How much does it cost to mitigate soil erosion after wildfires?
Bibliographic record
Abstract
Wildfires usually increase the hydrological and erosive response of forest areas, carrying high environmental, human, cultural, and financial on- and off-site effects. Post-fire soil erosion control measures have been proven effective at mitigating such responses, especially at the slope scale, but there is a knowledge gap as to how cost-effective these treatments are. In this work, we review the effectiveness of post-fire soil erosion mitigation treatments at reducing erosion rates over the first post-fire year and provide their application costs. This allowed assessing the treatments’ cost-effectiveness (CE), expressed as the cost of preventing 1 Mg of soil loss. This assessment involved a total of 63 field study cases, extracted from 26 publications from the USA, Spain, Portugal, and Canada, and focused on the role of treatment types and materials, and countries. Treatments providing a protective ground cover showed the best median CE (895 $ Mg−1), especially agricultural straw mulch (309 $ Mg−1), followed by wood-residue mulch (940 $ Mg−1) and hydromulch (2332 $ Mg−1). Barriers showed a relatively low CE (1386 $ Mg−1), due to their reduced effectiveness and elevated implementation costs. Seeding showed a good CE (260 $ Mg−1), but this reflected its low costs rather than its effectiveness to reduce soil erosion. The present results confirmed that post-fire soil erosion mitigation treatments are cost-effective as long as they are applied in areas where the post-fire erosion rates exceed the tolerable erosion rate thresholds (>1 Mg−1 ha−1 y−1) and are less costly than the loss of on- and off-site values that they are targeted to protect. For this reason, the proper assessment of post-fire soil erosion risk is vital to ensure that the available financial, human and material resources are applied appropriately.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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; both teacher heads agree on what is shown here.
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".