Adoption of On-Farm Plantation Forestry by Smallholder Farmers in Uganda
Bibliographic record
Abstract
<p>The study assessed the factors influencing adoption and intensity of adoption of on-farm plantation forestry by comparing results from a censored Tobit model and a Double-hurdle model. Analysis indicated that determinants of adoption and intensity of adoption of on-farm plantation forestry are different, thus indicating a double-hurdle process. Results from the double-hurdle model indicated that size of landholding, secondary school education, forestry skills training, extension services and farmers’ perceptions significantly explain the variation in the decision to invest in on-farm plantation forestry. On the other hand, gender of household head and size of landholding influenced the intensity of adoption. This study highlights some of the areas that should be considered in developing adoption strategies for on-farm plantation forestry. It also highlights the importance of farmers’ perceptions in influencing adoption of farm forestry. The study suggests that since the factors influencing adoption and intensity of farm forestry adoption are made separately, it is important that both stages are considered in developing adoption strategies for farm forestry.</p>
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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".