Key determinants of adaptive strategies of goats to a 2-day nutritional challenge during early lactation.

Little is known about the key determinants of the physiological adaptations to environmental challenges and how these determinants interact. We evaluated how the response/recovery profiles to a short-term nutritional challenge during early lactation are affected by early-life nutritional strategies in dairy goats divergently selected for functional longevity. We used 72 females, split into two cohorts, daughters of Alpine bucks divergently selected for functional longevity. The females from the two lines were fed with two divergent diets, normal vs low-energy, from weaning until the middle of first gestation, and then fed with the same standard diet. Individual BW, body condition score, morphology, and plasma samples were collected from birth to first kidding. The adaptative physiological strategy to a nutritional challenge was assessed via a 2-day feed restriction challenge, during early lactation, which consisted of a five-day control period on a standard lactation diet followed by a 2-day challenge with straw-only feeding and then a 10-day recovery period on a standard lactation diet. During the challenge, DM intake, BW, milk yield (MY), and plasma and milk metabolite composition were recorded daily. Linear mixed-effects models were used to analyze all traits, considering the individual nested in the cohort as a random effect and the 2 × 2 treatments (i.e., line and rearing diet) and litter size as fixed effects. Linear mixed-effects models using a piecewise arrangement were used to analyze the response/recovery profiles to nutritional challenge. Random parameters estimated for each individual, using the mixed-effects models without the fixed effects of rearing diet and genetic line, were used in a stepwise model selection based on R2 to identify key determinants of an individual's physiological adaptations to environmental challenges. Differences in stature and body reserves created by the two rearing diets diminished during late gestation and the 5-day control period. Genetic line did not affect body reserves during the rearing phase. Rearing diet and genetic line slightly affected the recovery profiles of evaluated traits and had no effects on prechallenge and response to challenge profiles. The prekidding energy status measures and MY before challenge were selected as strong predictors of variability in response-recovery profiles of milk metabolites that have strong links with body energy dynamics (i.e., isoCitrate, ß-hydroxybutyrate, choline, cholesterol, and triacylglycerols; R2 = 35%). Our results suggested that prekidding energy status and MY are key determinants of adult resilience and that rearing diet and genetic line may affect adult resilience insofar as they affect the animals' energy status.

Responses of selected plasma metabolites to a two-day nutritional challenge of goats divergently selected for functional longevity.

Understanding the extent to which genetics × environment plays a role in shaping individual strategies to environmental challenges is of considerable interest for future selection of more resilient animals. Accordingly, the objective of this study was to evaluate the metabolic responses to a nutritional challenge of goats divergently selected for functional longevity based on plasma metabolites and the repeatability of these responses across 2 experimental farms and years. We carried out 6 different experimental trials from years 2018 to 2022 (4 trials on site Bourges (2018-21) and 2 trials (2021-22) on site Grignon) in which 267 first kidding goats, daughters of Alpine bucks divergently selected for functional longevity, longevity plus (n = 137), and longevity minus (n = 130), were exposed to a 2-d nutritional challenge in early lactation. The experiments consisted of a 5 or 7-d control period (pre-challenge) on a standard lactation diet followed by a 2-d nutritional challenge with straw-only feeding and then a 7 or 10-d recovery period on a standard lactation diet, for site Bourges and Grignon, respectively. During the challenge plasma metabolite composition was recorded daily. Linear mixed-effects models were used to analyze all traits, considering the individual as a random effect and the 2x2 treatments (i.e., genetic line and year nested in site) and litter size as fixed effects. The linear mixed-effects model using a piecewise arrangement was used to analyze the response/recovery profiles to the nutritional challenge. Random parameters estimated for each individual, using the mixed-effects models without the fixed effects of genetic line, were used in a Sparse Partial Least Square Discriminant Analysis (sPLS-DA) to compare the goat metabolism response to the challenge on a multivariate scale. The plasma metabolites, glucose, ß-hydroxybutyrate (BHB), and nonesterified fatty acids (NEFA), and urea concentrations responded to the 2-d nutritional challenge. Selection for functional longevity did not affect plasma glucose, NEFA, BHB, and urea response/recoveries to a 2-d nutritional challenge. However, site, trial, and litter size affected these responses. Moreover, the plasma metabolites seem not to fully recover to prechallenge levels after the recovery phase. The sPLS-DA analysis did not discriminate between the 2 longevity lines. We observed meaningful between-individuals' variability in plasma BHB, especially on the prechallenge and rate of response and rate of recovery from the 2-d nutritional challenge (CV = 26.2%, 36.1%, and 41.2%, repeatability = 0.749, 0.322, and 0.741, respectively). Plasma NEFA recovery from challenge also demonstrated high between-individuals' variability (CV = 16.4%, repeatability = 0.323). Selection for functional longevity did not affect plasma metabolites responses to a 2-d nutritional challenge in dairy goats. Plasma NEFA and BHB response/recovery presented high between-individuals' variability, indicating individual adaptative characteristics to nutritional challenges not related to the environmental conditions but to inherent individual characteristics.

Combined mechanistic and genetic modelling to benchmark body reserve traits as proxies of dairy cows' lifetime efficiency in grass-based production systems.

Improving lifetime lactation efficiency of dairy cows by selection is difficult due to the complexity of this trait and the existence of genotype-by-environment interactions. This study aimed at assessing the relevance of traits derived from body reserves as lifetime efficiency indicators under contrasting nutritional environments. Given the absence of large-scale datasets covering a panel of feeding regimes, phenotypes were simulated for populations of 20 000 dairy cows using a mechanistic bioenergetic model. Ten phenotypes were computed for third-lactation cows. Analysed phenotypes comprised total milk production, lactation efficiency, BW at calving (BWcalv), DM intake (DMI) and interval between first insemination and conception. Five traits described levels and changes of body reserves at different periods during lactation. Lifetime lactation efficiency was computed for all cows (Life_Eff). Three nutritional environments were defined considering a grass-based production system with seasonal calving: a high non-limiting scenario (HS) mimicking ad libitum access to feed and two limiting environments with moderate (MS) and low (LS) feed offer. Variance components were estimated for all traits within and between environments using REML. Heritabilities estimated for milk production, lactation efficiency, BWcalv and DMI were moderate in the different environments (0.27-0.35 ± 0.04). The heritability of body reserve levels and dynamics were moderate in the HS and MS scenarios (0.23-0.30 ± 0.03) and lower in the LS scenario (0.14-0.25 ± 0.03). The heritability of Life_Eff was low in the HS environment (0.07 ± 0.01) and slightly increased in the limiting environments. All genetic correlations estimated between environments were moderate to high (≥0.66 ± 0.07), suggesting low to moderate genotype-by-environment interactions. Estimated genetic correlations were moderate between Life_Eff and body reserve levels (from 0.39 to 0.51 ± 0.08) and moderate but negative between Life_Eff and change in body reserves traits (-0.27 to -0.37 ± 0.09) in the HS environment. The genetic correlations between Life_Eff and body reserve levels increased to higher values in the limiting environments. In contrast, genetic correlations between Life_Eff and the changes in body reserves were closer to zero. In conclusion, this study showed that body reserve levels were relevant proxies of lifetime irrespective of the environment. In contrast, changes in body reserves that reflected energy mobilisation in early lactation were less informative about lifetime efficiency in environments with severe feed restrictions.

Analysis of reproduction success, growth and milk trajectories and response to nutritional challenge in two Alpine goat lines selected on divergent longevity.

This study aims to investigate whether the variation in reproduction success, growth, and milk trajectories is associated with different adaptive strategies in the short term (response to an acute nutritional challenge), using two Alpine goat lines. A total of 382 Alpine goats (179 low longevity (low_LGV) and 203 high longevity (high_LGV)), selected for divergent functional longevity from a commercial population, were monitored for 4 years and recorded for BW, reproduction and milking performance. Every year, an average of fifty primiparous goats were exposed to a 2-d nutritional challenge in early lactation. A polynomial model was used to analyse the lifetime trajectory of lactation and BW. A piecewise model was used to analyse the individual milk yield and responses of milk components to the nutritional challenges. The statistical analysis revealed that the two lines had a similar performance for total milk yield in the first lactation, BW at birth and at first kidding, litter size and weight, kidding interval and interval from the first insemination to conception. BW trajectories revealed that low_LGV goats had a greater BW in pregnancy but then lost more weight in early lactation compared to high_LGV goats, which showed a greater BW after kidding. Milk trajectories showed that the high_LGV goats had a higher initial milk yield, an earlier but less marked lactation peak and more persistency in milk production in late lactation than low_LGV goats. Except for milk protein content, quite similar response and recovery profiles of milk yield and milk fat content were observed during the challenge for both lines. The response to the challenge was positively correlated to the initial level of milk production in early lactation but negatively correlated with milk production decline after the peak. This finding suggests that the low_LGV goats were more adapted to allocate resources to meet an expected physiological change such as gestation and lactation. However, high_LGV goats allocate more than low_LGV goats for structural mass and may better cope with an unexpected environmental change such as nutritional deficit.

Animal board invited review: Quantification of resilience in farm animals.

Resilience, when defined as the capacity of an animal to respond to short-term environmental challenges and to return to the prechallenge status, is a dynamic and complex trait. Resilient animals can reinforce the capacity of the herd to cope with often fluctuating and unpredictable environmental conditions. The ability of modern technologies to simultaneously record multiple performance measures of individual animals over time is a huge step forward to evaluate the resilience of farm animals. However, resilience is not directly measurable and requires mathematical models with biologically meaningful parameters to obtain quantitative resilience indicators. Furthermore, interpretive models may also be needed to determine the periods of perturbation as perceived by the animal. These applications do not require explicit knowledge of the origin of the perturbations and are developed based on real-time information obtained in the data during and outside the perturbation period. The main objective of this paper was to review and illustrate with examples, different modelling approaches applied to this new generation of data (i.e., with high-frequency recording) to detect and quantify animal responses to perturbations. Case studies were developed to illustrate alternative approaches to real-time and post-treatment of data. In addition, perspectives on the use of hybrid models for better understanding and predicting animal resilience are presented. Quantification of resilience at the individual level makes possible the inclusion of this trait into future breeding programmes. This would allow improvement of the capacity of animals to adapt to a changing environment, and therefore potentially reduce the impact of disease and other environmental stressors on animal welfare. Moreover, such quantification allows the farmer to tailor the management strategy to help individual animals to cope with the perturbation, hence reducing the use of pharmaceuticals, and decreasing the level of pain of the animal.

Carbon footprint based on lifetime productivity for future cows selected for resilience to climate-related disturbances.

Droughts, which can affect feed production, are projected to become more common under future climate conditions. In light of this, breeding cattle resilient to changes in feeding regimens is increasingly becoming an important topic. Body reserves can play a crucial role when feed resources are limited. We simulated populations of dairy cows selected with 2 different breeding goals: one reflecting the current breeding goal and the other placing weight on minimum level of body reserves in early lactation and change in body reserves during lactation. We considered this latter as a breeding goal for resilience. We used the 2 dynamic simulation programs ADAM and AQAL to predict performance of the cows after selection. In AQAL, we modeled moderate and severe drought by decreasing feed quality and quantity offered to cows during one year. We compared cows selected with the 2 breeding goals under 3 environments: without disturbances related to climate and with moderate and severe drought. In the environments without disturbances and the moderate drought, the cows selected with the current breeding goal had higher lifetime lactation efficiency (energy invested in milk/energy acquired from feed) and lower carbon footprint per kilogram of protein in milk and meat than cows selected for resilience. However, with severe drought, cows selected for resilience had higher lifetime lactation efficiency and lower carbon footprint per kilogram of protein in milk and meat than those selected with the current breeding goal. This suggests that cows selected for high productive performance do not perform well under very limiting conditions, leading to increased climate impact. The importance of inclusion of body reserves as a resilience trait in dairy cattle breeding depends on the future environment in which the cows will be used.

A framework to estimate the environmentally attainable intake of dairy cows in constraining environments.

Most intake models for dairy cows have been developed to make predictions under normal conditions, in which animals can meet their nutritional requirements. To estimate intake under constraining conditions, i.e. when intake is defined by the environment and not by the animal's requirements, it is necessary to develop models that take into account environmentally driven effects. The aim of this work was to develop a framework to represent the links between environmental variables (food quality and quantity, as well as ambient temperature, season, and farm type) and intake. The framework integrates time as the major constraint on intake and proposes the environmentally attainable intake (EAI) as the product of the Eating Rate (ER) and the Eating Time (ET). ER is the maximum sustainable rate (gr DM/min) at which animals bite the food, and ET is the daily time (min/d) that animals have to eat. The architecture of the framework is easily extensible to add constraints such as predation pressure, reproductive costs, competition, parasitism, or diseases. Data from grazing and indoor dairy farms were used to test the usability of the framework. The results show that a time use-based framework is a reliable approach to estimate intake considering environmental variables with minimum use of animals' characteristics. In conclusion, a high-level framework of feeding behaviour, that captures the main underlying mechanisms of intake in constrained environments, can be used to predict the EAI and the effects of the environment on animal performance.

Animal board invited review: Specialising and intensifying cattle production for better efficiency and less global warming: contrasting results for milk and meat co-production at different scales.

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.

Exploring underlying drivers of genotype by environment interactions in feed efficiency traits for dairy cattle with a mechanistic model involving energy acquisition and allocation.

Feed efficiency (FE) is a complex phenotype made up of multiple traits for which there is potential for substantial genotype by environment interaction (G × E). The objective of this study is to evaluate the extent of G × 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 parameters 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 × 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 target performance environment.

Lactation curve model with explicit representation of perturbations as a phenotyping tool for dairy livestock precision farming.

In the context of dairy farming, ruminant females often face challenges inducing perturbations that affect their performance and welfare. A key issue is how to assess the effect of perturbations and provide metrics to quantify how animals cope with their environment. Milk production dynamics are good candidates to address this issue: i) they are easily accessible, ii) overall dynamics throughout lactation process are well described and iii) perturbations are visible through milk losses. In this study, a perturbed lactation model (PLM) with explicit representation of perturbations was developed. The model combines two components: i) the unperturbed lactation model that describes a theoretical lactation curve, assumed to reflect female production potential and ii) the perturbation model that describes all the deviations from the unperturbed lactation model with four parameters: starting date, intensity and shape (collapse and recovery). To illustrate the use of the PLM as a phenotyping tool, it was fitted on a data set of 319 complete lactations from 181 individual dairy goats. A total of 2 354 perturbations were detected, with an average of 7.40 perturbations per lactation. Loss of milk production for the whole lactation due to perturbations varied between 2 and 19% of the milk production predicted by the unperturbed lactation model. The number of perturbations was not the major factor explaining the loss of milk production, suggesting that there are different types of animal response to challenges. By incorporating explicit representation of perturbations in a lactation model, it was possible to determine for each female the potential milk production, characteristics of each perturbation and milk losses induced by perturbations. Further, it was possible to compare animals and analyze individual variability. The indicators produced by the PLM are likely to be useful to move from raw data to decision support tools in dairy production.

Review: To be or not to be an identifiable model. Is this a relevant question in animal science modelling?

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.

Review: Deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management.

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.

A dynamic model as a tool to describe the variability of lifetime body weight trajectories in livestock females.

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 ( = 202) from 45 dairy goats from the Alpine ( = 27) and Saanen ( = 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.

Introducing efficiency into the analysis of individual lifetime performance variability: a key to assess herd management.

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.

An individual-based model simulating goat response variability and long-term herd performance.

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.

Simple representation of physiological regulations in a model of lactating female: application to the dairy goat.

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.