An Integrated Hybrid-Stochastic Framework for Agro-Meteorological Prediction Under Environmental Uncertainty

Mohsen Pourmohammad Shahvar (), Davide Valenti, Alfonso Collura, Salvatore Micciche, Vittorio Farina and Giovanni Marsella
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Mohsen Pourmohammad Shahvar: Dipartimento di Fisica e Chimica “E. Segrè”, Università degli Studi di Palermo, 90128 Palermo, Italy
Davide Valenti: Dipartimento di Fisica e Chimica “E. Segrè”, Università degli Studi di Palermo, 90128 Palermo, Italy
Alfonso Collura: Istituto Nazionale di Astrofisica, Osservatorio Astronomico di Palermo, 90123 Palermo, Italy
Salvatore Micciche: Dipartimento di Fisica e Chimica “E. Segrè”, Università degli Studi di Palermo, 90128 Palermo, Italy
Vittorio Farina: Dipartimento di Scienze Agrarie, Alimentari e Forestali, Università degli Studi di Palermo, 90128 Palermo, Italy
Giovanni Marsella: Dipartimento di Fisica e Chimica “E. Segrè”, Università degli Studi di Palermo, 90128 Palermo, Italy

Stats, 2025, vol. 8, issue 2, 1-25

Abstract: This study presents a comprehensive framework for agro-meteorological prediction, combining stochastic modeling, machine learning techniques, and environmental feature engineering to address challenges in yield prediction and wind behavior modeling. Focused on mango cultivation in the Mediterranean region, the workflow integrates diverse datasets, including satellite-derived variables such as NDVI, soil moisture, and land surface temperature (LST), along with meteorological features like wind speed and direction. Stochastic modeling was employed to capture environmental variability, while a proxy yield was defined using key environmental factors in the absence of direct field yield measurements. Machine learning models, including random forest and multi-layer perceptron (MLP), were hybridized to improve the prediction accuracy for both proxy yield and wind components (U and V that represent the east–west and north–south wind movement). The hybrid model achieved mean squared error (MSE) values of 0.333 for U and 0.181 for V, with corresponding R 2 values of 0.8939 and 0.9339, respectively, outperforming the individual models and demonstrating reliable generalization in the 2022 test set. Additionally, although NDVI is traditionally important in crop monitoring, its low temporal variability across the observation period resulted in minimal contribution to the final prediction, as confirmed by feature importance analysis. Furthermore, the analysis revealed the significant influence of environmental factors such as LST, precipitable water, and soil moisture on yield dynamics, while wind visualization over digital elevation models (DEMs) highlighted the impact of terrain features on the wind patterns. The results demonstrate the effectiveness of combining stochastic and machine learning approaches in agricultural modeling, offering valuable insights for crop management and climate adaptation strategies.

Keywords: stochastic modeling; hybrid machine learning; proxy yield estimation; wind behavior analysis (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2025
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