Weed Control by Herbicides and Fertilizers Applied Separately or Combined on Kentucky Bluegrass Lawn
Bibliographic record
Abstract
Incorporating herbicides application into fertilization has several benefits including saving time and reducing traffics on the lawn. Premixed products of fertilizers and herbicides are commonly known as Weed & Feed in the lawn-care industry. To compare Weed & Feed with separate applications of fertilizers and herbicides on a Kentucky bluegrass (Poa pratensis L.) lawn, a Weed & Feed 28-3-3, containing 0.64% 2,4-D, 0.31% MCPP, and 0.03% dicamba of active ingredients, was used in this study. The first application was in May, with the second in June or Sept. Herbicides in forms of 2,4-D (LV-4, 4EC), MCPP (4EC), and dicamba (Clarity, 4EC) were applied at rates equal to the amounts in Weed & Feed or at half of the rates. The dominant weed in both locations was common dandelion (Taraxacum officinale Weber.) in 2005 and 2004. A secondary weed was Canada thistle (Cirsium arvense (L.) Scop.) in 2004 and broadleaf plantain (Plantago major L.) in 2005. When applied in May and June, fertilizer plus full rate of herbicides treatment achieved 112.3 and 83.7 days of acceptable turf quality in 2004 and 2005, respectively. During the same period, Weed & Feed resulted in 58.7 and 24.3 days of acceptable turf quality, respectively. Our study showed that Weed & Feed was generally as effective in weed control as the same amount of fertilizer plus half rates of herbicides sprayed although results may vary due to the timing of application. Fertilizer plus full rates of herbicides provided the same or better results of weed control than Weed & Feed.
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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 | 0.001 | 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".