Assessing gender disparities in farmers’ access and use of climate-smart agriculture in Southern Tanzania
Bibliographic record
Abstract
Abstract The importance of common bean in Tanzania is increasingly challenged by climate change, which increases women's vulnerability and undermines the contribution of the crop to food security and rural livelihoods. This study assessed gender differences in the use of climate-smart agriculture technologies and practices among bean farmers in Tanzania. A multi-stage sampling procedure was used to collect data from 364 smallholder bean farmers. Descriptive statistics and a multivariate probit model were employed to analyse the determinants of farmers’ adoption of climate-smart agricultural technologies and practices in common bean production. Results revealed that men dominated climate-adaptation decision-making processes at the household level because of their ownership and control over access to land, and access to agricultural support services. Older men farmers demonstrated a positive and significantly higher likelihood of adopting improved seeds (β = 0.026; p < 0.01), signifying they possess greater accumulated knowledge and wealth compared to women farmers and youths. Women farmers also had lower levels of education with fewer technological access contributing to their low uptake of climate-smart technologies, aggravating their vulnerability to climate change. Enhancing inclusive gender access to land and group-based approaches to information dissemination, and capacity building, would be relevant in enabling men, women, and young farmers to improve their adaptive and resilience capacities to climate change. Gender dynamics should be considered in designing climate-smart agriculture policies and implementation of climate-smart agriculture programs and policies to improve farmers’ resilience to climate change.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".