## 3 Climate-Related Production Prediction Model for Forage Crops
### Grass Production Prediction Model  
Unlike winter forage crops, grass is cultivated year-round. Its nature of being used for more than two years after initial growth means that any potential impact from climate factors is not significant. It can readily recover from such effects. Therefore, selecting climate factors for grass is more challenging than for winter forage crops. Fifteen climatic indicators that could affect the dry matter yield of grass were selected and tested primarily. However, it was judged that production prediction using the main indicators was desirable to study changes in the suitable cultivation area. Therefore, a production prediction model for grass was developed by selecting five climatic factors that were judged to have the most influence on grass production. Table 5 summarizes the descriptions of the five selected climate factors and the descriptive statistics of each variable. Among the climate factors, the drought indicator was obtained from the collected daily data, not the data crawled by the Korea Meteorological Administration. The DDAYS was obtained by counting the number of days without rain for more than 10 days. A correlation analysis was conducted based on the empirical knowledge that grass is influenced by precipitation over a longer duration compared to winter forage crops. The analysis involved the following variables: drought days (DDAYS), the sum of precipitation days in August (PREAUG), the growing degree days from January to December (GDDTOTAL), the maximum temperature in July (MAXJUL), the maximum temperature in August (MAXAUG), and the total dry matter yield (DMYT). The results of the correlation analysis between these five climate indicators and the DMYT are presented in Table 6. The dependent variable, DMYT, was confirmed to satisfy normality. The results of the correlation analysis between the total dry matter yield and five climate factors showed that the DDAYS, PREDAUG, MAXJUL, MAXAUG, and GDDTOTAL had slightly higher correlation coefficients. It could be seen that all independent variables had significant (\(p<0.01\)) correlations with the dependent variable, DMYT, the total dry matter yield. Figure 6 shows a graph of the correlation analysis results for each variable. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Variables** & **Coefficients** & **Intercept** & **R2** \\ \hline Multivariate regression (stepwise) (N = 304, X = 7) & DMY1– & & 6167.375 & \\  & PREOCT & 19.046 & & 0.34 \\  & MINITJAN & 231.498 & & \\  & GDDFJTA & 9.382 & & \\ \hline  & DMY1– & & 4765.62 & \\  & PREOCT & 18.87 & & \\  & PRESUM & 0.12 & & \\ Lasso regression & PREAFOVWIN & 9.459 & & \\  & MINTDEC & \(-\)122.77 & & 0.6019 \\  & MINITJAN & 90.58 & & \\  & MINTEFEB & 101.65 & & \\  & MINTMAR & 227.68 & & \\  & GDDFJTA & 7.51 & & \\ \hline  & DMY1– & & & \\ Lasso regression (N = 304, X = 4) & PREOCT & 18.4995 & 6208.17 & \\  & MINITJAN & 120.31 & & 0.627 \\  & MINITFEB & 140.11 & & \\  & GDDFJTA & 9.2 & & \\ \hline  & DMY1– & & 6441.169 & \\ Lasso regression (N = 304, X = 3) & PREOCT & 18.778 & & 0.629 \\  & MINITJAN & 239.335 & & \\  & GDDFJTA & 8.906 & & \\ \hline \end{tabular}
\end{table} 
Table 4: Comparison of IRG production prediction models. To predict production, a production prediction model was derived using multiple regression analysis and the Lasso model. These two models were then compared with each other. Two regression models were generated in the multivariate regression model with the stepwise selection method using SPSS. Both models were found to be significant. The second model was selected because its adjusted R\({}^{2}\) value was higher. A multiple regression