Factors influencing the adoption of AI-based technology in agriculture
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The global agricultural sector faces increasing pressure to adopt sustainable practices, making the integration of digital technologies and artificial intelligence (AI) critical for achieving food security and addressing the impacts of climate change. This thesis investigates the factors influencing the adoption of AI-based agricultural technologies, with a specific focus on an AI-based pest detection app. It employs both qualitative and quantitative approaches to examine preferences and willingness to pay (WTP) for various attributes of the technology among U.S. Midwestern farmers. Through a translational research process involving researchers, extension specialists, farmers, and commodity groups, the study identifies key attributes impacting technology adoption. Results from focus groups reveal that ease of use, data ownership, and increase in net returns per acre are the top attributes influencing technology adoption. This qualitative exploration informs the design of a Discrete Choice Experiment (DCE) aimed at eliciting farmers' valuation for various attributes of the pest detection app, including data governance, degree of autonomy, technical support, and yearly subscription costs. Results from the choice experiment, conducted among 104 Midwestern farmers, reveal a certain degree of heterogeneity in farmers' valuations, particularly for non-price attributes. While price significantly impacts farmers' likelihood of adopting the pest detection app, none of the non-price attributes seem to have an impact on the probability of adopting the technology. The WTP estimates indicate significant variation in preferences for non-price attributes such as data governance, degree of autonomy, and technical support, suggesting diverse valuations among farmers. The findings from this thesis underscore the importance of continuous researcher-stakeholder engagement and the need for tailored interventions to address the diverse preferences and challenges farmers face. By examining factors that influence adoption decisions, the study provides valuable insights for policymakers and stakeholders to promote broader adoption of AI-based technologies in agriculture, enhancing productivity and sustainability in the sector.
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M.S.