FACTORS THAT INFLUENCE ADOPTION OF INTEGRATED SOIL FERTILITY AND WATER MANAGEMENT PRACTICES BY SMALLHOLDER FARMERS IN THE SEMI-ARID AREAS OF EASTERN KENYA
Why this work is in the frame
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Bibliographic record
Abstract
<p>In arid and semi-arid lands (ASALs), low adoption of integrated soil fertility and water management (ISFWM) technologies has contributed to food and nutrition insecurity. A study was conducted to assess factors influencing smallholder farmers’ adoption decision of ISFWM technologies in Mwala and Yatta Sub-Counties. A questionnaire was administered to 248 respondents in the study region. Selection of household heads was done in ‘Farmer-led adoption approach’ sites otherwise known as Primary and Secondary Participatory Technology Evaluations (PPATEs and SPPATEs) and Non-PPATEs/SPATEs sites in both Sub-Counties. Relationships between different variables were determined by the Tobit model. The results revealed that group membership (P&lt;0.016), inaccessible credit services (P&lt;0.017), gender (P&lt;0.025), age and access to agricultural extension services (P&lt;0.027) influenced adoption of ISFWM technology significantly. Cost of inputs and access to radio information (P&lt;0.01), access to appropriate farm machines (p&lt;0.001), cost of labor and farmers’ perception on seasons’ reliability (P&lt;0.004) and out-put markets (P&lt;0.006) were reported to affect adoption of ISFWM practices highly significantly. Descriptive statistic results indicated that majority of the respondents (93.9%) in the project areas were adopting a combination of tied ridges, organic fertilizer and improved seed compared to only 6.1% in the non-project area. There was also significantly (P&lt;0.01) higher adoption (76.5%) of a combination of tied ridges, both fertilizer and improved seed in the project area in contrast to merely 23.5% in non-project area, as well as those adopting (80%) a combination of zai pit, both fertilizer and improved seed compared to only 20% in non-project area. Policy makers should focus on availability of affordable credit facilities and farm machines, ease access to information, labor and input-output markets for enhanced farm productivity and livelihoods of the smallholder farmers in ASALs.</p>
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it