Predicting footpad lesion scores of Dutch broiler flocks using routinely collected data

• Dynamic linear models and random forests are applied to footpad lesion data • Footpad lesions scores are predicted with 74.4% sensitivity and 60.2% specificity • Previous footpad lesion scores are an important predictor for the next flock score • Meteorological data improves the prediction of high footpad lesion scores Footpad lesions (FPL) are a prevalent welfare concern in broilers, influenced by various factors such as farm management practices and season. In the Netherlands, FPL scores are monitored at slaughter and linked to corrective measures. Early prediction of FPL scores could enable timely interventions. This study investigated the potential of routinely collected data to predict FPL scores at slaughter. Data from 592 broiler houses, each with at least 30 consecutive flocks, across 190 farms were included. The ability of various models to predict FPL score above or below the threshold of 80 was compared. These models included univariate dynamic linear models (DLMs); multivariate DLMs using weather data of the first week of the production cycle; and random forest models using previous flock scores or DLM output, first-week weather variables, and current and previous flock and farm characteristics. Incorporation of DLM output in the random forest model provided the numerically highest performance, although this was not significantly better than the random forest model with raw previous flock scores. This model achieved an ROC AUC of 0.70, with the best threshold yielding a sensitivity of 74.4% and specificity of 60.2%. Previous flock FPL was the most important predictor, followed by the fraction of birds thinned, flock size difference between previous and current flock, and outside humidity. These findings highlight the value of weather variables in predicting FPL scores. Future research should explore additional factors which could explain within-house variation, such as indoor climate and feed changes, to improve predictive accuracy.