Modeling soil cation exchange capacity in multiple countries
Abstract Cation exchange capacity (CEC), as an important indicator for soil quality, represents soil's ability to hold positively charged ions. We attempted to predict CEC using different statistical methods including monotone analysis of variance (MONANOVA), artificial neural networks (ANNs), principal components regressions (PCR), and particle swarm optimization (PSO) in order to compare the utility of these approaches and identify the best predictor. We analyzed 170 soil samples from four different nations (USA, Spain, Iran and Iraq) under three land uses (agriculture, pasture, and forest). Seventy percent of the samples (120 samples) were selected as the calibration set and the remainin…
Mapping Ash CaCO3, pH, and Extractable Elements Using Principal Component Analysis
Abstract Ash cover in fire-affected areas is an important factor in the reduction of soil erosion and increased availability of soil nutrients. Thus it is important to understand the spatial distribution of ash and its capacity for soil protection and to provide nutrients to the underlying soil. In this work, we aimed to map ash CaCO3, pH, and select extractable elements using a principal component analysis (PCA). Four days after a medium to severe wildfire, we established a grid in a 9 ×27 m area on a west facing slope and took ash samples every 3 m for a total of 40 sampling points. The PCA carried out retained five different factors. Factor 1 had high positive loadings for ash with elect…