Home > congress, GIS, holistic analysis, object-oriented programming, physiologically based models, Python, Remote sensing > Earth observation: bridging the gap to crop-pest systems

Earth observation: bridging the gap to crop-pest systems

November 16, 2016 Leave a comment Go to comments

The workshop “When Space Meets Agriculture” aimed at promoting a better understanding of the significance and potential of Europe’s space systems (EGNOS/Galileo and Copernicus) for the agricultural sector. While introducing Rural Development Programmes of selected regions and exploring opportunities to set synergies for the development of space applications for the agriculture sector, it will present the main strands of the European Agriculture Policy and more generally link the space community to the agriculture community. Our contribution identified recent and prospective holistic analyses of climate change effects on crop-pest systems in the Mediterranean Basin. The approach used in the analyses involves using physiologically based demographic modeling (PBDM) of crop-pest-natural enemy interactions in the context of a geographic information system (GIS). A major goal is to link the PBDM/GIS technology with increasingly available biophysical datasets from global modeling and satellite observations, and use them to bridge the gap between bottom-up (primarily physiological and population dynamics) and top-down (climatological) GIS approaches for assessing on ground ecosystem level problems, such as agricultural pests.

Ponti L., Gutierrez A.P., Iannetta M., 2016. Climate change and crop-pest dynamics in the Mediterranean Basin. When Space Meets Agriculture: Fostering Interregional collaborations, investments and definition of user requirements. Workshop organized by NEREUS, the Network of European Regions Using Space Technologies, Matera, Italy, 14‐15 November 2016. | Presentation freely available online

Conceptual diagram representing how physiologically-based demographic models bridge the gap between bottom-up (primarily physiological and population dynamics) and top-down (climatological, remote sensing, and ecological niche modeling) GIS approaches for assessing on-ground ecosystem-level problems such as agricultural pests (see Rocchini et al. 2015).

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