A spatial analysis: creating similarity domains for targeted research sites in Zimbabwe. Andries Potgieter
A Spatial Analysis:Creating similarity domains fortargeted research sites inZimbabwe“Overcoming poverty is not a gesture of charity, it is an act of justice”- Nelson Mandela, 2006 Photos – D Rodriguez Andries Potgieter and many others
ObjectiveAim:This spatial analysis has been commissioned by ACIAR to supplyspatial data layers (including climate, production, market accessand population) to develop similarity domains (on specificallyclimate and soil type) for research locations within the agriculturalland-use of Zimbabwe.- To Enhance the uptake of targeted farming systems technologies- To Assist funding bodies and policy makers to target those regions thatwill have the highest impact from intervention and investmentRisks & Uncertainties:- Lack of accurate agricultural information across temporal and spatial scales- Climate variability and change- Access to markets (input & output)- Political instability- High inflation
Did you know? - Area: 39 million ha (the size of Japan) flanked by RSA, Botswana, Mozambique and Zambia - Population: 13 million - Agriculture contributes 18% of GDP - Despite a 20% increase in area planted over the last year, 1.68 million people are currently in need food assistance (FAO 2011) NDVI AUC 1999,2000,2001 Inverse colours (blue low, red high)
Food Security Global Food Production 0.35 Global per capita harvested area [ ha person -1] Global cereal yields [kg ha-1] 3,000 0.25 2,000 1,000 0.15 source: Lui et al & FAO 1960 1970 1980 1990 2000 2010
Data• CLIMATE: Worldclim 1.4 database (http://www.worldclim.org/current)(Hijmans, Cameron et al. 2005)• SOIL: The SOTER soil database (Batjes 2004)• POPULATION: Population 2000 & 2005: SEDAC(http://sedac.ciesin.columbia.edu/gpw/global.jsp)• MARKET ACCESS: Cost distance grid for population greater than10,000 (Kai Sonder and Gomez 2010)• LAND USE: Global land cover - GLC2000(http://bioval.jrc.ec.europa.eu/products/glc2000/products.php)• CROP DISTRIBUTION: Gridded crop distributions(http://harvestchoice.org:8080/geonetwork/srv/en/main.home)• AGRICULTURAL POTENTIAL: Simulated maize yields (DSAT) fromJawoo Koo (IFPRI, http://harvestchoice.org)
Approach- Criteria for scaling out of locations to domains are (provided by CIMMYT): - Growing season rainfall (GSR) November to March (summer) - +/- 100mm precipitation in summer, - +/- 2o max temp (summer), - +/- 2o min temp (summer), - Soils: select similar soil classes of maximum rooting depth and soil texture to each location- Agricultural potential Simulated maize yields were generated using difference between Low Technology inputs (LI) (manure, manual labour etc.) and High Technology inputs (HI) (fertiliser, machinery etc.)- Overlaying of Climate and Soil layers where used to create the similarity domainswhile other data inputs where aggregated within each domain
Natural regions Final Site Locations - 3 sites moderate to high - 1 site moderate - 2 sites lowNRs II and III account for around 84% oftotal maize production (FAO 2006)
Total Average Rainfall Average total Winter, Summer & Annual rainfall
Average Summer Temperatures Minimum (10oC – 24oC) Maximum (15oC – 35oC)
Soils Maximum rooting depth and Soil texture. % of Soil Ag Land use VS - 18% S - 8% M - 20% D - 39% VD - 13%
Domain Extraction Rainfall (mm) Temperature oC AN SM WT SM Max WT Max SM Min WT Min AN Max AN MinGwanda 474 407 67 28 26 19 9 27 13Gweru 630 568 62 27 24 18 10 25 13Zimuto 647 575 72 26 24 18 8 25 12Wedza 816 709 107 29 26 19 9 27 13Madziwa 834 783 51 25 23 17 10 24 13Bindura 902 830 72 27 25 18 9 26 13 Rootable Soil Market Avg Domain PopulationLocation Depth (cm) Texture Access (hrs) travel time Area 2000 2005 Growth %ChangeGwanda M CL 0 3.4 283,184 50,409 51,356 947 2Bindura D CL 4 3.9 404,729 209,499 232,214 22,715 11Wedza S LS 2 4.2 694,153 193,099 204,221 11,122 6Madziwa M LS 3 4.5 870,442 330,721 363,542 32,821 10Zimuto S LS 8 4.6 1,645,682 516,219 553,792 37,573 7Gweru X S 4 4.3 1,696,315 536,207 589,658 53,451 10Very shallow (VS); <30cm (18%),Shallow (S); 30 – 50cm (8%),Moderate deep (M); 50 – 100cm (20%),Deep (D); 100 – 150cm (39%) andVery deep (X); >150cm (13%)
Agricultural PotentialLow input (manure, manual labour etc.) High input (fertiliser, machinery etc.) • Currently actual 3-year avg DOMAIN yield < 1.75 t/ha (FAO) • Potential Yield Gap remains high across all regionsHowever, farmers who applied good management e.g. buying and applying seeds and fertiliser intime, weeding and have access to hire machinery can get yields of more than 3 tonnes/ha (nationalaverage ~0.58 tonnes/ha) as was the case in parts of Mashonaland West during the 2002/2003season (FAO 2003).
Summary Successfully extracted similarity domains for six locations/sites in Zimbabwe. Most domains showed a relatively low average (~4 hours) travelling time to thenearest market hub compared to the remainder of the country. However, during some years farmers will need to travel even further to acquireseed and fertiliser, which can constrain their management ability and thus leading tomuch lower yield than what is achievable. Yield Gap between what is currently achieved and what potentially can be achievedremains high. Identified spatial regions that will benefit the most from extrapolating targetedfarming systems technologies from the selected research locations and thus havethe highest impact from intervention and investment. Participatory research targeting integrated farming systems is necessary todetermine more site specific and best fitted crop production risk and managementpractices for especially maize-legume systems as is currently being undertakenthrough the SIMLESA ACAIR funded project.