• 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.

• Korean Journal of Soil Science and Fertilizer
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• v.11 no.2
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• pp.97-104
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• 1979

According to Soil Taxonomy which has been developed over the past 20 years in the soil conservation service of the U. S. D. A, Soils in Korea are classified. This system is well suited for the classification of the most of soils. But paddy field soils have some difficulties in classification because Soil Taxonomy states no proposals have yet been developed for classifying artificially irrigated soils. This paper discusses some problems in the application of Taxonomy and suggestes the classification of paddy field soils in Korea. Following is the summary of the paper. 1. Anthro aquic, Aquic Udipsamments : The top soils of these soils are saturated with irrigated water at some time of year and have mottles of low chroma(2 or less) more than 50cm of the soil surface. (Ex. Sadu, Geumcheon series) 2. Anthroaquic Udipsamments : These sails are like Anthroaquic, Aquic Udipsamments except for the mottles of low chroma within 50cm of the soil surface. (Ex. Baegsu series) 3. Halic Psammaquents : These soils contain enough salts as distributed in the profile that they interfere with the growth of most crop plants and located on the coastal dunes. The water table fluctuates with the tides. (Ex. Nagcheon series) 4. Anthroaquic, Aquic Udifluvents : They have some mottles that have chroma of 2 or less in more than 50cm of the surface. The upper horizon is saturated with irrigated water at sometime. (Ex. Maryeong series) 5. Anthro aquic Udifluvents : These soils are saturated with irrigated water at some time of year and have mottles of low chroma(2 or less) within 50cm of the surface soils. (Ex. Haenggog series) 6. Fluventic Haplaquepts : These soils have a content of organic carbon that decreases irregularly with depth and do not have an argillic horizon in any part of the pedon. Since ground water occur on the surface or near the surface, they are dominantly gray soils in a thick mineral regolith. (Ex Baeggu, Hagseong series) 7. Fluventic Thapto-Histic Haplaquepts : These soils have a buried organic matter layer and the upper boundary is within 1m of the surface. Other properties are same as Fluventic Haplaquepts. (Ex. Gongdeog, Seotan series) 8. Fluventic Aeric Haplaquepts : These soils have a horizon that has chroma too high for Fluventic Haplaquepts. The higher chroma is thought to indicate either a shorter period of saturation of the whole soils with water or some what deeper ground water than in the Fluventic Haplaquepts. The correlation of color with soil drainage classes is imperfect. (Ex. Mangyeong, Jeonbug series) 9. Fluventic Thapto-Histic Aeric Haplaquepts : These soils are similar to Fluventic Thapto Histic Haplaquepts except for the deeper ground water. (Ex. Bongnam series) 10. Fluventic Aeric Sulfic Haplaquepts : These soils are similar to Fluventic Aeric Haplaquepts except for the yellow mottles and low pH (<4.0) in some part between 50 and 150cm of the surface. (Ex. Deunggu series) 11. Fluventic Sulfaquepts : These soils are extremely acid and toxic to most plant. Their horizons are mostly dark gray and have yellow mottles of iron sulfate with in 50cm of the soil surface. They occur mainly in coastal marshes near the mouth of rivers. (Ex. Bongrim, Haecheog series) 12. Fluventic Aeric Sulfaquepts : They have a horizon that has chroma too high for Fluventic Sulfaquepts. Other properties are same as Fluventic Sulfaquepts. (Ex. Gimhae series) 13. Anthroaquic Fluvaquentic Eutrochrepts : These soils have mottles of low chroma in more than 50cm of the surface due to irrigated water. The base saturation is 60 percent or more in some subhroizon that is between depth of 25 and 75cm below the surface. (Ex. Jangyu, Chilgog series) 14. Anthroaquic Dystric Fluventic Eutrochrepts : These soils are similar to Anthroaquic Fluvaquentic Eutrochrepts except for the low chroma within 50cm of the surface. (Ex. Weolgog, Gyeongsan series) 15. Anthroaquic Fluventic Dystrochrepts : These soils have mottles that have chroma of 2 or less within 50cm of the soil surface due to artificial irrigation. They have lower base saturation (<60 percert) in all subhorizons between depths of 25 and 75cm below the soil surface. (Ex. Gocheon, Bigog series) 16. Anthro aquic Eutrandepts : These soils are similar to Anthroaquic Dystric Fluventic Eutrochrepts except for lower bulk density in the horizon. (Ex. Daejeong series) 17. Anthroaquic Hapludalfs : These soils' have a surface that is saturated with irrigated water at some time and have chroma of 2 or less in the matrix and higher chroma of mottles within 50cm of the surface. (Ex. Hwadong, Yongsu series) 18. Anthro aquic, Aquic Hapludalfs : These soils are similar to Anthro aquic Hapludalfs except for the matrix that has chroma 2 or less and higher chroma of mottles in more than 50cm of the surface. (Ex. Geugrag, Deogpyeong se ries)

• 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.