• Journal of Distribution Science
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• v.15 no.11
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• pp.21-29
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• 2017

Purpose - Relationship between farm and county losses determines whether the county program provides too little, too much, or similar amount of assistance relative to the loss on an individual farm. A review of the literature finds limited analysis of the determinants of this relationship. This paper conducts such an analysis using farm-level yield data. Research design, data, and methodology - Farm-level yield data from Illinois and Kansas farm business management associations are used for to calculate the correlation between farm and county loss and the share of farm loss systemic with county loss, and also for the regression analysis. Results - Average share of farm loss systemic with the county loss lies between 42% and 68%. The correlation between farm and county yield/revenue deviation from expected value is statistically significant in all four models. The coefficient is positive, implying the higher the correlation, the larger the share of farm loss that is systemic with the county loss. Conclusions - The findings of this study are consistent with the existing literature which argues that county variability may not be closely associated with farm variability. The findings of this study thus raise questions about the efficacy of area yield and revenue insurance products in helping farmers manage their risk.

• Journal of the Korean Solar Energy Society
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• v.32 no.6
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• pp.37-43
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• 2012

Long-term variations in wind speed and capacity factor(CF) on Seongsan wind farm of Jeju Island, South Korea were derived statistically. The selected areas for this study were Subji, having a year wind data at 30m above ground level, Sinsan, having 30-year wind data at 10m above ground level and Seongsan wind farm, where long-term CF was predicted. The Measure-Correlate-Predict module of WindPRO was used to predict long-tem wind characteristics at Seongsan wind farm. Eachyear's CF was derived from the estimated 30-year time series wind data by running WAsP module. As a result, for the 30-year CFs, Seongsan wind farm was estimated to have 8.3% for the coefficien to fvariation, CV, and-16.5% ~ 13.2% for the range of variation, RV. It was predicted that the annual CF at Seongsan wind farm varied within about ${\pm}4%$.

• The Journal of Industrial Distribution & Business
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• v.8 no.6
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• pp.87-95
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• 2017

Purpose - The structural changes of Korean agriculture are complex due to heterogeneous production processes and farms' features. This study analyzed trends of dualism in Korean agriculture over the period 2000-15 based on farm-level data to clarify the specific trends of dualism in terms of farm income, farm-size, and farm operators' age. From the results of this study, we would be able to understand the features of structural changes in Korean agriculture more profoundly. Research design, data, and methodology - We incorporated farm-level data in South Korea: Agricultural census and Farm household economy survey. As measures of inequality, we used size-weighted quantiles, and normalized Gini coefficients as well as mean and conventional quantiles. The size-weighted quantiles are more robust to changes in the number of small farms, but they are more sensitive to changes in the distribution of farm-size. Thus, they would be more useful to identify trends of dualism of Korean agriculture. Results - The results show that the farmland distribution of crop farms became more skewed and dispersed. However, the herd distribution of livestock farms became more concentrated. To be specific, their mean and 1st quantile increases more rapidly than their size-weighted 2nd quantile and size-weighted 3rd quantile. Gini coefficients of livestock farms regarding their herd distribution decreased by 0.1 on average. In the case of income distribution, the results indicate that the polarization regarding farm household/agricultural/non-agricultural income became more severe. However, we also found that the distribution of transfer income became concentrated continuously. The results imply that transfer income including subsidies would decrease farm income polarization. Lastly, during the study periods, Korean farms were aging over time, and age distribution of them more concentrated. Conclusions - The structure of Korean agriculture has been changing, even though the absolute size of it decreased over time. Land (herd) distribution became more dispersed (concentrated). Inequality regarding agricultural income became more severe, and it made farm household income more polarized even though transfer income would decrease income gaps among farms. Lastly, farms continue to age regardless of farm types and this might affect the structural changes in Korean agriculture in the future.

• Journal of Agricultural Extension & Community Development
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• v.8 no.2
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• pp.245-261
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• 2001

The consulting industry had enjoyed growth rates over the past 20 years which show no signs of slowing in their momentum. Since 1995, it began to discuss institutionalization of consulting in the agricultural sector in Korea. Rural Development Adminstration has launched farm management consulting project by means of benchmarking for farming innovation in 1988, which consists of establishing consulting team, developing standard diagnosis form of sixty crops and materials, training consultants, having various public relations, and making information systems. Until October, 2001, sixty seven thousands of farmers have benefited on this project. It is now appreciated as a alternative for new agricultural extension approach. Analyzing the changes of farm management practice level through consulting shows improvement, which data have been stored in web database. It is expected that farm management consulting project be main stream of agricultural extension in this country.

• Journal of Veterinary Clinics
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• v.32 no.1
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• pp.78-84
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• 2015

Although researches have highlighted the important role of enhanced farm biosecurity to reduce the severity and prevalence of diseases in livestock, to date there has been little study in Korea on farmers' adoption of biosecurity measures to control porcine reproductive and respiratory syndrome virus (PRRSV) infection. To mitigate the risk of PRRSV infection in pigs, the risk factors by which PRRSV is introduced in pig farms must be determined. The primary aim of this study was to investigate pig producers' perceptions about on-farm biosecurity practices. We also analyzed data obtained from a cross-sectional study on 196 farrow-to-finish farms conducted between March 2013 and February 2014 to identify risk factors for PRRSV infection at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. Farms were classified as negative or positive through the use of infection profiles that combined data on PCR positive pigs and serological testing including antibody titer, sero-conversion pattern at each age category, and vaccination status. Data on biosecurity practices, farm management and environmental characteristics were analyzed using multivariate ordinal logistic regression. Generally, the biosecurity level in the pig farms included in this study were insufficient to reduce/prevent the risk of PRRSV infection given the high pig density areas and the considerable extent of vehicle movement. Factors associated with PRRSV infection were those where owners used on-farm vaccination programs had a lower risk of infection (OR = 0.19, 95% CI 0.06-0.61). The results from the analysis may guide to tailor biosecurity measures in the reduction or prevention of PRRS to the specific circumstances of pig farms in different localities of the world. To the best knowledge of the authors, this is the first study to report information on the biosecurity practices currently implemented on Korean pig farms.

• Korean Journal of Rural Living Science
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• v.6 no.1
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• pp.55-64
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• 1995

This study attempts to measure Quality of Life (QOL) of Korean farm households and examine the impacts of explanatory variables on QOL across residential areas. The data from 760 farm housewives were analyzed using means, Pearson's correlation, regression, one-way ANOVA, and Duncan's multiple range tests. The major findings of this study can be summarized as follows : 1. The general level of QOL of farm households was low. More specifically, among the three subcategories of QOL, the non for satisfaction in community life (SCL) was the highest and the score for satisfaction in home life (SHL) was the lowest. The score for the level of living in home life (LLHL) and SCL were high in the urban area and the score for the SHL was low in the mountainous area. 2. The significantly influenced variables fur the three subcategories of QOL were different in each residential areas. But it was common that the LLHL in the four residential areas was significantly determined by monetary asset and home management ability and the SHL was significantly determined by monetary asset in the three residential areas except the mountainous area.

• Journal of Veterinary Clinics
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• v.33 no.2
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• pp.107-112
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• 2016

The objective of this study was to determine potential contributing factors associated with biosecurity level of farrow-to-finish pig farms and to develop a classification tree model to explore how these factors related to each other based on prediction model. To this end, the author analyzed data (n = 193) extracted from a cross-sectional study of 344 farrow-to-finish farms which was conducted between March and September 2014 aimed to explore swine disease status at farm level. Standardized questionnaires with information about basic demographical data and management practices were collected in each farm by on-site visit of trained veterinarians. For the classification of the data sets regarding biosecurity level as a dependent variable and predictor variables, Chi-squared Automatic Interaction Detection (CHAID) algorithm was applied for modeling classification tree. The statistics of misclassification risk was used to evaluate the fitness of the model in terms of prediction results. Categorical multivariate input data (40 variables) was used to construct a classification tree, and the target variable was biosecurity level dichotomized into low versus high. In general, the level of biosecurity was lower in the majority of farms studied, mainly due to the limited implementation of on-farm basic biosecurity measures aimed at controlling the potential introduction and transmission of swine diseases. The CHAID model illustrated the relative importance of significant predictors in explaining the level of biosecurity; maintenance of medical records of treatment and vaccination, use of dedicated clothing to enter the farm, installing fence surrounding the farm perimeter, and periodic monitoring of the herd using written biosecurity plan in place. The misclassification risk estimate of the prediction model was 0.145 with the standard error of 0.025, indicating that 85.5% of the cases could be classified correctly by using the decision rule based on the current tree. Although CHAID approach could provide detailed information and insight about interactions among factors associated with biosecurity level, further evaluation of potential bias intervened in the course of data collection should be included in future studies. In addition, there is still need to validate findings through the external dataset with larger sample size to improve the external validity of the current model.

• Journal of Korean Society of Rural Planning
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• v.20 no.3
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• pp.89-100
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• 2014

The purpose of this study is to investigate whether the effect of the Strong Small Farm Development Project by Rural Development Administration is positively associated with participating farmers. The data that used in this study is a survey data that targets 442 farming households who have participated in the strong small farm development project. This study applies ordered-probit model to evaluate level of the participants' satisfaction of the project. This study found that participants' level of satisfaction is closely associated by satisfaction level of project contents that contains education and consulting, friendly attitude toward the project, active participation of the project, and understanding of the project's contents and their importances. Based on research findings, this research provides some implications of future upbringing policy for strong small farms and the principal point of the policy that leads to succeed in implementing the project when formulating agricultural policy in participants view. the strong small farms.

• Journal of The Korean Society of Agricultural Engineers
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• v.65 no.5
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• pp.69-80
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• 2023

Carbon neutrality in agriculture can be derived from systematic GHG reduction policies based on quantitative environmental impact analysis of GHG-emitting activities. This study is to explore how to advance the calculation of carbon emissions from agricultural activities to the detailed spatial level to a spatial Tier 3 level (Tier 2.5 level), methodologically beyond the Tier 2 approach. To estimate the GHG emissions beyond the Tier 2.5 level by region for detailed spatial units, we constructed available activity data on carbon emission impact factors such as rice cultivation, agricultural land use, and livestock. We also built and verified detailed data on emission activities at the field level through field surveys. The GHG emissions were estimated by applying the latest national emission factors and regional emission factors according to the IPCC 2019 GL based on the field-level activity data. This study has significance that it explored ways to build activity data and calculate GHG emissions through statistical data and field surveys based on parcels, one of the smallest spatial units for regional carbon reduction strategies. It is expected that by utilizing the activity data surveyed for each field and the emission factor considering the activity characteristics, it will be possible to improve the accuracy of GHG emission calculation and quantitatively evaluate the effect of applying reduction policies.

• Journal of Agricultural Extension & Community Development
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• v.27 no.1
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• pp.33-50
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• 2020

This study aims at shedding light on two questions: 1) how livelihood strategies hired by heterogeneous farm households differ and in what aspect, and 2) would the strategy change over time or remain identical across farm types? Using 2013-2017 Farm Economy Survey panel data, we divide the sample farms into 4 sub-groups based on income level and sources. Key findings are as follows. First, regardless of farm types, strong path dependency has been observed. That is, lots of farms are likely (enforced) to maintain the livelihood strategies, accounting for why many farms fail to response to market and/or policy signals. Second, along with compounding risks, farms are more vulnerable to specific sorts risks. Third, based on the findings, we made policy suggestions.