A teleonomic model describing performance (body, milk and intake) during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. 1. Trajectories of life function priorities and genetic scaling

The prediction of the control of nutrient partitioning, particularly energy, is a major issue in modelling dairy cattle performance. The proportions of energy channelled to physiological functions (growth, maintenance, gestation and lactation) change as the animal ages and reproduces, and according to its genotype and nutritional environment. This is the first of two papers describing a teleonomic model of individual performance during growth and over repeated reproductive cycles throughout the lifespan of dairy cattle. The conceptual framework is based on the coupling of a regulating sub-model providing teleonomic drives to govern the work of an operating sub-model scaled with genetic parameters. The regulating sub-model describes the dynamic partitioning of a mammal female's priority between life functions targeted to growth (G), ageing (A), balance of body reserves (R) and nutrient supply of the unborn (U), newborn (N) and suckling (S) calf. The so-called GARUNS dynamic pattern defines a trajectory of relative priorities, goal directed towards the survival of the individual for the continuation of the specie. The operating sub-model describes changes in body weight (BW) and composition, foetal growth, milk yield and composition and food intake in dairy cows throughout their lifespan, that is, during growth, over successive reproductive cycles and through ageing. This dynamic pattern of performance defines a reference trajectory of a cow under normal husbandry conditions and feed regimen. Genetic parameters are incorporated in the model to scale individual performance and simulate differences within and between breeds. The model was calibrated for dairy cows with literature data. The model was evaluated by comparison with simulations of previously published empirical equations of BW, body condition score, milk yield and composition and feed intake. This evaluation showed that the model adequately simulates these production variables throughout the lifespan, and across a range of dairy cattle genotypes.     

Modeling homeorhetic trajectories of milk component yields,body composition and dry-matter intake in dairy cows: Influence of parity,milk production potential and breed

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

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

Lifetime performance variability is a powerful tool for evaluating herd management. Although efficiency is a key aspect of performance, it has not been integrated into existing studies on the variability of lifetime performance. The goal of the present article is to analyse the effects of various herd management options on the variability of lifetime performance by integrating criteria relative to feed efficiency. A herd model developed for dairy goat systems was used in three virtual experiments to test the effects of the diet energy level, the segmentation of the feeding plan and the mean production potential of the herd on the variability of lifetime performance. Principal component analysis showed that the variability of lifetime performance was structured around the first axis related to longevity and production and the second related to the variables used in feed efficiency calculation. The intra-management variability was expressed on the first axis (longevity and production), whereas the inter-management variability was expressed on the second axis (feed efficiency) and was mainly influenced by the combination of the diet energy level and the mean production potential. Similar feed efficiencies were attained with different management options. Still, such combinations relied on different biological bases and, at the level of the individual, contrasting results were observed in the relationship between the obtained pattern of performance (in response to diet energy) and the reference pattern of performance (defined by the production potential). Indeed, our results showed that over-feeding interacted with the feeding plan segmentation: a high level of feeding plan segmentation generated a low proportion of individuals at equilibrium with their production potential, whereas a single ration generated a larger proportion. At the herd level, the diet energy level and the herd production potential had marked effects on production and efficiency due to dilution of fixed production costs (i.e. maintenance requirements). Management options led to similar production and feed efficiencies at the herd level while giving large contrasts in the proportions of individuals at equilibrium with their production potential. These results suggested that analysing individual variability on the basis of criteria related to production processes could improve the assessment of herd management. The herd model opens promising perspectives in studying whether individual variability represents an advantage for herd performance.     

LiGAPS-Beef,a mechanistic model to explore potential and feed-limited beef production 2: sensitivity analysis and evaluation of sub-models

The model LiGAPS-Beef (Livestock simulator for Generic analysis of Animal Production Systems – Beef cattle) has been developed to assess potential and feed-limited growth and production of beef cattle in different areas of the world and to identify the processes responsible for the yield gap. Sensitivity analysis and evaluation of model results with experimental data are important steps after model development. The first aim of this paper, therefore, is to identify which parameters affect the output of LiGAPS-Beef most by conducting sensitivity analyses. The second aim is to evaluate the accuracy of the thermoregulation sub-model and the feed intake and digestion sub-model with experimental data. Sensitivity analysis was conducted using a one-at-a-time approach. The upper critical temperature (UCT) simulated with the thermoregulation sub-model was most affected by the body core temperature and parameters affecting latent heat release from the skin. The lower critical temperature (LCT) and UCT were considerably affected by weather variables, especially ambient temperature and wind speed. Sensitivity analysis for the feed intake and digestion sub-model showed that the digested protein per kg feed intake was affected to a larger extent than the metabolisable energy (ME) content. Sensitivity analysis for LiGAPS-Beef was conducted for ¾ Brahman×¼ Shorthorn cattle in Australia and Hereford cattle in Uruguay. Body core temperature, conversion of digestible energy to ME, net energy requirements for maintenance, and several parameters associated with heat release affected feed efficiency at the herd level most. Sensitivity analyses have contributed, therefore, to insight which parameters are to be investigated in more detail when applying LiGAPS-Beef. Model evaluation was conducted by comparing model simulations with independent data from experiments. Measured heat production in experiments corresponded fairly well to the heat production simulated with the thermoregulation sub-model. Measured ME contents from two data sets corresponded well to the ME contents simulated with the feed intake and digestion sub-model. The relative mean absolute errors were 9.3% and 6.4% of the measured ME contents for the two data sets. In conclusion, model evaluation indicates the thermoregulation sub-model can deal with a wide range of weather conditions, and the feed intake and digestion sub-model with a variety of feeds, which corresponds to the aim of LiGAPS-Beef to simulate cattle in different beef production systems across the world.     

Feeding behavior improves prediction of dairy cow voluntary feed intake but cannot serve as the sole indicator

Low-cost feeding-behavior sensors will soon be available for commercial use in dairy farms. The aim of this study was to develop a feed intake model for the individual dairy cow that includes feeding behavior. In a research farm, the individual cows’ voluntary feed intake and feeding behavior were monitored at every meal. A feed intake model was developed based on data that exist in commercial modern farms: ‘BW,’ ‘milk yield’ and ‘days in milking’ parameters were applied in this study. At the individual cow level, eating velocity seemed to be correlated with feed intake (R²=0.93 to 0.94). The eating velocity coefficient varied among individuals, ranging from 150 to 230 g/min per cow. The contribution of feeding behavior (0.28) to the dry matter intake (DMI) model was higher than the contribution of BW (0.20), similar to the contribution of fat-corrected milk (FCM)/BW (0.29) and not as large as the contribution of FCM (0.49). Incorporating feeding behavior into the DMI model improved its accuracy by 1.3 (38%) kg/cow per day. The model is ready to be implemented in commercial farms as soon as companies introduce low-cost feeding-behavior sensors on commercial level.     

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^R. 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.     

Coupling a reproductive function model to a productive function model to simulate lifetime performance in dairy cows

Reproductive success is a key component of lifetime performance in dairy cows but is difficult to predict due to interactions with productive function. Accordingly, this study introduces a dynamic model to simulate the productive and reproductive performance of a cow during her lifetime. The cow model consists of an existing productive function model (GARUNS) which is coupled to a new reproductive function model (RFM). The GARUNS model simulates the individual productive performance of a dairy cow throughout her lifespan. It provides, with a daily time step, changes in BW and composition, fetal growth, milk yield and composition and food intake. Genetic-scaling parameters are incorporated to scale individual performance and simulate differences within and between breeds. GARUNS responds to the discrete event signals ‘conception’ and ‘death’ (of embryo or fetus) generated by RFM. In turn, RFM responds to the GARUNS outputs concerning the cow’s energetic status: the daily total processed metabolizable energy per kg BW (TPEW) and the net energy balance (EB). Reproductive function model models the reproductive system as a compartmental system transitioning between nine competence stages: prepubertal (PRPB), anestrous (ANST), anovulatory (ANOV), pre-ovulating (PREO), ovulating (OVUL), post-ovulating (PSTO), luteinizing (LUTZ), luteal (LUTL) and gestating (GEST). The transition from PRPB to ANST represents the start of reproductive activity at puberty. The cyclic path through ANST, PREO, OVUL, PSTO, LUTZ and LUTL forms the regime of ovulatory cycles, whereas ANOV and GEST are transient stages that interrupt this regime. Anovulatory refers explicitly to a stage in which ovulation cannot occur (i.e. interrupted cyclicity), whereas ANST is a pivotal stage within ovulatory cycles. Reproductive function model generates estradiol and progesterone hormonal profiles consistent with reference profiles derived from literature. Cyclicity is impacted by the GARUNS output EB and clearance of estradiol is impacted by TPEW. A farming system model was designed to describe different farm protocols of heat detection, insemination, feeding (amount and energy density), drying-off and culling. Results of model simulation (10 000 simulations of individual cows over 5000 days lifetime period, with randomly drawn genetic-scaling parameters and standard diet) are consistent with literature for reproductive performance. This model allows simulation of deviations in reproductive trajectories along physiological stages of the cow reproductive cycle. It thus provides the basis for evaluation of the relative importance of different factors affecting fertility at individual cow and herd levels across different breeds and management environments.     

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

Finding ways of increasing the efficiency of production systems is a key issue of sustainability. System efficiency is based on long-term individual efficiency, which is highly variable and management driven. To study the effects of management on herd and individual efficiency, we developed the model simulation of goat herd management (SIGHMA). This dynamic model is individual-based and represents the interactions between technical operations (relative to replacement, reproduction and feeding) and individual biological processes (performance dynamics based on energy partitioning and production potential). It simulates outputs at both herd and goat levels over 20 years. A farmer's production project (i.e. a targeted milk production pattern) is represented by configuring the herd into female groups reflecting the organisation of kidding periods. Each group is managed by discrete events applying decision rules to simulate the carrying out of technical operations. The animal level is represented by a set of individual goat models. Each model simulates a goat's biological dynamics through its productive life. It integrates the variability of biological responses driven by genetic scaling parameters (milk production potential and mature body weight), by the regulations of energy partitioning among physiological functions and by responses to diet energy defined by the feeding strategy. A sensitivity analysis shows that herd efficiency was mainly affected by feeding management and to a lesser extent by the herd production potential. The same effects were observed on herd milk feed costs with an even lower difference between production potential and feeding management. SIGHMA was used in a virtual experiment to observe the effects of feeding strategies on herd and individual performances. We found that overfeeding led to a herd production increase and a feed cost decrease. However, this apparent increase in efficiency at the herd level (as feed cost decreased) was related to goats that had directed energy towards body reserves. Such a process is not efficient as far as feed conversion is concerned. The underfeeding strategy led to production decrease and to a slight feed cost decrease. This apparent increase in efficiency was related to goats that had mobilised their reserves to sustain production. Our results highlight the interest of using SIGHMA to study the underlying processes affecting herd performance and analyse the role of individual variability regarding herd response to management. It opens perspectives to further quantify the link between individual variability, herd performance and management and thus further our understanding of livestock farming systems.     

Suitability of feeding and chewing time for estimation of feed intake in dairy cows

Monitoring of feeding and rumination behaviour can provide useful information for dairy herd management. The feeding behaviour of dairy cows can be recorded by different techniques, such as video cameras, weighing troughs or chewing sensors. Among feeding characteristics, individual feed intake of cows is of utmost interest, but as weighing troughs have high space and cost requirements they are used primarily in research studies. The objective of the present study was to evaluate whether records on feeding time or chewing activity or a combination of both contain enough information to estimate feed intake with sufficient accuracy. Feed intake and feeding time per cow were recorded by means of weighing troughs. Concurrently, chewing activity of seven cows was recorded by MSR-ART pressure sensors during five to eight measuring days per cow. Feeding and chewing behaviour were evaluated in time slots (1 min) and additionally assigned to feeding bouts for further analysis. The 1 min time slots were classified into feeding/no feeding or chewing/no chewing by the two systems, and agreement was found in 92.2% of the records. On average, cows spent 270±39 min/day at the feeding troughs and chewed 262±48 min/day. The average fresh matter intake (FMI) was 49.6±5.1 kg/day. Feed intake was divided into 9.7 bouts/day during which cows fed in average 27.8±21.7 min/bout and chewed 27.0±23.1 min/bout. The correlation between FMI and feeding time was r=0.891 and between FMI and chewing time r=0.780 overall cows. Hence, both systems delivered suitable information for estimating feed intake.     

Repeatability of traits for characterizing feed intake patterns in dairy goats: a basis for phenotyping in the precision farming context

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

Effects of the level of feed intake and ergot contaminated concentrate on ruminal fermentation and on physiological parameters in cows

The aim of the present study was to examine the effects of ergot contaminated feed concentrate at differing levels of feed intake on ruminal fermentation, and on various physiological parameters of dairy cows. Twelve double fistulated (in the rumen and the proximal duodenum) Holstein Friesian cows were fed either a control diet (on a dry matter (DM) base: 60% maize silage, 40% concentrate) or a diet containing ergot alkaloids (concentrate contained 2.25% ergot resulting in an ergot alkaloid concentration of the daily ration between 505 and 620 (μg/kg DM) over a period of four weeks. Daily feed amounts were adjusted to the current performance which resulted in a dry matter intake (DMI) variation between 6.0 and 18.5 kg/day. The resulting ergot alkaloid intake varied between 4.1 and 16.3 (μg/kg body weight when the ergot contaminated concentrate was fed. Concentrations of isovalerate, propionate and ammonia nitrogen in the rumen fluid were significantly influenced by ergot feeding, and the amount of ruminally undegraded protein, as well as the fermentation of neutral detergent fibre, tended to increase with the ergot supplementation at higher levels of feed intake, which might indicate a shift in the microbial population. Other parameters of ruminal fermentation such as ruminai pH, fermented organic matter as a percentage of intake, or the amount of non-ammonia nitrogen measured at the duodenum were not significantly influenced by ergot feeding. The activities of liver enzymes (aspartate aminotransferase, γ-glutamyltransferase, glutamate dehydrogenase, creatine kinase) in the serum were not affected by ergot feeding. The rectal measured body temperature of the cows significantly increased after ergot administration (p=0.019). Thus, body temperature can be regarded as a sensitive parameter to indicate ergot exposure of dairy cows.     

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 cow’s 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.     

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.     

On the effects ofFusarium-contaminated wheat and the feed intake level on ruminal fermentation and toxin-turnover of cows

The aim of the study was to examine the effects of dry matter intake level and the feeding ofFusarium-contaminated wheat on the toxin-turnover and ruminal fermentation of dairy cows. Fourteen dairy cows equipped with ruminal and duodenal cannulae were used. All animals were fed the same diet, only the daily feed amounts were adjusted to the current performance stage of the cow. On a dry matter basis, the diet consisted of 60% concentrate including 55% wheat (Fusarium-contaminated wheat [Mycotoxin period] or control wheat [Control period]). Each cow was fed with both the contaminated and the control wheat. TheFusarium-contamination of the wheat significantly decreased the flow of undegraded protein at the duodenum with increased intakes of organic matter. The duodenal flow of microbial protein and the activities of aspartate aminotransferase (ASAT), glutamate dehydrogenase (GLDH) and gamma glutamyl transferase (γ-GT) in the serum were not affected by dietary treatment, but increased with feed intake. The duodenal flow of deoxynivalenol (DON) and de-epoxy DON related to DON intake ranged between 12 and 77% when theFusarium-contaminated wheat was fed. DON was almost completely metabolized to de-epoxy DON independent of the feed intake level. The zearalenone (ZON) flow at the duodenum increased moderately with increasing ZON/feed intake.     

Rhythmic pattern of growth and nutrient retention in response to feeding time in the rainbow trout

The effect of six different feeding times was tested on feed intake, growth performance, proximate body composition and nutrient retention in the rainbow trout Oncorhynchus mykiss . Using a non‐linear regression model, a significant rhythmic pattern over a 24 h period was observed for feed gain ratio and nutrient retention responses to feeding time. Specific growth rate and protein growth rate responses were also rhythmic but the trends were not significant. There was no clear effect of feeding time on feed intake and proximate body composition. The study suggested that feed intake, at least under the experimental conditions encountered, was synchronized to feeding time while some physiological rhythms involved in nutrient metabolism were probably synchronized to photoperiod.     

Determining the economic value of daily dry matter intake and associated methane emissions in dairy cattle

Feed represents a substantial proportion of production costs in the dairy industry and is a useful target for improving overall system efficiency and sustainability. The objective of this study was to develop methodology to estimate the economic value for a feed efficiency trait and the associated methane production relevant to Canada. The approach quantifies the level of economic savings achieved by selecting animals that convert consumed feed into product while minimizing the feed energy used for inefficient metabolism, maintenance and digestion. We define a selection criterion trait called Feed Performance (FP) as a 1 kg increase in more efficiently used feed in a first parity lactating cow. The impact of a change in this trait on the total lifetime value of more efficiently used feed via correlated selection responses in other life stages is then quantified. The resulting improved conversion of feed was also applied to determine the resulting reduction in output of emissions (and their relative value based on a national emissions value) under an assumption of constant methane yield, where methane yield is defined as kg methane/kg dry matter intake (DMI). Overall, increasing the FP estimated breeding value by one unit (i.e. 1 kg of more efficiently converted DMI during the cow’s first lactation) translates to a total lifetime saving of 3.23 kg in DMI and 0.055 kg in methane with the economic values of CAD $0.82 and CAD $0.07, respectively. Therefore, the estimated total economic value for FP is CAD $0.89/unit. The proposed model is robust and could also be applied to determine the economic value for feed efficiency traits within a selection index in other production systems and countries.     

A machine vision system to predict individual cow feed intake of different feeds in a cowshed

Data on individual feed intake of dairy cows, an important variable for farm management, are currently unavailable in commercial dairies. A real-time machine vision system including models that are able to adapt to multiple types of feed was developed to predict individual feed intake of dairy cows. Using a Red-Green-Blue-Depth (RGBD) camera, images of feed piles of two different feed types (lactating cows' feed and heifers' feed) were acquired in a research dairy farm, for a range of feed weights under varied configurations and illuminations. Several models were developed to predict individual feed intake: two Transfer Learning (TL) models based on Convolutional Neural Networks (CNNs), one CNN model trained on both feed types, and one Multilayer Perceptron and Convolutional Neural Network model trained on both feed types, along with categorical data. We also implemented a statistical method to compare these four models using a Linear Mixed Model and a Generalised Linear Mixed Model, showing that all models are significantly different. The TL models performed best and were trained on both feeds with TL methods. These models achieved Mean Absolute Errors (MAEs) of 0.12 and 0.13 kg per meal with RMSE of 0.18 and 0.17 kg per meal for the two different feeds, when tested on varied data collected manually in a cowshed. Testing the model with actual cows’ meals data automatically collected by the system in the cowshed resulted in a MAE of 0.14 kg per meal and RMSE of 0.19 kg per meal. These results suggest the potential of measuring individual feed intake of dairy cows in a cowshed using RGBD cameras and Deep Learning models that can be applied and tuned to different types of feed.     

Analysis of feed intake and energy balance of high-yielding first lactating Holstein cows with fixed and random regression models

At the dairy research farm Karkendamm, the individual roughage intake was measured since 1 September 2005 using a computerised scale system to estimate daily energy balances as the difference between energy intake and calculated energy requirements for lactation and maintenance. Data of 289 heifers with observations between the 11th and 180th day of lactation over a period of 487 days were analysed. Average energy-corrected milk yield, feed intake, live weight and energy balance were 31.8kg, 20.6kg, 584 kg and 13.6 MJ NEL (net energy lactation), respectively, per day. Fixed and random regression models were used to estimate repeatabilities, correlations between cow effects and genetic parameters. The resulting genetic correlations in different lactation stages demonstrate that feed intake and energy balance at the beginning and the middle of lactation are genetically different traits. Heritability of feed intake is low with h2=0.06 during the first days after parturition and increases in the middle of lactation, whereas the energy balance shows the highest heritability with h2=0.34 in the first 30 days of lactation. Genetic correlations between energy balance and feed intake and milk yield, respectively, illustrate that energy balance depends more on feed intake than on milk yield. Genetic correlation between body condition score and energy balance decreases rapidly within the first 100 days of lactation. Hence, to avoid negative effects on health and reproduction as consequences of strong energy deficits at the beginning of lactation, the energy balance itself should be measured and used as a selection criterion in this lactation stage. Since the number of animals is rather small for a genetic analysis, the genetic parameters have to be evaluated on a more comprehensive dataset.     

The importance of hormonal circadian rhythms in daily feeding patterns: An illustration with simulated pigs

The interaction between hormonal circadian rhythms and feeding behaviour is not well understood. This study aimed to deepen our understanding of mechanisms underlying circadian feeding behaviour in animals, using pigs, Sus scrofa, as a case study. Pigs show an alternans feeding pattern, that is, a small peak of feed intake at the beginning of the day and a larger peak at the end of the day. We simulated the feeding behaviour of pigs over a 24 h period. The simulation model contained mechanisms that regulate feeding behaviour of animals, including: processing of feed in the gastrointestinal tract, fluctuation in energy balance, circadian rhythms of melatonin and cortisol and motivational decision-making. From the interactions between these various processes, feeding patterns (e.g. feed intake, meal frequency, feeding rate) emerge. These feeding patterns, as well as patterns for the underlying mechanisms (e.g. energy expenditure), fitted empirical data well, indicating that our model contains relevant mechanisms. The circadian rhythms of cortisol and melatonin explained the alternans pattern of feeding in pigs. Additionally, the timing and amplitude of cortisol peaks affected the diurnal and nocturnal peaks in feed intake. Furthermore, our results suggest that circadian rhythms of other hormones, such as leptin and ghrelin, are less important in circadian regulation of feeding behaviour than previously thought. These results are relevant to animal species with a metabolic and endocrine system similar to that of pigs, such as humans. Moreover, the modelling approach to understand feeding behaviour can be applied to other animal species.     

Effects of feeding high-quality hay with graded amounts of concentrate on feed intake,performance and blood metabolites of cows in early lactation

Dairy cows are commonly fed energy-dense diets with high proportions of concentrate feedstuffs to meet the increased energy needs of early lactation. However, feeding large amounts of concentrates may cause rumen acidosis and impact cow health. The hypothesis tested was that the energy supply and metabolic health of early-lactation Simmental cows can be maintained when high-quality hay rich in water-soluble carbohydrates (WSC) and crude protein (CP) is fed, despite the proportion of concentrates in the diet being reduced or even excluded. Twenty-four Simmental cows were allocated to one of four feeding groups beginning 10 d before the expected calving date, until 28 d thereafter. The feeding groups were 60CH (60% conventional fibre-rich hay plus 40% concentrate feed), 60HQH (60% high-quality hay plus 40% concentrate feed), 75HQH (75% high-quality hay plus 25% concentrate feed) and 100HQH (100% high-quality hay). The fibre-rich hay and high-quality hay differed in WSC content (110 g vs. 198 g of dry matter (DM)), neutral detergent fibre (646 g vs. 423 g of DM) and CP (65 g vs. 223 g of DM). Individual feed intake and milk production were monitored daily, and blood samples were collected weekly. Dry matter intake (DMI) and milk yield increased post partum, but 4 weeks post partum, the DMI of cows fed 100HQH only reached a daily mean DMI of 18.6 kg, whereas the DMI of the other groups averaged 21.9 kg (p < 0.046). The negative energy balance was less pronounced in cows fed 75HQH since they showed similar milk yields to the cows fed 60CH and 100HQH, but their energy intake was higher. Concentrations of milk components were similar across rations 60CH, 60HQH and 75HQH, as were most of blood parameters. Cows fed 100HQH responded to the energy deficit post partum with a higher ratio of non-esterified fatty acids to cholesterol and a higher concentration of ß-hydroxybutyrate (significant in comparison to cows fed 75HQH, p < 0.05). In conclusion, feeding high-quality hay with a WSC content of 20% in DM has the potential to decrease the proportion of concentrates in dairy cow feeding in early lactation, but cannot fully replace their supplementation due to a limited rumen capacity for forage intake.