CropBASE: Decision Support Knowledge Platform for Underutilised Crops
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1 CropBASE: Decision Support Knowledge Platform for Underutilised Crops Sue Walker with Sayed Azam-Ali & Asha Karunaratne Crops For the Future Research Centre Malaysia 3 rd International Bambara Groundnut Workshop Accra 4 September, 13
2 CropBASE: Decision Support Knowledge Platform for Underutilised Crops Web-based First `port-of-call for information on underutilised crops Decision Support Multi-stakeholder options on underutilised crops and end-uses Knowledge Platform Cross-cutting all CFFRC Programmes and Themes & research value chain Comparative Productivity and resource-use-efficiency of underutilised and major crops and cropping systems under current and future climates Interactive Tools to integrate data on underutilised and major crops with georeferenced information and databases
3 `provides a service to potential end-users interested in underutilised crops and products that can contribute to livelihoods Four Clusters: Decision Making Data Manipulation and Modelling
4 Agile Development Lightweight framework Iterative development Close links between development team & client Frequent communication Data Manipulation and Modelling
5 Set of Solution Components or Software Subsystems 1. User interface Interactive data capture and retrieval. IT and information storage system Data storage and management Conventional and alternative data sources 3. Analysis engine Data manipulation and modelling for end-use applications
6 1. User Interface Social networking style `accessibility Self-regulating community of experts legitimacy Quality control via editorial panel `filtering Scientific data from recognised sources `credible Geo-referenced information `comparative Crowd sourcing data input `novel data
7 YIELD (TONS/HA) CropBASE outputs Various formats depending on user/audience requirements Graphical, Diagrammatic, Tabular, Spatial maps via GIS tools, etc Yield Data for UN landrace at all Namibia Sites NA3 NA4 NA NA58 NA61 NA63 YEAR
8 . IT and Information Storage System Scientific Information Local Information Evidence-based research and Local knowledge & information
9 Current databases provide input for crop models within GIS framework Existing geospatial natural resource databases e.g. FAO for soils, crop statistics, food and water indices; e.g. WMO, NASA, NOAA for past, current and future climate scenarios. CFFRC working with crop modelling community e.g. AgMIP Comparative testing of models for range of crops CFFRC to spearhead underutilised crops in crop models e.g. APSIM, AquaCrop, DSSAT African Bambara groundnut (Azam-Ali & colleagues) Ethiopian Teff (Raes & colleagues) Peruvian Quinoa (Raes & colleagues) Calibrated for AquaCrop (FAO Irrigation and Drainage #66) Other leafy vegetables and root crops are in the pipeline.
10 Community information Local preferences via participatory community interactions Regional & global demand as driver to expand specific crops & niche markets Economic assessment of crops & cropping systems beyond current locations and centres of diversity. farmer extension health Met Service
11 3. Analysis engine Climate agroecological suitability Quality nutritional, food security and livelihood potentials of crops and products at field, farm and regional scales Economic socio-economic, trade and regulatory potential Crop-climate modelling is most advanced stage: link available databases as input for crop models in GIS framework. Decision Making Data Manipulation and Modelling
12 Crop-Climate Modelling input output Climatic conditions Climate scenarios Environ Data Soil Climate Climate Forecast Species genotype Suitability Maps Soil conditions Primary Data Base Underpinning Data Base Yield forecast Yield potentials Decision Support & Knowledgebase Crop Species characters Genotypic characters Market data Meta data Farmer Knowledge Crop Data Agronomy Physiology Genetics Farmer Fertilizer responses Pest disease tolerance Market potentials Inferences
13 AquaCrop Flow-Chart
14 AquaCrop Conceptual Framework Water Productivity WP (g m - mm -1 ) Biomass T C WP * Biomass T C ET O CO () (g m - ) Biomass (kg m - ) 3 1 Sorghum Sunflower Chickpea Wheat Biomass (kg m - ) 3 1 Sorghum Sunflower Chickpea Wheat ETc TC (mm x 1) (ETc/ETo) ( TC / O )
15 AquaCrop Conceptual Framework Canopy Cover CCo CCx canopy cover CGC CDC Canopy Cover (CC) exponential growth during early development and exponential decay during late development time to full canopy start of canopy senescence time to harvest time CC CGC t CC o e (1) CC CGC t CC x (CC x CC o ) e ()
16 CropBASE CFFRCPLUS Studentships Proposal No. Title Candidate CropB1-1 Ontology knowledge system Interviews CropB1-3 Geography X genetics Bambara Interviews CropB1-5 Trans-disciplinary communication Mumuni Eliasu CropB1-7 Value chain Integration Haixia Liu CropB1-8 Dimensional Informatics Mohammad Kamar CropB1-9 Remote sensing imagery Web tool Phan Kien Tuong CropB1-1 Geospatial P-use efficiency Diriba Kumssa CropB1-11 Interactive crowd-sourced data Dai C Huynh CropB1-1 Economic value prediction system Oh Mei Shin CropB1-17 (FoodP1-14) Winged bean genetics & food value Asiah Kamal
17 CropBASE Crop-Climate model challenges To simulate crop productivity at country to global scales under variable climates To capture crop-climate interactions for underutilised crops and trait identification across geographical boundaries General Circulation Model (GCM) Crop Model Coupling Climate Model + Crop Model
18 Simulated yield (t/ha) CropBASE Crop-Climate Model Example Simulation Example AquaCrop crop predictions under water deficit Bambara groundnut -Model evaluation.5 y =.839x +.11 R =.7 RMSE =.36 t/ha Vigna subterranea L. Verdc Measured yield (t/ha) Karunaratne, A.S., Azam-Ali, S. N., Izzi G. and Steduto P. 11.Calibration and validation of FAO-AquaCrop model for irrigated and water deficient bambara groundnut. Journal of Experimental Agriculture, v. 47 (3), pp., 59 57
19 CropBASE Crop-Climate Model Example Approach Select crops and genotypes (bambara groundnut and pearl millet) Select locations (districts in South Africa, Botswana & Namibia) CropBASE retrieves relevant Geospatial input data Select AquaCrop Geospatial database information into AquaCrop format Select geospatial data (199-1) and climate data from GCMs Compare AquaCrop output: dry matter & yield for -years for each genotype and location for historical & climate change scenario.
20 CropBASE Crop-Climate Model Example Yield Predictions: Historical Period (199-1) Landrace Site Yield (t/ha) Sd WP ET Yield (kg/m 3 ) Sd Bambara groundnut Uniswa Red Namibia S19-3 Namibia Pearl Millet Monyaloti Namibia Bambara groundnut Uniswa Red South Africa S19-3 South Africa Pearl Millet Monyaloti South Africa Bambara groundnut Uniswa Red Botswana S19-3 Botswana Pearl Millet Monyaloti Botswana
21 Yield (t/ha) Yield (t/ha) CropBASE Crop-Climate Model Example Bambara groundnut vs Pearl Millet: Baseline and A-CSIRO Botswana South Africa Baseline-Bambara(UN) A-CSIRO-Bambara(UN) Baseline-Bambara(S19) A-CSIRO-Bambara(S19) Baseline-PearlM(Mon) A-CSIRO-PearlM(Mon) 4 4
22 Yield (t/ha) Yield (t/ha) Yield (t/ha) Yield (t/ha) Yield (t/ha) Yield (t/ha) CropBASE Crop-Climate Model Example Namibia: Bambara groundnut landraces yield predictions 3 1 Simulated Yield trend at NA Simulated Yield trend at NA Simulated Yield trend at NA Simulated Yield trend at NA N S19-3 UN N 3 Simulated Yield trend at NA Simulated Yield trend at NA
23 CropBASE: Decision Support Knowledge Platform for Underutilised Crops A Web-based 1st `port-of-call for info on underutilised crops with Decision Support for multi-stakeholders on underutilised crops and end-uses, as a Knowledge Platform cutting across the research value chain, for Comparative Analysis of productivity & resource-use-efficiency of underutilised vs major crops and range of cropping systems under current & future climates, as Interactive Tools to integrate data on underutilised & major crops with geo-referenced information & databases. Data Manipulation and Modelling 3 rd International Bambara Groundnut Workshop Accra 4 Sept, 13
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