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The New Digital Soil Map of Sweden -Derived for Free Use in Precision Agriculture
K. Piikki, M. Söderström
SLU, Swedish University of Agricultural Sciences, Sweden

The Digital Soil Map of Sweden (DSMS) was finalized in 2015. The present paper describes the mapping strategy, the estimated uncertainty of the primary map layers and its potential use in precision agriculture. The DSMS is a geodatabase with information on the topsoil of the arable land in Sweden. The spatial resolution is 50 m × 50 m and it covers > 90% of the arable land of the country (~2.5 million ha). Non-agriculture land and areas with organic soil are excluded. Access to a number of national datasets made it possible to build the DSMS. Results from two soil sampling campaigns (~15 000 samples) were combined with remote sensing data (gamma radiation data from airborne radiometric scannings and a digital elevation model derived from airborne laser scanning) plus a Quaternary soil deposit map. Multivariate adaptive regression splines models were parameterized for clay content and sand content and the models were deployed for the 50 m square grid. A 10-fold bootstrap cross validation was performed to estimate the uncertainty of the map layers. The mean absolute errors (MAE) were 5.6 % for clay content and 10 % for sand content. In 75% of the validation samples the clay content MAE was < 8 % and the corresponding value for sand was 14 %. The DSMS data is provided for free by the Geological Survey of Sweden. Examples of applications developed based on the DSMS are a web application used by farmers to generate prescription files for variable-rate seeding and variable-rate liming based on the DSMS clay content information. Another potential use of the map data is to enhance the modelling of nutrient and pesticide leaching from agricultural land. We envisage a wide use of the DSMS by farmers, extension officers, private companies, researchers and expect it to expand with more data layers; the current database can be used as input to pedotransfer functions to derive secondary information.

Keyword: Digital soil map, precision agriculture, public data, remote sensing