A yield prediction model for Italian ryegrass (IRG) was constructed based on climatic data by locations in South Korea using a general linear model. The sample size of the final dataset was 312 during 25 years. The forage crop and climatic data were collected from the reports of two national research projects on forage crops and Korean meteorological administration, respectively. Five optimal climatic variables were selected through the stepwise multiple regression analysis with dry matter yield (DMY) as the response variable. Subsequently, three climatic variables were selected after considering the interpretability of the five variables. The three selected climatic variables were spring accumulated temperature, mean temperature in January and spring rainfall days. Then, the yield prediction model was constructed based on these three climatic variables using general linear model with the cultivated locations as dummy variables. The model constructed in this research could explain 73.6% of variation in DMY of IRG. The goodness‐of‐fit of the model was tested through residual diagnostics and 10‐fold cross‐validation. For climatic variables, the high partial eta squared value of spring accumulated temperature and spring rainfall may reflect the growth characteristics that spring is the main growing period for IRG and IRG has strong waterlogging tolerance and weak drought tolerance. The results may also support the possibility to sow IRG in the subsequent spring if autumnal seeding was missed in South Korea.

The purpose of this research is to identify the significance of climate factors related to the significance of change of dry matter yield (DMY) of whole crop maize (WCM) by year through the exploratory data analysis. The data (124 varieties; n=993 in 7 provinces) was prepared after deletion and modification of the insufficient and repetitive data from the results (124 varieties; n=1027 in 7 provinces) of import adaptation experiment done by National Agricultural Cooperation Federation. WCM was classified into early-maturity (25 varieties, n=200), mid-maturity (40 varieties, n=409), late-maturity (27 varieties, n=234) and others (32 varieties, n=150) based on relative maturity and days to silking. For determining climate factors, 6 weather variables were generated using weather data. For detecting DMY and climate factors, SPSS21.0 was used for operating descriptive statistics and Shapiro-Wilk test. Mean DMY by year was classified into upper and lower groups, and a statistically significant difference in DMY was found between two groups (p<0.05). To find the reasons of significant difference between two groups, after statistics analysis of the climate variables, it was found that Seeding-Harvesting Accumulated Growing Degree Days (SHAGDD), Seeding-Harvesting Precipitation (SHP) and Seeding-Harvesting Hour of sunshine (SHH) were significantly different between two groups (p<0.05), whereas Seeding-Harvesting number of Days with Precipitation (SHDP) had no significant effects on DMY (p>0.05). These results indicate that the SHAGDD, SHP and SHH are related to DMY of WCM, but the comparison of R 2 among three variables (SHAGDD, SHP and SHH) couldn't be obtained which is needed to be done by regression analysis as well as the prediction model of DMY in the future study.

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As a part of establishing suitability classification for forage production, use of the national soil and climate database was attempted for Italian ryegrass (Lolium multiflorum Lam., IRG) in Gangwon Province. The soil data base were from Heugtoram of the National Academy of Agricultural Science, and the climate data base were from the National Center for Agro-Meteorology, respectively. Soil physical properties including soil texture, drainage, slope available depth and surface rock contents, and soil chemical properties including soil acidity and salinity, organic matter content were selected as soil factors. The crieria and weighting factors of these elements were scored. Climate factors including average daily minimum temperature, average temperature from March to May, the number of days of which average temperature was higher than 5 from September to December, the number of days of precipitation and its amount from October to May of the following year were selected, and criteria and weighting factors were scored. The electronic maps were developed with these scores using the national data base of soil and climate. Based on soil scores, the area of Goseong, Sogcho, Gangreung, and Samcheog in east coastal region with gentle slope were classified as the possible and/or the proper area for IRG cultivation in Gangwon Province. The lands with gentle or moderate slope of Cheolwon, Yanggu, Chuncheon, Hweongseong, Pyungchang and Jeongsun in west side slope of Taebaeg mountains were classified as the possible and/or proper area as well. Based on climate score, the east coastal area of Goseong, Sogcho, Yangyang, Gangreung and Samcheog could be classified as the possible or proper area. Most area located on west side of the Taebaeg mountains were classified as not suitable for IRG production. In scattered area in Chuncheon and Weonju, where the scores exceeded 60, the IRG cultivation should be carefully managed for good production. For better application of electronic maps.

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