Relationship between Attitude, Knowledge, and Support towards the Acceptance of Sustainable Agriculture among Contract Farmers in Malaysia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 |
| 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 it