An Evaluation of Farmers’ Participation in Afforestation Programme in Kogi State, Nigeria
Bibliographic record
Abstract
Extensive deforestation has reduced the 65 million hectares of intact forest cover of 1897 in Nigeria to thepresent 4 million hectares. The consequences of this unhealthy development have resulted to environmentaldegradation and accelerated wind and water erosion of the fertile land that has also left Nigerian soil too poor forsustainable agricultural production. Reforestation through small-scale village based farmers’ participation nowform one of the strategies embarked upon by several agencies in Nigeria including Kogi afforestation project.This study attempts to evaluate farmers’ participation in afforestation project in Kogi State. Structuredquestionnaire was used to interview 120 participants. Descriptive statistics, adoption index and sigma methodwere used to describe socio-economic characteristics, participation methods and to measure the level of adoptionwhile chi-square was used to find differences between income generated from adoption of the variousafforestation technologies. Findings reveal that 67 percent of the farmers had little or no formal education, morethan 30 percent of the farmers underwent passive participation in afforestation while adoption of improvedseedlings, exotic trees and pure stand technologies received high score of 4.90, 4.74 and 4.44 respectively. Seedscarification and harvesting by chipping technologies received the least adoption score of 2.61 and 2.94. Thechi-square test adjudged that there was a significant difference between income generated and type of technologyadopted. This study recommends that more pragmatic interactive participation method that will give room forjoint analysis of action plan and formation of local institutions should be put in place.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".