Nitrogen effects on water use efficiency in the semi-arid Canadian prairies
Bibliographic record
Abstract
In the semiarid environment of the Canadian prairies, water is the main constraint to crop production. Few studies have examined the influence of fertilizer on water use efficiency (WUE) and fewer still have made comparisons on a cropping system basis. We assessed the impact of fertilizer N on WUE in a 39-year crop rotation experiment conducted on a Brown Chernozemic soil at Swift Current, Saskatchewan. The cropping systems included continuous wheat (Cont W) with N+P and P fertilizer alone, and a fallow-wheat-wheat (F-W-W) system with N+P and P only. All cropping systems were managed using conventional tillage practices. We developed an equation to asses WUE of the cropping systems that included water use during the fallow year. We also calculated precipitation use efficiency (PUE, i.e., yield/harvest-to-harvest precipitation). WUE and PUE values and fertilizer effects were greatest in the later third of the study period, due to the increase in recommended fertilizer N rates applied and the more favorable moisture conditions that prevailed. We converted PUE results into net return values ($ ha-1 mm-1 water) by assuming an average price over the 39-yr period and found that N fertilizer in the Cont W system earned an average return above fertilizer cost of $36.39 ha-1 yr-1 and $9.81 ha-1 yr-1in the F-W-W system.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".