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 manag ement and 305-day milk production. Second goal of the study was to investig ate advantages and disadvantages of principal components regression (PCR) a nd partial least squares (PLS) for livestock management research. Multicoll inearity was present in the data set and the number of variables was high c ompared to the number of observations. Out of 70 variables related to breed ing management and technical results at dairy farms, 19 were selected for P LS 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 compo nents (PCs) were selected for PC-regression with 305-day milk production be ing the goal variable. Related variables were combined into one so-called s ynthetic factor. All synthetic variables were used in a path-analysis. The same path-analysis was worked out with PLS. PLS forms synthetic factors cap turing most of the information for the independent X-variables that is usef ul for predicting the dependent Y-variable(s) while reducing the dimensiona lity. Both methodologies showed that milk production per cow is related to critical success factors of the producer, farm size, breeding value for pro duction and conformation. Milk production per cow was the result of the att itude of the farmer as well as the genetic capacity of the cow. It was foun d 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 th e possibility to include more than one Y-variable. Advantages of PCR are th at hypothesis testing can be performed, and that complete optimisation is u sed 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.