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The detection of auto-fluorescence in phytogenic, hydrated amorphous silica depositions (phytoliths) has been found to be a promising approach to verify if phytoliths were burnt or not, especially in archaeological contexts. However, it is unknown so far at what temperature and how auto-fluorescence is induced in phytoliths. We used fluorescence microscopy, scanning electron microscope-energy dispersive X-ray spectroscopy (SEM-EDX), and Fourier transform infrared spectroscopy to analyze auto-fluorescence in modern phytoliths extracted from plant samples or in intact leaves of winter wheat. Leaves and extracted phytoliths were heated at different temperatures up to 600 degrees C. The aims of our experiments were i) to find out what temperature is needed to induce auto-fluorescence in phytoliths, ii) to detect temperature-dependent changes in the molecular structure of phytoliths related to auto-fluorescence, and iii) to derive a mechanistic understanding of auto-fluorescence in phytoliths. We found organic compounds associated with phytoliths to cause auto-fluorescence in phytoliths treated at temperatures below approx. 400 degrees C. In phytoliths treated at higher temperatures, i.e., 450 and 600 degrees C, phytolith auto-fluorescence was mainly caused by molecular changes of phytolith silica. Based on our results we propose that auto-fluorescence in phytoliths is caused by clusterization-triggered emissions, which are caused by overlapping electron clouds forming non-conventional chromophores. In phytoliths heated at temperatures above about 400 degrees C dihydroxylation and the formation of siloxanes result in oxygen clusters that serve as non-conventional chromophores in fluorescence events. Furthermore, SEM-EDX analyses revealed that extractable phytoliths were dominated by lumen phytoliths (62%) compared to cell wall phytoliths (38%). Our findings might be not only relevant in archaeological phytolith-based examinations, but also for studies on the temperature-dependent release of silicon from phytoliths and the potential of long-term carbon sequestration in phytoliths.
Background
Non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) has received tremendous attention as a potential neuromodulator of cognitive and affective functions, which likely exerts its effects via activation of the locus coeruleus-noradrenaline (LC-NA) system. Reliable effects of taVNS on markers of LC-NA system activity, however, have not been demonstrated yet.
Methods
The aim of the present study was to overcome previous limitations by pooling raw data from a large sample of ten taVNS studies (371 healthy participants) that collected salivary alpha-amylase (sAA) as a potential marker of central NA release.
Results
While a meta-analytic approach using summary statistics did not yield any significant effects, linear mixed model analyses showed that afferent stimulation of the vagus nerve via taVNS increased sAA levels compared to sham stimulation (b = 0.16, SE = 0.05, p = 0.001). When considering potential confounders of sAA, we further replicated previous findings on the diurnal trajectory of sAA activity.
Conclusion(s)
Vagal activation via taVNS increases sAA release compared to sham stimulation, which likely substantiates the assumption that taVNS triggers NA release. Moreover, our results highlight the benefits of data pooling and data sharing in order to allow stronger conclusions in research.
Background
Non-invasive transcutaneous auricular vagus nerve stimulation (taVNS) has received tremendous attention as a potential neuromodulator of cognitive and affective functions, which likely exerts its effects via activation of the locus coeruleus-noradrenaline (LC-NA) system. Reliable effects of taVNS on markers of LC-NA system activity, however, have not been demonstrated yet.
Methods
The aim of the present study was to overcome previous limitations by pooling raw data from a large sample of ten taVNS studies (371 healthy participants) that collected salivary alpha-amylase (sAA) as a potential marker of central NA release.
Results
While a meta-analytic approach using summary statistics did not yield any significant effects, linear mixed model analyses showed that afferent stimulation of the vagus nerve via taVNS increased sAA levels compared to sham stimulation (b = 0.16, SE = 0.05, p = 0.001). When considering potential confounders of sAA, we further replicated previous findings on the diurnal trajectory of sAA activity.
Conclusion(s)
Vagal activation via taVNS increases sAA release compared to sham stimulation, which likely substantiates the assumption that taVNS triggers NA release. Moreover, our results highlight the benefits of data pooling and data sharing in order to allow stronger conclusions in research.
Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50% and 70% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling.
A detailed analysis of horizontal and vertical particulate matter (PM) fluxes during wind erosion has been done, based on measurements of PM smaller than 10, 2.5, and 1.0 mu mm, at windward and leeward positions on a measuring field. The three fractions of PM measurement are differently influenced by the increasing wind and shear velocities of the wind. The measured concentrations of the coarser fractions of the fine dust, PM10, and PM2.5, increase with wind and shear velocity, whereas the PM1.0 concentrations show no clear correlation to the shear velocity. The share of PM2.5 on PM10 depends on the measurement height and wind speed and varies between 4 and 12 m/s at the 1 m height ranging from 25% to 7% (average 10%), and at the 4 m height from 39% to 23% (average 30%). Although general relationships between wind speed, PM concentration, and horizontal and vertical fluxes could be found, the contribution of the measuring field was very low, as balances of incoming and outgoing fluxes show. Consequently, the measured PM concentrations are determined from a variety of sources, such as traffic on unpaved roads, cattle drives, tillage operations, and wind erosion, and thus, represent all components of land use and landscape structure in the near and far surroundings of the measuring field. The current results may reflect factors from the landscape scale rather than the influence of field-related variables. The measuring devices used to monitor PM concentrations showed differences of up to 20%, which led to considerable deviations when determining total balances. Differences up to 67% between the calculated fluxes prove the necessity of a previous calibration of the devices used. (c) 2022 International Research and Training Centre on Erosion and Sedimentation/the World Association for Sedimentation and Erosion Research.
From gustiness to dustiness
(2022)
This study delivers the first empirical data-driven analysis of the impact of turbulence induced gustiness on the fine dust emissions from a measuring field. For quantification of the gust impact, a new measure, the Gust uptake Efficiency (GuE) is introduced. GuE provides a percentage of over- or under-proportional dust uptake due to gust activity during a wind event. For the three analyzed wind events, GuE values of up to 150% could be found, yet they significantly differed per particle size class with a tendency for lower values for smaller particles. In addition, a high-resolution correlation analysis among 31 particle size classes and wind speed was conducted; it revealed strong negative correlation coefficients for very small particles and positive correlations for bigger particles, where 5 mu m appears to be an empirical threshold dividing both directions. We conclude with a number of suggestions for further investigations: an optimized field experiment setup, a new particle size ratio (PM1/PM0.5 in addition to PM10/PM2.5), as well as a comprehensive data-driven search for an optimal wind gust definition in terms of soil erosivity.
Wind erosion of agricultural soils affects their stock of essential elements for plants, like phosphorus (P). It is known that the composition of the eroded sediments varies with height, according to the size and density of the transported substances. Aim of this study was to analyze the concentration and enrichment ratios of P forms in sediments transported by the wind. A wind-tunnel study was performed on a sandy-and a sandy loam soil in order to measure P forms concentrations in the saltating sediments. P concentrations were also measured in the particulate matter (PM) of each soil, gained with the Easy Dust Generator. In both soils, inorganic-(Pi) and organic P (Po) were preferentially transported in PM, with enrichment ratios of 1.8 and 5.5, respectively. Nevertheless, a Pi/Po of 0.9 indicated that the accumulation of the minor Po in PM was more pronounced than Pi. This agrees with P-rich light and easily erodible organic compounds, almost exclusively accumulated in PM, and in relatively heavy and less erodible minerals, like apatites, in lower height sediments. Labile P (Pl) was preferentially transported in saltating sediments of both soils. This was attributed to the selective Bray & Kurtz I's extraction of the abundant inorganic P forms of these sediments. Total P (Pt) copied the transport trends of Pi, the major form. According to the transporting trends, Pi and Po would be re-sedimented at longer distances from the source than Pl. Outcomes become useful for modeling the influence of wind erosion on P cycling.
Remote sensing plays an increasingly key role in the determination of soil organic carbon (SOC) stored in agriculturally managed topsoils at the regional and field scales. Contemporary Unmanned Aerial Systems (UAS) carrying low-cost and lightweight multispectral sensors provide high spatial resolution imagery (<10 cm). These capabilities allow integrate of UAS-derived soil data and maps into digitalized workflows for sustainable agriculture. However, the common situation of scarce soil data at field scale might be an obstacle for accurate digital soil mapping. In our case study we tested a fixed-wing UAS equipped with visible and near infrared (VIS-NIR) sensors to estimate topsoil SOC distribution at two fields under the constraint of limited sampling points, which were selected by pedological knowledge. They represent all releva nt soil types along an erosion-deposition gradient; hence, the full feature space in terms of topsoils' SOC status. We included the Topographic Position Index (TPI) as a co-variate for SOC prediction. Our study was performed in a soil landscape of hummocky ground moraines, which represent a significant of global arable land. Herein, small scale soil variability is mainly driven by tillage erosion which, in turn, is strongly dependent on topography. Relationships between SOC, TPI and spectral information were tested by Multiple Linear Regression (MLR) using: (i) single field data (local approach) and (ii) data from both fields (pooled approach). The highest prediction performance determined by a leave-one-out-cross-validation (LOOCV) was obtained for the models using the reflectance at 570 nm in conjunction with the TPI as explanatory variables for the local approach (coefficient of determination (R-2) = 0.91; root mean square error (RMSE) = 0.11% and R-2 = 0.48; RMSE = 0.33, respectively). The local MLR models developed with both reflectance and TPI using values from all points showed high correlations and low prediction errors for SOC content (R-2 = 0.88, RMSE = 0.07%; R-2 = 0.79, RMSE = 0.06%, respectively). The comparison with an enlarged dataset consisting of all points from both fields (pooled approach) showed no improvement of the prediction accuracy but yielded decreased prediction errors. Lastly, the local MLR models were applied to the data of the respective other field to evaluate the cross-field prediction ability. The spatial SOC pattern generally remains unaffected on both fields; differences, however, occur concerning the predicted SOC level. Our results indicate a high potential of the combination of UAS-based remote sensing and environmental covariates, such as terrain attributes, for the prediction of topsoil SOC content at the field scale. The temporal flexibility of UAS offer the opportunity to optimize flight conditions including weather and soil surface status (plant cover or residuals, moisture and roughness) which, otherwise, might obscure the relationship between spectral data and SOC content. Pedologically targeted selection of soil samples for model development appears to be the key for an efficient and effective prediction even with a small dataset.
The precise and accurate assessment of carbon dioxide (CO2) exchange is crucial to identify terrestrial carbon (C) sources and sinks and for evaluating their role within the global C budget. The substantial uncertainty in disentangling the management and soil impact on measured CO2 fluxes are largely ignored especially in cropland. The reasons for this lies in the limitation of the widely used eddy covariance as well as manual and automatic chamber systems, which either account for short-term temporal variability or small-scale spatial heterogeneity, but barely both. To address this issue, we developed a novel robotic chamber system allowing for dozens of spatial measurement repetitions, thus enabling CO2 exchange measurements in a sufficient temporal and high small-scale spatial resolution. The system was tested from 08th July to 09th September 2019 at a heterogeneous field (100 m x 16 m), located within the hummocky ground moraine landscape of northeastern Germany (CarboZALF-D). The field is foreseen for a longer-term block trial manipulation experiment extending over three erosion induced soil types and was covered with spring barley. Measured fluxes of nighttime ecosystem respiration (R-eco) and daytime net ecosystem exchange (NEE) showed distinct temporal patterns influenced by crop phenology, weather conditions and management practices. Similarly, we found clear small-scale spatial differences in cumulated (gap-filled) R-eco, gross primary productivity (GPP) and NEE fluxes affected by the three distinct soil types. Additionally, spatial patterns induced by former management practices and characterized by differences in soil pH and nutrition status (P and K) were also revealed between plots within each of the three soil types, which allowed compensating for prior to the foreseen block trial manipulation experiment. The results underline the great potential of the novel robotic chamber system, which not only detects short-term temporal CO2 flux dynamics but also reflects the impact of small-scale spatial heterogeneity.
Various studies have been performed to quantify silicon (Si) stocks in plant biomass and related Si fluxes in terrestrial biogeosystems. Most studies are deliberately designed on the plot scale to ensure low heterogeneity in soils and plant composition, hence similar environmental conditions. Due to the immanent spatial soil variability, the transferability of results to larger areas, such as catchments, is therefore limited. However, the emergence of new technical features and increasing knowledge on details in Si cycling lead to a more complex picture at landscape and catchment scales. Dynamic and static soil properties change along the soil continuum and might influence not only the species composition of natural vegetation but also its biomass distribution and related Si stocks. Maximum likelihood (ML) classification was applied to multispectral imagery captured by an unmanned aerial system (UAS) aiming at the identification of land cover classes (LCCs). Subsequently, the normalized difference vegetation index (NDVI) and ground-based measurements of biomass were used to quantify aboveground Si stocks in two Si-accumulating plants (Calamagrostis epige-jos and Phragmites australis) in a heterogeneous catchment and related corresponding spatial patterns of these stocks to soil properties. We found aboveground Si stocks of C. epige-jos and P. australis to be surprisingly high (maxima of Si stocks reach values up to 98 g Sim(-2)), i.e. comparable to or markedly exceeding reported values for the Si storage in aboveground vegetation of various terrestrial ecosystems. We further found spatial patterns of plant aboveground Si stocks to reflect spatial heterogeneities in soil properties. From our results, we concluded that (i) aboveground biomass of plants seems to be the main factor of corresponding phytogenic Si stock quantities, and (ii) a detection of biomass heterogeneities via UAS-based remote sensing represents a promising tool for the quantification of lifelike phytogenic Si pools at landscape scales.