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Automatic feeders were used to characterize the feeding behaviour traits of two populations of ducks (White Pekin and Muscovy). In addition to feed conversion ratio, six phenotypes were derived at the scale of the day and the meal and genetic parameters were estimated. Feeding rate in the Pekin population was twice as large as in the Muscovy population (19 g/min vs. 9 g/min). Feeding traits exhibited moderate (0.38) to high (0.67) heritabilities. A similar pattern of correlations could be observed between these traits in the two populations. Feed conversion ratio did not show any remarkable correlation with feeding traits in the Pekin line, except with daily feed intake, but was negatively correlated with the number of meals and meal feed intake in the Muscovy population (0.68). Further studies will be needed to decipher how these traits can be incorporated in breeding programs without impairing ducks' abilities to produce fatty liver.

International audience; Feed efficiency is a complex trait of interest in animal breeding to reduce environmental impact of livestock herding and the cost of animal feed. A divergent selection on feed efficiency was carried out in pigs for more than 10 generations at an INRAE experimental unit, establishing lines of relative high- and low-feed efficiency in large white pigs. The objective of this study was to identify the molecular mechanisms underlying the divergence in feed efficiency between the two pig lines in the duodenum. We focused on the duodenum because of its sensory role in the hunger satiety pathways.Duodenum mucosal samples were collected in post-weaning pigs from the two divergent lines either after a 12 h period of feed restriction, or with a 10 h period of feed restriction followed by a 2 h period of ad libitum feed access (n = 6 per group and per line). Transcritomes and methylomes were analysed by poly-A RNA sequencing (RNA-seq) and by methylated DNA precipitation followed by sequencing (MeDP-seq), respectively. Transcriptomic reads were analysed with nf-core/rnaseq and limma-voom. MeDP-seq reads were analysed with nf-core/chipseq and the BayMeth method.A total of 962 genes were differentially expressed in the duodenum mucosa between the conditions (fasted vs fed), in contrast with 318 differentially expressed genes when comparing the divergent lines. The DNA methylome of the duodenum mucosa was unaffected by short term feed intake, but was distinct between the lines. More transcripts were affected by short term feed intake in the feed-efficient line than in the other line, including genes of the incretine secretion pathways involved in the production of GIP and GLP-1.Thus the duodenum transcriptomic response to short term feed intake was distinct in feed efficient and less efficient pig lines. These differences might in part be due to differences in DNA methylation profiles.

The aim of this study was to describe in detail the longitudinal growth and feedingtraits, that are key phenotypes for feed efficiency and force-feeding, in the threegenetically different types of ducks farmed for fatty liver production in France(Muscovy, Pekin, and mule ducks). Forty-one Muscovy, 35 Pekin, and 40 muleducks, all males, were reared with single-place electronic feeders to compare theirgrowth performances and feeding traits. Data were analyzed from 28 to 49 daysof age, after eliminating multiple-bird and unidentified visits (between 1 and 6%of data per genetic type). We studied the different traits at three time scales: forthe entire trial (covering 4 weeks), by week and by day. The effects of the genetictype (for the three time scales), the time scale and their interaction (for week andday scales only) were tested using linear mixed models with SAS software. At thetrial scale, the hybrid mule was characterized by a feeding behavior close to thatof Pekin ducks, and a production performance similar to Muscovy ducks. Like Pekinducks, mule ducks consumed two less per visit (26.8 g) than Muscovy ducks (P

Classical methods as genomic BLUP performs well for genomic prediction of polygenic trait, but does not consider interaction between genes or between genes and other information such as host genetic or microbial data. This study aims at comparing several methods including parametric and machine learning methods to predict digestive coefficient using genomic, microbial and both genomic and microbial information. Considering only microbial data led to the best prediction accuracies for digestive coefficients, whereas considering only genomic data performed worst. BLUP, RKHS and GSVM gave the best prediction accuracies except when combined genomic and microbial data was used. Combining microbial and genomic data did not improve prediction accuracies for all traits and methods considered in this study. Thus, considering microbial information is crucial to predict digestive efficiency and interactions between host genetic and faecal microbial information seem to be limited.

International audience; The resource allocation theory has long been of interest in livestock as a framework to explain the correlated responses to selection for high productivity. Increasing limitations on feed resources and genetic improvement of feed efficiency (FE) should further promote allocation constraints. Yet, predicting those consequences for breeding critically depends upon the possibility to get testable predictions from the current framework. Here we assessed the changes in energy allocation when selecting for FE, and the consequences of those changes on health and reproduction. For this, we used two approaches; a survey of selection experiments among various species and a case study in sheep focusing on a potential trade-off between FE and response to an infectious challenge (parasite infection). Overall, results provide weak evidence for the current allocation framework. We outline several directions to better appraise the consequences of breeding for FE in increasingly challenging environments.

Industrial livestock systems faces a major crisis of legitimacy and animal breeding contributes to this situation. Worldwide, breeding goals focus on economic performance and rarely consider the environmental and social impacts. Agroecology offers a conceptual framework to think alternative solutions to face this crisis and to guide the transition of livestock farming towards nature-based systems. Animal genetics, as an essential component of animal husbandry, should contribute to this evolution. Based on the agroecological principles applied to animal farming and the efficiency-substitution-redesign (ESR) framework, we showed that animal breeding mainly focused on the E-S levels of the ESR framework and few on the redesign of animal farming. We then present some perspectives for the redesign of breeding goals, the use of the G×E interaction information in the evaluation of animals, and the benefits of managing the available genetic diversity of livestock species to fit to the needs of nature-inclusive systems.

We performed a selection experiment to investigate the possibility to improve resistance to non-specific diseases in rabbits. The selection criterion was a binary trait (healthy versus sick or dead) obtained from clinical signs of diseases occurring naturally on farm. A herd of 116 to 132 does and 30 bucks was bred based on this selection criterion. The heritability of the trait was 0.035 ± 0.009. After four generations of selection, the genetic progress was 1.5 genetic standard deviation, indicating that breeding for general disease resistance is feasible.

International audience; We performed a genome wide association study for growth and feed efficiency using the GEMMA software on 679 rabbits genotyped with the Affimetrix Axiom Rabbit 200K Genotyping Array. No significant SNP was found for growth traits or feed intake. Two and 89 chromosome-wide significant SNPs were detected for feed conversion ratio and residual feed intake, respectively. The 89 significant SNPs for residual feed intake were all located on chromosome 18, where the putative functional candidate gene GOT1 could be identified.

International audience

International audience

International audience; Breeding pigs that can efficiently digest alternative diets with increased fibre content is a viable strategy to mitigate the feed cost volatility in pig production. This study aimed at determining the relative contribution of microbiome and genetics to the phenotypic variability of three digestibility coefficients (DC). Faecal samples were collected at sixteen weeks of age to sequence the V3-V4 regions of the 16S RNA gene and to predict DC with near-infrared spectrometry for energy, organic matter, and nitrogen. Data were available for 1,564 Large White pigs, and 14,366 identified Operational Taxonomic Units (OTUs) were identified. The proportions of phenotypic variance explained by the microbiome, or ‘microbiability’, and by additive genetic effects were first estimated independently and then jointly using a Bayesian approach. The microbiability was estimated under three scenarios: without filtering the OTUs, by filtering the OTUs presented in more than five samples and with an average abundance higher than 0.001% (2,399 OTUs) or greater than 0.01% (803 OTUs). Estimates of microbiability for the traits were reduced with increasingly stringent filtering, from 54±2 to 66±2% in the first scenario and from 25±1 to 35±1% for 803 OTUs. With intermediate filtering, the estimated microbiability ranged from 37±1 to 46±2% and was higher than heritability (from 26±1 to 29±1%) when estimated independently from genetics effects. The joint estimation of microbiability and heritability will point out how the two random effects overlap in explaining the trait variability. To conclude, our results show that the filtering of OTUs is important and influences microbiability. The microbiome explained a significant proportion of the phenotypic variance of digestive efficiency, which was even larger than heritability estimates.

International audience; The objective of the Feed-a-Gene project (2015-2020; H2020 grant agreement 633531) was to adapt the feed, the animal, and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems. For ‘feed’, we concluded that non-GMO European soybeans can be processed in small-scale processing plants to obtain soybean meals with similar or even better nutritional quality as imported soybean meals. Also, there is a potential for the use of green biomass to extract protein for monogastric animals to replace imported soybean meal in organic farming. Concerning the ‘animal’, monogastric animals have to rely more on alternative feed resources that are less or not in competition with other uses. These resources are typically more variable and of poorer nutritional quality. With new molecular and automated records, animals can be selected that do best with feed that is worse and transform these alternative feeds into meat, respecting their welfare and robustness and limiting environmental impacts. Precision livestock feeding is a novel technology that can be using to provide tailored diets to animals according to their (individual) requirements, thereby reducing nutrient excretion and the environmental impact of livestock production. The potential of precision livestock farming is much larger than what we explored in Feed-a-Gene (e.g. precision medication, design of housing systems). It is not a question of ‘if’ but of ‘when’ these systems will be implemented, and ‘how’ society will perceive trade-offs of improved resource use, reduced environmental impact, improved welfare versus a perceived further industrialization of livestock production.

International audience

Feature selection (FS, i.e., selection of a subset of predictor variables) is essential in high-dimensional datasets to prevent overfitting of prediction/classification models and reduce computation time and resources. In genomics, FS allows identifying relevant markers and designing low-density SNP chips to evaluate selection candidates. In this research, several univariate and multivariate FS algorithms combined with various parametric and non-parametric learners were applied to the prediction of feed efficiency in growing pigs from high-dimensional genomic data. The objective was to find the best combination of feature selector, SNP subset size, and learner leading to accurate and stable (i.e., less sensitive to changes in the training data) prediction models. Genomic best linear unbiased prediction (GBLUP) without SNP pre-selection was the benchmark. Three types of FS methods were implemented: (i) filter methods: univariate (univ.dtree, spearcor) or multivariate (cforest, mrmr), with random selection as benchmark; (ii) embedded methods: elastic net and least absolute shrinkage and selection operator (LASSO) regression; (iii) combination of filter and embedded methods. Ridge regression, support vector machine (SVM), and gradient boosting (GB) were applied after pre-selection performed with the filter methods. Data represented 5,708 individual records of residual feed intake to be predicted from the animal’s own genotype. Accuracy (stability of results) was measured as the median (interquartile range) of the Spearman correlation between observed and predicted data in a 10-fold cross-validation. The best prediction in terms of accuracy and stability was obtained with SVM and GB using 500 or more SNPs [0.28 (0.02) and 0.27 (0.04) for SVM and GB with 1,000 SNPs, respectively]. With larger subset sizes (1,000–1,500 SNPs), the filter method had no influence on prediction quality, which was similar to that attained with a random selection. With 50–250 SNPs, the FS method had a huge impact on prediction quality: it was very poor for tree-based methods combined with any learner, but good and similar to what was obtained with larger SNP subsets when spearcor or mrmr were implemented with or without embedded methods. Those filters also led to very stable results, suggesting their potential use for designing low-density SNP chips for genome-based evaluation of feed efficiency. info:eu-repo/semantics/publishedVersion

International audience; We evaluated the economic and environmental impacts of strategies that incorporated selection for pig feed:gain and dietary optimization based on a single or multiple objectives tailored to meet the population’s nutritional requirements, with the goal to optimize sustainable farm feed efficiency. The economic and environmental features of the strategy were evaluated using life cycle assessment (LCA) and bio-economic models. An individual trait-based LCA model was applied to evaluate global warming potential (GWP), terrestrial acidification potential (AP), freshwater eutrophication potential (EP), and land occupation (LO) of the combined genetics and nutrition optimization to produce 1kg of live pig weighing 120kg at the farm gate. A parametric individual trait-based bio-economic model was developed and applied to determine the cost breakdown, revenue and profit to be gained from a 120kg live pig at the farm gate. Applying the combined genetics and nutrition optimization, the individual performance traits of pigs from two genetic lines with contrasted levels of feed efficiency were simulated with InraPorc in response to diets formulated for least cost, least environmental impacts, or minimum combination of cost and environmental impacts objectives, and accounting for the nutritional requirements of each line. Significant differences in the environmental impacts (P < 0.0001) and profit (P < 0.05) between lines predicted the same reference diet showed that selection for feed efficiency (residual feed intake, RFI) in pigs improves pig production sustainability. When pig responses were simulated with their line optimized diets, except for EP, all the line environmental impacts were less (P < 0.05) than with the reference diet. The high correlations of feed conversion ratio (FCR) with the environmental impacts (> 0.82) and the profit (< -0.88) in both lines underline the importance of feed efficiency as a lever for the sustainability of pig production systems. Implementing combined genetics and nutrition optimization, the inherent profit and environmental differences between the genetic lines was predicted to be reduced from 23.4% with the reference diet to 7.6% with the diet optimized jointly for economic and environmental objectives (joint diet). Consequently, for increased pig sustainability, diet optimization for sustainability objectives should be applied to cover the specific nutritional requirements arising in the herd from the pigs’ genetic level.

International audience; This study aimed to evaluate the genetic relationship between faecal microbial composition and five feed efficiency (FE) and production traits, residual feed intake (RFI), feed conversion ratio (FCR), daily feed intake (DFI), average daily gain (ADG) and backfat thickness (BFT). A total of 588 samples from two experimental pig lines developed by divergent selection for RFI were sequenced for the 16 rRNA hypervariable V3-V4 region. The 75 genera with less than 20% zero values (97% of the counts) and two alpha-diversity indexes were analysed. Line comparison of the microbiota traits and estimations of heritability (h(2)) and genetic correlations (r(g)) were analysed. A non-metric multidimensional scaling showed line differences between genera. The alpha-diversity indexes were higher in the LRFI line than in the HRFI line (p < .01), with h(2) estimates of 0.19 +/- 0.08 (Shannon) and 0.12 +/- 0.06 (Simpson). Forty-eight genera had a significant h(2) (>0.125). The r(g) of the alpha-diversities indexes with production traits were negative. Some r(g) of genera belonging to the Lachnospiraceae, Ruminococcaceae, Prevotellaceae, Lactobacillaceae, Streptococcaceae, Rikenellaceae and Desulfovibrionaceae families significantly differed from zero (p < .05) with FE traits, RFI (3), DFI (7) and BFT (11). These results suggest that a sizable part of the variability of the gut microbial community is under genetic control and has genetic relationships with FE, including diversity indicators. It offers promising perspectives for selection for feed efficiency using gut microbiome composition in pigs.

International audience; Purpose Use a holistic individual life cycle assessment (LCA) to investigate possible mitigation of environmental impacts through optimisation of overall farm feed efciency by combining animal selection for feed efciency and formulation of diets with minimum environmental impacts tailored to pig nutritional requirements.Methods A linear multi-objective optimisation method was used to combine diet optimisation tailored to meet the representative nutritional requirements of genetic lines with environmental optimisation of the environmental impacts of the diet. Environmental optimisation was obtained by weighting the environmental impacts of the diet in a single environmental impact score. An individual trait-based LCA model with a cradle-to-farm-gate system boundary and functional unit of 1 kg live pig at the farm gate was applied to genetic lines selected for high (LRFI, high feed efcient line) and low (HRFI, low feed efcient line) feed efciency data. The production traits of each individual animal in response to the optimised diets were simulated with InraPorc® and imported into the individual LCA model to assess global warming potential (GWP), terrestrial acidifcation potential (AP), freshwater eutrophication potential (EP), and land occupation (LO) of the overall farm feed efciency approach.Results and discussion Integrating selection for feed efciency, nutritional requirements of genetic lines (HRFI and LRFI) and environmental diet optimisation resulted in overall mitigation of environmental impacts. Compared to the conventional diet, the environmental score of the optimised tailored diets was reduced by 5.8% and 5.2% for LRFI and HRFI lines, respectively. At the general production system level, the environmental impacts decreased by an average of 4.2% for LRFI and 3.8% for HRFI lines compared to environmental impacts of the lines fed the conventional diet (P < 0.05). The HRFI line with its optimised tailored diet had fewer impacts than the LRFI line with the conventional diet, except for EP. Individual LCA revealed high correlations between environmental impacts and feed efciency and protein deposition traits.Conclusions Implementation of overall farm feed efciency would efectively mitigate environmental impacts. A holistic economic evaluation of the resulting trade-of between diet costs and pig performances is now needed

International audience; The use of early biomarkers is an interesting approach to improve selection efficiency for traits that are difficult to measure. The aim of this study was to validate juvenile IGF1 and leptin as biomarkers of growth and feed efficiency in Large White (LW), Duroc and Piétrain purebred lines. To determine plasma concentrations of IGF1 and leptin, blood samples were obtained from 8-9-week-old piglets at the end of the post-weaning phase. After data curation, 337 LW, 192 Duroc and 189 Piétrain pigs had both plasma concentrations and production performance recorded during the growing-finishing phase, namely average daily gain (ADG), daily feed intake (DFI) and feed conversion ratio (FCR). Nested linear mixed models including successively the biomarker concentration and the weight at bleeding (BBW) as covariables in addition to the usual effects were compared using a likelihood ratio test. In the three lines, adding IGF1 concentration in prediction models of growth and feed efficiency traits increased the goodness-of-fit, with the covariable being significant (P < 0.001), except for FCR in the Piétrain line. Augmenting this model with BBW increased the model likelihood in the LW and Duroc breeds for any traits, as BBW was highly significant (P < 0.001) although IGF1 was not significant anymore. These results suggested that IGF1 is a biomarker of growth and feed efficiency that captured the same information as BBW. In the Piétrain line, IGF1 contributed to increase the likelihood of the model along with BBW for ADG and DFI. Hence, IGF1 seems to be a relevant biomarker for the Piétrain breed but to have limited utility for the LW and Duroc breeds if pigs can be weighed in the post-weaning phase. The same approach was applied to juvenile leptin, but it was found not to be a useful biomarker of growth or feed efficiency for the three lines.

International audience

Eberle, U., Smetana, S., Bos, U. (Eds.); International audience; Purpose. European pig production aims at reducing its environmental impacts with various mitigation strategies. Among them, the replacement of Brazilian soybean meal (BSM) associated to deforestation with European protein sources (EPS) combined with the genetic improvement of feedefficiency was tested in the H2020 Feed-A-Gene project. The goal was to estimate the environmental impacts of pig production resulting from incorporating EPS in fattening feeds.Methods. Four EPS were examined: fine fraction of rapeseed meal, partly defatted soybean meals obtained from crushing of dehulled or non-dehulled soybeans, and protein paste extracted from biomass. Environmental impacts per ton of protein for feedstuffs and per kilogram of pig at the farm gate were estimated using life cycle assessment. Benefits were calculated by comparing each EPS scenario with a reference using BSM and traditional protein sources. Least cost formulation defined feed compositions. For current feedstuffs, prices come from four European countries for four contrasting years. Prices of BSM and EPS were considered zero respectively in reference and innovative scenarios in order to maximize their incorporation. Improved genetic was assessed thanks to the comparison of two lines respectively with low and high feed efficiency.Results and discussion. At feedstuff level, the EPS reduce climate change impact more than twice compared to BSM but other impacts could be increased as acidification, eutrophication and land occupation. At pig level, the average incorporation of BSM in the reference fattening diet was 13%. Compared to this baseline, EPS scenarios reduce climate change impacts (by 8-9% for the European soybean meal and by 3-4% for the protein paste and the fine fraction of rapeseed meal) but still with a transfer of impact to land use (increase of 13%). With an additional improved genetic, the reduction on climate change with EPS rises 12-16% and limits the increase of land occupation below 5%. Theseresults show a potential interest of EPS.Conclusions. EPS can’t be considered competitive as a replacement for BSM in the current context in which less than 5% of BSM was currently used in fattening diets due to the relative prices of protein sources. EPS seem interesting for climate change in a context economically favorable to BSM. But because of the transfer of impacts, there is a need for more macroscopic analyses to capture indirect land use effects.

National audience

Discoffeery session pour professionnels porteurs d'enjeux; Towards biomarkers of feed efficiency Using –omics approaches for biomarkers discovery . Réunion finale du projet européen Feed-a-Gene

International audience; The objective of Feed-a-Gene (H2020 grant agreement 633531) was to adapt the feed, the animal and the feeding techniques to improve the efficiency and sustainability of monogastric livestock production systems. Efficiency is a complex trait affecting the components of sustainability. Imported soybean meal is a high-quality protein source, but its use can have environmental and social impacts. Technological treatments applied to European-grown protein sources (i.e. soybean meal, rapeseed meal, and green biomass) allow to obtain fractions with different nutritional characteristics that can be fed to different livestock species. Replacing Brazilian soybean meal with locally-produced protein sources reduced energy cost and impact on climate change, but resulted in a transfer of land-use and in more arable land use. Novel traits indicative for feed efficiency were developed and tested (e.g. individual feed intake in broilers, genomic information on nitrogen efficiency, large-scale measurements of digestibility in individual pigs, blood metabolites, and behavioural traits). These traits can be used in livestock management strategies, and digestibility, microbiota, and blood biomarkers appeared promising for genetic selection. Different mathematic models were developed to assess digestive and metabolic efficiency, animal robustness, and variation among animals. A userfriendly tool was developed allowing users to work with these models. Prototypes of precision feeding systems were developed for broilers, growing pigs, and sows. These systems adjust the type of feed to the daily changing nutritional requirements of individual or groups of animals. Precision feeding in pigs reduced key environmental impacts and increased profitability compared to conventional feeding systems.