TY - JOUR A1 - Vogel, Sebastian A1 - Bönecke, Eric A1 - Kling, Charlotte A1 - Kramer, Eckart A1 - Lück, Katrin A1 - Nagel, Anne A1 - Philipp, Golo A1 - Rühlmann, Jörg A1 - Schröter, Ingmar A1 - Gebbers, Robin T1 - 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 JF - Agronomy N2 - 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. KW - soil acidity KW - pH buffer capacity KW - soil-base titration KW - titration curve Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:eb1-opus-3370 SN - 2073-4395 VL - 10 IS - 6 PB - MDPI ER - TY - CHAP A1 - Kling, Charlotte A1 - Schröter, Ingmar A1 - Vogel, Sebastian A1 - Bönecke, Eric A1 - Rühlmann, Jörg A1 - Gebbers, Robin A1 - Lück, Katrin A1 - Schubert, Torsten A1 - Gerlach, Felix A1 - Philipp, Golo A1 - Scheibe, Dirk A1 - Zieger, Karin A1 - Palme, Stefan A1 - Kramer, Eckart ED - Leibniz-Zentrum für Agrarlandschaftsforschung (ZALF), T1 - 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 BT - Book of Abstracts T2 - Landscape 2021 Diversity for sustainable and resilient agriculture, Online conference, 20.-22.09.2021 N2 - 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 (Bönecke et al., 2020; Pätzold 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 (Bönecke 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. Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:eb1-opus-7741 SP - 292 ER - TY - JOUR A1 - Bönecke, Eric A1 - Meyer, Swen A1 - Vogel, Sebastian A1 - Schröter, Ingmar A1 - Gebbers, Robin A1 - Kling, Charlotte A1 - Kramer, Eckart A1 - Lück, Katrin A1 - Nagel, Anne A1 - Philipp, Golo A1 - Gerlach, Felix A1 - Palme, Stefan A1 - Scheibe, Dirk A1 - Zieger, Karin A1 - Rühlmann, Jörg T1 - Guidelines for precise lime management based on high-resolution soil pH, texture and SOM maps generated from proximal soil sensing data JF - Precision Agriculture N2 - 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. KW - Variable rate soil liming; Soil texture; Soil pH; Soil organic matter; Soil sensing; Site specific soil management Y1 - 2020 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:eb1-opus-4890 SN - 1385-2256 VL - 22 SP - 493 EP - 523 PB - Springer Nature ER - TY - JOUR A1 - Horf, Michael A1 - Bönecke, Eric A1 - Gebbers, Robin A1 - Kling, Charlotte A1 - Kramer, Eckart A1 - Rühlmann, Jörg A1 - Schröter, Ingmar A1 - Schwanghart, Wolfgang A1 - Vogel, Sebastian T1 - Utility of visible and near-infrared spectroscopy to predict base neutralizing capacity and lime requirement of quaternary soils JF - Precision Agriculture N2 - 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. KW - Soil acidification; pH buffer capacity; Soil-base titration; Precision agriculture; Chemometrics Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:eb1-opus-4916 SN - 1385-2256 SN - 1573-1618 VL - 24 SP - 288 EP - 309 PB - Springer Nature ER - TY - JOUR A1 - Vogel, Sebastian A1 - Bönecke, Eric A1 - Kling, Charlotte A1 - Kramer, Eckart A1 - Lück, Katrin A1 - Philipp, Golo A1 - Rühlmann, Jörg A1 - Schröter, Ingmar A1 - Gebbers, Robin T1 - 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 JF - Precision Agriculture N2 - 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. KW - Proximal soil sensing KW - Lime requirement KW - Precision farming KW - Sensor data fusion KW - Spatial variability KW - Sensitivity analysis Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:eb1-opus-4140 SN - 1385-2256 SN - 1573-1618 VL - 23 IS - 1 SP - 127 EP - 149 PB - Springer US ER - TY - JOUR A1 - Vogel, Sebastian A1 - Gebbers, Robin A1 - Oertel, Marcel A1 - Kramer, Eckart T1 - Evaluating Soil-Borne Causes of Biomass Variability in Grassland by Remote and Proximal Sensing JF - Sensors N2 - 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. KW - apparent electrical conductivity (ECa) KW - pH KW - UAV KW - boundary-line KW - quantile regression KW - law of minimum Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:eb1-opus-3294 SN - 1424-8220 VL - 19 IS - 20 PB - MDPI ER -