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 programmes.
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.