Adoption and Impact of Conservation Agriculture on Smallholder Farmers’ Crop Productivity and Income in Luapula Province, Zambia
Bibliographic record
Abstract
Despite efforts to systemically disseminate Conservation Agriculture (CA) technology in Luapula Province of Zambia, the adoption rate remains limited. Furthermore, no empirical evidence has been presented on the factors influencing adoption of the technology or the extent to which farmers’ livelihood has been influenced due to uptake of the technology. This study therefore examined the adoption and impact of CA on crop productivity and income on farming households in the Province. Using the 2012 Rural Agricultural Livelihood Survey (RALS) data, the study employed a probit regression model to identify factors influencing adoption of CA among the smallholder farmers in the Province. The probit regression analysis showed that advice on CA and access to wetlands/dambos by households increased the probability to adopt CA. The study also adopted the Propensity Score Matching (PSM) approach to help match the adopters and non-adopters based on observable covariates in order to assess technology impact by providing consistent estimates of the Average Treatment Effect on the Treated (ATT). The results showed a small but insignificant positive impact of CA on crop productivity and income. This suggests that adoption of CA has the potential to generate an improvement in farming households’ livelihood in Luapula Province, Zambia. Therefore, adoption of CA in Luapula Province should be explicitly encouraged. This can be further enhanced by increased access to quality extension services that incorporates promotion of CA practices among the smallholder farming households in the area.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".