1. Open access SPAM for Ghana: Challenges and opportunities Ulrike Wood-Sichra and Liangzhi You GEOSHARE: Geospatial Open Source Hosting of Agriculture, Resource & Environmental Data for Discovery and Decision Making Sep 10-11, 2014 International Food Policy Research Institute, Washington DC

2. Leading to “SPAM Challenges and Opportunities” What is SPAM The SPAM process Visualization of downscaling SPAM results Equations behind SPAM Crop list and statistical data coverage Challenges Opportunities

3. SPAM ? from http://www.cs4fn.org/ X X

4. Spatial Production Allocation Model (SPAM) Drawing on a variety of inputs, SPAM uses an entropy- based, data-fusion approach to “plausibly” assess cropping system distribution and performance at a “meso-gridded” scale: 5-minute globally, 30-seconds at country level (if data available) Major input layers in SPAM: – National and subnational production statistics: area, yield (production) – Production system – Rural population density – Cropland extent – Irrigation map – Crop suitability – Existing crop distribution maps – Crop prices

5. The SPAM process Sub-national area (crop) irrigated_ land agricultural_land suitable_area (crop) crop distribution (crop) SPAM other data (..,crop) pixelized area (crop)

6. Sub-national area (crop) irrigated_ land agricultural_land suitable_area (crop) crop distribution (crop) SPAM other data (..,crop) pixelized area (crop) PRIOR irrigated PRIOR subsistence PRIOR rainfed_high PRIOR rainfed_low 42 crops simultaneously The SPAM process

7. some more data Behind the scene adjustments/calculations Sub-national area (crops) irrigated_ land agricultural_land suitable_area (crop) FIXED!! Sum >= pix = max(ag,irr) pix = f(suit,ag,irr) Pre-process Sum >= irrigated subsistence rainfed-high rainfed-low PRIORS The SPAM process... continued

8. Visualization of Downscaling example: Ghana harvested area rice per region

9. layer 1: Northern Region - harvested area rice Visualization of Downscaling... continued

10. layer 2: Northern Region – irrigated area irrigated area harvested area rice Visualization of Downscaling... continued

11. layer 3: Northern Region – agricultural area agricultural area harvested area rice irrigated area Visualization of Downscaling... continued

12. layer 4: Northern Region – rural population rural population harvested area rice irrigated area agricultural area Visualization of Downscaling... continued

13. layer 5: Northern Region – suitable irrigated area rice suitable irrigated area rice harvested area rice irrigated area agricultural area rural population Visualization of Downscaling... continued

14. layer 6: Northern Region – suitable rainfed high area rice suitable rainfed high area rice harvested area rice irrigated area agricultural area rural population suitable irrigated area rice Visualization of Downscaling... continued

15. layer 7: Northern Region – suitable rainfed low area rice suitable rainfed low area rice harvested area rice irrigated area agricultural area rural population suitable irrigated area rice suitable rainfed high area rice Visualization of Downscaling... continued

16. some more data result 1: Northern Region – allocated irrigated area rice Visualization of Downscaling... continued

17. some more data result 2: Northern Region – allocated rainfed high area rice Visualization of Downscaling... continued

18. some more data result 3: Northern Region – allocated rainfed low area rice Visualization of Downscaling... continued

19. some more data result 4: Northern Region – allocated subsistence area rice Visualization of Downscaling... continued

20. some more data result 5: Northern Region – allocated rice area total Visualization of Downscaling... continued scaled to FAO Country Totals

21. Crop Distribution – Maize 2005 SPAM Results Crop Distribution – Vegetables 2005

22. Equations behind SPAM Minimize difference between prior and allocated area share for all pixels, crops and production systems in a cross entropy equation subject to constraints (limits) dictated by existing agricultural area irrigated area suitable area crop area statistics and solve in GAMS But first calculate the priors for each pixel, crop and production system as a function of potential revenue, irrigated area and agricultural land, where potential revenue is a function of percentage of crop area, crop price, rural population density and potential yield

23. Equations behind SPAM S ijl is the area share allocated to pixel i and crop j at input level l Potential Revenue Prior ∏ ijl = f(Potential revenue, Irrigated area, Cropland) now it becomes serious

24. SPAM Prior Calculation Subsistence crop area: Irrigated crop area: Rainfed crop area:

25. SPAM Optimization Subject to:

26. SPAM2005 Crop List and... 1wheat14dry beans28sugarcane 2rice15chickpea29sugar beet 3maize16cowpea 4barley17pigeon pea30cotton 5pearl millet18lentils31other fibres 6finger/small millets19other pulses32arabica coffee 7sorghum33robusta coffee 8other cereals20soybeans34cocoa 21groundnuts35tea 9potato22coconuts36tobacco 10sweet potato23oil palm37banana 11yam24sunflower38plantains 12cassava25rapeseed39tropical fruit 13other roots & tubers26sesame seed40temperate fruit 27other oil crops41vegetables 42rest cereals roots & tubers pulses oil crops sugar c. fibres stimulants fruits & veg all the rest “rest” accounts for only 2-5 % of global crop harvested area – with exceptions

27. ... Statistical Area Coverage – subnational

29. Crop Distribution – Maize/Vegetable 2005

30. Validation Validation process by other CGIAR centers (e.g. IRRI, CIAT, ILRI, CIP, CYMMT). Each focuses on the mandate crops. Crop map view ‘parties’ by local experts and agronomists Crowd-sourcing on a dedicated website (mapSPAM.info) Through implementation on the HUB with GEOSHARE

31. Challenges Different sources -> ‘contradictory’ information Raster data not at same scale Sub-national data complete, at least level1, better level2 Conform national crops -> FAO/SPAM crops Consistencies between layers – constraints met ag_land >= stats, irr >= crop_irr, suit_land >= ag_land >= stats Cropping intensities & production systems shares consistent with data and model Validation of results, eg through implementation on the HUB with GEOSHARE

32. Opportunities Driven by applications or user preferences: Accessibility for HUBZero community – move out of IFPRI ‘enclosure’ Include data at larger scale – 10x10 km -> 1x1 km Use most recent data - statistics, ag_land, irrigation, distribution, administrative units Provide and use national/sub-national prices Change crop list – expand/reduce (suitabilities?) Proprietory suitability conditions (modify model) Reproducibility of results Teaching tool for GIS, modelling, GAMS Validation ‘easier’ at large scale, reduced area

33. Thank you !

34. Minas Gerais missing Area of Industrial production There should be more in the Arg finger here Mismatch with cassava atlas Very good

35. Generally looks good

36. Path From Admin Units to Pixels 80 grids (20 crops x 4 systems)

37. Users and Applications CGIAR centers such as IRRI, CYMMT, CIP, CIAT, ILRI. FAO, World Bank, and universities HarvestPlus, Agricultural Water Management, AgFuture Fill the gaps between micro-macro linkage, between biophysical models and economic models The most critical comparative advantage we have as a team. Widely applied in country strategy work within IFPRI, regional priority settings such as ASARECA, CORAF, and in ReSAKSS, CAADP, AGRA

38. Issues Unreliable, often carelessly processed/validated statistics Challenges matching statistics with admin. area shape files Unreliable cropland extent/area intensity estimates Lack of data on cropping patterns and systems (e.g. cropping intensity – converting harvested to physical land footprint) Unsatisfactory data on location-specific biophysical conditions (e.g., soils) and economic behaviour (e.g., prices and risks) Lack of established validation data, methods, and protocols Scientific peer review does not imply data are “fit-for-purpose” Unspecified reliability of results Consistency of approach has potential tradeoff with reliability (e.g. patchwork of best national data vs consistent regional data)