Genotype by environment interaction under two production systems utilizing a random regression model for milk production traits in multiparous South African Holstein dairy cattle

BACKGROUND: Dairy cattle in South Africa (SA) have to produce in varying environments which can roughly be separated as total mixed ration (TMR) and pasture (PAST) production systems according to the level of rainfall in the herd area. Previous studies aimed to determine a possible genotype × environment interaction (GxE) using 305-day lactation records, finding no interaction, but different heritabilities. An alternative random regression (aRR) model was developed using SA Holstein test-day records of herds with known production system used (TMR or PAST, analysed independently). The aRR model separates the animal genetic (additive) effect into a general production level and a linear persistency effect and shows clear differences in the genetic variances and heritability estimates over the lactation between TMR and PAST. The aim of this study was to further investigate the existence of a possible GxE interaction, using comparable data, methods and RR model to differentiate each trait (milk, fat and protein production for the first 3 lactations) separately for the two production systems (TMR or PAST). The possible reranking of sires on resulting estimated breeding values from the analysis between TMR and PAST were also investigated. RESULTS: Genetic correlations (0.83 to 0.93) of the genetic general production level effect between TMR and PAST were strong, while corresponding correlations of the genetic linear persistency effect were moderate (0.40 to 0.60) for all traits. Significant reranking of sires between TMR and PAST are predicted for genetic linear persistency, as well as genetic total production. CONCLUSIONS: Genetic correlations show a significant genotype by environment interaction for the genetic linear persistency effect between the two production systems in SA Holsteins. Genetic evaluations should analyse production traits in the different systems as genetically different traits, utilizing an applicable random regression model that can effectively dissociate genetic general production level from genetic linear persistency. Estimating separate breeding values for persistency for each production system is justified.