Impact of Nitrogen Rate on Maize Yield and Nitrogen Use Efficiencies in Northeast China
Why this work is in the frame
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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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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 it