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심준용 ( Joon-yong Shim ),정재민 ( Jae-min Jung ),손찬수 ( Chan-soo Son ),조용빈 ( Yong-been Cho ) 한국농업기계학회 2020 한국농업기계학회 학술발표논문집 Vol.25 No.2
Cultivation area and consumption per person of watermelon are gradually decreasing, as well as attempts to switch to other crops are increasing due to high labor intensity of farm work. Hence, it is necessary to examine suitable cultivation area so that high quality crops can be more safely harvested. Therefore, the aim of this study was to identify a suitable site of watermelon cultivation by soil physical property. There are five physical properties of soil: subsoil properties, drainage class, slope, available soil depth, and gravel contents. Each of the optimal grade for cultivation is ‘well’ for drainage grade, ‘sandy loam’ or ‘silty sandy loam’ for subsoil properties, ‘50~100cm’ for available soil depth, ‘2~7%’ for slope, ‘10~35%’ for gravel content respectively. From the results, suitable sites of drainage class were Jeju, western coast including Chungnam while low productive sites were Gangwon, east coast including Gyeongbuk. In case of subsoil properties, Ganwon, Gyeonggi and central region had the optimal province to cultivate watermelon. The optimal region for slope were Jeju coast and west region, and a middle of vertical axis region and east coast for available soil depth were the best suitable cites for cultivation. suitable cites of gravel content appeared the west coast and evenly each province of all parts of the country. Finally, Jeju coast and west region were the best optimal cites to cultivate watermelon in terms of soil physical properties. Further study will be necessary to analyze the suitable sites according to climate condition.
김미옥(Mi Ok Kim),조용빈(Yong Been Cho) 한국농촌지도학회 2016 농촌지도와 개발 Vol.23 No.1
In this study, we examined the awareness of consumers purchasing Punica granatum by conducting a survey on con-sumption of Punica granatum for the consumer panel of the Rural Development Administration (RDA) and derived the purchasing characteristics from the actual purchase date analyzed in a Linear regression model and Tobit model. Mostconsumers had been purchasing Punica granatum for health and beauty, and the proportion of that consumers were willingto repurchase Punica granatum was 93.1%. The result of examining the biggest considerations in 5 point scale whenchoosing a Punica granatum was in the order of freshness (4.37)> price (4.15)> safety (4.13)> size(3.86)> brand (3.27)>discount event (2.76). When we compared the results between a linear regression model and tobit model, the signs of all variables are consistent with each other. However, it was estimated that all absolute values of the coefficient valuesin the results of the tobit model analysis were larger than the values in the linear regression model, except for the “favoritepurchasing place” of a weekday traditional markets. Punica granatum is known as a good fruit for postmenopausal womenand it seems that the higher age is, the more purchase there will be. The more income a housewife had, the greaterpurchase there was. In the case of the purchase amount, a selecting for a eating pleasure was bigger than a selecting for a need of health. Therefore, it is necessary to develop Punica granatum with a taste in consumer preferences.
Analysis of Purchasing Recognition and Purchasing Characteristics of a Plum Purchaser
김미옥(Mi-OK Kim),조성주(Sung-Ju Cho),조용빈(Yong-Been Cho) 한국유통과학회 2015 유통과학연구 Vol.13 No.2
Purpose – Given an increase in the consumption of plums, prices have fluctuated in an unstable manner, making it difficult for farmhouses to sell the product. This study intends to provide information on the cultivation and sale of plums to consumers, thus enabling producers to utilize relevant information to analyze the types of plums that are preferred and consumed by users. Research design, data, and methodology – In this study, a survey was conducted on plum consumption by a consumer panel established and operated by the Rural Development Administration in December 2009. The objective was to identify the purchasing awareness of plums and to analyze panel data from 2010 to 2013 using a linear regression model, a Tobit model, and a panel regression model to derive the purchase characteristics. Results – The outcome of the survey on plums is as follows. Plums are purchased because they are good for the health (90.6%), which means that most customers purchase plums for their health benefits. When plums are in season, the purchase rate is 94.8%, indicating that most plums are purchased when they are in season and that selling plums when they are out of season is difficult. Therefore, we sell most plums in the correct season, and the rest of the plums need to be processed and then sent to markets. The strongest reason for not purchasing plums is that they are difficult to process for consumption (63.1%), followed by the reason that the fruit is unfamiliar (15.5%). Regarding solutions for increasing the consumption of plums, the answers were as follows: distribute a recipe for plums (36.9%), advertise its effect through TV or the press (31.1%), and develop various processed products (15.6%). When customers decide to pick out plums, the major considerations were freshness (4.43), safe to eat (4.16), price (3.96), size (3.87), brand (3.28), and discount event (2.62). Freshness is important for decision making and safe to eat was more important than pr
다차원 데이터의 군집분석을 위한 차원축소 방법: 주성분분석 및 요인분석 비교
홍준호 ( Jun-ho Hong ),오민지 ( Min-ji Oh ),조용빈 ( Yong-been Cho ),이경희 ( Kyung-hee Lee ),조완섭 ( Wan-sup Cho ) (사)한국빅데이터학회 2020 한국빅데이터학회 학회지 Vol.5 No.2
본 논문은 농식품 소비자패널 데이터에서 소비자의 유형을 나눌 때에 변수간 연관성이 많은 장바구니 분석에서 전처리 방법과 차원축소의 방법을 제안한다. 군집분석은 다변량 자료에서 관측 개체를 몇 개의 군집으로 나눌 때 널리 사용되는 분석기법이다. 하지만 여러 개의 변수가 연관성을 가진 경우에는 차원축소를 통한 군집분석이 더 효과적일 수 있다. 본 논문은 1,987 가구를 대상으로 조사한 식품소비 데이터를 K-means 방법을 사용하여 군집화하였으며, 군집을 나누기 위해 17개의 변수를 선정하였고, 17개의 다중공선성 문제와 군집을 나누기 위한 차원축소의 방법 중 주성분 분석과 요인분석을 비교하였다. 본 연구에서는 주성분분석과 요인분석 모두 2개의 차원으로 축소하였으며 주성분분석에서는 3개의 군집으로 나뉘었지만 분석하고자 하였던 소비 패턴에 대한 군집의 특성이 잘 나타나지 않았으며 요인분석에서는 분석가가 보고자 하는 소비 패턴의 특징이 잘 나타났다. This paper proposes a pre-processing method and a dimensional reduction method in the analysis of shopping carts where there are many correlations between variables when dividing the types of consumers in the agri-food consumer panel data. Cluster analysis is a widely used method for dividing observational objects into several clusters in multivariate data. However, cluster analysis through dimensional reduction may be more effective when several variables are related. In this paper, the food consumption data surveyed of 1,987 households was clustered using the K-means method, and 17 variables were re-selected to divide it into the clusters. Principal component analysis and factor analysis were compared as the solution for multicollinearity problems and as the way to reduce dimensions for clustering. In this study, both principal component analysis and factor analysis reduced the dataset into two dimensions. Although the principal component analysis divided the dataset into three clusters, it did not seem that the difference among the characteristics of the cluster appeared well. However, the characteristics of the clusters in the consumption pattern were well distinguished under the factor analysis method.
소비자패널자료를 활용한 개별 소비자의 돈육 구입빈도, 구입부위 및 구입량 선택행위 분석
권오상 ( Oh Sang Kwon ),강혜정 ( Hyu Jung Kang ),서종석 ( Jong Seok Seo ),조용빈 ( Yong Been Cho ) 한국농업경제학회 2014 農業經濟硏究 Vol.55 No.3
This study estimates household`s demand for fresh pork cuts using a micro consumption data set in Korea. The choices for purchasing cut (=what), purchasing amount (=how much), and purchasing frequency (=how often) are modelled as interrelated discrete, continuous and count data choice behaviors, respectively. The short-run and long-run own and cross price elasticities are estimated, and the impacts of household characteristics are also analyzed. It is found that each cut has quite substantial cross as well as own price elasticity. The demand for belly which is the most popular cut in Korea is relatively inelastic in its own price change, but its impact on demand for other cuts is very high. Several cuts are normal while the others are inferior. Household size, age, family composition, housing type, purchasing season and time affect all the three interrelated choices. Our model is not based on a single coherent utility maximization process, but is more flexible than the popular marketing science research models based on the Hanemann`s dis-crete-continuous model.