Impact of Ephemeral Gully Erosion and Economics in Agricultural Fields of Ontario, Canada
Bibliographic record
Abstract
Abstract Agricultural soils in Ontario, Canada have greater erosion potential where 54% of cropland has a risk of erosion above the annual rate of soil regeneration. The estimated annual soil erosion cost to Ontario farmers is over $150 million including crop yield reductions, nutrient losses, pesticide losses, etc. (OMAFRA, 2016). Over the last few decades, overland erosion processes in agricultural fields have been studied extensively. However, Ephemeral Gully (EG) erosion due to concentrated flow that erodes topsoil and associated economic losses remains largely unstudied. In this study, the objectives were to: (1) evaluate the soil loss and corresponding topsoil depth reduction due to EG erosion; and (2) estimate the economic loss associated with crop production due to EG erosion. Three fields in southern Ontario were selected, and identified at 15178 Imperial Road (15.60 ha), 46844 Lyon Line (2.78 ha) and 46383 Wilson Line (15.58 ha) (Figure 1). In each field, EGs were visible from aerial images and also during field visits. Typical soil textures in these fields were mainly silty clay loams. The average annual precipitation in the area is about 850 mm. An Unmanned Aerial Vehicle (UAV) equipped with a high-resolution camera was used to obtain the Digital Elevation Model (DEM) of the agricultural fields and the DEM was used to identify potential ephemeral gullies (PEGs) of agricultural fields. Daily climate data was obtained from nearby weather stations, soil characteristics were developed from the Soil Landscapes of Canada (SLC) database and field management information was taken upon consultation with the farmers who own the fields. The selected fields were simulated with the AnnAGNPS model which calculates overland erosion using the Revised Universal Soil Loss Equation (RUSLE) and EG erosion using the Revised Ephemeral Gully Erosion Model (REGEM), respectively. The sediment yield generated by sheet and rill and EG were calibrated and validated with available measured data and or literature values so that the model simulated the sediment yield reasonably well. The economic impact of EG erosion was calculated based on the relationship between topsoil depth and corn yield developed by Fenton et al. (2005), and the reduction of topsoil depth driven by EG erosion. Results showed that soil losses from EG erosion were significant and were 6-10 times greater than sheet and rill erosion. The EGs developed not only due to rainfall but also due to snowmelt. The annual topsoil loss from EG erosion ranged from 1.00 to 2.53 cm in the studied fields and the annual topsoil loss from sheet and rill erosion was minimal (0.01 cm). The corresponding economic loss due to topsoil loss was calculated in terms of crop yield reduction by annually filling the EG using adjacent topsoil by tillage operation. Economic loss caused by EG erosion varied from $5.46 ha-1 to $12.66 ha-1. The results also indicate that EG erosion reduces annual farm income in the long term.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".