• 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 The Korean Society of Grassland and Forage Science
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• v.41 no.2
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• pp.110-118
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• 2021

This study was conducted to investigate the effects of species and varieties of summer forage crops on growth characteristics and productivity in Sihwa reclaimed land. The summer forage crops used in the trial were silage corn, sorghum×sudangrass hybrid(SSH), and proso millet. For each forage species, Gwangpyeongok(GPO), P15453, P1952 and P2088 were used for silage corn, and 877F, Green star, Honey chew, and Turbo gold cultivars were used for SSH. For proso millet, Ibaekchal, Geumsilchal and Manhongchal developed by the National Institute of Crop Science were used. Silage corn and SSH were sown on May 21, 2019 and proso millet on June 4, and harvested on September 2. There was no significant difference in plant and ear height of silage corn among varieties. P1543 was the highest and P2088 was the lowest in yield of silage corn, but there was no significant difference among treatments. Among the SSH, the plant height of 877F was the highest and Turbo gold variety had the smallest (p<0.05). As for the dry matter(DM) yields, 877F had the highest at 3,862 kg/ha and Green star had the lowest at 2,669 kg/ha (p<0.05). The fresh matter yield of proso millet was 15,778 kg/ha, which was higher than that of corn or SSH, The average dry matter yield was 4,780 kg/ha, and Ibaekchal variety had the highest DM yield compared to other varieties (p<0.05). P2088 had the highest TDN content and GPO was the lowest (p<0.05). As for the SSH, the TDN content of Green star and Honey chew varieties was significantly higher, and the RFV value was the lowest in Turbo gold. The average crude protein content of proso millet was 7.03%, and the highest TDN and RFV values were 64.36% and 106 in Geumsilchal. In the experiment of the germination rate of summer forage crops according to salt concentration, silage corn showed a germination rate of 83.1% even at 0.4% salinity. In particular, P2088 and P1921 varieties had more than 80% germination rate even at 0.6% salt concentration. As for the SSH, the germination rate of 877F was 93.3% even at 0.8% salinity, and 88.3% with Honey chew, indicating higher resistance to salt concentration compared to other varieties. Proso millet showed a high germination rate of 84.0 to 88.7% even at a salt concentration of 0.6%. Considering the above results, proso millet was recommended as the most suitable forage crop species in the Sihwa reclaimed land with high salt concentration, and the Ibaekchal variety is recommended as a suitable forage crop due to its high yield.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.23-46
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• 2021

Collaborative filtering, which is often used in personalization recommendations, is recognized as a very useful technique to find similar customers and recommend products to them based on their purchase history. However, the traditional collaborative filtering technique has raised the question of having difficulty calculating the similarity for new customers or products due to the method of calculating similaritiesbased on direct connections and common features among customers. For this reason, a hybrid technique was designed to use content-based filtering techniques together. On the one hand, efforts have been made to solve these problems by applying the structural characteristics of social networks. This applies a method of indirectly calculating similarities through their similar customers placed between them. This means creating a customer's network based on purchasing data and calculating the similarity between the two based on the features of the network that indirectly connects the two customers within this network. Such similarity can be used as a measure to predict whether the target customer accepts recommendations. The centrality metrics of networks can be utilized for the calculation of these similarities. Different centrality metrics have important implications in that they may have different effects on recommended performance. In this study, furthermore, the effect of these centrality metrics on the performance of recommendation may vary depending on recommender algorithms. In addition, recommendation techniques using network analysis can be expected to contribute to increasing recommendation performance even if they apply not only to new customers or products but also to entire customers or products. By considering a customer's purchase of an item as a link generated between the customer and the item on the network, the prediction of user acceptance of recommendation is solved as a prediction of whether a new link will be created between them. As the classification models fit the purpose of solving the binary problem of whether the link is engaged or not, decision tree, k-nearest neighbors (KNN), logistic regression, artificial neural network, and support vector machine (SVM) are selected in the research. The data for performance evaluation used order data collected from an online shopping mall over four years and two months. Among them, the previous three years and eight months constitute social networks composed of and the experiment was conducted by organizing the data collected into the social network. The next four months' records were used to train and evaluate recommender models. Experiments with the centrality metrics applied to each model show that the recommendation acceptance rates of the centrality metrics are different for each algorithm at a meaningful level. In this work, we analyzed only four commonly used centrality metrics: degree centrality, betweenness centrality, closeness centrality, and eigenvector centrality. Eigenvector centrality records the lowest performance in all models except support vector machines. Closeness centrality and betweenness centrality show similar performance across all models. Degree centrality ranking moderate across overall models while betweenness centrality always ranking higher than degree centrality. Finally, closeness centrality is characterized by distinct differences in performance according to the model. It ranks first in logistic regression, artificial neural network, and decision tree withnumerically high performance. However, it only records very low rankings in support vector machine and K-neighborhood with low-performance levels. As the experiment results reveal, in a classification model, network centrality metrics over a subnetwork that connects the two nodes can effectively predict the connectivity between two nodes in a social network. Furthermore, each metric has a different performance depending on the classification model type. This result implies that choosing appropriate metrics for each algorithm can lead to achieving higher recommendation performance. In general, betweenness centrality can guarantee a high level of performance in any model. It would be possible to consider the introduction of proximity centrality to obtain higher performance for certain models.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.26 no.1
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• pp.56-68
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• 1981

A maize line was selected in 1979 among 1000 Korean local maize lines collected in 1977. The selected maize line was characterized by having three to four tillers and eight to 10 ears on each individual plant. The line was assumed to have a great potential as a silage crop. The investigation was conducted as one of the serial studies on the Korean maize collected lines to provide basic information on the genetic variabilities of the multi-eared and tillered (MET) line and on other agronomic characters, prior to use the line as material for future breeding works for silage crop. The MET line and Suwon #19, single cross hybrid, as check variety were planted on May 1, 15 and 30, in three different levels of plant populations. The results obtained were summarized as follows: 1. The genetic variabilities of multi-ear and tillering habits were greater than environmental variabilities. 2. Total dry leaf weight of individual plant of MET line was also significantly higher than that of Suwon #19. 3. The mean number of tillers and ears bearing on the individual plant of MET line varied greatly with plant densities. The number of tillers and ears was on the average 2.9 and 7.0, respectively, when planted in 60cm. by 60cm. 4. The total dry matter and dried stem weight of the individual plant on MET line were comparable to those of Suwon #19. 5. The kernel weight from the individual plant of MET line was 5 to 40% less than that of Suwon #19, depending upon the plant densities. 6. The Kernel to stover ratio was higher for Suwon #19 than for the MET line. (41% to 35%). 7. The MET line had shown first tiller two weeks after planted on May 1. The second and third tillers appeared three to five days after the appearance of the first tiller. 8. The MET line was very specific in tillering habits. All the tillers were borne on the first few nodes of main stem below the soil surface. 9. The tillering habits of MET line were vigorous in the early part of the growing season, but less vigorous in the later part of the growing season. The number of efficient tillers bearing useable ears, was around two to three, when planted in 60cm. by 60cm. 10. The difference of plant height between main stem and first few tillers was around 10cm. 11. The ear size of MET line was around one-third of the major corn belt hybrids. The shape of ear of MET line was conical, with different diameter. 12. The kernel of the MET line was flinty with small soft starch patches on the endosperm part. 13. The 100 kernel weight was around 15gr., which is about one half of the major high yielding hybrids. 14. The ear height of MET line was comparatively higher than that of Suwon #19. 15. Significantly high and positive phenotypic correlation coefficients were obtained among major plant characters. 16. The growth rate of MET line was slower than that of Suwon #19. 17. MET line and Suwon #19 were both heavily infected with black streaked mosaic virus.

• Journal of Intelligence and Information Systems
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• v.24 no.1
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• pp.227-252
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• 2018

The evolution of instant communication has mirrored the development of the Internet and messenger applications are among the most representative manifestations of instant communication technologies. In messenger applications, senders use emoticons to supplement the emotions conveyed in the text of their messages. The fact that communication via messenger applications is not face-to-face makes it difficult for senders to communicate their emotions to message recipients. Emoticons have long been used as symbols that indicate the moods of speakers. However, at present, emoticon-use is evolving into a means of conveying the psychological states of consumers who want to express individual characteristics and personality quirks while communicating their emotions to others. The fact that companies like KakaoTalk, Line, Apple, etc. have begun conducting emoticon business and sales of related content are expected to gradually increase testifies to the significance of this phenomenon. Nevertheless, despite the development of emoticons themselves and the growth of the emoticon market, no suitable emoticon recommendation system has yet been developed. Even KakaoTalk, a messenger application that commands more than 90% of domestic market share in South Korea, just grouped in to popularity, most recent, or brief category. This means consumers face the inconvenience of constantly scrolling around to locate the emoticons they want. The creation of an emoticon recommendation system would improve consumer convenience and satisfaction and increase the sales revenue of companies the sell emoticons. To recommend appropriate emoticons, it is necessary to quantify the emotions that the consumer sees and emotions. Such quantification will enable us to analyze the characteristics and emotions felt by consumers who used similar emoticons, which, in turn, will facilitate our emoticon recommendations for consumers. One way to quantify emoticons use is metadata-ization. Metadata-ization is a means of structuring or organizing unstructured and semi-structured data to extract meaning. By structuring unstructured emoticon data through metadata-ization, we can easily classify emoticons based on the emotions consumers want to express. To determine emoticons' precise emotions, we had to consider sub-detail expressions-not only the seven common emotional adjectives but also the metaphorical expressions that appear only in South Korean proved by previous studies related to emotion focusing on the emoticon's characteristics. We therefore collected the sub-detail expressions of emotion based on the "Shape", "Color" and "Adumbration". Moreover, to design a highly accurate recommendation system, we considered both emotion-technical indexes and emoticon-emotional indexes. We then identified 14 features of emoticon-technical indexes and selected 36 emotional adjectives. The 36 emotional adjectives consisted of contrasting adjectives, which we reduced to 18, and we measured the 18 emotional adjectives using 40 emoticon sets randomly selected from the top-ranked emoticons in the KakaoTalk shop. We surveyed 277 consumers in their mid-twenties who had experience purchasing emoticons; we recruited them online and asked them to evaluate five different emoticon sets. After data acquisition, we conducted a factor analysis of emoticon-emotional factors. We extracted four factors that we named "Comic", Softness", "Modernity" and "Transparency". We analyzed both the relationship between indexes and consumer attitude and the relationship between emoticon-technical indexes and emoticon-emotional factors. Through this process, we confirmed that the emoticon-technical indexes did not directly affect consumer attitudes but had a mediating effect on consumer attitudes through emoticon-emotional factors. The results of the analysis revealed the mechanism consumers use to evaluate emoticons; the results also showed that consumers' emoticon-technical indexes affected emoticon-emotional factors and that the emoticon-emotional factors affected consumer satisfaction. We therefore designed the emoticon recommendation system using only four emoticon-emotional factors; we created a recommendation method to calculate the Euclidean distance from each factors' emotion. In an attempt to increase the accuracy of the emoticon recommendation system, we compared the emotional patterns of selected emoticons with the recommended emoticons. The emotional patterns corresponded in principle. We verified the emoticon recommendation system by testing prediction accuracy; the predictions were 81.02% accurate in the first result, 76.64% accurate in the second, and 81.63% accurate in the third. This study developed a methodology that can be used in various fields academically and practically. We expect that the novel emoticon recommendation system we designed will increase emoticon sales for companies who conduct business in this domain and make consumer experiences more convenient. In addition, this study served as an important first step in the development of an intelligent emoticon recommendation system. The emotional factors proposed in this study could be collected in an emotional library that could serve as an emotion index for evaluation when new emoticons are released. Moreover, by combining the accumulated emotional library with company sales data, sales information, and consumer data, companies could develop hybrid recommendation systems that would bolster convenience for consumers and serve as intellectual assets that companies could strategically deploy.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.4 no.1
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• pp.31-71
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• 1968

To clarify the breeding behavior of the hybrids between tropical and temperate area rice varieties, investigations were made on heading days and grain sterility. In this study, crosses were made in half way diallel involving 7 varieties: 2 photoperied sensitive Indicas, 2 less sensitive intermediate Indicas, 1 Ponlai Japonica and 2 high temperature sensitive Japonicas. The parents and $F_1$s were grown under 10 hours and 14 hours daylength controlled conditions at both IRRI(International Rice Research Institute, N$14^{\circ}$17') and Suwon(N$37^{\circ}$16'). F2s with their parents were grown at IRRI in the short day season, and at Suwon under natural conditions. Fa lines with their parents were grown at Suwon under natural conditions. Observations were made for heading days and sterility. The results are summarized as follow; 1. Heading days : 1. For the $F_1$s, earliness showed dominance or overdominance to lateness under the 10 hours condition, and dominance or partial dominance under the 14 hours conditions, at both IRRI and Suwon. 2. For the $F_2$s grown at IRRI during the shortday season earliness appeared to be dominant over lateness and segregation was not distinct and continuous. In the early season culture of $F_2$s at Suwon earliness showed partial dominance or was intermediate. In the proper season culture of $F_2$s lateness showed partial dominance or was intermediate. 3. In the combinations between late parental varieties which do not head at Suwon, transgressive segregants bearing effective panicles were obtained. 4. The crosses of parental varieties having long basic vegetative growth duration showed bigger variance in heading days, and significant correlation was found between of parental varieties and the mean coefficient of variance for parental arrays. 5. The means of heading days of F2 populations were significantly correlated with those of $F_1$ or mid-parents. The means of F 8 lines were also highly correlated with the means of $F_2$s, but, the means of $F_3$ lines grown at Suwon and of their parental $F_2$ individual, grown at IRRI were not correlated. 6. A faint heritability was calculated from the regression of $F_3$ lines grown at Suwon on the $F_2$ individuals grown at IRRI for most combinations, especially in the combinations involving shortday sensitive varieties. This implies low efficiency for the selection of heading days of $F_2$ individuals at IRRI to be grown in lines at Suwon. 7. No significant reciprocal effects were measured for $F_1$ and $F_2$ mean heading days. 8. Partitioning the observed photoperiod sensitivity. into two components, parental array mean md the deviation from this array mean, the parental photoperiod sensitivity contributing to the hybrids was measured in terms of general and specific combining ability for photoperiod sensitivity. 9. The photoperiod sensitivity of $F_1$s was higher than that of the parents, and it decreased as the generation progressed in most combinations of tested varieties. 10. The response of heading days to difference of temperature was weaker for $F_1$ hybrids than for the parents. The differences of temperature responses between the longday and shortday treatments were specific for the variety. 2. Sterility : 1. The $F_1$ sterility was specific for the combinations and not correlated to the parental sterility. The sterility of $F_1$s grown under the 10 hours condition was higher than of those grown under 14 hours. These results were the same at both locations, IRRI and Suwon. 2. The high sterile combinations in $F_1$ showed high sterility in $F_2$. The combinations between a high photoperiod sensitive variety and a high temperature sensitive variety showed high sterility and wider variance. 3. The mean sterility of $F_2$s was lower than of $F_1$s and the mean of $F_3$ lines was lower than of $F_2$s. Sterility decreased as the generation progressed, and the differences of $F_3$ sterility of different combinations were not significant. 4. A faint correlation between grain sterility and pollen sterility was observed in $F_2$ populations. 5. No significant reciprocal effects were measured in $F_1$ and $F_2$ sterility. 6. Following Griffing's method, specific combining ability effects were higher than general combining ability effects, especially in the combinations between highly photoperiod sensitive varieties and highly temperature sensitive varieties. 7. No distinct correlations were found between $F_2$ individual sterility grown at IRRI and $F_3$ line sterility grown at Suwon. 8. No distinct correlations were observed between heading days and sterility of $F_2$ individuals.

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.13
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• pp.1-40
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• 1973

These studies were aimed at clarification of genetic and ecological variation in culm length, panicle length and plant height of the $\textrm{F}_2$ plants in some selected crosses made between semi-dwarf rice varieties and tall Japonica ones. One Indica semi-dwarf, Taichung Native 1, one Indica $\times$ Japonica hybrid, IE51 and one Japonica semi-dwarf, Tankanbaekmang were used as short-gene donors while two of medium maturity varieties, Jinheung and Kwanok and one late veriety, Palkweng were used as the corresponding counterpart of respective dwarf varieties in a series of crosses. Five different crosses, Kwanok $\times$ Tankanbaekmang, Palkweng $\times$ Tankanbaekmang, Jinheung $\times$ T(N)1, Kwanok $\times$ T(N)1 and Kwanok $\times$ IE51, were made among the above six varieties. The $\textrm{F}_2$ plants of these crosses together with the concerned parental varieties were grown under several different conditions including three levels of each nitrogen and planting space, three planting seasons and three locations in 1968, to investigate variation in length of culm and panicle, and plant height. On the other hand, the F$_3$ progenies which were derived from the shortest 10 percent of the plants of three $\textrm{F}_2$ populations, Kwanok $\times$ T(N)1, Jinheung $\times$ T(N) 1 and Kwanok $\times$ IE51 grown in the previous year, were compared each other on the basis of selection efficiency in culm length. The experimental results could be summarized as follows; 1. Genetic behavior A. It was revealed that Tankanbaekmang, one of Japonica dwarf has a simple recessive gene responsible for short culm expression, showing a typical segregation ratio of three tall to one short culm plants in $\textrm{F}_2$ generation of the crosses either with Kwanok or Palkweng. B. In the both combinations, segregation pattern of the panicle length was exactly same as that of culm length. It seems that the same gene controls both culm length and panicle length. C. No difference between segregation of culm length and plant height in the above crosses was observed. D. T(N)1, one of Indica semi-dwarf did not show such a simple genetic behavior as detected from the crosses with Tankanbaekmang in segregation of culm length but formed a continuous and normal distribution curve. Therefore, some nonallelic genic actions might be involved in expression of culm length of the counterpart varieties of T(N)1. In particular, a transgressive segregation appeared toward the direction of longer culm length in case of Jinheung $\times$ T(N)1. The genetic behavior of panicle length and plant height generally coincided with that of culm length in all the cases. E. IE51 demonstrated exactly the same genetic behavior as that of T(N)1 when this variety was crossed with Kwanok. It was clearly clarified that the simple recessive gene controlling dwarfism from T(N)1 was well incorporated into this variety. 2. Ecological variation A. In general, there was a decreasing tendency in culm length and plant height of rice plant as seeding delayed while it was not so noticeable in panicle length. The decreasing magnitude varied from variety to variety and from cross to cross. Genetic behavior of the culm length and related characters of these materials was not disturbed by the variation of seeding season, nitrogen level, planting space and experimental location. E. The elongation mode of the upper three internodes was very similar to the segregation mode of culm length, panicle length and plant height in $\textrm{F}_2$ populations of . all the crosses investigated in this study. Accordingly, this result confirmed that the roles of the upper three internodes are very important in manifesting plant stature in rice. C. The effect of nitrogen on culm length and the related other two characters seemed to be meager. However, it was true to show an increasing tendency of those characters as nitrogen level got increased from 4 kg to 12kg per l0a, with different magnitude depending upon variety or cross. D. Also, the effect of planting space on culm length, panicle length and plant height was relatively small in all the cases. Those characters varied again depending upon variety or cross. However, a general increasing tendency was detected in manifestation of those traits under denser planting space condition. E. All the parental varieties produced shorter culm, panicle and plant height when they were grown at the lower latitude locations. It might be attributed to the fact that their reproductive growth accelerated with increased temperature prevailing at the lower latitude locations such as Iri and Mi1yang. On the countrary, $\textrm{F}_2$ population reacted differently to the different locations from the parental varieties. All the $\textrm{F}_2$ plants produced the longest culm, panicle and plant at Milyang. 3. Selection efficiency A. The heritability of culm length in Kwanok $\times$ T(N)1, Kwanok $\times$ IE51 and Jinheung$\times$T(N)1 was 92 percent, 74 percent and 55 percent, respectively. B. The actual genetic advance for culm length obtained from the progeny lines of the selected plants(10 precent) from the $\textrm{F}_2$ generation, was comparable to the expected advance calculated from the original $\textrm{F}_2$ populations. As compared with the $\textrm{F}_2$ population, the $\textrm{F}_3$ plants of Kwanok $\times$ T(N)l shortened on the average by 20.8cm, those of Kwanok $\times$ IE51 did 8.7cm and those of Jinheung$\times$T(N)1 20.0cm, respectively. C. Panicle length of the populations was differently affected from one cross to another by the selection based upon culm length in $\textrm{F}_2$ Kwanok $\times$ T(N)1 did not show any noticeable shortening of its culm length due to the selection pressure. On the other hand, both Kwanok $\times$ IE51 and Jinheung $\times$ T(N)1 showed a considerable shortening of their panicles in case of selection for culm length. Based upon the above results, it could be concluded that the ecological variation in culm length, panicle length and plant height was relatively small and fallen within the range of genetic variation. Considering from the fact that the simple recessive gene governing short height of Tankanbaekmang always accompanied with some undesirable characters such as short panicle and extremely small grain, the short gene of T(N)1 seemed to be more useful as dwarf gene source since it did not carry short gene together with such undesirable traits.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.14
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• pp.1-40
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• 1973

There is a general tendency to increase nitrogen level in rice production to insure an increased yield. On the other hand, percentage of ripened grains is getting decreased with such an increased fertilizer level. Decreasing of the percentage is one of the important yield limiting factors. Especially the newly developed rice variety, 'Tongil' is characterized by a relatively low percentage of ripened grains as compared with the other leading varieties. Therefore, these studies were aimed to finding out of some measures for the improvement of ripening in rice. The studies had been carried out in the field and in the phytotron during the period of three years from 1970 to 1972 at the Crop Experiment Station in Suwon. The results obtained from the experiments could be summarized as follows: 1. The spikelet of Tongil was longer in length, more narrow in width, thinner in thickness, smaller in the volume of grains and lighter in grain weight than those of Jinheung. The specific gravity of grain was closely correlated with grain weight and the relationship with thickness, width and length was getting smaller in Jinheung. On the other hand, Tongil showed a different pattern from Jinheung. The relationship of the specific gravity with grain weight was the greatest and followed by that with the width, thickness and length, in order. 2. The distribution of grain weight selected by specific gravity was different from one variety to another. Most of grains of Jinheung were distributed over the specific gravity of 1.12 with its peak at 1.18, but many of grains of Tongil were distributed below 1.12 with its peak at 1.16. The brown/rough rice ratio was sharply declined below the specific gravity of 1.06 in Jinheung, but that of Tongil was not declined from the 1.20 to the 0.96. Accordingly, it seemed to be unfair to make the specific gravity criterion for ripened grains at 1.06 in the Tongil variety. 3. The increasing tendency of grain weight after flowering was different depending on varieties. Generally speaking, rice varieties originated from cold area showed a slow grain weight increase while Tongil was rapid except at lower temperature in late ripening stage. 4. In the late-tillered culms or weak culms, the number of spikelets was small and the percentage of ripened grains was low. Tongil produced more late-tillered culms and had a longer flowering duration especially at lower temperature, resulting in a lower percentage of ripened grains. 5. The leaf blade of Tongil was short, broad and errect, having light receiving status for photosynthesis was better. The photosynthetic activity of Tongil per unit leaf area was higher than that of Jinheung at higher temperature, but lower at lower temperature. 6. Tongil was highly resistant to lodging because of short culm length, and thick lower-internodes. Before flowering, Tongil had a relatively higher amount of sugars, phosphate, silicate, calcium, manganese and magnesium. 7. The number of spikelets of Tongil was much more than that of Jinheung. The negative correlation was observed between the number of spikelets and percentage of ripened grains in Jinheung, but no correlation was found in Tongil grown at higher temperature. Therefore, grain yield was increased with increased number of spikelets in Tongil. Anthesis was not occurred below 21$^{\circ}C$ in Tongil, so sterile spikelets were increased at lower temperature during flowering stage. 8. The root distribution of Jinheung was deeper than that of Tongil. The root activity of Tongil evaluated by $\alpha$-naphthylamine oxidation method, was higher than that of Jinheung at higher temperature, but lower at lower temperature. It is seemed to be related with discoloration of leaf blades. 9. Tongil had a better light receiving status for photosynthesis and a better productive structure with balance between photosynthesis and respiration, so it is seemed that tongil has more ideal plant type for getting of a higher grain yield as compared with Jinheung. 10. Solar radiation during the 10 days before to 30 days after flowering seemed enough for ripening in suwon, but the air temperature dropped down below 22$^{\circ}C$ beyond August 25. Therefore, it was believed that air temperature is one of ripening limiting factors in this case. 11. The optimum temperature for ripening in Jinheung was relatively lower than that of Tongil requriing more than $25^{\circ}C$. Air temperature below 21$^{\circ}C$ was one of limiting factors for ripening in Tongil. 12. It seemed that Jinheung has relatively high photosensitivity and moderate thermosensitivity, while Tongil has a low photosensitivity, high thermosensitivity and longer basic vegetative phase. 13. Under a condition of higher nitrogen application at late growing stage, the grain yield of Jinheung was increased with improvement of percentage of ripened grains, while grain yield of Tongil decreased due to decreasing the number of spikelets although photosynthetic activity after flowering was. increased. 14. The grain yield of Jinheung was decreased slightly in the late transplanting culture since its photosynthetic activity was relatively high at lower temperature, but that of Tonil was decreased due to its inactive photosynthetic activity at lower temperature. The highest yield of Tongil was obtained in the early transplanting culture. 15. Tongil was adapted to a higher fertilizer and dense transplanting, and the percentage of ripened grains was improved by shortening of the flowering duration with increased number of seedlings per hill. 16. The percentage of vigorous tillers was increased with a denser transplanting and increasing in number of seedlings per hill. 17. The possibility to improve percentage of ripened grains was shown with phosphate application at lower temperature. The above mentioned results are again summarized below. The Japonica type leading varieties should be flowered before August 20 to insure a satisfactory ripening of grains. Nitrogen applied should not be more than 7.5kg/10a as the basal-dressing and the remained nitrogen should be applied at the later growing stage to increase their photosynthetic activity. The morphological and physiological characteristics of Tongil, a semi-dwarf, Indica $\times$ Japonica hybrid variety, are very different from those of other leading rice varieties, requring changes in seed selection by specific gravity method, in milling and in the cultural practices. Considering the peculiar distribution of grains selected by the method and the brown/rough rice ratio, the specific gravity criterion for seed selection should be changed from the currently employed 1.06 to about 0.96 for Tongil. In milling process, it would be advisable to bear in mind the specific traits of Tongil grain appearance. Tongil is a variety with many weak tillers and under lower temperature condition flowering is delayed. Such characteristics result in inactivation of roots and leaf blades which affects substantially lowering of the percentage of ripened grains due to increased unfertilized spikelets. In addition, Tongil is adapted well to higher nitrogen application. Therefore, it would be recommended to transplant Tongil variety earlier in season under the condition of higer nitrogen, phosphate and silicate. A dense planting-space with three vigorous seedlings per hill should be practiced in this case. In order to manifest fully the capability of Tongil, several aspects such as the varietal improvement, culural practices and milling process should be more intensively considered in the future.he future.