Adoption of Regenerative Farming Practices for Soil Restoration

Abstract

The uptake of regenerative agriculture to restore soil is drastically low despite purported environmental and financial benefits, due to little knowledge of adoption drivers. This study adopts a problem-oriented, multi-methods research design with binary logistic regression, Bayesian learning models, and utility threshold analysis to empirically estimate the drivers of adoption from 412 farm units in three different agro-ecological zones. The models show that the ensemble machine-learning model has the highest prediction accuracy and dominates the baseline models with no systemic factors. The most negative impacts on the probability of adoption are from risk aversion (λ) and transition cost shocks (γ), with elasticities of -47.3% and -37.8% per standard deviation increase, respectively, while subsidies and information quality (τ) are positive and sizeable. The Bayesian learning model shows a doubling of local sample size from n=1 to n=10 reduces the posterior variance by 82.5% and increases learning rates from 0.318 to 0.938, addressing the empirical evidence problem. Threshold analysis shows that adoption depends on minimum subsidies of $258 ha⁻¹ to induce adoption by farmers with λ > 2.0, which are not always offered by policies. There are substantial regional differences: the arid zone is more negatively affected by risk, while the humid tropics have stronger network effects. The machine learning process model for soil carbon accumulation has a root mean square error (RMSE) of 24.91 g C m⁻² yr⁻¹ and an index of agreement (IA) of 0.921. Economic, information, and policy deficits are most acute (0.873, 0.791, and 0.762). We propose that the adoption puzzle can be solved by offering targeted subsidies to ease the transition (local risk analysis), farmer-directed knowledge networks to reduce Bayesian uncertainty, and a comprehensive policy reform that accounts for non-market ecosystem services. Without this approach, regenerative agriculture will be an environmental ideal, but not an everyday reality.