Growing Soybean Prior to Corn Increased Soil Nitrogen Supply and N Fertilizer Efficiency for Corn in Cold and Humid Conditions of Eastern Canada
Bibliographic record
Abstract
<p>Growing soybean (<em>Glycine max L.)</em> prior to corn (<em>Zea mays</em> L) can enhance corn grain and nitrogen (N) use efficiency compared to continuous corn. This two year study (2007-2008) was conducted at 62 sites in Quebec (Eastern Canada) to assess the effect of crop rotations [soybean-corn, soybean-wheat (<em>Triticum aestivum</em> L.,)-corn and corn-corn] on corn yield, N uptake, N fertilizer efficiency (NFE), and the economic optimum N rate (EONR). Plots within each crop rotation received N fertilizer rates from 0 to 250 kg N ha<sup>-1</sup> to assess the N contribution from the preceding soybean crop. Corn grain yields ranged from 8.4 to 10.8 Mg ha<sup>-1</sup> and were lower in continuous corn than in the crop rotations. Corn N uptake and NFE varied from 89 to 164 kg N ha<sup>-1</sup> and from 45 to 80 kg grain per kg N fertilizer, respectively. A significant interaction of crop rotation and year on corn N uptake and NFE was obtained implying that annual variations influenced soil N supply. The EONR for corn was lower under crop rotations than continuous corn in 2008 only. No difference in corn yield, NFE and EONR was observed for soybean-corn and soybean-wheat-corn crop sequences. In conclusion, crop rotations including soybean increased soil N availability and reduced EONR from 32 to 45 kg ha<sup>-1</sup> for corn grown in 2008.</p>
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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 | 0.001 | 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 | 0.000 | 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".