The document discusses the development of the SLATE (Sustainable Livelihoods Asset Evaluation) tool by ILRI researchers aimed at improving livestock development in Ethiopian communities by evaluating diverse capital assets affecting household livelihoods. It highlights strategic lessons learned, such as the importance of incorporating farmers' experiences, the identification of common household typologies, and recognizing varied research paradigms. The SLATE tool assists in targeting effective interventions by providing a clearer understanding of community needs through locally derived indicators.

1. Unlocking livestock development potential through science, influence and capacity
development ILRI APM, Addis Ababa, 15-17 May 2013
Developing capacity
SLATE: A tool for sustainable livelihoods asset evaluation
This document is licensed for use under a Creative Commons Attribution-Noncommercial-Share Alike 3.0 Unported Licence May 2013
Some strategic lessons on:
1. Farmers’ understanding of livelihoods issues is clearer if
they use their own experiences as a starting point.
2. Benchmarking shows that capacity already exists within
communities and that there are researchable issues around
supporting peer-to-peer capacity development.
3. ILRI needs to understand that “Science” and “Research” are
not synonyms and that there is more than one valid
research paradigm. We are not ILSI; yet!
Identifying principal components in the SLATE dataset and then clustering
these allows us to generate a livelihoods-based typology for the target
community. In this example, a four point typology emerged clearly from
the analysis. This kind of typology is very important for a farming systems
research project like Africa RISING as it allows us to target our evaluations
of interventions on clearly defined and relatively homogeneous groups.
There are many approaches to identifying household typologies within
communities (and beyond) and Africa RISING will be exploring the
potential for using some of these in combination. An advantage of the
livelihoods-based approach described here is that the indicators used
come from the communities themselves and are, therefore, more likely to
reflect real community concerns.
Peter Thorne and Amare Haileslassie
Delivering science
SLATE data can be used for benchmarking by comparing the
characteristics of the groups with the strongest and weakest
overall asset scores. In this example, it is clear that community
leaders – who are generally drawn from the top 25 per cent –
have a higher opinion of themselves than do their peers!
In most cases, endowment scores for individual asset
indicators follow the general pattern of overall asset
endowments with the highest scores found amongst the top
25 per cent. In the example, an exception is the case of Idder
membership. Idder is an important community insurance
institution that is often the only avenue open to less well
endowed households for coping with unanticipated expenses
such as those associated with weddings and funerals.
1. “Science” for development is about context. Tools like SLATE
help to clarify context and underpin effective targeting.
2. Viewed from the farmer’s perspective, no problem is about
science in one dimension.
3. Farmers are individuals. Science needs to identify groups
whose members are sufficiently similar to be experiencing
common problems that might be soluble by science.
The SLATE (Sustainable Livelihoods AsseT Evaluation) software tool
has been developed by ILRI researchers working on the Africa
RISING project in Ethiopia to:
• characterize the diverse, capital assets (financial, human,
natural, physical, and social) that affect the livelihoods of
households within a target community;
• identify groups of households with similar patterns of livelihood
asset endowment. This will help the project’s on-farm research
to target common problems and common solutions within
those groups.
A SLATE analysis starts with the identification of a set of
community-specific, livelihoods asset indicators by a cohort of key
informants. Interviews are then conducted to evaluate these
individual indicators across a representative sample of
householders in order to generate the SLATE dataset.
What is SLATE?
Benchmarking with SLATE
Household typologies with SLATE
Cluster
characteristics