Abstract
The paper retraces the GRASPgfs endeavor (Geospatial Resource for Agricultural Species and Pests with integrated workflow modelling to support Global Food Security) between multiple disciplines around a common objective of facilitating research and model simulations for sustainable food security. Within this endeavor, the geospatial media has been the enabler for multidisciplinary research in crop modelling. Geospatial genetic-trait variations and associations with environmental forecasting were the main focus of the GRASPgfs. Designing the platform achieving this objective generated a transdisciplinary vision of modelling and forecasting for sustainable agriculture. Based on interoperability principles, seamless access as well as sharing for data, metadata and processing models, the design is described in this paper. This geospatial binding facilitates and supports new types of hypotheses and analysis as illustrated in the paper with a landscape genetic case study (bambara groundnut) and a crop disease modelling (eyespot disease). The approach and the eGRASP platform are generic enough to accommodate further complexity into the integrated modelling that this geospatial binding enables.
Highlights
A Food and Agriculture Organisation (FAO) report of the Commission on Genetic Resources for Food and Agriculture (CGRFA 13/11) clearly identified “spatial analysis to identify varieties likely to have climate-adapted traits as an aid to plant breeding” as one of the eight priorities in multidisciplinary research
Earth Observation data (EO) has proven the capacity to provide measurements of key environmental conditions to predict the production of the healthy crops and potential disease threats
Such agricultural modelling and simulations need access to elaborated geolocated genetic-trait information as well as complementary data sources coming from geospatial data providers and geospatial data hubs, e.g. soil moisture data, climate data
Summary
A FAO report of the Commission on Genetic Resources for Food and Agriculture (CGRFA 13/11) clearly identified “spatial analysis to identify varieties likely to have climate-adapted traits as an aid to plant breeding” as one of the eight priorities in multidisciplinary research.
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