@article{VogelBoeneckeKlingetal.2020, author = {Vogel, Sebastian and B{\"o}necke, Eric and Kling, Charlotte and Kramer, Eckart and L{\"u}ck, Katrin and Nagel, Anne and Philipp, Golo and R{\"u}hlmann, J{\"o}rg and Schr{\"o}ter, Ingmar and Gebbers, Robin}, title = {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}, series = {Agronomy}, volume = {10}, journal = {Agronomy}, number = {6}, publisher = {MDPI}, issn = {2073-4395}, doi = {10.3390/agronomy10060877}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-3370}, pages = {18}, year = {2020}, abstract = {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.}, language = {en} } @inproceedings{KlingSchroeterVogeletal.2021, author = {Kling, Charlotte and Schr{\"o}ter, Ingmar and Vogel, Sebastian and B{\"o}necke, Eric and R{\"u}hlmann, J{\"o}rg and Gebbers, Robin and L{\"u}ck, Katrin and Schubert, Torsten and Gerlach, Felix and Philipp, Golo and Scheibe, Dirk and Zieger, Karin and Palme, Stefan and Kramer, Eckart}, title = {Optimization of soil pH and crop yield based on a precision liming strategy guided by proximal soil sensing: Results of three on-farm field trails}, series = {Landscape 2021 Diversity for sustainable and resilient agriculture, Online conference, 20.-22.09.2021}, booktitle = {Landscape 2021 Diversity for sustainable and resilient agriculture, Online conference, 20.-22.09.2021}, editor = {Leibniz-Zentrum f{\"u}r Agrarlandschaftsforschung (ZALF),}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-7741}, pages = {292}, year = {2021}, abstract = {Soil acidity is a key factor for soil fertility as it simultaneously influences several yield-relevant soil properties and thus the productivity of agricultural soils. Besides natural pedogenetic processes, on agricultural land, soil acidification is amplified by the removal of the harvested biomass as well as by the application of acidifying fertilizers. Therefore, regular lime application on agricultural fields is inevitable to sustain soil quality and productivity. In Germany, the standard method for defining the lime requirement of a soil is the VDLUFA framework of the German Advisory Board for Agricultural Analytics. The VDLUFA method uses look-up tables that define the lime requirement according to soil texture, soil pH (CaCl2) and soil organic matter (SOM) content (LVLF et al., 2008). To determine these lime-relevant parameters, fields should be subdivided into subunits of 3 to 5 ha. Within a subunit, one mixed sample from 15 to 20 samples should form a composite sample for further reference lab analysis. However, a uniform lime requirement determined for subunits of 3 to 5 ha is often in contrast to the real soil variability observed in the field (Kling et al., 2019). In order to reflect the within-field soil variability, a few studies have demonstrated the use of high-resolution proximal soil sensing (PSS) data as tools for variable rate (VR) lime application (B{\"o}necke et al., 2020; P{\"a}tzold et al., 2020). However, there is a lack of studies that evaluate the feasibility and benefit of VR methods in on-farm field trails. Therefore, the aim of this study is to compare the performance of site-specific liming based on PSS to optimize soil pH and crop yield with that of commonly applied standard liming methods in Germany. To determine the effect and practicability of the VRA method, a multi-site-multi-year experiment was conducted between 2017 and 2020 on three sites in Brandenburg, Germany. The trial compared the effects on soil pH and crop yield of three lime management practices: variable-rate liming based on PSS data (VR-PS), uniform liming rate based on the VDLUFA standard method (UR), and no liming (ZR). Soil texture and soil pH were assessed with two mobile sensor platforms: the Geophilus system measuring apparent electrical resistivity (ERa) and Gamma-radiation and the Veris pH Manager (Veris Technologies Inc., Salina, KS, USA). Crop yields were obtained from revenue recordings of combine harvesters. Lime prescription maps were generated with an adapted and stepless VDLUFA algorithm, allowing a continuous CaO recommendation (B{\"o}necke et al., 2020). Based on these maps, management zones were delineated to perform lime spreading with state-of-the-art technique. In this study, we outline the conceptual framework of the VR liming approach and present first results from the on-farm field trials to verify the VR approach for an optimized soil acidity management as well as consider whether the higher economic revenue can compensate for added costs for mapping services and spreading technologies.}, language = {en} } @article{BoeneckeMeyerVogeletal.2020, author = {B{\"o}necke, Eric and Meyer, Swen and Vogel, Sebastian and Schr{\"o}ter, Ingmar and Gebbers, Robin and Kling, Charlotte and Kramer, Eckart and L{\"u}ck, Katrin and Nagel, Anne and Philipp, Golo and Gerlach, Felix and Palme, Stefan and Scheibe, Dirk and Zieger, Karin and R{\"u}hlmann, J{\"o}rg}, title = {Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data}, series = {Precision Agriculture}, volume = {22}, journal = {Precision Agriculture}, publisher = {Springer Nature}, issn = {1385-2256}, doi = {10.1007/s11119-020-09766-8}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-4890}, pages = {493 -- 523}, year = {2020}, abstract = {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.}, language = {en} } @article{HorfBoeneckeGebbersetal.2023, author = {Horf, Michael and B{\"o}necke, Eric and Gebbers, Robin and Kling, Charlotte and Kramer, Eckart and R{\"u}hlmann, J{\"o}rg and Schr{\"o}ter, Ingmar and Schwanghart, Wolfgang and Vogel, Sebastian}, title = {Utility of visible and near-infrared spectroscopy to predict base neutralizing capacity and lime requirement of quaternary soils}, series = {Precision Agriculture}, volume = {24}, journal = {Precision Agriculture}, publisher = {Springer Nature}, issn = {1385-2256}, doi = {10.1007/s11119-022-09945-9}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-4916}, pages = {288 -- 309}, year = {2023}, abstract = {Detailed knowledge of a soil's lime requirement (LR) is a prerequisite for a demand-based lime fertilization to achieve the optimum soil pH and thus sustainably increasing soil fertility and crop yields. LR can be directly determined by the base neutralizing capacity (BNC) obtained by soil-base titration. For a site-specific soil acidity management, detailed information on the within-field variation of BNC is required. However, soil-base titrations for BNC determination are too laborious to be extensively applied in routine soil testing. In contrast, visible and near-infrared spectroscopy (visNIRS) is a time and cost-effective alternative that can analyze several soil characteristics within a single spectrum. VisNIRS was tested in the laboratory on 170 air-dried and sieved soil samples of nine agricultural fields of a quaternary landscape in North-east Germany predicting the soil's BNC and the corresponding lime requirement (LRBNC) at a target pH of 6.5. Seven spectral pre-processing methods were tested including a new technique based on normalized differences (ND). Furthermore, six multivariate regression methods were conducted including a new method combining a forward stagewise subset selection algorithm with PLSR (FS-PLSR). The models were validated using an independent sample set. The best regression model for most target variables was FS-PLSR combined with the second Savitzky-Golay derivation as pre-processing method achieving R2s from 0.68 to 0.82. Finally, the performance of the direct prediction of LRBNC (R2 = 0.68) was compared with an indirect prediction that was calculated by the predicted BNC parameters. This resulted in slightly higher correlation coefficients for the indirect method with R2 = 0.75.}, language = {en} } @article{VogelBoeneckeKlingetal.2022, author = {Vogel, Sebastian and B{\"o}necke, Eric and Kling, Charlotte and Kramer, Eckart and L{\"u}ck, Katrin and Philipp, Golo and R{\"u}hlmann, J{\"o}rg and Schr{\"o}ter, Ingmar and Gebbers, Robin}, title = {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}, series = {Precision Agriculture}, volume = {23}, journal = {Precision Agriculture}, number = {1}, publisher = {Springer US}, issn = {1385-2256}, doi = {10.1007/s11119-021-09830-x}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-4140}, pages = {127 -- 149}, year = {2022}, abstract = {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.}, language = {en} } @incollection{TechenHelmingBrueggemannetal.2020, author = {Techen, Anja-K. and Helming, Katharina and Br{\"u}ggemann, Nicolas and Veldkamp, Edzo and Reinhold-Hurek, Barbara and Lorenz, Marco and Bartke, Stephan and Heinrich, Uwe and Amelung, Wulf and Augustin, Katja and Boy, Jens and Corre, Marife and Duttman, Rainer and Gebbers, Robin and Gentsch, Norman and Grosch, Rita and Guggenberger, Georg and Kern, J{\"u}rgen and Kiese, Ralf and Kuhwald, Michael and Leinweber, Peter and Schloter, Michael and Wiesmeier, Martin and Winkelmann, Traud and Vogel, Hans-J{\"o}rg}, title = {Chapter Four - Soil research challenges in response to emerging agricultural soil management practices}, series = {Advances in Agronomy}, booktitle = {Advances in Agronomy}, number = {161}, editor = {Sparks, Donald L.}, publisher = {Elsevier}, issn = {0065-2113}, doi = {10.1016/bs.agron.2020.01.002}, publisher = {Hochschule f{\"u}r nachhaltige Entwicklung Eberswalde}, pages = {179 -- 240}, year = {2020}, abstract = {Agricultural management is a key force affecting soil processes and functions. Triggered by biophysical constraints as well as rapid structural and technological developments, new management practices are emerging with largely unknown impacts on soil processes and functions. This impedes assessments of the potential of such emerging practices for sustainable intensification, a paradigm coined to address the growing demand for food and nonfood products. In terms of soil management, sustainable intensification means that soil productivity is increased while other soil functions and services, such as carbon storage and habitat for organisms, are simultaneously maintained or even improved. In this paper we provide an overview of research challenges to better understand how emerging soil management practices affect soil processes and functions. We distinguish four categories of soil management practices: spatial arrangements of cropping systems, crops and rotations, mechanical pressures, and inputs into the soil. Key research needs identified for each include nutrient efficiency in agroforestry versus conventional cropping systems, soil-rhizosphere microbiome elucidation to understand the interacting roles of crops and rotations, the effects of soil compaction on soil-plant-atmosphere interactions, and the ecotoxicity of plastics, pharmaceuticals and other pollutants that are introduced into the soil. We establish an interdisciplinary, systemic approach to soil science and include cross-cutting research activities related to process modeling, data management, stakeholder interaction, sustainability assessment and governance. The identification of soil research challenges from the perspective of agricultural management facilitates cooperation between different scientific disciplines in the field of sustainable agricultural production.}, language = {en} } @article{VogelGebbersOerteletal.2019, author = {Vogel, Sebastian and Gebbers, Robin and Oertel, Marcel and Kramer, Eckart}, title = {Evaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing}, series = {Sensors}, volume = {19}, journal = {Sensors}, number = {20}, publisher = {MDPI}, issn = {1424-8220}, doi = {10.3390/s19204593}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:eb1-opus-3294}, pages = {16}, year = {2019}, abstract = {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.}, language = {en} }