The document discusses spatial modeling and applications at the International Food Policy Research Institute (IFPRI). Some key applications discussed include spatial price modeling, mapping crop calendars and agricultural productivity trends, poverty mapping, and modeling the impacts of fertilizer policy options in East Africa. The presentation also discusses methodology for spatially modeling farmgate prices and crop yields at different locations.

1. Spatial modeling, analysis and its
applications in IFPRI
Zhe Guo (z.guo@cgiar.org)
Africa Agriculture GIS Week 2013(AAGW3)
March 11-March 16, 2013

2. Overview
 Spatial price modeling
 Mapping crop calendar
 Trends and spatial patterns of Ag. Productivity
 Spatial production allocation model (SPAM)
 Poverty mapping
 Mapping livestock from household and census data
 Arabic spatial
 Modeling Farmers’ Agricultural Knowledge Spillover
 The Economics of Land Degradation:
A Way Forward for An Action-Oriented Global
Economic Assessment
2

3. Overview
 Spatial price modeling
 Mapping crop calendar
 Trends and spatial patterns of Ag. Productivity
 Spatial production allocation model (SPAM)
 Poverty mapping
 Mapping livestock from household and census data
 Arabic spatial
 Modeling Farmers’ Agricultural Knowledge Spillover
 The Economics of Land Degradation:
A Way Forward for An Action-Oriented Global
Economic Assessment
3

4. Fertilizer policy options in East Africa
When developing its regional fertilizer strategy, AGRA requested
an assessment of the impacts of three strategies on local
fertilizer prices:
1. Reducing the landed cost of fertilizer through collective bulk
purchasing by Eastern and Southern Africa countries.
2. Reducing transport costs through improved road and related
transportation infrastructure and transport fleet.
3. Reduced transactions costs through improved
harmonization and streamlining of border crossing/customs
procedures.

5. Methodology/Data Development
1. Capture Heterogeneity of Location-Specific Effects over
a Large Geographic Region. Recognizing that adoption
is driven by local realities, such as the effective farmgate
prices of inputs and outputs, and site-specific impacts of
technologies.
2. Regional Application of a Site-Specific Crop Yield
Model (DSSAT) driven by location-specifc estimates of
weather, soils and crop management.
3. Collection and analysis of regional transport and
market prices (on-going for SSA)

6. Assessing Farm-gate Prices: 1. Imported
Inputs
Pfert, farm
Farm Farmgate
Fertilizer Price?
Off-road
X
Seasonal
Road
Dual
Carriageway
Single
Carriageway
National
Border
Port
Pfert, port
Farmgate Fertilizer Price:
Pfert, farm = Pfert, port + Build-up costs
(Handling + “Barriers” + Transport Costs)

7.  Core is a transport cost model
 Input parameters can include spatial and non spatial data
 Involves simulating every transportation path option from starting point to the end point.
 The model first identifies all possible paths from the starting point to the end point.
 The model calculates the cost of all possible pathways
 Only the least cost path is selected

8. Assessing Farmgate Prices: 2. Output Surplus to Local Markets
Pfert, farm
Pmaize, farm Farm Farmgate
Fertilizer Price?
Off-road
Farmgate
X
Maize Seasonal
Price? Road
Dual
Carriageway
Pmaize,market Single
Carriageway
Maize
National
Market Border
Port
Pfert, port
Farmgate Fertilizer Price:
Pfert, farm = Pfert, port + Build-up costs
(Handling + “Barriers” + Transport Costs)
Farmgate Maize Price
Pmaize, farm = Pmaize,market - Transport Costs

9. Estimating Farm-gate Maize Prices
Transport Costs to Markets Final Farmgate Price

10. *DSSAT Cropping System Model Ver. 4.0.2.000 May 21, 2009; 16:32:33
*RUN 1 : RAINFED LOW NITROGEN
*DSSAT Cropping System Model Ver. 4.0.2.000 May 21, 2009; 16:32:33
MODEL : MZCER040 - MAIZE
EXPERIMENT : UFGA8201 MZ NIT X IRR, GAINESVILLE 2N*3I
*RUN 1 : RAINFED LOW NITROGEN
TREATMENT *DSSAT Cropping LOW NITROGEN Ver. 4.0.2.000
1 10: RAINFED System Model May 21, 2009; 16:32:33
MODEL : MZCER040 - MAIZE
CROP 9: MAIZE CULTIVAR : McCurdy 84aa ECOTYPE :IB0002
EXPERIMENT : UFGA8201 MZ NIT X IRR, GAINESVILLE 2N*3I
STARTING DATE 8: 1
*RUN FEB 25 1982RAINFED LOW NITROGEN
:
TREATMENT 1 : RAINFED LOW NITROGEN
PLANTING DATE 7: FEB 26 1982MZCER040 - MAIZE 7.2
MODEL : PLANTS/m2 : ROW SPACING : 61.cm
CROP : MAIZE CULTIVAR : McCurdy 84aa ECOTYPE :IB0002
WEATHER EXPERIMENT
6
: UFGA 1982UFGA8201 MZ NIT X IRR, GAINESVILLE 2N*3I
:
STARTING DATE : FEB 25 1982
SOIL TREATMENT 1
Yield : IBMZ910014 RAINFED LOW NITROGEN - Millhopper Fine Sand
: TEXTURE :
PLANTING DATE : FEB 26 1982
(t/ha)
5 PLANTS/m2 : 7.2 ROW SPACING : 61.cm
SOIL INITIAL C : DEPTH:180cmMAIZE H2O:160.9mm NO3:: McCurdy 84aa
CROP : EXTR. CULTIVAR 2.5kg/ha NH4: 12.9kg/ha :IB0002
ECOTYPE
WEATHER 4 : UFGA 1982
WATER BALANCE : IRRIGATE : FEB 25 1982
STARTING DATE ON REPORTED DATE(S)
SOIL 3 : IBMZ910014 TEXTURE : - Millhopper Fine Sand
IRRIGATION PLANTING DATE : FEB 26 1982
: 13 mm IN 1 APPLICATIONS
PLANTS/m2 : 7.2 ROW SPACING : 61.cm
SOIL INITIAL C : DEPTH:180cm EXTR. H2O:160.9mm NO3: 2.5kg/ha NH4: 12.9kg/ha
NITROGEN BAL. 2: SOIL-N & : UFGA
WEATHER N-UPTAKE 1982 SIMULATION; NO N-FIXATION
WATER BALANCE : IRRIGATE ON REPORTED DATE(S)
N-FERTILIZER SOIL 1: 116 : IBMZ910014 3 APPLICATIONS
kg/ha IN TEXTURE : - Millhopper Fine Sand
IRRIGATION : 13 mm IN 1 APPLICATIONS
RESIDUE/MANURE 0:INITIAL C :: DEPTH:180cm ;EXTR. H2O:160.9mm NO3: APPLICATIONS
SOIL INITIAL 1000 kg/ha 0 kg/ha IN 0 2.5kg/ha NH4: 12.9kg/ha
NITROGEN BAL. 0 : SOIL-N & N-UPTAKE SIMULATION; NO N-FIXATION
ENVIRONM. OPT. : BALANCE 500.00 100
WATER DAYL= SRAD= 150 0.00 TMAX=
200
: IRRIGATE ON REPORTED DATE(S) 0.00 TMIN= 0.00
N-FERTILIZER : 116 kg/ha IN 3 APPLICATIONS
IRRIGATION RAIN= : Fertilizer (kg[N]/ha)=mm IN
0.00 CO2 13 R330.00 1 APPLICATIONS WIND=
DEW = 0.00 0.00
RESIDUE/MANURE : INITIAL : 1000 kg/ha ; 0 kg/ha IN 0 APPLICATIONS
SIMULATION NITROGEN BAL. :Y SOIL-N & N-UPTAKE SIMULATION; :N N-FIXATION
OPT : WATER : NITROGEN:Y N-FIX:N PHOSPH NO PESTS :N
ENVIRONM. OPT. : DAYL= 0.00 SRAD= 0.00 TMAX= 0.00 TMIN= 0.00
N-FERTILIZER :C ET 116 kg/ha IN
PHOTO : :R INFIL:S 3 APPLICATIONS
HYDROL :R SOM :G
RAIN= 0.00 CO2 = R330.00 DEW = 0.00 WIND= 0.00
MANAGEMENT RESIDUE/MANURE : INITIAL : :R1000 kg/ha ;
OPT : PLANTING:R IRRIG
SIMULATION OPT : WATER
*SUMMARY OF ENVIRONM. GENETIC DAYL= PARAMETERS
SOIL AND OPT. : INPUT
PHOTO
MANAGEMENT OPT : PLANTING:R IRRIG
SOIL LOWER UPPER
SIMULATION SAT : WATER
RAIN=
:C ET
OPT EXTR INIT:Y ROOT
OUTPUT FERT :R RESIDUE:N kg/ha IN
:Y NITROGEN:Y N-FIX:N PHOSPH :N PESTS :N
0.00 SRAD=
0 HARVEST:M WTH:M
0.00 TMAX=
:R INFIL:S HYDROL :R SOM
0.00 CO2 = R330.00 DEW =
NITROGEN:Y N-FIX:N PHOSPH :N PESTS :N
BULK pH NO3 NH4
0 APPLICATIONS
0.00 TMIN=
:G
0.00 WIND=
:R FERT :R RESIDUE:N HARVEST:M WTH:M
ORG
0.00
0.00
*SUMMARY OF SOIL AND GENETIC INPUT PARAMETERS
DEPTH LIMIT LIMIT SW PHOTO SW:C DIST
SW ET DENS INFIL:S HYDROL :R SOM C :G
:R
cm cm3/cm3
MANAGEMENT OPT : PLANTING:R IRRIG g/cm3 FERT :R ugN/g ugN/g HARVEST:M WTH:M
cm3/cm3 cm3/cm3 :R RESIDUE:N %
SOIL LOWER UPPER
cm cm3/cm3
SAT EXTR INIT
cm3/cm3
5- 15 0.025 SOIL LOWER UPPER 0.086 EXTR INIT
0.086 0.230 0.061 SAT
Phenology
*SUMMARY OF SOIL AND GENETIC INPUT PARAMETERS
DEPTH LIMIT LIMIT
0- 5 0.026 0.096 0.230 0.070 0.086
SW SW
cm3/cm3
SW
1.00
1.00
ROOT
-------------------------------------------------------------------------------
DIST
1.30
1.30ROOT
BULK
DENS
7.00
g/cm3
7.00BULK
pH
0.10
NO3
0.50
ugN/g ugN/g
0.10 pH 0.50 NO3
NH4
2.00
1.00 NH4
ORG
C
%
ORG
-------------------------------------------------------------------------------
flowering, grain/seed/tuber,
15- 30 0.025DEPTH LIMIT LIMIT 0.086
0.086 0.230 0.061
0- 5 0.026 0.096 0.230 0.070 0.086
30- 45 0.025 cm 0.086cm3/cm3 0.061 0.086
0.230 cm3/cm3
5- 15 0.025 0.086 0.230 0.061 0.086
45- 60 0.025 0.086 0.230 0.061 0.086
SW SW
0.70 SW
0.30
cm3/cm3
0.30
1.40DIST
1.00
1.40
1.00
1.40
7.00DENS
1.30
7.00
1.30
g/cm3
7.00
0.10
7.00
0.10
7.00
-------------------------------------------------------------------------------0.10
0.50
0.10
0.50
0.10
1.00
0.50
ugN/g ugN/g
0.50
0.50
0.50
0.50
2.00
1.00
C
%
15- 30 0.025 0.086 0.230 0.061 0.086 0.70 1.40 7.00 0.10 0.50 1.00
maturity
60- 90 0.0280- 5 0.026 0.096 0.230 0.070 0.086
0.090 0.230 0.062 0.076
30- 45 0.025 0.086 0.230 0.061 0.086
90-120 0.0285- 15 0.025 0.086 0.230 0.061 0.086
0.090 0.230 0.062 0.076
45- 60 0.025 0.086 0.230 0.061 0.086
120-150 0.029 0.1300.025 0.086 0.230 0.061 0.086
15- 30 0.230 0.101 0.130
0.05
0.03
0.00
1.451.00
0.30
1.451.00
0.30
1.450.70
7.001.30
1.40
7.001.30
1.40
7.001.40
0.107.00
7.00
0.107.00
7.00
0.107.00
0.600.10
0.10
0.500.10
0.10
0.100.50
0.50 0.50
0.100.50
0.50
0.500.10
0.50
0.040.50
2.00
1.00
1.00
60- 90 0.028 0.090 0.230 0.062 0.076 0.05 1.45 7.00 0.10 0.60 0.10
150-180 0.070 0.2580.025 0.086 0.230 0.061 0.086
30- 45 0.360 0.188 0.258 0.00 1.200.30 7.001.400.107.00 0.500.10 0.240.50 0.50
90-120 0.028 0.090 0.230 0.062 0.076 0.03 1.45 7.00 0.10 0.50 0.10
45- 60 0.025 0.086 0.230 0.061 0.086 0.30 1.40 7.00 0.10 0.50 0.50
TOT-180 60- 90
Yield component
120-150 0.029 0.130 0.230 0.101 0.130
6.2 22.20.028 0.090 0.230 0.062 0.076 kg/ha-->1.45 2.57.00
45.3 16.1 21.4 <--cm
150-180 0.070 0.258 0.360 0.188 0.258
SOIL ALBEDO 90-120 0.028 0.090 0.230 0.062 0.076
RUNOFF CURVE # :60.00
: 0.18 EVAPORATION LIMIT : 2.000.03
120-150 0.029 0.130 0.230 RATE
DRAINAGE 0.101 0.130
0.00
-
0.00
: 0.650.00
1.45
0.05
1.20
7.00
7.00
1.45
1.45
0.10
0.10
MIN. 7.00
FACTOR 0.10
FERT.7.00
0.50
12.90.10
0.50
0.04
870800.60
0.24
: 1.000.50
FACTOR0.10
: 0.800.50
0.10
0.10
0.04
TOT-180 6.2 22.2 45.3 16.1 21.4 <--cm - kg/ha--> 2.5 12.9 87080
MAIZE
P1
SOIL ALBEDO grain/seed/tuber, biomass, LAI
150-180 0.070 0.258 0.360 0.188 0.258
: 0.18
CULTIVAR :IB0035-McCurdy 84aa
RUNOFF CURVE # :60.00
EVAPORATION LIMIT : 2.00
DRAINAGE RATE
: 265.00 P2 6.2 :22.2 45.3 16.1 : 21.4 <--cm
TOT-180 0.3000 P5 920.00
0.00 1.20
ECOTYPE :IB0002
: 0.65
7.00 0.10 0.50
MIN. FACTOR : 1.00
FERT. FACTOR : 0.80
- kg/ha--> 2.5
0.24
12.9 87080
G2 : 990.00ALBEDO
SOIL G3 :: 0.18
8.500 PHINT : 39.000LIMIT : 2.00
EVAPORATION MIN. FACTOR : 1.00
MAIZE CULTIVAR :IB0035-McCurdy 84aa ECOTYPE :IB0002
RUNOFF CURVE # :60.00 DRAINAGE RATE : 0.65 FERT. FACTOR : 0.80
RUN NO.
P1
P1 1
: 265.00 P2 : 0.3000 P5
*SIMULATED CROP AND SOIL STATUS AT MAIN DEVELOPMENT STAGES
G2 : 990.00 G3
MAIZE Growth
: 8.500 PHINT : 39.000
CULTIVAR :IB0035-McCurdy 84aa
: 265.00 LOW NITROGEN
RAINFED P2 : 0.3000 P5
: 920.00
ECOTYPE :IB0002
: 920.00
*SIMULATED CROP AND SOIL STATUS AT MAIN DEVELOPMENT STAGES
RUN NO.
G2
CROP GROWTH
DATE AGE STAGE
1
grain/seed/tuber, biomass, LAI
: 990.00 G3
BIOMASS CROP N
RAINFED LOW NITROGEN
: 8.500 PHINT : 39.000
STRESS
*SIMULATED CROP AND SOIL STATUS AT %MAIN DEVELOPMENT STAGES
kg/ha LAI kg/ha H2O N
------ --- ---------- ----- ----- --- --- ---- ----
CROP GROWTH BIOMASS CROP N STRESS
25 FEB 0RUN NO. Sim 1
Start 0 RAINFED LOW NITROGEN
0.00 0 0.0 0.00 0.00
26 FEB
27 FEB
9 MAR
DATE AGE STAGE
0 Sowing
25 FEB 0 Start Sim
11 Emergence STAGE
DATE AGE
0
0
29
kg/ha
0.00
LAI kg/ha %
0 0.0 0.00 0.00
------ --- ---------- ----- ----- --- --- ---- ----
1 Germinate GROWTH 0.00
CROP BIOMASS 0 0.0 0.00 N
0
0.00
0.00
H2O
SoilN
CROP 0.00 STRESS
0 0.0 0.00 0.00
kg/ha 1 4.4 0.00 0.00 H2O
LAI kg/ha % N
26 FEB 0 Sowing 0 0.00 0 0.0 0.00 0.00
27 MAR
1 APR
27 FEB
9 MAR
26 FEB
nitrogen balance, water balance,
29------ --- ---------- ----- 4 ----- 0.00 --- ---- ----
End Juveni
1 Germinate
0
11 Emergence
251
3425 FEB Ini Start Sim
Floral 304
0 Sowing
0.43
0
0.44
29
0.00
1.6 --- 0.09
0 0.0 0.00 0.00
0 4 0.00 0.00 0.0 0.00 0.00
0.00
0
1.5 0 0.50
1 4.4 0.00 0.00
0.00 0 0.0 0.00 0.00
27 MAR 29 End Juveni 251 0.43 4 1.6 0.00 0.09
1 APR
27 FEB
34 Floral Ini
9 MAR
27 MAR
carbon balance
1 Germinate
11 Emergence
304
29 End Juveni
29
251
0
0.44
0.00 0 0.0 0.00 0.00
4 1.5 0.00 0.50
0.00
0.43
1 4.4 0.00 0.00
4 1.6 0.00 0.09
1 APR 34 Floral Ini 304 0.44 4 1.5 0.00 0.50
IFPRI HPC (80 CPU’s)
CULTIVAR
• Phenology
• Max # of kernels
• Kernel filling rate
10

11. Maize Yield Simulation Settings
 Resolution: 5 arc-minutes ( 10 km gridcells)
 Extent: Kenya, Tanzania, Rwanda, Burundi, Uganda
 Climate: 50 realizations of daily weather from historical
monthly mean climate
 Yield Model: CERES-Maize in DSSAT-CSM v4.5
 Soil: FAO HWSD v1.1 + ISRIC WISE v1.1
 Maize Variety: OPV (medium maturity)
 Planting window: HC Growing Seasons + IIASA GAEZ
v3.0
 Fertilizer rate: Basal and topdressing of urea (0, 10, 20,
…, 100 kg*N+/ha)
 Water: Not managed (i.e., rainfed system)

12. Estimating Value Cost Ratios (VCRs)
“Fertilizer markets have failed in Africa”
 Why?
– Scattered and small size of local market
– Weak demand for use with food staple crops
– High transportation cost – poor road and rail infrastructure,
particularly in landlocked countries
– Low profitability
World Bank ARD Note
Issue 21 (2007)
Value-Cost Ratio (VCR)
y(N)x,y Pricemaize
x,y
VCRx,y
N Pricefertilizer
x,y
 N = fertilizer application rate (kg/ha)
 y(N) = maize yield with fertilizer at N rate (t/ha)
 y(N) = y(N) – y(0) (t/ha)
“…IFDC suggests VCR>2 to accommodate price and climatic
risks and still provide an incentive to farmers”

13. Maximum VCRs and Corresponding N
Application Rates

14. VCRs in Northern and Central Corridors
Northern Corridor
Av. N appl. at
VCR max VCR Maize yield Urea price Maize price
Country (av. max) (kg/ha) (kg/ha) (US$/Ton) (US$/ton)
Kenya 3.31 26.0 1,650 446 186
Rwanda 1.54 20.0 1,473 587 156
Uganda 2.17 30.3 2,274 544 114
Corridor 2.80 27.1 1,849 489 160
Central Corridor
Av N appl. at
VCR max VCR Maize yield Urea price Maize price
Country (av. max) (kg/ha) (kg/ha) (US$/Ton) (US$/ton)
Burundi 4.03 28.9 3,343 601 198
Rwanda 1.71 22.2 1,808 601 146
Tanzania 2.09 27.5 2,607 499 90
Corridor 2.24 27.0 2,584 521 108

15. Urea Use VCRs: Country Means
VCR Av. N appl. at Maize yield Urea price Maize price
Country (av. max) max VCR (kg/ha) (kg/ha) (US$/Ton) (US$/ton)
Burundi 3.95 29.4 3,266 600 195
Kenya 1.59 26.4 1,203 513 126
Rwanda 1.67 22.4 1,795 601 146
Tanzania 2.18 31.8 2,821 549 117
Uganda 2.38 37.6 2,925 556 104
Region 1.93 29.9 1,943 534 121

16. Urea Use VCRs: AEZ means
Av. Yield Av. Fert. Rate
Avg. at Max. VCR at Max. VCR
Max. VCR (kg ha-1) (kg[N] ha-1)
Lowlands Arid 0.74 897 34
Lowlands Semi-Arid 2.00 1,636 30
Lowlands Sub-Humid 1.97 2,154 26
Lowlands Humid 2.90 3,243 39
Highlands Semi-Arid 2.31 2,414 30
Highlands Sub-Humid 2.65 2,530 29
Highlands Humid 2.80 2,255 28
Region 2.19 2,161 31

17. (1) Reduce
landed
cost of urea
SCENARIO ANALYSIS
(2) Reduce
transport
costs
(3) Streamline
customs/
border
regulations
Baseline 20% change 50% change

18. RAINFED WHEAT
2. Yield responses to fertilizer
Mean Yield (kg/ha)
High : 8000
1. Agro-climatic suitability
Variety: Digelu Variety: Veery
4000
No fert.
Ethiopia
100% Low : 1
Rec. Fert.
No
No fert.
Fertilizer
Kenya
100%
Rec. Fert.
Recommended
Yield Yield
Fertilizer Rate
Transport cost: Wheat farming
3. Modeling of farm-gate prices
Port to enterprise data
Farm-gate
Country Net economic return (US $/Ha) Incremental net economic return
(%)
4. Profitability analysis
T0 T1 T2 T0 to T1 T0 to T2 T1 to T2
Angola -198.60 -85.75 -22.11 56.82 88.87 74.22
Burundi 753.11 1096.98 1362.42 45.66 80.91 24.20
Ethiopia 59.62 173.80 233.87 191.51 292.27 34.56
Transport cost: International wheat and Kenya 741.03 976.46 1160.50 31.77 56.61 18.85
Capital to fertilizer prices Madagascar 161.46 239.31 267.92 48.22 65.94 11.96
Farm-gate 450
400
Mozambique -46.94 29.15 39.20 162.10 183.51 34.48
Wheat price (US$/ton)
350
300
Rwanda 1131.30 1377.55 1566.96 21.77 38.51 13.75
250
Tanzania 379.00 554.67 658.47 46.35 73.74 18.71
200
150
100
DRC 171.67 347.30 454.33 Profitability
102.31 164.65 30.82
50
0
Uganda 639.29 903.64 1103.94Sensitivity
41.35 72.68 22.17
Zambia 67.72 310.20 449.48 Analysis
358.06 563.73 44.90
Nominal world wheat price Real world wheat price Zimbabwe -25.72 236.49 400.16 Tool (Excel)
1019.48 1655.83 69.21
Source: CIMMYT – HarvestChoice “Wheat Potential for Africa “ (2011) Net Economic Return and Potential Production

19. General Conclusions
• Fertilizer import, transport, and transactions cost have a very
strong influence on farm-gate fertilizer prices
• Consequently, policies and investments that reduce these
costs can have very broad-scale economic benefits
• There is high spatial variation in the response to nitrogen
application as a consequence of the variation in climate and
soils
• The confounding of local variation in input and output prices,
weather and soil leads to greater spatial variation in the
profitability of production and, consequently, on the incentives
to adopt new technologies.
• While undergoing further validation and calibration, there
appear to be many locations in in East Africa where nitrogen
application appears not to be profitable*
* Analysis was limited to urea use only and higher N response can be obtained through a range of
complementary inputs and management practices

20. Issues and Next Steps
 On-going validation and refinement using a broader range of
empirical data, e.g.,
– Improved road network and fertilizer cost build up data
– Observed farm-gate prices
– Farm level fertilizer use efficiency data
– Including P as well as N in crop simulation
– Household survey data
 Improved empirical estimation approach under development (e.g.
Media, vector+ raster) including seasonality (temporal change in
surplus/deficit regions)
 Now running crop model with historic weather record to assess
(climate-induced) temporal variability in productivity and
profitability
 No plans (yet!) to assemble historic prices and assess joint impacts
of climate and price volatility on profitability
 Extending data and analytical base to most of SSA
 Feedback and collaborative opportunities to improve, extend and
apply approach welcomed by HarvestChoice team!

21. Overview
 Spatial price modeling
 Mapping crop calendar
 Trends and spatial patterns of Ag. Productivity
 Spatial production allocation model (SPAM)
 Poverty mapping
 Mapping livestock from household and census data
 Arabic spatial
 Modeling Farmers’ Agricultural Knowledge Spillover
 The Economics of Land Degradation:
A Way Forward for An Action-Oriented Global
Economic Assessment
21

22. Background
 Crop calendar is important:
1. to a large number of organizations and individuals who
are concerned with production, marketing, processing and
trade of food and feed products.
2. to seed and input suppliers
3. To crop growth modeler
4. To the stakeholders and farmers

23. Background
 FAO (2007) - Many countries, with an emphasis on developing
countries, especially Africa. Mostly national-level data, but some
 large countries are divided into two or three regions
 USDA (2006) - Many countries, with an emphasis on Europe, Asia
and North America. Mostly national-level data, but some large
countries are divided into two regions
 USDA-FAS (2008) - High-resolution, sub-national data for Russia and
Ukraine. National-level data for Argentina, Côte d’Ivoire, Ethiopia,
 Iran, Iraq, Kenya, Nigeria, Somalia, Syria, Tanzania, Turkey and
Zimbabwe
 USDA-NASS (1997) - State-level data for the United States
 IMD-AGRIMET (2008) Very high-resolution, district-level data for
India
 USDA-FAS (2003) - State-level data for Australia
Source: Center for Sustainability and the Global Environment (SAGE)