The Impact of Farm Forestry on Poverty alleviation and Food Security in Uganda
Bibliographic record
Abstract
<p>To address the problem of high rural poverty and food insecurity, government and international donors have funded on-farm plantation forestry projects as one of the tools for improving the welfare of rural communities. In the wake of climate change, on-farm plantation forestry has evolved to include carbon forestry, with the dual purpose of sequestering carbon and improving rural livelihoods. However, there is a dearth of empirical evidence regarding whether and under what conditions on-farm plantation forestry can deliver favorable livelihood outcomes.</p>Therefore, Propensity Score Matching (PSM) and endogenous switching regression models were used to estimate the average treatment effects of adopting eucalyptus and carbon forestry woodlots (under the planvivo system) on consumption expenditure per adult equivalent and daily calorie acquisition per adult equivalent. PSM and switching regression results consistently indicated that adoption of eucalyptus woodlots increased consumption expenditure by 32 and 28.3% respectively. PSM and switching regression results also indicated that adoption of eucalyptus woodlots increased calorie acquisition per adult equivalent by 36 and 13.1% respectively. Results also indicated that adoption of carbon forestry increased calorie acquisition per adult equivalent by between 22 and 26.9% but the impact on consumption expenditure per adult equivalent was mixed. The findings of this study provide empirical evidence that adoption of on-farm eucalyptus woodlots is an important pathway for smallholder farmers to escape poverty and improve food security. Similarly, adoption of carbon forestry woodlots under the planvivo system can improve food security. However, previous on-farm plantation forestry projects were not well targeted to the poor households.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".