Genetic relationships between weight loss in early lactation and daily milk production throughout lactation in Holstein cows.

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.

Association between body condition genomic values and feed intake, milk production, and body weight in French Holstein cows.

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.

The use of 3-dimensional imaging of Holstein cows to estimate body weight and monitor the composition of body weight change throughout lactation.

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.

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.

Short communication: Identifying key parameters for modelling the impacts of livestock health conditions on greenhouse gas emissions.

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.

Precision feed restriction improves feed and milk efficiencies and reduces methane emissions of less efficient lactating Holstein cows without impairing their performance.

A possible driver of feed inefficiency in dairy cows is overconsumption. The objective was therefore to test precision feed restriction as a lever to improve feed efficiency of the least efficient lactating dairy cows. An initial cohort of 68 Holstein lactating cows was monitored from calving to end of ad libitum feeding at 196 ± 16 d in milk, with the last 70 d being used to estimate feed efficiency. For a given expected dry matter (DM) intake (DMI) during ad libitum feeding, offered DMI during restriction was set to observed DMI of the 10% most efficient cows during ad libitum feeding for similar performance. Feed restriction lasted during 92 d, with only the last 70 d being used for data analyses. A single diet was fed during ad libitum and restriction periods, and was based on 64.9% of corn silage and 35.1% of concentrates on a DM basis. Individual DMI, body weight, milk production, milk composition, and body condition score were recorded, as well as methane emissions. Feed efficiency was defined as the repeatable part of the random effect of cow on the intercept in a mixed model predicting DMI with net energy in milk, maintenance and body weight gain and loss within parity, feeding level, and time. Milk energy efficiency was estimated in the same way, predicting net energy in milk instead of DMI. The 15 least efficient cows ate 2.6 kg of DM/d more than the 15 most efficient cows during ad libitum feeding with 2 g/kg of DMI lower methane yield, but similar daily methane emissions. Feed restriction decreased DMI by 2.6 kg of DMI/d for the least efficient cows, which was 1.8 kg of DMI/d more than the most efficient cows, and decreased daily methane emissions by 49.2 g/d for the least efficient cows, which was 22.4 g/d more than the most efficient cows. Feed restriction had no significant effect on milk, body weight, or body weight change. Feed restriction reduced the variability of both milk energy and feed efficiencies, as shown by a decrease of their standard deviation from 0.87 to 0.69 kg of DM/d for feed efficiency and from 1.14 to 0.65 UFL/d for milk energy efficiency. Despite narrow efficiency differences, the most efficient cows during ad libitum feeding remained more efficient during feed restriction (r = 0.46 for feed efficiency and 0.49 for milk energy efficiency). The 2 efficiency groups no longer differed in feed efficiency during precision feed restriction. Precision feed restriction seemed to bring the least efficient cows closer to the most efficient cows and to reduce their methane emissions without impairing their performance.

To what extent is climate change adaptation a novel challenge for agricultural modellers?

Modelling is key to adapting agriculture to climate change (CC), facilitating evaluation of the impacts and efficacy of adaptation measures, and the design of optimal strategies. Although there are many challenges to modelling agricultural CC adaptation, it is unclear whether these are novel or, whether adaptation merely adds new motivations to old challenges. Here, qualitative analysis of modellers' views revealed three categories of challenge: Content, Use, and Capacity. Triangulation of findings with reviews of agricultural modelling and Climate Change Risk Assessment was then used to highlight challenges specific to modelling adaptation. These were refined through literature review, focussing attention on how the progressive nature of CC affects the role and impact of modelling. Specific challenges identified were: Scope of adaptations modelled, Information on future adaptation, Collaboration to tackle novel challenges, Optimisation under progressive change with thresholds, and Responsibility given the sensitivity of future outcomes to initial choices under progressive change.

Influence of diet and manure management on ammonia and greenhouse gas emissions from dairy barns.

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.

Influence of particle size and density on mean retention time in the rumen of dairy cows.

Increasing rumen-undegraded protein is one challenge of ruminant nutrition to both meet protein requirements of animals and reduce nitrogen excretion in the environment by increasing nitrogen efficiency. Industrial processes using heat or tanning to reduce rumen protein degradation have certain limitations, such as difficulty in balancing low ruminal degradation and high intestinal digestibility. Reducing the mean retention time (MRT) in the rumen by varying the size and density of particles may be another promising way to increase the rumen-undegraded protein proportion of concentrate feeds and improve the effectiveness of industrial processes. Spherical plastic particles of 3 mean diameter sizes (1, 2, and 3 mm) and 4 densities (0.9, 1.1, 1.3, and 1.5) were used to study the combined effect of size and density on the MRT of particles without interactions with microbial fermentations. Dynamics of fecal excretion of particles were monitored over 106 h (17 sampling times) in a Latin square experiment with 4 lactating cows. Cumulative particle excretion curves were fitted to a double exponential model to calculate total MRT in the digestive tract (TMRT), MRT in 2 compartments (MRT1 and MRT2), and retention time in the intestines' tubular section (TT). Differences in density had a quadratic effect, with densities of 1.1 and 1.3 yielding lower TMRT (29.5 and 31.2 h, respectively) than the densities of 0.9 and 1.5 (TMRT = 64.0 and 51.2 h, respectively). Similar responses were observed for MRT1, which was assumed to be the ruminal MRT for densities 1.1 and 1.3 (8.9 and 10.5 h, respectively) compared with densities 0.9 and 1.5 (39.6 and 22.6 h, respectively). Differences in diameter had a linear effect on TMRT (12.9 h longer for 3 mm than for 1 mm) and on TT. A combined effect of size and density was observed and particle size had no effect on TMRT when density was 1.1 to 1.3; however, outside this range, an increase in particle diameter increased TMRT. Consequently, a density of 1.2 to 1.3 is optimal for the escape of particles. As smaller particles of concentrates lose functional specific gravity more rapidly than larger particles due to their higher fermentation rate, our results, obtained with plastic particles, suggest that a diameter slightly greater than 3 mm seems a compromise to delay the start of fermentation and allow for rapid passage through the reticulo-omasal orifice.

Identification of biological traits associated with differences in residual energy intake among lactating Holstein cows.

Residual feed intake, which is usually used to estimate individual variation of feed efficiency, requires frequent and accurate measurements of individual feed intake to be carried out. Developing a breeding scheme based on residual feed intake in dairy cows is therefore complicated, especially because feed intake is not measurable for a large population. Another solution could be to focus on biological determinants of feed efficiency, which could potentially be directly and broadband measurable on farm. Several phenotypes have been identified in literature as being associated with differences in feed efficiency. The present study therefore aims to identify which biological mechanisms are associated with residual energy intake (REI) differences among dairy cows. Several candidate phenotypes were recorded frequently and simultaneously throughout the first 238 d in milk for 60 Holstein cows fed on a constant diet based on maize silage. A multiple linear regression of the 238 d in milk average of net energy intake was fitted on the 238 d in milk averages for milk energy output, metabolic body weight, the sum over the 238 d in milk of both, body condition score loss and gain, and the residuals were defined as REI. A partial least square regression was fitted over all biological traits to explain REI variability. Linear multiple regression explained 93.6% of net energy intake phenotypic variation, with 65.5% associated with lactation requirement, 23.2% with maintenance, and 4.9% with body reserves change; the 6.4% residuals represented REI. Overall, measured biological traits contributed to 58.9% of REI phenotypic variability, which were mainly explained by activity (26.5%) and feeding behavior (21.3%). However, apparent confounding was observed between behavior, activity, digestibility, and rumen-temperature variables. Drawing a conclusion on biological traits that explain feed efficiency differences among dairy cows was not possible due to this apparent confounding between the measured variables. Further investigation is needed to validate these results and to characterize the causal relationship of feed efficiency with feeding behavior, digestibility, body reserves change, activity, and rumen temperature.

Isolating the cow-specific part of residual energy intake in lactating dairy cows using random regressions.

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.

An approach to holistically assess (dairy) farm eco-efficiency by combining Life Cycle Analysis with Data Envelopment Analysis models and methodologies.

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.

Low degradable protein supply to increase nitrogen efficiency in lactating dairy cows and reduce environmental impacts at barn level.

Generally, <30% of dairy cattle's nitrogen intake is retained in milk. Large amounts of nitrogen are excreted in manure, especially in urine, with damaging impacts on the environment. This study explores the effect of lowering dietary degradable nitrogen supplies--while maintaining metabolisable protein--on dairy cows' performance, nitrogen use efficiency and gas emissions (NH3, N2O, CH4) at barn level with tied animals. Two dietary N concentrations (CP: 12% DM for LowN; 18% DM for HighN) were offered to two groups of three lactating dairy cows in a split-plot design over four periods of 2 weeks. Diets were formulated to provide similar metabolisable protein supply, with degradable N either in deficit or in excess (PDIN of 84 and 114 g/kg DM for LowN and HighN, respectively). Cows ingested 0.8 kg DM/day less on the LowN diet, which was also 2.5% less digestible. Milk yield and composition were not significantly affected. N exported in milk was 5% lower (LowN: 129 g N/day; HighN: 136 g N/day; P<0.001) but milk protein yield was not significantly affected (LowN: 801 g/day; HighN: 823 g/day; P=0.10). Cows logically ingested less nitrogen on the LowN diet (LowN: 415 g N/day; HighN: 626 g N/day; P<0.001) resulting in a higher N use efficiency (N milk/N intake; LowN: 0.31; HighN: 0.22; P<0.001). N excreted in urine was almost four times lower on the LowN diet (LowN: 65 g N/day; HighN: 243 g N/day; P<0.001) while urinary urea N concentration was eightfold lower (LowN: 4.6 g/l; HighN: 22.9 g/l; P<0.001). Ammonia emission (expressed in g/h in order to remove periods of the day with potential interferences with volatile molecules from feed) was also lower on the LowN diet (LowN: 1.03 g/h per cow; HighN: 1.25 g/h per cow; P<0.05). Greenhouse gas emissions (N2O and CH4) at barn level were not significantly affected by the amount of dietary N. Offering low amounts of degradable protein with suitable metabolisable protein amounts to cattle improved nitrogen use efficiency and lowered ammonia emissions at barn level. This strategy would, however, need to be validated for longer periods, other housing systems (free stall barns) and at farm level including all stages of manure management.

Rear shape in 3 dimensions summarized by principal component analysis is a good predictor of body condition score in Holstein dairy cows.

Body condition is an indirect estimation of the level of body reserves, and its variation reflects cumulative variation in energy balance. It interacts with reproductive and health performance, which are important to consider in dairy production but not easy to monitor. The commonly used body condition score (BCS) is time consuming, subjective, and not very sensitive. The aim was therefore to develop and validate a method assessing BCS with 3-dimensional (3D) surfaces of the cow's rear. A camera captured 3D shapes 2 m from the floor in a weigh station at the milking parlor exit. The BCS was scored by 3 experts on the same day as 3D imaging. Four anatomical landmarks had to be identified manually on each 3D surface to define a space centered on the cow's rear. A set of 57 3D surfaces from 56 Holstein dairy cows was selected to cover a large BCS range (from 0.5 to 4.75 on a 0 to 5 scale) to calibrate 3D surfaces on BCS. After performing a principal component analysis on this data set, multiple linear regression was fitted on the coordinates of these surfaces in the principal components' space to assess BCS. The validation was performed on 2 external data sets: one with cows used for calibration, but at a different lactation stage, and one with cows not used for calibration. Additionally, 6 cows were scanned once and their surfaces processed 8 times each for repeatability and then these cows were scanned 8 times each the same day for reproducibility. The selected model showed perfect calibration and a good but weaker validation (root mean square error=0.31 for the data set with cows used for calibration; 0.32 for the data set with cows not used for calibration). Assessing BCS with 3D surfaces was 3 times more repeatable (standard error=0.075 versus 0.210 for BCS) and 2.8 times more reproducible than manually scored BCS (standard error=0.103 versus 0.280 for BCS). The prediction error was similar for both validation data sets, indicating that the method is not less efficient for cows not used for calibration. The major part of reproducibility error incorporates repeatability error. An automation of the anatomical landmarks identification is required, first to allow broadband measures of body condition and second to improve repeatability and consequently reproducibility. Assessing BCS using 3D imaging coupled with principal component analysis appears to be a very promising means of improving precision and feasibility of this trait measurement.

Effect of ambient temperature and sodium bicarbonate supplementation on water and electrolyte balances in dry and lactating Holstein cows.

The aim of this study was to quantify the effect of the interaction between 2 constant ambient temperatures [thermoneutrality (TN; 15°C) and high temperature (HT; 28°C)] and 2 levels of Na bicarbonate supplementation [calculated to provide diet Na contents of 0.20%DM (Na-) and 0.50%DM (Na+)] on water partitioning in dairy cows. Treatments were compared on 4 dry and 4mid-lactation Holstein cows according to 2 Latin squares (1 for each physiological stage) over the course of 4 periods of 15d. Diets consisted of a total mixed ration based on maize silage. Dry cows were restricted to their protein and energy requirements, whereas lactating cows were fed ad libitum. The daily average temperature-humidity index was 59.4 for TN and 73.2 for HT. Lactating and dry cows had higher vaginal temperatures at HT than at TN, but the increase was more pronounced in lactating cows (+1.05 vs. +0.12°C for vaginal temperature, respectively). Dry matter intake (DMI) of lactating cows decreased by 2.3kg/d at HT. Free water intake (FWI) and estimated volume of water lost to evaporation increased at HT in both lactating and dry cows; no interactions were observed between temperature and physiological stage. When expressed as a proportion of DMI, the increase in evaporation that occurred with increasing temperature was completely compensated for by an increase in FWI for both physiological stages. The urinary water excretion increased slightly at HT in lactating cows but not in dry cows, which may be related to the low chloride content of the offered diet. High Na supplementation increased DMI slightly in lactating cows, but milk yield was not affected. Sodium supplementation did not limit the decrease in DMI observed in lactating cows at HT; this observation is likely due to the high diet electrolyte balance of the offered diets. Sodium supplementation increased FWI in lactating cows and urinary flow in both physiological states. The interaction between ambient temperature and Na supplementation did not affect either water intake or water evaporation. This study demonstrates that the development of predictive models for water intake that include environmental variables could be based on mechanistic models of evaporation.

An individual reproduction model sensitive to milk yield and body condition in Holstein dairy cows.

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.

Significant differences in fertility between dairy cows selected for one QTL located on bovine chromosome 3 are not attributable to energy balance, although eating behaviour is affected.

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.

Advances in predicting nutrient partitioning in the dairy cow: recognizing the central role of genotype and its expression through time.

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.

Prediction of water intake and excretion flows in Holstein dairy cows under thermoneutral conditions.

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 r(2) of 0.51, DM was the first predictive parameter of free water intake (FWI), with a partial r(2) 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.

Milk protein synthesis in response to the provision of an "ideal" amino acid profile at 2 levels of metabolizable protein supply in dairy cows.

Providing a well-balanced supply of essential AA (EAA) can serve as an opportunity to reduce the protein intake for dairy cows by increasing the efficiency of metabolizable protein (or PDIE, its equivalent in the INRA feeding system) utilization for milk protein yield. Our objectives were to compare the effect of supplying an "ideal" EAA profile (EAA+) with an imbalanced AA profile (control) at 2 levels of PDIE/NE(L) (net energy for lactation) supplies to study the interaction between PDIE and AA profiles, and to compare this ideal profile with a simple mixture of the 4 most deficient EAA (4 EAA) in the diets of dairy cows. Six lactating multiparous Holstein cows received 6 treatments with 2 different levels of PDIE supplied by diets and AA infusions in the duodenum according to a changeover design with 3-wk periods. Within each PDIE supply level, the cows received 3 different AA infusions in the duodenum according to a 3×3 Latin square design with 1-wk subperiods, which corresponded to the following treatment groups: control (Glu), 4EAA (Lys, Met, His, Leu), and EAA+ (4 EAA plus Ile, Val, Phe, Trp, and Tyr). In the low and high PDIE treatments, diets and infusions provided 54.7 and 64.0 g/Mcal of PDIE/NE(L), respectively, which corresponded to crude protein levels of 13.6 and 15.2%, respectively. High-PDIE supplies increased the milk protein yield by 163 g/d, the milk protein content by 1.4 g/kg, the milk yield by 4.1 kg/d, and the lactose yield by 178 g/d and decreased the PDIE efficiency of utilization by 12.4%, whereas the N efficiency of utilization remained unaffected. Supplying the 2 EAA profiles (4EAA and EAA+) increased the milk protein yield by 67 g/d, the milk protein content by 1.3g/kg, and the milk yield by 0.9 kg/d, whereas the milk fat and milk lactose contents were decreased by 2.4 and 1.6g/kg, respectively. The responses regarding milk yield and its composition were similar whether the cows received the 4 EAA or the EAA+ treatment. The responses were similar for the milk yield and composition whether the EAA were supplied by low- or high-PDIE supplies. In conclusion, the efficiency of PDIE utilization was improved by 6.6% and the N efficiency was improved by 7.0% by correcting the EAA profiles, independent of the level of PDIE supplied. In addition, the increased efficiency observed, associated with provision of the 4 EAA, was similar to the provision of all EAA (EAA+) in this experiment.