Leaching of Mineral and Organic Nitrogen from Putting Green Profiles Supporting Various Turfgrasses
Bibliographic record
Abstract
Nitrate (NO 3 − ) 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 15 N. Leachates were analyzed for NO 3 − ‐N, NH 4 + ‐N, and DON. The 15 N 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 15 N revealed that one‐half to two‐thirds of NO 3 − ‐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, DO 15 N 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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".