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| Web URL(s): | https://scisoc.confex.com/scisoc/2020am/meetingapp.cgi/Paper/127796
Last checked: 09/14/2023 |
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| Publication Type:
| Report |
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| Content Type: | Abstract or Summary only |
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| Author(s): | Elmore, Matthew T.;
Prorock, Michael;
Tuck, Daniel P. |
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| Author Affiliation: | Elmore and Tuck: Department of Plant Biology, Rutgers University, New Brunswick, NJ; Prorock: Mesur.io, Chapel Hill, NC |
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| Title: | A model to predict goosegrass (Eleusine indica) seedling emergence in cool-season turfgrass |
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| Section: | Turfgrass pest management poster: Diseases, insects, weeds (includes student competition)
Other records with the "Turfgrass pest management poster: Diseases, insects, weeds (includes student competition)" Section
C05 turfgrass science
Other records with the "C05 turfgrass science" Section
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| Meeting Info.: | San Antonio, Texas: November 9-13, 2020 |
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| Source: | ASA, CSSA, SSSA International Annual Meeting. November 2020, p. 127796. |
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| Publishing Information: | [Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America] |
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| # of Pages: | 1 |
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| Abstract/Contents: | "The objective of this study was to determine if goosegrass (Eleusine indica) seedling emergence in cool-season turfgrass is associated with weather variables to develop an emergence model. Goosegrass emergence was assessed weekly in New Jersey, USA at three sites in 2017 and two sites in 2018. Goosegrass seedlings were counted and removed on a weekly basis from April through October within fixed circles. An ensemble modeling approach was used to derive meteorological and agronomic conditions at each site. Growing degree-days were calculated from winter solstice using a 10°C base temperature. Correlation analysis using logistic regression determined week-of-year, growing degree-days, and air temperature were most associated with goosegrass emergence. These variables were subjected to logistic regression using a Gompertz function to describe emergence. Models consisting of week-of-year and accumulated growing degree-days described emergence equally well. Emergence patterns were variable among locations and year. This research demonstrates that air temperature-based functions can explain goosegrass emergence. More advanced techniques are being incorporated into an improved model, including a seven-day moving average of air temperature and growing degree-days. This model will be validated across multiple locations in 2021." |
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| Language: | English |
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| References: | 3 |
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| Note: | This item is an abstract only!
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| ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Elmore, M. T., M. Prorock, and D. P. Tuck. 2020. A model to predict goosegrass (Eleusine indica) seedling emergence in cool-season turfgrass. Agron. Abr. p. 127796. |
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| Web URL(s):
https://scisoc.confex.com/scisoc/2020am/meetingapp.cgi/Paper/127796
Last checked: 09/14/2023 |
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