Bermudagrass, Wheat, and Tall Fescue Crude Protein Forage Estimation using Mobile‐Platform, Active‐Spectral and Canopy‐Height Data. Issue 2 (1st March 2016) - Pittman, J. Josh; Arnall, D. Brian; Interrante, Sindy M.; Wang, Ning; Raun, William R.; Butler, Twain J.
Abstract : Forage nutritive value analysis is typically performed via laboratory methods. Developing a system that provides crude protein (CP) estimates more quickly would be valuable. For evaluating feasibility of this system, N rate experiments containing bermudagrass [ Cynodon dactylon (L.) Pers.], tall fescue [ Lolium arundinaceum (Schreb.) Darbysh.], and wheat ( Triticum aestivum L .) were examined. A hyperspectral passive spectrometer collecting spectral data at 340 to 1030 nm and a sensor system developed for use from a mobile platform were employed to collect data for prediction of CP. The predicted CP from hyperspectral data regressed with those measured by near‐infrared spectroscopy (NIRS) in a laboratory produced R 2 = 0.80. Bermudagrass CP predictions from data collected using the mobile system showed no seasonal influence and were characterized with R 2 = 0.85. Wheat predictions from the mobile sensors exhibited R 2 = 0.27 for both fall and spring wheat when modeled as one data set. When split into two data sets for fall (Feekes 1–7) and spring (Feekes 7–10) growth, wheat model predictions of CP bore R 2 = 0.65 and 0.01, respectively. Tall fescue exhibited a similar pattern for mobile data, whereas all predictions regressed with measured values exhibited an R 2 = 0.63, spring and early summer an R 2 = 0.83, and fall observations an R 2 = 0.41. The results of this study indicate prediction of CP using hyperspectral data are accurate enough to be used for reporting forage CP for bermudagrass and are seasonally dependent for reporting forage CP in tall fescue and wheat.