• Title/Summary/Keyword: Quality model

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DISEASE DIAGNOSED AND DESCRIBED BY NIRS

  • Tsenkova, Roumiana N.
    • Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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    • 2001.06a
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    • pp.1031-1031
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    • 2001
  • The mammary gland is made up of remarkably sensitive tissue, which has the capability of producing a large volume of secretion, milk, under normal or healthy conditions. When bacteria enter the gland and establish an infection (mastitis), inflammation is initiated accompanied by an influx of white cells from the blood stream, by altered secretory function, and changes in the volume and composition of secretion. Cell numbers in milk are closely associated with inflammation and udder health. These somatic cell counts (SCC) are accepted as the international standard measurement of milk quality in dairy and for mastitis diagnosis. NIR Spectra of unhomogenized composite milk samples from 14 cows (healthy and mastitic), 7days after parturition and during the next 30 days of lactation were measured. Different multivariate analysis techniques were used to diagnose the disease at very early stage and determine how the spectral properties of milk vary with its composition and animal health. PLS model for prediction of somatic cell count (SCC) based on NIR milk spectra was made. The best accuracy of determination for the 1100-2500nm range was found using smoothed absorbance data and 10 PLS factors. The standard error of prediction for independent validation set of samples was 0.382, correlation coefficient 0.854 and the variation coefficient 7.63%. It has been found that SCC determination by NIR milk spectra was indirect and based on the related changes in milk composition. From the spectral changes, we learned that when mastitis occurred, the most significant factors that simultaneously influenced milk spectra were alteration of milk proteins and changes in ionic concentration of milk. It was consistent with the results we obtained further when applied 2DCOS. Two-dimensional correlation analysis of NIR milk spectra was done to assess the changes in milk composition, which occur when somatic cell count (SCC) levels vary. The synchronous correlation map revealed that when SCC increases, protein levels increase while water and lactose levels decrease. Results from the analysis of the asynchronous plot indicated that changes in water and fat absorptions occur before other milk components. In addition, the technique was used to assess the changes in milk during a period when SCC levels do not vary appreciably. Results indicated that milk components are in equilibrium and no appreciable change in a given component was seen with respect to another. This was found in both healthy and mastitic animals. However, milk components were found to vary with SCC content regardless of the range considered. This important finding demonstrates that 2-D correlation analysis may be used to track even subtle changes in milk composition in individual cows. To find out the right threshold for SCC when used for mastitis diagnosis at cow level, classification of milk samples was performed using soft independent modeling of class analogy (SIMCA) and different spectral data pretreatment. Two levels of SCC - 200 000 cells/$m\ell$ and 300 000 cells/$m\ell$, respectively, were set up and compared as thresholds to discriminate between healthy and mastitic cows. The best detection accuracy was found with 200 000 cells/$m\ell$ as threshold for mastitis and smoothed absorbance data: - 98% of the milk samples in the calibration set and 87% of the samples in the independent test set were correctly classified. When the spectral information was studied it was found that the successful mastitis diagnosis was based on reviling the spectral changes related to the corresponding changes in milk composition. NIRS combined with different ways of spectral data ruining can provide faster and nondestructive alternative to current methods for mastitis diagnosis and a new inside into disease understanding at molecular level.

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Effects of Water Temperature and Photoperiod on the Oxygen Consumption Rate of Fasted Juvenile Parrot Fish, Oplegnathus fasciatus (돌돔, Oplegnathus fasciatus 치어의 절식시 산소 소비율에 미치는 수온과 광주기의 영향)

  • Oh, Sung-Yong;Noh, Choong-Hwan;Kang, Rae-Seon;Myoung, Jung-Goo
    • Ocean and Polar Research
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    • v.28 no.4
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    • pp.407-413
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    • 2006
  • The effect of water temperature and photoperiod on the oxygen consumption of the fasted juvenile parrot fish, Oplegnathus fasciatus was investigated to provide empirical data for the early-stage culture management and bioenergetic growth model of the species. The mean body weight of the juvenile used for the experiment was $21.5{\pm}1.9g$, and the oxygen consumption rate was measured under four water temperatures (10, 15, 20 and $25^{\circ}C$) and three photoperiods (24L:0D, 12L:12D and OL:24D) with an interval of 5 minutes for 24 hours using a continuous flow-through respirometer. In each treatment three replicates were set up and 15 juveniles were totally involved. The oxygen consumption rates increased with increasing water temperature under all photoperiod treatments (P<0.001). Mean oxygen consumption rates at 10, 15, 20 and $25^{\circ}C$ ranged $202.1{\sim}403.4,\;306.7{\sim}502.2,\;536.7{\sim}791.0\;and\;879.9{\sim}1,077.4mg\;O_2\;kg^{-1}h^{-1}$, respectively. $Q_{10}$ values ranged $1.58{\sim}2.30$ between 10 and $15^{\circ}C,\;2.44{\sim}3.06$ between 15 and $20^{\circ}C\;and\;1.86{\sim}2.6y9$ between 20 and $25^{\circ}C$, respectively. Mean oxygen consumption rates of O. fasciatus were the highest in continuous light (24L:0D) followed by 12L:12D and 0L:24D (P<0.001). The oxygen consumption of fish exposed to the 12L:12D photoperiod was significantly higher during the light phase than during the dark phase under all temperature treatments (P<0.001). In summary, oxygen consumption rates of the juvenile parrot fish increase with increasing water temperature and lengthening daylight period; and, thereby, changes in water quality resulted from the depletion of oxygen under high temperature and long daylight photoperiod conditions should be monitored.

Global Temperature Trends of Middle and Upper Tropospheres Derived from Satellite Data and Model Reanalyses (위성자료와 모델 재분석에서 유도된 중간 및 상부 대류권의 전지구 온도 경향)

  • Yoo, Jung-Moon;Lee, Ji-Eun
    • Journal of the Korean earth science society
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    • v.21 no.5
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    • pp.525-540
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    • 2000
  • Global temperature trends of middle and upper tropospheres have been investigated using the data of satellite-observed Microwave Sounding Unit (MSU) channels 2-3(Ch2, Ch3) during the period of 1980-97 and three GCM (NCEP, ECMWF, GEOS) reanalyses during 1981-93. The global, hemispheric and tropical anomalies, computed from the data during the common period, have been intercompared in the following regions; ocean, land, and both ocean and land. The correlation with MSU in midtropospheric temperatures is the best (r=0.81${\sim}$0.95) in ECMWF, particularly over the tropics. The correlations in upper troposphere are lower (r=0.06${\sim}$0.34) due to poor quality of MSU Ch3 data consistent with previous result. The midtropospheric trends during 1981-93, obtained from MSU and three GCMs, show the global warming of 0.01${\sim}$0.18K decade$^{-1}$. The warmest years have been 1987 and 1991 in El Ni${\tilde{n}$o while the coolest 1993 and 1994 in La Ni${\tilde{n}$a. The warming (0.12${\sim}$0.13K decade$^{-1}$) in MSU over global ocean is similar to that over global land. The largest discrepancy in upper troposphere between MSU and GCMs has been found in the transition period (1984. 12-1985. 1) from NOAA 9 to 10, because of a sizable error in the MSU Ch3. The midtropospheric trends near the Korean peninsula during 1981-93 are almost negligible(-0.02K decade$^{-1}$) in MSU, but indicate significant warming (0.25-0.43K decade$^{-1}$) in GCMs. The trends are crosschecked and discussed with other two independent MSU data of Spencer and Christy (1992a, 1992b).

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The effect of semantic categorization of episodic memory on encoding of subordinate details: An fMRI study (일화 기억의 의미적 범주화가 세부 기억의 부호화에 미치는 영향에 대한 자기공명영상 분석 연구)

  • Yi, Darren Sehjung;Han, Sanghoon
    • Korean Journal of Cognitive Science
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    • v.28 no.4
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    • pp.193-221
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    • 2017
  • Grouping episodes into semantically related categories is necessary for better mnemonic structure. However, the effect of grouping on memory of subordinate details was not clearly understood. In an fMRI study, we tested whether attending superordinate during semantic association disrupts or enhances subordinate episodic details. In each cycle of the experiment, five cue words were presented sequentially with two related detail words placed underneath for each cue. Participants were asked whether they could imagine a category that includes the previously shown cue words in each cycle, and their confidence on retrieval was rated. Participants were asked to perform cued recall tests on presented detail words after the session. Behavioral data showed that reaction times for categorization tasks decreased and confidence levels increased in the third trial of each cycle, thus this trial was considered to be an important insight where a semantic category was believed to be successfully established. Critically, the accuracy of recalling detail words presented immediately prior to third trials was lower than those of followed trials, indicating that subordinate details were disrupted during categorization. General linear model analysis of the trial immediately prior to the completion of categorization, specifically the second trial, revealed significant activation in the temporal gyrus and inferior frontal gyrus, areas of semantic memory networks. Representative Similarity Analysis revealed that the activation patterns of the third trials were more consistent than those of the second trials in the temporal gyrus, inferior frontal gyrus, and hippocampus. Our research demonstrates that semantic grouping can cause memories of subordinate details to fade, suggesting that semantic retrieval during categorization affects the quality of related episodic memory.

An Adjustment of Cloud Factors for Continuity and Consistency of Insolation Estimations between GOES-9 and MTSAT-1R (GOES-9과 MTSAT-1R 위성 간의 일사량 산출의 연속성과 일관성 확보를 위한 구름 감쇠 계수의 조정)

  • Kim, In-Hwan;Han, Kyung-Soo;Yeom, Jong-Min
    • Korean Journal of Remote Sensing
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    • v.28 no.1
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    • pp.69-77
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    • 2012
  • Surface insolation is one of the major indicators for climate research over the Earth system. For the climate research, long-term data and wide range of spatial coverage from the data observed by two or more of satellites of the same orbit are needed. It is important to improve the continuity and consistency of the derived products, such as surface insolation, from different satellites. In this study, surface insolations based on Geostationary Operational Environmental Satellite (GOES-9) and Multi-functional Transport Satellites (MTSAT-1R) were compared during overlap period using physical model of insolation to find ways to improve the consistency and continuity between two satellites through comparison of each channel data and ground observation data. The thermal infrared brightness temperature of two satellites show a relatively good agreement between two satellites : rootmean square error (RMSE)=5.595 Kelvin; Bias=2.065 Kelvin. Whereas, visible channels shown a quite different values, but it distributed similar tendency. And the surface insolations from two satellites are different from the ground observation data. To improve the quality of retrieved insolations, we have reproduced surface insolation of each satellite through adjustment of the Cloud Factor, and the Cloud Factor for GOES-9 satellite is modified based on the analysis result of difference channel data. As a result, the insolations estimated from GOES-9 for cloudy conditions show good agreement with MTSAT-1R and ground observation : RMSE=$83.439W\;m^{-2}$ Bias=$27.296W\;m^{-2}$. The result improved accuracy confirms that the modification of Cloud Factor for GOES-9 can improve the continuity and consistency of the insolations derived from two or more satellites.

A study on the CRM strategy for medium and small industry of distribution (중소유통업체의 CRM 도입방안에 관한 연구)

  • Kim, Gi-Pyoung
    • Journal of Distribution Science
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    • v.8 no.3
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    • pp.37-47
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    • 2010
  • CRM refers to the operating activities that always maintain and promote good relationship with customers to ultimately maximize the company's profits by understanding the value of customers to meet their demands, establishing a strategy which may maximize the Life Time Value and successfully operating the business by integrating the customer management processes. In our country, many big businesses are introducing CRM initiatively to use it in marketing strategy however, most medium and small sized companies do not understand CRM clearly or they feel difficult to introduce it due to huge investment needed. This study is intended to present CRM promotion strategy and activities plan fit for the medium and small sized companies by analyzing the success factors of the leading companies those have already executed CRM by surveying the precedents to make the distributors out of the industries have close relation with consumers to overcome their weakness in scale and strengthen their competitiveness in such a rapidly changing and fiercely competing market. There are 5 stages to build CRM such as the recognition of the needs of CRM establishment, the establishment of CRM integrated database, the establishment of customer analysis and marketing strategy through data mining, the practical use of customer analysis through data mining and the implementation of response analysis and close loop process. Through the case study of leading companies, CRM is needed in types of businesses where the companies constantly contact their customers. To meet their needs, they assertively analyze their customer information. Through this, they develop their own CRM programs personalized for their customers to provide high quality service products. For customers helping them make profits, the VIP marketing strategy is conducted to keep the customers from breaking their relationships with the companies. Through continuous management, CRM should be executed. In other words, through customer segmentation, the profitability for the customers should be maximized. The maximization of the profitability for the customers is the key to CRM. These are the success factors of the CRM of the distributors in Korea. Firstly, the top management's will power for CS management is needed. Secondly, the culture across the company should be made to respect the customers. Thirdly, specialized customer management and CRM workers should be trained. Fourthly, CRM behaviors should be developed for the whole staff members. Fifthly, CRM should be carried out through systematic cooperation between related departments. To make use of the case study for CRM, the company should understand the customer and establish customer management programs to set the optimal CRM strategy and continuously pursue it according to a long-term plan. For this, according to collected information and customer data, customers should be segmented and the responsive customer system should be designed according to the differentiated strategy according to the class of the customers. In terms of the future CRM, integrated CRM is essential where the customer information gathers together in one place. As the degree of customers' expectation increases a lot, the effective way to meet the customers' expectation should be pursued. As the IT technology improved rapidly, RFID (Radio Frequency Identification) appears. On a real-time basis, information about products and customers is obtained massively in a very short time. A strategy for successful CRM promotion should be improving the organizations in charge of contacting customers, re-planning the customer management processes and establishing the integrated system with the marketing strategy to keep good relation with the customers according to a long-term plan and a proper method suitable to the market conditions and run a company-wide program. In addition, a CRM program should be continuously improved and complemented to meet the company's characteristics. Especially, a strategy for successful CRM for the medium and small sized distributors should be as follows. First, they should change their existing recognition in CRM and keep in-depth care for the customers. Second, they should benchmark the techniques of CRM from the leading companies and find out success points to use. Third, they should seek some methods best suited for their particular conditions by achieving the ideas combining their own strong points with marketing. Fourth, a CRM model should be developed that will promote relationship with individual customers just like the precedents of small sized businesses in Switzerland through small but noticeable events.

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A Study on the Effects of Meterological Factors on the Distribution of Agricultural Products: Focused on the Distribution of Chinese Cabbages (기상요인이 농산물 유통에 미치는 영향에 관한 연구: 배추 유통 사례를 중심으로)

  • Lee, Hyunjoung;Hong, Jinhwan
    • Journal of Distribution Research
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    • v.17 no.5
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    • pp.59-83
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    • 2012
  • Agriculture is a primary industry that influenced by the weather or meterological factors more than other industry. Global warming and worldwide climate changes, and unusual weather phenomena are fatal in agricultural industry and human life. Therefore, many previous studies have been made to find the relationship between weather and the productivity of agriculture. Meterological factors also influence on the distribution of agricultural product. For example, price of agricultural product is determined in the market, and also influenced by the weather of the market. However, there is only a few study was made to find this link. The objective of this study is to investigate the effects of meterological factors on the distribution of agricultural products, focusing on the distribution of chinese cabbages. Chinese cabbage is a main ingredient of Kimchi, and basic essential vegetable in Korean dinner table. However, the production of chinese cabbages is influenced by weather and very fluctuating so that the variation of its price is so unstable. Therefore, both consumers and farmers do not feel comfortable at the unstable price of chinese cabbages. In this study, we analyze the real transaction data of chinese cabbage in wholesale markets and meterological factors depending on the variety and geography. We collect and analyze data of meterological factors such as temperatures, humidity, cloudiness, rainfall, snowfall, wind speed, insolation, sunshine duration in producing and consuming region of chinese cabbages. The result of this study shows that the meterological factors such as temperature and humidity significantly influence on the volume and price of chinese cabbage transaction in wholesale market. Especially, the weather of consuming region has greater correlation effects on transaction than that of producing region in all types of chinese cabbages. Among the whole agricultural lifecycle of chinese cabbages, 'seeding - harvest - shipment - wholesale', meterological factors such as temperature and rainfall in shipment and wholesale period are significantly correlated with transaction volume and price of crops. Based on the result of correlation analysis, we make a regression analysis to verify the meterological factors' effects on the volume and price of chines cabbage transaction in wholesale market. The results of stepwise regression analysis are shown in

    and
    . The type of chinese cabbages are categorized by 5 types, i.e. alpine, gimjang for winter, spring, summer, and winter crop, and all of the regression models are shown significant relationship. In addition, meterological factors in shipment and wholesale period are entered more in regression model than those in seeding and harvest period. This result implies that weather in consuming region is also important in the distribution of chinese cabbages. Based on the result of this study, we find several implications and recommendations for policy makers of agricultural product distribution. The goal of agricultural product distribution policy is to insure proper price and production cost for farmers and provide proper price and quality, and stable supply for consumers. Therefore, coping with the uncertainty of weather is very essential to make a fruitful effect of the policy. In reality, very big part of consumer price of chinese cabbage is made up of the margin of intermediaries, because they take the risk. In addition, policy makers make efforts for farmers to utilize AWIS (Agricultural Weather Information System). In order to do that, it should integrate the relevant information including distribution and marketing as well as production. Offering a consulting service to farmers about weather management is also expected to be a good option in agriculture and weather industry. Reflecting on the result of this study, the distribution authorities can offer the guideline for the timing and volume of harvest, and it is expected to contribute to the stable equilibrium of supply and demand of agricultural products.

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  • The Effect of Meta-Features of Multiclass Datasets on the Performance of Classification Algorithms (다중 클래스 데이터셋의 메타특징이 판별 알고리즘의 성능에 미치는 영향 연구)

    • Kim, Jeonghun;Kim, Min Yong;Kwon, Ohbyung
      • Journal of Intelligence and Information Systems
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      • v.26 no.1
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      • pp.23-45
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      • 2020
    • Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

    Effects and Improvement of Carbon Reduction by Greenspace Establishment in Riparian Zones (수변구역 조성녹지의 탄소저감 효과 및 증진방안)

    • Jo, Hyun-Kil;Park, Hye-Mi
      • Journal of the Korean Institute of Landscape Architecture
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      • v.43 no.6
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      • pp.16-24
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      • 2015
    • This study quantified storage and annual uptake of carbon for riparian greenspaces established in watersheds of four major rivers in South Korea and explored desirable strategies to improve carbon reduction effects of riparian greenspaces. Greenspace structure and planting technique in the 40 study sites sampled were represented by single-layered planting of small trees in low density, with stem diameter at breast height of $6.9{\pm}0.2cm$ and planting density of $10.4{\pm}0.8trees/100m^2$ on average. Storage and annual uptake of carbon per unit area by planted trees averaged $8.2{\pm}0.5t/ha$ and $1.7{\pm}0.1t/ha/yr$, respectively, increasing as planting density got higher. Mean organic matter and carbon storage in soils were $1.4{\pm}0.1%$ and $26.4{\pm}1.5t/ha$, respectively. Planted trees and soils per ha stored the amount of carbon emitted from gasoline consumption of about 61 kL, and the trees per ha annually offset carbon emissions from gasoline use of about 3 kL. These carbon reduction effects are associated with tree growth over five years to fewer than 10 years after planting, and predicted to become much greater as the planted trees grow. This study simulated changes in annual carbon uptake by tree growth over future 30 years for typical planting models selected as different from the planting technique in the study sites. The simulation revealed that cumulative annual carbon uptake for a multilayered and grouped ecological planting model with both larger tree size and higher planting density was approximately 1.9 times greater 10 years after planting and 1.5 times greater 30 years after than that in the study sites. Strategies to improve carbon reduction effects of riparian greenspaces suggest multilayered and grouped planting mixed with relatively large trees, middle/high density planting of native species mixed with fast-growing trees, and securing the soil environment favorable for normal growth of planting tree species. The research findings are expected to be useful as practical guidelines to improve the role of a carbon uptake source, in addition to water quality conservation and wildlife inhabitation, in implementing riparian greenspace projects under the beginning stage.

    Effects of Natural Wetland in Reducing Nutrient Loadings from Rice Culture - Free-Range Ducks (RCFD) Paddy fields in Korea (오리농업재배 소유역내 자연습지가 오리농업시 유출되는 영양염류 부하량 저감에 미치는 영향)

    • Ko, Jee-Yeon;Lee, Jae-Saeng;Jung, Ki-Youl;Choi, Young-Dae;Yun, Eul-Soo;Woo, Koan-Sik;Seo, Myung-Chul;Nam, Min-hee
      • Korean Journal of Soil Science and Fertilizer
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      • v.42 no.4
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      • pp.249-256
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      • 2009
    • The amount of nutrients from the effluents of rice culture - free-range ducks (RCFD) paddy fields and the effects of natural wetlands located at downstream of RCFD on water quality and aquatic plants was evaluated. This was carried out in a 61.9 ha paddy fields in Ulsan, Gyeongnam, where downstream is a 5.9 ha natural wetland, 61% of which was covered with well-developed aquatic plants. The amounts of T-N and T-P in the effluent from paddy field with RCFD were 13.7 and $2.5kg\;ha^{-1}$, respectively, which is 1.2~2.5 times higher than those observed in conventional rice culture practice. The amount of runoff from the RCFD area, calculated using the revised TANK model, was $543mm\;ha^{-1}$ with 808 kg of T-N and 130 kg of T-P during rice cultivation period. The dominant aquatic plants in the wetland includes Phragmites communis, Zizania latifolia, Persicaria thunbergii. etc. The nutrient contents of the aquatic plants which amounted to 761 kg of T-N and 103 kg of T-P were almost equivalent to 94% and 79% of the T-N and T-P in RCFD and CRC effluent. Therefore, the use and maintenance of wetlands in RCFDs area could be a good solution to management the non-point pollution from duck feces in RCFD paddy fields.


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