#### A General Information About the dataset # 1. Dataset Title: Root and shoot physiology of six German winter wheat cultivars released between 1895 and 2002 # 2. Brief description of the dataset: # The dataset contains plant physiological measurements of six German winter wheat cultivars released between 1895 and 2002. # Measurements were conducted during two growing seasons between the years 2022-2024 and contain detailed measurements of shoot and root properties. # This includes measurements of leaf area index, specific leaf area, leaf level photosynthesis and stomatal conductance, leaf water potential, # canopy transpiration, biomass of individual plant organs, CN ratios of the biomass, number of tillers, light extinction coefficient, canopy height, # straw and grain yield, grain yield quality, root biomass, root biomass distribution, root hydraulic conductivity, axial and radial conductivity, # root length, root aerenchyma proportions, root cortical cell number and diameter, metaxylem number and diameter, root stele diameter. # 3. Author Information # Authors: Dominik Behrend 1*, Juan Carlos Baca Cabrera 2*, Thuy Huu Nguyen 1, Clara Oliva Gonçalves Bazzo 1,3, Hanna Bernartz 1,2, # Yann Boursiac 4, Frank Ewert 1,5, Hubert Hüging 1, Dylan H. Jones 6, Guillaume Lobet 7, Phillip Nachtweide 1,8, Hannah Schneider 6, # Jan Vanderborght 2°, Thomas Gaiser 1° # * These authors contributed equally # ° These authors contributed equally # For correspondence: dbehrend@uni-bonn.de, j.baca.cabrera@fz-juelich.de # Institutions: # 1. Institute of Crop Science and Resource Conservation (INRES) – Crop Science Group, University of Bonn, Katzenburgweg 5, 53115, Bonn Germany # 2. Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany # 3. INRES - Agro-ecological Modelling Group, University of Bonn, Niebuhrstr. 1a, 53113 Bonn # 4. Institute for Plant Sciences of Montpellier (IPSiM), Univ Montpellier, CNRS, INRAE, Institut Agro, Montpellier 34060, France # 5. Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, Germany # 6. Leibniz Institute of Plant Genetics & Crop Plant Research (IPK) OT Gatersleben, Corrensstr. 3, 06466 Seeland, Germany # 7. Earth and Life Institute, UC-Louvain, 1348 Louvain-la-Neuve, Belgium # 8. INRES - Agroecology and Organic Farming Group, University of Bonn, Auf dem Hügel 6, 53121 Bonn, Germany # 4. This file was generated on 2026-01-09 by Dominik Behrend # 5. Funding # Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – # SFB 1502/1–2022 - Projektnummer: 450058266 and was partially funded by the DFG - EXC 2070 – 390732324 (PhenoRob). # 6. Language of the dataset: English # 7. Spatial and temporal coverage # - Spatial extent: Point # - Temporal resolution: seasonal 2022-2024 ##### B Data structure and file overview # 1. File list: # The dataset is provided in CSV files which are separated into different subfolders, based # the data origen: # - One folder containing files with data that was measured using plants that were grown in a field experiment: # - Leaf area index: /FieldData/lai_plots_KA_2023-2024_v1.csv # - Leaf size: /FieldData/leafsize_plots_KA_2023-2024_V1.csv # - Specific leaf area: /FieldData/sla_plots_KA_2023-2024_v1.csv # - Biomass: /FieldData/biomass_plots_KA_2023-2024_v1.csv # - Yield: /FieldData/yield_plots_KA_2023-2024_v1.csv # - Light extinction coefficient: /FieldData/lightextinctioncoefficient_plots_KA_2023-2024_v1.csv # - Canopy height: /FieldData/height_plots_KA_2023-2024_v1.csv # - Transmitted Radiation: /FieldData/transmfrac_plots_KA_2023-2024_v1.csv # - Number of tillers: /FieldData/spikebearingtillers_plots_KA_2023-2024_V1.csv # - Grain Quality: /FieldData/grainquality_plots_KA_2024_V1.csv # - Transpiration: /FieldData/transpiration_plots_KA_2024_V1.csv # - Biomass Nitrogen and Carbon concentrations: /FieldData/biomassnitrogencarbon_field_KA_2023-2024_V1.csv # - Leaf gas exchange: /FieldData/leafgasexchange_field_KA_2023-2024_V1.csv # - Leaf water potential: /FieldData/leafwaterpotential_field_KA_2023-2024_V1.csv # - Root axes: /FieldData/rootaxes_plots_KA_2023-2024_v1.csv # - Root diameter: /FieldData/rootdiameter_plots_KA_2023-2024_v1.csv # - Root aerenchyma: /FieldData/aerenchyma_plots_KA_2023_2024_v1.csv # - Root apoplastic barriers: /FieldData/apoplasticbarriers_plots_KA_2023_2024_v1.csv # - Root cortical cell diameter: /FieldData/corticalcelldiameter_plots_KA_2023_2024_v1.csv # - Root cortical cell number: /FieldData/corticalcellnumber_plots_KA_2023_2024_v1.csv # - Root meta Xylem diameter: /FieldData/metaxylemdiameter_plots_KA_2023_2024_v1.csv # - Root meta Xylem number: /FieldData/metaxylemnumber_plots_KA_2023_2024_v1.csv # - Root cross section diameter: /FieldData/rootdiametercs_plots_KA_2023_2024_v1.csv # - Root stele diameter: /FieldData/stelediameter_plots_KA_2023_2024_v1.csv # - Root axial conductance: /FieldData/axialconductance_plots_klein_altendorf_2023_2024_v1.csv # - Root radial conductivity: /FieldData/radialconductivity_plots_klein_altendorf_2023_2024_v1.csv # # - One folder containing files with data that was measured using plants that were grown in Tubes inside the field: # - Tube - Plant biomass: /TubeData/plantbiomass_tubes_KA_2023-2024_V1.csv # - Tube - Root biomass for 3 depths: /TubeData/rootbiomassdepth_tubes_KA_2023-2024_V1.csv # - Tube - Biomass Nitrogen and Carbon concentrations: /TubeData/biomassnitrogencarbon_tubes_KA_2023-2024_V1.csv # # - Another folder contains files with data that was measured using plants that were sampled in a hydroponix experiment: # - Root hydraulic conductance: /RootHydroponicsData/Krs_hydroponics_ipsim_2023_v1.csv # - Root Surface area: /RootHydroponicsData/surfacearea_hydroponics_ipsim_2023_v1.csv # - Root length: /RootHydroponicsData/totallength_hydroponics_ipsim_2023_v1.csv # # Besides the main dataset, three additional compressed Folders containing root Images are included: # - A Folder containing root anatomy images: /root_anatomy_images.zip # - A Folder containing root architectural images: /root_architecture_images.zip # - A Folder containing Images of plants from the hydroponic experiemnt: /root_hydroponic_images.zip # 2. Are there multiple versions of the dataset?: No # 3. Relationship between files: # All files are containing data of the same winter wheat varieties. All files share some columns to clearly identify cultivars # used and experimental site, year, date, Season, Cultivar, Cultivar year of release, Repetition and days after sowing of sampling. # Detailed Information About the file columns can be found in the variable_list_Behrend_BacaCabrera.xlsx # 4. Additional related data: # This data was used for model calibration and Validation of the dataset released previously: # Behrend, Dominik; Nguyen, Thuy Huu; Baca Cabrera, Juan C.; Baumert, Josef; Gonçalves Bazzo, Clara Oliva; # Jones, Dylan; Lobet, Guillaume; Seidel, Sabine J.; Srivastava, Amit Kumar; Storm, Hugo; Vanderborght, Jan; # Ewert, Frank; Gaiser, Thomas, 2026, "Winter wheat simulation outputs across Europe (1991–2020)", # https://doi.org/10.60507/FK2/QCZ9KA, bonndata, V1 ##### C Sharing/Access Information # 1. Was data derived from another source? # - No # 2. Licenses/restrictions placed on the data: CC-BY licence # 3. Links to publications that cite or use the data: # - Behrend, D., Nguyen, T.H., Hüging, H., Baca Cabrera, J.C., Lobet, G., Seidel, S.J., Srivastava, A., # Gonçalves Bazzo, C.O., Kramer, N., Nachtweide, P., Vanderborght, J., Ewert, F., Gaiser, T., 2026. # Biomass partitioning and canopy architecture of six German winter wheat cultivars released between 1895 and 2002. Crop Sci. 66, e70263. # https://doi.org/10.1002/csc2.70263 # # - Behrend, D., Nguyen, T.H., Baca Cabrera, J.C., Baumert, J., Bazzo, C.O.G., Jones, D.H., Lobet, G., # Seidel, S.J., Srivastava, A.K., Storm, H., Vanderborght, J., Ewert, F., Gaiser, T., 2026. # Breeding changes water use of winter wheat across Europe. Npj Sustain. Agric. 4, 29. # https://doi.org/10.1038/s44264-026-00135-y # # - Baca Cabrera, J.C., Vanderborght, J., Boursiac, Y., Behrend, D., Gaiser, T., Nguyen, T.H., Lobet, G., 2025. # Decreased root hydraulic traits in German winter wheat cultivars over 100 years of breeding. Plant Physiol. 198, kiaf166. # https://doi.org/10.1093/plphys/kiaf166 # # - Baca Cabrera, J.C., Jones, D.H., Vanderborght, J., Behrend, D., Schneider, H.M., Lobet, G., 2025. # Root anatomical gradients and cultivar differences underlie variation in root hydraulic properties in German winter wheat. # https://doi.org/10.1101/2025.11.19.689226 # # - Jones, D.H., Baca Cabrera, J.C., Behrend, D., Wells, D.M., Swift, J.F., Atkinson, J.A., Schön, M., Lobet, G., Hanlon, M.T., Schneider, H.M., 2025. The Rapid Anatomics Tool (RAT): # A low-cost root anatomical phenotyping platform reveals changes in root anatomy along the root axis. Plant Phenomics 100150. # https://doi.org/10.1016/j.plaphe.2025.100150 ##### D Methodological Information # 1. Description of methods used for generation of data: # 1.1. Field Management: # - Experiments were conducted during two consecutive growing seasons (2022/2023 and 2023/2024) at Campus Klein-Altendorf, near Bonn, Germany (50°37’ N, 6°59’ E) # - Experiment was arranged as a complete randomized block design with four field repetitions # - Cultivars were sown with a row spacing of 10.4 cm and at a seed density designed to achieve 320 plants m⁻² # - Grown cultivars, sorted by their release date, were S. Dickkopf – 1895, SG v. Stocken – 1920, Heines II – 1940, Jubilar – 1961, Okapi – 1978, Tommi – 2002 # - The Experiment was managed in a conventional manner to minimize nutrient, pest, and disease stress and fertilized with 180 kg Nitrogen ha-1 # More detailed information About the crop Management during the experimental Seasons, and the weather and soil conditions can be found in Behrend et al. (2026) # 1.2. Sampling Methods used to generate data of Above Ground processes in the plots: # - This includes processes such as: Leaf area index, Specific leaf area, Biomass, Yield, Light extinction coefficient, Canopy height, # Transmitted Radiation, Number of tillers, Grain Quality, Transpiration, Biomass Nitrogen and Carbon concentrations # are described in Detail in the publication: Behrend et al. (2026) # - In short, the following methodologies were used to derive the data: # - Leaf area index: Green leaves were measured destructively using a LI-3100C Area Meter (LI-COR-Biogeoscience, Nebraska, USA). # - Specific leaf area: Was calculated using dry matter biomass and leaf area index measurements. # - Biomass: Dry matter biomass was estimated from Fresh matter cuts of 4 rows, each 50 cm, divided into leaves, stem and spike and then dried at 105°C for 24 Hours. # - Yield: Straw and grain yields were determined using a Combine harvester with a working width of 1.5 m # - Canopy height: Canopy height was measured using a ruler from soil to the top of the canopy. # - Transmitted Radiation: Was measured using a SunScan device (Delta-T Devices, Cambridge, UK) equipped with an external beam fraction sensor. # Measurements were taken around midday, at the same dates of the biomass cuts. # - Light extinction coefficient: Was calculated using Beer’s Law based on the destructively measured LAI and fractional light interception recorded with the SunScan. # - Number of tillers: Shortly before the harvest, spike bearing tillers (defined as all tillers with a spike) were measured in two rows per plot for a length of one meter. # - Grain Quality: Grain Quality indices were measured using a near infrared sensor installed within the Combine harvester. # - Transpiration: Canopy Transpiration was derived from 5 sap flow sensors for each cultivar (SAG3; Dynamax Inc., Houston USA), # Data collection was done using a CR1000 data logger and two AM 16/32 multiplexers (Campbell Scientific, Logan, Utah USA). # - Biomass Nitrogen and Carbon concentrations: Biomass subsamples for each organ were dried at 65 °C to analyze for nutrient carbon and nitrogen (CN) contents # using a CN analyzer (EuroEA3000, EuroVector S.p.A., Italy). # - Leaf size: Leaf width and length were measured for only for the flag leaf using a ruler. # - Leaf gas exchange: Leaf gas exchange measurements were done with a Licor6400XT Portable Photosynthesis System (Li-Cor Biosciences), # using a constant CO2 concentration of 410 ppm, constant flow rate of 500 (μmol s−1), and ambient weather conditions. # Measurements were conducted always at the youngest fully developed leaf. Leaf area within the system was adapted based on leaf width measurements. # - Leaf water potential: For the measurements of leaf water potential, the youngest fully developed leaves were cut by a sharp knife and inserted into a digital # pressure chamber [(SKPM 140/ (40-50-80), Skye Instrument Ltd, UK] with air pressure between 0-35 bars. Measurements were partly done pre dawn until midday. # 1.3. Sampling Methods used to generate data from plants in the tubes: # - This includes processes such as: Plant biomass, Root biomass for 3 depths, Biomass Nitrogen and Carbon concentrations # Measurements are described in detail in the publication: Behrend et al. (2026) # - In short, the following methodologies were used to derive the data: # - To measure biomass partitioning between root and shoot biomass in the field, tubes were burried inside the already sown field, allowing for realistic canopy conditions. # In each tube 1 plant was sown, tubes were harvested at three developmental stages according to the BBCH scale 30 (beginning of stem elongation), # 45 (late booting), 60 (beginning of flowering). # - Plant biomass: Fresh matter biomass was divided into leaves, stem and spike and then dried at 105°C for 24 Hours. # - Root biomass for 3 depths: Tubes were cut open, divided into depth sections of 0-33 cm, 33-66 cm and 66-100 cm and washed out from the bottom to the top. # Remaining soil particles on the roots were then removed using tweezers. Roots were then dried at 65 °C for at least 3 days or until no further water evaporated. # Dry matter root weight was then determined before roots were milled and analyzed for CN ratios. # - Biomass Nitrogen and Carbon concentrations: Biomass subsamples for each organ were dried at 65 °C to analyze for nutrient carbon and nitrogen (CN) contents # using a CN analyzer (EuroEA3000, EuroVector S.p.A., Italy). # 1.3. Sampling Methods used to generate the root images and data such as: # Root axes, Root diameter, Root aerenchyma, Root apoplastic barriers, Root cortical cell diameter, # Root cortical cell number, Root meta Xylem diameter, Root meta Xylem number, Root cross section diameter, # Root stele diameter, Root axial conductance, Root radial conductivity # Are described in Detail in the publications: # - Baca Cabrera, J.C., Vanderborght, J., Boursiac, Y., Behrend, D., Gaiser, T., Nguyen, T.H., Lobet, G., 2025. # Decreased root hydraulic traits in German winter wheat cultivars over 100 years of breeding. Plant Physiol. 198, kiaf166. # https://doi.org/10.1093/plphys/kiaf166 # - Baca Cabrera, Juan C., Jones, D.H., Vanderborght, J., Behrend, D., Schneider, H.M., Lobet, G., 2025. Root anatomical gradients and cultivar differences # underlie variation in root hydraulic properties in German winter wheat. # https://doi.org/10.1101/2025.11.19.689226 # - General root sampling method in the field: - Root sampling was conducted at the end of the tillering stage (BBCH < 30) in both growing seasons, using an adapted shovelomics approach for wheat (York 2018). # At each plot, a representative soil volume was excavated from the topsoil with an approximate diameter and depth of 20–30 cm, each excavation typically contained 5–10 individual plants # Samples were soaked in water and gently washed to remove adhering soil while preserving root integrity # Tillers were separated above the mesocotyl and manually counted prior to analysis. # Seminal and crown roots were carefully disentangled and scanned at 600 dpi using a flatbed scanner (Epson Expression 12000XL, Epson, Japan). # - Root morphological traits such as Root axes, Root diameter: Digital images were analysed using the SmartRoot software (Lobet et al., 2011) # - Root anatomical traits such as Root aerenchyma, Root apoplastic barriers, Root cortical cell diameter, Root cortical cell number, Root meta Xylem diameter, Root meta Xylem number, Root cross section diameter, # Root stele diameter: Were quantified from the same crown root samples collected for shovelomics-based morphological analysis. From each excavated plant, the longest crown root (approximately 20–25 cm in length) # was excised directly at the tiller junction and retained for anatomical characterization. For each selected crown root, 2–3 cm segments were excised from three standardized positions along the root axis: # a basal segment adjacent to the tiller junction (first 2.5 cm), a mid-root segment located approximately 10 cm from the base, and a distal segment located approximately 20 cm from the base. # Cross-sectional anatomical imaging was performed using the Rapid Anatomics Tool (RAT; Jones et al., 2025). Anatomical traits were quantified from cross-sectional images using Fiji (ImageJ) # (Schindelin et al., 2012). Linear dimensions were obtained using the line tool, and tissue areas were manually delineated using the polygon tool. Metaxylem lumens were segmented following # grayscale conversion and Gaussian blurring, with threshold-based selection applied to quantify individual vessel areas (see Jones et al.(Jones et al., 2025) for detailed image processing steps). # - Root axial conductance, Root radial conductivity: Were derived from the measured anatomical cross-sections using the integrated GRANAR–MECHA computational framework (Heymans et al., 2019). # For each crown root and sampling position, virtual root cross-sections were generated with GRANAR (v1.1) based on experimentally quantified anatomical traits, including root and stele diameter, # cortical cell file number and thickness, and the number and diameter of metaxylem vessels. The reconstructed anatomies were then processed with MECHA (Couvreur et al., 2018) to estimate # axial conductance (kx, m4 MPa-1 s-1) and radial conductivity (kr, m MPa-1 s-1) . Model inputs included the measured anatomical traits for each cross-section, apoplastic barrier scores, and default subcellular # hydraulic parameters. # 2. Methods for processing the data: # 2.1. Field Management: Some data types were additionally postprocessed # - Some data types were additionally postprocessed, in short, the following methodologies were used to process the data that: # - Transpiration: Vertical and temperature gradients within each sapflow sensor were recorded at 10-minute intervals and later aggregated to # hourly intervals. Temperature gradients of each sensor were used to calculate sap flow according to Langensiepen et al. (2014). # Upscaling of the measurements from the transpiration rates of each tiller (g h-1) to the canopy transpiration rates (mm h-1) # was performed using the mean tiller number. # 3. References for methodologies and data processing: # - Langensiepen, M., Kupisch, M., Graf, A., Schmidt, M. & Ewert, F. Improving the stem heat balance method for determining sap-flow in wheat. # Agric. For. Meteorol. 186, 34–42 (2014), https://doi.org/10.1016/j.agrformet.2013.11.007. # - Behrend, D., Nguyen, T.H., Hüging, H., Baca Cabrera, J.C., Lobet, G., Seidel, S.J., Srivastava, A., # Gonçalves Bazzo, C.O., Kramer, N., Nachtweide, P., Vanderborght, J., Ewert, F., Gaiser, T., 2026. # Biomass partitioning and canopy architecture of six German winter wheat cultivars released between 1895 and 2002. Crop Sci. 66, e70263. # https://doi.org/10.1002/csc2.70263 # - York LM. Phenotyping crop root crowns: general guidance and specific protocols for maize, wheat, and soybean. In: Ristova D, Barbez E, editors. # Root development: methods and protocols. New York (NY): Springer; 2018. p. 23–32. # https://doi.org/10.1007/978-1-4939-7747-5_2 # - Lobet G, Pagès L, Draye X. A novel image-analysis toolbox enabling quantitative analysis of root system architecture. Plant Physiol. 2011:157(1):29–39. # https://doi.org/10.1104/pp.111.179895 # - Jones, D.H., Baca Cabrera, J.C., Behrend, D., Wells, D.M., Swift, J.F., Atkinson, J.A., Schön, M., Lobet, G., Hanlon, M.T., Schneider, H.M., 2025. The Rapid Anatomics Tool (RAT): # A low-cost root anatomical phenotyping platform reveals changes in root anatomy along the root axis. Plant Phenomics 100150. # https://doi.org/10.1016/j.plaphe.2025.100150 # - Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J.-Y., White, D.J., # Hartenstein, V., Eliceiri, K., Tomancak, P., Cardona, A., 2012. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682. # https://doi.org/10.1038/nmeth.2019 # - Heymans, A., Couvreur, V., LaRue, T., Paez-Garcia, A., Lobet, G., 2019. GRANAR, a computational tool to better understand the functional importance of monocotyledon root anatomy. Plant Physiol. 182, 707–720. # https://doi.org/10.1104/pp.19.00617 # - Couvreur, V., Faget, M., Lobet, G., Javaux, M., Chaumont, F., Draye, X., 2018. Going with the Flow: Multiscale Insights into the Composite Nature of Water Transport in Roots. Plant Physiol. 178, 1689–1703. # https://doi.org/10.1104/pp.18.01006 ##### E Data specific information # 1. Variable list including full names and definitions of column headings and Units of the two main datasets # can be found in the file: variable_list_Behrend_BacaCabrera.xlsx # Explanations of variables: # All files include the following variables: # - Date: Sampling date # - Year: Experimental year of sampling. # - Season: Experimental season of sampling (since winter wheat are sown in autumn and cover 2 years). # - Site_ID: Experimental station at which measurements were taken # - Cultivar_ID: Cultivar name plus year of release. # - Cultivar_Year: Cultivar year of release # - PlotID: Plot ID of the randomized field experiment # - DAS: Days after sowing, number of days from sowing to measurement date