Extension Agents and Sustainable Cocoa Farming: A Case Study of Extension Agents in Sabah State, Malaysia
Bibliographic record
Abstract
Cocoa has been commercially planted in Malaysia since the 1950s. Over the years, the planting area has gradually reduced, which has resulted in the need to import cocoa beans to sustain the local grindings requirement. The Malaysian Cocoa Board (MCB) has introduced extension programmes to cocoa farmers to encourage sustainable farming of cocoa in the country. This study assessed the perception, knowledge, attitudes, and value of agricultural extension agents towards sustainable cocoa farming in Sabah State, Malaysia. Data were collected from extension agents working for the Malaysian Cocoa Board. A questionnaire was administered to all the front-line extension agents who deal directly with cocoa farmers. Findings revealed that cocoa extension agents’ perception, knowledge, attitudes, and value towards the concept of sustainable cocoa farming is favourable. In fact, they strongly support the concept. A significant relationship exists between knowledge on Sustainable Cocoa Farming (SCF) and their attitude towards the concept (r = 0.465). Perception also significantly correlates with the attitude of the agents (r = 0.425). The study concluded that policymakers should include SCF in training. Extension agents’ positive perceptions regarding selected sustainable cocoa farming have implications for in-service training for agricultural extension agents in the east of Malaysia to increase their specific knowledge in SCF. In addition, although cocoa extension agents possess general knowledge on the concept, further training on the application of SCF is highly recommended.
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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 | 0.001 | 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 | 0.000 |
| 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".