Informing Strategic Investments in Enhancing
Agricultural Technology Development and Use:
The Role of Agricultural Statistics
Stanley Wood
Senior Research Fellow,
International Food Policy Research Institute (IFPRI)
Co-Principal Investigator, HarvestChoice
Contribution of Partners in the Development of Agricultural Statistics in Africa
Twentieth Session, African Commission on Agricultural Statistics
Algiers, Algeria, 10-13th December 2007
Overview
• Reinvigorated engagement in agricultural
development in Africa
• What is HarvestChoice?
• Need for improved agricultural statistical
data to support strategy/policy/
investment analysis
• HarvestChoice/FAO initiatives related to
agricultural statistics in Africa
Re-engagement in Agriculture
(some examples)
• NEPAD’s explicit strategy on the role of agricultural
growth in economic growth → CAADP → ReSAKSS
→ National Strategic (planning, design, M&E)
Information System & Analysis Capacity
• World Bank: Multi-Country Agricultural Productivity
Program (MAPP), Rural Infrastructure,
[Re-emphasis: World Bank Assistance to Agriculture
in Sub-Saharan Africa: An IEG Review. 2007. World
Development Report. 2007]
• Bill and Melinda Gates Foundation: Agricultural
Development Program
BMGF Schematic of the Agricultural Development Program
Global
Development
Program
Financial
Services
Agricultural
Development
·
Science and
Technology
·
Farmer Productivity
·
Market Access
·
Data, Policy &
Advocacy
What is HarvestChoice?
• A BMGF-sponsored ($4M, 39 month) effort co-managed by
IFPRI and U. of Minnesota to compile, generate, harmonize, and
disseminate public-goods information on the potential payoffs
from improved crop production technologies and practices.
• Focus
on poor farm households in SSA and S. Asia, but
embedded in a perspective of national (social) welfare, and
international flows of knowledge, technology, and trade
• Institutionally-neutral
portal supported and accessible to a
growing number of R&D partners: FAO (Statistics Division),
CIMMYT, CIAT, IRRI, ICRISAT, & Universities (Pretoria, VT,
Georgia, Davis), World Bank.
What is HarvestChoice?
• Partner/user programs: HarvestPlus, Generation
Challenge Program, USAID/IPM-CRSP, USAID/IEHA,
(AGRA/PASS, WB/MAPP, Howard Buffet Foundation,
Sainsbury Family Trust)
• Regional partners and processes, e.g. CAADP
(ReSAKSS), ASARECA, (SADC, CORAF)
Some Strategic Questions
• Where are the poor and what is their welfare status?
• On what cropping systems do the poor most depend?
• What are the constraints to the productivity of those systems?
• What existing or potential technologies might best
address those constraints? Under what scenarios?
• What is the magnitude and distribution of potential payoffs to the
poor from different investment targeting strategies?
by, e.g., districts, AEZs, production systems, crops, constraints, technologies..
Harmonizing (Spatial) Thematic Data
Thematic Layers
Geopolitical
Production constraints
Ecosystems (ecosystem services)
Crop
production(climate,
systemssoil, water)
Agroecological
Cropland and rangelands
Aligning
Location
-specific
(georeferenced)
data
Cropland and
rangelands services)
Ecosystems
(ecosystem
Production systems
Agroecological (climate, soil, water)
Food
consumption
(macro-,
micronutrients)
Infrastructure
(Towns,
roads, irrigation)
Welfare (poverty, hunger, health)
Urban/rural
populations/densities
Demography (population density)
Infrastructure (Towns, roads, irrigation)
VulnerabilityUnits
(poverty, hunger,
Geopolitical
health, conflict)
Analytical (largely economic) tools
HarvestChoice Activities
1. Macro Trends:
Human Welfare &
Crop Systems
2. Micro Linkages:
Human Welfare &
Crop Systems
3. Crop Systems Evaluation Platform (Physical)
a. Baseline distribution & performance of crop systems
b. Distribution & severity of key productivity constraints
c. Potential responses to change (tech., man., climate.)
4. Technology
Landscape
5. (Economic) Evaluation
Other data (e.g,
prices, investments,
market, technology
spillover insights)
Dialogue with Stakeholder/User Groups on
Scenarios
Constraint-Scale
Evaluation
7. Outreach
(e.g., country delivery
-CountrySTAT)
Technology-Scale
Evaluation
6. Commercialization
Prospects
Change
Fixed
Geographies of Analysis
Flexible
Geographies of Analysis
e.g., IMPACT/WATER,
GTAP derivatives
e.g., DREAM,
MM models
(e.g., policy)
Market/Policy Analysis
Macro Scale, Usually aggregate,
Geo-political units
informs
Household
Characterization
Region
Urban/Rural
Income tercile
Consumption
Production
Inputs
Micro Scale
informs
Change
(e.g., climate,
technologies)
Production System
Analysis
Meso Scale,
Pixels as Units of Analysis
Aggregation
By Commodity
Infrastructure/Market Access
Production System
Ecosystem Services
Where are the Africa’s poor
and what is their welfare status?
Inequality
Compiling and harmonizing available,
sub-national datasets on
Expenditure, poverty, undernourishment,
child mortality and undernourishment,
Infant
Mortality Micronutrient deficiency, selected DALY’s
Children
Poverty
Underweight
Hunger Task Force/CIESIN 2005
CBS et al. 2003
Hunger Task Force/CIESIN 2005
Alderman et al 2002
Prepared by CIAT from WHO data
On what cropping systems do
RWANDA, 2000
Consumption: g. per cap. per day
the poor most
Maize depend?
Sorghum O. Grains
Rice
Wheat
Cassava Potato
Sw. Potato
National
65.0
38.2
1.3
27.3
8.2
107.4
275.6
392.2
CONSUMPTION
Butare
43.9
35.0
1.0
28.8
4.8
109.1
98.8
321.1
3.0
27.7
119.6
61.8
230.1
445.5
474.8
83.8
0.8
1.5
0.8
1.4
1.8
4.4
7.9
12.3
26.3
85.7
1.4
2.2
2.8
6.7
27.8
94.8
111.5
144.1
105.9
80.7
147.4
211.9
257.0
318.8
443.3
343.6
444.1
491.5
463.8
218.0
290.4
238.9
381.0
419.9
&
ns
9.3
5.5
107.1
108.2
rg
hu
So
ua
sh
sq
Be
30.1
20.3
pk
i
1.3
1.2
m
13.8
77.7
es
1.4
0.9
an
s
331.2
175.7
746.8
346.7
774.7
543.0
286.2
621.3
56.0
383.1
315.1
m
ee
t
po
ta
to
e
39.0
36.0
335.4
82.4
216.6
562.4
174.9
88.1
292.7
183.7
459.9
425.8
169.8
Pu
65.2
64.5
18.1
57.1
82.7
28.0
146.4
303.0
125.5
212.4
60.0
9.9
172.5
a
22.0
33.4
42.2
52.2
41.1
5.3
3.8
5.0
2.4
2.2
2.4
3.3
2.9
31.1
8.6
2.1
av
38.6
72.7
79.0
83.1
51.8
19.8
38.1
6.6
10.7
2000
15.5
19.0
11.0
14.9
79.3
6.3
16.2
as
s
41.9
24.1
1.8
2.1
0.4
1.4
Rwanda,
1.9
0.5
2.0
0.4
0.7
2.3
1.7
C
77.2
19.4
Sw
Pl
Male headed
Female headed
Po
Lowest
2
3
4
Greatest
an
ta
i
Expenditure
ns
0
90.1
17.4
35.9
30.9
27.3
42.3
42.6
41.9
24.2
42.3
51.2
ta
to
es
Rural
100 rural
Urban urban
54.4
83.0
37.9
58.0
62.5
140.7
88.7
67.6
12.8
72.3
155.5
s
Byumba
Cyangugu
Gikongoro
g. per cap.Gisenyi
per day
Gitarama
600
Region Kibungo
500 Kibuye
400 Kigali Ngali
Ville de Kigali
300 Ruhengeri
200 Umutara
Crop Consumption
(1st Admin * U/R * Expend. Class * M/F Headed)
For 17 countries in SSA
• Includes 73 % of SSA population
• All but 2 AGRA/PASS countries
• Testing extrapolation using country typology
HarvestPlus (CIAT & IFPRI), maps prepared by Glenn Hyman
Overview of Spatial Allocation
Forest
Shrublands, Savanna,
Grasslands
Croplands
Cropland/Natural
Vegetation
Water bodies
Initial
Representation
(a) Crop Production Statistics
(b) Land Cover
Percentage Ag.
>60%
40-60%
30-40%
<30%
(c) Agricultural Land Cover
Pre-Processing
Production shares (High/Low inputs)
Harvested to physical area (CI)
Potential gross revenue per pixel per crop
per input level
Final
Representation
Optimisation
(MAX: Gross Revenue)
MIN: Cross Entropy
(d) Crop * Input Level Specific
Biophysical Suitability
Simultaneous allocation across all crops
into agricultural share of each pixel
(e) Crop Area Allocation
Any other mapped crop
distribution evidence
On what cropping systems do
the poor most depend?
PRODUCTION
For 20+ major crops at 10km resolution
Av.
Maize Output
per distribution
hh)
“Plausible”
assessment
of the(kg
spatial
of
1000
Uganda
production systems
and1999-2000
performance of crops.
Av. Maize Output (kg/hh)
1200
800
Complemented
by available data on technology
600
Least Poor
Poorest
adoption, market participation,
land holding structure,
Quintile
Maize
Quintile
400
Maize
land
tenure & new data on input
Area use/costs (FAO)
Area
(2nd Level
Admin)
200
(1st Level
Admin)
0
1
3
West
5
7
9 11
North
13
15
17 19
Central
21
23 25
East
New Tools for
Distributing & Validating Crop Data
• SPAM Results Web Accessible through Google Earth
Evaluating the Payoffs to
Crop Improvement for the Poor
• Economic benefits of technical change arising from; higher
(on- and off-farm) productivity, lower unit costs, lower
variance of output, quality price premiums, commercialization
constraints and opportunities (using 2+ stage assessment)
• Share of benefits to poor producers and poor consumers
- Spatial incidence of benefits
- Implications for nutrition and incomes
• Potential sources of benefit – Local, spillins
• Economic implications of time lags, (e.g. R&D, regulation,
commercialization, adoption)
What yield response to N Application?
Increase in Potential Maize Yield per Kg N
What if...?
Baseline
Maize Yield Response to Fertilizer
–
–
–
–
kg[Maize Yield] / kg[N Fertilizer]
Maize in Year 2000 (medium maturity)
0.5-degree grid (about 50 km)
0 and 50 kg[N]/ha N fertilization
Kg maize /
Yield
Kg NGain (kg[Yield]/kg[N])
-26 - 0
1 - 11
12 - 20
21 - 29
30 - 39
40 - 47
48 - 55
56 - 64
65 - 72
73 - 86
?
“Site”-Specific Response: Ghana
Long Maturity Duration
6
5
4
yield
(t/ha)
3
2
1
0
10 8
irrigation rate
6
4
(mm/ha/week)
2
0
20
40
60
80
*
100
fertilization rate
(kg[N]/ha)
Short Maturity Duration
6
5
4
yield
(t/ha)
3
2
1
0
10 8
irrigation rate
(mm/ha/week)
6
4
2
0
20
40
60
80
100
fertilization rate
(kg[N]/ha)
BMGF: An (Unofficial) Guide to Selected Investments, and Strategy Ideas
with Potential Linkages to Agricultural Statistics Capacity in Africa
Global
Development
Program
Financial
Services
Agricultural
Development
·
Science and
Technology
·
Farmer Productivity
·
Market Access
·
Data, Policy &
Advocacy
Initial Ideas from Data Strategy
Brainstorming (Oct 2007)
•Regional network of harmonized (national)
panel datasets
AGRA (~16 countries)national agricultural
•Strengthening
·
Seeds (PASS)
statistical
services (especially ag. census
·
Market development
·
Soil
health
and
expenditure/welfare
indicators)
·
·
·
Water management
……...
Monitoring & Evaluation
•Use of “new” technologies/approaches to
data collection (satellite, GPS, PDA,..)
WFP/VAM Surveys
FAO/CountrySTAT
Digital Soil
Database
HarvestChoice/FAO activities related to
agricultural statistics in Africa
• Compilation and harmonization of agricultural census
data (including capture/digitization of older data when
necessary to better understand past trends)
[e.g., holdings, production systems, land tenure, cropping
patterns, technology and input use, labour use, productivity,
access to services, market participation]
• Standardized analysis of national consumption and
expenditure data
[e.g., household characteristics, expenditure/income,
consumption of agricultural goods, food security]
HarvestChoice/FAO activities related to
agricultural statistics in Africa
• Production system characterization
(e.g., orientation, output and input mixes, technologies,
management practices, cropping patterns, rotations/fallow
use, natural resource needs/impacts, productivity)
• Cost of production database
(to support more detailed productivity and profitability
analysis - particularly in the light of potential change, e.g.,
increased investment or policy change)
NB All processed data generated will be made available in digital
format and, wherever feasible, made available for national
CountrySTAT implementations
HarvestChoice/FAO activities
Learning/Partner Hopes from AFCAS
• Gather and consolidate information on the status of ongoing and planned nationally representative survey and
census activities of participating countries
• Identify opportunities for “data rescue” of past census/
survey data
• Start to identify potential synergies between country
statistical service development plans and potential funding
options of relevance to the Gates Foundation portfolio
• Find partner countries to help develop and test the Cost of
Production survey instrument to be administered by
FAO/ESSD
• Communicate new opportunities for investment in statistical
and monitoring systems at country level