System Pinpoints Stressed Turfgrass

Turfgrass managers spend a significant amount of time monitoring their turfgrass fertility and irrigation programs to ensure the most efficient use of their resources while minimizing potential environmental impacts. Characterizing the spatial variability of nutrients across a golf course or large sports facility requires careful observation and periodic collection of soil and tissue samples. Remote sensing techniques have been shown to be valuable tools in quickly and reliably identifying stressed plants through the use of various vegetative indices. Research has shown that remote sensing data can be related to turf chlorophyll content, turf injury and quality (Trenholm et al., 1999]. As a result, there has been an increased interest in using remote-sensing tools as a non-destructive tool for determining the nutrient status of plants due to the potential time savings that could result when compared to traditional sampling methods. Handheld chlorophyll meters have been used to rapidly assess plant nitrogen status in agronomic crops (Piekielek and Fox, 1992; Schepers et al., 1996; Wood et al., 1992) by measuring optical density at two wavelengths and converting to a value that has been positively correlated with chlorophyll and nitrogen. While handheld chlorophyll meters are an attractive option for monitoring turfgrass health, they are limited in the amount of spectral information collected from the turfgrass canopy. An alternative to chlorophyll meters is to measure light reflected from the turfgrass canopy with a multispectral radiometer that is capable of measurements at numerous wavelengths along the electromagnetic spectrum, thus increasing the amount of information that might be gathered and interpreted from the canopy. Research in turfgrass science often involves using controllable variable (factors) to explain or predict other variable (responses). For instance, we may be interested in the influence of nitrogen (N) concentration on the biomass production of a particular turfgrass. When these factors are few in number, not highly collinear and have a well-understood relationship to the responses, then multiple linear regression (MLR) can be a good way to turn data into information (Tobias, 1997). Partial leastsquares (PLS) is a method developed for constructing predictive models when there are a large number of highly collinear factors (Tobias, 1997). The research was conducted during a two-year field experiment at the Iowa State University Horticulture research station in Gilbert, Iowa, on a creeping bentgrass (Agrostis stolonifera L., Penncross) putting green constructed according to United States Golf Association specifications to determine the correlation between nitrogen concentration of plant tissue and remotely sensed multispectral scanner data. Plots were 5 feet by 5 feet in size and arranged in a randomized, complete-block design with four replications per treatment. Continued on page 50 IN THIS ISSUE