Livelihood Strategies of Female Indigenous Vegetable Farmers’ in Osun State, Nigeria
Bibliographic record
Abstract
This study examined the socioeconomic characteristics, income profile and pattern of livelihood diversification strategies of resource-constrained female indigenous vegetable farmers in Osun state, which may serve as entry point for poverty alleviation and women empowerment in the study area. A total of 240 female indigenous vegetable farmers were surveyed through the use of pre-tested structured questionnaire during the 2011/2012 production cycle. The study established that the majority of the respondents were below 50 years of age (73%), married (78%) with more than 7 years of formal education (51.7%) and cultivated less than 1 hectare of vegetables (65%). Vegetable production is the most important source of income contributing about half of the total income of the farmers but its share reduces as total income increases. Cluster analysis identified six livelihood portfolio combinations- sole vegetable farming, vegetable and livestock farming, part time vegetable and livestock farming, mixed farming, arable crop farming, part time mixed farming. Part time mixed farming was the most successful livelihood portfolio because it had the highest daily per capita income (N 378.55), the highest proportion of the rich (72.73%) and the lowest proportion of the poor (4.55%), while, vegetable and livestock farming was most common among the poorest farmers (53.85%). A major policy implication of the results of the study is that multiple complementary economic activities such as milling and other food processing activities should be encouraged among the female indigenous vegetable farmers, to enable them generate sufficient incomes to improve on their livelihood.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".