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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #155179


item Starks, Patrick - Pat
item Coleman, Samuel
item Phillips, William
item Brown, Michael
item Steiner, Jean

Submitted to: Agronomy Society of America, Crop Science Society of America, Soil Science Society of America Meeting
Publication Type: Abstract Only
Publication Acceptance Date: 8/1/2003
Publication Date: 12/1/2003
Citation: Starks, P.J., Coleman, S.W., Phillips, W.A., Brown, M.A., Steiner, J.L. 2003. Determining forage quality in situ using remote sensing [abstract]. American Society of Agronomy. Paper No. C06-starks123664-poster.

Interpretive Summary: Abstract Only

Technical Abstract: A critical shortcoming in the management of grazing lands is the inability to quantify the quality of live, standing forages on pastures in real-time. This information is needed to make informed land and livestock management decisions. The objective of this study was to determine the feasibility of estimating common forage quality parameters, directly in the field using a hand-held radiometer. Approaches commonly used in benchtop near infrared spectroscopy (NIRS) analysis were adapted to produce calibration equations from reflectance spectra collected using a hand-held hyperspectral radiometer. The reflectance data were collected from bermudagrass pastures during the summers of 1999, 2000, and 2001. Two-thirds of the reflectance data were used in calibration equation development, and the remaining one-third was used to validate the equations. For comparative purposes, both laboratory chemical and benchtop NIRS analyses were conducted on vegetation samples that were collected after scanning by the radiometer. The robustness of the calibration equations was then tested on a reflectance data set collected during the summer of 2002 from pastures containing mixtures of bermudagrass, smooth brome, and yellow bristlegrass. The analyses indicated that it is feasible to quantify forage quality in real-time using field remote sensing techniques.