Abstract
As crop productivity is greatly influenced by weather conditions, many attempts have been made to estimate crop yields using meteorological data and have achieved great progress with the development of machine learning. However, most yield prediction models are developed based on observational data, and the utilization of climate model output in yield prediction has been addressed in very few studies. In this study, we estimate rice yields in South Korea using the meteorological variables provided by ERA5 reanalysis data (ERA-O) and its dynamically downscaled data (ERA-DS). After ERA-O and ERA-DS are validated against observations (OBS), two different machine learning models, Support Vector Machine (SVM) and Long Short-Term Memory (LSTM), are trained with different combinations of eight meteorological variables (mean temperature, maximum temperature, minimum temperature, precipitation, diurnal temperature range, solar irradiance, mean wind speed, and relative humidity) obtained from OBS, ERA-O, and ERA-DS at weekly and monthly timescales from May to September. Regardless of the model type and the source of the input data, training a model with weekly datasets leads to better yield estimates compared to monthly datasets. LSTM generally outperforms SVM, especially when the model is trained with ERA-DS data at a weekly timescale. The best yield estimates are produced by the LSTM model trained with all eight variables at a weekly timescale. Altogether this study shows the significance of high spatial and temporal resolution of input meteorological data in yield prediction, which can also serve to substantiate the added value of dynamical downscaling.
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Data Availability
The data that support the findings of this study are available from the corresponding author upon reasonable request.
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Acknowledgements
This study was carried out with the support of the “Research Program for Agricultural Science & Technology Development (Project No. PJ014882)”, National Institute of Agricultural Sciences, Rural Development Administration, Republic of Korea. We would thank Prof. Axel Garcia y Garcia and Dr. Vijaya Raj Joshi who provided us the R code used for developing and evaluating the statistical models presented in Joshi et al. (2020). We would also like to express our gratitude to Prof. Kwon Hyun-Han for discussing the interpretation of the statistical analysis with us.
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Ha, S., Kim, YT., Im, ES. et al. Impacts of meteorological variables and machine learning algorithms on rice yield prediction in Korea. Int J Biometeorol 67, 1825–1838 (2023). https://doi.org/10.1007/s00484-023-02544-x
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DOI: https://doi.org/10.1007/s00484-023-02544-x