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The aim of this study was to quantify the effects of progesterone profile features and other cow-level factors on insemination success to provide a real-time predictor equation of probability of insemination success. Progesterone profiles from 26 dairy herds were analyzed and the effects of profile features (progesterone slope, cycle length, and cycle height) and cow traits (milk yield, parity, insemination during the previous estrus) on likelihood of artificial insemination success were estimated. The equation was fitted on a training data set containing data from 16 herds (6,246 estrous cycles from 3,404 lactations). The equation was tested on a testing data set containing data from 10 herds (8,105 estrous cycles from 3,038 lactations). Predictors were selected to be implemented in the final equation if adding them to a base model correcting for timing of insemination and parity decreased the overall likelihood distance of the model. Selected variables (cycle length, milk yield, cycle height, and insemination during the previous estrus) were used to build the final model using a stepwise approach. Predictors were added 1 by 1 in different order, and the model that had the smallest likelihood distance was selected. The final equation included the variables timing of insemination, parity, milk yield, cycle length, cycle height, and insemination during the previous estrus, respectively. The final model was applied to the testing data set and area under the curve (AUC) was calculated. On the testing data set, the final model had an AUC of 58%. When the farm effect was taken into account, the AUC increased to 63%. This equation can be implemented on farms that monitor progesterone and can support the farmer in deciding when to inseminate a cow. This can be the first step in moving the focus away from the current paradigm associated with poorer estrus detection, where each detected estrus is automatically inseminated, to near perfect estrus detection, where the question is which estrous cycle is worth inseminating?

International audience; As the environments in which livestock are reared become more variable, animal robustness becomes an increasingly valuable attribute. Consequently, there is increasing focus on managing and breeding for it. However, robustness is a difficult phenotype to properly characterise because it is a complex trait composed of multiple components, including dynamic elements such as the rates of response to, and recovery from, environmental perturbations. In this review, the following definition of robustness is used: the ability, in the face of environmental constraints, to carry on doing the various things that the animal needs to do to favour its future ability to reproduce. The different elements of this definition are discussed to provide a clearer understanding of the components of robustness. The implications for quantifying robustness are that there is no single measure of robustness but rather that it is the combination of multiple and interacting component mechanisms whose relative value is context dependent. This context encompasses both the prevailing environment and the prevailing selection pressure. One key issue for measuring robustness is to be clear on the use to which the robustness measurements will employed. If the purpose is to identify biomarkers that may be useful for molecular phenotyping or genotyping, the measurements should focus on the physiological mechanisms underlying robustness. However, if the purpose of measuring robustness is to quantify the extent to which animals can adapt to limiting conditions then the measurements should focus on the life functions, the trade-offs between them and the animal's capacity to increase resource acquisition. The time-related aspect of robustness also has important implications. Single time-point measurements are of limited value because they do not permit measurement of responses to (and recovery from) environmental perturbations. The exception being single measurements of the accumulated consequence of a good (or bad) adaptive capacity, such as productive longevity and lifetime efficiency. In contrast, repeated measurements over time have a high potential for quantification of the animal's ability to cope with environmental challenges. Thus, we should be able to quantify differences in adaptive capacity from the data that are increasingly becoming available with the deployment of automated monitoring technology on farm. The challenge for future management and breeding will be how to combine various proxy measures to obtain reliable estimates of robustness components in large populations. A key aspect for achieving this is to define phenotypes from consideration of their biological properties and not just from available measures.

Chantier qualité GA; Recently, the French National Institute for Agricultural Research appointed an expert committee to review the issue of pain in food-producing farm animals. To minimise pain, the authors developed a ‘3S’ approach accounting for ‘Suppress, Substitute and Soothe’ by analogy with the ‘3Rs’ approach of ‘Reduction, Refinement and Replacement’ applied in the context of animal experimentation. Thus, when addressing the matter of pain, the following steps and solutions could be assessed, in the light of their feasibility (technical constraints, logistics and regulations), acceptability (societal and financial aspects) and availability. The first solution is to suppress any source of pain that brings no obvious advantage to the animals or the producers, as well as sources of pain for which potential benefits are largely exceeded by the negative effects. For instance, tail docking of cattle has recently been eliminated. Genetic selection on the basis of resistance criteria (as e.g. for lameness in cattle and poultry) or reduction of undesirable traits (e.g. boar taint in pigs) may also reduce painful conditions or procedures. The second solution is to substitute a technique causing pain by another less-painful method. For example, if dehorning cattle is unavoidable, it is preferable to perform it at a very young age, cauterising the horn bud. Animal management and constraint systems should be designed to reduce the risk for injury and bruising. Lastly, in situations where pain is known to be present, because of animal management procedures such as dehorning or castration, or because of pathology, for example lameness, systemic or local pharmacological treatments should be used to soothe pain. These treatments should take into account the duration of pain, which, in the case of some management procedures or diseases, may persist for longer periods. The administration of pain medication may require the intervention of veterinarians, but exemptions exist where breeders are allowed to use local anaesthesia (e.g. castration and dehorning in Switzerland). Extension of such exemptions, national or European legislation on pain management, or the introduction of animal welfare codes by retailers into their meat products may help further developments. In addition, veterinarians and farmers should be given the necessary tools and information to take into account animal pain in their management decisions.

In ruminants, dietary lipids are extensively hydrogenated by rumen micro-organisms, and the extent of this biohydrogenation is a major determinant of long-chain fatty acid profiles of animal products (milk, meat). This paper reports on the duodenal flows of C18 fatty acids and their absorption in the small intestine, using a meta-analysis of a database of 77 experiments (294 treatments). We established equations for the prediction of duodenal flows of various 18-carbon (C18) fatty acids as a function of the intakes of their precursors and other dietary factors (source and/or technological treatment of dietary lipids). We also quantified the influence of several factors modifying rumen metabolism (pH, forage : concentrate ratio, level of intake, fish oil supplementation). We established equations for the apparent absorption of these fatty acids in the small intestine as a function of their duodenal flows. For all C18 unsaturated fatty acids, apparent absorption was a linear function of duodenal flow. For 18:0, apparent absorption levelled off for high duodenal flows. From this database, with fatty acid flows expressed in g/kg dry matter intake, we could not find any significant differences between animal categories (lactating cows, other cattle or sheep) in terms of rumen metabolism or intestinal absorption of C18 fatty acids.

The evolution of feeding systems for ruminants towards evaluation of diets in terms of multiple responses requires the updating of the calculation of nutrient supply to the animals to make it more accurate on aggregated units (feed unit, or UF, for energy and protein digestible in the intestine, or PDI, for metabolizable protein) and to allow prediction of absorbed nutrients. The present update of the French system is based on the building and interpretation through meta-analysis of large databases on digestion and nutrition of ruminants. Equations involved in the calculation of UF and PDI have been updated, allowing: (1) prediction of the out flow rate of particles and liquid depending on the level of intake and the proportion of concentrate, and the use of this in the calculation of ruminal digestion of protein and starch from in situ data; (2) the system to take into account the effects of the main factors of digestive interactions (level of intake, proportion of concentrate, rumen protein balance) on organic matter digestibility, energy losses in methane and in urine; (3) more accurate calculation of the energy available in the rumen and the efficiency of its use for the microbial protein synthesis. In this renewed model UF and PDI values of feedstuffs vary depending on diet composition, and intake level. Consequently, standard feed table values can be considered as being only indicative. It is thus possible to predict the nutrient supply on a wider range of diets more accurately and in particular to better integrate energy x protein interactions occurring in the gut.

Some dairy farmers opt to omit one milking, either incidentally or weekly, without changing other milking times. This practice entails an extended milking interval of 24 h (24h-MI), which is associated with a decrease in milk yield. This decrease varies among cows and could be partly due to factors such as stage of lactation and milk yield level. The aim of this study was to describe the average and individual responses in terms of loss and carryover effects of a 24h-MI on milk yield. The influence of factors such as parity, stage of lactation, and milk yield potential were investigated, together with response repeatability. Our trial used 292 Holstein-Friesian cows, and consisted of 3 successive periods: 1 wk of twice-daily milking (TDM) as a control, one 24h-MI, and then 13 d of TDM. The number of observations per cow ranged from 1 to 9, with no more than three 24h-MI per lactation. The 24h-MI reduced milk yield by 23% (7.8 kg on average) and milk lactose content by 2.6 g/kg on the 24h-MI day. Milk fat and protein content, and somatic cell score increased by 3.0 g/kg, 0.5 g/kg, and 0.4 units, respectively. No significant carryover effect was found of a 24h-MI on milk yield or milk composition 2 wk after resumption of TDM. Milk yield loss and recovery varied widely (coefficient of variation 62%), and the relationship between milk loss and milk recovery showed substantial variation (residual standard deviation 2.1 kg/d). Cows with a greater milk potential level lost more milk yield but recovered more milk, with no influence on recovery:loss ratio. Cows in early lactation recovered the lost milk yield faster. Repeatability of the responses to a 24h-MI was 44% for milk yield loss (kg/d), 57% for relative milk yield loss (%), 33% for milk yield recovery (kg/d), and 0% for milk recovery:loss ratio (%), suggesting a genetically determined ability to limit loss when one milking is omitted. To conclude, a 24h-MI caused higher milk yield losses than reported in previous studies. Stage of lactation, estimated potential milk yield level, and parity explained the cows' response to the 24h-MI, but did not account for all the individual variability.

The aim of this study was to gain a better understanding of the variability in shape and features of all progesterone profiles during estrus cycles in cows and to create templates for cycle shapes and features as a base for further research. Milk progesterone data from 1418 estrus cycles, coming from 1009 lactations, was obtained from the Danish Cattle Research Centre in Foulum, Denmark. Milk samples were analyzed daily using a Ridgeway ELISA-kit. Estrus cycles with less than 10 data points or shorter than 4 days were discarded, after which 1006 cycles remained in the analysis. A median kernel of three data points was used to smooth the progesterone time series. The time between start of progesterone rise and end of progesterone decline was identified by fitting a simple model consisting of base length and a quadratic curve to progesterone data, and this luteal-like phase (LLP) was used for further analysis. The data set of 1006 LLP's was divided into five quantiles based on length. Within quantiles, a cluster analysis was performed on the basis of shape distance. Height, upward and downward slope, and progesterone level on Day 5 were compared between quantiles. Also, the ratio of typical versus atypical shapes was described, using a reference curve on the basis of data in Q1–Q4. The main results of this article were that (1) most of the progesterone profiles showed a typical profile, including the ones that exceeded the optimum cycle length of 24 days; (2) cycles in Q2 and Q3 had steeper slopes and higher peak progesterone levels than cycles in Q1 and Q4 but, when normalized, had a similar shape. Results were used to define differences between quantiles that can be used as templates. Compared to Q1, LLP's in Q2 had a shape that is 1.068 times steeper and 1.048 times higher. Luteal-like phases in Q3 were 1.053 times steeper and 1.018 times higher. Luteal-like phases in Q4 were 0.977 times steeper and 0.973 times higher than LLP's in Q1. This article adds to our knowledge about the variability of progesterone profiles and their shape differences. The profile clustering procedure described in this article can be used as a means to classify progesterone profiles without recourse to an a priori set of rules, which arbitrarily segment the natural variability in these profiles. Using data-derived profile shapes may allow a more accurate assessment of the effects of, e.g., nutritional management or breeding system on progesterone profiles.

For energy feeding systems for ruminants to evolve towards a nutrient-based system, dietary energy supply has to be determined in terms of amount and nature of nutrients. The objective of this study was to establish response equations of the net hepatic flux and net splanchnic release of acetate, butyrate and beta-hydroxybutyrate to changes in diet and animal profiles. A meta-analysis was applied on published data compiled from the FLuxes of nutrients across Organs and tissues in Ruminant Animals database, which pools the results from international publications on net splanchnic nutrient fluxes measured in multi-catheterized ruminants. Prediction variables were identified from current knowledge on digestion, hepatic and other tissue metabolism. Subsequently, physiological and other, more integrative, predictors were obtained. Models were established for intakes up to 41 g dry matter per kg BW per day and diets containing up to 70 g concentrate per 100 g dry matter. Models predicted the net hepatic fluxes or net splanchnic release of each nutrient from its net portal appearance and the animal profile. Corrections were applied to account for incomplete hepatic recovery of the blood flow marker, para-aminohippuric acid. Changes in net splanchnic release (mmol/kg BW per hour) could then be predicted by combining the previously published net portal appearance models and the present net hepatic fluxes models. The net splanchnic release of acetate and butyrate were thus predicted from the intake of ruminally fermented organic matter (RfOM) and the nature of RfOM (acetate: residual mean square error (RMSE) = 0.18; butyrate: RMSE = 0.01). The net splanchnic release of beta-hydroxybutyrate was predicted from RfOM intake and the energy balance of the animals (RMSE = 0.035), or from the net portal appearance of butyrate and the energy balance of the animals (RMSE = 0.050). Models obtained were independent of ruminant species, and presented low interfering factors on the residuals, least square means or individual slopes. The model equations highlighted the importance of considering the physiological state of animals when predicting splanchnic metabolism. This work showed that it is possible to use simple predictors to accurately predict the amount and nature of ketogenic nutrients released towards peripheral tissues in both sheep and cattle at different physiological status. These results provide deeper insight into biological processes and will contribute to the development of improved tools for dietary formulation.

Recent proposals have updated systems of feed units of energy, protein, structure and rumen fill. However, consistency between the largely independently built units has not been checked, even though energy, structure and fill values of feedstuffs are linked throughout feeding in ruminants. The target of this work was to build a mechanistic model to ensure consistency across equations related to intake, mastication, fill value of feeds and diets, digestion of organic matter and transit times. The first step was to build a large database of published results on digestion, intake and chewing in cattle. This database was studied by meta-analyses to calculate more than 100 empirical regressions; about half of these equations were used to build the new French systems of net energy and metabolisable protein. The other equations were mainly related to mastication (per day or kg dry matter intake, DMI), as well as the sizes and adjacent flows of 12 compartments inside the rumen: neutral detergent fibre (NDF), degradable (D) or not (U), in large or small particles; protein, soluble (S) or D and U; starch, S or D; water; volatile fatty acids; and microbes. The rumen digesta load (RL) was closely linked with the forage NDF intake, but in this dataset, there was no maximum RL, so it does not seem to limit DMI. Close relationships were observed between long-forage intake (NDFI, % of liveweight) and dietary forage NDF (%DM) and between mastication (min/day) and dietary index of mastication (min/kg DMI). Unlike RL, each of these two relationships exhibited obvious asymptotic maximum values for the duration of mastication per day (~1000 min/day) and for the level intake of NDF from long forages (1.35% of liveweight). In these two equations, the slope represented the level of DMI. The two equations were added to the other structural equations. It is therefore possible to use a mechanistic model with 12 compartments to consistently integrate aspects dealing with fill, chewing, DMI and energy value of rations in cattle.

The major challenge of animal phenotyping is to acquire systemic knowledge of farm animal robustness. This will allow responding to a double objective in terms of exploiting the variability of animal aptitudes: selection for major objectives in order to improve efficiency and robustness of genotypes and precision farming that exploits individual variability of animals for an improved resilience on the herd level or to improve the management for a specific genotype. Two major challenges of our knowledge exist which are interdependent and common to all animal breeding, and which must be met in order to improve animal robustness. The first is understanding andpredicting the compromise amongst vital functions. By this we mean understanding how priority changes are made under specific limited resources. The second is understanding and exploiting the temporal aspects of robustness during an animal’s life in order to allow the animal to maintain its level of performance under different environments without compromising its reproduction, health or welfare. Attaining these objectives requires accomplishing the following: i) defining complex phenotypes through the integration of data obtained on the molecular or tissular scales of an animal or of one of its functions; ii) developing high-throughput technologiesto characterize at a lower cost and the most precisely as possible the largest number of animals; iii) developing important data bases and reliable methods and computing tools necessary for the treatment of information for modeling and predictive biology., L’enjeu majeur du phénotypage animal est d’acquérir une connaissance systémique de la robustesse des animaux d’élevage pour répondre au mieux à une double finalité en termes d’exploitation de la variabilité des aptitudes animales : une sélection éclairée vers des objectifs majeurs pour améliorer l’efficience et la robustesse des génotypes, et un élevage de précision exploitant la variabilité individuelle des animaux pour gagner en résilience à l’échelle du troupeau, ou pour améliorer la conduite à génotype donné. Deux défis majeurs de connaissance, interdépendants et communs à toutes les filières animales, sont à relever pour améliorer la robustesse des animaux : i) comprendre et prédire les compromis entre fonctions vitales, c’est-à-dire les changements de priorités dans l’allocation de ressources limitées ; ii) comprendre et exploiter les aspects temporels de la robustesse au cours de la vie de l’animal pour lui permettre de maintenir son niveau de production dans une large gamme d’environnements sans pour autant compromettre sa reproduction, sa santé et son bien-être. Atteindre ces objectifs nécessite de lever les verrous techniques suivants : i) définir des phénotypes complexes à partir de l’intégration de données obtenues à différentes échelles moléculaires, tissulaires, de l’animal ou d’une de ses fonctions ; ii) mettre en oeuvre des technologies à haut débit pour caractériser à moindre coût et le plus précisément possible le plus grand nombre d’animaux ; iii) développer d’importantes bases de données, des méthodes et outils informatiques performants, nécessaires aux traitements des informations à des fins de modélisation et de biologie prédictive.

L’évolution des systèmes d’unités d’alimentation des ruminants vers des systèmes de lois de réponses multiples nécessite d’actualiser le calcul des apports nutritifs à l’animal, afin de le rendre plus précis sur les unités agrégées (UF et PDI) et plus évolutif (prédiction des nutriments absorbés). Cette actualisation est basée sur la construction et l’interprétation par méta-analyse, de larges bases de données sur la digestion et la nutrition des ruminants. Les différentes équations utilisées pour le calcul des apports UF et PDI ont ainsi été mises à jour, ce qui permet en particulier : i) de prévoir le transit des particules et des liquides en fonction du niveau d’ingestion et de la proportion de concentré, et de prendre en compte les variations de transit dans le calcul des dégradations des protéines et de l’amidon dans le rumen, estimées à partir des données issues de mesures in situ; ii) de prendre en compte les effets des principaux facteurs d’interactions digestives (niveau d’ingestion, proportion de concentré, balance protéique du rumen) sur la digestibilité de la matière organique, les pertes d’énergie sous forme de méthane et sous forme d’urine ; iii) de calculer plus précisément l’énergie disponible dans le rumen et son efficacité d’utilisation pour la synthèse de protéines microbiennes. Ce modèle rénové de la digestion chez les ruminants conduit à considérer que les valeurs UF et PDI des aliments varient en fonction de la ration dans laquelle ils sont incorporés, les valeurs des tables étant indicatives. Il est ainsi possible de prédire plus précisément les apports sur une plus large gamme de régimes, et en particulier de mieux traiter des interactions entre les apports en énergie et en protéines., The evolution of feeding systems for ruminants towards multiple animal response systems requires the updating of the calculation of nutrient supply to the animals to make it more accurate on aggregated units (UF and PDI) and to allow prediction of absorbed nutrients. This updating is based on the building and interpretation through meta-analysis of large databases of digestion and nutrition of ruminants. Different equations involved in the calculation of UF and PDI have been updated, allowing the following: 1 ) predicting the passage rate of particles and liquid depending on the level of intake and the proportion of concentrate, and taking it into account in order to calculate the ruminal digestion of protein and starch from in situ data; 2) taking into account the effects of the main factors of digestive interactions (level of intake, proportion of concentrate, rumen protein balance) on organic matter digestibility, energy losses in methane and urine; 3) calculating in a more accurate way the energy available in the rumen and the efficiency of its use for microbial protein synthesis. This renewed model leads to the conclusion that UF and PDI values of feed vary depending on the diet, the feed table values being only indicative. It is thus possible to predict the nutrient supply on a wider range of diets more accurately and in particular to better address energy x protein interactions.

11th International Symposium on Ruminant Physiology (ISRP), 6–9 September, 2009 Clermont-Ferrand (France) Proceedings (International Symposium on the Physiology of Digestion and Metabolism in the Ruminant); Carbohydrates are the main source of energy in ruminants. Their site, extent and kinetics of digestion highly impact the amount and profile of nutrients delivered to peripheral tissues, and the responses of the animal, i.e. ingestion, efficiency of production, N and methane excretion, quality of products and welfare. Development of multi-objective feed evaluation systems thus requires a more integrated quantitative knowledge on carbohydrate digestion and yield of terminal products, as well as on their metabolism by splanchnic tissues. The objective of this paper is to review (1) quantitative knowledge on fibre, starch and sugar digestion, volatile fatty acids (VFA) and glucose production and splanchnic metabolism and (ii) modelling approaches which aim at representing and/or predicting nutrient fluxes in the digestive tract, portal and hepatic drainage. It shows that the representation of carbohydrate digestion and VFA yield is relatively homogeneous among models. Although published quantitative comparisons of these models are scarce, they stress that prediction of fibre digestion and VFA yield and composition is still not good enough for use in feed formulation, whereas prediction of microbial N yield and ruminal starch digestion seems to be more satisfactory. Uncertainties on VFA stoichiometric coefficients and absorption rates may partly explain the poor predictions of VFA. Hardly any mechanistic models have been developed on portal-drained viscera (PDV) metabolism whereas a few exist for liver metabolism. A qualitative comparison of these models is presented. Most are focused on dairy cows and their level of aggregation in the representation of nutrient fluxes and metabolism highly differs depending on their objectives. Quantitative comparison of these models is still lacking. However, recent advances have been achieved with the empirical prediction of VFA and glucose production and fluxes through PDV and liver based on the current INRA feed evaluation system. These advances are presented. They illustrate that empirical prediction of ruminal VFA and intestinal glucose production can be evaluated by comparison with measured net portal net fluxes. We also illustrate the potential synergy between empirical and mechanistic modelling. It is concluded that concomitant empirical and mechanistic approach may likely help to progress towards development of multi-objective feed evaluation systems based on nutrient fluxes.

The combined effects of lucerne (Medicago sativa L.) extract supplementation and ammonia treatment of rice straw (Oryza sativa, variety Thaibonnet) on the ruminal digestion of cell wall components were investigated in six continuous culture systems using a randomised complete block design. Data were fitted to second-order polynomial models. Untreated rice straw had higher contents of ash-free cell wall residues (CWR; 763 v. 687 g/kg dry matter (DM)) and non-cellulosic sugars (191 v. 166 g/kg DM) than treated rice straw. Ammoniation preferentially removed xylose, which resulted in a lower xylose-to-arabinose ratio (5.1 v. 5.8). In absence of lucerne supplementation and ammoniation, degradability coefficients were 0.54, 0.46, 0.58, 0.54, 0.42 and 0.60 for cellulose-glucose, xylose, arabinose, galactose, mannose and uronic acids, respectively. Both factors had significant effects on the microbial degradation of structural polysaccharides. With lucerne extract at an optimal level, ammonia treatment increased ash-free cell wall degradation by more than 10%. The degradability coefficients were increased by ammoniation without any significant interaction with lucerne extract, except for glucose, whose degradability was mostly influenced by lucerne extract in a curvilinear way. The comparison of regression coefficients in cell wall and CWR models suggested that ammoniation improved the degradabilities of xylose, galactose and mannose by partly solubilising the corresponding hemicelluloses and by improving the susceptibility of the remaining fraction to microbial attack, whereas it increased the degradability of arabinose only by favouring microbial attack.

The production of volatile fatty acids (VFA) is still poorly predicted by rumen models despite its major contribution to ruminant energy supply. We aimed at building an empirical model describing production rates (PR) of individual VFA in the rumen. Three distinct bibliographic data bases were used. They gathered published in vivo measurements on ruminal VFA-PR, digestive fluxes and molar proportion of VFA in the rumen, and net portal fluxes of VFA. These data bases cover a wide range of intake levels and dietary composition. A meta-analysis was performed, using within-experiment models. Models were derived, describing total VFA-PR and individual VFA molar proportions in the rumen from variation in dry matter intake level and dietary composition. The VFA-PR was explained by intake of rumen fermentable organic matter (RfOM), calculated from detailed knowledge of the chemical composition of diets according to INRA Feed Tables, with the effect of other factors remaining non significant. The VFA molar proportions were explained by dry matter intake, digestible organic matter, digestible neutral detergent fibre and rumen starch digestibility. Based on these results, we proposed an empirical model to predict individual VFA-PR from simple predictors which represent the nature of digested substrates, as well as their site and extent of digestion. The model can be used under a wide range of levels of feed intake and dietary composition. Evaluation of predicted VFA-PR indicated close relationships with observed net portal appearance of VFA. The relationships were consistent with quantitative knowledge on VFA metabolism by the digestive tissues.

In ruminant nutrition, metabolisable energy (ME) is an aggregated unit, whereas feed evaluation systems for ruminants have to evolve toward nutrient-based systems. Empirical models of net portal appearance (NPA) of nutrients have been established from dietary characteristics. This work firstly extended the meta-analysis to energy expenditure of portal drained viscera (PDV-EE) in relation with changes in dry matter intake (DMI) and dietary composition and, second, assessed the quantitative consistency between dietary ME defined according to INRA and the amount of absorbed energy (AE), calculated as the sum of the predicted NPA and PDV-EE. Publications presenting data on sheep and cattle were selected from the FLORA database and diets were described according to INRA tables of feedstuffs. For animals consuming on average 20.2 ± 7.2 g DM/d/kg BW and containing 25.6 ± 31.7% concentrate, the best adjustment of the PDV-EE prediction model was obtained with DMI. Adding parameters of diets composition did not improve the model. Concerning the assessment of the coherence between ME and AE, since urinary energy contributes to AE but not heat of fermentation, AE was compared by GLM to Y = ME + urinary energy – fermentation heat. Factors potentially responsible for this difference were investigated by testing the influence of DMI and dietary characteristics. For animals eating 18.1 ± 9.7 g DM/d/kg BW of diets containing 34 ± 34% concentrates, the amount of AE was linearly related to Y independently of the unit expression. On a DM basis, no difference was obtained between the two variables. When expressed in MJ/d/ kg BW the difference ranged from −0.03 to 0.13 and was negatively correlated to DMI and to the concentrate proportion in the diet, suggesting that differences could be partly due to digestive interactions. This work demonstrates that a quantitative coherence exists between models of ME and AE, and that energy systems can evolve towards systems having a more physiological basis.

Decrease of N intake (NI) with the aim of increasing efficiency of N utilization and decreasing the negative environmental effects of animal production requires assessment of the forms in which N is absorbed. A meta-analysis was conducted on 68 publications (90 experiments and 215 treatments) to study the effect of NI on net portal appearance (NPA) of nitrogenous nutrients [amino acids (AA), ammonia, and urea] in ruminants. In addition, the effect of several dietary energy and protein factors on this relationship was investigated. These factors were: dry matter intake; proportion of concentrate; diet concentrations and intakes of nonfiber carbohydrates and neutral detergent fiber (NDF); diet concentrations of total digestible nutrients (TDN) and crude protein; rumen-degradable protein and rumen-undegradable protein, as percent dry matter or percent crude protein. The effect of species and physiological stage was also investigated. Within-experiment analyses revealed that the NPA of AA-N and ammonia-N increased linearly, whereas the NPA of urea-N decreased (or recycling of urea-N increased) linearly with NI. Besides NI, many significant covariates could be introduced in each NPA model. However, only TDN and neutral detergent fiber intake (NDFi) were common significant covariates of NI in each NPA model. In this database, ruminants converted 60% of incremental NI into NPA of AA-N with no species effect on that slope. However, at similar NI, TDN, and NDFi, sheep absorbed more AA-N than did cattle and dairy cows. On the other hand, species tended to affect the slope of the relationship between NPA of ammonia-N and NI, which varied from 0.19 for the sheep to 0.38 for dairy cows. On average, the equivalent of 11% of incremental NI was recycled as urea-N to the gut through the portal-drained viscera, which excludes salivary contribution, and no species difference was detected. Overall, at similar TDN and NDFi, sheep and cattle increased their NPA of AA-N relative to NI increment by a similar magnitude. The higher absorption of AA-N observed in sheep compared with cattle, at similar NI, TDN, and NDFi, might result from lower losses of AA through portal-drained viscera metabolism.

The effects of permanent or random exposure to stray voltage on a water trough were evaluated in growing–finishing Romane male lambs between the age of 13 and 19 weeks. Ninety lambs were assigned during two 6-week experimental periods to one of three treatments, with 30 animals in each treatment (15 per experimental period). The treatments were permanent exposure to voltage (PERM, 3.5 V) on the water trough, random exposure to voltage (RAND, 3.5 V, 34 h/week) and no voltage exposure for the control group (CONT). No effects of voltage exposure were observed on production parameters: growth, average daily gain and water intake. The stress physiology seemed to be slightly modified with a lower plasma cortisol concentration at slaughter in PERM lambs compared to CONT lambs (P < 0.05) and a higher adrenal medulla weight in PERM lambs compared to CONT lambs (P < 0.05). However, no differences were observed between treatments in heart-rate, basal plasma cortisol concentration and tyrosine hydroxylase and phenylethanolamine-Nmethyl transferase activities. Carcass yield, temperature and the pH of the M. longissimus dorsi were not modified by voltage exposure during rearing. In the good carcass conformation class (R class in EUROP grading scheme), there were fewer fat carcasses (grade 4 of 5) in the PERM and RAND compared to the CONT group (P < 0.05). In conclusion, no major effects of voltage exposure were observed in male lambs on production, carcass parameters and stress physiology. Stray voltage could be considered as a mild stressor in growing–finishing lambs.

This review gives an update of available data on the effect of nutrition on milk fatty acid (FA) composition in dairy cow and goat. It starts by an overview of the main digestive and metabolic pathways involved in the milk fat secretion processes. Used data are originated from literature and from two databases developed by INRA. Altogether, analyses confirm the wide plasticity of milk FA profiles in dairy ruminant, peculiarly for the saturated FA (10–18 carbon), oleic (9c-18:1) and vaccenic (11t-18:1) acids, and other trans isomers of 18:1 and 18:2. Comparing effects of diets with large amount of concentrate and/or maize silage with grass-based diets (grazed or correctly preserved) shows a decrease in saturated FA, at the expense of 9c- and 11t-18:1, and to a lesser extent, 18:3 n-3 and 9c11t-CLA. Dietary supplements of plant oil or oilseeds have similar effects, sometimes more marked, but they increase simultaneously other trans isomers of 18:1 and 18:2, especially when added to maize silage and/or high-concentrate diets. Oils or seeds rich in 18:2 n-6 (sunflower, soybean…) increase particularly 10t-18:1, and 10t12c-, 8t10c-, 7t9c- and 9t11c-CLA, whereas those rich in 18:3 n-3 (linseed) enhance notably 13t/14t-18:1, and 9ct12-, 9ct13- and 11tc15-18:2. When comparing goats’ with cows’ results, it seems that they are less sensitive to the 11t- to 10t- ruminal shift, thus explaining the good stability and the large magnitude of the responses of their milk 11t-18:1 and 9c11t-CLA content to lipid supplementation of high-concentrate diets. Furthermore, goats seem to respond better than cows to 18:3 n-3 rich supplements. Ongoing studies are evaluating the use of milk FA profile as biomarkers for the authentication of milk according to production zone and type of feeding, or to predict methane enteric emissions by dairy cows receiving oilseeds. Future studies should also integrate the expected progress of knowledge on the potential effects on consumers of the different major and minor FA found in standard or modified dairy products, as well as putative secondary effects of feeding conditions on sensory quality of dairy products and ruminant health.; La composition en acides gras (AG) du lait est une composante importante de sa qualité nutritionnelle pour l’homme, qualité qui est fortement modulable à court terme par l’alimentation des ruminants. Après un rappel des principaux phénomènes digestifs et métaboliques qui concourent à la synthèse des matières grasses du lait, cette revue présente les données récentes sur les effets de l’alimentation sur la composition en AG du lait de vache, ainsi que sur quelques particularités de la chèvre. Ces données confirment et précisent la forte plasticité du profil en AG du lait de ruminant, notamment pour les AG saturés (C10–C18), l’acide oléique (9c-18:1), l’acide vaccénique (11t-18:1) et d’autres isomères trans du 18:1 et du 18:2. Par rapport à des rations riches en concentrés et/ou en ensilage de maïs, les rations à base d’herbe (pâturée ou correctement conservée) diminuent les AG saturés au profit des 9c- et 11t-18:1, et dans une moindre mesure du 18:3 n-3 et du 9c11t-CLA. Les suppléments d’huiles ou graines oléagineuses ont des effets similaires, parfois plus marqués, mais ils augmentent aussi d’autres isomères trans du 18:1 et du 18:2, notamment lorsqu’ils sont ajoutés à des rations riches en concentrés et/ou en ensilage de maïs. Les huiles et graines riches en 18:2 n-6 (tournesol, soja…) accroissent en particulier le 10t-18:1 et les 10t12c-, 8t10c-, 7t9c- et 9t11c-CLA, alors que celles qui sont riches en 18:3 n-3 (lin) accroissent, notamment, le 13t/14t-18:1 et les 9c12t-, 9c13t- et 11t15c-18:2. Des différences marquées existent entre les réponses de la lipogenèse mammaire et du profil des AG du lait de la vache et de la chèvre laitière. La chèvre est moins sujette que la vache à la déviation du métabolisme ruminal du 11t- vers le 10t-18:1, ce qui explique probablement la stabilité et l’ampleur des réponses à la supplémentation lipidique des 11t-18:1 et 9c11t-CLA de son lait, y compris avec des rations riches en concentrés. En outre, l’effet inhibiteur du 10t12c-CLA sur la lipogenèse mammaire est beaucoup moins marqué chez cette espèce que chez la vache. Enfin, la chèvre semble mieux répondre que la vache à la supplémentation du régime en 18:3 n-3. Des études en cours évaluent une possible utilisation du profil en AG du lait pour tracer l’origine des produits, ou pour prédire la réduction de la production de méthane d’origine digestive chez la vache laitière recevant des graines oléagineuses. Les études futures devront aussi intégrer l’avancée attendue des connaissances en nutrition humaine sur les effets potentiels des différents AG majeurs et mineurs des produits laitiers standards ou modifiés, ainsi que les éventuels effets secondaires des pratiques alimentaires sur la qualité sensorielle des produits et la santé des animaux.

This work set out to establish the response equations for hepatic blood flows in sheep and the contribution of hepatic arterial flow to hepatic venous blood flow due to changes in intake levels at constant diet composition. The FLORA (FLuxes across Organs and tissues in Ruminant Animals) database was used, and meta-analysis performed. The meta-analysis involved selection of published papers, identification of studies, description and coding of the selected dataset and statistical analysis using a covariance model. Meta-analyses were carried out using a within-study approach. To ensure absence of bias, the analysis incorporated interfering variables and factors studied in between-study comparisons. Variables concerned diet composition; qualitative factors concerned the physiological state of the animals and the methods used to measure blood flow. The results obtained showed that hepatic blood flows were positively related to intake in sheep. The magnitude of the response (as indicated by the slope) varied with the level of intake and the blood vessel (portal, hepatic venous or arterial). Nine linear relationships were established for the portal, hepatic venous and arterial blood flows as a function of dry matter intake (DMI) with below- and above-maintenance levels considered separately. Data obtained at below- and above-maintenance levels were considered together and four quadratic relationships were established for hepatic blood flows as a function of DMI. These relationships expressed a strong effect of intake on hepatic blood flows. The contribution of hepatic arterial to hepatic venous blood flow averaged 18.2%, with a wide variability. It did not vary significantly with level of intake. Although in between-study comparisons the arterial/venous blood flow was positively influenced by the organic matter digestibility of the diet, the relationships we obtained were robust. They can be used in models of net hepatic nutrient fluxes to predict variations and absolute values of hepatic blood flows from variations and absolute values of DMI.

A detailed knowledge of chewing behaviour is important in understanding the factors that can affect digestive function in high producing ruminants. The aim of this study was to compare chewing behaviour either measured with a portable automatic system (APEC) which records jaw movements or obtained by scan sampling video analysis in 12 individually housed dairy goats. One hour and daily time-intervals were used for the analysis. APEC and video showed better agreement for I h than for daily time-intervals but a very high individual variability was observed for all the parameters measured. Daily duration of rumination seemed to be assessed better by the APEC than by the video because it is sometimes difficult to determine if the goats are resting or ruminating on the video. Daily duration of intake was however assessed better by the video because the automatic recorder interpreted all oral behaviours as intake. However, total chewing time is directly related to the amount of saliva produced which an important factor in ruminant nutrition. The APEC allows continuous recording of activity independently of the animal's position in the pen or in a group or at pasture where animals cannot be filmed, however the device can be easily damaged by the animals. Video cannot be damaged by the animals but it can be difficult to determine the animal's activity if the animal is lying-down or not facing the camera, or in a dark part of its pen. Both systems were relevant to measure chewing behaviour of stall housed dairy goats, and to study the daily time-budget of feeding behaviour but results have to be interpreted carefully. (C) 2009 Elsevier B.V. All rights reserved.

La composition en Acides Gras (AG) du lait et de la viande est une composante importante de leur qualité nutritionnelle pour l’Homme, qui est fortement modulable à court terme par l’alimentation des animaux d’élevage. Cet article résume les grands axes de recherche et les principaux résultats obtenus à l’INRA sur ce sujet au cours des 10 dernières années. Chez le porc, le poulet et le lapin, les recherches ont principalement porté sur le transfert des AG polyinsaturés, en particulier le 18:3 n-3 du lin, de l’aliment à la viande (muscle et tissu adipeux) et ses conséquences sur la qualité des carcasses. Chez les ruminants, on a étudié les effets des principaux types de fourrages (herbe pâturée, foins, ensilages d’herbe ou de maïs) et de concentrés (céréales, oléagineux), et leurs interactions, sur les AG du lait et de la viande bovine, notamment les AG saturés et insaturés (oléique, trans, conjugués et 18:3 n-3). Des différences marquées existent entre les réponses des AG du lait de la vache et de la chèvre laitière. Des études en cours évaluent les éventuels effets secondaires des pratiques alimentaires sur la qualité sensorielle des produits et la santé des animaux, et sur une possible utilisation du profil en AG pour tracer l’origine des produits. Les études futures devront aussi intégrer l’avancée attendue des connaissances en nutrition humaine sur les effets potentiels des différents AG majeurs et mineurs des produits animaux standards ou enrichis., Milk and meat fatty acid (FA) composition is an important component of their nutritional quality for human consumption. This composition can be markedly and rapidly modulated by feeding factors in farm animals. This paper summarizes the main research directions and results obtained by INRA on these topics during the last 10 years. In the pig, chicken and rabbit, research was focused mainly on the transfer of polyunsaturated FA, particularly 18:3 n-3 from linseed, from feed to meat (muscle and adipose tissue) and its consequences on carcass quality. In ruminants, studies were on the effects of the main forage types (pasture, hays, grass or maize silages) and concentrates (cereals, oilseeds), and their interactions, on bovine milk and meat FA, particularly saturated and unsaturated FA (oleic, trans, conjugated and 18:3 n-3). Marked differences were observed between the dairy cow and goat milk FA responses. Current studies are being done to assess putative secondary effects of feeding practices on product sensory quality and animal health, as well as the potential of using FA profile to trace animal product origin. Future studies should also take into account expected progress in human nutrition knowledge on putative effects of major and minor FA, which are present in standard or enriched animal products.

Apres un rappel des principaux phenomenes digestifs et metaboliques qui concourent a la synthese des matieres grasses du lait, cette revue fait le point des donnees recentes sur les effets de l'alimentation sur la composition en acides gras (AG) du lait de vache. Ces donnees sont issues de la bibliographie ainsi que d'une base de donnees developpee a l'INRA. Elles confirment la forte plasticite du profil en AG du lait de vache, notamment pour les AG satures (C10 a C18), l'acide oleique (9c-18:1), l'acide vaccenique (11t-18:1) et d'autres isomeres trans du 18:1 et du 18:2. Par rapport a des rations riches en concentres et/ou en ensilage de mais, les rations a base d'herbe (pâturee ou correctement conservee) diminuent les AG satures au profit des 9c- et 11t-18:1, et dans une moindre mesure du 18:3 n-3 et du 9c11t-CLA. Les supplements d'huiles ou graines oleagineuses ont des effets similaires, parfois plus marques, mais ils augmentent aussi d'autres isomeres trans du 18:1 et du 18:2, notamment lorsqu'ils sont ajoutes a des rations riches en concentres et/ou en ensilage de mais. Les huiles et graines riches en 18:2 n-6 (tournesol, soja,...) accroissent en particulier le 10t-18:1 et les 10t12c-, 8t10c-, 7t9c- et 9t11c-CLA, alors que celles qui sont riches en 18:3 n-3 (lin) accroissent notamment le 13t/14t-18:1 et les 9c12t-, 9c13t- et 11t15c-18:2.

In a research programme aiming at characterising the energy value of ruminant diets by the net portal energy fluxes in ruminants, the present work focused on the relationships between intakes of both dry matter (DMI) and digestible organic matter (DOMI) and the portal blood flow and the net portal fluxes of oxygen (O-2), volatile fatty acids, beta-hydroxybutyrate, glucose and lactate in sheep and growing cattle. Response equations were generated, expressed as a function of liveweight (LW), using meta-analysis on published that were compiled into the FLORA (FLux of nutrients through the Organs and tissues of Ruminant Animals) database and that investigated changes in intake at constant dietary metabolisable energy (ME) contents within experimental groups.. The experimental groups in the selected data set were predominantly adult sheep (n = 17), with nine experimental groups on growing cattle and only one experimental group on lactating dairy cows. Intake range was larger in sheep than in growing cattle (0-35 g/d/kg LW versus 0-23 g/d/kg LW, respectively), and the types of diets fed also differed, with sheep predominantly being fed forage-rich diets (60% of experimental groups). Blood flow and portal 02 consumption showed curvilinear relationships; however the response equations generated for nutrients were best described by linear relationships. With the exception of glucose net portal flux, 70-95% of variation was explained by the models generated. The magnitude of the response equations was largely different between species, and this may also include differences in diets and/or physiological state between the datasets. Both DMI and DOMI were good predictors of the influence of a change in intake level on net portal fluxes of energy metabolites. Subsequent work needs to focus on the influence of diet composition (i.e., dietary ME content). (C) 2007 Elsevier B.V. All rights reserved.

International audience; Nutritional strategies, including feed management measures, are promising methods for CH4 and overall GHG reduction. Evidence from literature is reviewed in this article in relation to the effects of forage quality (digestible organic matter, DOM) and forage type (grasses vs. legumes, and maize). The major determinants of forage quality are botanical composition and phenological stage, i.e., at advanced growth stages of plants, the fibre content increases while DOM decreases. Methane yield (g/kg DMI) decreases with increased digestibility of forages in both dairy cattle and sheep, and also CH4 intensity (g/kg milk) decreases with increased digestibility of forages for dairy cattle. Using forage legumes in ruminant feeding systems can reduce overall GHG emissions due to decreased N fertilizer use and related emissions. Recommended dietary mitigation measures are often related to a reduction in N excretion such as better matching of dietary protein to animal needs, shifting N excretion from urine to faeces (by tannin inclusion at low levels) and reducing the amount of excreted fermentable organic matter. Methane decreases with increasing intake of forage legumes rich in tannins and there is a shift of N partition from urinary N towards faecal N. Reduced CH4 emissions from ruminants fed on forage-based diets will decrease the carbon footprint of livestock and agriculture and improve the efficiency of productive ruminants in both developing and developed countries. Likewise, estimations of net CH4 output should account for enteric CH4 emissions and soil carbon (C) sequestration of land used for feed production (i.e., grasslands and croplands).

International audience; Feed efficiency (FE) is a complex phenotype made up of multiple traits for which there is potential for substantial genotype by environment interaction (G & times; E). The objective of this study is to evaluate the extent of G & times; E for FE traits with a simulation approach. We used a mechanistic model of the dairy cow that simulates trajectories of phenotypes throughout lifetime, depending on trajectories of resource acquisition and allocation, driven by 4 genetic scaling parameters, and depending on the nutritional environment (quantity and quality of feed resources). The cow model, calibrated for a grass-based farming system and seasonal calving, was combined with a genetic module. This simulated genetic variation in the 4 genetic scaling parameters related to resource acquisition and allocation, based on a simple balanced pedigree structure (200 paternal half-sib groups each of 100 daughters). The population of 20,000 cows generated was simulated in 4 nutritional environment scenarios, representing a gradient of feeding constraints. In each scenario, 6 traits derived from the model outputs were analyzed to obtain population genetic parameters. Genetic correlations between second-lactation production and FE were positive and high in all scenarios and increased as the nutritional environment became more constraining. A measure of lifetime FE was positively correlated with second -lactation production under a less constrained environment, but these correlations decreased as the environment became more constraining. The genetic correlation between body reserves at second calving, and lifetime FE was positive and low in the least constraining scenario and increased as the environment became more constraining. In addition to genetic parameters, we looked at the distributions of acquisition and allocation pa rameters among the best performing cows for lactation and life FE, in the 2 most contrasted scenarios. The 4 subpopulations of best cows had acquisition and allocation strategies different from the whole population. In conclusion, this simulation study identifies the potential underlying biological basis for important G & times; E in FE traits. This highlights the importance of having a balanced breeding goal when undertaking selection that should also be based on phenotypes relevant to the environment.

A multidisciplinary research programme was developed to get a scientific expertise for the quality assessment of products obtained from cloned livestock. Thirty-seven bovine Holstein female clones of five different genotypes and their products were analysed in comparison with 38 control animals obtained by conventional artificial insemination and raised under the same conditions at the same experimental farm. Animal evaluation included over 150 criteria and more than 10 000 measurements to check the physiological status and health over a 3-year period. All the parameters studied were in the normal range for age and breed, but some significant differences were detected between clone and control groups in terms of delayed onset of puberty in clones, higher neutrophil counts in haematology or lower biochemical plasma concentrations of gamma glutamyl transferase. Milk and meat analyses were conformable to expected values. We, however, found some differences in fatty acid (FA) composition of milk and muscle suggesting a possible deviation in lipid metabolism as assessed by higher delta-9 desaturase activity indexes in both milk and muscles from clones compared with controls. Repeated muscle biopsies in the semitendinosus muscle of the same animals demonstrated a higher oxidative activity in muscle of young clones (8 months of age) compared with controls, suggesting a delayed muscle maturation in clones. Nutritional evaluation of milk and meat using the rat feeding trials did not show any difference between clone and control products for food intake, growth rate, body composition of the rats, nor for possible allergenicity. Possible reactivation of bovine endogenous retroviruses (BERVs) was analysed and compared between normal and cloned cattle. As expected, these BERV sequences are not transcribed and no RNA was detected in the blood of clones, donor animals or controls; therefore, it may be assumed that the sanitary risk associated with BERV sequences is not higher in cattle derived from somatic nuclear transfer than in cattle born from conventional reproduction. Our results confirm that the quality and safety of products (milk and meat) from adult and clinically healthy cloned cattle is globally similar to normal animals. However, from a strictly biological point of view, the slightly delayed maturation we observed in the muscle of clones together with some marginal differences identified in FA composition of both muscle and milk, point to the need for more refined analysis to totally exclude any risks from the consumption of those products.

Scientific expertise was developed during a 3-year study to evaluate a large number of bovine female clones (n = 37; from 4 to 36 months of age) and their products through a multidisciplinary approach and compare them to non-cloned breed, age and sex-matched contemporary control animals (n = 38) maintained under the same conditions at the same experimental farm of INRA. In clone and control groups, most parameters measured for health and development of the animals as well as evaluation of milk and meat products were within the normal range for the breed. The strict comparison between cloned animals and controls allowed us to detect slight significant differences between the two groups. Cloned heifers reached puberty significantly later (+62 days) and at higher body weight (+56 kg) than controls. There were slight differences in antigen-specific induced proliferation of lymphocytes after vaccination with ovalbumin before 10 months of age, but responses were normal responses in older animals. There were differences in the fatty acid (FA) composition of milk and muscle arising from two families of clones, suggesting a possible deviation in lipid metabolism as assessed by higher Delta-9 desaturase activity indices in both milk and muscle from clones compared to controls. Nutritional evaluation of milk and meat using the rat model did not reveal any difference between products derived from clones versus controls.

The specific composition of milk fat of the small ruminant makes it especially valuable nutritionally and for consumer health. The aspects of the nature and composition of the animal's diet that have an influence on the fat content of its milk and its composition has been reviewed. The various studies have been, fundamentally, aimed at optimising the production of milk. Particular account is taken of the industrial use made today of sheep and goat milk, as it comprises the raw material for the manufacture of different dairy products, especially of cheese. The physical form in which the forage proportion of the diet is presented and/or the forage/concentrate ratio in the diet are aspects for special consideration in determining the fat content of the milk. In the case of the goat, this seems to depend particularly on the energy intake achieved in each case. Dietary supplementation with different types of fat, unprotected or protected against the ruminal metabolism, is considered a crucial strategy. Not only does this alleviate certain limitations in the diet provided from the production system that is practiced. The semi-extensive production system of the small ruminant is most common in arid and semi-arid zones. It enables the producer to control the content and composition of milk fat. The scope of effect depends on the nature and amount of the dietary fat and on the existence of interactions between the basal diet (forage type, starchy concentrate, etc.) and dietary lipids. Besides the production system, the involvement of other factors, such as stage of lactation and the productive capability of the animal, should, also be taken into account. From the available information, it can be concluded that the nature of fat content of sheep and goat milk, in comparison with cow milk, presents advantages for consumer health. This composition makes them directly or in the form of dairy products, foods of choice for segments of the human population with specific requirements. Different nutritional strategies are designed with the aim of optimising the composition of these milks with respect to their fat content and composition. Experimental results in goat nutrition, show that lipid supplementation does not change net energy intake and milk yield, but increases in most cases, milk fat content and allows much less saturated fatty acids, much more oleic and/or vaccenic and rumenic acids, more linolenic acid, and other trans fatty acids. The responses of goats are different from cow's responses in many aspects of milk production and mammary lipid metabolism, and the same is probably true for sheep. The variation in milk fat composition is very large, with important interactions between forage, concentrates and oils, in almost all major and minor fatty acids. Supplementation of corn silage or high-concentrate diets with vegetable oils increases sharply trans fatty acids other than rumenic and vaccenic acids. The aim of future research is to improve the effects of different feeding strategies, in order to increase more selectively fatty acids that are of interest for human nutrition and health without increasing the less desired fatty acids, and without decreasing the sensory quality of dairy products.

High-producing ruminants need high-concentrate diets to satisfy their nutrient requirements and meet performance objectives. However, such diets induce sub-acute ruminal acidosis (SARA), which will adversely affect dry matter intake and lead to lower production performance. This work develops a novel modelling approach to quantify the capacity of dairy goats to adapt to a high-concentrate diet challenge at the individual level. The animal model used was dairy goats (from Saanen or Alpine breed), and rumen pH was used as the indicator of the response. A three-step modelling procedure was developed to quantify daily scores and produce a single global index for animals’ adaptive response to the new diet. The first step summarizes the post-prandial kinetics of rumen acid status using three synthetic variables. In the second step, the effect of time on the response of goats is described, in the short and long terms. In the last step, a metric based on phase trajectories ranks goats for their resilience capacity. This modelling procedure showed a high variability among the goats in response to the new diet, highlighting in particular their daily and general strategies to buffer the effect of the diet change. Two main categories of adaptive strategies were observed: (i) acid status increased, but the goats tried to minimize its variations, and (ii) acid status oscillated between increases and decreases. Such phenotyping, alongside other behavioral, digestive, and metabolic measures, can help to determine biomarkers of goats’ capacity to adapt to diets of higher nutritive value and to increase production performance without compromising their health status. Quantifying the capacity of goats to buffer the effect of highly fermentable diets helps to better adapt feed to animals in precision livestock farming. This procedure is generic and can be adapted to any indicator of animal health and performance. In particular, several indicators can be combined to assess multi-performance, which is of major interest in the context of selection for robust animals.

A dairy cow’s lifetime resilience and her ability to recalve gain importance on dairy farms, as they affect all aspects of the sustainability of the dairy industry. Many modern farms today have milk meters and activity sensors that accurately measure yield and activity at a high frequency for monitoring purposes. We hypothesized that these same sensors can be used for precision phenotyping of complex traits such as lifetime resilience or productive life span. The objective of this study was to investigate whether lifetime resilience and productive life span of dairy cows can be predicted using sensor-derived proxies of first-parity sensor data. We used a data set from 27 Belgian and British dairy farms with an automated milking system containing at least 5 yr of successive measurements. All of these farms had milk meter data available, and 13 of these farms were also equipped with activity sensors. This subset was used to investigate the added value of activity meters to improve the model’s prediction accuracy. To rank cows for lifetime resilience, a score was attributed to each cow based on her number of calvings, her 305-d milk yield, her age at first calving, her calving intervals, and the DIM at the moment of culling, taking her entire lifetime into account. Next, this lifetime resilience score was used to rank the cows within their herd, resulting in a lifetime resilience ranking. Based on this ranking, cows were classified in a low (last third), moderate (middle third), or high (first third) resilience category within farm. In total, 45 biologically sound sensor features were defined from the time series data, including measures of variability, lactation curve shape, milk yield perturbations, activity spikes indicating estrous events, and activity dynamics representing health events (e.g., drops in daily activity). These features, calculated on first-lactation data, were used to predict the lifetime resilience rank and, thus, to predict the classification within the herd (low, moderate, or high). Using a specific linear regression model progressively including features stepwise selected at farm level (cutoff P-value of 0.2), classification performances were between 35.9 and 70.0% (46.7 ± 8.0, mean ± SD) for milk yield features only, and between 46.7 and 84.0% (55.5 ± 12.1, mean ± SD) for lactation and activity features together. This is, respectively, 13.7 and 22.2% higher than what random classification would give. Moreover, using these individual farm models, only 3.5 and 2.3% of cows were classified high when they were actually low, or vice versa, whereas respectively 91.8 and 94.1% of wrongly classified animals were predicted in an adjacent category. The sensor features retained in the prediction equation of the individual farms differed across farms, which demonstrates the variability in culling and management strategies across farms and within farms over time. This lack of a common model structure across farms suggests the need to consider local (and evidence-based) culling management rules when developing decision support tools for dairy farms. With this study we showed the potential of precision phenotyping of complex traits based on biologically meaningful features derived from readily available sensor data. We conclude that first-lactation milk and activity sensor data have the potential to predict cows’ lifetime resilience rankings within farms but that consistency between farms is currently lacking. ispartof: JOURNAL OF DAIRY SCIENCE vol:103 issue:8 pages:7155-7171 ispartof: location:United States status: published

International audience; The objective of this review is to describe how dietary glucogenic precursors could stimulate ovarian activity in post-partum dairy cows and improve reproductive success. Although the nutrient requirements for the early resumption of ovarian cycles, and for follicle and embryo development are quantitatively small, reproductive success is deteriorated by post-partum negative energy balance. Since very little glucose is absorbed directly from the digestive tract of ruminants one of the targets for nutritional manipulation could be the glucogenic potential of the diet. This could be achieved by giving rumen-resistant starch or mono-propylene glycol. Both these adaptations increase glucose, insulin and insulin-like growth factor-1 plasma concentrations and stimulate ovarian follicle growth.

BackgroundResilience can be defined as the capacity of animals to cope with short-term perturbations in their environment and return rapidly to their pre-challenge status. In a perspective of precision livestock farming, it is key to create informative indicators for general resilience and therefore incorporate this concept in breeding goals. In the modern swine breeding industry, new technologies such as automatic feeding system are increasingly common and can be used to capture useful data to monitor animal phenotypes such as feed efficiency. This automatic and longitudinal data collection integrated with mathematical modelling has a great potential to determine accurate resilience indicators, for example by measuring the deviation from expected production levels over a period of time.ResultsThis work aimed at developing a modelling approach for facilitating the quantification of pig resilience during the fattening period, from approximately 34 kg to 105 kg of body weight. A total of 13 093 pigs, belonging to three different genetic lines were monitored (Pietrain, Pietrain NN and Duroc) since 2015, and body weight measures registered (approximately 11.1 million of weightings) with automatic feeding systems. We used the Gompertz model and linear interpolation on body weight data to quantify individual deviations from expected production, thereby creating a resilience index (ABC). The estimated heritabilities of ABC are low but not zero from 0.03 to 0.04 (± 0.01) depending on the breed.ConclusionsOur model-based approach can be useful to quantify pig responses to perturbations using exclusively the growth curves and should contribute to the genetic improvement of resilience of fattening pigs by providing a resilience index.

International audience; Cattle are the world’s largest consumers of plant biomass. Digestion of this biomass by ruminants generates high methane emissions that affect global warming. In the last decades, the specialisation of cattle breeds and livestock systems towards either milk or meat has increased the milk production of dairy cows and the carcass weight of slaughtered cattle. At the animal level and farm level, improved animal performance decreases feed use and greenhouse gas emissions per kg of milk or carcass weight, mainly through a dilution of maintenance requirements per unit of product. However, increasing milk production per dairy cow reduces meat production from the dairy sector, as there are fewer dairy cows. More beef cows are then required if one wants to maintain the same meat production level at country scale. Meat produced from the dairy herd has a better feed efficiency (less feed required per kg of carcass weight) and emits less methane than the meat produced by the cow-calf systems, because the intake of lactating cows is largely for milk production and marginally for meat, whereas the intake of beef cows is entirely for meat. Consequently, the benefits of breed specialisation assessed at the animal level and farm level may not hold when milk and meat productions are considered together. Any change in the milk-to-meat production ratio at the country level affects the numbers of beef cows required to produce meat. At the world scale, a broad diversity in feed efficiencies of cattle products is observed. Where both productions of milk per dairy cow and meat per head of cattle are low, the relationship between milk and meat efficiencies is positive. Improved management practices (feed, reproduction, health) increase the feed efficiency of both products. Where milk and meat productivities are high, a trade-off between feed efficiencies of milk and meat can be observed in relation to the share of meat produced in either the dairy sector or the beef sector. As a result, in developing countries, increasing productivities of both dairy and beef cattle herds will increase milk and meat efficiencies, reduce land use and decrease methane emissions. In other regions of the world, increasing meat production from young animals produced by dairy cows is probably a better option to reduce feed use for an unchanged milk-to-meat production ratio.

International audience; In the long term, resilient animals are able to maintain their normal biological processes when confronted with environmental perturbations, reducing their risk of being culled. Therefore, longevity can be proposed as an indicator of long-term resilience. Decisions to remove a given dairy cow from the herd are mainly related to low milk production (i.e., voluntary culling) or to reasons other than production (i.e., involuntary culling). The aptitude of animals to delay any culling is defined as true longevity (TL), whereas functional longevity (FL) is the ability to avoid involuntary culling. The aim of the study was to investigate the influence of production, reproduction, morphology, and health traits on TL and FL, to identify risk factors for culling. Data included 278,217 lactations from 122,461 Holstein Friesian cows reared in 640 herds. The length of productive life, calculated as the time between first calving and culling, or censoring, was used as the measure of longevity. Survival analysis was performed using proportional hazards models assuming a piecewise Weibull distribution of the baseline hazard function, with or without adjustment for milk production to evaluate FL and TL. Insemination status, calving ease, mastitis, somatic cell count, displaced abomasum, and udder depth had significant relationships with TL and FL. Differences in estimates of relative risk between TL and FL showed that milk production often influenced culling decisions: farmers are more prone to cull animals with low production even when they had good other characteristics. The culling risk factors identified in the present study can be used to study resilience in dairy cattle and to improve genetic evaluations of functional or total longevity.

International audience; In ruminants, feeding behaviour variables are parameters involved in feed efficiency that show variation among individuals. This study aimed to evaluate during the first two production cycles in ruminants the repeatability of feed intake pattern, which is an important aspect of feeding behaviour. Thirty-five dairy goats from Alpine or Saanen breeds were housed in individual pens at four periods (end of first gestation, middle of first and second lactations and middle of second gestation which is also the end of first lactation) and fed a total mixed ration (TMR) ad libitum. Individual cumulative dry matter intake (DMI) was automatically measured every 2 min during the last 4 days of each period. Feed intake pattern was characterized by several measures related to the quantity of feed eaten or to the rate of intake during the 15 h following the afternoon feed delivery. Two main methods were used: modelling cumulative DMI evolution by an exponential model or by a segmentation-clustering method. The goat ability to sort against dietary fibre was also evaluated. There was a very good repeatability of the aggregate measures between days within a period for a given goat estimated by the day effect within breed and goat, tested on the residual variance (P > 0.95). The correlations between periods were the highest between the second and either the third or fourth periods. With increasing age, goats sorted more against the fibrous part of the TMR and increased their initial rate of intake. Alpine goats ate more slowly than Saanen goats but ate during a longer duration. Principal component analysis (PCA) was performed on all the aggregate measures of feed intake patterns. The factor score plots generated by the PCA highlighted the opposition between the different measures of feed intake patterns and the sorting behaviour. The projection of the animals on the scoring plots showed a breed effect and that there was a continuum for the feed intake pattern of goats. In conclusion, this study showed that the feed intake pattern was highly repeatable for an animal in a given period and between periods. This means that phenotyping goats in a younger age might be of interest, either to select them on feeding behaviour and choose preferentially the slow eaters or to adapt the quantity offered and restrict feed delivery to the fast eaters in order to increase feed efficiency and welfare by limiting the occurrence of acidosis, for example.

Heat and water stress are rampant challenges in animal production as a result of climate change. Sixteen lactating goats of the Alpine and Saanen breeds were subjected to water restriction for 16 h on two consecutive days followed by rehydration, along with naturally rising temperature that culminated by the end of an experimental period of one week. Data were collected from the animals on four separate days representing their status under control conditions when temperature was still within their comfort zone, on the second day of the water restriction treatment, 24 h after rehydration and finally on the day when ambient temperature was above the normal average. Animals showed an increase in rectal temperature with time as ambient temperature increased. The heat stress was reflected by the decrease in pCO2 and HCO3 concentrations consistent with the onset of panting, while milk production was maintained but the concentration of major milk components decreased. Water stress, on the other hand, led to hemoconcentration with increased osmolality, Na+, urea, glucose and albumin concentrations and an increase in milk lactose. The changes in blood parameters were reversed 24 h after rehydration but were slightly lower than normal, indicating the need for more than 24 h to re-adjust water balance in these lactating animals. Finally, the latency between first feeding and the following drinking event after return from milking showed that the shortest latency was under the water restriction treatment indicating thirst, in addition, a trend for shorter latency before drinking over the experimental period was observed in response to the rise in temperature. This experiment showed the capacity of these highly productive breeds to withstand mild water and heat stress. However, subtle changes in physiology and milk composition warrant further research into their effect on milk quality and animal welfare.

International audience; The emergence of precision livestock farming (PLF), and with it the increasing capacity to record behaviour and production parameters automatically, makes it possible to monitor feeding behaviour of individual animals over time. This study reports some of the first quantifications of feeding behaviour variables for individual dairy goats whilst group housed. The feeding behaviour of 16 non-lactating goats (NoLact), 32 goats in the middle of their first lactation (MidLact) and 24 goats (including 20 goats from MidLact) at the end of their first lactation (EndLact) from two breeds (Alpine and Saanen) was recorded. The goats were housed in groups of four individuals and each had access to one weight-monitored feeding station where they received a total mixed ration ad libitum twice a day. Following a habituation period, feeding behaviour was measured for nine days (NoLact), 10 days (MidLact) and six days (EndLact). After merging feeder visits into meals (≥8-min inter-meal-interval), six variables were calculated for each individual: meal frequency (number of daily meals, NDM), size (feed intake per meal, FIM) and duration (DUM), as well as feeding rate (FR), daily feeding time (DFT) and daily feed intake (DFI). The inter- and intra-individual variabilities of these six variables were investigated and the intra-class correlation coefficients calculated. Particular attention was paid to the first meal following feed distribution and to the stability of individual feeding behaviour between lactation stages. Effects of body weight, breed, and stage of lactation on the six feeding behaviour variables were analysed using a general linear model. Our study confirmed previous findings from individually housed dairy goats that feeding behaviour variables are relatively stable within an individual but show greater variability between individuals. Although there were differences between the lactation stages studied, the feeding behaviour of individuals tested in two lactation stages (mid- and end of lactation) remained relatively stable. The feeding behaviour variables were normally distributed, and smaller meals were linked with a higher meal frequency and vice versa. The first meal after a feed distribution, in addition to being positively correlated with the DFI, appeared to be stable in size for individual goats with a larger variability among individuals and could potentially be used to characterize the individuals, whereas time-related feeding behaviour variables appeared less useful for this purpose. Characterization of individuals based on their feeding behaviour can potentially be used to refine individual management for disease prevention or as a trait used in genetic selection.

International audience; The objectives of this study were to quantify the effects on nitrogen metabolism and dairy performance of substituting soybean meal for proteaginous seeds or replacing raw with treated proteaginous seeds in dairy cow diets. This study was focused on three proteaginous seeds: faba bean, lupin and pea. Two databases were created, which gathered information on in vivo and in situ results, respectively. These were then used to analyze nitrogen ruminal degradability, nitrogen intestinal true digestibility, ruminal parameters, nitrogen partitioning and milk production and composition. A total of 32 and 36 articles were analyzed from the in vivo and in situ databases, respectively. Statistical analyses assessed the effects of substitution for each measured or calculated variable; t-tests were applied to compare the difference between the tested feed (i.e. raw or treated proteaginous seed, or treated proteaginous seed) and the control feed (i.e. soybean meal, or raw proteaginous seed, respectively). The proteaginous seeds contained less crude protein than soybean meal (on average -199 g/kg DM, P < 0.001). The use of raw proteaginous seeds instead of soybean meal led to higher nitrogen ruminal degradability (on average +16 g/100 g, P < 0.005), but treatment of these seeds led to a decrease in nitrogen ruminal degradability (-13 g/100 g on average) compared to raw seeds. Replacing soybean meal by raw faba bean, lupin or pea in the iso-crude protein diets of dairy cows led to an increase in NH3 in ruminal fluid (+20 mg/L, P < 0.040) and tended to decrease milk protein content. However, when those seeds were treated and their values compared to those of raw seeds, nitrogen in milk tended to increase, but milk fat content was decreased. This quantitative review has allowed some general trends to be highlighted, despite a limited amount of available data for some variables (in particular for pea). Moreover, treatment of seeds are variable (use of different processes, pressures and temperatures) and feeding practices of dairy cows are diverse (e.g. maize silage or grass silage as main forage, forage:concentrate ratio ranging from 84:16 to 40:60), which can dilute the effects of the addition of proteaginous seeds (added at up to 30 g/100 g of dietary dry matter) to the dairy cow diets.

International audience; Residual feed intake (RFI) is an increasingly used trait to analyze feed efficiency in livestock, and in some sectors such as dairy cattle, it is one of the most frequently used traits. Although the principle for calculating RFI is always the same (i.e., using the residual of a regression of intake on performance predictors), a wide range of models are found in the literature, with different predictors, different ways of considering intake, and more recently, different statistical approaches. Consequently, the results are not easily comparable from one study to another as they reflect different biological variabilities, and the relationship between the residual (i.e., RFI) and the underlying true efficiency also differs. In this review, the components of the RFI equation are explored with respect to the underlying biological processes. The aim of this decomposition is to provide a better understanding of which of the processes in this complex trait contribute significantly to the individual variability in efficiency. The intricacies associated with the residual term, as well as the energy sinks and the intake term, are broken down and discussed. Based on this exploration as well as on some recent literature, new forms of the RFI equation are proposed to better separate the efficiency terms from errors and inaccuracies. The review also considers the time period of measurement of RFI. This is a key consideration for the accuracy of the RFI estimation itself, and also for understanding the relationships between short-term efficiency, animal resilience, and long-term efficiency. As livestock production moves toward sustainable efficiency, these considerations are increasingly important to bring to bear in RFI estimations.

BackgroundUnderstanding the effects of environment on livestock provides valuable information on how farm animals express their production potential, and on their welfare. Ruminants often face perturbations that affect their performance. Evaluating the effect of these perturbations on animal performance could provide metrics to quantify how animals cope with their environment and therefore, better manage them. In dairy systems, milk production records can be used to evaluate perturbations because (1) they are easily accessible, (2) the overall dynamics throughout the lactation process have been widely described, and (3) perturbations often occur and cause milk loss. In this study, a lactation curve model with explicit representation of perturbations was developed.MethodsThe perturbed lactation model is made of two components. The first one describes a theoretical unperturbed lactation curve (unperturbed lactation model), and the second describes deviations from the unperturbed lactation model. The model was fitted on 319 complete lactation data from 181 individual dairy goats allowing for the characterization of individual perturbations in terms of their starting date, intensity, and shape.ResultsThe fitting procedure detected a total of 2,354 perturbations with an average of 7.40 perturbations per lactation. Loss of production due to perturbations varied between 2% and 19%. Results show that the number of perturbations is not the major factor explaining the loss in milk yield over the lactation, suggesting that there are different types of animal response to challenging factors.ConclusionsBy incorporating explicit representation of perturbations, the model allowed the characterization of potential milk production, deviations induced by perturbations (loss of milk), and thereby comparison between animals. These indicators are likely to be useful to move from raw data to decision support tools in dairy production.

International audience; Background: the red macroalgae Asparagopsis taxiformis is a potent natural supplement for reducing methane production from cattle.A. taxiformis contains several anti-methanogenic compounds including bromoform that inhibits directly methanogenesis. The positive and adverse effects of A. taxiformis on the rumen microbiota are dose-dependent and operate in a dynamic fashion. It is therefore key to characterize the dynamic response of the rumen microbial fermentation for identifying optimal conditions on the use of A. taxiformis as a dietary supplement for methane mitigation. Accordingly, the objective of this work was to model the effect of A. taxiformis supplementation on the rumen microbial fermentation under in vitro conditions. We adapted a published mathematical model of rumen microbial fermentation to account for A. taxiformis supplementation. We modelled the impact of A. taxiformis on the fermentation and methane production by two mechanisms, namely (i) direct inhibition of the growth rate of methanogens by bromoform and (ii) hydrogen control on sugars utilization and on the flux allocation towards volatile fatty acids production. We calibrated our model using a multi-experiment estimation approach that integrated experimental data with six macroalgae supplementation levels from a published in vitro study assessing the dose-response impact of A. taxiformis on rumen fermentation. Results: our model captured satisfactorily the effect of A. taxiformis on the dynamic profile of rumen microbial fermentation for the six supplementation levels of A. taxiformis with an average determination coefficient of 0.88 and an average coefficient of variation of the root mean squared error of 15.2% for acetate, butyrate, propionate, ammonia and methane. Conclusions: our results indicated the potential of our model as prediction tool for assessing the impact of additives such as seaweeds on the rumen microbial fermentation and methane production in vitro. Additional dynamic data on hydrogen and bromoform are required to validate our model structure and look for model structure improvements. We expect this model development can be useful to help the design of sustainable nutritional strategies promoting healthy rumen function and low environmental footprint.

International audience; Wild and farmed animals are key elements of natural and managed ecosystems that deliver functions such as pollination, pest control and nutrient cycling within the broader roles they play in contributing to biodiversity and to every category of ecosystem services. They are subjected to global changes with a profound impact on the natural range and viability of animal species, the emergence and spatial distribution of pathogens, land use, ecosystem services and farming sustainability. We urgently need to improve our understanding of how animal populations can respond adaptively and therefore sustainably to these new selective pressures. In this context, we explored the common points between animal production science and animal ecology to identify promising avenues of synergy between communities through the transfer of concepts and/or methodologies, focusing on seven concepts that link both disciplines. Animal adaptability, animal diversity (both within and between species), selection, animal management, animal monitoring, agroecology and viability risks were identified as key concepts that should serve the cross-fertilization of both fields to improve ecosystem resilience and farming sustainability. The need for breaking down interdisciplinary barriers is illustrated by two representative examples: i) the circulation and reassortment of pathogens between wild and domestic animals and ii) the role of animals in nutrient cycles, i.e. recycling nitrogen, phosphorus and carbon through, for example, contribution to soil fertility and carbon sequestration. Our synthesis identifies the need for knowledge integration techniques supported by programmes and policy tools that reverse the fragmentation of animal research toward a unification into a single Animal Research Kinship, OneARK, which sets new objectives for future science policy. At the interface of animal ecology and animal production science, our article promotes an effective application of the agroecology concept to animals and the use of functional diversity to increase resilience in both wild and farmed systems. It also promotes the use of novel monitoring technologies to quantify animal welfare and factors affecting fitness. These measures are needed to evaluate viability risk, predict and potentially increase animal adaptability and improve the management of wild and farmed systems, thereby responding to an increasing demand of society for the development of a sustainable management of systems.

International audience; Animal production systems (APSs) have long been transformed through intensification, specialization and geographical concentration, leading them to become major anthropogenic drivers of pollution, climate change, and biodiversity loss. Agroecology, organic farming and sustainable intensification have been proposed as alternative models to invert those trends. Diversity is highly valued in agroecology and organic farming, in which it is assumed not only to increase farm performance but also to strengthen farm resilience. Here, we examine how the diversity of system components and interactions among these components can increase productivity, resource-use efficiency and farm resilience in aquaculture, rabbit, monogastric and ruminant systems. In doing so, we reveal that the same processes can occur in very different systems. For instance, the higher performance of multi-species aquaculture or ruminant grazing systems results from (i) the complementary feeding habits of animal species that exploit resources from different ecological niches more efficiently; and (ii) facilitation or competitive exclusion, which results in a species increasing or decreasing resource availability for another species. The benefits of diversity are observed not only in relatively extensive systems but also in intensive indoor systems. For instance, genetic diversity is associated with herd and social immunity in rabbit production, while trade-offs between life functions play a major role in dairy herd performance. In the last section, we discuss how management options nested in system component diversity and their interactions can enhance system resilience. Strategic and tactical management of APS diversity can promote farm buffering and adaptive capabilities, respectively, via the abovementioned processes. By stabilizing the farm financial situation and facilitating access to short supply channels, transformative changes, such as a diversification of the animal species bred or development of a processing enterprise on farm, expand options for increasing the resilience of APSs to market price fluctuations and climatic shocks. However, the need for new technical skills and sometimes high initial investments can act as strong inhibitors of farm diversification. We conclude with a description of some of the research or action that is needed for these principles to be more widely implemented in commercial farms.

International audience; Histidine is an essential amino acid (EAA) in mammals, fish, and poultry. We aim to give an overview of the metabolism and physiological effects of histidine in humans and different animal species through a systematic review following the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In humans, dietary histidine may be associated with factors that improve metabolic syndrome and has an effect on ion absorption. In rats, histidine supplementation increases food intake. It also provides neuroprotection at an early stage and could protect against epileptic seizures. In chickens, histidine is particularly important as a limiting factor for carnosine synthesis, which has strong anti-oxidant effects. In fish, dietary histidine may be one of the most important factors in preventing cataracts. In ruminants, histidine is a limiting factor for milk protein synthesis and could be the first limiting AA for growth. In excess, histidine supplementation can be responsible for eating and memory disorders in humans and can induce growth retardation and metabolic dysfunction in most species. To conclude, the requirements for histidine, like for other EAA, have been derived from growth and AA composition in tissues and also have specific metabolic roles depending on species and dietary levels.

Modern ruminant production systems have to meet the sustainability challenge. Achieving this aim will require changes in all part of the production systems, including feeding. Today, diet formulation for ruminants has to supply a diet balanced in all nutrients and free from harmful substances, to meet production objectives and thereby generate, using ecological practices, animal products that are safe for humans. This requirement implies extending the ruminant diet evaluation concept to cover new dimensions other than feed value, such as impact on animal health, emitted wastes and the quality of ruminant products. Here, we present the concepts of a multi-criteria evaluation of ruminant diets, and the method we used to define the criteria and their indicators. Twenty-four criteria adapted to dairy ruminants and ranked in four categories of impact (animal production and health, wastes emitted and quality of the animal products) are proposed. Then, we discussed the type of indicators required to assess these criteria and identified the works to be carried out on indicators development. Finally, we discuss the steps remaining for the construction of the multi-objective diet evaluation tool., L’alimentation des ruminants ne peut plus être raisonnée uniquement comme la satisfaction des besoins nutritionnels, mais doit tenir compte d’autres réponses en lien avec la santé animale, l’environnement ou la qualité des produits. Cela nécessite d’identifier de nouveaux critères pour évaluer les rations sur ces réponses et pose la question des indicateurs à mesurer à l’échelle des aliments pour évaluer ces critères.

Summary Motivated by an application to the clustering of milking kinetics of dairy goats, we propose a novel approach for functional data clustering. This issue is of growing interest in precision livestock farming, which is largely based on the development of data acquisition automation and on the development of interpretative tools to capitalize on high throughput raw data and to generate benchmarks for phenotypic traits. The method that we propose in the paper falls in this context. Our methodology relies on a piecewise linear estimation of curves based on a novel regularized change-point-estimation method and on the k-means algorithm applied to a vector of coefficients summarizing the curves. The statistical performance of our method is assessed through numerical experiments and is thoroughly compared with existing experiments. Our technique is finally applied to milk emission kinetics data with the aim of a better characterization of interanimal variability and towards a better understanding of the lactation process.

International audience; A new method to estimate residual feed intake (RFI) was recently developed based on a multi-trait random regression model. This approach deals with the dynamic nature of the lactation, which is in contrast with classical linear approaches. However, an issue remains: pooling data across sites and years, which implies dealing with different (and sometimes unknown) diet energy contents. This will be needed for genomic evaluation. In this study, we tested whether merging two individual datasets into a larger one can lead to valuable results in comparison to analysing them on their own with the multi-trait random regression model. Three datasets were defined: the first one with 1 063 lactations, the second one with 205 lactations from a second farm and the third one combining the data of the two first datasets (1 268 lactations). The model was applied to the three datasets to estimate individual RFI as well as variance components and correlations between the four traits included in the model (fat and protein corrected milk production, BW, feed intake and body condition score), and a fixed month-year-farm effect was used to define the contemporary group. The variance components and correlations between animal effects of the four traits were very similar irrespective of the dataset used with correlations higher than 0.94 between the different datasets. The RFI estimates for animals from their single farm only were also very similar (r > 0.95) to the ones computed from the merged dataset (Dataset 3). This highlights that the contemporary group correction in the model adequately accounts for differences between the two feeding environments. The dynamic model can thus be used to produce RFI estimates from merged datasets, at least when animals are raised in similar systems. In addition, the 205 lactations from the second farm were also used to estimate the RFI with a linear approach. The RFI estimated by the two approaches were similar when the considered period was rather short (r = 0.85 for RFI for the first 84 days of lactation) but this correlation weakened as the period length grew (r = 0.77 for RFI for the first 168 days of lactation). This weakening in correlations between the two approaches when increasing the used time-period reflects that only the dynamic model permits the regression coefficients to evolve in line with the physiological changes through the lactation. The results of this study enlarge the possibilities of use for the dynamic RFI model.

The control of nutrient partitioning is complex and affected by many factors, among them physiological state and production potential. Therefore, the current model aims to provide for dairy cows a dynamic framework to predict a consistent set of reference performance patterns (milk component yields, body composition change, dry-matter intake) sensitive to physiological status across a range of milk production potentials (within and between breeds). Flows and partition of net energy toward maintenance, growth, gestation, body reserves and milk components are described in the model. The structure of the model is characterized by two sub-models, a regulating sub-model of homeorhetic control which sets dynamic partitioning rules along the lactation, and an operating sub-model that translates this into animal performance. The regulating sub-model describes lactation as the result of three driving forces: (1) use of previously acquired resources through mobilization, (2) acquisition of new resources with a priority of partition towards milk and (3) subsequent use of resources towards body reserves gain. The dynamics of these three driving forces were adjusted separately for fat (milk and body), protein (milk and body) and lactose (milk). Milk yield is predicted from lactose and protein yields with an empirical equation developed from literature data. The model predicts desired dry-matter intake as an outcome of net energy requirements for a given dietary net energy content. The parameters controlling milk component yields and body composition changes were calibrated using two data sets in which the diet was the same for all animals. Weekly data from Holstein dairy cows was used to calibrate the model within-breed across milk production potentials. A second data set was used to evaluate the model and to calibrate it for breed differences (Holstein, Danish Red and Jersey) on the mobilization/reconstitution of body composition and on the yield of individual milk components. These calibrations showed that the model framework was able to adequately simulate milk yield, milk component yields, body composition changes and dry-matter intake throughout lactation for primiparous and multiparous cows differing in their production level.

Pigs selected for high performance may be more at risk of developing diseases. This study aimed to assess the health and performance of two pig lines divergently selected for residual feed intake (RFI) (low RFI (LRFI) v. high RFI (HRFI)) and housed in two contrasted hygiene conditions (poor v. good) using a 2×2 factorial design (n=40/group). The challenge period (Period 1), started on week zero (W0) when 12-week-old pigs were transferred to good or poor housing conditions. At week 6 (W6), half of the pigs in each group were slaughtered. During a recovery period (Period 2) from W6 to W13 to W14, the remaining pigs (n=20/group) were transferred in good hygiene conditions before being slaughtered. Blood was collected every three (Period 1) or 2 weeks (Period 2) to assess blood indicators of immune and inflammatory responses. Pulmonary lesions at slaughter and performance traits were evaluated. At W6, pneumonia prevalence was greater for pigs housed in poor than in good conditions (51% v. 8%, respectively, P

The aim of this Research Reflection is to describe the basic rumen function of goats and its modification in response to environmental factors, as well as to discuss similarities and differences when compared to other ruminants. In so doing we shall reveal the adaptive capacity of goats to harsh environments. The basic rumen function in goats is similar to other species of ruminants, as stressed by the opportunity to apply the updates of feeding systems for ruminants to goats. The rumen epithelium acts as a protective barrier between the rumen and the host, but it can be damaged by toxic compounds or acidosis. The rumen also plays an important role in water balance, both for dehydration and rehydration. Recent studies show that the microbiota exhibits a high fractional stability due to functional redundancy and resilience, but this needs more investigation. The microbial community structure differs between goats and cows, which explains the difference in sensitivity to milk fat depression following intake of high lipid diets. Goats also differ from other ruminants by their enhanced ability to feed-sort, but as with cows they can suffer from acidosis. Nevertheless, goats can be considered to be very resistant to environmental factors such as water stress, salt stress or heat stress, and this is especially so in some endogenous breeds. They also are able to detoxify tannins, polyphenols and other secondary metabolites. Some new trials involving feeding behaviour, microbiota and omics or approaches by meta-analyses or modelling will improve our knowledge of rumen function in goats.