A Newly Practice to Mitigate N2O Emission from Winter Wheat Soil by Intercropping Isatis indigotica
Bibliographic record
Abstract
Greenhouse gas (GHG) emitted from agricultural field was received considerable attention worldwide, depending on differed land use and cropping system. An innovative strategy to mitigate agricultural N2O by intercropping traditional Chinese medicinal herb Isatis indigotica in winter wheat field was assessed. By exogenously applying root exudates of I. indigotica in a lab incubation study, we testify and quantify whether N2O emission was inhibited. Results demonstrated great reduction of N2O emission from winter field soil intercropping I. indigotica (NPKWR-N+P+K+wheat+I. indigotica) compared to CK (NPKW-N+P+K+wheat but no I. Indigotica) was found. N2O emission in treatment of NPKWR was decreased by 32% than that in CK during the whole winter wheat growth season, among which the best decreasing N2O emission was obtained in the stage of grain filling of winter wheat, N2O emitting from NPKWR was reduced by 60% than that in CK. The N2O emission intensity per kg of harvested wheat grain treated with I. indigotica was declined to 0.15 g N2O/kg grain from 0.24 g N2O/kg grain in CK. qPCR (quantitative fluorescent polymerase chain reaction) analysis indicated nitrifying microbial population in wheat soil was severely suppressed by I. indigotica. The number of qPCR gene copy in both soil intercropping I. indigotica and exogenously applying root exudates of I. indigotica was lower than in CK. Such trend of decreased microbial population number was in agreement with that of N2O emission from winter wheat field. This suggested that intercropping I. indigotica was a practical and simple technique to reduce N2O emission from winter wheat field which was an effective strategy for mitigating and adapting global change worldwide in agriculture.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".