• Journal of Distribution Science
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• v.21 no.9
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• pp.35-42
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• 2023

Purpose: The research aims at examining the impact of marketer capabilities and persistence on marketer performance and distribution of agricultural product facilities. Research design, data, and methodology: The research employs quantitative methods using a cross-sectional design survey by analyzing the marketer of agricultural production facilities. Sampling was done using the purposive sampling technique and data were taken from 235 respondents. The data were then processed using SEM-PLS. Results: The findings reveal that both marketer capabilities and marketer persistence significantly impact the performance of agricultural product facility marketers. Notably, marketer persistence exerts a more dominant influence on marketer performance than marketer capabilities. Effective communication and coordination between the sales team and the distribution center emerge as crucial factors determining the success of distributing agricultural equipment to reach farmers' land at the optimal time. Conclusions: The findings offer valuable managerial insights for agricultural product facility companies seeking to enhance marketer performance. To achieve this, companies should focus on increasing marketer persistence, with an emphasis on nurture-focused persistence rather than closure-focused persistence. Additionally, improving marketer capabilities is crucial, starting with relationship development, followed by trust building, customer retention, responsiveness, and acquisition. These strategies can collectively contribute to boosting marketer performance within the organization.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.626-631
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• 2005

Monitoring data from agricultural reservoirs throughout the country were analyzed to evaluate the limiting factor for algal growth and correlationship between Chl-a, total phosphorus (TP), total nitrogen (TN), and chemical oxygen demand (COD). It was used for the analysis to monitored data of 394 reservoirs such as TP, TN, Chl-a, and COD from 1999 to 2003. This study analyzed water quality items in terms of areas, seasons. It turned out that phosphorus in agricultural reservoirs (about 80%) was dominant limiting factor for algal growth. Therefor, it appears that the appropriate managements of phosphorus in the agricultural watershed are crucial to prevent excessive on algal growth. Generally, there is correlation between Chl-a and TP while Chl-a do not have effect on TN. Also, Chl-a have influence on COD. This study could be used beneficially for water quality management of agricultural reservoirs and related water quality modeling.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2002.10a
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• pp.281-284
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• 2002

Geographic Information System (GIS) has advantage of analyzing spatial distributed data and handling spatial data for hydrologic analysis. Hydrologic Engineering Center's Hydrologic Modeling System(HEC-HMS) with HEC-GeoHMS was used to analyze flood runoff at agricultural small watershed. HEC-GeoHMS, which is an ArcView GIS extension designed to process geospatial data for HEC-HMS, is a useful tool for storing, managing, analyzing, and displaying spatially distributed data. Hydroligical component including peak discharge, time to peak, direct runoff, baseflow for Balhan study watershed, which is located in Whasung city, Kyunggi province, having an area of $29.79km^2$, were calculated using the HEC-HMS model with HEC-GeoHMS.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.5
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• pp.622-626
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• 2002

Growth records over six generations of 686 pigs in SD-II Swine Line were used to estimate the genetic and phenotypic covariance functions for body weight as longitudinal data. A random regression model with Legendre polynomials of age as independent variables was used to estimate the (co)variances among the regression coefficients, thus the coefficients of genetic and permanent environmental covariance functions by restricted maximum likelihood employing the average information algorithm. The results showed that, using litter effect as additional random effect, a reduced order of fit did not describe the data adequately. For all five orders of fit, however, the change trends of genetic and phenotypic (co)variances were very similar from ${\kappa}$=3 onwards.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.479-484
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• 2005

A GIS-based integrated system for reuse of effluent from wastewater treatment plants was developed in this study. The GIS-supported program classified attribute data which the effluent's quantity and quality and agricultural thematic map data according to the 5 big river basin area. From the database, showing the spatial variation of the water quality of the effluent, thereby proposing proper mitigation strategies over the watershed. Also, this system enables the users who is going to reuse the reclaimed water for their paddies to provide of all the wastewater treatment plant data and agricultural structures and thematic map data.

• Smart Media Journal
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• v.12 no.5
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• pp.65-72
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• 2023

Smart Farm means creating new value in various fields related to agriculture, including not only agricultural production but also distribution and consumption through the convergence of agriculture and ICT. In Korea, a rental smart farm is created to spread smart agriculture, and a smart farm big data platform is established to promote data collection and utilization. It is pushing for digital transformation of agricultural products distribution from production areas to consumption areas, such as expanding smart APCs, operating online exchanges, and digitizing wholesale market transaction information. As such, although agricultural data is generated according to characteristics from various sources, it is only used as a service using statistics and standardized data. This is because there are limitations due to distributed data collection from agriculture to production, distribution, and consumption, and it is difficult to collect and process various types of data from various sources. Therefore, in this paper, we analyze the current state of domestic agricultural data collection and sharing for digital agriculture and propose a data collection and linkage method for artificial intelligence services. And, using the proposed data, we propose a deep learning-based environmental factor recommendation method.

• Journal of Biosystems Engineering
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• v.38 no.4
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• pp.248-254
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• 2013

Purpose: This study was performed in order to obtain basic data for policy development and R&D to sharpen competitiveness in domestic agricultural machinery industry by analyzing the recent status of demand and supply for tractor, rice transplanter(riding type), and combine. Methods: Basic data from 199,275 units of tractor, rice transplanter (riding type), and combine was offered by the National Agricultural Cooperative Federation and Korea Agricultural Machinery Industry Cooperative. Those agricultural machines were supplied by the government's loan support from 2003 to 2012. Results: Recent supply of tractor is only 13,000 units or so per annum, thereby being stagnated. Rice transplanter and combine in 2012 corresponded to 3,810 units and 2,490 units, respectively. The domestic market share of the imported agricultural machinery accounted for 60.0% in tractor, 99.5% in saddle rice transplanter, and 80.9% in combine, thereby having been sharply increased 33.1%p, 42.0%p and 53.6%p compared to the ones in 2003. Life spans of tractor, combine and saddle rice transplanter are 3.7, 3.7 and 4.2 years, respectively. Among the discontinued models, the one less than 300 units supplied was occupied up to 70~85%. Conclusions: The domestic demand and the export expansion are needed through developing a model of agricultural machinery of having competitiveness to domestically activate agricultural machinery industry.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.31-40
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• 2019

In order to implement practical alternatives to proactively cope with the agricultural drought, the potential vulnerability of irrigation pumping stations to agricultural drought was quantitatively evaluated. Data for the 124 pumping stations which are correlatable to the three proxy variables, i.e. exposure, sensitivity, and adaptive capacity was collected by the Korea Rural Community Corporation, and then standardized considering distribution of each data set. Finally, the vulnerability index was calculated by multiplying the weights determined by the expert survey. The results showed that the vulnerability index ranged from 0.709 to 0.331 and the most vulnerable pumping stations such as Judam, Wongoo and Jinahn were mostly located in Gyeongbuk province likely because of the climatological characteristics with high temperature and low rainfall around this area. In addition, it was found that the adaptive capacity was a dominant factor comparing to exposure or sensitivity proxy variables in contributing to the vulnerability. It is therefore recommended that more practical alternatives should be employed to effectively reduce the vulnerability of an individual pumping station to agricultural drought. Furthermore, the corresponding data related to adaptive capacity should be systematically organized and managed at a field level to design reliable adaptation strategies.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.63-72
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• 2020

With the implementation of integrated water management policies, the need for information sharing with respect to agricultural water use has increased, necessitating the quantification of irrigation water supply using monitoring data. This study aims to estimate the irrigation water supply amount based on the relationship between the water level and irrigation canal discharge, and evaluate the reliability of monitoring data for irrigation water supply in terms of hydrology. We conducted a flow survey in a canal and reviewed the applicability of the rating curve based on the exponential and parabolic curves. We evaluated the reliability of the monitoring data using a reservoir water balance analysis and compared the calculated results of the supply quantity in terms of the reservoir water reduction rate. We secured 26 readings of measurement data by varying the water levels within 80% of the canal height through water level control. The exponential rating curve in the irrigation canal was found to be more suitable than the parabolic curve. The irrigation water supplied was less than 9.3-28% of the net irrigation water from 2017 to 2019. Analysis of the reservoir water balance by applying the irrigation water monitoring data revealed that the estimation of the irrigation water supply was reliable. The results of this study are expected to be used in establishing an evaluation process for quantifying the irrigation water supply by using measurement information from irrigation canals in agricultural reservoirs.

• Korean Journal of Agricultural Science
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• v.49 no.2
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• pp.193-202
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• 2022

The recurrent neural network (RNN) algorithm has been widely used in water-related research areas, such as water level predictions and water quality predictions, due to its excellent time series learning capabilities. However, studies on water quality predictions using RNN algorithms are limited because of the scarcity of water quality data. Therefore, most previous studies related to water quality predictions were based on monthly predictions. In this study, the quality of the water in a reservoir in Nonsan, Chungcheongnam-do Republic of Korea was predicted using the RNN-LSTM algorithm. The study was conducted after constructing data that could then be, linearly interpolated as daily data. In this study, we attempt to predict the water quality on the 7th, 15th, 30th, 45th and 60th days instead of making daily predictions of water quality factors. For daily predictions, linear interpolated daily water quality data and daily weather data (rainfall, average temperature, and average wind speed) were used. The results of predicting water quality concentrations (chemical oxygen demand [COD], dissolved oxygen [DO], suspended solid [SS], total nitrogen [T-N], total phosphorus [TP]) through the LSTM algorithm indicated that the predictive value was high on the 7th and 15th days. In the 30th day predictions, the COD and DO items showed R² that exceeded 0.6 at all points, whereas the SS, T-N, and T-P items showed differences depending on the factor being assessed. In the 45th day predictions, it was found that the accuracy of all water quality predictions except for the DO item was sharply lowered.