Assessing plant water status accurately in both time and space is crucial for maintaining satisfactory crop yield and quality standards, especially in the face of a changing climate. Remote sensing technology offers a promising alternative to traditional in situ measurements for estimating stem water potential (Ψ_stem). In this study, we carried out field measurements of Ψ_stem in an irrigated olive orchard in southern Italy during the 2021 and 2022 seasons. Water status data were acquired at midday from 24 olive trees between June and October in both years. Reflectance data collected at the time of Ψ_stem measurements were utilized to calculate vegetation indices (VIs). Employing machine learning techniques, various prediction models were developed by considering VIs and spectral bands as predictors. Before the analyses, both datasets were randomly split into training and testing datasets. Our findings reveal that the random forest model outperformed other models, providing a more accurate prediction of olive water status (R² = 0.78). This is the first study in the literature integrating remote sensing and machine learning techniques for the prediction of olive water status in order to improve olive orchard irrigation management, offering a practical solution for estimating Ψ_stem avoiding time-consuming and resource-intensive fieldwork.

A field experiment was conducted in Southern Italy to study the response of durum wheat (Triticum turgidum L. var. durum) grain yield and quality traits to a no-tillage (NT) system and different nitrogen N fertilizer rates (30, 60, and 90 kg N ha⁻¹). The NT system was evaluated and compared to conventional (CT) and reduced (RT) tillage within continuous wheat (WW) and faba bean–wheat (FW) crop sequences over 3-years (2010–2012). The results showed a promising grain yield increase (30%) in the last year. The effect of the N rate on protein content was significant, while productive parameters were not significantly influenced due to both weather conditions and the previous crop. Tillage effect was significantly (p ≤ 0.05) positive on grain yield, yield components and quality parameters, especially in NT system, and was more pronounced when accompanied with faba bean in the rotation system. Despite producing a lower grain protein content (13%) compared to other systems, NT produced good semolina quality (with higher hectoliter weight and lower percentage of broken and shriveled grains). This study provides useful information for farmers on how to produce a satisfactory yield and good grain quality with minimum inputs, helping to design sustainable strategies for durum wheat cultivation in the dry regions.