Bermudagrass, Wheat, and Tall Fescue Crude Protein Forage Estimation using Mobile‐Platform, Active‐Spectral and Canopy‐Height Data

Abstract

F orage nutritive value analysis has traditionally been performed through wet and ignition laboratory (Kellems and Church, 1998) and NIRS laboratory analysis (Norris et al., 1976). These methods of forage analysis are accepted as accurate and are used for livestock feed ration estimation but require a number of days or weeks for results to be delivered. Remote sensing provides an alternative that could provide forage nutritive value estimates with less turnaround time and allow more rapid decision making for inclusion of a feeding supplement. The examination of hyperspectral reflectance for indicating N concentration in vegetation is somewhat extensive. Estimation of bermudagrass N concentration through the use of spectral sensors has been examined by Starks et al. (2004) for multiple wavebands in the 368-to 1100-nm range and produced estimates with R 2 = 0.76 as compared with laboratory analysis. An R 2 = 0.82 was reported by Starks et al. (2008) 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.

High‐Throughput Approaches for Phenotyping Alfalfa Germplasm under Abiotic Stress in the Field

“…The spider refers to a gasoline-powered high-clearance tractor (Lee Spider, LeeAgra) mounted with an array of sensors (Supplemental Fig. S2) and used for field-based data collection as previously described (Pittman et al, 2016). The spider's hydraulic, front-end platform contains two sensing platforms with identical components.…”

“…These surface models used the data points provided by the spider while driving through the field. Previously described models developed to estimate plant biomass (Pittman et al, 2015) and crude protein content (Pittman et al, 2016) in alfalfa were implemented in this study as described above. The frequency of data collection established was 2-wk intervals between 7 Dec. 2016 and 14 Sept. 2017, occasionally adjusted for weather conditions.…”

Improving Predictability of Multisensor Data with Nonlinear Statistical Methodologies

“…To assess the nutritional values of bermudagrass, the forage nutritive value analysis has been the traditional choice, which involves evaluations performed through wet and ignition laboratory (Kellems and Church, 2009) and near-infrared reflectance spectroscope (NIRS) laboratory analysis (Norris et al, 1976). Although the test results were considered accurate and reliable, the analysis process could take weeks to complete, leading to high turnaround time (Pittman et al, 2016).…”

Improving Dual-Purpose Winter Wheat in the Southern Great Plains of the United States

“…In addition, forage nutritive values can also be predicted by modeling plant crude protein (CP) contents using a hyperspectral passive spectrometer [80]. Forage quality analysis has often been performed using near-infrared spectroscopy (NIRS) [81].…”

“…Forage quality analysis has often been performed using near-infrared spectroscopy (NIRS) [81]. Positive high correlations were observed between CP measurements from NIRS and CP estimates from a hyperspectral reflectance model in wheat, bermudagrass, and tall fescue [80]. Therefore, sensor phenotyping platforms greatly increased the breeding efficiency, and facilitated the selection of forage biomass and quality traits during dual-purpose winter wheat breeding.…”