• Korean Journal of Environmental Agriculture
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• v.6 no.1
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• pp.67-75
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• 1987

The major objectives of this study were to find out the growth and yield of two ecotypes of Korean garlic in main garlic producing districts in Korea. The data were collected by the field survey which had been conducted at 270 township in 58 major garlic production countries throughout the country on 10th, 20th, and 30th day of every month from 1982 to 1984. The results of this study were as follow: 1) Sowing period of garlic of southern and northern ecotype were around September 20 and October 20, respectively. 2) Average number of plants per $3.3m^2$ of the southern and northern ecotype were 123 and 100, respectively. 3) Leaf emergence time of southern ecotype was before the beginning of winter (November) and that of northern ecotype was from early February till April. 4) Stepwise multiple regression analysis showed that the plant heights measured on November 30 for southern ecotype and on June 30 for northern ecotype most adequately predicted the yield of garlic. The relationship between yield and plant height were as follows: Southern ecotype; Y=571.56+5.34X Northern ecotype; Y=251.81+5.45X where Y is yield expressed in Kg/l0a and X is height in cm at the respective date. 5) The number of leaves increased until harvest in both ecotypes. At harvest, the number of leaves in southern and northern ecotype were 10 and 8, respectively. Number of leaves counted on January 20 for southern ecotype and on June 20 for northern ecotype correlated best with the yield of the ecotypes. 6) The highest senescent portion of southern ecotype and northern ecotype were seen on January 30 and May 30, respectively. Stepwise multiple regression analysis showed that the senescent portion of southern ecotype counted on January 30 and that of northern ecotype on June 20 mostly affected the yield. 7) Average yield of southern and northern ecotype at the main garlic producing districts were 771 and 652Kg/10a, respectively.

• The Korean Journal of Applied Statistics
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• v.36 no.1
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• pp.49-62
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• 2023

Solar energy, which is rapidly increasing in proportion, is being continuously developed and invested. As the installation of new and renewable energy policy green new deal and home solar panels increases, the supply of solar energy in Korea is gradually expanding, and research on accurate demand prediction of power generation is actively underway. In addition, the importance of solar radiation prediction was identified in that solar radiation prediction is acting as a factor that most influences power generation demand prediction. In addition, this study can confirm the biggest difference in that it attempted to predict solar radiation using medium-term forecast weather data not used in previous studies. In this paper, we combined the multi-linear regression model, KNN, random fores, and SVR model and the clustering technique, K-means, to predict solar radiation by hour, by calculating the probability density function for each cluster. Before using medium-term forecast data, mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to compare model prediction results. The data were converted into daily data according to the medium-term forecast data format from March 1, 2017 to February 28, 2022. As a result of comparing the predictive performance of the model, the method showed the best performance by predicting daily solar radiation with random forest, classifying dates with similar climate factors, and calculating the probability density function of solar radiation by cluster. In addition, when the prediction results were checked after fitting the model to the medium-term forecast data using this methodology, it was confirmed that the prediction error increased by date. This seems to be due to a prediction error in the mid-term forecast weather data. In future studies, among the weather factors that can be used in the mid-term forecast data, studies that add exogenous variables such as precipitation or apply time series clustering techniques should be conducted.