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Meta-analysis of factors affecting milk component yields in dairy cattle
The objectives of this study were thus to identify most significant factors that determine milk component yield (MCY) using a meta-analysis and, if possible, to develop equations to predict MCY using variables that can be easily measured in the field. A literature database was constructed based on t...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4534184/ https://www.ncbi.nlm.nih.gov/pubmed/26290694 http://dx.doi.org/10.1186/2055-0391-56-5 |
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author | Lee, Junsung Seo, Jakyeom Lee, Se Young Ki, Kwang Seok Seo, Seongwon |
author_facet | Lee, Junsung Seo, Jakyeom Lee, Se Young Ki, Kwang Seok Seo, Seongwon |
author_sort | Lee, Junsung |
collection | PubMed |
description | The objectives of this study were thus to identify most significant factors that determine milk component yield (MCY) using a meta-analysis and, if possible, to develop equations to predict MCY using variables that can be easily measured in the field. A literature database was constructed based on the research articles published in the Journal of Dairy Science from Oct., 2007 till May, 2010. The database consisted of a total of 442 observed means for MCY from 118 studies. The candidate factors that determine MCY were those which can be routinely measured in the field (e.g. DMI, BW, dietary forage content, chemical composition of diets). Using a simple linear regression, the best equations for predicting milk fat yield(MFY) and milk protein yield (MPY) were MFY = 0.351 (±0.068) + 0.038 (±0.003) DMI (R(2) = 0.27), and MPY = 0.552 (±0.071) + 0.031 (±0.002) DMI - 0.004 (±0.001) FpDM (%, forage as a percentage of dietary DM) (R(2) = 0.38), respectively. The best equation for predicting milk fat content (%) explained only 12% of variations in milk fat content, and none of a single variable can explain more than 5% of variations in milk protein content. We concluded that among the tested variables, DMI was the only significant factor that affects MFY and both DMI and FpDM significantly affect MPY. However, predictability of linear equations was relatively low. Further studies are needed to identify other variables that can predict milk component yield more accurately. |
format | Online Article Text |
id | pubmed-4534184 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-45341842015-08-19 Meta-analysis of factors affecting milk component yields in dairy cattle Lee, Junsung Seo, Jakyeom Lee, Se Young Ki, Kwang Seok Seo, Seongwon J Anim Sci Technol Research The objectives of this study were thus to identify most significant factors that determine milk component yield (MCY) using a meta-analysis and, if possible, to develop equations to predict MCY using variables that can be easily measured in the field. A literature database was constructed based on the research articles published in the Journal of Dairy Science from Oct., 2007 till May, 2010. The database consisted of a total of 442 observed means for MCY from 118 studies. The candidate factors that determine MCY were those which can be routinely measured in the field (e.g. DMI, BW, dietary forage content, chemical composition of diets). Using a simple linear regression, the best equations for predicting milk fat yield(MFY) and milk protein yield (MPY) were MFY = 0.351 (±0.068) + 0.038 (±0.003) DMI (R(2) = 0.27), and MPY = 0.552 (±0.071) + 0.031 (±0.002) DMI - 0.004 (±0.001) FpDM (%, forage as a percentage of dietary DM) (R(2) = 0.38), respectively. The best equation for predicting milk fat content (%) explained only 12% of variations in milk fat content, and none of a single variable can explain more than 5% of variations in milk protein content. We concluded that among the tested variables, DMI was the only significant factor that affects MFY and both DMI and FpDM significantly affect MPY. However, predictability of linear equations was relatively low. Further studies are needed to identify other variables that can predict milk component yield more accurately. BioMed Central 2014-06-05 /pmc/articles/PMC4534184/ /pubmed/26290694 http://dx.doi.org/10.1186/2055-0391-56-5 Text en © Lee et al.; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Lee, Junsung Seo, Jakyeom Lee, Se Young Ki, Kwang Seok Seo, Seongwon Meta-analysis of factors affecting milk component yields in dairy cattle |
title | Meta-analysis of factors affecting milk component yields in dairy cattle |
title_full | Meta-analysis of factors affecting milk component yields in dairy cattle |
title_fullStr | Meta-analysis of factors affecting milk component yields in dairy cattle |
title_full_unstemmed | Meta-analysis of factors affecting milk component yields in dairy cattle |
title_short | Meta-analysis of factors affecting milk component yields in dairy cattle |
title_sort | meta-analysis of factors affecting milk component yields in dairy cattle |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4534184/ https://www.ncbi.nlm.nih.gov/pubmed/26290694 http://dx.doi.org/10.1186/2055-0391-56-5 |
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