what data, from where?

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• Few quality assurance and documentation standards• Limited repositories• A outdated culture of data ownership rather than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




Natural resources



SectorsStandards and good practice for data generation

Standard sampling framesMinimum data setsDefinitionsLongitudinal and panel data

Possible structure




Natural resources



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




Natural resources



Sectors

Standards and good practice for data recording• Georeferencing• Codebooks• Quality assurance• Linking to other data sources

Possible structure




Natural resources



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


1860 Time 2000


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


1860 Time 2000


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

what data, from where?

Technology