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José","familyName":"Lacueva Pérez","affiliation":["Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-0998-2939","nameIdentifierScheme":"ORCID"}]},{"name":"Labata Lezaun, Gorka","nameType":"Personal","givenName":"Gorka","familyName":"Labata Lezaun","affiliation":["Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-8634-4124","nameIdentifierScheme":"ORCID"}]},{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-7073-219X","nameIdentifierScheme":"ORCID"}]},{"name":"del Hoyo Alonso, Rafael","nameType":"Personal","givenName":"Rafael","familyName":"del Hoyo Alonso","affiliation":["Universidad San Jorge","Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2755-5500","nameIdentifierScheme":"ORCID"}]},{"name":"Barriuso Vargas, Juan","nameType":"Personal","givenName":"Juan","familyName":"Barriuso Vargas","affiliation":["Universidad de Zaragoza"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2980-5454","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"A Multisource Grapevine Phenology Dataset for Smart Farming and AI Modeling"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Grapevine Phenology"},{"subject":"Copernicus Sentinel 2"},{"subject":"Supervised Machine Learning","subjectScheme":"MeSH"},{"subject":"Multisource"}],"contributors":[{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"contributorType":"Editor","nameIdentifiers":[{"nameIdentifier":"0000-0002-7073-219X","nameIdentifierScheme":"ORCID"}]},{"name":"Labata Lezaun, Gorka","nameType":"Personal","givenName":"Gorka","familyName":"Labata Lezaun","affiliation":["Instituto Tecnológico de 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Aragón"],"contributorType":"ContactPerson","nameIdentifiers":[{"nameIdentifier":"0000-0003-0998-2939","nameIdentifierScheme":"ORCID"}]},{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"contributorType":"DataManager","nameIdentifiers":[]}],"dates":[{"date":"2026-01-10","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1016/j.compag.2025.110018","resourceTypeGeneral":"JournalArticle","relatedIdentifierType":"DOI"},{"relationType":"IsReferencedBy","relatedIdentifier":"https://webdiis.unizar.es/~silarri/prot/DREAM/index.html","resourceTypeGeneral":"Other","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.17930722","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Description\n\nArtificial Intelligence and Machine Learning rely on large, high-quality datasets for accurate and robust models, yet data scarcity remains a major challenge—especially in smart farming. Agricultural data are highly diverse and heterogeneous, complicating model development. Phenology modeling, a key application, studies how plant biological events relate to climate and seasons. Accurate phenology models improve crop quality, support climate adaptation, and guide decisions such as pesticide use and harvesting, enhancing environmental and economic sustainability.This study introduces a georeferenced dataset for Machine Learning-based grapevine phenology prediction across 3 Protected Designations of Origin in Arag’on, Spain. Developed by a multidisciplinary team, the dataset combines 9 datasets from 8 sources—including meteorological time series, field phenology observations, and Copernicus Sentinel-2 multispectral imagery—covering the period 2016–2022. It supports both physical and ML-based phenology modeling and facilitates knowledge extraction in agronomy and plant biology. Its relevance lies in its comprehensive scope, the inclusion of 9 phenological stages, and a rigorous methodology ensuring reproducibility. This framework enables the creation of similar datasets for otherregions or crops, advancing smart farming through scalable, data-driven solutions. We further anticipate its potential contribution to developing Foundation Models as well as to the creation.\n\nDataset Structure\n\nThe dataset contains 2 main CSV files and 1 supporting folder:\n\n\n\nDIF Description.pdf- Description of the data set.\n\nDIF phenologicaletages.csv: it contains the links from the phenologystageid values in the file “DIF GrapevinePehologyDataset.csv:  with the correspoding BBCH values.\n\nDIF GrapevinePehologyDataset.csv: it contains the dataset used to train the models we presented.\n\nMetadata: this folder contains the JSON files describing the dataset and its content:\n\n\n\nDIF_DataSetDescription.json: the description contained if “DIF Description.pdf” but in JSON format.\n\nDIF GrapevinePehologyDataset.json: contains the dataset presented in this paper which was used to train the models we presented in [51, 50]. We describe the content of the file in next paragraphs.\n\n\n\n\nIntended Use\n\nThe goal of this dataset is to enable the development of models for predicting grapevine phenology in the three Protected Designations of Origin in Aragón (Spain), using data from field observations, meteorological stations, and NDVI derived from Copernicus Sentinel-2 multispectral imagery. Additionally, it supports the calibration of physical models for these regions by including the calculation of cold and heat accumulation indices. These calculations are performed using the traditional start dates of January 1 and February of the corresponding year, as well as from the date when plants enter dormancy: the first autumn day when the maximum temperature does not exceed 10 °C.\n\nAccess Conditions: This dataset is publicly available under the terms of the Creative Commons Attribution 4.0 International license. \n\nSpecifications Table\n\n\n\n\n\n\nSubject\n\n\n\nSmart farming\n\n\n\n\n\nSpecific subject area\n\n\n\nThe dataset is based on data from 3 Protected Designations of Origin—Calatayud, Cariñena and Campo de Borja, —in Aragón, northeastern Spain. Built by merging 9 georeferenced time-series datasets from 8 data sources considering the period from 2016 to 2022. It includes meteorological data (measurements, estimates, and forecasts), qualitative field phenology observations, and Copernicus Sentinel-2 multispectral imagery.\n\n\n\n\n\nType of data\n\n\n\nAnalyzedFilteredProcessedMulti-source\n\n\n\n\n\nData collection\n\n\n\nData merged in the dataset is obtained from 9 georeferenced datatsets obtained from 8 data sources.  The datasets considered are:\n\n·         Red FARA phenological registry [1]: this dataset has restricted access.  It provides phenology field observations on the control parcels.\n\n·         Spanish Cadastral Registry (Catastro) [2]: it is used to normalize Red FARA records and to obtain the NDVI of the control parcels from Copernicus Sentinel 2 images.\n\n·         Aragón Open Data Common Agrarian Policy Registry (CAP) [3]: together with the Catastro data is used to normalize of the Red FARA records.\n\n·         SIAR [4] and Grapevine [5] climatic station networks provide meteorological data.\n\n·         ERA5 real climatic estimations [6] and ECMWF IFS forecast [7] data used to replace failures in climatic data and forecast data to perform predictions.\n\n·         Copernicus Sentinel 2 multispectral images [8]: these images are used to determine the NDVI of the control parcels used to create the dataset. \n\nFor accessing these dataset we used available APIs.  All they are public and provide open access. The 2 exceptions were Red FARA which has restricted access, and ERA5 data which was accessed using openMeteo API [8] which eased our work.  The access to the data and the transformations performed in them were coded in Python.  A deep explanation of the transformation performed can be obtained in [9].\n\n\n\n\n\nData source location\n\n\n\nCountry: Spain.Region: Aragón.Protected Designation of Origin: Calatayud, Campo de Borja, Cariñena.Coordinates: Parallelepiped defined by points (41.98107, −2.177578) and (41.166320, −0.922575) in WGS84 coordinates.\n\n\n\n\n\nData accessibility\n\n\n\nRepository name: Zenodo\n\nData identification number: 10.5281/zenodo.17930723 Direct URL to data: https://doi.org/10.5281/zenodo.17930723  \n\n\n\n\n\nReferences\n\n\n\n\n\n\n\n\n[1]\n\n\n\nGovernment of Aragón. Red FARA Home Page. Last access: January 10, 2026. 2026. url: http://web.redfara.es.\n\n\n\n\n\n[2]\n\n\n\nSpanish Treasury. Spanish Cadastral Registry Electronic Home Page. Last access: January 10, 2026. 2026. url: https://www.sedecatastro.gob.es/.\n\n\n\n\n\n[3]\n\n\n\nGovernment of Aragón. Aragón Open Data Home Page. Last access: January 10, 2026. 2026. url: https://opendata.aragon.es.\n\n\n\n\n\n[4]\n\n\n\nSpanish Ministry of Agriculture, Fisheries and Food. Agro-climatic Information System for Irrigation (SIAR) Home Page. Last access: January 10, 2026. 2026. url: https://eportal.mapa.gob.es//websiar/Inicio.aspx .\n\n\n\n\n\n[5]\n\n\n\nGrapevine Project Consortium. Grapevine Project Home Page. Grant agreement ID: 863463. https://grapevine-project.eu (Last access: August 8, 2024), https://web.archive.org/web/20230922054033/https://grapevine- project.eu (Last access: January 10, 2026), https://www.egi.eu/case-study/grapevine (Last access: January 10, 2026). 2022.\n\n\n\n\n\n[6]\n\n\n\nCopernicus Climate Change Service (C3S). ERA5 hourly data on single levels from 1959 to present. Last access: January 10, 2026. 2023. url: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview .\n\n\n\n\n\n[7]\n\n\n\nEuropean Centre for Medium-Range Weather Forecasts (ECMWF). ECMWF Open Data. Last accessed January 10, 2026. 2026. url: https://www.ecmwf.int/en/forecasts/datasets/open-data .\n\n\n\n\n\n[8]\n\n\n\nF. Gascon et al. “Copernicus Sentinel-2 mission: products, algorithms and Cal/Val”. In: Earth Observing Systems XIX. Ed. by James J. Butler, Xiaoxiong (Jack) Xiong, and Xingfa Gu.SPIE, Sept. 2014, pp. 1–9. doi:10.1117/12.2062260. url: http://dx.doi.org/10.1117/12.2062260.\n\n\n\n\n\n[9]\n\n\n\nFrancisco Jos´e Lacueva-P´erez et al. “Developing machine learning models from multisourced real-world datasets to enhance smart-farming practices”. In: Computers and Electronics in Agriculture 231 (Apr. 2025), p. 110018. issn: 0168-1699. doi: 10.1016/j.compag.2025.110018. url: http://dx.doi.org/10.1016/j.compag.2025.110018 .\n\n\n\n\n\n \n\n\n\n\n\n \n\n \n\n\n\n\n \n\nFile “DIF_GrapevinePehologyDataset.csv” Description\n\nFile DIF_GrapevinePehologyDataset.csv contains the dataset presented in this paper.  Each of the records represents the data considered for a given parcel (vineyard) in each date. The following table provides a description of the fields contained in the dataset. For clarity, we simplified the table by using an abbreviated notation for the field names; specifically, for some field names we include an asterisk (“*”) with the name followed by a couple of numbers in brackets (“[…]”) that describe the range of integer values that can replace the “*” in the dataset; for example, we did this in fields which provide values of the given variable data for the n days before (days_after ) and after (days_adelante).  For clarity, we provide here some examples:\n\n·         tmed_min *_days_after  [1,13]: this name represents that the dataset contains all the fields tmed_min 1_days_after , tmed_min 2_days_after , ..., tmed_min 13_days_after , which represent, for the given field, the minimum temperature for each of the n days before the date of the record.\n\n·         wind_NE *_days_after [1,6]: this name represents that the dataset contains all the fields wind_NE 1_days_after, t wind_NE 2_days_after, ..., wind_NE 6_days_after, which represent, for the given field, the wind_NE index for each of the n days after the date of the record.\n\n·         gdd_4.5_t0_Tbase_sum *_weeks_before [1,2]: this name represents that the dataset contains all the fields gdd_4.5_t0_Tbase_sum 1_weeks_before and gdd_4.5_t0_Tbase_sum 2_weeks_before, which represent, for the given field, the GDD calculated using the base temperature 4.5º C and starting to accumulate at the beginning of the session.\n\nMoreover, we use “|” to denote choices (expressed within brackets “[…]”), which can represent several attributes. For example, “rad_[min|MAX|mean]” actually represents (in a condensed way) 3 different variables: “rad_min”, “rad_max” and “rad_mean”. Other notations can be interpreted similarly. The full list of variable names is shown in Appendix A.\n\n\n\n\n\n\nField Name (abbreviated notation)\n\n\n\nDescription\n\n\n\n\n\n\n\nphenologystageid\n\n\n\nId of the phenological stage of the parcel on the given date.  See file “DIF phenologicalstages.csv”.\n\n\n\n\n\nvariety\n\n\n\nGrapevine variety:  Cabernet Sauvignon, Chardonnay, Garnacha, Mazuela, Syrach, Tempranillo.\n\n\n\n\n\ncodigo\n\n\n\nId of the parcel in the Spanish Cadastral Registry.\n\n\n\n\n\nlongitude\n\n\n\nLongitude of the centroid of the parcel.\n\n\n\n\n\nlatitude\n\n\n\nLatitude of the centroid of the parcel.\n\n\n\n\n\naltitudeASL\n\n\n\nAltitudeASL of the centroid of the parcel.\n\n\n\n\n\nPDO_id\n\n\n\nId of the Protected Designation of Origin (PDO): Calatayud, Carinena and Campo de Borja.\n\n\n\n\n\ndate\n\n\n\nThe date of the record.\n\n\n\n\n\nstation\n\n\n\nThe name of the climatic station whose data are considered.\n\n\n\n\n\nseason\n\n\n\nThe season to which the record belongs.\n\n\n\n\n\nday\n\n\n\nThe DOY (day of the year).\n\n\n\n\n\n\"PDO_Borja\", \"PDO_Calatayud\", \"PDO_Carinena\", \"PDO_Somontano\"\n\n\n\nBoolean values which are true when the record corresponds to the given PDO.\n\n\n\n\n\n\"variety_CABERNET SAUVIGNON\", \"variety_CHARDONNAY\", \"variety_GARNACHA\", \"variety_MAZUELA\", \"variety_SYRACH\", \"variety_TEMPRANILLO\"\n\n\n\nBoolean values which are true when the record corresponds to a field with the given variety.\n\n \n\n\n\n\n\nmin, MAX, mean, std, medayn, diff\n\n\n\nValues derived from the NDVI indexes calculated for each parcel from the Copernicus Sentinel 2 multispectral images.  They represent the minimum, maximum, average, standard deviation, medayn and difference values.\n\n\n\n\n\ntmed_[min|MAX|mean]\n\n\n\n[Minimum|Maximum|Mean] temperature for the given date (ºC).\n\n\n\n\n\ntmed_[min|MAX|mean] *_days_after  [1,13]\n\n\n\n[Minimum|Maximum|Mean] temperatures for the 13 days before the given date.\n\n\n\n\n\ntmed_[min|MAX|mean] *_days_after [1,6]\n\n\n\n[Minimum|Maximum|Mean] temperatures for the 6 days following the given date.\n\n\n\n\n\nrad_[min|MAX|mean]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the given date (W/m²).\n\n\n\n\n\nrad_[min|MAX|mean] *_days_after  [1,13]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the 13 days before the given date.\n\n\n\n\n\nrad_[min|MAX|mean] *_days_after [1,6]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the 6 days following the given date.\n\n\n\n\n\nhr_ mean\n\n\n\nAverage air relative humidity for the given date (%).\n\n\n\n\n\nhr_mean *_days_after  [1,13]\n\n\n\nAverage air relative humidity radaytion for the 13 days before the given date.\n\n\n\n\n\nhr_mean *_days_after [1,6]\n\n\n\nAverage air relative humidity radaytion for the 6 days following the given date.\n\n\n\n\n\nwind_[N|NE|E|SE|S|SW|W|NW] *_days_after  [1,13]\n\n\n\nWind index for the North, North-East, East, South-East, South, South-West, West, North-West area for the 13 days before the given date.\n\n\n\n\n\nind_[N|NE|E|SE|S|SW|W|NW] *_days_after [1,6]\n\n\n\nWind index for the North, North-East, East, South-East, South, South-West, West, North-West area for the 6 days following the given date.\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum *_weeks_before [1|2]\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum * 1_weeks_after\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum *_weeks_before [1|2]\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum * 1_weeks_after\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum\n\n\n\nUtah cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum *_weeks_before [1|2]\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum * 1_weeks_after\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nrad_sum\n\n\n\nAccumulated radaytion since the beginning of the season until the given date.\n\n\n\n\n\nrad_sum *_weeks_before [1|2]\n\n\n\nAccumulated radaytion since the beginning of the season until 1 or 2 weeks before the given date.\n\n\n\n\n\nrad_sum 1_weeks_after\n\n\n\nAccumulated radaytion since the beginning of the season until the next week after the given date.\n\n\n\n\n\nprecip_sum\n\n\n\nAccumulated precipitation since the beginning of the season until the given date.\n\n\n\n\n\nprecip_sum *_weeks_before [1|2]\n\n\n\nAccumulated precipitation since the beginning of the season until 1 or 2 weeks before the given date.\n\n\n\n\n\nprecip_sum 1_weeks_after\n\n\n\nAccumulated precipitation since the beginning of the season until the next week after the given date.\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum *_weeks_before [1|2]\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum * 1_weeks_after\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week after the given day.\n\n\n\n\n\n \n\n \n\nThe GDD, Winkler and Chilling (Richardson and Utah) indexes are also calculated considering the contributions of the time units (periods) to the daily contribution. These fields (or columns of the file) have the same naming schema as their counterparts based on daily calculations but with the “cumm” suffix.\n\nFile “DIF phenologicalstages.csv” Description\n\nThis file contains a description of the different types of phenological stages considered. The fields are:\n\n\n\nitainnovaid: this is an identifier of the phenological stage.\n\nbbch: the number of stage in the BBCH phenological stage.\n\nDescripción BBCH: this is a textual description of the previous BBCH phenological stage.\n\n\nThe contents of the file are as follows:\n\n \n\n\n\n\n\n\nitainnovaid\n\n\n\nbbch\n\n\n\nDescripción BBCH\n\n\n\n\n\n0\n\n\n\n0\n\n\n\nWinter dormancy or resting period\n\n\n\n\n\n3\n\n\n\n63\n\n\n\nEarly flowering: 30% of flowerhoods fallen\n\n\n\n\n\n1\n\n\n\n11\n\n\n\nFirst leaf unfolded and spread away from shoot\n\n\n\n\n\n2\n\n\n\n15\n\n\n\n5 leaves unfolded\n\n\n\n\n\n4\n\n\n\n65\n\n\n\nFull flowering: 50% of flowerhoods fallen\n\n\n\n\n\n6\n\n\n\n71\n\n\n\nFruit set: young fruits begin to swell, remains of flowers\n\n\n\n\n\n5\n\n\n\n68\n\n\n\n80% of flowerhoods fallen\n\n\n\n\n\n7\n\n\n\n75\n\n\n\n50% of fruits have reached final size or fruit has reached 50% of final size\n\n\n\n\n\n8\n\n\n\n77\n\n\n\n70% of fruits have reached final size or fruit has reached 70% of final size\n\n\n\n\n\n9\n\n\n\n81\n\n\n\nBeginning of ripening or fruit colouration","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"NEAT-AMBIENCE - Next-gEnerATion dAta Management to foster suitable Behaviors and the resilience of cItizens against modErN ChallEnges","funderName":"Agencia Estatal de Investigación","awardNumber":"PID2020-113037RB-I00","funderIdentifier":"10.13039/501100011033","funderIdentifierType":"Crossref Funder ID"},{"awardTitle":"COSMOS, Computer Science for Complex System modelling","funderName":"Gobierno de Aragón","awardNumber":"T64_23R","funderIdentifier":"10.13039/501100010067","funderIdentifierType":"Crossref Funder ID"}],"url":"https://zenodo.org/doi/10.5281/zenodo.17930723","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-04-08T17:59:11Z","registered":"2026-04-08T17:59:11Z","published":null,"updated":"2026-04-26T20:16:10Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.17930722","type":"dois","attributes":{"doi":"10.5281/zenodo.17930722","identifiers":[],"creators":[{"name":"Lacueva Pérez, Francisco José","nameType":"Personal","givenName":"Francisco José","familyName":"Lacueva Pérez","affiliation":["Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-0998-2939","nameIdentifierScheme":"ORCID"}]},{"name":"Labata Lezaun, Gorka","nameType":"Personal","givenName":"Gorka","familyName":"Labata Lezaun","affiliation":["Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-8634-4124","nameIdentifierScheme":"ORCID"}]},{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-7073-219X","nameIdentifierScheme":"ORCID"}]},{"name":"del Hoyo Alonso, Rafael","nameType":"Personal","givenName":"Rafael","familyName":"del Hoyo Alonso","affiliation":["Universidad San Jorge","Instituto Tecnológico de Aragón"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2755-5500","nameIdentifierScheme":"ORCID"}]},{"name":"Barriuso Vargas, Juan","nameType":"Personal","givenName":"Juan","familyName":"Barriuso Vargas","affiliation":["Universidad de Zaragoza"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2980-5454","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"A Multisource Grapevine Phenology Dataset for Smart Farming and AI Modeling"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[{"subject":"Grapevine Phenology"},{"subject":"Copernicus Sentinel 2"},{"subject":"Supervised Machine Learning","subjectScheme":"MeSH"},{"subject":"Multisource"}],"contributors":[{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"contributorType":"Editor","nameIdentifiers":[{"nameIdentifier":"0000-0002-7073-219X","nameIdentifierScheme":"ORCID"}]},{"name":"Labata Lezaun, Gorka","nameType":"Personal","givenName":"Gorka","familyName":"Labata Lezaun","affiliation":["Instituto Tecnológico de Aragón"],"contributorType":"DataCurator","nameIdentifiers":[{"nameIdentifier":"0000-0002-8634-4124","nameIdentifierScheme":"ORCID"}]},{"name":"del Hoyo Alonso, Rafael","nameType":"Personal","givenName":"Rafael","familyName":"del Hoyo Alonso","affiliation":["Universidad San Jorge","Instituto Tecnológico de Aragón"],"contributorType":"DataCurator","nameIdentifiers":[{"nameIdentifier":"0000-0003-2755-5500","nameIdentifierScheme":"ORCID"}]},{"name":"Barriuso Vargas, Juan","nameType":"Personal","givenName":"Juan","familyName":"Barriuso Vargas","affiliation":["Universidad de Zaragoza"],"contributorType":"DataCurator","nameIdentifiers":[{"nameIdentifier":"0000-0003-2980-5454","nameIdentifierScheme":"ORCID"}]},{"name":"Lacueva Pérez, Francisco José","nameType":"Personal","givenName":"Francisco José","familyName":"Lacueva Pérez","affiliation":["Instituto Tecnológico de Aragón"],"contributorType":"ContactPerson","nameIdentifiers":[{"nameIdentifier":"0000-0003-0998-2939","nameIdentifierScheme":"ORCID"}]},{"name":"Ilarri, Sergio","nameType":"Personal","givenName":"Sergio","familyName":"Ilarri","affiliation":["Universidad de Zaragoza"],"contributorType":"DataManager","nameIdentifiers":[]}],"dates":[{"date":"2026-01-10","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1016/j.compag.2025.110018","resourceTypeGeneral":"JournalArticle","relatedIdentifierType":"DOI"},{"relationType":"IsReferencedBy","relatedIdentifier":"https://webdiis.unizar.es/~silarri/prot/DREAM/index.html","resourceTypeGeneral":"Other","relatedIdentifierType":"URL"},{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.17930722","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":"1.0","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Description\n\nArtificial Intelligence and Machine Learning rely on large, high-quality datasets for accurate and robust models, yet data scarcity remains a major challenge—especially in smart farming. Agricultural data are highly diverse and heterogeneous, complicating model development. Phenology modeling, a key application, studies how plant biological events relate to climate and seasons. Accurate phenology models improve crop quality, support climate adaptation, and guide decisions such as pesticide use and harvesting, enhancing environmental and economic sustainability.This study introduces a georeferenced dataset for Machine Learning-based grapevine phenology prediction across 3 Protected Designations of Origin in Arag’on, Spain. Developed by a multidisciplinary team, the dataset combines 9 datasets from 8 sources—including meteorological time series, field phenology observations, and Copernicus Sentinel-2 multispectral imagery—covering the period 2016–2022. It supports both physical and ML-based phenology modeling and facilitates knowledge extraction in agronomy and plant biology. Its relevance lies in its comprehensive scope, the inclusion of 9 phenological stages, and a rigorous methodology ensuring reproducibility. This framework enables the creation of similar datasets for otherregions or crops, advancing smart farming through scalable, data-driven solutions. We further anticipate its potential contribution to developing Foundation Models as well as to the creation.\n\nDataset Structure\n\nThe dataset contains 2 main CSV files and 1 supporting folder:\n\n\n\nDIF Description.pdf- Description of the data set.\n\nDIF phenologicaletages.csv: it contains the links from the phenologystageid values in the file “DIF GrapevinePehologyDataset.csv:  with the correspoding BBCH values.\n\nDIF GrapevinePehologyDataset.csv: it contains the dataset used to train the models we presented.\n\nMetadata: this folder contains the JSON files describing the dataset and its content:\n\n\n\nDIF_DataSetDescription.json: the description contained if “DIF Description.pdf” but in JSON format.\n\nDIF GrapevinePehologyDataset.json: contains the dataset presented in this paper which was used to train the models we presented in [51, 50]. We describe the content of the file in next paragraphs.\n\n\n\n\nIntended Use\n\nThe goal of this dataset is to enable the development of models for predicting grapevine phenology in the three Protected Designations of Origin in Aragón (Spain), using data from field observations, meteorological stations, and NDVI derived from Copernicus Sentinel-2 multispectral imagery. Additionally, it supports the calibration of physical models for these regions by including the calculation of cold and heat accumulation indices. These calculations are performed using the traditional start dates of January 1 and February of the corresponding year, as well as from the date when plants enter dormancy: the first autumn day when the maximum temperature does not exceed 10 °C.\n\nAccess Conditions: This dataset is publicly available under the terms of the Creative Commons Attribution 4.0 International license. \n\nSpecifications Table\n\n\n\n\n\n\nSubject\n\n\n\nSmart farming\n\n\n\n\n\nSpecific subject area\n\n\n\nThe dataset is based on data from 3 Protected Designations of Origin—Calatayud, Cariñena and Campo de Borja, —in Aragón, northeastern Spain. Built by merging 9 georeferenced time-series datasets from 8 data sources considering the period from 2016 to 2022. It includes meteorological data (measurements, estimates, and forecasts), qualitative field phenology observations, and Copernicus Sentinel-2 multispectral imagery.\n\n\n\n\n\nType of data\n\n\n\nAnalyzedFilteredProcessedMulti-source\n\n\n\n\n\nData collection\n\n\n\nData merged in the dataset is obtained from 9 georeferenced datatsets obtained from 8 data sources.  The datasets considered are:\n\n·         Red FARA phenological registry [1]: this dataset has restricted access.  It provides phenology field observations on the control parcels.\n\n·         Spanish Cadastral Registry (Catastro) [2]: it is used to normalize Red FARA records and to obtain the NDVI of the control parcels from Copernicus Sentinel 2 images.\n\n·         Aragón Open Data Common Agrarian Policy Registry (CAP) [3]: together with the Catastro data is used to normalize of the Red FARA records.\n\n·         SIAR [4] and Grapevine [5] climatic station networks provide meteorological data.\n\n·         ERA5 real climatic estimations [6] and ECMWF IFS forecast [7] data used to replace failures in climatic data and forecast data to perform predictions.\n\n·         Copernicus Sentinel 2 multispectral images [8]: these images are used to determine the NDVI of the control parcels used to create the dataset. \n\nFor accessing these dataset we used available APIs.  All they are public and provide open access. The 2 exceptions were Red FARA which has restricted access, and ERA5 data which was accessed using openMeteo API [8] which eased our work.  The access to the data and the transformations performed in them were coded in Python.  A deep explanation of the transformation performed can be obtained in [9].\n\n\n\n\n\nData source location\n\n\n\nCountry: Spain.Region: Aragón.Protected Designation of Origin: Calatayud, Campo de Borja, Cariñena.Coordinates: Parallelepiped defined by points (41.98107, −2.177578) and (41.166320, −0.922575) in WGS84 coordinates.\n\n\n\n\n\nData accessibility\n\n\n\nRepository name: Zenodo\n\nData identification number: 10.5281/zenodo.17930723 Direct URL to data: https://doi.org/10.5281/zenodo.17930723  \n\n\n\n\n\nReferences\n\n\n\n\n\n\n\n\n[1]\n\n\n\nGovernment of Aragón. Red FARA Home Page. Last access: January 10, 2026. 2026. url: http://web.redfara.es.\n\n\n\n\n\n[2]\n\n\n\nSpanish Treasury. Spanish Cadastral Registry Electronic Home Page. Last access: January 10, 2026. 2026. url: https://www.sedecatastro.gob.es/.\n\n\n\n\n\n[3]\n\n\n\nGovernment of Aragón. Aragón Open Data Home Page. Last access: January 10, 2026. 2026. url: https://opendata.aragon.es.\n\n\n\n\n\n[4]\n\n\n\nSpanish Ministry of Agriculture, Fisheries and Food. Agro-climatic Information System for Irrigation (SIAR) Home Page. Last access: January 10, 2026. 2026. url: https://eportal.mapa.gob.es//websiar/Inicio.aspx .\n\n\n\n\n\n[5]\n\n\n\nGrapevine Project Consortium. Grapevine Project Home Page. Grant agreement ID: 863463. https://grapevine-project.eu (Last access: August 8, 2024), https://web.archive.org/web/20230922054033/https://grapevine- project.eu (Last access: January 10, 2026), https://www.egi.eu/case-study/grapevine (Last access: January 10, 2026). 2022.\n\n\n\n\n\n[6]\n\n\n\nCopernicus Climate Change Service (C3S). ERA5 hourly data on single levels from 1959 to present. Last access: January 10, 2026. 2023. url: https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview .\n\n\n\n\n\n[7]\n\n\n\nEuropean Centre for Medium-Range Weather Forecasts (ECMWF). ECMWF Open Data. Last accessed January 10, 2026. 2026. url: https://www.ecmwf.int/en/forecasts/datasets/open-data .\n\n\n\n\n\n[8]\n\n\n\nF. Gascon et al. “Copernicus Sentinel-2 mission: products, algorithms and Cal/Val”. In: Earth Observing Systems XIX. Ed. by James J. Butler, Xiaoxiong (Jack) Xiong, and Xingfa Gu.SPIE, Sept. 2014, pp. 1–9. doi:10.1117/12.2062260. url: http://dx.doi.org/10.1117/12.2062260.\n\n\n\n\n\n[9]\n\n\n\nFrancisco Jos´e Lacueva-P´erez et al. “Developing machine learning models from multisourced real-world datasets to enhance smart-farming practices”. In: Computers and Electronics in Agriculture 231 (Apr. 2025), p. 110018. issn: 0168-1699. doi: 10.1016/j.compag.2025.110018. url: http://dx.doi.org/10.1016/j.compag.2025.110018 .\n\n\n\n\n\n \n\n\n\n\n\n \n\n \n\n\n\n\n \n\nFile “DIF_GrapevinePehologyDataset.csv” Description\n\nFile DIF_GrapevinePehologyDataset.csv contains the dataset presented in this paper.  Each of the records represents the data considered for a given parcel (vineyard) in each date. The following table provides a description of the fields contained in the dataset. For clarity, we simplified the table by using an abbreviated notation for the field names; specifically, for some field names we include an asterisk (“*”) with the name followed by a couple of numbers in brackets (“[…]”) that describe the range of integer values that can replace the “*” in the dataset; for example, we did this in fields which provide values of the given variable data for the n days before (days_after ) and after (days_adelante).  For clarity, we provide here some examples:\n\n·         tmed_min *_days_after  [1,13]: this name represents that the dataset contains all the fields tmed_min 1_days_after , tmed_min 2_days_after , ..., tmed_min 13_days_after , which represent, for the given field, the minimum temperature for each of the n days before the date of the record.\n\n·         wind_NE *_days_after [1,6]: this name represents that the dataset contains all the fields wind_NE 1_days_after, t wind_NE 2_days_after, ..., wind_NE 6_days_after, which represent, for the given field, the wind_NE index for each of the n days after the date of the record.\n\n·         gdd_4.5_t0_Tbase_sum *_weeks_before [1,2]: this name represents that the dataset contains all the fields gdd_4.5_t0_Tbase_sum 1_weeks_before and gdd_4.5_t0_Tbase_sum 2_weeks_before, which represent, for the given field, the GDD calculated using the base temperature 4.5º C and starting to accumulate at the beginning of the session.\n\nMoreover, we use “|” to denote choices (expressed within brackets “[…]”), which can represent several attributes. For example, “rad_[min|MAX|mean]” actually represents (in a condensed way) 3 different variables: “rad_min”, “rad_max” and “rad_mean”. Other notations can be interpreted similarly. The full list of variable names is shown in Appendix A.\n\n\n\n\n\n\nField Name (abbreviated notation)\n\n\n\nDescription\n\n\n\n\n\n\n\nphenologystageid\n\n\n\nId of the phenological stage of the parcel on the given date.  See file “DIF phenologicalstages.csv”.\n\n\n\n\n\nvariety\n\n\n\nGrapevine variety:  Cabernet Sauvignon, Chardonnay, Garnacha, Mazuela, Syrach, Tempranillo.\n\n\n\n\n\ncodigo\n\n\n\nId of the parcel in the Spanish Cadastral Registry.\n\n\n\n\n\nlongitude\n\n\n\nLongitude of the centroid of the parcel.\n\n\n\n\n\nlatitude\n\n\n\nLatitude of the centroid of the parcel.\n\n\n\n\n\naltitudeASL\n\n\n\nAltitudeASL of the centroid of the parcel.\n\n\n\n\n\nPDO_id\n\n\n\nId of the Protected Designation of Origin (PDO): Calatayud, Carinena and Campo de Borja.\n\n\n\n\n\ndate\n\n\n\nThe date of the record.\n\n\n\n\n\nstation\n\n\n\nThe name of the climatic station whose data are considered.\n\n\n\n\n\nseason\n\n\n\nThe season to which the record belongs.\n\n\n\n\n\nday\n\n\n\nThe DOY (day of the year).\n\n\n\n\n\n\"PDO_Borja\", \"PDO_Calatayud\", \"PDO_Carinena\", \"PDO_Somontano\"\n\n\n\nBoolean values which are true when the record corresponds to the given PDO.\n\n\n\n\n\n\"variety_CABERNET SAUVIGNON\", \"variety_CHARDONNAY\", \"variety_GARNACHA\", \"variety_MAZUELA\", \"variety_SYRACH\", \"variety_TEMPRANILLO\"\n\n\n\nBoolean values which are true when the record corresponds to a field with the given variety.\n\n \n\n\n\n\n\nmin, MAX, mean, std, medayn, diff\n\n\n\nValues derived from the NDVI indexes calculated for each parcel from the Copernicus Sentinel 2 multispectral images.  They represent the minimum, maximum, average, standard deviation, medayn and difference values.\n\n\n\n\n\ntmed_[min|MAX|mean]\n\n\n\n[Minimum|Maximum|Mean] temperature for the given date (ºC).\n\n\n\n\n\ntmed_[min|MAX|mean] *_days_after  [1,13]\n\n\n\n[Minimum|Maximum|Mean] temperatures for the 13 days before the given date.\n\n\n\n\n\ntmed_[min|MAX|mean] *_days_after [1,6]\n\n\n\n[Minimum|Maximum|Mean] temperatures for the 6 days following the given date.\n\n\n\n\n\nrad_[min|MAX|mean]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the given date (W/m²).\n\n\n\n\n\nrad_[min|MAX|mean] *_days_after  [1,13]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the 13 days before the given date.\n\n\n\n\n\nrad_[min|MAX|mean] *_days_after [1,6]\n\n\n\n[Minimum|Maximum|Mean] radaytion for the 6 days following the given date.\n\n\n\n\n\nhr_ mean\n\n\n\nAverage air relative humidity for the given date (%).\n\n\n\n\n\nhr_mean *_days_after  [1,13]\n\n\n\nAverage air relative humidity radaytion for the 13 days before the given date.\n\n\n\n\n\nhr_mean *_days_after [1,6]\n\n\n\nAverage air relative humidity radaytion for the 6 days following the given date.\n\n\n\n\n\nwind_[N|NE|E|SE|S|SW|W|NW] *_days_after  [1,13]\n\n\n\nWind index for the North, North-East, East, South-East, South, South-West, West, North-West area for the 13 days before the given date.\n\n\n\n\n\nind_[N|NE|E|SE|S|SW|W|NW] *_days_after [1,6]\n\n\n\nWind index for the North, North-East, East, South-East, South, South-West, West, North-West area for the 6 days following the given date.\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum *_weeks_before [1|2]\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\ngdd_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum * 1_weeks_after\n\n\n\nGDD heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum *_weeks_before [1|2]\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_[TBase|Tbasemin]_sum * 1_weeks_after\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum\n\n\n\nUtah cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum *_weeks_before [1|2]\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nChillingDD_7.0_[t0|1|2]_ Utah _sum * 1_weeks_after\n\n\n\nRichardson cold accumulation index, calculated with a base temperature of 7.0º C; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a minimum temperature threshold above which the cold accumulation stopped (Tbasemin, -7ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week to the given day.\n\n\n\n\n\nrad_sum\n\n\n\nAccumulated radaytion since the beginning of the season until the given date.\n\n\n\n\n\nrad_sum *_weeks_before [1|2]\n\n\n\nAccumulated radaytion since the beginning of the season until 1 or 2 weeks before the given date.\n\n\n\n\n\nrad_sum 1_weeks_after\n\n\n\nAccumulated radaytion since the beginning of the season until the next week after the given date.\n\n\n\n\n\nprecip_sum\n\n\n\nAccumulated precipitation since the beginning of the season until the given date.\n\n\n\n\n\nprecip_sum *_weeks_before [1|2]\n\n\n\nAccumulated precipitation since the beginning of the season until 1 or 2 weeks before the given date.\n\n\n\n\n\nprecip_sum 1_weeks_after\n\n\n\nAccumulated precipitation since the beginning of the season until the next week after the given date.\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum).\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum *_weeks_before [1|2]\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the 2 weeks previous to the given day.\n\n\n\n\n\nwinkler_[4.5|10.0]_[t0|1|2]_[TBase|TbaseMAX]_sum * 1_weeks_after\n\n\n\nWinkler heat accumulation index, calculated with a base temperature of 4.5ºC or 10.0 ºC; accumulated since the beginning of the season (t0), January the 1st of the date’s year (1) or February the 1st (2); considering a maximum temperature threshold over which the heat accumulation stopped (TbaseMAX, 35ºC) or not (TBase); and considering the daily contribution calculated considering the min temperature and max temperature of the given day (sum), for the next week after the given day.\n\n\n\n\n\n \n\n \n\nThe GDD, Winkler and Chilling (Richardson and Utah) indexes are also calculated considering the contributions of the time units (periods) to the daily contribution. These fields (or columns of the file) have the same naming schema as their counterparts based on daily calculations but with the “cumm” suffix.\n\nFile “DIF phenologicalstages.csv” Description\n\nThis file contains a description of the different types of phenological stages considered. The fields are:\n\n\n\nitainnovaid: this is an identifier of the phenological stage.\n\nbbch: the number of stage in the BBCH phenological stage.\n\nDescripción BBCH: this is a textual description of the previous BBCH phenological stage.\n\n\nThe contents of the file are as follows:\n\n \n\n\n\n\n\n\nitainnovaid\n\n\n\nbbch\n\n\n\nDescripción BBCH\n\n\n\n\n\n0\n\n\n\n0\n\n\n\nWinter dormancy or resting period\n\n\n\n\n\n3\n\n\n\n63\n\n\n\nEarly flowering: 30% of flowerhoods fallen\n\n\n\n\n\n1\n\n\n\n11\n\n\n\nFirst leaf unfolded and spread away from shoot\n\n\n\n\n\n2\n\n\n\n15\n\n\n\n5 leaves unfolded\n\n\n\n\n\n4\n\n\n\n65\n\n\n\nFull flowering: 50% of flowerhoods fallen\n\n\n\n\n\n6\n\n\n\n71\n\n\n\nFruit set: young fruits begin to swell, remains of flowers\n\n\n\n\n\n5\n\n\n\n68\n\n\n\n80% of flowerhoods fallen\n\n\n\n\n\n7\n\n\n\n75\n\n\n\n50% of fruits have reached final size or fruit has reached 50% of final size\n\n\n\n\n\n8\n\n\n\n77\n\n\n\n70% of fruits have reached final size or fruit has reached 70% of final size\n\n\n\n\n\n9\n\n\n\n81\n\n\n\nBeginning of ripening or fruit colouration","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"NEAT-AMBIENCE - Next-gEnerATion dAta Management to foster suitable Behaviors and the resilience of cItizens against modErN ChallEnges","funderName":"Agencia Estatal de Investigación","awardNumber":"PID2020-113037RB-I00","funderIdentifier":"10.13039/501100011033","funderIdentifierType":"Crossref Funder ID"},{"awardTitle":"COSMOS, Computer Science for Complex System modelling","funderName":"Gobierno de Aragón","awardNumber":"T64_23R","funderIdentifier":"10.13039/501100010067","funderIdentifierType":"Crossref Funder ID"}],"url":"https://zenodo.org/doi/10.5281/zenodo.17930722","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":2,"versionOfCount":1,"created":"2026-04-08T17:59:11Z","registered":"2026-04-08T17:59:11Z","published":null,"updated":"2026-04-26T20:16:10Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19660388","type":"dois","attributes":{"doi":"10.5281/zenodo.19660388","identifiers":[],"creators":[{"name":"Koopman, Christel","nameType":"Personal","givenName":"Christel","familyName":"Koopman","affiliation":["Delft University of Technology"],"nameIdentifiers":[{"nameIdentifier":"0009-0006-8967-7385","nameIdentifierScheme":"ORCID"}]},{"name":"Albertsma, Jelco","nameType":"Personal","givenName":"Jelco","familyName":"Albertsma","affiliation":["Delft University of Technology"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-1842-8406","nameIdentifierScheme":"ORCID"}]},{"name":"van der Veen, Monique","nameType":"Personal","givenName":"Monique","familyName":"van der Veen","affiliation":["Delft University of Technology"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-0316-4639","nameIdentifierScheme":"ORCID"}]},{"name":"Vermaas, David","nameType":"Personal","givenName":"David","familyName":"Vermaas","affiliation":["Delft University of Technology","SeaO2","AquaBattery"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-4705-6453","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Electrochemically-mediated separation of carbon monoxide using a Ni-based redox couple"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-04-20","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19660388","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This is a short instruction file for the datasets that were used for the paper:\"Electrochemically-mediated separation of carbon monoxide using a Ni-based redox couple\"by Christel Koopman, Jelco Albertsma, Monique van der Veen, David A. Vermaas**Corresponding Author\n\n\n\nThe data for all figures and tables are included in excel files here2. The raw data sets of the CV measurements are presented in folder \"CV_raw_datasets\" as ivium files.\n\n\nThe ReadMe document contains a more detailed explanation of the files.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"ReCoVR – Recovery and Circularity of Valuable Resources","funderName":"Dutch Research Council","awardNumber":"37401","funderIdentifier":"10.13039/501100003246","funderIdentifierType":"Crossref Funder ID"}],"url":"https://zenodo.org/doi/10.5281/zenodo.19660388","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":2,"versionOfCount":1,"created":"2026-04-26T20:01:48Z","registered":"2026-04-26T20:01:48Z","published":null,"updated":"2026-04-26T20:01:48Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19660389","type":"dois","attributes":{"doi":"10.5281/zenodo.19660389","identifiers":[{"identifier":"oai:zenodo.org:19660389","identifierType":"oai"}],"creators":[{"name":"Koopman, Christel","nameType":"Personal","givenName":"Christel","familyName":"Koopman","affiliation":["Delft University of Technology"],"nameIdentifiers":[{"nameIdentifier":"0009-0006-8967-7385","nameIdentifierScheme":"ORCID"}]},{"name":"Albertsma, Jelco","nameType":"Personal","givenName":"Jelco","familyName":"Albertsma","affiliation":["Delft University of Technology"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-1842-8406","nameIdentifierScheme":"ORCID"}]},{"name":"van der Veen, Monique","nameType":"Personal","givenName":"Monique","familyName":"van der Veen","affiliation":["Delft University of Technology"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-0316-4639","nameIdentifierScheme":"ORCID"}]},{"name":"Vermaas, David","nameType":"Personal","givenName":"David","familyName":"Vermaas","affiliation":["Delft University of Technology","SeaO2","AquaBattery"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-4705-6453","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Electrochemically-mediated separation of carbon monoxide using a Ni-based redox couple"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-04-20","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19660388","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This is a short instruction file for the datasets that were used for the paper:\"Electrochemically-mediated separation of carbon monoxide using a Ni-based redox couple\"by Christel Koopman, Jelco Albertsma, Monique van der Veen, David A. Vermaas**Corresponding Author\n\n\n\nThe data for all figures and tables are included in excel files here2. The raw data sets of the CV measurements are presented in folder \"CV_raw_datasets\" as ivium files.\n\n\nThe ReadMe document contains a more detailed explanation of the files.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"ReCoVR – Recovery and Circularity of Valuable Resources","funderName":"Dutch Research Council","awardNumber":"37401","funderIdentifier":"10.13039/501100003246","funderIdentifierType":"Crossref Funder ID"}],"url":"https://zenodo.org/doi/10.5281/zenodo.19660389","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":1,"created":"2026-04-26T20:01:48Z","registered":"2026-04-26T20:01:48Z","published":null,"updated":"2026-04-26T20:01:48Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19570551","type":"dois","attributes":{"doi":"10.5281/zenodo.19570551","identifiers":[],"creators":[{"name":"Arbelaez Gaviria, Juliana","nameType":"Personal","givenName":"Juliana","familyName":"Arbelaez Gaviria","affiliation":["Mendel University in Brno","International Institute for Applied Systems Analysis","Czech Academy of Sciences, Global Change Research Institute"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-8440-1933","nameIdentifierScheme":"ORCID"}]},{"name":"Palazzo, Amanda","nameType":"Personal","givenName":"Amanda","familyName":"Palazzo","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-8167-9403","nameIdentifierScheme":"ORCID"}]},{"name":"Boere, Esther","nameType":"Personal","givenName":"Esther","familyName":"Boere","affiliation":["Vrije Universiteit Amsterdam"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-3863-8731","nameIdentifierScheme":"ORCID"}]},{"name":"Trnka, Miroslav","nameType":"Personal","givenName":"Miroslav","familyName":"Trnka","affiliation":["Mendel University in Brno","Global Change Research Institute - Czech Academy of Sciences"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-4727-8379","nameIdentifierScheme":"ORCID"}]},{"name":"Havlik, Petr","nameType":"Personal","givenName":"Petr","familyName":"Havlik","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-5551-5085","nameIdentifierScheme":"ORCID"}]},{"name":"Fischer, Milan","nameType":"Personal","givenName":"Milan","familyName":"Fischer","affiliation":["Mendel University in Brno","Mendel University in Brno, Faculty of AgriSciences","Global Change Research Institute CAS"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-7841-9317","nameIdentifierScheme":"ORCID"}]},{"name":"Bartošová, Lenka","nameType":"Personal","givenName":"Lenka","familyName":"Bartošová","affiliation":["Mendel University in Brno","Global Change Research Institute CAS"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-9843-0807","nameIdentifierScheme":"ORCID"}]},{"name":"Burek, Peter","nameType":"Personal","givenName":"Peter","familyName":"Burek","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-6390-8487","nameIdentifierScheme":"ORCID"}]},{"name":"Folberth, Christian","nameType":"Personal","givenName":"Christian","familyName":"Folberth","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-6738-5238","nameIdentifierScheme":"ORCID"}]},{"name":"Balkovic, Juraj","nameType":"Personal","givenName":"Juraj","familyName":"Balkovic","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2955-4931","nameIdentifierScheme":"ORCID"}]},{"name":"Wu, Yazhen","nameType":"Personal","givenName":"Yazhen","familyName":"Wu","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-6869-8327","nameIdentifierScheme":"ORCID"}]},{"name":"Leclère, David","nameType":"Personal","givenName":"David","familyName":"Leclère","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-8658-1509","nameIdentifierScheme":"ORCID"}]},{"name":"Frank, Stefan","nameType":"Personal","givenName":"Stefan","familyName":"Frank","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-5702-8547","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Data for: The global limits of sustainable irrigation as an adaptation to climate change"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-04-26","dateType":"Issued"},{"date":"2026-04-26","dateType":"Submitted"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19570551","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This repository provides the datasets supporting the paper:\n\n“The global limits of sustainable irrigation as an adaptation to climate change.”\n\nHere is the dataset supporting the analysis of irrigation, agricultural production, water use, and trade under climate change scenarios using the Global Biosphere Management Model (GLOBIOM). The dataset is designed to assess the role and limits of irrigation as an adaptation to climate change, providing spatially explicit and aggregated outputs across multiple climate forcing pathways.\n\nThe dataset combines model outputs from an integrated modelling framework linking agricultural production, hydrology, land use, and international trade. It includes projections up to 2050 under a consistent socioeconomic pathway (SSP2) and multiple climate scenarios derived from CMIP6 projections. The dataset enables the evaluation of irrigation dynamics, water availability constraints, and trade-mediated water flows across regions based on 2 types of datasets:\n\n\n\nSpatial explicit results: the spatial component of the dataset is provided at the native GLOBIOM resolution, where each spatial unit represents a grid cell characterized by homogeneous biophysical conditions (e.g., soil, slope, and climate). These spatial units are linked to a shapefile through a unique identifier (LUID), allowing the mapping of model outputs.The repository contains spatially explicit results for four main domains: cropland area, agricultural production, crop yields, and irrigation water withdrawals. These outputs are provided as separate CSV files, each containing harmonized data structures. Across all files, each row represents a unique combination of spatial unit, scenario, variable, and year, including the associated model output value.\n\nMacroregional results: aggregate model outputs across world regions. These datasets provide indicators related to trade flows and water use, including virtual water trade and associated metrics, allowing the analysis of interregional dependencies and water transfers embedded in agricultural trade. The main CSV files contain all input-derived and model-generated outputs used in the analysis. \n\n\nEach row corresponds to a unique observation defined by a spatial or regional unit, a scenario combination (climate model and forcing pathway), and a specific variable.  The spatial reference layer (provided as a shapefile) contains the geometries of the GLOBIOM spatial units and enables the visualization and mapping of all spatial results by linking to the tabular data through the spatial unit identifier.\n\nData description \n\nGLOBIOM_spatial_results\n\nvariable\n\n\n\nRainfed cropland area\n\nIrrigated cropland area\n\nRainfed production\n\nIrrigated production\n\nRainfed yield\n\nIrrigated yield\n\nIrrigation Water Withdrawal\n\n\nunit\n\n\n\n1000 ha\n\n1000 t\n\nt/ha\n\nkm3\n\n\ncountry\n\n\n\nMultiple country names (all countries represented in GLOBIOM spatial units)\n\n\nspatial_unit_id\n\n\n\nUnique numeric identifiers (LUID)\n\n\nitem\n\n\n\nCrop aggregated\n\n\ngcm\n\n\n\nGCM mean ensemble\n\nGFDL-ESM4\n\nIPSL-CM6A-LR\n\nMPI-ESM1-2-HR\n\nMRI-ESM2-0\n\nUKESM1-0-LL\n\n\nrcp\n\n\n\nRCP 2.6\n\nRCP 7.0\n\nRCP 8.5\n\n\nssp\n\n\n\nSSP2\n\n\nyear\n\n\n\n2050\n\n\nvalue\n\n\n\nContinuous numeric values\n\n\nMacroRegions_GLOBIOM_results\n\nvariable\n\n\n\nCropland area\n\nRainfed cropland area\n\nIrrigated cropland area\n\nTotal crop production\n\nAverage crop yield\n\nImports of crop products\n\nExports of crop products\n\nGrereenhouse gas emissions\n\nRainfed crop yield\n\nIrrigated crop yield\n\nExogenous total yield\n\nExogenous irrigated yield\n\nExogenous rainfed yield\n\nIrrigation Water Withdrawal\n\nUndernourished population\n\nValue added from agriculture\n\nBlue water efficiency\n\nVirtual water content\n\nIrrigated crop production\n\nRainfed crop production\n\n\nunit\n\n\n\n1000 ha\n\n1000 t\n\nt/ha\n\nkm3\n\nMt CO2eq/yr km3\n\nMn USD 2000\n\nkm3/1000 t \n\nt/ha km3  \n\n\nitem\n\n\n\nCrop aggregated\n\nTotal\n\nLand use change\n\n\nregions\n\n\n\nWorld\n\nLatin America and the Caribbean\n\nNorth America\n\nCommonwealth of Independent States\n\nSub-Saharan Africa\n\nOceania\n\nSouthwest Asia and North Africa\n\nEuropean Economic Area\n\nSoutheast Asia\n\n\ngcm\n\n\n\nGCM mean ensemble\n\nGFDL-ESM4\n\nIPSL-CM6A-LR\n\nMPI-ESM1-2-HR\n\nMRI-ESM2-0\n\nUKESM1-0-LL\n\n\nrcp\n\n\n\nRCP 2.6\n\nRCP 7.0\n\nRCP 8.5\n\n\nssp\n\n\n\nSSP2\n\n\nyear\n\n\n\n2000\n\n2010\n\n2020\n\n2030\n\n2040\n\n2050\n\n\nvalue\n\n\n\nContinuous numeric values","descriptionType":"Abstract"},{"description":"Water underpins agricultural adaptation to climate change, yet irrigation accounts for nearly 70% of global freshwater withdrawals. Whether water systems can sustain further irrigation expansion under climate change remains uncertain. We quantify when irrigation crosses soft and hard adaptation limits under alternative climate futures by 2050. Using an integrated modeling framework linking crop cultivations, hydrology, land use and trade, we evaluate future contributions and sustainability of  irrigation across water, land and economic indicators. Under low warming, irrigation stabilizes global production while withdrawals decline by 17%, and blue water use efficiency improves by 25%, functioning as an efficiency-enhancing adaptation. As warming intensifies, irrigation shifts from efficiency to dependency: sustaining production increasingly requires rising withdrawals, with efficiency declining by 27–32% in major producing regions. Climate change also weakens global trade-related blue water savings, reinforcing unequal virtual water exchanges between the Global South and North. Water availability, not agronomy alone, ultimately defines the limits of irrigation-based adaptation. ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"AdAgriF - Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation","funderName":"Ministry of Education Youth and Sports","awardNumber":"CZ.02.01.01/00/22_008/0004635","funderIdentifier":"10.13039/501100001823","funderIdentifierType":"Crossref Funder ID"},{"awardTitle":"Assessing Climate Change Risk in EUrope","funderName":"European Commission","awardNumber":"101081358","funderIdentifier":"10.13039/501100000780","funderIdentifierType":"Crossref Funder ID"}],"url":"https://zenodo.org/doi/10.5281/zenodo.19570551","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"api","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":1,"versionOfCount":1,"created":"2026-04-26T19:40:43Z","registered":"2026-04-26T19:40:43Z","published":null,"updated":"2026-04-26T19:40:43Z"},"relationships":{"client":{"data":{"id":"cern.zenodo","type":"clients"}}}},{"id":"10.5281/zenodo.19570552","type":"dois","attributes":{"doi":"10.5281/zenodo.19570552","identifiers":[{"identifier":"oai:zenodo.org:19570552","identifierType":"oai"}],"creators":[{"name":"Arbelaez Gaviria, Juliana","nameType":"Personal","givenName":"Juliana","familyName":"Arbelaez Gaviria","affiliation":["Mendel University in Brno","International Institute for Applied Systems Analysis","Czech Academy of Sciences, Global Change Research Institute"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-8440-1933","nameIdentifierScheme":"ORCID"}]},{"name":"Palazzo, Amanda","nameType":"Personal","givenName":"Amanda","familyName":"Palazzo","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-8167-9403","nameIdentifierScheme":"ORCID"}]},{"name":"Boere, Esther","nameType":"Personal","givenName":"Esther","familyName":"Boere","affiliation":["Vrije Universiteit Amsterdam"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-3863-8731","nameIdentifierScheme":"ORCID"}]},{"name":"Trnka, Miroslav","nameType":"Personal","givenName":"Miroslav","familyName":"Trnka","affiliation":["Mendel University in Brno","Global Change Research Institute - Czech Academy of Sciences"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-4727-8379","nameIdentifierScheme":"ORCID"}]},{"name":"Havlik, Petr","nameType":"Personal","givenName":"Petr","familyName":"Havlik","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-5551-5085","nameIdentifierScheme":"ORCID"}]},{"name":"Fischer, Milan","nameType":"Personal","givenName":"Milan","familyName":"Fischer","affiliation":["Mendel University in Brno","Mendel University in Brno, Faculty of AgriSciences","Global Change Research Institute CAS"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-7841-9317","nameIdentifierScheme":"ORCID"}]},{"name":"Bartošová, Lenka","nameType":"Personal","givenName":"Lenka","familyName":"Bartošová","affiliation":["Mendel University in Brno","Global Change Research Institute CAS"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-9843-0807","nameIdentifierScheme":"ORCID"}]},{"name":"Burek, Peter","nameType":"Personal","givenName":"Peter","familyName":"Burek","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-6390-8487","nameIdentifierScheme":"ORCID"}]},{"name":"Folberth, Christian","nameType":"Personal","givenName":"Christian","familyName":"Folberth","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-6738-5238","nameIdentifierScheme":"ORCID"}]},{"name":"Balkovic, Juraj","nameType":"Personal","givenName":"Juraj","familyName":"Balkovic","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0003-2955-4931","nameIdentifierScheme":"ORCID"}]},{"name":"Wu, Yazhen","nameType":"Personal","givenName":"Yazhen","familyName":"Wu","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-6869-8327","nameIdentifierScheme":"ORCID"}]},{"name":"Leclère, David","nameType":"Personal","givenName":"David","familyName":"Leclère","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0002-8658-1509","nameIdentifierScheme":"ORCID"}]},{"name":"Frank, Stefan","nameType":"Personal","givenName":"Stefan","familyName":"Frank","affiliation":["International Institute for Applied Systems Analysis"],"nameIdentifiers":[{"nameIdentifier":"0000-0001-5702-8547","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Data for: The global limits of sustainable irrigation as an adaptation to climate change"}],"publisher":"Zenodo","container":{},"publicationYear":2026,"subjects":[],"contributors":[],"dates":[{"date":"2026-04-26","dateType":"Issued"},{"date":"2026-04-26","dateType":"Submitted"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsVersionOf","relatedIdentifier":"10.5281/zenodo.19570551","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This repository provides the datasets supporting the paper:\n\n“The global limits of sustainable irrigation as an adaptation to climate change.”\n\nHere is the dataset supporting the analysis of irrigation, agricultural production, water use, and trade under climate change scenarios using the Global Biosphere Management Model (GLOBIOM). The dataset is designed to assess the role and limits of irrigation as an adaptation to climate change, providing spatially explicit and aggregated outputs across multiple climate forcing pathways.\n\nThe dataset combines model outputs from an integrated modelling framework linking agricultural production, hydrology, land use, and international trade. It includes projections up to 2050 under a consistent socioeconomic pathway (SSP2) and multiple climate scenarios derived from CMIP6 projections. The dataset enables the evaluation of irrigation dynamics, water availability constraints, and trade-mediated water flows across regions based on 2 types of datasets:\n\n\n\nSpatial explicit results: the spatial component of the dataset is provided at the native GLOBIOM resolution, where each spatial unit represents a grid cell characterized by homogeneous biophysical conditions (e.g., soil, slope, and climate). These spatial units are linked to a shapefile through a unique identifier (LUID), allowing the mapping of model outputs.The repository contains spatially explicit results for four main domains: cropland area, agricultural production, crop yields, and irrigation water withdrawals. These outputs are provided as separate CSV files, each containing harmonized data structures. Across all files, each row represents a unique combination of spatial unit, scenario, variable, and year, including the associated model output value.\n\nMacroregional results: aggregate model outputs across world regions. These datasets provide indicators related to trade flows and water use, including virtual water trade and associated metrics, allowing the analysis of interregional dependencies and water transfers embedded in agricultural trade. The main CSV files contain all input-derived and model-generated outputs used in the analysis. \n\n\nEach row corresponds to a unique observation defined by a spatial or regional unit, a scenario combination (climate model and forcing pathway), and a specific variable.  The spatial reference layer (provided as a shapefile) contains the geometries of the GLOBIOM spatial units and enables the visualization and mapping of all spatial results by linking to the tabular data through the spatial unit identifier.\n\nData description \n\nGLOBIOM_spatial_results\n\nvariable\n\n\n\nRainfed cropland area\n\nIrrigated cropland area\n\nRainfed production\n\nIrrigated production\n\nRainfed yield\n\nIrrigated yield\n\nIrrigation Water Withdrawal\n\n\nunit\n\n\n\n1000 ha\n\n1000 t\n\nt/ha\n\nkm3\n\n\ncountry\n\n\n\nMultiple country names (all countries represented in GLOBIOM spatial units)\n\n\nspatial_unit_id\n\n\n\nUnique numeric identifiers (LUID)\n\n\nitem\n\n\n\nCrop aggregated\n\n\ngcm\n\n\n\nGCM mean ensemble\n\nGFDL-ESM4\n\nIPSL-CM6A-LR\n\nMPI-ESM1-2-HR\n\nMRI-ESM2-0\n\nUKESM1-0-LL\n\n\nrcp\n\n\n\nRCP 2.6\n\nRCP 7.0\n\nRCP 8.5\n\n\nssp\n\n\n\nSSP2\n\n\nyear\n\n\n\n2050\n\n\nvalue\n\n\n\nContinuous numeric values\n\n\nMacroRegions_GLOBIOM_results\n\nvariable\n\n\n\nCropland area\n\nRainfed cropland area\n\nIrrigated cropland area\n\nTotal crop production\n\nAverage crop yield\n\nImports of crop products\n\nExports of crop products\n\nGrereenhouse gas emissions\n\nRainfed crop yield\n\nIrrigated crop yield\n\nExogenous total yield\n\nExogenous irrigated yield\n\nExogenous rainfed yield\n\nIrrigation Water Withdrawal\n\nUndernourished population\n\nValue added from agriculture\n\nBlue water efficiency\n\nVirtual water content\n\nIrrigated crop production\n\nRainfed crop production\n\n\nunit\n\n\n\n1000 ha\n\n1000 t\n\nt/ha\n\nkm3\n\nMt CO2eq/yr km3\n\nMn USD 2000\n\nkm3/1000 t \n\nt/ha km3  \n\n\nitem\n\n\n\nCrop aggregated\n\nTotal\n\nLand use change\n\n\nregions\n\n\n\nWorld\n\nLatin America and the Caribbean\n\nNorth America\n\nCommonwealth of Independent States\n\nSub-Saharan Africa\n\nOceania\n\nSouthwest Asia and North Africa\n\nEuropean Economic Area\n\nSoutheast Asia\n\n\ngcm\n\n\n\nGCM mean ensemble\n\nGFDL-ESM4\n\nIPSL-CM6A-LR\n\nMPI-ESM1-2-HR\n\nMRI-ESM2-0\n\nUKESM1-0-LL\n\n\nrcp\n\n\n\nRCP 2.6\n\nRCP 7.0\n\nRCP 8.5\n\n\nssp\n\n\n\nSSP2\n\n\nyear\n\n\n\n2000\n\n2010\n\n2020\n\n2030\n\n2040\n\n2050\n\n\nvalue\n\n\n\nContinuous numeric values","descriptionType":"Abstract"},{"description":"Water underpins agricultural adaptation to climate change, yet irrigation accounts for nearly 70% of global freshwater withdrawals. Whether water systems can sustain further irrigation expansion under climate change remains uncertain. We quantify when irrigation crosses soft and hard adaptation limits under alternative climate futures by 2050. Using an integrated modeling framework linking crop cultivations, hydrology, land use and trade, we evaluate future contributions and sustainability of  irrigation across water, land and economic indicators. Under low warming, irrigation stabilizes global production while withdrawals decline by 17%, and blue water use efficiency improves by 25%, functioning as an efficiency-enhancing adaptation. As warming intensifies, irrigation shifts from efficiency to dependency: sustaining production increasingly requires rising withdrawals, with efficiency declining by 27–32% in major producing regions. Climate change also weakens global trade-related blue water savings, reinforcing unequal virtual water exchanges between the Global South and North. Water availability, not agronomy alone, ultimately defines the limits of irrigation-based adaptation. ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"awardTitle":"AdAgriF - Advanced methods of greenhouse gases emission reduction and sequestration in agriculture and forest landscape for climate change mitigation","funderName":"Ministry of Education Youth and Sports","awardNumber":"CZ.02.01.01/00/22_008/0004635","funderIdentifier":"10.13039/501100001823","funderIdentifierType":"Crossref Funder ID"},{"awardTitle":"Assessing Climate Change Risk in EUrope","funderName":"European Commission","awardNumber":"101081358","funderIdentifier":"10.13039/501100000780","funderIdentifierType":"Crossref Funder 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