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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]

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]

Sonication is widely used for disruption of suspended soil aggregates. Calorimetric calibration allows for determining sonication power and applied energy as a measure for aggregate disrupting forces. Yet other properties of sonication devices (e.g., oscillation frequency and amplitude, sonotrode diameter) as well as procedure details (soil‐to‐water ratio, size, shape, and volume of used containers) may influence the extent of aggregate disruption in addition to the applied energy. In this study, we tested potential bias in aggregate disruption when different devices or procedures are used in laboratory routines. In nine laboratories, three reference soil samples were sonicated at 30 J mL−1 and 400 J mL−1. Aggregate disruption was estimated based on particle size distribution before and after sonication. Size distribution was obtained by standardized submerged sieving for particle size classes 2000–200 and 200–63 µm, and by dynamic imaging for particles < 63 µm. Despite differences in sonication devices and protocols used by the participants, only 16 in 216 tests of samples of the size fractions 2000–200 and 200–63 µm were identified as outliers. For the size fraction < 63 µm, fewer outliers were detected (8 in 324 tests). Four out of nine laboratories produced more than two outliers. In these laboratories, sonication devices differed from the others regarding oscillation frequencies (24 or 30 kHz compared to 20 kHz), sonotrode diameters (10 and 14 mm compared to 13 mm), and sonication power (16 W compared to > 45 W). Thus, these sonication device properties need to be listed when reporting on sonication‐based soil aggregate disruption. The overall small differences in the degree of disruption of soil aggregates between different laboratories demonstrate that sonication with the energies tested (30 and 400 J mL−1) provides replicable results despite the variations regarding procedures and equipment. [ABSTRACT FROM AUTHOR]

Past land-use changes, intensive cropping with large proportions of root crops, and preferred use of mineral fertilizer have been made responsible for proceeding losses of soil organic C (SOC) in the plough layer. We hypothesized that in intensive agriculturally managed regions changes in SOC stocks would be detectable within a decade. To test this hypothesis, we tracked the temporal development of the concentrations and stocks of SOC in 268 arable sites, sampled by horizon down to 60 cm in the Cologne-Bonn region, W Germany, in 2005 and in 2013. We then related these changes to soil management data and humus balances obtained from farmers' surveys. As we expected that changes in SOC concentrations might at least in part be minor, we fractionated soils from 38 representative sites according to particle size in order to obtain C pools of different stability. We found that SOC concentrations had increased significantly in the topsoil (from 9.4 g kg−1 in 2005 to 9.8 g kg−1 in 2013), but had decreased significantly in the subsoil (from 4.1 g kg−1 in 2005 to 3.5 g kg−­1 in 2013). Intriguingly, these changes were due to changes in mineral-bound SOC rather than to changes in sand-sized organic matter pools. As bulk density decreased, the overall SOC stocks in the upper 60 cm exhibited a SOC loss of nearly 0.6 t C (ha · y)−1 after correction by the equivalent soil mass method. This loss was most pronounced for sandy soils [−0.73 t SOC (ha · y)−1], and less pronounced for loamy soils [−0.64 t SOC (ha · y)−1]; silty soils revealed the smallest reduction in SOC [−0.3 t SOC (ha · y)−1]. Losses of SOC occurred even with the overall humus balances having increased positively from about 20 kg C (ha · y)−1 (2003-2005) to about 133 kg C (ha · y)−1 (2005-2013) due to an improved organic fertilization and intercropping. We conclude that current management may fail to raise overall SOC stocks. In our study area SOC stocks even continued to decline, despite humus conservation practice, likely because past land use conversions (before 2005) still affect SOC dynamics. [ABSTRACT FROM AUTHOR]

The challenges of Vis-NIR spectroscopy are permanent soil surface variations of moisture and roughness. Both disturbance factors reduce the prediction accuracy of soil organic carbon (SOC) significantly. For improved SOC prediction, both disturbance effects have to be determined from Vis-NIR spectra, which is especially challenging for roughness. Thus, an approach for roughness quantification under varying moisture and its impact on SOC assessment using Support Vector Machines is presented here. [ABSTRACT FROM AUTHOR]

A letter to the editor is presented in response to the comment by C. Brock on an article about the monitoring of organic carbon stocks from agricultural soils in Western Germany.