Comparing direct and indirect approaches to predicting soil texture class

Abstract

Soil texture, or the relative proportions of sand, silt, and clay, is a key soil attribute that influences many important physical, chemical, and biological properties of soils. Digital soil mapping is increasingly used to predict soil texture; however, few comparisons have been made between direct prediction of a texture class, and the indirect prediction of texture class by first predicting sand, silt, and clay content, and subsequently converting the predictions to a texture class. We predicted soil texture class for the 5–15 and 30–60 cm depth intervals of the Ottawa soil survey project using direct and indirect approaches which yielded a similar overall accuracy (28–36%) and kappa (0.19–0.27). The predicted soil maps had a similar spatial distribution of soil texture classes. We then used the Euclidean distance between the texture classes to adjust the model performance metrics, revealing the indirect approach provided the better soil texture class prediction. When comparing the predictions, the 5–15 and 30–60 cm maps were in perfect agreement for 53% and 42% of the study area, respectively, and in both cases texture class predictions were within one texture class for over 87% of the map area. For many studies, including legacy soil surveys, texture class information is available, and particle size distribution data are generally lacking. This study confirms that direct prediction of soil texture class performs almost equally with indirect prediction.