Application of a Choice Experiment to Estimate Farmers Preferences for Different Land Use Options in Northern Tajikistan
Bibliographic record
Abstract
Based on farmers’ preferences this study estimates the non-market values of agri-environmental attributes and their changes within the study area. The analysis is carried out using a choice experiment technique of stated-preference to conduct investigations regarding different land use options within the agricultural sector of the Konibodom region of Tajikistan. The dataset was constructed using a detailed household level survey amongst 117 representative farmers throughout the district, including all agriculturally important settlements. Detailed focus group discussions and a combination of personal interview and ‘pick and drop’ approaches were selected as the appropriate surveying techniques. In order to compliment the survey data, secondary data was collected from official statistics, key informants and experts from the field. Several types of models were specified and estimated such as Conditional Logit and Random Parameter Logit (RPL) Models. Significant improvements were achieved through the inclusion of interaction terms into the RPL model. The results of both the RPL models reveal that preference heterogeneity exists amongst farmers in the study area, implicating that a decision for land allocation under different crops is jointly associated with other socio-economic and environmental factors, influencing one another.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".