Articles
Climate change multi-risk assessment for mango cultivation in Sicily, Italy, by using Bayesian Network
Article number
1415_15
Pages
135 – 144
Language
English
Abstract
Ensuring food security poses a significant challenge for organizations and consultant companies involved in the agriculture industry or responsible for food programs.
This challenge is particularly relevant in Sicily, Italy, which has a semi-tropical climate.
Given the favorable weather conditions for mango cultivation and other tropical crops, it becomes crucial to consider measures for safeguarding against potential climate change impacts in the future.
Climate change is expected to bring changes and increased risks in terms of temperature, extreme events, soil salinity, and irregular rainfall.
Amidst this looming threat, there is a growing demand for a fresh approach and supportive tools to manage risks and mitigate potential damages in policy-making and decision-making circles.
In this study, we employ a robust method known as Bayesian Network (BN) to effectively capture and model multiple risks under various future scenarios.
By exploring what-if situations, such as the maximum levels of climate-related variables, the projected BN model is trained and validated using spatially resolved data from the Messina region in Sicily.
This approach enables us to understand the dynamic variations in local-scale temperature and precipitation, as well as the underlying driving forces, within the timeframe of 2009-2022. The outputs of the Bayesian Network aid in predicting future trends in temperature and precipitation levels, thereby supporting the prioritization of mango cultivation and conservation efforts.
In general, the findings derived from the BN analysis provide valuable support for disaster risk management and mitigation strategies in the face of climate change and extreme events.
This tool can further enhance decision-making processes by integrating the spatial results of the developed model into a user-friendly interface such as Geographic Information System (GIS), thereby assisting policymakers and decision-makers in prioritizing disaster risk management and climate change adaptation plans.
This challenge is particularly relevant in Sicily, Italy, which has a semi-tropical climate.
Given the favorable weather conditions for mango cultivation and other tropical crops, it becomes crucial to consider measures for safeguarding against potential climate change impacts in the future.
Climate change is expected to bring changes and increased risks in terms of temperature, extreme events, soil salinity, and irregular rainfall.
Amidst this looming threat, there is a growing demand for a fresh approach and supportive tools to manage risks and mitigate potential damages in policy-making and decision-making circles.
In this study, we employ a robust method known as Bayesian Network (BN) to effectively capture and model multiple risks under various future scenarios.
By exploring what-if situations, such as the maximum levels of climate-related variables, the projected BN model is trained and validated using spatially resolved data from the Messina region in Sicily.
This approach enables us to understand the dynamic variations in local-scale temperature and precipitation, as well as the underlying driving forces, within the timeframe of 2009-2022. The outputs of the Bayesian Network aid in predicting future trends in temperature and precipitation levels, thereby supporting the prioritization of mango cultivation and conservation efforts.
In general, the findings derived from the BN analysis provide valuable support for disaster risk management and mitigation strategies in the face of climate change and extreme events.
This tool can further enhance decision-making processes by integrating the spatial results of the developed model into a user-friendly interface such as Geographic Information System (GIS), thereby assisting policymakers and decision-makers in prioritizing disaster risk management and climate change adaptation plans.
Publication
Authors
M. Pourmohammad Shahvar, D. Scuderi, D. Valenti, A. Collura, S. Miccichè, V. Farina, G. Marsella
Keywords
multi-risk assessment, climate change, bayesian network, mango farms, agriculture, Sicily
Groups involved
Online Articles (38)