Bionote
Kaouter Essakkat is a researcher specializing in agricultural economics, with a strong focus on technology adoption, ecosystem services, and sustainable farming practices. Her work blends discrete choice experiments, mixed logit models, and agent-based modeling to analyze farmers’ decision-making. She holds a PhD in Agricultural Economics from the University of Nebraska-Lincoln and is currently engaged in postdoctoral research at the University of Illinois Urbana-Champaign. Passionate about policy-relevant science, Kaouter aims to bridge academic insights with real-world applications.
Presentation Abstract
Semiautonomous robots using artificial intelligence designed to eliminate weeds offer a promising solution to herbicide resistance. However, their adoption, once commercially available, will depend on a complex interplay of technological, biophysical, and socioeconomic factors. This study combines a discrete choice experiment with an agent-based model to assess how Midwestern U.S. farmers make adoption decisions over time. We examine key drivers including robot effectiveness, annual cost, prevalence of herbicide-resistant weeds, neighbor weed pressure, and service frequency. Findings indicate a positive willingness to pay for highly effective robots, particularly when farmers face high resistance on their crops, but a reduced incentive to adopt when neighboring fields experience substantial weed pressure. Simulations highlight strong peer effects. Insights from this study inform pricing strategies and incentive programs to induce the adoption of these technologies. Targeting early adopters and leveraging peer influence can help amplify positive externalities and accelerate adoption.
Co-Authors: L. Wu, S. Atallah, M. Khanna