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
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
It is well acknowledged that interactions between the soil–vegetation system and the atmosphere will have major impacts on regional climate and that our knowledge of processes and feedbacks is insufficient (Pielke et al., 2007; Thornton et al., 2014). Process models enable testing of hypotheses concerning the governing processes, identifying epistemic and aleatory uncertainties and highlighting the need for further investigations (Porter and Semenov, 2005; Godfray et al., 2010; Challinor et al., 2014; Tao et al., 2017; Schalge et al., 2020). Predicting the impacts of climate change on agro-ecosystems and the land surface exchange of water, energy and momentum and vice versa requires process models to understand and study land–atmosphere feedbacks (Ingwersen et al., 2018; Monier et al., 2018). There is consensus that fully coupled climate, land surface, crop and hydrological models facilitate the prediction of climate change impacts on agricultural productivity as well as its feedbacks on climate change projections themselves (Marland et al., 2003; Hansen, 2005; Perarnaud et al., 2005; Levis, 2010). This implies the continuous improvement of models and process understanding. In relation to the water balance this includes, in particular, partitioning evaporation and transpiration (Kool et al., 2014; Stoy et al., 2019), modelling crop transpiration (Heinlein et al., 2017), investigating impacts on groundwater resources (Riedel and Weber, 2020), improving the representation of the green-vegetation-fraction dynamics of croplands in the Noah-Multiparameterization Land Surface Model (NOAH-MP LSM; Imukova et al., 2015; Bohm et al., 2020), determining the dynamic root growth of crops (Gayler et al., 2014) and assessing the relevance of subsurface processes (Gayler et al., 2013), and evaluating the energy balance closure problem in eddy-covariance (EC) measurements (Ingwersen et al., 2015; Imukova et al., 2016) and associated minor storage terms (Eshonkulov et al., 2019), as well as incorporating crop growth in land surface models (Ingwersen et al., 2011, 2018), investigating the carbon balance and turnover of agro-ecosystems (Demyan et al., 2016; Poyda et al., 2019), evaluating crop model performances (Bassu et al., 2014; Kimball et al., 2019), responding to changes in environmental drivers (Biernath et al., 2011, 2013), quantifying the effect of different intensities of free-air carbon dioxide and temperatures on grain yield and grain quality (Högy et al., 2010, 2019), evaluating the worth of observed data (Wöhling et al., 2013b), and developing data model integration techniques (Wöhling et al., 2013a).
However, the effects are further reaching than just to the biophysical environment. Regional climate projections typically neglect changes and adaptation of the agents of land use, namely the farmers, meaning that the concomitant projections of future crop yields are based on crude simplifications (Hermans et al., 2010). Multi-agent system modelling has reached a level of maturity such that empirical bio-economic simulators can be run on high-performance computer clusters (Schreinemachers and Berger, 2011; Kelly et al., 2013). As a result, integrated model systems (Fig. 1) can now be built that simulate both biophysical and socioeconomic processes with comparable process detail, accounting for the complex reality of local/regional human adaptation and feedback to global changes (Troost and Berger, 2015).
Diagram of the cardinal land modelling system compartments and relations. The presented dataset contains time series of quantified land surface, crops, and soil processes and properties. This serves as a unique backbone for model validation and model development in the soil–vegetation–atmosphere continuum and robust land systems modelling.
To enable an understanding of feedbacks within bio-economic modelling systems, the models employed for the representation of processes of different complexity in the soil–vegetation–atmosphere continuum require calibration and validation against observed state variables or fluxes at the field level (Kersebaum et al., 2015). For this, high-quality observed data on the state variables or fluxes of interest are required, which should encompass grain and biomass yields and soil organic carbon and nitrogen stocks and turnover in soils, as well as the water, carbon dioxide and energy fluxes between land surface and atmosphere. Still very few model intercomparison studies include, in addition to crop growth, soil water flux relevant variables to calibrate their agro-ecosystem models (Seidel et al., 2018) because datasets that include all these variables and fluxes are rare (Kersebaum et al., 2015). The dataset presented here is intended to help close this data gap, leading to better process representation on the one hand, while, on the other hand, facilitating model selection (Wöhling et al., 2015) and tackling the question of required and sufficient model complexity in the light of available data (Guthke, 2017).
To study the effects of regional climate change and to facilitate parameterization and validation to continuously improve model components, extensive collaborative field measurements and controlled exposure experiments were carried out in two study areas in southwestern Germany. Field research was part of two wider integrated research projects funded by the German Research Foundation (DFG), Package Request (PAK) 346 Structure and Functions of Agricultural Landscapes under Global Climate Change – Processes and Projections on a Regional Scale (Regional Climate Change) and Research Unit (RU) 1695 Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale.
In this section, the full dataset, which is composed of many individual datasets spanning diverse types of data sources, temporal and spatial measurement resolution, and origins, is individually described. Both research areas were intensively used agricultural landscapes: (1) Kraichgau, with a mild climate and moderate precipitation and which is dominated by intensive row crop agriculture, and (2) the Central Swabian Alb (Mittlere Schwäbische Alb), with a harsh climate and higher precipitation. Animal fattening, row crop agriculture and heathland areas are important features to the Central Swabian Alb agro-economic setting. Within the scope of this publication we present a high-quality dataset spanning a time period of nine cropping seasons from 2009 to 2018 intensely characterizing the two respective agro-ecosystems. The backbone of the investigation was formed by six eddy-covariance 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 (i.e.
Schema of the measurement campaign at the research sites:
1 – soil profile characteristics;
2 – management and cultivation data (sowing date, harvest date, crop type and variety, fertilization, and pesticide application including amount and type) and soil tillage;
3 – meteorological data (rain and air temperature at 2 m height and relative humidity); 4 – soil–/biosphere–atmosphere fluxes using fully equipped eddy-covariance stations for carbon, energy and water vapour flux measurements, as well as wind speed and wind direction;
5 – soil state measurements including water content, temperature and matric potential, soil profile depth permitting at 5, 15, 45, 75, 90 and 130 cm soil depth;
6 – five plots per research site for carbon and nitrogen measurements integrated over depths of 0–30, 30–60 and 60–90 cm; and
7 – plant performance also determined at the plots (phenology, height and leaf area index; yield; aboveground biomass; and carbon and nitrogen in vegetative and generative biomass). A detailed GIS (geographic information system) data model is included in the dataset including fields, measurements locations and plots. Illustration by Holger Vanselow (
Measurements were performed in two research areas in two study regions, Kraichgau (48.9
Kraichgau is a hilly region with fertile soils in the northwest of the state of Baden-Württemberg, southwestern Germany. It is part of the Neckar catchment and borders on the Odenwald low mountains in the north, the Neckar Valley in the northeast, the Stromberg and Heuchelberg downlands in the southeast, the Black Forest in the southwest, and the Rhine Valley in the west. The natural geographic region of Kraichgau is located at an altitude of 100–400 m a.s.l. and covers approximately 1600
Due to its location in a basin surrounded by low-mountain ranges, Kraichgau is characterized by a mild climate with an annual mean temperature of more than 9
Soil characteristics at the six research sites EC1 to EC6 (data are presented in profile_data.csv, and methods are described in Sect. 2.3.3).
EC: eddy-covariance station, i.e. research site. Ht: top depth of soil horizon. Hl: lower depth of soil horizon. bd: bulk density. por: porosity. fc: field capacity. wp: wilting point. stc: stone content. S: sand. u: silts. t: clay. class: soil texture class (soil organic matter at the beginning of the research period in 2009). lc: lime content. wt %: weight percentage. NA: not available. The soil texture and texture classes are in reference to the German soil classification (Sponagel, 2005). Model parameters for the van Genuchten–Mualem (van Genuchten, 1980) soil hydraulic functions can be derived with a new pedotransfer function (Szabó et al., 2021), which includes uncertainties. Description of soil hydraulic properties over the full moisture range can be achieved using the Brunswick model (Weber et al., 2019; Streck and Weber, 2020) in conjunction with the pedotransfer function by Weber et al. (2020).
The low-mountain range of the Swabian Alb is a region with an approximate width of up to 40 km that stretches in a southwest–northeast direction over approximately 220 km, from the Black Forest in the southwest to the Franconian Alb in the northeast, covering an area of ca. 5700
The Swabian Alb is the largest contiguous karst region in Germany. The foothills are mostly formed by Black Jurassic and the escarpment by Brown Jurassic, whereas the plateau consists of White Jurassic. The in situ unlayered reef limestones (
The intensive agricultural land use in this area (Wöhling et al., 2013a) is characterized by a relatively balanced mix of crop production, dairy farming, bull fattening, pig production and biogas production. Most farm holdings simultaneously produce three to five different crops, with spring barley, winter wheat, winter barley and winter rapeseed being the dominant crops, while dairy and cattle farmers tend to also grow silage maize, clover and field grass (Troost and Berger, 2015). Three EC stations (EC4, EC5 and EC6) were installed at research sites 4–6 with respective areas of 8.7, 16.7 and 13.4 ha (Fig. 3): EC4 (
Basic field management information was provided by the farmers directly as field card index data. These contained information on crop rotations (Table 2), fertilization, soil management and pesticide usage (Table 3). Crop yield is reported as total generative biomass at harvest by the farmers. In Germany, grain yields are expected to have a residual water content of around 14 %. Separately, vegetative and generative biomass was also determined by plot sampling as part of the biomass characterization (cf. Sect. 2.3.4). From Table 3 it can be seen that the yields reported by the farmers are commonly lower than those reported by the scientists, which were determined at the experimental plots (cf. Sect. 2.3.4). High discrepancies of yields are found in the data of 2018 at EC2, EC4 and EC6, with inexplicably low yields reported for the plot replicates. In 2013, EC4 had a winter rapeseed yield of 1.2
Research sites EC1 to EC6 and land use from 2010 to 2018.
SM: silage maize. GM: grain maize. WR: winter rapeseed. WW: winter wheat. SP: spelt. CC: cover crop. WB: winter barley. SB: spring barley.
Summary of field management and nitrogen and organic matter input.
Continued.
Under the assumptions that no silage maize was used as fodder, that the water content of the harvested maize was 70 % by mass, that the amount of carbon in the biogas digestate was 17.4 % of the carbon exported from the field (Lindorfer and Frauz, 2015) and that the organic carbon content was 58 % of maize organic matter, we note the following: harvest and fertilization data provided by the farmers and included in the dataset indicate that between the silage maize carbon exported and returned to the field with the biogas digestate, referenced to the 15 silage maize cultivation periods, on average, approximately 900
Note that the yield values reported in last two columns of Table 3 differ from each other. In the “field” column values are farmer reported yields for the entire field. These are affected by harvest losses, no yields on tractor tracks and reduced yields due to side effects on the field. In the “plot” column the reported values stem from observations on experimental plots located far away from the edge of the field and between tractor tracks. This explains why the farmer values are mostly smaller than the plot values. Values in brackets are standard deviations over the plots. For silage maize in the Kraichgau region, the farmer values are reported as fresh mass.
Crop management was fairly typical for conventional intensive crop production in the areas. Noteworthy is the importance of biogas production, both as a motive for silage maize production and as a supplier of organic fertilizer. Choice of maize and wheat varieties in the sample reflects the climatic differences between the two locations. Due to the shorter growing season, early-maturing silage maize varieties (S220–S240) were preferred at the Swabian Alb sites, while the Kraichgau sites are dominated by medium- to late-maturing varieties (S240–S310). With respect to winter wheat, the full spectrum of varieties ranging from hard (high protein/gluten, German classification group E) to soft (low protein, group C) varieties can be found. Wheat variety choice tends towards the higher-quality end of the spectrum (groups E and A) in Kraichgau and more towards the lower-quality spectrum (groups B and C) in the Swabian Alb locations. Production of marketable quality wheat requires reliably favourable production conditions. Wheat yields are slightly higher (0.5
Table 3 indicates the number of pest and plant control operations between the harvest date of the previous crop and the harvest of the current crop. The number before the colon indicates the number of application; the uppercase letters indicate the type of agent; and the numbers in brackets indicate the number of agents applied. For example, at EC1 in 2010 herbicides were applied once, with three different agents. In the file plant_protection.csv, active substances of agent and application rates are further specified (cf. Table 3).
Meteorological data were measured at all eddy-covariance stations and recorded on CR3000 data loggers (Campbell Scientific Inc., Logan, UT, USA) in 30 min intervals. Global radiation (Rg) and net radiation were measured with four-component net radiometers (NR01, Hukseflux Thermal Sensors B.V., Delft, the Netherlands) that were installed about 1.5 m above the canopy. Air temperature and humidity were measured at 2 m height (HMP45, Vaisala Inc., Helsinki, Finland; EC2 from September 2016 and EC1 from December 2016: HC2S3 HygroClip2, Rotronic GmbH, Ettlingen, Germany) and precipitation at 1 m height (ARG100, EML, North Shields, UK). During the period 11 April–2 November 2017, different sensors were used at EC1. During this time, long- and short-wave radiation was measured with a four-component CNR4 net radiometer (Kipp & Zonen B.V., Delft, the Netherlands) and an HMP155 probe (Vaisala Inc., Helsinki, Finland) was used to measure air temperature and humidity. Data were stored on an XLite 9210 data logger (Sutron Corporation, Sterling, VA, USA). The convention used in this dataset is that all energy components directed away from the surface are positive. April–June mean temperature and precipitation sum is presented in Fig. 4, which highlights the mean differences between the two regions. The interannual variability of precipitation is high, whereas that of temperature is low. Mean air June temperatures gradually increased over the reported 10 years. Rain and soil water content as well as soil temperature are shown as an example in Fig. 5. The weather data gap filling and flags were done using an automated Fortran programme, which we summarize here. For all variables no gap filling is marked by flag 0. Gap filling was first tried by using data from an adjacent station. The gap-filled data were then flagged as 11, 12, 13, 14, 15 or 16 for data from EC1 through EC6, respectively. If no wind speed or wind direction data from the adjacent stations were available, a random wind speed was sampled from the data of the previous 12 h (
All six EC stations were equipped with the same equipment (Table 4), except for the number of soil sensors which was variable (Table 5). Surface–atmosphere fluxes (net
The instrumentation of the eddy-covariance stations (Wizemann et al., 2015) used until 2018. Occasional changes to the general layout are detailed in the text.
Installation depths (in cm) of the soil sensors. During selected periods, additional sensors were installed in greater depths, particularly at EC1 to EC3.
The EC data from April 2009 to December 2012 were processed using the EC software package TK2 and after January 2013 using version TK3.1 (Mauder and Foken, 2015). Fluxes were computed from 30 min covariances between vertical wind velocity and the corresponding scalar (
Data gaps occurred in times of sensor excavation for harvest and sowing and due to browsing by animals. The stony ground at the SA sites made it necessary to install soil sensors at EC4 and EC5 at a maximum of 45 cm depth and at EC6 at 15 cm depth. By way of example, time series of
Soil water content (black dots), precipitation (grey bars) and soil temperature (orange line) at research site EC1 in 2010 at 5 cm soil depth. The dashed orange line indicates 0
At selected sites some additional measurement campaigns to determine soil surface
Adjacent to the EC stations but in the tilled soil, temperature sensors (model 107, Campbell Scientific Inc., UK) were installed at 2, 6, 15, 30 and 45 cm soil depth. To measure the volumetric soil water content and soil matric potential, we installed FDR (frequency domain reflectometry) probes (CS616, Campbell Scientific Inc., UK) and matric-potential sensors (model 253, Campbell Scientific Inc., UK) at 5, 15, 30, 45 and 75 cm depth and selective extra depths at some locations. Three soil heat flux plates (HFP01, Hukseflux Thermal Sensors, the Netherlands) were installed 8 cm below the ground surface. At EC1, self-calibrating heat flux plates (HFP01SC, Campbell Scientific Inc., Logan, UT, USA) at 8 cm depth and HydraProbe II sensors (Stevens Water Monitoring Systems Inc., Portland, OR, USA) for soil volumetric water content and soil temperature at 5, 10 and 15 cm depth were used during the period 11 April–2 November 2017. Soil water content and temperature at 5 cm depth and precipitation are presented in Fig. 5 for EC1 in 2010, where the strong drop in soil water contents around DOY 75 (day of the year) and DOY 350 are both attributed to soil freezing. We did not exclude these data from our dataset intentionally. At EC1 to EC3, the soil water content sensors were calibrated to in situ gravimetric soil water content data. In EC4 to EC6, only the factory-calibrated time series are provided. The remaining sites and years are presented in the Supplement.
To determine total and mineral nitrogen (
At each field, five plots of 4
For ground truth, green vegetation fraction (GVF) was determined based on photos at EC1, EC2 and EC3 fields in 2012 and 2013 (Imukova et al., 2015). The photos are available as part of the dataset. Within each study field, five plots (
Soil microbial biomass C and N (
Plant material was separated into vegetative and generative fractions. Vegetative parts were dried to a constant weight at 60
In environmental sciences, observations are afflicted with random and systematic errors and additionally by uncertainty due to spatial heterogeneity of the system of interest. In principle, errors and uncertainties can be approximated quantitatively by theoretical and practical approaches. Identifying which part of a measured value has to be attributed to the random error, systematic error or uncertainty can prove highly challenging and is scale dependent. One common approach is by replicating the measurement process. For the weather and eddy-covariance data, details were already given in Sect. 2.3.1 and 2.3.2., respectively. Quality flags in both datasets are qualitative indicators, and for the weather data, only the instrumental measurement uncertainty is known (Table 4), since replicate measurements were not made. Generally, uncertainties in determined height, direction and orientation of measurement devices as well as installation depths of sensors are unresolved. In the predominant cases for the soil and plant measurements, including the soil chamber flux measurements, errors and uncertainties can be deduced from (a) replicate measurements/sampling within a plot or (b) replicate plots in a field. In most cases, replicate measurements are directly provided in the data files; exceptions are the soil profile characterization and the leaf area measurements where the replicate measurements were averaged and their standard deviations are reported. No replicate measurements exist for the time series of soil water content, soil temperature and matric potentials. An exception is the measured ground heat fluxes which were determined in replicates of three at each station. For some of the analytical instruments, the uncertainties as determined by the manufacturer are given in Table 4. The uncertainties of the remaining measurement devices are not explicitly covered, as they are considered negligible or implicitly covered in replicate measurements. In most cases, systematic errors are sometimes even impossible to quantify. We consider the random measurement error to be captured by the replicate measurements/samples from within a plot, while those between plot replicates are an indicator for effects of heterogeneity. For obvious reasons, over 10 years, different persons were involved in sampling, installing sensors and handling the experiments, sometimes within a season. While the methods remained the same, this has the potential to induce systematic errors, which are not further resolved, since the information is no longer retrievable.
We provide figures and tables alluding to the scope and nature of the datasets for download at
Folder structure of the dataset.
Determined variables and description of the field cultivation data files (cultivation.csv) including farmer reported yield.
Determined variables and description of the soil management data files (soil_management.csv).
Determined variables and description of the soil carbon and nitrogen measurement files (soil_cn.csv).
Determined variables and description of the fertilizer data files (fertilization.csv).
The farmer-reported type and total amount of applied fertilizer type. Based on information provided from the fertilizer suppliers, analyses on the organic matter content of the organic fertilizers (slurry) and selected gap filling by expert knowledge, the dataset can be considered complete. However, it has to be acknowledged that the data on the organic fertilizers contain a non-quantified uncertainty. Further details on assumptions and calculations are given in Appendix A.
Determined variables and description of the farmer-reported plant protection measures. The active substances and respective units were added based on expert knowledge (plant_protection.csv).
Determined variables and description of the weather data files (weather.csv).
Determined variables and description of the eddy-covariance measurement data files (flux_data.csv). For the variables nee_filtered, le_filtered and h_filtered data points were removed according to the following rule: for the respective quality
Determined variables and description of the soil water content, temperature, heat storage and matric-potential measurements. Partially, the research sites had different numbers of sensors of a given type. All had temperature sensors installed at 2, 6, 15, 30 and 45 cm soil depth, and matric-potential and volumetric water content sensors were installed at 5, 15, 30, 45 and 75 cm depth, which are given separately in the individual files. For this reason, each data file (soil_site
Determined biomass variables and data description (biomass.csv).
Determined variables and description of carbon and nitrogen content data of the crop biomass (cn.csv).
Determined leaf area index and data description (lai.csv).
Determined plant development stage and height measurement and data description (phenology.csv).
Determined chamber flux measurements; the suffixes are identical to the ones in Table 9, as are the plot references here and in the data description.
Description of the attribute tables of the four GIS data model files which identify the location of the research areas, stations and plots in Tables 6–18. The main research plots are given in 03_research_plots.gpkg, identified as “veg” in the “sub_plot_type” column, and additionally “b09”, “b10” and “b12” relate to the bare-soil plots in 04_research_plots_chambers.gpkg.
The digital database is available freely for download from the BonaRes Data Centre
We provide a comprehensive dataset on agricultural crop growth and land surface exchange on arable soils in Germany. The continuous eddy-covariance measurements on adjacent fields and the long duration of our measurements (2009–2018) is unique and allows for new insights into the role of crop rotations for land surface exchange processes. According to a recent report by the Alliance of Science Organisations in Germany our installations have been the only ones on agricultural land throughout southern Germany that fulfil the criteria for becoming part of the intended national observatory network for terrestrial ecosystem research (Kögel-Knabner et al., 2018). One research site per region (EC2 and EC4) is still fully operational, while the remaining sites were dismantled after completion of the project at the end of the growing season in 2018.
We recognized that the interannual variability within locations exceeds the effect of regional climate. In other words, a direct comparison of fluxes measured in the two study regions is only possible if the measurements are performed in the same year under comparable large-scale weather conditions (Wizemann et al., 2015). Although some drier growing seasons were identified with sometimes low soil volumetric water contents in the upper soil layers, it became apparent that the deep loess soil profiles in the Kraichgau region and the soils in the cooler and wetter Swabian Alb region were generally not severely water-deficient. An exception to that was the very early ripening and subsequently harvest of maize in the Kraichgau region in 2018.
The dataset was used extensively to calibrate soil–crop models and land surface models. In spite of the high data quality and the extensive coverage of crops and years, we would like to draw the attention to some possible improvements for future campaigns like the one presented. First, it became apparent that it would be beneficial to include measurements to infer information on the partitioning between the evaporation and transpiration of the crops. Also, we notice that, due to a solar-power shortage in winter, we have some data gaps in the EC measurements. We think it would be worthwhile to extend the research by extending the measurements on soil (hydraulic) properties (transience, hydrophobicity, structure, etc.). In the future, it would be beneficial to properly quantify the contribution of the cover crops to the overall fluxes and budgets, as well as to include sensors that capture the
Further information on the quantification rules to calculate the amount and type of mineral N from the reported applied mineral fertilizers on the 54 site years and the organic matter content.
n/a: not applicable. rv: reported value.
Further information on the quantification rules to calculate the amount and type of mineral N from the reported applied mineral fertilizers on the 54 site years and the organic matter content.
TS: total solids. OM: organic matter. Nutrient contents determined in the laboratory in 2015 and 2016 for Kraichgau and 2014, 2015 and 2016 for research site EC6 (SA). Otherwise, average values were assumed based on expert knowledge, as given below.
Average yields of the Kraichgau district Enzkreis for the years 2010–2018. Values that are not available are indicated by NA. Data from the Statistisches Landesamt Baden-Württemberg accessible at
Average yields of the Swabian Alb district Alb-Donau for the years 2010–2018. Values that are not available are indicated by NA. Data from Statistisches Landesamt Baden-Württemberg accessible at
Average yields of the Swabian Alb district Reutlingen for the years 2010–2018. Values that are not available are indicated by NA. Data from Statistisches Landesamt Baden-Württemberg accessible at
The supplement related to this article is available online at:
The project was conceptualized (ideas, formulation or evolution of overarching research goals and aims) by GC, PH, TS, AF, TM, JI, VW. The data were curated (management activities to annotate, i.e. produce metadata, and scrub data and maintain research data, including software code, where necessary for interpreting the data itself, for initial use and later re-use) by TW, CT, HDW, PH, ML, YFN, MSD, AP, PK and JI. The formal analysis was completed (application of statistical, mathematical, computational or other formal techniques to analyse or synthesize study data) by TW, HDW, PH, AP and PK. The funding was acquired (acquisition of the financial support for the project leading to this publication) by TM, AF, GC, PH, JI, VW and TS. The investigation was completed (conducting a research and investigation process, specifically performing the experiments or data/evidence collection) by KB, HDW, TW, RE, PH, YFN, IW, ML, MSD, AP, PK and TS. The methodology was developed (development or design of methodology and creation of models) by TM, PH, TS and JI. The project was administered (management and coordination responsibility for the research activity planning and execution) by PH, VW, TS and JI. The resources were acquired (provision of study materials, reagents, materials, patients, laboratory samples, animals, instrumentation, computing resources or other analysis tools) by TM, GC, PH, AF, JI and TS. The software was managed (programming and software development, computer programme design, computer code and supporting algorithm implementation, and existing code component testing) by TW, GC, HDW and JI. The project was supervised (oversight and leadership responsibility for the research activity planning and execution, including mentorship external to the core team) by TM, GC, PH, AP, JI, VW and TS. The results were validated (verification, whether as a part of the activity or separate, of the overall replication/reproducibility of results/experiments and other research outputs) by TW, CT, KB, PH, AP, PK and JI. The results were visualized (preparation, creation and/or presentation of the published work, specifically visualization/data presentation) by TW, PH and JI. The original draft was written (preparation, creation and/or presentation of the published work, specifically writing the initial draft, including substantive translation) by TW, PH, YFN, ML, AP and JI. The writing was reviewed and edited (preparation, creation and/or presentation of the published work by those from the original research group, specifically critical review, commentary or revision, including during the pre- or post-publication stages) by TW, TM, CT, AP, JI, PH, TS and MSD. TR wrote an outstandingly knowledgeable review which was precise, constructive, and very comprehensive. The review resulted in a significant contribution to the manuscript. In recognition of his contribution, he was added as a co-author.
In recognition of his contribution as a referee during peer review, Tim Reichenau was added as co-author after acceptance of the manuscript. The contact author has declared that neither they nor their co-authors have any other competing interests.
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We specifically acknowledge the distinguished farmers who enabled this research, namely the elder Hans Bosch (senior; deceased) and the younger Günter Bosch (junior) (EC1 to EC3), Hans-Gerhard Fink (EC4), Gerhard Hermann (EC5), and Hans Reichart (EC6), without whom the collection of this dataset would have not been possible. This dataset is the result of the DFG (German Research Foundation) integrated project PAK 346 Structure and Functions of Agricultural Landscapes under Global Climate Change – Processes and Projections on a Regional Scale (Regional Climate Change) and the DFG-funded Research Unit 1695 Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale. Tobias K. D. Weber was funded by the Collaborative Research Center 1253 CAMPOS (project 7: Stochastic Modeling Framework of Catchment-Scale Reactive Transport), funded by the German Research Foundation (DFG, grant agreement SFB 1253/1 2017). Further, we thank the technical staff team members Benedikt Prechter and Thomas Schreiber and student helpers Felix Baur and Christian Schade. KB was financed by a scholarship in the frame of the Erasmus Mundus IAMONET-RU programme. The authors are grateful to Walter Damsohn, Gina Gensheimer, Zorica Kauf, Erhard Strohm (deceased) and the students for assistance with the field experiments. Parts of this study were funded by the Federal Ministry of Education and Research (grant no. 01PL11003) for the Humboldt Reloaded projects at the University of Hohenheim, Germany. Lastly, we thank Andreas Klumpp and Joachim Auerbacher for their support. We also kindly acknowledge the support of the BonaRes Data Centre team, in particular Nikolai Svoboda, Marcus Schmidt and Thomas Kühnert.
The study was supported by the German Research Foundation (DFG) in the framework of the research unit PAK 346 (Structure and Functions of Agricultural Landscapes under Global Climate Change – Processes and Projections on a Regional Scale (Regional Climate Change)) and FOR 1695 (Agricultural Landscapes under Global Climate Change – Processes and Feedbacks on a Regional Scale, project no. 193709899).
This paper was edited by Birgit Heim and reviewed by two anonymous referees.