Soil Science Society of America Journal Soil Sci. Soc. Am. J. 77:860–876 doi:10.2136/sssaj2012.0275 Received 28 Aug. 2012. *Corresponding author (kabindra.adhikari@agrsci.dk). © Soil Science Society of America, 5585 Guilford Rd., Madison WI 53711 USA All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher. High-Resolution 3-D Mapping of Soil Texture in Denmark Pedology S oil, essentially a nonrenewable natural resource, is a dynamic and complex bio- material (Young and Crawford, 2004), supports all terrestrial life on earth, and acts as a foundation for several environmental processes. Soil texture, which is spatially variable in nature (Burrough, 1993), is one of the most important physical properties, as it governs several physical, chemical, biological, and hydrological prop- erties and processes in soils. Spatial variability in texture contributes to variations Kabindra Adhikari* Rania Bou Kheir Mette B. Greve Dep. of Agroecology Faculty of Science and Technology Aarhus Univ. Blichers Allé 20 P.O. Box 50, DK-8830 Tjele Denmark Peder K. Bøcher Ecoinformatics and Biodiversity Group Dep. of Bioscience Aarhus Univ. Ny Munkegade 114 DK-8000 Aarhus C Denmark Brendan P. Malone Budiman Minasny Alex B. McBratney Dep. of Environmental Sciences Faculty of Agriculture and Environment The Univ. of Sydney, Biomedical Building C81 1 Central Avenue Eveleigh, NSW 2015 Australia Mogens H. Greve Dep. of Agroecology Faculty of Science and Technology Aarhus Univ. Blichers Allé 20 P.O. Box 50, DK-8830 Tjele Denmark Soil texture which is spatially variable in nature, is an important soil physi- cal property that governs most physical, chemical, biological, and hydrologi- cal processes in soils. Detailed information on soil texture variability both in vertical and lateral dimensions is crucial for proper crop and land manage- ment 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 profles (up to a 2-m depth) using equal-area quadratic splines and predicted clay, silt, fne 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., R 2 = 0.54, RMSE = 33.7 g kg −1 for silt 0–5 cm and R 2 = 0.29, RMSE = 38.8 g kg −1 from 100–200 cm). Danish soils have a high sand content (mean values for clay, silt, fne 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 fne 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 elu- viated with subsequent accumulation at lower depths. We found equal-area quadratic splines and regression rules to be promising tools for soil profle harmonization and spatial prediction of texture properties at national exten- tacross Denmark. Abbreviations: AIC, akaike information criteria; C, categorical data; DEM, digital elevation model; DSM, digital soil mapping; GIS, geographic information system; lidar, light detection and ranging; LSP, land surface parameters; ME, mean error; MFD, multiple-fow direction; MRVBF, multi-resolution index of valley bottom fatness; Q, quantitative data; RMSE, root mean square error; RNE, relative nugget effect; SAGA, system for automated geoscientifc analyses; SD, standard deviation; TIN, triangular irregular network; TWI, topographic wetness index. Published March 25, 2013