1 Department of Agroecology - Soil Physics and Hydropedology, Department of Agroecology, Science and Technology, Aarhus University2 Department of Agroecology - Climate and Water, Department of Agroecology, Science and Technology, Aarhus University3 Department of Bioscience - Ecoinformatics and Biodiversity, Department of Bioscience, Science and Technology, Aarhus University4 University of Sydney5 Department of Agroecology - Soil Physics and Hydropedology, Department of Agroecology, Science and Technology, Aarhus University6 Department of Agroecology - Climate and Water, Department of Agroecology, Science and Technology, Aarhus University7 Department of Bioscience - Ecoinformatics and Biodiversity, Department of Bioscience, Science and Technology, Aarhus University
Soil texture which is spatially variable in nature, is an important soil physical property that governs most physical, chemical, biological, and hydrological processes in soils. Detailed information on soil texture variability both in vertical and lateral dimensions is crucial for proper crop and land management and environmental studies, especially in Denmark where mechanized agriculture covers two thirds of the land area. We modeled the continuous depth function of texture distribution from 1958 Danish soil profiles (up to a 2-m depth) using equal-area quadratic splines and predicted clay, silt, fine sand, and coarse sand content at six standard soil depths of GlobalSoilMap project (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) via regression rules using the Cubist data mining tool. Seventeen environmental variables were used as predictors and their strength of prediction was also calculated. For example, in the prediction of silt content at 0 to 5 cm depth, factors that registered a higher level of importance included the soil map scored (90%), landscape types (54%), and landuse (27%), while factors with lower scores were direct insolation (17%) and slope aspect (14%). Model validation (20% of the data selected randomly) showed a higher prediction performance in the upper depth intervals but increasing prediction error in the lower depth intervals (e.g., R2 = 0.54, RMSE = 33.7 g kg−1 for silt 0–5 cm and R2 = 0.29, RMSE = 38.8 g kg−1 from 100–200 cm). Danish soils have a high sand content (mean values for clay, silt, fine sand, and coarse sand content for 0- to 5-cm depth were 79, 84, 324, and 316 g kg−1, respectively). Northern parts of the country have a higher content of fine sand compared to the rest of the study area, whereas in the western part of the country there was little clay but a high coarse sand content at all soil depths. The eastern and central parts of the country are rich in clay, but due to leaching, surface soils are clay eluviated with subsequent accumulation at lower depths. We found equal-area quadratic splines and regression rules to be promising tools for soil profile harmonization and spatial prediction of texture properties at national extentacross Denmark.
Soil Science Society of America. Journal, 2013, Vol 77, Issue 3, p. 860-876