USE OF NEAR INFRARED REFLECTANCE SPECTRA OF SOILS FOR OBJECTIVE SELECTION OF SAMPLES

Abstract

A low cost strategy for objective and rapid selection of soil samples from a large population was evaluated. The purpose of the strategy was to retain a maximum of the original variation in important soil properties with only a small selection of samples. The evaluation was made with emphasis on clay content, soil organic matter, cation exchange capacity, and base saturation, all of which are important factors for biochemical activities in the soil and, therefore, for soil fertility. The strategy involved use of near infrared (NIR) spectroscopy combined with principal component analysis (PCA). A 2-nm interval spectrum between 1300 and 2398 nm was recorded on 146 air-dried soil samples from the most important cultivated areas in Sweden. The samples were considered mainly Cambisols and Regosols. The first derivative of each NIR spectrum was used for PCA. Twenty soils were selected by visual examination of two-dimensional score-plots from PCA. Score-plots were made from NIR data alone, from NIR data combined with pH, and from the eight significant score vectors from PCA on NIR data, combined with pH. Two criteria for selection from these plots were applied: (i) one sample from each apparent group was selected and (ii) samples evenly distributed at the periphery of the total sample population, and one in the center, were selected. In all, six selections were made. The distributions in soil properties in the selections were compared with random selection and with the original population. It was clear that NIR could help to improve the diversity in sample selections compared with random selection. In general, peripheral selections generated a higher recovery of range and a more even distribution in soil parameters than cluster selections. For clay content and cation exchange capacity, PCA on NIR data alone gave the best results, but to improve the distribution in pH and the pH-dependent base saturation, pH had to be included in PCA. To select soil samples that are distributed in all five soil parameters to the best extent possible, we propose peripheral selection from a two-dimensional PCA plot calculated from score vectors and pH data. In the present study, this method would have reduced costs about 70% compared with wet chemistry analyzes.