Assessing the Downstream Socioeconomic Impacts of Agroforestry in Kenya
Bibliographic record
Abstract
Agroforestry is widely purported to improve the livelihoods of smallholder farmers, rehabilitate degraded landscapes, and enhance the provisioning of ecosystem services. Yet, evidence supporting these longer-term impacts is limited. Using a quasi-experimental impact evaluation design informed by a theory-based and mixed methods framework, we investigated both the intermediate effects and household welfare outcomes of a nine-year effort led by Vi Agroforestry (herein Vi), a Swedish nongovernmental organization (NGO), to promote agroforestry in large sections of Bungoma and Kakamega counties in western Kenya. We compared households belonging to 432 pre-existing farmer groups operating in 60 program villages and 61 matched comparison villages. To address potential selection bias, we used program targeting as an instrument for program participation, combined with the difference-in-differences approach to control for time-invariant differences between our treatment and comparison groups. We complemented the above with semi-structured interviews with a sub-sample of 40 purposively selected program participants. Despite evidence of variable program exposure and agroforestry uptake, we found modest, yet statistically significant, effects of Vi's program on intermediate outcomes, such as agroforestry product income, fuelwood access, and milk yields among dairy farmers. We also found that this program modestly increased asset holdings, particularly among households represented by female program participants.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".