A Bayesian Network Analysis for the Factors Affecting the 305-day Milk Productivity of Holstein Friesians

Abstract

The variables affecting the milk productivity have been discussed in various articles through different methods. A recent study using path analysis shows that three variables significantly affect the 305-day milk yield of Holstein Friesian cows. These variables are parity, first calving year and lactation length. Calving season is another variable which appears to be significant in a different study. The aim of this study is to provide a simultaneous multilateral analysis among the milk yield, these three variables and a new variable calving season. The analysis was realized through a Bayesian network built over the findings of the path analysis. 17,109 records of Holstein Friesian cows calved between 2001-2011 years were analyzed. The estimated Bayesian network showed that younger cows produced more milk. Lactation length and parity do not depend on each other. Cows reached their highest amount of milk yield on their 4 th parities. Milk yield is mostly affected by lactation length. Finally, first calving year, parity, lactation length and calving season should be considered as criteria in a selection study to increase the milk yield.

Effects of wearable therapies on jump performance in sport horses

“…In consideration of the complex and interconnected nature of equine physiological and biomechanical responses, relationships between variables were explored via a Bayesian learning network (BLN). While a novel approach to this specific type of investigation in equines, Bayesian networks have been successfully applied in other specialties of animal and veterinary science, including nutrition ( 19 ), reproduction ( 20 ), lactation ( 21 ), genetics ( 22 ), and epidemiology ( 23 , 24 ). Bayesian networks possess several advantages over traditional linear models for analyzing highly variable responses that are difficult to interpret in isolation.…”