Effect of crop rotation and cropping history on net nitrogen mineralization dynamics of a clay loam soil
Bibliographic record
Abstract
Estimating soil nitrogen (N) mineralization is critical to balance fertilizer N requirements and their environmental impacts. In this study, net N mineralization was examined in soils under different crop rotations with each phase of the rotation present every year with biologically based incubations in 2011 and 2015. Net N mineralization was significantly different among treatments when the current crop was soybean, and the effect was dependent upon the previous crop and the cropping sequence. In particular, net increases in inorganic N were greater when the previous crop was winter wheat with or without red clover than if it were corn, and greater for the first year of soybean compared with the second year for rotations with two consecutive years of soybean in the 2011 incubation. However, cropping history did not influence net soil N mineralization when the current crop was corn, winter wheat, or winter wheat with red clover. In 2015, the presence of red clover in the rotation increased net N mineralization in all phases of the rotation. These results suggest both current and previous crops should be considered when estimating the N supplying capacity (net mineralization) of the soil. Net mineralizable N was found to be significantly correlated with total amino sugars (P < 0.001), glucosamine (P < 0.001), and galactosamine (P = 0.003), which suggests that amino sugars could be used as an indicator of the N supplying capacity of soil.
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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 | 0.000 | 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 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".