The impact of rural land management changes on soil hydraulic properties and runoff processes: results from experimental plots in upland UK
Bibliographic record
Abstract
Abstract To develop an evidence base to help predict the impacts of land management change on flood generation, four experimental sites were established on improved grassland used for sheep grazing at the Pontbren catchment in upland Wales, UK. At each site, three plots were established where surface runoff was measured, supplemented by measurements of soil infiltration rates and soil and vegetation physical properties. Following baseline monitoring, treatments were applied to two of the plots: exclusion of sheep (ungrazed) and exclusion of sheep and planting with native broadleaf tree species (tree planted), with the third plot acting as a control (grazed pasture). Due to a particularly dry summer that occurred pre‐treatment, the soil hydrological responses were initially impacted by the effects of the climate on soil structure. Nevertheless, treatments did have a clear influence on soil hydrological response. On average, post‐treatment runoff volumes were reduced by 48% and 78% in ungrazed and tree‐planted plots relative to the control, although all results varied greatly over the sites. Five years following treatment application, near‐surface soil bulk density was reduced and median soil infiltration rates were 67 times greater in plots planted with trees compared to grazed pasture. The results illustrate the potential use of upland land management for ameliorating local‐scale flood generation but emphasise the need for long‐term monitoring to more clearly separate the effects of land management from those of climatic variability. Copyright © 2013 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".