Coupling genetic and mechanistic models to benchmark selection strategies for feed efficiency in dairy cows: sensitivity analysis validating this novel approach

Abstract

Coupling genetic and mechanistic models is appealing to explore the impact of energy trade-offs on the expression of feed efficiency traits in dairy cattle and predict selection response. The objective of this study was to evaluate the sensitivity of genetic (co)variances among milk production and feed efficiency (FE) traits simulated with a mechanistic dairy cow model depending on the genetic variability assumed for input parameters. The cow model was calibrated for a grass-based production and included a genetic module. Four genetically driven input parameters described the energy acquisition and allocation to different biological functions of cows. In each simulation, a population of 20 000 cows from 200 unrelated sires was simulated. The nutritional environment was an input of the model and was tailored by modulating feed offer and quality. A non-limiting nutritional environment was simulated to mimic a situation of ad libitum feeding and was used as a reference. Two other scenarios were simulated by imposing a moderate and a high DM intake restriction on simulated cows. Five phenotypes related to milk production and FE were considered: milk production, BW at calving, DM intake, lactation efficiency and body reserves during early lactation. These traits were estimated both in first and third lactations. A baseline scenario was defined considering a heritability of 0.35 and a phenotypic CV of 10% for acquisition and allocation parameters (AAPs). Different scenarios were explored by reducing the heritability to 0.15 or increasing CV to 20 and 30% or both. Heritabilities and genetic correlations between simulated traits were estimated using animal linear mixed models. Each scenario was replicated 20 times. Simulated performance and genetic parameters for these traits were compared across scenarios using an ANOVA. The heritability of AAPs only influenced the heritability of simulated traits. The phenotypic CV of AAPs mainly influenced the variability of simulated traits. However, increasing the CV also affected the number of cows reaching first and third lactation, due to the early culling of females with extreme AAPs profiles. Compared to other input parameters, the nutritional environment had the largest effect on both performance and genetic correlations between traits. Using a heritability value of 0.35 and a CV of 10% for all four AAPs enabled the simulation of milk production and FE performance with a realistic mean, variance and genetic correlations among traits in the three considered environments.