An approach of a data-driven Spatial Decision Support System to manage the effects of the Climate Change on agriculture

Abstract

Climate change is an important challenge of our era. Enhancing resilience to climate change, particularly to reduce its effects on agriculture is crucial to improve food security and achieve sustainable development. Persistent drought and extreme weather events affect the agricultural calendar and crops, particularly in developing countries. Crop harvesting is being affected due to higher air temperatures that reduce the daily temperature variability and increase plagues and sicknesses. Sea-level rise and frequent flooding are other factors that threaten food security and efforts to eradicate poverty and achieve sustainable development. Land degradation is one of the indirect effects of Climate Change. Within the framework of the Project “Environmental Bases for Local Alimentary Sustainability”, sponsored by EU and UNDP (2012-2017), this work is aimed to provide decision-making tools to support new Climate Change adaptation policies. Particularly, a decision support solution to analyze land degradation as one of the driving force Climate Change effects is discussed. An index of degradation is computed by a weighted sum according to Saaty prioritization mechanism. Common spatial analyses from Geographical Information Systems have been used in the study area to combine different factors (layers) and produce the layer of degradation indexes. Such a layer is used as input data to the ETL component in a Business Intelligence solution. As a typical data-driven Spatial Decision Support System, other components as data warehousing, OLAP and spatial reporting are also presented; as well as, the software tools developed to support this BI solution.