Effects of two kinds of variable‐rate nitrogen application strategies on the production of winter wheat ( <i>Triticum aestivum</i> )
Bibliographic record
Abstract
Abstract To improve nitrogen‐use efficiency (NUE) is crucial to agriculture; it benefits agricultural production and reduces the impact on the environment. In past decades, a lot of variable‐rate nitrogen (VRN) application strategies have been proposed to improve NUE. The concern of this study is whether the specific N management strategy based on using in‐season predicted grain yield (ISPGY) and in‐season N uptake (ISNU) is more efficient than the VRN strategy based on grain yield goal (GYG) and ISNU. For this purpose, 2‐year (2005–06 and 2006–07) winter wheat ( Triticum aestivum ) experiments with the cultivar ‘Jingdong8’ were conducted at the China National Experimental Station for Precision Agriculture, located in the Changping district of Beijing, China. Four VRN application methods, SPAD chlorophyll meter method 1 (SCM1), SPAD chlorophyll meter method 2 (SCM2), vegetation index method 1 (VI1), and vegetation index method 2 (VI2), were compared with a random block design with 10 replications. The differences between SCM1 and SCM2 and between VI1 and VI2 were used to estimate the potential grain yield for each plot. SCM1 and VI1 used ISPGY, whereas SCM2 and VI2 adopted GYG. Economic benefits and soil residual NO 3 ‐N were analysed for the four methods. The results showed that the SCM2 and V12 performed better than the corresponding SCM1 and VI1, indicating that the GYG‐based VRN strategy is better than the ISPGY‐based VRN strategy for conducting specific N management.
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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".