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This article in SSSAJ

  1. Vol. 77 No. 3, p. 860-876
     
    Received: Aug 28, 2012
    Published: March 25, 2013


    * Corresponding author(s): kabindra.adhikari@agrsci.dk
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doi:10.2136/sssaj2012.0275

High-Resolution 3-D Mapping of Soil Texture in Denmark

  1. Kabindra Adhikari *a,
  2. Rania Bou Kheira,
  3. Mette B. Grevea,
  4. Peder K. Bøcherb,
  5. Brendan P. Malonec,
  6. Budiman Minasnyc,
  7. Alex B. McBratneyc and
  8. Mogens H. Greved
  1. a Dep. of Agroecology Faculty of Science and Technology Aarhus Univ. Blichers Allé 20 P.O. Box 50, DK-8830 Tjele Denmark
    b Ecoinformatics and Biodiversity Group Dep. of Bioscience Aarhus Univ. Ny Munkegade 114 DK-8000 Aarhus C Denmark
    c Dep. of Environmental Sciences Faculty of Agriculture and Environment The Univ. of Sydney, Biomedical Building C81 1 Central Avenue Eveleigh, NSW 2015 Australia
    d Dep. of Agroecology Faculty of Science and Technology Aarhus Univ. Blichers Allé 20 P.O. Box 50, DK-8830 Tjele Denmark

Abstract

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.

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Copyright © 2013. Copyright © by the Soil Science Society of America, Inc.