SDM- and GIS-Based Prediction of Citrus Suitability in Southern Italy: Evaluating the Influence of Local Versus Global Climate Datasets

Giuseppe Antonio Catalano, Provvidenza Rita D’Urso () and Claudia Arcidiacono
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Giuseppe Antonio Catalano: Department of Veterinary Sciences, University of Messina, Polo Universitario dell’Annunziata Viale Giovanni Palatucci n. 23, 98168 Messina, Italy
Provvidenza Rita D’Urso: Department of Agriculture, Food and Environment, University of Catania, Via S. Sofia n. 100, 95123 Catania, Italy
Claudia Arcidiacono: Department of Agriculture, Food and Environment, University of Catania, Via S. Sofia n. 100, 95123 Catania, Italy

Land, 2025, vol. 14, issue 11, 1-27

Abstract: This study investigated the application of Species Distribution Models (SDMs), based on Boosted Regression Tree (BRT) and Random Forest (RF), to predict the distribution of citrus crops in a Mediterranean climate by comparing climate data from WorldClim with those from the Regional Territorial Information System of Sicily (S.I.T.R.). To this aim, 19 bioclimatic variables were calculated from monthly temperature and precipitation data in the period 2003–2021 by using the biovars package in R software version 2023.12.0+369. Soil properties, terrain elevation, slope, and soil water retention capacity were considered to adequately simulate pedoclimatic conditions in the Syracuse area in Sicily (Italy). The SDM algorithms performed well (AUC: 0.84–0.93; TSS: 0.51–0.69), and Random Forest was selected to compare global and local outcomes. Using data from local meteorological stations increased the model’s reliability, resulting in a difference of approximately ~800 ha in the predicted citrus distribution compared to WorldClim data. This approach also provided a more accurate representation of precipitation patterns, for instance, in the municipality of Augusta, where WorldClim underestimated the average annual rainfall by 284 mm. These findings emphasise the importance of incorporating local environmental data into SDMs to improve prediction accuracy and inform future hybrid approaches to enhance model robustness in the context of climate change. Finally, the results contribute to expanding knowledge of citrus soil and climate conditions, with potential implications for land-use planning.

Keywords: precision agriculture; local climate data; citrus distribution; Geographic Information Systems; predictors’ territorial analysis; regional climate models (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:14:y:2025:i:11:p:2223-:d:1791508

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