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A presentation prepared by Ric Coe
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What data, from where?
Presented by Ric Coe (ICRAF/ILRI Research Methods Group, [email protected]) at the Workshop on Dealing with Drivers of Rapid Change in Africa: Integration of Lessons from Long-term Research on INRM, ILRI, Nairobi, June 12-13, 2008
What data?
• Formal, codified knowledge about specific times and places
• Qualitative or quantitative
The data system
1. Official (government+ generated) datao Some regularityo National coverage, some international standardisationo Increasing international coverage and complianceo Initiatives to increase integration
Paris21 Marrakech Action Plan for Stats
o Initiatives to increase availability of disaggregate data International Household Survey Network
o Much still of doubtful qualityo IP culture at national finer scales still rewards data ownership not
data supplyo Coverage patchyo Beyond the direct scope of CG
2. Remotely Sensed data• Rapidly increasing free collections• raw and processed layers• multiple scales• multiple time points• standardisation in formats• web and open source processing/display tools
3. Research Data
Example:• ICRISAT compiled data for Fakara, Niger• 90 data sets compiled• meta data available, searchable• Protocols• Ownership, access rules,…
3. Research data• Collected to examine specific questions
o including complex questions• one-off, rarely repeated• limited geographic scope
o even if multi-country• little standardisation in indicators and
definitions• Often very small sample size with poorly
defined sampling frame• …
…and unlikely to be available after the end of the project
• The Responsibility Gapo between short term funded projects and their host organisations
• Note OECD standards for public availability of data from public funded research
• Few incentives for ensuring long term security and access
• Few quality assurance and documentation standards• Limited repositories• A outdated culture of data ownership rather than
provisiono “What we share as important as what we own” (Leadbetter)
Hot news!
• CGIAR to catch up with the rest of the world!
• Inter-centre Research Data Management Workshop agreedo Data provision and sharing should become an
objective of the CG centreso Incentives to share data to be developedo Start with sharing tools, guides, standards,…
An ESA research data platform?
• Maximise long term value of one-off surveys by allowing them to be better targeted, integrated and reused
• Maximise value of other initiatives to collate studies by providing a facility for long term archiving and access
• Provide links to other data sources, particularly those that put agriculture in context (environmental, social, economic)
• Develop methods to increase data generation efficiency• Support development of national and regional data
generation and integration services• Champion the need for quality, relevant agricultural data
on which to base R+D decisions and track changes.
Possible structure
Archiving and access
Intersector integration
Standards and methods
Natural resources
Consumption and welfare
Production and markets
Sectors
Possible structure
Archiving and access
Intersector integration
Standards and methods
Natural resources
Consumption and welfare
Production and markets
SectorsStandards and good practice for data generation
Standard sampling framesMinimum data setsDefinitionsLongitudinal and panel data
Possible structure
Archiving and access
Intersector integration
Standards and methods
Natural resources
Consumption and welfare
Production and markets
Sectors
Develop methods for farm surveys• Use of RS data for stratification, sampling
efficiency and interpolation• Methods that build on other wide scale
surveys• Rapid methods for key indicators
(avoiding the 40 page questionnaire)
Possible structure
Archiving and access
Intersector integration
Standards and methods
Natural resources
Consumption and welfare
Production and markets
Sectors
Standards and good practice for data recording• Georeferencing• Codebooks• Quality assurance• Linking to other data sources
Possible structure
Archiving and access
Intersector integration
Standards and methods
Natural resources
Consumption and welfare
Production and markets
SectorsStandards for archiving and access• Meta data • Confidentiality• Ownership and other IP issues
Physical archive and means of access1.Long term secure archive2.Catalogues, visualisation of coverages3.Retrieval and dissemination
What it takes
• Long term commitment and funding!
Data for drivers of change• Demonstrating a possible driver is easy(the data supports shows X →Y as plausible)
Data to ‘discover’ or ‘prove’ drivers very much harder…
• What is a driver – what is a cause?• Problems:
o confounderso multiple causeso proximal and underlyingo feedback and time scales
A
B
C
At-1
Bt-1
Ct-1
At
Bt
Ct
At+1
Bt+1
Ct+1
Data requirement driven precisely by the question
• How do you get more convinced?
II. CASE STUDIES
A. Rangeland ManagementAustralia / NSW:
1860 Time 2000
Scale of Drivers & Responses
Local
Global
= Sheep #
Rabbits+ drought
• Kangaroo
(water pts.)
Extinctionbrowsing
marsupials
= Ecological drivers& responses
II. CASE STUDIES
A. Rangeland ManagementAustralia / NSW:
1860 Time 2000
Scale of Drivers & Responses
Local
Global
= Sheep #
Sub-regionalNetworking(reciprocity, knowledge exchange)
= Social drivers &responses
Expansion of watering points to accessnew rangeland; reduced stocking density
Political organizing
II. CASE STUDIES
A. Rangeland ManagementAustralia / NSW:
1860 Time 2000
Scale of Drivers & Responses
Local
Global
= Sheep #KoreanWar
(wool $$)= Political-economicdrivers & responses
Pricesupport
endsRural political dominance
Urban dominance,“Closer Settlement”
Publicly funded water supplies / stock routes
Strongeconomy
Multiple information sources
• Every explanation is partial• Starts with the conceptual frame…• Leads to numerous proposed
components, links, processes• Formal data used to examine and quantify
each of those hypotheses.
Collecting further data
“Policies are hypotheses.Management options are experimental treatments”Remember all you learnt (+some) on experimental design when planning AM/AR activities