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Mid‐infrared spectroscopy (MIRS) has proven to be a cost‐effective, high throughput measurement technique for soil analysis. After multivariate calibration mid‐infrared spectra can be used to predict various soil properties, some of which are related to lime requirement (LR). The objective of this study was to test the performance of MIRS for recommending variable rate liming on typical Central European soils in view of precision agriculture applications. In Germany, LR of arable topsoils is commonly derived from the parameters organic matter content (SOM), clay content, and soil pH (CaCl2) as recommended by the Association of German Agricultural Analytical and Research Institutes (VDLUFA). We analysed a total of 458 samples from six locations across Germany, which all revealed large within‐field soil heterogeneity. Calcareous topsoils were observed at some positions of three locations (79 samples). To exclude such samples from LR determination, peak height at 2513 cm−1 of the MIR spectrum was used for identification. Spectra‐based identification was accurate for carbonate contents > 0.5%. Subsequent LR derivation (LRSPP) from MIRS‐PLSR predictions of SOM, clay, and pH (CaCl2) for non‐calcareous soil samples using the VDLUFA look‐up tables was successful for all locations (R2 = 0.54–0.82; RMSE = 857–1414 kg CaO ha−1). Alternatively, we tested direct LR prediction (LRDP) by MIRS‐PLSR and also achieved satisfactory performance (R2 = 0.52–0.77; RMSE = 811–1420 kg CaO ha−1; RPD = 1.44–2.08). Further improvement was achieved by refining the VDLUFA tables towards a stepless algorithm. It can be concluded that MIRS provides a promising approach for precise LR estimation on heterogeneous arable fields. Large sample numbers can be processed with low effort which is an essential prerequisite for variable rate liming in precision agriculture. [ABSTRACT FROM AUTHOR]

Background: Mid‐infrared spectroscopy (MIRS) is commonly recognized as a rapid and high throughput measurement technology for numerous soil properties, given that appropriate prediction models are calibrated. Soil spectral libraries (SSL) may reduce effort and costs for MIRS practical application. Aims: To calibrate MIR‐SSL‐based prediction models for soil properties and to test their applicability to independent sample sets at regional scale (e.g., for soil survey) and at field scale (i.e., for precision agriculture, PA). Methods: Spectra of 1013 arable topsoil samples of the European Land Use/Land Cover Area Frame Survey 2009 (LUCAS) from Belgium, the Netherlands, Luxembourg, and Germany formed the basis for the MIR‐SSL. Leave‐one‐out cross‐validation (LOOCV) via partial least squares regression served to calibrate (1) generic prediction models including all samples, and (2) stratified models for different parent materials. Test‐set validation (TSV) was conducted on samples from independent campaigns at (1) regional scale with a sample set from Schleswig‐Holstein (Germany; n = 385) and (2) field scale for four individual fields in Germany (n = 513). Results: Generic LOOCV models successfully predicted soil organic carbon, total nitrogen, sand, silt, clay, carbonate, and pH. Calibration for available nutrients failed. The TSV was successful for the regional sample set for all variables (2.5 ≤ RPIQ ≤ 5.9), except for carbonate (RPIQ = 0). At field scale, the validation was highly variable for different sites and parameters. Stratified models using soil parent material as auxiliary variable improved only occasionally the applicability at field scale, that is, on single fields and only for clay and carbonate. Conclusions: Although the MIR‐SSL in its present state cannot be recommended for nutrient management, it provides valuable support for soil survey and PA. [ABSTRACT FROM AUTHOR]

Abstract: A detailed knowledge on the heterogeneity of the soil organic carbon (SOC) content in agricultural soils is required to support applications such as precision agriculture and soil C monitoring. Imaging spectroscopy in the visible (VIS) and near-infrared (NIR) region has proven to be highly sensitive to organic soil components and can efficiently provide data with high spatial resolution. The objectives of our study were (i) to test the suitability of airborne hyperspectral imaging for the characterisation of the spatial heterogeneity of the SOC content at the field-scale, (ii) to investigate the impact of various soil surface conditions (roughness, vegetation) on SOC prediction and (iii) to produce SOC maps for arable fields on a pixel-wise basis. The soil reflectance was recorded by the aircraft-mounted hyperspectral sensor HyMap (450–2500nm) on test sites with the following varying soil surface conditions: bare soil, fine seed-bed; ploughed, bare soil; volunteer crops; straw residues. A partial least squares regression (PLSR) was performed for data analysis. Our results reveal an accurate prediction of the SOC content at a comparatively small concentration range (8.3 to 18.5gSOCkg−1) on long-term uniformly cultivated fields. Site-specific characteristics influenced the calibration models; highest prediction accuracy was performed over a bare, fine soil (RMSEP=0.76gSOCkg−1; RPD=2.08). A generated pixel-wise map (8m×8m) allows the detection of small-scale spatial variability of SOC content and comparatively more realistic than an interpolated map. Thus, airborne hyperspectral imaging constitutes a substantial progress compared to point observations and facilitates well-directed applications in precision agriculture. [Copyright &y& Elsevier]