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

Navarro-Ferrer, David Gerardo (Enviromental Agency)
Search for David Gerardo Navarro-Ferrer in Research Programmes Information System
Delgado, Tatiana (Industrial Engineering Faculty)
Search for Tatiana Delgado in Research Programmes Information System
Martin, Gustavo (Tropical Geography Institute)
Search for Gustavo Martin in Research Programmes Information System
Jaimez, Efrén (Geophysical and Astronomy Institute)
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Chapter Climate Change and Scarce Resources
Volume Proceedings of the 28th Conference on Environmental Informatics - Informatics for Environmental Protection, Sustainable Development and Risk Management
Conference EnviroInfo 2014 - ICT for Energy Effieciency
Oldenburg, 2014
Year 2014
Abstract of the Article
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.
Pages 197 - 204
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