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Agriculture

Farming start with a proper understanding of the soil. Most farmers globally know composition of their fields, but also look for methods that can precisely map the composition of their soils for applications as in precision farming of the selection of proper crops for their fields. This approach has been used by famers with larger fields and using precision farming, but also by famers that turn into biological farming. A European project on improving marginal Spanish soil showed this application. A poster shows the results of of this EU project Crops for Better soils. 

Gamma-ray spectrometry has become a valuable tool for precision agriculture, particularly for assessing soil characteristics that directly impact crop yield and soil health. Extensive research has established its effectiveness in mapping various soil properties, ranging from physical properties as clay content, cation exchange capacity (CEC), soil moisture and soil organic carbon (SOC) but also chemical properties as pH, and plant-available phosphorus (P), −magnesium (Mg) and −potassium (K) (see e.g. Schmidinger et. al.,2024), all of which are crucial for optimized agricultural management.

Important parameters used in agriculture

Mapping Soil Texture (Clay Content)

Gamma-ray spectrometry detects naturally occurring radionuclides such as potassium-40, uranium-238, and thorium-232, which are frequently enriched in clay particles. This correlation enables the spectrometry technique to provide accurate estimations of clay content in soil, making it a powerful proxy for assessing soil texture. Studies have shown that clay-rich areas generally have higher gamma emission rates due to radionuclide concentration, which can be used to map texture variability within agricultural fields.

Cation Exchange Capacity (CEC)

CEC is a critical property linked to soil fertility, reflecting the soil's ability to retain essential cations. As gamma-ray spectrometry is sensitive to mineral compositions that influence CEC, it provides a rapid, non-invasive method for assessing this property across large fields. This enables farmers to adjust soil amendments precisely, ensuring optimal soil health and crop productivity.

Soil Moisture Content

Soil moisture levels significantly affect gamma-ray emissions, as wet soils attenuate gamma radiation differently than dry soils. Thus, gamma spectrometry can offer estimates of soil moisture distribution, assisting with irrigation management in precision agriculture. Real-time data on moisture variability allows for precision irrigation practices that conserve water and improve crop health.

Gamma-ray spectrometry, particularly when used in mobile systems, facilitates on-the-go mapping, enabling rapid, high-resolution soil surveys. This approach reduces the need for extensive physical sampling, providing an economical and efficient solution for precision agriculture.

Soil Bulk density

Soil bulk density is a critical parameter in agriculture, as it measures how compact the soil is by considering pore space, organic matter, and mineral particles. This has direct effects on root penetration, aeration, and water infiltration; factors essential for plant growth and soil health. Compacted soils can lead to restriction for root growth and limiting the movement of air and water, which can negatively impact crop yields. Managing soil bulk density is therefore vital for maintaining a healthy soil structure and supporting sustainable farming practices.

Practical applications

The company SoilOptix® employs gamma-ray spectrometry to map soil properties with high precision, enabling farmers to make informed decisions for optimal crop management. Soil Optix developed models that translate the high resolution field measurements to to detailed maps of soil texture, nutrient levels, and organic matter content. These high-resolution measurements are integrated into models that provide actionable insights, facilitating variable-rate applications of inputs such as fertilizers and seeds, thereby enhancing yield potential and promoting sustainable farming practices.

References

Schmidinger, J., Barkov, V., Tavakoli, H., Correa, J., Ostermann, M., Atzmueller, M., Gebbers, R., & Vogel, S. (2024). Which and how many soil sensors are ideal to predict key soil properties: A case study with seven sensors. Geoderma, 450, 117017. https://doi.org/10.1016/j.geoderma.2024.117017
Wehrle, R., & Pätzold, S. (2024). Site-Independent Mapping of Clay Content in Vineyard Soils via Mobile Proximal Gamma-Ray Spectrometry and Machine Learning Calibrations. Sensors, 24(14). https://doi.org/10.3390/s24144528

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