The use of a cluster analysis in across herd genetic evaluation for beef cattle

Neser, F.W.C.; Erasmus, G.J.; Scholtz, M. M.

doi:10.4314/sajas.v38i1.4109

Cited by 12 publications

(13 citation statements)

References 11 publications

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“…Regression analysis indicate that higher rainfall resulted in a decrease in BW_E, 18 MW_E, MW_E (P <0.05), and an increase in RI (P <0.05). The relationship between rainfall and growth in the present study agrees with the findings of Neser et al (2008), who reported that rainfall explained 10% of the weaning weight of Bonsmara weaner calves. Fynn & O'Conner (2000) also found a curvilinear relationship between rainfall and cattle production.…”

Section: Figure 1 Geographic Locations Of the Cluster Component Bonsmsupporting

confidence: 93%

Influence of bioregion and environmental factors on the growth, size and reproduction of Bonsmara cows

Webb

Visagie

Westhuizen³

et al. 2017

SA J. An. Sci.

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A better understanding of the influences of the bioregion (production environment) and environmental factors on the productivity of beef cows is vital in the pursuit of improving beef production. The influence of bioregion and important environmental factors in South Africa (temperature, rainfall, soil pH, soil cation exchange capacity, soil organic carbon content, soil phosphorus (P) concentration, and grazing capacity) on production and reproduction traits of Bonsmara cows were analysed by means of cluster analysis and analysis of variance (ANOVA). Stepwise regression analyses were used to determine the influence of the different environmental factors (and possible non-additive genetic effects) on the production and reproduction efficiency of the cows. Results of the effects of bioregion and non-additive genetic effects (the contribution of which was not quantified per se) on beef cow productivity indicate that bioregion influenced production and reproduction traits of cows, while significant farm effects were also noted. Bonsmara farm influenced the production efficiency of cows through the implementation of management practices and breeding objectives. Environmental factors significantly influenced all production traits measured for Bonsmara cows. The extent of the effects of the combined influences of environmental factors on cow productivity depended on the physiological stage of animal growth and reproduction. Environmental effects on production efficiency were buffered by maternal effects at birth, but became more significant after weaning (9%) to yearling age (10%) and maturity. Most environmental factors studied influenced the production traits of cows, with rainfall and temperature having the largest influence. The negative influence of rainfall on cow productivity was attributed to its negative influence on grazing quality, through effects on soil pH, phosphorus and soil organic carbon content. The small negative influence of temperature on the productivity of cows indicates that this breed is well adapted to the main South African beef production regions. It should be noted that in this study the non-additive genetic component was not separated from the environmental components. ______________________________________________________________________________________

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Section: Figure 1 Geographic Locations Of the Cluster Component Bonsmsupporting

confidence: 93%

Influence of bioregion and environmental factors on the growth, size and reproduction of Bonsmara cows

Webb

Visagie

Westhuizen³

et al. 2017

SA J. An. Sci.

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“…The studies carried out by Weigel and Rekaya (2000) and Espasandin et al (2013) are two examples where clusters of farm environments were identified across different countries, thus allowing genetic correlations to be estimated between cluster groups rather than between countries. Similar methods have also been used within countries (Neser et al, 2008;Huquet et al, 2012b). The variables used to cluster the farms, in the cattle studies mentioned above, often included information such as climatic conditions, performance indicators (such as milk yield or weight at weaning) and herd size.…”

Section: Discussionmentioning

confidence: 99%

“…Providing there are suitable genetic links across clusters, the use of a cluster model would allow an EBV for each animal to be predicted for each environment, thus allowing the identification of animals best suited to specific environments, or animals that will perform consistently across a number of environments, as suggested by Neser et al (2008). This would potentially reduce any unwanted effects of G × E and allow farmers to select animals best suited to their farm environment.…”

Section: Discussionmentioning

confidence: 99%

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Characterisation of terminal sire sheep farm systems, based on a range of environmental factors: a case study in the context of genotype by environment interactions using Charollais lambs

McLaren

Lambe

Morgan-Davies

et al. 2014

Animal

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The objective of this study was to define different terminal sire flock environments, based on a range of environmental factors, and then investigate the presence of genotype by environment interactions (G × E) between the environments identified. Data from 79 different terminal sire flocks (40 Texel, 21 Charollais and 18 Suffolk), were analysed using principal coordinate and non-hierarchical cluster analyses, the results of which identified three distinct environmental cluster groups. The type of grazing, climatic conditions and the use of vitamins and mineral supplements were found to be the most important factors in the clustering of flocks. The presence of G × E was then investigated using data from the Charollais flocks only. Performance data were collected for 12 181 lambs, between 1990 and 2010, sired by 515 different sires. Fifty six of the sires had offspring in at least two of the three different cluster groups and pedigree information was available for a total of 161 431 animals. Traits studied were the 21-week old weight (21WT), ultrasound muscle depth (UMD) and log transformed backfat depth (LogUFD). Heritabilities estimated for each cluster, for each trait, ranged from 0.32 to 0.45. Genetic correlations estimated between Cluster 1 and Cluster 2 were all found to be significantly lower than unity, indicating the presence of G × E. They were 0.31 (±0.17), 0.68 (±0.14) and 0.18 (±0.21) for 21WT, UMD and LogUFD, respectively. Evidence of sires re-ranking across clusters was also observed. Providing a suitable strategy can be identified, there is potential for the optimisation of future breeding programmes, by taking into account the G × E observed. This would enable farmers to identify and select animals with an increased knowledge as to how they will perform in their specific farm environment thus reducing any unexpected differences in performance.

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“…It has been also benefited from clustering analysis in studies regarding selection (3,10,19). Dogan (10) states that handling the features of individuals in selection methods one by one creates weakness, and this weakness can be overcome by evaluating the variables simultaneously in clustering methods.…”

Section: Discussionmentioning

confidence: 99%

Classification of Karayaka and Bafra (Chios x Karayaka B1) sheep according to body measurements by different clustering methods

İbrahim;ÖZBEYAZ

2011

Ankara Üniversitesi Veteriner Fakültesi Dergisi

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Summary:This study has been carried out to classify Karayaka and Bafra (Chios x Karayaka B 1 ) sheep according to body measurements by k-Means within nonhierarchical clustering methods and single linkage, average linkage and complete linkage within hierarchical clustering methods. In this research, age-adjusted data of body measurements of 223 Karayaka and 462 Bafra sheep in various ages breed in Gökhöyük Agricultural Enterprise that belongs to General Directorate of Agricultural Enterprises were used. At the end of the study, it was detected that it was obtained the best parameter value when Bafra sheep were divided into 7 clusters and Karayaka into 3 according to k-Means method, and in hierarchical clustering methods, it was obtained different clusters according to various levels of similarity and distance. These results put forward that according to classifications made by different clustering methods, Bafra has a more heterogeneous structure than Karayaka in terms of body measurements.

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The use of a cluster analysis in across herd genetic evaluation for beef cattle

Cited by 12 publications

References 11 publications

Influence of bioregion and environmental factors on the growth, size and reproduction of Bonsmara cows

Influence of bioregion and environmental factors on the growth, size and reproduction of Bonsmara cows

Characterisation of terminal sire sheep farm systems, based on a range of environmental factors: a case study in the context of genotype by environment interactions using Charollais lambs

Classification of Karayaka and Bafra (Chios x Karayaka B1) sheep according to body measurements by different clustering methods

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