Nitrogen loss on tile-drained Mollisols as affected by nitrogen application rate under continuous corn and corn-soybean rotation systems
Bibliographic record
Abstract
Helmers, M. J., Zhou, X., Baker, J. L., Melvin, S. W. and Lemke, D. W. 2012. Nitrogen loss on tile-drained Mollisols as affected by nitrogen application rate under continuous corn and corn-soybean rotation systems. Can. J. Soil Sci. 92: 493–499. Nitrate-nitrogen (NO 3 -N) loss from production agricultural systems through subsurface drainage networks is of local and regional concern throughout the Midwestern United States. The increased corn acreage and the practice of growing continuous corn instead of a corn-soybean rotation system due to the increasing demand for food and energy have raised questions about the environmental impacts of this shift in cropping systems. The objective of this 4-yr (1990–1993) study was to evaluate the effect of nitrogen (N) application rate (0–168 kg N ha −1 for corn following soybean and 0–224 kg N ha −1 for corn following corn) on NO 3 -N concentration, NO 3 -N losses, and crop yields in continuous corn and corn-soybean production systems on tile-drained Mollisols in north central Iowa. The results show that NO 3 -N concentrations from the continuous corn system were similar to NO 3 -N concentrations from the corn-soybean rotation at equivalent N application rates.When extra N fertilizer (approximately 56 kg N ha −1 ) was applied to continuous corn than the corn-soybean rotation, this resulted in 14–36% greater NO 3 -N concentrations in subsurface drainage from the continuous corn system. While corn yield increased as N application rate increased, corn yields at the recommended N application rates (112–168 kg N ha −1 ) in the corn-soybean rotation were up to 3145 kg ha −1 greater than corn yields at the recommended application rates (168–224 kg N ha −1 ) in the continuous corn system. The corn-soybean rotation with recommended N application rates (168–224 kg N ha −1 ) appeared to be beneficial environmentally and economically.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".