Evaluation of a Binary Classification Approach to Detect Herbage Scarcity Based on Behavioral Responses of Grazing Dairy Cows

Abstract

In precision grazing, pasture allocation decisions are made continuously to ensure demand-based feed allowance and efficient grassland utilization. The aim of this study was to evaluate existing prediction models that determine feed scarcity based on changes in dairy cow behavior. During a practice-oriented experiment, two groups of 10 cows each grazed separate paddocks in half-days in six six-day grazing cycles. The allocated grazing areas provided 20% less feed than the total dry matter requirement of the animals for each entire grazing cycle. All cows were equipped with noseband sensors and pedometers to record their head, jaw, and leg activity. Eight behavioral variables were used to classify herbage sufficiency or scarcity using a generalized linear model and a random forest model. Both predictions were compared to two individual-animal and day-specific reference indicators for feed scarcity: reduced milk yields and rumen fill scores that undercut normal variation. The predictive performance of the models was low. The two behavioral variables “daily rumination chews” and “bite frequency” were confirmed as suitable predictors, the latter being particularly sensitive when new feed allocation is present in the grazing set-up within 24 h. Important aspects were identified to be considered if the modeling approach is to be followed up.