Agroecological practices of legume residue management and crop diversification for improved smallholder food security, dietary diversity and sustainable land use in Malawi
Bibliographic record
Abstract
The role of agroecological practices in addressing food security has had limited investigation, particularly in Sub-Saharan Africa. Quasi-experimental methods were used to assess the role of agroecological practices in reducing food insecurity in smallholder households in Malawi. Two key practices – crop diversification and the incorporation of organic matter into soil – were examined. The quasi-experimental study of an agroecological intervention included survey data from 303 households and in-depth interviews with 33 households. The survey sampled 210 intervention households participating in the agroecological intervention, and 93 control households in neighboring villages. Regression analysis of food security indicators found that both agroecological practices significantly predicted higher food security and dietary diversity for smallholder households: the one-third of farming households who incorporated legume residue soon after harvest were almost three times more likely to be food secure than those who had not incorporated crop residue. Qualitative semi-structured interviews with 33 households identified several pathways through which crop diversification and crop residue incorporation contributed to household food security: direct consumption, agricultural income, and changes in underlying production relations. These findings provide evidence of agroecology’s potential to address food insecurity while supporting sustainable food systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 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".