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International audience; 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.

International audience; The production of methane by the rumen microbiota is a complex biological process. When tackling the modelling of methane production, the modeller decides what complexity is needed to answer the scientific question for which the model is intended. Such a choice results in a diversity of possible models spanning both empirical and mechanistic approaches. Within the framework of precision livestock farming, simple dynamic models offer great advantages for integrating online data (e.g., feed intake) to predict individual methane emissions from cattle. Accordingly, we previously developed, with satisfactory results, a simple dynamic model that uses DM intake kinetics as a single predictor of methane emissions from finishing beef steers. The objective of the present work was to assess the capability of the previously developed model to predict the dynamic pattern of methane production from dairy cows fed a diet containing either wheat grain or corn grain. We showed that the simple dynamic model in its original form enables a description of the dynamics of individual methane emissions from dairy cows with an average determination coefficient (r2) of 0.65 and an average concordance correlation coefficient of 0.81 and RMSE of 16% and 26% for the corn-based and wheat-based diets, respectively. Additionally, we performed a principal component analysis associating the parameters of the methane model with variables characterising the feeding behaviour of the cows. The results showed the effect of the diet type on the feeding behaviour of the animals. This impact was propagated on the dynamics of methane emissions. Interestingly, our model enabled us to determine that the differences in patterns of methane emissions between the diets result simply from the dependency of the methane yield and rate constant of methane eructation on the grain type.

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

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

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

Rats detect and use odorant molecules as a source of information about their environment. Some of these molecules come from conspecifics, and many arise as by-products from microbial activity. Thus, compared to conventionally housed rats, germ-free rats are raised in an environment with fewer odorants, but this reduction is rarely quantified. Using gas chromatography-mass spectrometry, we found that germ-free rat faeces samples contained half as many volatile molecules than conventional rat faeces (52 vs 109 (±2.4) molecules; P < 0.001) and overall these were only 12% as abundant. We then investigated if odours from female germ-free rats in oestrus would have pro-erectile effects in conventional male rats. For this aim, conventionally housed Brown Norway (BN) rats (n = 16) with sexual experience with either Fischer or BN females, were exposed to four different odour types: faeces from germ-free Fischer rat in oestrus, faeces from conventional rats in oestrus and di-oestrus (either from Fischer or BN), and a control (either 1-hexanol or male rat faeces). The number of penile erections per test as well as the duration of freezing behaviour was significantly higher with the oestrous odours (germ-free and conventional) compared to the control, with intermediate responses to the di-oestrous faeces. The findings indicate that, despite a significantly reduced composition in terms of volatiles compared to conventionally housed rats, the faeces of germ-free rats contain sufficient odorants to evoke sexual responses in conventional male rats. Oestrous odours of rats thus appear not to be of microbial origin.

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

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

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.

Seaweeds are macroalgae, which generally reside in the littoral zone and can be of many different shapes, sizes, colours and composition. They include brown algae (Phaeophyceae), red algae (Rhodophyceae) and green algae (Chlorophyceae). Seaweeds have a long history of use as livestock feed. They have a highly variable composition, depending on the species, time of collection and habitat, and on external conditions such as water temperature, light intensity and nutrient concentration in water. They may contain non-protein nitrogen, resulting in an overestimation of their protein content, and nitrogen-to-protein conversion factors lower than 6.25, normally used for feed ingredients, have been advocated. They contain considerable amount of water. Most essential amino acids are deficient in seaweeds except the sulphur containing amino acids. Seaweeds concentrate minerals from seawater and contain 10–20 times the minerals of land plants. They contain only small amounts of lipids (1–5%), but majority of those lipids are polyunsaturated n-3 and n-6 fatty acids. Brown seaweeds have been more studied and are more exploited than other algae types for their use in animal feeding because of their large size and ease of harvesting. Brown algae are of lesser nutritional value than red and green algae, due to their lower protein content (up to approx. 14%) and higher mineral content; however brown algae contain a number of bioactive compounds. Red seaweeds are rich in crude protein (up to 50%) and green seaweeds also contain good protein content (up to 30%). Seaweeds contain a number of complex carbohydrates and polysaccharides. Brown algae contain alginates, sulphated fucose-containing polymers and laminarin; red algae contain agars, carrageenans, xylans, sulphated galactans and porphyrans; and green algae contain xylans and sulphated galactans. In ruminants, step-wise increase in the levels of seaweeds in the diet may enable rumen microbes to adapt and thus enhance energy availability from these complex carbohydrates. In monogastrics, those polysaccharides may impact the nutritional value but the addition of enzyme cocktails might help. In vivo studies on ruminants, pigs, poultry and rabbits reveal that some seaweeds have the potential to contribute to the protein and energy requirements of livestock, while others contain a number of bioactive compounds, which could be used as prebiotic for enhancing production and health status of both monogastric and ruminant livestock. Seaweeds tend to accumulate heavy metals (arsenic), iodine and other minerals, and feeding such seaweeds could deteriorate animal and human health. Regular monitoring of minerals in seaweeds would prevent toxic and other undesirable situations.

Robustness is a complex trait difficult to characterize and phenotype. In the present study, two features of robustness in rainbow trout were investigated: sensitivity and resilience to an acute stressor. For that purpose, oxygen consumption, cortisol release, group dispersion and group activity of two isogenic lines of juvenile rainbow trout were followed before and after an environmental challenge. The effect of a 4h confinement protocol (~140kg/m(3)), which is generally considered as a highly stressful challenge, was investigated. Temporal patterns produced by this experiment were analyzed using multivariate statistics on curve characteristics to describe physiological and behavioral adaptive systems for each isogenic line. The two isogenic lines were found to be highly divergent in their corticosteroid reactivity. However, no correlation between physiological and behavioral sensitivity or resilience was observed. Furthermore, the multivariate analysis results indicated two separate and independent fish group coping strategies, i.e. by favoring either behavioral or physiological responses. In addition, considerable intra-line variabilities were observed, suggesting the importance of micro-environment effects on perturbation sensitivities. In this context, cortisol release rate variability was found to be related to the pre-stress social environment, with a strong correlation between pre-stress aggressiveness and cortisol release rate amplitude. Overall, this approach allowed us to extract important characteristics from dynamic data in physiology and behavior to describe components of robustness in two isogenic lines of rainbow trout.

Monitoring fish shoal behaviour is a growing concern for scientists studying fish stress and welfare. This study presents an algorithm developed to calculate, from videos taken from above aquaria, two indexes characterizing fish shoal behaviour. These two indexes quantify the dispersion and the swimming activity of the fish shoal in the aquaria. The reliability of these indexes was tested on fish shoal simulations following the rules of Reynolds' model on flocks, herds and schools. Since coordinates of each simulated fish in the shoal was known, these simulations provided true values of dispersion and swimming speed of each fish in the shoal, which were compared to values calculated using the presented algorithm. Further, the two indexes were tested on videos of rainbow trout in aquaria. Behavioural variations of the shoal were estimated before and after food distribution in one test, and before and after a four hours confinement stress in a second test. Data resulting from simulations indicate that the two indexes are sensitive to the simulated changes in the cohesion or the swimming speed of the group. Thus, indexes faithfully translated true values in simulations, with a minimum of 94% of the total variation in true values explained by indexes. Furthermore, the two indexes were sensitive to shoal behaviour modifications observed in the two case studies. Indeed, as expected, a strong group dispersion decrease associated with an important swimming activity increase could be detected just after food distribution using our method. Similarly, our indexes were sensitive to a group behaviour change observed after the four hours confinement stress. Finally, our method was compared to Israeli's, and was found to be more sensitive and more accurate in our conditions. This method provides therefore a sensitive, non-invasive, simple and widely applicable tool to quantify behavioural changes associated with various challenges in aquacultural conditions.

Genetic selection for increased milk production has resulted in cows that lose more BCS in early lactation and that, consequently, are more susceptible to health disorders after calving. Excessive BCS at calving results in greater BCS loss after calving, increasing the risk of milk fever, ketosis, and fatty liver. Recent experiments during the transition period highlight benefits from controlling (ie, high-dietary roughage) energy intake precalving, with restricted cows being less prone to ketosis, fatty liver, and milk fever. The collective data indicate that a calving BCS of 3.0 for mature cows and 3.25 for heifers with a moderate energy restriction precalving will minimize the risk of BCS-related health disorders around calving.

The fat content of milk is an important indication of quality, both economically and physiologically. Existing analytical methods of milk fat are based on physical determination, i.e. gravimetric determination or near infrared spectroscopy (IR). Triacylglycerols (TAG) constitute up to 98% of total fat in milk. The present study describes a new method for determination of triacylglycerols in milk. The present method is a chemical, i.e. enzymatic–fluorometric assessment of glycerol, the content of TAG is consequently given in mol per litre. The method demonstrates a good accuracy and precision and the correlation with standard IR methods shows a fine association between the different assessment methods (n = 228; r = 0.905). Fatty acid profiles of sixty individual milk samples indicate that parameters co-linearly connected to specific fatty acids may explain a considerable amount of the variation between the methods. Furthermore, discrepancies between the present method and the established IR method may partly be due to the fact that IR spectroscopy is calibrated individually against gravimetric methods not taking fatty acid profiles and thereby molar weight of TAG into account The fat content of milk is an important indication of quality, both economically and physiologically. Existing analytical methods of milk fat are based on physical determination, i.e. gravimetric determination or near infrared spectroscopy (IR). Triacylglycerols (TAG) constitute up to 98% of total fat in milk. The present study describes a new method for determination of triacylglycerols in milk. The present method is a chemical, i.e. enzymatic-fluorometric assessment of glycerol, the content of TAG is consequently given in mol per litre. The method demonstrates a good accuracy and precision and the correlation with standard IR methods shows a fine association between the different assessment methods (n = 228; r = 0.905). Fatty acid profiles of sixty individual milk samples indicate that parameters co-linearly connected to specific fatty acids may explain a considerable amount of the variation between the methods. Furthermore, discrepancies between the present method and the established IR method may partly be due to the fact that IR spectroscopy is calibrated individually against gravimetric methods not taking fatty acid profiles and thereby molar weight of TAG into account.

The objectives of the present study were to investigate whether a dietary supplement of B-carotene given to dairy cows during the dry period was able to: (1) increase their B-carotene status, (2) increase the amount of B-carotene in colostrum, (3) increase the concentrations of immunoglobulinGincolostrum and (4) modify metabolic hormone,enzyme and metabolite status in their calves at birth. Forty Holstein cows were allocated to one of two dietary treatments: a control diet (C, n = 20) or the same diet plus 1 g B-carotene/ cow/day (BC, n = 20) starting on the day of drying-off. The B-carotene supplement was given individually to the cows throughout the dry period. From week 2 after the start of supplementation, blood concentrations of B-carotene were higher in BC compared to C cows (P

This study investigated the combined effects of once-daily milking and feeding level ( ad libitum or adjusted) on goat welfare and milk emission characteristics in late lactation. Forty dairy goats housed in individual pens were managed under twice-daily milking and fed ad libitum during a pre-experimental period (2 weeks). The experiment was then divided into two successive periods: a 1-week period of milking frequency reduction and a 3-week period of feed adjustment. During the first period, half the goats were managed under once-daily milking. During the second period, half the goats of each type of milking treatment was fed ad libitum , whereas the other half received a quantity of feed adjusted weekly to milk production. The following measurements were taken for all goats: milk yield, dry matter intake (DMI), somatic cell count (SCC), plasma cortisol concentrations before morning milking, milk flow parameters [latency (i.e., the time from teat cup attachment to first measurement of the milk with a jar), the maximum milk flow rate (MAXFR) and the time taken to reach this maximum (TMAX)] and goat behaviour at morning milking (number of goats ruminating, back hunching, foot moving or kicking at milking). In addition, the time–budget (time spent drinking, eating, standing, lying or climbing) of 16 goats was measured by video recording. Milk yield was reduced by once-daily milking (18 and 21% in the first and the second period, respectively) but not by feed adjustment. DMI was reduced by once-daily milking (12%) only during the second period or by feed adjustment (28%) during the same period. Both SCC and latency were not modified by once-daily milking nor feed adjustment. MAXFR was increased by once-daily milking but not modified by feed adjustment. TMAX was increased by once-daily milking only during the first period and reduced by feed adjustment during the second one. The time spent eating was reduced by once-daily milking or feed adjustment during the second period, in agreement with the reduction in DMI in this period. There were no effects of milking frequency or feeding level on plasma cortisol concentrations, the behaviour at morning milking or the time spent drinking, standing, lying or climbing. No welfare impairment was demonstrated under our experimental conditions in late lactation dairy goats under a once-daily milking management strategy whatever the feeding level.

An improved understanding of the animal factors that affect measures such as conception rate would contribute to solving the problem of impaired reproductive performance in modern dairy cows. A question of particular interest relates to the observed improvement in conception rates from first to second and third oestrus cycle: is the increase in conception rate related to cycle number per se or to increasing days from calving? A 5-year study using three breeds (Holstein, Jersey and Danish Red) allowed this issue to be examined. In 560 lactations, from calving until confirmed pregnancy or until 290 days after calving all cows were observed daily for signs of oestrus. Inseminations were carried out to all clear cases of oestrus after 35 days from calving until the cow was judged to be pregnant or until 290 days post-calving. Days from calving to the first oestrus was modeled using a frailty model. Danish Red cows had a significantly greater rate of occurrence of first oestrus over time. Generalized linear mixed models defined using a binomial distribution and logit link function were used to estimate probability of pregnancy as affected by: breed, parity, oestrus number and days from calving to first oestrus. In order to account for the paradox that the incidence of cows with inherently low fertility increases with oestrus number, different random components for cow were included. There was a significant increase in probability of pregnancy from first to second oestrus but no significant change in probability of pregnancy thereafter. There was a significant increase in probability of pregnancy with increasing days to first oestrus. This indicates that both oestrus number and days from calving play a role in determining the fertility of dairy cows. Correcting for inherent differences in fertility was shown to be important Udgivelsesdato: April An improved understanding of the animal factors that affect measures such as conception rate would contribute to solving the problem of impaired reproductive performance in modern dairy cows. A question of particular interest relates to the observed improvement in conception rates from first to second and third oestrus cycle: is the increase in conception rate related to cycle number per se or to increasing days from calving? A 5-year study using three breeds (Holstein, Jersey and Danish Red) allowed this issue to be examined. In 560 lactations, from calving until confirmed pregnancy or until 290 days after calving all cows were observed daily for signs of oestrus. Inseminations were carried out to all clear cases of oestrus after 35 days from calving until the cow was judged to be pregnant or until 290 days post-calving. Days from calving to the first oestrus was modeled using a frailty model. Danish Red cows had a significantly greater rate of occurrence of first oestrus over time. Generalized linear mixed models defined using a binomial distribution and logit link function were used to estimate probability of pregnancy as affected by: breed, parity, oestrus number and days from calving to first oestrus. In order to account for the paradox that the incidence of cows with inherently low fertility increases with oestrus number, different random components for cow were included. There was a significant increase in probability of pregnancy from first to second oestrus but no significant change in probability of pregnancy thereafter. There was a significant increase in probability of pregnancy with increasing days to first oestrus. This indicates that both oestrus number and days from calving play a role in determining the fertility of dairy cows. Correcting for inherent differences in fertility was shown to be important

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.

The whole-animal model described in this paper is intended to be a research model with an intermediary structure between sophisticated and simple cow models. The mechanistic model structure integrates the main metabolic pathways of the lactating dairy cow. Milk yield and related feed intake for varying production potentials were considered to be the driving forces and were empirically defined. The model was designed to explain the main metabolic flows and variations in body reserves associated with the push of nutrients from dry matter intake and their pull by the mammary gland to synthesise milk components throughout lactation. The digestive part of the model uses either known feed unit systems (e.g. PDI system for protein) or published empirical equations for the prediction of digestive flows of organic matter, starch and fatty acids and ruminal volatile fatty acid production. The metabolic sub-system is made up of four tissue compartments (body protein, body lipid, protein in the uterus and triglycerides in the liver) and five circulating metabolites (glucose, amino acids, acetate + butyrate, propionate, and triglycerides and non-esterified fatty acids). A major original aspect of the model is its homeorhetic control system. It was assumed that flows relating to adipose and protein tissue anabolism and catabolism were driven by a couple of linked theoretical hormones controlling mobilisation and anabolism. The evolution in body composition and body weight were outcomes of this control. Another originality of the model is its suitability for milk yields varying from 10 to 50 kg at peak production. This was achieved by homeorhetic control of milk potential acting on several key metabolic flows. Homeostatic regulation was also introduced in order to confine the behaviour of the model within realistic physiological values. For some basic aspects (e.g. Uterine involution, standard kinetics of body protein and fat), it was necessary to build specific databases from the literature and interpret these by meta-analysis. Fairly realistic simulated kinetics were obtained for body composition, liver triglycerides, blood plasma metabolite concentrations, milk protein and fat contents, and also for major groups of milk fatty acids.

WOS:000393538500020; International audience; Embryo transfer between breeds of different sizes impacts fetal growth in horses. We have shown that it elicits various postnatal adaptations in terms of growth and glucose metabolism until weaning. Postweaning effects remain to be described. Pony (P), saddlebred (S), and draft (D) horses were used. Control Pony-in-Pony (P-P; n = 21) and Saddlebred-in-Saddlebred (S-S; n = 28) pregnancies were obtained by artificial insemination. Enhanced and restricted pregnancies were obtained by transferring P or S embryos into D mares (Pony-in-Draft [P-D], n = 6 and Saddlebred-in-Draft [S-D], n = 8) and S embryos into P mares (Saddlebred-in-Pony n = 6), respectively. Control and experimental foals were raised by their dams and recipient mothers, respectively and weaned on day 180. Weight gain, growth hormones, and glucose metabolism were investigated in foals between days 180 and 540. Pony-in-Draft (P-D) remained heavier than P-P on days 180, 360, and 540, with lower glucose and higher non-esterified fatty-acids on days 180, 360, and 540 and higher T-3 on day 180. Insulin sensitivity was similar between pony groups on days 200 and 540. S-P were lighter than S-D on day 180 but caught up by day 540. S-P had higher glucose than S-D on days 180, 360, and 540, as well as lower non-esterified fatty acids and higher T3 on day 180. Insulin sensitivity was higher in S-P than in S-D on day 200. No difference was observed between saddlebred groups thereafter. In conclusion, in horses, fetal growth is determinant for postnatal metabolism, especially for energy availability. (C) 2016 Elsevier Inc. All rights reserved.