Michael Herbst

Ecotron facilities are key tools to emulate and monitor environmental conditions for improving the understanding of terrestrial ecosystems. Although their conditions can be precisely modified by cutting-edge controlling systems, the external factors influencing the boundary conditions of such ecotron units remain uncertain under reconstructed scenarios (e.g., climate change). We assessed how an agroecosystem model can be used to simulate the bottom boundary conditions of the newly developed ecotron facility AgraSim. The facility comprises six identical ecotron units composed of a climate-controlled plant chamber on top of a cylindrical lysimeter (1 m2 by 150 cm depth). An automatic suction cup–pumping system installed below each lysimeter is used to control the bottom boundary condition of the system depending on the measured pressure head near its bottom. The first experimental trial of AgraSim is aimed to quantify the key climate responses of a typical agricultural field located in North-Rhine Westphalia (Germany) to transient climate change. A set of four climate scenarios were derived from storyline simulations with the regional atmospheric circulation model ICON, imposing a transient temperature gradient of +1oC to +4oC within the climate chambers. While the atmospheric forcings and the soil texture and hydraulic properties are well-characterized, the bottom boundary condition at 150 cm depth is unknown for the different climate change scenarios. We thus used an agroecosystem model (AgroC) to numerically solve water movement within the soil column (0-150 cm) and investigated the impact of choosing one of the following bottom boundary conditions: fixed pressure heads (FP); free-drainage (FD); and a modified seepage face at h=-100hPa (SP). A simulation that assumes a 500 cm deep soil column and free drainage was taken as a reference for assessing the performance of each of the different bottom boundary conditions. This setup was replicated for two rainfed cropping systems (winter wheat and maize), each parameterized with three types of rooting profile representing shallow, deeper and homogeneous root profiles, respectively. A 1-year spin-up run was performed to minimize the effect of the initial conditions. Our model simulations showed that the different boundary conditions only affected soil moisture below 30 cm, while topsoil moisture was mainly controlled by atmospheric forcings. The FP and FD scenarios tended to underestimate soil water availability in the 150 cm column, especially during critical summer drought periods. The modified SP showed the best agreement with the reference simulations, keeping the soil unsaturated during winter and maintaining moisture and fluxes closer to the reference levels in summer. This result was consistent across both crops and rooting profiles. At the crop level, the different boundary conditions had no significant effect on key crop variables (e.g., biomass and leaf area) and their respective response to the climate scenarios. Although our results are limited by observation availability and model parameterization uncertainty, they demonstrate the potential application of process-based models for decision-making in controlled-system facilities.

Increasing soil organic carbon (SOC) stocks in agricultural soils can enhance soil health, support higher crop productivity with fewer inputs, and contribute to offsetting greenhouse gas emissions. SOC can be increased through carbon-farming practices, such as cover crops, residue retention, and carbon-rich organic amendments. However, SOC changes are difficult to monitor at relevant temporal and spatial scales, and existing modelling and data-driven approaches often struggle to capture the complex interactions between management, soil, and future climate conditions.In this poster, we provide details on the implementation of a farm-scale digital twin, defined as an accurate and dynamic representation of a real-world agricultural system that is consistently updated, evaluates future scenarios, and provides actionable insights for stakeholders. The current version of the digital twin supports decision-making for carbon farming and agricultural management while accounting for key interactions within the soil-crop-atmosphere continuum. It is built on the process-based agroecosystem model AgroC, which couples SoilCO2 for water, heat, gas, and solute transport in a one-dimensional soil column, SUCROS for organ-specific crop growth, and RothC for soil carbon turnover. The digital twin was applied to 14 fields of varying size (1.5–15 ha) from a farm in western Germany. Field-specific crop rotation, seeding and harvest dates, fertilization, and application of mushroom compost were implemented in close collaboration with the farmer, enabling simulation of SOC dynamics over periods of 5 to 16 years depending on field data availability.Although within-field soil heterogeneity can be represented using spatially distributed simulations, each field was modelled using a single soil unit, as regional soil maps indicated relatively homogeneous conditions. This assumption was supported by Electromagnetic Induction (EMI) measurements performed on two representative fields in August 2025. For model validation, SOC data from previous years were available for eight fields. Additionally, ten fields were sampled between August and November 2025 at 56 locations, resulting in 164 soil samples for which SOC was estimated via the loss-on-ignition method. Further validation was performed using harvest data. The digital twin simulations well matched measured values, and it was confirmed that the sustainable practices implemented by the farmer had positively influenced SOC trajectories over the study period. Other unique information could be provided to the farmer, such as the rate at which SOC would decrease if regenerative practices were interrupted or the relative importance of individual actions in each field, with residue retention being the most prominent.Taken together, these results show that the developed farm-scale digital twin allows to account for complex interactions within the soil–crop system, can provide holistic, tailored analyses with the potential to not only support SOC management, but also adapt agricultural practices to climate change, improve water regulation, and enhance soil ecosystem functions for sustainable agriculture.

Carbon farming aims to sequester soil organic carbon (SOC) in agroecosystems by increasing soil organic matter content to improve soil health, while contributing to reducing greenhouse gas emissions. In this context, a promising approach is the use of agroecosystem models that can inform and optimize farmer’s practices but also offer a holistic perspective to enhance both agronomic and environmental outcomes while accounting for climate change. In this study, we developed a farm-scale digital twin of 14 fields with a combined area of 80.4 ha for an arable farm in western Germany. The twin uses a spatialized version of the process-based agroecosystem model AgroC, driven by detailed soil, climate, and management data since 2009.In its current version, the digital twin provides a reconstruction of past SOC dynamics and can be used to explore future management scenarios focused on regenerative agricultural practices, such as cover cropping, harvest residue management, and the application of organic amendments. It can thus provide decision support for optimizing carbon sequestration at the field-to-farm scale. The analysis of simulated SOC development for the past 16 years showed that simulated SOC stocks increased by 30.9 Mg C y-1 in the period 2009-2025. This is equivalent to a farm sequestration rate of 0.4 Mg C ha-1 y-1, corresponding to a relative gain of 6.5‰ y-1. To attribute the SOC sequestration achieved by regenerative management to individual practices, we compared SOC trajectories simulated for a system without regenerative management, the farm’s actual management, and each practice considered separately. Across all regenerative practices, cover cropping accounted for 4.2% of the additionally sequestered SOC, harvest residue retention for 69.7%, and organic fertilizer applications for 15.1%, with the remaining 11% attributable to interaction effects among these practices. For a moderate future climate scenario (RCP 4.5 ensemble), an additional carbon sequestration potential of 7.5±0.7 Mg C ha-1 by 2050 is predicted for a representative field under present management. Sequestration rates were found to slow after around 2028–2032 and to stagnate thereafter.Beyond these results for current management, the digital twin provides a decision-support environment in which farmers can explore future SOC management options, such as effects of new cover crops, altered crop rotation sequences, and improved cultivars. The available options can be compared for their long-term SOC sequestration potential, and the farmer can select the strategies that fit the farm-specific objectives and constraints best. When new observations on SOC, biomass, and yield become available, the digital twin can be updated and used to reevaluate the scenarios.In conclusion, the digital twin for carbon farming introduced here is a promising tool to identify locally adapted, farm-specific management strategies that further increase and sustain SOC and to evaluate their robustness under current and future climate conditions. These management changes may also have implications for other ecosystem services, including nutrient leaching and agricultural productivity. Our process-based approach already represents these processes, and in future work we aim to extend the digital twin to explore trade-offs and synergies between a range of ecosystem services beyond carbon sequestration.

Phosphorus (P) is removed from agroecosystems through harvesting, and sustainable management must include P fertilization as P availability affects crop performance. However, accurate assessment of plant‐available P is challenging. In this study, two promising approaches are combined to assess the plant‐available P of a 22‐year long‐term experiment (LTE) near Rostock, Germany. We hypothesize agreement between a modern P test method and process‐based model estimates of plant‐available P. The diffusive gradients in thin films (DGT) technique offers an accurate P test method because it mimics the diffusion and desorption of soil P in the presence of root uptake. This was applied in a synergetic combination with a state‐of‐the‐art agroecosystem model that was extended with a P cycling module. The simulations and yearly DGT‐P analyses comprise 4 treatments: no P fertilization, mineral P fertilization with triple‐superphosphate, organic P fertilization with compost, and mineral plus organic P fertilization. Soils at 0–30 cm depth were sampled in four replicates on a yearly basis between 1999 and 2021. In addition, a P fractionation was applied for 2015 using the Hedley approach, which made it possible to link non‐plant‐available, steady P fractions with the respective model pools. The comparison between DGT‐P determined plant‐available P up to a depth of 30 cm and that estimated from the pools of the agroecosystem model AgroC showed agreement with respect to the differences between the treatments and with respect to the temporal evolution ( R ² between 0.65 and 0.7). Less agreement was detected for DGT‐P and the respective model pools in deeper soil. A closer match over soil depth was found between grouped Hedley P fractions and AgroC model pools. Both, model and DGT‐P analyses indicate that a new plant‐available P equilibrium will be established under the new P management after about 12 years for the Rostock site, which points to the resilience of P cycling in agroecosystems. We conclude that the combined application of DGT‐P analysis and agroecosystem modeling offers a robust and accurate quantification of plant‐available P in the plough layer and can be used to create an agricultural digital twin with respect to soil P availability and its impact on crop yield.

Carbon farming aims to sequester carbon in agroecosystems by increasing soil organic matter content and reducing greenhouse gas emissions, while also improving soil health. It also affects a range of other ecosystem services, such as water regulation, nutrient cycling, and agricultural productivity. Robust methods are needed to assess the potential and effectiveness of carbon farming approaches and to analyze potential trade-offs between soil carbon sequestration and other ecosystem services. In this context, agroecosystem models that inform and optimize farmer’s practices while offering a holistic perspective for the optimization of both agronomic and environmental outcomes in current and future climatic conditions seem a promising tool. This work presents the first steps towards the development of a digital twin for a real-world farm (Damianshof, Rommerskirchen, Germany) using a spatialized version of the agroecosystem model AgroC, which simulates soil water, heat, carbon and nitrogen fluxes. The digital twin is driven by high-resolution soil, climate and farm management data from the year 2010 onwards. This enables the evaluation of different management options to increase soil carbon sequestration, with a particular focus on regenerative management practices such as the use of cover crops and different crop genotypes with more recalcitrant root systems. By simulating and analyzing these scenarios, the digital twin will provide insights for optimizing management decisions while considering multiple ecosystem services. In future studies, the digital twin will serve as a valuable tool for assessing the broader impacts of global change on the ecosystem services and soil health associated with carbon farming.

Water and N availability are key factors limiting crop yield, particularly in marginal soils. This study evaluated the effects of water and N stress on barley grown in marginal soils using field trials and the AgroC model. Experiments from 2020 to 2022 in Lithuania with spring barley cv. KWS Fantex under two N fertilization treatments on sandy soil provided data for model parameterization. The AgroC model simulated barley growth to assess yield potential and yield gaps due to water and N stress. Potential grain yields (assuming no water or N stress) ranged from 4.8 to 6.02 t DW ha⁻¹, with yield losses up to 54.4% assuming only N stress and 59.2% assuming only water stress, even with the N100 treatment (100 kg N ha⁻¹ yr⁻¹). A synthetic case study varying N fertilization from 0 to 200 kg N ha⁻¹ yr⁻¹ showed that increasing N still enhanced yield, but the optimal rate of 100–120 kg N ha⁻¹ yr⁻¹ depended on climatic conditions, leading to uncertainty in fertilization recommendations. This study underscores the importance of integrating advanced modeling techniques with sustainable agricultural practices to boost yield potential and resilience in marginal soils. Incorporating remote sensing data to capture soil and crop variability is recommended for improving simulation accuracy, contributing to sustainable agriculture strategies in the Baltic–Nordic region.

Climate change poses a challenge for resource utilization and environmental pollution issues caused by agricultural production, especially in arid to semi-arid regions. Farmland water, carbon and nitrogen (WCN) balances are closely related to these resource and environmental issues. Thus, the Agro-Hydrological & chemical and Crop systems simulator (AHC) was used to assess the response of WCN balances to climate change in a spring wheat farmland of arid Northwest China and to propose adaptation strategies. Five Global Climate Models from the Coupled Model Intercomparison Project 6 and two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5) were used to establish scenarios with the AHC model to simulate farmland WCN balances for the 2025–2100 period. Various irrigation amounts and nitrogen fertilization rates were tested as compensation strategies. Results indicated that precipitation showed an increasing trend, thus percolation increased and soil water consumption decreased from 2025 to 2100. However, for the carbon budget, although the soil CO2 emissions tend to decrease, the net primary production (NPP) was also significantly reduced, which resulted in declining the net ecosystem carbon budget (NECB) under future climatic conditions. In addition, higher temperature and increased precipitation enhanced soil inorganic nitrogen leaching and N2O emissions but reduced NH3 volatilization from 2025 to 2100. Overall, the soil total nitrogen loss was increased over time, whereas crop nitrogen uptake (CNU) was significantly reduced. In relation to the SSP1-2.6 scenario, the SSP5-8.5 scenario accelerated the increase rates of soil water percolation and total nitrogen loss over time, as well as the decrease rates of CNU and NPP over time. The negative effects caused by climate change can be mitigated by reducing irrigation and increasing nitrogen fertilization. For the SSP1-2.6 scenario, 30% irrigation reduction and 30% nitrogen fertilization increase can effectively decrease soil water percolation and the related nitrogen losses while CNU, NPP and NECB increase in relation to the current management (240 mm irrigation and 200 kg ha–1 nitrogen fertilization). For SSP5-8.5 the strategy with 45% irrigation reduction and 45% nitrogen fertilization increase can also decrease nitrogen losses and increase CNU, NPP and NECB.

To reach their net‐zero targets, countries will have to compensate hard‐to‐abate CO₂ emissions through carbon dioxide removal (CDR). Yet, current assessments rarely include socio‐cultural or institutional aspects or fail to contextualize CDR options for implementation. Here we present a context‐specific feasibility assessment of CDR options for the example of Germany. We assess 14 CDR options, including three chemical carbon capture options, six options for bioenergy combined with carbon capture and storage (BECCS), and five options that aim to increase ecosystem carbon uptake. The assessment addresses technological, economic, environmental, institutional, social‐cultural and systemic considerations using a traffic‐light system to evaluate implementation opportunities and hurdles. We find that in Germany CDR options like cover crops or seagrass restoration currently face comparably low implementation hurdles in terms of technological, economic, or environmental feasibility and low institutional or social opposition but show comparably small CO₂ removal potentials. In contrast, some BECCS options that show high CDR potentials face significant techno‐economic, societal and institutional hurdles when it comes to the geological storage of CO₂. While a combination of CDR options is likely required to meet the net‐zero target in Germany, the current climate protection law includes a limited set of options. Our analysis aims to provide comprehensive information on CDR hurdles and possibilities for Germany for use in further research on CDR options, climate, and energy scenario development, as well as an effective decision support basis for various actors.

Essential soil functions such as plant productivity, C storage, nutrient cycling and the storage and purification of water all depend on soil biological processes. Given this insight, it is remarkable that in modeling of these soil functions, the various biological actors usually do not play an explicit role. In this review and perspective paper we analyze the state of the art in modeling these soil functions and how biological processes could more adequately be accounted for. We do this for six different biologically driven processes clusters that are key for understanding soil functions, namely i) turnover of soil organic matter, ii) N cycling, iii) P dynamics, iv) biodegradation of contaminants v) plant disease control and vi) soil structure formation. A major conclusion is that the development of models to predict changes in soil functions at the scale of soil profiles (i.e. pedons) should be better rooted in the underlying biological processes that are known to a large extent. This is prerequisite to arrive at the predictive models that we urgently need under current conditions of Global Change.