Nitrogen and phosphorus effects on water use efficiency of spring wheat grown in a semi-arid region of the Canadian prairies
Bibliographic record
Abstract
Kröbel, R., Campbell, C. A., Zentner, R. P., Lemke, R., Steppuhn, H., Desjardins, R. L. and De Jong, R. 2012. Nitrogen and phosphorus effects on water use efficiency of spring wheat grown in a semi-arid region of the Canadian prairies. Can. J. Soil Sci. 92: 573–587. Water use efficiency (WUE) has often been analyzed for semiarid environments, but fallow-containing cropping systems were assessed inappropriately. Further, these short-term studies are unlikely to correctly assess weather variability impacts in such environments. We assessed the impact of fertilizer N and P on water use efficiency (WUE) and precipitation use efficiency (PUE) of spring wheat (Triticum aestivum L.) from a 39-yr long-term crop rotation study in semi-arid southwestern Saskatchewan. In the rotation experiment, continuous wheat (Cont W) with N+P or P fertilizer only, and fallow-wheat-wheat (F-W-W) with N+P, P only, or N only were studied. We calculated WUE using: (i) Yield (Y)/[water use (WU)/potential water use (PET)]; (ii) Y/WU; (iii) Y/WU with a fallow phase element added; and (iv) Y/harvest-to-harvest precipitation (PUE). The WUEs in the rotation experiment were generally greater for treatments with N+P fertilizer, and greatest after an increase of N application coupled with favourable soil water conditions in the final decades of this study. In cases (i) and (ii), WUE for F-W-W was greater than for the Cont W-treatment. In case (iii), the WUEs were 5.7, 4.5, 3.9, 3.6, and 3.6 kg ha−1 mm−1 water for Cont W (N+P), Cont W (P), F-W-W (N+P), F-W-W (P), and F-W-W (N), respectively. For PUE [case (iv)] the values were 4.0, 3.1, 3.4, 3.0, and 2.9, respectively. We concluded that case (ii) was most appropriate for continuous cropping and case (iii) for systems including fallow, while case (iv) was usable in general.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".