Open AccessModelling lactation curves for fat-to-protein ratio of milk in the first three lactations of Polish Holstein-Friesian cows
Author(s) -
A. Satoła
Publication year - 2021
Publication title -
south african journal of animal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 33
eISSN - 2221-4062
pISSN - 0375-1589
DOI - 10.4314/sajas.v51i3.4
Subject(s) - akaike information criterion , lactation , goodness of fit , statistics , mathematics , correlation coefficient , milk fat , residual , zoology , biology , food science , pregnancy , genetics , algorithm , linseed oil
Among milk traits, fat-to-protein ratio (FPR) is considered a potential measure of a cow’s energy status and is one of the selection criteria necessary to improve metabolic stability. Further genetic analyses require an appropriate model that describes the pattern of FPR changes throughout lactation. The objective of the study was to examine five mathematical functions to describe the lactation curve for FPR in the first three lactations of Polish Holstein-Friesians. The dataset contained FPR records for 5690 cows in the first lactation, 4081 cows in the second, and 2636 cows in the third lactation based on 48908, 34706, and 22097 test-day (TD) records, respectively. Using the MIXED procedure of SAS statistical analytics software, ten linear models (five with fixed effects only, and five with the additional random effect of cow) were fitted to the TD records. The goodness of fit was tested with Akaike's information criterion, residual variances and the correlation coefficient between the actual and estimated values. The model proposed by Ali and Schaeffer (1987) had the best fit to FPR in the first three parities, and the model of Wilmink (1987) provided the worst fit. The correlation coefficient between the actual and the estimated values of FPR was higher for models that included the random cow effect compared with models without this effect.