Pasquale Campi

Sustainable irrigation in water-limited regions requires timely, field-scale estimates of soil water content (SWC). Yet, field-scale SWC studies leveraging near-daily satellite imagery of regenerative systems—particularly cotton under Mediterranean conditions—are lacking, and explainable integrations of Planet SuperDove with agrometeorological inputs remain underexplored. In this study, we evaluated a machine learning framework that integrates near-daily multispectral features from Planet SuperDove with agrometeorological variables to estimate the daily SWC of regenerative cotton under Mediterranean conditions across two seasons (2023–2024). Six regression models were compared; extreme gradient boosting achieved the highest accuracy (R² = 0.73 ± 0.08; RMSE = 4.60 mm ± 0.81; nRMSE = 0.035 ± 0.01), with limited bias and stable performance across the years and moisture conditions. The model interpretability via SHAP indicated that agrometeorological drivers contributed over half of the predictive power, while the NDVI and NIR provided the most informative satellite inputs, followed by the NDRE and PSRI. The results show that combining high-frequency satellite data with meteorological inputs can deliver accurate and interpretable SWC estimates at the homogeneous plot level, supporting irrigation optimization of regenerative systems. This approach is practical, transferable, and suited for operational decision-making where frequent, high-resolution observations are available.

The Po valley (northern Italy) is the leading European region for processing tomato (Solanum lycopersicum L.) production. Although historically characterized by abundant water availability, this area is now increasingly affected by drought risk. This study presents a two-year evaluation of regulated deficit irrigation (RDI) on processing tomatoes in northern Italy. In 2019 (Parma) and 2022 (Piacenza), full irrigation (IRR, restoring 100% crop evapotranspiration) and RDI (100% IRR until the color-breaking stage, followed by 50% IRR) strategies were compared within a completely randomized block design. Overall, RDI resulted in a 25% reduction in water use without compromising yield, which was maintained through unchanged plant fertility and fruit size compared to IRR. Remote sensing data from PlanetScope imagery confirmed the absence of water stress in RDI-treated plants. Furthermore, increased soluble solids and dry matter contents under RDI suggest a physiological adaptation of processing tomatoes to late-season water deficit. Remarkably, environmental and economic sustainability indicators—namely water productivity and yield quality—were enhanced under RDI management. This study validates a simple, sustainable, and readily applicable irrigation approach for tomato cultivation in the Po valley. Future research should refine this method by investigating plant physiological responses to optimize water use in this key agricultural region.

The growing demand for renewable energy sources and the need to optimize water use in agriculture, particularly in water-scarce regions, highlights the importance of growing species suitable for semi-arid areas, such as sorghum (Sorghum bicolor L. Moench). Deficit irrigation strategies allow water savings by optimizing water use efficiency. However, the potential of sorghum for bioethanol production with deficit irrigation strategies is still not well studied. This work investigates the impact of three irrigation strategies (full, deficit, and regulated deficit) on the biomass yield, ethanol production, and water use efficiency of sorghum (‘KWS Bulldozer’) in a semi-arid Mediterranean area (the Apulia region, Southeastern Italy) over three growing seasons (2013, 2014, and 2017); irrigation needs were calculated from crop evapotranspiration using standard crop coefficients and soil water content measurements. Harvested biomass was analyzed for cellulose and hemicellulose content, and ethanol production was estimated using conversion models. The full irrigation treatment resulted in the highest biomass and ethanol production in all seasons (22,633 kg × ha⁻¹, 28,367 kg × ha⁻¹, and 23,835 kg × ha⁻¹, in 2013, 2014, and 2017, respectively), highlighting the relationship between a full water supply and yield optimization. However, deficit irrigation showed a higher biomass and ethanol water productivity (10.93 kg × m⁻³ and 3.23 L × m⁻³, respectively) than other treatments, suggesting that moderate irrigation strategies can effectively balance production and sustainable water use. The results suggest the importance of adjusting irrigation practices to specific environmental conditions to improve the efficiency and productivity of sorghum.

Sustainable fertilization techniques are essential in Mediterranean farming systems, where the depletion of organic matter, influencing soil water and nutrient availability, is becoming an increasing concern. In this context, organic fertilizers offer an effective strategy to restore soil fertility while reducing environmental impacts. This research aimed to evaluate the effects of different organic fertilizers on soil quality and tree performance in a sweet cherry (Prunus avium L.) orchard. This study was conducted in two growing seasons (2021–2022) in an organic orchard in Southern Italy, comparing four treatments: (i) compost, (ii) compost combined with compost tea, (iii) mixed manure, and (iv) an unfertilized control. The results indicated that compost tea, applied both to the soil and as a foliar spray, significantly improved tree water status, particularly under water stress conditions, as reflected by more negative stem water potential values. Moreover, this treatment enhanced photosynthetic performance, yield, and fruit quality, achieving the highest ratio of soluble solids content/total acidity. The findings suggest that compost tea, in combination with compost, could be a sustainable and valuable fertilization option for Mediterranean organic tree orchards. However, further studies are necessary to understand the benefits of other fruit orchards as well as the long-term effects on soils.

Climate change and water scarcity bring significant challenges to agricultural systems in the Mediterranean region. Novel methods are required to rapidly monitor the water stress of the crop to avoid qualitative losses of agricultural products. This study aimed to predict the stem water potential of cotton (Gossypium hirsutum L., 1763) using Sentinel-2 satellite imagery and machine learning techniques to enhance monitoring and management of cotton’s water status. The research was conducted in Rutigliano, Southern Italy, during the 2023 cotton growing season. Different machine learning algorithms, including random forest, support vector regression, and extreme gradient boosting, were evaluated using Sentinel-2 spectral bands as predictors. The models’ performance was assessed using R2 and root mean square error (RMSE). Feature importance was analyzed using permutation importance and SHAP methods. The random forest model using Sentinel-2 bands’ reflectance as predictors showed the highest performance, with an R2 of 0.75 (±0.07) and an RMSE of 0.11 (±0.02). XGBoost (R2: 0.73 ± 0.09, RMSE: 0.12 ± 0.02) and AdaBoost (R2: 0.67 ± 0.08, RMSE: 0.13 ± 0.02) followed in performance. Visible (blue and red) and red edge bands were identified as the most influential predictors. The trained RF model was used to model the seasonal trend of cotton’s stem water potential, detecting periods of acute and moderate water stress. This approach demonstrates the prospective for high-frequency, non-invasive monitoring of cotton’s water status, which could support smart irrigation strategies and improve water use efficiency in Mediterranean cotton production.

Climate change is making water management increasingly difficult due to rising temperatures and unpredictable rainfall patterns, impacting crop water availability and irrigation needs. This study investigated the ability of machine learning and satellite remote sensing to monitor water status and physiology. The research focused on predicting different eco-physiological parameters in an irrigated peach orchard under Mediterranean conditions, utilizing multispectral reflectance data and machine learning algorithms (extreme gradient boosting, random forest, support vector regressor); ground data were acquired from 2021 to 2023 in the south of Italy. The random forest model outperformed in predicting net assimilation (R² = 0.61), while the support vector machine performed best in predicting electron transport rate (R² = 0.57), Fv/Fm ratio (R² = 0.66) and stomatal conductance (R² = 0.56). Random forest also proved to be the most effective in predicting stem water potential (R² = 0.62). These findings highlighted the potential of integrating machine learning techniques with high-resolution satellite imagery to assist farmers in monitoring crop health and optimizing irrigation practices, thereby addressing the challenges determined by climate change.

Improving water use efficiency is gaining relevance for the sustainability of agricultural practices. In semi-arid Mediterranean areas, recent studies highlighted that future climatic scenarios will be even more critical for crops, given the increase in water scarcity. In this context, the rationalization of irrigation water is necessary to sustain processing tomato (Solanum lycopersicum L.) yield and quality since this crop requires large volumes of water. The present research aimed to identify the effects of a regulated deficit irrigation (RDI) strategy on the environmental and economic sustainability and fruit technological and functional quality of the processing tomato crop in the Mediterranean area. A two-year, open-field experiment was carried out to compare full irrigation management (IRR, restoring 100% ET_c) with an RDI strategy based on restoring 50% ET_c when the first fruit cluster reached the typical size (BBCH 701 phenological stage, relative to Solanaceous fruits). Remarkable water saving (21.46%, average of the two years) was achieved under RDI without significant variations in total and marketable yield compared to the IRR regime. Consequently, improved economic water productivity (+23.17%) was observed, allowing enhanced processing tomato sustainability. The RDI strategy boosted the glucose content (+17.78%), soluble solids content (SSC, +10.17%), and dry matter of the fruits (+10.03 g%). Furthermore, a higher SSC-to-titratable acidity ratio (+15.47%) and a negative shift in fructose/glucose balance (−7.71%) were observed in RDI-treated plants. Higher levels of the drought stress markers proline (+38.99%) and total polyphenols (+20.58%) were detected in RDI- compared to IRR-irrigated tomato fruits. These findings suggested the RDI strategy as an effective and sustainable approach for increasing both water productivity and the fruit quality of the processing tomato crop under semi-arid Mediterranean climatic conditions.

Background: Sustainable water management for table grape has the primary goal of optimizing irrigation through Smart Irrigation (SI) approaches, particularly in Mediterranean regions. In addition, extending the shelf life of table grapes through effective cold storage practices is crucial to meet consumer demands year-round. This research examined the journey “from farm to fork” of Sugrathirtyfive variety (Autumn Crisp^® brand), exploring the combined effects of Irrigation Volumes (IV), SO₂-Generating Pads (SGPs) and Cold Storage Duration (CSD) on the quality of grapes. Methods: Normal Irrigation (NI—based on the farmer’s experience) and SI (100% vine evapotranspiration restored) were supplied in 2023 to Sugrathirtyfive variety white table grape, trained to an overhead tendone system. Yield and quality parameters, berry texture, CIELAB colour coordinates, phenolic content, flavonoids, antioxidant activity and sensory attributes were evaluated on grapes subjected to different times and methods of cold storage. Results: SI grapes showed higher Total Soluble Solids (TSSs) and nutraceutical content, as well as improved CIELAB coordinates with interesting improved berry texture parameters. No differences emerged between single- or dual-release SGPs after 15 days (T1) and 40 days (T2) of CSD. Conclusions: Under our cold storage conditions (3 °C, 85% U.R.), 40 days represent the maximum temporal limit for the cold storage of Sugrathirtyfive variety, regardless of IV, provided they are refrigerated with the aid of SGPs.

Reduction of water availability imposes the agronomic issues of increasing the storage capacity of the soil and improving the use of rainwater or irrigation water. A field experiment in 2021 was conducted in a 5-year-old peach orchard in a Mediterranean environment to study the effect of mixed composed amendments (ACM), applied in different amounts, on the dynamics of soil water status. Water balance was monitored during the peach vegetative reproductive cycle on a daily scale. Three treatments of mixed composed amendments (ACM) were compared: A0, control; A1, with amendment (10 t ha⁻¹); and A2, with half dose of amendment (5 t ha⁻¹). On a seasonal scale, soil water content increased by 27% and 33% in A1 and A2 compared to A0, while relative extractable water varied between 0.41 (A0) and 0.65 (A1 and A2). Both soil water balance indicators show that storage capacity increases with the addition of amendment. Improved soil storage capacity was associated with higher values of stem water potential (throughout the growing season) and stomatal conductance (at the end of the season). Shoot and fruit growth observations were consistent with soil water content dynamics.

Semi-arid conditions are favorable for the cultivation of late ripening peach cultivars; however, seasonal water scarcity and reduction in soil biological fertility, heightened by improper soil management, are jeopardizing this important sector. In the present two-year study, four soil managements were compared on a late ripening peach orchard: (i) completely tilled (control); (ii) mulched with reusable reflective plastic film; (iii) mulching with a Leguminosae cover-crop flattened after peach fruit set; (iv) completely tilled, supplying the water volumes of the plastic mulched treatment, supposed to be lower than the control. Comparison was performed for soil features, water use, tree functionality, fruit growth, fruit quality, yield and water productivity. Even receiving about 50% of the regular irrigation, reusable reflective mulching reduced water loss and soil carbon over mineralization, not affecting (sometimes increasing) net carbon assimilation, yield, and fruit size and increasing water productivity. The flattening technique should be refined in the last part of the season as in hot and dry areas with clay soils and low organic matter, soil cracking increased water evaporation predisposing the orchard at water stress. The development and implementation of appropriate soil management strategies could be pivotal for making peach production economically and environmentally sustainable.