Relationship between Attitude, Knowledge, and Support towards the Acceptance of Sustainable Agriculture among Contract Farmers in Malaysia
Bibliographic record
Abstract
Sustainable agriculture practices are known as the best techniques by which to cultivate crops. To ensure the continuity of such practices, farmers should accept and apply this method on their yield. There is an abundance of international studies which have found that attitude, knowledge and support are the main factors to impinge on the acceptance of sustainable agriculture among farmers, but studies on the same scenario are lacking for Malaysia. Filling this research gap is the main objective of this study, which seeks to elucidate the relationship between attitude, knowledge and support towards the acceptance of sustainable agriculture among contract farmers in Malaysia. This is a quantitative study, and a total of 326 respondents were involved in the data collection process. The data were gained through a developed questionnaire. The resulting analysis proves that there is a significant relationship between contract farmers’ attitudes and their acceptance of sustainable agriculture (r=0.498, p=0.00).Contract farmers’ knowledge and their acceptance of sustainable agriculture are also shown to demonstrate a significant relationship (r= 0.348, 0.00).Additionally, there is support for a significant correlation between knowledge and acceptance of sustainable agriculture (r=0.365, p=0.00). In conclusion, farmers should have positive attitudes and adequate knowledge, and should obtain support from several parties to encourage them to embed sustainable agriculture within their farming practices.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".