A global review of the development and application of soil erosion control techniques
Bibliographic record
Abstract
Abstract Various soil erosion control techniques (SECTs) have been applied for decades. Yet, dynamic development of SECTs on a global scale has not been fully explored in the literature. We identified 779 publications to summarize spatial and temporal patterns of SECT development across the world. To achieve this goal, we asked (a) how many SECTs have been applied in the real world? (b) How do susceptible erosion areas use SECTs? And (c) what are the temporal patterns of SECT development? We found 183 sub-categories of SECTs, including 85 sub-categories of engineering techniques, 76 sub-categories of cropping techniques, and 22 sub-categories of biological techniques. In contrast, there is a great deal of interest in the evaluation of biological techniques and cropping techniques for soil erosion control. SECT research has evolved from an initial focus on a single SECT evaluation to a combination of SECTs evaluations (e.g. a combination of conservation tillage and mulch). Likewise, 64% of SECT cases were found in six countries with a different focal SECT among them: China and Spain targeted vegetation restoration, Brazil and the United States focused on conservation tillage, Ethiopia prioritized mixed SECTs, and India emphasized on check dam. Lastly, SECT application started from site erosion control (1930s–1980s), watershed management (1980s–2010s), to sustainable management (after 2010s). We identify the gaps between SECT application and research and a lack of an international platform for knowledge sharing, and propose that a combination of different SETCs in a balanced way is a reliable approach to obtaining the goal of sustainable soil management.
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 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".