This file was generated on 2024-11-12 by Lars Caspersen A GENERAL INFORMATION 1. Title of the dataset: Supporting Information for manuscript: Contrasting responses to climate change – predicting bloom of major temperate fruit tree species in the Mediterranean region and Central Europe 2. Brief description of the research project and its aims: This dataset contains supporting information for the manuscript Contrasting responses to climate change – predicting bloom of major temperate fruit tree species in the Mediterranean region and Central Europe. In entails the model estimated model parameters of PhenoFlex for each cultivar and repetition, the model performance for each set of estimated model parameters, as well as the future and baseline weather created combining the weather generator RMAWGEN and temperature scenarios based on output from CMIP6. 3. Author Information A. Investigator Contact Information Name: Lars Caspersen Institution: Department of Horticultural Sciences, Institute of Crop Science and Resource Conservation (INRES), University of Bonn Address: Auf dem Hügel 6, 53121 Bonn, Germany Email: lcaspers@uni-bonn.de B. Project Supervisor (Principal Investigator) Contact Information Name: Luedeling, Eike Institution: Department of Horticultural Sciences, Institute of Crop Science and Resource Conservation (INRES), University of Bonn Address: Auf dem Hügel 6, 53121 Bonn, Germany Email: luedeling@uni-bonn.de C. In case of questions related to this dataset, please contact: Name: Luedeling, Eike Institution: Department of Horticultural Sciences, Institute of Crop Science and Resource Conservation (INRES), University of Bonn Address: Auf dem Hügel 6, 53121 Bonn, Germany Email: luedeling@uni-bonn.de 4. Date of data collection : 1958-01-01 to 2022-12-31 5. Information about funding sources that supported the collection of the data: We thank the Partnership for Research and Innovation in the Mediterranean Area (PRIMA), a program supported under H2020, the European Union’s Framework program for research and innovation, for funding this research within the AdaMedOr project (grant number 01DH20012 of the German Federal Ministry of Education and Research). We also thank national donors from the partner countries: the Spanish Ministry of Science and Innovation (Agencia Estatal de Investigación 10.13039/501100011033) and NextGeneration EU/PRTR (grants PCI2020-111966 and PID2020-115473RR-I00 in the case of CITA; grant PCI2020-112113 in the case of CEBAS); the Ministère de l’Education Nationale, de la Formation Professionnelle, de l’Enseignement Supérieur et de la Recherche Scientifique, Département de l’Enseignement Supérieur et de la Recherche Scientifique (MENFPESRS/DESRS- Maroc) in the case of Morocco; and the Tunisian Ministry of Higher Education and Scientific Research in the case of Tunisia. 6. Language of the dataset: English 7. Geographic location of data collection : Spain, Tunisia, Morocco, Germany B DATA & FILE OVERVIEW 1. File List: future_weather.zip Collection of csv files containing weather generator output when combining local observed weather with projected future climate by CMIP6 . File names indicate location, scenario year (either 2050 or 2085), shared socioeconomic pathway, and name of global circulation model. hist-sim-weather.zip Collection of csv files for each modeled location, containg weather generator output for baseline conditions resembling 2015 conditions. File names indicate the modeled location. parameter_cultivars.csv Contains estimated PhenoFlex model parameters for the cultivars. performance_fitted_models.csv Contains model performance scores (RMSE, mean bias, RPIQ) for calibration and validation dataset. master_phenology_repeated_splits.csv Contains phenology observation, split for calibration and validation data for each cultivar and repetition. 2. Are there multiple versions of the dataset? no 3. Relationship between files : not relevant 4. Additional related data collected that was not included in the current data package: Local temperature observations for each modeled location, published in the dataset: Long-term phenology observations for temperate fruit trees in the Mediterranean region (and Germany) Luedeling, Eike; Caspersen, Lars; Delgado Delgado, Alvaro; Egea, Jose A.; Ruiz, David; Ben Mimoun, Mehdi; Benmoussa, Haïfa; Ghrab, Mohamed; Kodad, Ossama; El Yaacoubi, Adnane; Fadón, Erica; Rodrigo, Javier, 2024, "Long-term phenology observations for temperate fruit trees in the Mediterranean region (and Germany)", https://doi.org/10.60507/FK2/MZIELI, bonndata, V2 C SHARING/ACCESS INFORMATION 1. Was data derived from another source? yes/no : Model parameter were calibrated based on phenology observations available at: Long-term phenology observations for temperate fruit trees in the Mediterranean region (and Germany) Luedeling, Eike; Caspersen, Lars; Delgado Delgado, Alvaro; Egea, Jose A.; Ruiz, David; Ben Mimoun, Mehdi; Benmoussa, Haïfa; Ghrab, Mohamed; Kodad, Ossama; El Yaacoubi, Adnane; Fadón, Erica; Rodrigo, Javier, 2024, "Long-term phenology observations for temperate fruit trees in the Mediterranean region (and Germany)", https://doi.org/10.60507/FK2/MZIELI, bonndata, V2 2. Licenses/restrictions placed on the data: CC-BY 4.0 The dataset was compiled as part of the Adapting Mediterranean Orchards (AdaMedOr project) https://mel.cgiar.org/projects/adamedor 3. Links to publications that cite or use the data : Caspersen, Lars and Schiffers, Katja and Picornell, Antonio and Egea, Jose A. and Delgado, Alvaro and El Yaacoubi, Adnane and Benmoussa, Haïfa and Rodrigo, Javier and Fadón, Erica and Ben Mimoun, Mehdi and Ghrab, Mohamed and Kodad, Ossama and Luedeling, Eike, Contrasting Responses to Climate Change – Predicting Bloom of Major Temperate Fruit Tree Species in the Mediterranean Region and Central Europe. Available at SSRN: https://ssrn.com/abstract=4913208 or http://dx.doi.org/10.2139/ssrn.4913208 4. Links to other publicly accessible locations of the data : 5. Links/relationships to ancillary datasets : Phenology observation and local temperature observation are derived from: Long-term phenology observations for temperate fruit trees in the Mediterranean region (and Germany) Luedeling, Eike; Caspersen, Lars; Delgado Delgado, Alvaro; Egea, Jose A.; Ruiz, David; Ben Mimoun, Mehdi; Benmoussa, Haïfa; Ghrab, Mohamed; Kodad, Ossama; El Yaacoubi, Adnane; Fadón, Erica; Rodrigo, Javier, 2024, "Long-term phenology observations for temperate fruit trees in the Mediterranean region (and Germany)", https://doi.org/10.60507/FK2/MZIELI, bonndata, V2 D METHODOLOGICAL INFORMATION 1. Description of methods used for collection/generation of data: 2. Methods for processing the data: The model PhenoFlex was calibrated using a combination of enhanced scatter search and dynamic hill climbing. Weather data was generated using the package RMAWGEN. For future weather we combined output of CMIP6 with the weather generator. Scripts to follow the data processing can be found in the GitHub repository https://github.com/larscaspersen/AdaMedOr_climate-change-projections 3. Instrument- and/or software-specific information needed to interpret the data: No special software needed to read the stored files. Data processing was done using R. 4. People involved in sample collection, processing, analysis and/or submission: Project partners include: Caspersen, Lars; Schiffers, Katja, Picornell, Antonio; Delgado Delgado, Alvaro; Egea, Jose A.; Ruiz, David; Ben Mimoun, Mehdi; Benmoussa, Haïfa; Ghrab, Mohamed; Kodad, Ossama; El Yaacoubi, Adnane; Fadón, Erica; Rodrigo, Javier; Luedeling, Eike Furthermore numerous technical staff was involved in the data collection. 5. Describe any quality-assurance procedures performed on the data: not applicable 6. Standards and calibration information : not applicable 7. Environmental/experimental conditions : not applicable E DATA-SPECIFIC INFORMATION FOR: [FILENAME] future_weather.zip 1. Variable list including full names and definitions (please spell out abbreviated words) of column headings for tabular data: file name follows te following structure: future_weather_*location name*counter*_*location name*.*Shared socioeconomic scenario*GCM name*.*scenario year* changing variable names are marked between star sign (*) each file contains the following columns - DATE, month/day/year hh:mm:ss, though the hour minutes and seconds are not relevant - Year, this does not contain the real modelled year, it is simply a name-tag for the weather generator - Month, month of the modelled weather - Day, day of the modelled weather - nodata, - not relevant - - Tmin, daily minimum temperature generated - Tmax, daily maximum temperature generated 2. Units of measurement used: DATE (format: (mm/dd/YYYY) Tmin, Tmax: degree centigrade 3. Missing data codes/symbols: NA = missing observation 4. Specialized formats or other abbreviations used: not applicable hist-sim-weather.zip 1. Variable list including full names and definitions (please spell out abbreviated words) of column headings for tabular data: file name follows te following structure: hist_gen_*scenario year*_*counter*_*location name* changing variable names are marked between star sign (*) each file contains the following columns, same structure as for future weather - DATE, month/day/year hh:mm:ss, though the hour minutes and seconds are not relevant - Year, this does not contain the real modelled year, it is simply a name-tag for the weather generator - Month, month of the modelled weather - Day, day of the modelled weather - nodata, - not relevant - - Tmin, daily minimum temperature generated - Tmax, daily maximum temperature generated 2. Units of measurement used: DATE (format: (mm/dd/YYYY) Tmin, Tmax: degree centigrade 3. Missing data codes/symbols: NA = missing observation 4. Specialized formats or other abbreviations used: not applicable master_phenology_repeated_splits.csv 1. Variable list including full names and definitions (please spell out abbreviated words) of column headings for tabular data: - species, name of the fruit tree species - cultivar, name of cultivar - split, indicate if data part of calibration or validation - location, name of location the observation was taken from - repetition, different numbers indicate different repetition. different random split for each repetition - year, indicates in which year phenological observation was made - pheno, day of the year the phenology observation was made - measurement_type, indicates the phenology stage, either "flowering_f50" for full flowering (50% buds open, BBCH stage 65) or "begin_flowering_f5" for begin of flowering (10% buds open, BBCH stage 61) 2. Units of measurement used: pheno measured as day of the year, with first of January being 1 and 31st of December being 365 (or 366 in a leap year) 3. Missing data codes/symbols: NA = missing observation but should not contain missing observations 4. Specialized formats or other abbreviations used: not applicable parameter_cultivars.csv 1. Variable list including full names and definitions (please spell out abbreviated words) of column headings for tabular data: species repetition cultivar yc zc s1 Tu theta_c tau pie_c Tf Tb slope - species, name of the fruit tree species - cultivar, name of cultivar - repetition, different numbers indicate different repetition. different random split for each repetition - yc, chill requirement parameter - zc, heat requirement parameter - s1, chill and heat transition parameter - Tu, optimal temperature for heat accumulation - theta_c, Critical temperature above which no portions can be accumulated - tau, Time interval for accumulating one portion at the optimal temperature - pie_c, Critical period in a combined temperature cycle leading to chilling negation - Tf, transition temperature for converting degradable to stable chill portions - Tb, minimum temperature for heat accumulation - slope, transition parameter converting degradable to stable chill portions 2. Units of measurement used: yc = chill portions zc = growing degree hours Tu = temperature in degree centigrade theta_c = temperatue in Kelvin tau = hours pie_c = hours Tf = temperature in degree centigrade Tb = temperaute in degree centigrade slope = no unit 3. Missing data codes/symbols: NA = missing observation but should not contain missing observations 4. Specialized formats or other abbreviations used: not applicable performance_fitted_models.csv 1. Variable list including full names and definitions (please spell out abbreviated words) of column headings for tabular data: - species, name of the fruit tree species - repetition, different numbers indicate different repetition. different random split for each repetition - cultivar, name of cultivar - split, indicate if data part of calibration or validation - iqr, interquartile range of phenology observation - rmse, root mean square error of predicted bloom dates - mean_bias, mean difference between predicted and observed bloom day - rpiq_aj, ratio of performance to interquartile distance, calculated by dividing iqr by rmse 2. Units of measurement used: iqr, rmse, mean_bias = day of the year rpiq_adj = no unit 3. Missing data codes/symbols: NA = missing observation but should not contain missing observations 4. Specialized formats or other abbreviations used: not applicable