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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".