"From Hydrocarbons to Harvests: A Machine Learning-Based Zonation Model for Climate-Smart Agriculture."

With rising climate volatility threatening agricultural sustainability, Climate-Smart Agriculture (CSA) has emerged as a critical strategy to ensure food security. While CSA practices are proven effective, their large-scale implementation is often hindered by fragmented data and limited decision-support tools. This study introduces a novel machine learning-driven framework, originally developed for subsurface reservoir characterization in the petroleum sector, and adapts it for enhancing CSA adoption, productivity, and resource-use efficiency.

Using geospatial, soil, and weather data from diverse agro-climatic zones, we implemented zonation-based predictive modeling to classify land into CSA suitability regions. A suite of ML algorithms—Random Forest, Gradient Boosting, and spatial interpolation models—was applied to estimate moisture stress, optimize irrigation zones, and predict crop-specific productivity under varying climate scenarios. The model was trained on multi-source data and validated using ground truths from 320 smallholder farms. Additionally, stochastic frontier analysis (SFA) was used to assess the efficiency impacts of CSA interventions.

The ML-adapted framework enabled high-resolution mapping of optimal CSA practices, achieving over 85% accuracy in zone classification and a 20% gain in predictive precision compared to baseline methods. Farms implementing model-guided CSA strategies showed a 23% improvement in productivity and a 17% increase in technical efficiency. The system also proved valuable in identifying vulnerable zones to climate shocks, aiding in targeted intervention planning.

This work demonstrates the cross-domain applicability of AI/ML models from petroleum engineering to climate-smart agriculture. By translating subsurface zonation logic to surface-level agroecological analysis, we offer a scalable, data-driven solution for accelerating CSA adoption. Future directions include integrating real-time satellite feeds and farmer feedback loops to evolve the framework into a dynamic advisory tool for climate-resilient farming.