Exploring the Non-Use Value of Important Agricultural Heritage System: Case of Lingnan Litchi Cultivation System (Zengcheng) in Guangdong, China
Bibliographic record
Abstract
For the past decade, Important Agricultural Heritage Systems (IAHSs) have become research hotspots because of their rapidly increasing number. The non-use value is an important part of the value of an IAHS, and if ignored, the total value of an IAHS may be underestimated in part. Litchi is native to southern China, and its farming system is an important agricultural heritage with Chinese characteristics and global influence. In this context, the present study attempts to investigate the willingness to pay (WTP) of local residents and assess the non-use value of the Lingnan Litchi Cultivation System (Zengcheng) in Guangdong, China. To this aim, a survey was implemented on four sites in Zengcheng with the application of the contingent valuation method (CVM). Based on the analysis of 458 questionnaires, the WTP rate of residents in the heritage site is 66.6%, and the mean WTP is 62.5 Chinese yuan (CNY) per year. The total non-use value of the Lingnan Litchi Cultivation System (Zengcheng) is 49.9 million CNY. The option, bequest, and existence values in 2018 are estimated to be 20.1, 13.7, and 16.1 million CNY, respectively. Results of the logistic regression analysis indicate that variables of age, education level, financial burden, and heritage value cognition are significant factors of WTP for protecting litchi heritage. Compared with similar studies in China, the mean WTP and positive payment rate in this study are at a medium level. Resource attributes and local cultures may have significant impacts on the composition and estimate of the non-use value of an IAHS. The results of this study can be beneficial to the dynamic conservation and adaptive management of IAHSs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".