PRODUCTION AND CONSUMPTION OF UNDERUTILISED INDIGENOUS VEGETABLES (UIVs) AMONG MEN AND WOMEN FARMERS: EVIDENCE FROM SOUTHWEST NIGERIA
Bibliographic record
Abstract
The study determined the variables influencing involvement of selected Nigeria-Canada Vegetable (NiCanVeg) project farmers in the production, processing and consumption of underutilised indigenous vegetables (UIVs) in Southwest Nigeria. Gender Equality Framework was used to draw a clear distinction and demonstrate inter-relationships among men and women farmers under the project. Seventy eight participating farmers in four purposively-selected sites (54 women and 24 men) under the Nigeria’s Obafemi Awolowo University coordinating team were interviewed for the study using a well structured interview schedule. The mean age of the male and female farmers were 41.9±1.39 and 37.17±11.03 years, respectively. The farmers were dominated by Christians; 74.4% of the total men population and 63.37% of the total women population, with mean annual income of N7,000 and N 5,800 from vegetable production for men and women farmers, respectively. Consumption of the various UIVs is high as majority (67.5% men and 85.7% women) of the farmers indicated that NiCanVeg project has increased awareness on the agronomic practices as well as the nutritional compositions of the various UIVs under study through the numerous expository programs of the project. Among the ten UIVs investigated, Igbagba/garden egg (Solanum macrocarpon) was the major vegetable grown by male farmers while Apiroko/Fluted pumpkin (Telfairia occidentalis) was the major vegetable grown by female farmers. Results of Pearson’s Correlation showed that farm size (r = 0.528; P ≤ 0.002) and previous experience(r = 0.671; P ≤ 0.000) were established as the correlates of the level of involvement in production and consumption of UIVs among men while farm size (r = 0.081; P ≤ 0.006); age (r = 0.813; P ≤ 0.048) and years of formal education (r = 0.053; P ≤ 0.05) correlated significantly with women’s level of involvement in production and consumption of UIVs. This is expected to increase the food security base of the people, in conformation with some studies that established that women play significant role in fighting hunger and food insecurity in developing countries as they are the ‘heart’ of agricultural activities in the region. It was therefore suggested that NiCanVeg should target more women involvement in production and encourage equal distribution of assets between gender with a view to increasing food production and reducing hunger in Nigeria.
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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 | 0.000 | 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 | 0.000 | 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".