Piloting SLATE in the Ethiopian Highlands: process and key

lessonsAmare Haileslassie (Dr.)

Training of Trainers (ToT) on the use of Livelihoods Characterization/Benchmarking Tool (SLATE)

Jeldu, Ethiopia, 1-5 April 2013

Low adoption of technologies and lack of mechanisms for transfer of knowledge increasingly became a major concern

Determinants of adoption of land and water management technologies

Background: why targeting?

Traditional practices: spatial and temporal targeting Most often the social dimension is missing

Integrating social and biophysical dimension: livelihood framework

The hypothesis: Households stratified by livelihood endowments access and manage feed resources in different ways

More robust development outcomes will result from identifying practises that are transferable amongst strata and augmenting these with “external” innovations

Background: the hypothesis

Location (Oromia; Arsi-zone, Limu Bilbilo, Bokoji Negeso)

Altitude( 2500-3300 masl) Soils ( vertisols, luvisols) Mean annual rainfall ( ~1000mm) Agricultural systems: mixed

crop-livestock but with the different degree of combination

Where we test the hypothesis : the study area

SLATE – data : multi-step process

Stratified Bokoji Negeso kebel into three, geographically dispersed production systems

The process: strata building

Three people involved in facilitating and two for checking consistencies

Discussion was held between experts involved

Five key informants were selected from each of the stratum

Engaging farmers

Identification of livelihood indicators

Key informants were introduced to the concept of livelihood assets

Clustering key informants into their respective strata

Draft checklist of indicator was used to guide key informants

Farmers sampling and indicators scoring

~ 50 farmers : 15 crop based; 10 crop-livestock based and 20 dairy based Indicators were scored using a continues value

approaches Major parameters for indicators scoring

i. Importance of certain indicator in livelihood strategies of a farm (0-10)

ii. Whether owning/having access to a certain indicator had positive or negative effects and its magnitude ( -5, +5)

iii. Vulnerability to on going changes ( -5, +5); depending on whether it affects a farm negatively or positively

Application of SLATE:benchmarking farmers

~Biophysical based starta: Tulu-negeso, Chefa-woligela, Mirti-leman

SLATE- Integrated livelihood benchmarking: top 25% versus bottom 25% in terms of livelihood assets endowment)

Linkage with the PRA

Key lessons-result Variation in the mean values of livestock and

crop based livelihood capital across the livelihood index based farm clusters

Livelihood index clusters

Land area (ha)

Non-crop land (ha)

Livestock units

Large : small ruminants

Productive family

members

High 4.6 1.6 7.2 0.73 4.6

Medium 4.9 2.3 7.6 0.68 4.6

Low 3.4 1.4 5.2 0.82 4.1

Dependency on single livelihood asset ?

Key lessons-result Share of livelihood

assets based farm cluster across the biophysical strata

Lessons: biophysical based clustering may be generalization

Key lessons-result Variation of livelihood

assets index the across the livelihood status cluster

Distinct differences between clusters

The importance of different assets is different across clusters

Key lessons-result Vulnerability: the

low livelihood status cluster are more vulnerable

But still expect more from the same livelihood assets: lack of alternative?

Access to feed Availability of grazing

Current Five years time

Current Five years time

High 4.69 3.15 4.54 1.84

Medium 4.04 3.48 4.17 0.61

Low 3.07 1.30 3.46 0.62

Income from livestock

Current Five years time

High 3.85 2.31

Medium 3.87 3.57

Low 3.31 3.46

Key lessons-linkage with PRA Contribution (%) of livelihood activities to household

income ( for above average cluster)

Key lessons –linkage with PRA Contribution (%) of livelihood activities of below average

group to household income (for below average cluster)

Key lessons –linkage with PRA Contribution of various feedstuffs to the CP content of total diet

of livestock of the above (for above average-left ; below average-right )

How can we improve: tips for extracting information effectivelyPublicity, it may be necessary to arrange meetings with local opinion leaders in selected areas.

Ask the leaders to persuade people in their respective areas to provide requested information to the interviewers.

Prior orientation to the farmers

Gain the confidence of farmer: introduce purpose of the survey

Simple medium of interaction

How can we improve: tips for extracting information effectivelyShould not rigid to the sequence of questions.

Do probing to get exact answer.

Give space for farmer to speak.

The questions should be clear, precise

Thank for their time, ask if she /he has question to ask or idea to share

Explain to farmers on what the follow-up will be

How can we improve: quality control (sources of errors)

In general, there are two types of errors: non-sampling errors and sampling errors.

Non-sampling errors arise from: Defects in the sampling frame. Wrong question, responses or wrong recording.

Key lesson :quality control (defects in the sampling frame )

These occur when there is an omission, duplication or wrongful inclusion of units in the sampling frame ( e.g. gender?).

Omissions are referred to as ‘under coverage’ while duplications and wrongful inclusions are called ‘over coverage’.

Coverage errors may also occur in field operations, that is, when an enumerator misses several households or persons during the interviewing process.

How can we improve :quality control (interviewer bias)

An interviewer may influence the way a respondent answers survey questions.

Interviewers must remain neutral throughout the interviewing process and must pay close attention to the way they ask each question

How can improve: quality control(non-responses)

A respondent may refuse to answer if; They find questions particularly sensitive, or if They have been asked too many questions.

To reduce non-response, the following approaches can be used: Pilot testing of the questionnaire. Explaining survey purposes and uses. Assuring confidentiality of responses. Public awareness activities including discussions with key

organisations and interest groups

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