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This article in AJ

  1. Vol. 98 No. 6, p. 1640-1645
    Received: Jan 25, 2006

    * Corresponding author(s): jason.kruse@ces.uwex.edu
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Remote Sensing of Nitrogen Stress in Creeping Bentgrass

  1. Jason K. Kruse *a,
  2. Nick E. Christiansb and
  3. Michael H. Chaplinb
  1. a Univ. of Wisconsin Extension, Winnebago County, 625 East County Rd. Y, Suite 600, Oshkosh, WI 54901
    b Dep. of Horticulture, Iowa State Univ., 106 Horticulture Hall, Ames, IA 50011


Development of a remote sensing system that can reliably identify nutrient deficiencies may reduce time spent sampling turfgrass areas and allow for site-specific applications of fertilizers. The objectives of this research were to evaluate the use of a ground-based remote sensing system and partial least-squares (PLS) regression to predict the N concentration, biomass production, chlorophyll content, and visual quality of creeping bentgrass (Agrostis stolonifera L. ‘Penncross’) growing under varying N rates, and to compare PLS regression to other vegetative indices. The study consisted of three N treatments (0.0, 12.2, and 24.4 kg ha−1 15 d−1) arranged in a randomized complete block design. Spectral radiance measurements were obtained from plots using a fiber-optic spectrometer to calculate vegetative indices. The PLS regression analysis yielded a strong relationship between actual and predicted N concentration of creeping bentgrass plant tissue during 2002 and 2003 (r 2 = 0.95 and 0.71 respectively). However, PLS regression failed to produce a prediction for the chlorophyll concentration. Regressing the normalized vegetation index (NDVI), Stress1 (R 706/R 760), and Stress2 (R 706/R 813) ratios against N concentration yielded better results in 2003 when there were distinct differences in N concentration between the N rates. These results indicate that the traditional vegetation indices like NDVI might be better suited for determining the relative N status of turfgrass plants when compared against a well-fertilized control. More research will be required to determine if the PLS regression analysis produces prediction models that are able to specifically identify a particular nutrient deficiency or plant stress, and how the results will vary between grass species.

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