This document summarizes various analyses conducted on agricultural development domains in sub-Saharan Africa, including: 1) Analysis of existing land cover and potential agricultural land based on factors like rainfall using satellite data. 2) Analysis of market access based on distance to ports and incorporation of transportation costs. 3) Crop modeling and analysis of potential impacts of climate change on yields and food availability through 2050.

1. Development Domain Analysis Agricultural Potential Land Cover Market Access Crop Systems Modeling Farm-gate Price Modeling Spatial Analysis at IFPRI ZHE GUO*, EMILY SCHMIDT, JAWOO KOO, RIA TENORIO * GIS COORDINATOR (z.guo@cgiar.org) Environment and Production Technology Division International Food Policy Research Institute AAGW2010/NAIROBI 9 JUNE 2010

2. Domain Analysis Overview

4. Land cover

5. Market access Development Domain Analysis

7. Existing: land cover * Rural Population *[Market access]

8. Potential: Ag-potential (eg. LGP, NDVI) * Rural Population*[Market access]

9. Further economic analysis (e.g. DREAM model)existing cropland *rural population density potential cropland *rural population density 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. 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.

10. AGRICULTURAL POTENTIAL Domain Analysis

11. Ag-potential variables

12. PLANTING WINDOW OF RAINFED CROPS SSA MODIS Greenness Up/Down Season A/B Weekly window

13. Land Cover Domain Analysis

14. Existing cropland variables GLC2000 (2000) Globcover (2005) MODIS (2001) Africover (~2000)

16. Existing cropland variables

17. Market Access Domain Analysis

18. Market access

19. Crop systems modeling Biophysical Evaluation

20. SIMULATED CLIMATE CHANGE IMPACTS ON CROP YIELD 2000 and 2050 DSSAT 4.5 Maize Subsistence FutureClim 1.0 MIROC 3.2 (IPCC4) A2 SRES

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.ASSESSING CLIMATE CHANGE IMPACT ON AGRICULTURE AND COSTS OF ADAPTATION IMPACT Partial equilibrium ag. sector model Base year 2005; projection to 2050 Model changes in crop area and yields production, value of production, food availability per capita, child malnutrition and hunger impacts DSSAT 4.02 Yield changes of staple crops IFPRI Food Policy Report 21 “Climate change impact on agriculture and costs of adaptation”

23. 1965 SIMULATED LONG-TERM CHANGES IN CROP YIELD Rainfed Maize SSA DSSAT 4.5 1965-2000 Climate: CRU-Mashup Soil: HC27 2000

27. Land cover types

28. Elevation and Slope

29. Regulation cost

30. Storage cost

31. Marketing Margins20

32. Farm-gate maize price modeling

33. Spatial analysis at ifpri IFPRI Spatial Ignite | 3 June 2010

34. SPATIAL ECONOMETRIC ANALYSES 1991-2006 Neighbor’s Growth Effect (%)

35. AGRICULTURAL WATER MANAGEMENT SOLUTIONS SPAM SWAT EPIC

36. AGRICULTURE AND MALARIA RISK Maize, tree crops, and cattle Household survey 2006 7,426 households 14,000 parcels Geographically-weightedregression High Risk?: Cattle and Maize within 2-km radius Low Risk?: No Agriculture within 2-km radius

37. MODELING ACCESSIBILITY TO HEALTH CARE SERVICES Yemen Gravity model Accessibility Hospitals Rural health centers Educational programs

38. DATA VISUALIZATION Tableau Public http://tableausoftware.com