Phosphorus (P) fertilisation recommendations rely primarily on soil content of plant available P (P avl) that vary spatially within farm fields. Spatially optimized P fertilisation for precision farming requires reliable, rapid and non-invasive P avl determination. This laboratory study aimed to test and to compare visible-near infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy for P avl prediction with emphasis on future application in precision agriculture. After calibration with the conventional calcium acetate lactate (CAL) extraction method, limitations of Vis-NIRS and MIRS to predict P avl were evaluated in loess topsoil samples from different fields at six localities. Overall calibration with 477 (Vis-NIRS) and 586 (MIRS) samples yielded satisfactory model performance (R 2 0.70 and 0.72; RPD 1.8 and 1.9, respectively). Local Vis-NIRS models yielded better results with R 2 up to 0.93 and RPD up to 3.8. For MIRS, results were comparable. However, an overall model to predict P avl on independent test data partly failed. Sampling date, pre-crop harvest residues and fertilising regime affected model transferability. Varying transferability could partly be explained after deriving the cellulose absorption index from the Vis-NIR spectra. In 62 (Vis-NIRS) and 67% (MIRS) of all samples, prediction matched the correct P avl content class. Rapid discrimination between high, optimal and low P classes could be carried out on many samples from single fields thus marking an improvement over the common practice. However, P avl determination by means of IR spectroscopy is not yet satisfactory for determination of precision fertilizer dosage. For introduction into agricultural practice, a standardized sampling protocol is recommended to help achieve reliable spectroscopic P avl prediction.
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.
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