An Evaluation of Farmers’ Participation in Afforestation Programme in Kogi State, Nigeria
Why this work is in the frame
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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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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 | 0.005 | 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 | 0.001 |
| 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 it