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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.
Etablierung von Luzerne mit Landsberger Gemenge und Wickroggen unter Bedingungen in Brandenburg
(2023)
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.
Concepts to include farmers as co-researchers – the living lab approach
of the project “NutriNet”
(2021)
A key characteristic of current and future challenges towards a climate-resilient and sustainable agricultural landscape is their complexity, that leads to difficulties in implementation of scientific findings in agricultural practice. The implementation of new strategies in nutrient management on organic farms is such a complex challenge due to regional differences in perception and soil resources as well as individual prerequisites on farms. The project “NutriNet: competence- and co-research network for development of nutrient management in organic farming” meets this challenge by building a living lab according to Dell’Erra & Landoni (2014) and Rose et al. (2018) and aims i) to derive region-specific nutrient management advice for organic farms from scientific findings in field trials (nutrient management research), ii) to ensure implementation of these advice through group motivation and enable an exchange of expert knowledge in Field Schools (transformative research), iii) as well as to evaluate key methods, roles, competences and resources for an implementation-oriented co-research process (process research).
Sixty farmers organized in six regions in Germany build the basis of the “NutriNet” living lab, each accompanied by a regional consultant and a group of scientists on a joint level. All actors are involved in the co-research process to varying degrees depending on the stage of the process and can take on different roles. Process steps are ideation, development of the experimental question and design, trial implementation, data analysis, interpretation of data and implementation and transfer of results. The living lab character is especially addressed in the structure of the co-research process for conducting field trials in the “NutriNet” project as it describes a learning system within the system.
The co-research concept allows to take different experimental designs into account, namely field trials with a demonstrative character that provide methodological skills for farmers and pre-test results as well as field trials with spatial and temporal repetitions that meet scientific requirements. In order to reduce the scientific framework conditions to such an extent that, on the one hand, scientifically valid results and, on the other hand, the highest possible feasibility of the experiments by the farmers can be guaranteed, so-called regional and network trials are being tested in NutriNet. The concept is based on taking randomization and spatial repetition into account by repeatedly setting up the trial at several locations. In order to take environmental influences (soil, climate) into account, the possible sites are divided into groups according to site characteristics by using a cluster analysis. By involving not only one but many farmers throughout the whole co-research process and enabling exchange among them in Field-Schools, methodological training and implementation of findings are addressed as a part of the research.
After one year of field trials two regional trials with ten and seven farmers were set up as well as one network trial on thirteen farms in four regions. Methodological evaluation of the process led to adjustments in experimental design, data collection and coordination between experts that are implemented in a new series of network trials starting in autumn 2021.
Societies’ calls for addressing the urgent problems of climate change, biodiversity loss and other environmental crises are becoming ever louder. Responding to this, and to the growing severity of global threats to agricultural and food systems, national and international research funders are increasingly demanding that funded projects achieve real-world transformation of farming systems towards true sustainability and resilience. This development is not only a new funding opportunity for researchers. Because separating the roles of knowledge generation and knowledge exchange with practice will often fail to achieve the necessary transformation, (at least some) researchers will need to engage in this transdisciplinary process of transformative agricultural research. As a consequence, the roles of these researchers need to shift towards stronger integration in farming practice, and deeper exchange with practitioners, adding a new key role to teaching and research. We argue that meeting the goals of the mission of transformative research in agriculture requires three essential freedoms. First, researchers need to be free to engage in this transformative research. This entails a freed mindset, which enables researchers to leave behind traditional roles, e.g. for true knowledge exchange, co-design of projects, and learning from farmers. In addition, there needs to be a stronger appreciation for transdisciplinary and transformative research in the research community, accompanied by a reduced pressure to perform in other currently dominating assessment categories. Second, farmers need to be free to engage in this process as well. Again, this requires an open mindset, e.g. to engage in, and learn from collaborative research, but also available funds and time to engage in the process. Third, transformative agricultural research needs freedom in the project structure and in administrative rules. This includes the permission to fail and to tolerate errors, so that the people involved in the process are able to learn from mistakes. In addition, this is required so that projects are not set up to avoid risks, and can really be transformative and contain innovative elements. Finally, funders will need to show trust in the parties and to reduce administrative burden. Within this framework we discuss the success factors and limitations of transformative agricultural research using examples from various recent national and international projects.
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.
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.
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.