Antimicrobial resistance (AR) represents a global threat in human and veterinary medicine. In that regard, AR proliferation and dissemination in agricultural soils after manure application raises concerns on the enrichment of endogenous soil bacterial population with allochthonous antibiotic resistance genes (ARGs). Natural resilience of agricultural soils and background concentrations of ARGs play key roles in the mitigation of AR propagation in natural environments. In the present study, we carried out a longitudinal sampling campaign for two crop vegetation periods to monitor spatial and temporal changes in the abundance of seven clinically relevant ARGs (sul1, ermB, vanA, aph(3')-IIa, aph(3')-IIIa, blaTEM-1 and tet(W)) and ribosomal 16S RNA. The absolute and relative abundances of the selected ARGs were quantified in total community DNA extracted from agricultural (manured and non-manured) and forest soils, fresh pig faeces and manure slurry. We observed that ARG concentrations return to background levels after manure-induced exposure within a crop growing season, highlighting the resilience capacity of soil. Naturally occurring high background concentrations of ARGs can be found in forest soil in due distance under low anthropogenic influences. It was observed that pesticide application increases the concentrations of three out of seven ARGs tested (ermB, aph(3')-IIIa and tet(W)). Moreover, we noticed that the absolute abundances of sul1, vanA, ermB and blaTEM-1 resistance genes show an increase by 100- to 10,000- fold, from maturation of fresh pig faeces to manure. Outcomes of our study suggest that agricultural soil environments show a strong capacity to alleviate externally induced disturbances in endogenous ARG concentrations. Naturally occurring high concentrations of ARGs are present also in low human impacted environments represented by the indigenous resistome.

Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and scepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project the GROW Observatory (GROW; https://growobservatory.org, last access: 30 March 2020). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within 24 high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low-cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias, and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low intersensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes; only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science.

Antimicrobial resistance (AMR) is a public health issue attributed to the misuse of antibiotics in human and veterinary medicine. Since AMR surveillance requires a One Health approach, we sampled nine interconnected compartments at a hydrological open-air lab (HOAL) in Austria to obtain six bacterial species included in the WHO priority list of antibiotic-resistant bacteria (ARB). Whole genome sequencing-based typing included core genome multilocus sequence typing (cgMLST). Genetic and phenotypic characterization of AMR was performed for all isolates. Eighty-nine clinically-relevant bacteria were obtained from eight compartments including 49 E. coli, 27 E. faecalis, 7 K. pneumoniae and 6 E. faecium. Clusters of isolates from the same species obtained in different sample collection dates were detected. Of the isolates, 29.2% were resistant to at least one antimicrobial. E. coli and E. faecalis isolates from different compartments had acquired antimicrobial resistance genes (ARGs) associated with veterinary drugs such as aminoglycosides and tetracyclines, some of which were carried in conjugative and mobilizable plasmids. Three multidrug resistant (MDR) E. coli isolates were found in samples from field drainage and wastewater. Early detection of ARGs and ARB in natural and farm-related environments can identify hotspots of AMR and help prevent its emergence and dissemination along the food/feed chain.

Exploring the contributions of new and old water to runoff during precipitation events in agricultural catchments is essential for understanding runoff generation, solute transport, and soil erosion. The aim of this study was to investigate the variability in the isotopic composition of precipitation and runoff in the 66 ha agricultural catchment in Austria, in the Hydrological Open Air Laboratory (HOAL), in order to compare two isotope hydrograph separation methods. The classical two‐component (IHS) and the ensemble hydrograph separation (EHS) were applied to multiple large events in May–October of 2013–2018 using δ¹⁸O and δ²H. The peak flow new water contributions obtained by IHS were compared with the average new water fraction from EHS. The average new water fraction calculated with EHS based on regular weekly sampling was close to zero, which can be explained by the large diffuse groundwater discharge into the stream between the events. When only investigating events with high temporal resolution sampling, the results suggest that EHS provided average new water fractions during peak flows (0.46 ± 0.04 for δ¹⁸O, 0.47 ± 0.03 for δ²H) that were close to the averages obtained by IHS (0.47 for δ¹⁸O, 0.50 for δ²H). New water fractions tended to be higher for larger rainfall intensities. High peak flow new water fractions could be explained by the agricultural land use and soils with low permeability promoting overland flow generation and by some of the tile drainage systems contributing to the delivery of water. In conclusion, a weekly sampling frequency was not sufficient in the HOAL but instead high‐resolution sampling during events was necessary to estimate the average new water contributions during events. While EHS may be a more robust approach compared to IHS, as it relaxes some of the assumptions of IHS, IHS can provide information on the variability of new water contributions of individual events.