Piloting SLATE in the Ethiopian  Highlands: process and key            lessons              Amare Haileslassie (Dr.)Traini...
Background: why targeting? Low adoption of technologies and lack of mechanisms for transfer  of knowledge increasingly be...
Background: the hypothesis Integrating social and biophysical  dimension: livelihood framework The hypothesis: Household...
Where we test the hypothesis :the study area Location (Oromia; Arsi-zone,  Limu Bilbilo, Bokoji Negeso) Altitude( 2500-3...
The process: strata building SLATE – data : multi-step  process Stratified Bokoji Negeso kebel  into three, geographical...
Engaging farmers Three people involved in  facilitating and two for  checking consistencies Discussion was held  between...
Identification of livelihood                 indicators Key informants were  introduced to the concept of  livelihood ass...
Farmers sampling and indicators               scoring   ~ 50 farmers : 15 crop based; 10 crop-livestock based    and 20 d...
Application of SLATE:benchmarking              farmers  ~Biophysical based starta: Tulu-negeso, Chefa-   woligela, Mirti-...
Key lessons-result Variation in the mean values of livestock and  crop based livelihood capital across the  livelihood in...
Key lessons-result   Share of livelihood    assets based farm    cluster across the    biophysical strata   Lessons: bio...
Key lessons-result   Variation of livelihood    assets index the across    the livelihood status    cluster   Distinct d...
Key lessons-result                                    Access to feed       Availability of grazing   Vulnerability: the  ...
Key lessons-linkage with PRA   Contribution (%) of livelihood activities to household    income ( for above average clust...
Key lessons –linkage with PRA   Contribution (%) of livelihood activities of below average    group to household income (...
Key lessons –linkage with PRA   Contribution of various feedstuffs to the CP content of total diet    of livestock of the...
How can we improve: tips forextracting information effectivelyPublicity, it may be necessary to arrangemeetings with local...
How can we improve: tips forextracting information effectivelyShould not rigid to the sequence ofquestions.Do probing to g...
How can we improve: quality control (sources of errors)In general, there are two types of errors:      non-sampling erro...
Key lesson :quality control (defects      in the sampling frame )   These occur when there is an omission, duplication or...
How can we improve :quality control        (interviewer bias)  An interviewer may influence  the way a respondent answers...
How can improve: quality        control(non-responses)A respondent may refuse to answer if;       They find questions par...
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Piloting SLATE in the Ethiopian Highlands: Process and key lessons

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Presented by Amare Haileslassie on the Training of Trainers workshop on the use of Livelihoods Characterization/ Benchmarking Tool (SLATE), Jeldu, Ethiopia, 1-5 April 2013

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Piloting SLATE in the Ethiopian Highlands: Process and key lessons

  1. 1. Piloting SLATE in the Ethiopian Highlands: process and key lessons Amare Haileslassie (Dr.)Training of Trainers (ToT) on the use of Livelihoods Characterization/Benchmarking Tool (SLATE) Jeldu, Ethiopia, 1-5 April 2013
  2. 2. Background: why targeting? 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 Traditional practices: spatial and temporal targeting Most often the social dimension is missing
  3. 3. Background: the hypothesis 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
  4. 4. Where we test the hypothesis :the study area 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
  5. 5. The process: strata building SLATE – data : multi-step process Stratified Bokoji Negeso kebel into three, geographically dispersed production systems
  6. 6. Engaging farmers 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
  7. 7. 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
  8. 8. 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
  9. 9. 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
  10. 10. Key lessons-result Variation in the mean values of livestock and crop based livelihood capital across the livelihood index based farm clustersLivelihood Land area Non-crop Livestock Large : small Productiveindex (ha) land (ha) units ruminants familyclusters membersHigh 4.6 1.6 7.2 0.73 4.6Medium 4.9 2.3 7.6 0.68 4.6Low 3.4 1.4 5.2 0.82 4.1 Dependency on single livelihood asset ?
  11. 11. Key lessons-result Share of livelihood assets based farm cluster across the biophysical strata Lessons: biophysical based clustering may be generalization
  12. 12. 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
  13. 13. Key lessons-result Access to feed Availability of grazing Vulnerability: the low livelihood Current Five years Current Five years status cluster are time time more vulnerable High 4.69 3.15 4.54 1.84 But still expect Medium 4.04 3.48 4.17 0.61 more from the Low 3.07 1.30 3.46 0.62 same livelihood Income from livestock assets: lack of Current Five years time alternative? High 3.85 2.31 Medium 3.87 3.57 Low 3.31 3.46
  14. 14. Key lessons-linkage with PRA Contribution (%) of livelihood activities to household income ( for above average cluster)
  15. 15. Key lessons –linkage with PRA Contribution (%) of livelihood activities of below average group to household income (for below average cluster)
  16. 16. 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 )
  17. 17. How can we improve: tips forextracting information effectivelyPublicity, it may be necessary to arrangemeetings with local opinion leaders inselected areas.Ask the leaders to persuade people intheir respective areas to providerequested information to the interviewers.Prior orientation to the farmersGain the confidence of farmer: introducepurpose of the surveySimple medium of interaction
  18. 18. How can we improve: tips forextracting information effectivelyShould not rigid to the sequence ofquestions.Do probing to get exact answer.Give space for farmer to speak.The questions should be clear, preciseThank for their time, ask if she /he hasquestion to ask or idea to shareExplain to farmers on what the follow-upwill be
  19. 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. 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. 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. 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
  23. 23. Africa Research in Sustainable Intensification for the Next Generation africa-rising.net

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