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CCAFS Regional Agricultural Forecasting Toolbox (CRAFT)
 

CCAFS Regional Agricultural Forecasting Toolbox (CRAFT)

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At the FAO Workshop, held in Panama City on August 6 - 8th the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Head of Research James Hansen gave a presentation on ...

At the FAO Workshop, held in Panama City on August 6 - 8th the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), Head of Research James Hansen gave a presentation on CRAFT tool. More info: http://ow.ly/ocIqJ

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    CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) Presentation Transcript

    • 1 The CCAFS Regional Agricultural Forecasting Toolbox (CRAFT) James Hansen, Theme 2 Leader International Research Institute for Climate and Society Herramientas para la Adaptación y Mitigación del Cambio Climático en la Agricultura en Centroamérica Panamá, 6-8 de Agosto 2013 Date
    • 2 What is CRAFT? • Software platform to support within-season forecasting of crop production; secondarily, risk analysis and climate change impacts • Functions: • Manage spatial data, crop simulation (currently DSSAT) • Integrate seasonal forecasts (CPT) • Spatial aggregation • Probabilistic analysis • Post-simulation calibration • Visualization • Analyses: risk, forecast, hindcasts, climate change • Current version preliminary
    • 3 What is CCAFS? • Strategic partnership of international agriculture (CGIAR) and global change (Future Earth) research communities
    • 4 What is CCAFS? • Strategic partnership of international agriculture (CGIAR) and global change (Future Earth) research communities • World’s largest research program addressing the challenge of climate change and food security  Mechanism for organizing, funding climate-related work across CGIAR  Involves all 15 CGIAR Centers  $67M per year
    • 5 What is CCAFS? • Strategic partnership of international agriculture (CGIAR) and global change (Future Earth) research communities • World’s largest research program addressing the challenge of climate change and food security • 5 target regions across the developing world
    • 6 What is CCAFS? • Strategic partnership of international agriculture (CGIAR) and global change (Future Earth) research communities • World’s largest research program addressing the challenge of climate change and food security • 5 target regions across the developing world • Organized around 4 Themes: • Adaptation to progressive change • Adaptation through managing climate risk • Pro-poor climate change mitigation • Integration for decision-making
    • 7 Risk analysis Inputsupply management Farmer advisories Food security early warning, planning Trade planning, strategic imports Insurance evaluation, payout Insurance design Time of year Uncertainty(e.g.,RMSEP) seasonal forecast planting marketing harvest anthesis growing season EVENT APPLICATION Why CRAFT? Support adaptation opportunities
    • 8 Why CRAFT? Meets an unmet need • Platform to facilitate research, testing and implementation of crop forecasting methods • Target researchers and operational institutions in the developing world • Accessible: free, open-source (eventually) • Adaptable: support multiple crop model families
    • 9 Basics of yield forecasting: Uncertainty • Consider yields simulated with monitored weather thru current date, then sampled historic weather • Uncertainty diminishes as season progresses • Model error the non- climatic component • Relative contribution of climate, model uncertainty changes through the season forecast date harvest season onset Time of growing season 1 2 . . . n T Weatherdatayear monitored weather historic weather 0.5 1.0 1.5 2.0 Grainyield,Mg/ha 1May 1Jun 1Jul 1Aug Harvest Forecast Date 90th 75th 50th 25th 10th 1989 climatology-based Qld. Australia wheat forecast. Observed, and forecast percentiles. Hansen et al., 2004. Agric. For. Meteorol. 127:77-92 Uncertainty planting anthesis harvest Time model uncertainty climate uncertain SIMULATION ◄——— PREDICTION ——— Hansen, J.W., Challinor, A., Ines, A.V.M, Wheeler, T., Moron, V., 2006. Climate Research 33:27-41.
    • 10 Basics of yield forecasting: Reducing uncertainty Uncertainty planting anthesis harvest Time model uncertainty climate uncertainty Uncertainty planting anthesis harvest Time 2b. N-limited 3. Actual 1. Potential pests, disease, micronutrients, toxicities H, T, crop charac- teristics water2a. Water-limited ?????? soil N dynamics, plant N use, stress response photosynthesis, respiration, phenology water balance, transpiration, stress response Level of production Processes nitrogen after Rabbinge, 1993 • Reduce model error: • Improve model • Improve inputs • Assimilate monitored state • Greatest benefit late in season • Reduce climate uncertainty • Incorporate seasonal forecasts for remainder of season • Greatest benefit early in season Uncertainty planting anthesis harvest Time model uncertainty climate uncertainty Uncertainty planting anthesis harvest Time
    • 11 Incorporating seasonal forecasts: Queensland wheat study (2004) • WSI-type crop model • PC1 of GCM (ECHAM4.5) rainfall, persisted SSTs • Yields by cross-validated linear regression with normalizing transformation • Probabilistic, updated • Demonstrated yields more predictable than rainfall • One of several potential methods tested 200 0 200 400 km Correlation < 0.34 (n.s.) 0.34 - 0.45 0.45 - 0.50 0.50 - 0.55 0.55 - 0.60 0.60 - 0.65 > 0.65 Rain Yield Hansen, Pogieter, Tippett, 2004. Agric. For. Meteorol. 127:77-92 N 200 0 200 400 km 1 July 1 June 1 August 1 May Correlation <0.34 (n.s.) 0.34-0.45 0.45-0.55 0.55-0.65 0.65-0.75 0.75-0.85 > 0.85 0.5 1.0 1.5 2.0 Grainyield,Mg/ha 1May 1Jun 1Jul 1Aug Harvest Forecast Date 0.5 1.0 1.5 2.0 1May 1Jun 1Jul 1Aug Harvest Forecast Date 1982 Queensland, Australia wheat yield forecast. climatology only + GCM forecast Forecast date Grainyield(Mgha-1)
    • 12 Linking crop simulation models and seasonal climate forecasts statistically forecast date harvest model initialization fittedstatisticalmodel yn,1 yn,2 yn,3 . . . yn,n-1 Time of year 1 2 . . . n } Weatherdatayear ˆky
    • 13 Versions: • Windows 95+ • Linux batch • Windows batch (for CRAFT) Incorporating seasonal forecasts: CPT Climate Predictability Tool (CPT) is an easy-to-use software package for making tailored seasonal climate forecasts.
    • 14 Why CPT? Address problems that arose in RCOFs: • Slow production made pre-forum workshops expensive and prohibited monthly updates • Multiplicity, colinearity, artificial skill, lack of rigorous evaluation made forecasts questionable • Little use of GCM predictions (http://www.wmo.int/pages/prog/wcp/wcasp/clips/outlooks/climate_forecasts.html)
    • 15 What CPT does • Statistical forecasting • Statistical downscaling coarse resolution fine resolution statistical model dynamical model
    • 16 What CPT does • Statistical forecasting • Statistical downscaling • Designed to use gridded data (GCM output and SSTs) as predictors • Uses principal components (PCs, or EOFs) as predictors • Rigorous cross-validation to avoid artificial skill • Diagnostics and evaluation • New multi-model support
    • 17 CPT: Principal Components (PCs) • Explain maximum amounts of variance within data • Capture important patterns of variability over large areas • Uncorrelated, which reduces regression parameter errors • Few PCs need be retained, reducing dangers of “fishing” • Corrects spatial biases First PC of Oct-Dec 1950 -1999 sea-surface temperatures
    • 18 CPT: Canonical Correlation Analysis (CCA) July (top) and December (bottom) tropical Pacific sea-surface temperature anomaly, 1950-1999 December July
    • 19 CPT: Which method? Predictor Predictand (simulated yield) Method Point-wise Point-wise Multiple regression Spatial pattern Point-wise Principal component regression Spatial pattern Spatial pattern Canonical correlation analysis
    • 20 CCAFS structure: Yield forecast work flow WEATHER SOIL CULTIVAR MANAGEMENT CROP MODEL (DSSAT CSM) SIMULATED YIELDS STATISTICAL MODEL (CPT) SEASONAL PREDICTORS FORECAST YIELDS AGGREGATION CALIBRATION CALIBRATED YIELDS AGGREGATED YIELDS OBSERVED YIELDS 3 4 1 2
    • 21 CROP SIMULATOR IMPORT PROCESS MANAGER U S E R I N T E R F A C E CROP MODEL MANAGER CCAFS Modules EXTERNAL ENGINES INPUT/OUTPUT FILES CENTRAL RDBMS EXPORT AGGREGATOR CPT TOOL SEASONAL FORECAST MANAGER MS Windows MS .NET MySQL DB CRAFT Architecture
    • 22 Steps: yield forecast run • Step 1 – Prepare/Review Data Sets • Step 2 – Create Project & Run • Step 3 – Link Data Sources • Step 4 – Enter Crop Management Data • Step 5 – Setup & Execute Crop Model Run • Step 6 – View Crop Model Run Results • Step 7 – Seasonal Forecast Run • Step 8 – View Forecast Yield Results
    • 23 Home Page • Main Menu • Connect application to desired database, and test • Lists 5 most recent projects • Project Name link will direct the user to the current state of the workflow
    • 24 Data Upload • CCAFS pre-loaded with default data (currently South Asia). • Users can upload data: crop mask, cultivar, fertilizer, field history, planting, irrigation mask & management, soil. • These data are input data to DSSAT and CPT engines during run. • Version control of user-supplied data.
    • 25 Management Define input levels – Field, Cultivar, Planting, Irrigation, Fertilizer – using Management menu from the menu bar.
    • 26 Project • Search or select existing project, or create new project • Navigate to data source form for active run of active project
    • 27 Data Sources • Customize run by selecting uploaded or default data sources • Drop-down lists of previously uploaded data • This screen is not shown if Default is selected when creating Project and Run.
    • 28 Apply Inputs • Tabbed details of available Levels, and a grid to show the applied levels for specified ‘Type of Data’ • Green = input applied, • Pink = input not applied • Mandatory fields must be applied
    • 29 Run Project • Executes current active project with configured data sources and applied inputs • On successful execution, prompts to save run results, view result maps • 2 steps: • Crop simulation • Seasonal forecast
    • 30 Visualization • Displays user- selected output variables and statistics • Interactive grid cell selection • Display, map results by grid cell or polygon
    • 31 User interface summary SPATIAL DATA MANAGEMENT DATA PROJECT & RUN SETUP RUN PROJECT RESULTS Import default data sets – admin only Import gridded user data sets Export default data sets Export gridded user data sets Define Cultivar Define Planting Dates Define Irrigation Application Define Fertilizer Application Define Field History Create a project Search and Select Project & Run Create Run(s) Identify data sources Apply UI based inputs Run crop model Run Seasonal Forecast module Run Calibration module Single Project Run • Select project • Select outputs to view • View/Export Results Compare Project Runs • Select the two projects • Select outputs to compare • View/Export Results
    • 32 Major planned enhancements • Generalize locations, grid schemes, user inputs • Crop model interoparability (AgMIP) • Additional crop models: • APSIM • AquaCrop • ORYZA2000 • SARA-H • InfoCrop • … • Hindcast analysis and validation statistics • De-trending and post-simulation calibration