A three-year data set of gaseous field emissions from crop sequence at three sites in Germany

The purpose of the StaPlaRes project was to evaluate two innovative techniques of urea fertiliser application and to quantify greenhouse gas (GHG) emissions. All GHG emissions, as well as other gaseous emissions, agronomic and environmental variables were collected for three years (2016/2017–2018/2019) at three experimental field sites in Germany. All management activities were consistently documented. Multi-variable data sets of gas fluxes (N2O and NH3), crop parameters (grain and straw yield, N content, etc.), soil characteristics (NH4-N, NO3-N, etc.), continuously recorded meteorological variables (air and soil temperatures, radiation, precipitation, etc.), management activities (sowing, harvest, soil tillage, fertilization, etc.), were documented and metadata (methods, further information about variables, etc.) described. Additionally, process-related tests were carried out using lab (N2 emissions), pot and lysimeter experiments (nitrate leaching). In total, 2.5 million records have been stored in a Microsoft Access database (StaPlaRes-DB-Thuenen). The database is freely available for (re)use by others (scientists, stakeholders, etc.) on the publication server and data repository OpenAgrar for meta-analyses, process modelling and other environmental studies. Measurement(s) air temperature • air humidity • air pressure • global radiation • precipitation • Grain yield • nitrous oxide concentrations (gas field measurements) • emissions of NH3 • soil moisture • ammonium in soil • nitrate in soil Technology Type(s) weather station • Weighing • static closed chamber technique • method of calibrated Passive sampling • SENTEK sensor • Extraction with 0.0125 M CaCl2 solution or 0.1 M KCl (VDLUFA method handbook I A 6.1.4.1) Factor Type(s) Fertilizer Sample Characteristic - Environment gaseous field emissions Sample Characteristic - Location Germany Measurement(s) air temperature • air humidity • air pressure • global radiation • precipitation • Grain yield • nitrous oxide concentrations (gas field measurements) • emissions of NH3 • soil moisture • ammonium in soil • nitrate in soil Technology Type(s) weather station • Weighing • static closed chamber technique • method of calibrated Passive sampling • SENTEK sensor • Extraction with 0.0125 M CaCl2 solution or 0.1 M KCl (VDLUFA method handbook I A 6.1.4.1) Factor Type(s) Fertilizer Sample Characteristic - Environment gaseous field emissions Sample Characteristic - Location Germany


Background & Summary
Worldwide use of urea has increased more than 100-fold in the past four decades and now constitutes more than 50% of global nitrogenous fertiliser usage 1 . The global urea market demand reached a volume of nearly 187.8 million metric tons in 2020. From 2021 to 2026 the demand is expected to grow by 2% annually 2 . A large percentage of urea-N used for food production is lost to the environment in many different forms, including NH 3 , N 2 O and N 2 emissions [3][4][5][6][7] . Nitrous oxide contributes to both, the greenhouse effect 8 and stratospheric ozone depletion 9,10 . More than half of the entire anthropogenic N 2 O emission originates from agricultural soils 11 . Ammonia (NH 3 ) emission from agricultural sources significantly contributes to air pollution, soil acidification, water eutrophication, biodiversity loss, and declining human health 12 . There are numerous options for reducing NH 3 emission from urea-fertilised agricultural systems. Inhibitors, for example, are a promising tool for N 2 O and NH 3 mitigation. However, the effectiveness is highly variable and some measures depend widely on site-specific conditions such as weather, soil properties and management practices 13 . Moreover, there can be trade-offs such as simultaneous NH 3 reduction and N 2 O increase.
In order to combine NH 3 and N 2 O measurements, yield analyses, and soil sampling, a three-part experimental setup (see detailed explanation in Methods) was designed at three experimental field sites in Germany.
During the project period (autumn 2016 to autumn 2019) the weather was exceptionally warm and dry. The average annual temperature was 3 K higher compared to the long-term annual temperature at all experimental sites. A high deficit in annual precipitation occurred also at all sites, which was mainly caused by the lack of precipitation in spring. The second and third investigation years were significantly drier than the first one (see Table 1). These weather conditions were in line with the increasingly frequent droughts in Central Europe over the past 14 years 14 .
Overall, the site-specific emission factors (EFs) for N 2 O range from 0% to 0.54%. These EFs are lower than the EFs according to The Global Nitrous Oxide calculator (GNOC) 15,16 at the sites (EF 0.67-0.77%) and significantly lower than the default value of 1.0% according to the IPCC Refinement 17 . The trial-specific EFs fall within the lower uncertainty range of the aggregate N 2 ON-EF according to IPCC Refinement, which is reported to be 0.1-1.8%.
The NH 3 -N emission factor average over the three trial years and the three crops for the benchmark treatment "surface" is highest in Cunnersdorf (0.032 kg NH 3 -N kg N −1 ) and lowest in Roggenstein (0.012 kg NH 3 -N kg N −1 ). Overall, the specific measured NH 3 -N emission factors at all experimental sites during the project period from 2016 to 2019 are significantly below the default values for urea according to EMEP/EEA 18 or Rösemann, et al. 19 (0.142 kg NH 3 -N kg N −1 , uncertainty range 0.03-0.43 kg NH 3 -N kg N −1 ).
We introduce multi-variable datasets of GHG emissions as well as other gaseous emissions and agronomic variables. All variables were collected for three years (2016/2017-2018/2019) at three experimental field sites in Germany. In total 2.5 million records have been stored and archived in the database StaPlaRes-DB-Thuenen to quantify and to evaluate GHG for winter oilseed rape, winter wheat and winter barley. The database is publicly available at the OpenAgrar repository 20 (https://doi.org/10.3220/DATA20220119144442). A virtual final event was organised, where project results were presented. The final report 21 , posters and presentations of the event are available at the website 22 . Some project results have already been published [23][24][25] .

Methods
Study field sites. The StaPlaRes project consists of three sites spread across Germany. The main soil characteristics of each field site are shown in Table 2.
The project was established in late summer 2016 to evaluate two innovative technologies of urea fertilization. At all field sites, oat (Avena sativa L.) was cultivated as the preceding crop to achieve comparable conditions. The experiment at each field site was designed as a uniform field trial with an identical crop sequence consisting of winter oilseed rape (Brassica napus L.; short: OSR) -winter wheat (Triticum aestivum L.; short: WW) -winter barley (Hordeum vulgare L.; short: WB). The experiment was divided in three plot experiments: plot experiment I (short: PVI), large plot experiment (short: GPV) and plot experiment II (short: PVII) (see Fig. 1). Randomization of the test elements was performed in each of the three plot-trials through Latin squares (n = 4). One crop was grown at one plot each year (see Table 3).
The GPV experiment consisted of four plots (marked in green) with an area of 9 m × 9 m each for every treatment (T1 to T4, see below). Each plot contained three separate areas (3 m × 9 m) for (a) yield evaluation, (b) gas measurements, and (c) other samplings. In accordance with the requirements of the NH 3 measurement method, all plots of GPV were surrounded by specially managed interspaces (9 m × 9 m, exemplified by a blue arrow in Fig. 1). This design allows a comprehensive evaluation of plant development, soil conditions and gaseous  Table 2. Soil characteristics of the experimental field sites.
emissions. The experiments PVI and PVII made use of only one plot per treatment in order to evaluate the yield of the two other crops in the respective year. The whole experiment was set up as a randomized design with four replicated plots and four treatments (T): (T1) Control -No N fertilization, (T2) Stabilised -double stabilised urea fertilization, (T3) Incorporated -subsurface placement, and (T4) Surface -granular urea surface application without UI + NI, without. All activities on the fields were conducted according to best agricultural management practices.

Management.
All management activities at each field plot were documented from late summer 2016 until late summer 2019. Mandatory data on management events were emergence, sowing, harvest with crop name, soil tillage with soil depth and type, applications of mineral and/or organic fertilization (including total amount of fertiliser and quantity of N-input from the fertiliser) as well as crop protection. Each activity and the associated device were described. Additionally, dates of crop development, damages as well as nutrition supply and previous crop were reported.
For cereals, the first fertiliser application took place at the same time in all fertilised treatments. The number of split applications was reduced from three to two in winter wheat and from two to one in winter oilseed   www.nature.com/scientificdata www.nature.com/scientificdata/ rape and winter barley for (T2) Stabilised. The stabilised one-time fertilisation for OSR was applied approx. two to three weeks earlier. The scheduling of the application of stabilised urea was studied with two fertiliser treatments: (a) granular stabilised urea (ALZON ® neo-N -combined use of urease and nitrification inhibitors (short: stabilised) also as surface application without incorporation, (b) granular stabilised urea using ALZON ® neo-N as a very early initial application (before the beginning of vegetation) and a flexible timing of the second dressing (shoot). An additional experiment was conducted in Cunnersdorf and Roggenstein for winter wheat and winter barley to optimise the timing of N-stabilised fertilisation (T2).

Meteorological measurements.
All meteorological parameters were measured in 60-minute resolution by different weather stations at each experimental site (see Table 5). The measurements included air humidity, air pressure, air temperature, global radiation, precipitation and wind speed.
Crop field sampling. At the end of each cropping season, yield grain (all crops) and straw (for winter wheat and winter barley) were harvested on each field plot. All crop materials were weighed. Subsequently, quality parameters such as the nitrogen or crude protein content as well as dry matter content of all grain samples were determined. For winter oilseed rape, the oil content was also analysed. Furthermore, crop development parameters like BBCH, grains per ear, plants per m², etc. have been recorded. All crop parameters (quality and development) were determined by methods as specified in Table 6.
Soil field sampling. The topsoil (0-30 cm) was analysed at the beginning of the experiment. For each site, soil moisture data were collected hourly beside the field plots on a grass covered plot using SENTEK sensors based on the FDR methodology. The soil moisture was also directly measured during the Large plot experiment (GPV) in Cunnersdorf. Additionally, every month, soil samples were determined gravimetrically to calibrate the sensors. Soil samples were taken to determine NH 4 -N and NO 3 -N before the beginning of vegetation and after the harvest at 0-30 cm and 30-60 cm soil depth. After the first fertiliser application, mineral nitrogen in the soils was measured weekly and simultaneously with the gas flux measurements. Thus, with each gas flux measurement campaign, soil ammonium-N and soil nitrate-N content are related. All soil samples were stored at −20 °C until lab analysis (see Table 7).
Crop and soil sampling of lab, pot and lysimeter experiments. In addition to the field experiments, process-related investigations were conducted. Under standardized laboratory conditions (20 °C) without plants, soil tests were applied to investigate effects of urea with or without inhibitors on the nitrogen turnover dynamic and urease activity. Furthermore, ammonia volatilization potential (AVP) was also tested under different temperature regimes (5 °C and 20 °C). All methodological details about AVP have been described by Ohnemus,   www.nature.com/scientificdata www.nature.com/scientificdata/ et al. 29 . Several pot experiments with oat, silage maize, spring barley, spring wheat and summer oilseed rape using Mitscherlich containers were installed to analyse the nitrate leaching potential and/or ammonia volatilization potential. Lysimeter experiments served to quantify the amount of nitrate leaching for two fertiliser treatments (T2 and T3).
Gas field measurements. The static closed chamber technique (modified based on [30][31][32] ) was installed at all three sites to measure N 2 O, CO 2 and CH 4 during the crop cultivation period of winter oilseed rape, winter wheat and winter barley only for the "Large plot experiment" (see Fig. 1). Gaseous emissions were measured weekly and event-related in the morning until noon, i.e. weekly from the beginning after sowing and two times per week in loss-prone phases -wetness, fertilization, freeze-thaw. The chambers equipped with four sampling valves on the top were placed on chamber frames, which were installed in the ground shortly before the start of measurement and remained closed there for 60 minutes. The gas samples taken at twenty-minute intervals from the closed chambers were pumped out using 50 ml syringes and transferred to closed 20 ml crimp-top vials with rubber septa. In the end, four gas samples per plot were collected and analysed with a gas chromatograph. The field flux measurements and analysis of measurements have been described in detail by Vinzent, et al. 33 , Ruser, et al. 34 , Flessa, et al. 35 , Kesenheimer, et al. 13 . They were used at all experimental sites. At Bernburg and Cunnersdorf, N 2 O and CO 2 were measured, while at Roggenstein CH 4 was also analysed. There were differences of the chamber system (e.g. chamber area and chamber volume -both mentioned for each measurement) and the GHG flux calculation (details provided in Table 8 for the three field sites).
Ammonia field measurements. Emissions of NH 3 after fertilization were recorded using the method of Calibrated Passive Sampling -a combination of Dynamic Tube Method (DTM) and Passive Samplers 36 . The basic idea of this approach is to combine a simple qualitative measurement method on many field plots with a quantitative method with parallel measurements on a few plots. I.e. passive samplers 37 filled with diluted sulphuric acid continuously absorb ammonia. DTM [38][39][40] was applied in short measurement periods throughout the day. All details about the experimental design, operational instructions, preparations and flux calculation have been described with video instructions and material list by Pacholski 36 .    Table 9) to measure and to analyse N 2 and N 2 O flux in a fully automated system with the N 2 -free helium-oxygen incubation method. Previous N 2 studies by Fiedler, et al. 41 , Butterbach-Bahl, et al. 42 , Buchen-Tschiskale, et al. 43 . outlined the principle of the investigation. The described procedure has been applied here for the first time.
This method includes three soil cores with a volume of 250 cm³ for the incubation and nine soil cores with a volume of 100 cm³ for N min -analyses. Analyses were conducted at the beginning of gas flux measurement (t 0 ), at the peak of the N 2 O release (t 1 ), at the peak of the N 2 release (t 2 ) and at the end of the gas flux measurement.
Dry soil and water were mixed to obtain a water filled pore space (WFPS) of 70% (TR1) and 90% (TR2) for experiment 1 and 2. For 2 days, the soil cores (250 cm³) were left at 20 °C. Subsequently, the soil cores and fertiliser solution were cooled down to 1 °C and then the fertiliser solution (TR3 and TR4) was injected with five punctures (250 cm³) and four punctures (100 cm³) by a hole template. Soil samples were placed in a helium incubation system and incubated at 1 °C. The normal air was removed from the system and replaced by a helium-oxygen mixture three times. The change in N 2 concentration was measured for two to three days. When consistently low N 2 values were reached, the helium-oxygen mixture was replaced by a more complex N 2 -free gas mixture (He/O 2 /trace gases). After that the temperature in the system was increased to 20 °C. The measurements of N 2 and N 2 O were carried out up to two weeks until concentrations had levelled off again, i.e. the measured concentrations were similar to the level of the He/O 2 /trace gas mixture used for incubation. A detailed description of the preparation and incubation is stored with StaPlaRes-DB-Thuenen.
Modelling data. Soil moisture and seepage of each experimental site was modelled using the agricultural meteorological hydrologic budget model METVER. Meteorological and soil physical data as well as data on the crop phenological development is required for METVER. The meteorological data include daily mean air temperature, daily sunshine duration and daily precipitation. Further information about METVER is published by Böttcher, et al. 44 .

Data Records
All data are stored in the relational database StaPlaRes-DB-Thuenen and are available on the publication server and data repository OpenAgrar (OA) 20  The open access repository publishes, stores, archives and distributes publications, publication references and research data. Its resources can be searched and used by everyone. It contains theses, reports, conference proceedings, journal articles, books, institutional documents, research datasets, videos and interviews. The repository is registered in re3data.org to improve data finding.
StaPlaRes-DB-Thuenen has been designed with Microsoft Access 2019. The database provides stored and archived data (in total 2.5 million records) spread over 38 separate tables (see Table 10). The database tables are related to each other via primary and secondary keys. For simplification, all tables are organised in categories: "experimental design", "driving forces", "measurements -raw data", "measurements -processed data", "specific statistics" and "metadata". Figure 2 shows the data structure of the database. More details about the database are provided in its documentation.
Category -EXPERIMENTAL DESIGN. The category "experimental design" contains the basic information ("key of the database"). The table "5_Plot" represents the organizing principle of the database and contains a Plot_ID (the primary key) describing the unique positioning or affiliation of each measured value and the associated information of the database. For each "Measurements" table in the StaPlaRes-DB-Thuenen there is a 1:n relation to the table "5_Plot". This means that the tables are linked by the foreign key Plot_ID (with the exception of   www.nature.com/scientificdata www.nature.com/scientificdata/ the tables "R_Conc_incubation" and "P_Flux_incubation"). These measurements-tables and the metadata-tables "M_Site_info", "M_Straw_info", "M_BelowLOQ_info", "M_Yield_info", "M_P_and_K_info", "M_Soilprofile_info" and "D_Soil_profile" (Driving forces) are linked to the table "1_Site" via the Site_ID as a foreign key. "D_Management" describes what event or what activity (Management_Name) was performed on a specific plot for a particular crop, at a given time (as date) with a certain intensity (Intensity), the used device and the amount of N in case of nitrogen fertilisation. The columns Intensity and N_amount are complemented by a unit as index.

Category -DRIVING FORCES. A dataset in the table
"D_Soil_profile" describes the composition of the soil profile at each site (location) consisting of horizons (Horizont_nr, Horizont_name) and relevant parameters (soil texture, measured value, unit as index, soil depth from, soil depth to, method as index, source of data, comment). All meteorological parameters, displayed in Table 5, are stored in the table "D_Meteo". Category -MEASUREMENTS. All "measurements" tables are structured with the following eight columns. If necessary each table can be complemented by more columns.
Column names are sometimes underlined at the end because they differ from the reserved words in the Access database and to avoid problems/error messages. Reserved words are words and symbols with a special meaning for Microsoft Access. The metadata tables "M_Variables", "M_Units" and "M_Methods" are always linked to each "measurements" table. Please note that not all measurements are available across all field sites.

Data of crop and soil samplings.
The tables "R_Plant" and "R_Soil_periodic" contain all event-related plant and soil field samples. "R_Soil_periodic" is additionally equipped with "soil depth from" and "soil depth to" as well as with three Boolean columns (switching variable). "Aggregated" column indicates whether a measured value was aggregated based on several values or not. Whether a measured value was adopted from another plot or not will be shown by "Inherited" as a second Boolean column (if a value was adopted, a comment indicates from which plot). A further Boolean column "Below_LOQ" in this table indicates whether a measured value is below the limit of quantification (LOQ) or not. "R_Soil_continuous" stores all soil sensor values which were measured in an hourly interval. In addition to field samples, laboratory samples for soil tests and pot experiments were conducted. For a clear differentiation of the different "scale" of measurements, all lab or pot measured values are stored in the table "R_soil_lab_pot" and "R_Plant_pot".
Gas emission data. The database contains raw data of gas flux measurements (table "R_Conc") and processed data ("P_N2O_flux" and "P_NH3_flux"). Table "R_conc" lists the specific concentration of the gases measured by gas chromatography and used for the calculation of the respective gas fluxes. table is supplemented by the columns "time step", "chamber area", "chamber volume" and "vial number". The gas fluxes of N 2 O, CH 4 and CO 2 are stored in the table "P_N2O_flux". "P_N2O_flux_daily" provides interpolated and aggregated daily N 2 O fluxes. "P_NH3_flux" table contains NH 3 fluxes.
Additional gas data and modelling data. In additional laboratory experiments concentrations and fluxes of N 2 , N 2 O, CO 2 and CH 4 were quantified using the described incubation method. The experimental results are displayed in the table "R_Conc_incubation" and "P_Flux_incubation". Modelled values of soil moisture and seepage are stored in the database table "P_Modelled_SM_SP".
Category -METADATA. All variables, units and methods used in the StaPlaRes-DB-Thuenen are listed in the metadata tables "M_Variables", "M_Units" and "M_Methods". "M_Variables_info" displays all variables used. The information about variables contains a brief description and is supplemented by value plausibility and reference to time and space. The data type of each variable is also defined (raw data, processed or general data). The table "M_Information" defines descriptive information on all columns of the StaPlaRes-DB-Thuenen, except for the column "Variable_". All table names include "info" at the end. Further metadata tables provide additional information; which is described below.
• sites -All field sites are described with general information about the site, such as coordinates, • straw yield -the handling of straw after the harvest (whether the straw was incorporated or removed from the field). • fertiliser application -Due to weather conditions, it was not always possible to apply the urea with subsurface placement. Plot by plot it is described on which fertilisation date the fertilisation was "incorporated" or defined as "surface" fertiliser application. If the table does not contain an entry there was no deviation in fertiliser application from the treatment. • phosphor & potassium fertilization -the handling of phosphor and potassium fertiliser application at each field site (stock or annual fertilization). • limits of quantification -for different measured variables (e.g. NH4-N) limits of quantification (LOQ) are documented.

Technical Validation
Data have been collected by tailored data templates which have been compiled in an iterative manner. By pre-defining experiment names, treatment names, measurement variables, units and methods in the data templates, it was possible to reduce errors. In addition, a two-level data quality control was elaborated. The flow chart in Fig. 3 illustrates the procedure.  www.nature.com/scientificdata www.nature.com/scientificdata/ A document was provided with detailed explanations on the procedure for data delivery and data quality control. Moreover, we plotted each variable to identify and correct errors in data entry, as well as to identify and remove potential erroneous measurements.
Multiple steps were taken to ensure the technical quality of the dataset. Most importantly, consistent field and laboratory protocols were employed. For example, ring tests for gas chromatograph analysis of N 2 O have been conducted at all laboratory.
The results from helium incubations were specifically checked for the following measurement errors: erratic, synchronous changes in all gas concentrations, measurement gaps, negative CO 2 and N 2 concentration values, deviations between the measured and expected concentration values for internal gas standards.
Crop yield data were compared to yield from other experiments conducted at the same location or to yield data from national variety trails. A one-way ANOVA was conducted to compare the mean crop yield of each treatment. A Tukey's post-hoc test was performed for a pairwise comparison of the treatments with a statistical difference at p < 0.05.
Due to irregularities in the hourly precipitation data from the measurement technology at the CUN and BER sites, daily precipitation data from parallel existing weather stations were supplemented (see Table 5).

Usage Notes
The data described are stored in the database StaPlaRes-DB-Thuenen and will be freely available for (re)use by others at the publication server and data repository OpenAgrar 20 (https://doi.org/10.3220/ DATA20220119144442).
Database protection and data reproducibility was guaranteed by dividing the StaPlaRes-DB-Thuenen into a frontend database (FE) and a backend database (BE). The frontend (labelled with "fe" in the database name) represents the application database. The backend (labelled with "be" in the database name) embodies the base of the data in the background and is not intended for application. StaPlaRes