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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.

Lack of understanding the effects of single- and multiple-weed interference on soybean yield has led to inadequate weed management in Primorsky Krai, resulting in much lower average yield than neighboring regions. A 2 yr field experiment was conducted in a soybean field located in Bogatyrka (43.82°N, 131.6°E), Primorsky Krai, Russia, in 2013 and 2014 to investigate the effects of single and multiple interference caused by naturally established weeds on soybean yield and to model these effects. Aboveground dry weight was negatively affected the most by weed interference, followed by number of pods and seeds. Soybean yield under single-weed interference was best demonstrated by a rectangular hyperbolic model, showing that common ragweed and barnyardgrass were the most competitive weed species, followed by annual sowthistle, American sloughgrass, and common lambsquarters. In the case of multiple-weed interference, soybean yield loss was accurately described by a multivariate rectangular hyperbolic model, with total density equivalent as the independent variable. Parameter estimates indicated that weed-free soybean yields were similar in 2013 and 2014, i.e., estimated as 1.72 t and 1.75 t ha−1, respectively, and competitiveness of each weed species was not significantly different between the two years. Economic thresholds for single-weed interference were 0.74, 0.66, 1.15, 1.23, and 1.45 plants m−2for common ragweed, barnyardgrass, annual sowthistle, American sloughgrass, and common lambsquarters, respectively. The economic threshold for multiple-weed interference was 0.70 density equivalent m−2. These results, including the model, thus can be applied to a decision support system for weed management in soybean cultivation under single and multiple-weed interference in Primorsky Krai and its neighboring regions of Russia.

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Canopy cover (CC) is a good predictor variable for plant growth parameters such as leaf area index and aboveground biomass. A nondestructive, low-cost, and convenient method is presented for estimating CC using digital camera image analysis. CC was estimated by the ratio of plant pixels to total pixels of digital camera image of rice field. To determine the criteria for segmenting the rice plant from variable soil background, three mosaic images for rice plant, flooded/bare soil, and algae-infested background were prepared from digital camera images that were taken in various field conditions. An image analysis program was developed in Visual Basic to extract red, green, and blue (RGB) features from the mosaic images, calculate RGB-based color indices, and compute the minimum segmentation error for separating rice plant from background. When judged by the segmentation error, modified excessive green index (MEGI) showed the highest potential for segmenting rice plant from flooded/bare soil background, followed by normalized green (g) and excessive green index (EGI). At the threshold MEGI value of 0.03, the segmentation error was the lowest as 0.13%. Any single index considered was not satisfactory in segmenting rice plant from algae-infested background. However, a discriminant function of 1.2553EGI + 0.01735G -0.01474B was successful in segmenting rice plant from flooded/bare soil and algaeinfested background with segmentation errors of 0.34 and 1.17%, respectively. CC for four rice varieties from tillering to booting stage was estimated based on the threshold value of MEGI and discriminant function and also manually using commercial software. Both estimates of CC showed good relationship of r 2 = 0.94, suggesting that a digital camera could be used efficiently for measuring the CC of rice field.