits to the station but modifies feeding behavior of animals. Secondly, for most existing designs, the emphasis was placed on feed intake and feeding behavior but did not include body weight measurement (Howie et al., 2011; Yan et al., 2019). The feed efficiency data are then calculated on the periods between two manual weighing of animals. Finally, as feed intake per visit is very low at early ages, many designs provides data only after a few weeks, e.g. after 2 weeks in Howie et al. (2011) and after 8 weeks in Yan et al. (2019). Another design has been developed recently permitting to measure simultaneously body weight and feed intake of animals, without animal isolation (Guettier et al., 2020). Using this design, we recently showed that it was possible to obtain daily performances of feed intake, growth and feed efficiency and establish profiles of genetic parameters of these traits (Berger et al., 2021; Mignon-Grasteau et al., 2021). This is opening new possibilities in terms of strategies of selection of feed efficiency. Indeed, in addition to the classical selection on the final feed efficiency or growth, we can select on feed efficiency at an earlier age depending on its heritability and its genetic correlation with final feed efficiency. We can also take advantage of daily measures of feed efficiency to distinguish different profiles of animals and select them on these profiles.

The aim of this study was thus to compare different selection strategies to improve final feed efficiency. We used data from two broiler lines divergently selected on breast meat pH, from which we obtained daily performances of weight and feed intake throughout their life.

## Materials and methods

The present study was performed in agreement with the French National Regulation for human care and use of animals for research purposes and received the authorization number 2019071715406895_V2-21517. Animals were reared at the PEAT INRAE poultry experimental facility (2018, https://doi.org/10.15454/1.5572326250887292E12) registered by the French Ministry of Agriculture under license number C-37-175-1 for animal experimentation (INRAE,
Centre Val de Loire, Nouzilly, France). Raw data can be found in the Data INRAE repository, under the unique persistent identifier https://doi.org/10.15454/QBNHAD.

<table><caption>Table 5.1. Composition and age of distribution of diets.</caption><thead><tr><th>Ingredient (%)</th><th>1-8 d</th><th>9-20 d</th><th>21-42 d</th></tr></thead><tbody><tr><td>Corn</td><td>20.42</td><td>19.89</td><td>23.50</td></tr><tr><td>Wheat</td><td>30.10</td><td>30.10</td><td>30.10</td></tr><tr><td>Fava bean</td><td>-</td><td>12.00</td><td>13.00</td></tr><tr><td>Soybean meal</td><td>24.22</td><td>11.61</td><td>7.13</td></tr><tr><td>Rapeseed meal</td><td>5.00</td><td>5.00</td><td>8.00</td></tr><tr><td>Wheat DDGS</td><td>3.00</td><td>5.00</td><td>5.00</td></tr><tr><td>High fiber sunflower meal</td><td>8.12</td><td>7.73</td><td>5.19</td></tr><tr><td>Soybean oil</td><td>5.00</td><td>5.00</td><td>5.00</td></tr><tr><td>Corn gluten</td><td></td><td></td><td></td></tr><tr><td>Calcium carbonate</td><td>0.655</td><td>0.142</td><td>0.00</td></tr><tr><td>Bicalcic phosphate</td><td>2.05</td><td>1.73</td><td>1.40</td></tr><tr><td>Salt</td><td>0.192</td><td>0.15</td><td>0.158</td></tr><tr><td>Vitamins and minerals</td><td>0.40</td><td>0.40</td><td>0.40</td></tr><tr><td>Sodium carbonate</td><td>0.196</td><td>0.262</td><td>0.250</td></tr><tr><td>DL-Methionine</td><td>0.234</td><td>0.285</td><td>0.234</td></tr><tr><td>HCL Lysine</td><td>0.176</td><td>0.264</td><td>0.250</td></tr><tr><td>Threonine</td><td>0.088</td><td>0.157</td><td>0.135</td></tr><tr><td>Valine</td><td>0.038</td><td>0.130</td><td>0.106</td></tr><tr><td>Tryptophane</td><td>-</td><td>-</td><td>0.005</td></tr><tr><td colspan="4"><strong>Calculated composition</strong></td></tr><tr><td>AMEn, kcal/kg</td><td>2850</td><td>2900</td><td>2950</td></tr><tr><td>CP, g/kg</td><td>215</td><td>194</td><td>181</td></tr><tr><td>Lys, g/kg</td><td>11.2</td><td>10.9</td><td>10</td></tr><tr><td>Met + Cys, g/kg</td><td>8.4</td><td>8.17</td><td>7.5</td></tr><tr><td>Trp, g/kg</td><td>2.28</td><td>1.84</td><td>1.7</td></tr></tbody></table>
## Birds and housing

In this study, we used animals from two experimental broiler lines divergently selected for a high (pHu+) or a low (pHu-) ultimate pH of the *Pectoralis major* muscle during fourteen generations. These lines came from a fast-growing grandparental female broiler line selected for both growth and reproduction traits. A complete description of the breeding scheme and performance of these lines can be found in Alnahhas et al. (2014). Two batches were reared in parallel for 42 days for the current study, spaced a week apart. The first and second batches respectively included 183 and 156 pHu- and 144 and 167 pHu+ of both sexes.

The temperature was set at 32°C at hatch and decreased progressively to reach 20-23°C from 29 d on. The light program was 24D:0N at hatch, 23D:1N at 1 d and 18D:6N from 3 to 42 d. At hatch, the animals were identified with a wing band and an electronic Radio Frequency Identification Device (RFID) chip, then weighed and placed in one pen on a floor covered with wooden chips. The RFID chip was placed at the base of the neck and secured with a plastic string passing under the skin.

The animals were fed *ad libitum* with a diet containing a high proportion of sunflower, rapeseed, and fava bean in order to reduce the soybean meal proportion in the diet. The detailed composition is shown in Table 5.1. A starter diet was given from hatch to 8 d (2850 kcal.kg⁻¹ DM; 21.5 % CP), a grower diet from 9 to 19 d (2900 kcal.kg⁻¹ DM; 20.0 % CP), and a finisher diet from 20 to 42 d (2950 kcal.kg⁻¹ DM; 18.5 % CP).

Body weight and feed intake were individually and continuously recorded throughout the experiment thanks to electronic feed stations.

## Body weight, feed intake, weight gain, and feed conversion ratio

Body weight (BW), daily feed intake (DFI), and daily cumulative feed conversion ratio (DCFCR) were calculated as in Berger et al. (2021). Briefly, for each animal i at day j, average daily gain (ADG<sub>ij</sub>) and daily cumulative feed conversion ratio (DCFCR<sub>ij</sub>) were calculated as:

$$ADG_{ij} = \frac{BW_{i(j+2)} - BW_{i(j-2)}}{5}$$

$$DCFCR_{ij} = \frac{\sum_{k=3}^{k=j} DFI_{ik}}{BW_{ij} - BW_{i3}}$$

where DFI<sub>ij</sub> and BW<sub>ij</sub> are the daily feed intake and body weight of animal i for day j, respectively. DCFCR<sub>42</sub> stands for the cumulative feed conversion ratio over the whole rearing period.
## Meat ultimate pH

At 42 d, the animals were weighed after 8 hours of feed withdrawal and transferred to the slaughterhouse of the PEAT INRAE poultry experimental facility (2018, https://doi.org/10.15454/1.5572326250887292E12).

After 24 hours of chilling, the right *Pectoralis major* and *Pectoralis minor* were cut and weighed. Breast meat yield (BMY) was calculated as:

$$BMY = 100 \times \frac{2 \times (Pectoralis major + Pectoralis minor)}{BW_{42}}$$

The ultimate pH of the *Pectoralis major* (pHu) was measured with a portable pH meter (model 506, Crison Instruments SA, Alella, Barcelona, Spain).

## Phenotypic clusters and slaughter performance

Using the PCA and HCPCP functions of the FactomineR package of R (Lê et al., 2008), 5 clusters have been identified for each line, in order to differentiate the evolution of the DCFCR throughout the life of the animals. The number of clusters retained was determined by combining the inertia gain and the number of animals in each cluster. For the creation of these clusters, data from day 5 to day 42 have been taken in account. As animals have been blood sampled on the 16th day, which altered their performances, data collected this day were not included in the PCA. Clusters then have been sorted from one (the most interesting) to five (the least interesting), based on the number of days during which DCFCR value was the lowest.

The effect of cluster on weight gain, body weight, feed efficiency, breast yield and breast meat pH at 42 days was evaluated with an analysis of variance including the fixed effects of hatch, sex and cluster (PROC GLM, SAS, 2013).

## Genetic analyses

Genetic parameters were estimated using VCE6.0 software (Neumaier and Groeneveld, 1998, Groeneveld et al., 2010). In addition to data collected during this experiment, data of pHu, BMY and body weight at 42 d (BW42) collected from previous generations of selection were included in the genetic analyses, leading to a variable number of data depending on the trait: 578 for the different clusters, DFI and DCFCR42 and 9,541 for pHu, BMY and BW42. The pedigree file contained all animals from the beginning of the selection experiment, i.e. 23,319 animals.

Genetic analyses were conducted separately for each line. In addition to the direct genetic effect of animal, the model included the fixed effects of the sex (N=2) and batch (N=31). When the maternal permanent environment effect (N=1,961) was not null, it was also included in the model of analysis.
## Cluster selection and comparison to other scenarios

To find the best strategy of selection of final feed efficiency, we compared expected response to selection of this trait after selection of different selection criteria: final cumulative feed conversion ratio (DCFCR$_{42}$), final BW (BW$_{42}$), cluster 1 of DCFCR (CLUST_NUMB), one daily DCFCR before the second diet change (DCFCR$_{17}$) and one daily DCFCR after the second diet change (DCFCR$_{26}$ for pHu+ and DCFCR$_{30}$ for pHu-). In the latter two cases, the ages were chosen as compromises between the highest heritability and the highest genetic correlation with DCFCR$_{42}$ (Berger et al., 2021). In order to compare scenarios, the expected response to selection has been calculated using the following equation:

$$E(\Delta G_{DCFCR_{42}}) = \frac{i \times \sigma_{p_{DCFCR_{42}}} \times r_g(ST, DCFCR_{42}) \times \sqrt{h^2_{ST} h^2_{DCFCR_{42}}}}{\Delta t}$$

where $h^2_{ST}$ is the heritability of the selected trait, $h^2_{DCFCR_{42}}$ the heritability of DCFCR$_{42}$, i the intensity of selection, $r_g(ST, DCFCR_{42})$ the genetic correlation between the selected trait and DCFCR$_{42}$, $\sigma_{p_{DCFCR_{42}}}$ the standard deviation of DCFCR$_{42}$ and $\Delta t$ the interval of generation.

As i and $\sigma_{p_{DCFCR_{42}}}$ and $\Delta t$ were identical for all scenarios, they have been removed from our calculus for the comparison of scenarios. The comparison of scenarios thus depends only on heritability of the two traits and on the genetic correlation between them, we thus compared scenarios based on the simplified equation:

$$E(\Delta G_{DCFCR_{42}}) \propto r_g(ST, DCFCR_{42}) \times \sqrt{h^2_{ST} h^2_{DCFCR_{42}}}$$
<table><caption>Table 5.2. Feed efficiency clusters characteristics of the two pHu lines.</caption><thead><tr><th></th><th>Age (d)</th><th>Cluster 1</th><th>Cluster 2</th><th>Cluster 3</th><th>Cluster 4</th><th>Cluster 5</th></tr></thead><tbody><tr><td colspan="7" style="text-align: center; font-weight: bold;">Line pHu+</td></tr><tr><td>N</td><td></td><td>65</td><td>88</td><td>48</td><td>64</td><td>16</td></tr><tr><td>ADG (g.d<sup>-1</sup>)</td><td>1-8</td><td>13.8 ± 0.7</td><td>14.6 ± 0.6</td><td>13 ± 0.8</td><td>14.3 ± 0.7</td><td>13.2 ± 1.6</td></tr><tr><td></td><td>9-20</td><td>35.1 ± 0.4</td><td>35.9 ± 0.4</td><td>33.5 ± 0.5</td><td>34.7 ± 0.4</td><td>32.6 ± 0.9</td></tr><tr><td></td><td>21-42</td><td>70.5 ± 0.3<sup>a</sup></td><td>68.5 ± 0.3<sup>b</sup></td><td>71.8 ± 0.4<sup>a</sup></td><td>66.6 ± 0.3<sup>b</sup></td><td>67.4 ± 0.6<sup>b</sup></td></tr><tr><td>DFI (g.d<sup>-1</sup>)</td><td>1-8</td><td>16.0 ± 1.9</td><td>16.6 ± 1.6</td><td>17.7 ± 2.1</td><td>17.6 ± 1.8</td><td>18.1 ± 3.7</td></tr><tr><td></td><td>9-20</td><td>58.6 ± 1.2</td><td>62.8 ± 1.1</td><td>61.3 ± 1.5</td><td>63.9 ± 1.2</td><td>63.5 ± 2.5</td></tr><tr><td></td><td>21-42</td><td>155.1 ± 0.9<sup>b</sup></td><td>161.5 ± 0.7<sup>a</sup></td><td>161.3 ± 1.0<sup>a</sup></td><td>164.1 ± 0.9<sup>a</sup></td><td>166.7 ± 1.7<sup>a</sup></td></tr><tr><td>FCR</td><td>1-8</td><td>1.56 ± 0.02<sup>b</sup></td><td>1.47 ± 0.01<sup>c</sup></td><td>1.85 ± 0.02<sup>a</sup></td><td>1.62 ± 0.02<sup>b</sup></td><td>1.88 ± 0.03<sup>a</sup></td></tr><tr><td></td><td>9-20</td><td>1.33 ± 0.01<sup>e</sup></td><td>1.34 ± 0.01<sup>d</sup></td><td>1.59 ± 0.01<sup>b</sup></td><td>1.44 ± 0.01<sup>c</sup></td><td>1.67 ± 0.02<sup>a</sup></td></tr><tr><td></td><td>21-42</td><td>1.51 ± 0.01<sup>d</sup></td><td>1.60 ± 0.01<sup>c</sup></td><td>1.61 ± 0.01<sup>c</sup></td><td>1.70 ± 0.01<sup>b</sup></td><td>1.79 ± 0.01<sup>a</sup></td></tr><tr><td colspan="7" style="text-align: center; font-weight: bold;">Line pHu-</td></tr><tr><td>N</td><td></td><td>48</td><td>121</td><td>54</td><td>68</td><td>29</td></tr><tr><td>ADG (g.d<sup>-1</sup>)</td><td>1-8</td><td>15.2 ± 0.8</td><td>15.8 ± 0.5</td><td>15.2 ± 0.8</td><td>15.5 ± 0.7</td><td>14.3 ± 1.2</td></tr><tr><td></td><td>9-20</td><td>37.5 ± 0.5<sup>a</sup></td><td>37.1 ± 0.3<sup>a</sup></td><td>36.1 ± 0.5<sup>ab</sup></td><td>36.1 ± 0.4<sup>ab</sup></td><td>33.5 ± 0.7<sup>b</sup></td></tr><tr><td></td><td>21-42</td><td>73.4 ± 0.4<sup>a</sup></td><td>68.4 ± 0.2<sup>c</sup></td><td>70.7 ± 0.3<sup>b</sup></td><td>65.8 ± 0.3<sup>d</sup></td><td>65.8 ± 0.5<sup>d</sup></td></tr><tr><td>DFI (g.d<sup>-1</sup>)</td><td>1-8</td><td>16.3 ± 1.0</td><td>17.5 ± 0.6</td><td>19.0 ± 0.9</td><td>18.5 ± 0.8</td><td>19.6 ± 1.4</td></tr><tr><td></td><td>9-20</td><td>59.3 ± 1.4</td><td>62.0 ± 0.9</td><td>61.3 ± 1.3</td><td>63.7 ± 1.2</td><td>62.4 ± 2.1</td></tr><tr><td></td><td>21-42</td><td>152.4 ± 2.1<sup>b</sup></td><td>154.6 ± 1.3<sup>ab</sup></td><td>152.3 ± 1.9<sup>ab</sup></td><td>156.6 ± 1.7<sup>a</sup></td><td>155.3 ± 3.1<sup>a</sup></td></tr><tr><td>FCR</td><td>1-8</td><td>1.40 ± 0.02<sup>d</sup></td><td>1.44 ± 0.01<sup>d</sup></td><td>1.71 ± 0.01<sup>b</sup></td><td>1.51 ± 0.01<sup>c</sup></td><td>2.02 ± 0.02<sup>a</sup></td></tr><tr><td></td><td>9-20</td><td>1.25 ± 0.01<sup>e</sup></td><td>1.30 ± 0.01<sup>d</sup></td><td>1.44 ± 0.01<sup>b</sup></td><td>1.39 ± 0.01<sup>c</sup></td><td>1.66 ± 0.01<sup>a</sup></td></tr><tr><td></td><td>21-42</td><td>1.44 ± 0.01<sup>d</sup></td><td>1.53 ± 0.01<sup>c</sup></td><td>1.52 ± 0.01<sup>c</sup></td><td>1.63 ± 0.01<sup>b</sup></td><td>1.72 ± 0.01<sup>a</sup></td></tr></tbody></table>
<caption>Figure 5.2. Clusters of DCFCR for the pHu- lines (a) and pHu+ line (b).</caption>

5.2.a.

<figure><img src="image_52.png" /></figure>

5.2.b.

<figure><img src="image_53.png" /></figure>
**Results**

Phenotypic evolution on considered traits as well as their profile of heritability and genetic correlation with final feed efficiency can be found in Berger et al. (submitted).

*Profiles of DCFCR for the two lines*

Kinetics of DCFCR in each cluster is presented in

Figure 5.2. The distribution of chickens into clusters can be found in Table 5.2. For both lines, the most interesting cluster included 15.3% and 23.1% of the pHu- and pHu+ animals, respectively. This cluster 1 presented the lowest values of DCFCR throughout the life of the animals, except for pHu+ line, for which feed efficiency values were slightly better before 13 d in cluster 2. Final DCFCR value in cluster 1 was 1.57 in pHu- and 1.67 in pHu+, respectively. Feed efficiency in cluster 1 is thus better than in other clusters by 0.09 to 0.28 in pHu- line and by 0.07 to 0.22 in pHu+ line. Consistently with the fact that lines were issued from commercial broiler lines selected for growth and efficiency, the two clusters with the best feed efficiencies represented more than half of birds (54%), but there were still 29 to 31% of birds in the 2 clusters with the worst feed efficiency.

In all clusters, feed efficiency decreased between hatch and the first diet change and increased after the second diet change. The difference between clusters is thus due to the level of DCFCR at the first diet change and to the slope of evolution of DCFCR between the two diet changes. In cluster 1, DCFCR at the 1st diet change was low, and DCFCR remained stable between the diet changes. Clusters 2 and 4 had similar features, with a low value of DCFCR at the 1st diet change followed by an increasing DCFCR between the diet changes, with a higher starting value in cluster 4 than in cluster 2. Clusters 3 and 5 both started from high values of DCFCR at the 1st diet change, but DCFCR remained stable at a high value in cluster 5 between the two diet changes whereas it decreased in cluster 3. Due to this slope, cluster 3, which was the second worst before the 1st diet change, became better than cluster 4 between 17 and 22 d and better than cluster 2 between 27 and 32 d (Figure 5.2).

Final DCFCR was thus significantly better in cluster 1 than in all other clusters, and clusters 4 and 5 did not differ at this age (Figure 5.2.a, b).
<figure><img src="image_54.png" /><figcaption>Figure 5.3.Slaughter performances of the different clusters studied DCFCR42 (a, b) BW42 (c, d) and BMY (e, f) in pHu+ line (full bars) and pHu- lines (dashed bars).

**Bars with different letters are significantly different (P<0.05).**</figcaption></figure>
## *Differences between clusters at slaughter*

BMY and pHu did not significantly differ between the different clusters in the line pHu- (Figure 5.3.f). For the line pHu+, BMY did not significantly differ between clusters but breast meat pHu of the cluster 1 was 0.06 higher than in cluster 4 (Figure 5.3.e).

Body weight at 42 d differed between clusters only for pHu-, animals being lighter in cluster 5 than in cluster 1 (2456 g vs 2782 g, cf.

Figure 5.2.d). In clusters 2 to 4, BW₄₂ had intermediate values, not significantly different from clusters 1 and 5.

## *Genetic Parameters*

**Heritability estimates.** Genetic parameters of feed efficiency, profile of feed efficiency, breast meat yield and pHu are presented in Table 5.3. Heritability of feed efficiency traits were moderate to high, with much higher estimates in pHu- than in pHu+ line. At the opposite, DFI was more heritable in pHu+ than in pHu-. Finally, body weight, breast yield and pH and the cluster number got similar heritability estimates in both lines.

**Genetic correlations between feed efficiency traits.** The genetic correlations between DCFCR₄₂ and other feed efficiency traits was moderate to high (0.51 to 0.96), with much higher values in pHu- than in pHu+ line. In both cases, the cluster was highly correlated to DCFCR₄₂ (0.76 in pHu+, 0.89 in pHu-).

**Genetic correlations between feed efficiency traits and BW₄₂.** The genetic correlations between feed efficiency and BW₄₂ were nearly null in pHu+ but moderately negative un pHu- (Table 5.3). In pHu- line, BW₄₂ was surprisingly less correlated with final feed efficiency (-0.32) than with efficiency at earlier ages and with clusters of feed efficiency (-0.46 to -0.56).

**Genetic correlations between feed efficiency traits and BMY.** The correlations between feed efficiency traits and BMY were low in pHu- line (-0.07 to 0.17) and with final feed efficiency in pHu+ line (0.01). In the latter line, feed efficiency at 17 and 30 d and the cluster number showed moderate correlations with BMY (0.32 to 0.45).

**Genetic correlations between feed efficiency traits and pHu.** Correlations with pHu showed opposite signs between the two lines, which could be expected as the two lines have been selected divergently on pHu. In pHu+ line, the correlations with pHu were low to moderate for feed efficiency at 17 d, 30 d and cluster (-0.24 to -0.57) but high with DCFCR₄₂ (-0.95). At the opposite, correlations were moderately positive pHu (0.42 to 0.61).
<table><caption>Table 5.3. Heritability (diagonal) and genetic correlations (upper right triangle) estimates for the different traits of feed efficiency, body weight, breast yield and breast meat pH in pHu- lines.</caption><thead><tr><th></th><th colspan="8">Line pHu-</th></tr><tr><th></th><th>DCFCR<sub>42</sub></th><th>BW<sub>42</sub></th><th>DFI</th><th>DCFCR<sub>17</sub></th><th>DCFCR<sub>M</sub></th><th>Profile</th><th>BMY</th><th>pHu</th></tr></thead><tbody><tr><td>DCFCR<sub>42</sub></td><td>0.56 ± 0.09</td><td>-0.32 ± 0.1</td><td>0.34 ± 0.12</td><td>0.82 ± 0.06</td><td>0.96 ± 0.02</td><td>0.89 ± 0.04</td><td>-0.06 ± 0.12</td><td>0.61 ± 0.10</td></tr><tr><td>BW<sub>42</sub></td><td></td><td>0.34 ± 0.03</td><td>0.77 ± 0.05</td><td>-0.46 ± 0.09</td><td>-0.50 ± 0.10</td><td>-0.56 ± 0.09</td><td>0.29 ± 0.06</td><td>0.04 ± 0.06</td></tr><tr><td>DFI</td><td></td><td></td><td>0.26 ± 0.04</td><td>0.06 ± 0.15</td><td>0.12 ± 0.15</td><td>0.01 ± 0.15</td><td>0.25 ± 0.08</td><td>0.46 ± 0.12</td></tr><tr><td>DCFCR<sub>17</sub></td><td></td><td></td><td></td><td>0.49 ± 0.07</td><td>0.90 ± 0.10</td><td>0.93 ± 0.03</td><td>0.17 ± 0.08</td><td>0.56 ± 0.11</td></tr><tr><td>DCFCR<sub>M</sub><sup>1</sup></td><td></td><td></td><td></td><td></td><td>0.64 ± 0.03</td><td>0.97 ± 0.10</td><td>-0.07 ± 0.11</td><td>0.56 ± 0.11</td></tr><tr><td>Profile</td><td></td><td></td><td></td><td></td><td></td><td>0.47 ± 0.09</td><td>-0.01 ± 0.10</td><td>0.42 ± 0.12</td></tr><tr><td>BMY</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.59 ± 0.04</td><td>0.23 ± 0.06</td></tr><tr><td>pHu</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.54 ± 0.04</td></tr></tbody><tbody><tr><th></th><th colspan="8">Line pHu+</th></tr><tr><td>DCFCR<sub>42</sub></td><td>0.31 ± 0.09</td><td>0.07 ± 0.09</td><td>0.50 ± 0.09</td><td>0.53 ± 0.10</td><td>0.51 ± 0.08</td><td>0.76 ± 0.06</td><td>0.01 ± 0.11</td><td>-0.95 ± 0.04</td></tr><tr><td>BW<sub>42</sub></td><td></td><td>0.37 ± 0.03</td><td>0.90 ± 0.03</td><td>-0.04 ± 0.09</td><td>-0.02 ± 0.08</td><td>0.05 ± 0.09</td><td>0.31 ± 0.05</td><td>-0.07 ± 0.06</td></tr><tr><td>DFI</td><td></td><td></td><td>0.36 ± 0.04</td><td>0.17 ± 0.10</td><td>0.21 ± 0.10</td><td>0.39 ± 0.10</td><td>0.27 ± 0.07</td><td>-0.48 ± 0.07</td></tr><tr><td>DCFCR<sub>17</sub></td><td></td><td></td><td></td><td>0.35 ± 0.04</td><td>0.89 ± 0.06</td><td>0.83 ± 0.06</td><td>0.40 ± 0.07</td><td>-0.30 ± 0.08</td></tr><tr><td>DCFCR<sub>M</sub><sup>1</sup></td><td></td><td></td><td></td><td></td><td>0.43 ± 0.04</td><td>0.92 ± 0.03</td><td>0.45 ± 0.1</td><td>-0.24 ± 0.13</td></tr><tr><td>Profile</td><td></td><td></td><td></td><td></td><td></td><td>0.47 ± 0.05</td><td>0.32 ± 0.09</td><td>-0.57 ± 0.10</td></tr><tr><td>BMY</td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.55 ± 0.03</td><td>0.20 ± 0.05</td></tr><tr><td>pHu</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>0.57 ± 0.03</td></tr></tbody></table>

<footnote n="1">¹ M: age at maximum heritability of feed efficiency (26 d in pHu+ line. 30 d in pHu- line)</footnote>
**Expected response to selection to the different scenarios**

Expected response to selection on DCFCR<sub>42</sub>, BW<sub>42</sub>, FI, BMY and pHu are represented in the Figure 5.4.a for pHu+ line and Figure 5.4.b for pHu- line.

**Impact on DCFCR<sub>42</sub>.** In both lines, selection on cluster number would give a good expected response to selection on DCFCR<sub>42</sub>, equivalent to 82 to 94% of the response expected from direct selection. In the pHu- line, selection on feed efficiency at 30 days would even be more efficient than selection on DCFCR<sub>42</sub>. Selection on feed efficiency at an early age would give lower but still high responses to selection on DCFCR<sub>42</sub> (55 to 76% of the response to direct selection). Due to the very low correlations between BW<sub>42</sub> and DCFCR<sub>42</sub>, selection on BW<sub>42</sub> would have nearly no impact on DCFCR<sub>42</sub> (0.02 for pHu+, -0.14 for pHu-).

**Impact on BW<sub>42</sub> and feed intake.** The 4 strategies of selection of feed efficiency (DCFCR<sub>17</sub>, DCFCR<sub>26</sub> or DCFCR<sub>30</sub>, DCFCR<sub>42</sub> or cluster of feed efficiency) would have different impacts on body weight and feed intake in both lines. In pHu+ line, whatever the selection criterion, improving feed efficiency would be done mainly by a reduction of feed intake at a nearly constant final body weight. Reduction of feed intake would we twice as high by selecting DCFCR<sub>42</sub> or clusters than by selecting DCFCR<sub>17</sub> and DCFCR<sub>26</sub>. At the opposite, in pHu- line, improvement of feed efficiency would mainly be obtained by reducing body weight, especially if the selection criterion is DCFCR<sub>30</sub>. In this line, selecting on DCFCR<sub>42</sub> is the only case in which a better feed efficiency would be obtained both by an increase of body weight and a decrease of feed intake.

**Impact on BMY and pHu.** Whatever the selection criterion, selecting for more efficient birds would lead to more acid breast meat in pHu- line and less acid meat in pHu+ line, the highest impact being obtained with DCFCR<sub>42</sub>. In pHu+ line, selection of a better DCFCR<sub>17</sub>, DCFCR<sub>26</sub> or on cluster would decrease breast yield but selection on DCFCR<sub>42</sub> would not modify breast yield. In pHu- line, selection on DCFCR<sub>17</sub> would not change breast yield, but selection at later ages (30 days, 42 days or on cluster) would lead to a highest breast yield.
<figure><img src="image_55.png" /><figcaption>Figure 5.4. Response to selection of DCFCR42, BW42, DFI, BMY and pHu in pHu+ (a) and pHu- (b) lines, depending of selected trait</figcaption></figure>

<figure><img src="image_56.png" /></figure>
Discussion

Our results showed a 4.2% global better feed efficiency in pHu-, consistently with the
7.1% difference already found by Alnahhas et al. (2017) in the same lines. This is consistent
with the fact that the most efficient cluster 1 includes more animals in the pHu+ line (23%) than
in the pHu- line (15%) whereas the less efficient cluster 5 includes more animals in the pHu+
than in the pHu- line (6% vs 9%). This is probably linked to the difference of metabolism
between the two lines, as energy production is mostly done through more efficient metabolic
pathways in the pHu- line (carbohydrate use) than in pHu+ line (lipid oxidation, muscle protein
degradation, amino acid catabolism, Beauclercq et al., 2017).

Clusters of feed efficiency can be distinguished both on their ability to cope with the
first diet change and on the level of feed efficiency at the first diet change, the latter reflecting
the quality of starting period. The importance of starting period on the final performances has
already be highlighted in chickens (Willemsen et al., 2008), including in the pHu+/pHu- lines
(Métayer-Coustard et al., 2021). During this period, maintenance costs are proportionally low
and most of the differences in feed efficiency are due to lower feed intake and/or higher growth
rate (Marks, 1991). During this starting phase, the better DCFCR of cluster 1 was primarily due
to a 4 to 17% lower feed intake than in all other clusters. Clusters 3 to 5 also showed a 3 to 7%
reduced growth during the same period. During the grower phase, trends were less clear, but
clusters 2 to 5 showed most often an equivalent or slower growth and equivalent or higher feed
intake than cluster 1. During the finisher phase, a difference between the two lines was present,
a better efficiency being clearly associated to lower feed intake in pHu+ line and to a higher
growth in pHu- line.

Among the different scenarios of selection tested, and with a fixed intensity of selection
of 1 (approximately 38% of the best individuals), selecting on BW42 is the less interesting to
improve global feed efficiency. Moreover, in both lines, the gain on body weight is obtained
through an increased feed intake, as classically stated in the literature (Marks, 1991). Despite
this drawback, when considering price paid per kg of live weight and feed cost (0.896€/kg and
306€/t, respectively, Méda et al., 2021), the economic gain of selection of body weight is still
high and is even the best among the tested scenarios in pHu+ line (+6.1%). In the pHu+ line,
the progress on body weight will also be associated to an increase of breast weight, which, in
this line, is associated to a higher proportion of muscle defects such as white striping, due to
lower reserves of glycogen in the muscle (Le Bihan-Duval et al., 2008; Alnahhas et al., 2017).

Due to contrasted evolutions of body weight and feed intake in both lines following
selection of feed efficiency at 17, 26/30 d, 42 d or on clusters, the best strategy to improve feed
efficiency is not the same in the two lines. Previous results in the literature also showed that,
depending on genotype and age, the genetic correlations between feed efficiency, feed intake
and body weight vary largely and that variations of feed efficiency might thus be attributed mostly to body weight, weight gain or feed intake (Aggrey et al., 2010; Marchesi et al., 2021). In our case, in the pHu+ line, all these strategies would not change body weight but would decrease feed intake, the reduction being twice as high with a selection on DCFCR42 or clusters than with selection on DCFCR17 and DCFCR26. Therefore, the economic gain obtained through selection of DCFCR42 or on clusters would be similar (+2.1%) and slightly better than with DCFCR17 or DCFCR26 (+1.5-1.6%). In the pHu- line at the opposite, the improvement of feed efficiency would mostly be due to an increase in body weight at constant feed intake. Selection on DCFCR30 and on clusters would thus more improve the economic gain (10.2% and 6.1%, respectively) than selection on DCFCR17 or DCFCR42 (5.4 and 5.7%, respectively).

Conclusion

Two scenarios of selection have been retained for pHu-, selection on kinetic of feed efficiency and on DCFCR30. Selection on the kinetic of feed efficiency seems also interesting in the pHu+ lines, alongside DCFCR42. Despite being interesting, they underline different ways of selecting animals and different impact on a global level.

References

Aggrey, S. E., A. B. Karnuak, B. Sebastian, and N. B. Anthony. 2010. Genetic properties of feed efficiency parameters in meat-type chickens. Genet Sel Evol 42:25.

Alnahhas, N., C. Berri, M. Boulay, E. Baéza, Y. Jégo, Y. Baumard, M. Chabault, and E. Le Bihan-Duval. 2014. Selecting broiler chickens for ultimate pH of breast muscle: Analysis of divergent selection experiment and phenotypic consequences on meat quality, growth, and body composition traits. J Anim Sci 92:3816-3824.

Alnahhas, N., C. Berri, M. Chabault-Dhuit, M. Bourin, C. Arnould, and E. Le Bihan-Duval. 2017. Combined effect of divergent selection for breast muscle ultimate pH and dietary amino acids on chicken performance, physical activity and meat quality. Animal 11:335-344.

Basso, B., M. Lagüe, G. Guy, E. Ricard, and C. Marie-Etancelin. 2014. Detailed analysis of the individual feeding behavior of male and famle mule ducks. J Anim Sci 92:1639-1646.

Beauclercq, S., C. Hennequet-Antier, C. Praud, E. Godet, A. Collin, S. Tesseraud, S. Métayer-Coustard, M. Bourin, M/ Moroldo, F. martins, S. Lagarrigue, E. Le Bihan-Duval, and C.
Berri. 2017. Muscle transcriptome analysis reveals molecular pathways and biomarkers involved in extreme ultimate pH and meat defect occurrence in chicken. Sci Rep 7:6447.

Berger, Q., E. Guettier, S. Urvoix, J. Bernard, P. Ganier, M. Chahnamian, E. Le Bihan-Duval, and S. Mignon-Grasteau. 2021. The kinetics of growth, feed intake, and feed efficiency reveal a good capacity of adaptation of slow- and rapid-growing broilers to alternative diets. Poult Sci 100:101010.

Berger, Q., E. Guettier, J. Bernard, P. Ganier, M. Chahnamian, E. Le Bihan-Duval, and S. Mignon-Grasteau. Profiles of genetic parameters of body weight and feed efficiency in two divergent broiler lines for meat ultimate pH. BMC Genet (submitted).

Bley, T. A. G., and W. Bessei. 2008. Recording of individual feed intake and feeding behavior of pekin ducks kept in groups. Poult Sci 87:215-221.

Groeneveld, E., M. Kovač, and N. Mielenz. 2010. VCE User's Guide and Reference Manual, version 6.0. Institute of Farm Animal Genetics, Neustadt, Germany.

Guettier, E., S. Urvoix, P. Servant, A. Mika, Q. Berger, and S. Mignon-Grasteau. 2020. Mise au point d'un automate de consommation alimentaire pour volaillesnomméBIRD-e: Bird Individual Ration Dispenser-electronic. Cahier Tech INRA 99:1-12.

Howie, J. A., S. Avendano, B. J. Tolkamp, and I. Kyriazakis. 2011. Genetic parameters of feeding behavior traits and their relationship with live performance traits in modern broiler lines. Poult Sci 90:1197-1205.

Lê, S., J. Josse, and F. Husson. 2008. FactoMineR: A Package for Multivariate Analysis. *J Stat Soft* 25:1-18.

Le Bihan-Duval, E., M. Debut, C. M. Berri, N. Sellier, V. Santé-Lhoutellier, Y. Jégo, and C. Beaumont. 2008. Chicken meat quality: genetic variability and relationship with growth and muscle characteristics. BMC Genet 9:53.

Lecce, J. G. 1969. Rearing colostrum-free pigs in an automatic feeding device. J Anim Sci 28:27-33.

Li, G. M., Y. Zhao, J. L. Purswell, and C. Magee. 2021. Effects of feeder space on broiler feeding behaviors. Poult Sci 100:101016.
Marchesi, J. A. P., R. K. Ono, M. E. Cantao, A. M. G. Guaratini, J. D. Oliveira, G. C. Monteiro Moreira, T. F. Godoy, L. L. Couthino, D. P. Munari, and M. C. Ledur. 2021. Exploring the genetic architecture of feed efficiency traits in chickens. Sci Rep 11:4622.

Marks, H. L. 1991. Feed efficiency changes accompanying selection for body weight in chickens and quail. W Poult Sci J 47:197-212.

Méda, B., F. Garcia-Launay, L. Dusart, P. Ponchant, S. Espagnol, A. Wilfart. 2021. Reducing environmental impacts of feed using multiobjective formulation: what benefits at the farm gate for pig and broiler production? Animal 15:100024.

Métayer-Coustard, S., S. Tesseraud, C. Praud, D. Royer, T. Bordeau, E. Coudert, E. Cailleau-Audouin, E. Godet, J. Delaveau, E. Le Bihan-Duval, and C. Berri. 2021. Early growth and protein-energy metabolism in chicken lines divergently selected on ultimate pH. Front Physiol 12:643580.

Mignon-Grasteau, S., Q. Berger, E. Guettier, and E. Le Bihan-Duval. 2021. New advances based on our feed station and its use for feed efficiency phenotyping. 72th Eur Assoc Anim Prod, Davos, 30/08/2021-03/09/2021.

Neumaier, A., and E. Groeneveld. 1998. Restricted maximum likelihood estimation of covariances in sparse linear models. Genet Sel Evol 30:3.

Ozhan, N., U. G. Simsek, and M. Ozcelik. 2016. Comparison of floor and cage housing systems in terms of some welfare assessments in broiler. Ank Univ Vet Fak Derg 63:317-322.

Randall, M., M. Herbert, and R. Hepherd. 1976. Automatic pneumatic conveying and weight-dispensing system for floor-feeding pellets to pigs. J Agric Eng Res 21:233-245.

Roller, W., and H. Teague. 1970. Automatic paste feeding system for pigs. Agric Eng 51:304.

SAS Institute Inc. 2013. SAS/STAT® 13.1 User's Guide. Cary, NC: SAS Institute Inc.

Willemsen, H., N. Everaert, A. Witters, L. DE Smit, M. Debonne, F. Verschuere, P. Garain, D. Berckmans, E. Decuypere, and V. Bruggeman. 2008. Critical assessment of chick quality measurements as indicator of posthatch performance. Poult Sci 87:2358-2366.

Yan, W., C. Sun, C. Wen, C. Ji, D. Zhang, and N. Yang. 2019. Relationships between feeding behaviors and performance traits in slow-growing yellow broilers. Poult Sci 98:548-555.
Résumé des principaux résultats et conclusion

***Clusters d'efficacité alimentaire*** – L’observation des profils d’efficacité alimentaire montre que les deux points qui permettent de distinguer les animaux les uns des autres sont leur niveau d’efficacité alimentaire au 1er changement d’aliment (qui traduit la qualité de leur démarrage) et la pente d’évolution de l’indice de consommation entre les 2 changements d’aliment. Les plus efficaces sont ceux avec un faible niveau d’indice de consommation au moment du 1er changement d’aliment et ceux dont l’indice de consommation diminue entre les deux changements d’aliment.

***Stratégie de sélection*** – Le déterminisme génétique des caractères étudiés différant fortement entre les deux lignées (cf. chapitre 4), il n’est pas surprenant de trouver que les meilleures stratégies de sélection pour améliorer l’indice de consommation ne sont pas les mêmes chez les pHu+ et les pHu-. Pour les pHu+, une sélection directe sur l’indice à 42 jours ou sur les profils d’efficacité donnerait des résultats comparables. Chez les pHu-, la meilleure stratégie de sélection indirecte serait d’utiliser un indicateur précoce de l’efficacité finale, l’indice cumulé à 30 jours. L’amélioration de l’indice passerait plutôt par une plus faible consommation alimentaire chez les pHu+ et par un poids plus élevé chez les pHu-. Cette amélioration de l’indice induira, chez les pHu-, une baisse du pHu et une augmentation du BMY, et chez les pHu+, une simple augmentation du pHu.
# CHAPITRE 6 : Discussion générale

L'objectif de ce travail était de mettre en avant de nouveaux critères de sélection de l'efficacité alimentaire, en mettant à profit les nouvelles possibilités offertes par les outils de phénotypage à haut débit (identification de critères précoces, prise en compte de la cinétique d'évolution avec le temps). Il s'inscrivait dans le cadre de la promotion de la durabilité de la production avicole, ce qui impliquait de mener cette réflexion en intégrant les impacts en termes économiques, environnementaux et sociaux. L'efficacité alimentaire en aviculture a en effet un impact majeur sur ces 3 aspects (Figure 6.1).

<figure><img src="image_57.png" /><figcaption>Figure 6.1. Liens entre efficacité alimentaire et durabilité.</figcaption></figure>

La composition des régimes impacte à la fois les aspects sociaux et environnementaux de la durabilité au travers de la dépendance aux importations et de la concurrence entre alimentation animale et humaine, mais le recours aux matières premières alternatives peut affecter les performances. Nous avons donc placé la réflexion sur la recherche de nouveaux
critères de sélection de l'efficacité dans ce contexte de substitution des matières premières classiques par des matières premières alternatives.

L'accès aux données de consommation et d'efficacité alimentaire quotidiennes a tout d'abord permis de mettre en avant l'importance des transitions alimentaires.

# 1. Importance des périodes de transitions alimentaires

Les différentes études de la thèse ont permis de montrer que l'utilisation des mangeoires Bird-e permettait un suivi journalier fiable des performances au niveau du lot et de l'individu. Elles ont ainsi mis en évidence l'importance des transitions alimentaires, qui ressortent comme des moments critiques impactant la réponse des animaux. Lorsque les animaux ont été distingués selon leur profil d'évolution de l'indice de consommation avec l'âge, les deux facteurs de classification les plus importants étaient le niveau d'efficacité au moment de la première transition alimentaire, qui traduit la qualité au démarrage, et la pente d'évolution de l'indice de consommation entre les deux transitions, au moment où la vitesse de croissance est maximale.

L'observation des performances à l'approche des transitions pourrait donc être un outil d'aide au pilotage de l'élevage. En effet, une dégradation des performances (baisse du gain de poids, augmentation de la consommation alimentaire) dans les jours précédant les transitions alimentaires pourrait indiquer que l'aliment distribué ne répond plus complètement aux besoins des animaux. C'est par exemple ce que nous avons observé dans la 1ère étude, au cours de laquelle les performances de croissance des poulets label ont été dégradées dans les 3 jours précédant la 2ème transition alimentaire avant de retrouver leur niveau attendu après le changement d'aliment. Dans ce cas, l'observation des données quotidiennes pourrait amener à avancer l'âge de la transition par rapport aux préconisations habituelles et permettre ainsi aux animaux d'exprimer pleinement leur potentiel génétique.

A l'opposé, un ralentissement des performances après une transition alimentaire pourrait signaler une difficulté d'adaptation au nouvel aliment et suggérer de retarder la transition ou modifier la composition du régime distribué pour mieux l'adapter aux besoins des animaux. C'est par exemple ce que l'on observe après la 2ème transition alimentaire dans l'étude des lignées pHu+ et pHu-. En effet, si la croissance des deux lignées est identique après cette transition, c'est au prix d'une consommation alimentaire plus élevée des pHu+, ce qui pourrait indiquer une inadéquation du régime aux besoins de cette lignée.
Au-delà des transitions alimentaires, on peut également signaler que le suivi quotidien des performances va permettre de connaître la réaction des animaux à diverses perturbations. C'est par exemple ce que nous avons pu observer à J17 dans l'étude sur les lignées pHu, où une baisse transitoire des performances a été observée suite à la prise de sang effectuée sur les animaux la veille.

## 2. Amélioration de l'efficacité alimentaire en contexte alternatif

L'amélioration de l'efficacité alimentaire en contexte alternatif peut permettre de progresser sur les différents aspects de la durabilité soit au travers de la composition du régime, même sans amélioration des performances, soit par la sélection pour améliorer l'indice de consommation, soit en cumulant les avantages des deux stratégies. La thèse permet d'apporter de nouvelles données pour évaluer les conséquences sur les différents piliers de la durabilité.

### 2.1. Régime alternatif

#### 2.1.1. Impact sur le coût de production

Afin d'évaluer l'évolution du coût de production lié au passage d'un aliment à base de blé, maïs et de soja à un aliment contenant des matières premières alternatives et locales (féverole, tourteau de colza et de tournesol, drèches de blé), nous avons dans un premier temps calculé le prix des deux types d'aliment (Tableau 6.1) en utilisant les prix actuels des matières premières (Tableau 6.2). Ces éléments permettent de calculer le coût des aliments classiques (CD) et alternatifs (AD) utilisés dans la première étude pour les animaux labels et standards (Tableau 6.3). Les coûts des aliments CD et AD sont équivalents pour la période de démarrage du poulet label rouge et pour la période croissance du poulet standard. Dans les autres cas, l'aliment alternatif est 0.5 à 2.1% plus cher que l'aliment classique. Pour estimer le coût réel de l'utilisation de ces régimes alternatifs, nous avons pris en compte les consommations alimentaires mesurées dans la première étude, le poids final des animaux ainsi que les coûts de production hors aliment par kg de poids vif (0.420 € chez les animaux STD, 0.898 € chez les animaux label, chiffres ITAVI (2016)). Nous avons alors pu calculer le coût alimentaire par kg de poids vif pour les deux aliments et les deux types de production.
<table><caption>Tableau 6.1.Composition des différents régimes classiques (CD) et alternatifs (AD) pour des animaux à croissance rapide (STD) et à croissance lente (LR) utilisés au cours de nos études</caption><thead><tr><th rowspan="2">Ingredient (%)</th><th colspan="6">STD</th><th colspan="6">LR</th></tr><tr><th colspan="3">CD</th><th colspan="3">AD</th><th colspan="3">CD</th><th colspan="3">AD</th></tr><tr><th></th><th>Démarrage</th><th>Croissance</th><th>Finition</th><th>Démarrage</th><th>Croissance</th><th>Finition</th><th>Démarrage</th><th>Croissance</th><th>Finition</th><th>Démarrage</th><th>Croissance</th><th>Finition</th></tr></thead><tbody><tr><td>Maïs</td><td>30.650</td><td>35.970</td><td>39.800</td><td>20.420</td><td>18.890</td><td>23.500</td><td>29.620</td><td>42.920</td><td>46.620</td><td>18.250</td><td></td><td>16.950</td></tr><tr><td>Blé</td><td>30.100</td><td>30.100</td><td>30.100</td><td>30.100</td><td>30.100</td><td>30.100</td><td>38.550</td><td>30.100</td><td>30.100</td><td>40.100</td><td>57.950</td><td>45.100</td></tr><tr><td>Féverole</td><td></td><td></td><td></td><td></td><td>12.000</td><td>13.000</td><td></td><td></td><td></td><td>10.000</td><td>13.000</td><td>12.000</td></tr><tr><td>Tourteau de soja</td><td>32.860</td><td>28.520</td><td>25.150</td><td>24.220</td><td>11.610</td><td>7.130</td><td>28.080</td><td>23.160</td><td>19.840</td><td>18.540</td><td>6.730</td><td>5.200</td></tr><tr><td>Tourteau de colza</td><td></td><td></td><td></td><td>5.000</td><td>5.000</td><td>8.000</td><td></td><td></td><td></td><td>5.000</td><td>5.000</td><td>5.000</td></tr><tr><td>Drèches de blé</td><td></td><td></td><td></td><td>3.000</td><td>5.000</td><td>5.000</td><td></td><td></td><td></td><td>3.000</td><td>5.000</td><td></td></tr><tr><td>Tourteau de tournesol</td><td></td><td></td><td></td><td>8.120</td><td>7.730</td><td>5.190</td><td></td><td></td><td></td><td>5.020</td><td>8.000</td><td></td></tr><tr><td>Huile de soja</td><td>2.210</td><td>1.900</td><td>1.990</td><td>5.000</td><td>5.000</td><td>5.000</td><td></td><td>0.360</td><td>0.570</td><td>1.420</td><td>3.820</td><td>3.800</td></tr><tr><td>Gluten</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>1.100</td></tr><tr><td>Calcium carbonate</td><td>0.710</td><td>0.169</td><td>0.002</td><td>0.655</td><td>0.142</td><td></td><td>0.600</td><td>0.274</td><td>0.300</td><td>0.590</td><td>0.390</td><td>0.350</td></tr><tr><td>Phosphate bicalcique</td><td>2.160</td><td>1.850</td><td>1.540</td><td>2.050</td><td>1.730</td><td>1.400</td><td>1.970</td><td>1.870</td><td>1.560</td><td>1.880</td><td>1.540</td><td>1.350</td></tr><tr><td>Sel</td><td>0.236</td><td>0.207</td><td>0.211</td><td>0.192</td><td>0.150</td><td>0.158</td><td>0.270</td><td>0.246</td><td>0.280</td><td>0.254</td><td>0.180</td><td>0.230</td></tr><tr><td>Prémix</td><td>0.400</td><td>0.400</td><td>0.400</td><td>0.400</td><td>0.400</td><td>0.400</td><td>0.400</td><td>0.400</td><td>0.400</td><td>0.400</td><td>0.400</td><td>0.400</td></tr><tr><td>Carbonate de sodium</td><td>0.135</td><td>0.173</td><td>0.175</td><td>0.196</td><td>0.262</td><td>0.250</td><td>0.100</td><td>0.129</td><td>0.081</td><td>0.114</td><td>0.227</td><td>0.160</td></tr><tr><td>DL-Methionine</td><td>0.269</td><td>0.275</td><td>0.231</td><td>0.234</td><td>0.285</td><td>0.234</td><td>0.204</td><td>0.211</td><td>0.114</td><td>0.207</td><td>0.230</td><td>0.114</td></tr><tr><td>HCL Lysine</td><td>0.176</td><td>0.264</td><td>0.250</td><td>0.287</td><td>0.414</td><td>0.392</td><td>0.154</td><td>0.243</td><td>0.125</td><td>0.183</td><td>0.373</td><td>0.214</td></tr><tr><td>Threonine</td><td>0.076</td><td>0.111</td><td>0.094</td><td>0.088</td><td>0.157</td><td>0.135</td><td>0.052</td><td>0.087</td><td>0.010</td><td>0.062</td><td>0.140</td><td>0.032</td></tr><tr><td>Valine</td><td>0.021</td><td>0.061</td><td>0.041</td><td>0.038</td><td>0.130</td><td>0.106</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Tryptophane</td><td></td><td></td><td></td><td></td><td></td><td>0.005</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Composition calculée</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>AMEn, kcal/kg</td><td>2850</td><td>2900</td><td>2950</td><td>2850</td><td>2900</td><td>2950</td><td>2750</td><td>2850</td><td>2900</td><td>2750</td><td>2850</td><td>2890</td></tr><tr><td>CP, g/kg</td><td>215.0</td><td>200.4</td><td>187.1</td><td>215.0</td><td>194.3</td><td>181.3</td><td>200.0</td><td>179.8</td><td>165.0</td><td>200.0</td><td>179.0</td><td>164.9</td></tr><tr><td>Lys, g/kg</td><td>11.200</td><td>10.900</td><td>10.000</td><td>11.200</td><td>10.900</td><td>10.000</td><td>10.000</td><td>9.500</td><td>7.800</td><td>10.000</td><td>9.500</td><td>7.810</td></tr><tr><td>Met + Cys, g/kg</td><td>8.400</td><td>8.170</td><td>7.500</td><td>8.400</td><td>8.170</td><td>7.500</td><td>7.500</td><td>7.200</td><td>6.000</td><td>7.500</td><td>7.200</td><td>6.000</td></tr><tr><td>Trp, g/kg</td><td>2.280</td><td>2.060</td><td>1.890</td><td>2.280</td><td>1.840</td><td>1.700</td><td>2.100</td><td>1.790</td><td>1.620</td><td>1.990</td><td>1.730</td><td>1.490</td></tr></tbody></table>
<table><caption>Tableau 6.2. Prix des matières premières utilisées (€/t).</caption><thead><tr><th></th><th>Coût (€/t)</th><th></th><th>Coût (€/t)</th></tr></thead><tbody><tr><td>Maïs</td><td>161.14</td><td>Phosphate bicalcique</td><td>470</td></tr><tr><td>Blé</td><td>163.17</td><td>Sel</td><td>72.42</td></tr><tr><td>Fèverole</td><td>240</td><td>Prémix</td><td>2700</td></tr><tr><td>Tourteau de Soja</td><td>380.79</td><td>Carbonate de calcium</td><td>552</td></tr><tr><td>Tourteau de Colza</td><td>270.75</td><td>DL-Méthionine</td><td>3050</td></tr><tr><td>DDGS</td><td>117.4</td><td>HCL Lysine</td><td>1416.67</td></tr><tr><td>Tourteau de tournesol</td><td>184.11</td><td>Thréonine</td><td>1600</td></tr><tr><td>Huile de soja</td><td>736.5</td><td>Valine</td><td>5000</td></tr><tr><td>Gluten</td><td>188.76</td><td>Tryptophane</td><td>15000</td></tr><tr><td>Carbonate de calcium</td><td>50</td><td></td><td></td></tr></tbody></table>

Pour les animaux à croissance rapide, ce dernier s'élevait à 0.406 €/kg de poids vif et 0.389 €/kg de poids vif, respectivement avec le régime alternatif et le régime classique. Pour les animaux à croissance lente, ce coût alimentaire s'élevait à 0.615 € et 0.616 € par kg de poids vif pour les régimes alternatif et classique. Il en ressort un coût de production identique quel que soit le régime, de 1.514 €/kg de poids vif, pour les animaux à croissance lente, et plus important de 2.9% pour les animaux à croissance rapide nourris avec le régime alternatif (0.826 €/kg de poids vif versus 0.809 €/kg de poids vif).

<table><caption>Tableau 6.3. Coût des aliments classique (CD) et alternatif (AD) en €/tonne, en production label rouge (LR) ou standard (STD).</caption><thead><tr><th rowspan="2">Phase</th><th colspan="2">LR</th><th colspan="2">STD</th></tr><tr><th>AD</th><th>CD</th><th>AD</th><th>CD</th></tr></thead><tbody><tr><td>Démarrage</td><td>247.6</td><td>247.9</td><td>279.6</td><td>275.1</td></tr><tr><td>Croissance</td><td>242.3</td><td>241.0</td><td>267.2</td><td>267.3</td></tr><tr><td>Finition</td><td>233.2</td><td>228.3</td><td>259.0</td><td>257.0</td></tr></tbody></table>

### 2.1.2. Impact environnemental

Pour calculer l'impact environnemental du changement d'aliment, nous nous sommes appuyés sur les données d'impact environnemental de chaque matière première (Wilfart et al., 2016) et avons comparé les émissions de CO2, le potentiel d'acidification, d'eutrophisation, l'énergie non renouvelable utilisée au cours du cycle de production, l'occupation des terres et finalement la demande en phosphore.
Comme pour le calcul de l'impact sur le coût de production, nous avons utilisé les données de croissance et de consommation des poulets standard et label de la première expérience (Tableau 6.4).

<table><caption>Tableau 6.4. Impact environnemental par kg de poids vif des régimes alternatif (AD) et classique (CD) utilisés pour les souches Label Rouge (LR) et standard (STD).</caption><thead><tr><th rowspan="2">Critère<fnref n="1" /></th><th rowspan="2">Unité</th><th colspan="2">LR<fnref n="2" /></th><th colspan="2">STD<fnref n="2" /></th></tr><tr><th>AD</th><th>CD</th><th>AD</th><th>CD</th></tr></thead><tbody><tr><td>CC</td><td>kg équivalent-CO2</td><td>1.72</td><td>1.99</td><td>1.15</td><td>1.27</td></tr><tr><td>AC</td><td>mol H+</td><td>0.026</td><td>0.031</td><td>0.016</td><td>0.018</td></tr><tr><td>EU</td><td>kg PO₄³⁻</td><td>0.0100</td><td>0.0106</td><td>0.0062</td><td>0.0064</td></tr><tr><td>CEDNR</td><td>MJ</td><td>15.05</td><td>17.22</td><td>10.66</td><td>10.84</td></tr><tr><td>LO</td><td>m<fnref n="2" />/an</td><td>3.37</td><td>3.91</td><td>2.36</td><td>2.06</td></tr><tr><td>PD</td><td>kg P</td><td>0.029</td><td>0.032</td><td>0.018</td><td>0.019</td></tr></tbody></table>

L'utilisation d'un régime alternatif permet de réduire l'impact environnemental sur tous les postes (Tableau 6.4). Le gain est particulièrement important sur le changement climatique et l'acidification des sols. On note un impact plus important chez les labels que chez les standards pour la consommation d'énergie fossile et la dépendance en phosphore, alors que l'effet est plus fort chez les standards pour l'eutrophisation (Figure 6.2). Cet effet favorable de l'utilisation du régime alternatif vient principalement de sa plus faible teneur en soja et en maïs que le régime classique. En effet, ces cultures nécessitent de nombreux intrants, notamment en azote (qui génèrent l'acidification des sols) et phosphore (d'où l'impact sur les besoins en phosphore) et une forte mécanisation (qui génère de la consommation d'énergie fossile). Les matières premières alternatives sont de plus produites plus localement (diminuant ainsi fortement les émissions de carbone et l'énergie utilisée pour produire et apporter ces matières aux bassins de production).

Seule l'occupation des sols est plus importante dans le cadre de l'utilisation de régimes alternatifs, et ceci uniquement sur les souches à croissance rapide, les poulets élevés avec le régime alternatif ayant besoin de 0.30 m<fnref n="2" /> de plus de surface de culture par an et par kg de poids vif. Cette augmentation est majoritairement due à la forte utilisation de féveroles dans les

<footnote n="1">¹: CC : changement climatique ; AC : Acidification ; EU : Eutrophisation ; CEDNR : Demande en énergie non renouvelable ; LO : Occupation des sols ; PD : Demande en phosphore</footnote>

<footnote n="2">²: LR : Label rouge ; STD : Standard</footnote>
régimes alternatifs en remplacement du soja. La féverolle occupe en effet 28% de surface en plus que le soja, car sa productivité est faible, du fait d'un ratio cosse-fleurs inférieur à 30%, alors que celui du soja peut aller jusque 61% (Alharbi et Adhikari, 2020 ; Kim et al., 2020).

<figure><img src="image_58.png" /></figure>

CC: changement climatique ; AC: acidification; EU: eutrophisation; CEDNR: Besoin en énergies non renouvelables; LO: Occupation des sols ; PD: Besoin en phosphore

<caption>Figure 6.2. Différence d'impact environnemental du régime alternatif par rapport au régime classique (en % des impacts du régime classique) en production label rouge ou standard.</caption>

### 2.1.3. Impact sur le volet social du développement durable

Du point de vue social, l'utilisation de matières premières alternatives va permettre de réduire la dépendance de l'Europe vis-à-vis des différents pays producteurs de soja et de maïs tout en favorisant le travail local. De plus, dans le contexte de production actuel, des questionnements se posent sur les nombreux flux de marchandises qui sont nécessaires au bon fonctionnement de la production mondiale. En effet, du point de vue économique, l'absence de souveraineté alimentaire concernant l'alimentation des volailles rend la production européenne extrêmement sensible aux moindres perturbations que peuvent subir ces flux mondiaux. Par ailleurs, du point de vue sanitaire, ces flux importants sont des facteurs de transmission de virus, humain ou animal, entre pays avec des régulations différentes (Hafez et Attia, 2020). Ces interrogations renforcent l'idée qu'une production plus locale serait favorable pour la durabilité de la production de volaille européenne. L'utilisation des matières premières alternatives va aussi diminuer la compétition homme-animal sur les sources d'énergie et de protéines. En effet,
les régimes alternatifs utilisés dans cette thèse contiennent 1.4% moins de protéines utilisables par l'homme et 1.8 à 3.5% moins d'énergie utilisable par l'homme par rapport aux régimes conventionnels. Ces régimes n'ayant pas ou peu d'impact sur les rendements en filet, leur utilisation ne devrait pas entraîner de surcoût pour les consommateurs.

## 2.2. Nouvelles stratégies de sélection

La seconde partie de la thèse a porté sur l'étude des possibilités d'amélioration génétique de l'efficacité à utiliser un régime alternatif (comparable à celui utilisé pour les poulets standards de la 1ère étude) et sur la comparaison de plusieurs stratégies de sélection basées sur les paramètres génétiques estimés dans les lignées pHu.

### 2.2.1. Impact sur le coût de production

Avec leurs performances actuelles et le coût de l'aliment, le coût de production est actuellement de 0.872 €/ kg de poids vif pour les pHu+ et de 0.855 €/ kg de poids vif pour les pHu-. Afin d'évaluer l'impact sur le coût de production des différentes stratégies de sélection proposées au chapitre 5, nous avons pris en compte l'évolution suite à la sélection du poids vif à l'abattage (avec un prix de vente de 0.896 €/kg de poids vif, Méda et al., 2021), de la consommation alimentaire (en reprenant le calcul du coût de l'aliment du §2.1.1.) et ajouté le coût de productions hors-alimentation (0.420 €/kg de poids vif, ITAVI, 2016). Les différentes stratégies de sélection testées ont toutes démontré un impact économique favorable, que ce soit en diminuant les coûts alimentaires, en augmentant le poids vif ou en cumulant les deux (Tableau 6.4).
<table><caption>Tableau 6.4. Prix de vente, coûts de production et gain économique par animal et par kg de poids vif attendus par génération de sélection avec les différentes stratégies de sélection (i=1).</caption><thead><tr><th>Lignée</th><th>Stratégie<fnref n="1" /></th><th>Prix de vente (€/animal)</th><th>Coût alimentaire (€/animal)</th><th>Coût hors-aliment (€/animal)</th><th>Gain économique (€/animal)</th><th>Gain économique (€/kg poids vif)</th></tr></thead><tbody><tr><td rowspan="6">pHu-</td><td>Situation initiale</td><td>2.322</td><td>1.126</td><td>1.089</td><td>0.108</td><td>0.041</td></tr><tr><td>BW42</td><td>2.416</td><td>1.156</td><td>1.133</td><td>0.128</td><td>0.047</td></tr><tr><td>DCFCR17</td><td>2.374</td><td>1.123</td><td>1.113</td><td>0.138</td><td>0.052</td></tr><tr><td>DCFCR30</td><td>2.419</td><td>1.120</td><td>1.134</td><td>0.165</td><td>0.061</td></tr><tr><td>DCFCR42</td><td>2.362</td><td>1.109</td><td>1.107</td><td>0.145</td><td>0.055</td></tr><tr><td>CLUST_NUM</td><td>2.384</td><td>1.126</td><td>1.117</td><td>0.140</td><td>0.053</td></tr><tr><td rowspan="6">pHu+</td><td>Situation initiale</td><td>2.304</td><td>1.163</td><td>1.080</td><td>0.061</td><td>0.024</td></tr><tr><td>BW42</td><td>2.422</td><td>1.212</td><td>1.135</td><td>0.075</td><td>0.028</td></tr><tr><td>DCFCR17</td><td>2.309</td><td>1.154</td><td>1.082</td><td>0.073</td><td>0.028</td></tr><tr><td>DCFCR26</td><td>2.306</td><td>1.150</td><td>1.081</td><td>0.074</td><td>0.029</td></tr><tr><td>DCFCR42</td><td>2.296</td><td>1.138</td><td>1.076</td><td>0.082</td><td>0.032</td></tr><tr><td>CLUST_NUM</td><td>2.297</td><td>1.139</td><td>1.077</td><td>0.081</td><td>0.032</td></tr></tbody></table>

Chez les pHu-, la meilleure stratégie est la sélection de l'indice cumulé à 30 jours, qui augmente le gain économique de 0.02 € par kg de poids vif. L'amélioration est principalement due au gain de poids et donc à l'augmentation du prix de vente des animaux qui est plus importante que l'augmentation du coût de production (coût alimentaire + coût hors aliment). En revanche, chez les pHu+, les gains les plus importants sont obtenus par la sélection sur le profil et l'indice à 42 jours (+0.008 €/kg poids vif, soit 34% d'augmentation) ou dans une moindre mesure à 26 jours (+0.005 €/kg de poids vif) et viennent principalement de la réduction du coût alimentaire, de 1.1 à 2.1% en fonction de la stratégie. Dans cette lignée, bien que n'étant pas la plus rentable économiquement, la sélection de l'indice à 26 jours permet de ne pas perdre sur le poids vif, contrairement à la sélection sur l'indice à 42 jours ou sur le profil. L'effort de sélection devra être plus important chez les pHu+ que chez les pHu- puisque non seulement les gains attendus de la sélection sont moins importants chez les pHu+ mais aussi que leur coût de production initial est plus élevé

<footnote n="1">¹ BW42 : poids vif à 42 jours ; DCFCRX : indice de consommation cumulé à X jours ; CLUST_NUM : profil d'évolution de l'indice de consommation cumulé</footnote>
### 2.2.2. Impact environnemental

Les différentes stratégies de sélection envisagées sont toutes intéressantes du point de vue environnemental (Tableau 6.5).

<table><caption>Tableau 6.5. Impact environnemental des différentes stratégies de sélection envisagées (par kg de poids vif)</caption><thead><tr><th>Lignée</th><th>Stratégie<sup>2</sup></th><th>CC<sup>1</sup><br>(kg CO2-eq)</th><th>AC<sup>1</sup><br>(mol H+)</th><th>EU<sup>1</sup><br>(kg PO43-)</th><th>CEDNR<sup>1</sup><br>(MJ)</th><th>LO<sup>1</sup><br>(m<sup>2</sup>.yr)</th><th>PD<sup>1</sup><br>(kg P)</th></tr></thead><tbody><tr><td rowspan="6">pHu-</td><td>Situation initiale</td><td>1.189</td><td>0.017</td><td>0.0068</td><td>11.075</td><td>2.472</td><td>0.0184</td></tr><tr><td>DCF CR42</td><td>1.153</td><td>0.016</td><td>0.0066</td><td>10.738</td><td>2.398</td><td>0.0178</td></tr><tr><td>Profil</td><td>1.159</td><td>0.016</td><td>0.0066</td><td>10.796</td><td>2.410</td><td>0.0179</td></tr><tr><td>DCF CR 17</td><td>1.161</td><td>0.016</td><td>0.0066</td><td>10.814</td><td>2.414</td><td>0.0179</td></tr><tr><td>DCF CR30</td><td>1.137</td><td>0.016</td><td>0.0065</td><td>10.588</td><td>2.364</td><td>0.0176</td></tr><tr><td>BW42</td><td>1.172</td><td>0.017</td><td>0.0067</td><td>10.913</td><td>2.435</td><td>0.0181</td></tr><tr><td rowspan="6">pHu+</td><td>Situation initiale</td><td>1.240</td><td>0.018</td><td>0.0071</td><td>11.553</td><td>2.580</td><td>0.0191</td></tr><tr><td>DCF CR42</td><td>1.218</td><td>0.017</td><td>0.0069</td><td>11.353</td><td>2.537</td><td>0.0188</td></tr><tr><td>Profil</td><td>1.218</td><td>0.017</td><td>0.0069</td><td>11.357</td><td>2.537</td><td>0.0188</td></tr><tr><td>DCF CR 17</td><td>1.228</td><td>0.017</td><td>0.0070</td><td>11.445</td><td>2.556</td><td>0.0190</td></tr><tr><td>DCF CR26</td><td>1.226</td><td>0.017</td><td>0.0070</td><td>11.428</td><td>2.553</td><td>0.0189</td></tr><tr><td>BW42</td><td>1.233</td><td>0.017</td><td>0.0070</td><td>11.479</td><td>2.562</td><td>0.0190</td></tr></tbody></table>

La stratégie qui apporte la plus forte amélioration par kg de poids vif est la sélection sur le DFCR30 pour les animaux pHu-, avec une diminution de 4.4% des différents impacts environnementaux. Dans la lignée pHu+, sélectionner sur DFCR42 ou sur les profils permet un progrès de 1.6% à 1.8% sur les différents impacts environnementaux (CC, AC, EU, CEDNR, LO et PD, Tableau 6.5). Dans les deux lignées, la sélection sur le BW42 ressort comme la moins intéressante, car elle est la seule à induire une augmentation de la consommation, et ne diminue donc l'impact de la production que de 0.5 à 1.4%.

<footnote n="1">¹ CC: changement climatique ; AC: acidification; EU: eutrophisation; CEDNR: Besoin en énergies non renouvelables; LO: Occupation des sols ; PD: Besoin en phosphore</footnote>

<footnote n="2">² DCFCRX : indice de consommation cumulé à X jours, Profil : profil d'évolution de l'indice de consommation au cours de la vie de l'animal, BW42 : poids vif à 42 jours</footnote>
### 2.2.3. Impact sur le volet social de la durabilité

Dans les deux lignées, seule la sélection sur le poids ne modifierait pas le pH ultime du filet. Dans tous les autres cas, le pH augmenterait dans la lignée pHu+, augmentant la fréquence de viandes de types DFD et diminuerait le pH dans la lignée pHu-, augmentant la fréquence de viandes de type acide (Alnahhas et al., 2014). Chez les pHu+, la sélection de l'indice à 42 jours est la seule stratégie qui ne dégraderait pas le rendement en filet. Chez les pHu-, la sélection sur le profil ne modifierait pas le rendement en filet et la sélection sur l'indice à 30 ou 42 jours l'améliorerait.

## Conclusions sur l'amélioration de la durabilité de la production

L'utilisation du régime alternatif entraînera un léger surcoût chez les animaux standard mais sera neutre chez les labels. Une simulation de sélection chez des animaux standard a montré qu'améliorer l'indice de consommation pouvait entrainer un gain économique de 4.6 à 9.2% suivant le critère et la lignée sélectionnée.

Sur le plan environnemental, le changement de composition du régime atténue l'impact sur le changement climatique, l'eutrophisation, l'acidification, la consommation d'énergie non renouvelable et le besoin en phosphore de 2.4 à 15.2%. La sélection des différents critères d'indice de consommation a un impact environnemental positif plus ou moins important (0.5 à 4.4% selon la lignée, le critère de sélection et l'indicateur d'impact environnemental considérés). La sélection sur le poids augmente la consommation alimentaire et améliore donc faiblement les performances environnementales.

La modification des régimes alimentaires et la sélection de l'indice auront un impact social plutôt positif car ils vont globalement réduire la dépendance aux importations de soja. Il faut cependant rester vigilant sur les impacts qu'ils pourraient avoir sur les fonctions productives (c'est à dire la quantité et la qualité du filet), dont les variations semblent dépendantes de la souche.

La sélection d'animaux sur leur efficacité alimentaire avec un régime alternatif permettra de cumuler les progrès dus à la nutrition et à la sélection. Selon le type génétique considéré, la sélection de l'indice de consommation à un âge précoce (26/30 jours) et sur le profil d'évolution de l'indice de consommation permet un bon compromis entre les performances économiques, environnementales et sociales.
# 3. Perspectives d'utilisation de l'automate

## 3.1. Sélection des animaux labels

L'accès des animaux à l'extérieur voire à un parcours est de plus en plus plébiscité par les consommateurs (voir projet ACCEPT, Delanoue et al. (2018)). L'élevage en plein-air est ainsi un critère important dans la qualité d'image des poulets Label Rouge, mais il également un facteur de variation de leur efficacité alimentaire (N'Dri et al., 2007). Il existe notamment une variabilité dans l'utilisation des parcours, avec selon les études de 15 à 50% d'animaux explorateurs (Jones et al. 2006; Zeltner and Hirt, 2008, Castellini et al., 2016). L'analyse génétique de l'efficacité alimentaire des souches alternatives implique donc de pouvoir mesurer le caractère au sein de bâtiments d'élevage donnant accès au parcours, ce qui semble possible selon des essais récents à INRAE avec l'automate BIRD-e. Ceci permettra de préciser le lien phénotypique et génétique entre activité sur le parcours et efficacité alimentaire, et plus largement d'évaluer la résilience des animaux face aux variations environnementales.

Concernant les souches de type Label, dont l'âge d'abattage est réglementé et ne doit pas être inférieur à 81 jours pour le poulet (Beaumont et al., 2004), la sélection sur l'efficacité alimentaire doit autant que possible se faire à vitesse de croissance équivalente. Pour ces génétiques, il serait intéressant d'évaluer les possibilités de sélection de la consommation résiduelle plutôt que l'indice de consommation, et les effets sur les autres caractères (croissance, qualité de la viande, comportement...). Dans tous les cas, une modélisation plus fine de la cinétique d'efficience sera un atout pour adopter les meilleures stratégies de sélection de ces souches à croissance lente, dont l'indice de consommation est aujourd'hui 1,8 plus important que celui du poulet standard (Malher et al., 2018).

## 3.2. Une sélection encore plus précise des animaux

Dans le cadre de cette thèse, nous nous sommes intéressés uniquement aux cinétiques journalières des différentes performances. Toutefois, les données étant acquises pour chaque repas, il est possible d'étudier la cinétique alimentaire au sein d'une journée. D'après des premiers résultats, cela permet de séparer des animaux de consommation totale identique mais avec des profils d'alimentation différents. A titre d'exemple, la Figure 6.3 montre les consommations observées pour 4 animaux pHu+ âgés de 17 jours (2 à forte consommation, 2 à faible consommation) qui présentent un nombre de visites, des durées de visites et des quantités ingérées par repas très différents (Tableau 6.6).
<figure><img src="image_59.png" /><figcaption>Figure 6.3. Cinétique d'ingestion d'animaux à forte et à faible consommation journalière.</figcaption></figure>

Les relations entre le comportement alimentaire et efficacité sont encore peu connues, mais Howie et al. (2011) ont montré pour différents génotypes de poulets que la durée et la taille du repas influencent l'efficacité alimentaire (corrélation de 0.20 à 0.25 selon le génotype). L'accès en continu aux données de performances et de consommation devrait permettre de raffiner la connaissance des liens entre comportement et efficacité alimentaires. Ceci sera d'autant plus important dans le contexte actuel de réchauffement climatique (Hertig et Jacobheit, 2014) et pour les productions avec un accès à l'extérieur. En effet, la température impacte les poulets de manière non négligeable, ces derniers présentant une consommation et une efficacité plus faibles lorsque la température est élevée (Syafwan et al., 2012 ; Liu et al., 2016). Dans ce contexte, la sélection devra également intégrer la répartition des repas dans la journée et évaluer les liens avec l'efficacité mais aussi la physiologie et les capacités d'adaptation de l'animal.

<table><caption>Tableau 6.6. Comportement alimentaire et performances d'animaux pHu+ de 17 jours étudiés pour la journée du 20/12/2019.</caption><thead><tr><th rowspan="2"></th><th colspan="2">Faible consommation</th><th colspan="2">Forte consommation</th></tr><tr><th>Peu de repas</th><th>Nombreux repas</th><th>Peu de repas</th><th>Nombreux repas</th></tr></thead><tbody><tr><td>Consommation (g.jour<sup>-1</sup>)</td><td>57.4</td><td>54.0</td><td>112.8</td><td>112.1</td></tr><tr><td>Nombre repas</td><td>11</td><td>97</td><td>12</td><td>31</td></tr><tr><td>Consommation par repas (g)</td><td>5.2</td><td>1.2</td><td>9.3</td><td>1.8</td></tr><tr><td>Durée Repas (s)</td><td>180</td><td>54</td><td>324</td><td>86</td></tr><tr><td>Vitesse Ingestion (g/s)</td><td>0.029</td><td>0.021</td><td>0.029</td><td>0.021</td></tr><tr><td>Poids vif</td><td>525.79</td><td>410</td><td>676.32</td><td>527.9</td></tr><tr><td>Indice de consommation</td><td>1.29</td><td>1.26</td><td>1.38</td><td>2.83</td></tr></tbody></table>
Conclusion générale

Les modes d'élevage et de sélection des volailles de chair doivent évoluer afin de mieux intégrer les enjeux du développement durable. Que ce soit sur le volet environnemental avec l'impact de régimes riches en maïs et soja, sur le volet social avec la meilleure prise en compte du bien-être de l'animal en élevage, ou sur la question de la compétition entre nutrition humaine et animale, de nombreux progrès peuvent encore être apportées par la filière avicole. Les questions touchant à l'impact environnemental de la production peuvent être adressées en modifiant le régime de ces animaux pour réduire la part des matières premières pouvant nourrir l'homme et augmenter celle de matières alternatives peu valorisables en nutrition humaine. Cette modification impacte aussi bien l'amont de la filière, avec la nécessité de mettre en place une sélection adaptée au nouveau contexte alimentaire et d'organiser les filières d'approvisionnement en matières premières alternatives, que l'aval dans la mesure où les performances économiques et la qualité des produits doivent autant que possible être maintenues suite à cette transition.

Au niveau européen, la transition alimentaire nécessitera des adaptations à différents échelons de la filière (modifications des lignes de productions d'aliment, réflexions sur les rotations de cultures, répercussion sur le prix d'achat des volailles...), dont les répercussions sur les coûts de production pourraient être plus importantes que celles envisagées dans nos travaux (Pampouille et al., 2021). L'introduction d'une taxe sur l'impact carbone ou de subventions pour favoriser la production de matières alternatives locales pourrait cependant à terme rendre la production européenne plus attractive. Du point de vue écologique, les impacts environnementaux positifs trouvés au cours de cette thèse pourraient évoluer avec l'augmentation de la demande en matières premières alternatives, du fait de la pression sur les rendements des nouvelles cultures et l'accroissement potentiel des intrants. L'utilisation de l'automate de consommation BIRD-e a permis l'obtention de cinétiques d'efficience alimentaire et l'étude de leur déterminisme génétique au sein de deux lignées d'animaux à croissance rapide nourries avec un régime alternatif. Les résultats ont montré des différences entre lignées dont le profil métabolique varie, confirmant que l'efficience alimentaire est un caractère complexe. La simulation de différentes stratégies confirme cependant que des gains significatifs peuvent être attendus d'une sélection sur l'efficacité alimentaire appliquée en conditions nutritionnelles alternatives, tout en mettant en évidence des réponses variées sur les fonctions productives (quantité et qualité de la viande). Des approches originales, basées sur l'identification de profils d'évolution de l'indice de consommation ou sur la recherche d'indicateurs précoces, ont été testées et peuvent amener à revisiter les pratiques de sélection de l'efficience alimentaire. Ces nouvelles approches, aujourd'hui permises par les technologies
de phénotypage à haut-débit de l'efficience alimentaire, seront particulièrement intéressantes à développer sur des souches alternatives à croissance plus lente, dont l'impact environnemental est plus élevé. Le cadre de ces futures recherches devra inclure les liens avec des indicateurs de la résilience et du bien-être des animaux et la qualité de leurs produits, afin de contribuer à une efficience alimentaire durable.
ABASHT, B., MIGNON-GRASTEAU, S., BOTTJE, W. & LAKE, J., 2020. Genetics and genomics of feed utilization efficiency in poultry species. Dans S.E. AGGREY, H. ZHOU, M. TIXIER-BOICHARD & D.D. RHOADS (eds.), *Advances in poultry genetics and genomics*. S.l.: Burleigh Dodds Science Publishing, pp. 183-220.

ABASHT, B., ZHOU, N., LEE, W.R., ZHUO, Z. & PERIPOLLI, E., 2019. The metabolic characteristics of susceptibility to wooden breast disease in chickens with high feed efficiency. *Poultry Science*, Vol. 98, n° 8, pp. 3246-3256.

ABUDABOS, A.M., AL-ATIYAT, R.M., STANLEY, D., ALJASSIM, R. & ALBATSHAN, H.A., 2017. The effect of corn distiller's dried grains with solubles (DDGS) fortified with enzyme on growth performance of broiler. *Environmental Science and Pollution Research*, Vol. 24, n° 26, pp. 21412-21421.

AGGREY, S., GONZÁLEZ-CERÓN, F. & REKAYA, R., 2014. Association of SNPs with components of residual feed intake parameters in a meat-type chicken population. *10th World Congress on Genetics Applied to Livestock Production*. Vancouver, BC, Canada.

AGGREY, S.E., KARNUAH, A.B., SEBASTIAN, B. & ANTHONY, N.B., 2010. Genetic properties of feed efficiency parameters in meat-type chickens. *Genetics Selection Evolution*, Vol. 42, pp. 25-30.

AHMADI, M., 2016. The Effect of Different Levels of Rapeseed Meal with and without Enzyme on the Performance and the Serum Level of Triiodothyronine (T3), Thyroxine (T4) and Thyroid Stimulating Hormone (TSH) in Broiler Chickens. *Iranian Journal of Applied Animal Science*, Vol. 6, n° 1, pp. 203-209.

AKPOBOME, G.O. & FANGUY, R.C., 1992. Evaluation of cage floor systems for production of commercial broilers. *Poultry Science*, Vol. 71, n° 2, pp. 274-280.

ALAGAWANY, M., ATTIA, A.I., IBRAHIM, Z.A., MAHMOUD, R.A. & EL-SAYED, S.A., 2017. The effectiveness of dietary sunflower meal and exogenous enzyme on growth, digestive enzymes, carcass traits, and blood chemistry of broilers. *Environmental Science and Pollution Research International*, Vol. 24, n° 13, pp. 12319-12327.

ALHARBI, N. & ADHIKARI, K., 2020. Factors of yield determination in faba bean (Vicia faba). *Crop and Pasture Science*, Vol. 71, pp. 305-321.

ALIZADEH, M., RODRIGUEZ-LECOMPTE, J.C., ROGIEWICZ, A., PATTERSON, R. & SLOMINSKI, B.A., 2016. Effect of yeast-derived products and distillers dried grains with
solubles (DDGS) on growth performance, gut morphology, and gene expression of pattern
recognition receptors and cytokines in broiler chickens. *Poultry Science*, Vol. 95, n° 3, pp. 507-
517.

ALJUOBORI, A., ZULKIFLI, I., SOLEIMANI, A.F., ABDULLAH, N. & LIANG, J.B., 2016. Growth performance, carcass characteristics and blood parameters of broiler chickens fed on canola meal under high environmental temperature. *European Poultry Science*, Vol. 80, pp. 127-138.

ALNAHHAS, N., BERRI, C., BOULAY, M., BAÉZA, E., JÉGO, Y., BAUMARD, Y., CHABAULT, M. & LE BIHAN-DUVAL, E., 2014. Selecting broiler chickens for ultimate pH of breast muscle: Analysis of divergent selection experiment and phenotypic consequences on meat quality, growth, and body composition traits. *Journal of Animal Science*, Vol. 92, n° 9, pp. 3816-3824.

ALNAHHAS, N., BERRI, C., CHABAULT, M., CHARTRIN, P., BOULAY, M., BOURIN, M.C. & LE BIHAN-DUVAL, E., 2016. Genetic parameters of white striping in relation to body weight, carcass composition, and meat quality traits in two broiler lines divergently selected for the ultimate pH of the pectoralis major muscle. *Bmc Genetics*, Vol. 17, pp. 61-69

ALNAHHAS, N., BERRI, C., CHABAULT-DHUIT, M., BOURIN, M., ARNOULD, C. & LE BIHAN-DUVAL, E., 2017. Combined effect of divergent selection for breast muscle ultimate pH and dietary amino acids on chicken performance, physical activity and meat quality. *Animal*, Vol. 11, n° 2, pp. 335-344.

ALNAHHAS, N., LE BIHAN-DUVAL, E., BAÉZA, E., CHABAULT, M., CHARTRIN, P., BORDEAU, T., CAILLEAU-AUDOUIN, E., METEAU, K. & BERRI, C., 2015. Impact of divergent selection for ultimate pH of pectoralis major muscle on biochemical, histological, and sensorial attributes of broiler meat. *Journal of Animal Science*, Vol. 93, n° 9, pp. 4524-4531.

AMERAH, A.M., VAN DE BELT, K. & VAN DER KLIS, J.D., 2015. Effect of different levels of rapeseed meal and sunflower meal and enzyme combination on the performance, digesta viscosity and carcass traits of broiler chickens fed wheat-based diets. *Animal*, Vol. 9, n° 7, pp. 1131-1137.

AN, B.K., JUNG, J.H., OH, S.T., KANG, C.W., LEE, K.W. & LEE, S.R., 2016. Effects of Diets with Graded Levels of Canola Meal on the Growth Performance, Meat Qualities, Relative Organ Weights, and Blood Characteristics of Broiler Chickens. *Brazilian Journal of Poultry Science*, Vol. 18, n° 2, pp. 351-356.
ASHAYERIZADEH, A., DASTAR, B., SHARGH, M.S., MAHOONAK, A.R.S. & ZEREHDARAN, S., 2018. Effects of feeding fermented rapeseed meal on growth performance, gastrointestinal microflora population, blood metabolites, meat quality, and lipid metabolism in broiler chickens. *Livestock Science*, Vol. 216, pp. 183-190.

ASHAYERIZADEH, A., DASTAR, B., SHARGH, M.S., MAHOONAK, A.S. & ZEREHDARAN, S., 2017. Fermented rapeseed meal is effective in controlling Salmonella enterica serovar Typhimurium infection and improving growth performance in broiler chicks. *Veterinary Microbiology*, Vol. 201, pp. 93-102

BAÉZA, E., ARNOULD, C., JLALI, M., CHARTRIN, P., GIGAUD, V., MERCERAND, F., DURAND, C., METEAU, K., LE BIHAN-DUVAL, E. & BERRI, C., 2012. Influence of increasing slaughter age of chickens on meat quality, welfare, and technical and economic results. *Journal of Animal Science*, Vol. 90, n° 6, pp. 2003-2013.

BAILEY W. M. & H. S. SIEGEL, 1984. Instrumentation for Measuring Continuous Activity, Feed and Water Consumption, and Growth Rate of Chickens. *Transactions of the ASAE*, Vol. 27, n° 1, pp. 201-204.

BAREKATAIN, R., SWICK, R.A., TOGHYANI, M. & DE KONING, C.T., 2017. Interactions of full-fat canola seed, oat hulls as an insoluble fiber source and pellet temperature for nutrient utilization and growth performance of broiler chickens. *Poultry Science*, Vol. 96, n° 7, pp. 2233-2242.

BASSO, B., LAGÜE, M., GUY, G., RICARD, E. & MARIE-ETANCELIN, C., 2014. Detailed analysis of the individual feeding behavior of male and female mule ducks,. *Journal of Animal Science*, Vol. 92, n° 4, pp. 1639-1646.

BEAUCLERCQ, S., HENNEQUET-ANTIER, C., PRAUD, C., GODET, E., COLLIN, A., TESSERAUD, S., METAYER-COUSTARD, S., BOURIN, M., MOROLDO, M., MARTINS, F., LAGARRIGUE, S., LE BIHAN-DUVAL, E. & BERRI, C., 2017. Muscle transcriptome analysis reveals molecular pathways and biomarkers involved in extreme ultimate pH and meat defect occurrence in chicken. *Scientific Reports*, Vol. 7, pp. 6447-6460.

BEAUCLERCQ, S., NADAL-DESBARATS, L., HENNEQUET-ANTIER, C., COLLIN, A., TESSERAUD, S., BOURIN, M., LE BIHAN-DUVAL, E. & BERRI, C., 2016. Serum and Muscle Metabolomics for the Prediction of Ultimate pH, a Key Factor for Chicken-Meat Quality. *Journal of Proteome Research*, Vol. 15, n° 4, pp. 1168-1178.

BEAUMONT, C., BIHAN-DUVAL, E., LE, JUIN, H. et MAGDELAINE, P., 2004. Productivité et qualité du poulet de chair. *INRAE Productions Animales*. Vol. 17, n° 4, pp. 265-273
BEAUMONT, C., GUILLAUMIN, S., GERAERT, P.A., MIGNON-GRASTEAU, S. & LECLERCQ, B., 1998. Genetic parameters of body weight of broiler chickens measured at 22°C or 32°C. *British Poultry Science*, Vol. 39, n° 4, pp. 488-491.

BELLAMY, D. & LEONARD, R.A., 1965. Effect of cortisol on the growth of chicks. *General and Comparative Endocrinology*, Vol. 5, n° 4, pp. 402-410.

BERGER, Q., GUETTIER, E., BERNARD, J., GANIER, P., CHAHNAMIAN, M., LE BIHAN-DUVAL, E. & MIGNON-GRASTEAU, S., Non Paru. Profiles of genetic parameters of body weight and feed efficiency in two divergent broiler lines for meat ultimate pH.

BERGER, Q., GUETTIER, E., URVOIX, S., BERNARD, J., GANIER, P., CHAHNAMIAN, M., LE BIHAN-DUVAL, E. & MIGNON-GRASTEAU, S., 2021. The kinetics of growth, feed intake, and feed efficiency reveal a good capacity of adaptation of slow and rapid growing broilers to alternative diets. *Poultry Science*, Vol. 100, n° 4, pp. 101010-101023.

BERWANGER, E., NUNES, R., DE OLIVEIRA-BRUXEL, T., BAYERLE, D. & GIUSTI BRUNO, L.D., 2017. Performance and carcass yield of broilers fed increasing levels of sunflower cake. *Revista Caatinga*, Vol. 30, pp. 201-212

BIESEK, J., KUZNIACKA, J., BANASZAK, M., KACZMAREK, S., ADAMSKI, M., RUTKOWSKI, A., ZMUDZINSKA, A., PERZ, K. & HEJDYSZ, M., 2020. Growth Performance and Carcass Quality in Broiler Chickens Fed on Legume Seeds and Rapeseed Meal. *Animals*, Vol. 10, n° 5, pp. 846-862.

BLEY, T.A.G. & BESSEI, W., 2008. Recording of Individual Feed Intake and Feeding Behavior of Pekin Ducks Kept in Groups. *Poultry Science*, Vol. 87, n° 2, pp. 215-221.

BOGGIA, A., PAOLOTTI, L. & CASTELLINI, C., 2010. Environmental impact evaluation of conventional, organic and organic-plus poultry production systems using life cycle assessment. *World's Poultry Science Journal*, Vol. 66, n° 1, pp. 95-114.

BOKKERS, E. a. M. & KOENE, P., 2003. Eating behaviour, and preprandial and postprandial correlations in male broiler and layer chickens. *British Poultry Science*, Vol. 44, n° 4, pp. 538-544.

BOSCO, A.D., RUGGERI, S., MATTIOLI, S., MUGNAI, C., SIRRI, F. & CASTELLINI, C., 2013. Effect of Faba Bean (Vicia Faba Var. Minor) Inclusion in Starter and Growing Diet on Performance, Carcass and Meat Characteristics of Organic Slow-Growing Chickens. *Italian Journal of Animal Science*, Vol. 12, n° 4, pp. 76-83.
BÖTTGER, C. & SÜDEKUM, K.-H., 2017. European distillers dried grains with solubles (DDGS): Chemical composition and in vitro evaluation of feeding value for ruminants. *Animal Feed Science and Technology*, Vol. 224, pp. 66-77.

BUENAVISTA, R.M.E., SILIVERU, K. & ZHENG, Y., 2021. Utilization of Distiller's dried grains with solubles: A review. *Journal of Agriculture and Food Research*, Vol. 5, pp. 100195-100203.

CAPPER, J.L., 2012. Is the Grass Always Greener? Comparing the Environmental Impact of Conventional, Natural and Grass-Fed Beef Production Systems. *Animals*, Vol. 2, n° 2, pp. 127-143.

CASTELLINI, C., MUGNAI, C., MOSCATI, L., MATTIOLI, S., GUARINO AMATO, M., CARTONI MANCINELLI, A. et DAL BOSCO, A., 2016. Adaptation to organic rearing system of eight different chicken genotypes: behaviour, welfare and performance. *Italian Journal of Animal Science*. Vol. 15, n° 1, pp. 37-46.

CHEGENI, A., TORKI, M. & KAMYAB, A., 2011. Effects of beta-mannanase-based enzyme in corn-soy and corn-soy-canola diets on broiler performance. *Journal of Applied Animal Research*, Vol. 39, n° 3, pp. 261-268.

CHENUT, R., 2017. Prévision et estimation du coût de production et du revenu en élevage avicole. *ITAVI*, pp. 1-4.

CHENUT, R., COUTELET, G. & MAGDELAINE, P., 2013. Structure et organisation des filières volailles de chair en Europe : analyse comparée des filières allemande, britannique, espagnole et néerlandaise et belge. *ITAVI*, pp. 1-102.

CHEVA-ISARAKUL, B. & TANGTAWEEWIPAT, S., 1991. Effect of Different Levels of Sunflower Seed in Broiler Rations. *Poultry Science*, Vol. 70, n° 11, pp. 2284-2294.

CHIANG, G., LU, W.Q., PIAO, X.S., HU, J.K., GONG, L.M. & THACKER, P.A., 2010. Effects of Feeding Solid-state Fermented Rapeseed Meal on Performance, Nutrient Digestibility, Intestinal Ecology and Intestinal Morphology of Broiler Chickens. *Asian-Australasian Journal of Animal Sciences*, Vol. 23, n° 2, pp. 263-271.

CHOBANOVA, S., 2019. Effects of compound poultry feed with different content of high-protein sunflower meal on growth performance of broiler chickens. *Bulgarian Journal of Agricultural Science*, Vol. 25, n° Suppl. 3, pp. 91-94.

CIURESCU, G., VASILACHI, A., GRIGORE, D. & GROSU, H., 2019. Growth performance, carcass traits, and blood biochemistry of broiler chicks fed with low-fibre sunflower meal and phytase. *South African Journal of Animal Science*, Vol. 49, n° 4, pp. 735-745.
COZANNET, I.P., LESSIRE, M., METAYER, J.P., GADY, C., PRIMOT, Y., GERAERT, P.A., TUTOUR, L.L., SKIBA, F. & NOBLET, J., 2010. Valeur nutritive des drêches de blé et de maïs pour les volailles. *INRAE Productions Animales*, Vol. 23, n° 5, pp. 405-414.

CRISAN, M., 2020. Pratiques culturales en grandes cultures. *Agreste Chiffres et Données*, Vol. 2019, n° 3, pp. 1-30.

DE OLIVEIRA, D.D., PINHEIRO, J.W., NICOLAO FONSECA, N.A. & OBA, A., 2012. Performance of broiler chickens fed with sunflower cake. *Semina-Ciencias Agrarias*, Vol. 33, n° 5, pp. 1979-1990.

DELANOUE, E., DOCKES, A.-C., CHOUTEAU, A., ROGUET, C. et PHILIBERT, A., 2018. Regards croisés entre éleveurs et citoyens français : vision des citoyens sur l’élevage et point de vue des éleveurs sur leur perception par la société. *INRAE Productions Animales*. Vol. 31, n° 1, pp. 51-68.

DIAZ, D., MORLACCHINI, M., MASOERO, F., MOSCHINI, M., FUSCONI, G. & PIVA, G., 2006. Pea seeds (Pisum sativum), faba beans (Vicia faba var. minor) and lupin seeds (Lupinus albus var. multitalia) as protein sources in broiler diets: effect of extrusion on growth performance. *Italian Journal of Animal Science*, Vol. 5, n° 1, pp. 43-53.

DISETLHE, A.R.P., MARUME, U. & MLAMBO, V., 2018. Humic acid and enzymes inclusion in canola-based diets generate different responses in growth performance, protein utilization dynamics, and hemato-biochemical parameters in broiler chickens. *Poultry Science*, Vol. 97, n° 8, pp. 2745-2753.

DRURY, L.N., BEARD, C.W. & HOPKINS, S.R., 1969. An Isolation Cabinet System for Avian Disease Research. *Avian Diseases*, Vol. 13, n° 2, pp. 400-412.

EUROPEAN COMMISSION, 2019. United States is Europe's main soya beans supplier with imports up by 112%. *European Commission*. Disponible sur: https://ec.europa.eu/commission/presscorner/detail/en/IP_19_161.

EUROPEAN PARLIAMENT, 2011. The EU protein deficit: what Solution for a long-standing Problem? Disponible sur : https://www.europarl.europa.eu/doceo/document/A-7-2011-0026_EN.pdf?redirect.

EVENSON, R.E. & GOLLIN, D., 2003. Assessing the Impact of the Green Revolution, 1960 to 2000. *Science*, Vol. 300, n° 5620, pp. 758-762.

FALLON, D., 1965. Eatometer: a device for continuous recording of free-feeding behavior. *Science* (*New York, N.Y.*), Vol. 148, n° 3672, pp. 977-978.
FARIA, P.B., VIEIRA, J.O., SILVA, J.N., RODRIGUES, A.Q., SOUZA, X.R., SANTOS, F.R. & PEREIRA, A.A., 2011. Performance and carcass characteristics of free-range broiler chickens fed diets containing alternative feedstuffs. *Brazilian Journal of Poultry Science*, Vol. 13, n° 3, pp. 211-216.

FIGUEIREDO, D., MURAKAMI, A., PEREIRA, M., FURLAN, A. & TORAL, F., 2003. Performance and duodenum mucous morphometry of broiler chickens fed canola meal, during the starting period. *Revista Brasileira de Zootecnia*, Vol. 32, pp. 1321-1329.

FOLTYN, M., RADA, V., LICHOVNIKOVA, M. & DRAČKOVÁ, E., 2013. Effect of corn DDGS on broilers performance and meat quality. *Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis*, Vol. 61, pp. 59-64.

FAOSTAT, 2021. Disponible sur: http://www.fao.org/faostat/fr/#data/QA/visualize.

FRIES-CRAFT, K. & BOBECK, E.A., 2019. Evaluation of a high-protein DDGS product in broiler chickens: performance, nitrogen-corrected apparent metabolisable energy, and standardised ileal amino acid digestibility. *British Poultry Science*, Vol. 60, n° 6, pp. 749-756.

GATES, R.S. & XIN, H., 2008. Extracting poultry behaviour from time-series weigh scale records. *Computers and Electronics in Agriculture*, Vol. 62, n° 1, pp. 8-14.

GIULIANO, S., RYAN, M.R., VÉRICEL, G., RAMETTI, G., PERDRIEUX, F., JUSTES, E. & ALLETTO, L., 2016. Low-input cropping systems to reduce input dependency and environmental impacts in maize production: A multi-criteria assessment. *European Journal of Agronomy*, Vol. 76, pp. 160-175.

GOH, Y.K., SHIRES, A., ROBBLEE, A.R. & CLANDININ, D.R., 1982. Influence of supplementary cellulolytic enzymes (driselase) on the performance of broilers fed rations containing rapeseed meal. *Canadian Journal of Animal Science*, Vol. 62, n° 1, pp. 269-273.

GOPINGER, E., XAVIER, E.G., ELIAS, M.C., CATALAN, A. a. S., CASTRO, M.L.S., NUNES, A.P. & ROLL, V.F.B., 2014. The effect of different dietary levels of canola meal on growth performance, nutrient digestibility, and gut morphology of broiler chickens. *Poultry Science*, Vol. 93, n° 5, pp. 1130-1136.

GORDY, J.F., 1974. Broilers. Dans: O.A. HANKE & J.L. SKINNER, *American poultry history, 1823-1973: an anthology overview of 150 years: people-places-progress*: American Poultry Historical Society, pp. 370-433.

GROENEVELD, L.F., LENSTRA, J.A., EDING, H., TORO, M.A., SCHERF, B., PILLING, D., NEGRINI, R., FINLAY, E.K., JIANLIN, H., GROENEVELD, E. & WEIGEND, S., 2010. Genetic diversity in farm animals – a review. *Animal Genetics*, Vol. 41, n° s1, pp. 6-31.
GROENEVELD E, KOVAČ M & MIELENZ N, 2010. *VCE User's Guide and Reference Manual, version 6.0*. Institute of Farm Animal Genetics; Neustadt, Germany:

GRUMBLES, L.C., DELAPLANE, J.P. & HIGGINS, T.C., 1948. Continuous Feeding of Low Concentrations of Sulfaquinoxaline for the Control of Coccidiosis in Poultry. *Poultry Science*, Vol. 27, n° 5, pp. 605-608.

GUETTIER, E., URVOIX, S., SERVANT, P., MIKA, A., BERGER, Q. & MIGNON-GRASTEAU, S., 2020. Mise au point d'un automate de consommation alimentaire pour volailles nommé BIRD-e: Bird Individual Ration Dispenser-electronic. *Cahier des Techniques de l'INRA*, Vol. 99, pp. 12-23.

GUILL, R.A. & WASHBURN, K.W., 1974. Genetic Changes in Efficiency of Feed Utilization of Chicks Maintaining Body Weight Constant. *Poultry Science*, Vol. 53, n° 3, pp. 1146-1154.

HAFEZ, M. & ATTIA, Y., 2020. Challenges to the Poultry Industry: Current Perspectives and Strategic Future After the COVID-19 Outbreak. *Frontiers in Veterinary Science*, Vol. 7, pp. 516-531.

HAVENSTEIN, G., FERKET, P. & QURESHI, M., 2003. Carcass composition and yield of 1957 versus 2001 broilers when fed representative 1957 and 2001 broiler diets. *Poultry Science*, Vol. 82, n° 10, pp. 1509-1518.

HAVENSTEIN, G., FERKET, P., SCHEIDELER, S. & LARSON, B., 1994a. Growth, Livability, and Feed Conversion of 1957 Vs 1991 Broilers When Fed Typical 1957 and 1991 Broiler Diets. *Poultry Science*, Vol. 73, n° 12, pp. 1785-1794.

HAVENSTEIN, G., FERKET, P., SCHEIDELER, S. & RIVES, D., 1994b. Carcass Composition and Yield of 1991 Vs 1957 Broilers When Fed Typical 1957 and 1991 Broiler Diets. *Poultry Science*, Vol. 73, n° 12, pp. 1795-1804.

HENRIQUEZ, B., OLSON, M., HOY, C., JACKSON, M. & WOUDA, T., 2018. Frost tolerance of faba bean cultivars (Vicia faba L.) in central Alberta. *Canadian Journal of Plant Science*, Vol. 98, n° 2, pp. 509-514.

HERNANDEZ, F., LOPEZ, M.J., GARCIA, V., MARTINEZ, S., MEGIAS, M.D. & MADRID, J., 2011. Influence of cereal type and the inclusion of sunflower meal as a source of additional dietary fibre on nutrient retention, growth performance and digestive organ size in broilers from one to twenty-one days of age. *Animal Feed Science and Technology*, Vol. 165, n° 3-4, pp. 251-257

HERTIG, E. & JACOBEIT, J., 2014. Atlantic-European area: past and future. *Atmospheric Science Letters*, Vol. 15, n° 4, pp. 314-320.
HORVATOVIC, M.P., GLAMOCIC, D., ZIKIC, D. & DAPCEVIC HADNADJEV, T., 2015. Performance and Some Intestinal Functions of Broilers Fed Diets with Different Inclusion Levels of Sunflower Meal and Supplemented or Not with Enzymes. *Brazilian Journal of Poultry Science*, Vol. 17, n° 1, pp. 25-30.

HOWIE, J.A., AVENDANO, S., TOLKAMP, B.J. & KYRIAZAKIS, I., 2011. Genetic parameters of feeding behavior traits and their relationship with live performance traits in modern broiler lines. *Poultry Science*, Vol. 90, n° 6, pp. 1197-1205.

HOWIE, J.A., TOLKAMP, B.J., AVENDANO, S. & KYRIAZAKIS, I., 2009. A novel flexible method of split feeding behaviour into bouts. *Applied Animal Behaviour Science*, Vol. 116, pp. 101-109.

HOWIE, J.A., TOLKAMP, B.J., BLEY, T. & KYRIAZAKIS, I., 2010. Short-term feeding behaviour has a similar structure in broilers, turkeys and ducks. *British Poultry Science*, Vol. 51, n° 06, pp. 714-724.

HUBBARD, 2020. *Poulet de chair : Guide et recommandations nutritionnelles*. Disponible sur :
https://www.hubbardbreeders.com/media/hep_guide_broiler_fr.pdf

HUSSAIN, M., MIRZA, M.A., NAWAZ, H., ASGHAR, M. & AHMED, G., 2019. Effect of Exogenous Protease, Mannanase, and Xylanase Supplementation in Corn and High Protein Corn DDGS Based Diets on Growth Performance, Intestinal Morphology and Nutrient Digestibility in Broiler Chickens. *Brazilian Journal of Poultry Science*, Vol. 21, n° 4, pp. 1-10.

HUYNH-TRAN, V.H., GILBERT, H. & DAVID, I., 2017. Genetic structured antedependence and random regression models applied to the longitudinal feed conversion ratio in growing Large White pigs. *Journal of Animal Science*, Vol. 95, n° 11, pp. 4752-4763.

INRAE, 2020. Bird-e. Automate de consommation alimentaire pour volailles. Disponible sur :
https://www feed-a-gene eu/media/bird-e-automate-de-consommation-alimentaire-pour-volailles

ITAVI, 2016. Performances techniques et coûts de production - Résultats 2015. Disponible sur :
https://www.itavi.asso.fr/content/performances-techniques-et-couts-de-production-resultats-2015

ITAVI, 2019. *Performances techniques et coûts de production en volailles de chair*. 2019.

JENSEN, E.S., PEOPLES, M.B. & HAUGGAARD-NIELSEN, H., 2010. Faba bean in cropping systems. *Field Crops Research*, Vol. 115, n° 3, pp. 203-216.
JEZIERNY, D., MOSENTHIN, R., SAUER, N. & EKLUND, M., 2010. In vitro prediction of standardised ileal crude protein and amino acid digestibilities in grain legumes for growing pigs. *Animal: An International Journal of Animal Bioscience*, Vol. 4, n° 12, pp. 1987-1996.

JIMÉNEZ-MORENO, E., GONZÁLEZ-ALVARADO, J.M., GONZÁLEZ-SERRANO, A., LÁZARO, R. & MATEOS, G.G., 2009. Effect of dietary fiber and fat on performance and digestive traits of broilers from one to twenty-one days of age1. *Poultry Science*, Vol. 88, n° 12, pp. 2562-2574.

JONES, P.J., TAHAMTANI, F.M., PEDERSEN, I.J., NIEMI, J.K. & RIBER, A.B., 2020. The Productivity and Financial Impacts of Eight Types of Environmental Enrichment for Broiler Chickens. *Animals: an open access journal from MDPI*, Vol. 10, n° 3, pp. 378-391.

JUNG, B., BATAL, A.B. & MITCHELL, R., 2010. Effect of exogenous enzyme supplementation on performance and carcass characteristics of broilers fed distillers dried grains with solubles (DDGS). *Journal of Dairy Science*, Vol. 93, pp. 551-552.

JUNG, B., MITCHELL, R.D. & BATAL, A.B., 2012. Evaluation of the use of feeding distillers dried grains with solubles in combination with canola meal on broiler performance and carcass characteristics. *Journal of Applied Poultry Research*, Vol. 21, n° 4, pp. 776-787.

KACZMAREK, S.A., HEJDYSZ, M., KUBIŚ, M., NOWACZEWSKI, S., MIKUŁA, R. & RUTKOWSKI, A., 2020. Effects of feeding intact, ground and/or pelleted rapeseed on nutrient digestibility and growth performance of broiler chickens. *Archives of Animal Nutrition*, Vol. 74, n° 3, pp. 222-236.

KALINOWSKI, A., MORAN, E. & WYATT, C., 2003. Methionine and cystine requirements of slow- and fast-feathering broiler males from three to six weeks of age. *Poultry Science*, Vol. 82, n° 9, pp. 1428-1437.

KARUNAJEEWA, H., IJAGBUJI, E. & REECE, R., 1990. Effect of Dietary Levels of Rapeseed Meal and Polyethylene-Glycol on the Performance of Male Broiler-Chickens. *British Poultry Science*, Vol. 31, n° 3, pp. 545-555.

KHAJALI, F., TAHMASEBI, M., HASSANPOUR, H., AKBARI, M.R., QUJEQ, D. & WIDEMAN, R.F., 2011. Effects of supplementation of canola meal-based diets with arginine on performance, plasma nitric oxide, and carcass characteristics of broiler chickens grown at high altitude. *Poultry Science*, Vol. 90, n° 10, pp. 2287-2294.

KIISKINEN, T., 1983. The Effect of Diets Supplemented with Regent Rapeseed Meal on Performance of Broiler Chicks. *Annales Agriculturae Fenniae*, Vol. 22, n° 4, pp. 206-213.

KIM, J.S., HOSSEINDOUST, A., INGALE, S.L., RATHI, P.C., LEE, S.H., CHOI, Y.H., KIM, M.J., KUMAR, A., PARK, S.O., OHH, S.J. & CHAE, B.J., 2016. Effects of diet form and xylanase
supplementation in a high maize distillers dried grains with solubles (DDGS) diet on
performance, nutrient digestibility and non-starch polysaccharide degradation in broiler.
*European Poultry Science*, Vol. 80, pp. 298-305.

KIM, J.W., SANJAYAN, N., LETERME, P. & NYACHOTI, C.M., 2019. Relative bioavailability of phosphorus in high-protein sunflower meal for broiler chickens and effects of dietary phytase supplementation on bone traits, growth performance, and apparent ileal digestibility of nutrients. *Poultry Science*, Vol. 98, n° 1, pp. 298-305.

KIM, Y.-U., CHOI, D.-H., BAN, H.-Y., SEO, B.-S., KIM, J. & LEE, B.-W., 2020. Temporal Patterns of Flowering and Pod Set of Determinate Soybean in Response to High Temperature. *Agronomy*, Vol. 10, n° 3, pp. 414-422.

KOIVUNEN, E., PARTANEN, K., PERTTILA, S., PALANDER, S., TUUNAINEN, P. & VALAJA, J., 2016. Digestibility and energy value of pea (Pisum sativum L.), faba bean (Vicia faba L.) and blue lupin (narrow-leaf) (Lupinus angustifolius) seeds in broilers. *Animal Feed Science and Technology*, Vol. 218, pp. 120-127.

KONIECZKA, P., CZERWINSKI, J., JANKOWIAK, J., ZABEK, K. & SMULIKOWSKA, S., 2019. Effects of Partial Replacement of Soybean Meal with Rapeseed Meal, Narrow-Leaved Lupin, Ddgs, and Probiotic Supplementation, on Performance and Gut Microbiota Activity and Diversity in Broilers. *Annals of Animal Science*, Vol. 19, n° 4, pp. 1115-1131.

KUBENA, L.F., CHEN, T.C., DEATON, J.W. & REECE, F.N., 1974. Factors Influencing the Quantity of Abdominal Fat in Broilers: 3. Dietary Energy Levels. *Poultry Science*, Vol. 53, n° 3, pp. 974-978.

KÜÇÜKYILMAZ, K., BOZKURT, M., ÇINAR, M., ÇATLI, A.U., BINTAS, E. & ERKEK, R., 2014. The effects of an organic rearing system and dietary supplementation of an essential oil mixture on performance and meat yield of slow-growing broilers in two seasons. *South African Journal of Animal Science*, Vol. 44, n° 4, pp. 360-370.

KYRIAZAKIS, I. & TOLKAMP, B., 2011. Hunger and thirst. Dans: M.C. APPLEBY, J.A. MENCH, I.A.S. OLSSON & B.O. HUGHES (eds.), *Animal welfare*. Wallingford: CABI, pp. 44-63.

LAISSE, S., BAUMONT, R., DUSART, L., GAUDRÉ, D., ROUILLÉ, B., BENOIT, M., VEYSSET, P., RÉMOND, D. & PEYRAUD, J.-L., 2019. L'efficience nette de conversion des aliments par les animaux d'élevage : une nouvelle approche pour évaluer la contribution de l'élevage à l'alimentation humaine. *INRA Productions Animales*, Vol. 31, n° 3, pp. 269-288.
LATHUILLIÈRE, M.J., MIRANDA, E.J., BULLE, C., COUTO, E.G. & JOHNSON, M.S., 2017. Land occupation and transformation impacts of soybean production in Southern Amazonia, Brazil. *Journal of Cleaner Production*, Vol. 149, pp. 680-689.

LAUDADIO, V., BASTONI, E., INTRONA, M. & TUFARELLI, V., 2013. Production of low-fiber sunflower (Helianthus annuus L.) meal by micronization and air classification processes. *CyTA - Journal of Food*, Vol. 11, n° 4, pp. 398-403.

LAWLOR, T.J., TSURUTA, S., KLEI, L. & MISZTAL, I., 2002. Use of a random regression model to investigate changes in genetic parameters over time. *7th World Congress on Genetics Applied to Livestock Production*, 19-23 Aout 2002, Montpellier, France, pp. 6-9.

LE BIHAN-DUVAL, E., DEBUT, M., BERRI, C.M., SELLIER, N., SANTÉ-LHOUTELLIER, V., JÉGO, Y. & BEAUMONT, C., 2008. Chicken meat quality: genetic variability and relationship with growth and muscle characteristics. *BMC Genetics*, Vol. 9, n° 1, pp. 53-59.

LÊ, S., JOSSE, J. & HUSSON, F., 2008. FactoMineR: An R Package for Multivariate Analysis. *Journal of Statistical Software*, Vol. 25, n° 1, pp. 1-18.

LECCE, J., 1969. Rearing Colostrum-Free Pigs in an Automatic Feeding Device. *Journal of Animal Science*, Vol. 28, n° 1, pp. 27-33.

LEE, K.H., OLOMU, J.M. & SIM, J.S., 1991. Live performance, carcass yield, protein and energy retention of broiler chickens fed canola and flax full-fat seeds and the restored mixtures of meal and oil. *Canadian journal of animal science*, Vol. 71, n° 3, pp. 897-903.

LEINONEN, I. & KYRIAZAKIS, I., 2016. How can we improve the environmental sustainability of poultry production? *The Proceedings of the Nutrition Society*, Vol. 75, n° 3, pp. 265-273.

LEINONEN, I., WILLIAMS, A.G., WALLER, A.H. & KYRIAZAKIS, I., 2013. Comparing the environmental impacts of alternative protein crops in poultry diets: The consequences of uncertainty. *Agricultural Systems*, Vol. 121, pp. 33-42.

LESSIRE, M., MÉDA, B., DUSART, L., HALLOUIS, J.-M., BORDEAU, T., SOUCHET, C., MERCERAND, F., HATTÉ, C., BOUVAREL, I. & BERRI, C., 2015. Remplacement du tourteau de soja par des matières premières riches en proteines : évaluation multicritère chez le poulet de chair à croissance intermédiaire. *Onzièmes Journées de la Recherche Avicole et Palmipèdes à Foie Gras, Tours, les 25 et 26 mars 2015*. pp. 893-898.

LESSIRE, M., PRIMOT, Y., CORRENT, E., PAULINE, F., TESSERAUD, S. & BERRI, C., 2013. Lysine supply in finishing broilers: effect on performances and meat quality. Dans: J.W. OLTJEN, E. KEBREAB & H. LAPIERRE (eds.), *Energy and protein metabolism and nutrition in sustainable animal production*. Wageningen: Wageningen Academic Publishers. pp. 209-210.
LI, G., ZHAO, Y., HAILEY, R., ZHANG, N., LIANG, Y. & PURSWELL, J.L., 2019. An ultra-high frequency radio frequency identification system for studying individual feeding and drinking behaviors of group-housed broilers. *Animal*, Vol. 13, n° 9, pp. 2060-2069.

LI, G., ZHAO, Y., PURSWELL, J.L. & MAGEE, C., 2021. Effects of feeder space on broiler feeding behaviors. *Poultry Science*, Vol. 100, n° 4, pp. 101016-101026.

LIAQAT, S., MAHMOOD, S., AHMAD, S., KAMRAN, Z. & KOUTOULIS, K.C., 2016. Replacement of canola meal with Moringa oleifera leaf powder affects performance and immune response in broilers. *Journal of Applied Poultry Research*, Vol. 25, n° 3, pp. 352-358.

LIU, Q.W., FENG, J.H., CHAO, Z., CHEN, Y., WEI, L.M., WANG, F., SUN, R.P. & ZHANG, M.H., 2016. The influences of ambient temperature and crude protein levels on performance and serum biochemical parameters in broilers. *Journal of Animal Physiology and Animal Nutrition*, Vol. 100, n° 2, pp. 301-308.

LIU, Y.L., YI, G.F., SONG, G.L., HOU, Y.Q., HUANG, J.W., VÁZQUEZ-AÑÓN, M. & KNIGHT, C.D., 2007. Impact of feeding 2-hydroxy-4-(methylthio)butanoic acid and DL-methionine supplemented maize-soybean-rapeseed meal diets on growth performance and carcase quality of broilers. *British Poultry Science*, Vol. 48, n° 2, pp. 190-197.

LOAR, R.E., MORITZ, J.S., DONALDSON, J.R. & CORZO, A., 2010. Effects of feeding distillers dried grains with solubles to broilers from 0 to 28 days posthatch on broiler performance, feed manufacturing efficiency, and selected intestinal characteristics. *Poultry Science*, Vol. 89, n° 10, pp. 2242-2250.

LOSINGER, W.C., 1998. Feed-conversion ratio of finisher pigs in the USA. *Preventive Veterinary Medicine*, Vol. 36, n° 4, pp. 287-305.

LOTT, B.D., SIMMONS, J.D. & MAY, J.D., 1992. An Automated Feed and Water Consumption Measuring System for Poultry Research. *Applied Engineering in Agriculture*, Vol. 8, n° 4, pp. 521-523.

MAGDELAINE, P., COUTELET, G. & DUVALEIX-TREGUER, S., 2015. La contractualisation dans le secteur aviculture chair. *Économie rurale. Agricultures, alimentations, territoires*, n° 345, pp. 73-86.

MACLEOD, M., GERBER, P., MOTTET, A., TEMPIO, G., FALCUCCI, A., OPIO, C., VELLINGA, T., HENDERSON, B. et STEINFELD, H., 2013. *Greenhouse gas emissions from pig and chicken supply chains – a global life cycle assessment*. Rome : Food and Agriculture Organization of the United Nations.

MALHER, X., MAGDELAINE, P. & DUCLOS M., J., 2018. Diversité et résilience dans les filières avicoles. *Innovations Agronomiques*, Vol. 68, pp. 107-127.
MANYEULA, F., MLAMBO, V., MARUME, U. & SEBOLA, N.A., 2019. Nutrient digestibility, haemo-biochemical parameters and growth performance of an indigenous chicken strain fed canola meal-containing diets. *Tropical Animal Health and Production*, Vol. 51, n° 8, pp. 2343-2350.

MARCHESI, J.A.P., ONO, R.K., CANTÃO, M.E., IBELLI, A.M.G., PEIXOTO, J. de O., MOREIRA, G.C.M., GODOY, T.F., COUTINHO, L.L., MUNARI, D.P. & LEDUR, M.C., 2021. Exploring the genetic architecture of feed efficiency traits in chickens. *Scientific Reports*, Vol. 11, n° 1, pp. 4622-4634.

MARKS, H.L., 1991. Feed efficiency changes accompanying selection for body weight in chickens and quail. *World's Poultry Science Journal*, Vol. 47, n° 3, pp. 197-212.

MCNAUGHTON, J., ROBERTS, M., RICE, D., SMITH, B., HONG, B., DELANEY, B. & IIAMS, C., 2014. Comparison of broiler performance and carcass yields when fed diets containing genetically modified canola meal from event DP-circle divide 73496-4, near-isogenic canola meal, or commercial canola meals. *Poultry Science*, Vol. 93, n° 7, pp. 1713-1723.

MÉDA, B., DUSART, L., LESSIRE, M., HALLOUIS, J.M., BORDEAU, T., SOUCHET, C., MERCERAND, F., HATTÉ, C., BOUVAREL, I. & BERRI, C., 2015. Replacing soybean meal by alternative protein sources: multicriteria assessment of a «Label Rouge» chicken production system. *Actes des 11èmes Journées de la Recherche Avicole et Palmipèdes à Foie Gras, Tours, France, les 25 et 26 mars 2015*, pp. 1082-1087.

MÉDA, B., GARCIA-LAUNAY, F., DUSART, L., PONCHANT, P., ESPAGNOL, S. & WILFART, A., 2021. Reducing environmental impacts of feed using multiobjective formulation: What benefits at the farm gate for pig and broiler production? *Animal*, Vol. 15, n° 1, pp. 100024-100035.

MENDES, M. & AKKARTAL, E., 2007. Canonical correlation analysis for studying the relationships between pre- and post slaughter traits of Ross 308 broiler chickens. *Archiv Fur Geflugelkunde*, Vol. 71, n° 6, pp. 267-271.

METAYER-COUSTARD, S., FRANZONI, A., RÉHAULT-GODBERT, S., COLLIN, A., TRAVEL, A., RAYNAUD, E., BAUMARD, Y., DELAVEAU, J., RAT, C., AUDOUIN, E., GODET, E., BRIONNE, A., BERRI, C. & DUVAL, E., 2019. Variation in energy stores: impact on reproduction and egg characteristics in meat-type strains. *13e Journées de la recherche avicole et des palmipèdes à foie gras*, Mars 2019, Tours, France, pp. 206-209.
MÉTAYER-COUSTARD, S., TESSERAUD, S., PRAUD, C., ROYER, D., BORDEAU, T., COUDERT, E., CAILLEAU-AUDOUIN, E., GODET, E., DELAVEAU, J., LE BIHAN-DUVAL, E. & BERRI, C., 2021. Early Growth and Protein-Energy Metabolism in Chicken Lines Divergently Selected on Ultimate pH. *Frontiers in Physiology*, Vol. 12, pp. 144-157.

MIGNON-GRASTEAU, S., 2007. Prise en compte des interactions entre génotype et environnement dans les productions avicoles. *Septièmes Journées de la Recherche Avicole, Tours, 28 et 29 mars 2007*. Tours. pp 386-454

MIGNON-GRASTEAU, S., BERGER, Q., GUETTIER, E. & LE BIHAN-DUVAL, E., 2021. Mignon-Grasteau, S., Q. Berger, E. Guettier, and E. Le Bihan-Duval. 2021. New advances based on our feed station and its use for feed efficiency phenotyping. *72th Eur Assoc Anim Prod*. Davos.

MIKA, A., GUETTIER, E., BERGER, Q., BIHAN-DUVAL, E.L., BERNARD, J., PAMPOUILLE, E., BOUVAREL, I. y MIGNON-GRASTEAU, S., 2021. Eva-HD - Mise au point d'un automate de consommation alimentaire pour volailles « BIRD-e: Bird Individual Ration Dispenser-electronic ». *Innovations Agronomiques*, Vol. 82, pp. 137-149.

MIN, Y.N., HANCOCK, A., YAN, F., LU, C., COTO, C., KARIMI, A., PARK, J.H., LIU, F.Z. & WALDROUP, P.W., 2009. Use of combinations of canola meal and distillers dried grains with solubles in broiler starter diets. *Journal of Applied Poultry Research*, Vol. 18, n° 4, pp. 725-733.

MIN, Y.N., LI, L.L., LIU, S.K., ZHANG, J., GAO, Y.P. & LIU, F.Z., 2015. Effects of dietary distillers dried grains with solubles (DDGS) on growth performance, oxidative stress, and immune function in broiler chickens. *Journal of Applied Poultry Research*, Vol. 24, n° 1, pp. 23-29.

MIRAKZEHI, M.T., TAHMASBI, A. & GHAZI, S., 2010. The Effect of Different Treatments of Rapeseed Meal on Nitrogen Digestibility and Metabolizable Energy in Broilers and Chicks Performance. *Journal of Animal and Veterinary Advances*, Vol. 9, n° 19, pp. 2517-2521.

MOLENAAR, R., REIJRINK, I. a. M., MEIJERHOF, R. & VAN DEN BRAND, H., 2009. Correlation Between Chick Length and Chick Weight at Hatch and Slaughter Weight and Breast Yield in Broilers. Dans: P.M. HOCKING (ed.), *Biology of Breeding Poultry*. Wallingford: Cabi Publishing-C a B Int, pp. 446-486.

MONTAZER-SADEGH, R., EBRAHIM-NEZHAD, Y. & MAHERI-SIS, N., 2008. Replacement of Different Levels of Rapeseed Meal with Soybean Meal on Broilers Performance. *Asian Journal of Animal and Veterinary Advances*, Vol. 3, n° 5, pp. 278-285.
MORAES, P.O., NOVELINI, L., KRABBE, E.L., KAWSKI, V.L., NUNES, K., CORCINI, C.D., VARELLA, A.S. & XAVIER, E.G., 2018. Growth performance, morphometric analysis of the intestinal mucosa and thyroid of broiler fed canola meal. *Arquivo Brasileiro De Medicina Veterinaria E Zootecnia*, Vol. 70, n° 1, pp. 187-194.

MOTTET, A. & TEMPIO, G., 2017. Global poultry production: current state and future outlook and challenges. *World's Poultry Science Journal*, Vol. 73, n° 2, pp. 245-256.

MUSHTAQ, T., SARWAR, M., AHMAD, G., MIRZA, M.A., AHMAD, T., NOREEN, U., MUSHTAQ, M.M.H. & KAMRAN, Z., 2009. Influence of sunflower meal based diets supplemented with exogenous enzyme and digestible lysine on performance, digestibility and carcass response of broiler chickens. *Animal Feed Science and Technology*, Vol. 149, n° 3-4, pp. 275-286.

MUSHTAQ, T., SARWAR, M., AHMAD, G., MIRZA, M.A., NAWAZ, H., MUSHTAQ, M.M.H. & NOREEN, U., 2007. Influence of canola meal-based diets supplemented with exogenous enzyme and digestible lysine on performance, digestibility, carcass, and immunity responses of broiler chickens. *Poultry Science*, Vol. 86, n° 10, pp. 2144-2151.

MUSHTAQ, T., SARWAR, M., AHMAD, G., NISA, M.U. & JAMIL, A., 2006. The influence of exogenous multienzyme preparation and graded levels of digestible lysine in sunflower meal-based diets on the performance of young broiler chicks two weeks posthatching. *Poultry Science*, Vol. 85, n° 12, pp. 2180-2185.

NALLE, C.L., RAVINDRAN, V. & RAVINDRAN, G., 2010. Evaluation of Faba Beans, White Lupins and Peas as Protein Sources in Broiler Diets. *International Journal of Poultry Science*, Vol. 9, n° 6, pp. 567-573.

NASSIRI MOGHADDAM, H., SALARI, S., ARSHAMI, J., GOLIAN, A. et MALEKI, M., 2012. Evaluation of the nutritional value of sunflower meal and its effect on performance, digestive enzyme activity, organ weight, and histological alterations of the intestinal villi of broiler chickens. *Journal of Applied Poultry Research*. Vol. 21, n° 2, pp. 293-304.

N'DRI, A.L., SELLIER, N., TIXIER-BOICHARD, M., BEAUMONT, C. & MIGNON-GRASTEAU, S., 2007. Genotype by environment interactions in relation to growth traits in slow growing chickens. *Genetics Selection Evolution*, Vol. 39, n° 5, pp. 513-528.

NEUMAIER, A. & GROENEVELD, E., 1998. Restricted Maximum Likelihood Estimation of Covariances in Sparse Linear Models. *Genetics Selection Evolution*, Vol. 30, pp. 3-26.
NGUYEN, C.V., SMULIKOWSKA, S. & MIECZKOWSKA, A., 2003. Effect of linseed and rapeseed or linseed and rapeseed oil on performance, slaughter yield and fatty acid deposition in edible parts of the carcass in broiler chickens. *Journal of Animal and Feed Sciences*, Vol. 12, n° 2, pp. 271-288.

NGUYEN, T.T.H., BOUVAREL, I., PONCHANT, P. & VAN DER WERF, H.M.G., 2012. Using environmental constraints to formulate low-impact poultry feeds. *Journal of Cleaner Production*, Vol. 28, pp. 215-224.

OCDE/FAO, 2017. *OECD-FAO Agricultural Outlook 2017-2026*. Éditions OCDE, Paris.

OLUKOSI, O.A., KASPRZAK, M.M., KIGHTLEY, S., CARRE, P., WISEMAN, J. & HOUDIJK, J.G.M., 2017. Investigations of the nutritive value of meals of double-low rapeseed and its influence on growth performance of broiler chickens. *Poultry Science*, Vol. 96, n° 9, pp. 3338-3350.

OZHAN, N., SIMSEK, U.G. & OZCELIK, M., 2016. Comparison of floor and cage housing systems in terms of some welfare assessments in broiler. *Ankara Universitesi Veteriner Fakultesi Dergisi*, Vol. 63, n° 3, pp. 317-322.

OZTURK, E., 2017. Performance of broilers fed with different levels of sunflower meal supplemented with or without enzymes. *Indian Journal of Animal Research*, Vol. 51, n° 3, pp. 495-500.

PAMPOUILLE, E., DUSART, L., BONNOUVRIER, A., DANEL, J., DAUGUET, S., DÉSOLÉ, M., HEUZE, V., LAMBERT, W., BOURHIS, L., CADRE, L., MARTIN, N., MÉDA, B., MICHAUD, C., MIGNON-GRASTEAU, S., NARCY, A., PEYRONNET, C., QUINSAC, A., RECOULES, E., SCHOULER, C., TORMO, E., TRAN, G., VILARIÑO, M., WILFART, A. & BOUVAREL, I., 2021. VOCALIM -Mieux valoriser des matières premières métropolitaines dans l'alimentation des poulets de chair pour améliorer l'autonomie protéique française. *Innovations Agronomiques*, Vol. 82, pp. 425-440.

PAYVASTEGAN, S., FARHOOMAND, P., DANESHYAR, M. & GHAFFARI, M., 2017. Evaluation of Different Levels of Canola Meal on Performance, Organ Weights, Hepatic Deiodinase Gene Expression and Thyroid Morphology in Broiler Chickens. *Journal of Poultry Science*, Vol. 54, n° 4, pp. 282-291.

PAYVASTEGAN, S., FARHOOMAND, P. & DELFANI, N., 2013. Growth Performance, Organ Weights and, Blood Parameters of Broilers Fed Diets Containing Graded Levels of Dietary Canola Meal and Supplemental Copper. *Journal of Poultry Science*, Vol. 50, n° 4, pp. 354-363.
PELLETIER, N., PIROG, R. & RASMUSSEN, R., 2010. Comparative life cycle environmental impacts of three beef production strategies in the Upper Midwestern United States. *Agricultural Systems*, Vol. 103, n° 6, pp. 380-389.

PERELLA, F., MUGNAI, C., BOSCO, A.D., SIRRI, F., CESTOLA, E. & CASTELLINI, C., 2009. Faba bean (Vicia faba var. minor) as a protein source for organic chickens: performance and carcass characteristics. *Italian Journal of Animal Science*, Vol. 8, n° 4, pp. 575-584.

PETRACCI, M., MUDALAL, S., SOGLIA, F. & CAVANI, C., 2015. Meat quality in fast-growing broiler chickens. *Worlds Poultry Science Journal*, Vol. 71, n° 2, pp. 363-373.

PEYRONNET, C., LACAMPAGNE, J.-P., CADRE, P.L. & PRESSENDA, F., 2014. Les sources de protéines dans l'alimentation du bétail en France : la place des oléoprotéagineux. *OCL*, Vol. 21, n° 4, pp. D402.

PFIRTER, H., HALTER, H. & LUTHY, J., 1982. Residues of Rapeseed Processing as Feedstuffs for Growing-Pigs and Poultry. 1. Growth-Performance and Organ Development. *International Journal for Vitamin and Nutrition Research*, Vol. 52, n° 2, pp. 217-217.

PLAVNIK, I., MACOVSKY, B. & SKLAN, D., 2002. Effect of feeding whole wheat on performance of broiler chickens. *Animal Feed Science and Technology*, Vol. 96, n° 3, pp. 229-236.

POKROVSKY, V. & LE MAGNEN, J., 1963. *Réalisation d'un dispositif d'enregistrement graphique continu et automatique de la consommation alimentaire du rat blanc*. Masson Editeur, Paris, France.

PUMA, M.C., XIN, H., GATES, R.S. & BURNHAM, D.J., 2001. An instrumentation system for studying feeding and drinking behavior of individual poultry. *Applied Engineering in Agriculture*, Vol. 17, n° 3, pp. 365-374.

PYM, R.A.E. & NICHOLLS, P.J., 1979. Selection for food conversion in broilers: Direct and correlated responses to selection for body-weight gain, food consumption and food conversion ratio. *British Poultry Science*, Vol. 20, n° 1, pp. 73-86.

QUINTON, J.N., GOVERS, G., VAN OOST, K. & BARDGETT, R.D., 2010. The impact of agricultural soil erosion on biogeochemical cycling. *Nature Geoscience*, Vol. 3, n° 5, pp. 311-314.

RAMA RAO, S.V., RAJU, M.V.L.N., PANDA, A.K. & REDDY, M.R., 2006. Sunflower seed meal as a substitute for soybean meal in commercial broiler chicken diets. *British Poultry Science*, Vol. 47, n° 5, pp. 592-598.
RANDALL, M., HERBERT, M. & HEPHERD, R., 1976. Automatic Pneumatic Conveying and Weight-Dispensing System for Floor-Feeding Pellets to Pigs. *Journal of Agricultural Engineering Research*, Vol. 21, n° 3, pp. 233-245.

RAZA, S., ASHRAF, M., PASHA, T.N., LATIF, F., BABAR, M.E. & HASHMI, A.S., 2009. Effect of Enzyme Supplemented High Fibre Sunflower Meal on Performance of Broilers. *Pakistan Journal of Zoology*, Vol. 41, n° 1, pp. 57-64.

REBOLE, A., RODRIGUEZ, M.L., ORTIZ, L.T., ALZUETA, C., CENTENO, C., VIVEROS, A., BRENES, A. & ARIJA, I., 2006. Effect of dietary high-oleic acid sunflower seed, palm oil and vitamin E supplementation on broiler performance, fatty acid composition and oxidation susceptibility of meat. *British Poultry Science*, Vol. 47, n° 5, pp. 581-591.

REED, W.L. & CLARK, M.E., 2011. Beyond Maternal Effects in Birds: Responses of the Embryo to the Environment. *Integrative and Comparative Biology*, Vol. 51, n° 1, pp. 73-80.

REZAEIPOUR, V., AGHARAJABI, A., GHAREVEISI, S. & NOROZI, M., 2015. Effects of Full-Fat Canola Seed with an Exogenous Enzyme Supplementation on Performance, Carcass Characteristics and Thyroid Hormones of Broiler Chickens. *Journal of Animal and Plant Sciences*, Vol. 25, n° 5, pp. 1233-1237.

ROLLER, W. & TEAGUE, H., 1970. Automatic Paste Feeding System for Pigs. *Agricultural Engineering*, Vol. 51, n° 5, pp. 301.

ROY, S. & SARKAR, S.K., 2016. RFID based real time system for early detection of Avian Influenza for poultry based industry. *2016 International Conference on Microelectronics, Computing and Communications (MicroCom)*. Durgapur, India: IEEE, pp. 1-6.

RUTKOWSKI, A., KACZMAREK, S., SZCZYRKOWSKA, A. & JOZEFIAK, D., 2012. The effect of particle size of full-fat rapeseed and of multi-carbohydrase enzyme supplementation on nutrient digestibility and performance in broilers. *Journal of Animal and Feed Sciences*, Vol. 21, n° 2, pp. 324-333.

SADEGHI, A., TOGHYANI, M. & GHEISARI, A., 2015. Effect of various fiber types and choice feeding of fiber on performance, gut development, humoral immunity, and fiber preference in broiler chicks. *Poultry Science*, Vol. 94, n° 11, pp. 2734-2743.

SAKI, A.A., MAHMOUDI, H., TABATABAEI, M.M. & AHMADI, A., 2008. Amino acid digestibility and energy value of yellow-seeded rapeseed meal in broiler chicken performance. *Journal of Applied Animal Research*, Vol. 33, n° 1, pp. 21-24.

SALEH, A.A., HAYASHI, K., IJIRI, D. & OHTSUKA, A., 2015. Effect of feeding Aspergillus awamori and canola seed on the growth performance and muscle fatty acid profile in broiler chicken. *Animal Science Journal*, Vol. 86, n° 3, pp. 305-311.
SANTOS, F.B.O., SHELDON, B.W., SANTOS, A.A. & FERKET, P.R., 2008. Influence of Housing System, Grain Type, and Particle Size on Salmonella Colonization and Shedding of Broilers Fed Triticale or Corn-Soybean Meal Diets1. *Poultry Science*, Vol. 87, n° 3, pp. 405-420.

SAS INSTITUTE INC., 2013. SAS/STAT® 13.1 User's Guide. Cary, NC: SAS Institute Inc.

SCHAEFFER, L.R., 2004. Application of random regression models in animal breeding. *Livestock Production Science*, Vol. 86, n° 1, pp. 35-45.

SCHAT, K.A., 2016. History of the First-Generation Marek's Disease Vaccines: The Science and Little-Known Facts. *Avian Diseases*, Vol. 60, n° 4, pp. 715-724.

SCHAUBERGER, B., BEN-ARI, T., MAKOWSKI, D., KATO, T., KATO, H. & CIAIS, P., 2018. Yield trends, variability and stagnation analysis of major crops in France over more than a century. *Scientific Reports*, Vol. 8, n° 1, pp. 16865-16877.

SHARIF, M., SHAHZAD, M.A., REHMAN, S., KHAN, S., ALI, R., KHAN, M.L. & KHAN, K., 2012. Nutritional Evaluation of Distillery Sludge and Its Effect as a Substitute of Canola Meal on Performance of Broiler Chickens. *Asian-Australasian Journal of Animal Sciences*, Vol. 25, n° 3, pp. 401-409.

SHARIFI, S.D., GOLESTANI, G., YAGHOBFAR, A., KHADEM, A. & PASHAZANUSSI, H., 2013. Effects of supplementing a multienzyme to broiler diets containing a high level of wheat or canola meal on intestinal morphology and performance of chicks. *Journal of Applied Poultry Research*, Vol. 22, n° 4, pp. 671-679.

SIBBALD, I.R., 1976. A bioassay for true metabolizable energy in feedingstuffs. *Poultry Science*, Vol. 55, n° 1, pp. 303-308.

SIEGEL, P., HONAKER, C.F. & RAUW, W., 2008. Selection for high production in poultry. Dans: RAUW, W., *Resources Allocation Theory Applied to Farm Animal Production*, pp. 230-242

SIEGEL, P.B., 2014. Evolution of the Modern Broiler and Feed Efficiency. *Annual Review of Animal Biosciences*, Vol. 2, n° 1, pp. 375-385.

SIMSEK, U.G., ERISIR, M., CIFTCI, M. & TATLI SEVEN, P., 2014. Effects of Cage and Floor Housing Systems on Fattening Performance, Oxidative Stress and Carcass Defects in Broiler Chicken. *Kafkas Universitesi Veteriner Fakultesi Dergisi*, Vol. 20, n° 5, pp. 727-733.

SKLAN, D., SMIRNOV, A. & PLAVNIK, I., 2003. The effect of dietary fibre on the small intestines and apparent digestion in the turkey. *British Poultry Science*, Vol. 44, n° 5, pp. 735-740.

SMITH, J.S.C., SMITH, O.S. & LAMKEY, K.R., 2005. Maize Breeding. *Maydica*, Vol. 50, n° 3-4, pp. 185-192.
SMITH, S.N., DAVIS, M.E. & LOERCH, S.C., 2010. Residual feed intake of Angus beef cattle divergently selected for feed conversion ratio. *Livestock Science*, Vol. 132, n° 1, pp. 41-47.

STEINFELD, H., GERBER, P., WASSENAAR, T.D., NATIONS, F. and A.O. of the U., CASTEL, V., ROSALES, M., M, M.R. & HAAN, C. de, 2006. *Livestock's Long Shadow: Environmental Issues and Options*. Food & Agriculture Org.

SWIATKIEWICZ, S. & KORELESKI, J., 2008. The use of distillers dried grains with soluble (DDGS) in poultry nutrition. *World's Poultry Science Journal*, Vol. 64, pp. 257-266.

SYAFWAN, S., WERMINK, G.J.D., KWAKKEL, R.P. & VERSTEGEN, M.W.A., 2012. Dietary self-selection by broilers at normal and high temperature changes feed intake behavior, nutrient intake, and performance. *Poultry Science*, Vol. 91, n° 3, pp. 537-549.

*Tables de composition et de valeur nutritionnelle des aliments pour animaux INRA CIRAD AFZ*,
2021. Disponible à l'adresse : https://feedtables.com/fr

TAHERI, H.R. & TAHERKHANI, S., 2015. Effect of Phytase Superdoses and Citric Acid on Growth Performance, Plasma Phosphorus and Tibia Ash in Broilers Fed Canola Meal-Based Diets Severely Limited in Available Phosphorus. *Poultry Science Journal*, Vol. 3, n° 1, pp. 27-36.

TALLENTIRE, C.W., LEINONEN, I. & KYRIAZAKIS, I., 2016. Breeding for efficiency in the broiler chicken: A review. *Agronomy for Sustainable Development*, Vol. 36, n° 4, pp. 66-82.

TALLENTIRE, C.W., MACKENZIE, S.G. & KYRIAZAKIS, I., 2018. Can novel ingredients replace soybeans and reduce the environmental burdens of European livestock systems in the future? *Journal of Cleaner Production*, Vol. 187, pp. 338-347.

TAVERNARI, F. de C., DUTRA JUNIOR, W.M., TEIXEIRA ALBINO, L.F., ROSTAGNO, H.S., VIEIRA, R.A. & DA SILVA, C.R., 2009. Effect of different levels of sunflower meal in diets on the performance of broiler chickens. *Revista Brasileira De Zootecnia-Brazilian Journal of Animal Science*, Vol. 38, n° 9, pp. 1745-1750.

TAVERNARI, F.C., ALBINO, L.F.T., MORATA, R.L., DUTRA JÚNIOR, W.M., ROSTAGNO, H.S. & VIANA, M.T.S., 2008. Inclusion of sunflower meal, with or without enzyme supplementation, in broiler diets. *Brazilian Journal of Poultry Science*, Vol. 10, n° 4, pp. 233-238.
TAYLOR, M.L., STANISIEWSKI, E.P., RIORDAN, S.G., NEMETH, M.A., GEORGE, B. & HARTNELL, G.F., 2004. Comparison of broiler performance when fed diets containing roundup ready (Event RT73), nontransgenic control, or commercial canola meal. *Poultry Science*, Vol. 83, Vol. 83, n° 3, pp. 456-461.

TEAM, R.C., 2013. *R: A language and environment for statistical computing*. : Vienna, Austria.

THACKER, P. & WIDYARATNE, G., 2012. Effects of expeller pressed camelina meal and/or canola meal on digestibility, performance and fatty acid composition of broiler chickens fed wheat-soybean meal-based diets. *Archives of Animal Nutrition*, Vol. 66, n° 5, pp. 402-415.

TOGHYANI, M., MOHAMMADSALEHI, A., GHEISARI, A. & TABEIDIAN, S.A., 2009. The effect of low-glucosinolate rapeseed meal in diets with multi-enzyme supplement on performance and protein digestibility in broiler chicks. *Journal of Animal and Feed Sciences*, Vol. 18, n° 2, pp. 313-321.

TOGHYANI, M., WU, S.B., PÉREZ-MALDONADO, R.A., IJI, P.A. & SWICK, R.A., 2017. Performance, nutrient utilization, and energy partitioning in broiler chickens offered high canola meal diets supplemented with multicomponent carbohydrate and mono-component protease. *Poultry Science*, Vol. 96, n° 11, pp. 3960-3972.

TONA, K., BAMELIS, F., DE KETELAERE, B., BRUGGEMAN, V., MORAES, V., BUYSE, J., ONAGBESAN, O. & DECUYPERE, E., 2003. Effects of egg storage time on spread of hatch, chick quality, and chick juvenile growth. *Poultry Science*, Vol. 82, n° 5, pp. 736-741.

TU, X., DU, S., TANG, L., XIN, H. & WOOD, B., 2011. A real-time automated system for monitoring individual feed intake and body weight of group housed turkeys. *Computers and Electronics in Agriculture*, Vol. 75, n° 2, pp. 313-320.

TUUNAINEN, P., KOIVUNEN, E., VALAJA, J., VALKONEN, E., HIIDENHOVI, J., TUPASELA, T. & HONGISTO, M., 2016. Effects of dietary rapeseed meal and peas on the performance and meat quality of broilers. *Agricultural and Food Science*, Vol. 25, n° 1, pp. 22-33.

USAYRAN, N.N., SHA'AR, H., BARBOUR, G.W., YAU, S.K., MAALOUF, F. & FARRAN, M.T., 2014. Nutritional value, performance, carcass quality, visceral organ size, and blood clinical chemistry of broiler chicks fed 30% tannin-free fava bean diets. *Poultry Science*, Vol. 93, n° 8, pp. 2018-2027.

VAN HORNE, P.L.M., 2018. Competitiveness of the EU poultry meat sector, base year 2017 : international comparison of production costs. Wageningen : Wageningen Economic Research. Disponible à l'adresse : https://research.wur.nl/en/publications/7b6c31d2-2daa-4b95-8d3b-6a78d5ca0d82.
WANG, L., ZHANG, Y., KONG, L., WANG, Z., BAI, H., JIANG, Y., BI, Y., CHANG, G. & CHEN, G., 2021. Effects of rearing system (floor vs. cage) and sex on performance, meat quality and enteric microorganism of yellow feather broilers. *Journal of Integrative Agriculture*, Vol. 20, n° 7, pp. 1907-1920.

WANG, M.-S., THAKUR, M., PENG, M.-S., JIANG, Y., FRANTZ, L.A.F., LI, M., ZHANG, J.-J., WANG, S., PETERS, J., OTECKO, N.O., SUWANNAPOOM, C., GUO, X., ZHENG, Z.-Q., ESMAILIZADEH, A., HIRIMUTHUGODA, N.Y., ASHARI, H., SULADARI, S., ZEIN, M.S.A., KUSZA, S., SOHRABI, S., KHARRATI-KOOPAEE, H., SHEN, Q.-K., ZENG, L., YANG, M.-M., WU, Y.-J., YANG, X.-Y., LU, X.-M., JIA, X.-Z., NIE, Q.-H., LAMONT, S.J., LASAGNA, E., CECCOBELLI, S., GUNWARDANA, H.G.T.N., SENASIGE, T.M., FENG, S.-H., SI, J.-F., ZHANG, H., JIN, J.-Q., LI, M.-L., LIU, Y.-H., CHEN, H.-M., MA, C., DAI, S.-S., BHUIYAN, A.K.F.H., KHAN, M.S., SILVA, G.L.L.P., LE, T.-T., MWAI, O.A., IBRAHIM, M.N.M., SUPPLE, M., SHAPIRO, B., HANOTTE, O., ZHANG, G., LARSON, G., HAN, J.-L., WU, D.-D. & ZHANG, Y.-P., 2020. 863 genomes reveal the origin and domestication of chicken. Dans: Bandiera_abtest: a Cg_type: Nature Research Journals number: 8 Primary_atype: Research publisher: Nature Publishing Group Subject_term: Comparative genomics;Structural variation Subject_term_id: comparative-genomics;structural-variation, *Cell Research*, Vol. 30, n° 8, pp. 693-701.

WEI, X., SHAO, M., GALE, W. & LI, L., 2014. Global pattern of soil carbon losses due to the conversion of forests to agricultural land. *Scientific Reports*. Vol. 4, n° 1, pp. 4062-4067.

WEN, C., YAN, W., ZHENG, J., JI, C., ZHANG, D., SUN, C. & YANG, N., 2018. Feed efficiency measures and their relationships with production and meat quality traits in slower growing broilers. *Poultry Science*, Vol. 97, n° 7, pp. 2356-2364.

WESTHOEK, H., LESSCHEN, J.P., ROOD, T., WAGNER, S., DE MARCO, A., MURPHY-BOKERN, D., LEIP, A., VAN GRINSVEN, H., SUTTON, M.A. & OENEMA, O., 2014. Food choices, health and environment: Effects of cutting Europe's meat and dairy intake. *Global Environmental Change*, Vol. 26, pp. 196-205.

WILFART, A., ESPAGNOL, S., DAUGUET, S., TAILLEUR, A., GAC, A. & GARCIA-LAUNAY, F., 2016. ECOALIM: A Dataset of Environmental Impacts of Feed Ingredients Used in French Animal Production. *PLOS ONE*, Vol. 11, n° 12, e0167343.

WILLEMSEN, H., EVERAERT, N., WITTERS, A., DE SMIT, L., DEBONNE, M., VERSCHUERE, F., GARAIN, P., BERCKMANS, D., DECUYPERE, E. & BRUGGEMAN, V., 2008. Critical assessment of chick quality measurements as an indicator of posthatch performance. *Poultry Science*, Vol. 87, n° 11, pp. 2358-2366.
WILSON, S., 1969. Genetic Aspects of Feed Efficiency in Broilers. *Poultry Science*, Vol. 48, n° 2, pp. 487-495.

WOYENGO, T.A., KIARIE, E. & NYACHOTI, C.M., 2011. Growth performance, organ weights, and blood parameters of broilers fed diets containing expeller-extracted canola meal. *Poultry Science*, Vol. 90, n° 11, pp. 2520-2527.

XIN, H., BERRY, I.L., BARTON, T.L. & TABLER, G.T., 1993. Feeding and Drinking Patterns of Broilers Subjected to Different Feeding and Lighting Programs1. *Journal of Applied Poultry Research*, Vol. 2, n° 4, pp. 365-372.

XU, F.Z., ZENG, X.G. & DING, X.L., 2012. Effects of Replacing Soybean Meal with Fermented Rapeseed Meal on Performance, Serum Biochemical Variables and Intestinal Morphology of Broilers. *Asian-Australasian Journal of Animal Sciences*, Vol. 25, n° 12, pp. 1734-1741.

YAN, W., SUN, C., WEN, C., JI, C., ZHANG, D. & YANG, N., 2019. Relationships between feeding behaviors and performance traits in slow-growing yellow broilers. *Poultry Science*, Vol. 98, n° 2, pp. 548-555.

YI, Z., LI, X., LUO, W., XU, Z., JI, C., ZHANG, Y., NIE, Q., ZHANG, D. & ZHANG, X., 2018. Feed conversion ratio, residual feed intake and cholecystokinin type A receptor gene polymorphisms are associated with feed intake and average daily gain in a Chinese local chicken population. *Journal of Animal Science and Biotechnology*, Vol. 9, n° 1, pp. 50-58.

YO, T., VILARIÑO, M., FAURE, J.M. & PICARD, M., 1997. Feed Pecking in Young Chickens: New Techniques of Evaluation. *Physiology & Behavior*, Vol. 61, n° 6, pp. 803-810.

YOUVALARI, S.A., FARHOOMAND, P., KANANI, P.B. & GHASEMABAD, B.H., 2017. Effects of Copper Sulfate and Arginine Supplements on Performance and Carcass Traits in Broiler Chickens Fed with Canola Meal Based Diet. *Iranian Journal of Applied Animal Science*, Vol. 7, n° 4, pp. 647-654.

ZACHARIAS, B., BENZ, S., SCHRADE, H. & ZACHARIAS, T., 2016. The performance of growing pigs fed with soybeans, peas and rapeseed meal grown in Central Europe. *Züchtungskunde*, Vol. 88, n° 3, pp. 189-198.

ZAJAC, M., KICZOROWSKA, B., SAMOLINSKA, W. & KLEBANIUK, R., 2020. Inclusion of Camelina, Flax, and Sunflower Seeds in the Diets for Broiler Chickens: Apparent Digestibility of Nutrients, Growth Performance, Health Status, and Carcass and Meat Quality Traits. *Animals*, Vol. 10, n° 2, pp. 321-337.

ZATARI, I. & SELL, J., 1990. Effects of Pelleting Diets Containing Sunflower Meal on the Performance of Broiler-Chickens. *Animal Feed Science and Technology*, Vol. 30, n° 1-2, pp. 121-129.
ZEB, A., SATTAR, A., SHAH, A.B., BIBI, N. & MEULEN, U., 2002. Effects of feeding increased levels of heat processed rapeseed meal on performance of broiler chicks. *Archiv fur Geflugelkunde*, Vol. 66, pp. 158-163.

ZELTNER, E. et HIRT, H., 2008. A note on fear reaction of three different genetic strains of laying hens to a simulated hawk attack in the hen run of a free-range system. *Applied Animal Behaviour Science - APPL ANIM BEHAV SCI.* Vol. 113, pp. 69-73.

ZHANG, X., TSURUTA, S., ANDONOV, S., LOURENCO, D.A.L., SAPP, R.L., WANG, C. & MISZTAL, I., 2018. Relationships among mortality, performance, and disorder traits in broiler chickens: a genetic and genomic approach. *Poultry Science*, Vol. 97, n° 5, pp. 1511-1518.

ZHANG, Y., SHAN, A., JIANG, W., BI, C. & LI, Z., 2013. The effect of vitamin E on growth performance and meat quality in broilers given diets containing distillers' dried grain with solubles (DDGS). *British Poultry Science*, Vol. 54, n° 1, pp. 138-143.

ZHAO, X., REN, W., SIEGEL, P.B., LI, J., YIN, H., LIU, Y., WANG, Y., ZHANG, Y., HONAKER, C.F. & ZHU, Q., 2015. Housing systems interacting with sex and genetic line affect broiler growth and carcass traits. *Poultry Science*, Vol. 94, n° 7, pp. 1711-1717.

ZUIDHOF, M.J., FEDORAK, M.V., OUELLETTE, C.A. & WENGER, I.I., 2017. Precision feeding: Innovative management of broiler breeder feed intake and flock uniformity. *Poultry Science*, Vol. 96, n° 7, pp. 2254-2263.

ZUIDHOF, M.J., SCHNEIDER, B.L., CARNEY, V.L., KORVER, D.R. & ROBINSON, F.E., 2014. Growth, efficiency, and yield of commercial broilers from 1957, 1978, and 2005. *Poultry Science*, Vol. 93, n° 12, pp. 2970-2982.
# Annexes

## Annexe 1 : Liste des publications

### Publications

**2022**
**BERGER, Q.**, GUETTIER, E., BOUVAREL, I., MÉDA, B., BERNARD, J., GANIER, P., CHAHNAMIAN, M., LE BIHAN-DUVAL, E. & MIGNON-GRASTEAU, S., Comparison of different strategies of selection of feed efficiency in broilers. Journal of Animal Science (en préparation)

**2022**
**BERGER, Q.**, GUETTIER, E., BERNARD, J., GANIER, P., CHAHNAMIAN, M., LE BIHAN-DUVAL, E. & MIGNON-GRASTEAU, S. Profiles of genetic parameters of body weight and feed efficiency in two divergent broiler lines for meat ultimate pH. BMC Genomic Data (en révision)

**2021**
**BERGER, Q.**, GUETTIER, E., URVOIX, S., BERNARD, J., GANIER, P., CHAHNAMIAN, M., LE BIHAN-DUVAL, E. & MIGNON-GRASTEAU, S., 2021. The kinetics of growth, feed intake, and feed efficiency reveal a good capacity of adaptation of slow and rapid growing broilers to alternative diets. *Poultry Science*. 2021. vol. 100, no. 4, pp. 101010-101023.

**2021**
GUETTIER, E., URVOIX, S., SERVANT, P., MIKA, A., **BERGER, Q.** & MIGNON-GRASTEAU, S., 2021. BIRD-e : individualiser la mesure de la consommation d'aliments par les volailles. *Revue des sciences et techniques de l'animal de laboratoire*, vol 49, pp 21-26

**2021**
MIKA, A., GUETTIER, E., **BERGER, Q.**, BIHAN-DUVAL, E.L., BERNARD, J., PAMPOUILLE, E., BOUVAREL, I. & MIGNON-GRASTEAU, S., 2021. Eva-HD - Mise au point d'un automate de consommation alimentaire pour volailles « BIRD-e : Bird Individual Ration Dispenser-electronic ». *Innovations Agronomiques*. 2021. vol. 82, pp. 137.

**2020**
GUETTIER, E., URVOIX, S., SERVANT, P., MIKA, A., **BERGER, Q.** & MIGNON-GRASTEAU, S., 2020. Mise au point d'un automate de consommation alimentaire pour volailles nommé BIRD-e: Bird Individual Ration Dispenser-electronic. *Cahier des Techniques de l'INRA*. 2020. vol. 99, pp. 12 p.
**2022**
**BERGER, Q.**, GUETTIER, E., URVOIX, S., LE BIHAN-DUVAL, E. & MIGNON-GRASTEAU, S., 2022. Adaptation of slow- and rapid-growing broilers to alternative diets. *World Poultry Congress*. 07-11 août 2022. Paris, France;

**2022**
MIGNON-GRASTEAU, S., **BERGER, Q.**, GUETTIER, E. & LE BIHAN-DUVAL, E., 2022. Using high throughput phenotyping of growth and feed intake to improve adaptation of chickens to sustainable diets. *7th Summit of Mediterranean Poultry Production* 8-10 juin 2022, Córdoba, Espagne

**2021**
MIGNON-GRASTEAU, S., **BERGER, Q.**, GUETTIER, E. & LE BIHAN-DUVAL, E., 2021. New advances based on our feed station and its use for feed efficiency phenotyping. *72th Eur Assoc Anim Prod.* 2021. Davos.

**2020**
**BERGER, Q.** & MIGNON-GRASTEAU, S. Bird-e : Développement d'un automate de pesée et mesure de consommation en conditions d'élevage. *3èmes Journées Techniques SYSAAF*. 2020, 8-9 Décembre 2020.

**2020**
PAMPOUILLE, E., MIKA, A., BERNARD, J., GUETTIER, E., **BERGER, Q.**, BOUVAREL, I. & MIGNON-GRASTEAU, S. BIRD-e - The Poultry E-Feeding System: Basis and Applications. *2020 Animal Nutrition Conference of Canada*. 2020. 26-28 mai 2020.

**2019**
**BERGER, Q.**, GUETTIER, E., URVOIX, S., DUVAL, E. & MIGNON-GRASTEAU, S. Using high throughput phenotyping of growth and feed intake to improve adaptation of chickens to sustainable diets. *XIth EUROPEAN SYMPOSIUM ON POULTRY GENETICS*. 2019, 23-25 octobre 2019, Prague.
Annexe 2 : Poster présenté au Congrès ESPG (Prague) du 23 au 25 octobre 2019

<figure><img src="image_60.png" /></figure>
# Mise au point d'un automate de consommation alimentaire pour volailles nommé BIRD-e: Bird Individual Ration Dispenser-electronic

Elodie Guettier, Séverine Urvoix, Philippe Servant, Amandine Mika, Quentin Berger, Sandrine Mignon-Grasteau

<figure><img src="image_61.png" /></figure>

## To cite this version:

Elodie Guettier, Séverine Urvoix, Philippe Servant, Amandine Mika, Quentin Berger, et al.. Mise au point d'un automate de consommation alimentaire pour volailles nommé BIRD-e: Bird Individual Ration Dispenser-electronic. Cahier des Techniques de l'INRA, INRA, 2020, 99, 12 p. hal-02627687

HAL Id: hal-02627687

https://hal.inrae.fr/hal-02627687

Submitted on 26 May 2020

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

<figure><img src="image_62.png" /></figure>
# Mise au point d'un automate de consommation alimentaire pour volailles nommé BIRD-e : Bird Individual Ration Dispenser-electronic

Elodie Guettier¹*, Séverine Urvoix¹*, Philipe Servant², Amandine Mika³, Quentin

Berger¹, Sandrine Mignon-Grasteau¹

1 INRAE, Université de Tours, BOA, 37380 Nouzilly, France ;
severine.urvoix@inrae.fr

et elodie.guettier@inrae.fr

2 INRAE, PEAT, 37380 Nouzilly, France

3 ITAVI, 37380 Nouzilly, France

<figure><img src="image_63.png" /></figure>

Zootechnicienne de formation (Master PA), j'ai débuté ma carrière à l'INRA en tant que Directrice d'unité expérimentale (UE PAO, Centre Val de Loire). A l'issue de 3 mandats, j'ai intégré l'UMR BOA à mi-temps pour y développer une activité de recherche à l'interface Elevage de précision et bien-être animal. Je partage le reste de mon temps de travail entre de l'animation transversale (coordinatrice des SBEA de l'INRAE) et une mission d'expertise sur le dispositif expérimental INRAE en tant que membre de la

CNUE-DISC.

<figure><img src="image_64.png" /></figure>

Je suis Séverine Urvoix, technicienne en expérimentation animale dans l'équipe AQSel de l'UMR-BOA. Je suis très sensible aux problématiques liées aux conditions d'élevages et à l'expérimentation animale. J'aime les défis. Après 16 années en tant que zootechnicienne, puis technicienne en histopathologie j'ai été recrutée à l'INRA en

<footnote>* Co-premières auteures</footnote>
2009 et intégré le groupe de travail de développement d'outils de phénotypage en collaboration avec l'UE-PEAT. En 2012, je me suis lancée dans un nouveau challenge et suis partie en disponibilité sur un projet de protection et de conservation de l'Outarde Houbara aux EAU. A mon retour dans l'équipe AQSel en 2015 le projet mangeoire instruit depuis quelques années a été relancé et naturellement je me suis à nouveau impliquée dans cette aventure.

**Résumé.** L'UMR-Biologie des Oiseaux et Aviculture et l'UE-PEAT du centre Val de Loire ont travaillé sur le développement et la conception d'une mangeoire électronique pour les poulets élevés au sol. Cet outil innovant pour l'espèce permet des mesures précises et continues de l'alimentation et du poids des animaux. Cette avancée permet d'entrevoir de nouvelles perspectives dans la recherche et le développement de l'élevage avicole de précision. Cela permettra par exemple d'affiner la sélection des animaux sur l'efficacité alimentaire ou de mettre au point de nouvelles formulations d'aliments. De plus, en se libérant de la contrainte de la mesure en cage, cet outil permet des mesures respectueuses du bien-être animal.

**Mots-clés.** consommation alimentaire, pesage, volaille, haut-débit, comportement, bien-être

**Abstract.** The unit UMR-Biologie des Oiseaux and Aviculture (Poultry) and the unit PEAT from Val de Loire center have worked together to develop and design an electronic feeding dish for chicken on the ground. This innovative device allows exact and continuous weighings of the feeding and weigh of the animals. This beakthrough allows thinking about new perspectives in the research and developing of accurate poultry sector.

This will allow for example to improve animal selection on the feed efficiency or to set out new feed formulas. Moreover, this device allows well-being measuring of animals.

**Keywords.** feed consumption, weighing, poultry, high output, behaviour, well-being

**Introduction**

La maîtrise des coûts de production et le bien-être des animaux sont des facteurs importants pour les éleveurs d'animaux de rente. Aujourd'hui, l'alimentation représente environ 70 % du coût de production de la viande de poulet. L'indice de consommation (IC), qui est le ratio de la consommation alimentaire sur la production de viande ou d'œufs, est le principal critère de rentabilité de ces élevages. C'est aussi un indicateur de l'impact environnemental de la production. Par exemple, une réduction de 10 % de l'IC permet une réduction du coût de production du poulet vif d'environ 6 %, mais aussi une réduction de l'impact environnemental de 12 % pour l'excrétion d'azote et de 17 % pour le phosphore (De Verdal et al., 2011).

Jusqu'à récemment, on ne pouvait mesurer la consommation alimentaire des animaux qu'en les isolant en cages individuelles. Cela est néfaste au bien-être des animaux car cela empêche
l'expression normale des comportements alimentaires, sociaux et de l'activité physique des animaux,
ce qui biaise les résultats. Le biais est particulièrement important dans le cas des productions
alternatives de type poulets labels ou biologiques. De plus, cette méthode d'hébergement dessert le
bien-être animal. L'alternative consistait en une mesure au sol dans les conditions de production,
mais seulement au niveau du groupe. Or, les données individuelles sont nécessaires aux généticiens
pour la sélection. De plus, pour les nutritionnistes, la mise au point de nouveaux aliments grâce à des
mesures au niveau des groupes implique l'utilisation d'un nombre élevé d'animaux. Des outils pour
bovins, porcins ou canards existaient, mais il manquait encore un outil fiable et accessible pour les
poulets.

A partir de 2005, un groupe de travail a donc été constitué avec des chercheurs, des ingénieurs et
des techniciens pour réfléchir au développement d'un outil permettant de mesurer la consommation
alimentaire individuelle de Gallus élevés en groupe et au sol.

Depuis, l'équipe AQSel travaille avec l'UE-PEAT sur le développement d'un outil qui permet
l'enregistrement de données individuelles de consommation, comportement alimentaire et poids de
volailles élevées en groupe. Grâce à un système d'émetteur (puce RFID) fixé à l'animal et de
récepteur (antenne) placés sur la mangeoire, nous pouvons enregistrer les données dès la phase de
démarrage sans biaiser le comportement des animaux.

En 2012, un brevet d'invention a été déposé pour le caractère innovant de l'outil.

Depuis, grâce à différentes expérimentations, nous avons considérablement amélioré la partie
électronique, l'enregistrement et le traitement des données ainsi que l'ergonomie et les matériaux
utilisés. Nous nous sommes attachés à des améliorations techniques comme par exemple : le
revêtement antidérapant et les bords antiperchage des plateaux de pesée des aliments ou les capots
placés au-dessus des accès à l'aliment.

Des essais ont permis de comparer les comportements et les performances animaux élevés au sol et
en groupe sur différents génotypes ou différents régimes alimentaires. Les résultats très
encourageants ont confirmé l'accomplissement d'années de travail et de réflexion sur l'automate et
nous permettent de vous le présenter.

Cet article décrit le processus de développement de la conception jusqu'à la validation avec une
description détaillée de l'automate et de son fonctionnement.

Les étapes clés de développement pour un dispositif adapté

Pour réaliser l'automate de consommation pour volailles, plusieurs étapes ont été nécessaires,
reprises dans la figure ci-dessous.
<figure><img src="image_65.png" /><figcaption>Figure 1. Processus de mise au point de l'outil</figcaption></figure>

La contrainte que représentait la mesure de la consommation alimentaire individuelle en cage a permis d'exprimer la nécessité d'expérimenter dans des conditions d'élevage réelles, c'est-à-dire avec des animaux en groupe et au sol. Une fois le besoin exprimé, un premier état des lieux a été réalisé pour recenser l'ensemble des dispositifs existants sur le marché. N'ayant pas identifié d'équipement répondant à notre objectif, nous avons considéré qu'il était opportun de développer notre propre projet.

Pour rédiger un cahier des charges qui réponde précisément au besoin, il est important de bien comprendre les usages. Dans un premier temps, nous avons identifié les différents utilisateurs de l'outil :

* les volailles utilisent l'automate de consommation pour s'alimenter ;

* l'expérimentateur l'utilise comme distributeur d'aliment et comme substitut aux pesées manuelles des animaux ;

* le scientifique l'utilise comme un outil de phénotypage permettant d'obtenir des mesures individuelles et en continu de poids d'animal et de poids d'aliment consommé.

La description et l'analyse de ces différents besoins a permis de concevoir un premier prototype conçu et fabriqué en interne dans l'atelier de l'unité PEAT. Ce premier prototype a fait l'objet d'un brevet d'invention (n°1255242) détaillant essentiellement le concept.

Celui-ci a été testé à de nombreuses reprises, puis amélioré, permettant de concevoir un second prototype, plus performant, répondant à une grande partie des besoins exprimés. Les premiers essais mis en place avaient pour principal objectif de tester si les animaux acceptent de manger avec cette mangeoire, sans modifier leur comportement et sans que leur croissance soit affectée.