Observations and management implications of crop and water interactions in cold water-limited regions
Bibliographic record
Abstract
• Quantified 19 site-years of crop-water balance interactions in the Canadian Prairies. • Crop water use is greater than or equal to growing season precipitation. • Up to 70% of crop water use is from non-growing season sources. • Importance of antecedent soil water is inversely related to growing season rainfall. • Stubble and residue management can increase crop water availability between 4 and 48% Crop and water interactions strongly influence crop production in water-limited dryland agricultural systems in cold regions, such as the Canadian Prairies. A water balance approach was used to quantify crop water use, identify the source of water and corresponding hydrological processes, and evaluate the effectiveness of management techniques to increase agricultural productivity. Detailed water balance observations for 19 site-years were collected at four sites. Crop water use was consistently greater than or equal to growing season precipitation and displayed substantial interannual variation. On average, growing season precipitation provided 66% of crop water use whilst antecedent soil moisture from water surpluses in shoulder and winter seasons and preceding wet years supplied the remainder. Up to 70% of crop water use was derived from non-growing season water sources when high precipitation winters preceded dry growing seasons. Observations of soil moisture, snow accumulation, precipitation, and evaporative fluxes showed substantial spatial and temporal variability in antecedent soil moisture contributions to crop growth, which has implications for agricultural management. The relative importance of antecedent soil water to crop growth decreased with increased growing season precipitation. The water balance observations were used to constrain the water-limited yield potential associated with the optimisation of stubble and crop residue management practices. Increasing retention of snowfall with stubble management and suppression of soil evaporation with increased crop residue cover was estimated to increase potential crop water availability on average by 20% but, depended on seasonal dynamics, ranging between 4 and 48%. These results articulate the complex interactions between cold and warm season hydrological processes that drive dryland agricultural production in Western Canada and constrain the potential for stubble and residue management practices to mitigate crop water extremes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 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 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".