Unravelling the factors affecting agriculture profitability enterprise: Evidence from coconut smallholder production
Bibliographic record
Abstract
The coconut palm or scientific name Cocos nucifera L. has been called as 'Tree of Life' because of its multiuse.Malaysia remains as one of top ten coconut producing countries in the world and coconut is one of important industrial crops after oil palm, paddy and rubber.As coconut plays a significant source of income and employment for majority of smallholders, this study has therefore been undertaken in order to recommend strategies for policy decisions and formulate suitable schemes and programs to ameliorate socio-economic conditions of the coconut smallholders.The present study has brought into focus and issues relating to socio economic status, profitability and production of coconut in Batu Pahat district, Johor.A sample of 152 farmers was selected through a random sampling technique.In addition, the study uses Cost Benefit Analysis and multiple regression model to estimate the factors affecting the profitability of coconut production in Malaysia.The results reveal that the profitability was influenced by different factors including land, labor, fungicides, experience, education and extension visit.While the result for cost benefit analysis showed that in the study area, a cultivation of coconut was a profitable enterprise as indicated by benefit cost ratio, ranging from 5.0-8.4.On that basis, the article proposes some recommendations to improve profitability of coconut smallholders in the future.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".