Full TGIF Record # 310842
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Web URL(s):https://archive.lib.msu.edu/tic/ressum/2019/2019.pdf#page=149
    Last checked: 04/17/2020
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Publication Type:
i
Report
Author(s):Soldat, Doug
Author Affiliation:University of Wisconsin-Madison
Title:Building a better growth model to optimize nitrogen applications to bentgrass putting greens
Section:Integrated turfgrass management
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Ecophysiology: Light and temperature
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Source:Turfgrass and Environmental Research Program: 2019 Research Summaries. 2019, p. 141-144.
# of Pages:4
Publishing Information:[New York, New York]: The United States Golf Association Green Section
Language:English
References:0
See Also:Other Reports from this USGA research project: 2019-10-680
Note:Figures
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USGA Summary Points:Bentgrass growth rate is negatively correlated to soil moisture on sand root zones. Bentgrass growth rate is negatively correlated to traffic level, but across the range of typical traffic levels, this factor is not as significant as others. Annual bentgrass growth on sand greens with different soil organic matter levels varies substantially. Management practices and weather both play highly significant roles in controlling bentgrass growth. A machine learning approach to accurately predict bentgrass growth appears feasible.
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Soldat, D. 2019. Building a better growth model to optimize nitrogen applications to bentgrass putting greens. USGA Turfgrass Environ. Res. Summ. p. 141-144.
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Web URL(s):
https://archive.lib.msu.edu/tic/ressum/2019/2019.pdf#page=149
    Last checked: 04/17/2020
    Requires: PDF Reader
    Notes: Item is within a single large file
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MSU catalog number: b3609415
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