Full TGIF Record # 333501
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Web URL(s):https://scisoc.confex.com/scisoc/2023am/meetingapp.cgi/Paper/150720
    Last checked: 12/06/2023
Publication Type:
Content Type:Abstract or Summary only
Author(s):Errickson, William; Gaylert, Spencer; Jin, Yanhong; Cuite, Cara; Huang, Bingru
Author Affiliation:Errickson: Rutgers University, Freehold, NJ; Gaylert, Jin, Cuite, and Huang: Rutgers University, New Brunswick, NJ
Title:Needs assessment for remote sensing- and machine learning-guided precision turfgrass irrigation programs: Findings from a socioeconomic survery
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Turfgrass water conservation and management poster (includes student competition)
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C05 turfgrass science
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Meeting Info.:St. Louis, Missouri: October 29-November 1, 2023
Source:ASA, CSSA, SSSA International Annual Meeting. 2023, p. 150720.
Publishing Information:[Madison, Wisconsin]: [American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America]
# of Pages:1
Abstract/Contents:"New technologies including mobile remote sensing and artificial intelligence-guided precision irrigation management (PIM) programs may offer turfgrass managers additional tools to reduce water use and improve turf quality. However, adoption of these new practices may be limited by factors such as startup costs, perceived importance, and the learning curve associated with new technology. This project conducted an industry-wide survey of turfgrass professionals to investigate their current irrigation management strategies, and how likely they may be to adopt new PIM technologies. Survey participants included turfgrass researchers, superintendents, sod farmers, and landscape professionals. Survey responses indicated current water use and cost for existing operations, importance ratings of various characteristics associated with PIM and water conservation, and likelihood of adoption based on cost-benefit scenarios. The results from this survey will help to guide research, development, training, and education for turfgrass PIM and its associated decision support systems."
Note:This item is an abstract only!
ASA/CSSA/SSSA Citation (Crop Science-Like - may be incomplete):
Errickson, W., S. Gaylert, Y. Jin, C. Cuite, and B. Huang. 2023. Needs assessment for remote sensing- and machine learning-guided precision turfgrass irrigation programs: Findings from a socioeconomic survery. Agron. Abr. p. 150720.
Fastlink to access this record outside TGIF: https://tic.msu.edu/tgif/flink?recno=333501
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    Last checked: 12/06/2023
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