“Climate-smart agriculture and food security: Cross-country evidence from West Africa”
Bibliographic record
Abstract
In the face of climate change and extreme weather events which continue to have significant impacts on agricultural production, climate-smart agriculture (CSA) has emerged as one important entry point in reducing the emission of greenhouse gases and building climate resilience while ensuring increases in agricultural productivity with ensuing implications on food and nutrition security. We examine the relationship between CSA, land productivity (yields), and food security using a survey of farm households in Ghana, Mali, and Nigeria. To understand the correlates of the adoption of these CSA practices as well as the association between CSA, yields, and food security, we use switching regressions that account for multiple endogenous treatments. We find a positive association between the adoption of CSA practices and yields. This increase in yields translate to food security as we observe a positive association between CSA and food consumption scores. Although we show modest associations between the independent use of CSA practices such as adopting climate-smart groundnut varieties, cereal-groundnut intercropping, and the use of organic fertilizers, we find that bundling these practices may lead to greater yield and food security gains. Under the different combinations, the use of climate-smart groundnut varieties exhibit the strongest association with yields and food security. We also estimate actual-counterfactual relationships where we show that the adoption of CSA practices is not only beneficial to CSA adopters but could potentially be beneficial to non-CSA adopters should they adopt. These results have implications for reaching some of the sustainable development targets, especially the twin goals of increasing agricultural productivity and maintaining environmental sustainability.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".