Construction of a yield prediction model for whole crop maize on the basis of climatic data in South Korea

The objective of this study was to construct a dry matter yield (DMY) prediction model for whole crop maize (WCM, Zea mays) on the basis of climatic data by location in South Korea. The forage crop and climatic data were collected from the reports of national research projects on forage crops and the website of Korea Meteorological Administration, respectively. The data set (n=775) of 22 years(1988-2011) was used to construct the model after a four-step data preparation. Two optimal climatic variables were selected through stepwise multiple regression analysis, with DMY as the dependent variable. Subsequently, using a general linear model, the final model, whichincluded the two climatic variables and cultivated location (in the form of a dummy variable), was constructed as follows: DMY=11.298SHAGDD-3.651SHP+1 089.870+Location, where SHAGDD refers to seeding-harvest accumulated growing degree days and SHP, seeding-harvest precipitation. The homoscedasticity and assumption that the mean of the residualsequal to zero was satisfied and the reliability of the model were good,since most scatters of the predicted DMY values fell within the 95% confidence interval. The model was tested using residual diagnostics and 10-fold cross-validation. The results showed that the variables related to temperature and precipitation had significant effects on the WCM yield. Therefore, accurate determination of sowing and harvest dates is important to ensure that the crops grow under appropriate accumulative temperature conditions. Furthermore, land with good drainage and cultivars with strong tolerance to water logging are necessary for the cultivation of WCM in South Korea because of excessive precipitation during the growing season.