BUSCADOR RECOLECTA

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