Valuing Ecosystem Services in Semi‐arid Rangelands through Stochastic Simulation
Bibliographic record
Abstract
Abstract Ecosystem services and economic returns from semi‐arid rangelands are threatened by land degradation. Policies to improve ecosystem service delivery often fail to consider uncertainty in economic returns gained through different land uses and management practices. We apply an analytical framework using stochastic simulation to estimate the range of potential monetary outcomes of rangeland ecosystem services under different land uses, including consideration of the uncertainty and variability of model parameters. We assess monetary and non‐monetary dimensions, including those ecosystem services with uncertain and missing information, for communal rangelands, commercial ranches, game farms and Wildlife Management Areas in southern Kgalagadi District, Botswana. Public land uses (communal grazing areas and protected conservation land in Wildlife Management Areas) provide higher economic value than private land uses (commercial ranches and game farms), despite private land uses being more profitable in their returns from meat production. Communal rangelands and protected areas are important for a broader range of ecosystem services (cultural/spiritual services, recreation, firewood, construction material and wild food), which play a key role in sustaining the livelihoods of the largest share of society. The full range of ecosystem services should therefore be considered in economic assessments, while policies targeting sustainable land management should value and support their provision and utilisation. By forecasting the range of plausible ecosystem values of different rangeland land uses in monetary terms, our analysis provides policymakers with a tool to assess outcomes of land use and management decisions and policies. Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".