Under‐ or Over‐Application of Nitrogen Impact Corn Yield, Quality, Soil, and Environment
Bibliographic record
Abstract
Core Ideas Nitrogen application mid‐season cannot overcome drought stress later in the season. Except for crude protein, under‐application of N did not impact forage quality. Soil organic matter decreases in a chisel‐disked corn silage system regardless of N fertilizer rate. Use of compost, cover crops, and conservation tillage can offset soil organic matter losses. Under‐applying N by 30 kg N ha−1 was economically more detrimental than over‐applying. Under‐ or over‐application of N fertilizer to corn (Zea mays L.) has adverse economic and environmental consequences. A 5‐yr study was conducted to determine the impact of N fertilizer on corn silage yield, quality, soil properties, farm economics, and nitrogen‐use efficiency (NUE). Corn silage yields were 12.9, 14.2, and 14.7 Mg ha−1 with most economic rate of nitrogen (MERN) of 90, 95, and 114 kg N ha−1 in 2001, 2003, and 2004 (the three responsive years), respectively. In 2002 and 2005 (non‐responsive years), yields averaged 9.1 Mg ha−1. Yield increased by 3.3 Mg ha−1 with each 10 cm of precipitation in July and August. At the MERN, NUE ranged from 16 (2001) to 25.8 kg DM kg N−1 (2004), reflected in greater soil NO3–N (0–20‐cm depth) at harvest in 2001 as well (23 vs. 8.9 mg kg−1 in 2004). Soil NO3–N at silage harvest in responsive years ranged from 8.9 (2004) to 23 mg kg−1 (2001) in 2001 and in non‐responsive years averaged 22 mg kg−1. Soil NO3–N at harvest was not a good indicator of crop N responsiveness or NUE. Nitrogen addition beyond the MERN decreased NUE and soil pH, and increased crude protein (CP). Under‐application decreased CP and yield in N‐responsive years and increased NUE. Soil organic matter (SOM) was decreased regardless of N rate. Overall, application of 30 kg N ha−1 below the MERN was economically more detrimental than fertilizing the same amount above the MERN.
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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".