Long‐Term Manure Application and Forages Reduce Nitrogen Fertilizer Requirements of Silage Corn–Cereal Cropping Systems
Bibliographic record
Abstract
Assessment of the soil N supply capacity is essential to optimize fertilizer N use. We investigated soil N supply capacity and fertilizer N recovery for three cropping systems established in 1977: silage corn (Zea mays L.)–cereal without (CC) and with 20 Mg ha−1 yr−1 manure (CCM), and silage corn–forage (3‐yr) with manure (CFM). During the present study (2005–2008), manure applications were suspended and a silage corn–silage corn–barley (Hordeum vulgaris L.)–wheat (Triticum aestivum L.) sequence was imposed to all systems. Fertilizer (15NH415NO3, 3.1 atom % 15N) was applied in 2005 to silage corn (160 kg N ha−1) and in 2007 to barley (80 kg N ha−1). The 15N recovery in silage corn and barley ranged from 40 to 59%, with the lowest values measured in CFM. Compared to the CC systems (47 kg N ha−1) in 2005, soil‐derived N in silage corn was two times higher under CCM (98 kg N ha−1), and four times higher under CFM (208 kg N ha−1). These differences decreased over years, but were still noticeable at the end of the experiment. Twenty‐two to 58% of applied 15N was recovered in the soil at harvest. More than 50% of this residual N was present in macroaggregates (>0.25 mm), whereas <20% was present in particulate organic matter (POM). Up to 75% of residual 15N was lost during the winter period, and little residual N was transferred to the following crop. These findings indicate that soil macroaggregates may be a preferential sink for residual fertilizer N, but this N is not present in stable forms and is vulnerable to environmental loss.
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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".