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RESUMO Objetivou-se estimar a associação fenotípica entre a eficiência alimentar e o ganho em acabamento de carcaça de touros jovens da raça Nelore. Utilizaram-se informações de 98 touros jovens da raça Nelore, participantes de duas provas de eficiência alimentar que empregaram o sistema de cochos eletrônicos Growsafe®, as quais foram realizadas nos anos de 2015 e 2016, na fazenda Capim Branco, da Universidade Federal de Uberlândia. As provas de eficiência alimentar tiveram duração de 91 dias, sendo 21 dias de adaptação e 70 dias de avaliação. Os animais apresentavam idade média ao início e ao término do confinamento de 19,98 e 22,16 meses e peso inicial e final médio de 428,17 e 542,27kg, respectivamente. Verificou-se que os animais mais eficientes depositaram menos gordura, e os animais menos eficientes depositaram mais gordura durante o período de avaliação em confinamento. Observou-se que há uma associação fenotípica de baixa magnitude (0,29) entre o consumo alimentar residual (CAR) e o ganho em acabamento de carcaça (GACAB). Assim, pode-se inferir que, ao serem selecionados menores valores de CAR, haverá alguma interferência negativa em GACAB. Concluiu-se que animais mais eficientes (CAR negativo) podem apresentar menor deposição de gordura subcutânea, influenciando na qualidade de carcaça de bovinos da raça Nelore.

Meals are clusters of feedbunk visit (BV) events that are differentiated from the next meal by a nonfeeding interval that is longer compared with the nonfeeding intervals within a meal. The longest nonfeeding interval considered to be part of a meal is defi ned as the meal criterion. The objective of this study was to determine which combination of 2 probability density functions ((PDF): Gaussian normal (G), Weibull (W), Log-Normal, Gamma, and Gumbel) used in a bimodal distribution model had the best fi t of nonfeeding interval data collected in beef heifers. Feeding behavior traits (572,627 total BV events) were measured in 119 heifers fed a high-grain diet (3.08 Mcal ME/kg DM), using a GrowSafe system for 66 d. The frequency and duration of BV events averaged 75 ± 15 events/d and 73.0 ± 22.3 min/d, respectively. The bimodal PDF combinations were fi tted to the log 10 -transformed interval lengths between BV events for each animal, using R mixdist package (2.13). The Akaike Information Criterion (AIC) was used to assess goodness of fi t of the 25 bimodal PDF combinations. The PDF model with the least AIC value was selected as the best fi t for each individual. A χ 2 analysis of the selected best PDF distribution across individuals revealed that 78.2% of the heifers best fi t were G-W or W-W PDF models. The likelihood probability estimates were calculated from the average AIC deviation of each model from the standard G-G model. The G-W likelihood probability estimate was greater (P = 0.001) than the W-W combination (0.997 vs. 0.727). Our analysis indicated the G-W model had a statistically better fi t and is most likely the best approach to defi ne meal criterion in beef heifers fed high-grain diets.

The objective of this study was to evaluate the use of an electronic radio-frequency-identification-based system (GrowSafe System Ltd., Airdrie, Alberta, Canada) to measure feeding behavior traits in beef cattle fed a high-grain diet. Feeding behavior data were recorded by the GrowSafe system and time-lapse video using 10 heifers over a 6-d period. Observed bunk visit (BV) and meal event data (frequency and duration) were compared with electronic feeding behavior data generated by the GrowSafe system at 5 parameter settings (MPS; 30, 60, 100, 150, and 300 s), which are used to define the maximum duration between consecutive electronic identification recordings to initiate a subsequent BV event. A random coefficient model was used to compare video and electronic data using orthogonal contrasts. Video data were regressed on the electronic feeding behavior data to obtain an estimate of precision (r(2)) and other statistical estimates, including mean square error of prediction and concordance correlation coefficient, to access the adequacy of the electronic system predictions. The variation in MPS values affected BV data, but not meal event data. Electronic meal frequency and duration data were not different (P > 0.50) from observed values, and were not affected by electronic MPS values. The optimal MPS value for prediction of BV and meal event frequency and duration traits was 100 s. Our evaluation indicated the GrowSafe system 4000E was able to predict BV and meal event data when the 100-s MPS was used to analyze the feeding behavior data.

The objective of this study was to evaluate the use of an electronic radio-frequency-identification-based system (GrowSafe System Ltd., Airdrie, Alberta, Canada) to measure feeding behavior traits in beef cattle fed a high-grain diet. Feeding behavior data were recorded by the GrowSafe system and time-lapse video using 10 heifers over a 6-d period. Observed bunk visit (BV) and meal event data (frequency and duration) were compared with electronic feeding behavior data generated by the GrowSafe system at 5 parameter settings (MPS; 30, 60, 100, 150, and 300 s), which are used to define the maximum duration between consecutive electronic identification recordings to initiate a subsequent BV event. A random coefficient model was used to compare video and electronic data using orthogonal contrasts. Video data were regressed on the electronic feeding behavior data to obtain an estimate of precision (r(2)) and other statistical estimates, including mean square error of prediction and concordance correlation coefficient, to access the adequacy of the electronic system predictions. The variation in MPS values affected BV data, but not meal event data. Electronic meal frequency and duration data were not different (P0.50) from observed values, and were not affected by electronic MPS values. The optimal MPS value for prediction of BV and meal event frequency and duration traits was 100 s. Our evaluation indicated the GrowSafe system 4000E was able to predict BV and meal event data when the 100-s MPS was used to analyze the feeding behavior data.