Reducing nitrate leaching losses from turfgrass fertilization of residential lawns
Bibliographic record
Abstract
Abstract Fertilizer applications on lawns have raised environmental concerns in many Canadian municipalities. In this greenhouse study, NO 3 –N leaching losses from Kentucky bluegrass ( Poa pratensis L.) lawns were evaluated on two soils (a schist loam and a clay loam) and on a sand/peat moss rootzone mix (80% sand, 20% peat moss). Eight different fertilizer N sources (urea, Polyon 8 and 12‐wk release, Duration 45 and 90‐d release, XCU, corn gluten meal, and UFLEXX) were assessed at five application rates (25–200 kg N ha –1 yr –1 ) and two application frequencies over two 8‐wk trials. Average NO 3 –N concentration in leachate were measured at levels of 3.5, 7.4, and 1.4 mg L –1 from turf grown in loam, clay, and sand respectively, but losses from loam and clay were mostly affected by N mineralization from organic matter. Turf fertilized with rates ≥100 kg N ha –1 generally resulted in acceptable visual quality on both soils, but coated‐urea fertilizers were more efficient to reduce leaching. In sand, UFLEXX and urea (150 and 200 kg N ha –1 ) as well as XCU (200 kg N ha –1 ) resulted in higher NO 3 –N losses, varying from 8.5 to 23.7 mg L –1 , and losses from other N sources were consistently below 3 mg L –1 . Our results show that it is possible to maintain good quality turfgrass while keeping low NO 3 –N leaching losses (i.e., <4 mg L –1 ) in loam, clay, and sand by selecting the ideal combination of N source, N rate, and application frequency.
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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 | 0.001 | 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) | 0.004 | 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".