Closing Gender Gaps in Climate-Smart Agriculture through Strengthening Women Rice Seed Farmer’s Capacities and Access to Quality Stress-tolerant Seed in Benin
Bibliographic record
Abstract
Climate change and stress conditions (drought; submergence, salinity, iron toxicity, and cold) disproportionately affect the poorest and most disadvantaged rice farmers, forcing them deeper into poverty. Recent advances in genetics and breeding enable the development of rice varieties tolerant of these abiotic stresses and their cultivation can substantially contribute to poverty alleviation in unfavourable environments and for poor rice consumers globally. Through the program Stress-Tolerant Rice for Africa and South Asia (STRASA), fourteen new stress-tolerant varieties were released, produced and distributed in Sub-Saharan Africa to reach millions of poor farmers. However, ignoring women’s contributions to agriculture and particular in seed production and failing to design strategies to reach them with new varieties miss significant opportunities to reduce poverty. This study investigates on gender issues in rice seed production in Benin through a gender analysis of the division of labour, access and control of resources, livelihood, and constraints and opportunities faced. Both qualitative and quantitative data were collected with 29 women and 29 men seeds producers using both the Harvard Analytical and the Sustainable Livelihoods Frameworks. Data showed that women are central in rice seed production; but are marginalized in their access and control of resources. Given to women resources property rights as well as improving their control on resources will help them to be more performant as seed producers. These areas for action are important in designing and implementing activities in gender-responsive ways for sustainable Stress-Tolerant Rice seed multiplication, dissemination and out scaling in Africa.
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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 | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".