Adaptation of regional digital soil mapping for precision agriculture

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 9 50 m 2 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.

Internet of underground things in precision agriculture: Architecture and technology aspects

“…Spatial resolution of remote sensing techniques is a major issue; however, when combined with in-situ IOUT sensing approaches, they may result in fine resolution, which can be used to produce field maps for analysis and decision making in precision agriculture. Examples of these include soil moisture, yield [72], texture [140], pesticides applications [95], and nutrient field maps [2,112,120]. Remote sensing has also applications in satellite data fusion, crop structure and condition monitoring.…”

Soil Organic Carbon Baselines for Land Degradation Neutrality: Map Accuracy and Cost Tradeoffs with Respect to Complexity in Otjozondjupa, Namibia

“…By using residual kriging, Söderström et al [62] tested local adaptation of the SOC and pH layers of the AfSoilgrids250m, an earlier version of Soilgrids250m covering the African continent [63], for the country of Rwanda. Similarly, Söderström et al [64] updated a national soil texture map of Sweden [65] by re-parameterization of a MARSplines model that was used for prediction with local samples (farm extent) added to the calibration dataset, according to the principles of spiking [66]. In all cases the large-extent maps were improved by local adaptation.…”

A Review and Recommendations for Promoting Environmentally-Friendly Agriculture in Korea

“…Since 2016, the ASIS users and farmers have been able to receive fertilizer recommendations on their agricultural land through mobile phones. More importantly, the ASIS enables the adaptation of digital soil maps for precision agriculture and smart farms [30].…”