Accuracy Evaluation of the Crop-Weather Yield PredictiveModels of Italian Ryegrass and Forage Rye Using Cross-Validation

摘要

The objective of this study was to evaluate the accuracy of the yield predictive models of Italian ryegrass (IRG, Loliummultiflorum Lam.) and forage rye (FR, Secale cereale L.) reported in previous studies through K-fold cross-validationmethod. In previous studies, statistical models were constructed for dry matter yield prediction of IRG and FR using generallinear model based on climatic data by locations in the Republic of Korea. The yield predictive model for IRG cultivated inthe southern region of the Korean Peninsula and Jeju Island were DMY = 78.178AGD – 254.622MTJ + 64.156SGD –76.954PAT150 + 4.711SAP + 1028.295 + Location and DMY = – 8.044AAT + 18.640SDS – 7.542SAT + 9.610SAP +17282.191, respectively. The yield predictive model for FR was as follows: DMY = 20.999AGD + 163.705LTJ + 113.716SGD+ 64.379PAT100 – 4964.728 + Location. However, accuracy evaluation was not performed in the previous research. In thisstudy, the reported models and the data set used for model construction were investigated. Subsequently, K-fold crossvalidationwas performed to assess the accuracy of the models. The results showed that the yield predictive models fit to thedata sets well, while the accuracy of these models was in the common level since the data sources might keep major variancesin cultivars, climatic conditions, and cultivated locations. Therefore, models with better fitness and accuracy might beconstructed based on a data set with smaller variance. Hence, the standardization of the crop cultivation experiments is verynecessary to decrease the variance in the historical data used for future crop yield modeling.