CY-Bench: A comprehensive benchmark dataset for subnational crop yield forecasting

CY-Bench: A comprehensive benchmark dataset for sub-national crop yield forecasting Overview CY-Bench is a dataset and benchmark for subnational crop yield forecasting, with coverage of major crop growing countries of the world for maize and wheat. By subnational, we mean the administrative level where yield statistics are published. When statistics are available for multiple levels, we pick the highest resolution. The dataset combines sub-national yield statistics with relevant predictors, such as growing-season weather indicators, remote sensing indicators, evapotranspiration, soil moisture indicators, and static soil properties. CY-Bench has been designed and curated by agricultural experts, climate scientists, and machine learning researchers from the AgML Community, with the aim of facilitating model intercomparison across the diverse agricultural systems around the globe in conditions as close as possible to real-world operationalization. Ultimately, by lowering the barrier to entry for ML researchers in this crucial application area, CY-Bench will facilitate the development of improved crop forecasting tools that can be used to support decision-makers in food security planning worldwide. * Crops : Wheat & Maize* Spatial Coverage : Wheat (29 countries), Maize (38). See CY-Bench paper appendix for the list of countries.* Temporal Coverage : Varies. See country-specific data Data Data format The benchmark data is organized as a collection of CSV files (with the exception of location information, see below), with each file representing a specific category of variable for a particular country. Each CSV file is named according to the category and the country it pertains to, facilitating easy identification and retrieval. The data within each CSV file is structured in tabular format, where rows represent observations and columns represent different predictors related to a category of variable. Data content All data files are provided as .csv. Data Description Variables (units) Temporal Resolution Data Source (Reference) crop_calendar Start and end of growing season sos (day of the year), eos (day of the year) Static World Cereal (Franch et al, 2022) fpar fraction of absorbed photosynthetically active radiation fpar (%) Dekadal (3 times a month; 1-10, 11-20, 21-31) European Commission's Joint Research Centre (EC-JRC, 2024) ndvi normalized difference vegetation index - approximately weekly MOD09CMG (Vermote, 2015) meteo temperature, precipitation (prec), r