Quantifying growth perturbations over the fattening period in swine via mathematical modelling

Abstract

Background: Resilience can be defined as the capacity of animals to cope with short-term perturbations in their environment and return rapidly to their pre-challenge status. In a perspective of precision livestock farming, it is key to create informative indicators for general resilience and therefore incorporate this concept in breeding goals. In the modern swine breeding industry, new technologies such as automatic feeding system are increasingly common and can be used to capture useful data to monitor animal phenotypes such as feed efficiency. This automatic and longitudinal data collection integrated with mathematical modelling has a great potential to determine accurate resilience indicators, for example by measuring the deviation from expected production levels over a period of time. Results: This work aimed at developing a modelling approach for facilitating the quantification of pig resilience during the fattening period. A total of 13 093 pigs, belonging to three different genetic lines were monitored, and body weight measures registered with automatic feeding systems. We used the Gompertz model and linear interpolation on body weight data to quantify individual deviations from expected production, thereby creating a resilience index. The approach was able to quantify different degrees of perturbation. Further, we evaluated the heritability of the resilience index in the different lines analyzed. Conclusions: Our model-based approach can be useful to quantify pig responses to perturbations using exclusively the growth curves and should contribute to the improvement of swine productive performance. Keywords: modelling, perturbation, resilience, robustness, body weight, big data, pig, precision livestock farming

Design-Based Approach for Analysing Survey Data in Veterinary Research

“…Secondly, in a well-planned sample survey the target population can be framed in advance and followed by a well-designed sampling process so that the samples are representative of the population [7]. This representativeness is often not achieved during the passive "big data" collection process, with data often being collected only from a particular subset of the target population-e.g., Revilla, et al [8] analysed more than 10.5 million measurements from~13,000 pigs obtained using automatic feeding systems. However, this dataset was collected from only one boar testing station, making generalisation to the wider population potentially difficult.…”