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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.
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.
Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.
Processes driving the production, transformation and transport of methane (CH4 / in wetland ecosystems are highly complex. We present a simple calculation algorithm to separate open-water CH4 fluxes measured with automatic chambers into diffusion-and ebullition-derived components. This helps to reveal underlying dynamics, to identify potential environmental drivers and, thus, to calculate reliable CH4 emission estimates. The flux separation is based on identification of ebullition-related sudden concentration changes during single measurements. Therefore, a variable ebullition filter is applied, using the lower and upper quartile and the interquartile range (IQR). Automation of data processing is achieved by using an established R script, adjusted for the purpose of CH4 flux calculation. The algorithm was validated by performing a laboratory experiment and tested using flux measurement data (July to September 2013) from a former fen grassland site, which converted into a shallow lake as a result of rewetting. Ebullition and diffusion contributed equally (46 and 55 %) to total CH4 emissions, which is comparable to ratios given in the literature. Moreover, the separation algorithm revealed a concealed shift in the diurnal trend of diffusive fluxes throughout the measurement period. The water temperature gradient was identified as one of the major drivers of diffusive CH4 emissions, whereas no significant driver was found in the case of erratic CH4 ebullition events.
Carbon (C) sequestration in soils plays a key role in the global C cycle. It is therefore crucial to adequately monitor dynamics in soil organic carbon (Delta SOC) stocks when aiming to reveal underlying processes and potential drivers. However, small-scale spatial (10-30 m) and temporal changes in SOC stocks, particularly pronounced in arable lands, are hard to assess. The main reasons for this are limitations of the well-established methods. On the one hand, repeated soil inventories, often used in long-term field trials, reveal spatial patterns and trends in Delta SOC but require a longer observation period and a sufficient number of repetitions. On the other hand, eddy covariance measurements of C fluxes towards a complete C budget of the soil-plant-atmosphere system may help to obtain temporal Delta SOC patterns but lack small-scale spatial resolution. To overcome these limitations, this study presents a reliable method to detect both short-term temporal dynamics as well as small-scale spatial differences of Delta SOC using measurements of the net ecosystem carbon balance (NECB) as a proxy. To estimate the NECB, a combination of automatic chamber (AC) measurements of CO2 exchange and empirically modeled aboveground biomass development (NPPshoot / were used. To verify our method, results were compared with Delta SOC observed by soil resampling. Soil resampling and AC measurements were performed from 2010 to 2014 at a colluvial depression located in the hummocky ground moraine landscape of northeastern Germany. The measurement site is characterized by a variable groundwater level (GWL) and pronounced small-scale spatial heterogeneity regarding SOC and nitrogen (Nt) stocks. Tendencies and magnitude of Delta SOC values derived by AC measurements and repeated soil inventories corresponded well. The period of maximum plant growth was identified as being most important for the development of spatial differences in annual Delta SOC. Hence, we were able to confirm that AC-based C budgets are able to reveal small-scale spatial differences and short-term temporal dynamics of Delta SOC.
Carbon (C) stored in soils represents the largest C pool of terrestrial ecosystems and consequently plays a crucial role in the global C cycle. So far, it is widely unclear to what extent different land uses and land use change influence soil C storage. The hummocky ground moraine landscape of northeastern Germany is characterized by distinct small-scale soil heterogeneity on the one hand, and intensive energy crop cultivation on the other. Both factors are assumed to significantly influence gaseous C exchange; as such, they likely drive soil organic carbon (SOC) stock dynamics in terrestrial agricultural ecosystems. To date, it is not clear to what extent N fertilization forms, which are associated with energy crop cultivation (e.g., application of biogas fermentation residues) and soil type relative to soil erosion state, influence soil C dynamics, nor is it clear whether one of these factors is more important than the other. To investigate the influence of soil erosion state and N fertilization form on soil C dynamics, we present dynamic and seasonal net ecosystem carbon balances (NECB) as a proxy for changes in soil organic carbon stocks. Measurements were conducted for maize (Zea mays L.) at five sites in the "CarboZALF-D" experimental field during the 2011 growing season. Measurement sites represent different soil erosion states (non-eroded Albic Luvisols, extremely eroded Calcaric Regosols and depositional Endogleyic Colluvic Regosols) and N fertilization forms (100% mineral fertilizer, 50% mineral and 50% organic fertilizer, and 100% organic fertilizer). Fertilization treatments were established on the Albic Luvisol. Net ecosystem CO2 exchange (NEE) and ecosystem respiration (R-eco) were measured every four weeks using a dynamic flow-through non-steady-state closed manual chamber system. Gap filling was performed based on empirical temperature and PAR dependency functions and was used to derive daily NEE values. In parallel, daily above-ground biomass production (NPFshoot) was estimated using a logistic growth equation, fitted on periodic biomass samples. Finally, C dynamics were calculated as the balance of daily NEE and NPPshoot based on the initial C input due to organic fertilization. Resulting NECB varied from pronounced soil C losses at the Endogleyic Colluvic Regosol (592 g C m(-2)) to soil C gains at the Calcaric Regosol (-124 g C m(-2)). Minor to modest C losses were observed for the Albic Luvisol. Compared to N fertilization forms, soil erosion states generally had a stronger impact on derived NECB. However, interannual variations in plant phonology and interactions between soil erosion states and fertilization forms might result in different NECB values over multiple years. Hence, long-term measurements of different fertilization treatments on characteristic soil landscape elements are needed.
Silicon (Si) is considered as a quasiessential element for higher plants as its uptake increases plant growth and resistance against abiotic as well as biotic stresses. Foliar application of fertilizers generally is assumed to be a comparably environment-friendly form of fertilization because only small quantities are needed. The interest in foliar fertilization and the use of Si as a fertilizer in general increased significantly within the last decades, but there are only few publications dealing with the foliar application of Si at all. In the present review, the effects of Si foliar fertilization, including nano-Si fertilizers, on the three most important crops on a global scale, that is, maize, rice, and wheat, are summarized. Additionally, different pathways (i.e., cuticular pathways, stomata, and trichomes) of foliar uptake and functioning of Si foliar fertilizers against biotic (i.e., fungal diseases and harmful insects), as well as abiotic (i.e., water stress, macronutrient imbalance, and heavy metal toxicity) stressors are discussed. Future research should especially focus on (1) the gathering of empirical data from field and greenhouse experiments, (2) the intensification of co-operations between practitioners and scientists, (3) interdisciplinary research, and (4) the analysis of results from multiple studies (meta-analysis, big data) to fully understand effects, uptake, and functioning of Si foliar fertilizers and to evaluate their potential in modern sustainable agriculture concepts.
To better assess ecosystem C budgets of croplands and understand their potential response to climate and management changes, detailed information on the mechanisms and environmental controls driving the individual C flux components are needed. This accounts in particular for the ecosystem respiration (R-eco) and its components, the autotrophic (R-a) and heterotrophic respiration (R-h) which vary tremendously in time and space. This study presents a method to separate R-eco into R-a [as the sum of R-a (shoot) and R-a (root)] and R-h in order to detect temporal and small-scale spatial dynamics within their relative contribution to overall R-eco. Thus, predominant environmental drivers and underlying mechanisms can be revealed. R-eco was derived during nighttime by automatic chamber CO2 flux measurements on plant covered plots. R-h was derived from CO2 efflux measurements, which were performed in parallel to R-eco measurements on a fallow plot using CO2 sampling tubes in 10 cm soil depth. R-a (root) was calculated as the difference between sampling tube CO2 efflux measurements on a plant covered plot and R-h. R-a (shoot) was calculated as R-eco - R-a (root) - R-h. Measurements were carried out for winter wheat (Triticum aestivum L.) during the crop season 2015 at an experimental plot located in the hummocky ground moraine landscape of NE Germany. R-eco varied seasonally from < 1 to 9.5 g C m(-2) d(-1), and was higher in adult (a) and reproductive (r) than juvenile (j) stands (gC m(-2) d(-1): j = 1.2, a = 4.6, r = 5.3). Observed R-a and R-h were in general smaller compared to the independently measured R-eco, contributing in average 58% and 42% to R-eco. However, both varied strongly regarding their environmental drivers and particular contribution throughout the study period, following the seasonal development of soil temperature and moisture (R-h) as well as crop development (R-a). Thus, our results consistently revealed temporal dynamics regarding the relative contribution of R-a (root) and R-a (shoot) to R-a, as well as of R-a and R-h to R-eco. Based on the observed results, implications for partitioning of R-eco in croplands are given.
Agricultural soil landscapes of hummocky ground moraines are characterized by 3D spatial patterns of soil types that result from profile modifications due to the combined effect of water and tillage erosion. We hypothesize that crops reflect such soil landscape patterns by increased or reduced plant and root growth. Root development may depend on the thickness and vertical sequence of soil horizons as well as on the structural development state of these horizons at different landscape positions. The hypotheses were tested using field data of the root density (RD) and the root lengths (RL) of winter wheat using the minirhizotron technique. We compared data from plots at the CarboZALF-D site (NE Germany) that are representing a non-eroded reference soil profile (Albic Luvisol) at a plateau position, a strongly eroded profile at steep slope (Calcaric Regosol), and a depositional profile at the footslope (Anocolluvic Regosol). At each of these plots, three Plexiglas access tubes were installed down to approx. 1.5 m soil depth. Root measurements were carried out during the growing season of winter wheat (September 2014-August 2015) on six dates. The root length density (RLD) and the root biomass density were derived from RD values assuming a mean specific root length of 100 m g(-1). Values of RD and RLD were highest for the Anocolluvic Regosol and lowest for the Calcaric Regosol. The maximum root penetration depth was lower in the Anocolluvic Regosol because of a relatively high and fluctuating water table at this landscape position. Results revealed positive relations between below-ground (root) and above-ground crop parameters (i.e., leaf area index, plant height, biomass, and yield) for the three soil types. Observed root densities and root lengths in soils at the three landscape positions corroborated the hypothesis that the root system was reflecting erosion-induced soil profile modifications. Soil landscape position dependent root growth should be considered when attempting to quantify landscape scale water and element balances as well as agricultural productivity.
Subsurface lateral flow in hillslope soils depends on lower permeability or texture-contrasting soil horizons. In the arable hummocky soil landscape, erosion processes caused glacial till appearance closer to the soil surface at upslope positions. The objective of this work was to quantify the potential for subsurface lateral flow depending on the erosion-affected spatial hydropedological complexity. The eroded Haplic Luvisol profile was studied due to the presence of a relatively dense C horizon that varied in depth, thickness, and sloping angle. A two-dimensional numerical modeling and sensitivity analysis for the saturated hydraulic conductivity (K-s) of the C horizon and the depth to C horizon (i.e., soil solum thickness) was performed for rainstorms in 2011 and 2012 using HYDRUS-2D. A K-s value of <2.5 cm d(-1) for the C horizon was required for lateral flow initiation. Lateral flow was (i) increasing with decreasing solum thickness, indicating an erosion-induced feedback on subsurface lateral flow, and (ii) dependent on the soil moisture regime prior to rainstorms. The effect of lateral flow on the movement of a conservative tracer was simulated in the form of a "virtual experiment". Simulation scenarios revealed only a relatively small lateral shift of the tracer plume caused by a local decoupling of water (already lateral) from subsequent tracer movement (still vertical). Longer term simulations suggested that, for the present conditions, lateral flow was limited mostly to occasional summer storm events. Even without considering preferential flow contribution to lateral flow, highly complex hydropedologic interactions are present in erosion-affected heterogeneous soil profiles.