Estimates of variance components for feedlot traits of the Simmentaler breed in South Africa (original) (raw)
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Genetic relationship between feed efficiency and profitability traits in beef cattle
South African Journal of Animal Science, 2004
Genetic selection to improve feed efficiency aims to reduce the cost of feeding costs in beef cattle production and thereby improve profitability. The aim of this study was to estimate genetic (co)variances to compare residual feed intake (RFI) and feed conversion ratio (FCR) with growth, reproductive and profitability traits measured in growth tests of young bulls. The heritability estimated for FCR was 0.34 and for RFI 0.31 with a genetic correlation estimate of 0.75 between the traits. The estimated genetic correlation between profitability and FCR and RFI were-0.92 and-0.59, respectively. The genetic correlations and expected correlated responses between RFI and FCR with post-wean profitability (M-value) suggest that indirect selection for M-value through the direct selection for FCR and/or RFI will result in slower genetic progress in M-value than direct selection for M-value. However, where the M-value cannot be calculated and/or direct selection for M-value is not possible, it would be better to select indirectly for M-value through the use of FCR rather than RFI.
Journal of Animal Science, 2011
Variance components and further phenotypic and genetic parameters for reproduction traits including litter size at birth (LSB) and at weaning (LSW), gestation period (GP) and litter weight at birth (LWB) were estimated by use of the MTGSAM procedure fitting twotrait animal model (y = Xb + Z a a + Z P P + e) in Boer dams. The calculations were based on 1205 parities of 435 Boer dams at Boer Goat Breeding Station in Yidu, China, during 2002China, during -2007. Influencing factors such as parity and age of dam, kidding year and season, litter size at birth, sex of kids and some significant interactions were investigated as the fixed effects by PROC GLM (SAS8.1). The results showed that kidding year, parity and age of dam significantly influenced all the reproduction traits. Kidding season only affected the traits of GP and LWB. Litter size at birth had significant effect on LWB. The mean values of LSB, LSW, LWB and GP were 1.76 ± 0.67, 1.62 ± 0.62, 6.54 ± 2.51 kg and 151.7 ± 4.43 days. The estimates of direct additive heritability for above traits were 0.12 ± 0.01, 0.10 ± 0.01, 0.14 ± 0.02 and 0.09 ± 0.01, respectively. The estimates of all the correlations among LSB, LSW and LWB were higher than 0.52, and the genetic correlations were the highest among all the correlations. The estimates of genetic correlations between GP and other traits were medium (0.33-0.48) and lower than the permanent environmental correlations (0.60-0.72). In conclusion, compared with other goat breeds, the Boer goat has good characteristic in prolificacy. The gestation period is not significantly different in dams with increase of litter size, which is similar to the multiple birth species such as pig. However, the heritability estimates for all these reproduction traits obtained in the Boer dams are lower than 0.14; both LSB and LWB are traits with the highest heritability among 4 reproduction traits estimated. High genetic and permanent environmental correlations indicate that these traits were mainly attributable to different genetic effects or influenced by the permanent environmental effect for the different parity.
Tropical Animal Health and Production, 2004
Estimates of (co)variance and genetic parameters of birth, weaning (205 days) and yearling (365 days) weight were obtained using single-trait animal models. The data were analysed by restricted maximum likelihood, ¢tting an animal model that included direct and maternal genetic and permanent environmental e¡ects. The data included records collected between 1976 and 2001. The pedigree information extended as far back as early 1960s. The heritabilities for direct e¡ects of birth, weaning and yearling weights were 0.36, 0.29 and 0.25, respectively. Heritability estimates for maternal e¡ects were 0.13, 0.16 and 0.15 for birth, weaning and yearling weights, respectively. The correlations between direct and maternal additive genetic e¡ects were negative for all traits analysed. The results indicate that both direct and maternal e¡ects should be included in a selection programme for all the traits analysed.
Kansas Agricultural Experiment Station Research Reports, 2015
Feed is the greatest cost for a beef cattle production enterprise. Data collection to determine feed efficiency of animals is also costly, because both gain and intake records are needed to calculate feed efficiency. Electronic intake monitoring systems such as GrowSafe or Insentec to collect feed intake data are expensive and thus limit the number of animals that can be tested. Scientists have worked to pinpoint optimal test durations for collecting both weight gain and feed intake records to lessen costs. A 70-day performance test is currently recommended for accurate calculation of efficiency, with growth data as the limiting factor. Research has suggested that a 35-day test is adequate to measure feed intake, but a test period of at least 70 days is suggested to measure gain with sufficient accuracy. The objective of this study was to estimate genetic parameters for growth and intake traits with particular attention to the relationship between on-test average daily gain (ADG) and national cattle evaluation postweaning gain (PWG). If the correlation between these two traits is strong, it could allow for the use of PWG as a proxy for ADG in the genetic evaluation of feed efficiency. This substitution would allow producers to reduce the length of the test required to measure feed intake accurately.
Tropical animal health and production, 2018
This study aimed to compare feed efficiency measures of Nellore beef cattle on different residual intake and gain (RIG) classes. We used data from 610 animals weighing on average 236.33 kg and average of 283 days of age from feedlot performance tests carried out between 2005 and 2012. Animals were grouped based on RIG into three different classes: high RIG (> mean + 0.5 standard deviation (SD), most efficient; n = 193), medium RIG (mean ± 0.5 SD; n = 235), and low RIG (< mean - 0.5 SD, least efficient; n = 182). Residual feed intake (RFI), residual gain (RG), feed conversion ratio (FCR), feed efficiency (FE), relative growth rate (RGR), and Kleiber ratio (KR) of animals in each RIG class were compared by Tukey test at 1% of probability. Phenotypic correlations between variables were evaluated as well. Animals on high RIG class showed lower dry matter intake (P < 0.01) and higher average daily gain (P < 0.01) than low RIG animals. Consequently, high RIG animals had lower ...
Livestock Science, 2014
This study estimated genetic parameters and (co)variance components for dry matter intake (DMI), average daily gain (ADG), feed conversion rate (FCR), residual feed intake (RFI), residual body weight gain (RWG) and residual intake and body weight gain (RIG) in Nellore cattle. We also estimated the genetic and phenotypic correlations between these traits with growth and carcass traits. We used data on feed efficiency of 1038 Nellore males (Bos indicus), being 147 castrated and 891 young bulls. The animals were progenies of 176 sires and 779 dams, composing a relationship matrix of 3521 animals. The (co) variance components and genetic parameters were estimated by GIBBS2F90 software, using the Bayesian approach. The heritability estimates for DMI, RFI and RIG were 0.40, 0.38 and 0.54, respectively. The genetic correlations between all feed efficiency and carcass traits were low. The traits analyzed showed enough genetic variability and heritability, thus the inclusion of feed efficiency in animal breeding programs of Nellore cattle is feasible. The RIG showed higher heritability and a selection for feed efficiency does not have a negative effect on carcass traits.
Journal of Animal Science, 2020
This study aimed to estimate genetic parameters, including genomic data, for feeding behavior, feed efficiency, and growth traits in Nellore cattle. The following feeding behavior traits were studied (861 animals with records): time spent at the feed bunk (TF), duration of one feeding event (FD), frequency of visits to the bunk (FF), feeding rate (FR), and dry matter intake (DMI) per visit (DMIv). The feed efficiency traits (1,543 animals with records) included residual feed intake (RFI), residual weight gain (RWG), and feed conversion (FC). The growth traits studied were average daily gain (ADG, n = 1,543 animals) and selection (postweaning) weight (WSel, n = 9,549 animals). The (co)variance components were estimated by the maximum restricted likelihood method, fitting animal models that did (single-step genomic best linear unbiased prediction) or did not include (best linear unbiased prediction) genomic information in two-trait analyses. The direct responses to selection were calculated for the feed efficiency traits, ADG, and WSel, as well as the correlated responses in feed efficiency and growth by direct selection for shorter TF. The estimated heritabilities were 0.51 ± 0.06, 0.35 ± 0.06, 0.27 ± 0.07, 0.34 ± 0.06, and 0.33 ± 0.06 for TF, FD, FF, FR, and DMIv, respectively. In general, TF and FD showed positive genetic correlations with all feed efficiency traits (RFI, RWG, and FC), ADG, DMI, and WSel. Additionally, TF showed high and positive genetic and phenotypic correlations with RFI (0.71 ± 0.10 and 0.46 ± 0.02, respectively) and DMI (0.56 ± 0.09 and 0.48 ± 0.03), and medium to weak genetic correlations with growth (0.32 ± 0.11 with ADG and 0.14 ± 0.09 with WSel). The results suggest that TF is a strong indicator trait of feed efficiency, which exhibits high heritability and a weak positive genetic correlation with growth. In a context of a selection index, the inclusion of TF to select animals for shorter TF may accelerate the genetic gain in feed efficiency by reducing RFI but with zero or slightly negative genetic gain in growth traits.
Modelling Feed Intake and Efficiency in Feedlot Cattle
Preliminary results are presented for growth, feed intake and feed efficiency of 1165 feedlot-finished cattle. When intake is modelled as a function of an animal's metabolic weight and weight gain, more variation is explained if gain is estimated (for the period intake was measured) by modelling growth for most of the time animals were in the feedlot. This approach also leads to partial regression coefficients for weight and weight gain which are closer to values calculated from nutritional requirements for maintenance and gain. However, even using modelled gain, the partial coefficients for weight were approximately twice as high as those based on maintenance needs. Unless weight gains can be measured very accurately, eg by automatic weighing, or by a long test period, it may be advisable not to draw any conclusions about the efficiency of an individual animal. Preliminary estimates of genetic correlations for intake, weight, weight gain, fatness and feed efficiency, presented ...
Genetic evaluation using multi-trait and random regression models in Simmental beef cattle
Genetics and molecular research: GMR
The Brazilian Association of Simmental and Simbrasil Cattle Farmers provided 29,510 records from 10,659 Simmental beef cattle; these were used to estimate (co)variance components and genetic parameters for weights in the growth trajectory, based on multi-trait (MTM) and random regression models (RRM). The (co)variance components and genetic parameters were estimated by restricted maximum likelihood. In the MTM analysis, the likelihood ratio test was used to determine the significance of random effects included in the model and to define the most appropriate model. All random effects were significant and included in the final model. In the RRM analysis, different adjustments of polynomial orders were compared for 5 different criteria to choose the best fit model. An RRM of third order for the direct additive genetic, direct permanent environmental, maternal additive genetic, and maternal permanent environment effects was sufficient to model variance structures in the growth trajector...