Key determinants of adaptive strategies of goats to a 2-day nutritional challenge during early lactation

Little is known about the key determinants of the physiological adaptations to environmental challenges and how these determinants interact. We evaluated how the response/recovery profiles to a short-term nutritional challenge during early lactation are affected by early-life nutritional strategies in dairy goats divergently selected for functional longevity. We used 72 females, split into two cohorts, daughters of Alpine bucks divergently selected for functional longevity. The females from the two lines were fed with two divergent diets, normal vs low-energy, from weaning until the middle of first gestation, and then fed with the same standard diet. Individual BW, body condition score, morphology, and plasma samples were collected from birth to first kidding. The adaptative physiological strategy to a nutritional challenge was assessed via a 2-day feed restriction challenge, during early lactation, which consisted of a five-day control period on a standard lactation diet followed by a 2-day challenge with straw-only feeding and then a 10-day recovery period on a standard lactation diet. During the challenge, DM intake, BW, milk yield (MY), and plasma and milk metabolite composition were recorded daily. Linear mixed-effects models were used to analyze all traits, considering the individual nested in the cohort as a random effect and the 2 × 2 treatments (i.e., line and rearing diet) and litter size as fixed effects. Linear mixed-effects models using a piecewise arrangement were used to analyze the response/recovery profiles to nutritional challenge. Random parameters estimated for each individual, using the mixed-effects models without the fixed effects of rearing diet and genetic line, were used in a stepwise model selection based on R2 to identify key determinants of an individual's physiological adaptations to environmental challenges. Differences in stature and body reserves created by the two rearing diets diminished during late gestation and the 5-day control period. Genetic line did not affect body reserves during the rearing phase. Rearing diet and genetic line slightly affected the recovery profiles of evaluated traits and had no effects on prechallenge and response to challenge profiles. The prekidding energy status measures and MY before challenge were selected as strong predictors of variability in response-recovery profiles of milk metabolites that have strong links with body energy dynamics (i.e., isoCitrate, ß-hydroxybutyrate, choline, cholesterol, and triacylglycerols; R2 = 35%). Our results suggested that prekidding energy status and MY are key determinants of adult resilience and that rearing diet and genetic line may affect adult resilience insofar as they affect the animals' energy status.

Responses of selected plasma metabolites to a two-day nutritional challenge of goats divergently selected for functional longevity

Understanding the extent to which genetics × environment plays a role in shaping individual strategies to environmental challenges is of considerable interest for future selection of more resilient animals. Accordingly, the objective of this study was to evaluate the metabolic responses to a nutritional challenge of goats divergently selected for functional longevity based on plasma metabolites and the repeatability of these responses across 2 experimental farms and years. We carried out 6 different experimental trials from years 2018 to 2022 (4 trials on site Bourges (2018-21) and 2 trials (2021-22) on site Grignon) in which 267 first kidding goats, daughters of Alpine bucks divergently selected for functional longevity, longevity plus (n = 137), and longevity minus (n = 130), were exposed to a 2-d nutritional challenge in early lactation. The experiments consisted of a 5 or 7-d control period (pre-challenge) on a standard lactation diet followed by a 2-d nutritional challenge with straw-only feeding and then a 7 or 10-d recovery period on a standard lactation diet, for site Bourges and Grignon, respectively. During the challenge plasma metabolite composition was recorded daily. Linear mixed-effects models were used to analyze all traits, considering the individual as a random effect and the 2x2 treatments (i.e., genetic line and year nested in site) and litter size as fixed effects. The linear mixed-effects model using a piecewise arrangement was used to analyze the response/recovery profiles to the nutritional challenge. Random parameters estimated for each individual, using the mixed-effects models without the fixed effects of genetic line, were used in a Sparse Partial Least Square Discriminant Analysis (sPLS-DA) to compare the goat metabolism response to the challenge on a multivariate scale. The plasma metabolites, glucose, β-hydroxybutyrate (BHB), and nonesterified fatty acids (NEFA), and urea concentrations responded to the 2-d nutritional challenge. Selection for functional longevity did not affect plasma glucose, NEFA, BHB, and urea response/recoveries to a 2-d nutritional challenge. However, site, trial, and litter size affected these responses. Moreover, the plasma metabolites seem not to fully recover to prechallenge levels after the recovery phase. The sPLS-DA analysis did not discriminate between the 2 longevity lines. We observed meaningful between-individuals' variability in plasma BHB, especially on the prechallenge and rate of response and rate of recovery from the 2-d nutritional challenge (CV = 26.2%, 36.1%, and 41.2%, repeatability = 0.749, 0.322, and 0.741, respectively). Plasma NEFA recovery from challenge also demonstrated high between-individuals' variability (CV = 16.4%, repeatability = 0.323). Selection for functional longevity did not affect plasma metabolites responses to a 2-d nutritional challenge in dairy goats. Plasma NEFA and BHB response/recovery presented high between-individuals' variability, indicating individual adaptative characteristics to nutritional challenges not related to the environmental conditions but to inherent individual characteristics.

Genetic analysis of lactation consistency in US Holsteins using temporal variation in daily milk weights

The ability of a dairy cow to perform reliably over time is an interesting trait to include in dairy cattle breeding programs aimed at improving dairy cow resilience. Consistency, defined as the quality of performing as expected each day of the lactation, could be highly associated with resilience, defined as animal's ability to maintain health and performance in the presence of environmental challenges, including pathogens, heat waves, and nutritional changes. A total of 51,415,022 daily milk weights collected from 2018 to 2023 were provided for 255,191 multiparous Holstein cows milked 3 times daily in conventional parlor systems on farms in 32 states. The temporal variance (TEMPVAR) of milk yield from 5 to 305 d postpartum was computed as the log-transformed variance of daily deviations between observed and expected individual milk weights. Lower values of TEMPVAR imply smaller day-to-day deviations from expectations, indicating consistent performance, whereas larger values indicate inconsistent performance. Expected daily milk weights were computed using 3 nonparametric and parametric regression models: (1) loceally estimated scatterplot smoothing regression with a 0.75 span; (2) polynomial quantile regression using the median (0.5 quantile), and (3) polynomial quantile regression using a 0.7 quantile. The univariate statistical model included age at first calving and herd-year-season as fixed effects and cow as a random effect. Heritability estimates (standard errors) of TEMPVAR phenotypes calculated over the entire lactation ranged between 0.227 (0.011) and 0.237 (0.011), demonstrating that cows are genetically predisposed to display consistent or inconsistent performance. Estimated genetic correlations calculated using a multiple-trait model between TEMPVAR traits and between lactations were high (>0.95), indicating TEMPVAR is repeatable across lactations and robust to the model used to compute expected daily milk yield. Higher TEMPVAR phenotypes reflect more variation in performance, hence greater inconsistency, which is undesirable. Therefore, correlations between predicted transmitting abilities (PTA) for TEMPVAR and milk yield of 0.57 indicate that high-producing cows exhibit more day-to-day variation in performance. Correlations with productive life and livability were -0.38 and -0.48, respectively. Correlations between PTA for TEMPVAR and those of postpartum health traits were also negative, ranging from -0.41 to -0.08. Given that health traits are derived from disease resistance measurements, and higher health trait PTA are preferred, our results indicate that more consistent cows tend to have fewer health problems and greater longevity. Overall, our findings suggest that temporal variation in daily milk weights can be used to identify consistent animals that maintain expected performance throughout the lactation, which will enable selection for greater resilience to management and environmental perturbations.

Short-term responses of meat ewes facing an acute nutritional challenge in early-mid lactation

Simulating a consequence of a climate change event on feed availability, responses of Mediterranean meat ewes facing an acute undernutritional challenge (CHA; i.e., fed only low nutritional value cereal straw) were evaluated at a sensitive physiological stage (i.e., early suckling). Forty Romane ewes were chosen at early-mid pregnancy (around 2 mo) according to parity (20 primiparous, PRIM; 20 multiparous, MULT); feed efficiency genetic line of their sires (residual feed intake [RFI]; efficient, RFI-, n = 10 per parity; inefficient, RFI+, n = 10 per parity); litter size (i.e., bearing twins, diagnosed by ultrasonography); body weight (BW, kg) and body condition score (BCS) (initial BW and BCS [mean ± SD]: 51.6 ± 7.41 kg; 2.5 ± 0.20, respectively; representing flock' averages per parity). Effects on dry matter intake (DMI), ewes' BW and BCS, subcutaneous dorsal fat thickness (DFT), energy metabolism (plasma non-esterified fatty acids [NEFA], β-hydroxybutyrate (β-OHB), glucose, urea, triiodothyronine [T3]), and lambs' growth (BW and average daily gain [ADG]; g/d) were examined before, during and after CHA. Individuals' profiles of the response-recovery to CHA were described using a piecewise mixed-effects model. The fixed effect of parity and genetic line and the random effect of individual (ewe) were considered. A linear mixed-effects model was fitted to explore the effects on lambs' growth. The 2-d straw-only CHA had significant effects on most of the recorded parameters. Meaningful drops and recoveries were observed on ewes' DMI, BW, and DFT with effect on postchallenge levels. BW, BCS, DFT, or DMI were also affected by parity (MULT > PRIM) but not by genetic line. Plasma NEFA, β-OHB, glucose, urea, and T3 responded well to CHA with drops in T3, urea, and glucose levels, whereas NEFA and β-OHB significantly increased after CHA. MULT ewes presented sharper β-OHB recovery from CHA than PRIM (P ≤ 0.05). With this study, we provide tangible and necessary data for an emerging field of research. Our results give new insights into how such a short and abrupt CHA affects some key zootechnical and physiological parameters, and to what extent the impacts of CHA and the ewes' response-recovery are influenced. It also revealed potential between-individual differences in the adaptive capacities of ewes, which require further exploration.

Between-individual variability on reticulorumen digesta passage rate in goats

Identifying factors that influence an individual's ability to utilize nutrients is a strategy for enhancing livestock sustainability. Digesta passage rate (kp) is one of the most important determinants that influence nutrient utilization. However, there is limited knowledge regarding the individual's variability on kp. The objective of this study was to investigate between-individual variability of reticulorumen (RR) kp of solutes and particles in goats, and its relationship to total NDF digestibility (NDFd). A dataset consisting of 103 individual records from two studies of castrated male (n = 36), female (n = 34), and intact male (n = 33) growing Saanen goats fed ad libitum and slaughtered at around 16.62 ± 0.96 (n = 21), 23.09 ± 1.33 (n = 19), 30.67 ± 2.25 kg (n = 29), 38.02 ± 1.61 (n = 16), and 44.12 ± 1.95 (n = 18) kg BW was used. The RR kp of solutes and particles were measured using Cr-EDTA and indigestible NDF (iNDF), respectively. Using mixed-effect models, considering sex and BW as fixed effects and study as random effect, we aimed to identify meaningful between-individual variance of RR kp of solutes and particles. Given the characteristic of our dataset (i.e., one observation per individual), our approach focused on exploring the variance between individuals described by the residuals of our fitted models. The RR kp of solutes and particles showed significant (P < 0.05) between-individual variation (CV ≈ 27%). Only 10% of RR kp of solutes variance was explained by DM intake (DMI) (g/d and g/d and g/kg BW) and NDF intake (NDFI); g/d), while 51.8% of the variance in RR kp of particles was explained by DMI and NDFI (g/d and g/kg BW), RR wet pool size, and iNDF:NDF intake ratio. The between-individual variation of RR kp of particles explained 19.5% of the between-individual variation in NDFd (P < 0.001). However, the nutritional and individual traits cited above (i.e., DMI, NDFI, RR wet pool size, and iNDF:NDF intake ratio) explained only 51.8% of the between-individual variation of RR kp of particles, while the remaining 48.2% was attributed to the individual's inherent characteristics, which in turn explained only 1.24% of the between-individual variation of NDFd (P = 0.146). The RR kp of particles and solutes showed meaningful between-individuals variance, which was partially related to both individual inherent characteristics and nutritional and animal traits. However, the NDFd was found to be unrelated to the inherent characteristics of RR kp of particles.

Multivariate analysis of milk metabolite measures shows potential for deriving new resilience phenotypes

In a context of growing interest in breeding more resilient animals, a noninvasive indicator of resilience would be very valuable. We hypothesized that the time-course of concentrations of several milk metabolites through a short-term underfeeding challenge could reflect the variation of resilience mechanisms to such a challenge. We submitted 138 one-year-old primiparous goats, selected for extreme functional longevity (i.e., productive longevity corrected for milk yield [60 low longevity line goats and 78 high longevity line goats]), to a 2-d underfeeding challenge during early lactation. We measured the concentration of 13 milk metabolites and the activity of 1 enzyme during prechallenge, challenge, and recovery periods. Functional principal component analysis summarized the trends of milk metabolite concentration over time efficiently without preliminary assumptions concerning the shapes of the curves. We first ran a supervised prediction of the longevity line of the goats based on the milk metabolite curves. The partial least square analysis could not predict the longevity line accurately. We thus decided to explore the large overall variability of milk metabolite curves with an unsupervised clustering. The large year × facility effect on the metabolite concentrations was precorrected for. This resulted in 3 clusters of goats defined by different metabolic responses to underfeeding. The cluster that showed higher β-hydroxybutyrate, cholesterol, and triacylglycerols increase during the underfeeding challenge was associated with poorer survival compared with the other 2 clusters. These results suggest that multivariate analysis of noninvasive milk measures show potential for deriving new resilience phenotypes.

Analysis of reproduction success, growth and milk trajectories and response to nutritional challenge in two Alpine goat lines selected on divergent longevity

This study aims to investigate whether the variation in reproduction success, growth, and milk trajectories is associated with different adaptive strategies in the short term (response to an acute nutritional challenge), using two Alpine goat lines. A total of 382 Alpine goats (179 low longevity (low_LGV) and 203 high longevity (high_LGV)), selected for divergent functional longevity from a commercial population, were monitored for 4 years and recorded for BW, reproduction and milking performance. Every year, an average of fifty primiparous goats were exposed to a 2-d nutritional challenge in early lactation. A polynomial model was used to analyse the lifetime trajectory of lactation and BW. A piecewise model was used to analyse the individual milk yield and responses of milk components to the nutritional challenges. The statistical analysis revealed that the two lines had a similar performance for total milk yield in the first lactation, BW at birth and at first kidding, litter size and weight, kidding interval and interval from the first insemination to conception. BW trajectories revealed that low_LGV goats had a greater BW in pregnancy but then lost more weight in early lactation compared to high_LGV goats, which showed a greater BW after kidding. Milk trajectories showed that the high_LGV goats had a higher initial milk yield, an earlier but less marked lactation peak and more persistency in milk production in late lactation than low_LGV goats. Except for milk protein content, quite similar response and recovery profiles of milk yield and milk fat content were observed during the challenge for both lines. The response to the challenge was positively correlated to the initial level of milk production in early lactation but negatively correlated with milk production decline after the peak. This finding suggests that the low_LGV goats were more adapted to allocate resources to meet an expected physiological change such as gestation and lactation. However, high_LGV goats allocate more than low_LGV goats for structural mass and may better cope with an unexpected environmental change such as nutritional deficit.

Relationship between feed efficiency and resilience in dairy ewes subjected to acute underfeeding

Selection of dairy sheep based on production levels has caused a loss of rusticity, which might compromise their future resilience to nutritional challenges. Although refocusing breeding programs toward improved feed efficiency (FE) is expected, more-efficient ewes also seem to be more productive. As a first step to examine the relationship between FE and resilience in dairy sheep, in this study we explored the variation in the response to and the recovery from an acute nutritional challenge in high-yielding Assaf ewes phenotypically divergent for FE. First, feed intake, milk yield and composition, and body weight changes were recorded individually over a 3-wk period in a total of 40 sheep fed a total mixed ration (TMR) ad libitum. Data were used to calculate their FE index (FEI, defined as the difference between the actual and predicted intake estimated through net energy requirements for maintenance, production, and weight change). The highest and lowest FE ewes (H-FE and L-FE groups, respectively; 10 animals/group) were selected and then subjected to the nutritional challenge (i.e., withdrawing the TMR and limiting their diet only to the consumption of straw for 3 d). Afterward, sheep were fed again the TMR ad libitum. Temporal patterns of variation in performance traits, and ruminal fermentation and blood parameters were examined. A good consistency between FEI, residual feed intake, and feed conversion ratio was observed. Results supported that H-FE were more productive than L-FE sheep at similar intake level. Average time trends of milk yield generated by a piecewise model suggest that temporal patterns of variation in this trait would be related to prechallenge production level (i.e., H-FE presented quicker response and recovery than L-FE). Considering all studied traits, the overall response to and recovery from underfeeding was apparently similar or even better in H-FE than in L-FE. This would refute the initial hypothesis of a poorer resilience of more-efficient sheep to an acute underfeeding. However, the question remains whether a longer term feed restriction might impair the ability of H-FE ewes to maintain or revert to a high-production status, which would require further research.

Animal board invited review: Quantification of resilience in farm animals

Resilience, when defined as the capacity of an animal to respond to short-term environmental challenges and to return to the prechallenge status, is a dynamic and complex trait. Resilient animals can reinforce the capacity of the herd to cope with often fluctuating and unpredictable environmental conditions. The ability of modern technologies to simultaneously record multiple performance measures of individual animals over time is a huge step forward to evaluate the resilience of farm animals. However, resilience is not directly measurable and requires mathematical models with biologically meaningful parameters to obtain quantitative resilience indicators. Furthermore, interpretive models may also be needed to determine the periods of perturbation as perceived by the animal. These applications do not require explicit knowledge of the origin of the perturbations and are developed based on real-time information obtained in the data during and outside the perturbation period. The main objective of this paper was to review and illustrate with examples, different modelling approaches applied to this new generation of data (i.e., with high-frequency recording) to detect and quantify animal responses to perturbations. Case studies were developed to illustrate alternative approaches to real-time and post-treatment of data. In addition, perspectives on the use of hybrid models for better understanding and predicting animal resilience are presented. Quantification of resilience at the individual level makes possible the inclusion of this trait into future breeding programmes. This would allow improvement of the capacity of animals to adapt to a changing environment, and therefore potentially reduce the impact of disease and other environmental stressors on animal welfare. Moreover, such quantification allows the farmer to tailor the management strategy to help individual animals to cope with the perturbation, hence reducing the use of pharmaceuticals, and decreasing the level of pain of the animal.

Exploring the value of genomic predictions to simultaneously improve production potential and resilience of farmed animals

Sustainable livestock production requires that animals have a high production potential but are also highly resilient to environmental challenges. The first step to simultaneously improve these traits through genetic selection is to accurately predict their genetic merit. In this paper, we used simulations of sheep populations to assess the effect of genomic data, different genetic evaluation models and phenotyping strategies on prediction accuracies and bias for production potential and resilience. In addition, we also assessed the effect of different selection strategies on the improvement of these traits. Results show that estimation of both traits greatly benefits from taking repeated measurements and from using genomic information. However, the prediction accuracy for production potential is compromised, and resilience estimates tends to be upwards biased, when families are clustered in groups even when genomic information is used. The prediction accuracy was also found to be lower for both traits, resilience and production potential, when the environment challenge levels are unknown. Nevertheless, we observe that genetic gain in both traits can be achieved even in the case of unknown environmental challenge, when families are distributed across a large range of environments. Simultaneous genetic improvement in both traits however greatly benefits from the use of genomic evaluation, reaction norm models and phenotyping in a wide range of environments. Using models without the reaction norm in scenarios where there is a trade-off between resilience and production potential, and phenotypes are collected from a narrow range of environments may result in a loss for one trait. The study demonstrates that genomic selection coupled with reaction-norm models offers great opportunities to simultaneously improve productivity and resilience of farmed animals even in the case of a trade-off.

PhenoBR: a model to phenotype body condition dynamics in meat sheep

In situations of negative energy balance (NEB) due to feed scarcity or high physiological demands, body energy reserves (BRs), mainly stored in adipose tissues, become the main sources of energy for ruminants. The capacity to mobilise and restore such BRs in response to different challenges is of major concern in the current context of breeding for resilience. Body condition score (BCS) is a common, practical indicator of BR variations throughout successive productive cycles, and quantitative tools for characterising such dynamics at the individual level are still lacking. The main objective of this work was to characterise body condition dynamics in terms of BR mobilisation and accretion capacities of meat sheep during their productive lifespan through a modelling approach, using BCS measurements. The animal model used in this work was the reproductive meat ewe (n = 1 478) reared in extensive rangeland. Regular measurements of BCS for each productive cycle were used as the indicator of BR variations. A hybrid mathematical model and a web interface, called PhenoBR, were developed to characterise ewes' BCS variations through four synthetic and biologically meaningful parameters for each productive cycle i: BR accretion rate (k_bⁱ), BR mobilisation rate (k_pⁱ), plus the time of onset and the duration of the BR mobilisation, t_bⁱ and ΔTⁱ, respectively. The model PhenoBR converged for all the ewes included in the analysis. Estimation of the parameters indicated the inter-individual variability for BR accretion and mobilisation rates, and the length of the mobilisation period. The present study is a proof of concept that the combination of data-driven and concept-driven models is required for the estimation of biologically meaningful parameters that describe body reserve dynamics through consecutive productive cycles. Individual characterisation of animals by these parameters makes it possible to rank them for their efficiency in the use of body reserves when facing NEB challenges. Such parameters could contribute to better management and decision-making by farmers and advisors, e.g. by adapting feeding systems to the individual characteristics of BR dynamics, or by geneticists as criteria to develop future animal breeding programmes including BR dynamics for more robust and resilient animals.

Milk metabolites can characterise individual differences in animal resilience to a nutritional challenge in lactating dairy goats

The aim of this study is built in two phases: to quantify the ability of novel milk metabolites to measure between-animal variability in response and recovery profiles to a short-term nutritional challenge, then to derive a resilience index from the relationship between these individual variations. At two different stages of lactation, sixteen lactating dairy goats were exposed to a 2-d underfeeding challenge. The first challenge was in late lactation, and the second was carried out on the same goats early in the following lactation. During the entire experiment period, samples were taken at each milking for milk metabolite measures. For each metabolite, the response profile of each goat was characterised using a piecewise model for describing the dynamic pattern of response and recovery profiles after the challenge relative to the start of the nutritional challenge. Cluster Analysis identified three types of response/recovery profiles per metabolite. Using cluster membership, multiple correspondence analyses (MCAs) were performed to further characterise response profile types across animals and metabolites. This MCA analysis identified three groups of animals. Further, discriminant path analysis was able to separate these groups of multivariate response/recovery profile type based on threshold levels of three milk metabolites: β-hydroxybutyrate, free glucose and uric acid. Further analyses were done to explore the possibility of developing an index of resilience from milk metabolite measures. Different types of performance response to short-term nutritional challenge can be distinguished using multivariate analyses of a panel of milk metabolites.

Exploring milk loss and variability during environmental perturbations across lactation stages as resilience indicators in Holstein cattle

Genetic selection for resilience is essential to improve the long-term sustainability of the dairy cattle industry, especially the ability of cows to maintain their level of production when exposed to environmental disturbances. Recording of daily milk yield provides an opportunity to develop resilience indicators based on milk losses and fluctuations in daily milk yield caused by environmental disturbances. In this context, our study aimed to explore milk loss traits and measures of variability in daily milk yield, including log-transformed standard deviation of milk deviations (Lnsd), lag-1 autocorrelation (Ra), and skewness of the deviations (Ske), as indicators of general resilience in dairy cows. The unperturbed dynamics of milk yield as well as milk loss were predicted using an iterative procedure of lactation curve modeling. Milk fluctuations were defined as a period of at least 10 successive days of negative deviations in which milk yield dropped at least once below 90% of the expected values. Genetic parameters of these indicators and their genetic correlation with economically important traits were estimated using single-trait and bivariate animal models and 8,935 lactations (after quality control) from 6,816 Chinese Holstein cows. In general, cows experienced an average of 3.73 environmental disturbances with a milk loss of 267 kg of milk per lactation. Each fluctuation lasted for 19.80 ± 11.46 days. Milk loss traits are heritable with heritability estimates ranging from 0.004 to 0.061. The heritabilities differed between Lnsd (0.135-0.250), Ra (0.008-0.058), and Ske (0.001-0.075), with the highest heritability estimate of 0.250 ± 0.020 for Lnsd when removing the first and last 10 days in milk in a lactation (Lnsd2). Based on moderate to high genetic correlations, lower Lnsd2 is associated with less milk losses, better reproductive performance, and lower disease incidence. These findings indicate that among the variables evaluated, Lnsd2 is the most promising indicator for breeding for improved resilience in Holstein cattle.

Modelling beef cows' individual response to short nutrient restriction in different lactation stages

Short-term nutrient restrictions can occur naturally in extensive beef cattle production systems due to low feed quality or availability. The aims of the study were to (1) model the curves of milk yield, plasma non-esterified fatty acids (NEFAs) and β-hydroxybutyrate (BHB) contents of beef cows in response to short nutritional challenges throughout lactation; (2) identify clusters of cows with different response profiles; (3) quantify differences in cows' response between the clusters and lactation stages. Data of BW, body condition score (BCS), milk yield, NEFA, and BHB plasma concentration from 31 adult beef cows (626 ± 48 kg at calving) were used to study the effect of 4-day feed restriction repeated over months 2, 3 and 4 of lactation. On each month, all cows received a single diet calculated to meet the requirements of the average cow: 100 % requirements for 4 days (d-4 to d-1, basal period), 55 % requirements on the next 4 days (d0 to d3, restriction period) and 100 % requirements for 4 days (d4 to d7, refeeding period). Natural cubic splines were used to model the response of milk yield, NEFA and BHB to restriction and refeeding in the 3 months. The new response variables [baseline value, peak value, days to peak and to regain baseline, and areas under the curve (AUC) during restriction and refeeding] were used to cluster cows according to their metabolic response (MR) into two groups: Low MR and High MR. The month of lactation affected all the traits, and basal values decreased as lactation advanced. Cows from both clusters had similar BW and BCS values, but those in the High MR cluster had higher basal milk yield, NEFA and BHB contents, and responded more intensely to restriction, with more marked peaks and AUCs. Reaction times were similar, and baseline values recovered during refeeding in both clusters. Our results suggest that the response was driven by cows' milk potential rather than size or body reserves, and despite high-responding cattle's higher milk yield, they were able to activate metabolic pathways to respond to and recover from the challenge.

Fluorometric determination of isocitrate dehydrogenase (EC 1.1.1.42; 1; NADP+ dependent) in ruminant milk

The enzyme isocitrate dehydrogenase (EC 1.1.1.42; 1; NADP⁺ dependent) located in the mammary cell cytosol mediates the synthesis of the majority of reducing equivalents for the energetically demanding milk fat and cholesterol synthesis in mammary cell cytosol. The present article presents a novel fluorometric method for quantification of the activity of this enzyme (IDH) in ruminant milk without pretreatment of the sample. Further, 493 goat milk samples - harvested before, during and after a nutritional restriction - were analysed for IDH activity i) with addition of extra substrate (isocitrate), and ii) with the intrinsic isocitrate solely. The IDH activity ranged from 0.22 to 15.4 units [nano moles product/(ml * min)] (un-supplemented) and from 0.22 to 45.6 units (isocitrate supplemented). The IDH activity increased considerably in milk during the nutritional restriction period concomitant with the increase in the metabolite isocitrate concentration and somatic cell count and returned to the initial level shortly after restriction period. The present 'high through-put' analytical method may be beneficial in future studies to phenotype modifications in mammary energy metabolism and milk fat synthesis, for which IDH activity may be a biomarker.

Emphasis on resilience in dairy cattle breeding: Possibilities and consequences

This study aimed to investigate dairy cattle breeding goals with more emphasis on resilience. We simulated the consequences of increasing weight on resilience indicators and an assumed true resilience trait (TR). Two environments with different breeding goals were simulated to represent the variability of production systems across Europe. Ten different scenarios were stochastically simulated in a so-called pseudogenomic simulation approach. We showed that many modern dairy cattle breeding goals most likely have negative genetic gain for TR and promising resilience indicators such as the log-transformed, daily deviation from the lactation curve (LnVAR). In addition, there were many ways of improving TR by increasing the breeding goal weight of different resilience indicators. The results showed that adding breeding goal weight to resilience indicators, such as body condition score and LnVAR, could reverse the negative trend observed for resilience indicators. Loss in the aggregate genotype calculated with only current breeding goal traits was 12 to 76%. This loss was mainly due to a reduction in genetic gain in milk production. We observed higher genetic gain in beef production, fertility, and udder health when breeding for more resilience, but from an economical point of view, this was not high enough to compensate for the reduction in genetic gain in milk production. The highest genetic gain in TR was obtained when adding the highest breeding goal weight to LnVAR or TR, both with 0.29 genetic standard deviation units. The indicators we used, body condition score and LnVAR, can be measured on a large scale today with relatively cheap methods, which is crucial if we want to improve these traits through breeding. Economic values for resilience have to be estimated to find the most optimal breeding goal for a more resilient dairy cow in the future.

Exploration of robustness indicators using adaptive responses to short-term feed restriction in suckling primiparous beef cows

Animal robustness is a complex trait of importance for livestock production systems and genetic selection. Phenotyping is essential for evaluation of the adaptation of different genotypes to changing environments. This study tested an experimental framework to induce marked deviations in the adaptive responses of suckling beef cows and to identify relevant indicators of responses to characterise individual differences in the robustness of cows. The production and metabolic responses of primiparous suckling Charolais cows to two periods of feed restriction (FR, 50% of their net energy requirements) of different durations were monitored. After calving, 13 cows (aged 39 ± 2 months, BW of 680 ± 42 kg at calving) had ad libitum access to a diet composed of hay and supplemented with concentrate to meet their energy and protein requirements. Starting at 54 ± 6 days postcalving, the cows underwent two periods of FR: 4 days of FR (FR4), which was followed by 17 days of ad libitum intake to study the recovery from FR4, and 10 days of FR (FR10), which was followed by 18 days of ad libitum intake to study the recovery from FR10. The milk yield (MY), BW, body condition score and plasma non-esterified fatty acid (NEFA), β-hydroxybutyrate, glucose and urea concentrations were measured before, during and after each FR. Among all measured variables, the MY and NEFA concentrations showed the most significant changes in response to FR. A functional data analysis approach was applied to the MY and NEFA data to model the adaptive responses and extract quantifiable indicators of deviation and recovery. Linear correlations (P < 0.03-0.07) between FR4 and FR10 were found for some indicators describing MY and NEFA levels before and after FR. The overall repeatability of MY and NEFA responses between both FR accounted for 46% based on quartile analysis performed on average responses. Moreover, the variance in both the MY and NEFA variables did not differ significantly between FR4 and FR10, despite a trend for higher variances in FR10. Altogether, (1) the calculated variables derived from the functional data analysis of the time patterns of the MY and NEFA accounted for the differences in the cow responses to FR, and (2) the animal responses appeared to show concordance between FR4 and FR10. In conclusion, short-term FR is a relevant framework for studying productive and metabolic adaptive responses in suckling cows and allows the identification of potential robustness indicators.

Dairy goats adjust their meal patterns to the fibre content of the diet

Few studies have investigated how meal patterns of ruminants are affected by diet fibre content. Dairy goats (N = 32) in late lactation and early gestation were housed in eight groups of four goats, with all combinations of breed (Alpine and Saanen) and lactation number (1 and 2) represented in each group. Each goat had access to its own individual feed trough placed on a weigh scale with data logged automatically. All goats were fed the same total mixed ration (TMR; 30% concentrate and 44.6% NDF in DM) ad libitum for a control period of 22 days. Using the same feed ingredients, half of the groups were then offered a High fibre diet (20% concentrate; 47.3% NDF), and the other half a Low fibre diet (40% concentrate; 41.5% NDF) for a treatment period of 16 days. Daily meal patterns (meal frequency, duration and size, feeding rate, daily feed intake and daily feeding time) were computed for each animal using a meal criterion of 8 min. The last 10 days for each period (control and treatment) were used to calculate individual period means and individual differences between the two periods. During the control period, the goats ate on average 12.1 ± 0.49 meals/day, consuming 4.2 ± 0.10 kg fresh TMR daily. When the ration changed, all measures of feeding behaviour except meal size changed asymmetrically for the goats on the two diets. Goats fed the High fibre diet reduced their meal frequency by 10%, and the first meal after feed distribution lasted 11% longer, leading to a 9% reduction in feeding rate and no significant changes in daily feed intake and daily feeding time. Goats on the Low fibre diet did not significantly change their meal frequency or meal size, but the combined changes nevertheless led to a 9% increase in daily feed intake. On the Low fibre diet, goats were able to increase their feeding rate by a third, leading to a reduction in meal durations, thus reducing daily feeding time by 13%. Goats adapt their feeding behaviour to the fibre proportion of the offered diet, with more changes when fibre content is lowered, which needs to be taken into account when comparing phenotypes and adaptability of small ruminants to different diets.

Validation of resilience indicators by estimating genetic correlations among daughter groups and with yield responses to a heat wave and disturbances at herd level

Resilient cows are minimally affected in their functioning by disturbances, and if affected, they quickly recover. Previously, the variance and autocorrelation of daily deviations from a lactation curve were proposed as resilience indicators. These traits were heritable and genetically associated with good health and longevity. However, it was unknown if selection for these indicators would lead to desired changes in the phenotype. The first aim of this study was to investigate if forward prediction of the resilience indicators in another environment was possible. Therefore, the resilience indicator records were split into 2 subsets, each containing half of the daughters of each sire, split within sire into cows that calved in early year-seasons and cows that calved in more recent year-seasons. Genetic correlations between the subsets were then estimated for each resilience indicator. The second aim was to estimate genetic correlations between the resilience indicators and traits describing production responses to actual disturbances. The disturbances were a heat wave in July 2015 and yield disturbances at herd level. The latter were selected by decreases in mean yield of all primiparous cows in a herd, indicating that a disturbance occurred. The data set used for calculation of the resilience indicators and the traits describing yield responses contained 62,932,794 daily milk yield records on 199,104 primiparous cows. Genetic correlations (r_g) between recent and earlier daughter groups were 1 for both resilience indicators, which suggests that selection will result in changes in the phenotype in the next generation. Furthermore, low variance was genetically correlated with weak response in milk yield to both the heat wave and herd disturbances (r_g 0.47 to 0.97). Low autocorrelation was genetically correlated with reduced perturbation length and quick recovery after the heat wave and herd disturbances (0.28 to 0.97). These results suggest that variance and autocorrelation cover different aspects of resilience, and should be combined in a resilience index. In conclusion, genetic selection for the resilience indicators will likely result in favorable changes in the traits themselves, and in response and recovery to actual disturbances, which confirms that they are useful resilience indicators.

Nutritional Regulation of Mammary Gland Development and Milk Synthesis in Animal Models and Dairy Species

In mammals, milk is essential for the growth, development, and health. Milk quantity and quality are dependent on mammary development, strongly influenced by nutrition. This review provides an overview of the data on nutritional regulations of mammary development and gene expression involved in milk component synthesis. Mammary development is described related to rodents, rabbits, and pigs, common models in mammary biology. Molecular mechanisms of the nutritional regulation of milk synthesis are reported in ruminants regarding the importance of ruminant milk in human health. The effects of dietary quantitative and qualitative alterations are described considering the dietary composition and in regard to the periods of nutritional susceptibly. During lactation, the effects of lipid supplementation and feed restriction or deprivation are discussed regarding gene expression involved in milk biosynthesis, in ruminants. Moreover, nutrigenomic studies underline the role of the mammary structure and the potential influence of microRNAs. Knowledge from three lactating and three dairy livestock species contribute to understanding the variety of phenotypes reported in this review and highlight (1) the importance of critical physiological stages, such as puberty gestation and early lactation and (2) the relative importance of the various nutrients besides the total energetic value and their interaction.

Detection of unrecorded environmental challenges in high-frequency recorded traits, and genetic determinism of resilience to challenge, with an application on feed intake in lambs

BACKGROUND

Resilient animals can remain productive under different environmental conditions. Rearing in increasingly heterogeneous environmental conditions increases the need of selecting resilient animals. Detection of environmental challenges that affect an entire population can provide a unique opportunity to select animals that are more resilient to these events. The objective of this study was two-fold: (1) to present a simple and practical data-driven approach to estimate the probability that, at a given date, an unrecorded environmental challenge occurred; and (2) to evaluate the genetic determinism of resilience to such events.

METHODS

Our method consists of inferring the existence of highly variable days (indicator of environmental challenges) via mixture models applied to frequently recorded phenotypic measures and then using the inferred probabilities of the occurrence of an environmental challenge in a reaction norm model to evaluate the genetic determinism of resilience to these events. These probabilities are estimated for each day (or other time frame). We illustrate the method by using an ovine dataset with daily feed intake (DFI) records.

RESULTS

Using the proposed method, we estimated the probability of the occurrence of an unrecorded environmental challenge, which proved to be informative and useful for inclusion as a covariate in a reaction norm animal model. We estimated the breeding values for sensitivity of the genetic potential for DFI of animals to environmental challenges. The level and slope of the reaction norm were negatively correlated (- 0.46 ± 0.21).

CONCLUSIONS

Our method is promising and appears to be viable to identify unrecorded events of environmental challenges, which is useful when selecting resilient animals and only productive data are available. It can be generalized to a wide variety of phenotypic records from different species and used with large datasets. The negative correlation between level and slope indicates that a hypothetical selection for increased DFI may not be optimal depending on the presence or absence of stress. We observed a reranking of individuals along the environmental gradient and low genetic correlations between extreme environmental conditions. These results confirm the existence of a G [Formula: see text] E interaction and show that the best animals in one environmental condition are not the best in another one.

Lactation curve model with explicit representation of perturbations as a phenotyping tool for dairy livestock precision farming

In the context of dairy farming, ruminant females often face challenges inducing perturbations that affect their performance and welfare. A key issue is how to assess the effect of perturbations and provide metrics to quantify how animals cope with their environment. Milk production dynamics are good candidates to address this issue: i) they are easily accessible, ii) overall dynamics throughout lactation process are well described and iii) perturbations are visible through milk losses. In this study, a perturbed lactation model (PLM) with explicit representation of perturbations was developed. The model combines two components: i) the unperturbed lactation model that describes a theoretical lactation curve, assumed to reflect female production potential and ii) the perturbation model that describes all the deviations from the unperturbed lactation model with four parameters: starting date, intensity and shape (collapse and recovery). To illustrate the use of the PLM as a phenotyping tool, it was fitted on a data set of 319 complete lactations from 181 individual dairy goats. A total of 2 354 perturbations were detected, with an average of 7.40 perturbations per lactation. Loss of milk production for the whole lactation due to perturbations varied between 2 and 19% of the milk production predicted by the unperturbed lactation model. The number of perturbations was not the major factor explaining the loss of milk production, suggesting that there are different types of animal response to challenges. By incorporating explicit representation of perturbations in a lactation model, it was possible to determine for each female the potential milk production, characteristics of each perturbation and milk losses induced by perturbations. Further, it was possible to compare animals and analyze individual variability. The indicators produced by the PLM are likely to be useful to move from raw data to decision support tools in dairy production.

A novel modelling approach to quantify the response of dairy goats to a high-concentrate diet

High-producing ruminants need high-concentrate diets to satisfy their nutrient requirements and meet performance objectives. However, such diets induce sub-acute ruminal acidosis (SARA), which will adversely affect dry matter intake and lead to lower production performance. This work develops a novel modelling approach to quantify the capacity of dairy goats to adapt to a high-concentrate diet challenge at the individual level. The animal model used was dairy goats (from Saanen or Alpine breed), and rumen pH was used as the indicator of the response. A three-step modelling procedure was developed to quantify daily scores and produce a single global index for animals' adaptive response to the new diet. The first step summarizes the post-prandial kinetics of rumen acid status using three synthetic variables. In the second step, the effect of time on the response of goats is described, in the short and long terms. In the last step, a metric based on phase trajectories ranks goats for their resilience capacity. This modelling procedure showed a high variability among the goats in response to the new diet, highlighting in particular their daily and general strategies to buffer the effect of the diet change. Two main categories of adaptive strategies were observed: (i) acid status increased, but the goats tried to minimize its variations, and (ii) acid status oscillated between increases and decreases. Such phenotyping, alongside other behavioral, digestive, and metabolic measures, can help to determine biomarkers of goats' capacity to adapt to diets of higher nutritive value and to increase production performance without compromising their health status. Quantifying the capacity of goats to buffer the effect of highly fermentable diets helps to better adapt feed to animals in precision livestock farming. This procedure is generic and can be adapted to any indicator of animal health and performance. In particular, several indicators can be combined to assess multi-performance, which is of major interest in the context of selection for robust animals.

Exploring simultaneous perturbation profiles in milk yield and body weight reveals a diversity of animal responses and new opportunities to identify resilience proxies

Livestock husbandry aims to manage the environment in which animals are reared to enable them to express their production potential. However, animals are often confronted with perturbations that affect their performance. Evaluating effects of these perturbations on animal performance could provide metrics to quantify and understand how animals cope with their environment, and therefore to better manage them. Body weight (BW) and milk yield (MY) dynamics over lactation may be used for this purpose. The goal of this study was to estimate an unperturbed performance trajectory using a differential smoothing approach on both MY and BW time series, and then to identify the perturbations and extract their phenotypic features. Daily MY and BW records from 490 primiparous Holstein cows from 33 commercial French herds were used. From the fitting procedure, estimated unperturbed performance trajectories of BW and MY were clustered into 3 groups. After the fitting procedure, 1,754 deviations were detected in the MY time series and 964 were detected in the BW time series across all cows. Overall, 425 of these deviations were detected during the same period (±10 d) in both MY and BW time series, 76 of which started at the same time. Results suggest that combining various individual dynamic measures and revealing the relationship that exists between them could be of great value in obtaining reliable estimates of resilience components in large populations.

Composition, Mineral and Fatty Acid Profiles of Milk from Goats Fed with Different Proportions of Broccoli and Artichoke Plant By-Products

In the Mediterranean region, artichoke and broccoli are major crops with a high amount of by-products that can be used as alternative feedstuffs for ruminants, lowering feed costs and enhancing milk sustainability while reducing the environmental impact of dairy production. However, nutritional quality of milk needs to be assured under these production conditions and an optimal inclusion ratio of silages should be determined. This work aimed to evaluate the effect of three inclusion levels (25%, 40%, and 60%) of these silages (artichoke plant, AP, and broccoli by-product, BB) in goat diets on milk yield, composition, and mineral and fatty profiles. Treatments with 60% inclusion of AP and BB presented the lowest milk yield. No differences were found on the milk mineral profile. Inclusion of AP in the animals' diet improved the milk lipid profile from the point of view of human health (AI, TI) compared to BB due to a lower saturated fatty acid content (C12:0, C14:0, and C16:0) and a higher concentration of polyunsaturated fatty acids (PUFA), especially vaccenic acid (C18:1 trans11) and rumenic acid (CLA cis9, trans11), without any differences with the control treatment.

Milk metabolites as noninvasive indicators of nutritional status of mid-lactation Holstein and Montbéliarde cows

The objective was to investigate the effects of feed restriction on concentrations of selected milk metabolites in mid-lactation Holstein and Montbéliarde cows and to explore their correlations with energy balance and classic plasma and milk indicators of nutritional status. Eight Holstein and 10 Montbéliarde cows (165 ± 21 d in milk) underwent 6 d of feed restriction during which feed allowance was reduced to meet 50% of their net energy for lactation (NE_L) requirements. The experiment was divided in 4 periods: control (CON; d -3 to -1), restriction (RES; d 1 to 6), wk 1 (W1; d 7 to 13), and wk 2 (W2; d 14 to 18) after refeeding at ad libitum intake. Intake, milk production, energy balance and plasma metabolites were used to validate the feed restriction model. Concentrations of 7 milk metabolites: β-hydroxybutyrate (BHB), glucose, glucose-6-phosphate, isocitrate, glutamate, uric acid, and free amino groups were measured in morning milk samples, and fatty acids were measured in pooled p.m. and a.m. samples. Feed restriction induced a negative energy balance (-42.5 ± 4.4 MJ/d), increased plasma nonesterified fatty acids and BHB, and decreased plasma glucose concentrations. Feed restriction increased milk glucose-6-phosphate and isocitrate (+38% and +39%, respectively) and decreased milk BHB, glucose, glutamate, uric acid and free amino group concentrations (-20%, -57%, -65%, -42%, and -14%, respectively), compared with pre- restriction. Milk concentrations of medium-chain fatty acids (e.g., sum of C10 to C15) decreased and those of long chain (e.g., 18:0, cis-9 18:1) increased during restriction. Breed differences were not detected for the majority of variables. All studied milk metabolites were significantly correlated with energy balance (Spearman correlation = 0.48, 0.63, -0.31, -0.45, and 0.61 for BHB, glucose, glucose-6-phosphate, isocitrate, and glutamate, respectively). Milk glucose and glutamate showed the strongest correlations with plasma metabolites and milk FA associated with lipomobilization. These results suggest that milk metabolites may be used as noninvasive indicators of negative energy balance and metabolic status of dairy cows.

A procedure to quantify the feed intake response of growing pigs to perturbations

Improving robustness of farm animals is one of the goals in breeding programmes. However, robustness is a complex trait and not measurable directly. The objective of this study was to quantify and characterize (elements of) robustness in growing pigs. Robustness can be analysed by examining the animal’s response to perturbations. Although the origin of perturbations may not be known, their effect on animal performance can be observed, for example, through changes in voluntary feed intake. A generic model and data analysis procedure was developed (1) to estimate the target trajectory of feed intake, which is the amount of feed that a pig desires to eat when it is not facing any perturbations; (2) to detect potential perturbations, which are deviations of feed intake from the estimated target trajectory; and (3) to characterize and quantify the response of the growing pigs to the perturbations using voluntary feed intake as response criterion. The response of a pig to a perturbation is characterized by four parameters. The start and end times of the perturbation are ‘imposed’ by the perturbing factor, while two other parameters describe the resistance and resilience potential of the pig. One of these describes the immediate reduction in daily feed intake at the start of the perturbation (i.e., a ‘resistance’ trait) while another parameter describes the capacity of the pig to adapt to the perturbation through compensatory feed intake to rejoin the target trajectory of feed intake (i.e., a ‘resilience’ trait). The procedure has been employed successfully to identify the target trajectory of feed intake in growing pigs and to quantify the pig’s response to a perturbation.

Review: Deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management

As the environments in which livestock are reared become more variable, animal robustness becomes an increasingly valuable attribute. Consequently, there is increasing focus on managing and breeding for it. However, robustness is a difficult phenotype to properly characterise because it is a complex trait composed of multiple components, including dynamic elements such as the rates of response to, and recovery from, environmental perturbations. In this review, the following definition of robustness is used: the ability, in the face of environmental constraints, to carry on doing the various things that the animal needs to do to favour its future ability to reproduce. The different elements of this definition are discussed to provide a clearer understanding of the components of robustness. The implications for quantifying robustness are that there is no single measure of robustness but rather that it is the combination of multiple and interacting component mechanisms whose relative value is context dependent. This context encompasses both the prevailing environment and the prevailing selection pressure. One key issue for measuring robustness is to be clear on the use to which the robustness measurements will employed. If the purpose is to identify biomarkers that may be useful for molecular phenotyping or genotyping, the measurements should focus on the physiological mechanisms underlying robustness. However, if the purpose of measuring robustness is to quantify the extent to which animals can adapt to limiting conditions then the measurements should focus on the life functions, the trade-offs between them and the animal's capacity to increase resource acquisition. The time-related aspect of robustness also has important implications. Single time-point measurements are of limited value because they do not permit measurement of responses to (and recovery from) environmental perturbations. The exception being single measurements of the accumulated consequence of a good (or bad) adaptive capacity, such as productive longevity and lifetime efficiency. In contrast, repeated measurements over time have a high potential for quantification of the animal's ability to cope with environmental challenges. Thus, we should be able to quantify differences in adaptive capacity from the data that are increasingly becoming available with the deployment of automated monitoring technology on farm. The challenge for future management and breeding will be how to combine various proxy measures to obtain reliable estimates of robustness components in large populations. A key aspect for achieving this is to define phenotypes from consideration of their biological properties and not just from available measures.