Nitrogen Uptake and Leaching under Annual Bluegrass Ecotypes and Bentgrass Species: A Lysimeter Experiment
Bibliographic record
Abstract
Nitrate (NO3−) can leach from golf greens, potentially causing the degradation of surface and ground water quality. A greenhouse experiment was conducted with 11 annual bluegrass (Poa annua var. reptans Hausskn.) ecotypes from eastern Canada (Quebec and Ontario) and the USA, and three bentgrass (Agrostis spp.) species to compare N uptake and potential for N leaching. Two‐month‐old grasses were established for a 6‐wk period in lysimeter columns simulating a golf‐green profile. An unplanted root zone control was included. Water‐soluble fertilizer was applied at 25 kg N ha−1 (NH4NO3) every 14 d for 57 d. Leachate samples were collected every second day and analyzed for NO3–N and ammonium N (NH4–N) content. Dry weight and N concentration were determined on clippings, shoots, and roots. Ammonium N leaching was negligible for all grasses. Less NO3–N leaching losses occurred under bentgrasses (6–11% of applied N) than under annual bluegrasses (28–71% of applied N). Differences in NO3–N leaching were also found within annual bluegrasses; Quebec P. annua > Ontario P. annua > USA P. annua Grasses with a greater aboveground biomass developed a larger and deeper root system and were associated with a greater N uptake (r = 0.94) and, therefore, a lower NO3–N leaching (r = −0.94). Breeding programs and management practices to improve turfgrass root development appear to be critical to reduce fertilizer N leaching under sand‐based putting greens.
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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) | 0.001 | 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 | 0.001 |
| 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".