Mehlich 3 extractable nutrients as determined by near-infrared reflectance spectroscopy

Abstract

extractable nutrients as determined by near-infrared reflectance spectroscopy. Can. J. Soil Sci. 89: 579Á587. Near-infrared reflectance spectroscopy (NIRS) is a cost-effective and environmentally friendly technique of soil analysis that is particularly advantageous in intensive soil sampling and soil nutrient management as well. This study evaluated the potential of NIRS for predicting P, K, Ca, Mg, Cu, Zn, Mn, Fe, and Al extracted by Mehlich 3. We used 150 air-dried samples collected from a 15-ha site dominated by Orthic Humic Gleysol and Gleyed Dystric Brunisol soils. Calibration equations were developed using modified partial least squares regression. The accuracy of NIRS prediction was evaluated using the coefficient of determination (R 2 ), the ratio of performance deviation (RPD), and the ratio of error range (RER). Reliable calibrations were found for Ca, Cu, and Mg (R 2 ]0.7, RPD ]1.75, and RER ]8). Less-reliable calibrations were found for Al, Fe, K, Mn, P, and Zn (R 2 B 0.7, RPDB1.75, and RER B8). In the validation with independent samples, acceptable regression coefficients (i.e., 0.8 5 slope 51.2) were only found for Ca, Mg, and Mn. We presumed that the pH of the Mehlich 3 extractant (2.590.1) may affect the solubility of most of these nutrients, regardless the soil texture and, consequently, the potential of NIRS to predict them. The more a nutrient was correlated to clay content, the more it was likely predictable by NIRS. The prediction models obtained for Al, Ca, Cu, Fe, K, Mg, and Mn could still be used for screening purposes in cases where high accuracy is not required. These NIRS prediction models should be validated across larger geographic areas of geological homogeneity.

Predicting Soil Phosphorus‐Related Properties Using Near‐Infrared Reflectance Spectroscopy

“…This result agrees with Nduwamungu et al (2009b) who reports a poor calibration performance for M3P when analyzed by ICP emission spectroscopy on a limited number of soil samples {n = 150). Chang et al (2001) also report a low accuracy for M3P_ICP prediction using NIRS with a principal component regression technique {R^^ = 0.40, RPD = 1.18).…”

“…Compared with our results, Nduwamungu et al (2009b) report lower NIRS prediction performance for Al, Fe, Ca, Mn, and Zn and greater calibration accuracy for Mg and Cu when extracted by the Mehlich 3 method and analyzed by atomic absorption spectroscopy. The ability of NIRS to predict certain soil properties may be related to their correlation with primary soil properties such as texture (Chang et al, 2001;Nduwamungu et al, 2009a) or organic matter content (Dalai and Henry, 1986).…”

“…The ability of NIRS to predict certain soil properties may be related to their correlation with primary soil properties such as texture (Chang et al, 2001;Nduwamungu et al, 2009a) or organic matter content (Dalai and Henry, 1986). The CV values in the reference data used by Nduwamungu et al (2009b) were actually greater than those from our data set www.soils.org/publications/sssaj Near-infrared refiectance spectroscopy (NIRS) predicted values against measured values of (a) soil P content extracted using the Mehlich 3 method and analyzed by colorimetry (M3P_Col); (b) soil P content extracted using the Mehlich 3 method and analyzed by ICP (M3P_ICP); (c) soil P content extracted with water and analyzed by colorimetry (Cp); (d) total soil P; (e) annual timothy crop P-uptake; and (f) annual P-budget. Based on validation statistics reported here and in Table 2, NIRS predictions were considered moderately useful (MU) when 0.70 < R.^ < 0.80 and 1.75 < RPD < 2.25 and less reliable (LR) when R^^ < 0.70 and RPD < 1.75 (Malley et al, 2004).…”

“…Although some results appear promising, most studies use a limited number of samples (Malley et al, 2004;Nduwamungu et al (2009aNduwamungu et al ( , 2009bNduwamungu et al ( , 2009c. Nduwamungu et al (2009b) report moderately useful NIRS predictions for Mehlich 3 extractable Ca, Cu, and Mg and less reliable predictions for Al, Fe, K, Mn, P, and Zn. They conclude that further studies should incorporate larger sample sizes and more diverse soils.…”

Near Infrared Reflectance Spectroscopy Prediction of Soil Nitrogen Supply in Humid Temperate Regions of Canada

“…These three numbers (e.g., 0-0-1) are derivative treatments and were used to remove baseline variation and accentuate spectral features (Reeves et al, 2002). The first number stands for the order of the derivative, the second for the gap over which the derivative is to be calculated and, the third for the smoothing of the points (Nduwamungu et al, 2009a). The best spectral pre-treatment was selected based on two criteria: simultaneous low standard error (SE) and high coefficient of determination (R^) in both cross-validation and validation sets.…”