Farm-level analysis of environmental descriptors to investigate genotype by environment interaction in pasture-raised beef cattle

• Ano de defesa: 2024
• Autor(a) principal: Santana, Talita Estéfani Zunino
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Viçosa; Zootecnia
• Palavras-chave em Português: Bovinos de corte - Melhoramento genético; Genômica - Modelos matemáticos; Interação genótipo-ambiente; CIENCIAS AGRARIAS::ZOOTECNIA::GENETICA E MELHORAMENTO DOS ANIMAIS DOMESTICOS
• Link de acesso: https://locus.ufv.br/handle/123456789/32961; https://doi.org/10.47328/ufvbbt.2024.539

In beef cattle breeding programs, the environmental effects are commonly adjusted by considering the combined effects of herd, year, and season, referred to as the contemporary group (CG). Nonetheless, several other macro-environmental factors are known, such as climate, soil moisture, temperature, precipitation, farm management practices and facilities, etc. Such known environmental effects can be directly accounted for in the genetic evaluation models. The first objective of this study was to assess environmental and farm management factors for the evaluation of yearling weight (YW) in pasture-raised Nellore cattle across tropical savanna in South America. The dataset analyzed encompassed animal phenotypes, pedigree, climate and soil classifications, elevation, and detailed information related to farm management practices at the animal-rearing locations. Explanatory variables were selected based on three steps: (1) evaluation of each variable's contribution to explaining the variance among farms, (2) assessment of collinearity among farm management descriptors, and finally (3) comparison of models using a stepwise selection procedure. The results indicate that soil classification (SOIL), elevation (ELE), animal breeding technician (TEC), years enrolled in the breeding program (YEN), no-till farming (NTI), period of the breeding season (PBS), and reproduction technique (RTC) are deemed important to better describe the macro- environmental effects contributing to variation across farms. Indeed, when environmental and farm management descriptors were simultaneously included in the model, they explained 41.5% of the farm variance. This finding reveals the real source of environmental variation commonly accounted for by CG in the genetic evaluations. This suitable characterization of environmental factors might be especially important in the context of genotype by environmental interaction (GxE). In this sense, we also aimed to identify relevant environmental conditions (EC) for Nellore cattle using farm-level environmental descriptors via divisive hierarchical clustering analyses, estimate genetic parameters related to growth, reproductive, and carcass traits, and investigate the presence of GxE by comparing rankings of estimated breeding value (EBV) of bulls among identified ECs using either BLUP or ssGBLUP methods. The evaluated traits included YW, scrotal circumference (SC), age at first calving (AFC), ribeye area (REA), backfat thickness (FAT), and marbling score (MARB). The optimal clustering of farm-level descriptors grouped farms into two EC. Subsequently, a bi-trait linear model was used to investigate the GxE. The lowest genetic correlation was observed for AFC (0.31 ± 0.09), followed by YW (0.37 ± 0.05), and REA (0.62 ± 0.08), indicating traits largely affected by GxE. The Spearman’s correlations for EBVs of bulls were generally low across evaluated traits using either BLUP or ssGBLUP. The percentage of common bulls for EBVs ranked within the TOP5%, TOP10%, and TOP25% categories was most pronounced within the TOP5% ranking using either BLUP or ssGBLUP. AFC exhibited the highest degree of re- ranking, followed by YW and REA, across both methods and all ranking categories, indicating a higher influence of GxE on these traits. These findings highlight the importance of including environmental factors in genetic evaluations of AFC, YW, and REA traits to select animals more adapted to different environmental conditions. Keywords: climate, farm management practices, Nellore, GxE interaction, survey research.