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The CCAFS Regional Agricultural Forecasting Tool Box (CRAFT)
 

The CCAFS Regional Agricultural Forecasting Tool Box (CRAFT)

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The CCAFS Regional Agricultural Forecasting Tool Box (CRAFT)

The CCAFS Regional Agricultural Forecasting Tool Box (CRAFT)
craft james hansen

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    The CCAFS Regional Agricultural Forecasting Tool Box (CRAFT) The CCAFS Regional Agricultural Forecasting Tool Box (CRAFT) Presentation Transcript

    • 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 1
    • 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 2
    • What is CCAFS? • Strategic partnership of international agriculture (CGIAR) and global change (Future Earth) research communities 3
    • 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 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 5 target regions across the developing world 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 Organized around 4 Themes: • • • • Adaptation to progressive change Adaptation through managing climate risk Pro-poor climate change mitigation Integration for decision-making 6
    • Farmer advisories Input supply management Insurance design Risk analysis APPLICATION Time of year Food security early warning, planning Trade planning, strategic imports Insurance evaluation, payout Uncertainty (e.g., RMSEP) marketing harvest anthesis planting seasonal forecast EVENT Why CRAFT? Support adaptation opportunities growing season 7
    • 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 8
    • • Model error the nonclimatic component Relative contribution of climate, model uncertainty changes through the season Hansen, J.W., Challinor, A., Ines, A.V.M, Wheeler, T., Moron, V., 2006. Climate Research 33:27-41. monitored weather . . . historic weather model uncertainty climate uncertain n T season onset Time of growing season forecast date harvest 90th 2.0 planting 1.5 75th 50th 25th 10th anthesis Time harvest ◄——— PREDICTION ——— 1.0 SIMULATION • Uncertainty diminishes as season progresses 1 2 Grain yield, Mg/ha • Consider yields simulated with monitored weather thru current date, then sampled historic weather Weather data year • Uncertainty Basics of yield forecasting: Uncertainty 1989 climatology-based Qld. Australia wheat forecast. Observed, and forecast percentiles. Hansen et al., 2004. Agric. For. Meteorol. 0.5 127:77-92 1May 1Jun 1Jul 1Aug Harvest 9 Forecast Date
    • Basics of yield forecasting: Reducing uncertainty Improve model 1. Potential Improve inputs Assimilate monitored state 2a. Water-limited • water Greatest benefit late in season Reduce climate uncertainty • H, T, crop characteristics Incorporate seasonal forecasts for remainder of season 2b. N-limited 3. Actual Greatest benefit early in season nitrogen pests, disease, micronutrients, toxicities photosynthesis, respiration, phenology water balance, transpiration, stress response soil N dynamics, plant N use, stress response ?????? after Rabbinge, 1993 model uncertainty climate uncertainty planting anthesis Time harvest Uncertainty • • • • • Processes Level of production Reduce model error: Uncertainty • planting anthesis Time harvest 10
    • Incorporating seasonal forecasts: Queensland wheat study (2004) Rain • • • WSI-type crop model 2.0 2.0 Grain yield, Mg/ha • climatology only Grain yield (Mg ha-1) • • PC1 of GCM (ECHAM4.5) rainfall, persisted SSTs 1.5 Yields by cross-validated 1.0 linear regression with normalizing transformation Probabilistic, updated 0.5 N 1.5 1.0 Yield 1982 Queensland, Australia wheat yield forecast. Correlation 1 May 1May 1Jun 1Jul 1Aug 0.5 1 June Harvest 1May 1Jun 1Jul 1Aug ForecastoDateation C rrel Forecast date Forecast < 0.34 (n .s.) 0.34 - 0 .4 5 0.45 - 0 .5 0 0.50 - 0 .5 5 0.55 - 0 .6 0 0.60 - 0 .6 5 > 0. 6 5 Demonstrated yields more predictable than rainfall One of several potential methods tested Hansen, Pogieter, Tippett, 2004. Agric. For. Meteorol. 127:77-92 + GCM forecast 20 0 1 July 0 1 August 200 0 200 400 km 20 0 <0.34 (n.s.) Harvest 0.34-0.45 Date 0.45-0.55 0.55-0.65 0.65-0.75 0.75-0.85 > 0.85 400 km 11
    • harvest yn,1 yn,2 yn,3 . . . n . . . yn,n-1 } fitted statistical model Weather data year 1 2 forecast date model initialization Linking crop simulation models and seasonal climate forecasts statistically ˆ yk Time of year 12
    • Incorporating seasonal forecasts: CPT Climate Predictability Tool (CPT) is an easy-to-use software package for making tailored seasonal climate forecasts. Versions: • Windows 95+ • Linux batch • Windows batch (for CRAFT) 13
    • 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) 14
    • What CPT does • • Statistical forecasting Statistical downscaling coarse resolution statistical model fine resolution dynamical model 15
    • 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 16
    • 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 17
    • CPT: Canonical Correlation Analysis (CCA) July July (top) and December (bottom) tropical Pacific sea-surface temperature anomaly, 1950-1999 December 18
    • CPT: Which method? Predictor Predictand Point-wise Point-wise Multiple regression Spatial pattern Point-wise Principal component regression Spatial pattern Spatial pattern Canonical correlation analysis (simulated yield) Method 19
    • CCAFS structure: Yield forecast work flow 1 WEATHER CROP MODEL (DSSAT CSM) SOIL SEASONAL PREDICTORS SIMULATED YIELDS 2 STATISTICAL MODEL (CPT) CULTIVAR MANAGEMENT 4 FORECAST YIELDS OBSERVED YIELDS CALIBRATION 3 AGGREGATED YIELDS AGGREGATION CALIBRATED YIELDS 20
    • CRAFT Architecture CCAFS Modules PROCESS MANAGER CROP MODEL MANAGER AGGREGATOR SEASONAL FORECAST MANAGER IMPORT EXPORT CENTRAL RDBMS EXTERNAL ENGINES CROP SIMULATOR U S E R I N T E R F A C E MS Windows MS .NET MySQL DB CPT TOOL INPUT/OUTPUT FILES 21
    • 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 22
    • 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 23
    • 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. 24
    • Management Define input levels – Field, Cultivar, Planting, Irrigation, Fertilizer – using Management menu from the menu bar. 25
    • Project • Search or select existing project, or create new project • Navigate to data source form for active run of active project 26
    • 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. 27
    • 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 28
    • 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 29
    • Visualization • Displays userselected output variables and statistics • Interactive grid cell selection • Display, map results by grid cell or polygon 30
    • User interface summary SPATIAL DATA MANAGEMENT DATA PROJECT & RUN SETUP RUN PROJECT RESULTS Import default data sets – admin only Define Cultivar Create a project Run crop model Search and Select Project & Run Run Seasonal Forecast module Single Project Run • Select project • Select outputs to view • View/Export Results Import gridded user data sets Define Planting Dates Define Irrigation Application Create Run(s) Export default data sets Define Fertilizer Application Export gridded user data sets Define Field History Identify data sources Apply UI based inputs Run Calibration module Compare Project Runs • Select the two projects • Select outputs to compare • View/Export Results 31
    • 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 32