Time of nitrogen application affects nitrogen use efficiency of wheat in the humid pampas of Argentina
Bibliographic record
Abstract
Nitrogen (N) fertilization is an important management practice to increased grain yield; however, it is imperative to increase nitrogen use efficiency (NUE) in order to diminish risks of environmental pollution. The objective of this study was to determine the effect of fertilization times on wheat grain yield, grain N accumulation and grain N recovery efficiency (RE) in different sites and years at the south-eastern wheat belt of the Pampas. The experiments were a factorial combination of N rates and fertilization times (sowing and tillering). Grain yield ranged from 1600 to 7900 kg ha -1 and fertilization at tillering increased grain yield compared with fertilization at sowing (5465 vs. 5110 kg ha -1 ), similar behavior was observed for grain N accumulation (95 vs. 86 kg ha -1 ) and RE (0.41 vs. 0.32). Predicted grain yield by CERES-Wheat model for different N rates and fertilization times was correlated with observed grain yield (r 2 = 0.71). While fertilization at tillering significantly increased grain yield, CERES-Wheat model estimated nitrate leaching losses that ranged from 12 to 62 kg N ha -1 and from 7 to 16 kg N ha -1 for fertilization at sowing and tillering, respectively. However, denitrification losses ranged from 1.2 to 3.9 and from 0.5 to 2.4 kg N ha -1 for fertilization at sowing and tillering, respectively. Leaching losses for fertilization at sowing are a consequence of water excess early in the growing season and would be the main N loss factor. Therefore, N application at tillering is an appropriate strategy to improve NUE in the south-eastern wheat belt of the Pampas. Key words: Wheat, fertilization time, nitrogen use efficiency, N losses, CERES-Wheat
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".