Assessing genotype-by-environment interaction in dairy cattle in case of a wide range of production environments, unstructured crossbreeding and no pedigree information

Genetic variation in environmental sensitivity (ES) among breeds underscores the importance of accounting for genotype-by-environment (G × E) interactions in breeding programmes. This is especially true for countries where there is a large variation in location production systems and environmental conditions and where farms use crossbreeding. We present a systematic framework for assessing G × E by combining genomic reconstruction of an individual animal's breed composition with characterisation of production environment diversity, using factor analysis of mixed data (FAMD) and linear mixed models. Data from 288 farms in Ethiopia were analysed as a case study, which included weekly performance records, household parameters, daily meteorological data, and genotypes from 780 crossbred cows. To test G × E, the following context parameters were considered: agroecological zone (AEZ), market proximity, and farm size. Results showed that the best performances for milk yield (MY), age at first calving (AFC), and calf BW were generally observed on large, urban farms. However, MY, number of services per conception (NSC), and AFC improved with increasing exotic ancestry up to about 60%, beyond which these traits levelled off or declined. Antagonistic relationships among performance traits and their interactions with breed composition were also observed. While MY, calf BW, and AFC generally improved with increasing exotic ancestry, NSC was negatively affected by some ancestral breeds. Specifically, Holsteins exhibited consistently high NSC (3.52-4.66 ± 1.12) across all AEZ and market proximities, highlighting a limitation of their use in crossbreeding strategies with respect to fertility. Our study demonstrates that breed-by-environment interactions can be assessed using field data and genotypes collected from crossbred animals in highly diverse production systems. However, the study design is inevitably unbalanced for breed composition, which limits the ability to assess the performance of breed combinations not well represented in the studied population.