objective of the current study was to evaluate the change of rice yield under the projected climate change condition. The rice model included in "Decision Support System for Precise Management of Rice Culture” developed in Crop Environment and Production Technology Lab. of Seoul National University was validated prior to simulation experiment. For model input, the daily weather data were generated by SIMMETEO method from the monthly normal maximum and minimum temperatures and precipitation of the current period, 1971-2000 and the three periods in the future, 2011-2040, 2041-2070, and 2071-2100. The climate change projected using A1B emission scenario by Korea National Meteorological Institute was used for the periods in the future. Simulation experiments were carried out using three cultivars, Odaebyeo, Hwasungbyeo and Dongjinbyeo under six transplanting dates from May 10 to June 30. The vegetative and ripening period is expected to decrease respectively by 10 and 30 days in 2071-2100. High temperature-induced sterility is projected to increase by about 8% until 2071-2100. Rice yield on national average was simulated to decrease by 3, 7, and 13 % in 2011-2040. 2041-2070, and 2071-2100 periods, respectively. Though adaptation strategies that select the cultivar among the current cones and change the transplanting date would alleviate the yield decrease, the yield decrease of about 7% is still anticipated in 2071-2100.

Early diagnosis of crop growth at various growth stages will help to make an optimum fertilization. If we can diagnose crop growth at around the time of topdressing of N fertilizer, N fertilization can be made based on crop growth and target crop yield, which may provide economic and environmental benefits as compared with fixed rate fertilization. In this study we devised methods to diagnose rice growth non-destructively at panicle initiation stage and to determine N topdressing rate. SPAD-502, Field Scout CM1000 and Green Seeker GNDVI were used to diagnose the growth status of rice grown at different soil N fertilities. The values measured by the diagnostic equipments at rice panicle initiation stage were then regressed to rice grain yield. It was found that CM1000 and GNDVI were more efficient than SPAD to diagnose rice growth. Therefore, a multivariate model with CM1000 and GNDVI values was developed to make a decision of N fertilization at rice panicle initiation stage. In a subsequent field study, N fertilization determined by non-destructive growth diagnose by CM1000 and GNDVI, and the multivariate model could minimize N fertilizer use to achieve our target yield, resulted in significant reduction of N fertilizer as compared with fixed rate N fertilization.