Optimizing fertilizer nitrogen use efficiency by intensively managed spring wheat in humid regions: Effect of split application
Bibliographic record
Abstract
Velasco, J. L., Rozas, H. S., Echeverría, H. E. and Barbieri, P. A. 2012. Optimizing fertilizer nitrogen use efficiency by intensively managed spring wheat in humid regions: Effect of split application. Can. J. Plant Sci. 92: 847–856. Efficient N fertilizer management is critical for the economical production of wheat and the long-term protection of the environment. Six experiments were conducted at three locations in the south-east of the province of Buenos Aires (SE), Argentina, during a 4-yr period, on Typic Argiudoll and Petrocalcic Paleudoll. The study was designed to evaluate the effects of splitting nitrogen (N) fertilizer on N use efficiency (NUE) in wheat (Triticum aestivum L.). Rates of 0 to 150 kg N ha−1were used, applied at tillering (Z24) or split between Z24 and flag leaf (Z39). The experimental design was a randomized complete block with three replications. Grain yield ranged from 3522 to 8185 kg ha−1, according to N availability and application time. In the experiments without water stress (three out of six), average grain yield (across experiments) was 6669 and 6989 kg ha−1for full and split fertilization, respectively. In four out of six experiments, average N in above-ground biomass (NAB), N recovery fraction (NRF), and grain protein content (GPC) for split N application were greater than for full N at Z24 (NAB, 176 and 157 kg N ha−1; NRF, 66 and 51%; GPC, 100 and 92 g kg−1, for split and full N application, respectively). In years without water stress, splitting N between Z24 and Z39 is an appropriate strategy to improve NRF, reducing N losses, and minimizing the environmental impact of fertilization.
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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) | 0.000 | 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".