Herd-specific random regression carcass profiles for beef cattle after adjustment for animal genetic merit

Englishby, Tanya; Kl, Moore; Berry, D.P.; Coffey, Mike; Banos, Georgios

doi:10.1016/j.meatsci.2017.03.005

Cited by 4 publications

(5 citation statements)

References 29 publications

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“…A relatively simple approach to increasing the value of a carcass is to select for heavier carcasses; carcass weight is measured on almost all slaughtered animals globally and, in many cases, these data are available for genetic evaluations (Pabiou et al, 2012;Englishby et al, 2017). Based on the genetic parameters estimates for carcass weight, Table 3.…”

Section: Potential Genetic Gain Achievable In Cut Weightsmentioning

confidence: 99%

Potential exists to change, through breeding, the yield of individual primal carcass cuts in cattle without increasing overall carcass weight1

Judge

Pabiou

Murphy

et al. 2019

Journal of Animal Science

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The ability to alter the morphology of cattle towards greater yields of higher value primal cuts has the potential to increase the value of animals at slaughter. Using weight records of 14 primal cuts from 31,827 cattle, the objective of the present study was to quantify the extent of genetic variability in these primal cuts; also of interest was the degree of genetic variability in the primal cuts adjusted to a common carcass weight. Variance components were estimated for each primal cut using animal linear mixed models. The coefficient of genetic variation in the different primal cuts ranged from 0.05 (bavette) to 0.10 (eye of round) with a mean coefficient of genetic variation of 0.07. When phenotypically adjusted to a common carcass weight, the coefficient of genetic variation of the primal cuts was lesser ranging from 0.02 to 0.07 with a mean of 0.04. The heritability of the 14 primal cuts ranged from 0.14 (bavette) to 0.75 (topside) with a mean heritability across all cuts of 0.48; the heritability estimates reduced, and ranged from 0.12 (bavette) to 0.56 (topside), when differences in carcass weight were accounted for in the statistical model. Genetic correlations between each primal cut and carcass weight were all ≥0.77; genetic correlations between each primal cut and carcass conformation score were, on average, 0.59 but when adjusted to a common carcass weight, the correlations weakened to, on average, 0.27. The genetic correlations among all 14 primal cut weights was, on average, strong (mean correlation of 0.72 with all correlations being ≥0.37); when adjusted to a common carcass weight, the mean of the genetic correlations among all primal cuts was 0.10. The ability of estimated breeding values for a selection of primal cuts to stratify animals phenotypically on the respective cut weight was demonstrated; the weight of the rump, striploin, and fillet of animals estimated to be in the top 25% genetically for the respective cut, were 10 to 24%, 12 to 24%, and 7 to 17% heavier than the weight of cuts from animals predicted to be in the worst 25% genetically for that cut. Significant exploitable genetic variability in primal carcass cuts was clearly evident even when adjusted to a common carcass weight. The high heritability of many of the primal cuts infers that large datasets are not actually required to achieve high accuracy of selection once the structure of the data and the number of progeny per sire is adequate.

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Section: Potential Genetic Gain Achievable In Cut Weightsmentioning

confidence: 99%

Potential exists to change, through breeding, the yield of individual primal carcass cuts in cattle without increasing overall carcass weight1

Judge

Pabiou

Murphy

et al. 2019

Journal of Animal Science

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“…Similarly, Caccamo et al (2008) proposed that by identifying sources of herd-level variation in milk components and SCC, management decision support tools could be developed that would quantify how different levels of management contribute to the identified sources of variation. In fact, the benefit of using BLUE to disentangle the contribution of both genetic merit and management to the observed performance has the potential to be applicable to a whole range of different performance characteristics and species; Englishby et al (2017), for example, proposed using BLUE from random regression models on carcass weight as a tool to quantify herd-level effects on carcass growth rates and other beef carcass trait profiles.…”

Section: Tailored Decision Supportmentioning

confidence: 99%

“…Most genetic evaluations consider as fixed effects some measure of temporal herd groupings such as the contemporary group of herd-year-season (van Bebber et al, 1997;Calus et al, 2005) or just herd-year (Englishby et al, 2017). Bastin et al (2009) proposed using such herd-year BLUE to provide dairy producers with retrospective advice on feeding and management Characterization of best linear unbiased estimates generated from national genetic evaluations of reproductive performance, survival, and milk yield in dairy cows strategies.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, Caccamo et al (2008) included a herd-test-day effect in the statistical analyses of dairy cow milk performance data to account for short-term management changes such as changes in feed ration. Englishby et al (2017) used BLUE to independently quantify the association between management environments and the variability in beef carcass characteristics after accounting for differences in additive genetic merit; Englishby et al (2017) achieved this by including the random effects of both animal genetic merit and the contemporary group of finishing herd-year in the statistical model. Studies are however lacking, in dairy cattle at least, on the macro-environmental and management characteristics describing the BLUE.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of best linear unbiased estimates generated from national genetic evaluations of reproductive performance, survival, and milk yield in dairy cows

Dunne

Kelleher²,

Walsh

et al. 2018

Journal of Dairy Science

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Genetic evaluations decompose an observed phenotype into its genetic and nongenetic components; the former are termed BLUP with the solutions for the systematic environmental effects in the statistical model termed best linear unbiased estimates (BLUE). Geneticists predominantly focus on the BLUP and rarely consider the BLUE. The objective of this study, however, was to define and quantify the association between 8 herd-level characteristics and BLUE for 6 traits in dairy herds, namely (1) age at first calving, (2) calving to first service interval (CFS), (3) number of services, (4) calving interval (CIV), (5) survival, and (6) milk yield. Phenotypic data along with the fixed and random effects solutions were generated from the Irish national multi-breed dairy cow fertility genetic evaluations on 3,445,557 cows; BLUE for individual contemporary groups were collapsed into mean herd-year estimates. Data from 5,707 spring-calving herds between the years 2007 and 2016 inclusive were retained; association analyses were undertaken using linear mixed multiple regression models. Pearson coefficient correlations were used to quantify the relationships among individual trait herd-year BLUE, and transition matrices were used to understand the dynamics of mean herd BLUE estimates over years. Based on the mean annual trends in raw, BLUP, and BLUE, it was estimated that BLUE were associated with at least two-thirds of the improvement in CIV and milk production over the past 10 yr. Milk recording herds calved heifers for the first time on average 15 d younger, had an almost 2 d longer CFS but 2.3 d shorter CIV than non-milk-recording herds. Larger herd sizes were associated with worse BLUE for both CFS and CIV. Expanding herds and herds that had the highest proportion of cows born on the farm itself, on average, calved heifers younger and had shorter CIV. By separating the raw performance of a selection of herds into their respective BLUE and BLUP, it was possible to identify herds with inferior management practices that were being compensated by superior genetics; similarly, herds were identified with superior BLUE, but because of their inferior genetic merit, were not reaching their full potential. This suggests that BLUE could have a pivotal role in a tailored decision support tool that would enable producers to focus on the most limiting factor hindering them from achieving their maximum performance.

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“…Bullock breeding soundness evaluations (BSEs) play a crucial economic element of beef management [17]- [19], [7], [16] and are conducted to determine a bullocks capability to breed calves under natural mating management [16], [12] Precise conduct and reporting of BSE supports bull buyers, owners, and managers to make appropriate bull selection and management decisions [8], [20]- [26]. Sub-fertility of bulls is a major issue in in the cattle production sector in Africa and elsewhere [27].…”

Section: Introductionmentioning

confidence: 99%

Breeding Soundness Inspection and Production Features of Bullocks under Ecological Conditions in Free State Region, South Africa

Zuma-Netshiukhwi

Netshilema

Nongovhela

2022

EJFOOD

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Assessing the fertility of bullocks preceding to use as breeding animals make apparent economic if it were possible. Fertility is not possible predicted by means of a breeding soundness evaluation (BSE), but by the application of minimum standards to a set of accepted procedures, which supports informed decision makers to the breeding potential of the bullock. An bullocks found to satisfy these criteria in Central Performance Testing (CPT) known as Phase C, is reckoned to be breeding sound (BS). Furthermore, breeding success depends on the reproductive performance of both the cow and the bull. Breeding bull is likely to service a large number of heifers or cows in planned breeding season. The efficiency of a bullock can be based on the average daily gain (ADG) and feed conversion ratio (FCR) but poor breeding sound evaluation can be regarded as infertile. Bull breeding soundness evaluations (BSEs) are significant economic management of beef herd management and depends on adequate semen quality, physical soundness and serving capacity. Furthermore, assessment of scrotal circumference (SC), libido, testicular measurement and semen parameters often not done during a routine breeding soundness examination. The aim of this study was to depict using BSE whether bullocks would be fertile under Phase C. The relationship of BSE and production characteristics in central performance testing is challenging to identify fertility in bullocks within 112 given days of CPT. This study investigated performance data extracted from Integrated Registration and Genetic Information System (Intergis) managed by Agricultural Research Council Glen Bull Testing Centre. There were three breeds investigated, namely, Simmentaler, Santa Gertrudis and Limousin for the period of 2007 to 2017, were analysed to determine the dominance and importance of factors affecting breeding soundness classification.

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Herd-specific random regression carcass profiles for beef cattle after adjustment for animal genetic merit

Cited by 4 publications

References 29 publications

Potential exists to change, through breeding, the yield of individual primal carcass cuts in cattle without increasing overall carcass weight1

Potential exists to change, through breeding, the yield of individual primal carcass cuts in cattle without increasing overall carcass weight1

Characterization of best linear unbiased estimates generated from national genetic evaluations of reproductive performance, survival, and milk yield in dairy cows

Breeding Soundness Inspection and Production Features of Bullocks under Ecological Conditions in Free State Region, South Africa

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