Policy Integration and Coherence for Conservation Agriculture Initiatives in Malawi
Bibliographic record
Abstract
In sub-Saharan Africa, development and dissemination of perceived new agricultural innovations dominate the development agenda yet hunger and poverty remain widespread. A conducive policy environment is essential to support these efforts. Despite that national policies are a critical component in the functioning of an agricultural innovation system, studies have often overlooked their relevance in farmers’ adoption of agricultural innovations. There is an urgent need to enhance understanding of how policies affect long-term adoption of agricultural innovations aimed at increasing productivity and incomes of smallholder farmers. This study utilises thematic content analysis to examine the extent of integration of Conservation Agriculture (CA) and coherence in Malawi’s national agricultural policies, and their implication for CA adoption among smallholder farmers.Results indicate that inadequate integration of CA in the National Agricultural Policy (NAP), coupled with a lack of coherence of agricultural department policies, undermines farmers’ CA adoption. While inadequate integration constrains resource allocation for supporting CA activities, lack of coherence of agricultural policies radiates conflicting and confusing agricultural extension messages to smallholder farmers. We argue that inadequate CA integration and incoherence of policies are institutional constraints which prevent farmers’ sustained adoption. To facilitate long-term adoption of CA among smallholders, there is need to: (1) strengthen CA integration in agricultural policies; (2) improve departmental coordination to enhance coherence of agricultural strategies and extension messages disseminated to farmers; and (3) strengthen government’s role in supporting multi-disciplinary research to generate and disseminate best practices capable of sustaining CA adoption.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".