Impact of Nitrogen Rate on Maize Yield and Nitrogen Use Efficiencies in Northeast China
Bibliographic record
Abstract
Optimizing N fertilization is important to improve both maize (Zea mays L.) yield and nitrogen use efficiencies (NUEs). A 3‐yr maize field experiment (2008–2010) was conducted to evaluate the response of grain yield, aboveground biomass, plant N concentration, N uptake, and NUEs to fertilizer N rates from 0 to 280 kg N ha−1 at three different rain‐fed Haplic Phaeozem soils (FAO classification) in Northeast China. When N application rate increased from 70 to 280 kg N ha−1 across all site‐years, N recovery efficiency, N agronomic efficiency, N internal efficiency and N partial factor productivity decreased from 76.5 to 9.0%, 25.3 to 0.1 kg kg−1, 70.7 to 40.8 kg kg−1, and 145.6 to 22.8 kg kg−1, respectively. Differences observed among the years and experimental sites were primarily caused by variability in rainfall and soil characteristics. The maximal grain yield of 11.0 Mg ha−1 was achieved at an N rate of 210 kg N ha−1 with normal rainfall. Nitrogen application beyond the optimal N rate did not consistently increase grain yield, and caused a decrease in NUEs. The range of optimal N rate for maize grain yield fell between 140 and 210 kg N ha−1 at the three sites from 2008 to 2010 in Northeast China based on the best fitted models (quadratic, linear plus plateau, and quadratic plus plateau). The results provide guidelines for selecting N application rates to optimize both maize yield and NUEs in Northeast China.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".