This study aimed to discuss the optimal seeding and harvesting dates with growing degree days(GDD) via meta-data of whole crop maize(WCM). The raw data (n=3,152) contains cultivation year, cultivars, location, seeding and harvesting dates collected from various reports such as thesis, science journals and research reports (1982-2012). The processing was: recording, screening and modification of errors; Then, the final dataset (n=121) consists of seeding cases (n=29), and harvesting cases (n=92) which were used to detect the optimum. In addition, the optimal periods considering tolerance range and GDD also were estimated. As a result, the optimum seeding and harvesting periods were 14th April ~ 3rd May and 15th August ~ 4th September, respectively; where, their GDDs were 23.7~99.6℃ and 1,328.7~ 1,602.1℃, respectively. These GDDs could be used as a judge standard for selecting the seeding and harvesting dates.

Yield prediction model for mixed pasture was developed with a shortage that the relationship between dry matter yield (DMY) and days of summer depression (DSD) was not properly reflected in the model in the previous research. Therefore, this study was designed to eliminate the data of the regions with distinctly different climatic conditions and then investigate their relationships DMY and DSD using the data in each region separately of regions with distinct climatic characteristics and classify the data based on regions for further analysis based on the previous mixed pasture prediction model. The data set used in the research kept 582 data points from 11 regions and 41 mixed pasture types. The relationship between DMY and DSD in each region were analyzed through scatter plot, correlation analysis and multiple regression analysis in each region separately. In the statistical analysis, DMY was taken as the response variable and 5 climatic variables including DSD were taken as explanatory variables. The results of scatter plot showed that negative correlations between DMY and DSD were observed in 7 out of 9 regions. Therefore, it was confirmed that analyzing the relationship between DMY and DSD based on each region is necessary and 5 regions were selected (Hwaseong, Suwon, Daejeon, Siheung and Gwangju) since the data size in these regions is large enough to perform the further statistical analysis based on large sample approximation theory. Correlation analysis showed that negative correlations were found between DMY and DSD in 3 (Hwaseong, Suwon and Siheung) out of the 5 regions, meanwhile the negative relationship in Hwaseong was confirmed through multiple regression analysis. Therefore, it was concluded that the interpretability of the yield prediction model for mixed pasture could be improved based on constructing the models using the data from each region separately instead of using the pooled data from different regions.