Contribution of measurement error in calibration and validation data to the prediction accuracy of pedotransfer functions

Abstract

<p>Modelling of soil processes is dependent on the availability of high-quality soil data. Soil properties that are considered difficult to measure are frequently determined through pedotransfer functions (PTFs). Here, readily available data are translated into data that are needed. Aside from these input soil properties, a reference wet chemistry dataset of the soil property of interest is required. However, these calibration and validation data are imperfect due to measurement error caused by various sources. Until now, the uncertainty of calibration and validation data has been ignored when deriving PTFs, and uncertainty quantification remains limited to the propagation of model input, parameter and structural uncertainty. In this contribution, we aimed to take uncertainty analysis one step further by studying how measurement error in wet chemistry calibration and validation soil data affects PTF predictions and associated prediction uncertainty. We focused on PTFs to predict the soil’s cation-exchange capacity (CEC), which is an important indicator of soil fertility and nutrient retention capacity. To predict CEC through PTFs, soil properties such as clay percentage, organic carbon content and pH are commonly included. We aimed to study the effect of measurement error in CEC data on the accuracy of multiple linear regression and random forest prediction models. PTFs were developed for the entire USA, subdivided per soil taxonomic order. Here, wet chemistry data from the National Cooperative Soil Survey’s (NCSS) Soil Characterization Database were used. The PTFs were fitted with and without including measurement error. However, the majority of the samples from the NCSS Soil Characterization Database were not measured in duplicate, which was needed to quantify measurement error. Alternatively, we used data from the Wageningen Evaluating Programmes for Analytical Laboratories (WEPAL) to provide a best estimate for the measurement error. Comparison of PTFs with and without measurement error showed significant differences in model accuracy metrics.</p>