Factors influencing adoption of postharvest practices among underutilised indigenous vegetables producers in Southwest Nigeria
Bibliographic record
Abstract
Summary Background of the study – The adoption of improved postharvest practices among poor rural Underutilised Indigenous Vegetable (UIV) households aimed at improving their nutritional status and welfare can be facilitated if the said postharvest techniques are understood in terms of the current practices and the factors influencing their adoption. Objectives – This study examines the factors that influence the adoption of postharvest practices of UIV farmers in Southwest Nigeria. Materials and methods – Secondary data was extracted from the “synergizing fertilizer micro-dosing and indigenous vegetable production to enhance food and economic security of West Africa” (the MICROVEG project) data bank, with a total of 627 respondents. Descriptive and the Poisson Count Regression was used to analyse the data. Results – The result revealed that adopters of postharvest practices were predominantly male producers and marketers of UIV who are married and preferred dewing and aeration. In addition, religion, household working class demography, marital status, distance to water, farm size and revenue significantly increase the number of postharvest methods adopted. Conclusion – Promoters of adoption of postharvest methods of preservation of UIVs could utilise adult social networks such as religious or other social groups as entry point to facilitate the adoption process and elucidate on the universal need for postharvest method of UIVs preservation irrespective of farm size to increase the revenue accruable to the producers.
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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 | 0.000 |
| 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) | 0.001 | 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".