Predicting the extent of andic soils across western Haleakalā, Maui

Abstract

Maui is one of five Hawaiian Islands affected by orographic climate effect, exhibiting a massive precipitation gradient across western Haleakalā. However, high variability of volcanic ash deposits as a parent material across the study area complicates the ability to isolate the influence of climate on soil formation. Little is documented about the spatial extent of ash deposition, frequency and intensity of volcanic ejecta events, and composition of ash. Therefore, andic soils, which contain short range order (SRO) aluminosilicates and iron oxides that result in unique soil chemical and physical properties, are challenging to map. Using environmental and andic property data from 16 pedons sampled in the study area—bulk density, phosphate retention, and aluminum plus ½ iron extracted by ammonium oxalate—we applied multiple linear regression to create spatial prediction models of these three soil properties. Predicted soil properties were then used to classify andic soils. The mean prediction for an independent set of pedons showed a soil classification accuracy of 50% in the study area for Andisols (data to 60 cm), andic intergrades (data to 75 cm), and non-andic soils. Soil property predictions using depth-weighted average data to 1 m increased soil classification user accuracy of Andisols to 87.5%, andic-intergrades to 100%, and non-andic soils to 83.3%. Whether a soil exhibits andic soil properties within 60 or 75 cm is irrelevant when considering prior or current presence of ash in a soil. Accounting for all available pedon data with depth proves most important when attempting to predict andic properties and classes.