Influence of Constant and Fluctuating Water Contents on Nitrous Oxide Emissions from Soils under Varying Crop Rotations
Bibliographic record
Abstract
Soil water contents and cropping history influence soil organic matter decomposition, N cycling, and NO 3 − losses. However, soil water content fluctuates within and between seasons, which adds complexity to these biochemical processes. The impacts of constant soil water contents (30, 45, 60, 75, and 90% water‐filled pore space, WFPS), wet–dry (WD) cycles (90–30% WFPS), and crop rotation (monoculture corn [ Zea mays L.], a 2‐yr corn–soybean [ Glycine max (L.) Merr.] rotation, or a 3‐yr corn–soybean–winter wheat [ Triticum aestivum L.] rotation) on N 2 O emissions, inorganic N, and denitrification enzyme activity (DEA) were investigated using repacked soil cores incubated for 50 d. The 30% WFPS treatment resulted in a net accumulation of inorganic N after 50 d. In the constant‐WFPS treatments, increasing water content increased DEA, reduced soil inorganic N, and enhanced N 2 O emissions. When water contents fluctuated in five WD cycles, there was a considerable decrease in NO 3 − concentrations, high DEA activity, but low N 2 O emissions, which were similar to the emissions for the 30% WFPS treatment. Hence, fluctuating soil water content provided a soil environment that may have stimulated N 2 O reduction to N 2 or other microbial processes such as NO 3 − immobilization. At the wettest WFPS (90% WFPS), N 2 O emissions were greater under monoculture corn than the 3‐yr rotation, which is consistent with the larger decreases in inorganic N and higher DEA levels. Nitrous oxide emissions accounted for between 7.5 and 27.4% of NO 3 − loss when soils were incubated at 90% WFPS but only 3.86 to 5.12% with the WD cycles. Nitrous oxide emissions were found to be influenced by soil water content to a greater extent than crop rotation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".