Impact of Agroforestry Types‐Induced Microtopography on Hillslope Erosion in Alpine Canyon Areas
Bibliographic record
Abstract
ABSTRACT Surface conditions, including vegetation cover and microtopography, affect soil erosion significantly. However, research on the hydrological processes of different agroforestry types on sloping farmland in southwest alpine canyon regions remains insufficient. The microtopographic evolution of different agroforestry types and a bare slope (CK) was investigated by field‐based in situ scouring experiments. Agroforestry types were divided into Zanthoxylum + Plum + Canadian fleabane (ZPC), Zanthoxylum + Cherry + Artemisia indica (ZCA), Zanthoxylum + Green bean (ZG) and Plum + Soybean (PS). Structure from motion (SfM) photogrammetry was used to measure the microtopography of each slope under different scour discharge rates (6, 10 and 14 L·min −1 ). The influence of microtopography on surface runoff and sediment yield was analysed. The results revealed that the ZPC type exhibited the greatest intensity of spatial variation in microtopography, while the PS type showed the smallest. The elevation of each hillslope under different agroforestry types varied from −100 to 100 mm, and the erosion distribution rate accounted for 38.37% to 80.77% of the total. Compared to the pre‐experiment, the variation range of soil surface roughness (SSR), surface cutting depth (SCD), surface relief (SR) and microslope (MS) index were −16.49% to 11.56%, −24.79% to 32.32%, −22.72% to 33.44% and −17.36% to 19.42%, respectively. Under different scour discharge rates, the ZPC type effectively reduced runoff, while the ZCA type significantly decreased sediment yield. At a scour discharge of 14 L·min −1 , the initial runoff production time of the ZCA and ZPC types was significantly delayed compared to that of the CK hillslope, demonstrating a notable runoff reduction benefit. SSR and MS were positively correlated with sediment yield and runoff. SSR can be used to predict runoff and sediment yield in agroforestry areas. These findings provide a theoretical basis for the effective prevention and control of soil loss and the construction of prediction models for sloping farmland in alpine canyon areas.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".