A Dual-Ensemble Machine Learning Framework for Coconut Yield Projection Under CMIP6 Climate Scenarios in the Andaman and Nicobar Islands

Climate change directly affects agricultural productivity, particularly in small island systems where ecosystems and livelihoods are highly exposed to climate variability. This study presents a comprehensive analysis of climate variability for the three districts North and Middle Andaman, South Andaman, and Nicobar, using a six-model CMIP6 ensemble under four SSP scenarios (SSP126, SSP245, SSP370, and SSP585), coupled with ensemble tree-based machine learning algorithms to project coconut yield responses. The historical data was analysed from 1981 to 2025 and the projection was from 2026 to 2100. Observed rainfall reveals a persistent north-to-south gradient, with South Andaman recording the highest mean annual rainfall (3408.40 mm) and Nicobar recording the lowest (2442.13 mm), alongside pronounced inter-annual variability and a discernible drying tendency post-2015. Nicobar consistently records the warmest mean Tmax (30.89 °C) and Tmin (24.11 °C), while North and Middle Andaman exhibit the greatest inter-annual temperature variability. Future projections indicate a robust and statistically significant warming across all districts and scenarios, with end-of-century Tmax increases reaching up to 4.05 °C (Nicobar, SSP585) and Tmin increases up to 3.73 °C (North and Middle Andaman, SSP585), accompanied by a progressive compression of the diurnal temperature range. Precipitation projections show modest wetting in the Andaman districts under most scenarios, while Nicobar exhibits a muted response, with SSP370 uniquely projecting a decline of approximately 69 mm below the observed baseline. Among the ten evaluated CMIP6 models, six (ACCESS-CM2, CMCC-ESM2, CNRM-ESM2-1, EC-Earth3-Veg-LR, GFDL-ESM4, and NorESM2-MM) were selected based on composite skill scores across rainfall, Tmax, and Tmin. Model selection was optimized independently for each district via Leave-One-Year-Out cross-validation with hyperparameter tuning, yielding district-specific best performers: GradientBoost for North and Middle Andaman (R2 = 0.471), RandomForest for South Andaman (R2 = 0.609), and ExtraTrees for Nicobar (R2 = 0.289). K-Nearest Neighbours demonstrated competitive predictive skill in all three districts, confirming that instance-based learning can capture non-linear climate–yield relationships, though tree-based ensembles were preferred for their robustness and interpretability. Ensemble tree-based ML models and instance-based learning consistently outperformed all linear and kernel-based approaches, confirming the non-linear nature of climate–yield relationships in this setting. Coconut yield projections indicate above-baseline productivity gains of 3.4–21.5% in North and Middle Andaman and 24.6–36.8% in South Andaman, driven by favourable warming and precipitation trends, while Nicobar yields plateau at 7.7–13.7% above baseline, indicating thermal saturation of the climate yield response under already near-optimal thermal conditions. Notably, Nicobar exhibits a reversed yield–emission relationship wherein lower-emission pathways marginally outperform high-emission scenarios, likely reflecting avoidance of thermal stress thresholds. Inter-CMIP6-model uncertainty emerges as the dominant source of projection spread, exceeding scenario uncertainty across most districts, underscoring the critical importance of multi-model ensemble frameworks for robust agricultural climate impact assessments in data-sparse tropical island environments.