Economic values of reproductive, growth, feed efficiency and carcass traits in Nellore cattle

Souza, F. M. de; Lopes, Fernando Brito; Rosa, Guilherme J. M.; Magnabosco, C. de U.

doi:10.1111/jbg.12652

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…Fertility and reproductive performance directly impact the beef cattle industry's profitability [ 1 ]. Numerous environmental effects commonly influence fertility and reproductive traits (e.g., diet and nutritional management, climatic conditions, year season, reproductive management) [ 2 – 4 ], which are genetically controlled by many genes with small effects [ 5 – 8 ].…”

Section: Introductionmentioning

confidence: 99%

Genetic parameters for various semen production and quality traits and indicators of male and female reproductive performance in Nellore cattle

et al. 2023

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Background Given the economic relevance of fertility and reproductive traits for the beef cattle industry, investigating their genetic background and developing effective breeding strategies are paramount. Considering their late and sex-dependent phenotypic expression, genomic information can contribute to speed up the rates of genetic progress per year. In this context, the main objectives of this study were to estimate variance components and genetic parameters, including heritability and genetic correlations, for fertility, female precocity, and semen production and quality (andrological attributes) traits in Nellore cattle incorporating genomic information. Results The heritability estimates of semen quality traits were low-to-moderate, while moderate-to-high estimates were observed for semen morphological traits. The heritability of semen defects ranged from low (0.04 for minor semen defects) to moderate (0.30 for total semen defects). For seminal aspect (SMN_ASPC) and bull reproductive fitness (BULL_FIT), low (0.19) and high (0.69) heritabilities were observed, respectively. The heritability estimates for female reproductive traits ranged from 0.16 to 0.39 for rebreeding of precocious females (REBA) and probability of pregnancy at 14 months (PP14), respectively. Semen quality traits were highly genetically correlated among themselves. Moderate-to-high genetic correlations were observed between the ability to remain productive in the herd until four years of age (stayability; STAY) and the other reproductive traits, indicating that selection for female reproductive performance will indirectly contribute to increasing fertility rates. High genetic correlations between BULL_FIT and female reproductive traits related to precocity (REBA and PP14) and STAY were observed. The genetic correlations between semen quality and spermatic morphology with female reproductive traits ranged from -0.22 (REBA and scrotal circumference) to 0.48 (REBA and sperm vigor). In addition, the genetic correlations between REBA with semen quality traits ranged from -0.23 to 0.48, and with the spermatic morphology traits it ranged from -0.22 to 0.19. Conclusions All male and female fertility and reproduction traits evaluated are heritable and can be improved through direct genetic or genomic selection. Selection for better sperm quality will positively influence the fertility and precocity of Nellore females. The findings of this study will serve as background information for designing breeding programs for genetically improving semen production and quality and reproductive performance in Nellore cattle.

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Section: Introductionmentioning

confidence: 99%

Genetic parameters for various semen production and quality traits and indicators of male and female reproductive performance in Nellore cattle

et al. 2023

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“…In the beef cattle systems, the feed costs represent the largest effective operational cost (Souza et al., 2021). Therefore, the profitability and sustainability of beef cattle production rely on feed efficiency.…”

Section: Introductionmentioning

confidence: 99%

Genomic analysis of feed efficiency traits in beef cattle using random regression models

Ramos,

de Oliveira Menezes,

da Silva

et al. 2023

J Animal Breeding Genetics

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Feed efficiency plays a major role in the overall profitability and sustainability of the beef cattle industry, as it is directly related to the reduction of the animal demand for input and methane emissions. Traditionally, the average daily feed intake and weight gain are used to calculate feed efficiency traits. However, feed efficiency traits can be analysed longitudinally using random regression models (RRMs), which allow fitting random genetic and environmental effects over time by considering the covariance pattern between the daily records. Therefore, the objectives of this study were to: (1) propose genomic evaluations for dry matter intake (DMI), body weight gain (BWG), residual feed intake (RFI) and residual weight gain (RWG) data collected during an 84‐day feedlot test period via RRMs; (2) compare the goodness‐of‐fit of RRM using Legendre polynomials (LP) and B‐spline functions; (3) evaluate the genetic parameters behaviour for feed efficiency traits and their implication for new selection strategies. The datasets were provided by the EMBRAPA–GENEPLUS beef cattle breeding program and included 2920 records for DMI, 2696 records for BWG and 4675 genotyped animals. Genetic parameters and genomic breeding values (GEBVs) were estimated by RRMs under ssGBLUP for Nellore cattle using orthogonal LPs and B‐spline. Models were compared based on the deviance information criterion (DIC). The ranking of the average GEBV of each test week and the overall GEBV average were compared by the percentage of individuals in common and the Spearman correlation coefficient (top 1%, 5%, 10% and 100%). The highest goodness‐of‐fit was obtained with linear B‐Spline function considering heterogeneous residual variance. The heritability estimates across the test period for DMI, BWG, RFI and RWG ranged from 0.06 to 0.21, 0.11 to 0.30, 0.03 to 0.26 and 0.07 to 0.27, respectively. DMI and RFI presented within‐trait genetic correlations ranging from low to high magnitude across different performance test‐day. In contrast, BWG and RWG presented negative genetic correlations between the first 3 weeks and the other days of performance tests. DMI and RFI presented a high‐ranking similarity between the GEBV average of week eight and the overall GEBV average, with Spearman correlations and percentages of individuals selected in common ranging from 0.95 to 1.00 and 93 to 100, respectively. Week 11 presented the highest Spearman correlations (ranging from 0.94 to 0.98) and percentages of individuals selected in common (ranging from 85 to 94) of BWG and RWG with the average GEBV of the entire period of the test. In conclusion, the RRM using linear B‐splines is a feasible alternative for the genomic evaluation of feed efficiency. Heritability estimates of DMI, RFI, BWG and RWG indicate enough additive genetic variance to achieve a moderate response to selection. A new selection strategy can be adopted by reducing the performance test to 56 days for DMI and RFI selection and 77 days for BWG and RWG selection.

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“…In beef cattle systems, the feed costs represent the largest effective operational cost (Souza et al, 2021). Therefore, the profitability and sustainability of livestock activities rely on feed efficiency.…”

Section: Introductionmentioning

confidence: 99%

Genomic analysis for feed efficiency traits in beef cattle and algorithms to approximate accuracies for single-step genomic best linear unbiased predictor

Ramos

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Feed efficiency plays a major role in the overall profitability and sustainability of the beef cattle industry, as it is directly related to the reduction of the animals' input requirements. Traditionally, the average daily feed intake and weight gain are used to calculate feed efficiency traits. The phenotypes are collected in feedlot systems with electronic feed bunks, requiring high financial costs, which limits the data collection. Therefore, new methodologies capable of predicting breeding values with high accuracy for these traits become crucial for selecting efficient animals. In this sense, feed efficiency traits can be analyzed longitudinally using random regression models (RRM), which allow fitting random genetic and environmental effects over time by considering the covariance pattern between the daily records. RRM can be a feasible alternative to evaluate genetic parameters as a function of the evaluation period and increase the accuracy of estimated breeding values (EBV) for feed efficiency. The accuracy of EBV is calculated to help make selection decisions, representing the correlation between the true breeding value and EBV. The theoretical accuracy can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for an extensive system of equations, especially if genomic information is available. Thus, different algorithms to approximate accuracies have been proposed. Therefore, the first objective of this study was to propose genomic evaluations for dry matter intake (DMI), body weight gain (BWG), residual feed intake (RFI), and residual weight gain (RWG) data collected during an 84-day feedlot test period via random regression models and evaluate the genetic parameters behavior for feed efficiency traits and their implication for new selection strategies. Genetic parameters and genomic breeding values (GEBV) were estimated by random regression models under ssGBLUP for Nellore cattle using orthogonal Legendre polynomials and B-spline. The random regression model using linear B-splines proved a feasible alternative for the genomic evaluation of feed efficiency. Heritability estimates of DMI, RFI, BWG, and RWG indicate enough additive genetic variance to achieve a moderate response to selection. Based on genetic correlations and ranking comparisons between the test days, a new selection strategy can be adopted by reducing the performance test to 56 days for DMI and RFI selection and 77 days for BWG and RWG selection. The second objective of this study was to compare the approximated accuracies from two algorithms implemented in the BLUPF90 suite of programs and compare the approximated accuracies from the two algorithms against the exact accuracy based on the inversion of the LHS of MME. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, Algorithm 2 uses block sparse inversion of 𝐆−𝟏 . The Data were provided by the American Angus Association and included three datasets of growth, carcass, and marbling traits. For the genomic evaluations, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. Accuracies from Algorithm 2 presented a higher correlation with the exact accuracies than from Algorithm 1. Additionally, Algorithm 2's accuracies were, in general, closer to the exact accuracies according to the mean square error and demonstrated similar changes in behavior when adding new genotyped animals to the analysis. In summary, the random regression model is a feasible alternative for the genomic evaluation of feed efficiency in Nellore cattle and the Algorithm 2 is more suitable for approximating accuracies of GEBV and should be used for routine ssGBLUP evaluations. Keywords: Feed intake; Longitudinal data; B-splines; Accuracy approximation; Genomic evaluation.

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Economic values of reproductive, growth, feed efficiency and carcass traits in Nellore cattle

Cited by 7 publications

References 27 publications

Genetic parameters for various semen production and quality traits and indicators of male and female reproductive performance in Nellore cattle

Genetic parameters for various semen production and quality traits and indicators of male and female reproductive performance in Nellore cattle

Genomic analysis of feed efficiency traits in beef cattle using random regression models

Genomic analysis for feed efficiency traits in beef cattle and algorithms to approximate accuracies for single-step genomic best linear unbiased predictor

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