Step by step to higher yields? Adoption and impacts of a sequenced training approach for climate-smart coffee production in Uganda
Bibliographic record
Abstract
Climate change further exacerbates sustainability challenges in coffee cultivation. Addressing these requires effective delivery mechanisms for sustainable farming practices, particularly in smallholder contexts. We assess a novel public-private extension approach in Uganda, called Stepwise, comprising a sequence of climate-smart and good agricultural practices in four incremental steps. Using a mixed-method approach, an index that captures adoption intensity rather than binary uptake, and survey data from 915 Robusta and Arabica coffee farmers, we find adoption levels around 46% and relatively uniform amongst treated, spillover and comparison farmers. Regional variations suggest differing benefits across coffee varieties. Qualitative findings identify barriers to adoption, including financial and labour constraints, suboptimal training delivery, and input and output market imperfections. Despite relatively low uptake, adoption of more than half of the Stepwise practices is associated with substantial gains: inverse probability weighted regression adjustment reveals a 23% increase in yield and a 32% increase in revenue. Our findings add to the adoption literature, which often highlights limited uptake, and have important policy implications. Strengthening producer organizations, delivering targeted training but also innovative solutions for access to inputs and fair pricing, hold considerable potential to increase the adoption of climate-smart practices, particularly among resource-constrained farmers.
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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 | 0.001 | 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 | 0.001 |
| 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 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".