The role of artificial intelligence in sustainable agriculture in Costa Rica: An integrated evaluation using structural equation modeling, text mining, and scenario analysis
2025
Okot, Tom | Pérez, Edward
This study examines the increasing role of artificial intelligence (AI) in Costa Rica’s agricultural sector, emphasizing its potential to enhance sustainability, resource management, and market competitiveness. Using a mixed-methods approach, the research integrates structural equation modeling (SEM), multivariate regression analysis, text mining, and scenario analysis to provide a comprehensive evaluation of AI adoption. AI-driven solutions optimize key agricultural processes, including climate pattern prediction, soil condition monitoring, crop disease detection, and pest management. Quantitative findings indicate a strong correlation between AI adoption and improved productivity, economic benefits, and environmental conservation, particularly through optimized fertilizer and pesticide use and enhanced water management. However, challenges such as high implementation costs, limited digital infrastructure, and farmer resistance remain significant barriers. Text mining analysis reveals widespread concerns over data privacy, technical complexity, and financial investment, highlighting the importance of targeted training programs. Scenario analysis further suggests that government support and technological advancements could significantly accelerate AI adoption over the next decade. The study underscores the need for strategic partnerships among government agencies, educational institutions, and technology providers to bridge the digital divide and encourage AI adoption. These findings not only inform Costa Rican agricultural policy and innovation strategies but also provide a replicable model for other emerging economies aiming to integrate AI sustainably into agricultural systems.
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