This synthesis paper is one of a series that has been developed to
assist research managers use the key lessons learnt between 1995 and
2005 from natural resource research and implementation under the
Renewable Natural Resource Research Srategy (RNRRS), funded by the UK
Department of International Development (DFID). The aim of these papers
is to provide practical guidance to enable institutions and research
projects incorporate these lessons into their current and future
The aim of this synthesis is to show how poverty analysis and mapping
has been dealt with in the RNRRS programmes and how this experience can
be applied to develop poverty analyses for the rural economy and beyond.
Three case study clusters have been selected to highlight the poverty
issues discussed throughout the paper. Experience of research projects
from DFID's Aquaculture and Fish Genetics Research Programme (AFGRP),
the Forestry Research Programme (FRP), the Animal Health Programme
(AHP), and the Livestock Production Programme (LPP) are drawn upon. The
International Livestock Research Institute (ILRI) worked on a series of
projects with AHP and LPP, which forms the AHP/LPP cluster case study.
These research clusters were selected as examples of successful or
significant utilisation of poverty analysis. Reasons for success are
analysed and areas for improvement highlighted.
The major Lessons Learnt are:
- In order to be effective, poverty measurement, analysis and mapping
must be conducted using a coordinated approach by governments, civil
societies, the R&D community and investors.
- Due to the amorphous and diverse nature of poverty alleviation, and
the scarcity of resources, priorities are needed to ensure that
research interventions are appropriate and focused.
- It is essential that a multidisciplinary approach be used as well as
adopting an integrated methodology (such as the consideration of
watersheds as an overall spatial framework).
- Causal Diagrams are a useful and reasonably rapid tool to display
connections between the problems which collectively cause poverty, and
to focus attention on root causes.
- Poverty Mapping can be used as a valuable decision support tool to
help identify and evaluate investment choices, as well as for policy
and project design.
- Scenario Building and the generation of likely progressions enable
researchers to evaluate long-term developments.
Poverty Mapping and Analysis: An RNRRS Synthesis