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  1. Piloting SLATE in the Ethiopian Highlands: process and key lessons Amare Haileslassie (Dr.) SLATE Training for Africa RISING / NBDCAddis Ababa / Jeldu. 1 - 5 April 2013

  2. 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

  3. 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

  4. Location (Oromia; Arsi-zone, LimuBilbilo, BokojiNegeso) • 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

  5. SLATE – data : multi-step process • Stratified BokojiNegesokebel into three, geographically dispersed production systems • The process: strata building

  6. 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

  7. 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 • Identification of livelihood indicators

  8. ~ 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 • Importance of certain indicator in livelihood strategies of a farm (0-10) • Whether owning/having access to a certain indicator had positive or negative effects and its magnitude ( -5, +5) • Vulnerability to on going changes ( -5, +5); depending on whether it affects a farm negatively or positively • Farmers sampling and indicators scoring

  9. ~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 • Application of SLATE:benchmarking farmers

  10. Variation in the mean values of livestock and crop based livelihood capital across the livelihood index based farm clusters • Key lessons-result • Dependency on single livelihood asset ?

  11. Share of livelihood assets based farm cluster across the biophysical strata • Lessons: biophysical based clustering may be generalization • Key lessons-result

  12. 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

  13. Vulnerability: the low livelihood status cluster are more vulnerable • But still expect more from the same livelihood assets: lack of alternative? • Key lessons-result

  14. Contribution (%) of livelihood activities to household income ( for above average cluster) • Key lessons-linkage with PRA

  15. Contribution (%) of livelihood activities of below average group to household income (for below average cluster) • Key lessons –linkage with PRA

  16. Contribution of various feedstuffs to the CP content of total diet of livestock of the above (for above average-left ; below average-right ) • Key lessons –linkage with PRA

  17. How can we improve: tips for extracting information effectively Publicity, 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

  18. How can we improve: tips for extracting information effectively Should 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

  19. 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.

  20. 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.

  21. 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

  22. 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