Factors Affecting the Joint Adoption of Herbicides and Conservation Tillage Technologies among Smallholder Farmers in Zambia
Bibliographic record
Abstract
For over three decades, Zambian private and public organizations have been promoting Conservation Agriculture (CA) among smallholder farmers, throughout the country. CA, as a technology, involves Conservation Tillage (CT) methods and use of herbicides, particularly in the first 3 to 4 years of its adoption. Despite the long-term CA dissemination effort from stakeholders, adoption of both herbicides and CT technologies has been relatively low. Using the 2012 Rural Agricultural Livelihood Survey (RALS12) data collected country-wide, a bivariate probit analytical model investigated whether Zambian smallholder farmers make a joint adoption decision in the uptake of herbicides and CT methods. Results indicate that the adoption of herbicides is jointly linked to the adoption of CT technologies. Factors such as being a male-headed household, belonging to a cooperative society, larger sizes of cultivated areas, owning productive tools such as a knapsack sprayer and receiving advice on CA technologies, were critical in positively influencing the joint-adoption of herbicide and CT technologies. Overall, results imply that if the adoption of herbicides is to be enhanced, the promotion of CT technologies have to be intensified. The best promotional channels include agricultural groups such as farmer cooperatives, which are good conduits and platforms for smallholder farmers’ knowledge-sharing. These promotional efforts should support gender balancing and emphasize the inclusion and use of herbicides in CA. There is also need for government to enhance farmers’ access to productive tools that support the use of herbicides such as knapsack sprayers.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".