Roberto Daniel Sainz

This study quantified production risks and economic feasibility in tropical pasture-based beef systems by integrating a rule-based Minimum Module (MM) framework with Monte Carlo simulation (@Risk 8.0) in Excel 365 using a Microsoft Visual Basic 6.0 algorithm. Twelve scenarios combining low (0.5 animal unit [AU] ha⁻1), medium (1.0 AU ha⁻1), and high (1.5 AU ha⁻1) stocking rates across 2017–2020 were each run with 10,000 stochastic iterations to identify the smallest viable herd size and pasture area that ensured non-negative [...] ABSTRACT This study quantified production risks and economic feasibility in tropical pasture-based beef systems by integrating a rule-based Minimum Module (MM) framework with Monte Carlo simulation (@Risk 8.0) in Excel 365 using a Microsoft Visual Basic 6.0 algorithm. Twelve scenarios combining low (0.5 animal unit [AU] ha⁻1), medium (1.0 AU ha⁻1), and high (1.5 AU ha⁻1) stocking rates across 2017–2020 were each run with 10,000 stochastic iterations to identify the smallest viable herd size and pasture area that ensured non-negative

This study aimed to evaluate the impact of different genomic prediction approaches on the predictive ability for meat tenderness in Nellore cattle. Phenotypic ( n = 73,286), pedigree ( n = 4,141,892) and genomic ( n = 15,300) data from animals belonging to the genetic improvement program of the National Association of Breeders and Researchers (ANCP) were used. Six models were tested: (1) standard ssGBLUP (Single‐step Genomic Best Linear Unbiased Prediction), considering direct additive genetic and residual effects as random, contemporary group (CG) as a fixed effect, and slaughter age as a linear and quadratic covariate; (2) Model 1 + ssGBLUP weighted with SNP effects from the first WssGWAS iteration; (3) Model 1 + ssGBLUP weighted with SNP effects from the second WssGWAS iteration; (4) Model 1 + body weight as a covariate; (5) Model 1 as a bi‐trait model with body weight at 450 days (W450); (6) Model 1 as a multi‐trait model with carcass traits: ribeye area (REA), backfat thickness (BFT) and rump fat thickness (RFT). Predictive ability was evaluated using linear regression, in which the dataset was divided into a complete and a partial subset ( n = 374) dataset. Accuracy ranged from 0.04 (Models 2 and 3) to 0.37 (Model 6). Bias was low for all models, with Models 2 and 3 showing the least bias (−0.001). Model 6 showed the best performance in terms of accuracy and correlation (0.897), suggesting it was more effective in capturing genetic variability of meat tenderness, while reducing bias and increasing the precision of the estimates. Multi‐trait models may offer more robust genomic predictions by leveraging trait correlations to increase prediction accuracy.

We estimated heritabilities and genetic and phenotypic correlation estimates for maintenance energy requirements (NEmR), residual feed intake (RFI), growth, carcass and reproductive indicator traits, using data from 41 feed efficiency trials in Brazil, comprising 4381 males and females. Continuous traits were analysed using a linear animal model and threshold traits were analysed using a threshold animal model. The heritability estimates were low for RFI (0.190) and NEmR (0.193); other heritabilities were mainly moderate (growth and carcass traits) or high (sexual precocity traits). The genetic correlation of RFI with NEmR was high (0.701). The genetic correlations of NEmR were low with carcass and reproductive traits, and moderate with growth traits. Thus, selection to improve weaning weight and female sexual precocity indicator traits would not affect maintenance energy requirement. Genetic selection to reduce maintenance energy requirements is feasible and would also reduce DMI and RFI. Selection to improve RFI can be used to identify animals with lower maintenance energy requirements. Long‐term selection to reduce RFI and NEmR would have favourable effects on yearling weight, carcass muscle indicator traits and female sexual precocity. Genetic (co)variance component estimates for NEmR, in conjunction with economic values of selection criteria, may be used to develop novel approaches for genetic selection to improve efficiency of beef production.

Growth and carcass traits are essential selection criteria for beef cattle breeding programs. However, it is necessary to combine these measurements with body composition traits to meet the demand of the consumer market. This study aimed to estimate the genetic parameters for visual scores, growth (pre and post-weaning weights), and carcass (rib eye area (REA), back and rump fat thickness) traits in Nellore cattle using Bayesian inference. Data from 12,060 animals belonging to the HoRa Hofig Ramos herd were used. Morphological traits were evaluated by the MERCOS methodology. The heritability estimates obtained ranged from low to high magnitude, from 0.15 to 0.28 for visual scores, 0.13 to 0.44 for growth, and from 0.42 to 0.46 for carcass traits. Genetic correlations between visual scores and growth traits were generally of moderate to high magnitudes, however, visual scores showed low correlations with carcass traits, except between sacral bone and structure and REA. Selection for visual score traits can lead to favorable responses in body weight and vice versa, but the same is not true for carcass traits. Morphological categorical traits can be used as complementary tools that add value to selection.

The use of morphological traits assessed using visual scores as indirect selection criteria in cattle has the advantage of evaluating young animals regarding potential productive and reproductive performance. This enables breeders to make earlier decisions compared to later measurements, such as scrotal circumference at 450 days (SC450) and stayability (STAY). The aim of this study was to estimate the genetic parameters for visual score traits and their associations with reproductive traits: scrotal circumference at 365 days of age (SC365), SC450, STAY, probability of precocious calving (PPC30) and age at first calving (AFC) in Nellore cattle. Visual score data from 4,175 Nellore cattle, with an average age of 22 months, and reproductive data from 3,075 cattle belonging to the HoRa Genetics Provada herd were used. The morphological traits were evaluated by the MERCOS methodology. The heritability estimates obtained ranged from 0.15 to 0.28 for visual scores and 0.10 to 0.54 for reproductive traits. Genetic correlations between visual scores and reproductive traits were generally low, except between: muscularity and PPC30; structure and STAY; racial and SC450; conformation and SC365, SC450, STAY, and AFC; navel and STAY and AFC; and sacrum and SC365, STAY, and AFC, which were moderate to high. The identification of animals with flat sacral bone (not protruding or sloping) can also be an efficient characteristic in the identification for early pregnancy, and together with the musculature score, they can be related to animals with lower age at the first calving.

Resumo Um novo sistema de escore para estrutura corporal foi desenvolvido para bovinos Nelore, baseado em pesos de abate ideais para atingir os graus desejados de cobertura de gordura da carcaça. Dados de 688 registros completos incluindo sexo, idade, peso, altura do quadril e gordura subcutânea de bovinos Nelore em crescimento (190 machos e 498 fêmeas) foram obtidos da Associação Nacional de Criadores e Pesquisadores (ANCP), Ribeirão Preto – SP, Brasil. Os dados de peso foram regredidos por sexo, idade, altura do quadril e cobertura de gordura como covariáveis. A idade foi então fixada em 550 dias e a cobertura de gordura foi fixada em 6 mm, e novas equações para escores de estrutura corporal (1 a 11) em bovinos Nelore foram desenvolvidas: FrameNelore (machos) = -42,43 + 0,04919 × Idade + 0,3368 × Altura – 0,0003369 × Idade × Altura e FrameNellore (fêmeas) = -41,76 + 0,04919 × Idade + 0,3368 × Altura – 0,0003369 × Idade × Altura (Idade e Altura em dias e cm, respectivamente). As equações de Nelore resultaram em estrutura corporal médio de 5,3, variando de 2,3 a 7,7. Os valores foram semelhantes para machos (5,4) e fêmeas (5,3). Por outro lado, as equações anteriores deram valores mais altos, que eram muito diferentes entre machos e fêmeas. As novas pontuações de estruturacorporal no Nelore parecem razoáveis, principalmente em relação à composição corporal.

A new frame score system was developed for Nellore cattle, based upon ideal slaughter weights to achieve desired degrees of carcass fat cover. Data consisting of 688 complete records including sex, age, weight, hip height, and subcutaneous fat on growing Nellore cattle (190 males and 498 females) were obtained from the National Association of Breeders and Researchers (ANCP), Ribeirão Preto – SP, Brazil. Weight data were regressed on sex, age, hip height, and fat cover as covariates. Age was then fixed at 550 days and fat cover was set to 6 mm, and new equations for frame scores (1 to 11) in Nellore cattle were developed: FrameNellore (males) = -42.43 + 0.04919 × Age + 0.3368 × Height – 0.0003369 × Age × Height and FrameNellore (females) = -41.76 + 0.04919 × Age + 0.3368 × Height – 0.0003369 × Age × Height (Age and Height in days and cm, respectively). The Nellore equations resulted in average frame of 5.3, ranging from 2.3 to 7.7. Values were similar for males (5.4) and females (5.3). By contrast, previous equations gave higher values, that were very different between males and females. The new Nellore frame scores appear reasonable, particularly regarding body composition.

Resumo A utilização de características morfológicas de bovinos, pelo uso de escores visuais como critério de seleção indireta tem como vantagem a avaliação em animais jovens quanto ao potencial desempenho produtivo e reprodutivo, antecipando a tomada de decisão em comparação a medidas tomadas de forma tardia, como perímetro escrotal aos 450 dias (PE450) e stayability (STAY). Objetivou-se estimar os parâmetros genéticos para características de escores visuais e a associação dessas com características reprodutivas, perímetro escrotal aos 365 (PE365) dias de idade, PE450, STAY, probabilidade de parto precoce (3P) e idade ao primeiro parto (IPP) em bovinos Nelore. Foram utilizadas informações de escores visuais e de reprodução de 4.175 e 3.075 bovinos, respectivamente, com idade média de 22 meses, pertencentes a fazenda HoRa Genética Provada. As características morfológicas foram avaliadas pela metodologia MERCOS. As estimativas de herdabilidade obtidas apresentam grande amplitude, variando de 0,15 a 0,28 para escores visuais e 0,10 a 0,54 para características reprodutivas. As correlações genéticas entre característica de escores visuais e reprodução foram, de maneira geral baixas (0.03-0.66), com exceção entre a musculosidade e 3P, estrutura e STAY, racial e PE450, conformação com PE365, PE450, STAY e IPP, ônfalo com STAY e IPP, e sacro com PE365, STAY e IPP, que foram moderadas a altas. A identificação de animais com melhor osso sacro (mesmo nível das ancas), ou seja, não saliente ou inclinado pode ser uma característica eficiente na identificação para prenhez precoce, e juntamente ao escore de musculatura poderão ser relacionados a animais com menor idade ao primeiro parto.

Frame score (FS) systems for beef cattle generally represent the relationships among growth, body composition, reproduction and mature size, in a simple and practical form. This study aimed to: 1) develop a FS system for Nelore cattle that is biologically sound, easy to interpret, and useful for producers; and 2) estimate the genetic parameters of the FS with productive and reproductive traits. An arbitrary scale (1 to 12) was devised so that each unit corresponds to 15 kg of carcass weight (1 @), as this is a common measure used for marketing beef cattle in Brazil. Therefore, ideal carcass weight, defined as having 6 mm of backfat, would be 18 @ (269 kg) and 15 @ (224 kg) for FS = 6 males and females, respectively. Data from 36,030 animals (22,405 males, 13,565 females) raised on pasture were obtained from participating herds of the National Association of Breeders and Researchers (ANCP). Genetic parameters were estimated in uni- and bicharacteristic analyses under an animal model, using the EM-REML algorithm (AIREMLF90) and Bayesian inference (GIBBS1F90). The heritability estimate for the new FS was 0.38, and its additive genetic correlations were 0.70, 0.72, 0.77, 0.33, -0.57, 0.27, and 0.28 with BW at 365 d, BW at 450 d, hip height, longissimus muscle area, subcutaneous fat thickness, scrotal circumference at 450 d, and age at first calving, respectively. The estimated heritability and genetic correlations indicate that there is enough additive genetic variability to allow for genetic response to selection. The estimates support the notion that larger frame animals are taller, heavier, leaner and later maturing, both in body composition as well as sexually. The new frame score may be a useful tool for genetic selection of animals that are best suited to their environment.