Determinants of the level of compliance with recommended production practices among rice farmers in Osun state, Nigeria
Bibliographic record
Abstract
Despite the expansion of rice production in Nigeria over the past decade, a marked discrepancy in yields between farmers’ fields and demonstration sites threatens food security and economic growth by limiting domestic supply. This suggests that rice farmers are not fully implementing recommended practices. This study therefore employed the Fractional Response Probit Model (FRM), suited for analyzing bounded dependent variables, to examine the factors influencing the level of compliance with recommended practices among rice farmers in Osun State. The study utilized primary data gathered through interviews using a structured questionnaire administered to 180 rice farmers. The study revealed that most rice farmers demonstrated only moderate adherence to recommended practices, with a mean compliance level of 0.48, representing a substantial 52% shortfall from optimal yield. Several factors were identified as positively and statistically significantly influencing compliance, including age, sex, household size, years of education, and frequency of visits from extension agents. High input costs and limited access to credit were among the key obstacles to compliance encountered by the farmers. This study concludes that rice farmers in the study area are on average, 52% below maximum compliance with recommended practices, underscoring the need for improved adoption. It is recommended that rice sector stakeholders such as government agencies, extension agents, non-governmental organisations, and farmer associations support farmers through fertilizer subsidies and by creating platforms to reduce the cost and difficulty of accessing essential production inputs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".