Influence of Farmer Capacity Building in Financial Resource Mobilization on Performance of Smallholder Irrigation Projects in Migori County, Kenya
Bibliographic record
Abstract
The study examined influence of farmer capacity building in financial resource mobilization on performance of smallholder irrigation projects in Migori County, Kenya. The study adopted pragmatism as its philosophy, and used cross sectional and correlation research design. The target population was 2,815, and comprised farmers from fifteen smallholder irrigation projects that receive water from River Kuja through Lower Kuja Project. The sample size was 341 farmers. The study used systematic random sampling to draw the sample, used questionnaire to collect data, and analyzed data using descriptive and inferential statistics. The results showed that farmer capacity building in financial resource mobilization has a significant influence on performance of smallholder irrigation projects (r = 801, R2 = 0.641, F (5, 331) = 118.405, 0.000 < p < 0.05). Therefore, the study concluded that financial resource mobilization is a critical factor in performance of smallholder irrigation projects in Migori County. Consequently, the study recommends that Migori County Government educate farmers in smallholder irrigation projects on loan facilities by financial institutions. Further, the study recommends that Migori County develop a framework to assist farmers in smallholder irrigation projects to qualify for loans facilities operated by financial institutions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".