Smallholder Farmer Innovation and Contexts in Maize-Based Conservation Agriculture Systems in Central Malawi
Bibliographic record
Abstract
The increased threat of food insecurity and climate change requires more sustainable ways of agriculture intensification in African smallholder farming systems. Ample evidence confirms that maize-based conservation agriculture (CA) systems lead to increased soil health and yield enhancement yet their overall uptake remains low in Africa. An array of studies on challenges and solutions to CA systems conducted in southern Africa principally focussed on the views of scientists, often neglecting the views of CA farmers. Therefore, this study assessed farmer decision making, innovation and contexts during implementation of maize-based CA systems in communities of central Malawi. A survey involving interviews with 226 CA farmers was deployed, triangulated with key informants comprising extension workers and policy makers. The study showed that about 58% of smallholder farmers did not adapt CA practices to their circumstances because they were strictly following change agents’ recommendations. The major challenge noted was competition for crop residues due to mice hunters and grazing livestock. Local by-laws initiated by the communities have started to privatise the crop residues and its grazing. However, other innovations were often not documented by extension workers, consequently neglecting more than half of the potential solutions provided by farmers. The establishments of a National Conservation Agriculture Task Force and CA guidelines are positive developments for coordination of stakeholders and harmonisation of CA messages in Malawi. However, for greater adoption, non-linear interaction and learning must be encouraged in practice by fully embracing innovative farmers and the voices of the pool of stakeholders with varying experiences.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".