Water use efficiency of spring wheat in the semi-arid Canadian prairies: Effect of legume green manure, type of spring wheat, and cropping frequency
Bibliographic record
Abstract
Kröbel, R., Lemke, R., Campbell, C. A., Zentner, R., McConkey, B., Steppuhn, H., De Jong, R. and Wang, H. 2014. Water use efficiency of spring wheat in the semi-arid Canadian prairies: Effect of legume green manure, type of spring wheat, and cropping frequency. Can. J. Soil Sci. 94: 223-235. In the semi-arid Canadian prairie, water is the main determinant of crop production; thus its efficient use is of major agronomic interest. Previous research in this region has demonstrated that the most meaningful way to measure water use efficiency (WUE) is to use either precipitation use efficiency (PUE) or a modified WUE that accounts for the inefficient use of water in cropping systems that include summer fallow. In this paper, we use these efficiency measures to determine how cropping frequency, inclusion of a legume green manure, and the type of spring wheat [high-yielding Canada Prairie Spring (CPS) vs. Canada Western Red Spring (CWRS)] influence WUE using 25 yr of data (1987-2011) from the “New Rotation” experiment conducted at Swift Current, Saskatchewan. This is a well-fertilized study that uses minimum and no-tillage techniques and snow management to enhance soil water capture. We compare these results to those from a 39-yr “Old Rotation” experiment, also at Swift Current, which uses conventional tillage management. Our results confirmed the positive effect on WUE of cropping intensity, and of CPS wheat compared with CWRS wheat, while demonstrating the negative effect on WUE of a green manure crop in wheat-based rotations in semiarid conditions. Furthermore, we identified a likely advantage of using reduced tillage coupled with water conserving snow management techniques for enhancing the efficiency of water use.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".