Factors Influencing Adoption and Area under Conservation Agriculture: A Mixed Methods Approach
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Bibliographic record
Abstract
<p>Adoption of conservation agriculture (CA) is quite low in most parts of Africa. However, Zambia has been quite successful in increasing adoption of CA among smallholder farmers. Few studies using both quantitative and qualitative approaches have been conducted in Zambia to determine factors influencing adoption of CA. This study uses mixed methods approach to document factors influencing adoption of CA among smallholder farmers under the Conservation Agriculture Project (CAP) in Zambia. From a random sample of 415 smallholder farmers, results showed that 71% had adopted CA. Quantitative analysis indicated that CA trainings, previous experience in minimum tillage, membership in farmer organisations, and ownership of CA tillage equipment significantly increased the likelihood of CA adoption. Number of CA trainings attended, farm size, number of rippers owned and use of herbicide had a significant positive influence on area under CA. Qualitative approaches showed that good rapport with farmers, trust, reciprocity and altruism, monitoring and evaluations, extension strategy, quality and extent of technical knowledge in CA within CFU, and artificial incentives positively influenced adoption of CA. Traditional leadership was reported to enhance adoption of CA in most cases. Prestige was reported to withhold some men from adopting CA basins. Women were very involved in CA basins while men were mostly involved in ADP ripping. Some worldviews of farmers had negative influence on adoption of CA. Donor support and collaboration with the Zambia National Farmers Union and private sector were other contextual factors for the high adoption of CA among sampled smallholder farmers. In the promotion of CA it is important to pay attention to both quantitative and qualitative factors influencing adoption. A mixed methods approach thus can lead to a better understanding of the adoption of CA than a single research strategy approach.</p>
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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 | 0.003 | 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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 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 it