Weather-driven groundnut price forecasting and profitability assessment of cropping patterns in Tamil Nadu using boosting algorithms.

This research explores the integration of weather-based forecasting and profitability analysis to optimize groundnut-based cropping patterns in Tamil Nadu. Groundnut, a crucial oilseed crop, is significantly influenced by weather variability, which impacts its price and profitability. The study leverages advanced boosting algorithms, including Light Gradient Boost, XGBoost, HistGradientBoosting, and CatBoost, to forecast groundnut prices using a multivariate approach that incorporates weather parameters like Minimum and Maximum Temperature, Relative Humidity and Rainfall. Weather parameters were discretized using [Formula: see text]-means clustering. Crop prices were decomposed using Seasonal-Trend decomposition based on LOESS and each component was forecasted separately. HistGradientBoosting consistently outperforms other models, achieving the lowest multivariate Mean Absolute Error (MAE) across most crops and districts, underscoring its capability to handle complex, high-dimensional data effectively. The results reveal a substantial improvement in forecasting accuracy with multivariate models compared to univariate ones, establishing the importance of integrating weather features. Groundnut-related cropping patterns were analyzed for profitability using forecasted prices, with patterns involving high-value crops, such as onion in Namakkal, achieving the highest benefit-cost ratio (BCR) of 2.18. Patterns involving black gram also consistently outperformed green gram in economic efficiency. The findings emphasize the need for region-specific, weather-informed cropping strategies to maximize returns for farmers while mitigating risks.