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Within the context of Agriculture 4.0, the importance of predicting species distribution is increasing due to climatic change. The use of predictive species distribution models represents an essential tool for land planning and resource conservation. However, studies in the literature on Suitability Distribution Models (SDMs) under specific conditions are required to optimize the model accuracy in a specific context through map inspection and sensitivity analyses. The aim of this study was to optimize the simulation of the citrus distribution probability in a Mediterranean area based on presence data and a random background sample, in relation to several predictors. It was hypothesized that different parameter settings affected the SDM. The objectives were to compare different parameter settings and assess the effect of the number of input points related to species presence. Simulation of citrus occurrence was based on five algorithms: Boosted Regression Tree (BRT), Generalized Linear Model (GLM), Multivariate Adaptive Regression Splines (MARS), Maximum Entropy (MaxEnt), and Random Forest (RF). The predictors were categorized based on 19 bioclimatic variables, terrain elevation (represented by a Digital Terrain Model), soil physical properties, and irrigation. Sensitivity analysis was carried out by (a) modifying the values of the main models’ parameters; and (b) reducing the input presence points. Fine-tuning the parameters for each model according to the literature in the field produced variations in the selection of predictors. Consequently, probability changed in the maps and values of the accuracy measures modified. Results obtained by using refined parameters showed a reduced overfitting for BRT, yet associated with a decrease in the AUC value from 0.91 to 0.81; minor variations in AUC for GLM (equal to about 0.85) and MARS (about 0.83); a slight AUC reduction for MaxEnt (from 0.86 to 0.85); a slight AUC increase for RF (from 0.88 to 0.89). The reduction in presence points produced a decrease in the surface area for citrus probability of presence in all the models. Therefore, for the case study analyzed, it is suggested to keep input presence points above 250. In these simulations, we also analyzed which covariates and related ranges contributed most to the predicted value of citrus presence, for this case study, for different amounts of input presence points. In RF simulations, for 250 points, isothermality was one of the major predictors of citrus probability of presence (up to 0.8), while at increasing of the input points the contribution of the covariates was more uniform (0.4–0.6) in their range of variation. Full article

South Italy is characterised by a semi-arid climate with scarce rain and high evaporative demand. Since climate change could worsen this condition, the need to optimise water resources in this area is crucial. In citrus cultivation, which involves one of the most important crops bred in Southern Italy, and more generally in Mediterranean regions, deficit irrigation strategies are implemented in order to cope with limited resource availability. On this basis, knowledge on how the territorial distribution of citrus would change in relation to these strategies represents valuable information for stakeholders. Therefore, the objective of this study was to determine the probability of the presence of citrus in Sicily based on changes in the percentage of water deficit in order to identify and analyse change in the surface area as well as the location of the crop. The methodology was based on the application of species distribution models (SDM) and Geographic Information Systems (GIS) to the case study of the province of Syracuse in Sicily. Different geostatistical and machine learning models were applied based on bioclimatic variables measured over three decades, a Digital Terrain Model and irrigation. Assessment of the outcomes was carried out using classification evaluation metrics. The analysis of the outcomes showed that uncorrelated predictor layers mainly included water input that most affected the probability of the presence of citrus fruits. Moreover, GIS analyses showed that deficit irrigation strategies would generate an overall reduction of cultivation surfaces in the territory (e.g., for the Random Forest model the surface reduction was equal to 41.15%) and a decrease of citrus presence in southern areas of the considered territory. In this area, climate conditions are less favourable in terms of temperature and precipitation; thus, these analyses provide useful information for decision support tools in agriculture and land use policy. Full article