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Introducing a mechanistic model in digital soil mapping to predict soil organic matter stocks in the Cantabrian region ( Spain )
Author(s) -
Hendriks Chantal Mechtildis Johanna,
Stoorvogel Jetse Jacob,
ÁlvarezMartínez Jose Manuel,
Claessens Lieven,
PérezSilos Ignacio,
Barquín José
Publication year - 2021
Publication title -
european journal of soil science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.244
H-Index - 111
eISSN - 1365-2389
pISSN - 1351-0754
DOI - 10.1111/ejss.13011
Subject(s) - mean squared error , soil map , statistical model , soil science , soil organic matter , variance (accounting) , correlation coefficient , environmental science , statistics , computer science , mathematics , soil water , accounting , business
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.