Cross‐scale monitoring and assessment of land degradation and sustainable land management: A methodological framework for knowledge management
Bibliographic record
Abstract
Abstract For land degradation monitoring and assessment (M&A) to be accurate and for sustainable land management (SLM) to be effective, it is necessary to incorporate multiple knowledges using a variety of methods and scales, and this must include the (potentially conflicting) perspectives of those who use the land. This paper presents a hybrid methodological framework that builds on approaches developed by UN Food & Agriculture Organisation's land degradation Assessment in Drylands (LADA), the World Conservation Approaches and Technologies (WOCAT) programme and the Dryland Development Paradigm (DDP), and is being applied internationally through the EU‐funded DESIRE project. The framework suggests that M&A should determine the progress of SLM towards meeting sustainability goals, with results continually and iteratively enhancing SLM decisions. The framework is divided into four generic themes: (i) establishing land degradation and SLM context and sustainability goals; (ii) identifying, evaluating and selecting SLM strategies; (iii) selecting land degradation and SLM indicators and (iv) applying SLM options and monitoring land degradation and progress towards sustainability goals. This approach incorporates multiple knowledge sources and types (including land manager perspectives) from local to national and international scales. In doing so, it aims to provide outputs for policy‐makers and land managers that have the potential to enhance the sustainability of land management in drylands, from the field scale to the region, and to national and international levels. The paper draws on operational experience from across the DESIRE project to break the four themes into a series of methodological steps, and provides examples of the range of tools and methods that can be used to operationalise each of these steps. Copyright © 2011 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 | 0.078 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.015 |
| Science and technology studies | 0.005 | 0.028 |
| Scholarly communication | 0.021 | 0.017 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.004 | 0.003 |
| 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".