Soil Sensors as a Tool for Improving Decisions in Crop Production

Dear Colleagues,

Sampling and laboratory testing of soils (e.g., for plant-available nutrients, pH, soil organic matter content or soil texture), along with plant analysis, is an important component of the decision-making process for growing agricultural and horticultural crops. However, the spatial resolution does often not account for within-field soil variability and thus does not meet the requirements of site-specific soil management.

In recent years, technologies that measure soil properties directly in the field using proximal sensors have been increasingly offered to practitioners. These so-called "on-site methods" are attracting growing interest, because they considerably increase the spatial resolution of available soil data at minimum effort in comparison to standard expensive and time-consuming soil sampling and laboratory analysis. The use of these technologies significantly contributes to improving crop production, conserving natural resources and reducing the environmental pollution.

Over the past three decades, a variety of measurement techniques and instruments for on-site soil characterization have been developed and tested under field conditions. Comparability with reference methods is used to benchmark these soil sensors; alternatively, the extent to which soil health, crop yield and/or quality can be improved is evaluated using on-site methods.

Most soil sensing approaches still use site-specific reference analysis for sensor calibration, but site-independent (global) models are already being used for some sensors. Furthermore, sensor data fusion and the application of machine learning are increasingly used to improve prediction performance. Finally, sensor data are incorporated into dynamic soil/crop models and decision support systems in order to transfer data into knowledge.

This Special Issue aims to share knowledge on current developments in proximal soil sensing and how it contributes to improved crop production. This may include, but is not restricted to:

• New sensor technologies or multi-sensor platforms for soil mapping;
• New approaches or workflows for sensor data (pre-)processing;
• Improving soil property prediction by using sensor data fusion and machine learning;
• Developing generalized calibration models (field-wise vs. global calibration);
• Real-time control of agricultural machinery using soil sensors;
• Combining soil sensing, dynamic modeling and decision support systems for generating prescription maps;
• Field trials validating economic efficiency, yield effects and environmental benefits of sensor-based site-specific soil management.

In addition to original research articles, both review articles and opinion papers are welcome.

Prof. Dr. Hans-Werner Olfs
Dr. Sebastian Vogel
Guest Editors

Manuscript Submission Information

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• proximal soil sensing
• digital soil mapping
• multi-sensor platforms
• sensor data fusion
• sensor calibration
• decision support systems
• field trials