Regular Nitrogen Application Increases Nitrogen Utilization Efficiency and Grain Yield in Indica Hybrid Rice
Bibliographic record
Abstract
On the basis of research and practical production experience of many years, regular nitrogen fertilizer application (RFA) was used to improve rice (Oryza sativa L.) yield and increase nitrogen utilization efficiency (NUE). Two field experiments (manually transplanted rice with three indica hybrid rice cultivars and four N treatments, mechanically transplanted rice with eight N applications) were conducted in 2011 and 2012, to investigate the effects of RFA on rice yield and NUE. The results showed that RFA increased the total amount of N uptake in the plants and improved NUE when compared with the farmers’ nitrogen fertilizer practices (FFP). The average increment in agronomy efficiency (AE), recovery efficiency (RE), and partial factor productivity for applied nitrogen (PFP) were 12.16, 10.91, and 6.01% for the mechanically transplanted rice and 30.81, 65.57, 6.18% for the manually transplanted rice. The main reason for the increase was the improvement in N concentration of the plant organs. For RFA, the grains per panicle was 12.15 and 7.59% higher than that of FFP, which was the main reason for the 6.19 and 3.33% increase in the yield of the manually and mechanically transplanted rice, respectively. To the best of our knowledge, this is the first RFA strategy to be applied, and the jointing stage and 15 to 20 d after the jointing stage are the key periods for increasing the total amount of N uptake and grain yield; this strategy should be widely applied for indica hybrid rice production. Core Ideas Regular nitrogen fertilizer application significantly increased grains per panicle and then improve rice yield. Regular nitrogen fertilizer application improved plant N concentration and then increased N use efficiency. Jointing stage and 15 to 20 d after jointing stage are the two key stages to apply N fertilizers. Regular N fertilizer application is suitable for both manually and mechanically transplanted rice.
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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) | 0.001 | 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".