A crop water stress index for tall fescue (Festuca arundinacea Schreb.) irrigation decision-making ¯¯ a fuzzy logic method

Sensors are needed to document the spatial variability of soil parameters for successful implementation of Site-Specific Management (SSM). This paper reports research conducted to document the ability of a previously developed near infrared (NIR) reflectance sensor to predict soil organic matter and soil moisture contents of surface and subsurface soils. Three soil cores (5.56 cm dia.×1.5 m long) were collected at each of 16 sites across a 144000 km2 area of the US Cornbelt. Cores were subsampled at eight depth increments, and wetted to six soil moisture levels ranging from air-dry to saturated. Spectral reflectance data (1603¯2598 nm) were obtained in the laboratory on undisturbed soil samples. Data were collected on a 6.6 nm spacing with each reflectance value having a 45 nm bandpass. The data were normalized, transformed to optical density [OD, defined as log10 (1/normalized reflectance)], and analyzed using stepwise multiple linear regression. Standard errors of prediction for organic matter and soil moisture were 0.62 and 5.31%, respectively. NIR soil moisture prediction can be more easily commercialized than can soil organic matter prediction, since a reduced number of wavelength bands are required (four versus nine, respectively).