Nicolas Charles Friggens

On-farm estimation of energy balance in dairy cows using only frequent body weight measurements and body condition score

Precise energy balance estimates for individual cows are of great importance to monitor health, reproduction, and feed management. Energy balance is usually calculated as energy input minus output (EB(inout)), requiring measurements of feed intake and energy output sources (milk, maintenance, activity, growth, and pregnancy). Except for milk yield, direct measurements of the other sources are difficult to obtain in practice, and estimates contain considerable error sources, limiting on-farm use. Alternatively, energy balance can be estimated from body reserve changes (EB(body)) using body weight (BW) and body condition score (BCS). Automated weighing systems exist and new technology performing semi-automated body condition scoring has emerged, so frequent automated BW and BCS measurements are feasible. We present a method to derive individual EB(body) estimates from frequently measured BW and BCS and evaluate the performance of the estimated EB(body) against the traditional EB(inout) method. From 76 Danish Holstein and Jersey cows, parity 1 or 2+, on a glycerol-rich or a whole grain-rich total mixed ration, BW was measured automatically at each milking. The BW was corrected for the weight of milk produced and for gutfill. Changes in BW and BCS were used to calculate changes in body protein, body lipid, and EB(body) during the first 150 d in milk. The EB(body) was compared with the traditional EB(inout) by isolating the term within EB(inout) associated with most uncertainty; that is, feed energy content (FEC); FEC=(EB(body)+EMilk+EMaintenance+Eactivity)/dry matter intake, where the energy requirements are for milk produced (EMilk), maintenance (EMaintenance), and activity (EActivity). Estimated FEC agreed well with FEC values derived from tables (the mean estimate was 0.21 MJ of effective energy/kg of dry matter or 2.2% higher than the mean table value). Further, the FEC profile did not suggest systematic bias in EB(body) with stage of lactation. The EB(body) estimated from daily BW, adjusted for milk and meal-related gutfill and combined with frequent BCS, can provide a successful tool. This offers a pragmatic solution to on-farm calculation of energy balance with the perspective of improved precision under commercial conditions.

Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms

Lameness in dairy herds is traditionally detected by visual inspection, which is time-consuming and subjective. Compared with healthy cows, lame cows often spend longer time lying down, walk less and change behaviour around feeding time. Accelerometers measuring cow leg activity may assist farmers in detecting lame cows. On four commercial farms, accelerometer data were derived from hind leg-mounted accelerometers on 348 Holstein cows, 53 of them during two lactations. The cows were milked twice daily and had no access to pasture. During a lactation, locomotion score (LS) was assessed on average 2.4 times (s.d. 1.3). Based on daily lying duration, standing duration, walking duration, total number of steps, step frequency, motion index (MI, i.e. total acceleration) for lying, standing and walking, eight accelerometer means and their corresponding coefficient of variation (CV) were calculated for each week immediately before an LS. A principal component analysis was performed to evaluate the relationship between the variables. The effects of LS and farm on the principal components (PC) and on the variables were analysed in a mixed model. The first four PC accounted for 27%, 18%, 12% and 10% of the total variation, respectively. PC1 corresponded to Activity variability due to heavy loading by five CV variables related to standing and walking. PC2 corresponded to Activity level due to heavy loading by MI walking, MI standing and walking duration. PC3 corresponded to Recumbency due to heavy loading by four variables related to lying. PC4 corresponded mainly to Stepping due to heavy loading by step frequency. Activity variability at LS4 was significantly higher than at the lower LS levels. Activity level was significantly higher at LS1 than at LS2, which was significantly higher than at LS4. Recumbency was unaffected by LS. Stepping at LS1 and LS2 was significantly higher than at LS3 and LS4. Activity level was significantly lower on farm 3 compared with farms 1 and 2. Stepping was significantly lower on farms 1 and 3 compared with farms 2 and 4. MI standing indicated increased restlessness while standing when cows increased from LS3 to LS4. Lying duration was only increased in lame cows. In conclusion, Activity level differed already between LS1 and LS2, thus detecting early signs of lameness, particularly through contributions from walking duration and MI walking. Lameness detection models including walking duration, MI walking and MI standing seem worthy of further investigation.

Towards an improved estimation of the biological components of residual feed intake in growing cattle

Residual feed intake (RFI) is the difference between observed and predicted feed intake. It is calculated as the residuals from a multiple regression model of DMI on the various energy expenditures (e.g., maintenance, growth, activity). Residual feed intake is often cited to be indicative of feed efficiency differences among animals. However, explaining a large proportion of the (phenotypic and genetic) interanimal variation in RFI remains difficult. Here we first describe a biological framework for RFI dwelling on similarities between RFI and energy balance. Alternative phenotypic and genetic statistical models are subsequently applied to a dataset of 1,963 growing bulls of 2 British and 3 Continental breeds. The novel aspect of this study was the use of a mixed model framework to quantify the heritable interanimal variation in the partial regression coefficients on the energy expenditure traits within the RFI equation. Heritable genetic variation in individual animal regression coefficients for metabolic live weight existed. No significant genetic variation in animal-level regression coefficients for growth or body fat level, however, existed in the study population. The presence of genetic variation in the partial regression coefficient of maintenance suggests the existence of interanimal variation in maintenance efficiency. However, it could also simply reflect interanimal genetic variation in correlated energy expenditure traits not included in the statistical model. Estimated breeding values for the random regression coefficient could be useful phenotypes in themselves for studies wishing to elucidate the underlying mechanisms governing differences among animals in RFI.

Factors affecting energy and nitrogen efficiency of dairy cows: a meta-analysis.

A meta-analysis was performed to explore the correlation between energy and nitrogen efficiency of dairy cows, and to study nutritional and animal factors that influence these efficiencies, as well as their relationship. Treatment mean values were extracted from 68 peer-reviewed studies, including 306 feeding trials. The main criterion for inclusion of a study in the meta-analysis was that it reported, or permitted calculation of, energy efficiency (Eeff; energy in milk/digestible energy intake) and nitrogen efficiency (Neff; nitrogen in milk/digestible nitrogen intake) at the digestible level (digestible energy or digestible protein). The effect of nutritional and animal variables, including neutral detergent fiber, acid detergent fiber (ADF), digestible energy, digestible protein, proportion of concentrate (PCO), dry matter intake, milk yield, days in milk, and body weight, on Eeff, Neff, and the Neff:Eeff ratio was analyzed using mixed models. The interstudy correlation between Eeff and Neff was 0.62, whereas the intrastudy correlation was 0.30. The higher interstudy correlation was partly due to milk yield and dry matter intake being present in both Eeff and Neff. We, therefore, also explored the Neff:Eeff ratio. Energy efficiency was negatively associated with ADF and PCO, whereas Neff was negatively associated with ADF and digestible energy. The Neff:Eeff ratio was affected by ADF and PCO only. In conclusion, the results indicate a possibility to maximize feed efficiency in terms of both energy and nitrogen at the same time. In other words, an improvement in Eeff would also mean an improvement in Neff. The current study also shows that these types of transverse data are not sufficient to study the effect of animal factors, such as days in milk, on feed efficiency. Longitudinal measurements per animal would probably be more appropriate.

A multivariate analysis using physiology and behavior to characterize robustness in two isogenic lines of rainbow trout exposed to a confinement stress

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

e-Cow: an animal model that predicts herbage intake, milk yield and live weight change in dairy cows grazing temperate pastures, with and without supplementary feeding

This animal simulation model, named e-Cow, represents a single dairy cow at grazing. The model integrates algorithms from three previously published models: a model that predicts herbage dry matter (DM) intake by grazing dairy cows, a mammary gland model that predicts potential milk yield and a body lipid model that predicts genetically driven live weight (LW) and body condition score (BCS). Both nutritional and genetic drives are accounted for in the prediction of energy intake and its partitioning. The main inputs are herbage allowance (HA; kg DM offered/cow per day), metabolisable energy and NDF concentrations in herbage and supplements, supplements offered (kg DM/cow per day), type of pasture (ryegrass or lucerne), days in milk, days pregnant, lactation number, BCS and LW at calving, breed or strain of cow and genetic merit, that is, potential yields of milk, fat and protein. Separate equations are used to predict herbage intake, depending on the cutting heights at which HA is expressed. The e-Cow model is written in Visual Basic programming language within Microsoft Excel®. The model predicts whole-lactation performance of dairy cows on a daily basis, and the main outputs are the daily and annual DM intake, milk yield and changes in BCS and LW. In the e-Cow model, neither herbage DM intake nor milk yield or LW change are needed as inputs; instead, they are predicted by the e-Cow model. The e-Cow model was validated against experimental data for Holstein-Friesian cows with both North American (NA) and New Zealand (NZ) genetics grazing ryegrass-based pastures, with or without supplementary feeding and for three complete lactations, divided into weekly periods. The model was able to predict animal performance with satisfactory accuracy, with concordance correlation coefficients of 0.81, 0.76 and 0.62 for herbage DM intake, milk yield and LW change, respectively. Simulations performed with the model showed that it is sensitive to genotype by feeding environment interactions. The e-Cow model tended to overestimate the milk yield of NA genotype cows at low milk yields, while it underestimated the milk yield of NZ genotype cows at high milk yields. The approach used to define the potential milk yield of the cow and equations used to predict herbage DM intake make the model applicable for predictions in countries with temperate pastures.

Reproductive robustness differs between generalist and specialist maternal rabbit lines: the role of acquisition and allocation of resources

BackgroundFarm animals are normally selected under highly controlled, non-limiting conditions to favour the expression of their genetic potential. Selection strategies can also focus on a single trait to favour the most ‘specialized’ animals. Theoretically, if the environment provides enough resources, the selection strategy should not lead to changes in the interactions between life functions such as reproduction and survival. However, highly ‘specialized’ farm animals can be required for breeding under conditions that differ largely from selection conditions. The consequence is a degraded ability of ‘specialized’ animals to sustain reproduction, production and health, which leads to a reduced lifespan. This study was designed to address this issue using maternal rabbit lines. A highly specialized line with respect to numerical productivity at weaning (called V) and a generalist line that originated from females with a long reproductive life (called LP) were used to study the strategies that these lines develop to acquire and use the available resources when housed in different environments. In addition, two generations of line V, generations 16 and 36, were available simultaneously, which contributed to better understand how selection criteria applied in a specific environment changed the interplay between functions related to reproduction and survival.ResultsWe show that, under constrained conditions, line LP has a greater capacity for resource acquisition than line V, which prevents excessive mobilization of body reserves. However, 20 generations of selection for litter size at weaning did not lead to an increased capacity of nutrient (or resource) acquisition. For the two generations of line V, the partitioning of resources between milk production, body reserves preservation or repletion or foetal growth differed.ConclusionsCombining foundational and selection criteria with a specific selection environment resulted in female rabbits that had a different capacity to deal with environmental constraints. An increased robustness was considered as an emergent property of combining a multiple trait foundational criterion with a wide range of environmental conditions. Since such a strategy was successful to increase the robustness of female rabbits without impairing their productivity, there is no reason that it should not be applied in other livestock species.

Environmental sensitivity differs between rabbit lines selected for reproductive intensity and longevity

To better understand the mechanisms that allow some animals to sustain their productive effort in harsh environmental conditions, rabbit does from two selection lines (LP and V) were housed in normal (NC), nutritional (NF) or heat (HC) challenging environmental conditions from first to third partum. The LP line (n=85) was founded on reproductive longevity criteria by selecting does from commercial farms that had a minimum of 25 partum with more than 7.5 kits born alive per parity. Line V (n=79) was constituted from four specialised maternal lines into a composite synthetic line and then selected by litter size at weaning for 36 generations. Female rabbits in NC and NF environments were housed at normal room temperature (18°C to 24°C) and fed with control [11.6 MJ digestible energy (DE)/kg dry matter (DM)] or low-energy diets (9.1 MJ DE/kg DM). HC does were housed at high room temperatures (25°C to 35°C) and fed the control diet. Female rabbits in the HC and NF environments ingested 11.5% and 6% less DE than NC does, respectively (P<0.05). These differences between environments occurred in both lines, with the differences being higher for LP than for V does (+6%; P<0.05). Milk yield responses followed those of energy intake also being higher for LP does (+21.3 g/day; P<0.05). The environmental conditions did not affect the perirenal fat thickness (PFT), but a genotype by environment interaction was observed. In NC and HC, the PFT was higher for line V (+0.23 and +0.35 mm, respectively; P<0.05) than for LP does, but this was not the case at NF (-0.01 mm). Moreover, the PFT evolution was different between them. In the NC environment, LP does used the accreted PFT in late lactation (-0.29 mm), whereas V does did not (-0.08 mm). Conversely, in the HC environment, LP does showed a flat PFT evolution in late lactation, whereas V does accumulated PFT. In the NF environment, LP and V does had a similar PFT evolution. There was also a litter size reduction for V does of -2.59 kits total born in HC and -1.78 kits total born in NF environments, whereas this was not observed for LP does. The results for LP does indicate a direct use of DE ingested for reproduction with little PFT change, whereas V does actively use the PFT reserves for reproduction.

Data-derived reference profiles with corepresentation of progesterone, estradiol, LH, and FSH dynamics during the bovine estrous cycle

Subfertility in cows is often associated with alterations in the hormonal patterns involved in the regulation of the estrous cycle. Reference profiles are needed to ground modeling projects aimed at describing these alterations and to develop tools for detecting abnormal dynamics. Various schematic views of LH, FSH, progesterone (P4) and estradiol (E2) patterns have been published but with no clear indication of the extent to which they are derived from real data. The objective of this study was to generate standard profiles for the main reproductive hormones that can be proposed as reliable references to represent the normal dynamics of these hormones over the estrous cycle. A database of hormonal profiles was compiled with 40, 23, 33, and 34 profiles for LH, FSH, E2, and P4, respectively, derived from publications in which changes over time of at least three of these four hormones, including LH, were reported. These profiles were digitalized and standardized over the time throughout the estrous cycle, considering the interval between two successive LH surges to be 21 days. After this standardization on the x-axis, a transformation on the y-axis was performed to center the profiles around their common dynamics. For each hormone, the reference profile was then considered to be the median of the adjusted profiles. Quartiles were reported to account for the time evolution of the variability around each reference profile. The reference profiles obtained showed that the procedure used was satisfactory for extracting the overall changes over time of LH, P4, and E2. Results were less satisfactory for FSH, because of a higher variability observed between the original profiles in our database. The corepresentation of the reference profiles, i.e., when depicted together on the same scale, emphasizes the interplay between these hormones more precisely than most of the schematic views available in literature. These data-derived profiles can be considered to be generic and useful for benchmarking the normal dynamics of gonadotrophins and steroid hormones over the estrous cycle in cow.

Modelling impacts of performance on the probability of reproducing, and thereby on productive lifespan, allow prediction of lifetime efficiency in dairy cows

Reproductive success is a key component of lifetime efficiency - which is the ratio of energy in milk (MJ) to energy intake (MJ) over the lifespan, of cows. At the animal level, breeding and feeding management can substantially impact milk yield, body condition and energy balance of cows, which are known as major contributors to reproductive failure in dairy cattle. This study extended an existing lifetime performance model to incorporate the impacts that performance changes due to changing breeding and feeding strategies have on the probability of reproducing and thereby on the productive lifespan, and thus allow the prediction of a cows lifetime efficiency. The model is dynamic and stochastic, with an individual cow being the unit modelled and one day being the unit of time. To evaluate the model, data from a French study including Holstein and Normande cows fed high-concentrate diets and data from a Scottish study including Holstein cows selected for high and average genetic merit for fat plus protein that were fed high- v. low-concentrate diets were used. Generally, the model consistently simulated productive and reproductive performance of various genotypes of cows across feeding systems. In the French data, the model adequately simulated the reproductive performance of Holsteins but significantly under-predicted that of Normande cows. In the Scottish data, conception to first service was comparably simulated, whereas interval traits were slightly under-predicted. Selection for greater milk production impaired the reproductive performance and lifespan but not lifetime efficiency. The definition of lifetime efficiency used in this model did not include associated costs or herd-level effects. Further works should include such economic indicators to allow more accurate simulation of lifetime profitability in different production scenarios.