Social-Ecological Attributes of Conservation Agriculture in Southern Malawi
Bibliographic record
Abstract
Sustainable agriculture strategies should actively involve communities to leverage their knowledge, address challenges with intangible outcomes, and avert reliance on external support systems for innovations. The study aimed to identify farmers' perceptions of conservation agriculture social and ecological attributes (CA) and how personal demographics influence the value placed on these qualities. Multiple linear regression following principal component analysis was used to aggregate attitudinal factors underpinning farmers' attitudes toward CA's social-ecological features. Multiple correspondence analysis was performed to assess missed opportunities for CA valourisation based on farmers' perceptions of the functioning of organisations promoting CA. Extension guidance, CA area, and years of village residence were significantly correlated with assessments of enhanced surface soil and water dynamics, moisture maintenance, and nutrient recycling regarding ecological features. Concerning social qualities, CA, extension advice, and livestock ownership produced significant correlations associated with views of enhanced social connections, knowledge strengthening, and well-being and health, respectively. Organisations supporting CA were perceived as insufficient in providing interactional platforms that support acceptance of and collective action for CA. These findings indicate the possibility of incorporating information on social-ecological values into CA design and management.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".