Determinants of Smallholder Maize Farmer’s Perception on Use of Improved Weed Control Technologies in Eswatini
Bibliographic record
Abstract
Environmental concerns, increase labour cost and increase in demand for food has urged farmer to use the most economic and concomitant method to their farming objectives. Therefore, this study focused on ascertaining maize farmer’s perceptions on the weed control methods they choose and the socio-economic characteristics that determine these perceptions. This study was conduct countrywide in the four agro-ecological zones of Eswatini. Primary data was collected from 240 randomly selected maize farmers in the four Agro-ecological Zone of Eswatini. Factor analysis was used to estimate principal components about farmer’s perception on the different weed control methods. Multiple regression method was used to ascertain the socio-economic factors determining farmers’ perceptions. Farmers were interviewed about their perception on the usage of herbicides and integrated weed control method. The five likert scale of attitudinal statements related to herbicide and integrated weed control methods was developed. Four principal components were generated by the analysis from the positive attitudinal statement on the use of herbicides. These include productivity and economical, inclusive and confidence, knowledgeable and readiness, environmental impact. Farmer perceived that herbicides are associated with increased productive, can be used with confidence, farmers were ready to use herbicides, on the other hand, farmers perceived herbicides to have a negative impact on the environment. The socio-economic drivers of these farmers’ perceptions included sex of the farmer, education level, farming experience, access to agricultural trainings, amount of farm incomes and group membership. Integrated weeding method (hand hoe weeding + herbicide use) was perceived to be labour saving, productive and has no harm on the environment. The socio-economic drivers of these farmers’ perception towards the use of integrated weed control method included group membership and education. The study recommends that government should increase the number of extension officers to extend extension contact to more farmers, thus improve information sharing to farmers on best agricultural practices. Sensitization workshops, trainings and On-farm demonstration related to the usage of the improved weed control technologies is desired to increase farmers’ access to knowledge about the use of these improved weeding technologies.
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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 | 0.000 |
| 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".