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Increased temperature means and fluctuations associated with climate change are predicted to exert profound effects on the seed yield of soybean. We conducted an experiment to evaluate the impacts of global warming on the phenology and yield of two determinate soybean cultivars in a temperate region (37.27°N, 126.99°E; Suwon, South Korea). These two soybean cultivars, Sinpaldalkong [maturity group (MG) IV] and Daewonkong (MG VI), were cultured on various sowing dates within a four-year period, under no water-stress conditions. Soybeans were kept in greenhouses controlled at the current ambient temperature (AT), AT+1.5°C, AT+3.0°C, and AT+5.0°C throughout the growth periods. Growth periods (VE–R7) were significantly prolonged by the elevated temperatures, especially the R1–R5 period. Cultivars exhibited no significant differences in seed yield at the AT+1.5°C and AT+3.0°C treatments, compared to AT, while a significant yield reduction was observed at the AT+5.0°C treatment. Yield reductions resulted from limited seed number, which was due to an overall low numbers of pods and seeds per pod. Heat stress conditions induced a decrease in pod number to a greater degree than in seed number per pod. Individual seed weight exhibited no significant variation among temperature elevation treatments; thus, seed weight likely had negligible impacts on overall seed yield. A boundary line analysis (using quantile regression) estimated optimum temperatures for seed number at 26.4 to 26.8°C (VE–R5) for both cultivars; the optimum temperatures (R5–R7) for single seed weight were estimated at 25.2°C for the Sinpaldalkong smaller-seeded cultivar, and at 22.3°C for the Daewonkong larger-seeded cultivar. The optimum growing season (VE–R7) temperatures for seed yield, which were estimated by combining the two boundary lines for seed number and seed weight, were 26.4 and 25.0°C for the Sinpaldalkong and Daewonkong cultivars, respectively. Considering the current soybean growing season temperature, which ranges from 21.7 (in the north) to 24.6°C (in the south) in South Korea, and the temperature response of potential soybean yields, further warming of less than approximately 1°C would not become a critical limiting factor for soybean production in South Korea.

For the site-specific prescription of fertilizer topdressing in rice cultivation, a non-destructive diagnosis of the rice growth and nutrition status is necessary. Three experiments were done to develop and test a model using canopy reflectance for the nondestructive diagnosis of plant growth and N status in rice. Two experiments for model development were conducted, one in 2000 and another in 2003 in Suwon, Korea, including two rice varieties and four nitrogen (N) rates in 2000 and four rice varieties and 10 N treatments in 2003. Hyperspectral canopy reflectance (300-1,100 nm) data recorded at various growth stages before heading were used to develop a partial least squares regression (PLS) model to calculate plant biomass and N nutrition status. The 342 observations were split for model calibration (75%) and validation (25%). The PLS model was then tested to calculate within-field statistical variation of four crop variables: shoot dry weight (SDW), shoot N concentration (SN), shoot N density (SND) and N nutrition index (NNI) using measured canopy reflectance data from a field of 6,500 m 2 in 2004. Results showed that PLS regression using logarithm reflectance had better performance than both the PLS and multiple stepwise linear regression (MSLR) models using original reflectance data to calculate the four plant variables in year 2000 and 2003. It produced values with an acceptable model coefficient of determination (R 2 ) and relative error of calculation (REC). The model R 2 and REC ranged from .83 to .89 and 13.4% to 22.8% for calibration, and .76 to .87 and 14.0% to 24.4% for validation, respectively. The PLS regression model R 2 was reduced in the test data of year 2004 but the root mean square error of calculation (RMSEC) was smaller, suggesting that the PLS regression model using 123 canopy reflectance data could be a promising method to calculate within-field spatial variation of rice crop growth and N status.