9. ifpri aagw2010 - 9 june 2010


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  • Users can simulate a range of market, technology adoption, research spillover, and trade policy scenarios based on a flexible, multi-market, partial equilibrium model.With DREAM you can define a range of technology investment, development, and adoption scenarios and save them in an integrated database. Scenarios are described using market, R&D, and adoption information for any number of separate “regions.”
  • 9. ifpri aagw2010 - 9 june 2010

    1. 1. Development Domain Analysis<br />Agricultural Potential<br />Land Cover<br />Market Access<br />Crop Systems Modeling<br />Farm-gate Price Modeling<br />Spatial Analysis at IFPRI<br />ZHE GUO*, EMILY SCHMIDT, JAWOO KOO, RIA TENORIO <br />* GIS COORDINATOR (z.guo@cgiar.org)<br />Environment and Production Technology Division<br />International Food Policy Research Institute<br />AAGW2010/NAIROBI<br />9 JUNE 2010<br />
    2. 2. Domain Analysis<br />Overview<br />
    3. 3. To use a set of domain criteria to identify focus area and regions which have similarity or dissimilarity of conditions of relevance to agricultural development.<br />To capture and analyze the region patterns of agriculture productivity and potentials.<br /><ul><li>Ag-potential
    4. 4. Land cover
    5. 5. Market access </li></ul>Development Domain Analysis<br />
    6. 6. Development domain analysis<br /><ul><li>Development domains
    7. 7. Existing: land cover * Rural Population *[Market access]
    8. 8. Potential: Ag-potential (eg. LGP, NDVI) * Rural Population*[Market access]
    9. 9. Further economic analysis (e.g. DREAM model)</li></ul>existing cropland *rural population density<br />potential cropland *rural population density<br />Potential cropland density are derived from statistical variables (e.g. mean, min, max, standard deviation) of monthly NDVI and classified into high, med, low . The rural population density is classified into high, med and low classes. Development domain classes are developed from the intersection of the two variables. <br />Existing cropland density is derived from Afri-cover datasets and are classified into high and low classes. The rural population density is classified into high, med and low classes. Domain classes (2*3=6) are developed from the intersection of the two variables. <br />
    10. 10. AGRICULTURAL POTENTIAL<br />Domain Analysis<br />
    11. 11. Ag-potential variables<br />
    12. 12. PLANTING<br />WINDOW<br />OF<br />RAINFED CROPS<br />SSA<br />MODIS<br />Greenness Up/Down<br />Season A/B<br />Weekly window<br />
    13. 13. Land Cover<br />Domain Analysis<br />
    14. 14. Existing cropland variables<br />GLC2000<br />(2000)<br />Globcover<br />(2005)<br />MODIS<br />(2001)<br />Africover<br />(~2000)<br />
    15. 15.
    16. 16. Existing cropland variables<br />
    17. 17. Market Access<br />Domain Analysis<br />
    18. 18. Market access<br />
    19. 19. Crop systems modeling<br />Biophysical Evaluation<br />
    20. 20. SIMULATED<br />CLIMATE CHANGE<br />IMPACTS ON<br />CROP YIELD<br />2000 and 2050<br />DSSAT 4.5<br />Maize<br />Subsistence<br />FutureClim 1.0<br />MIROC 3.2 (IPCC4)<br />A2 SRES<br />
    21. 21. <ul><li>By 2050, the decline in calorie availability will increase child malnutrition by 20 percentrelative to a world with no climate change.
    22. 22. Thus, aggressive agricultural productivity investments of US$7.1–7.3 billion are neededto raise calorie consumption enough to offset the negative impacts of climate change on the health and well-being of children.</li></ul>ASSESSING <br />CLIMATE CHANGE<br />IMPACT ON AGRICULTURE<br />AND<br />COSTS OF ADAPTATION<br />IMPACT <br />Partial equilibrium ag. sector model<br />Base year 2005; projection to 2050<br />Model changes in crop area and yields<br />production, value of production, <br />food availability per capita, child malnutrition and hunger impacts<br />DSSAT 4.02<br />Yield changes of staple crops<br />IFPRI Food Policy Report 21 “Climate change impact on agriculture and costs of adaptation”<br />
    23. 23. 1965<br />SIMULATED<br />LONG-TERM<br />CHANGES IN<br />CROP YIELD<br />Rainfed<br />Maize<br />SSA<br />DSSAT 4.5<br />1965-2000<br />Climate: CRU-Mashup<br />Soil: HC27<br />2000<br />
    24. 24. Cumulative<br />Probability (%)<br />SIMULATED<br />LONG-TERM<br />CHANGES IN<br />CROP YIELD<br />Rainfed<br />Maize<br />Ethiopia<br />DSSAT 4.5<br />1961-2000<br />Climate: CRU-MashupSoil: HC27<br />Cultivar: <br /><ul><li> Traditional
    25. 25. Hybrid</li></li></ul><li>MAIZE Price modeling<br />Estimating Farm Gate Prices<br />
    26. 26. Price modeling overview<br /><ul><li>Ports locations
    27. 27. Land cover types
    28. 28. Elevation and Slope
    29. 29. Regulation cost
    30. 30. Storage cost
    31. 31. Marketing Margins</li></ul>20<br />
    32. 32. Farm-gate maize price modeling<br />
    33. 33. Spatial analysis at ifpri<br />IFPRI Spatial Ignite | 3 June 2010<br />
    34. 34. SPATIAL<br />ECONOMETRIC<br />ANALYSES<br />1991-2006<br />Neighbor’s Growth Effect (%)<br />
    35. 35. AGRICULTURAL<br />WATER<br />MANAGEMENT<br />SOLUTIONS<br />SPAM<br />SWAT<br />EPIC<br />
    36. 36. AGRICULTURE<br />AND<br />MALARIA <br />RISK<br />Maize, tree crops, and cattle<br />Household survey 2006<br />7,426 households<br />14,000 parcels<br />Geographically-weightedregression<br />High Risk?: Cattle and Maize within 2-km radius<br />Low Risk?: No Agriculture within 2-km radius<br />
    37. 37. MODELING<br />ACCESSIBILITY TO<br />HEALTH CARE<br />SERVICES<br />Yemen<br />Gravity model<br />Accessibility<br />Hospitals<br />Rural health centers<br />Educational programs<br />
    38. 38. DATA<br />VISUALIZATION<br />Tableau Public<br />http://tableausoftware.com<br />