FACTORS THAT INFLUENCE ADOPTION OF INTEGRATED SOIL FERTILITY AND WATER MANAGEMENT PRACTICES BY SMALLHOLDER FARMERS IN THE SEMI-ARID AREAS OF EASTERN KENYA
Bibliographic record
Abstract
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<0.016), inaccessible credit services (P<0.017), gender (P<0.025), age and access to agricultural extension services (P<0.027) influenced adoption of ISFWM technology significantly. Cost of inputs and access to radio information (P<0.01), access to appropriate farm machines (p<0.001), cost of labor and farmers’ perception on seasons’ reliability (P<0.004) and out-put markets (P<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<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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".