Wheat yield prediction for multiple cultivars based on agroclimatic factors

Abstract

This study aimed to evaluate the performance of four durum wheat cultivars Odysseo, Saragola, Irid and Maestrale using two machine learning techniques: classification and regression trees and random trees. Classification tree and regression analysis showed that mean annual temperature is the dominant factor influencing yield in all cultivars. For the Saragola, Irid and Maestrale cultivars, yield increased significantly when the mean annual temperature exceeded 17.25 °C, particularly when emergence density was optimal. In contrast, the Odysseo cultivar showed sensitivity to both average annual temperature and seeds per spike, with higher yields associated with an average annual temperature above 17.25 °C and seeds per spike above 33.6. The random tree analysis confirmed the importance of average annual temperature and emergence density, highlighting their strong predictive power. The models provided greater robustness and generalizability by reducing prediction variance, making them reliable tools for yield prediction. These findings highlight cultivar-specific responses to agroclimatic conditions, with Odysseo influenced by both mean annual temperature and seeds per ear, while Saragola, Irid and Maestrale demonstrate a critical interaction between mean annual temperature and emergence density. Integrating random tree models improves prediction accuracy and provides valuable information for developing precision agriculture strategies tailored to environmental conditions.