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There has been an increasing interest in using multi-model ensembles over the past decade. While it has been shown that ensembles often outperform individual models, there is still a lack of methods that guide the choice of the ensemble members. Previous studies found that model similarity is crucial for this choice. Therefore, we introduce a method that quantifies similarities between models based on so-called energy statistics. This method can also be used to assess the goodness-of-fit to noisy or deterministic measurements. To guide the interpretation of the results, we combine different visualization techniques, which reveal different insights and thereby support the model development. We demonstrate the proposed workflow on a case study of soil–plant-growth modeling, comparing three models from the Expert-N library. Results show that model similarity and goodness-of-fit vary depending on the quantity of interest. This confirms previous studies that found that “there is no single best model” and hence, combining several models into an ensemble can yield more robust results.

Microorganisms possess the ability to adapt to different environmental conditions through the use of various strategies. This diversity in strategies allows us to categorize them based on their functions in the ecosystem. Copiotrophs have a fast growth rate but a low carbon use efficiency (CUE), while oligotrophs have a slow growth rate but a high CUE. In the rhizosphere, the effect of root exudation on different functional microbial groups is not well understood. Process-based modeling is a useful tool to analyze the complex feedback between roots and soil in the rhizosphere. Here, we present a rhizosphere model that explicitly considers two different microbial groups (oligotrophs and copiotrophs) classified based on their microbial traits that correlates each other due to physiological trade-offs and organic carbon accessibility (dissolved organic carbon, mucilage and sorbed carbon). The model is one-dimensional axisymmetric, simulating a soil cylinder around individual root segments. The model was conditioned using a novel constraint-based Markov chain Monte Carlo parameter sampling method. Applying this approach enabled the identification of parameter sets that led to plausible model results in agreement with experimental findings from a comprehensive literature review. The conditioned model predicts organic matter concentration curves from the root surface into the soil driven by root exudation. Our simulations show a decreasing pattern of dissolved organic carbon, which is utilized by oligotrophs and copiotrophs, away from the root surface. Furthermore, we observe a slightly higher proportion of copiotrophs than oligotrophs near the root surface and dominance of copiotrophic biomass at very high nutrient availability conditions as expected from ecological theory and experimental evidence. However, the model predictions are still highly uncertain. Thus, further experimental data and observations are required for model conditioning.

In Poyda et al. (2019), we determined the carbon balance of six cropland sites in Southwest Germany on the basis of half-hourly net ecosystem exchange (NEE) fluxes measured with the eddy covariance (EC) method over a period of eight years from 2010 to 2017. We came up with the finding, that the sites lost on average about one ton of carbon per hectare and year. This huge loss was surprising, because the sites have been used as croplands already over several decades, and one would expect that the sites should be close to steady-state. In April 2022, we performed a soil organic carbon inventory at one of the six sites, and compared it with carbon data collected in April 2009. The soil inventory data do not give any evidence for a carbon loss in the order of one ton of carbon per hectare and year. Based on the data collected in April 2009, the carbon stock size of the plowing horizon at that time was 39.1 t C ha-1 yr-1. The carbon stock size determined in April 2022 was in the same range and amounted 39.9 t C ha-1 yr-1 with a standard error of 1.9 t C ha-1 yr-1, what means that these data indicate that the carbon budget of the cropland is indeed in or close to steady-state. In Poyda et al. (2019), the NEE flux data were gap-filled with the widely used software tool REddyProc. In REddyProc, the user has the option to apply or not to apply the friction velocity (u*) filter before gap-filling. In Poyda et al. (2019), the u* filter was applied before gap-filling. We reprocessed the data for the year 2016, the only year with no winter time gaps, because a methanol fuel cell supplied the station with additional power, without applying the u* filter. Without applying the u* filter the cumulated annual NEE was ‑2080 kg C ha-1. Applying the u* filter increased the NEE by 836 kg C ha-1 to -1244 kg C ha-1. We did the same comparison for NEE fluxes measured at the same site over the years 2019, 2020 and 2021, and we got a similar result. The application of the u* filter increased the mean NEE by +730 kg C ha-1. The cumulated NEE was -2832 kg C ha-1 without u* filter and -2102 kg C ha-1 with u* filter. This positive bias is in the range of the EC based derived carbon loss and is able to explain a major part of the suspected positive bias. We recommend all REddyProc users to check their NEE data for this phenomena simply by processing the data once with and once without enabling the u* filter and comparing both results.

The turnover and stabilization of soil organic matter (SOM) is the key to highly productive agriculture and to climate change mitigation. The pathways of matter and energy use by soil microorganisms are controlled by their functional traits and interactions. We present the concept of a novel data-driven bioenergetic soil organic matter model and shed light on integrating ecological trait-based ecological frameworks with process-based models. The model links SOM thermodynamics with key microbial traits, corresponding substrate-specific degradation pathways and extracellular enzymatic depolymerization processes. Carbon and energy flows will be quantitatively traced by modeling mass fractions of specific C source pools, C isotopologues and thermodynamic properties (degree of reduction) as dynamic state variables. The model uses functional microbial pools (generalists, specialists and stress tolerators) with specific trait trade-offs to reflect microbial life and growth strategies in line with current ecological theory. We use microbial transition state theory to reflect microbial growth kinetics and test this approach compared to empirical Monod kinetics. Resource use (i.e., enzyme synthesis) is modeled assuming that microbes are naturally selected to maximize their reproductive success (i.e., growth rate). Using a case study, we further demonstrate the applicability of a constraint-based model conditioning method for simulating microbial SOM processing. Based on this, we explain how constraint-based model conditioning could be used to parameterize bioenergetic models for predicting SOM stabilization in agroecosystems.

Microbial pesticide degraders are heterogeneously distributed in soil. Their spatial aggregation at the millimeter scale reduces the frequency of degrader-pesticide encounter and can introduce transport limitations to pesticide degradation. We simulated reactive pesticide transport in soil to investigate the fate of the widely used herbicide 4-chloro-2-methylphenoxyacetic acid (MCPA) in response to differently aggregated distributions of degrading microbes. Four scenarios were defined covering millimeter scale heterogeneity from homogeneous (pseudo-1D) to extremely heterogeneous degrader distributions and two precipitation scenarios with either continuous light rain or heavy rain events. Leaching from subsoils did not occur in any scenario. Within the topsoil, increasing spatial heterogeneity of microbial degraders reduced macroscopic degradation rates, increased MCPA leaching, and prolonged the persistence of residual MCPA. In heterogeneous scenarios, pesticide degradation was limited by the spatial separation of degrader and pesticide, which was quantified by the spatial covariance between MCPA and degraders. Heavy rain events temporarily lifted these transport constraints in heterogeneous scenarios and increased degradation rates. Our results indicate that the mild millimeter scale spatial heterogeneity of degraders typical for arable topsoil will have negligible consequences for the fate of MCPA, but strong clustering of degraders can delay pesticide degradation.

Important topics in Land-Atmosphere (L-A) feedback research are water and energy balances and heterogeneities of fluxes at the land-surface and in the atmospheric booundary layer. To target these questions, the Land-Atmosphere Feedback Observatory (LAFO) has been installed in Southwest Germany. The instrumentation allows comprehensive and high-resolution measurements from the bedrock to the lower free troposphere. Grouped in three components: atmosphere, soil and land-surface and vegetation, the LAFO observation strategy aims for simultaneous measurements in all three compartments. For that the LAFO sensor synergy contains lidar systems to measure the atmospheric key variables humidity, temperature and wind. At the land-surface eddy covariance stations operated to record the energy distribution of radiation, sensible, latent and ground heat fluxes. With a water and temperature sensor network the soil water and temperature is monitored in the agricultural investigation area. The observations in LAFO are organized in operational measurements and intensive observation periods (IOPs). Operational measurements aim for long timeseries dataset to investigate statistics as we present as example the correlation between mixing layer height and surface fluxes. The potential of IOPs is demonstrated with a 24 hour case study with dynamic and thermodynamic profiles with lidar as well as a surface layer observation with the scanning differential absorption lidar to relate atmospheric humidity patterns to soil water structures. Both long-term observations and IOPs are important for improving the representation of L-A feedbacks in climate and numerical weather prediction models.

We present a comprehensive, high-quality dataset characterizing soil–vegetation and land surface processes from continuous measurements conducted in two climatically contrasting study regions in southwestern Germany: the warmer and drier Kraichgau region with a mean temperature of 9.7 ∘C and annual precipitation of 890 mm and the cooler and wetter Swabian Alb with mean temperature 7.5 ∘C and annual precipitation of 1042 mm. In each region, measurements were conducted over a time period of nine cropping seasons from 2009 to 2018. The backbone of the investigation was formed by six eddy-covariance (EC) stations which measured fluxes of water, energy and carbon dioxide between the land surface and the atmosphere at half-hourly resolution. This resulted in a dataset containing measurements from a total of 54 site years containing observations with a multitude of crops, as well as considerable variation in local growing-season climates. The presented multi-site, multi-year dataset is composed of crop-related data on phenological development stages, canopy height, leaf area index, vegetative and generative biomass, and their respective carbon and nitrogen content. Time series of soil temperature and soil water content were monitored with 30 min resolution at various points in the soil profile, including ground heat fluxes. Moreover, more than 1200 soil samples were taken to study changes of carbon and nitrogen contents. The dataset is available at https://doi.org/10.20387/bonares-a0qc-46jc (Weber et al., 2021). One field in each region is still fully set up as continuous observatories for state variables and fluxes in intensively managed agricultural fields.

Plant phenology models are important components in process-based crop models, which are used to assess the impact of climate change on food production. For reliable model predictions, parameters in phenology models have to be accurately known. They are usually estimated by calibrating the model to observations. However, at regional scales in which different cultivars of a crop species may be grown, not accounting for inherent differences in phenological development between cultivars in the model and the presence of model deficits lead to inaccurate parameter estimates. To account for inherent differences between cultivars and to identify model deficits, we used a Bayesian multi-level approach to calibrate a phenology model (SPASS) to observations of silage maize grown across Germany between 2009 and 2017. We evaluated four multi-level models of increasing complexity, where we accounted for different combinations of ecological, weather, and year effects, as well as the hierarchical classification of cultivars nested within ripening groups of the maize species. We compared the calibration quality from this approach to the commonly used pooled approach in which none of these factors are considered. Our approach proved successful in improving calibration quality by incorporating the hierarchical classification of cultivars within ripening groups of maize. Our findings have implications for regional model calibration and data-gathering studies, since it emphasizes that ripening group and cultivar information is essential. Furthermore, we found that if this information is not available, at least weather, ecological regions and year effects should be taken into account. Our results can facilitate model improvement studies since we identified possible model limitations related to temperature effects in the reproductive (post-flowering) phase and to soil-moisture. We demonstrate that Bayesian multi-level calibration of a phenology model facilitates the incorporation of hierarchical dependencies and the identification of model limitations. Our approach can be extended to full crop models at different spatial scales.

Pesticides are widely used in agriculture despite their negative impact on ecosystems and human health. Biogeochemical modeling facilitates the mechanistic understanding of microbial controls on pesticide turnover in soils. We propose to inform models of coupled microbial dynamics and pesticide turnover with measurements of the abundance and expression of functional genes. To assess the advantages of informing models with genetic data, we developed a novel "gene-centric" model and compared model variants of differing structural complexity against a standard biomass-based model. The models were calibrated and validated using data from two batch experiments in which the degradation of the pesticides dichlorophenoxyacetic acid (2,4-D) and 2-methyl-4-chlorophenoxyacetic acid (MCPA) were observed in soil. When calibrating against data on pesticide mineralization, the gene-centric and biomass-based models performed equally well. However, accounting for pesticide-triggered gene regulation allows improved performance in capturing microbial dynamics and in predicting pesticide mineralization. This novel modeling approach also reveals a hysteretic relationship between pesticide degradation rates and gene expression, implying that the biodegradation performance in soils cannot be directly assessed by measuring the expression of functional genes. Our gene-centric model provides an effective approach for exploiting molecular biology data to simulate pesticide degradation in soils.

Crop models are tools used for predicting year-to-year crop development on field to regional scales. However, robust predictions are hampered by uncertainty in crop model parameters and in the data used for calibration. Bayesian calibration allows for the estimation of model parameters and quantification of uncertainties, with the consideration of prior information. In this study, we used a Bayesian sequential updating (BSU) approach to progressively incorporate additional data at a yearly time-step in order to calibrate a phenology model (SPASS) while analysing changes in parameter uncertainty and prediction quality. We used field measurements of silage maize grown between 2010 and 2016 in the regions of Kraichgau and the Swabian Alb in southwestern Germany. Parameter uncertainty and model prediction errors were expected to progressively be reduced to a final, irreducible value. Parameter uncertainty was reduced as expected with the sequential updates. For two sequences using synthetic data, one in which the model was able to accurately simulate the observations, and the other in which a single cultivar was grown under the same environmental conditions, prediction error was mostly reduced. However, in the true sequences that followed the actual chronological order of cultivation by the farmers in the two regions, prediction error increased when the calibration data were not representative of the validation data. This could be explained by differences in ripening group and temperature conditions during vegetative growth. With implications for manual and automatic data streams and model updating, our study highlights that the success of Bayesian methods for predictions depends on a comprehensive understanding of the inherent structure in the observation data and of the model limitations.

Exudation of organic carbon triggers complex spatial and temporal patterns of biophysical and biochemical processes in the root-influenced soil (rhizosphere). We use process-based modeling as a tool to gain insights into microbial interactions and carbon cycling in the rhizosphere. Here, we present a trait-based rhizosphere model that accounts for two different functional microbial groups (copiotrophs, oligotrophs) that differ according to life-history strategies, microbial physiology (e.g., dormancy) and carbon turnover (small and large polymers). The model is calibrated and validated against experimental data from the literature. We apply a parameter search algorithm that identifies plausible parameter spaces by conditioning model outputs to parameter and process constraints that reflect current ecological knowledge. We show the general concept of the model, first simulations after model conditioning, and a concept for coupling the rhizosphere model with the structural-functional plant model CPlantBox to cover the whole-plant scale.

Mechanistic models facilitate understanding complex biogeochemical interactions and process chains in soil. However, biogeochemical soil models often have weakly constraint parameters and show sloppiness. That means the dimensionality of parameter spaces is overly large and parameters often cannot be inferred based on available experimental data. Thus, equifinality arises, i.e. many different parameter combinations lead to very similar or identical model predictions.Expert knowledge represents a synthesis of existing knowledge on processes in soil systems that can be used to find viable parameter regions such that models give plausible predictions in line with evidence-based expectations. Here, we present an approach to leverage expert knowledge. This is achieved by formulating expert knowledge in terms of parameter and process constraints that must be fulfilled. Viable parameter sets are then identified by model conditioning using a novel Bayesian constraint-based parameter search algorithm that extends a previously published iterative constraint-based parameter search method. The algorithm successively applies stricter conditions by increasing the minimum acceptable number of process constraints to be satisfied in each iteration. We present the concept of the algorithm and demonstrate a successful application to a complex model simulating biodegradation of the herbicide Atrazine that has a high-dimensional parameter space. The presented approach can be widely applied to other soil biogeochemical models and provides a powerful tool to leverage expert knowledge for constructing robust prior parameter distributions for model sensitivity analysis or calibration.

The exchange of energy, moisture and momentum between the atmosphere and the land-surface as well as the associated feedback processes are decisive for the development of the planetary boundary layer. Inaccurate representation and parameterization of these processes are a weakness of current weather and climate models. Improvements in these areas will contribute significantly to better simulations of cloud formation on all temporal and spatial scales. This requires simultaneous measurements of the land-atmosphere system in all compartments. Both the LAFE and the new LAFO design with their instrument synergies have already made important contributions to this. With comparisons between model parameterizations and observations, e.g. the applicability of the Monin-Obukhov similarity theory (MOST) in the case of natural heterogeneous land surface can be investigated or new parameterizations can be developed.The Land-Atmosphere Feedback Experiment (LAFE, Wulfmeyer et al., 2018) was performed in August 2017 as a measurement campaign at the Atmospheric Radiation Measurements (ARM) Program Southern Great Plains site in Oklahoma, USA. For boundary layer observations, scanning Doppler lidar systems for wind measurements, rotational Raman lidar for temperature and humidity measurements, and differential absorption lidar for water vapor measurements were setup. At the land-surface, meteorological and plant dynamics variables, energy balance, and soil moisture and temperature were recorded at eddy covariance stations. These measurements are also executed at the Land-Atmosphere Feedback Observatory (LAFO, lafo.uni-hohenheim.de) at the University of Hohenheim in Stuttgart (Germany) to collect long-term time series in addition to field experiments. Here, lidar measurements are operationally operated and complemented by measurements from a Doppler cloud radar. At the land surface we measure with eddy covariance stations and a network of soil moisture and temperature sensors and the vegetation status is recorded in the study area. This sensor synergy in LAFO is prototype for GLAFOs (Gewex LAFOs, Wulfmeyer et al. 2020) to establish these measurements in different climate regions in the world.In this contribution, we present the measurement concept and how observations can be used to study and improve boundary layer and turbulence parameterizations. We demonstrate this with measurement results from LAFE and LAFO with estimates of fluxes determined by combining the moisture, temperature, and wind profiles near the ground, allowing the derivation of appropriate similarity relationships for both entrainment fluxes and MOST. Wulfmeyer et al., 2018, doi: 10.1175/BAMS-D-17-0009.1Wulfmeyer et al. 2020, GEWEX Quarterly Vol. 30, No. 1.

Der Austausch von Energie, Feuchte und Impuls zwischen der Atmosphäre und der Landoberfläche sowie die damit verbundenen Rückkopplungsprozesse sind maßgeblich für die Entwicklung der planetarischen Grenzschicht. Eine ungenaue Darstellung und Parametrisierung dieser Prozesse stellen eine Schwäche der heutigen Wetter- und Klimamodelle dar. Verbesserungen in diesen Bereichen werden einen signifikanten Beitrag zu besseren Simulationen der Wolkenbildung auf allen zeitlichen und räumlichen Skalen leisten. Dazu ist es notwendig, das System Land-Atmosphäre simultan in allen Kompartimenten zu vermessen. Dazu haben sowohl das LAFE- als auch das neue LAFO-Design mit deren Messgerätesynergien schon wichtige Beiträge geliefert. Mit Vergleichen zwischen Modellparametrisierungen und Beobachtungen können z.B. die Anwendbarkeit der Monin-Obukhov Ähnlichkeitstheorie (MOST) bei natürlicher heterogener Landoberfläche überprüft oder neue Parametrisierungen entwickelt werden. Das LAFE (Land-Atmosphere Feedback Experiment, Wulfmeyer et al. 2018) wurde im August 2017 als Messkampagne am Standort des Atmospheric Radiation Measurements (ARM) Program Southern Great Plains in Oklahoma, USA, umgesetzt. Für Grenzschichtbeobachtungen kamen scannende Dopplerlidar-Systeme für Windmessungen, Rotations-Ramanlidar für Temperatur- und Feuchtemessungen und Differentielles Absorptionslidar zur Wasserdampfmessung in der Grenzschicht zum Einsatz. An der Landoberfläche wurden meteorologische und pflanzendynamische Variablen, Energiebilanz, sowie Bodenfeuchte und -temperatur an Eddy-Kovarianz-Stationen erfasst. Diese Messungen verfolgen wir auch am Land-Atmosphäre Feedback Observatorium (LAFO, lafo.uni-hohenheim.de) an der Universität Hohenheim in Stuttgart (Deutschland) um neben Feldexperimenten auch lange Zeitreihen zu erfassen. Hier werden Lidarmessungen operationell betrieben und ergänzt durch Messungen eines Doppler-Wolkenradars. An der Landoberfläche messen Eddy-Kovarianz-Stationen und ein Netzwerk von Bodenfeuchte- und -temperatursensoren, sowie wird der Vegetationsstatus im Untersuchungsgebiet erfasst. Diese Sensorsynergie im LAFO ist Prototyp für GLAFOs (Gewex LAFOs, Wulfmeyer et al. 2020) zur Etablierung dieser Messungen in verschiedenen Klimaregionen der Erde. In diesem Beitrag stellen wir das Messkonzept vor und wie Beobachtungen für die Untersuchung und Verbesserung von Grenzschicht- und Turbulenz-Parametrisierungen eingesetzt werden können. Dies zeigen wir mit Messergebnissen von LAFE und LAFO mit Abschätzungen der Flüsse, die durch Kombination der Feuchte-, Temperatur- und Windprofile in Bodennähe bestimmt werden und die Ableitung entsprechenden Ähnlichkeitsbeziehung sowohl für Entrainment-Flüsse als auch für MOST ermöglichen.

Funding Information: This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modeling Framework), funded by the German Research Foundation ( DFG , Grant Agreement SFB 1253/1 2017 ), the Academy of Finland through projects AICropPro ( 316172 ) and DivCSA ( 316215 ) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes project ''Soil3'' ( BOMA 03037514 ) of the Federal Ministry of Education and Research ( BMBF ), Germany, the Deutsche Forschungsgemeinschaft ( DFG , German Research Foundation ) under Germany's Excellence Strategy - EXC 2070–390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B ) funded by the Federal Ministry of Education and Research ( BMBF , Germany), the INRA ACCAF meta-programme, the German Federal Ministry of Education and Research ( BMBF ) in the framework of the funding measure “Soil as a Sustainable Resource for the Bioeconomy – BonaRes”, project “BonaRes (Module B): BonaRes Centre for Soil Research , subproject B” (grant 031B0511B ), the National Key Research and Development Program of China ( 2017YFD0300205 ), the National Science Foundation for Distinguished Young Scholars ( 31725020 ), the Priority Academic Program Development of Jiangsu Higher Education Institutions ( PAPD ), the 111 Project ( B16026 ), and China Scholarship Council , the Agriculture and Agri-Food Canada's Project 1387 under he Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 ‘Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale, the U.S. Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133 ) and USDA / NIFA HATCH grant N. MCL02368 , the National Key Research and Development Program of China ( 2016YFD0300105 ), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries , the Academy of Finland through project AI-CropPro ( 315896 ), the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies ( D.M. 24064/7303/15 of 6/Nov/2015), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797 ). The order in which the donors are listed is arbitrary. Funding Information: This work was in part supported by the Collaborative Research Center 1253 CAMPOS (Project 7: Stochastic Modeling Framework), funded by the German Research Foundation (DFG, Grant Agreement SFB 1253/1 2017), the Academy of Finland through projects AICropPro (316172) and DivCSA (316215) and Natural Resources Institute Finland (Luke) through a strategic project BoostIA, the BonaRes project ''Soil3'' (BOMA 03037514) of the Federal Ministry of Education and Research (BMBF), Germany, the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy - EXC 2070?390732324 EXC (PhenoRob), the project BiomassWeb of the GlobeE programme (Grant number: FKZ031A258B) funded by the Federal Ministry of Education and Research (BMBF, Germany), the INRA ACCAF meta-programme, the German Federal Ministry of Education and Research (BMBF) in the framework of the funding measure ?Soil as a Sustainable Resource for the Bioeconomy ? BonaRes?, project ?BonaRes (Module B): BonaRes Centre for Soil Research, subproject B? (grant 031B0511B), the National Key Research and Development Program of China (2017YFD0300205), the National Science Foundation for Distinguished Young Scholars (31725020), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), the 111 Project (B16026), and China Scholarship Council, the Agriculture and Agri-Food Canada's Project 1387 under he Canadian Agricultural Partnership, the DFG Research Unit FOR 1695 ?Agricultural Landscapes under Global Climate Change ? Processes and Feedbacks on a Regional Scale, the U.S. Department of Agriculture National Institute of Food and Agriculture (award no. 2015-68007-23133) and USDA/NIFA HATCH grant N. MCL02368, the National Key Research and Development Program of China (2016YFD0300105), The Broadacre Agriculture Initiative, a research partnership between University of Southern Queensland and the Queensland Department of Agriculture and Fisheries, the Academy of Finland through project AI-CropPro (315896), the JPI FACCE MACSUR2 project, funded by the Italian Ministry for Agricultural, Food, and Forestry Policies (D.M. 24064/7303/15 of 6/Nov/2015), the Ministry of Education, Youth and Sports of Czech Republic through SustES - Adaption strategies for sustainable ecosystem services and food security under adverse environmental conditions (project no. CZ.02.1.01/0.0/0.0/16_019/000797). The order in which the donors are listed is arbitrary. Publisher Copyright: © 2021 Elsevier Ltd Calibration, the estimation of model parameters based on fitting the model to experimental data, is among the first steps in many applications of process-based models and has an important impact on simulated values. We propose a novel method of developing guidelines for calibration of process-based models, based on development of recommendations for calibration of the phenology component of crop models. The approach was based on a multi-model study, where all teams were provided with the same data and asked to return simulations for the same conditions. All teams were asked to document in detail their calibration approach, including choices with respect to criteria for best parameters, choice of parameters to estimate and software. Based on an analysis of the advantages and disadvantages of the various choices, we propose calibration recommendations that cover a comprehensive list of decisions and that are based on actual practices.

The carbon (C) sequestration potential of croplands has recently become a subject of debate because it may contribute significantly to global climate change mitigation. By contrast, carbon dioxide (CO2) emissions from German croplands have continuously increased over the past decades as a result of soil organic carbon (SOC) losses. Contrasting results, however, have been obtained on the C sink or source function of European croplands based on long-term experiments and rather recent eddy covariance (EC) measurements. Over a period of eight years (2010–2017), we measured the net ecosystem exchange (NEE) of CO2 on six intensively managed cropland sites in two climatically different regions of southwest Germany (Kraichgau (KR) and Swabian Jura (SJ)) using the EC technique. Additionally, we measured aboveground crop biomass at three development stages and estimated management-related C inputs and exports. The inter-annual on-site variability of cumulated annual NEE was large, and neither the region nor the different sites significantly affected NEE budgets. Winter rapeseed showed the lowest CO2 uptake capacity among the observed crops, and the mean annual NEE in the years with winter rapeseed harvest was significantly lower compared to winter wheat, silage maize and winter barley. On average over 46 site-years, annual NEE showed a distinct CO2 uptake of −2580 kg CO2-C ha−1 yr−1. Considering management-related C fluxes, the resulting net biome productivity (NBP) indicated a C source function of the study sites with mean annual losses of 1190 kg C ha−1 yr−1. Due to high C removals after whole plant harvests, silage maize cropping resulted in significantly higher C losses of 4280 kg C ha−1 yr−1 compared to winter rapeseed, winter wheat and winter barley, with mean annual NBPs of 1430, −188 and −1340 kg C ha−1 yr−1, respectively. Consequently, a higher share of exported C in annual NBP resulted in higher C losses. We conclude that the recently increased importance of silage maize in crop rotations destabilizes SOC stocks, threatening the efforts in enhancing soil C sequestration. This calls for further investigations on the C sequestration potentials of more diverse crop rotations including perennial phases.

From hypothesis testing to impact assessment, soil-crop models serve a multitude of purposes in researching climate change impact on the water cycle in cropping systems. As such, model complexity undeniably differs amongst approaches and intended purpose but must include the full range of soil-water states. Climate change is predicted to increase prolonged periods of dry conditions, however, soil-crop models, often misrepresented drying of soils, inducing misquantifications in root water uptake, evapotranspiration, and concomitantly, water use efficiency and ground water recharge rates. While in soil hydrology, this has been recognised and effective solutions exist, soil-crop models typically neglect these more comprehensive approaches.Our study serves two aims: For one, we shed light into the chaotic multitude of soil hydraulic property models, by representing the models in a systematic framework, of sub-models and processes. This model is called the Brunswick Soil Hydraulic Property Model Framework. With this we hope to make soil hydrology more tangible, for non-experts. Secondly, we i) demonstrate that representing soil hydraulic properties over the entire moisture range, i.e. from fully saturated to completely dry soils, changes simulated root water uptake under dry soil conditions, which sensitively affects crop yields. We quantify these effects by simulating root water uptake using the agro-ecosystem models Expert-N and Daisy, comparing the standard as well as oversimplified approach to a comprehensive model (of the Brunswick model framework). This was possible by using new pedotransfer functions to obtain all required model parameters. The framework and pedotransfer functions are available in the R package spsh on CRAN.

A new Land-Atmosphere Feedback Observatory (LAFO) was established at the University of Hohenheim, Stuttgart, Germany. It is considered as a role model for a network of GEWEX LAFOs (GLAFOs) that is a central project and proposed by the GEWEX Global Land/Atmosphere System Study (GLASS) panel (Wulfmeyer et al. 2020). Its main objective is to observe directly land-atmosphere (L-A) feedback for process understanding and improving its representation in weather and climate models. The set up phase of this research facility was funded as infrastructure project of the Carl Zeiss Foundation. The main goals are to1) investigate the diurnal cycle of the planetary boundary layer (PBL) including its turbulent properties,2) improve parameterizations based of vegetation dynamics, surface and PBL fluxes, and3) verify mesoscale and turbulence permitting models,4) characterize L-A feedback by suitable metrics.LAFO brings together a sensor synergy with unequaled spatial and temporal resolution. An extended set of soil physical, plant dynamic as well as meteorological variables throughout the PBL are measured focusing on evapotranspiration and turbulent exchange processes over an agricultural landscape. Observations are recorded with state-of-the-art instruments on a long-term basis as well as with a more sophisticated sensor setup campaign based.The first key component of the LAFO sensor synergy consists of 3D scanning lidar systems: A scanning water vapor differential absorption lidar and a scanning temperature and humidity rotational Raman lidar, both developed at the Institute of Physics and Meteorology. Both systems are worldwide unique and provide water vapor and temperature remote sensing data in the surface layer up to the lower free troposphere with very high resolution up to the turbulent scale (Behrendt et al. 2015, Wulfmeyer et al. 2015, Muppa et al. 2016, Späth et al. 2016, Lange et al. 2019). Additionally, two scanning Doppler lidars measure the horizontal and vertical wind profiles and turbulent wind fluctuations. The lidar measurements are complemented by a 3D scanning Doppler cloud radar.The second key component is a soil water and soil temperature sensor network distributed over the agricultural study area combined with two eddy-covariance stations (Imukova et al. 2016) to observe fluxes at the land surface.The third key component consists of devices for vegetation characterization. As an example, the “BreedVision” phenotyping platform (Busemeyer et al. 2013) based on an innovative sensor-setup provides an extensive set of sensor-data for field phenotyping and feature prediction without vegetation destruction. Unman aerial vehicles (UAVs) with spectroscopic cameras are also available.For specific campaigns studying L-A feedback with particularly high detail, research partners are highly welcome to join our research team. Following the FAIR (Findable, Accessible, Interoperable, Reusable) data principle, our data will be made available on a website. We present first measurement examples and show how these can be used to reach our research goals. Wulfmeyer et al. 2020, GEWEX Quarterly Vol. 30, No. 1.Behrendt et al. 2015, doi:10.5194/acp-15-5485-2015Wulfmeyer et al. 2015, doi:10.1002/2014RG000476Muppa et al. 2016, doi:10.1007/s10546-015-0078-9Späth et al. 2016, doi:10.5194/amt-9-1701-2016Lange et al. 2019, doi:10.1029/2019GL085774Imukova et al. 2016, doi:10.5194/bg-13-63-2016Busemeyer et al. 2013, doi:10.3390/s130302830

Crop models are used to evaluate the impact of climate change on food security by simulating plant phenology, yield, biomass and leaf area index. Plant phenology defines the timing of crucial growth stages and physiological processes that influence organ appearance and assimilate partitioning. It is governed by environmental factors such as solar radiation, temperature and water availability. Plant phenology is not only specific for the crop species, but also depends on the cultivar. Additionally, growth of a cultivar could vary depending on the environment. Common crop models cannot fully capture the influence of the environment on phenology, resulting in cultivar-specific parameters that are environment-dependent. These parameter estimates may be unreliable in case of limited data. Moreover, crucial species-specific information is ignored. On the other hand, in large regional-scale models covering multiple cultivars and environments, information about the cultivars grown is generally not available. In this case, a shared set of parameters for the crop species would suppress within-species differences leading to unreliable predictions.A Bayesian hierarchical framework enables us to alleviate these problems by honouring the multi-level data structure. Additionally, we can reflect the uncertainty from different sources, for example, model inputs and measurements. In this study we implement a Bayesian hierarchical framework to estimate parameters of the Soil-Plant-Atmosphere System Simulation (SPASS) model for simulating phenological development of different cultivars of silage maize grown over all the contrasting climatological regions of Germany.We used data from the German weather service on the phenological development stages of silage maize grown across Germany between 2009 and 2019. During this period, silage maize was grown in over 3000 unique location-year combinations. Maize crops were differentiated into early, mid-early, mid-late and late ripening groups and were further classified into cultivars within each ripening group. Within the hierarchical framework, we estimate maize species-specific parameters as well as parameters per ripening group and cultivar, through Bayesian model calibration. We analyse the influence of environmental conditions on parameter estimates, to further develop the hierarchical structure. We perform cross-validation to assess the prediction quality of the parameterized model.With this approach, we show that robust parameter estimates account for differences between cultivars, ripening groups as well as different environmental conditions. The parameterized model can be used for large-scale phenology predictions of silage maize grown across Germany. These parameter estimates may perform better than independent species- or cultivar-specific estimates, in predicting phenology of future cultivars where specific cultivar characteristics are not known.

Despite all legislative efforts, pesticides persist in soils at low concentrations and are leached to groundwater. This environmental issue has previously been associated with control factors relevant in natural soils but elusive in lab experiments and standard modeling approaches. One such factor is the small-scale spatial distribution of pesticide-degrading microorganisms in soil. Microbes are distributed heterogeneously in natural soils. They are aggregated in biogeochemical “hotspots” at the centimeter scale. The aim of our study is to investigate the relevance of such aggregation for pesticide degradation. For this, we upscaled the effect of the heterogeneity-induced accessibility limitations to degradation to the soil-column scale and analyzed kinetic constraints and amplifying factors under contrasting unsaturated flow regimes.We performed a 2D spatially explicit, site-specific model-based scenario analysis for bioreactive transport of the model pesticide 4-chloro-2-methylphenoxyacetic acid (MCPA) in an arable soil (Luvisol). Stochastic centimeter-scale spatial distributions of microbial degraders were simulated with a spatial statistical model (log Gaussian Cox process), parametrized to meet experimentally observed spatial distribution metrics. Three heterogeneity levels were considered, representing homogenized soil conditions, and the lower and upper limit of expected microbial spatial aggregation in natural soils. Additionally, two contrasting precipitation scenarios (continuous light rain vs. heavy rain events directly following MCPA application) were assessed. A reactive transport model was set up to simulate a 0.3 m x 0.9 m soil column based on hydraulic and bioreactive measurements from a soil monitoring station (Germany, SM#3/ DFG CRC 1253 CAMPOS).Our simulations revealed that heavy precipitation events were the main driver of pesticide leaching. Leached amounts from the topsoil increased by two to five orders of magnitude compared to the light rain scenario and at max. ca. 20 ng was leached from 90 cm after one year. With the increasing spatial aggregation of microbial degraders, upscaled pesticide degradation rates decreased, and considerable differences emerged between homogeneous and highly aggregated scenarios. In the latter, leaching from the plow layer into the subsoil was more pronounced and MCPA was detectable (LOD = 4 µg/kg) 5-6 times longer. In heterogeneous scenarios, degradation in microbial hotspots was mainly diffusion-limited during “hot moments” (times of high substrate availability), with a fraction of MCPA simultaneously “locked in” in coldspots with low microbial abundance. During intense precipitation events MCPA was remobilised from these coldspots by advective-dispersive transport, thereby increasing pesticide accessibility.Our results indicate that predicted environmental concentrations and detectability of pesticides might be underestimated if spatial heterogeneity of microbial degraders is neglected, and they highlight the importance of heavy rain events as drivers of leaching and substrate accessibility.

The rhizosphere shows complex spatial and temporal patterns of biophysical and biochemical processes. Process-based modeling that accounts for functional microbial traits provides a tool to gain a better understanding of microbial interactions involved in carbon cycling in the rhizosphere. Here, we present a trait-based rhizosphere model that accounts for microbial life-history strategies (copiotrophs, oligotrophs), microbial physiology (e.g., dormancy), and organic carbon bioaccessibility (small and large polymers). The model reflects the mm-scale microbial and carbon dynamics around a cylindrical root segment and will be linked with a structural-functional soil-plant model (CPlantBox), which enables to connect water, carbon and nitrogen dynamics in the rhizosphere to plant and bulk soil dynamics. We show the concept of trait-based rhizosphere modeling, first simulations, and our model coupling approach to CPlantBox.

Soil microbial functional traits control carbon (C) decomposition and stabilization in soil. Integrating metabolic trade-offs and life-history strategies of microbial communities into models enhances the representation of feedbacks between microbial diversity and soil biogeochemical functions. This has great potential to improve our understanding of microbial C allocation and how microbial processes affect C storage and use efficiency in soil. The current challenge is, however, to quantify and identify ecologically meaningful microbial traits. This study utilizes data from a 13C pulse-labelling litter decomposition experiment to inform a new soil C turnover model that captures microbial life-history traits and dormancy in combination with soil organic matter accessibility. Quantitative data from 13C DNA stable isotope probing and high-throughput sequencing is used to parameterize the C utilization of copiotrophic and oligotrophic microorganisms. The new model is then applied to quantify C utilization of functional microbial groups and C turnover in soil. In scenario analyses we investigate the sensitivity of functional microbial groups and its feedback on C cycling to C input.

The natural degradation pathways of the herbicide atrazine (AT) are highly complex. These pathways involve the metabolic activity of several bacterial guilds (that use AT as a source of carbon, nitrogen or both) and abiotic degradation mechanisms. The co-occurrence of multiple degradation pathways, combined with challenges in quantifying bacterial guilds and relevant intermediate metabolites, has led to the development of competing model formulations, which all represent valid descriptions of the fate of AT. A proper understanding of the fate of this complex compound is needed to develop effective management and mitigation strategies.Here, we propose a model discrimination process in combination with prospective optimal design of experiments. We performed Monte-Carlo simulations using a first-order model that reflects a simple reaction chain of complete AT degradation and a set of Monod-based model variants that consider different bacterial consortia and degradation pathways. We used a Bayesian statistical analysis of these simulation ensembles to simulate virtual degradation experiments and chemical analysis strategies, thus obtaining predictions on the utility of experiments to deliver conclusive data for model discrimination. To do so, we defined different experimental protocols including a combination of: i) the metabolites to measure (AT, metabolites and CO2), ii) sampling frequency (sampling every day, every two days and every four days), iii) features difficult to quantify (specific bacterial guilds). As a statistical metric to measure the conclusiveness of these virtual experiments, we used the so-called energy distance.Our results show that simulated AT degradation pathways following first-order reaction chains can be clearly distinguished from simulations using Monod-based models. Within the Monod-based models, we detected three clusters of models that differ in the number of bacterial guilds involved in AT degradation. Experimental designs considering main AT metabolites and sampling frequencies of once every two or four days at durations of 50 or 100 days provided the most informative data to discriminate models. Including measurements of bacterial guilds only slightly improved model discrimination. Our study highlights that environmental fate studies should prioritize measuring metabolites to elucidate active AT degradation pathways in soil and identify robust model formulations supporting risk assessment and mitigation strategies.

We present a comprehensive, high-quality dataset characterising soil-vegetation and land-surface processes from continuous measurements conducted in two climatically contrasting study regions in South West Germany: the warmer and drier Kraichgau region with a mean temperature of 9.7 °C and annual precipitation of 890 mm, and the cooler and wetter Swabian Alp with mean temperature 7.5 °C and annual precipitation 1042 mm. In each region, measurements were conducted over a time period of nine cropping seasons from 2009 to 2018. The backbone of the investigation was formed by six eddy-covariance stations (EC) which measured fluxes of water, energy and carbon dioxide between the land surface and the atmosphere at half-hourly resolution. This resulted in a dataset containing measurements from a total of 54 site*years containing observations with a multitude of crops, as well as considerable variation in local growing season climates. The presented multi-site, multi-year data set is composed of crop-related data on phenological development stages, canopy height, leaf area index, vegetative and generative biomass and their respective carbon and nitrogen content. Time series of soil temperature and soil water content were monitored with 30-min resolution at various points in the soil profile, including ground heat fluxes. Moreover, more than 1,200 soil samples were taken to study changes of carbon and nitrogen contents. The data set was uploaded to the Pangaea database and can be accessed at https://doi.org/10.20387/bonares-a0qc-46jc (for the review process, please refer to the data availability section). One station in each region has now been set up as continuous observatories of state variables and fluxes in intensively managed agricultural fields.

Plant phenology, which describes the timing of plant development, is a major aspect of plant response to environment and for crops, a major determinant of yield. Since climate change is projected to alter crop phenology worldwide, there is a large effort to predict phenology as a function of environment. Many studies have focused on comparing model equations for describing how phenology responds to weather but the effect of crop model calibration, also expected to be important, has received much less attention. The objective here was to obtain a rigorous evaluation of prediction capability of wheat crop phenology models, and to analyze the role of calibration. The 27 participants in this multi-model study were provided experimental data for calibration and asked to submit predictions for sites and years not represented in those data. Participants were instructed to use and document their 99usual99 calibration approach. Overall, the models provided quite good predictions of phenology (median of mean absolute error of 6.1 days) and did much better than simply using the average of observed values as predictor. Calibration was found to compensate to some extent for differences between models, specifically for differences in simulated time to emergence and differences in the choice of input variables. Conversely, different calibration approaches led to major differences in prediction error between models with the same structure. Given the large diversity of calibration approaches and the importance of calibration, there is a clear need for guidelines and tools to aid with calibration. Arguably the most important and difficult choice for calibration is the choice of parameters to estimate. Several recommendations for calibration practices are proposed. Model applications, including model studies of climate change impact, should focus more on the data used for calibration and on the calibration methods employed.