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.

Valérie Arnould,Hélène Soyeurt,Jeanne Bormann,Nicolas Gengler,Romain Reding

doi:10.3390/ani5030377

Abstract

Simple SummaryReducing 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.

Highlights

According to Arnould et al [1], milk yield, and, milk fat composition, may facilitate the development of strategies to prevent and monitor milk production dysfunction in dairy cattle, and may improve the sustainability of dairy production systems
To develop equations which permit the estimation of fatty acids (FA) daily yields from one milking, measurements of milk yield and milk composition at each milking are needed, as well as milk composition data from 50% alternate morning (AM) and 50% PM milk samples
The models proposed in the present study demonstrated that it is possible to estimate milk, fat, and FA yields without the use of milking intervals (MI) recorded on site

Summary

Introduction

According to Arnould et al [1], milk yield, and, milk fat composition, may facilitate the development of strategies to prevent and monitor milk production dysfunction in dairy cattle, and may improve the sustainability of dairy production systems. Positive correlations between saturated FA (SFA) and methane output has been observed (r = 0.87–0.91) [2]. In reports by van Haelst et al and Gross et al [3,4]), a high proportion of long chain FA (LCFA; especially if combined with a lower proportion of medium chain FA (MCFA)), and especially a high proportion of C18:1 cis-9, in milk fat were found to be good predictors of subclinical ketosis. A regular quantification of FA in milk is relevant

Objectives

Methods

Conclusion