A behavioural agent-based modelling approach for the ex-ante assessment of policies supporting precision agriculture

Abstract

Precision agriculture technologies can help reduce nitrogen losses and the associated negative environmental impacts. As the adoption rate of such technologies in small-scale farming systems is still low, additional policy measures are required to support their broader application. We provide an ex-ante assessment of policy measures (payments for reduced nitrogen, subsidy for the technology or area subsidies) to incentivize the adoption of sensing technologies for site-specific nitrogen fertilization with a specific focus on farmers' behavioural characteristics such as reluctance to change and their individual perception of the policy measures. We combine a bio-economic optimization model with data from a choice experiment, survey, and census data in an agent-based modelling framework. We simulate the impact of the policy measures on farmers' adoption decisions in Swiss wheat production. Simulations suggest that for the same level of nitrogen reduction a results-based payment (paying farmers for reduced nitrogen) is 1.5 times more cost-efficient than area-based subsidies and subsidies for technology use. Our results also suggest that considering how farmers perceive costs and benefits decreases the potential to reduce nitrogen input by ∼20%. We conclude that disregarding behavioural factors such as the perception of the instrument may result in a significant overestimation of the policy effect.