CYCleSS: the Crop Yields, Climate, Soils, and Satellites Dataset

Here, we present the Crop Yields, Climate, Soils, and Satellites (CYCleSS) dataset, a large-scale crop yield dataset derived from precision yield data for 934 fields across England on which a variety of crops are grown. In addition, the data also contains satellite-derived remote sensing data, weather data, and data on soil type, all aligned at a grid resolution of 10km. This effort has been made possible through careful anonymisation of the yield data while preserving the alignment with remote sensing, weather, and soil data. This data will be useful both to train machine learning models of yield prediction as well as to parameterize mechanistic crop growth models. Furthermore, the anonymisation procedure itself will be of interest to the research community, as it represents a solution to a common problem on the interface of agricultural research and farming practice. The dataset is described in detail in following paper: Corcoran et al. (2024) 'CYCleSS: A dataset for developing coarse-grained UK-wide crop yield models with machine learning', Nature Scientific Data (under review).