Predictions of Daily Milk and Fat Yields, Major Groups of Fatty Acids, and C18:1 cis-9 from Single Milking Data without a Milking Interval

Animals

Valérie M. R. Arnould³  Romain Reding³  Jeanne Bormann²  Nicolas Gengler¹  Hélène Soyeurt⁴ 

[1] Gembloux Agro Bio-Tech, Agriculture, Bio-engineering and Chemistry Department Animal Science and Nutrition Unit, University of Liège, Passage des Déportés 2, Gembloux B-5030, Belgium; E-Mail:;Administration des Services Techniques de l'Agriculture ASTA, Luxembourg L-1019, Luxembourg; E-Mail:;CONVIS s.c., Zone Artisanale et Commerciale, 4, Ettelbruck, L-9085, Luxembourg; E-Mails:;Gembloux Agro Bio-Tech, Department of Agricultural Science, Statistics, Informatics, and Applied Modeling Unit, University of Liège, Passage des Déportés 2, Gembloux B-5030, Belgium

关键词: milk recording;    fatty acid groups;    prediction model;    single milking;   

DOI  :  10.3390/ani5030377

来源: mdpi

【 摘 要 】

Reducing the frequency of milk recording decreases the costs of official milk recording. However, this approach can negatively affect the accuracy of predicting daily yields. Equations to predict daily yield from morning or evening data were developed in this study for fatty milk components from traits recorded easily by milk recording organizations. The correlation values ranged from 96.4% to 97.6% (96.9% to 98.3%) when the daily yields were estimated from the morning (evening) milkings. The simplicity of the proposed models which do not include the milking interval should facilitate their use by breeding and milk recording organizations.

Reducing the frequency of milk recording would help reduce the costs of official milk recording. However, this approach could also negatively affect the accuracy of predicting daily yields. This problem has been investigated in numerous studies. In addition, published equations take into account milking intervals (MI), and these are often not available and/or are unreliable in practice. The first objective of this study was to propose models in which the MI was replaced by a combination of data easily recorded by dairy farmers. The second objective was to further investigate the fatty acids (FA) present in milk. Equations to predict daily yield from AM or PM data were based on a calibration database containing 79,971 records related to 51 traits [milk yield (expected AM, expected PM, and expected daily); fat content (expected AM, expected PM, and expected daily); fat yield (expected AM, expected PM, and expected daily; g/day); levels of seven different FAs or FA groups (expected AM, expected PM, and expected daily; g/dL milk), and the corresponding FA yields for these seven FA types/groups (expected AM, expected PM, and expected daily; g/day)]. These equations were validated using two distinct external datasets. The results obtained from the proposed models were compared to previously published results for models which included a MI effect. The corresponding correlation values ranged from 96.4% to 97.6% when the daily yields were estimated from the AM milkings and ranged from 96.9% to 98.3% when the daily yields were estimated from the PM milkings. The simplicity of these proposed models should facilitate their use by breeding and milk recording organizations.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.