DyMEP: R package for weather data-based phenology prediction for ten crops
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Date
2025-10
Publication Type
Journal Article
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yes
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Abstract
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 (DyMEP) that facilitates such predictions. DyMEP 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. DyMEP 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 DyMEP. 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 DyMEP-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.
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published
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Journal / series
Volume
237
Pages / Article No.
110536
Publisher
Elsevier
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Software
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Subject
Crop phenology model; Weather data; R-CRAN package; Remote sensing; Crop insurance
Organisational unit
03648 - Buchmann, Nina / Buchmann, Nina
Notes
Funding
200756 - PHENOFLOW: A multifaceted workflow of high-throughput field phenotyping for improved prediction of wheat performance in future climate scenarios based on assessment of dynamic changes of phenology (SNF)