Gender differences in climate-smart adaptation practices amongst bean-producing farmers in Malawi: The case of Linthipe Extension Planning Area
Bibliographic record
Abstract
Agriculture is amongst the vulnerable sectors to climate change and its associated impacts. Most women are more vulnerable to the impacts of climate change than men. Climate Smart Agriculture ensures increased productivity thereby enabling food security, income security and wealth creation amongst the farming households. A study was carried out to understand the gender differences in access and use of climate-smart agriculture, challenges and solutions that men and women farmers use to adapt to climate change. Data was collected from 246 randomly sampled households from 14 villages at Linthipe Extension Planning in Dedza district in Malawi. The multivariate probit model was employed to understand the influence of sociodemographic, farm-level, and institutional factors in the application of climate-smart agriculture in the study area. Findings from this study indicate that there are differences in the adoption and use of climate-smart agriculture technologies in bean production amongst different gender categories. More women compared to men and youths tend to use fertilizer, use improved seeds and plant early in order to mitigate and adapt to climate change. Most men adopt and use irrigation, whilst the youth mostly adopted and used pesticides and conservation agriculture practices. The study recommends policies that would ensure the promotion of gender-responsive climate-smart agriculture technologies, improved access to inputs, and capacity building through training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".