Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Core Training Presentations- 6 IMPACT Data-Model Philosophy

275 views

Published on

Global Futures & Strategic Foresight (GFSF) program enhances and uses a coordinated suite of biophysical and socioeconomic models to assess potential returns to investments in new agricultural technologies and policies. These models include IFPRI’s International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT), hydrology and water supply-demand models, and the DSSAT suite of process-based crop models.

The program also provides tools and trainings to scientists and policy makers to undertake similar assessments.

GFSF program is a Consultative Group on International Agricultural Research (CGIAR) program led by the International Food Policy Research Institute (IFPRI)

Published in: Government & Nonprofit
  • Be the first to comment

  • Be the first to like this

Core Training Presentations- 6 IMPACT Data-Model Philosophy

  1. 1. 1 Introducing IMPACT 3: Modeling Philosophy and Environment Sherman Robinson Daniel Mason-D’Croz Shahnila Islam
  2. 2. Global Futures and IMPACT • Objective: Use IMPACT for ex-ante analysis of potential agricultural technologies to help policy makers prioritize agricultural investments • Phase 1: IMPACT Developments: – Welfare Module – Benefit-Cost Analysis – Technology Adoption Module – Tracking progress against MDGs • Challenges identified in Phase 1: – Insufficient geographic disaggregation – Need to model more CG-mandate crops – 2000 base year outdated – Model needed to be recoded to allow for better integration with new modules under development (water, livestock, fish, biofuels) 2
  3. 3. What is IMPACT 3? • More than a new FAO download and cleaner code • A modeling-data platform built on modularity and interoperability – Harmonized Data – Data driven model specifi- cation – More flexible to meet user needs 3
  4. 4. • IMPACT integrates various models, which often use similar input data • Better data sharing, common definitions, and clear responsibility of data processing removes redundancy and improves quality control 4 Why Data Harmonization? IMPACT 3 FAO Database Data Processing Spatial disaggregation Balance Demand, and Trade with Production Data Cleaning Crop Production Livestock Production Commodity Demand and Trade FAO Data Collection Bulk Download
  5. 5. • IMPACT integrates various models, which often use similar input data • Better data sharing, common definitions, and clear responsibility of data processing removes redundancy and improves quality control 5 Why Data Harmonization? IMPACT 3 FAO Database Data Processing Spatial disaggregation Balance Demand, and Trade with Production Data Cleaning Crop Production Livestock Production Commodity Demand and Trade FAO Data Collection Bulk Download SPAM
  6. 6. • IMPACT integrates various models, which often use similar input data • Better data sharing, common definitions, and clear responsibility of data processing removes redundancy and improves quality control 6 Why Data Harmonization? IMPACT 3 FAO Database Data Processing Spatial disaggregation Balance Demand, and Trade with Production Data Cleaning Crop Production Livestock Production Commodity Demand and Trade FAO Data Collection Bulk Download SPAM IMPACT
  7. 7. Shared Data Data Processing Data Users FAO Climate Data Exogenous IMPACT Parameters Geospatial and Subnational Data SPAM IMPACT Models Hydrology Crop Models Land-Use Model 7 IMPACT Data-Model Environment
  8. 8. • FAO – Crop Production – Livestock Production – Supply-Utilization – Food Balance Sheets – Water Stress • Climate Data – GCMS – Generated Weather • Geospatial and Subnational Data – Irrigation – Subnational Statistics – Crop suitability maps – Population Density • Exogenous IMPACT Parameters – Yield, Area Growth – Elasticities – Prices (AMAD) – Population – GDP 8 Share Data
  9. 9. • SPAM - Spatial Production Allocation Model • Land-Use Model • DSSAT Crop Models • Biofuel Model • Hydrology Model • Water Basin Management Model • Water Stress Model • Food Model – Crops – Livestock – Sugar – Oilseeds 9 Models
  10. 10. Direct Users of FAO Using Processed FAO SPAM FAO: Estimation FAO Climate Data Exogenous IMPACT Parameters Geospatial and Subnational Data IMPACT •Food •Water Stress •Water Demand Shared Data 10 FAO Data
  11. 11. • FAO Bulk Download for 3-year average around 2005 (04-06) • Harmonized SPAM/IMPACT commodity, and geographic definitions • Bayesian Work Plan – Iterate with new information 11 Processing FAO Data Source Data (FAO, SPAM) Feedback to data source Priors on values and estimation errors of production, demand, and trade Estimation by Cross- Entropy Method Check results against priors and identify potential data problems New information to correct identified problems
  12. 12. Data Harmonization and Quality • Too many cooks – Climate change is modeled in Water and Crop models for IMPACT – Need to use same initial and processed climate data – Ensure crop shocks and water shocks are compatible 12
  13. 13. Users of Climate Data Use Aggregated Processed Climate Data Crop Models Hydrology FAO Climate Data Exogenous IMPACT Parameters Geospatial and Subnational Data IMPACT •Food •Water Demand •Water Stress Shared Data 13 Climate Change Consistency
  14. 14. Data Harmonization and Quality • Building common geographical definitions • Standardize mapping of data • Share data (initial and processed) 14
  15. 15. Users of Geospatial and Subnational Data Use Aggregated Outputs from direct users SPAM Hydrology Crop Models Land-Use Model FAO Climate Data Exogenous IMPACT Parameters Geospatial and Subnational Data IMPACT •Food •Water Demand •Water Stress Shared Data 15 Geospatial Data Users
  16. 16. Modularity – Data Partitioning • IMPACT model is now data driven – General code built on specific data structures • Each dataset has unique problems – Detox drivers vs. self-driving car – Data Processing is source-specific – Model Inputs are model-specific 16
  17. 17. Modularity – IMPACT Partitioning • IMPACT model is now data driven – General code built on specific data structures • Each dataset has unique problems – Detox drivers vs. self-driving car – Data Processing is source-specific – Model Inputs are model-specific 17
  18. 18. Benefits of Data Independence • Cleaner Model Code – Facilitate model transfer and training • Data Processing and Model design are independent tasks • Model can run different data sources and aggregations without modification 18

×