Gendered adaptation of Eritrean dryland farmers
Bibliographic record
Abstract
Purpose This paper aims to report findings of a study of vulnerability that identified adaptation strategies of male and female farmers in two regions of Eritrea. The country is suffering from food shortage because of climate and non-climate stressors. As such, erratic rainfall, chronic droughts and extreme weather adversely affect crop production. This paper answers the question of how policy instruments and cultural practices, and their interaction, increase or reduce the vulnerabilities of male and female agricultural producers, including producer perceptions of how instruments and culture can be improved. Design/methodology/approach Interviews and focus groups were conducted in the two study regions in Eritrea. Documents and transcripts of the interviews and focus groups were coded by theme and analyzed. Findings Findings revealed that the main rainy season has reduced from four to two months, and the minor rainy season has often failed. As a result, exposure and sensitivity to climate change affects all farmers. These climate change impacts together with Eritrean government policy instruments, including the limited availability, affordability and accessibility of agricultural inputs such as land, fertilizer, seeds, and male labor exacerbate the vulnerability of agricultural producers. Tigrinya farm women are the least able to adapt to extreme weather because of an unequal distribution of resources resulting from cultural, patriarchal views of women which have prevented them from being regarded as equal primary farmers and further limit their access to the resources mentioned. This vulnerability is exacerbated by the prescribed military service of men in their community (which is not prescribed in the matrilineal Kunama community). Producers perceive that addressing this gender inequality and improving government instruments, most importantly getting rid of mandatory military service, will improve adaptation. Practical implications Concrete recommendations made by the community are reported. Originality/value This paper presents important findings from qualitative research conducted in Eritrea.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".