Saturated paste (SP) and 1:1 soil/water extractions (1:1) are commonly used to assess soil salinity for field remediation. Correlation of electrical conductivity (EC) and other analytes between the SP and 1:1 extraction methods have been documented, except the relationships were based on limited soil types and require further examination to be adequately evaluated. This study examined these relationships using 170 soils from petroleum and agriculture production sites. Saturated pastes and 1:1 extracts were prepared and analyzed for EC, major cations (Na+, K+, Mg2+, Ca2+), and major anions Cl−, SO42− Relationships of all analytes were established between the two methods using linear regression. Saturated paste extract EC (ECSP) was highly correlated with that of 1:1 extract EC (EC1:1) (r2 = 0.85, P < 0.001). Significant relationships also existed (r2 > 0.73, P < 0.001) between different ions in SP and 1:1 extracts. An independent validation set of 22 soils showed that the slopes of the regressions between predicted EC, Na+, and Cl− of SP equivalents from 1:1 extract measurements and direct SP extract measurements were very close to 1.0 suggesting that the regressions developed can accurately assess soil salinity in salt affected soils using 1:1 extract analysis instead of using the more expensive and time‐consuming SP extraction.

Non-destructive biomass estimation of vegetation has been performed via remote sensing as well as physical measurements. An effective method for estimating biomass must have accuracy comparable to the accepted standard of destructive removal. Estimation or measurement of height is commonly employed to create a relationship between height and mass. This study examined several types of ground-based mobile sensing strategies for forage biomass estimation. Forage production experiments consisting of alfalfa (Medicago sativa L.), bermudagrass [Cynodon dactylon (L.) Pers.], and wheat (Triticum aestivum L.) were employed to examine sensor biomass estimation (laser, ultrasonic, and spectral) as compared to physical measurements (plate meter and meter stick) and the traditional harvest method (clipping). Predictive models were constructed via partial least squares regression and modeled estimates were compared to the physically measured biomass. Least significant difference separated mean estimates were examined to evaluate differences in the physical measurements and sensor estimates for canopy height and biomass. Differences between methods were minimal (average percent error of 11.2% for difference between predicted values versus machine and quadrat harvested biomass values (1.64 and 4.91 t·ha−1, respectively), except at the lowest measured biomass (average percent error of 89% for harvester and quad harvested biomass < 0.79 t·ha−1) and greatest measured biomass (average percent error of 18% for harvester and quad harvested biomass >6.4 t·ha−1). These data suggest that using mobile sensor-based biomass estimation models could be an effective alternative to the traditional clipping method for rapid, accurate in-field biomass estimation.

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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.