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Proximal Mobile Gamma Spectrometry as Tool for Precision Farming and Field Experimentation (2020)

Pätzold S., Leenen M., Heggemann T.

Soil Systems, 4 (2), 31



Soils naturally emit gamma radiation that can be recorded using gamma spectrometry. Spectral features are correlated with soil mineralogy and texture. Recording spectra proximally and in real-time on heterogeneous agricultural fields is an option for precision agriculture. However, the technology has not yet been broadly introduced. This study aims to evaluate the current state-of-the art by (i) elucidating limitations and (ii) giving application examples. Spectra were recorded with a tractor-mounted spectrometer comprising two 4.2 L sodium iodide (NaI) crystals and were evaluated with the regions of interest for total counts, 40Potassium, and 232Thorium. A published site-independent multivariate calibration model was further extended, applied to the data, and compared with site-specific calibrations that relied on linear correlation. In general, site-specific calibration outperformed the site-independent approach. However, in specific cases, different sites could also replace each other in the site-independent model. Transferring site-specific models to neighbouring sites revealed highly variable success. However, even without data, post-processing gamma surveys detected spatial texture patterns. For most sites, mean absolute error of prediction in the test-set validation was below 5% for single texture fractions. On this basis, thematic maps for agricultural management were derived. They showed quantitative information for lime requirement in the range from 1068 to 3560 kg lime ha−1 a−1 (equivalent to 600–2000 kg calcium oxide (CaO) ha−1 a−1 if converted to the legally prescribed unit) and for field capacity (26−44% v/v). In field experimentation, spatially resolved texture data can serve (i) to optimize the experimental design or (ii) as a complementary variable in statistical evaluation. We concluded that broadening the database and developing universally valid prediction models is needed for introduction into agricultural practice. Though, the current state-of-the-art allows valuable application in precision agriculture and field experimentation, at least on the basis of site-specific or regional basis. Intelligence for Soil