VIII. International Congress on Domestic Animal Breeding, Genetics and Husbandry - 2024 (ICABGEH-24), Antalya, Türkiye, 23 - 25 Eylül 2024, ss.301-321
In this study, it is aimed to examine the possibilities of using Multivariate Adaptive Regression Splines algorithms in the field of animal science for more than one result. For this purpose, attachment time (min/day) and milk yield (lt) were used as multiple outcome variables, and cabin residence time (min/day), milking speed (lt/min) and lactation day (days) were used as explanatory variables in a data set taken from the Polish Holstein population. The methodology and goodness-of-fit criteria of multioutcome MARS were examined in detail. According to the results obtained, it was determined that the explanatory variables of duration of stay in the cabin (min/day), milking speed (lt/min) and lactation day (days) were capable of explaining the attachment time variable by 0.72% and milk yield by 79.6%. It was determined that while the success of predicting milk yield was high, the success of predicting the attachment time variable was quite low. It has been evaluated that the success of multi-outcome prediction may also depend on the relationship between outcome variables. When the findings and similar literature were evaluated, it was understood that multi-outcome multivariate adaptive regression curves (MARS) can be used successfully in the field of dairy cattle studies.