Quality Analysis of Perennial Grasses for Use as Bioenergy Feedstock.

Replicated small plot trials of both cool and warm season perennial grasses were established in 2006-2007 in diverse locations in New York State for evaluation as potential bioenergy feedstock. Currently, very little is known about the variability in biomass quality components in these grasses that may be important for the emerging bioenergy industry conversion technologies, or how these relate to soil type and various environmental conditions. The overall goals of the project are to identify traits and quality characteristics associated with high yield, gross energy content, efficient conversion to liquid fuels, combustible materials and other bio-based products and to breed for improvement of those traits in perennial grasses for the Northeastern (NE) region of the US. However, in order to breed cultivars for the bioenergy industry in the NE, rapid screening methods for these traits need to be developed. The Cornell Forage Breeding Project has successfully used Near Infra Red (NIR) spectroscopy technology to strategically select germplasm for breeding alfalfa. We are now developing a rapid compositional analysis screening method similar to the process used for forage quality. Over one thousand perennial grass samples harvested from trials were oven dried, ground to a 1 mm particle size and scanned on a Foss NIR Systems spectrophotometer (Foss NIR Systems, Model 5000, Silver Spring, MD). Approximately 10% of the samples were selected for calibration by principal component analysis method using WINISI II software (Intrasoft International, Port Matilda, PA).  These calibration samples were analyzed through wet-chemistry by Dairy One Forage Testing Laboratory, Ithaca, NY.  NIR equations were then developed for the sample set using ISI software (Intrasoft International, CAL version 1.5 and higher, Port Matilda, PA) to predict values of all perennial grass samples. Data from this analysis will be presented.