Socio-Economic Factors Influencing Woodlot Farming Adoption from Crop Farming in Tanzania: A Case of Mufindi District
Bibliographic record
Abstract
The study stressed to examine socio – economic factors influencing woodlot farming adoption in Mufindi district, Tanzania. The study used questionnaire to collect data from 40 woodlot owners (sample size). The collected data were analyzed using multinomial logistic model. Findings shown that the high income generating expectation, land size, and assets acquisitions both influence woodlot farming adoption positively and significantly while education level influence woodlot farming adoption negatively and significantly. Basing on the findings, the study concluded that socio-economic factors that influence woodlot farming positively and significantly are income generation expectation, land size, and assets acquisitions meanwhile education level influence woodlot farming adoption negatively and significantly. Based on the findings, this study recommends that there is a need for government to support the woodlot owners by providing subsidies including inputs such as land, fertilizers, and quality seeds through Mufindi district officials so as to create employment opportunities among majority of rural households and raise their standard of living. If all these recommendations are implemented, then improvement in the income from woodlot farming would ultimately be realized.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".