Non - biased prediction of soil organic carbon and total nitrogen with vis - 1 NIR spectroscopy , as affected by soil moisture content and texture

8 This study was undertaken to evaluate the effects of moisture content (MC) and texture on 9 the prediction of soil organic carbon (OC) and total nitrogen (TN) with visible and near 10 infrared (vis-NIR) spectroscopy under laboratory and on-line measurement conditions. An 11 AgroSpec spectrophotometer was used to develop calibration models of OC and TN using 12 laboratory scanned spectra of fresh and processed soil samples collected from five fields on 13 Silsoe Farm, UK. A previously developed on-line vis-NIR sensor was used to scan these 14 fields. Based on residual prediction deviation (RPD), which is the standard deviation of the 15 prediction set (S.D.) divided by the root mean square error of prediction (RMSEP), the 16 validation of partial least squares (PLS) models of OC and TN prediction using on-line 17 spectra was evaluated as very good (RPD = 2.01 2.24) and good to excellent (RPD = 1.86 18 2.58), respectively. A better accuracy was obtained with fresh soil samples for OC (RPD = 19 2.11 2.34) and TN (RPD = 1.91 2.64), whereas the best accuracy for OC (RPD = 2.66 20 3.39) and TN (RPD = 2.85 3.45) was obtained for processed soil samples. Results also 21 showed that MC is the main factor that decreases measurement accuracy of both on-line and 22 fresh samples, whilst the accuracy was greatest for soils of high clay content. It is 23