Glenn Hyman Centro Internacional de Agricultura Tropical

1. Análisis Espacial y Sistemas de Información Geográfica para establecer prioridades Regionales en la Investigación y Desarrollo Agropecuario Glenn Hyman Centro Internacional de Agricultura Tropical

2. OUTLINE • Mapping, GIS and Spatial Analysis for Targeting • Political and Agroecological Units • New Information for Targeting and Priority-setting • Some directions for the Future

3. Mapping, GIS and Spatial Analysis for Targeting

4. Thematic Mapping of “Necesidades Basicas Insatisfechas” at different scales in Honduras

5. Village-level mapping based on multivariate statistical analysis in the Central Peruvian Amazon

6. Boolean Analysis Preliminary overlay of areas of high population density [shown in lavender, > 25 people/km2] and low rice yields [shown in yellow < 1.5 tons/ha ] Purple colors show areas meeting both conditions

7. Spatial Clustering Methods based on village-level census data in Honduras

8. Fungicide Applications Model based on data Source: Hijmans et al. 2000

9. Political and Agroecological Units Most interventions are carried out in countries, departments, municipios, villages Biological, soil and climate processes occur in agroecological zones Priority setting exercises should analyze conditions according to both political and agroecological units Significant improvements can be made by using information at higher spatial resolution (e.g. TAC priority setting exercises)

10. Most interventions occur according to political units 10,400 units most are municipios Municipio (canton) level Parroquia level As a general principal, geographical targeting improves with greater spatial resolution (e.g. leakage)

11. CIAT Agroecological Zones from climate classificiation (cluster analysis method)

12. Source: Jeff White, CIMMYT Classification based on length of growing season

13. Population from Census Improving socioeconomic data for further analysis Modifiable Areal Unit Problem - estimation techniques improve spatial resolution Accessibility Model of Population

14. Modeled population counts compared to actual census data for Peru Modeled estimates reduced overall error by one half in Peru Sum of errors when modeled population is compared to actual census data PERU

15. Population and Vegetation Types in Honduras - with better resolution maps, we can more easily estimate population in vegetation zones CCAD Vegetation Map

16. Potential Agricultural Productivity Population summed by zones Note: Areas en black are cities. Zones in Purple are higher rural population densities. Zones in green are low rural population densities. Raster Population Surface (digital map)

17. Agricultural potential in calories/ha/yr From a spatial overlay, we can estimate the number of rural people living in different classes of agricultural potential.

18. Overlay of Land Degradation and Population Maps Sources: GLASSOD, CIAT Population Data

20. New Information for Targeting and Priority-setting

21. Global Land Cover from 1 km AVHRR Source: USGS

22. Source: WRI, IFPRI

23. New Climate Data and Application Tools, Including IWMI’s World Water Atlas and CIAT’s MarkSim Example:Weather Stations used in calibration set for Markov climatic models

24. Global Population Data Set At 1 km spatial resolution Source: LandScan

25. Global Nighttime Lights Database From U.S. National Geophysical Data Center

26. Collection Dates from Last Census Round Year of Census

27. Most countries plan to conduct their next census within the next few years.

28. NATIONAL SPATIAL DATA INFRASTRUCTURES GIS is moving to the Internet Survey for 21 countries • Leadership + Participation • Core Data • Components •  Pricing • Legal restrictions • Challenges http://www.procig.org http://www.gsdi.org

29. Gateways to Geographic InformationNSDI Clearinghouse Growth 1999 Source: United States Geological Survey 200 180 160 140 120 100 80 60 40 20 0 Jun-95 Jun-96 Feb-97 Dec-97 Sep-98 Mar-99 Jun-99 Sep-99 Nov-99 Intn'l Domestic Gateway Total

30. Source: United States Geological Survey

31. Instituto Nacional de Estadística Secretaría de Agricultura y Ganadería Secretaría de Recursos Naturales y Ambiente Instituto Nacional de Estadística Ministerio de Agricultura, Ganadería y Alimentación Comisión Nacional de Medio Ambiente Instituto Geográfico Nacional Instituto Nacional de Estadística y Censos Ministerio Agropecuario y Forestal Ministerio de Ambiente y Recursos Naturales INETER Dirección General de Estadística y Censos Ministerio de Agricultura y Ganadería Ministerio de Medio Ambiente y Recursos Naturales Viceministerio de Vivienda y Desarrollo Urbano Instituto Geográfico Nacional CIAT Instituto Nacional de Estadística y Censos Ministerio de Agricultura y Ganadería Ministerio de Ambiente y Energía Instituto Geográfico Nacional CATIE Dirección de Estadística y Censos Ministerio de Desarrollo Agropecuario Autoridad Nacional de Ambiente Instituto Geográfico Nacional http://www.procig.org PROCIG 26 instituciones participantes

32. Some directions for the Future • Computational Geography • Better data, and power to analyze it • Combining Census and Survey Methods • LSMS • DHS • Better Integration between the • Social, Economic, Biological • and Geographical

33. A relatively small investment in improved geographical targeting could yield large gains in poverty reduction