Selection criteria for frame score and its association with growth-, reproductive-, feed efficiency-and carcass-related traits in Nellore cattle

• Autor(a) principal: Negreiros, M. P.
• Data de Publicação: 2022
• Outros Autores: Peripolli, E. [UNESP], Espigolan, R., Londoño-Gil, M. [UNESP], Rodriguez, J. D. [UNESP], Brunes, L., Magnabosco, C. U., Guimarães, N. C., Sainz, R. D., Pereira, A. S.C., Lobo, R. B., Baldi, F. [UNESP]
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1071/AN22054; http://hdl.handle.net/11449/240507
• topic: animal size; beef cattle; Bos indicus; fertility; genetic correlation; genetic parameters; response to selection; selection indexes

Context: A frame score prediction equation developed specifically for Nellore cattle could be an auxiliary tool to improve mating decisions on the basis of feed resources and production-system objectives. Aims: Estimate genetic parameters for frame by using a prediction equation developed for Nellore cattle and genetic associations between frame score (FRAME) and growth-, reproductive-, carcass-and feed efficiency-related traits, and five bioeconomic indexes. Methods: Birth weight (BW), adjusted weight at 120 (W120), 210 (W210) and 450 (W450) days of age, adult weight (AW), age at first calving (AFC), probability of precocious calving (PPC30), stayability (STAY), accumulated cow productivity (ACP), adjusted scrotal circumference at 365 (SC365) and 450 (SC450) days of age, rib eye area (REA), subcutaneous backfat thickness (BFT), rump fat thickness (RFT), intramuscular fat percentage (IMF), residual feed intake (RFI) and dry-matter intake (DMI) were included in the analyses. Frame score was calculated using the multiple linear regression (MLR) prediction method. The estimation of genetic parameters was performed using a linear animal model, except for PPC30 and STAY, which were estimated through a threshold animal model. The correlated response in FRAME considering selection for growth-, reproductive-, carcass-and feed efficiency-indicator traits were obtained in the context of single-trait selection and a multiple-trait context. Key results: Heritability estimated for FRAME was moderate (0.30 ± 0.09). Frame score showed moderate genetic correlations with growth traits, BW (0.51 ± 0.08), W120 (0.41 ± 0.07), W210 (0.35 ± 0.07) and W450 (0.29 ± 0.08). The genetic correlation estimates between FRAME and RFT was high (-0.84 ± 0.02), but low with ACP (0.25 ± 0.08) and RFI (0.10 ± 0.13). In the single-trait and multi-trait contexts, there was a lower correlated gain for FRAME when the selection was applied for traits commonly measured in beef cattle breeding programs. Conclusion: Selection to increase growth traits would lead to an increase in frame size and herd nutritional requirements, and it would reduce the carcass fatness level and early heifer sexual precocity. FRAME could be an alternative trait to monitor calf birth weight. Implications: Selection for FRAME is feasible, and the most suitable frame score value depends on the production system objectives and feed resources.