A socio-technical agent-based simulation model for predicting smart agriculture adoption dynamics.

Traditional technology adoption models in agriculture fail to adequately capture the complex interplay of socio-technical factors that drive farmer decision-making, resulting in limited predictive accuracy and insufficient understanding of diffusion dynamics. Existing approaches predominantly rely on static econometric frameworks or simplified diffusion models that overlook the dynamic social interactions, trust networks, and heterogeneous decision-making processes that characterize real-world agricultural technology adoption. This gap hinders effective policy design and technology deployment strategies. To address these limitations, this paper presents AdoptAgriSim, a Socio-Technical Agent-Based Simulation Model for Predicting Smart Agriculture Adoption Dynamics, which integrates economic, social, and technological dimensions into a unified framework. The model employs multi-agent reinforcement learning and socio-economic network modelling to capture how individual farmers, peer networks, and market forces interact during the diffusion of technology. AdoptAgriSim incorporates a multi-objective decision mechanism that balances rational economic reasoning with social learning shaped by trust-based network structures. Using three real-world datasets from Iowa (USA), Europe, and India, the model achieves 94.2% prediction accuracy for five-year adoption intervals, outperforming existing diffusion and econometric models. It effectively reproduces emergent adoption behaviours such as technology clustering, peer-driven influence cascades, and region-specific diffusion trajectories. Significant contributions include