Promoting fast-growing species sawlog plantations by smallholder farms: evidence from a choice experiment study in Vietnam
Bibliographic record
Abstract
Smallholders’ planting fast-growing trees for pulpwood production has substantially contributed to forest rehabilitation, wood production, and rural incomes worldwide. Government incentive programs have attempted to increase the productivity and value of such plantations and enhance sawlog supply to the furniture industry. Designing effective incentive programs to encourage sawlog plantations requires a deeper understanding of smallholder preferences and socio-economic characteristics. This study examines preferences for different designs of incentive programs, including technical support, rotation length, financial subsidy, committed area, and timber insurance. A scale-adjusted latent class model is used to investigate preference heterogeneity using data on 300 smallholders in the Central Highland and Northern Upland regions, representing two development stages of plantation forestry in Vietnam. The analysis identified four preference classes, which value attributes differently. Preference for sawlog plantation programs depends on individual characteristics and psychological factors such as perception of benefits, obstacles, and risks. The longest rotation preferred by a significant proportion of smallholders for sawlog-oriented plantations is a medium rotation of 8–10 years. Our study further determines feasible incentive programs that meet smallholder preferences and government budget constraints, draws important policy implications for promoting the value of smallholder plantations and livelihoods.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".