Adoption of climate-resilient groundnut varieties increases agricultural production, consumption, and smallholder commercialization in West Africa
Bibliographic record
Abstract
As part of the climate-smart agriculture approach, the adoption of climate-resilient crop varieties has the potential to build farmers' climate resilience but could also induce agricultural transformation in developing nations. We investigate the relationship between adoption of climate-resilient groundnut varieties and production, consumption, and smallholder commercialization using panel data from Ghana, Mali, and Nigeria. We find adoption of climate-resilient groundnut varieties to increase smallholder production, consumption, and commercialization. The biggest adoption impact gains are observed under the sustained use of these climate-resilient varieties. We show that adoption benefits all households, but the biggest gains are found among smaller producers, suggesting that adoption is inclusive. Furthermore, we provide suggestive evidence that yield increases could explain commercialization, although household consumption also matters. We conclude that adoption of climate-resilient groundnut varieties can at least partially reduce production constraints and promote smallholder consumption and commercialization, with implications for agricultural transformation.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".