Beyond rice: the rise of salt-tolerant potatoes and sweet potatoes in Bangladesh?
Bibliographic record
Abstract
Abstract In Southern Bangladesh, where rice dominates as the staple crop, the introduction of salt-tolerant potato and sweet potato varieties aims to enhance agricultural productivity and address food and nutrition insecurity in response to climate change and soil salinization. This study evaluates the impact of an intervention that disseminated improved varieties alongside agronomy and nutrition training. Using ex-post data from 1,621 farmers, treated and untreated, recalling the 2022/2023 and 2018/2019 seasons, a matched difference-in-difference analysis reveals an Average Treatment Effect on the Treated on sweet potato yield of 4.8 tonnes/ha (33 per cent increase) but no effect on potato yield. Yet, there is evidence for widespread disadoption of the improved varieties. Results from a Heckman selection model, including robustness checks for heterogeneity, suggest that positive yield effects stem mostly from training. Although no significant difference in food and nutrition security was observed between treated and comparison households, we note a shift in cultivation patterns. Potatoes, traditionally grown by men as cash crops, were increasingly cultivated by women to combat food insecurity, whilst sweet potatoes, traditionally grown for consumption, became more commercialized. This study shows the importance of timely planned evaluations of agriculture projects that carefully consider the interplay of adoption, training, consumption, and gender, highlighting the need for locally targeted initiatives to address food and nutrition insecurity in climate-vulnerable regions.
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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 | 0.001 |
| 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) | 0.005 | 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".