Effect of climate and economic factors on rice production: machine learning approach

Abstract

Purpose. This study examines how climatic factors such as land surface temperature, precipitation, relative humidity, solar radiation, soil moisture, and the Index of Prices Paid by Farmers (Ib) influence rice production in Banten Province, Indonesia. The study used panel data from 2008 to 2024 to analyse these effects.

Results. Among all models tested, the Random Forest algorithm demonstrated the highest predictive performance in modelling rice production in Banten Province. It achieved the lowest Root Mean Square Error (RMSE) of 0.239 and the highest coefficient of determination (R² = 0.758), indicating that the model could explain approximately 75.8% of the variation in rice yields. In contrast, traditional linear regression produced lower accuracy (R² = 0.559; RMSE = 0.312). The Random Forest model effectively captured nonlinear interactions and multicollinearity between climatic and economic variables. Variable importance analysis revealed that land surface temperature and relative humidity were the most influential predictors, while precipitation and solar radiation had moderate effects. The Index of Prices Paid by Farmers (Ib), although statistically insignificant in the linear model, showed a nonlinear threshold effect in the Random Forest model, negatively affecting production beyond certain levels. Accumulated Local Effects (ALE) plots further confirmed these nonlinear and threshold-based relationships, visually interpreting how each predictor influenced rice output across its distribution. These findings underscore the capability of machine learning not only to enhance predictive accuracy but also to interpret variable importance in a nuanced manner.

Scientific novelty. This study offers a methodological advancement by integrating climatic and economic variables into a comprehensive predictive framework using ensemble machine learning models. While previous studies often relied solely on linear models or focused only on climatic data, this research includes the Index of Prices Paid by Farmers (Ib) to reflect input cost pressures, a factor rarely explored in similar contexts. Moreover, Accumulated Local Effects (ALE) plots enhance model interpretability by illustrating the marginal effects of predictors, overcoming common limitations of black-box machine learning models. The study’s novelty lies in its dual contribution: achieving high predictive accuracy and delivering interpretable insights relevant to agricultural planning in climate-sensitive regions.

Practical value. The findings provide critical evidence to support the formulation of climate-resilient and economically sensitive agricultural policies. The strong influence of temperature and humidity implies an urgent need to invest in adaptive technologies such as heat-tolerant rice varieties and region-specific irrigation systems. Furthermore, identifying a cost threshold in the Ib variable suggests that rice production may decline beyond certain input price levels, highlighting the importance of timely and targeted subsidies, input price control, or financial support mechanisms for smallholder farmers. The machine learning framework developed in this study can also be adopted as a decision-support tool for early warning systems, helping policymakers predict potential yield shortfalls and plan proactive interventions to maintain food security under uncertain climatic conditions.