PubMed2003
The influence of social stressors on pig performance, although undeniable, is frequently underestimated, and in pig growth modeling is generally ignored. The aims here were to quantify the effects of the main social stressors (i.e., group size, space allowance, feeder space allowance, and mixing) on the performance of growing pigs and to incorporate these relationships into a general growth simulation model. Effects of the individual stressors were described by conceptual equations derived on biological grounds. Parameter values were estimated from experimental data, while taking steps to avoid the problems of using a strictly empirical approach. It was assumed that social stress decreases the capacity of the animal to attain its potential. This is equivalent to lowering the maximum rate of daily gain (ADGp, kg/d). Because it is generally assumed that animals eat to attain their potential, a decrease in ADGp necessarily leads to a decrease in intake. Genetic variation among genotypes in their ability to cope with social stressors was accounted for by introducing an extra genetic parameter (EX) into the model. The value of EX adjusts both the intensity of stressor at which the animal becomes effectively stressed and the extent to which stress decreases performance and increases energy expenditure at a given stressor intensity. Rather than using an empirical adjustment to predict values for the model output variables, such as intake and gain, the chosen functional forms were integrated into a general growth model as mechanistic equations. This allowed the effects of interactions that exist between social stressors and the other variables, such as the genotype, feed composition, and environment on pig intake and growth, to be explored and, at least in principle, predicted. The adapted model is able to predict the performance of pigs differing in both the potential and ability to cope with environmental stressors when raised under given dietary, physical, and social environmental conditions. The social stressor equations developed here can be incorporated into other pig growth simulation models.