Foliar and Granular Fertilizer Effects on Creeping Bentgrass and Soil Nutrient Levels
Bibliographic record
Abstract
Core Ideas Foliar fertilizer treatments did not deplete soil nutrient levels after three years of applications. Combining foliar and granular fertilizers resulted in the best turfgrass quality. Biweekly application of foliar fertilizers resulted in higher turf quality than monthly granular fertilizer applications. Research was conducted to determine the effects of foliar and granular fertilizers on creeping bentgrass [Agrostis stolonifera var. L. palustris (Huds.) Farw. ‘Penn A4’] quality and soil nutrient levels of two putting green rootzones. The hypothesis was foliar fertilizers would decrease soil nutrient levels and result in lower turfgrass quality over 3 yr compared to granular fertilizers. The fertilizer treatments were granular urea, methylene urea, and natural organic, foliar fertilizer at two rates, foliar‐granular combination, and an untreated control. The two rootzones were a United States Golf Association (USGA) guideline rootzone and a sandy clay loam (58% sand, 20.5% silt, and 21.5%) native soil. The urea, methylene urea, natural organic, and foliar‐granular treatments were applied at 0.5 lb N/1000 ft2/month from May through October. The foliar treatment was applied at two rates, 0.25 and 0.5 lb N/1000 ft2/month from May through October. Soil samples were collected from 2009 to 2011 and turfgrass quality was rated every 2 weeks. Turfgrass growing on native soil had consistently higher quality than turfgrass on the USGA rootzone and native soil had higher soil NO3–N, P, and K levels than the USGA rootzone. Neither the foliar, granular, or combination treatments decreased soil NO3–N, P, or K levels compared to initial soil test levels over 3 year of research. Every two weeks, applications of both the combination and foliar 2X treatments produced higher turfgrass quality than monthly methylene urea, urea, and organic treatments. Although the 0.25 lb N/1000 ft2/month foliar treatment had a lower N rate than all fertilizer treatments, it had acceptable quality throughout the research.
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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".