An Empirically Derived Conceptual Framework to Assess Dis-Adoption of Conservation Agriculture: Multiple Drivers and Institutional Deficiencies
Bibliographic record
Abstract
Efforts of national governments and international agencies aimed at alleviating hunger and poverty are often undermined by lack of long-term adoption of agricultural innovations. Studies commonly explain farmers’ adoption decisions using household general determinants, yet decision-making, particularly for under-resourced smallholder farmers in sub-Saharan Africa, is a complex process. Using the case of conservation agriculture [CA], this article analyses dis-adoption of agricultural technologies by examining multiple domains of Malawi’s CA innovation system and how these influence farmer decision-making. It analyses institutional arrangements of CA promoters, national policies and farmers’ experiences. From this, we empirically derive a multifaceted dis-adoption drivers’ framework to explain CA dis-adoption in smallholder farming systems. Our findings reveal that adverse features in national policies, institutional arrangements, technological attributes and social cultural dimensions all lead to unfavourable experiences of CA for smallholder farmers, which can culminate in dis-adoption. The CA dis-adoption drivers’ framework we develop in this study provides a useful troubleshooting tool. It can be used to guide improvements in the design and implementation of project-based interventions seeking long-term adoption of agricultural innovations across sub-Saharan Africa.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".