Abstract
In the initial phase of a national project to map clay, sand and soil organic matter (SOM) content in arable topsoil in Sweden, a study area in south-west Sweden comprising about 100 000 ha of arable land was assessed. Models were created for texture, SOM and two estimated variables for lime requirement determination (target pH and buffering capacity), using a data mining method (multivariate adaptive regression splines). Two existing reference soil datasets were used: a grid dataset and a dataset created for individual farms. The predictor data were of three types: airborne gamma-ray spectrometry data, digital elevation from airborne laser scanning, and legacy data on Quaternary geology. Validations were designed to suit applicability assessments of prediction maps for precision agriculture. The predictor data proved applicable for regional mapping of topsoil texture at 50 × 50 m2 spatial resolution (root mean square error: clay = 6.5 %; sand = 13.2 %). A novel modelling strategy, ‘Farm Interactive’, in which soil analysis data for individual farms were added to the regional data, and given extra weight, improved the map locally. SOM models were less satisfactory. Variable-rate application files for liming created from derived digital soil maps and locally interpolated soil data were compared with ‘ground truth’ maps created by proximal sensors on one test farm. The Farm Interactive methodology generated the best predictions and was deemed suitable for adaptation of regional digital soil maps for precision agricultural purposes.
Highlights
There are no detailed general maps adapted for use at farm level on topsoil texture in arable land in Sweden
By adding samples from a farm and giving these soil analyses extra weight, the variation in the regional model was better adapted to the local conditions on the farm, resulting in improved validation results compared with other Digital soil mapping (DSM) methods and with the current method of interpolation of data obtained on the farm itself
For the particular application of direct use in precision agriculture, improved accuracy was achieved on many farms through a novel approach where as few as nine local soil samples from a farm were added to the regional calibration model
Summary
There are no detailed general maps adapted for use at farm level on topsoil texture in arable land in Sweden (which comprises about 2.6 million hectares in total). In a joint project run by the Swedish University of Agricultural Sciences (SLU) and SGU, of which this study forms part, the intention was to combine the data from these soil sampling programmes with information in other publicly available datasets in order to make predictions of soil textural properties at a spatial resolution that enables discrimination of within-field variability and thereby use in precision agriculture. Jenny (1941) identified five major soil-forming factors and formulated a mechanistic model for soil development These so-called ‘clorpt’ factors were: climate (cl), organisms (o), relief (r) parent material (p) and time (t). McBratney et al (2003) proposed a generic framework for digital soil mapping based on Jenny’s approach, and taking spatial dependence into account. Piikki et al (2013) found that predictions of topsoil clay content were improved when data from an electromagnetic induction (EMI)
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