Women-Led Pulse Agriculture for Enhanced Household Nutrition Security in East African Countries
Bibliographic record
Abstract
Pulse crops are significant sources of starch, fiber, protein and micronutrients for the human population. Four East African countries (Ethiopia, Tanzania, Uganda, and Rwanda) have huge potential of pulse production but are constrained by several challenges in the production-consumption chain. This scoping review has assessed the challenges and opportunities related to women-led, nutrition-sensitive pulse agriculture in the four East African countries. This scoping review is based on data from major scientific databases, such as PubMed, Scopus, and Web of Science, which are commonly used for research purposes, as well as grey literature. The criteria used were studies conducted in Ethiopia, Tanzania, Uganda, or Rwanda; grey literature which includes reports, theses, and other unpublished materials that may be relevant to a particular topic (years 2010 to 2023); observational studies, including case-control, cohort, and cross-sectional studies that assessed pulse crop production and consumption; and those published in English. Evidence from these countries shows that the historical gender gap and low level of participation by women in the sector have had adverse effects on the production of pulses. There are also social and cultural barriers that severely constrain women’s role in pulse agriculture, such as poor knowledge of the benefits of pulses, constraining cultural practice and gender-based norms in the pulse sector, limited access to market, land and finance, underdeveloped delivery/supply chain and extension services, less developed value addition culture, and the stigma of pulse consumption. This study identifies multiple avenues to ameliorate the identified socioeconomic, cultural and policy constraints, including promoting women-led pulse production through increasing access to financing for small-scale pulse crop farmers, improving market access through better marketing and distribution networks, and investing in infrastructure to support pulse production and consumption.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".