• Korean Journal of Medicinal Crop Science
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• v.1 no.2
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• pp.109-114
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• 1993

This study was carried out to determine the optimum planting time for in vitromultiplied tuber of Pinellia ternata(Thunb.) Breit. The tubers were planted on April 20, May 20, June 20, July 20, August 20 and September 20 in 1990. Emergence ratios were 68 to 87% in any planting time except planting on July 20. The number of tubers per $m^2$ at harvest in plantings on May 20 and June 20 were significantly higher with 1,110 and 1,021, respectively, while in plantings after July 20, those were drastically decreased. As compared with fresh yield of planting on April 20(352kg /10a), that of May 20 was 109% and June 20 was 103%, while those of after July 20 were from 41% to 19%. There was a highly positive correlation between dry tuber yield and the number of tubers per $m^2(r=0.991^{**})$. Tuber yields for commercial use(diameter over 7.1mm) were high in planting on May 20(322kg /10a) and on June 20(299kg /10a). It was suggested that optimum field planting time for in vitro multiplied tuber of Pinellia ternata(Thunb.) Breit was from May 20 to June May 20.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.35-48
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• 2014

According to the 2013 construction market outlook report, the liquidation of construction companies is expected to continue due to the ongoing residential construction recession. Bankruptcies of construction companies have a greater social impact compared to other industries. However, due to the different nature of the capital structure and debt-to-equity ratio, it is more difficult to forecast construction companies' bankruptcies than that of companies in other industries. The construction industry operates on greater leverage, with high debt-to-equity ratios, and project cash flow focused on the second half. The economic cycle greatly influences construction companies. Therefore, downturns tend to rapidly increase the bankruptcy rates of construction companies. High leverage, coupled with increased bankruptcy rates, could lead to greater burdens on banks providing loans to construction companies. Nevertheless, the bankruptcy prediction model concentrated mainly on financial institutions, with rare construction-specific studies. The bankruptcy prediction model based on corporate finance data has been studied for some time in various ways. However, the model is intended for all companies in general, and it may not be appropriate for forecasting bankruptcies of construction companies, who typically have high liquidity risks. The construction industry is capital-intensive, operates on long timelines with large-scale investment projects, and has comparatively longer payback periods than in other industries. With its unique capital structure, it can be difficult to apply a model used to judge the financial risk of companies in general to those in the construction industry. Diverse studies of bankruptcy forecasting models based on a company's financial statements have been conducted for many years. The subjects of the model, however, were general firms, and the models may not be proper for accurately forecasting companies with disproportionately large liquidity risks, such as construction companies. The construction industry is capital-intensive, requiring significant investments in long-term projects, therefore to realize returns from the investment. The unique capital structure means that the same criteria used for other industries cannot be applied to effectively evaluate financial risk for construction firms. Altman Z-score was first published in 1968, and is commonly used as a bankruptcy forecasting model. It forecasts the likelihood of a company going bankrupt by using a simple formula, classifying the results into three categories, and evaluating the corporate status as dangerous, moderate, or safe. When a company falls into the "dangerous" category, it has a high likelihood of bankruptcy within two years, while those in the "safe" category have a low likelihood of bankruptcy. For companies in the "moderate" category, it is difficult to forecast the risk. Many of the construction firm cases in this study fell in the "moderate" category, which made it difficult to forecast their risk. Along with the development of machine learning using computers, recent studies of corporate bankruptcy forecasting have used this technology. Pattern recognition, a representative application area in machine learning, is applied to forecasting corporate bankruptcy, with patterns analyzed based on a company's financial information, and then judged as to whether the pattern belongs to the bankruptcy risk group or the safe group. The representative machine learning models previously used in bankruptcy forecasting are Artificial Neural Networks, Adaptive Boosting (AdaBoost) and, the Support Vector Machine (SVM). There are also many hybrid studies combining these models. Existing studies using the traditional Z-Score technique or bankruptcy prediction using machine learning focus on companies in non-specific industries. Therefore, the industry-specific characteristics of companies are not considered. In this paper, we confirm that adaptive boosting (AdaBoost) is the most appropriate forecasting model for construction companies by based on company size. We classified construction companies into three groups - large, medium, and small based on the company's capital. We analyzed the predictive ability of AdaBoost for each group of companies. The experimental results showed that AdaBoost has more predictive ability than the other models, especially for the group of large companies with capital of more than 50 billion won.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Korean Journal of Veterinary Research
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• v.35 no.3
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• pp.543-552
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• 1995

This study was undertaken to investigate the eftects of Cd administered ad libitum for 6 weeks on the cellular immune responses of Balb/c mice. The results were summarized as follows; 1. The mice fed 25, 50 and 100ppm Cd drank as much as control, but the mice fed 200ppm Cd drank significantly less water after Cd exposure than did control. Increasing rates of body weight of Cd-fed mice for 6 weeks were as this, control group 27.0%, Cd administered groups(25, 50, 100 and 200ppm) 28.54%, 28.31%, 20.49% and 18.04%, respectively. 2. Absolute spleen to body weight(mg/g) of control, 25, 50, 100 and 200ppm Cd administered groups were $4.34{\pm}0.23$, $4.20{\pm}0.54$, $4.80{\pm}0.87$, $4.25{\pm}0.32$ and $4.40{\pm}0.32$, respectively. Splenic cellularity(${\times}10^7$) of control was $24.29{\pm}5.98$ but increased to $27.72{\pm}5.48$, $32.96{\pm}8.44$, $28.32{\pm}8.76$ and $29.64{\pm}4.08$ in 25, 50, 100 and 200ppm Cd-fed groups, respectively. 3. Total $CD_4{^+}$ cells(${\times}10^7$) of control, 25, 50, 100 and 200ppm Cd-fed groups were $9.15{\pm}2.24$, $10.40{\pm}2.04$, $12.04{\pm}3.08$, $10.20{\pm}3.16$ and $10.80{\pm}1.48$, respectively and total $CD_8{^+}$ cells(${\times}10^7$) of these groups were $2.32{\pm}0.56$, $2.54{\pm}0.27$, $3.12{\pm}0.80$, $2.25{\pm}0.70$ and $2.24{\pm}0.28$, in order. On the other hand, $CD_4{^+}/CD_8{^+}$ ratios in total cells were increased significantly except for 50ppm Cd-fed group($3.88{\pm}0.01$). And that of control was $3.97{\pm}0.02$, but those of 25, 100 and 200ppm were $4.35{\pm}0.01$, $4.54{\pm}0.03$ and $4.81{\pm}0.03$. 4. Phagocytosis rates of peritoneal macrophages were increased significantly in 25 and 50ppm Cd groups($36.34{\pm}9.45$ and $37.15{\pm}9.22$, respectively), but 100 and 200ppm groups showed similar rates($18.20{\pm}3.04$ and $19.48{\pm}3.22$ respectively) to that of control($21.43{\pm}3.62$). 5. In mitogen-induced splenocyte proliferation, various concentraions of $CdCl_2(10^{-4}-10^{-7}M)$ were added to mitogen-stimulated culture in vitro. Splenocyte proliferation induced by LPS was decreased dose dependently, but proliferation by Con-A was increased slightly in concentrations of $10^{-7}-10^{-6}M$. 6. Significant cytotoxicity of splenocytes with $CdCl_2$ were shown at $10^{-4}M$ treated group, especially at 24 hrs. From these results, it could be concluded that Cd might modulate the immune responses by modifying a distribution of T cell subpopulations.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.21 no.2
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• pp.281-313
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• 1976

To obtain fundamental informations on cotton breeding efficiences for Korea, individual genetic relationships and interrelationships between the agronomic characteristics of Upland cotton were investigated. These experiments were couducted at the Mokpo Branch Station $(34^{\circ}48'N, $ $126^{\circ}23'E$ and altitude of 10m above sea level) from 1969 through 1972. Heterosis, combining ability, dominance and recessive gene action, genetic variance, and phenotypic and genotypic correlation were investigated by $F_1'S$ from an 11-parent partial diallel cross and the segregating $F_2$ and $F_3$ populations of the cross Paymaster times Heujueusseo Trice. The following points resulted from this study, 1. Heteroses for number of bolls per plant and lint yield were significant at 27, 84% and 37.26%, respectively. No other character had significant heteroses. 2. The GCA estimates for all studied characteristics were higher than the SCA estimates. Varieties with high GCA effects were Suwon 1 for earliness, Paymaster and Arijona for high lint percent, and Arijona for long fiber, etc, 3. SCA estimates for lint yield varied widely in crosses with Mokpo 4, Mokpo 6 and Heujueusseo Trice. Those crosses with the highest SCA effects were combinations with large characteristics differences, Example of these crosses are Mokpo 4 times Acala 1517W, Mokpo 4 times D. P. L. and Heujueusseo Trice aud Paymaster. 4. Early-maturing varieties were completely dominant to late-maturing varieties in some combinations while other crosses gave intermediate phenotypes. These results suggest additive genetic action by multi-genes. Heujueusseo Trice, Mokpo 6, and Suwon 1 showed highest degree of dominance for earliness. 5. There were no significant trends for inheritance of weight of boll and 100 seeds weight. 6. Long staple was partially to completely dominant to short staple. Though there were single gene ratios the rate of dominance decreased in the $F_2$ and $F_3$ populations in the cross between the long staple variety Paymaster and the short staple variety Heujueusseo Trice. Diallel cross $F_1$ hybrids showed complicated allelic gene action for staple length. Various dominance degree were shown by varieties. 7. Number of bolls per plant indicated strong over-dominance and small non-allelic additive gene action. 8. Lint Yield was characterized by over-dominance and by multiple non-allelic-gene action. High-yielding varieties were dominant to low-yielding ones. However, the low-yielding variety Heujueusseo Trice showed over-dominance, indicating different reactions according to the varieties and combinations. 9. Broad sense heritability for days to flowering was 34-39% while narrow sense heritability was 11%. Large variations of individual plants caused by Korean climatic conditions cause this situation. Heritability estimates for weight of boll was 30% for broad sense and 22% for narrow sense. 10. Heritability estimates for staple length and lint percent were very high suggesting strong selection effects. 11. Narrow sense heritability estimates for number of bolls per plant was 30% in the diallel cross $F_1$ hybrids and 36% in the $F_2$ population of the special cross. Broad sense heritability was estimated at 67% suggesting that. 12. Heritability estimates for lint yield was low due to high over-dominance in the diallel cross $F_1$ hybrids. Heritability estimates for yield was low in the $F_1$ hybrids but high in the $F_2$ and $F_3$ populations. 13. Phenotypic and genotypic correlations between lint percent and days to flowering and between staple length and days to flowering were high in the $F_1, $ $F_2$ and $F_3$ populations. Late-maturing varieties and individuals had long staple and high lint percent in general. As the correlation between days to flowering and lint yield was extremely low, the two traits were considered independent of each other. Days to flowering and number of bolls per plant were negatively correlated in the $F_3$ population, indicating early-maturing individual plants with many bolls may be readily selected. 14. Phenotypic and genotypic correlations between lint percent and staple length were high in $F_1, $ $F_2$ and $F_3$ populations. Accordingly, long staple varieties were high in lint percent. It was recognized that lint yield and lint percent were positively correlated in the diallel cross $F_1$ hybrids, and lint percent and staple length were positively correlated in the $F_2$ population, indicating that lint percent and staple length affect lint yield. 15. Lint yield was significantly and positively phenotypically correlated with number of bolls per plant in $F_1, $ $F_2$ and $F_3$ populations. A high genotypic correlation was also noted indicating a close genetic relationship. The selection efficiencies for a high-yielding variety can be increased when individual plants with many bolls are selected in later generations. The selection efficiencies for good fiber quality can be enhanced when individuals with long staple and high lint percent are selected in early generations.

• Journal of the Korean Wood Science and Technology
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• v.2 no.4
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• pp.4-14
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• 1974

The total forest land area in Korea accounts for some 67 percent of the nation's land total. Its productivity, however, is very low. Consequently, forest production accounts for only about 2 percent of the gross national product and a minor proportion of no more than about 5 percent versus primary industry. In this case, however, only the direct income from forestry is taken into account, making no reference to the forestry output induced by other industrial sectors. The value added Or the induced forestry output in manufacturing the primary wood products into higher quality products, makes a larger contribution to the economy than direct contribution. So, this author has tried to analyze the structure of forestry and compute the repercussion effect and the induced output of primary forest products when utilized by other industries for their raw materials, Hsing the input-output table and attached tables for 1963 and 1966 issued by the Bank of Korea. 1. Analysis of forestry structure A. Changes in total output Durng the nine-year period, 1961-1969, the real gross national product in Korea increased 2.1 times, while that of primary industries went up about 1. 4 times. Forestry which was valued at 9,380 million won in 1961, was picked up about 2. 1 times to 20, 120 million won in 1969. The rate of the forestry income in the GNP, accordingly, was no more than 1.5 percent both in 1961 and 1962, whereas its rate in primary industries increased 3.5 to 5.4 percent. Such increase in forestry income is attributable to increased forest production and rise in timber prices. The rate of forestry income, nonetheless, was on the decrease on a gradual basis. B. Changes in input coefficient The input coefficient which indicates the inputs of the forest products into other sectors were up in general in 1966 over 1963. It is noted that the input coefficient indicating the amount of forest products supplied to such industries closely related with forestry as lumber and plywood, and wood products and furniture, showed a downward trend for the period 1963-1966. On the other hand, the forest input into other sectors was generally on the increase. Meanwhile, the input coefficient representing the yolume of the forest products supplied to the forestry sector itself showed an upward tendency, which meant more and more decrease in input from other sectors. Generally speaking, in direct proportion to the higher input coefficient in any industrial sector, the reinput coefficient which denotes the use of its products by the same sector becomes higher and higher. C. Changes in ratio of intermediate input The intermediate input ratio showing the dependency on raw materials went up to 15.43 percent m 1966 from 11. 37 percent in 1963. The dependency of forestry on raw materials was no more than 15.43 percent, accounting for a high 83.57 percent of value added. If the intermediate input ratio increases in any given sector, the input coefficient which represents the fe-use of its products by the same sector becomes large. D. Changes in the ratio of intermediate demand The ratio of the intermediate demand represents the characteristics of the intermediary production in each industry, the intermediate demand ratio in forestry which accunted for 69.7 percent in 1963 went up to 75.2 percent in 1966. In other words, forestry is a remarkable industry in that there is characteristics of the intermediary production. E. Changes in import coefficient The import coefficient which denotes the relation between the production activities and imports, recorded at 4.4 percent in 1963, decreased to 2.4 percent in 1966. The ratio of import to total output is not so high. F. Changes in market composition of imported goods One of the major imported goods in the forestry sector is lumber. The import value increased by 60 percent to 667 million won in 1966 from 407 million won in 1963. The sales of imported forest products to two major outlets-lumber and plywood, and wood products and furniture-increased to 343 million won and 31 million won in 1966 from 240million won and 30 million won in 1963 respectively. On the other hand, imported goods valued at 66 million won were sold to the paper products sector in 1963; however, no supply to this sector was recorded in 1963. Besides these major markets, primary industries such as the fishery, coal and agriculture sectors purchase materials from forestry. 2. Analysis of repercussion effect on production The repercussion effect of final demand in any given sector upon the expansion of the production of other sectors was analyzed, using the inverse matrix coefficient tables attached to the the I.O. Table. A. Changes in intra-sector transaction value of inverse matrix coefficient. The intra-sector transaction value of an inverse matrix coefficient represents the extent of an induced increase in the production of self-support products of the same sector, when it is generated directly and indirectly by one unit of final demand in any given sector. The intra-sector transaction value of the forestry sector rose from 1.04 in 1963 to 1, 11 in 1966. It may well be said, therefore, that forestry induces much more self-supporting products in the production of one unit of final demand for forest products. B. Changes in column total of inverse matrix coefficient It should be noted that the column total indicates the degree of effect of the output of the corresponding and related sectors generated by one unit of final demand in each sector. No changes in the column total of the forestry sector were recorded between the 1963 and 1966 figures, both being the same 1. 19. C. Changes in difference between column total and intra-sector transaction amount. The difference between the column total and intra-sector transaction amount by sector reveals the extent of effect of output of related industrial sector induced indirectly by one unit of final demand in corresponding sector. This change in forestry dropped remarkable to 0.08 in 1966 from 0.15 in 1963. Accordingly, the effect of inducement of indirect output of other forestry-related sectors has decreased; this is a really natural phenomenon, as compared with an increasing input coefficient generated by the re-use of forest products by the forestry sector. 3. Induced output of forestry A. Forest products, wood in particular, are supplied to other industries as their raw materials, increasng their value added. In this connection the primary dependency rate on forestry for 1963 and 1966 was compared, i. e., an increase or decrease in each sector, from 7.71 percent in 1963 to 11.91 percent in 1966 in agriculture, 10.32 to 6.11 in fishery, 16.24 to 19.90 in mining, 0.76 to 0.70 in the manufacturing sector and 2.79 to 4.77 percent in the construction sector. Generally speaking, on the average the dependency on forestry during the period 1963-1966 increased from 5.92 percent to 8.03 percent. Accordingly, it may easily be known that the primary forestry output induced by primary and secondary industries increased from 16, 109 million won in 1963 to 48, 842 million won in 1966. B. The forest products are supplied to other industries as their raw materials. The products are processed further into higher quality products. thus indirectly increasing the value of the forest products. The ratio of the increased value added or the secondary dependency on forestry for 1963 and 1966 showed an increase or decrease, from 5.98 percent to 7.87 percent in agriculture, 9.06 to 5.74 in fishery, 13.56 to 15.81 in mining, 0.68 to 0.61 in the manufacturing sector and 2.71 to 4.54 in the construction sector. The average ratio in this connection increased from 4.69 percent to 5.60 percent. In the meantime, the secondary forestry output induced by primary and secondary industries rose from 12,779 million Wall in 1963 to 34,084 million won in 1966. C. The dependency of tertiary industries on forestry showed very minor ratios of 0.46 percent and 0.04 percent in 1963 and 1966 respectively. The forestry output induced by tertiary industry also decreased from 685 million won to 123 million won during the same period. D. Generally speaking, the ratio of dependency on forestry increased from 17.68 percent in 1963 to 24.28 percent in 1966 in primary industries, from 4.69 percent to 5.70 percent in secondary industries, while, as mentioned above, the ratio in the case of tertiary industry decreased from 0.46 to 0.04 percent during the period 1963-66. The mining industry reveals the heaviest rate of dependency on forestry with 29.80 percent in 1963 and 35.71 percent in 1966. As it result, the direct forestry income, valued at 8,172 million won in 1963, shot up to 22,724 million won in 1966. Its composition ratio lo the national income rose from 1.9 percent in 1963 to 2.3 per cent in 1966. If the induced outcome is taken into account, the total forestry production which was estimated at 37,744 million won in 1963 picked up to 105,773 million won in 1966, about 4.5 times its direct income. It is further noted that the ratio of the gross forestry product to the gross national product. rose significantly from 8.8 percent in 1963 to 10.7 percent in 1966. E. In computing the above mentioned ratio not taken into consideration were such intangible, indirect effects as the drought and flood prevention, check of soil run-off, watershed and land conservation, improvement of the people's recreational and emotional living, and maintenance and increase in the national health and sanitation. F. In conclusion, I would like to emphasize that the forestry sector exercices an important effect upon the national economy and that the effect of induced forestry output is greater than its direct income.

• Korean Journal of Agricultural Science
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• v.10 no.2
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• pp.221-234
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• 1983

A study was conducted to devise a method to estimate the edible meat weight in live broilers. White Cornish broiler chicks CC, Single Comb White Leghorn egg strain chicks LL, and two reciprocal cross breeds of these two parent stocks (CL and LC) were employed A total of 240 birds, 60 birds from each breed, were reared and sacrificed at 0, 2, 4, 6, 8 and 10 weeks of ages in order to measure various body parameters. Results obtained from this study were summarized as follows. 1) The average body weight of CC and LL were 1,820g and 668g, respectively, at 8 weeks of age. The feed to gain ratios for CC and LL were 2.24 and 3.28, respectively. 2) The weight percentages of edible meat to body weight were 34.7, 36.8 and 37.5% at 6, 8 and 10 weeks of ages, respectively, for CC. The values for LL were 30.7, 30.5 and 32.3%, respectively, The CL and LC were intermediate in this respect. No significant differences were found among four breeds employed. 3) The CC showed significantly smaller weight percentages than did the other breeds in neck, feather, and inedible viscera. In comparison, the LL showed the smaller weight percentages of leg and abdominal fat to body weight than did the others. No significant difference was found among breeds in terms of the weight percentages of blood to body weight. With regard to edible meat, the CC showed significantly heavier breast and drumstick, and the edible viscera was significantly heavier in LL. There was no consistent trend in neck, wing and back weights. 4) The CC showed significantly larger measurements body shape components than did the other breeds at all time. Moreover, significant difference was found in body shape measurements between CL and LC at 10 weeks of age. 5) All of the measurements of body shape components except breast angle were highly correlated with edible meat weight. Therefore, it appeared to be possible to estimate the edible meat wight of live chickens by the use of these values. 6) The optimum regression equations for the estimation of edible meat weight by body shape measurements at 10 weeks of age were as follows. $$Y_{cc}=-1,475.581 +5.054X_{26}+3.080X_{24}+3.772X_{25}+14.321X_{35}+1.922X_{27}(R^2=0.88)$$ $$Y_{LL}=-347.407+4.549X_{33}+3.003X_{31}(R^2=0.89)$$ $$Y_{CL}=-1,616.793+4.430X_{24}+8.566X_{32}(R^2=0.73)$$ $$Y_{LC}=-603.938+2.142X_{24}+3.039X_{27}+3.289X_{33}(R^2=0.96)$$ Where $X_{24}$=chest girth, $X_{25}$=breast width, $X_{26}$=breast length, $X_{27}$=keel length, $X_{31}$=drumstick girth, $X_{32}$=tibotarsus length, $X_{33}$=shank length, and $X_{35}$=shank diameter. 7) The breed and age factors caused considerable variations in assessing the edible meat weight in live chicken. It seems however that the edible meat weight in live chicken can be estimated fairly accurately with optimum regression equations derived from various body shape measurements.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.17
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• pp.45-78
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• 1974

To provide useful information for developing new high yielding soybean varieties and for improving cultural practices, an investigation was made on variation of dry matter production and on relationship among several agronomic characters of soybean plants grown under different planting times and densities as well as under different fertilizer levels, using Kwang-kyo, Dong puk-tae, and Suke ＃ 51 as determinate types and Shelby, SRF-300 and Harosoy as indeterminate types at the Crop Experiment Station during the period of 1972 and 1973. The results obtained were summarized as follow: 1. The dry weight, CGR and LAI at the initial flowering stage were high in the high plant population irrespective of varieties, planting times, and fertilizer levels. However, those characters of the indeterminate type were lower than those of the determinate types. The same characters of the indererminate type at the terminal leaf stage were either same or higher than those of the determinate types. 2. The dry weight of the determinate type at the initial flowering stage was similar to the indeterminate, type, when planting times were May 21 or June 15. The dry weights of both types of varieties were low when planted on July 10. When fertilizer levels were increased, the CGR, dry weight and LAI at the initial flowering stages were also increased. 3. Even though significant differences of LAI were obtained among the varieties within the same plant type, the indeterminate type was in general lower than that of the determinate type regardless of planting time and densities, or fertilizer levels, while the yield of the indeterminate type was comparable to the yield of the determinate type. 4. The high degree of leaf- and petiole-fall at the greenbean stage was highly associated with early planting and high levels of fertilizers. However, less amount of leaf- or petiole-fall was found when planted on July 10 or under low plant population. 5. The percent of stem weight was high under higher plant population, while the percent of leaf weight was high under lower plant population. When planting time was late, the percent of stem and petiole weight were reduced, while the leaf weight was increased. 6. The percent of pod weight of the determinate type at the terminal leaf stage was about 2% when planted on May 21, about 8% when planted on June 15, and about 9% when planted on July 10. The percent of pod weight of the indeterminate type at the terminal leaf stage were about 6 % when planted on May 21, 14% when planted on June 15 and 21% when planted on July 10. 7. Kwang kyo showed less degree of leaf-fall even when lodged due to high levels of fertilizer applied, while SRF-300 showed great damage due to lodging. 8. High yields were obtained when planted on May 21, but there were little yield differences between yields from May 21 and June 15 plantings. The reduction of yield due to late planting of July 10 was less apparent in the determinate type of varieties, while it was high in the indeterminate type. 9. The optimum plant population per are for high yield was 1, 250 to 2, 500 plants when planted on May 21, 2, 500 plants when planted on June 15, and 3, 333 plants when planted on July 10. 10. High correlation coefficients were obtained between dry matter weight and LAI at the terminal leaf stages, and between the dry matter weight and yield at the greenbean stages. The optimum dry weight for high yield in the determinate type was expected to be 25 kg. per are at the initial flowering stage and 50 kg. per are at the terminal leaf stage. In the indeterminate type the LAI and dry weight at the greenbean stage were 4 to 5 and 80 kg. per are, respectively. 11. Under the high plant population plant height was increased, while the stem diameter and the number of nodes and branches were reduced. Consequently, the percent of mainstem to main stem plus branches were increased, and the length of internode was also elongated. The ratios of stem weight, number of nodes and pods, and yield of main stem were increased when high plant population was associated with the early planting. The percent of main stem to branches for the indeterminate type was higher than that of the determinate type. 12. Under the high plant densities and late planting, the percent of the pod number and yields of main stem were increased, indicating that varieties with no or less branches were better adaptable under such conditions. 13. High degree of simple correlation coefficients was obtained between the LAI at the initial flowering stage and terminal leaf stage, and the total node number, dry matter and dry stem weight of both determinate and indeterminate types. Even though no significant correlation was found between the LAI at the initial flowering stage of the determinate type and the stem length and pod number per are, highly significant correlation coefficients were obtained between such characters in the indeterminate type of varieties. 14. The dry matter was positively correlated with the LAI, CGR, stem length, and pod number, node number and dry stem weight per are, while no significant correlation was found between the dry matter and stem diameter. 15. The correlation coefficients between lodging index and the LAI, dry weight, stem length and dry stem weight were highly significant. Negative correlation was obtained for the indeterminate type between the stem diameter and lodging index. The correlation coefficient between the stem diameter and lodging index was non-significant for the determinate type, while positive correlation was obtained between the yield and lodging index in the determinate type. The lodging index was also positively correlated with average length of internode of main stem. 16. The 100 seed weight appeared to be lowered under the high plant population and no fertilizer condition, and when planted late. Apparent differences of 100 seed weight were found between main stem and branches, being higher for the main stem than for the branches. 17. No variation of protein content was found due to different cultural practices. However, the oil content was apparently reduced when planted late.

• Journal of the Korean Wood Science and Technology
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• v.2 no.2
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• pp.32-34
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• 1974

The important results which have been obtained in the investigation can be recapitulated as follows. 1. As demonstrated by the experimental results and analyses concerning their effects in the on-ground type mushroom house, the constructions in relation to the side wall and ceiling of the experimental house showed a sufficient heat insulation on effect to protect insides of the house from outside climatic conditions. 2. As the effect on the solar type experimental mushroom house which was constructed in a half basement has been shown by the experimental results and analyses, it has been proved to be effective for making use of solar heat. However there were found two problems to be improved for putting solar house to practical use in the farm mushroom growing: (1) the construction of the roof and ceiling should be the same as for the on ground type house, and (2) the solar heat generating system should be reconstructed properly. 3. Among several ventilation systems which have been studied in the experiments, the underground earthen pipe and ceiling ventilation, and vertical side wall and ceiling ventilation systems have been proved to be most effective for natural ventilation. 4. The experimental results have shown that ventilation systems such as the vertical side wall and underground ventilation systems are suitable to put to practical use as natural ventilation systems for farm mushroom house. These ventilation systems can remarkably improve the temperature of fresh air which is introduced into the house by heat transfers within the ventilation passages, so as to approach to the desired temperature of the house without any cooling or heating operation. For example, if it is assuming that X is the outside temperature and Y is the amount of temperature adjustment made by the influence of the ventilation system, the relationships that exist between X and Y can be expressed by the following regression lines. Underground iron pipe ventilation system. Y=0.9X-12.8 Underground earthen pipe ventilation system. Y=0.96X-15.11 Vertical side wall ventilation system. Y=0.94X-17.57 5. The experimental results have 8hown that the relationships existing between the admitted and expelled air and the $CO_2$ concentration can be described with experimental regression lines or an exponent equation as follows: 5.1 If it is assumed that X is an air speed cm/sec. and Y is an expelled air speed in cm/sec. in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the regression lines shown below: 5.2 If it IS assumed that X is an admitted volume of air in $m^3$/hr. and Y is an expelled volume of air in $m^3$/hr. in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the regression lines shown below. 5.3 If it is assumed that expelled air speed in emisec. and replacement air speed in cm/sec. at the bed surface in a natural ventilation system are shown as X and Y. respectively, since the Y is a function of the X. the relationships that exist between X and Y can be expressed by the following regression line: GE(100%)-CV (50%) ventilation system. Y=-0.54X+0.84 5.4 If it is assumed that the replacement air speed in cm/sec. at the bed surface is shown as X, and $CO_2$ concentration which is expressed by multiplying 1000 times the actual value of $CO_2$ % is shown as Y, in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the following regression line: GE(100%)-CV(50%) ventilation system. Y=114.53-6.42X 5.5 If it is assumed that the expelled volume of air is shown as X and the $CO_2$ concencration which is expressed by multiplying 1000 times the actual of $CO_2$% is shown as Y in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the following exponent equation: GE(100%)-CV(50%) ventilation system. Y=$127.18{\times}1.0093^{-x}$ 5.6 The experimental results have shown that the ratios of the cross sectional area of the GE and CV vent to the total cubic capacity of the house, required for providing an adequate amount of air in a natural ventilation system, can be estimated as follows: GE(admitting vent of the underground ventilation) 0.3-0.5% (controllable) CV(expelling vent of the ceiling ventilation) 0.8-1.0% (controllable) 6. Among several heating devices which were studied in the experiments, the hot-water boilor which wasmodified to be fitted both as hot-water boiler and as a pressureless steam-water was found most suitable for farm mushroom growing.