Relation of yield of corn (<i>Zea mays</i> L.) to nitrogen in shoot and soil during the early-season following manure application to field plots
Bibliographic record
Abstract
We investigated corn grain yield responses to early-season soil mineral N and plant N content following application of a variety of manures in Eastern Canada. Liquid cattle, liquid swine, straw-bedded poultry, wood-bedded poultry, and solid cattle manures were each applied at 100, 200, and 300 kg N ha-1 prior to planting corn in a field experiment repeated 3 consecutive years. Additional treatments were urea applied at 50, 100, and 150 kg N ha-1, and liquid cattle and solid cattle manures at 200 kg N ha-1 with bedding amendments added to the field before manure spreading. Control plots received no manure, no urea, and no bedding amendment. Part of the ammonium-N applied in manures and a portion of that added as urea was volatilized or immobilized shortly after application and was not recovered as soil mineral N at planting. Across all treatments, soil mineral N in the top 30 cm on Jun. 10 in each year gave a better relationship to corn grain yield than soil mineral N measured at planting or on Jul. 10. Inclusion of data for plant N content on Jun. 10 or Jul. 10 did not improve the regressions. With remarkable consistency across all manure types, across manure rates, and over the three years, similar values for soil mineral N on Jun. 10 in plots given manure at planting had yields typically 0.5-1.0 t ha-1 greater than those in plots given urea at planting. This result indicates that mineralization of N from manure after the time of sidedress N and undetected as soil mineral N on Jun. 10 can contribute to yield increases of corn. Recent manure history should be considered when using soil tests for mineral N that are taken 3-4 wk after planting to calculate sidedress N fertilization. Key words: Sidedress, soil test, slurry, solid, cattle, swine, poultry
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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) | 0.001 | 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".