Determinants of Smallholder Farmers’ Willingness to Pay for Irrigation Water in Kerio Valley Basin, Kenya
Bibliographic record
Abstract
Food security is the major outcome of irrigation development activities. However, this cannot be achieved without sustainable water resources management. With the increasing budgetary constraints in many developing countries, governments have recognized the need to delegate irrigation scheme management to Irrigation Water Users’ Associations (IWUA’s) as much as possible. Despite the majority of these associations being operational, the major challenge has been poor performance due to inadequate farmer participation. This study examines the factors which influence farmers’ willingness to pay for irrigation water in a smallholder irrigation scheme in Kerio Valley Basin, Kenya. Using a multi-stage sampling method, a representative sample of 216 smallholder farmers from the Basin were interviewed. Results show that education level, access to training on irrigation, participation in construction of the irrigation system, crop income from irrigation and membership in IWUA significantly and positively influence farmers’ decisions to pay for irrigation water. Distance to the water source reduces the willingness to pay for irrigation water. Differential pricing based on income levels of farmers, rather than uniform pricing is recommended. We further recommend formulation of policies to train farmers in water management and to support farmer participation in IWUA’s.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".