A characterization of soil erosion in cultivated watersheds in Manitoba's Red River Valley using sediment budgeting, and its implications for managing soil erosion’s impacts
Bibliographic record
Abstract
Soil erosion accelerated by agriculture reduces agricultural productivity and compromises the function of drainage infrastructure and downstream water quality. In Manitoba, the relationship between soil erosion and water quality is of particular concern, following measurable declines in Lake Winnipeg’s water quality since the 1990s. Understanding the state of soil erosion, transportation, and deposition in the Red River Valley is of interest, due to the extensive cultivation of the river’s watershed and its contribution to total flow (and by extension, sediment flux) into the lake. Sediment budgets were drafted for two sub-watersheds of the well-studied Boyne-Morris and La Salle River watersheds, located in the Red River Valley, and coded 05OF024 and 05OG008 by the Water Survey of Canada (WSC), respectively. To characterize soil erosion, the sediment budgets used published National Agri-Environmental Health Analysis and Reporting Program (NAHARP) soil erosion risk estimates calculated with the SoilERI model. Sediment transportation was quantified using flow and total suspended solids (TSS) measurements made by the WSC and other organizations. Deposition within the sub-watersheds was quantified through measurements made in road-side ditches (a common sediment sink in the Red River watershed) and inferred through imbalances in the sediment budgets. Rates of soil erosion, deposition within, and transportation out of the 05OF024 sub-watershed were an order of magnitude greater than in the 05OG008 sub-watershed due to differences in basin scale, but relative differences in their rates in each basin were the same. Rates of erosion were 1 order of magnitude greater than rates of deposition in road-side ditches and 3 orders of magnitude greater than transportation past the sub-watershed outlets. Differences between rates of soil erosion and deposition in road-side ditches were noted and attributed to unmeasured deposition in cultivated fields. Rates of deposition in such settings were of the same order of magnitude but less than rates of erosion. Both sediment budgets quantified rates of road-side ditch dredging, which were 1 order of magnitude greater than rates of deposition in road-side ditches and directed soil back into cultivated fields. Rates of water, wind, and tillage erosion characterized by NAHARP erosion risk estimates were not mirrored by related, measured rates of deposition in roadside ditches in either watershed. The stark differences between rates of soil erosion and rates of sediment transportation past the outlets of both sub-watersheds suggested the downstream impacts of eroded soil on water quality may be minimal at coarse temporal scales in watersheds of similar or greater size in the Red River Valley. Greater degrees of sediment delivery to road-side ditches suggested that sediment may have more meaningful impacts on the function of drainage infrastructure, especially at the same temporal scales in smaller watersheds in the region. Differences in estimated rates of water, wind, and tillage erosion and related rates of deposition in road-side ditches suggest the SoilERI model may not adequately characterize rates of soil erosion. This does not invalidate the SoilERI model, but highlights its limitations which should be considered when it is used to estimate rates of soil erosion in such settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".