Mapping Soil Strength Markers at Field Scale Using Proximal Sensors

The current agricultural system allows farm machinery to operate randomly, thus compacting around 23-33% of the land with critical levels in Europe. To tackle this issue, the European Union (EU) has launched the mission ‘A Soil Deal for Europe’ under the Horizon Europe program, this mission aims to have healthy soil by 2030. Mitigating soil compaction to improve soil structure has been selected as one of the eight objectives of this mission. It is evident from past research that with the increasing size and weight of farm machinery, soil compaction has become a significant threat to top and sub-soil; however, subsoil compaction is even more persistent and cumulative than topsoil. Therefore, within the SOLGRAS project, we emphasize both surface and sub-surface soil compaction. The ability of soil to withstand the soil compaction is governed by its soil strength. This soil strength depends on many soil properties, such as soil water content, bulk density, texture, and organic matter content. We aim to map these soil properties as a first step before predicting soil strength at the field level. Since proximal sensors provide rapid, low-cost, non-destructive measurements, they have significantly enhanced digital soil mapping. In this project, we have used geophysical sensors based on electromagnetic induction and gamma-ray radiometric principles to predict the above-mentioned soil properties at the field level in Denmark. The geophysical survey and collection of soil samples were performed on the same day for each of the three chosen farmer fields. Here, we present our results of predicted soil properties obtained via the proximal sensors and a limited number of laboratory-measured values. Samples of 100 cc undisturbed soil cores and bags were collected from the surface (15-cm depth) and sub-surface (40-cm depth) at 23 sampling sites for each field. Data from these sampling sites are used to train and validate our models for predicting soil properties associated with soil strength. These predicted high-resolution maps produced at the field level will enable us to set up optimum vehicular specifications and routes before entering the field.