Integrating in situ conservation of plant genetic resources with ex situ conservation management: Involving custodian farmers, benefits and their willingness to accept compensation
Bibliographic record
Abstract
Compensation for custodian farmers to provide agrobiodiversity-conservation services in their farms (in situ) is an emerging global approach because it can generate public benefits when it comes to important plant genetic resources (PGRs) for food and agriculture. This review focused on: i) the integration of in situ conservation to the PGR management involving custodian farmers in a systematic conservation approach; ii) the private benefits obtained by custodian farmers to increase their willingness to actively participate in the conservation service; and iii) the willingness to accept (WTA) compensation of the farmers in providing conservation service relative to opportunity costs. The most recent approaches suggest the integration of in situ conservation with ex situ conservation management for efficient conservation programmes, especially in relation to governance of local agrobiodiversity. Involving custodian farmers in the comprehensive genetic-improvement system appears to be an activity that increases their willingness to participate in agrobiodiversity conservation schemes. Farmers receive private benefits, which are simultaneously reflected in the provision of public benefits. Therefore, WTA compensation, which employs mechanisms in the design of payments for better agro-environmental conservation services, controls the opportunity costs. Overall, farmers seem willing to participate in any type of compensation scheme that is proposed in the different countries. Furthermore, it is possible to capture the WTA, therefore significant progress requires more studies of primary schemes, indepth analysis to capture farmers' preferences on economic, socio-cultural, and environmental factors in scheme design, and the implementation of incentive policies designed with pragmatic tools.
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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 | 0.003 | 0.004 |
| 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 | 0.002 | 0.002 |
| 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".