Food Security and Productivity of Urban Food Crop Farming Households in Southern Nigeria
Bibliographic record
Abstract
The study investigated the effect of urban food crop farming household’s productivity on household food security in Cross River State, Southern Nigeria. A two-stage sampling technique was used to obtain a sample size of 217 urban food crop farmers. The data was analyzed using food security index, food insecurity/surplus gap index, head count index, productivity index and logistic regression. The result showed that 53.5 % of the households were food insecure while 46.5 % were food secure. The average daily per capita calorie intake for food secure households was 8732.29 kcal, which is higher than the national average; and 880.26 kcal for food insecure households, which is far lower than the national average and the recommended minimum requirement by FAO. The food insecurity gap/surplus index result showed that food secure households exceeded the calorie requirement by 218% while the food insecure households fell short of the calorie requirement by 89%. The logistic regression estimates revealed that the productivity of urban farming households had a significant and positive effect on household’s food security status. This means that the higher the productivity of urban farming household’s, the higher is the probability that households would be food secure. The study therefore recommends that, to reduce food insecurity in the study area government must make
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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 | 0.001 |
| 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".