2016, Schirrmann, Michael, Joschko, Monika, Gebbers, Robin, Kramer, Eckart, Zörner, Mirjam, Barkusky, Dietmar, Timmer, Jens

Background: Earthworms are important for maintaining soil ecosystem functioning and serve as indicators of soil fertility. However, detection of earthworms is time-consuming, which hinders the assessment of earthworm abundances with high sampling density over entire fields. Recent developments of mobile terrestrial sensor platforms for proximal soil sensing (PSS) provided new tools for collecting dense spatial information of soils using various sensing principles. Yet, the potential of PSS for assessing earthworm habitats is largely unexplored. This study investigates whether PSS data contribute to the spatial prediction of earthworm abundances in species distribution models of agricultural soils. Methodology/Principal Findings: Proximal soil sensing data, e.g., soil electrical conductivity (EC), pH, and near infrared absorbance (NIR), were collected in real-time in a field with two management strategies (reduced tillage / conventional tillage) and sandy to loam soils. PSS was related to observations from a long-term (11 years) earthworm observation study conducted at 42 plots. Earthworms were sampled from 0.5 x 0.5 x 0.2 m³ soil blocks and identified to species level. Sensor data were highly correlated with earthworm abundances observed in reduced tillage but less correlated with earthworm abundances observed in conventional tillage. This may indicate that management influences the sensor-earthworm relationship. Generalized additive models and state-space models showed that modelling based on data fusion from EC, pH, and NIR sensors produced better results than modelling without sensor data or data from just a single sensor. Regarding the individual earthworm species, particular sensor combinations were more appropriate than others due to the different habitat requirements of the earthworms. Earthworm species with soil-specific habitat preferences were spatially predicted with higher accuracy by PSS than more ubiquitous species. Conclusions/Significance: Our findings suggest that PSS contributes to the spatial modelling of earthworm abundances at field scale and that it will support species distribution modelling in the attempt to understand the soil-earthworm relationships in agroecosystems.

2020, Erler, Alexander, Riebe, Daniel, Beitz, Toralf, Löhmannsröben, Hans-Gerd, Gebbers, Robin

Precision agriculture (PA) strongly relies on spatially differentiated sensor information. Handheld instruments based on laser-induced breakdown spectroscopy (LIBS) are a promising sensor technique for the in-field determination of various soil parameters. In this work, the potential of handheld LIBS for the determination of the total mass fractions of the major nutrients Ca, K, Mg, N, P and the trace nutrients Mn, Fe was evaluated. Additionally, other soil parameters, such as humus content, soil pH value and plant available P content, were determined. Since the quantification of nutrients by LIBS depends strongly on the soil matrix, various multivariate regression methods were used for calibration and prediction. These include partial least squares regression (PLSR), least absolute shrinkage and selection operator regression (Lasso), and Gaussian process regression (GPR). The best prediction results were obtained for Ca, K, Mg and Fe. The coefficients of determination obtained for other nutrients were smaller. This is due to much lower concentrations in the case of Mn, while the low number of lines and very weak intensities are the reason for the deviation of N and P. Soil parameters that are not directly related to one element, such as pH, could also be predicted. Lasso and GPR yielded slightly better results than PLSR. Additionally, several methods of data pretreatment were investigated.

2021, Skiba, Ute, Medinets, Sergiy, Cardenas, Laura M., Carnell, Edward John, Hutchings, Nick, Amon, Barbara

Atmospheric NOx concentrations are declining steadily due to successful abatement strategies predominantly targeting combustion sources. On the European continent, total NOx emissions fell by 55% between 1990 and 2017, but only modest reductions were achieved from the agricultural sector; with 7.8% from 20 Eastern European countries and 19.1% from 22 Western European countries. Consequently, the share of agricultural NOx emissions for these 42 European countries have increased from 3.6% to 7.2%. These values are highly uncertain due to serious lack of studies from agricultural soils and manure management. The emission factor (EFNO 1.33%), currently used for calculating soil NOx emissions from European agricultural categories ‘N applied to soils’ and ‘manure management’ was evaluated here by including recently published data from temperate climate zones. The newly calculated EFNO (average 0.60%, 0.0625th%/0.5475th%, n = 65 studies) is not notably different from the current value, given the large uncertainties associated with the small pool of studies, and therefore continued use of EFNO (1.33%) is recommended until more data become available. An assessment of the contribution of agricultural and non-agricultural NOx sources found that of the 42 European countries, the 8 most populated countries achieved considerable reductions (1990–2017) from categories ‘non-agricultural sources’ (55%), ‘N applied to soils’ (43%) and ‘manure management’ (1.2%), compared to small reductions from the remaining 34 countries. Forests are also large sources of soil NOx. On average, emissions from Eastern European forests were 4 times larger than from ‘N applied agricultural soil’, whereas Western European NOx emissions from ‘N applied agricultural soil’ were two times larger than from forest soils. Given that non-agricultural sources of NOx continue to decline, soil related emissions from agriculture, forests and manure management become more important, and require rigorous investigation in order to improve atmospheric pollution forecasts.

2020, Siemon, Bernhard, Ibs-von Seht, Malte, Steuer, Annika, Deus, Nico, Wiederhold, Helga

The knowledge of the subsurface down to about one hundred meters is fundamental for a variety of economic, ecological, and geoscientific tasks, particularly in coastal zones. Marine and terrestrial processes influence coastal zones and both seawater intrusion and submarine freshwater discharges may occur. The Federal Institute for Geosciences and Natural Resources (BGR) conducted airborne geophysical surveys in the coastal region of the German Bight between 2000 and 2014. The helicopter-borne system used simultaneously collected electromagnetic (HEM), magnetic (HMG), and radiometric (HRD) data. An area of about 5900 km2 was covered with parallel flight lines at 250 m line separation and additional tie-lines at larger separations. In total, about 25,000 km of data at sampling distances of 4 m (HEM, HMG) and 40 m (HRD) were acquired. The electrical resistivity (HEM), the anomalies of the magnetic field (HMG), and the exposure rate (HRD) are the resulting geophysical parameters derived from the data. The results are displayed as maps of the geophysical parameters as well as vertical resistivity sections (only HEM). Both data and products are publicly available via BGR’s product center. The airborne geophysical results helped to outline the fresh–saline groundwater interface, freshwater lenses on islands, submarine groundwater discharges, buried tunnel valleys, mires, and ancient landscapes.

2015, Jochum, Tobias, Michalzik, Beate, Bachmann, Anne, Popp, Jürgen, Frosch, Torsten

Soil and groundwater contamination with benzene can cause serious environmental damage. However, many soil microorganisms are capable to adapt and are known to strongly control the fate of organic contamination. Innovative cavity enhanced Raman multi-gas spectroscopy (CERS) was applied to investigate the short-term response of the soil micro-flora to sudden surface contamination with benzene regarding the temporal variations of gas products and their exchange rates with the adjacent atmosphere. 13C-labeled benzene was spiked on a silty-loamy soil column in order to track and separate the changes in heterotrophic soil respiration – involving 12CO2 and O2 – from the natural attenuation process of benzene degradation to ultimately form 13CO2. The respiratory quotient (RQ) decreased from a value 0.98 to 0.46 directly after the spiking and increased again within 33 hours to a value of 0.72. This coincided with the maximum 13CO2 concentration rate (0.63 μmol m−2 s−1), indicating the highest benzene degradation at 33 hours after the spiking event. The diffusion of benzene in the headspace and the biodegradation into 13CO2 were simultaneously monitored and 12 days after the benzene spiking no measurable degradation was detected anymore. The RQ finally returned to a value of 0.96 demonstrating the reestablished aerobic respiration.