This spatial analysis created six similarity domains for targeted research sites in Zimbabwe based on climate, soil type, population, market access, and agricultural potential. The domains showed relatively low average travel times to markets but noted constraints to input access could still limit yields. While current average yields are below 1.75 tons/ha, the analysis found a continued high potential yield gap across the regions. It identified spatial areas that would most benefit from extrapolating targeted farming technologies from the research locations.

1. A Spatial Analysis:
Creating similarity domains for
targeted research sites in
Zimbabwe
“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

2. Objective
Aim:
This spatial analysis has been commissioned by ACIAR to supply
spatial data layers (including climate, production, market access
and population) to develop similarity domains (on specifically
climate and soil type) for research locations within the agricultural
land-use of Zimbabwe.
- To Enhance the uptake of targeted farming systems technologies
- To Assist funding bodies and policy makers to target those regions that
will have the highest impact from intervention and investment
Risks & Uncertainties:
- Lack of accurate agricultural information across temporal and spatial scales
- Climate variability and change
- Access to markets (input & output)
- Political instability
- High inflation

3. 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)

4. 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

5. 1000
1500
2000
2500
3000
0
500
1980/81
1981/82
1982/83
1983/84
1984/85
1985/86
1986/87
1987/88
1988/89
Food Security
1989/90
1990/91
Production (Tons ' 000)
1991/92
1992/93
Year
1993/94
1994/95
1995/96
1996/97
Zimbabwe: Maize production
Area (Ha ' 000)
1997/98
1998/99
1999/00
2000/01
2001/02
2002/03
Yield (Kgs/ha)
2003/04
2004/05
2005/06
Funk & Brown 2010

6. Challenges
Likely impact of climate change on maize yields for Africa
Impact on maize yields by 2050 - Percent loss relative to 1990 (Schlenker and
Lobell, 2009)

7. Food Security Security
Zimbabwe: Food
20000
15000
10000
5000
0
94
95
96
97
98
99
00
01
02
03
04
05
06
19
19
19
19
19
19
20
20
20
20
20
20
20
-5000
-10000
-15000 Zimbabwe; Funk & Brown 2010
Population Yield (Kgs/ha) Prod ' 000 FII ' 000

8. 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 than
10,000 (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) from
Jawoo Koo (IFPRI, http://harvestchoice.org)

9. 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 domains
while other data inputs where aggregated within each domain

10. Natural regions
Final Site Locations
- 3 sites moderate to high
- 1 site moderate
- 2 sites low
NRs II and III account for around 84% of
total maize production (FAO 2006)

11. Total Average Rainfall
Average total Winter, Summer & Annual rainfall

12. Average Summer Temperatures
Minimum (10oC – 24oC) Maximum (15oC – 35oC)

13. Soils
Maximum rooting depth and Soil texture.
% of Soil Ag Land use
VS - 18%
S - 8%
M - 20%
D - 39%
VD - 13%

14. Domain Extraction
Rainfall (mm) Temperature oC
AN SM WT SM Max WT Max SM Min WT Min AN Max AN Min
Gwanda 474 407 67 28 26 19 9 27 13
Gweru 630 568 62 27 24 18 10 25 13
Zimuto 647 575 72 26 24 18 8 25 12
Wedza 816 709 107 29 26 19 9 27 13
Madziwa 834 783 51 25 23 17 10 24 13
Bindura 902 830 72 27 25 18 9 26 13
Rootable Soil Market Avg Domain Population
Location Depth (cm) Texture Access (hrs) travel time Area 2000 2005 Growth %Change
Gwanda M CL 0 3.4 283,184 50,409 51,356 947 2
Bindura D CL 4 3.9 404,729 209,499 232,214 22,715 11
Wedza S LS 2 4.2 694,153 193,099 204,221 11,122 6
Madziwa M LS 3 4.5 870,442 330,721 363,542 32,821 10
Zimuto S LS 8 4.6 1,645,682 516,219 553,792 37,573 7
Gweru X S 4 4.3 1,696,315 536,207 589,658 53,451 10
Very shallow (VS); <30cm (18%),
Shallow (S); 30 – 50cm (8%),
Moderate deep (M); 50 – 100cm (20%),
Deep (D); 100 – 150cm (39%) and
Very deep (X); >150cm (13%)

15. Similarity Domains
Gwanda Gweru Zimuto
Madziwa Wedza Bindura

16. Agricultural Potential
Low 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 regions
However, farmers who applied good management e.g. buying and applying seeds and fertiliser in
time, weeding and have access to hire machinery can get yields of more than 3 tonnes/ha (national
average ~0.58 tonnes/ha) as was the case in parts of Mashonaland West during the 2002/2003
season (FAO 2003).

17. Summary
 Successfully extracted similarity domains for six locations/sites in Zimbabwe.
 Most domains showed a relatively low average (~4 hours) travelling time to the
nearest market hub compared to the remainder of the country.
 However, during some years farmers will need to travel even further to acquire
seed and fertiliser, which can constrain their management ability and thus leading to
much lower yield than what is achievable.
 Yield Gap between what is currently achieved and what potentially can be achieved
remains high.
 Identified spatial regions that will benefit the most from extrapolating targeted
farming systems technologies from the selected research locations and thus have
the highest impact from intervention and investment.
 Participatory research targeting integrated farming systems is necessary to
determine more site specific and best fitted crop production risk and management
practices for especially maize-legume systems as is currently being undertaken
through the SIMLESA ACAIR funded project.

18. thank you