Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region (Spain)

Abstract

Digital soil mapping (DSM) is an effective mapping technique that supports the increased need for quantitative soil data. In DSM, soil properties are correlated with environmental characteristics using statistical models such as regression. However, many of these relationships are explicitly described in mechanistic simulation models. Therefore, the mechanistic relationships can, in theory, replace the statistical relationships in DSM. This study aims to develop a mechanistic model to predict soil organic matter (SOM) stocks in Natura2000 areas of the Cantabria region (Spain). The mechanistic model is established in four steps: (a) identify major processes that influence SOM stocks, (b) review existing models describing the major processes and the respective environmental data that they require, (c) establish a database with the required input data, and (d) calibrate the model with field observations. The SOM stocks map resulting from the mechanistic model had a mean error (ME) of −2 t SOM ha−1 and a root mean square error (RMSE) of 66 t SOM ha−1. The Lin's concordance correlation coefficient was 0.47 and the amount of variance explained (AVE) was 0.21. The results of the mechanistic model were compared to the results of a statistical model. It turned out that the correlation coefficient between the two SOM stock maps was 0.8. This study illustrated that mechanistic soil models can be used for DSM, which brings new opportunities. Mechanistic models for DSM should be considered for mapping soil characteristics that are difficult to predict by statistical models, and for extrapolation purposes. Highlights Theoretically, mechanistic models can replace the statistical relationships in digital soil mapping. Mechanistic soil models were used to develop a mechanistic model for digital soil mapping that predicted SOM stocks. The applicability of the mechanistic approach needs to be explored for different soil properties and regions.

Accounting for analytical and proximal soil sensing errors in digital soil mapping

“…Among the three soil properties, the clay content model had poor performance compared with pH and SOC, with the lowest MEC value. This could be attributed to one or a combination of the following: (a) the spatial resolution of some of the environmental variables was not detailed enough to capture the variation (Maleki et al, 2020); (b) the set of covariates retained was not suitable and other environmental variables need to be included; or (c) the sampling protocol was too clustered to capture variability across the study area, since observations were taken from three main clusters and various processes operate at different spatial scales (Hendriks et al, 2021).…”

A review on digital mapping of soil carbon in cropland: progress, challenge, and prospect

“…The most basic incorporation is using pedological knowledge in the selection of environmental covariates. For example, Hendriks et al (2021) introduced a mechanistic model to predict SOM stocks, which started with identifying major processes that influenced SOM stocks, then collecting the input data for the model. The other alternative method is incorporating pedological knowledge into the conceptual model, such as structural equation modeling (Angelini et al 2016).…”