Everyday climate adaptation practices in agriculture contribute to food security in Sub-Saharan Africa
Bibliographic record
Abstract
Sub-Saharan Africa (SSA) faces considerable threats to its food security because of the adverse effects of climate change. Agriculture, which both influences and is influenced by climate change, requires a thorough understanding of how it impacts and is impacted by these changes. Such understanding is essential for guiding everyday adaptation strategies that uphold sustainable practices and food security. This study explores the impact of various climate adaptation strategies, demographic, and economic factors on dietary diversity across SSA by using Household Dietary Diversity Score (HDDS) as an objective and standardized measure. The research integrates everyday adaptation practices such as tree management, home gardening, crop diversity, intercropping, and composting, alongside demographic factors to assess their influence on food security. The findings reveal tree management and home gardening consistently show a positive influence on HDDS, regardless of seasonal variability. Crop diversity and intercropping also positively impact HDDS, although their effectiveness varies across seasons. Meanwhile, irrigation emerges as a critical factor in maintaining dietary diversity during challenging seasons. Female control within households emerges as a significant demographic factor positively associated with HDDS. Moreover, dietary diversity is generally lower in West Africa, particularly during adverse seasons, because of less stable and extreme agricultural conditions. Despite these adaptation practices, the study identifies a significant policy gap, as existing agricultural policies in the region do not fully support the integration of these everyday practices or address gender-specific needs. Therefore, there is a critical need for sustainable, gender-responsive, and region-specific agricultural policies that effectively incorporate these everyday climate adaptation practices to enhance resilience and food security in SSA.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".