description
- Access to a wide range of information, from rigorous scientific results to 'hear-say' farmer's knowledge is becoming critical to be able to target efforts in achieving food security planning at community or country levels. Also, designing scientific and intervention strategies within changing climates and markets is a fundamental challenge. Developments of technologies for data collection in mobile communications, sensor platforms, spatial search and pervasive computing are fundamentally changing research in agriculture. However, inter-disciplinary research needed to transform raw data into useful intelligence and knowledge to improve the planet's environmental, economic and societal well being is still constrained by disciplinary and organisational silos and legacy concepts and an non-existent or non-rigorous approach to quantifying the uncertainty intrinsic in any collected dataset. GRASP-GFS will use a geospatially-anchored 'genotype' database integration principle to query such multidimensional data information, including papers, reports, indigenous, socio-economic and farmer's knowledge. This framework will enable uncertainty assessments through the use of quality weighting descriptors of the different components within a chosen geo-workflow model for food security. Cross-disciplinary expertise driven from geospatial sciences methodologies will be used to develop this integrating framework across all subjects relevant to Food Security. The driving focus will be the agricultural species germplasm for genotype characteristics with the data ordered by geospatial origin with the higher level descriptor being the 'agricultural trait'. A particular novel aspect is the combined use of climate records or scenarios and land ground condition data with known (and new) sources of traits in crop, animal and microbial species of agricultural importance. This will permit new perspectives on genetic diversity, identifying new sources of germplasm and sources of trait variation, geolocating suitable germplasm by a combination of agro-ecological modelling and matching principles, planning breeding objectives with the greatest likely impact by taking into acccount the added information of local market and farmer knowledge. These modelling capabilities will come from framing each above model within a generic approach allowing workflow composing based on semantic description of data and processes and workflow quality assessment for uncertainty/error propagation. Two use cases modelling with wheat crop in the UK and bambara groundnut in Malaysia will demonstrate the approach with crop specific data and processing models to forecast geospatial trait variation for these two crops. Supporting the Crops for the Future Research Centre (CFFRC) in Malaysia, the GRASP integrated geospatial platform for agricultural species, including major pests and diseases, will allow future investigators to shape the data handling and integration according to their subject requirements, before contributing to the population of the prototype database. Data capture from sensor network to remote sensing, including crowd-sourcing from farmers will be further integrated within the GRASP platform allowing other refinements of the workflow modelling and multiple scale scenario risk assessments. Using open standards and interoperability principles developed by the Open Geospatial Consortium (OGC), the platform deliverable software will be released under open source license to enable wide use and further developments also ensuring sustainability of the project. Both desktop interfaces and web interfaces with compiled current databases (with updating facilities) will be released, enabling wide audience usage even from remote places with weak internet connections. The GRASP-GFS aims to link with other global initiatives such as GEOSS (Global Earth Observation System of Systems) and GeoNode to develop productive interaction between bench, economic and social scientists.