Understanding the factors contributing to food security among under-recognised indigenous vegetable farming households in Nigeria
Bibliographic record
Abstract
Integrating under-recognised indigenous vegetables into cropping systems presents a viable strategy for enhancing household food security. Despite their potential, these native crops remain under-researched. This study examined the determinants of food security among households engaged in the cultivation of under-recognised indigenous vegetables in Nigeria. A total of 302 respondents were selected using a simple random sampling technique from the NiCanVeg farmers’ lists. The data were analysed using a Zero-One Inflated Beta (ZOIB) regression model, which is appropriate for handling proportions with a considerable number of zero outcomes – common in food security indicators. The use of the ZOIB model helped correct for the bias introduced by zero responses. The analysis revealed that socio-economic factors – including sex, age, education, household size, savings, association membership, marital status, total income, income derived from under-recognised vegetables, market participation, and asset value – significantly influenced both the probability and intensity of food security. The findings underscore the importance of promoting education and market participation as strategic interventions to improve food security among farming households cultivating under-recognised indigenous vegetables.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".