Economics of salt‐induced land degradation and restoration
Bibliographic record
Abstract
Abstract Food security concerns and the scarcity of new productive land have put productivity enhancement of degraded lands back on the political agenda. In such a context, salt‐affected lands are a valuable resource that cannot be neglected nor easily abandoned even with their lower crop yields, especially in areas where significant investments have already been made in irrigation and drainage infrastructure. A review of previous studies shows a very limited number of highly variable estimates of the costs of salt‐induced land degradation combined with methodological and contextual differences. Simple extrapolation suggests that the global annual cost of salt‐induced land degradation in irrigated areas could be US $ 27.3 billion because of lost crop production. We present selected case studies that highlight the potential for economic and environmental benefits of taking action to remediate salt‐affected lands. The findings indicate that it can be cost‐effective to invest in sustainable land management in countries confronting salt‐induced land degradation. Such investments in effective remediation of salt‐affected lands should form part of a broader strategy for food security and be defined in national action plans. This broader strategy is required to ensure the identification and effective removal of barriers to the adoption of sustainable land management, such as perverse subsidies. Whereas reversing salt‐induced land degradation would require several years, interim salinity management strategies could provide a pathway for effective remediation and further showcase the importance of reversing land degradation and the rewards of investing in sustainable land management.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".