Trade-Offs in Multi-Purpose Land Use under Land Degradation
Bibliographic record
Abstract
Land provides a host of ecosystem services, of which the provisioning services are often considered paramount. As the demand for agricultural products multiplies, other ecosystem services are being degraded or lost entirely. Finding a sustainable trade-off between food production and one or more of other ecosystem services, given the variety of stakeholders, is a matter of optimizing land use in a dynamic and complex socio-ecological system. Land degradation reduces our options to meet both food demands and environmental needs. In order to illustrate this trade-off dilemma, four representative services, carbon sinks, water storage, biodiversity, and space for urbanization, are discussed here based on a review of contemporary literature that cuts across the domain of ecosystem services that are provided by land. Agricultural research will have to expand its focus from the field to the landscape level and in the process examine the cost of production that internalizes environmental costs. In some situations, the public cost of agriculture in marginal environments outweighs the private gains, even with the best technologies in place. Land use and city planners will increasingly have to address the cost of occupying productive agricultural land or the conversion of natural habitats. Landscape designs and urban planning should aim for the preservation of agricultural land and the integrated management of land resources by closing water and nutrient cycles, and by restoring biodiversity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".