Building climate resilience in the MENA region: a feminist political ecology of agroecology and neglected and underutilized crop species
Bibliographic record
Abstract
This study explores the factors associated with the cultivation of neglected and underutilized crop species (NUS) and agroecology adoption (crop rotation with legumes) amid the climate crisis in rural Lebanon and Morocco. A mixed-methods approach (interviews and questionnaire survey) was used. Five key informant interviews as well as a survey of 923 farmers: 465 in Lebanon (242 men and 223 women) and 458 in Morocco (238 men and 220 women) were conducted. This paper addresses two key questions: (1) what are the predictors of agroecology and NUS cultivation, and (2) what are the primary gendered NUS varietal preferences of farmers and why? Using insights from feminist political ecology, and applying logistic regression analysis, key predictors of adoption include gender, with Moroccan women more likely to adopt agroecology, and age, as older farmers in Morocco are less likely to engage. In Lebanon, marital status influences adoption, with divorced or widowed farmers more engaged in NUS cultivation. Education and wealth also play significant roles, with higher levels of both positively associated with agroecology and NUS adoption. Amid the climate crisis, men and women farmers both indicated that traits like drought resistance and suitability for low rainfall conditions are key drivers for NUS and agroecology adoption. Some key reasons for NUS varietal preferences included, market demands, the need to reduce burden on women, taste and cooking qualities, and to adjust to the climate. Farmers, especially women, also indicated importance of flour blending in their uptake of barley cultivation. These findings highlight the complex motivations behind NUS varietal selection, and the importance of context-specific strategies to support sustainable agriculture in the MENA region.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".