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In this dataset, artificial weekly periods are created dividing each month into four periods (days: 1-8; 9-15; 16-22; 23-end). There are 4 files in Netcdf format: 1) ETo.nc containing weekly reference evapotranspiration estimations; 2) ETo_var.nc containing uncertainty estimation of weekly reference evapotranspiration estimations; 3) ETo_Ae.nc containing estimations of the aerodynamic component of weekly reference evapotranspiration and 4) ETo_Ra.nc containing estimations of the radiative component of weekly reference evapotranspiration., SPanish reference evapotranspiration (SPETo) is a weekly gridded reference evapotranspiration dataset for Continental Spain and Balearic Islands, at 1.1 km of spatial resolution, covering the 1961-2014 period. Reference evapotranspiration was calculated using Penman-Monteith FAO-56 method using gridded data of maximum temperature, minimum temperature, dewpoint temperature, wind speed and sunshine duration., This work was supported by research projects CGL2014-52135-C03-01, CGL2014-52135-C03-02 and CGL2014-52135-C03-03 financed by Ministerio de Economía y Competitividad (España), There are 4 files in Netcdf format: 1) ETo.nc containing weekly reference evapotranspiration estimations; 2) ETo_var.nc containing uncertainty estimation of weekly reference evapotranspiration estimations; 3) ETo_Ae.nc containing estimations of the aerodynamic component of weekly reference evapotranspiration and 4) ETo_Ra.nc containing estimations of the radiative component of weekly reference evapotranspiration., No

The dataset is freely available on the web repository of the Spanish National Research Council (CSIC). There are 12 files in NetCDF format: 3 with maximum temperature estimations and 3 minimum temperatures. Each of 3 files represent peninsular Spain (_pen), Balearic islands (_bal) and Canary islands (_can). Same files exist for the uncertainty estimates., Spanish TEmperature At Daily scale (STEAD) is a new daily gridded maximum and minimum temperature dataset for Spain. It covers the whole territory of peninsular Spain and Balearic and Canary Islands at a 5x5 kilometre spatial resolution. Daily temperature was estimated for each point of the grid from 1901-01-01 to 2014-12-31 in peninsular Spain and from 1971-01-01 to 2014-12-31 in the Balearic and Canary Islands. The grid was built using a previously reconstructed station-based dataset. The observed temperature information comprised more than 5,000 stations provided by Spanish Meteorological Agency (AEMET) and Ministry of Agriculture and Environment (MAGRAMA)., Ministerio de Economía y Competitividad (MINECO): CGL2015-69985-R and CGL2017-83866-C3-3-R, Ministerio de Ciencia, Innovación y Universidades: FJCI-2017-31595, No

Los detalles de la metodología y los resultados se pueden encontrar en: Domínguez-Castro, F., Vicente-Serrano, S.M., Tomás-Burguera, M., Peña-Gallardo M., Beguería S., El Kenawy A., Luna, Y., Morata, A. High-spatial-resolution probability maps of drought duration and magnitude across Spain. https://doi.org/10.5194/nhess-2018-289 Acceso mediante licencia: http://opendatacommons.org/licenses/odbl/1-0/, This dataset includes 1.1 km spatial resolution of the Generalized Pareto parameters (X0, alpha, Kappa) of drought duration and magnitude for SPI and SPEI at 1, 3, 6 and 12 months timescales and the maximum drought duration and magnitude risk maps for 50 and 100 years for SPI and SPEI at 1, 3, 6 and 12 months timescales. This dataset contains two zip files “parameters” and “risk_maps”. Parameters zip file contains 16 netCDF3 (8 for drought magnitude and 8 for drought duration) each one provides the Generalized Pareto parameters (1:X0, 2:alpha, 3:Kappa) for one index (SPI or SPEI) and one timescale (1, 3, 6 and 12 months). Risk_maps zip file contains 8 netCDF3 (4 for maximum drought magnitude and 4 for maximum drought duration) each one provides the risk maps for 4 time scales (1, 3, 6, 12 months) of one index (SPI or SPEI) and one time period (50 or 100 years). Each netCDF file contains 1115 longitudes and 834 latitudes., This work was supported by the following research projects: CGL2014-52135-C03-01 and PCIN-2015-220 financed by the Spanish Commission of Science and Technology and FEDER, 1560/2015; Herramientas de monitorización de la vegetación mediante modelización ecohidrológica en parques continentales financed by the Red de Parques Nacionales. IMDROFLOOD financed by Water Works 2014; a co-funded call of the European Commission and INDECIS, Peer reviewed

Dataset and code of the article., Reliable crop monitoring systems provide critical information to detect and track anomalies in the status of crops. These systems are fundamental for the development of integrated methodologies that inform agricultural policy, market analysis, or producer decision-making. They are also used in the development of early warning systems that permit to anticipate drought conditions and trigger action to mitigate short term food shortages or to stabilize the structure and pricing of agricultural markets. Current efforts to develop crop monitoring systems exploit meteorological and crop growth models, and satellite imagery. However, legacy sources of information such as operational crop rating surveys that have long and uninterrupted records receive less attention. We argue that crop rating data, despite its subjective and non-quantitative nature, captures the complexities of assessing the 'status' of a crop better than any model or remote sensing retrieval. This is because crop rating data naturally represents the broad expert knowledge of many individual surveyors spread throughout the country. Crop rating surveys in effect constitute a sophisticated network of "humans as sensors" that provide consistent and accurate information on crop progress. We analyze data from the USDA Crop Progress and Condition (CPC) survey between 1987 and 2019 for four major crops across the US (corn, soybeans, winter wheat, and upland cotton). We show how the original qualitative data can be transformed into a continuous, probabilistic variable better suited to quantitative analysis, and demonstrate it can be used to monitor crop status and provide early predictions of crop yields., Peer reviewed

The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. The Global SPEI database, SPEIbase, offers long-time, robust information on the drought conditions at the global scale, with a 0.5 degrees spatial resolution and a monthly time resolution. It has a multi-scale character, providing SPEI time-scales between 1 and 48 months. The Standardized Precipitatin-Evapotranspiration Index (SPEI) expresses, as a standardized variate (mean zero and unit variance), the deviations of the current climatic balance (precipitation minus evapotranspiration potential) with respect to the long-term balance. The reference period for the calculation, in the SPEIbase, corresponds to the whole study period. Being a standardized variate means that the SPEI condition can be compared across space and time. Calculation of the evapotranspiration potential in SPEIbase is based on the FAO-56 Penman-Monteith method. Data type: float; units: z-values (standard deviations). No land pixels are assigned a value of 1.0x10^30. In some rare cases it was not possible to achieve a good fit to the log-logistic distribution, resulting in a NAN (not a number) value in the database. Dimensions of the dataset: lon = 720; lat = 360; time = 1356. Resolution of the dataset: lon = 0.5º; lat = 0.5º; time = 1 month. Created in R using the SPEI package (http://cran.r-project.org/web/packages/SPEI)., Global gridded dataset of the Standardized Precipitation-Evapotranspiration Index (SPEI) at time scales between 1 and 48 months.-- Spatial resolution of 0.5º lat/lon.-- This is an update of the SPEIbase v2.4 (http://digital.csic.es/handle/10261/128892).-- What’s new in version 2.5: 1) Data has been extended to the period 1901-2015 (it was 1901-2014 in v 2.4), based on the CRU TS3.24.01 dataset. 2) A bug on versions 2.2 to 2.4 of the dataset has been corrected that prevented correctly reading the ETo data in mm/month-- For more details on the SPEI visit http://sac.csic.es/spei., No

Los 18 indicadores agroclimáticos están calculados sobre una malla de 5x5 km para el periodo 1981-2010 cubriendo todo el territorio de la España peninsular. Se ofrecen los datos a resolución anual excepto para los valores de probabilidad, que tienen un único valor para todo el periodo., Base de datos de indicadores agroclimáticos para la España peninsular, No

The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. The Global SPEI database, SPEIbase, offers long-time, robust information on the drought conditions at the global scale, with a 0.5 degrees spatial resolution and a monthly time resolution. It has a multi-scale character, providing SPEI time-scales between 1 and 48 months. The Standardized Precipitatin-Evapotranspiration Index (SPEI) expresses, as a standardized variate (mean zero and unit variance), the deviations of the current climatic balance (precipitation minus evapotranspiration potential) with respect to the long-term balance. The reference period for the calculation, in the SPEIbase, corresponds to the whole study period. Being a standardized variate means that the SPEI condition can be compared across space and time. Calculation of the evapotranspiration potential in SPEIbase is based on the FAO-56 Penman-Monteith method. Data type: float; units: z-values (standard deviations). No land pixels are assigned a value of 1.0x10^30. In some rare cases it was not possible to achieve a good fit to the log-logistic distribution, resulting in a NAN (not a number) value in the database. Dimensions of the dataset: lon = 720; lat = 360; time = 1356. Resolution of the dataset: lon = 0.5º; lat = 0.5º; time = 1 month. Created in R using the SPEI package (http://cran.r-project.org/web/packages/SPEI)., Global gridded dataset of the Standardized Precipitation-Evapotranspiration Index (SPEI) at time scales between 1 and 48 months.-- Spatial resolution of 0.5º lat/lon.-- This is an update of the SPEIbase v2.6 (https://digital.csic.es/handle/10261/202305).-- What’s new in version 2.7: 1) Based on the CRU TS 4.05 dataset, spanning the period between January 1901 to December 2020. Using TLMoments::PWM instead of lmomco::pwm.ub for calculating distribution parameters. For more details on the SPEI visit http://sac.csic.es/spei, Peer reviewed

The Global 0.5° gridded SPEI dataset is made available under the Open Database License. Any rights in individual contents of the database are licensed under the Database Contents License. Users of the dataset are free to share, create and adapt under the conditions of attribution and share-alike. The Global SPEI database, SPEIbase, offers long-time, robust information on the drought conditions at the global scale, with a 0.5 degrees spatial resolution and a monthly time resolution. It has a multi-scale character, providing SPEI time-scales between 1 and 48 months. The Standardized Precipitatin-Evapotranspiration Index (SPEI) expresses, as a standardized variate (mean zero and unit variance), the deviations of the current climatic balance (precipitation minus evapotranspiration potential) with respect to the long-term balance. The reference period for the calculation, in the SPEIbase, corresponds to the whole study period. Being a standardized variate means that the SPEI condition can be compared across space and time. Calculation of the evapotranspiration potential in SPEIbase is based on the FAO-56 Penman-Monteith method. Data type: float; units: z-values (standard deviations). No land pixels are assigned a value of 1.0x10^30. In some rare cases it was not possible to achieve a good fit to the log-logistic distribution, resulting in a NAN (not a number) value in the database. Dimensions of the dataset: lon = 720; lat = 360; time = 1356. Resolution of the dataset: lon = 0.5º; lat = 0.5º; time = 1 month. Created in R using the SPEI package (http://cran.r-project.org/web/packages/SPEI)., Global gridded dataset of the Standardized Precipitation-Evapotranspiration Index (SPEI) at time scales between 1 and 48 months.-- Spatial resolution of 0.5º lat/lon.-- This is an update of the SPEIbase v2.7 (https://digital.csic.es/handle/10261/268088).-- What’s new in version 2.8: 1) Based on the CRU TS 4.06 dataset, spanning the period between January 1901 to December 2021. For more details on the SPEI visit http://sac.csic.es/spei, No

[EN] A monthly precipitation gridded data set over mainland Spain between December 1915 and December 2020. The dataset combines ground observations from the National Climate Data Bank (NCDB) of the Spanish national climate and weather service (AEMET) and new data rescued from meteorological yearbooks published prior to 1951 that was never incorporated into the NCDB. The yearbooks data represented a significant improvement of the dataset, as it almost doubled the number of weather stations available during the first decades of the 20th century, the period when the dataset was more scarce. The final dataset contains records from 11,312 stations. Spatial interpolation was performed using geostatistical techniques over a regular 0.1° × 0.1° grid, using a two-stage process: estimation of the probability of zero-precipitation (dry month), and estimation of precipitation magnitude., [ES] Conjunto de datos en rejilla de precipitación mensual en la España peninsular, entre diciembre de 1915 y diciembre de 2020. El conjunto de datos utilizado combina observaciones del Banco Nacional de Datos de AEMET y nuevos datos rescatados de los anuarios climáticos publicados con anterioridad a 1951, y que casi duplican la información existente sobre la primera mitad del siglo 20. El conjunto final contiene información de un total de 11.312 observatorios. Se utilizaron técnicas geoestadísticas para interpolar espacialmente las observaciones sobre una rejilla regular de 0.1° × 0.1°, utilizando un proceso en dos pasos: en primer lugar se interpoló la probabilidad de mes seco (precipitación igual a cero), y en un segundo paso la magnitud de la precipitación., Projects CGL2017-83866-C3-3-R (CLICES: Climate of the last Century in the Spanish mainland) and PID2020-116860RB-C22 EXE: Extremos térmicos y pluviométricos en la España peninsular 1916-2020), funded by the Spanish Ministry of Science., Mean grid; standard deviation grid; netCDF., No

Table S6 Candidate genes mapped within the genomic regions associated with the significantly associated SNPs detected in response to U. pisi. Chromosomal linkage disequilibrium (LD) decay was considered to limit the genomic regions were to look for candidate genes., Uromyces pisi ([Pers.] D.C.) Wint. is an important foliar biotrophic pathogen infecting grass pea (Lathyrus sativus L.), compromising their yield stability. To date, few efforts have been made to assess the natural variation in grass pea resistance and to identify the resistance loci operating against this pathogen, limiting its efficient breeding exploitation. To overcome this knowledge gap, the genetic architecture of grass pea resistance to U. pisi was investigated using a worldwide collection of 182 accessions through a genome-wide association approach. The response of the grass pea collection to rust infection under controlled conditions and at the seedling stage did not reveal any hypersensitive response but a continuous variation for disease severity, with the identification of promising sources of partial resistance. A panel of 5,651 high-quality single-nucleotide polymorphism (SNP) markers previously generated was used to test for SNP-trait associations, based on a mixed linear model accounting for population structure. We detected seven SNP markers significantly associated with U. pisi disease severity, suggesting that partial resistance is oligogenic. Six of the associated SNP markers were located in chromosomes 4 and 6, while the remaining SNP markers had no known chromosomal position. Through comparative mapping with the pea reference genome, a total of 19 candidate genes were proposed, encoding for leucine-rich repeat, NB-ARC domain, and TGA transcription factor family, among others. Results presented in this study provided information on the availability of partial resistance in grass pea germplasm and advanced our understanding of the molecular mechanisms of quantitative resistance to rust in grass pea. Moreover, the detected associated SNP markers constitute promising genomic targets for the development of molecular tools to assist disease resistance precision breeding., Peer reviewed