Leaching of Mineral and Organic Nitrogen from Putting Green Profiles Supporting Various Turfgrasses
Bibliographic record
Abstract
Nitrate (NO3−) leached from golf greens has the potential to impair water quality. Dissolved organic N (DON) is also increasingly recognized as a form leached from fertilized soils. A controlled experiment was conducted to determine (i) the significance of DON in total N leaching losses under simulated golf‐green profiles and (ii) the short‐term contribution of fertilizer to leaching of inorganic and organic N forms. Various turfgrasses were grown in lysimeter columns designed to simulate a golf‐green profile. Fertilizer was applied at 25 kg N ha−1 every 14 d for 55 d, and the last application was labeled with 15N. Leachates were analyzed for NO3−‐N, NH4+‐N, and DON. The 15N recovery was assessed in plant, soil, and leachates. In the presence of plants, 10 to 70% (average 40%) of total N leached was accounted for by DON. Application of 15N revealed that one‐half to two‐thirds of NO3−‐N leached in the following 14 d was derived from the fertilizer, whereas the majority of leached DON was derived from soil residual N. Nevertheless, DO15N was present in most leachate samples collected for 14 d after fertilizer application, indicating that only a few days were required to convert mineral fertilizer to leachable organic forms. We conclude that DON may be a significant component of total N leaching losses from putting greens and would account for part of the N losses traditionally attributed to volatilization and denitrification.
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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".