Search

ABSTRACT: After calving, high-yielding dairy cows mobilize body reserves for energy, sometimes to the detriment of health and fertility. This study aimed to estimate the genetic correlation between body weight loss until nadir and daily milk production (MY24) in first- (L1) and second-lactation (L2) Holstein cows. The data set included 859,020 MY24 records and 570,651 daily raw body weight (BWr) phenotypes from 3,989 L1 cows, and 665,361 MY24 records and 449,449 BWr phenotypes from 3,060 L2 cows, recorded on 36 French commercial farms equipped with milking robots that included an automatic weighing platform. To avoid any bias due to change in digestive content, BWr was adjusted for variations in feed intake, estimated from milk production and BWr. Adjusted body weight was denoted BW. The genetic parameters of BW and MY24 in L1 and L2 cows were estimated using a 4-trait random regression model. In this model, the random effects were fitted by second-order Legendre polynomials on a weekly basis from wk 1 to 44. Nadir of BW was found to be earlier than reported in the literature, at 29 d in milk, and BW loss from calving to nadir was also lower than generally assumed, close to 29 kg. To estimate genetic correlations between body weight loss and production, we defined BWL5 as the loss of weight between wk 1 and 5 after calving. Genetic correlations between BWL5 and MY24 ranged from −0.26 to 0.05 in L1 and from −0.11 to 0.10 in L2, according to days in milk. These moderate to low values suggest that it may be possible to select for milk production without increasing early body mobilization.

ABSTRACT: Body condition score (BCS) offers a good estimate of the amount of stored fat on the body, and its variations can be used as a proxy for energy balance. Many countries have implemented a genomic evaluation of BCS, including France, where estimated breeding values are based on an individual BCS determination during the first lactation. In this article, we investigate the degree to which this genomic estimated breeding value based on a single phenotype record per cow might reflect different profiles of body reserves throughout lactation and be used to predict, and perhaps limit, their mobilization during early lactation. We also investigate whether selection on BCS affects other traits. A data set including 686 lactations of 435 Holstein cows from 3 experimental farms not used in the reference population for genomic evaluation was used to estimate the effects of the BCS direct genomic value (iBCS) on BCS, body weight, feed intake, milk production, and fat and protein contents throughout the lactation period. For each trait, the model included different iBCS regressions and an effect of the direct genomic value of the trait itself when available. It thus appeared that cows with a positive iBCS always had a higher BCS than negative iBCS cows, whatever the lactation stage, and that this difference increased during the first 6 mo to reach a difference of 0.8 point. A similar effect was seen regarding body weight, but it was the opposite for milk production, with negative iBCS cows producing slightly more milk (difference of about 3% over lactation). Feed intake increased slightly faster at the beginning of lactation for cows with positive iBCS. Therefore, iBCS is a promising tool that could help to limit intense mobilization during early lactation. Should feed efficiency be included in the breeding goal, greater attention should be paid to BCS to avoid further body mobilization in early lactation.

ABSTRACT: Three-dimensional (3D) imaging offers new possibilities in animal phenotyping. Here, we investigated how this technology can be used to study the morphological changes that occur in dairy cows over the course of a single lactation. First, we estimated the individual body weight (BW) of dairy cows using traits measured with 3D images. To improve the quality of prediction, we monitored body growth (via 3D imaging), gut fill (via individual dry matter intake), and body reserves (via body condition score) throughout lactation. A group of 16 Holstein cows—8 in their first lactation, 4 in their second lactation, and 4 in their third or higher lactation—was scanned in 3D once a month for an entire lactation. Values of morphological traits (e.g., chest depth or hip width) increased continuously with parity, but cows in their first lactation experienced the largest increase during the monitoring period. Values of partial volume, estimated from point of shoulder to pin bone, predicted BW with an error of 25.4 kg (R2 = 0.92), which was reduced to 14.3 kg when the individual effect of cows was added to the estimation model. The model was further improved by the addition of partial surface area (from point of shoulder to pin bone), hip width, chest depth, diagonal length, and heart girth, which increased the R2 of BW prediction to 0.94 and decreased root mean square error to 22.1 kg. The different slopes for individual cows were partly explained by body condition score and morphological traits, indicating that they may have reflected differences in body density among animals. Changes in BW over the course of lactation were mostly due to changes in growth, which accounted for around two-thirds of BW gain regardless of parity. Body reserves and gut fill had smaller but still notable effects on body composition, with a higher gain in body reserves and gut fill for cows in their first lactation compared with multiparous cows. This work demonstrated the potential for rapid and low-cost 3D imaging to facilitate the monitoring of several traits of high interest in dairy livestock farming.

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.

Dairy systems are a source of pollutant emissions, such as greenhouse gases (GHG) and NH3 that are associated with impacts on the environment. Gas emissions in barns are related mainly to diet intake and chemical composition, N excretion and manure management. A reduction in dietary N is known to be an effective way to reduce N excretion and the resulting NH3 emissions. However, most studies consider manure in liquid form with frequent removal from the barn. In deep litter systems, several processes can occur during the accumulation of solid manure that result in variable gas emissions. The objective of this experiment was to investigate the influence of the interaction between dietary CP (low or high) and manure management (liquid or solid) on gas emissions (NH3, N2O, CH4) at the barn level. Dietary treatments provided either low (LowN; 12% CP) or high (HighN; 18% CP) degradable protein to modify the amount of total ammonia nitrogen (TAN) excreted. The cows were housed for two 8-week periods in two mechanically ventilated rooms equipped to manage manure either in liquid (LM; slurry) or solid form (SM; deep litter). In the LM treatment, N balance was measured for 4 days. As expected, animals fed the LowN diet ingested 35% less N and excreted 65% less N in their urine, with no reduction in faecal N excretion and N secretion in milk. On the LowN diet, excretion of urea-N and NH3-N emissions were reduced regardless of the manure management. On the HighN diet, urinary urea-N excretion was three times as high, while NH3-N emissions were 3.0 and 4.5 times as high in LM and SM, respectively. Manure management strongly influenced CH4-C emissions, which were 30% higher in SM than in LM, due to the accumulation of litter. Moreover, gas emissions from solid manure increased over the accumulation period, except for NH3 on the LowN diet. Finally, our results suggest that methods used for national inventories would become more accurate by considering the variability in TAN excretion, which is the primary factor that influences NH3 emissions.

The choice of rearing strategy for dairy cows can have an effect on production yield, at least during the first lactation. For this reason, it is important to closely monitor the growth and development of young heifers. Unfortunately, current methods for evaluation can be costly, time-consuming, and dangerous because of the need to physically manipulate animals, and as a result, this type of monitoring is seldom performed on farms. One potential solution may be the use of tools based on three-dimensional (3D) imaging, which has been studied in adult cows but not yet in growing individuals. In this study, an imaging approach that was previously validated for adult cows was tested on a pilot population of five randomly selected growing Holstein heifers, from 5 weeks of age to the end of the first gestation. Once a month, all heifers were weighed and an individual 3D image was recorded. From these images, we estimated growth trends in morphological traits such as heart girth or withers height (188.1 ± 3.7 cm and 133.5 ± 6.0 cm on average at one year of age, respectively). From other traits, such as body surface area and volume (5.21 ± 0.32 m2 and 0.43 ± 0.05 m3 on average at one year of age, respectively), we estimated body weight based on volume (402.4 ± 37.5 kg at one year of age). Body weight estimates from images were on average 9.7% higher than values recorded by the weighing scale (366.8 ± 47.2 kg), but this difference varied with age (19.1% and 1.8% at 6 and 20 months of age, respectively). To increase accuracy, the predictive model developed for adult cows was adapted and completed with complementary data on young heifers. Using imaging data, it was also possible to analyze changes in the surface-to-volume ratio that occurred as body weight and age increased. In sum, 3D imaging technology is an easy-to-use tool for following the growth and management of heifers and should become increasingly accurate as more data are collected on this population.

Les nouvelles technologies des capteurs, de la communication et du traitement des données ont fait leur entrée dans les élevages. Elles constituent une nouvelle source d’informations à la fois plus nombreuses et diversifiées, à plus haute fréquence, sur des durées plus longues et pour plus d’entités. Elles soulèvent dans le même temps des problèmes de qualification, de fiabilité et de maintenance des informations qui restent largement à explorer. Dans la théorie de la décision, le terme information ne concerne que ce qui est susceptible d’avoir un impact sur la décision. Lorsque les informations acquises par des dispositifs automatiques se substituent à des informations obtenues par des activités de surveillance ou de mesure manuelles, ces dispositifs trouvent rapidement leur place s’ils sont fiables. À l’opposé, pour d’autres informations concernant les alertes de santé, le bien-être ou l’environnement, les dispositifs sont encore peu utilisés et leur intégration dans des outils performants d’aide à la décision est encore insuffisante. De plus, aujourd’hui, l’information sort du cadre strict de l’élevage. Les consommateurs veulent savoir d’où vient leur aliment, comment il a été produit, dans quel respect de l’environnement ou du bien-être animal, avec des garanties sur ces informations. En conclusion, les nouvelles technologies de l’information permettent l’accès à beaucoup de nouvelles informations en élevage, mais avec une idée encore trop imprécise de leurs utilisations dans les processus de décision de l’éleveur ou d’autres acteurs.

L’efficience alimentaire est un caractère de première importance pour l’élevage, tant du point de vue économique que comme élément de sa durabilité. Si l’efficience alimentaire peut s’étudier à différentes échelles, le choix est fait de situer cette synthèse au niveau de l’animal et des variations individuelles. Dans un premier temps, les différentes définitions de l’efficience et des critères utilisés pour l’estimer sont abordées, en comparant les possibilités offertes par les critères de type ratio et ceux de type résidu. L’ingéré résiduel (ou « Residual Feed Intake » : RFI), critère le plus plébiscité aujourd’hui dans les études sur les ruminants, est présenté. Dans un deuxième temps, les mécanismes biologiques impliqués dans l’efficience alimentaire sont passés en revue, ainsi que les moyens de les étudier. On retrouve tout d’abord ce qui a trait à l’efficience digestive, avec notamment l’impact du temps de séjour ou de la population microbienne. Puis, les déterminants de l’efficience métabolique tels que la production de chaleur, le taux de renouvellement protéique, la respiration mitochondriale, ou encore l’influence de la composition corporelle sont également détaillés. Dans un troisième temps, nous évoquons les défis à relever afin de mettre en place une potentielle sélection sur le caractère, la difficulté majeure résidant dans le phénotypage d’un nombre suffisant d’individus. Enfin, les liens entre efficience et résilience sont explorés, afin de prendre en compte la performance de l’animal sur un pas de temps plus long et d’inclure l’importance de la durabilité.

Improved animal health can reduce greenhouse gas (GHG) emissions intensity in livestock systems while increasing productivity. Integrated modelling of disease impacts on farm-scale emissions is important in identifying effective health strategies to reduce emissions. However, it requires that modellers understand the pathways linking animal health to emissions and how these might be incorporated into models. A key barrier to meeting this need has been the lack of a framework to facilitate effective exchange of knowledge and data between animal health experts and emissions modellers. Here, these two communities engaged in workshops, online exchanges and a survey to i) identify a comprehensive list of disease-related model parameters and ii) test its application to evaluating models. Fifty-six parameters were identified and proved effective in assessing the potential of farm-scale models to characterise livestock disease impacts on GHG emissions. Easy wins for the emissions models surveyed include characterising disease impacts related to feeding.

The ability to properly assess and accurately phenotype true differences in feed efficiency among dairy cows is key to the development of breeding programs for improving feed efficiency. The variability among individuals in feed efficiency is commonly characterised by the residual intake approach. Residual feed intake is represented by the residuals of a linear regression of intake on the corresponding quantities of the biological functions that consume (or release) energy. However, the residuals include both, model fitting and measurement errors as well as any variability in cow efficiency. The objective of this study was to isolate the individual animal variability in feed efficiency from the residual component. Two separate models were fitted, in one the standard residual energy intake (REI) was calculated as the residual of a multiple linear regression of lactation average net energy intake (NEI) on lactation average milk energy output, average metabolic BW, as well as lactation loss and gain of body condition score. In the other, a linear mixed model was used to simultaneously fit fixed linear regressions and random cow levels on the biological traits and intercept using fortnight repeated measures for the variables. This method split the predicted NEI in two parts: one quantifying the population mean intercept and coefficients, and one quantifying cow-specific deviations in the intercept and coefficients. The cow-specific part of predicted NEI was assumed to isolate true differences in feed efficiency among cows. NEI and associated energy expenditure phenotypes were available for the first 17 fortnights of lactation from 119 Holstein cows; all fed a constant energy-rich diet. Mixed models fitting cow-specific intercept and coefficients to different combinations of the aforementioned energy expenditure traits, calculated on a fortnightly basis, were compared. The variance of REI estimated with the lactation average model represented only 8% of the variance of measured NEI. Among all compared mixed models, the variance of the cow-specific part of predicted NEI represented between 53% and 59% of the variance of REI estimated from the lactation average model or between 4% and 5% of the variance of measured NEI. The remaining 41% to 47% of the variance of REI estimated with the lactation average model may therefore reflect model fitting errors or measurement errors. In conclusion, the use of a mixed model framework with cow-specific random regressions seems to be a promising method to isolate the cow-specific component of REI in dairy cows.

L’ambition de l’Atelier de Réflexion Prospective sur l’élevage a été de repenser la place, les rôles de l’élevage et en quoi il doit se transformer pour contribuer pleinement au développement de systèmes agri-alimentaires plus durables ainsi que d’identifier des thématiques de recherches interdisciplinaires permettant d’explorer des fronts de science ou méthodologiques porteurs d’avenir pour lever les verrous de connaissances. La réflexion a été animée par un groupe de 15 chercheurs et en a mobilisé une centaine. À partir d’une analyse du contexte et des drivers d’évolution des systèmes nous proposons un nouveau cadre conceptuel et des voies de progrès pour penser l’élevage de demain. L’élevage doit s’inscrire dans le cadre de systèmes agri-alimentaires circulaires dans lesquels il doit contribuer, au-delà d’une efficience accrue des moyens de production, à la préservation de la qualité des ressources et à la production d’une alimentation à un prix abordable. Il faut repenser les systèmes pour qu’ils soient climato-intelligents et répondent aux enjeux de santé et du bien-être des animaux et des Hommes ; repenser les liens entre élevage, production végétale et territoire pour maximiser les recyclages et repenser les liens entre élevage, transformation et consommation des produits de l’élevage. Les innovations doivent être basées sur les principes de l’agroécologie complétés par ceux de l’économie circulaire et par la mobilisation des leviers des (bio)technologies et de l’innovation organisationnelle. Le texte décrit quatre grandes priorités scientifiques avec leurs enjeux de recherche et produit des recommandations pour un plan d’action.

L’élevage est soumis à des défis considérables pour s’adapter au problème de compétition sur les ressources, notamment alimentaires, et aux problèmes environnementaux. L’amélioration de l’efficience de conversion des ressources alimentaires en produits animaux est une voie importante susceptible de répondre simultanément aux deux enjeux. Cette revue définit cette notion d’efficience en la plaçant dans le cadre plus global du triangle de la performance : pertinence, efficience et efficacité. Elle montre l’importance de mettre en perspective ces questions d’efficience alimentaire des animaux en abordant la question du changement d’échelle. Dans les secteurs de production de viande et de lait, elle fait le point sur les travaux conduits pour améliorer l’efficience à la fois par la sélection d’animaux capables de mieux transformer les aliments en produits animaux et par une conduite alimentaire plus précise et mieux équilibrée. Elle évoque les principales avancées pour réduire les émissions à risques pour l’environnement. Raisonner l’efficience alimentaire et les émissions de rejets à l’échelle de l’animal pour la production de lait ou de viande offre des perspectives de progrès importants, mais ne garantit pas toujours une amélioration de l’efficience à une échelle plus globale ni une réduction des impacts sur l’environnement. Il faut donc bien prendre soin de vérifier que les stratégies proposées sont efficaces et permettent d’atteindre les objectifs recherchés avec des indicateurs appropriés. La prise en compte des effets du changement d’échelle pour améliorer la performance globale est un vrai nouveau défi pour la recherche.

Generally,

Eco-efficiency is a useful guide to dairy farm sustainability analysis aimed at increasing output (physical or value added) and minimizing environmental impacts (EIs). Widely used partial eco-efficiency ratios (EIs per some functional unit, e.g. kg milk) can be problematic because (i) substitution possibilities between EIs are ignored, (ii) multiple ratios can complicate decision making and (iii) EIs are not usually associated with just the functional unit in the ratio’s denominator. The objective of this study was to demonstrate a ‘global’ eco-efficiency modelling framework dealing with issues (i) to (iii) by combining Life Cycle Analysis (LCA) data and the multiple-input, multiple-output production efficiency method Data Envelopment Analysis (DEA). With DEA each dairy farm’s outputs and LCA-derived EIs are aggregated into a single, relative, bounded, dimensionless eco-efficiency score, thus overcoming issues (i) to (iii). A novelty of this study is that a model providing a number of additional desirable properties was employed, known as the Range Adjusted Measure (RAM) of inefficiency. These properties altogether make RAM advantageous over other DEA models and are as follows. First, RAM is able to simultaneously minimize EIs and maximize outputs. Second, it indicates which EIs and/or outputs contribute the most to a farm’s eco-inefficiency. Third it can be used to rank farms in terms of eco-efficiency scores. Thus, non-parametric rank tests can be employed to test for significant differences in terms of eco-efficiency score ranks between different farm groups. An additional DEA methodology was employed to ‘correct’ the farms’ eco-efficiency scores for inefficiencies attributed to managerial factors. By removing managerial inefficiencies it was possible to detect differences in eco-efficiency between farms solely attributed to uncontrollable factors such as region. Such analysis is lacking in previous dairy studies combining LCA with DEA. RAM and the ‘corrective’ methodology were demonstrated with LCA data from French specialized dairy farms grouped by region (West France, Continental France) and feeding strategy (regardless of region). Mean eco-efficiency score ranks were significantly higher for farms with 30% maize in the total forage area before correcting for managerial inefficiencies. Mean eco-efficiency score ranks were higher for West than Continental farms, but significantly higher only after correcting for managerial inefficiencies. These results helped identify the eco-efficiency potential of each region and feeding strategy and could therefore aid advisors and policy makers at farm or region/sector level. The proposed framework helped better measure and understand (dairy) farm eco-efficiency, both within and between different farm groups.

In recent years, it has become increasingly clear that understanding nutrient partitioning is central to a much broader range of issues than just being able to predict productive outputs. The extent to which nutrients are partitioned to other functions such as health and reproduction is clearly important, as are the efficiency consequences of nutrient partitioning. Further, with increasing environmental variability, there is a greater need to be able to predict the ability of an animal to respond to the nutritional limitations that arise from the environment in which it is placed. How the animal partitions its nutrients when resources are limited, or imbalanced, is a major component of its ability to cope, that is, its robustness. There is mounting evidence that reliance on body reserves is increased and that robustness of dairy cows is reduced by selection for increased milk production. A key element for predicting the partition of nutrients in this wider context is to incorporate the priorities of the animal, that is, an explicit recognition of the role of both the cow's genotype (genetic make-up), and the expression of this genotype through time on nutrient partitioning. Accordingly, there has been a growing recognition of the need to incorporate in nutritional models these innate driving forces that alter nutrient partitioning according to physiological state, the genetically driven trajectories. This paper summarizes some of the work carried out to extend nutritional models to incorporate these trajectories, the genetic effects on them, as well as how these factors affect the homeostatic capacity of the animal. At present, there are models capable of predicting the partition of nutrients throughout lactation for cows of differing milk production potentials. Information concerning genotype and stage of lactation effects on homeostatic capacity has not yet been explicitly included in metabolic models that predict nutrient partition, although recent results suggest that this is achievable. These developments have greatly extended the generality of nutrient partitioning models with respect to the type of animal and its physiological state. However, these models remain very largely focussed on predicting partition between productive outputs and body reserves and, for the most part, remain research models, although substantial progress has been made towards developing models that can be applied in the field. The challenge of linking prediction of nutrient partitioning to its consequences on health, reproduction and longevity, although widely recognized, is only now beginning to be addressed. This is an important perspective for future work on nutrient partitioning.

Improvement of reproduction in dairy cows has become a major challenge in dairy production. We have recently shown that dairy cows carrying the ‘fertil−’ haplotype for one quantitative trait locus (QTL), affecting female fertility and located on the bovine chromosome 3, had a significantly lower conception rate after the first artificial insemination than cows carrying the ‘fertil+’ haplotype. The objective of this paper was to study other phenotypic modifications linked to this QTL. In the present study, 23 ‘fertil+’ and 18 ‘fertil−’ cows were characterized for live weight, milk production, food intake, eating behaviour and plasma metabolites. These parameters were measured during the first lactation, from calving to 40 weeks postpartum (wkpp). In the first 7 weeks of lactation, ‘fertil+’ primiparous cows had a significantly higher live BW and milk production than ‘fertil−’ cows. Dry matter intake tended to be slightly higher for ‘fertil+’ than for ‘fertil−’ primiparous cows in this period. However, energy balance was similar for the two haplotypes in the whole lactation, except in the first wkpp, and consequently, could not explain their different fertility. The major observation concerned the eating behaviour. ‘Fertil+’ primiparous cows had a significantly lower eating rate than ‘fertil−’ cows during the 40 weeks of lactation. In parallel, ‘fertil+’ cows spent significantly more time at the feeder for a similar number of visits than ‘fertil−’ cows. Furthermore, no differences in plasma concentrations of non-esterified fatty acids and insulin were observed between the two haplotypes. Plasma glucose was significantly lower in ‘fertil+’ than in ‘fertil−’ cows in the second wkpp. Taken together, our results show that ‘fertil+’ and ‘fertil−’ dairy cows, with different fertility, have also different eating behaviour without any variation in energy balance, except in the first week of lactation.

To simulate the consequences of management in dairy herds, the use of individual-based herd models is very useful and has become common. Reproduction is a key driver of milk production and herd dynamics, whose influence has been magnified by the decrease in reproductive performance over the last decades. Moreover, feeding management influences milk yield (MY) and body reserves, which in turn influence reproductive performance. Therefore, our objective was to build an up-to-date animal reproduction model sensitive to both MY and body condition score (BCS). A dynamic and stochastic individual reproduction model was built mainly from data of a single recent long-term experiment. This model covers the whole reproductive process and is composed of a succession of discrete stochastic events, mainly calving, ovulations, conception and embryonic loss. Each reproductive step is sensitive to MY or BCS levels or changes. The model takes into account recent evolutions of reproductive performance, particularly concerning calving-to-first ovulation interval, cyclicity (normal cycle length, prevalence of prolonged luteal phase), oestrus expression and pregnancy (conception, early and late embryonic loss). A sensitivity analysis of the model to MY and BCS at calving was performed. The simulated performance was compared with observed data from the database used to build the model and from the bibliography to validate the model. Despite comprising a whole series of reproductive steps, the model made it possible to simulate realistic global reproduction outputs. It was able to well simulate the overall reproductive performance observed in farms in terms of both success rate (recalving rate) and reproduction delays (calving interval). This model has the purpose to be integrated in herd simulation models to usefully test the impact of management strategies on herd reproductive performance, and thus on calving patterns and culling rates.

We aimed at quantifying the extent to which agricultural management practices linked to animal production and land use affect environmental outcomes at a larger scale. Two practices closely linked to farm environmental performance at a larger scale are farming intensity, often resulting in greater off-farm environmental impacts (land, non-renewable energy use etc.) associated with the production of imported inputs (e.g. concentrates, fertilizer); and the degree of self-sufficiency, i.e. the farm's capacity to produce goods from its own resources, with higher control over nutrient recycling and thus minimization of losses to the environment, often resulting in greater on-farm impacts (eutrophication, acidification etc.). We explored the relationship of these practices with farm environmental performance for 185 French specialized dairy farms. We used Partial Least Squares Structural Equation Modelling to build, and relate, latent variables of environmental performance, intensification and self-sufficiency. Proxy indicators reflected the latent variables for intensification (milk yield/cow, use of maize silage etc.) and self-sufficiency (home-grown feed/total feed use, on-farm energy/total energy use etc.). Environmental performance was represented by an aggregate 'eco-efficiency' score per farm derived from a Data Envelopment Analysis model fed with LCA and farm output data. The dataset was split into two spatially heterogeneous (bio-physical conditions, production patterns) regions. For both regions, eco-efficiency was significantly negatively related with milk yield/cow and the use of maize silage and imported concentrates. However, these results might not necessarily hold for intensive yet more self-sufficient farms. This requires further investigation with latent variables for intensification and self-sufficiency that do not largely overlap- a modelling challenge that occurred here. We conclude that the environmental 'sustainability' of intensive dairy farming depends on particular farming systems and circumstances, although we note that more self-sufficient farms may be preferable when they may benefit from relatively low land prices and agri-environment schemes aimed at maintaining grasslands.

The increase in the worldwide demand for dairy products, associated with global warming, will emphasize the issue of water use efficiency in dairy systems. The evaluation of environmental issues related to the management of animal dejections will also require precise biotechnical models that can predict effluent management in farms. In this study, equations were developed and evaluated for predicting the main water flows at the dairy cow level, based on parameters related to cow productive performance and diet under thermoneutral conditions. Two datasets were gathered. The first one comprised 342 individual measurements of water balance in dairy cows obtained during 18 trials at the experimental farm of Méjussaume (INRA, France). Predictive equations of water intake, urine and fecal water excretion were developed by multiple regression using a stepwise selection of regressors from a list of seven candidate parameters, which were milk yield, dry matter intake (DMI), body weight, diet dry matter content (DM), proportion of concentrate (CONC) and content of crude protein (CP) ingested with forage and concentrate (CPf and CPc, g/kg DM). The second dataset was used for external validation of the developed equations and comprised 196 water flow measurements on experimental lots obtained from 43 published papers related to water balance or digestibility measurements in dairy cows. Although DMI was the first predictor of the total water intake (TWI), with a partial r2 of 0.51, DM was the first predictive parameter of free water intake (FWI), with a partial r2 of 0.57, likely due to the large variability of DM in the first dataset (from 11.5 to 91.4 g/100 g). This confirmed the compensation between water drunk and ingested with diet when DM changes. The variability of urine volume was explained mainly by the CPf associated with DMI (r.s.d. 5.4 kg/day for an average flow of 24.0 kg/day) and that of fecal water was explained by the proportion of CONC in the diet and DMI. External validation showed that predictive equations excluding DMI as predictive parameters could be used for FWI, urine and fecal water predictions if cows were fed a well-known total mixed ration. It also appeared that TWI and FWI were underestimated when ambient temperature increased above 25°C and possible means of including climatic parameters in future predictive equations were proposed.

In regions of intensive pig and dairy farming, nutrient losses to the environment at farm level are a source of concern for water and air quality. Dynamic models are useful tools to evaluate the effects of production strategies on nutrient flows and losses to the environment. This paper presents the development of a new whole-farm model upscaling dynamic models developed at the field or animal scale. The model, called MELODIE, is based on an original structure with interacting biotechnical and decisional modules. Indeed, it is supported by an ontology of production systems and the associated programming platform DIESE. The biotechnical module simulates the nutrient flows in the different animal, soil and crops and manure sub-models. The decision module relies on an annual optimization of cropping and spreading allocation plans, and on the flexible execution of activity plans for each simulated year. These plans are examined every day by an operational management sub-model and their application is context dependent. As a result, MELODIE dynamically simulates the flows of carbon, nitrogen, phosphorus, copper, zinc and water within the whole farm over the short and long-term considering both the farming system and its adaptation to climatic conditions. Therefore, it is possible to study both the spatial and temporal heterogeneity of the environmental risks, and to test changes of practices and innovative scenarios. This is illustrated with one example of simulation plan on dairy farms to interpret the Nitrogen farm-gate budget indicator. It shows that this indicator is able to reflect small differences in Nitrogen losses between different systems, but it can only be interpreted using a mobile average, not on a yearly basis. This example illustrates how MELODIE could be used to study the dynamic behaviour of the system and the dynamic of nutrient flows. Finally, MELODIE can also be used for comprehensive multi-criterion assessments, and it also constitutes a generic and evolving framework for virtual experimentation on animal farming systems.

Dans le cadre de la révision des unités d’alimentation des ruminants (projet INRA « Systali »), les besoins protéiques ainsi que les réponses aux variations des apports protéiques ont été actualisés. Le calcul des besoins d’entretien intègre les trois principales voies de pertes obligatoires azotées, qui aboutissent aux besoins protéiques non productifs : les protéines endogènes fécales, les pertes azotées urinaires endogènes et les pertes de protéines par les phanères. Ces actualisations ont été effectuées par approche factorielle et à partir de méta-analyses de larges bases de données de la littérature. Les nouvelles valeurs de ces besoins en protéines digestibles dans l’intestin (PDI) « non productifs » ont ensuite été appliquées à de nombreuses expériences conduites sur vaches ou chèvres laitières avec pour objectif d’étudier les variations des réponses aux apports protéiques. Pour l’estimation de la réponse de l’efficacité des PDI en protéine de lait (EffPDI), il a été décidé d’appliquer une valeur commune pour les différentes productions de protéines ainsi que pour l’accrétion et la mobilisation des protéines corporelles, liées au bilan d’énergie. EffPDI est une fonction décroissante non linéaire de la concentration en PDI, identique entre les vaches et les chèvres. Il est aussi possible de prédire la réponse de la production des protéines laitières en fonction du niveau d’apport des PDI disponibles et en utilisant, comme situation pivot, soit la production pour EffPDI = 67%, soit le potentiel de production. Les données recueillies ont également permis de calculer, de façon factorielle, les pertes azotées urinaires dont les deux principales causes sont l’inefficacité des PDI et l’état nutritionnel azoté du rumen évalué par le critère de la balance protéique du rumen (BalProRu).

The dairy farming systems of Western Europe are based on a simple feeding system composed of grazed and preserved grass, maize silage and concentrates in variable proportions. There is, nevertheless, a great diversity of feeding strategies between dairy farms. Over 5 years, we studied the direct and delayed effects of four feeding strategies on the lactation and reproduction performances of Holstein and Normande dairy cows. The four feeding strategies (denoted Hh, Hl, Lh and Ll) correspond to two total mixed rations applied in winter from calving to turnout (maize silage with 30% concentrate or grass silage with 15% concentrate), which were subsequently crossed with two levels of concentrate supplementation at grazing to 210 days. Each year, 72 dairy cows managed in grouped winter calving were assigned to the four strategies. Finally, the results of 325 lactations and 295 inseminated cows were analysed. The four strategies resulted in considerable variation in nutrient intake and, in particular, in differences in concentrates consumed, with values of 1407, 1026, 773 and 392 kg dry matter per cow for strategies Hh, Lh, Hl and Ll, respectively. Total milk production (7567, 7015, 6720 and 6238 kg per cow for treatments Hh, Lh, Hl and Ll, respectively), milk fat content (39.0, 37.1, 40.3 and 38.5 g/kg, respectively), milk protein content (33.0, 31.8, 33.1 and 31.6 g/kg, respectively), and the character of the lactation and body condition curves were all highly sensitive to the strategies applied. While no significant interaction was detected on total lactation yield, the Holstein cows reacted more dramatically to each dietary change at each period, compared with the Normande cows. Winter feeding did not affect the production of milk at pasture whereas, at pasture, the milk from the cows of the H groups in winter was higher in milk fat and protein content. Reproduction performance was unaffected by feeding strategy. The Holstein cows, well fed and producing the most milk (Hh and Hb), had the lowest rate of success at first artificial inseminations (21.5%). The dual-purpose Normande cows had a pregnancy rate 10 points higher than Holstein cows. This comparison of strongly contrasting feeding strategies confirms the immediate reactivity of dairy cows (in terms of milk performance and body condition) to variations of nutritive intake throughout lactation, with a weak carryover effect from feeding levels early in lactation. In contrast, reproduction performance was less sensitive to variation in nutrient supply.

Rumen fill may be a strong intake constraint for dairy cows fed on pasture, even though pasture is highly digestible in the grasslands of temperate climates. This constraint may also depend on the cows’ maturity. Moreover, indoor feeding of fresh herbage may not always be a good model for the study of intake regulation at grazing. To test these hypotheses, four mature (6.3 ± 0.72 year old) and four young (3.8 ± 0.20 year old) dairy cows were offered fresh perennial ryegrass indoors or at grazing. The impact of rumen fill on intake was evaluated by addition of rumen inert bulk (RIB; coconut fiber, 15 l) compared to a control. The experimental design was a double 4 × 4 Latin square with four 14-day periods and a 2 × 2 factorial arrangement of two feeding methods (indoor feeding v. grazing), combined with the addition, or not, of RIB (RIB v. control), repeated for four mature and four young cows. Digestibility of offered herbage was 0.81. The average ytterbium measured dry matter intake (Yb DMI) was 19.0 and 15.5 kg/day for mature and young cows respectively (P = 0.019). The effect of RIB on predicted Yb DMI interacted with feeding method and cow age (P = 0.043). The presence of RIB decreased Yb DMI by 4.4 kg/day in mature cows at grazing and by 3.4 kg/day in young cows indoors, whereas it did not affect the Yb DMI of mature cows indoors or grazing young cows. Both grazing and young age constituted a clear constraint on the feeding behavior of the cows. Grazing cows had fewer ingestion and rumination sequences, which were longer and less evenly distributed throughout the day and night. Young cows had lower intake rates that were less adaptable to the feeding method and the presence of RIB. Mature cows clearly decreased their daily intake rate at grazing compared to indoor feeding, and with RIB compared to control, whereas the intake rate of young cows did not vary. These results indicate that rumen fill can represent a constraint on intake in grazing cows, even when highly digestible perennial ryegrass is offered. The study also shows that the impact of RIB on intake is highly dependent upon other constraints applied to the chewing behavior, which in this experiment were methods of offering herbage and cow age.

Applying holistic indicators to assess dairy farm efficiency is essential for sustainable milk production. Data Envelopment Analysis (DEA) has been instrumental for the calculation of such indicators. However, ‘additive’ DEA models have been rarely used in dairy research. This study presented an additive model known as slacks-based measure (SBM) of efficiency and its advantages over DEA models used in most past dairy studies. First, SBM incorporates undesirable outputs as actual outputs of the production process. Second, it identifies the main production factors causing inefficiency. Third, these factors can be ‘priced’ to estimate the cost of inefficiency. The value of SBM for efficiency analyses was demonstrated with a comparison of four contrasting dairy management systems in terms of technical and environmental efficiency. These systems were part of a multiple-year breeding and feeding systems experiment (two genetic lines: select vs. control; and two feeding strategies: high forage vs. low forage, where the latter involved a higher proportion of concentrated feeds) where detailed data were collected to strict protocols. The select genetic herd was more technically and environmentally efficient than the control herd, regardless of feeding strategy. However, the efficiency performance of the select herd was more volatile from year to year than that of the control herd. Overall, technical and environmental efficiency were strongly and positively correlated, suggesting that when technically efficient, the four systems were also efficient in terms of undesirable output reduction. Detailed data such as those used in this study are increasingly becoming available for commercial herds through precision farming. Therefore, the methods presented in this study are growing in importance.

Face au double défi du secteur de l’élevage qui devra répondre à l’augmentation future de la demande en protéines animales tout en réduisant ses impacts environnementaux, l’analyse énergétique fossile apparaît comme une méthode d’évaluation pertinente. Elle permet de fournir à la fois des références sur les consommations et l’efficience d’utilisation de l’énergie fossile, mais également sur les voies d’intensification écologique de ces systèmes. Son application dans le cadre de deux précédentes études à des systèmes d’élevage en Afrique de l’Ouest a nécessité des améliorations méthodologiques. Celles-ci ont concerné la prise en compte de la mobilité des troupeaux, l’analyse à l’échelle du troupeau afin de produire des comparaisons pertinentes entre systèmes, l’affectation d’un coût en énergie fossile au travail humain et animal et l’ouverture du panel des productions aux effluents et à la traction animale produite. D’autres perspectives d’amélioration, liées notamment à l’étude d’autres systèmes d’élevage et de nouveaux territoires, sont également proposées.

L’eau d’abreuvement est un besoin incompressible des élevages laitiers. Elle représente un coût non négligeable et sa restriction conduit immédiatement à des baisses de performances et à une altération du bien-être des troupeaux. Le bilan hydrique de l’animal est équilibré grâce à la régulation de l’excrétion rénale d’eau et des volumes d’eau bue en réponse à des consignes de pression osmotique plasmatique et de volume sanguin. De nombreuses équations de prédiction des besoins en eau des vaches laitières ont été publiées. La teneur en matière sèche de la ration ressort comme le premier paramètre prédictif des volumes d’eau bue parce qu’elle détermine la part relative de l’eau provenant de l’aliment et bue dans l’eau totale ingérée. La quantité de matière sèche ingérée, les teneurs en sodium, potassium et azote de la ration ainsi que la température ambiante sont les principaux paramètres prédictifs de l’eau totale ingérée. La comparaison des erreurs de prédiction des équations des besoins en eau des vaches laitières sur un jeu de données commun montre des performances très hétérogènes. Les critères de choix des équations doivent être la nature des paramètres prédictifs disponibles mais aussi les conditions dans lesquelles elles ont été établies. Les équations de prédiction des besoins en eau des vaches laitières ont souvent été établies à partir de mesures individuelles et leur application sur un troupeau disposant d’abreuvoirs à niveau constant peut entrainer un biais de prédiction. Quelques pistes sont évoquées pour quantifier les besoins en eau des génisses.