Effect of N, P and cropping frequency on nitrogen use efficiencies of spring wheat in the Canadian semi-arid prairie
Bibliographic record
Abstract
Kröbel, R., Campbell, C. A., Zentner, R. P., Lemke, R., Desjardins, R. L. and Karimi-Zindashty, Y. 2012. Effect of N, P and cropping frequency on nitrogen use efficiencies of spring wheat in the Canadian semi-arid prairie. Can. J. Plant Sci. 92: 141–154. Knowledge of nitrogen use efficiency (NUE) is useful for determining fertilizer requirements. We used balance and difference methods to determine the effect of N and P fertilizer on nitrogen use efficiencies for continuous wheat (Triticum aestivum L.) (Cont W) and fallow-wheat-wheat (F-W-W) in a 39-yr crop rotation study conducted on a Brown Chernozem at Swift Current in semi-arid southwestern Saskatchewan. In the balance method, NUE was estimated as yield (Y), or grain N content (GN), divided by either fertilizer N (FN), or FN+soil test N (SN), or FN+SN+growing season net N mineralization (N min ). Most reasonable results [calculating NUE as either (Y or GN)/(FN+SN+N min )] were unaffected by fertilizer or rotation and averaged 10.9 kg grain kg −1 available N and 0.3 kg grain N kg −1 available N, respectively, for the different fertilizer treatments of Cont W and F-W-W. Using the difference method, where check values are deducted from treatment values in the numerator, Cont W had greater NUE than F-W-W (roughly 2:1). Variations in NUE were not easily explained in the rotation experiment because of the confounding effect of concurrent increases in available moisture and FN availability in the last decade. However, results from a semi-controlled lysimeter experiment at Swift Current showed that irrigation increased NUE, while increasing FN decreased NUE curvilinearly. Of the methods used to assess NUE, the simplest (Y/FN) was the least accurate. However, data needed for more accurate estimates are less likely to be available to the farming community.
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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 | 0.002 | 0.001 |
| 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".