Grain Corn and Soil Nitrogen Responses to Sidedress Nitrogen Sources and Applications
Bibliographic record
Abstract
The efficiency of synthetic N fertilization can be improved by selecting the fertilizer source and application that best matches the soil N supply and crop demand. A field experiment was conducted for 3 yr (2004–2006) on a clay soil near Québec City, QC, Canada, to evaluate the effects of N fertilizer source and application on corn (Zea mays L.) yield, plant N accumulation, and residual soil inorganic N. Treatments consisted of an unfertilized control (0 N) and three sources of N fertilizer (urea ammonium nitrate 32% [UAN], calcium ammonium nitrate [CAN], and aqua ammonia [AA]) applied at three different concentrations (100, 150, and 200 kg N ha−1). Nitrogen fertilizers were banded 5 cm below the soil surface between corn rows at the six‐leaf stage every year. Fertilizer source affected grain corn with the highest mean yields (8.9 Mg ha−1) and total plant N accumulation achieved with UAN at any application. For all fertilizer sources, the linear‐plus‐plateau model best described the corn response to N application with optimum rate at 100, 124, and 128 kg N ha−1 for UAN, CAN, and AA, respectively. At harvest each year, the concentration of residual soil inorganic N increased in the upper layer. Under the cool and humid climatic prevailing conditions, UAN was the most efficient synthetic N fertilizer when banded into the soil at sidedress.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".