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

Abstract

Weaning is a critical transition phase in swine production in which piglets must cope with different stressors that may affect their health. During this period, the prophylactic use of antibiotics is still frequent to limit piglet morbidity, which raises both economic and public health concerns such as the appearance of antimicrobial-resistant microbes. With the interest of developing tools for assisting health and management decisions around weaning, it is key to provide robustness indexes that inform on the animals’ capacity to endure the challenges associated with weaning. This work aimed at developing a modelling approach for facilitating the quantification of piglet resilience to weaning. A total of 325 Large White pigs weaned at 28 days of age were monitored and further housed and fed conventionally during the post-weaning period without antibiotic administration. Body weight and diarrhoea scores were recorded before and after weaning, and blood was sampled at weaning and 1 week later for collecting haematological data. A dynamic model was constructed based on the Gompertz–Makeham law to describe live weight trajectories during the first 75 days after weaning, following the rationale that the animal response is partitioned in two time windows (a perturbation and a recovery window). Model calibration was performed for each animal. Our results show that the transition time between the two time windows, as well as the weight trajectories are characteristic for each individual. The model captured the weight dynamics of animals at different degrees of perturbation, with an average coefficient of determination of 0.99, and a concordance correlation coefficient of 0.99. The utility of the model is that it provides biologically meaningful parameters that inform on the amplitude and length of perturbation, and the rate of animal recovery. Our rationale is that the dynamics of weight inform on the capability of the animal to cope with the weaning disturbance. Indeed, there were significant correlations between model parameters and individual diarrhoea scores and haematological traits. Overall, the parameters of our model can be useful for constructing weaning robustness indexes by using exclusively the growth curves. We foresee that this modelling approach will provide a step forward in the quantitative characterisation of robustness.

Body Weight Deviations as Indicator for Resilience in Layer Chickens

“…Janhunen et al, 2012;Sae-Lim et al, 2015;Iung et al, 2017; see also Hill and Mulder, 2010 for a review). This fits within the proposed idea of high-frequency BW measurements (Friedman et al, 2012;Revilla et al, 2019) and the strong increase in heritability estimates with the addition of a few observations for an individual (Figure 2 from Berghof et al, 2019b). Moreover, the current technological developments are expected to allow more frequent (i.e.…”

“…Deviations and their patterns in production traits have been shown before as indicative for health-related traits, although these approaches use only relatively-large, short-time deviations (e.g. De Haas et al, 2004;Codrea et al, 2011;Fischer et al, 2018;Nguyen-Ba et al, 2019;Revilla et al, 2019;Byrd et al, 2019). Berghof et al (2019b), however, used a different approach to define three resilience indicators based on all production deviations due to unknown disturbances during a production cycle using concepts to study resilience of ecosystems (Scheffer et al, 2009;Scheffer et al, 2015;Scheffer et al, 2018): natural logarithm-transformed variance [ln(variance)] of deviations, skewness of deviations, and autocorrelation of deviations (Berghof et al, 2019b).…”

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

“…This would be expected as the divergence in birthweight from the current study is greater than that employed by Beaulieu et al (2010) . This finding, combined with only 78% of all trial animals fitting the exponential relationship generated by the model, suggests that a modeling approach is better suited to studies conducted under strict experimental conditions such as those given by Lopez-Verge et al (2018) and Revilla et al (2019) .…”

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

“…Other models have been developed to quantify animals’ response to perturbations, using time series data 13 – 16 . In all these models, data were available at a high frequency, and on a given time scale, whereas in the present work, the model had to consider two embedded time scales.…”

“…The indicator of rumen acid status was pH, the post-prandial kinetics of which were measured throughout the 9 days of sampling. Various authors have quantified animals’ response to different types of perturbations, such as feed restriction, weaning, response to mycotoxin challenges or general perturbations with unknown origins 13 – 16 . In these studies, the indicator of the animal response (body weight, feed intake, biochemical samples in plasma, milk yield, etc.)…”