Regression Techniques for Modelling Conception in Seasonally Calving Dairy Cows

Abstract

Reproductive performance is important for the economic efficiency of pasture-based dairy farms. In these seasonal calving systems, a concise period of breeding is essential to ensure the alignment of peak grass availability with peak lactating cow energy demands. Trials and statistical analysis have identified the factors affecting overall reproductive performance, but few studies have analysed performance at the individual service level. In this paper, four binary models of service outcome are described, incorporating age, stage of lactation, calving events, and measures of energy balance and milk production. Random effects at the cow, sire and herd level were included. Logistic regression and generalised additive models were created, both as stand-alone predictors and using ensemble learning in the form of bagging. The four models were evaluated in terms of calibration and discrimination using an external dataset of nine dairy herds representing the typical Irish pasture-based system. Logistic regression (with and without bagging) and generalised additive modelling with bagging all performed satisfactorily and would be useful as stand-alone models or in whole-farm simulation. Logistic regression is suggested as the most useful model for farmers and their advisers due to ease of interpretation. This model will be used as part of a PhD project to create simulation software for seasonally calving dairy animals.