Field Evaluation of Nitrogen-use Efficient Rice Varieties under Varying Fertilizer Regimes
Bibliographic record
Abstract
Improving the nitrogen use efficiency (NUE) of rice is of great significance for ensuring crop yield while reducing the environmental impact caused by excessive nitrogen application. This study focuses on the field performance assessment of high-nitrogen-efficient rice varieties under different fertilization patterns, covering strategies such as conventional fertilization, reduced fertilization, slow-release fertilization, and organic combined fertilization. It analyzes the performance of the varieties in terms of nitrogen absorption, utilization efficiency, and yield composition, and screens the varieties suitable for the development of green agriculture - the optimal fertilization combination Through field trials conducted in major rice-growing areas in China (such as the middle and lower reaches of the Yangtze River, the main production areas in Northeast China, and the double-cropping rice-growing areas in South China), the responses and adaptability of different varieties to fertilization patterns were compared. Research has found that different rice varieties with high nitrogen efficiency show significant differences under different nitrogen application conditions, indicating that species-specific fertilization optimization is of great significance for improving nitrogen recovery rate and yield. This study provides a theoretical basis and practical path for constructing an efficient and low-input rice cultivation system, and offers scientific support for subsequent research on the genotype-fertilization interaction mechanism and the formulation of green agricultural policies.
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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 | 0.001 | 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".