Towards Food Sovereignty: The Role of Smallholder Farmers’ Seed Security in Improving Climate Change Resilience in Northern Malawi
Bibliographic record
Abstract
With climate extreme events increasing in frequency and intensity in Malawi, the future of local food production faces serious threats, necessitating renewed efforts to build the adaptive capabilities of the majority poor smallholder farmers. In this context, seed security is critical to improving rural livelihoods and agrobiodiversity; however, knowledge of its role in climate change resilience is sparse. Drawing insights from vulnerability and resilience literature, this paper examines the role of seed security in enhancing climate change resilience in northern Malawi. Using a cross-sectional survey of 1,090 smallholder farmers and applying logistic regression analysis, the study found that households that are seed-secure were significantly more likely to report stronger resilience to climate change than those that were not seed-secure, even after controlling for theoretically relevant variables (OR = 1.89; p < .01). Other noteworthy predictors of climate change resilience included level of education, wealth, agroecological practice, and seed sources. Based on the findings, we advocate for promoting seed security as part of broader localized and place-specific action plans to foster resilience to climate change in agricultural regions.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".