Enhancing Nitrogen Use Efficiency in Rice for Sustainable Agriculture
Bibliographic record
Abstract
The primary goal of this study is to enhance nitrogen use efficiency (NUE) in rice ( Oryza sativa L.) to promote sustainable agricultural practices. This involves reducing the dependency on nitrogen fertilizers while maintaining or improving rice productivity and minimizing environmental impacts. Key discoveries include the identification of genetic and agronomic strategies to improve NUE. Genetic approaches, such as the manipulation of NIN-like proteins (OsNLP1 and OsNLP3), have shown promise in enhancing NUE and grain yield under varying nitrogen conditions. Additionally, site-specific nutrient management (SSNM) and digital decision support tools like Rice Crop Manager have been effective in optimizing nitrogen application, thereby improving NUE and reducing environmental pollution. The integration of conventional breeding, molecular genetics, and alternative farming techniques has also been highlighted as essential for achieving sustainable improvements in NUE. The findings underscore the importance of a multifaceted approach combining genetic, agronomic, and technological innovations to enhance nitrogen use efficiency in rice. These strategies not only improve rice productivity but also contribute to environmental sustainability by reducing nitrogen losses and pollution. Future research should focus on refining these approaches and promoting their adoption among farmers to achieve long-term sustainability in rice production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".