Using electromagnetic induction to inform precision turfgrass management strategies in sand-capped golf course fairways

被引:0
|
作者
Williams, Dallas M. [1 ]
Straw, Chase M. [1 ]
Smith, A. Peyton [1 ]
Watkins, Kathryn L. [1 ]
Hong, Sarah G. [1 ]
Floyd, Weston F. [1 ]
Wyatt, Briana M. [1 ]
机构
[1] Texas A&M Univ, Dept Soil & Crop Sci, College Stn, TX 77840 USA
关键词
APPARENT ELECTRICAL-CONDUCTIVITY; SPECTRAL REFLECTANCE; SOIL; SALINITY; GROWTH; ZONES;
D O I
10.1002/agg2.70020
中图分类号
S3 [农学(农艺学)];
学科分类号
0901 ;
摘要
To meet the turfgrass standards that players expect, golf course superintendents rely on intense irrigation, fertilization, and cultivation programs. However, the overapplication of irrigation water and fertilizer has been shown to have negative effects on water quality. Precision turfgrass management (PTM) is an emerging area of interest as more golf course superintendents are looking to increase input efficiency while simultaneously reducing water and fertilizer input costs, as well as environmental impacts. Our objectives were to (1) use electromagnetic induction (EMI) to determine the spatial variability of apparent electrical conductivity (EC) in sand-capped fairways and (2) correlate EC to measured soil and turfgrass characteristics to determine the applicability of mapping EC for PTM. Soil samples and EC data were collected in spring 2021 on four sand-capped fairways from two golf courses (one hybrid bermudagrass and one zoysiagrass) belonging to the same facility in southeast Texas. Apparent EC was found to be positively and significantly correlated with soil volumetric water content (VWC, 0.40 < r > 0.62) and turfgrass normalized difference vegetation index (NDVI; 0.21 < r > 0.46) in three of four fairways, while EC was negatively and significantly correlated with penetration resistance (PR, -0.29 < r > -0.48) in two of four fairways studied. The strengths of these relationships were corroborated by strong visual similarities when comparing spatial maps of EC with those of VWC, NDVI, and PR, indicating that EMI-based EC data have potential for use in delineating site-specific management zones for water and fertilizer applications, as well as targeted aeration.
引用
收藏
页数:14
相关论文
共 8 条
  • [1] Factors leading to spatiotemporal variability of soil moisture and turfgrass quality within sand-capped golf course fairways
    Hejl, Reagan
    Straw, Chase
    Wherley, Benjamin
    Bowling, Rebecca
    McInnes, Kevin
    PRECISION AGRICULTURE, 2022, 23 (05) : 1908 - 1917
  • [2] Factors leading to spatiotemporal variability of soil moisture and turfgrass quality within sand-capped golf course fairways
    Reagan Hejl
    Chase Straw
    Benjamin Wherley
    Rebecca Bowling
    Kevin McInnes
    Precision Agriculture, 2022, 23 : 1908 - 1917
  • [3] Golf course superintendents' knowledge of variability within fairways: a tool for precision turfgrass management
    Straw, Chase M.
    Wardrop, William S.
    Horgan, Brian P.
    PRECISION AGRICULTURE, 2020, 21 (03) : 637 - 654
  • [4] Golf course superintendents’ knowledge of variability within fairways: a tool for precision turfgrass management
    Chase M. Straw
    William S. Wardrop
    Brian P. Horgan
    Precision Agriculture, 2020, 21 : 637 - 654
  • [5] A review of precision management for golf course turfgrass
    Carlson, Michael G. G.
    Gaussoin, Roch E. E.
    Puntel, Laila A. A.
    CROP FORAGE & TURFGRASS MANAGEMENT, 2022, 8 (02)
  • [6] Is organic golf course management a hole in one?: Using microbial analysis to evaluate the turf phytobiome under different management strategies
    Allan-Perkins, E.
    Manter, D.
    Jung, G.
    PHYTOPATHOLOGY, 2016, 106 (12) : 171 - 171
  • [7] Geostatistical monitoring of soil salinity for precision management using proximally sensed electromagnetic induction (EMI) method
    Rong-Jiang Yao
    Jing-Song Yang
    Dan-Hua Wu
    Wen-Ping Xie
    Peng Gao
    Xiang-Ping Wang
    Environmental Earth Sciences, 2016, 75
  • [8] Geostatistical monitoring of soil salinity for precision management using proximally sensed electromagnetic induction (EMI) method
    Yao, Rong-Jiang
    Yang, Jing-Song
    Wu, Dan-Hua
    Xie, Wen-Ping
    Gao, Peng
    Wang, Xiang-Ping
    ENVIRONMENTAL EARTH SCIENCES, 2016, 75 (20)