Soil bulk density pedotransfer functions of the humus horizon in arable soils

Soil bulk density (Db) is an important indicator of soil quality, site productivity and soil compaction and is widely used as an input variable for various models. However, Db is often not determined during actual soil surveys, and many attempts have been made to predict it from other more easily and routinely measured variables. The aim of this study was to develop a predictive model for Db estimation for the humus horizon of arable soils. In addition, we hypothesised that the mixed model approach would enable more accurate predictions than the multiple regression model. Estonian National Soil Monitoring data (1983–2008) were used for Db modelling. The dataset contains 17,293 entries for the humus horizon of Estonian arable soils. Mixed model methodology and linear multiple regression analysis were used to develop prediction models. Soil sampling depth, soil organic carbon and water content had the greatest effects on Db estimation. Db is determined through interactions of explanatory variables, and their contribution to Db variation in mixed model differs from variable contribution indicated by the simple regression model. We found that the regression model overestimates the importance of SOC. The mixed model prediction was an improvement compared to multiple regression models (MSE = 0.009 compared to MSE = 0.014). In addition, the predicted values in the mixed model showed good agreement with observed values, while the multiple regression model underestimated Db values above 1.6 g cm−3 and overestimated Db values below 1.2 g cm−3. We conclude that the mixed model approach enables higher prediction accuracy than multiple regression analysis. We propose a mixed model with 20 variables as a prediction model for the humus horizon of Estonian arable soils. The methodology is also applicable for other soil conditions.