Characterizing individual differences in animal responses to a nutritional challenge: Toward improved robustness measures

Abstract

The aim of this paper was to explore the variation between individuals in the response to and recovery from a nutritional challenge, the repeatability of responses between lactation stages, and the use of shape-clustering methods to classify similar individuals. Sixteen dairy goats were exposed to a 2-d nutritional challenge (underfeeding) at 2 different stages of lactation. Each challenge consisted of a 7-d control period with standard total mixed ration (TMR), 2d of straw-only feeding, and a 10-d recovery period on the TMR. All feeds were offered ad libitum, as was water. The first challenge was in late lactation on primiparous goats (mean days in milk=249), and the second challenge was carried out on the same goats early in the following lactation (mean days in milk=28). The main energetic response traits dry matter intake (DMI), milk yield, body weight, milk fat and protein contents, and plasma glucose, fatty acids (NEFA), β-hydroxybutyrate (BHB), urea, and insulin, were measured daily throughout. A clustering procedure linked to a piecewise mixed model was used to characterize different types of response. As expected, straw feeding caused a large decline in DMI and milk yield, and substantial increases in milk fat and milk protein composition, relative to the prechallenge period on the control TMR. For both DMI and milk yield, the slope of the response, and hence the size of the drop, was strongly related to the prechallenge values, indicating that these 2 measures were tightly constrained by the challenge. Regression slopes between lactation stages for responses to the same nutritional challenge were significant for DMI, milk protein content, plasma BHB and urea, and body weight, indicating that within-animal responses in late and early lactation were repeatable. The clustering procedure generally performed well, classifying both scaling differences and differences in shape. The extent of reranking of cluster designations between late lactation and the following early lactation period was examined. For milk yield, DMI, body weight, and urea, relatively little reranking occurred; the numbers of goats not changing class number were 10, 12, 10, and 13, respectively. In contrast, for milk contents of fat and protein, and also for BHB, no clear association was found between late and early lactation class numbers. For NEFA and glucose, these comparisons were not relevant because either the vast majority of goats were in 1 cluster (NEFA) or because an outlier goat skewed the cluster designation (glucose in late lactation). For insulin, 9 out of 16 goats kept the same rank.

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

“…Review: Deciphering animal robustness nutritional challenges lasting 2 to 4 days, tracking performance and blood metabolites of animals before, during and after a shift from a standard feed to a very poor quality feed (Bjerre-Harpøth et al, 2012;Friggens et al, 2016). Both studies documented inter-animal variability in responses in dry matter intake and milk production which was largely related to pre-challenge levels.…”

“…Højsgaard and Friggens (2010) showed that using multivariate time-series statistics to combine different mastitis indicators (electrical conductivity, somatic cell count and lactate dehydrogenase), a degree of infection index could be constructed. This approach has promise for combining indicators of 'the various things' that are key components of robustness (or at least robustness to a particular type of environmental perturbation), with different statistical and modelling approaches proposed (Sadoul et al, 2015b;Friggens et al, 2016). The key issue is to know which robustness components should be included in a given index and which measures contain information about these components.…”

“…These genetically driven changes in Figure 6 Example of individual animal trajectories before, during and after an environmental perturbation for (a) milk yield and (b) plasma glucose in 16 dairy goats. The perturbation, replacement of the normal feed by straw only commenced on day 0 and lasted until day 2, full details given in Friggens et al (2016) (reproduced with permission).…”

“…The key issue is to know which robustness components should be included in a given index and which measures contain information about these components. In the case of Højsgaard and Friggens (2010), this choice was made using existing physiological knowledge; in the case of Friggens et al (2016) a much more exploratory approach was taken. In both cases, it is worth noting that the usefulness of a measure as a biological indicator can be greatly affected by the measurement frequency.…”

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

“…Recent developments in monitoring technologies allow the continuous recording of animal performance (Neethirajan, 2017). Although several studies have explored these technologies to study the impacts of perturbations on animal performance (Codrea et al, 2011;Munsterhjelm et al, 2015;Friggens et al, 2016), these technologies have not yet been used to develop a generic method that detects perturbations and that allows to quantify the animal's response to perturbations. Therefore, the objective of this study was to propose a data analysis and modelling procedure to detect the impact of perturbations in growing pigs and quantify the feed intake response in terms of resistance and resilience.…”