The relation between breeding management and 305-day milk production, determined via principal components regression and partial least squares

A field study was set up to investigate the relation between breeding management and 305-day milk production. Second goal of the study was to investigate advantages and disadvantages of principal components regression (PCR) and partial least squares (PLS) for livestock management research. Multicollinearity was present in the data set and the number of variables was high compared to the number of observations. Out of 70 variables related to breeding management and technical results at dairy farms, 19 were selected for PLS and PCR, based on a correlation of greater than or equal to 0.25 or less than or equal to - 0.25 with 305-day milk production. Five principal components (PCs) were selected for PC-regression with 305-day milk production being the goal variable. Related variables were combined into one so-called synthetic factor. All synthetic variables were used in a path-analysis. The same path-analysis was worked out with PLS. PLS forms synthetic factors capturing most of the information for the independent X-variables that is useful for predicting the dependent Y-variable(s) while reducing the dimensionality. Both methodologies showed that milk production per cow is related to critical success factors of the producer, farm size, breeding value for production and conformation. Milk production per cow was the result of the attitude of the farmer as well as the genetic capacity of the cow. It was found that at high producing farms the producer put relatively much emphasis on the quality of the udder and less on the kg of milk. Advantages of PLS are the optimization towards the Y-variable, resulting in a higher R-2, and the possibility to include more than one Y-variable. Advantages of PCR are that hypothesis testing can be performed, and that complete optimisation is used in determining the PCs. It is concluded that PLS is a good alternative for PCR when relations are complex and the number of observations is small. (C) 2000 Elsevier Science BN. All rights reserved.