Which Perennial Crop Farm Approach Generates More Profitability? A Case Study in Dak Lak province, Vietnam
Bibliographic record
Abstract
Dak Lak Province, Vietnam has been identified as the optimal growing area region of cash crops. However, in recent years, perennial crops have faced some challenges need to have a new approach to maintain production sustainability. This study primarily provides a comparative analysis of the economic performance of crop cultivation by two approaches, mono-crop approaches including mono-coffee farms (MCFs) and mono-pepper farms (MPFs); intercropped approaches comprising intercropped coffee farms (ICFs) and intercropped pepper farms (IPFs). Additionally, this paper identifies the main factors affecting the farmer’s adoption decision on different intercropped farm types. Based on an investigation of 120 selected farms, focus group discussions (FGDs) and participant assessments, from January to April 2019, the information about farming operations, costs and profits also were collected. The findings indicated that intercropped farms (include ICFs and IPFs) had a higher reduction of variable costs than mono-crop farms (MCFs and MPFs). Likewise, between two intercropped farm types, ICFs that wasted fewer input costs seem to be more appropriate for the poor and small saving households than that of IPFs. Moreover, ICFs and IPFs generate more profitability, increase by 62 % and 25.7 % as compared with MCFs and MPFs, respectively. Going forward, the study revealed factors influencing farmers’ decision-making on applying different approaches for intercropped farms. These comprise farm profiles (pest and disease status; the age of the tree), farmers’ characteristics (training) and economic factors (profits and other income). The findings devote information to intercropped farms in terms of enhancing economic benefits should be promoted for the coming years. Looking beyond, this empirical evidence is likely a useful contribution to farming management. What’s more, the factors highlighted here demonstrate the need for continued improvement in such farming strategies.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".