Best management practices adoption to mitigate non-point source pollution : A conditional frailty model

Purpose The purpose of this paper is to address the issues of correlated events and individual heterogeneity in multiple best management practices (BMPs) adoption. Design/methodology/approach The authors used survey data collected from broiler producers in Louisiana, USA. The authors estimated several duration models that either considered event dependence or heterogeneity or both. Findings Results from the conditional frailty model indicated that large farms adopt BMPs earlier, farmers who have been in broiler farming profession for a long time are late to adopt BMPs and more informed farmers, through contact with extension agents and education, are early adopters of BMPs. Research limitations/implications The limitation of this study is that the authors did not validate the robustness of the conditional frailty model using a more rigorous approach, such as empirical simulation method. Practical implications Many farmers do not adopt a new technology immediately after it becomes available. Duration models help to understand why farmers wait and how long they wait before adopting a new technology. In case of correlated events, where farmers adopt more than one technology, it is important to know the driving factors behind multiple technologies adoption. The findings from this study should help to properly target farmers to increase the adoption rate of a desired BMP. Originality/value This is the first study in agriculture technology adoption literature that uses a conditional frailty model to understand why farmers wait to adopt a new technology. This study also addresses both dependence in BMP adoption and heterogeneity in farmers’ quality that impact technology adoption.