Modifying the lactation curve to improve lactation milk and persistency.
Daily, stage and lactation estimated breeding values (EBV) and the shape of the lactation curve for each cow are controlled by a unique set of random (genetic) regression coefficients under a test day model, thus providing a basis for genetic improvement of these characteristics. Three selection procedures were developed for simultaneous improvement of total lactation milk and persistency: 1) index selection based on daily EBV, 2) index selection based on stage EBV, and 3) index selection based on random regression (RR) coefficients. A numerical example was given to demonstrate the computation of indexes based on stage EBV and based on RR coefficients. A conversion equation was derived to convert between genetic changes in EBV and RR coefficients. Index selection based on daily EBV would require the finding of 305 weighting factors for a lactation period of 305 d, making it impractical to determine the weighting factors on a daily basis. Alternatively, a lactation period was partitioned into a few stages to facilitate the construction of index selection based on stage EBV and index selection based on RR coefficients. These selection procedures make use of the annual genetic gains routinely computed in national genetic evaluations to restrict the genetic gains between different lactation stages to achieve the desired curve. When there is no prior knowledge of annual genetic gains, the proportional restriction of genetic gains between stages may be used. In summary, this study provides a simple means of modifying the lactation curve by manipulating genetic changes in different lactation stages at a pre-specified rate.
Togashi
K
K
National Agricultural Research Center for Hokkaido Region, Hitsujigaoka 1, Toyohiraku, Sapporo, Japan 0628555.
Lin
C Y
CY
D000818:Animals; D001947:Breeding; D002417:Cattle / Q000235:genetics* / Q000502:physiology; D005260:Female; D007774:Lactation / Q000235:genetics*; D008433:Mathematics; D008892:Milk / Q000737:chemistry; D012044:Regression Analysis