Study on the Physiological Basis of Efficient Nitrogen Utilization and Green Fertilization Strategy of Rice
Bibliographic record
Abstract
In the context of the current green transformation of agriculture and sustainable ecological development, improving the nitrogen use efficiency of rice has become an important issue to ensure food security and reduce environmental pollution. This study systematically explored the key physiological mechanisms of rice in the process of nitrogen absorption, transport and metabolism, focusing on the expression and regulatory role of key genes such as OsNRT1.1B , OsAMT1.2 , and OsGS1;1 , as well as the functions of transcription factors such as NLP, DOF, and MYB in the regulation of nitrogen metabolism. Combined with the development trend of green agriculture in recent years, this study further evaluated the practical effects of the "one base and one topdressing" fertilization mode of controlled-release fertilizers and the integrated management with green control technology, and analyzed the synergistic effect of high-efficiency varieties and green fertilization modes through a typical case of the demonstration field in Dahao Village, Jiashan. The study showed that indica-japonica hybrid rice and excellent late japonica rice varieties have a strong responsiveness to nitrogen supply, and can significantly improve nitrogen use efficiency and yield stability under reasonable cultivation management and precision fertilization. This study not only provides theoretical support and practical path for reducing nitrogen fertilizer and increasing its efficiency, but also provides a reference for the construction of regional rice ecological planting system.
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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 | 0.001 |
| 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 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".