Estimation of genetic parameters for dry matter intake, energy- corrected milk, metabolic body weight, and milk yield using a random regression model.

No Thumbnail Available

Date

20-Jun

Authors

Houlahan, K.
Baes, C.F.
Miglior, F.
Oliveira Jr., G.A.
Schenkel, F.S.
Chud, T.C.S.

Journal Title

Journal ISSN

Volume Title

Publisher

2020 American Dairy Science Association® Annual Meeting

Abstract

Feed efficiency has been heavily researched in recent years. As feed costs rise and the environmental impacts of agriculture become more apparent, improving the efficiency with which dairy cows convert feed to milk is increasingly important. However, feed intake is expensive to accurately measure on large populations, making the inclusion of feed efficiency into breeding programs difficult. Through technological advancements, accurate measurements on traits related to feed efficiency, such as dry matter intake, BW, body condition score, and milk components have become more readily available. Understanding how the genetic parameters of traits related to feed efficiency vary throughout a lactation curve is valuable. In this study, 75,255 daily feed intake records, 16,786 milk production records, and 30,615 weekly BW records were collected on 610, 827, and 331 Canadian first lactation cows, respectively, from 2007 to 2019. Genetic parameters were calculated for these 3 traits by week of lactation using a random regression model. Heritability estimates for all traits were lower in the first stage of lactation compared with the later stages of lactation. The results of this study contributed a better understanding of the change in genetic parameters across the first lactation, providing insight on potential selection strategies to include feed efficiency into breeding programs.

Description

Oral Presentation at the 2020 ADSA Annual Meeting (online).

Keywords

dairy cattle, feed efficiency, genetics

Citation

K. Houlahan, et al. Estimation of genetic parameters for dry matter intake, energy- corrected milk, metabolic body weight, and milk yield using a random regression model. J. Dairy Sci. 103 (Suppl. 1). p. 74, 195, 2020.

Collections