Determinants of Youth Farmers’ Participation in Agricultural Activities in Akwa Ibom State, Nigeria
Bibliographic record
Abstract
A refocus on agriculture is considered a pertinent resort for the youths because it is generally believed to be a panacea for sustainable development in any nation. To help generate suitable policies to encourage youth farmers to be involved in agricultural activities, the study analysed factors that influence youth farmers’ participation in agricultural activities in Akwa Ibom State, Nigeria. Through a list of farmers obtained with the assistance of Akwa Ibom State Agricultural Development Programme, 120 youth farmers were randomly selected for the study using simple random sampling technique. The study used descriptive and inferential tools to analyse information collected. The majority (59.2%) of youth farmers were male and 42.5% were between the ages of 36-39 years. Only 8.3% had access to credit. About 71% of the youth farmers were involved in on-farm activities and only 29.2% in both on- and off-farm activities. The major determinants of youth agricultural activities were household size and membership of social organizations. The state government and other relevant agencies and organizations should create platforms to educate youth farmers on the need for more involvement and diversification in their agricultural livelihood strategies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".