Biological nitrification inhibition by rice root exudates and its relationship with nitrogen‐use efficiency
Bibliographic record
Abstract
Summary Microbial nitrification in soils is a major contributor to nitrogen (N) loss in agricultural systems. Some plants can secrete organic substances that act as biological nitrification inhibitors ( BNI s), and a small number of BNI s have been identified and characterized. However, virtually no research has focused on the important food crop, rice ( Oryza sativa ). Here, 19 rice varieties were explored for BNI potential on the key nitrifying bacterium Nitrosomonas europaea . Exudates from both indica and japonica genotypes were found to possess strong BNI potential. Older seedlings had higher BNI abilities than younger ones; Zhongjiu25 ( ZJ 25) and Wuyunjing7 ( WYJ 7) were the most effective genotypes among indica and japonica varieties, respectively. A new nitrification inhibitor, 1,9‐decanediol, was identified, shown to block the ammonia monooxygenase ( AMO ) pathway of ammonia oxidation and to possess an 80% effective dose ( ED 80 ) of 90 ng μl −1 . Plant N‐use efficiency ( NUE ) was determined using a 15 N‐labeling method. Correlation analyses indicated that both BNI abilities and 1,9‐decanediol amounts of root exudates were positively correlated with plant ammonium‐use efficiency and ammonium preference. These findings provide important new insights into the plant–bacterial interactions involved in the soil N cycle, and improve our understanding of the BNI capacity of rice in the context of NUE .
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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 | 0.000 |
| 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".