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This experiment aimed to evaluate the effect of weight gain, body condition score and antral follicle count on the fertility of cows subjected to fixed-time artificial insemination. To this study, it was used 194 Nelore, pluriparous cows, between the 30th and the 45th day of the postpartum period. The cows were weighed, was classified by the body condition score and a transrectal ultrasound was performed in the ovaries to identify the corpus luteum and for counting the antral follicles. Then, the females were submitted to a hormonal protocol to perform the artificial insemination in fixed time. Thirty days after the artificial insemination, the cows were weighed again and the pregnancy was diagnosed by transrectal ultrasound. The pregnancy rate did not interact (P>0.05) with the body score condition, nor the presence of the corpus luteum, neither with the antral follicles count. The weight gain between the artificial insemination moment and the pregnancy diagnosis showed an interaction (P = 0.02) with the pregnancy rate. The weight gain average between the FTAI and the pregnancy diagnosis was 9.0 kg. The animals that gained up to 73 kg, showed higher pregnancy rate. In conclusion, the weight gain between the artificial insemination in fixed time and the pregnancy diagnosis influenced the pregnancy rate.

RESUMONEGREIROS, M. P.M. 2022. 61f. Novos critérios de seleção para avaliação de bovinos de corte. Dissertação (Mestrado) -

We evaluated a novel frame score system for Nellore cattle raised on pasture (Guimarães et al., 2020 J, Anim. Sci. 98 (Suppl. 4)). Growth traits included age-adjusted weights (kg) at birth (W0), 120 (W120), 210 (W210) and 450 (W450) days of age, and adult weight (AW). Carcass traits (by ultrasound) included ribeye area (REA, cm2), 12th-13th rib backfat (BF, mm), rump fat (RF, mm), and intramuscular fat (IMF, %). Feed efficiency traits included residual feed intake (RFI, kg of DM/d) and DM intake (DMI, kg/d). We applied the previously developed equations (that also include hip height (HH, cm) and age to 12,049 records from the National Association of Breeders and Researchers (ANCP) database:FSmales= -20.35 + 0.1305*REA + 0.2633*BF - 0.5901*RF+ 0.1139*HH + 0.0056*AGEFSfemales = -11.87 + 0.1316*REA - 0.2457*BF - 0.6218*RF + 0.1139*HH + 0.0009507*AGEThe (co)variance components and genetic parameters were estimated using a linear animal model or a threshold animal model depending on the trait. The heritability estimate for frame score was moderate (0.30). Estimated genetic correlations were moderate for growth traits (W0, 0.51; W120, 0.41; W210, 0.35; W450, 0.29; AW, 0.39). Estimated genetic correlations for carcass and efficiency traits ranged from low to high (REA, 0.50; BF, -0.25; RF, -0.84; IMF, -0.15; DMI, 0.29; RFI, 0.10). The estimates support the notion that larger frame animals are heavier, leaner and later maturing, and less feed efficient. The new frame score may be a useful tool for genetic selection of animals that are best suited to their environment.

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.