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Efficiency of Different Selection Criteria for Persistency and Lactation Milk Yield

¹ National Agricultural Research Center for Hokkaido Region, Hitsujigaoka 1, Toyohiraku, Sapporo, Japan 0628555
² Dairy and Swine Research and Development Centre, Agriculture and Agri-Food Canada

Corresponding author: K. Togashi; e-mail: tkenji{at}naro.affrc.go.jp.

A conversion formula was developed to convert the genetic covariance matrices of daily yields and of random regression coefficients between 305-d and 335-d production periods under a random regression test day model. Five selection criteria were compared in terms of genetic improvement in persistency and lactation milk: 1) lactation estimated breeding value (EBVL), 2) ratio of daily estimated breeding value (EBV)(r_280/65 = D₂₈₀/D₆₅), 4) ratio of partial lactation EBV (P_280/65 = D_66~280/D_5~65), and 5) differential daily EBV (d_65–280 = D₆₅ – D₂₈₀), where D_i refers to EBV at days in milk (DIM) i. Fundamental differences among these 5 selection criteria were interpreted conceptually with a graph. Persistency, defined as k = (G₆₅ – G₂₈₀)/215, was the average daily rate of decline in selection gain from DIM 65 to 280, which is free from the effect of lactation milk on the rate of decline. Parameter k provides an objective measure of persistency, which increases when k < 0 and decreases when k > 0. Of the 5 selection criteria compared, d_65–280 and P6 achieved greater persistency at the expense of genetic gain in lactation milk, whereas selection based on EBV_L achieved the highest response in lactation milk, but was coupled with greatest decline in persistency. Selection on P_280/65 or r_280/65 improved both lactation milk and persistency and, thus, is recommended for simultaneous improvement of these 2 economically important traits. Further study of the relative economic values of persistency and lactation milk in order to combine both traits into an index for selection decision is warranted.

Key Words: persistency • lactation milk • selection criterion • test day model

Abbreviation key: RR = random regression

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