A Bayesian Belief Network to Infer Incentive Mechanisms to Reduce Antibiotic Use in Livestock Production

Abstract

Efficient policy intervention to reduce antibiotic use in livestock production requires knowledge about potential causal factors of antibiotic use. Animal health status and management quality were considered the two most important factors that influence farmers’ decision-making concerning antibiotic use. The objective of this paper was to develop a Bayesian belief network (BBN) to analyze how these factors can directly and indirectly influence antibiotic use. Since both factors are not directly observable (i.e., latent), they were inferred from related observable variables (i.e., manifest variables). Using farm accounting data and registration data on antibiotic use and veterinary services in specialized finisher pig farms over the period 2007-2010, a confirmatory factor analysis was carried out to construct the two latent factors. Antibiotic use is quantified as the number of days per year in which an average pig is treated with antibiotics according to their standard daily dosages (NDD). Descriptive analysis on the data revealed that for the finisher pig farms, NDD averaged about 17 days, with substantial year-to-year variations and between-farm variations within the same year.The conditional probabilities for the BBN model were obtained through regression analysis between the constructed factors, NDD, and a number of technical and economic variables. The BBN model showed that antibiotic use was simultaneously influenced by the two latent factors, but in varying degrees depending on other variables. Therefore interventions targeting only to improve one factor are likely to lead to unsatisfactory outcomes of antibiotic use.