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Comparison of reproductive performance of AI- and natural service-sired beef females under commercial management

The objective of this study was to assess differences in reproductive performance of natural service and artificial insemination (AI) sired beef females based on pregnancy outcomes, age at first calving, and calving interval. Data were sourced from 8,938 cows sired by AI bulls and 3,320 cows sired b...

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Autores principales: Marrella, Mackenzie A, White, Robin R, Dias, Nicholas W, Timlin, Claire, Pancini, Stefania, Currin, J, Clark, Sherrie, Stewart, Jamie L, Mercadante, Vitor R G, Bradford, Heather L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309952/
https://www.ncbi.nlm.nih.gov/pubmed/34316542
http://dx.doi.org/10.1093/tas/txab114
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author Marrella, Mackenzie A
White, Robin R
Dias, Nicholas W
Timlin, Claire
Pancini, Stefania
Currin, J
Clark, Sherrie
Stewart, Jamie L
Mercadante, Vitor R G
Bradford, Heather L
author_facet Marrella, Mackenzie A
White, Robin R
Dias, Nicholas W
Timlin, Claire
Pancini, Stefania
Currin, J
Clark, Sherrie
Stewart, Jamie L
Mercadante, Vitor R G
Bradford, Heather L
author_sort Marrella, Mackenzie A
collection PubMed
description The objective of this study was to assess differences in reproductive performance of natural service and artificial insemination (AI) sired beef females based on pregnancy outcomes, age at first calving, and calving interval. Data were sourced from 8,938 cows sired by AI bulls and 3,320 cows sired by natural service bulls between 2010 and 2017. All cows were in a commercial Angus herd with 17 management units located throughout Virginia and represented spring and fall calving seasons. All calves were born to dams managed with estrus synchronization. Pregnancy was analyzed with generalized linear mixed models and other reproductive measures with linear mixed models in R. Six models were evaluated with the dependent variables of pregnancy status at the first diagnosis, pregnancy status at the second diagnosis, pregnancy type (AI or natural service) at the first diagnosis, pregnancy type at the second diagnosis, calving interval, and age at first calving. Independent variables differed by model but included sire type of the female (AI or natural service), prebreeding measures of age, weight, and body condition score, postpartum interval, sex of the calf nursing the cow, and management group. No differences were observed between AI- and natural service-sired females based on pregnancy status at first and second pregnancy diagnosis (P > 0.05). Sire type was only found to be significant for age at first calving (P < 0.05) with AI-sired females being 26.6 ± 1.6 d older at their first calving, which was expected because AI-sired females were born early in the calving season making them older at breeding. Surprisingly, age and body condition score were not significant predictors of pregnancy (P > 0.05). Body weight at breeding was not significant for pregnancy (P > 0.05) but was significant for age at first calving (P < 0.05). These data suggested that lighter heifers calved earlier which contradicts our original hypothesis. Overall, commercial Angus females sired by AI or natural service bulls had similar reproductive performance. Factors that were commonly associated with reproductive success were not significant in this commercial Angus herd managed with estrus synchronization. Given the size of these data, the importance of body condition, age, and weight should be reassessed in modern genetics and management practices.
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spelling pubmed-83099522021-07-26 Comparison of reproductive performance of AI- and natural service-sired beef females under commercial management Marrella, Mackenzie A White, Robin R Dias, Nicholas W Timlin, Claire Pancini, Stefania Currin, J Clark, Sherrie Stewart, Jamie L Mercadante, Vitor R G Bradford, Heather L Transl Anim Sci Animal Genetics and Genomics The objective of this study was to assess differences in reproductive performance of natural service and artificial insemination (AI) sired beef females based on pregnancy outcomes, age at first calving, and calving interval. Data were sourced from 8,938 cows sired by AI bulls and 3,320 cows sired by natural service bulls between 2010 and 2017. All cows were in a commercial Angus herd with 17 management units located throughout Virginia and represented spring and fall calving seasons. All calves were born to dams managed with estrus synchronization. Pregnancy was analyzed with generalized linear mixed models and other reproductive measures with linear mixed models in R. Six models were evaluated with the dependent variables of pregnancy status at the first diagnosis, pregnancy status at the second diagnosis, pregnancy type (AI or natural service) at the first diagnosis, pregnancy type at the second diagnosis, calving interval, and age at first calving. Independent variables differed by model but included sire type of the female (AI or natural service), prebreeding measures of age, weight, and body condition score, postpartum interval, sex of the calf nursing the cow, and management group. No differences were observed between AI- and natural service-sired females based on pregnancy status at first and second pregnancy diagnosis (P > 0.05). Sire type was only found to be significant for age at first calving (P < 0.05) with AI-sired females being 26.6 ± 1.6 d older at their first calving, which was expected because AI-sired females were born early in the calving season making them older at breeding. Surprisingly, age and body condition score were not significant predictors of pregnancy (P > 0.05). Body weight at breeding was not significant for pregnancy (P > 0.05) but was significant for age at first calving (P < 0.05). These data suggested that lighter heifers calved earlier which contradicts our original hypothesis. Overall, commercial Angus females sired by AI or natural service bulls had similar reproductive performance. Factors that were commonly associated with reproductive success were not significant in this commercial Angus herd managed with estrus synchronization. Given the size of these data, the importance of body condition, age, and weight should be reassessed in modern genetics and management practices. Oxford University Press 2021-06-30 /pmc/articles/PMC8309952/ /pubmed/34316542 http://dx.doi.org/10.1093/tas/txab114 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the American Society of Animal Science. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Animal Genetics and Genomics
Marrella, Mackenzie A
White, Robin R
Dias, Nicholas W
Timlin, Claire
Pancini, Stefania
Currin, J
Clark, Sherrie
Stewart, Jamie L
Mercadante, Vitor R G
Bradford, Heather L
Comparison of reproductive performance of AI- and natural service-sired beef females under commercial management
title Comparison of reproductive performance of AI- and natural service-sired beef females under commercial management
title_full Comparison of reproductive performance of AI- and natural service-sired beef females under commercial management
title_fullStr Comparison of reproductive performance of AI- and natural service-sired beef females under commercial management
title_full_unstemmed Comparison of reproductive performance of AI- and natural service-sired beef females under commercial management
title_short Comparison of reproductive performance of AI- and natural service-sired beef females under commercial management
title_sort comparison of reproductive performance of ai- and natural service-sired beef females under commercial management
topic Animal Genetics and Genomics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8309952/
https://www.ncbi.nlm.nih.gov/pubmed/34316542
http://dx.doi.org/10.1093/tas/txab114
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