This document discusses using spatial data and analysis to improve agricultural policy and planning in sub-Saharan Africa. It outlines work to develop an updated dataset and characterization of farming systems for the region. The methodology uses numerous spatial and tabular datasets to delineate systems, characterize them, and perform statistical analysis. The goals are to better understand risks, opportunities, and system performance over time to inform interventions and management strategies.
1. Spatial Data and
Analysis in Support
of Improved Policy
and Planning
Christopher Auricht
chris@auricht.com
John Dixon
John.Dixon@aciar.gov.au
ACIAR
Canberra
21 June 2012
2. 2
Talk outline
Context and Background
Needs
Issuesand status of spatial data
Methodology used in developing an updated
farming systems dataset and analysis for Sub-
Saharan Africa
Status and future work
3. 3
Facts
According to CGIAR analysis
One billion of the worlds poor within Africa and
Asia (those living on less than $1 per day) are fed
primarily by:
hundreds of millions of small-holder farmers (often with
less than 2 ha of land, several crops, and a cow or
two), or
Herders (most with fewer than five large animals)
Solution
Developsustainable farming systems that
improve efficiency gains to produce increased
food production
5. 5
Scale of Rural Hunger
Nearly one billion people experience
debilitation, health-threatening hunger each year
4 out of 5 of these people are rural farmers
Trends in maize shortage in Zambia
Percentage of farm households with maize shortage
The Hunger
Period
7. 8
Background
Business as usual investments in agriculture unlikely to
deliver sustainable solutions in many countries
Numerous issues often identified as barriers to
progress e.g. inefficiencies in program
delivery, political uncertainty etc. These are not the
only problem!
Existing systems (often under stress) have been, and
are expected to continue to accommodate large
increases in population, increasing urbanisation, rising
demand for animal products and competition for
land and water
Forecasts suggesting that current practices will not
stay abreast with population growth, environmental
change and increasing demand for animal products.
8. 9
Needs
Requiresa strategic approach, an appreciation of
scale, and an understanding of the interactions
between and within systems
9. 10
The current ACIAR project
Builds
on the work of Dixon et al 2001
www.fao.org/farmingsystems/
10. 11
2001 Farming Systems and Poverty
Global study – part of the World Bank Rural Sector Review
Widely accepted as pioneering body of work – looked at
things as a ‘surface’ across landscape not confined by
country borders
Largely driven by LGP/AEZ and market access,
supplemented by expert opinion
Extensively used to guide investment at the program level
and frame analysis in numerous global studies
Approach focused on high level farming systems within six
developing regions
Involved use of various thematic data layers to underpin
the delineation, characterisation / description and
subsequent analysis of systems
11. 12
Program Application
Major rivers
Major Lakes
National Boundaries
Regional Programme
Countries
#
Major Farming Systems
1. Irrigation
2. Tree crop
3. Forest based
Uganda
4. Rice-tree crop Kenya
Rwanda
5. Highland perennial
6. Highland temperate mixed Tanzania
Malawi
7. Root crops
8. Cereal-root crops mixed
e
qu
Zambia
bi
9. Maize mixed
am
oz
Zimbabwe
M
11. Agro-pastoral millet/sorghum
10. Large commercial and smallholder
12. Pastoral N
13. Sparse (arid)
900 0 900 Kilomete rs
14. Coastal artisanal fishing
13. 14
Sub-Saharan Update
Farming systems website in FAO still one of the
most visited sites within the organisation
Previous study 10 years old
Consistent seamless datasets somewhat
limited in original work
In need of updating as spatial extent of
systems and frame conditions changed e.g.
climate, population, urbanisation, market
access etc.
Many updated and new datasets available
14. 15
Current Situation
2012 – Large quantity of potential datasets – approx. 300
alone in the Harvest Choice database longitudinal and
some predictive data now available
GAEZ 3.0 - 1,000’s of datasets representing 100’s of
thematic layers
Challenge – which ones to use and how
Strategic approach
Access and collation
Assess (fit-for-purpose) and Prioritise (currency, coverage,
scale etc)
Process Products
Disseminate
15. 16
Methodology
Work in collaborative fashion with authors and other large
data providers e.g. IFPRI – Harvest Choice, UN-FAO,
ILRI, ICRAF, IIASA, CGIAR others
Delineate new Farming
System Boundaries –
Iterative process based
on concept of central
Spatial tendancy
and
Tabular
Characterise and
. Data
describe systems
Statistics and Analysis
16. 17
Approach
Integration of new datasets –
LGP and Market access
Supporting Datasets
Population (rural, urban, total)
Livestock – cattle, sheep, goats, poultry, LU and
TLU
Crop areas and production
Yield gaps
Protected areas
Poverty $2.00 and $1.25 /day
Nutrition
17. 18
Hunger, Poverty & Productivity
Spatial Covariates/Proxies & Analytical Flow
Terrain, Demograph
Production Production Interventions/ Linkage to
y,
Environment & Systems & Responses Macro
Infrastructure, Admi
Constraints Performance Models
n Units
7
Maize
Yield
Potential 6
t[DM]/ha
5
4
3
2
40
1 30
20
0 10
Irrigation
0
100 Threshold
80 NA % of Available
60
40
Fertilizer Application Rate 20 Soil Water
0
kg[N]/ha
Settlements, ports, & Cropland Runoff Crop Yield Responses to Inputs, Management, CC
Slope, travel times marketsDiseases (Maize Stem Farming & Rural of small Aggregate to FPUs
Port travel times & costs Drought extent & CropProduction perQuantity of Nutrients Removed
Market Administrative costsAgroecological intensityBorer)
Road, rail, river, ICT networks IncidenceZones Suitability: SystemsFertilizerscale irrigation
Elevation
aspect, drainage &
Pests
Units Value of Distribution Rainfed Wheat ofProfitability
& Severity Profitability Person Welfare Benefits
Distribution
Yields
Source: HarvestChoice 2010
22. 23
Big questions for management and policy
What is it?
Where is it?
What are its characteristics and how does it
operate ?
What are the risks/threats ?
What are the opportunities (Research / Extension) ?
How changing with time ?
Evaluation and Performance
23. 24
Spatial data
Tool to support process
Understand
Analyse
Develop interventions
Monitor
Not the answer in itself
has limitations
Fit for purpose
Complement with expert knowledge