Full TGIF Record # 313727
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Web URL(s):https://archive.lib.msu.edu/tic/ressum/2020/2020.pdf#page=111
    Last checked: 08/05/2021
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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: 2020 Research Summaries. 2020, p. 103-106.
Publishing Information:[New York, New York]: The United States Golf Association Green Section
# of Pages:4
Language:English
References:0
See Also:Other Reports from this USGA research project: 2019-10-680
Note:Graphs
USGA Summary Points:Temperature, relative humidity and evapotranspiration are key weather factors determining bentgrass growth Foot traffic, nitrogen rate and soil moisture are weakly correlated with bentgrass growth. However, model accuracy substantially increased when these variables were included. Potentially mineralizable nitrogen in the top 5 cm of soil was positively correlated with bentgrass clipping production. A data-driven statistical model using the random forest machine learning algorithm can accurately predict bentgrass yield. However, the model was only effective for the location where the model was built, suggesting that individual golf courses should build a customized growth prediction models to manage nitrogen adaptively.
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Soldat, D. 2020. Building a better growth model to optimize nitrogen applications to bentgrass putting greens. USGA Turfgrass Environ. Res. Summ. p. 103-106.
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Web URL(s):
https://archive.lib.msu.edu/tic/ressum/2020/2020.pdf#page=111
    Last checked: 08/05/2021
    Requires: PDF Reader
    Notes: Item is within a single large file
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MSU catalog number: b3609415
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