Equity‐Calibrated Technology Forecasting for Sustainable Development: A Portable <scp>ADOPT</scp> Framework Integrating Bayesian‐Ising Diagnostics

ABSTRACT Climate‐smart agriculture (CSA) is widely promoted to enhance resilience and productivity among smallholder farmers, yet its diffusion remains uneven due to structural barriers and heterogeneous adoption contexts. Existing forecasting tools, such as the Adoption and Diffusion Outcome Prediction Tool (ADOPT), estimate adoption trajectories but rely largely on expert‐driven assumptions that may overlook empirical disparities in access, capability, and opportunity. This study develops an equity‐calibrated forecasting framework by integrating empirical diagnostics from a Bayesian‐Ising ensemble model into ADOPT. Using survey data from 569 smallholder households in Western Kenya, stratified by gender and agroecological zone, we forecast adoption trajectories for six CSA practices while accounting for structural inequalities in adoption pathways. The model predicts a peak adoption level of 84% after 19 years, with 50% of peak adoption reached by year 8.2. Sensitivity analysis identifies perceived economic benefit as the strongest determinant of adoption, followed by awareness, trialability, and relative advantage. Results reveal a pronounced asymmetry: a one‐step negative shift in perceived income benefit reduces peak adoption by 19.9 percentage points, more than double the 8.9‐point increase generated by an equivalent positive shift, consistent with Prospect Theory's prediction of loss aversion. Compared with an expert‐only scenario, the equity‐calibrated model lowers the projected adoption ceiling by 15 percentage points, highlighting the consequences of ignoring structural barriers. By linking empirical diagnostics with technology forecasting, the study provides a replicable framework for equity‐sensitive innovation governance and inclusive scaling strategies.