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.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".