Characterizing animal response to environmental challenges: new traits of more efficient animals

Farm animals are constantly facing perturbations due to changing environmental and farm conditions. Characterization of the animal response when it is facing these perturbations that influence animal performance and health is of main concern to ensure sustainable livestock production. Indeed, a better understanding of the adaptation mechanisms used by the animal to cope with a single and multiple challenges (through resistance and resilience) is a prerequisite to propose adequate farm management strategies and for the development of precision livestock farming systems. Several experimental studies have been conducted to investigate the influence of the environment on animal performance. Mathematical models can be used to consider and to quantify the systemic aspects of the animal’s response to a perturbation. Existing models of farm animal performance have accounted to a limited extent for environmental perturbations. With novel monitoring technologies, it is now possible to evaluate the impact of these perturbations on animal performance in real time and with a high frequency. 

We propose a mechanistic model to describe the influence of a generic and perturbation of unknown origin on feed intake of growing pigs. The model is based on two sub-models: InraPorc, a model to describe growing pig performance in a standard environment, and the well-known spring-and-damper system used in physics to describe the behavior of a system in presence of external force. The InraPorc model was used to describe the phenotypic performance of the animal in the absence of acute perturbing factors. The spring-and-damper system included two parameters to characterize the adaptive response of animals when facing a perturbation. The main interest of this characterization is to define new standards to rank animals based on feed efficiency together with their adaptive capacity, and to find out potential correlation between these parameters. Future development of the model will adapt it to simulate successive perturbations of known or unknown origins. Moreover, based on these new parameters, the model can propose model-derived traits for genetic selection of more efficient and robust animals.