Submitted to: Meeting Abstract
Publication Type: Other
Publication Acceptance Date: 6/21/2018
Publication Date: N/A
Technical Abstract: Selective breeding has been practiced since domestication, but early breeders commonly selected on appearance (e.g., coat color and pattern) rather than quantitative phenotypes (e.g., milk yield). A breeding index converts information about several traits of a cow – for example, how much she milks and how fertile she is – into 1 number used for selection and also to predict the animal’s own performance. Calculation of most numbers needed to compute a selection index is straightforward when the phenotype and pedigree data are available. Prediction of likely economic values 3 to 10 years in the future, when the offspring of matings planned using the index will be lactating, is more challenging. The first USDA selection index, introduced in 1971, included only milk and fat yield, while the latest version of the lifetime net merit (NM$) index includes 14 traits, with some traits actually composites of other traits. Selection indices are revised to reflect improved knowledge of cow biology, new techniques to collect and analyze data, and evolving economic conditions. Single-trait selection often suffers from antagonistic correlations with traits not in the selection objective. Multiple-trait selection avoids those problems but at the cost of less than maximal progress for individual traits. How many and which traits to include is not simple to determine because traits are not independent. Many countries use total merit indices (TMI) such as NM$ that reflect the needs of different producers in different environments. While the emphasis placed on trait groups differs, most TMI include yield, fertility, health, and conformation traits. Addition of milk composition, feed intake, greenhouse gas emission, and other traits is possible but are more costly to collect, and many are not yet directly rewarded in the marketplace, such as with incentives from milk processing plants. Traditional selection required recording lots of cows across many farms, but genomic selection favors collecting more detailed information from cooperating farms. A similar strategy may be useful in less developed countries. With genomic prediction, recording important new traits on a small fraction of cows can quickly benefit the whole population.