Prediction of the Italian Ryegrass (Lolium multiflorum Lam.) Yield via Climate Big Data and Geographic Information System in Republic of Korea
2017
Kim, M., Kangwon National University, Chuncheon, Republic of Korea | Oh, S.M., Kangwon National University, Chuncheon, Republic of Korea | Kim, J.Y., Kangwon National University, Chuncheon, Republic of Korea | Lee, B.H., Kangwon National University, Chuncheon, Republic of Korea | Peng, J., Kangwon National University, Chuncheon, Republic of Korea | Kim, S.C., Kangwon National University, Chuncheon, Republic of Korea | Befekadu Chemere, Kangwon National University, Chuncheon, Republic of Korea | Jalil Ghassemi Nejad, Kangwon National University, Chuncheon, Republic of Korea | Kim, K.D., Gangwondo Agricultural Research and Extension Services, Chuncheon, Republic of Korea | Jo, M.H., Foundation for the Rural Youth, Seoul, Republic of Korea | Kim, B.W., Kangwon National University, Chuncheon, Republic of Korea | Sung, K.I., Kangwon National University, Chuncheon, Republic of Korea
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.
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