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Now showing 1 - 9 of 9
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    Optical Spectrometry to Determine Nutrient Concentrations and other Physicochemical Parameters in Liquid Organic Manures: A Review
    (Basel : MDPI, 2022) Horf, Michael; Vogel, Sebastian; Drücker, Harm; Gebbers, Robin; Olfs, Hans-Werner
    Nutrient concentrations in livestock manures and biogas digestates show a huge variability due to disparities in animal husbandry systems concerning animal species, feed composition, etc. Therefore, a nutrient estimation based on recommendation tables is not reliable when the exact chemical composition is needed. The alternative, to analyse representative fertilizer samples in a standard laboratory, is too time-and cost-intensive to be an accepted routine method for farmers. However, precise knowledge about the actual nutrient concentrations in liquid organic fertilizers is a prerequisite to ensure optimal nutrient supply for growing crops and on the other hand to avoid environmental problems caused by overfertilization. Therefore, spectrometric methods receive increasing attention as fast and low-cost alternatives. This review summarizes the present state of research based on optical spectrometry used at laboratory and field scale for predicting several parameters of liquid organic manures. It emphasizes three categories: (1) physicochemical parameters, e.g., dry matter, pH, and electrical conductivity; (2) main plant nutrients, i.e., total nitrogen, ammonium nitrogen, phosphorus, potassium, magnesium, calcium, and sulfur; and (3) micronutrients, i.e., manganese, iron, copper, and zinc. Furthermore, the commonly used sample preparation techniques, spectrometer types, measuring modes, and chemometric methods are presented. The primarily promising scientific results of the last 30 years contributed to the fact that near-infrared spectrometry (NIRS) was established in commercial laboratories as an alternative method to wet chemical standard methods. Furthermore, companies developed technical setups using NIRS for on-line applications of liquid organic manures. Thus, NIRS seems to have evolved to a competitive measurement procedure, although parts of this technique still need to be improved to ensure sufficient accuracy, especially in quality management.
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    Base Neutralizing Capacity of Agricultural Soils in a Quaternary Landscape of North-East Germany and Its Relationship to Best Management Practices in Lime Requirement Determination
    (Basel : MDPI AG, 2020) Vogel, Sebastian; Bönecke, Eric; Kling, Charlotte; Kramer, Eckart; Lück, Katrin; Nagel, Anne; Philipp, Golo; Rühlmann, Jörg; Schröter, Ingmar; Gebbers, Robin
    Despite being a natural soil-forming process, soil acidification is a major agronomic challenge under humid climate conditions, as soil acidity influences several yield-relevant soil properties. It can be counterbalanced by the regular application of agricultural lime to maintain or re-establish soil fertility and to optimize plant growth and yield. To avoid underdose as well as overdose, lime rates need to be calculated carefully. The lime rate should be determined by the optimum soil pH (target pH) and the response of the soil to lime, which is described by the base neutralizing capacity (BNC). Several methods exist to determine the lime requirement (LR) to raise the soil pH to its optimum. They range from extremely time-consuming equilibration methods, which mimic the natural processes in the soil, to quick tests, which rely on some approximations and are designed to provide farmers with timely and cost-efficient data. Due to the higher analytical efforts, only limited information is available on the real BNC of particular soils. In the present paper, we report the BNC of 420 topsoil samples from Central Europe (north-east Germany), developed on sediments from the last ice age 10,000 years ago under Holocene conditions. These soils are predominantly sandy and low in humus, but they exhibit a huge spatial variability in soil properties on a small scale. The BNC was determined by adding various concentrations of Ca(OH)2 and fitting an exponential model to derive a titration curve for each sample. The coefficients of the BNC titration curve were well correlated with soil properties affecting soil acidity and pH buffer capacity, i.e., pH, soil texture and soil organic matter (SOM). From the BNC model, the LRs (LRBNC) were derived and compared with LRVDLUFA based on the standard protocol in Germany as established by the Association of German Agricultural Analytic and Research Institutes (VDLUFA). The LRBNC and LRVDLUFA correlated well but the LRVDLUFA were generally by approximately one order of magnitude higher. This is partly due to the VDLUFA concept to recommend a maintenance or conservation liming, even though the pH value is in the optimum range, to keep it there until the next lime application during the following rotation. Furthermore, the VDLUFA method was primarily developed from field experiments where natural soil acidification and management practices depressed the effect of lime treatment. The BNC method, on the other hand, is solely based on laboratory analysis with standardized soil samples. This indicates the demand for further research to develop a sound scientific algorithm that complements LRBNC with realistic values of annual Ca2+ removal and acidification by natural processes and N fertilization.
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    Bibliometric Analysis of Soil and Landscape Stability, Sensitivity and Resistivity
    (Basel : MDPI, 2022) Bettoni, Manuele; Maerker, Michael; Bosino, Alberto; Schillaci, Calogero; Vogel, Sebastian
    In times of global change, it is of fundamental importance to understand the sensitivity, stability and resistivity of a landscape or ecosystem to human disturbance. Landscapes and ecosystems have internal thresholds, giving them the ability to resist such disturbance. When these thresholds are quantified, the development of countermeasures can help prevent irreversible changes and support adaptations to the negative effects of global change. The main objective of this analysis is to address the lack of recent studies defining terms like sensitivity, resistivity and stability in reference to landscapes and ecosystems through a Bibliometric analysis based on Scopus and Web of Science peer-reviewed articles. The present research also aims to quantify landscape statuses in terms of their sensitivity, stability and resistivity. The term “landscape stability” is mainly related to quantitatively measurable properties indicating a certain degree of stability. In contrast, the term “landscape sensitivity” is often related to resilience; however, this definition has not substantially changed over time. Even though a large number of quantification methods related to soil and landscape stability and sensitivity were found, these methods are rather ad hoc. This study stresses the importance of interdisciplinary studies and work groups.
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    Viticulture in the Laetanian Region (Spain) during the Roman Period: Predictive Modelling and Geomatic Analysis
    (Basel : MDPI AG, 2020) Stubert, Lisa; Oliveras, Antoni Martín i; Märker, Michael; Schernthanner, Harald; Vogel, Sebastian
    Geographic information system (GIS)-based predictive modelling is widely used in archaeology to identify suitable zones for ancient settlement locations and determine underlying factors of their distribution. In this study, we developed predictive models on Roman viticulture in the Laetanian Region (Hispania Citerior-Tarraconensis), using the location of 82 ancient wine-pressing facilities or torcularia as response variables and 15 topographical and 6 socio-economic cost distance datasets as predictor variables. Several predictor variable subsets were selected either by expert knowledge of similar studies or by using a semi-automatization algorithm based on statistical distribution metrics of the input data. The latter aims at simplifying modelling and minimizing the necessity of a priori knowledge. Both approaches predicted the distribution of archeological sites sufficiently well. However, the best prediction performance was obtained by an expert knowledge model utilizing a predictor variable combination based on recommendations on viticulture by Lucius Junius Moderatus Columella, the prominent ancient Roman agronomist. The results indicate that the accessibility of a location and its connectivity to trade routes and distribution centres, determined by terrain steepness, was decisive for the settlement of viticultural facilities. With the knowledge gained, the ancient cultivated area and number of wine-pressing facilities needed for processing the vineyard yields were extrapolated for the entire study region.
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    Direct prediction of site-specific lime requirement of arable fields using the base neutralizing capacity and a multi-sensor platform for on-the-go soil mapping
    (Dordrecht [u.a.] : Springer Science + Business Media B.V, 2021-7-26) Vogel, Sebastian; Bönecke, Eric; Kling, Charlotte; Kramer, Eckart; Lück, Katrin; Philipp, Golo; Rühlmann, Jörg; Schröter, Ingmar; Gebbers, Robin
    Liming agricultural fields is necessary for counteracting soil acidity and is one of the oldest operations in soil fertility management. However, the best management practice for liming in Germany only insufficiently considers within-field soil variability. Thus, a site-specific variable rate liming strategy was developed and tested on nine agricultural fields in a quaternary landscape of north-east Germany. It is based on the use of a proximal soil sensing module using potentiometric, geoelectric and optical sensors that have been found to be proxies for soil pH, texture and soil organic matter (SOM), which are the most relevant lime requirement (LR) affecting soil parameters. These were compared to laboratory LR analysis of reference soil samples using the soil’s base neutralizing capacity (BNC). Sensor data fusion utilizing stepwise multi-variate linear regression (MLR) analysis was used to predict BNC-based LR (LRBNC) for each field. The MLR models achieved high adjusted R2 values between 0.70 and 0.91 and low RMSE values from 65 to 204 kg CaCO3 ha−1. In comparison to univariate modeling, MLR models improved prediction by 3 to 27% with 9% improvement on average. The relative importance of covariates in the field-specific prediction models were quantified by computing standardized regression coefficients (SRC). The importance of covariates varied between fields, which emphasizes the necessity of a field-specific calibration of proximal sensor data. However, soil pH was the most important parameter for LR determination of the soils studied. Geostatistical semivariance analysis revealed differences between fields in the spatial variability of LRBNC. The sill-to-range ratio (SRR) was used to quantify and compare spatial LRBNC variability of the nine test fields. Finally, high resolution LR maps were generated. The BNC-based LR method also produces negative LR values for soil samples with pH values above which lime is required. Hence, the LR maps additionally provide an estimate on the quantity of chemically acidifying fertilizers that can be applied to obtain an optimal soil pH value.
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    Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data
    (Dordrecht [u.a.] : Springer Science + Business Media B.V, 2020) Bönecke, Eric; Meyer, Sven; Vogel, Sebastian; Schröter, Ingmar; Gebbers, Robin; Kling, Charlotte; Kramer, Eckart; Lück, Katrin; Nagel, Anne; Philipp, Golo; Gerlach, Felix; Palme, Stefan; Scheibe, Dirk; Zieger, Karin; Rühlmann, Jörg
    Soil acidification is caused by natural paedogenetic processes and anthropogenic impacts but can be counteracted by regular lime application. Although sensors and applicators for variable-rate liming (VRL) exist, there are no established strategies for using these tools or helping to implement VRL in practice. Therefore, this study aimed to provide guidelines for site-specific liming based on proximal soil sensing. First, high-resolution soil maps of the liming-relevant indicators (pH, soil texture and soil organic matter content) were generated using on-the-go sensors. The soil acidity was predicted by two ion-selective antimony electrodes (RMSEpH: 0.37); the soil texture was predicted by a combination of apparent electrical resistivity measurements and natural soil-borne gamma emissions (RMSEclay: 0.046 kg kg−1); and the soil organic matter (SOM) status was predicted by a combination of red (660 nm) and near-infrared (NIR, 970 nm) optical reflection measurements (RMSESOM: 6.4 g kg−1). Second, to address the high within-field soil variability (pH varied by 2.9 units, clay content by 0.44 kg kg−1 and SOM by 5.5 g kg−1), a well-established empirical lime recommendation algorithm that represents the best management practices for liming in Germany was adapted, and the lime requirements (LRs) were determined. The generated workflow was applied to a 25.6 ha test field in north-eastern Germany, and the variable LR was compared to the conventional uniform LR. The comparison showed that under the uniform liming approach, 63% of the field would be over-fertilized by approximately 12 t of lime, 6% would receive approximately 6 t too little lime and 31% would still be adequately limed. © 2020, The Author(s).
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    Determination of Nutrients in Liquid Manures and Biogas Digestates by Portable Energy-Dispersive X-ray Fluorescence Spectrometry
    (Basel : MDPI AG, 2021) Horf, Michael; Gebbers, Robin; Vogel, Sebastian; Ostermann, Markus; Piepel, Max-Frederik; Olfs, Hans-Werner
    Knowing the exact nutrient composition of organic fertilizers is a prerequisite for their appropriate application to improve yield and to avoid environmental pollution by over-fertilization. Traditional standard chemical analysis is cost and time-consuming and thus it is unsuitable for a rapid analysis before manure application. As a possible alternative, a handheld X-ray fluorescence (XRF) spectrometer was tested to enable a fast, simultaneous, and on-site analysis of several elements. A set of 62 liquid pig and cattle manures as well as biogas digestates were collected, intensively homogenized and analysed for the macro plant nutrients phosphorus, potassium, magnesium, calcium, and sulphur as well as the micro nutrients manganese, iron, copper, and zinc using the standard lab procedure. The effect of four different sample preparation steps (original, dried, filtered, and dried filter residues) on XRF measurement accuracy was examined. Therefore, XRF results were correlated with values of the reference analysis. The best R2s for each element ranged from 0.64 to 0.92. Comparing the four preparation steps, XRF results for dried samples showed good correlations (0.64 and 0.86) for all elements. XRF measurements using dried filter residues showed also good correlations with R2s between 0.65 and 0.91 except for P, Mg, and Ca. In contrast, correlation analysis for liquid samples (original and filtered) resulted in lower R2s from 0.02 to 0.68, except for K (0.83 and 0.87, respectively). Based on these results, it can be concluded that handheld XRF is a promising measuring system for element analysis in manures and digestates.
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    What makes soil landscape robust? Landscape sensitivity towards land use changes in a Swiss southern Alpine valley
    (Amsterdam [u.a.] : Elsevier Science, 2022) Bettoni, Manuele; Maerker, Michael; Sacchi, Roberto; Bosino, Alberto; Conedera, Marco; Simoncelli, Laura; Vogel, Sebastian
    Landscape sensitivity is a concept referring to the likelihood that changes in land use may affect in an irreversible way physical and chemical soil properties of the concerned landscape. The objective of this study is to quantitatively assess the sensitivity of the southern Alpine soil landscape regarding land use change-induced perturbations. Alpine soil landscapes can be considered as particularly sensitive to land use changes because their effects tend to be enhanced by frequent extreme climatic and topographic conditions as well as intense geomorphologic activity. In detail, the following soil key properties for soil vulnerability were analysed: (i) soil texture, (ii) bulk density, (iii) soil organic carbon (SOC), (iv) saturated hydraulic conductivity (Ksat), (v) aggregate stability and (vi) soil water repellency (SWR). The study area is characterized by a steep, east-west oriented valley, strongly anthropized in the last centuries followed by a progressive abandonment. This area is particularly suitable due to constant lithological conditions, extreme topographic and climatic conditions as well as historic land use changes. The analysis of land use change effects on soil properties were performed through a linear mixed model approach due to the nested structure of the data. Our results show a generally high stability of the assessed soils in terms of aggregate stability and noteworthy thick soils. The former is remarkable, since aggregate stability, which is commonly used for detecting land use-induced changes in soil erosion susceptibility, was always comparably high irrespective of land use. The stability of the soils is mainly related to a high amount of soil organic matter favouring the formation of stable soil aggregates, decreasing soil erodibility and hence, reducing soil loss by erosion. However, the most sensitive soil property to land use change was SWR that is partly influenced by the amount of soil organic carbon and probably by the quality and composition of SOM.
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    Evaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing
    (Basel : MDPI AG, 2019) Vogel, Sebastian; Gebbers, Robin; Oertel, Marcel; Kramer, Eckart
    On a grassland field with sandy soils in Northeast Germany (Brandenburg), vegetation indices from multi-spectral UAV-based remote sensing were used to predict grassland biomass productivity. These data were combined with soil pH value and apparent electrical conductivity (ECa) from on-the-go proximal sensing serving as indicators for soil-borne causes of grassland biomass variation. The field internal magnitude of spatial variability and hidden correlations between the variables of investigation were analyzed by means of geostatistics and boundary-line analysis to elucidate the influence of soil pH and ECa on the spatial distribution of biomass. Biomass and pH showed high spatial variability, which necessitates high resolution data acquisition of soil and plant properties. Moreover, boundary-line analysis showed grassland biomass maxima at pH values between 5.3 and 7.2 and ECa values between 3.5 and 17.5 mS m−1. After calibrating ECa to soil moisture, the ECa optimum was translated to a range of optimum soil moisture from 7% to 13%. This matches well with to the plant-available water content of the predominantly sandy soil as derived from its water retention curve. These results can be used in site-specific management decisions to improve grassland biomass productivity in low-yield regions of the field due to soil acidity or texture-related water scarcity.