DyMEP: R package for weather data-based phenology prediction for ten crops

For most digital agriculture applications, such as in-season yield predictions, information on crop phenology is a prerequisite. Phenology is largely determined by environmental factors, e.g., temperature, precipitation, and global radiation. Consequently, weather data can be used to predict phenology. Here, we introduce the R package Dynamic Multi-Environmental Phenology ( D y M E P ) that facilitates such predictions. D y M E P was trained for ten crops, among others winter wheat, spring wheat, barley, green peas, beans and oat, with a large dataset representing the Central European climate. D y M E P fills the gap between complex, highly parameterized crop growth models that are difficult to use by non-experts, and extremely simplified models such as the Growing Degree Day approach. By carefully selecting the environmental covariates to use for each phenological phase, the user can reach suitable prediction accuracy for most applications in D y M E P . If temperature, precipitation, relative humidity, and global radiation are available as covariates to select from, the package achieves absolute errors ranging from 0 to 6 days across all applied phenology phases and root mean square errors ranging from 7 to 17 days on an independent test set. Combining D y M E P -based phenology predictions with ground-based or remote sensing observations holds promise to facilitate digital agriculture applications such as large-scale yield forecasting or monitoring of fields for crop insurance. • Phenology predictions are essential components for digital agriculture applications. • We provide a comprehensive R package predicting crop phenology based on weather data. • The package includes pre-trained phenology models for ten major crops. • The package is hosted by CRAN and, therefore, easy to install.