Towards the quantitative characterisation of piglets' robustness to weaning: a modelling approach

The genetics of resilience and its relationships with egg production traits and antibody traits in chickens

BACKGROUND

Resilience is the capacity of an animal to be minimally affected by disturbances or to rapidly return to its initial state before exposure to a disturbance. Resilient livestock are desired because of their improved health and increased economic profit. Genetic improvement of resilience may also lead to trade-offs with production traits. Recently, resilience indicators based on longitudinal data have been suggested, but they need further evaluation to determine whether they are indeed predictive of improved resilience, such as disease resilience. This study investigated different resilience indicators based on deviations between expected and observed egg production (EP) by exploring their genetic parameters, their possible trade-offs with production traits, and their relationships with antibody traits in chickens.

METHODS

Egg production in a nucleus breeding herd environment based on 1-week-, 2-week-, or 3-week-intervals of two purebred chicken lines, a white egg-laying (33,825 chickens) and a brown egg-laying line (34,397 chickens), were used to determine deviations between observed EP and expected average batch EP, and between observed EP and expected individual EP. These deviations were used to calculate three types of resilience indicators for two life periods of each individual: natural logarithm-transformed variance (ln(variance)), skewness, and lag-one autocorrelation (autocorrelation) of deviations from 25 to 83 weeks of age and from 83 weeks of age to end of life. Then, we estimated their genetic correlations with EP traits and with two antibody traits.

RESULTS

The most promising resilience indicators were those based on 1-week-intervals, as they had the highest heritability estimates (0.02-0.12) and high genetic correlations (above 0.60) with the same resilience indicators based on longer intervals. The three types of resilience indicators differed genetically from each other, which indicates that they possibly capture different aspects of resilience. Genetic correlations of the resilience indicator traits based on 1-week-intervals with EP traits were favorable or zero, which means that trade-off effects were marginal. The resilience indicator traits based on 1-week-intervals also showed no genetic correlations with the antibody traits, which suggests that they are not informative for improved immunity or vice versa in the nucleus environment.

CONCLUSIONS

This paper gives direction towards the evaluation and implementation of resilience indicators, i.e. to further investigate resilience indicator traits based on 1-week-intervals, in breeding programs for selecting genetically more resilient layer chickens.

Identification and characterization of unknown disturbances in a structured population using high-throughput phenotyping data and measurement of robustness: application to growing pigs

Improving the robustness of animals has become a priority in breeding due to climate change, new societal demands, and the agroecological transition. Components of animal robustness can be extracted from the analysis of the adaptive response of an animal to disturbance using longitudinal data. Nonetheless, this response is a function of animal robustness as well as of disturbance characteristics (intensity and duration). To correctly assess an animal's robustness potential, it is therefore useful to know the characteristics of the disturbances it faces. The UpDown method, which detects and characterizes unknown disturbances at different levels of organization of the population (e.g., individual, pen, and batch disturbances), has been proposed for this purpose. Furthermore, using the outputs of the method, it is possible to extract proxies of the robustness of animals. In this context, the objective of the study was to evaluate the performances of the UpDown method to detect and characterize disturbances and quantify the robustness of animals in a genetic framework using different sets of simulations, and to apply this method to real pig longitudinal data recorded during the fattening period (body weight, cumulative feed intake, and feeding rate). Based on the simulations, the specificity of the UpDown method was high (>0.95). Its sensitivity increased with the level of organization exposed (from 0.23 to 0.32 for individual disturbances, from 0.45 to 0.59 for pen disturbances, and from 0.77 to 0.88 for batch disturbances). The UpDown method also showed a good ability to characterize detected disturbances. The average time interval between the estimated and true start date or duration of the disturbance was lower than 3 d. The correlation between the true and estimated intensity of the disturbance increased with the hierarchical level of organization (on average, 0.41, 0.78, and 0.83 for individual, pen, and batch disturbance, respectively). The accuracy of the estimated breeding values of the proxies for robustness extracted from the analysis of individual trajectories over time were moderate (lower than 0.33). Applied to real data, the UpDown method detected different disturbances depending on the phenotype analyzed. The heritability of the proxies of robustness were low to moderate (ranging from 0.11 to 0.20).

Disentangling the dynamics of energy allocation to develop a proxy for robustness of fattening pigs

BACKGROUND

There is a growing need to improve robustness of fattening pigs, but this trait is difficult to phenotype. Our first objective was to develop a proxy for robustness of fattening pigs by modelling the longitudinal energy allocation coefficient to growth, with the resulting environmental variance of this allocation coefficient considered as a proxy for robustness. The second objective was to estimate its genetic parameters and correlations with traits under selection and with phenotypes that are routinely collected. In total, 5848 pigs from a Pietrain NN paternal line were tested at the AXIOM boar testing station (Azay-sur-Indre, France) from 2015 to 2022. This farm is equipped with an automatic feeding system that records individual weight and feed intake at each visit. We used a dynamic linear regression model to characterize the evolution of the allocation coefficient between the available cumulative net energy, which was estimated from feed intake, and cumulative weight gain during the fattening period. Longitudinal energy allocation coefficients were analysed using a two-step approach to estimate both the genetic variance of the coefficients and the genetic variance in their residual variance, which will be referred to as the log-transformed squared residual (LSR).

RESULTS

The LSR trait, which could be interpreted as an indicator of the response of the animal to perturbations/stress, showed a low heritability (0.05 ± 0.01), a high favourable genetic correlation with average daily growth (- 0.71 ± 0.06), and unfavourable genetic correlations with feed conversion ratio (- 0.76 ± 0.06) and residual feed intake (- 0.83 ± 0.06). Segmentation of the population in four classes using estimated breeding values for LSR showed that animals with the lowest estimated breeding values were those with the worst values for phenotypic proxies of robustness, which were assessed using records routinely collected on farm.

CONCLUSIONS

Results of this study show that selection for robustness, based on estimated breeding values for environmental variance of the allocation coefficients to growth, can be considered in breeding programs for fattening pigs.

An improved transmissibility model to detect transgenerational transmitted environmental effects

BACKGROUND

Evolutionary studies have reported that non-genetic information can be inherited across generations (epigenetic marks, microbiota, cultural inheritance). Non-genetic information is considered to be a key element to explain the adaptation of wild species to environmental constraints because it lies at the root of the transgenerational transmission of environmental effects. The "transmissibility model" was proposed several years ago to better predict the transmissible potential of each animal by taking these diverse sources of inheritance into account in a global transmissible potential. We propose to improve this model to account for the influence of the environment on the global transmissible potential as well. This extension of the transmissibility model is the "transmissibility model with environment" that considers a covariance between transmissibility samplings of animals sharing the same environment. The null hypothesis of "no transmitted environmental effect" can be tested by comparing the two models using a likelihood ratio test (LRT).

RESULTS

We performed simulations that mimicked an experimental design consisting of two lines of animals with one exposed to a particular environment at a given generation. This enabled us to evaluate the performances of the transmissibility model with environment so as to detect and quantify transgenerational transmitted environmental effects. The power and the realized type I error of the LRT were compared to those of a T-test comparing the phenotype of the two lines, three generations after the environmental exposure for different sets of parameters. The power of the LRT ranged from 45 to 94%, whereas that of the T-test was always lower than 26%. In addition, the realized type I error of the T-test was 15% and that of the LRT was 5%, as expected. Variances, the covariance between transmissibility samplings, and path coefficients of transmission estimated with the transmissibility model with environment were close to their true values for all sets of parameters.

CONCLUSIONS

The transmissibility model with environment is effective in modeling vertical transmission of environmental effects.

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.

ASAS-NANP symposium: Mathematical Modeling in Animal Nutrition: The power of identifiability analysis for dynamic modeling in animal science:a practitioner approach

Constructing dynamic mathematical models of biological systems requires estimating unknown parameters from available experimental data, usually using a statistical fitting procedure. This procedure is usually called parameter identification, parameter estimation, model fitting, or model calibration. In animal science, parameter identification is often performed without analytic considerations on the possibility of determining unique values of the model parameters. These analytical studies are related to the mathematical property of structural identifiability, which refers to the theoretical ability to recover unique values of the model parameters from the measures defined in an experimental setup and use the model structure as the sole basis. The structural identifiability analysis is a powerful tool for model construction because it informs whether the parameter identification problem is well-posed (i.e., the problem has a unique solution). Structural identifiability analysis is helpful to determine which actions (e.g., model reparameterization, choice of new data measurements, and change of the model structure) are needed to render the model parameters identifiable (when possible). The mathematical technicalities associated with structural identifiability analysis are very sophisticated. However, the development of dedicated, freely available software tools enables the application of identifiability analysis without needing to be an expert in mathematics and computer programming. We refer to such a non-expert user as a practitioner for hands-on purposes. However, a practitioner should be familiar with the model construction and software implementation process. In this paper, we propose to adopt a practitioner approach that takes advantage of available software tools to integrate identifiability analysis in the modeling practice in the animal science field. The application of structural identifiability implies switching our regard of the parameter identification problem as a downstream process (after data collection) to an upstream process (before data collection) where experiment design is applied to guarantee identifiability. This upstream approach will substantially improve the workflow of model construction toward robust and valuable models in animal science. Illustrative examples with different levels of complexity support our work. The source codes of the examples were provided for learning purposes and to promote open science practices.

Effects of dietary Glycyrrhiza polysaccharide on growth performance, blood parameters and immunity in weaned piglets

The purpose of this study was to evaluate the effects of dietary Glycyrrhiza polysaccharide (GCP) on growth performance, blood parameters and immunity in weaned piglets. A total of 240 (10.33 ± 0.62 kg body weight) 35-day-old (Duroc × Landrace × White) weaned piglets were randomly assigned to four dietary treatments, with six replicate pens per treatment and 10 piglets per pen (five males and five females). The dietary treatments continued for 21 days and comprised a basal diet supplemented with 0 (control group), 500, 1000 and 2000 mg/kg GCP. The results showed that the inclusion of 1000 and 2000 mg/kg GCP increased the average daily gain and decreased the feed conversion rate compared with the control group (p < 0.05). The piglets treated with 500 and 1000 mg/kg GCP had a lower diarrhoeal incidence than the control group (p < 0.05). Moreover, supplementation with 1000 mg/kg GCP increased the counts of white blood cells, neutrophils, red blood cells, and platelets, and elevated alkaline phosphatase, total protein, globulin, glucose, triglyceride, immunoglobulin A, immunoglobulin G, and total antioxidant capacity levels (p < 0.05), and decreased malondialdehyde content compare with the control group (p < 0.05). In addition, relative to the control group, piglets fed 500 and 1000 mg/kg GCP had significantly lower expression of interleukin-6 mRNA in spleen (p < 0.05). Our results indicate that dietary supplementation with GCP can improve growth performance, blood parameters and immunity in weaned piglets. Our study suggests that adding 1000 mg/kg GCP to the diet had the most beneficial effect.

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.

Effect of Resin Acid and Zinc Oxide on Immune Status of Weaned Piglets Challenged With E. coli Lipopolysaccharide

With the ban of zinc oxide (ZnO) at high dosages in piglet diets in Europe by 2022, alternative nutritional solutions are being tested to support piglet immune defence during their weaning, the most critical and stressful moment of pig production. The present study evaluated the effect of zinc oxide (ZnO; 2,500 mg/kg diet) and resin acid concentrate (RAC; 200 mg/kg diet) on the immune defence of weaned piglets challenged with lipopolysaccharide (LPS). Piglets were challenged at days 7 and 21 post-weaning, and blood was sampled 1.5 and 3.0 h after each challenge to determine serum levels of pro- and anti-inflammatory cytokines. The levels of serum tumour necrosis factor alpha (TNF-α) and interleukin 8 (IL-8) increased at days 7 and 21, and those of IL-6 at day 21 when challenged piglets were fed a diet supplemented with ZnO. In challenged piglets fed with RAC, the serum levels of IL-1β, IL-6, IL-8, IL-10 and TNF-α were increased at days 7 and 21, except for that of IL-1β, which was not affected at day 21. The increased levels of these cytokines indicate the successful immune-modulatory effect of ZnO and RAC, which appears as a candidate to replace ZnO in weaned piglets' diets.

Physiological response to the weaning in two pig lines divergently selected for residual feed intake

Breeding efficient pigs is a way to reduce dietary costs and environmental waste. However, optimization of feed efficiency must not be linked to a decrease of the ability of animals to cope with stress, such as the weaning. This study characterizes the response after weaning of pigs from two lines divergently selected for residual feed intake (RFI) during growth. Animals of the low (L) RFI line are more efficient than animals from the high (H) RFI line. Thirty-six piglets from each line weaned at 28 days of age were individually housed and fed a conventional dietary sequence. Their performance, behaviour, health and oxidative status, immune and nutritional parameters were followed during three weeks. Daily feed intake and growth rate of pigs from the LRFI line were 35% and 40% lower compared with HRFI (p < 0.001). Pigs from the LRFI-line had lower total tract apparent digestibility (-6% for OM) and suffered more from undernutrition with a 167 and 55% higher plasmatic concentration of NEFA and urea compared with HRFI (p < 0.01). In the first week after the weaning, they had more diarrhoea and had a higher inflammatory status with concentration of haptoglobin 52% higher (p < 0.001). These piglets then seemed to adapt to the weaning conditions and to recover during the second and third weeks. Both lines had similar zootechnical performance and physiological characteristics at the end of the post-weaning period. To conclude, the physiological responses to the weaning differed between lines. Pigs from the LRFI line, selected for greater feed efficiency, were more sensitive to the weaning stress. They were also more resilient as they finally adapted to the new condition and recovered to show similar performance results as pigs of the HRFI line.

Investigation of Early Supplementation of Nucleotides on the Intestinal Maturation of Weaned Piglets

Simple Summary

Nucleotides represent a group of bioactive compounds essential for the development of the gastrointestinal tract and immune function. This study aimed to evaluate the short-term effect of oral administration of nucleotides before and after weaning on growth performance, health, development of the intestinal immunity and microbiome of piglet. A nucleotide-based product (NU) was orally given four times before weaning and once after to one group of piglets, while a second group was used as a control (CO). The NU pigs did not grow more than the control until 12 days post-weaning but had increased hemoglobin and hematocrit values. At weaning, feces of NU piglets had a microbial profile more typical of growing pigs, while those of CO were more representative of suckling pigs. The upregulation of genes in the blood of control pigs at weaning was indicative of more activation towards an inflammatory response, while genes of erythropoiesis were more active in NU pigs post-weaning. NU supplementation stimulated genes for proliferative activity in the intestinal immune system, a sign of possible anticipated maturation. NU supplementation did not influence the growth performance of piglets but may have expressed a positive effect on pig microbiota anticipating its maturation at weaning, with possible immunostimulant activity on the intestinal immune system.

Abstract

Nucleotides are essential for the development of the gastrointestinal tract and immune function, but their intake with milk by piglets could be insufficient. The effect of nucleotides on growth and health was tested on 98 piglets divided into two groups: NU, orally administrated with 4 mL of a nucleotide-based product (SwineMOD^®) at 10, 15, 18, 21, 27 days, or not (CO). Blood and feces were sampled at weaning (26 d, T1), and at 38 d (T2). Per each group and time-point, eight piglets were slaughtered and jejunal Peyer’s patches (JPPs) were collected. NU increased hemoglobin content and hematocrit, but not growth. At weaning, the NU fecal microbiota was characterized by the abundance of Campylobacteraceae, more typical of the growing phase, compared to CO, with a greater abundance of Streptococcaceae. For the blood transcriptome, an initial greater inflammatory activation was seen in CO, while at T2, NU enriched gene sets related to erythropoiesis. The activation of gene groups ranging from epigenetic response to transcriptional regulation evidenced an intense proliferative activity in NU JPPs. NU supplementation did not influence the growth performance of piglets but could have expressed a positive effect on pig microbiota anticipating its maturation at weaning. This immunostimulant activity in the JPPs could moderate the inflammation in the immediate pre-weaning.

The fecal microbiota of piglets during weaning transition and its association with piglet growth across various farm environments

This study describes the fecal microbiota from piglets reared in different living environments during the weaning transition, and presents the characteristics of microbiota associated with good growth of piglets after weaning. Fecal samples were collected pre- (d26) and post-weaning (d35) from 288 male piglets in 16 conventional indoor commercial farms located in the West of France. The changes one week after weaning on the most abundant microbial families was roughly the same in all farms: alpha diversity increased, the relative abundance of Bacteroidaceae (-61%), Christensenellaceae (-35%), Enterobacteriaceae (-42%), and Clostridiaceae (-32%) decreased, while the relative abundance of Prevotellaceae (+143%) and Lachnospiraceae (+21%) increased. Among all the collected samples, four enterotypes that were ubiquitous in all farms were identified. They could be discriminated by their respective relative abundances of Prevotella, Faecalibacterium, Roseburia, and Lachnospira, and likely corresponded to a gradual maturational shift from pre- to post-weaning microbiota. The rearing environment influenced the frequency of enterotypes, as well as the relative abundance of 6 families at d26 (including Christensenellaceae and Lactobacillaceae), and of 21 families at d35. In all farms, piglets showing the highest relative growth rate during the first three weeks after weaning, which were characterized as more robust, had a higher relative abundance of Bacteroidetes, a lower relative abundance of Proteobacteria, and showed a greater increase in Prevotella, Coprococcus, and Lachnospira in the post-weaning period. This study revealed the presence of ubiquitous enterotypes among the farms of this study, reflecting maturational stages of microbiota from a young suckling to an older cereal-eating profile. Despite significant variation in the microbial profile between farms, piglets whose growth after weaning was less disrupted were, those who had reached the more mature phenotype characterized by Prevotella the fastest.

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.

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.

Modelling the feed intake response of growing pigs to diets contaminated with mycotoxins

Quantifying robustness of farm animals is essential before it can be implemented in breeding and management strategies. A generic modelling and data analysis procedure was developed to quantify the feed intake response of growing pigs to perturbations in terms of resistance and resilience. The objective of this study was to apply this procedure to quantify these traits in 155 pigs from an experiment where they received diets with or without cereals contaminated with the mycotoxin deoxynivalenol (DON). The experimental pigs were divided equally in a control group and three DON-challenged groups. Pigs in each of the challenged groups received a diet contaminated with DON for 7 days early on (from 113 to 119 days of age), later on (from 134 to 140 days of age) or in both periods of the experiment. Results showed that the target feed intake trajectory of each pig could be estimated independently of the challenge. The procedure also estimated relatively accurately the times when DON was given to each challenged group. Results of the quantification of the feed intake response indicated that age and previous exposure to DON have an effect on the resilience capacity of the animals. The correlation between resistance and resilience traits was modest, indicating that these are different elements of robustness. The feed intake analysis procedure proved its capacity to detect and quantify the response of animals to perturbations, and the resulting response traits can potentially be used in breeding strategies.

What is the current significance of low birthweight pigs on commercial farms in Northern Ireland in terms of impaired growth and mortality?

There is little modern data addressing the differential lifetime growth of commercially reared low and average birthweight pigs born into large litters (>14 piglets). As such, the main aim of this study was to quantify the lifetime growth and mortality rate of low and average birthweight pigs on commercial farms in Northern Ireland. It was also aimed to analyze the level, stage and cause of mortality within each birthweight category. A total of 328 low birthweight (low BW; <1 kg) and 292 average birthweight (Av BW; 1.3 to 1.7 kg) pigs were individually identified across four commercial farms and one research farm. Animal growth and mortality were monitored on an individual basis from birth until slaughter age. Av BW pigs were heavier than low BW pigs throughout the trial (P < 0.001), with a weight advantage of 1.16 kg at weaning increasing to over 9 kg at slaughter age. Av BW pigs recorded a superior average daily gain (ADG) to low BW pigs throughout the trial (P < 0.05), with the greatest difference recorded immediately postweaning between weeks 4 and 8 and weeks 8 and 12 when a 77 and 85 g/d difference was recorded, respectively. AV BW pigs which were cross-fostered were significantly lighter than those remaining with their birth mother at weaning (0.9 kg), week 8 (1.7 kg), and week 12 (3.1 kg) (P < 0.05, respectively). The variance of weight was significantly greater for the AV BW pig population than the low BW pig population at week 4 (P < 0.001) and 8 (P < 0.05). Preweaning mortality of low BW pigs was over three times greater than that of Av BW pigs (21% vs. 6%; P < 0.001), with low BW deaths occurring earlier (9.2 d vs. 15.4 d; P < 0.001) and at a lighter weight (1.2 vs. 2.4 kg; P < 0.001) than Av BW pigs. There was a clear association between birthweight and cause of preweaning death (P < 0.05), with starvation (49%) and overlying (28%) accounting for the majority of low BW mortalities. Birthweight had no effect on rate, age, or weight of postweaning mortalities (P > 0.05). The alimentary tract (27%) and respiratory tract (27%) were the most commonly implicated body systems following postmortem examination of postweaning deaths. In conclusion, this study quantified the inferior weight, growth rate, and mortality of low BW pigs, identifying the lactation and immediate postweaning periods as having the greatest potential in reducing this birthweight associated growth differential.

Body Weight Deviations as Indicator for Resilience in Layer Chickens

Resilience is the capacity of an animal to be minimally affected by disturbances or to rapidly return to the state pertained before exposure to a disturbance. Less resilient animals are expected to be more susceptible to environmental perturbations, such as diseases, and will consequently show more and/or greater fluctuations in production than more resilient animals. Natural antibodies (NAb) are antibodies recognizing antigens without previous exposure to these, and are hypothesized to be an indication of general disease resistance. The objective of this research was to investigate genetic parameters of resilience indicators based on standardized body weight (BW) deviations and to investigate its relation with immunity (i.e. NAb) and disease resistance. Keyhole limpet hemocyanin-binding NAb were measured in layer chickens, which were selectively bred for high and low keyhole limpet hemocyanin-binding NAb levels during six generations. In addition, BW data of these layers were collected on a four-weekly interval from 4 weeks of age until 32 weeks of age. Standardized deviations of BW from an individual were compared to lines’ average BW (i.e. across individuals), and these were used to calculate resilience indicators: natural logarithm-transformed variance [ln(variance)], skewness, and lag-one autocorrelation of deviations (i.e. all within an individual). Heritabilities of resilience indicators were between 0.09 and 0.11. Genetic correlations between the three resilience indicators were between -0.20 and 0.40 (with high SE), which might suggest that the resilience indicators capture different aspects of resilience. Genetic correlations between resilience indicators and NAb were close to zero, which suggests that the resilience indicators and NAb capture different aspects of immunity. This might indicate that, in this dataset, environmental perturbations are only to a small extent affected by disease incidence, possibly due to a lack of disease occurrence. However, a lower estimated breeding value for ln(variance) was predictive for lower lesion scores after an avian pathogenic Escherichia coli inoculation and vice versa. In conclusion, this study shows that there is genetic variation in resilience indicators based on BW deviations in layer chickens, which opens up possibilities to improve resilience by means of selective breeding.