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Resilience, when defined as the capacity of an animal to respond to short term environmental challenges and to return to the pre-challenge status, is a dynamic and complex trait. Resilient animals contribute to reinforce the capacity of the herd to cope with often fluctuating and unpredictable environmental conditions. Quantification of resilience at individual scale enables potentially including this trait into future breeding programs, for the animals with a better capacity to adapt to the changing environment, and therefore potentially reduce the risk for disease and improve the welfare of animals. The ability of modern technologies for longitudinal monitoring of multiple indicators of animal performance, at individual scale is a huge step forward to evaluate resilience of farm animals. Resilience is not directly measurable and the development of interpretive models is necessary to quantify indicators associated with resilience. In addition to the quantification of resilience capacity, another application of models described in this paper are detection of periods of perturbations as perceived by the animal, which helps the farmer to adapt an adequate management strategy to help the animal coping with the perturbation, and in this way reducing the use of medicines, and decrease the level of the pain of the animal. These applications do not require explicit knowledge of the origin of the perturbations, and are developed with case studies in real-time and post-treatment of data. The main objective of this paper is to illustrate with examples, different modeling approaches to get the most out of this new generation of data (i.e., with high frequency recording) to detect and quantify animals’ responses to perturbations. In addition, perspectives on the use of hybrid models for better understanding and predicting animal resilience are presented., The R code of the examples are available at https://quantanimal.github.io/ .

The objective of this study was to present a new methodology to predict selection response on resilience and feed efficiency (FE) in dairy cattle. This approach combines genetic and mechanistic modelling to describe the biological mechanisms underlying these traits. A dairy cattle breeding scheme was simulated considering a non-limiting nutritional environment and two different breeding goals focusing either on milk production or FE. Selection response was predicted within the non-limiting environment but also for a prospective low-input system (LS). Predictions obtained with conventional and mechanistic-based methods were consistent for milk production, body weight and FE within the non-limiting environment. However, genetic trends predicted for fertility were different. Selection response achieved on milk production was much smaller in the LS than in the breeding nucleus due to the increased nutritional constraint. The breeding goal with emphasis on FE enabled a better transfer of genetic gain to the LS environment.

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.

What is a good (useful) mathematical model in animal science? For models constructed for prediction purposes, the question of model adequacy (usefulness) has been traditionally tackled by statistical analysis applied to observed experimental data relative to model-predicted variables. However, little attention has been paid to analytic tools that exploit the mathematical properties of the model equations. For example, in the context of model calibration, before attempting a numerical estimation of the model parameters, we might want to know if we have any chance of success in estimating a unique best value of the model parameters from available measurements. This question of uniqueness is referred to as structural identifiability; a mathematical property that is defined on the sole basis of the model structure within a hypothetical ideal experiment determined by a setting of model inputs (stimuli) and observable variables (measurements). Structural identifiability analysis applied to dynamic models described by ordinary differential equations (ODEs) is a common practice in control engineering and system identification. This analysis demands mathematical technicalities that are beyond the academic background of animal science, which might explain the lack of pervasiveness of identifiability analysis in animal science modelling. To fill this gap, in this paper we address the analysis of structural identifiability from a practitioner perspective by capitalizing on the use of dedicated software tools. Our objectives are (i) to provide a comprehensive explanation of the structural identifiability notion for the community of animal science modelling, (ii) to assess the relevance of identifiability analysis in animal science modelling and (iii) to motivate the community to use identifiability analysis in the modelling practice (when the identifiability question is relevant). We focus our study on ODE models. By using illustrative examples that include published mathematical models describing lactation in cattle, we show how structural identifiability analysis can contribute to advancing mathematical modelling in animal science towards the production of useful models and, moreover, highly informative experiments via optimal experiment design. Rather than attempting to impose a systematic identifiability analysis to the modelling community during model developments, we wish to open a window towards the discovery of a powerful tool for model construction and experiment design.

Until now, the development of precision livestock farming has been largely based on data acquisition automation. The future challenge is to develop interpretative tools to capitalize on high-throughput raw data and to generate benchmarks for phenotypic traits. We developed a dynamic model of female BW that converts BW time series into a vector of biologically meaningful parameters. The model is based on a first submodel that split a female's weight into elementary mass changes related to biological functions: growth (G component), reserves balance (R component), uterine load (U component), and maternal investment (M component). These elementary weight components are linked to the second submodel, which represents the litter developmental stages (oocyte, fetus, neonate, and juvenile) that drive elementary components of dam weight over each reproductive cycle. The so-called GRUM model is based on ordinary differential equations and laws of mass action. Input data are BW measures, age, and litter weight at birth for each parturition. Outputs of the fitting procedure are a vector of parameters related to each GRUM component and indexed by reproductive cycle. We illustrated the potential application of the model with a case study including growth and successive lactations (n = 202) from 45 dairy goats from the Alpine (n = 27) and Saanen (n = 18) breeds. The fitting procedure converged for all individuals, including goats that went through extended lactations. We analyzed the fitted parameters to quantify breed and parity effects over 4 reproductive cycles. We found significant differences between breeds regarding gestation components (fetal growth and reserves balance). We also found significant differences among reproductive cycles for reserves balance. Although these findings are based on a small sample, they illustrate how use the model can be to adapt herd management and implement grouping strategies to account for individual variability.

Recirculating aquaculture systems (RASs) are seen as a promising technology to address the societal and environmental challenges of aquaculture. However, this technology is mainly used in intensive monoculture and little knowledge is available in polyculture approaches. In this study, we studied survival, growth performance, and behaviour of juvenile pikeperch Sander lucioperca reared in RAS either in monoculture or in polyculture (associated with sterlet Acipenser ruthenus or tench Tinca tinca or both sterlet and tench). After 30 days, the survival rate was 100 % for both monoculture and polyculture reared pikeperch. The mean final weights and the biomass gain of pikeperch were significantly lower for pikeperch alone (respectively 75.7 ± 2.7 g and 25.2 %) or with sterlet (respectively 81.0 ± 3.2 g and 38.3 %) than in combination with tench (respectively 85.7 ± 8.1 g and 50.1 %) or with sterlet and tench (respectively 90.3 ± 16.4 g and 51.5 %). Behavioural changes were also detected depending on the fish combination: there are fewer interactions between individuals of pikeperch when reared alone (0.34 ± 0.10) or in polyculture with sterlet (0.40 ± 0.02) in comparison to the other polyculture conditions (with tench [0.80 ± 0.20], or the two other species [0.70 ± 0.15]). The group structure also differed with a lower cohesion and homogeneity of the pikeperch group when they were reared in monoculture compared to polyculture modalities. No aggressive interaction was detected between pikeperch regardless of rearing modalities. In conclusion, due to positive effects on growth parameters and few behavioural changes in pikeperch, this study highlights that RAS polyculture is a relevant alternative production strategy for pikeperch compared to monoculture.

The main effect of climate change on livestock production systems in the Mediterranean area is to induce changes in feed resource availability. In face of constraining and changing environmental conditions, the challenge is to find practical solutions for herd management to minimize farmer's risk. These solutions should be good enough in terms of efficiency (E), to ensure sufficient income, and good enough in terms of resilience (R), to ensure farm sustainability. The objective of the ADAPT-HERD project is to develop simulation tools capable of evaluating the consequences of management strategies at the herd level, under contrasting and varying environments (Egypt, France, Spain and Tunisia). Our approach considers that biological adaptive capacities of small ruminants can be fully integrated within management strategies to improve R and E by: i) using reproduction practices that provide the best match between herd demand and feed supply; ii) managing groups of animals based on their adaptive capacities (targeting interventions) and iii) managing herd demography to adapt animal numbers to future feed resources. The project will implement data acquisition at animal level (fine-grained experiments on adaptive capacities and phenotyping of local breeds) and at production system level (herd management, feed resource, climate and market conditions). Data will be combined and used in computer simulations to explore different climate change scenarios and management strategies and evaluate their effects on herd R and E. Interfacing modelling tools will be a key aspect of the project, fostering knowledge exchange and collaboration, especially with ongoing projects focusing on genetic selection and breeding solutions to improve R&E in small ruminants.

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.

Laying hens are likely to be exposed to a wide range of persistent organic pollutants (POPs), including polychlorinated biphenyls (PCBs). To improve the safety of poultry farming systems in terms of POPs, the present research focused on assessing the impact of physiological characteristics of the hen on the transfer of ingested PCBs to eggs. Modeling was used as a research tool to explore the impact of some physiological characteristics on the transfer of PCBs in the laying hen. The mathematical model simulates the dynamics of the size of the lipid compartments in the animal and the frequency of laying, with the PCB concentrations in egg yolk and adipose tissue being model outputs. Simulations were run to assess effects of animal characteristics on the transfer of PCBs to eggs. Laying rate proportionally influenced the PCB level of eggs and adipose tissue at steady state. Body fat diluted absorbed PCBs in the absence of laying and significantly influenced the decontamination rate of tissues during depuration after an exposure period. Application of the present model to actual exposure cases highlights its value in improving the support of risk management in livestock farming. Environ Toxicol Chem 2015;34:173-183. (c) 2014 SETAC

Methods to assess the consequences of mitigation scenarios on greenhouse gas (GHG) emissions in the cattle sector have been widely developed. A crucial issue of these methods is handling the co-products in emissions allocation. In the cattle sector, milk and beef production systems are closely interconnected because of the meat obtained from dairy culled cows and fattened surplus calves. To fully evaluate a change in the dairy sector, the change in the meat production induced should also be taken into account. With this objective, a national cattle population model was developed, based on three sub-models. The herd functioning sub-model represents the bunch of cattle trajectories generated by reproductive and finishing processes depending on breeds, production levels and finishing types. Assuming a steady state herd, the demographics sub-model represents the number of animals in age groups depending on trajectories length. The production sub-model represents the number of animals slaughtered or exported, the quantity of carcasses, milk and direct GHG emissions. The model was calibrated on French data. The simulation of a reference scenario reflecting the French situation in 2010 confirmed that the model allowed linking cattle trajectories, demographics and production of milk, meat and direct GHG emissions. Scenarios for the dairy sector were simulated to illustrate the potential of such modelling approach to evaluate direct GHG emission at the national level. The results confirmed that the interconnection between dairy and beef sectors can modify the benefit of strategies such as dairy intensification. Further, the results also demonstrated that the compensation between beef and dairy cattle to achieve production objectives lead to a change in the type of carcass produced due to differences between breeds and finishing strategies. Accounting for such differences allows deepening the evaluation of the consequences of GHG mitigation options.

LCA FOR AGRICULTURE; International audience; Purpose In agricultural life cycle assessment (LCA), the allocation method chosen to divide impacts among co-products is an important issue, since it may change conclusions about a product's impacts. We developed a biophysical allocation method to assign upstream environmental burdens and the use of raw materials at farm gate to the livestock co-products at the slaughterhouse based on their metabolic energy requirements. Methods Biophysical allocation is designed to build a relationship between co-products of a meat-production system and their associated net metabolic energy requirements. A metabolic growth model (Gompertz function) was combined with an energy calculation model to estimate metabolic energy requirements for the growth of an animal from birth to slaughter age. Allocation factors were calculated based on the energy required to maintain and produce body tissues (excluding waste), as a function of their chemical (protein and lipid) and physiological properties. This method was applied for an average beef cow and then compared to other allocation methods (e.g., mass, dry matter, protein, and economic). Results and discussion At slaughter age, carcass tissues required the most energy (44 %) due to their high quantity of protein; the gastrointestinal tract and liver required about 28 and 5 %, respectively, of total metabolic energy requirements due to their roles in body metabolism. Biophysical allocation considers the energy cost of building and maintaining the tissues, regardless of their final uses. It reflects physical relationships among co-products as well as other allocation methods do. It also reveals the cause-effect relationship between tissues according to the energy required to maintain physiological functions. Once the growing time until slaughter is set, biophysical allocation factors are not influenced over time, unlike those of economic allocation, which is highly influenced by price variability. Conclusions This study provides a generic and robust biophysical allocation method for estimating environmental burdens of co-products, in accordance with ISO allocation rules. The method can be considered an original contribution to international debates on allocation methods applied to livestock products in LCA. In this paper, it is applied to cattle-related product, but it is generic and the principles can be adapted to any kind of livestock species. It should be considered and discussed by stakeholders in livestock production industries.

Livestock farming systems (LFS) are facing different challenging issues, questioning our viewpoint: ratherthan technical objects to be optimized, LFS are components of the agro-ecosystem (AES). This change in ourviewpoint involves: (1) integrating dynamical, multidimensional and multifunctional aspects; (2) facilitatingcollaboration among disciplines; and (3) developing and re-using computer models. In this work, we developedArchiMod, a metamodel describing structure and functioning of any LFS based on a set of concepts, usedin a protocol of description and a graphical language. The graphical language represents the biophysicalfunctioning of an AES, seen as made of entities (non-living components) and biomasses of living organisms,in a given life stage of a given species, interacting by way of processes within 4 contexts of interactions. Theconcept of context for processes enables progressively organizing the diversity of processes, each of themcorresponding to a defined level of organization, where a given type of processes occurs. The Organism Stagecontext describes input and output flows at the interface of a living organism with its external environment (e.g.feed intake). The Species pathway context describes transitions between organism stages within each specieslife cycle (e.g. non-lactating to lactating for dairy female). The Environmental Interactions context describesbiotic (e.g. pollen dispersal by insects) and abiotic (e.g. N leaching) processes within a physical environment.Finally, the Management operation context describes processes driven by the farmer (e.g. drying-off). Keyprinciples of ArchiMod are: (1) each process is a building block used to describe the whole system dynamics;(2) the system’s functioning is described by defining the contextualization of all elementary processes; (3)it gives a graphical display of the whole set of components to be defined in a mathematical and/or computermodel; (4) it provides the rules to articulate elementary models, independent of the mathematical formalismchose to describe processes. ArchiMod was tested on contrasted LFS with various species (fish, rabbit, goat,sheep, cattle, chicken and pig).

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Finding ways of increasing the efficiency of production systems is a key issue of sustainability. System efficiency is based on long-term individual efficiency, which is highly variable and management driven. To study the effects of management on herd and individual efficiency, we developed the model simulation of goat herd management (SIGHMA). This dynamic model is individual-based and represents the interactions between technical operations (relative to replacement, reproduction and feeding) and individual biological processes (performance dynamics based on energy partitioning and production potential). It simulates outputs at both herd and goat levels over 20 years. A farmer's production project (i.e. a targeted milk production pattern) is represented by configuring the herd into female groups reflecting the organisation of kidding periods. Each group is managed by discrete events applying decision rules to simulate the carrying out of technical operations. The animal level is represented by a set of individual goat models. Each model simulates a goat's biological dynamics through its productive life. It integrates the variability of biological responses driven by genetic scaling parameters (milk production potential and mature body weight), by the regulations of energy partitioning among physiological functions and by responses to diet energy defined by the feeding strategy. A sensitivity analysis shows that herd efficiency was mainly affected by feeding management and to a lesser extent by the herd production potential. The same effects were observed on herd milk feed costs with an even lower difference between production potential and feeding management. SIGHMA was used in a virtual experiment to observe the effects of feeding strategies on herd and individual performances. We found that overfeeding led to a herd production increase and a feed cost decrease. However, this apparent increase in efficiency at the herd level (as feed cost decreased) was related to goats that had directed energy towards body reserves. Such a process is not efficient as far as feed conversion is concerned. The underfeeding strategy led to production decrease and to a slight feed cost decrease. This apparent increase in efficiency was related to goats that had mobilised their reserves to sustain production. Our results highlight the interest of using SIGHMA to study the underlying processes affecting herd performance and analyse the role of individual variability regarding herd response to management. It opens perspectives to further quantify the link between individual variability, herd performance and management and thus further our understanding of livestock farming systems.

A dynamic model of the lactating dairy goat, combining a minimum of mechanistic representations of homeorhetic regulations and a long-term approach, was developed. It describes (i) the main changes in body weight, dry-matter intake, milk production and composition of a dairy goat; (ii) the succession of pregnancy and lactation throughout the productive life; and (iii) the major changes in dynamics induced by the female profile (production potential and body weight at maturity). The model adopts a ‘pull’ approach including a systematic expression of the production potential and not representing any feed limitation. It involves three sub-systems. The reproductive events sub-system drives the dynamics through time with three major events:service, kidding and drying off. It also accounts for the effect of production potential (kg of milk at the peak of lactation) and lactation number (potential reached at the fourth lactation). The regulating sub-system represents the homeorhetic mechanisms during pregnancy and lactation with two sets of theoretical hormones, one representing gestation and the other lactation. The operating sub-system describes the main physiological flows and the energetic requirements linked to these functions through a compartmental structure. Simulations were run in order to test (i) the behaviour of the model at the scale of the productive life for an average profile of female (60 kg at maturity and 4 kg of milk at peak); (ii) the sensitivity of the simulated dynamics(mainly milk production and body reserves) to the production potential and body weight at maturity; (iii) external validation with comparison of model outputs to data from the experimental flock of Grignon and data from the French milk record organization (French organism in charge of animal recording for dairy farmers). The results at the scale of one productive life show the model simulates a relevant set of dynamics. The sensitivity analysis suggests that the model fairly well simulates the link between a female’s ability to produce and mobilise reserves. Finally, external validation confirms the model’s ability to simulate a relevant set of physiological dynamics while pointing out some limits of the model (simulation of milk fat and protein content dynamics, for example). The results illustrate the relevance of the model in simulating biological dynamics and confirm the possibility of including minimum representations of homeorhetic regulations with a simple structure. This simplicity gives an opportunity to integrate this basic element in a herd simulator and test interactions between females’ regulations and management rules.

Background Feed efficiency of farm animals has greatly improved through genetic selection for production. Today, we are faced with the limits of our ability to predict the effect of selection on feed efficiency, partly because the relative importance of the components of this complex phenotype changes across environments. Thus, we developed a dairy cow model that incorporates the dynamic interplay between life functions and evaluated its behaviour with a global sensitivity analysis on two definitions of feed efficiency. A key model feature is to consider feed efficiency as the result of two processes, acquisition and allocation of resources. Acquisition encapsulates intake and digestion, and allocation encapsulates partitioning rules between physiological functions. The model generates genetically-driven trajectories of energy acquisition and allocation, with four genetic-scaling parameters controlling these processes. Model sensitivity to these parameters was assessed with a complete factorial design. Results Acquisition and allocation had contrasting effects on feed efficiency (ratio between energy in milk and energy acquired from the environment). When measured over a lactation period, feed efficiency was increased by increasing allocation to lactation. However, at the lifetime level, efficiency was increased by decreasing allocation to growth and increasing lactation acquisition. While there is a strong linear increase in feed efficiency with more allocation to lactation within a lactation cycle, our results suggest that there is an optimal level of allocation to lactation beyond which increasing allocation to lactation negatively affects lifetime feed efficiency. Conclusions We developed a model to predict lactation and lifetime feed efficiency and show that breaking-down feed conversion into acquisition and allocation, and introducing genetically-driven trajectories that control these mechanisms, permitted quantification of their relative roles on feed efficiency. The life stage at which feed efficiency is evaluated appears to be a key aspect for selection. In this model, body reserves are also a key component in the prediction of lifetime feed efficiency since they integrate the feedback of acquisition and allocation on survival and reproduction. This modelling approach provided new insights into the processes that underpin lifetime feed efficiency in dairy cows. Electronic supplementary material The online version of this article (doi:10.1186/s12711-016-0251-8) contains supplementary material, which is available to authorized users.

Laying hens are likely to be exposed to a wide range of persistent organic pollutants (POPs), including polychlorinated biphenyls (PCBs). To improve the safety of poultry farming systems in terms of POPs, the present research focused on assessing the impact of physiological characteristics of the hen on the transfer of ingested PCBs to eggs. Modeling was used as a research tool to explore the impact of some physiological characteristics on the transfer of PCBs in the laying hen. The mathematical model simulates the dynamics of the size of the lipid compartments in the animal and the frequency of laying, with the PCB concentrations in egg yolk and adipose tissue being model outputs. Simulations were run to assess effects of animal characteristics on the transfer of PCBs to eggs. Laying rate proportionally influenced the PCB level of eggs and adipose tissue at steady state. Body fat diluted absorbed PCBs in the absence of laying and significantly influenced the decontamination rate of tissues during depuration after an exposure period. Application of the present model to actual exposure cases highlights its value in improving the support of risk management in livestock farming.

Séance : Capacité d’adaptation des exploitations d’élevage, synthèseSéance : Capacité d'adaptation des exploitations d'élevage, synthèse; La capacité des élevages de ruminants à faire face aux perturbations de leur environnement est une composante essentielle de leur durabilité. Cette aptitude repose d'une part sur la robustesse des animaux, c'est-à-dire leur capacité à faire face à des perturbations, et d'autre part sur les décisions de conduite de l'éleveur qui orientent et influencent les trajectoires productives des animaux. Si le concept de robustesse a été largement abordé dans la littérature, il reste toutefois peu opérationnel en pratique car sa mesure est complexe et la recherche d'indicateurs quantifiables n'a fait l'objet que de peu de travaux jusqu'à présent. Cette synthèse discute et propose quelques principes importants à considérer afin d'aboutir à une évaluation quantitative de la robustesse à deux niveaux d'organisation : l'animal et le troupeau. Concernant l'animal, nous montrons qu'il est pertinent d'évaluer la robustesse en explorant de façon conjointe différentes composantes de la performance (production laitière, mobilisation des réserves corporelles, fertilité) susceptibles d'être déviées sous l'effet d'une perturbation ou en situation d'environnement contraignant. La relation entre le potentiel génétique et la robustesse est également discutée, en utilisant la notion de compromis entre fonctions. Pour illustrer cet objectif, les perturbations retenues sont surtout de nature alimentaire. Au niveau du troupeau, nous discutons de l'intérêt de faire cohabiter des animaux ayant des types de robustesse différents pour accroître sa capacité à faire face à des perturbations inattendues, de natures et d'amplitudes différentes.

L’alimentation des chèvres laitières est un domaine en évolution technique rapide qui demande de plus en plus de recommandations alimentaires précises. La dernière mise à jour a été effectuée en 2007 et une actualisation s’avérait nécessaire. Les caractéristiques zootechniques de base (variations de la production de lait, des taux butyreux et protéique et du poids vif) sont mieux connues et désormais modélisées au sein du logiciel INRAtion. Les valeurs des besoins en énergie, protéines et minéraux au cours du cycle gestation-lactation sont globalement confirmées ou légèrement modifiées. En revanche, les lois de réponses aux apports de concentrés ont été précisées, en relation notamment avec les apports et les bilans d’énergie. Dans le domaine des lipides, de nombreuses données ont été publiées récemment ce qui permet de disposer de nouvelles lois de réponses ayant une bonne précision. Les rejets d’éléments polluants (méthane et azote) ont été abordés et des équations de prévision simples et utilisables de ces flux sont proposées. Enfin, dans les domaines du comportement alimentaire, de l’acidose et des besoins en eau, des travaux récents permettant de disposer de valeurs repères applicables sur le terrain., Feeding dairy goats is an area in which rapid technological changes require more and more specific feeding and nutritional recommendations. The last update was completed in 2007 and an update is now of interest. The basic animal characteristics (dynamic changes in milk, fat and protein productions and body weight) are now better understood and modeled in the INRAtion software. The requirement values for energy, protein and minerals were generally confirmed or slightly modified (pregnancy, milk). However, the laws of responses to inputs of concentrates have been focused, particularly in relation with inputs and energy balances. For lipids, much data have been published recently providing new laws of responses with a good accuracy. Outflows of possible polluting elements (methane and phosphorus) are discussed and simple equations are proposed and used. Finally, in the areas of feeding behavior, acidosis and water requirements, recent works have allowed progress and part of these data are quite applicable on farms.

International audience

Cet article présente un simulateur du fonctionnement du troupeau caprin laitier qui permet d’évaluer les effets des pratiques de conduite sur les performances du troupeau. Le simulateur est basé sur l’interaction entre un système biotechnique et un système décisionnel. Le système biotechnique représente chaque animal grâce à un modèle de chèvre laitière. Le système décisionnel représente les opérations techniques d’alimentation, de reproduction et de renouvellement. Trois plans d’expériences ont testé les effets i) du niveau des apports alimentaires et du potentiel de production du troupeau, ii) de la segmentation du plan d’alimentation et de la durée de la lutte et iii) d’une conduite simplifiée du troupeau. Le simulateur a permis d’évaluer la production laitière et l’efficacité alimentaire à l’échelle du troupeau et des individus. Le niveau des apports et le potentiel de production du troupeau ont des effets marqués sur la production et l’efficacité alimentaire via la dilution des coûts de production par la quantité de lait produite. La segmentation du plan d’alimentation module les effets du niveau des apports alimentaires. L’examen d’indicateurs biologiques individuels permet de distinguer des conduites a priori équivalentes en termes de performance du troupeau. Ces conduites sont en fait associées à des niveaux de risques biologiques différents, susceptibles de moduler les capacités d’adaptation du troupeau en réponse à une perturbation., This paper describes a dairy goat herd model, which simulates the effects of management practices on herd performance. The model is based on the interaction between a decisional system and a biological system. The biological system represents each animal based on a dairy goat model. The decisional system is based on the technical operations of feeding, reproduction and replacement. Three experimental plans were run to test the effects of i) the energy level of the diet and the mean herd production potential; ii) the number of steps within the feeding plan and the length of the breeding season and iii) a simplified management of the herd. The model enables assessing milk production and feed efficiency at both herd and individual levels. The results showed that the feeding level and the mean herd potential have marked effects on milk production and feed efficiency through a dilution effect of the production cost by the quantity of milk produced. The results also showed that the degree of segmentation of the feeding plan modulates the effects of the feeding level. By looking at biological individual variables, it was possible to discriminate management practices which led to similar performances at the herd level. These practices generated different biological risks, which can potentially modulate herd adaptive abilities to environmental perturbations.

Individual variability is a key element to understand and predict herd response to management practices. To study how individual variability is generated within the herd, an individual-based herd model was developed. It was applied to intensive dairy goat systems. The model combines a decisional with a biological sub-model. The decisional sub-model represents technical decisions relative to reproduction, feeding and replacement management. The biological sub-model represents each individual within the herd and is based on a dairy goat model. This latter simulates body weight and milk production dynamics throughout each goat’s productive life. Individual goat performance depends on its production potential, physiological regulations and responses to diet. As each goat model has its own individual performance scaling and its own operation driven by management decisions, individual variability is an output of simulation. In the present work, the herd model was used to simulate two feeding strategies involving two-step (2S) or five-step (5S) feeding sequences. The 5S feeding sequence is based on a regular adjustment of the quantity of concentrate feedstuff throughout lactation whereas the 2S feeding sequence is based on a single adjustment in late lactation. Results show that increasing the number of steps within the feeding sequence did not lead to a major difference in terms of milk production and feed cost. When focusing at individual level, results show that the 2S feeding sequence led to a greater proportion of goats whose body weight was lower than their potential body weight. For the 2S feeding sequence, production and efficiency relied on goats which tended to lose more weight than those fed with the 5S sequence. Hence, the same level of herd production and efficiency can be achieved with different biological underlying processes. This result stresses the importance of understanding the mechanisms behind overall performance to achieve a sound evaluation of management effects.

Lifetime performance variability is a powerful tool for evaluating herd management. Although efficiency is a key aspect of performance, it has not been integrated into existing studies on the variability of lifetime performance. The goal of the present article is to analyse the effects of various herd management options on the variability of lifetime performance by integrating criteria relative to feed efficiency. A herd model developed for dairy goat systems was used in three virtual experiments to test the effects of the diet energy level, the segmentation of the feeding plan and the mean production potential of the herd on the variability of lifetime performance. Principal component analysis showed that the variability of lifetime performance was structured around the first axis related to longevity and production and the second related to the variables used in feed efficiency calculation. The intra-management variability was expressed on the first axis (longevity and production), whereas the inter-management variability was expressed on the second axis (feed efficiency) and was mainly influenced by the combination of the diet energy level and the mean production potential. Similar feed efficiencies were attained with different management options. Still, such combinations relied on different biological bases and, at the level of the individual, contrasting results were observed in the relationship between the obtained pattern of performance (in response to diet energy) and the reference pattern of performance (defined by the production potential). Indeed, our results showed that over-feeding interacted with the feeding plan segmentation: a high level of feeding plan segmentation generated a low proportion of individuals at equilibrium with their production potential, whereas a single ration generated a larger proportion. At the herd level, the diet energy level and the herd production potential had marked effects on production and efficiency due to dilution of fixed production costs (i.e. maintenance requirements). Management options led to similar production and feed efficiencies at the herd level while giving large contrasts in the proportions of individuals at equilibrium with their production potential. These results suggested that analysing individual variability on the basis of criteria related to production processes could improve the assessment of herd management. The herd model opens promising perspectives in studying whether individual variability represents an advantage for herd performance.

Agricultural intensification drastically reduces diversity at different scales of livestock farming systems (LFS). This homogenization process leads to environmental degradation and ignores the fact that multiple performance criterions often come in conflict. Taking advantage of diversity at different scales of LFS is a new challenge to improve sustainability. The diversity is considered here on a functional viewpoint, i.e. a variety of functions played by individual production entities involved in overall performance. The objective of this study was to examine how the diversity of individual production entities shapes the relationship between different performance criterions at the upper scale. We used two types of dynamic models representing contrasting LFS: extensive grassland suckling farms and intensive dairy herds. Both models include a certain level of functional diversity with production entities corresponding to different types of management practices (grassland farm model) or different types of females (herd model). With the grassland farm model, we studied how the biodiversity/production relationship was influenced by the functional diversity of management practices. With the herd model, we explored how production/efficiency relationship was influenced by the functional diversity of females. At the farm scale, we showed that interactions between the diverse management practices shaped the relationship between ecological and productive performance from a convex to a concave trade-off. Complementary effects resulting from the diversity of management practices thus improved the trade-off. Trade-off shape also depended on the level of farm intensity and it was less costly for the extensive farms to achieve viable levels of ecological performance. Increasing the complementary effect through increased diversity in management practices is likely to improve the conciliation between productive and ecological performance. At the herd scale, we observed a win–win situation between production and efficiency. However, at the highest levels of production, there was a production/efficiency trade-off; increases in either production or efficiency corresponded to distinct functional groups of females. Managing the functional groups of females with targeted practices is likely to improve the production/efficiency trade-off. We concluded from both case studies that functional diversity plays a key role in the relationship between multiple performance criterions. The next steps on the research agenda include exploring new spatial and temporal scales and accounting for environmental variability. A better understanding of the role of functional diversity can benefit from multi-criterions and multi-scale modelling approaches.

Interests and limits of the representation of individual variability in dynamic herd models. The variability of individual responses is a key element to understand and evaluate herd performance. It has a biological basis (responses generated by physiological regulations and genotype) and a decisional basis (different management practices involving different herd organisation levels). This review analyses if current dynamic herd models were appropriate to study individual variability and produce knowledge to benefit from it. After analysing the type of biological responses represented in models and how these responses are modelled and then the type of management practices and the herd levels involved to perform these practices, the conclusion presents the models’ shortcomings to study individual variability. They emerge from a partial view of the biological basis (only one type of biological response represented, no response to feeding and no regulations) and of the decisional one (no individual information, a single type of practices and no link between farmer’s project, groups of animals and practices). As a result, simulating the emergence of individual variability from herd operation requires developing agent-based herd model with two essential characteristics: first, representing the animal as a regulated organism in a nutritional environment and second, formalizing groups of animals as management entities, targeted by a set of feeding, reproductive and replacement practices., La variabilité des réponses individuelles est un élément clé pour comprendre le fonctionnement du troupeau et évaluer ses performances. Cette variabilité repose sur une composante biologique (élaboration de réponses productives liées à l’expression des régulations physiologiques et du génotype) et sur une composante décisionnelle (ensemble de pratiques s’appuyant sur différents niveaux d’organisation du troupeau). Cette synthèse examine dans quelle mesure les modèles de fonctionnement du troupeau existants sont opérationnels pour étudier la variabilité individuelle et faire ainsi progresser notre capacité à prédire les réponses du troupeau aux pratiques. La première partie synthétise les types de réponses biologiques représentés dans les modèles et l’élaboration de ces réponses. La seconde passe en revue les types de pratiques représentés et les niveaux d’information qu’elles mobilisent. La conclusion présente les limites des modèles pour l’étude de la variabilité individuelle. Celles-ci résident dans une vision partielle de la composante biologique (un seul type de réponse biologique, absence des réponses à l’alimentation et des régulations physiologiques) et de la composante décisionnelle (absence d’informations individuelles, un seul type de pratiques représenté, pas de lien entre projet d’élevage, lots et pratiques). Par conséquent, simuler la construction de la variabilité individuelle générée par le fonctionnement du troupeau nécessite de développer un modèle individu-centré avec deux caractéristiques essentielles. La première concerne la formalisation de l’animal en tant qu’entité régulée répondant à son environnement nutritionnel. La seconde concerne la formalisation explicite du lot comme entité de gestion, support d’un système de pratiques d’alimentation, de reproduction et de renouvellement.

Sustainability of livestock farming systems (LFS) based on grassland depends on their abilities to buffer environmental variations while maintaining targeted production project. These abilities come from the regulating properties of LFS. On one side, regulations can arise from adaptive grazing management in order to take advantage of resource diversity under climatic hazard (Andrieu et al., 2007). On the other side, regulations can also arise from female adaptive abilities driven by farmer practices. At the herd level, variable female responses generate a diversity of lifetime performances. The objective of this study was to assess the contribution of lifetime performance diversity to the sustainability of LFS based on grassland. We relied on the example of dairy goat herd. Total milk production in year 1 was similar for both cases (Case 1: 120 601 kg; Case 2: 119 016 kg). The decline in fertility led to a decrease of production in both cases. However, in case 1 this decline was around 17% of herd production in year 1 and around 8% in case 2. In case 2, the perturbation due to low fertility was buffered at herd level due to an increase in the number of females maintained in extended lactation. These females contributed to 14% and 24% of overall production in years 1 and 2 respectively. Both management cases illustrate that herd can have regulating properties arising from the diversity of female lifetime performances. This aspect can be of great interest for grassland systems where feed supply is highly variable and female regulating properties are strongly solicited.

Le concept de chaîne d’approvisionnement est appliqué à un ensemble composé d’un bassin de collecte laitière adossé à un grand périmètre irrigué marocain pour en comprendre le fonctionnement. Quatre grands types d’acteurs sont identifiés, à savoir : le gestionnaire de périmètre, l’industriel laitier, les coopératives de collecte et les éleveurs irrigants. Leurs relations sont analysées autour des dispositifs mis en place pour coordonner leurs décisions par rapport aux objectifs d’économie et de valorisation de l’eau (côté périmètre), de qualité, saisonnalité et productivité (côté bassin de collecte). Ils s’articulent autour de la planification et du pilotage des flux d’eau et de lait, d’outils économiques (système de tarification de l’eau, système de paiement du lait), d’échanges d’informations et de services liés (appui technique, fourniture d’intrants et de crédit). Les performances observées sont cependant perfectibles et différents axes de recherche intervention sont proposés en conclusion : aide à la conception de système d’alimentation des vaches laitières sous contraintes, gestion de la qualité du lait de l’étable à l’usine et gestion des coopératives., The supply chain concept is used for analyzing the management of a dairy plant supply area located in a large Moroccan irrigation scheme. Four main stakeholders are identified, i.e. the scheme manager, the dairy company, milk collection co-operatives and dairy farmers using irrigation water. The tools and processes implemented in order to co-ordinate their decisions are analyzed considering objectives such as saving irrigation water and increasing the water productivity on the water supplier side, and improving the milk quality, the seasonality and the productivity as far as the dairy supply chain is concerned. It includes: i) planning and operating both the water and milk flows; ii) implementing adapted economic tools, i.e. water pricing and milk payment system; and iii) providing services to farmers such as technical advice and input/credit supply. Never the less, the system performances could be improved. Various research and intervention topics are suggested in the conclusion, mainly: i) the design of a decision support system dedicated to the cows’ feeding system; ii) the milk quality management along the supply chain; and iii) the co-operatives’ management system.

L’ambition de l’étude Inra "EFESE-EA" est de décrire les mécanismes et déterminants de la fourniture des services écosystémiques par les écosystèmes agricoles sur la base d'une revue des connaissances existantes, et de procéder à leur évaluation à l’échelle nationale sur la base d’indicateurs définis dans le cadre de l’étude. L’organisation du travail, telle que prévue en début d’étude, se voulait séquentielle : (1) identification et spécification biophysiques d’une liste de biens agricoles et services écosystémiques ; (2) évaluation biophysique : quantification du niveau de fourniture des biens et services identifiés à l’étape (1) (3) évaluation économique : quantification de la valeur économique des services (le plus souvent dans une unité monétaire) Dans le temps imparti à l’étude, le collectif d’experts a donné la priorité aux volets biophysiques (1) et (2) afin : - d’instruire de façon robuste la conceptualisation des biens et services (volet 1) : ce travail constitue un front de recherche actuel, associé à une littérature académique abondante mais parfois non stabilisée, que le collectif d’experts s’est attaché à analyser de façon à proposer des choix de conceptualisation argumentés ; - de pousser au maximum l’exercice d’évaluation biophysique (volet 2) dans le cadre de la demande initiale formulée par le MEEM : cartographier la production d’un large panel de biens agricoles et les SE rendus par les écosystèmes agricoles à la résolution spatiale la plus fine possible, et à l’échelle France entière. A noter que le présent exercice ne constituant pas un projet de recherche mais bien une étude institutionnelle Inra (au sens des procédures DEPE), l’ensemble des évaluations développées dans le présent rapport est réalisée à partir de données existantes, aucun travail d’expérimentation visant à acquérir de nouvelles données de terrain n’ayant été conduit. Il résulte de ce choix de priorisation que : - le volet d’évaluation économique (3) est initié pour quelques SE mais peu développé en comparaison des volets (1) et (2) ; - tout en veillant à élaborer des méthodologies d’évaluation biophysiques traçables et robustes, les experts ont pris le parti de proposer des méthodologies plus exploratoires pour quelques SE pour lesquels les données actuelles ne permettent pas d’évaluer directement le niveau de fourniture à l’échelle France entière : dans ces cas particuliers (signalés explicitement dans les sections du rapport dont ils font l’objet), les méthodologies ont été mises en œuvre jusqu’à la réalisation des cartographies dans le but de donner à voir le potentiel qu’offrent ces méthodologies et la nature des résultats qu’elles peuvent produire sous condition de leur validation France entière, plutôt que dans le but d’interpréter pour eux-mêmes les résultats obtenus. Les experts se sont alors particulièrement attachés à relativiser les résultats quantitatifs ainsi produits, et à accompagner les cartographies d’un descriptif détaillé des protocoles de validation qu’il faudrait mettre en œuvre dans les suites de l’étude pour stabiliser et valider ces méthodologies exploratoires. Ce parti pris du groupe de travail EFESE-écosystèmes agricoles est compatible avec l’objectif poursuivi dans le programme EFESE, qui se donne pour objectif de produire un guide méthodologique pour l’évaluation des biens et SE en en pointant les limites, difficultés, précautions et améliorations possibles associées à chacune des pistes avancées.

The objective of this paper is to bring insights into the contribution and advance of modelling for livestock farming systems (LFS). First, we rely on a few examples to illustrate how models can be powerful tools to increase our understanding of livestock production. Second, we argue that: (i) the choice of model type depends on the goal that modeller wants to achieve; (ii) any model is a compromise among generality, precision or realism; and (iii) any model requires hypotheses, data and careful evaluation against reality. Third, we develop two examples showing that new challenges faced by LFS demand to incorporate new scales of analysis. The dairy goat model shows that it is possible to simply formalize basic biological processes at female level. This representation is crucial to increase our understanding on the role of the variability of female's lifetime performance at herd level. The co-viability model shows that accounting for grazing impact on the conservation of biodiversity requires enlarging spatial and temporal scale of LFS analysis. It thus implies a simplification of grazing process formalization. Both examples open new fields of collaborations between animal production sciences and other disciplines such as animal nutrition and population ecology.