Modeling selection for production traits under constant infection pressure

This article presents a model describing the relationship between level of disease resistance and production under constant infection pressure. The mo del assumes that given a certain infection pressure, there is a threshold f or resistance below which animals will stop producing, and that there is al so a threshold for resistance above which animals produce at production pot ential. In between both thresholds animals will show a decrease in producti on, the size of decrease depending on the severity of infection and the lev el of resistance. The dynamic relationship between production and resistanc e when level of resistance changes, such as due to infection, is modeled bo th stochastically and deterministically. Selection started in a population with very poor level of resistance introduced in an environment with consta nt infection pressure. Mass selection on observed production was applied, w hich resulted in a nonlinear selection response for all three traits consid ered. When resistance is poor, selection for observed production results in increased level of resistance. With increasing level of resistance, select ion response shifts to production potential and eventually selection for ob served production is equivalent to selection; for production potential. The rate at which resistance is improved depends on its heritability, the diff erence between both thresholds, and selection intensity. The model also rev ealed that when a zero correlation between resistance and production potent ial is assumed, the phenotypic correlation between resistance and observed production level increases for low levels of resistance and subsequently as ymptotes to zero, whereas the phenotypic correlation between production pot ential and observed production asymptotes to 1.0. For most breeding schemes investigated, the deterministic model performed well in relation to the st ochastic Simulation results. Experimental results reported in literature su pport the model predictions.