An Overview of Crop and Crop Residue Management Impacts on Crop Water Use and Runoff in the Canadian Prairies
Bibliographic record
Abstract
Although crop and crop residue management practices are mainly used for increasing crop yield, they and the resulting changes in crop growth affect one or more hydrological components, including runoff. Based on published research in the Canadian Prairies, this paper reviews the effects of crop type, quantity of crops and crop residues, crop variability within landscapes, tillage, and stubble management practices on crop water use (termed including evaporation, transpiration and interception), snow trapping, and water infiltration, with the aim to discuss major impacts of crop and residue management on runoff. Rainfall runoff is influenced by rain interception and crop water use, and it can be reduced by choosing appropriate crop types, increasing above-ground biomass, or increasing coverage on the soil surface, activities which coincide with the farmer’s efforts of increasing crop productivity. However, although high stubble and reduced tillage for maintaining good residue cover help conserve soil moisture and improve soil health, they increase snowmelt runoff potential. The review emphasizes the need of future research to assess the agronomic and environmental trade-offs of crop residue management, the linkage between crop water use and runoff, and the impacts of crop and residue management on runoff across various temporal and spatial scales.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".