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A b s t r a c t. As population rises, more people need to be fed. With increasing income, the potential exists for increases in the demand for cereals (i.e., barley). Since barley has a high level of tolerance to environmental stressors, this crop has been recommended as a potential crop for food security in marginal environments. In this study, a crop growth Agricultural Land Management Alternatives with Numerical Assessment Criteria model, was parameterized and used to simulate the yields of two barley types grown in a temperate environment at a latitude of 35°N. In order to apply this crop model to barley, 19 years of field data were used to model calibration and validation. As a result, the ALMANAC model accurately simulated yields for both barley types. The validated model was used to predict yields under three diverse seasonal rainfall scenarios associated with different patterns of the Central Pacific El Niño influence. According to the simulation results, excessively high seasonal rainfall decreased barley yields. Crop price and annual revenue of the two barley types were also evaluated using a non-linear regression model. For the malt type, the food price was higher with a higher rainfall, while naked barley had a higher revenue under the conditions of a lower rainfall.K e y w o r d s: barley, rainfall, simulation, food cost, grain yield

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This study was conducted to analyze seasonal and annual variations in rice quality and factors affecting the quality, for quality evaluation of the brand rice varieties produced in Jeonnam region. Coefficient of variation (CV) values for the seasonal variation in the rice quality were 3.1% in Toyo value, 2.1% in whiteness, 1.6% in protein content, 1.0% in moisture content, and 0.4% in head rice ratio. Quality characteristics of the brand rice varieties generally showed a decreasing tendency after April, as the months progressed. CV values for the annual variation in the rice quality were relatively high at 5.6% in protein content and 5.2% in Toyo value whereas those for whiteness and head rice ratio were relatively low, at 2.7% and 1.8%, respectively. Palatability and protein content showed high correlations with minimum air temperature, sunshine hours, rainfall, and daily temperature range. Head rice ratio had a negative correlation with daily temperature range whereas chalky rice ratio had a positive correlation with rainfall. Based on these results, we formulated a multiple regression equation to estimate palatability of cooked rice using protein content, whiteness, head rice ratio, and moisture content as follows: y ＝-6.71a ＋ 2.27b ＋ 1.29c ＋ 0.51d -15.34 (R 2 =0.51*) (y: palatability of cooked rice, a: protein content, b: moisture content, c: whiteness, d: head rice ratio).

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