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http://dx.doi.org/10.5532/KJAFM.2019.21.2.75

Rice yield prediction in South Korea by using random forest  

Kim, Junhwan (Divison of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration)
Lee, Juseok (Bio-Evaluation Center, Korea Research Institute of Bioscience and Biotechnology)
Sang, Wangyu (Divison of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration)
Shin, Pyeong (Divison of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration)
Cho, Hyeounsuk (Divison of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration)
Seo, Myungchul (Divison of Crop Physiology and Production, National Institute of Crop Science, Rural Development Administration)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.21, no.2, 2019 , pp. 75-84 More about this Journal
Abstract
In this study, the random forest approach was used to predict the national mean rice yield of South Korea by using mean climatic factors at a national scale. A random forest model that used monthly climate variable and year as an important predictor in predicting crop yield. Annual yield change would be affected by technical improvement for crop management as well as climate. Year as prediction factor represent technical improvement. Thus, it is likely that the variables of importance identified for the random forest model could result in a large error in prediction of rice yield in practice. It was also found that elimination of the trend of yield data resulted in reasonable accuracy in prediction of yield using the random forest model. For example, yield prediction using the training set (data obtained from 1991 to 2005) had a relatively high degree of agreement statistics. Although the degree of agreement statistics for yield prediction for the test set (2006-2015) was not as good as those for the training set, the value of relative root mean square error (RRMSE) was less than 5%. In the variable importance plot, significant difference was noted in the importance of climate factors between the training and test sets. This difference could be attributed to the shifting of the transplanting date, which might have affected the growing season. This suggested that acceptable yield prediction could be achieved using random forest, when the data set included consistent planting or transplanting dates in the predicted area.
Keywords
Random forest; Rice yield; Yield prediction; Empirical;
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