Does Adopting the Bean Technology Bundle Enhance Food Security and Resilience for Smallholder Farmers in Ethiopia?
Bibliographic record
Abstract
The analysis of the differential impacts of multiple improved technologies has largely accounted for selective adoption, considering either the full application of a bundle or its individual components. The impacts of adopting agricultural technology bundles on household welfare are less understood when considering a partial adoption of either the entire bundle or its individual components on a portion of crop area. We assess simultaneous adoption and the impacts of multiple improved technologies promoted as a bundle and recommended for legume intensification systems for smallholder farmers in Ethiopia. We use DNA fingerprinting data to precisely identify our key treatment—“adoption of improved bean varieties”—in this study. Using an endogenous multivariate treatment effects model, we found significant positive impacts of adopting bundled interventions on agricultural incomes and household food security but vulnerability to food insecurity persists for many households. We find that growing improved varieties with fertilizers increased household agricultural revenue, allowing for more legume consumption and enhancing their likelihood of achieving adequate food consumption and food security outcomes; however, the vulnerability to food insecurity of the adopters remains high due to pre-existing resource degradation issues. Given the similarity in production contexts in Sub-Saharan Africa, our results provide perspective for similar development interventions. We use the results of our analysis to discuss potential policy implications and programs to support technological intensification among smallholder farmers.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".