This study was aimed to find yield prediction model of Italian ryegrass using climate big data and geographic information. After that, mapping the predicted yield results using Geographic Information System (GIS) as follows; First, forage data were collected; second, the climate information, which was matched with forage data according to year and location, was gathered from the Korean Metrology Administration (KMA) as big data; third, the climate layers used for GIS were constructed; fourth, the yield prediction equation was estimated for the climate layers. Finally, the prediction model was evaluated in aspect of fitness and accuracy. As a result, the fitness of the model (R2) was between 27% to 95% in relation to cultivated locations. In Suwon (n=321), the model was; DMY = 158.63AGD –8.82AAT +169.09SGD - 8.03SAT +184.59SRD -13,352.24 (DMY: Dry Matter Yield, AGD: Autumnal Growing Days, SGD: Spring Growing Days, SAT: Spring Accumulated Temperature, SRD: Spring Rainfall Days). Furthermore, DMY was predicted as 9,790±120 (kg/ha) for the mean DMY(9,790 kg/ha). During mapping, the yield of inland areas were relatively greater than that of coastal areas except of Jeju Island, furthermore, northeastern areas, which was mountainous, had lain no cultivations due to weak cold tolerance. In this study, even though the yield prediction modeling and mapping were only performed in several particular locations limited to the data situation as a startup research in the Republic of Korea.

The objective of this study was to select a model showing high-levels of interpretability which is high in R-squared value in terms of predicting the yield in the mixed pasture using the factors of fertilization, seeding rate and years after pasture establishment in steps, as well as the climate as a basic factor. The processes of constructing the yield prediction model for the mixed pasture were performed in the sequence of data collection (forage and climatic data), preparation, analysis, and model construction. Through this process, six models were constructed after considering climatic variables, fertilization management, seeding rates, and periods after pasture establishment years in steps, thereafter the optimum model was selected through considering the coincidence of the models to the forage production theories. As a result, Model VI (R squared = 53.8%) including climatic variables, fertilization amount, seeding rates, and periods after pasture establishment was considered as the optimum yield prediction model for mixed pastures in South Korea. The interpretability of independent variables in the model were decreased in the sequence of climatic variables(24.5%), fertilization amount(17.8%), seeding rates(10.7%), and periods after pasture establishment(0.8%). However, it is necessary to investigate the reasons of positive correlation between dry matter yield and days of summer depression (DSD) by considering cultivated locations and using other cumulative temperature related variables instead of DSD. Meanwhile the another research about the optimum levels of fertilization amounts and seeding rates is required using the quadratic term due to the certain value-centered distribution of these two variables

The objective of this research was to determine the effects of harvesting frequency and fertilization levels on botanical composition, dry matter yield, and forage feed compositions of Alpine grassland at 800 m altitude. This research lasted for three years at National Alpine Agricultural Research Institute in Pyeongchang with two harvesting frequency schedules (two and three times annually) and two levels of fertilizer application (conventional level of fertilizer at 280-200-240 kg/ha and a lower level of fertilizer at 200-200-200 kg/ha for N, P2O5, and K2O). Mixture combinations with seeding rate (kg/ha) were as follows: Orchardgrass 18, Tall fescue 9, Timothy 8, Kentucky bluegrass 3, and Ladino Clover 2. The gramineae ratio ranged from 93.2 to 95.3%. Therefore, gramineae forage was considered as the dominant plant in this experiment. No significant (p>0.05) difference was observed in forage dry matter yield between the two harvesting frequency treatments (two times at 9.8 ton/ha and three times at 8.6 ton/ha). However, forage dry matter yield in the two times of harvesting frequency tended to be greater than that in the three times of harvesting frequency. Significantly (p<0.05) higher forage dry matter yield in the standard fertilization level group than the lower fertilization level group (9.8 ton/ha vs. 8.7 ton/ha) was observed. However, there was no significant (p>0.05) difference in forage crude protein concentration between the two harvesting frequency treatment groups, although the concentration in the group with three times of harvesting frequency tended to be higher. In contrast, crude fiber concentration in the group with two times of harvesting frequency tended to be higher, although the difference was not statistically significant (p>0.05). Crude protein, ether extract, crude fiber, and organic matter concentrations were not significantly (p>0.05) different between the two groups with different fertilization levels. Based on these results, it was concluded that the group with two times of harvesting frequency with conventional fertilization level might be proper for obtaining better forage productivity for Alpine grassland at 800 m altitude.