Full TGIF Record # 317412
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Web URL(s):https://archive.lib.msu.edu/tic/ressum/2021/2021.pdf#page=247
    Last checked: 04/13/2022
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Publication Type:
i
Report
Author(s):Wherley, Benjamin; Hejl, Reagan; McInnes, Kevin; Grubbs, B.
Author Affiliation:Texas A&M University, College Station, TX
Title:Data-driven irrigation scheduling techniques for managing sand-capped fairways
Section:Integrated turfgrass management
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Ecophysiology: Water
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Source:Mike Davis Program for Advancing Golf Course Management: 2021 Progress Reports. 2021, p. 240-247.
Publishing Information:[New York, New York]: The United States Golf Association Green Section
# of Pages:8
Language:English
References:0
See Also:Other Reports from this USGA research project: 2018-05-665
Note:Pictures, color
Graphs
USGA Summary Points:All irrigation scheduling approaches produced similar levels of acceptable turfgrass quality and percent green cover with no apparent differences in root development by the end of the project. Over the two-year study, NOAA Forecasted Reference ET (FRET) was shown to be a reliable predictor of (R2=0.97) onsite weather station Penman-Monteith reference evapotranspiration (ET┬░). Seasonal water use was 23% lower for the on-site ET┬░ based approach compared to SMS-based scheduling, althrough thi sdid not result in elevated electrical conductivity within the sand cap. Under wilt-based irrigation, the volumetric water content at which wilt occurred was highest mid-summer (4.4%), but declined during early and late season months (2.0%), suggesting different thresholds may be used throughout the season when using SMS-based irrigation scheduling.
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Wherley, B., R. Hejl, K. McInnes, and B. Grubbs. 2021. Data-driven irrigation scheduling techniques for managing sand-capped fairways. USGA Turfgrass Environ. Res. Summ. p. 240-247.
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Web URL(s):
https://archive.lib.msu.edu/tic/ressum/2021/2021.pdf#page=247
    Last checked: 04/13/2022
    Requires: PDF Reader
    Notes: Item is within a single large file
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