• Korean Journal of Soil Science and Fertilizer
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• v.24 no.4
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• pp.278-285
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• 1991

This study was conducted to manufacture a good qualified organic fertilizer with cow dung through rapid composting process, and the proposal guideline of the degree of maturity could be estimated with the measurement of final product. It included total nitrogen content of above 2% on an oven-dry material basis, C/N ratio of below 20, CEC of more than about 60 me/100g, ratio of carbon in reducing sugar to the total carbon of below 35%, and temperature in pile of above $60^{\circ}C$. The total nitrogen content, the C/N ratio, and the Paper Chromatographic method couldn't be the guideline to evalute the maturity of cow dung compost. CEC was increased in increased fermentation and it was high in the high fermented temperature plots which were cow dung+ shredded bark in 1988, cow+dung+wood chips in 1989, and cow dung+rice straws in 1990. The ratio of carbon in reducing sugar to total carbon in 1990 was lower in cow dung+saw dust than cow dung+rice straws that was the highest temperature in pile. Generally cow dung was mixed well with saw dust and thus the total carbon of the product was high. The measurement of the temperature in pile seems to be a indirect guideline of maturity.

• Korean Journal of Organic Agriculture
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• v.20 no.3
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• pp.297-311
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• 2012

Since 1997, Korean Ministry of Knowledge Economy and Ministry of Environment have established data on some 400 basic raw and subsidiary materials and process like energy, petro-chemical, steel, cement, glass, paper, construction materials, transportation, recycling and disposal etc by initiating establishment of LCI database. Regarding agriculture, Rural Development Administration has conducted establishment of LCI database for major farm products like rice, barley, beans, cabbage and radish etc from 2009, and released that they would establish LCI database for 50 items until 2020 later on. The domestic LCI database for seeds, seedling, agrochemical, inorganic fertilizer and organic fertilizer etc is only at initial stage of establishment, so overseas LCI databases are brought and being used. However, since the domestic and overseas natural environments differ, they fall behind in reliability. Therefore, this study has the purpose to select organic farming materials, survey the production process for various types of organic farming materials and establish LCI database for the effects of greenhouse gas emitted during the process in order to select carbon basic units for agricultural production system compliant in domestic situation instead of relying on overseas data and apply life cycle assessment of greenhouse gas emitted by each crop during the process. As for selecting methods, in this study organic farming materials were selected in the method of direct observation of material and bottom-up method a survey method with focus on the organic farming materials admitted into rice production. For the basic unit of carbon emission amount by the production of 1kg of organic farming material, the software PASS 4.1.1 developed by Korea Accreditation Board under Ministry of Knowledge Economy was used. The study had the goal to ultimately provide basic unit to calculate carbon emission amount in executing many institutions like goal management system and carbon performance display system etc in agricultural sector to be conducted later on. As a result, emission basic units per 1kg of production were calculated to be 0.0088kg-$CO_2$ for charcoal, 0.1319kg-$CO_2$ for grass liquid, and 0.2804kg-$CO_2$ for microbial agent.

• Journal of the Korean Society of Food Culture
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• v.5 no.3
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• pp.349-359
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• 1990

This study was carried out to understand and analyze the cooking and processing methods presented in CHE MIN YO SUL, Chinese books of husbandary was written in sixth century. This book was composed of two parts-part I is Agricultural production and part II is product-Utilization. Especially, wines and yeast(NU RUK) written in part II were studied at this study paper. Most of yeast was made of barley and wheat. These materials had been prepared as raw, steamed, and roasted state by proper ratio with kinds of yeast and then fermented as dough state. Occasionally, various kinds of soup made from cocklebur, leaves of mulberry tree, wormwood etc. put into yeast dough. Yeast doughs were shaped round and square with or without hole in the center, made in July of the lunar calendar and fermented for 3 or 4 weeks. There were 43 kinds of wines in this book. Most of them were made of all kinds of cereals grown at that time-rice, waxy rice, millet, waxy millet etc. These cereals had been steaming or cooking gruel with grain or powder state and then fermented with yeast. These wines were prepared by single or double brewing methods and the kinds of double brewing wines were more than single brewing wines by two times. There were none of wines made from fruit and distilled wines. Generally, single brewing wines were not made in Apr., Nov., Dec., of the lunar calendar and double brewing wines were not made in Aug., Oct., Nov., of the lunar calendar. And ripenning periods of wine brewing were various, from 1 day to 7 months for single brewing, from 2 days to 8 months for double brewing.

• The Korean Journal of Food And Nutrition
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• v.15 no.3
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• pp.228-234
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• 2002

In this paper, we proposed a preparation of Jeung- pyun (Korean fermented steamed rice cake with sour taste and spongy texture) using fuzzy theory. Before this preparation was introduced, it thoroughly analyzed the existing data of Jeung-pyun preparation with sensory evaluation and instrumental measurement. It defined a membership auction of Fuzzy set by analyzed three sorts of data on Jeung-pyun. And it established the Fuzzy model using the quantity of materials as input, such as rice, flour, wheat flour and fermentation time, and the sensory test scores as output, such as grain, softness, sourness, chewiness, overall quality, pH value and volume, respectively. We got the results that the Fuzzy model was accord with the conventional method with sensory evaluation. And the validity of this method is shown through the computer simulation of the test data. Therefore, the proposed method by Fuzzy model will apply to make Jeung-pyun without sensory evaluation. This study will contribute to develop standard preparation for korean foods and expert system of preparation using computer system.

• Food Science and Preservation
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• v.14 no.3
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• pp.263-268
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• 2007

This paper was study to develop an extract of Elaeagnus multiflora as a materials of beverage, and was part of a wider project to use Elaeagnus multiflora for the development of processing foods. This was sought to optimize various sensory characteristics of color and flavor. The highest color score was 5.15 points. This was attained with 5.2%(v/v) Elaeagnus multiflora extract and 3.2%(v/v) brown rice vinegar. The highest flavor score was 4.06 points, and was arrived which added 10.8%(v/v) Elaeagnus multiflora extract and 0.4%(v/v) brown rice vinegar.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.23 no.4
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• pp.69-84
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• 2020

Up to date, the majority research on the major landscape elements in organic farming has been mainly focused on the practice of seeking efficiency. The problem is that this type of study contributes to polluting the agricultural environment and damaging the ecological circulation system. As an alternative, there is a growing body of research on organic farming, but it is not widely applied that research on how to manage the landscape considering the scenic characteristics of farming villages practicing organic farming. Hence, in this paper we utilized landscape adjectives as a way to enhance the objectivity of the organic agricultural complex landscape assessment. More specifically, not only this study used a landscape image of an organic agricultural complex to identify a landscape adjective suitable for the landscape elements but also this study confirmed the suitability of landscape adjectives comparing to the opinions of experts and the public. To carry out, this study performed the experts survey which is composed of 12 major landscape elements, including rice paddies and fields, monoculture and diverse crops, dirt roads, windbreak trees, accent planting, dum-bung(small pond), natural small river, natural waterways, plastic film houses, one-storied houses, and pavilion. As a result of deriving the landscape adjectives from the main landscape elements, there were nine landscape adjectives that were consistent with experts and the public, including "clear" and "Artless" for rice paddies and fields, while the mismatched landscape adjectives were 'traditional'. The accent planting was a combination of landscape adjectives such as 'natural' and 'clear', while the windbreak trees was a consensus of all landscape adjectives. Only two adjectives, 'friendly' and 'wild', agreed on the dirt load, nine dum-bung(small pond), ten natural small river, nine duckery, eight one-storied houses, 10 pavilion, eight monoculture and diverse crops, and three natural waterways. The most common landscape adjectives were windbreak trees, pavilions, and natural small river, all 10 landscape adjectives. However, it is considered that only three of the 10 landscape types on the dirt road and the natural number are matched. Thus, additional management measures will be needed. In addition, it was analyzed that the most common landscape adjectives were "Artless" and "friendly" 13 times. The landscape adjectives of the organic farming complex responded by experts were analyzed to be suitable for natural, clear, zingy, silent, traditional, artless, friendly, wild and Leisurely, and consistent with the general public's opinion.

• Journal of Plant Biotechnology
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• v.45 no.4
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• pp.289-298
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• 2018

The Biosafety Division of the National Academy of Agricultural Science has developed a 'Crop Composition DB' that provides analytical data on commercialized crops. It can be used as a reference in the 'Comparative Evaluation by Compositional Analysis' for the safety assessment of genetically modified (GM) crops. This database provides the composition of crops cultivated in Korea, and thus upgrades the data to check the extent of changes in the compositional content depending on the cultivated area, varieties and year. The database is a compilation of data on the antioxidant, nutrient and secondary metabolite compositions of rice and capsicum grown in two or more cultivation areas for a period of more than two years. Data analysis was conducted under the guidelines of the Association of Official Analytical Chemists or methods previously reported on papers. The data was provided as average, minimum and maximum values to assess whether the statistical differences between the GM crops and comparative non-GM crops fall within the biological differences or tolerances of the existing commercial crops. The Crop Composition DB is an open-access source and is easy to access based on the query selected by the user. Moreover, functional ingredients of colored crops, such as potatoes, sweet potatoes and cauliflowers, were provided so that food information can be used and utilized by general consumers. This paper introduces the feature and usage of 'Crop Composition DB', which is a valuable tool for characterizing the composition of conventional crops.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1357-1369
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• 2022

Cloud removal is an essential image processing step for any task requiring time-series optical images, such as vegetation monitoring and change detection. This paper presents a two-stage cloud removal method that combines conditional generative adversarial networks (cGANs) with regression-based calibration to construct a cloud-free time-series optical image set. In the first stage, the cGANs generate initial prediction results using quantitative relationships between optical and synthetic aperture radar images. In the second stage, the relationships between the predicted results and the actual values in non-cloud areas are first quantified via random forest-based regression modeling and then used to calibrate the cGAN-based prediction results. The potential of the proposed method was evaluated from a cloud removal experiment using Sentinel-2 and COSMO-SkyMed images in the rice field cultivation area of Gimje. The cGAN model could effectively predict the reflectance values in the cloud-contaminated rice fields where severe changes in physical surface conditions happened. Moreover, the regression-based calibration in the second stage could improve the prediction accuracy, compared with a regression-based cloud removal method using a supplementary image that is temporally distant from the target image. These experimental results indicate that the proposed method can be effectively applied to restore cloud-contaminated areas when cloud-free optical images are unavailable for environmental monitoring.

• The Journal of Society for e-Business Studies
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• v.27 no.2
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• pp.37-64
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• 2022

As the social acceptance of artificial intelligence increases, the number of cases of applying machine learning methods to companies is also increasing. Technical factors such as accuracy and interpretability have been the main criteria for selecting machine learning methods. However, the success of implementing machine learning also affects management factors such as IT departments, operation departments, leadership, and organizational culture. Unfortunately, there are few integrated studies that understand the success factors of machine learning selection in which technical and management factors are considered together. Therefore, the purpose of this paper is to propose and empirically analyze a technology-management integrated model that combines task-tech fit, IS Success Model theory, and John Rice's algorithm selection process model to understand machine learning selection within the company. As a result of a survey of 240 companies that implemented machine learning, it was found that the higher the algorithm quality and data quality, the higher the algorithm-problem fit was perceived. It was also verified that algorithm-problem fit had a significant impact on the organization's innovation and productivity. In addition, it was confirmed that outsourcing and management support had a positive impact on the quality of the machine learning system and organizational cultural factors such as data-driven management and motivation. Data-driven management and motivation were highly perceived in companies' performance.

• Journal of Life Science
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• v.17 no.12
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• pp.1660-1668
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• 2007

The non-ionic surfactant (NIS) Tween 80 was evaluated for its ability to influence invitro cumulative gas production, dry matter digestibility, cellulolytic enzyme activities, anaerobic microbial growth rates, and adhesion to substrates by mixed rumen microorganisms on rice straw, alfalfa hay, cellulose filter paper and tall fescue hay. The addition of NIS Tween 80 at a level of 0.05% increased significantly (P<0.05) in vitro DM digestibility, cumulative gas production, microbial growth rate and cellulolytic enzyme activity from all of substrates used in this study. In vitro cumulative gas production from the NIS-treated substrates; rice straw, alfalfa hay, filter paper and tall fescue hay was significantly (P<0.05) improved by 274.8, 235.2, 231.1 and 719.5% compared with the control, when substrates were incubated for 48 hr in vitro. The addition of 0.05% NIS Tween 80 to cultures growing on alfalfa hay resulted in a significant increase in CMCase (38.1%), xylanase (121.4%), Avicelase (not changed) and amylase (38.2%) activities after 36 h incubation. These results indicated that the addition of 0.05% Tween 80 could greatly stimulate the release of some kinds of cellulolytic enzymes without decreasing cell growth rate in contrast to trends reported with aerobic microorganism. Our SEM observation showed that NIS Tween. 80 did not influence the microbial adhesion to substrates used in the study. Present data clearly show that improved gas production, DM digestibility and cellulolytic enzyme activity by Tween 80 is not due to increased bacterial adhesion on the substrates.