Clippings return decreases mineral nitrogen requirements for bermudagrass (<i>Cynodon</i> spp.) lawns in Mediterranean Europe
Bibliographic record
Abstract
Abstract The use of bermudagrass [Cynodon dactylon (L.) Pers.] and hybrid bermudagrass (C. dactylon x C. transvaalensis Burtt Davy) in lawns is rapidly increasing in Mediterranean Europe; however, the identification of optimal N fertilization practices is needed to shorten the long dormancy periods some cultivars undergo in these environments. A 2‐yr study was conducted at the agricultural experimental farm of Padova University from May 2016 until June 2018 to compare the effects of three N fertilization rates (160 kg N ha−1 without clippings returned, 80 kg N ha−1 with clippings returned, and 80 kg N ha−1 without clippings returned applied with a controlled‐release fertilizer) on two bermudagrass cultivars (‘La Paloma’, ‘Yukon’) and two hybrid bermudagrass cultivars (‘Patriot’, ‘Tifway’) by measuring summer and fall quality, spring green‐up, and root morphology. Bermudagrasses fertilized at 160 kg N ha−1 without clippings returned slightly increased turfgrass quality in the summer, but higher turf quality was recorded in plots fertilized at 80 kg N ha−1 with clippings returned after weekly mowing events in the fall. The same N rates had a positive effect on spring green‐up for ‘La Paloma’ and ‘Tifway’; however, no benefits of increased N rates were detected on root morphology. Results suggest that returning clippings can be a powerful tool for reducing mineral N applications and increase growing season length in northern Italy.
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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".