Geospatial Resource for Agricultural Species and Pests with integrated workflow modelling to support Global Food Security (GRASP-GFS): a prototype

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.

Targeting Global Food Security issues with sustainable agriculture, related to crop selection and climate change needs the development of models integrating a range of disciplines such as genetic, agro-ecological modelling and land-climate forecasts. Geospatial science can be the mediating component of an e-infrastructure enabling data and processing to be retrieved, integrated and made available within a geospatial workflow modelling interface with uncertainty management.
The objective is to facilitate the use and reuse of resources of trait diversity in crop, animal and microbial species of agricultural importance, together with dynamic climate records within a cutting-edge geocomputational integrated framework (GRASP).
The focused entry points of the GRASP will be genotype(s) of agricultural species germplasm by geospatial origin, with the higher level descriptor being the agricultural trait coming from various sources, e.g. the USA germplasm database ars-grin.gov., the Pathogen Host Interactions database cataloguing pathogenicity, virulence and effector genes developed at Rothamsted Research (RRES), Plantwise knowledge bank (CABI), Species 2000 and the catalogue of life (University of Reading), and other partner data, but also data coming from regular monitoring such as RRES yearly surveys for live monitoring of insect populations in the UK, the Crop Monitor project (Fera) gathering real-time data of crop pests and disease activity in arable crops throughout England.
Research related to data and models integration semantically and syntactically within an interoperable framework compatible with the GEOSS initiative will also investigate the role of uncertainty and data quality in Food security workflow modelling outcomes. The flexibility of this platform will allow considering other data-types to enrich the existing information, such as crowd-sourcing data (farmers), enabling richer interaction and knowledge transfer.

The University of Nottingham in the UK and the University's campuses in Malaysia and China are playing a growing part in its Global Food Security research, with this now one of the five main themes identified for the next 10 years.. In June 2011, the University of Nottingham Malaysia Campus announced that it was to co-host the first ever Crops for the Future Research Centre (CFFRC) in partnership with the Government of Malaysia. This centre will support research on underutilised crops that contribute to High Value Agriculture, Nutritional Security and Digital Knowledge Systems, which will each use aspects of geospatial data integration for spatial crop forecast modelling under various genetic-trait hypothesis and climate scenarios.
Within this pump-prime funding to develop this prototype, only a minimum participation from the social and economic disciplines represented by the above named academic will be possible but it is expected to involve them from the last three months of the project within workshops to fully integrate their themes for future research and developments of the platform.
Besides the direct academic beneficiaries and the support to the CFFRC this project will benefit researchers and stakeholders in food security, as they will be able to test sustainable strategies for crop development under different threats of pathogens and pests in interaction with climate changes. This modelled information will be available at different scale allowing various planning mechanisms and validation with quality management for input and outputs of the models, therefore improving the decision-making process.
The framework developed will facilitate communications at various levels between end-users and project scientists for example to capture data from the field to support and improve the quality and precision of models giving feedback on the expected yields.
The outcomes of the project will be made available from a dedicated website platform allowing the wider use of resources developed (database queries, modelling runs). The software deliverables from the project will be released under an open source license to enable policy makers and the wider community all over the world who are interested to make use of the resources for their benefit and further development, therefore ensure the long-term sustainability of the project and also improve the software quality because of peer participation.
This work will be linked to other global initiatives such as GEOSS (Global Earth Observation System of Systems), GeoNode etc. Such an approach will help to develop a Food Security research community and permit productive interaction and integration between bench, economic and social scientists.
The open approach (both in terms of datatype and species) will mean that we develop a platform and set of tools which are potentially relevant to most research groups in the food security area and crop developments and would lead to the development of additional tools and analyses from other stake-holders, as well as breeding and research companies. A successful implementation of this project will form one of the novel approaches currently being developed by Crops for the Future.