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 (r2 = 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 (R706/R760), and Stress2 (R706/R813) 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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A procedure was developed for the rapid quantitative determination of K, P, Ca, Mg, Mn, Fe, Cu, B, and Zn content in plant tissue by direct reading emission spectrometry using spark excitation and the rotating disc electrode technique. Aliquots of standard reference material, ground to pass a 40 mesh screen, are weighed into high form porcelain crucibles and ashed at 450°C for 6 h. Five milliliters of an internal standard-buffer solution (0.2% cobalt and 0.5% lithium in 1 N HCl) are added to the remaining ash. The resulting solution is subjected to a 30-sec burn on the spectrometer, and the intensity ratios for each element are recorded. Known concentrations ( X) and intensity ratio units ( Y) are entered into a stepwise regression computer program, and the linear, quadratic, and cubic regressions of Y on X are determined. Sample values are entered as Y into the appropriate regression equation which is then solved for X. If quadratic or cubic regression equations are used, the program will select the appropriate root. Relative standard deviations for samples determined over a several-day period generally were less than 10%.