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Conventional laboratory analysis of soil properties is often expensive and requires much time if various soil properties are to be measured. Visual and near-infrared (vis–NIR) spectroscopy offers a complementary and cost-efficient way to gain a wide variety of soil information at high spatial and temporal resolutions. Yet, applying vis–NIR spectroscopy requires confidence in the prediction accuracy of the infrared models. In this study, we used soil data from six agricultural fields in eastern Switzerland and calibrated (i) field-specific (local) models and (ii) general models (combining all fields) for soil organic carbon (SOC), permanganate oxidizable carbon (POXC), total nitrogen (N), total carbon (C) and pH using partial least-squares regression. The 30 local models showed a ratio of performance to deviation (RPD) between 1.14 and 5.27, and the root mean square errors (RMSE) were between 1.07 and 2.43 g kg -1 for SOC, between 0.03 and 0.07 g kg -1 for POXC, between 0.09 and 0.14 g kg -1 for total N, between 1.29 and 2.63 g kg -1 for total C, and between 0.04 and 0.19 for pH. Two fields with high carbonate content and poor correlation between the target properties were responsible for six local models with a low performance (RPD < 2). Analysis of variable importance in projection, as well as of correlations between spectral variables and target soil properties, confirmed that high carbonate content masked absorption features for SOC. Field sites with low carbonate content can be combined with general models with only a limited loss in prediction accuracy compared to the field-specific models. On the other hand, for fields with high carbonate contents, the prediction accuracy substantially decreased in general models. Whether the combination of soils with high carbonate contents in one prediction model leads to satisfying prediction accuracies needs further investigation. [ABSTRACT FROM AUTHOR]

In order to study the soil aggregate distributions and soil organic matter (SOM), we sampled top- and subsoils in four intensively farmed croplands (two organic (Org-OB and Org-LA), and two conventional (Con-OB and Con-LA)) on Haplic Chernozems located in Marchfeld in the east of Vienna (Austria). Soil structure and SOM quantity, quality and distribution between free and occluded particulate organic matter and aggregate size fractions (

Mycobacterium avium ssp. paratuberculosis (MAP) is the causative agent of a chronic enteric disease of ruminants. Available diagnostic tests are complex and slow. In vitro, volatile organic compound (VOC) patterns emitted from MAP cultures mirrored bacterial growth and enabled distinction of different strains. This study was intended to determine VOCs in vivo in the controlled setting of an animal model. VOCs were pre-concentrated from breath and feces of 42 goats (16 controls and 26 MAP-inoculated animals) by means of needle trap microextraction (breath) and solid phase microextraction (feces) and analyzed by gas chromatography/ mass spectrometry. Analyses were performed 18, 29, 33, 41 and 48 weeks after inoculation. MAP-specific antibodies and MAP-specific interferon-γ-response were determined from blood. Identities of all marker-VOCs were confirmed through analysis of pure reference substances. Based on detection limits in the high pptV and linear ranges of two orders of magnitude more than 100 VOCs could be detected in breath and in headspace over feces. Twenty eight substances differed between inoculated and non-inoculated animals. Although patterns of most prominent substances such as furans, oxygenated substances and hydrocarbons changed in the course of infection, differences between inoculated and non-inoculated animals remained detectable at any time for 16 substances in feces and 3 VOCs in breath. Differences of VOC concentrations over feces reflected presence of MAP bacteria. Differences in VOC profiles from breath were linked to the host response in terms of interferon-γ-response. In a perspective in vivo analysis of VOCs may help to overcome limitations of established tests. [ABSTRACT FROM AUTHOR]

Quantity and quality of soil organic matter (SOM) affect physical, chemical, and biological soil properties, and are pivotal to productive and healthy grasslands. Thus, we analyzed the distribution of soil aggregates and assessed quality, quantity, and distribution of SOM in two unimproved and improved (two organic and two conventional) grasslands in subarctic Iceland, in Haplic and Histic Andosols. We also evaluated principal physicochemical and biological soil properties, which influence soil aggregation and SOM dynamics. Macroaggregates (>250 µm) in topsoils were most prominent in unimproved (62–77%) and organically (58–69%) managed sites, whereas 20–250 µm aggregates were the most prominent in conventionally managed sites (51–53%). Macroaggregate stability in topsoils, measured as mean weight diameter, was approximately twice as high in organically managed (12–20 mm) compared with the conventionally managed (5–8 mm) sites, possibly due to higher organic inputs (e.g., manure, compost, and cattle urine). In unimproved grasslands and one organic site, macroaggregates contributed between 40% and 70% of soil organic carbon (SOC) and nitrogen to bulk soil, whereas in high SOM concentration sites free particulate organic matter contributed up to 70% of the SOC and nitrogen to bulk soil. Aggregate hierarchy in Haplic Andosols was confirmed by different stabilizing mechanisms of micro- and macroaggregates, however, somewhat diminished by oxides (pyrophosphate-, oxalate-, and dithionite-extractable Fe, Al, and Mn) acting as binding agents for macroaggregates. In Histic Andosols, no aggregate hierarchy was observed. The higher macroaggregate stability in organic farming practice compared with conventional farming is of interest due to the importance of macroaggregates in protecting SOM and soils from erosion, which is a prerequisite for soil functions in grasslands that are envisaged for food production in the future. [ABSTRACT FROM PUBLISHER]

Laboratory imaging spectroscopy can be used to explore physical and chemical variations in soil profiles on a submillimetre scale. We used a hyperspectral scanner in the 400 to 1000nm spectral range mounted in a laboratory frame to record images of two soil cores. Samples from these cores were chemically analyzed, and spectra of the sampled regions were used to train chemometric PLS regression models. With these models detailed maps of the elemental concentrations in the soil cores could be produced. Eight different spectral pretreatments were applied to the sample spectra and to the resulting images in order to explore the influence of these pre-treatments on the estimation of elemental concentrations. We found that spectral preprocessing has a minor influence on chemometry results when powerful regression algorithms like PLSR are used. [ABSTRACT FROM AUTHOR]

Land-use change affects not only the amount of soil organic matter (SOM) but also its composition. We performed cross-polarization magic angle spinning (CPMAS) 13C and 15N nuclear magnetic resonance (NMR) spectroscopy to investigate the chemical composition of bulk SOM in topsoils (0–15 cm) under different land use, namely native forest (NF), 27-year cropland with wheat/soybean rotation (CL) and 27-year rangelands with guineagrass (RG) and with bahiagrass (RB), in south-east Bolivia. The findings of this study showed only a subtle alteration of composition of bulk SOM despite the large changes in carbon (C) content. Nevertheless, NF and RB showed a slightly lower abundance of aromatic C but a higher proportion of alkyl C compared to CL and RG where the loss of organic matter was substantial. This suggests that relatively stable components dominated by aromatic structures had relatively enriched during SOM decomposition under agricultural practices. A slight disparity of SOM composition observed between RG and RB (less O-alkyl C but more aromatic C in RG than RB) suggests that grass species influenced SOM quality even under the same land use, namely the rangeland. On the other hand, organic N composition was less affected by land use or management practice than C forms. [ABSTRACT FROM AUTHOR]

Analysis of the spatial variability of soil properties is important to interpret the site-specific ecosystems not only with respect to process investigations but also to model upscaling. This paper aims to study the effects of the grazing intensity on soil physical and mechanical properties and their interactions in a Leymus chinensis steppe of the Xilin River Basin, Inner Mongolia, China. The investigated sites were subjected to five grazing intensities (ungrazed since 1979, ungrazed since 1999, winter grazing, continuous grazing and heavy grazing). Soil water content (SWC), hydraulic conductivity (K), water drop penetration time (WDPT), shear strength (SS), soil organic carbon (SOC) concentration, bulk density (BD), and soil texture were measured at a grid with 15m sampling distance on the surface soil during the period of 2004–2005. The data were analyzed using descriptive statistics and geostatistics. The correlation and interaction between soil properties were analyzed by the methods of Pearson correlation, partial correlation and multiple regression analysis. The results showed that spatial distributions of soil properties could be well described by spherical or exponential models. The ranges of spatial dependence were the highest for WDPT and the lowest for SS. Grazing decreased SWC, SOC and WDPT but increased BD and SS. Multiple regression analysis showed significant correlations among SWC, K, WDPT, SOC and BD; as well as between SS and silt content. Soil compaction induced by sheep trampling, especially in the heavily grazed site, inclined to a homogenous spatial distribution of soil properties, which will possibly enhance soil vulnerability to water and nutrient loss, and consequently reduce the plant available water and thus grassland productivity. [Copyright &y& Elsevier]