• Animal Bioscience
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• v.35 no.1
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• pp.147-152
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• 2022

Objective: This study was conducted to obtain an objective index that can be quantified and used for establishing an animal welfare certification standard in Korea. For this purpose rumen pH, ruminating time, milk yield, milk quality, and blood components of cows reared in farms feeding high forage level (90%) and farms feeding low forage level (40%) were compared. Methods: Data on rumen pH, rumination time, milk yield, milk fat ratio, milk protein ratio, and blood metabolism were collected from 12 heads from a welfare farm (forage rate 88.5%) and 13 heads from a conventional farm (forage rate 34.5%) for three days in October 2019. Results: The rumination time was longer in cattle on the welfare farm than on the conventional farm (p<0.01), but ruminal pH fluctuation was greater in the cattle on conventional farm than the welfare farm (p<0.01). Conventional farms with a high ratio of concentrated feed were higher in average daily milk yield than welfare farms, but milk fat and milk production efficiency (milk fat and milk protein corrected milk/total digestible nutrients) was higher in cattle on welfare farms. Blood test results showed a normal range for both farm types, but concentrations of total cholesterol and non-esterified fatty acid were significantly higher in cows from conventional farms with a high milk yield (p<0.01). Conclusion: The results of this study confirmed that cows on the animal welfare farm with a high percentage of grass feed had higher milk production efficiency with healthier rumen pH and blood metabolism parameters compared to those on the conventional farm.

• Korean Journal of Organic Agriculture
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• v.17 no.1
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• pp.19-33
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• 2009

This paper evaluates the technical efficiency of organic farm management practices and studies its main determinants in Hongdongmyeon of Hongsung county, Chungnam province. The analysis is performed in two stages. First, the efficiency is measured via the nonparametric "Data Envelopment Analysis" (DEA) technique. The DEA models are constructed not only to assess the overall technical efficiency of organic fanning practices but also to evaluate the management efficiencies. In a second stage critical determinants of efficiency are determined using a Tobit model. In this analysis the focus is on technical and socio-economic variables. The analytical results show that technically efficient farms is about 13 percent and the mean technical efficiency is found to be 0.73 indicating that many farms are not operating at an efficient scale.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.119-128
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• 1993

Effective machine capacity is affected by the physical and geometrical conditions of the fields. In the small and scattered farmland structure field efficiency is greatly influenced by plot geometry. In this paper, a method for estimating field efficiency and effective machine capacity was developed . The developed method was applied to Korean paddy cultivation. Various time elements related to farm operations for small and scattered plots are discussed in this paper . Available working time is divided into two parts, viz. the preparation time for machine operation and actual working time. Two kinds of machine efficiencies, namely , Machine Efficiency 1, applicable on a single large plot or set of well consolidated plots ; and Machine Efficiency 2, applicable on small and scattered multiple plots, are considered. Based assumptions made and steps followed to construct the model are discussed. Effective capacity of each machine based on different plot geometries are calculated y the model. Machine efficiency on a single plot increases with increase in the dimension of longer side of the plot . Low speed, low theoretical capacity machines have higher machine efficiency which is only slightly influenced by plot geometry. As plot geometry is improved , the machine efficiency of high speed, high capacity machines increases rapidly. The effects of short side length and plot size on machine efficiency on a single plot depend on the type of farm operation. For a particular plot shape, as plot size increases, machine efficiency on multiple plots increases rapidly. The effects of consolidation on machine efficiency is highly significant if the plot size is small and/or machine size is large.

• Journal of Digital Convergence
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• v.17 no.6
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• pp.145-157
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• 2019

This study is to look at the influential factors associated with the acceptance intention of smart farm and suggest a proposal for spreading adoption of smart farms. The research questionnaire distributed to the farmers were used for the research analysis by statistical program SPSS v22.0 and Process macro v3.0. The technical characteristics of smart farm, which are availability, reliability and economic efficiency were selected as independent variables to analyze the influential factors on acceptance intention of smart farm and the mediating effect of effort expectation was observed. As a result, availability and economic efficiency have a positive(+) influence on acceptance intention and reliability have no influence on acceptance intention. And availability, reliability and economic efficiency have a positive(+) influence on effort expectation. Effort expectation mediates the relationship between the technical characteristics of smart farm and acceptance intention. The results of the study are expected to be utilized at the seeking direction of policy for potential adopters of smart farm, the training and consulting in actual field of smart farm.

• ANNALS OF ANIMAL RESOURCE SCIENCES
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• v.29 no.4
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• pp.158-165
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• 2018

The present study was undertaken to investigate the reproductive performance of the female breeding pigs after artificial insemination (AI) using the frozen boar semen imported from Canada, thereby finding insights into improving the efficiency of AI using the frozen semen (FSAI). Analyzed in the present study were the records of a total of 626 FSAI in a great grandparent (GGP) farm beginning from May through November of the year of 2016 (Farm A) and 2,024 FSAI beginning from 2015 through 2017 from a second GGP farm (Farm B). Both the total number of piglets born (TNB) and the number born alive (NBA) were greater during May than during September within FSAI (p<0.05) in Farm A (p<0.01 for the effect of the month). In Farm B, no difference was detected between the years in any of the farrowing rate, TNB, and NBA. When the records from Farm A and Farm B were pooled, the farrowing rate was greater for Farm A vs. Farm B (p<0.01), with no difference between the two farms in TNB and NBA. Moreover, TNB and NBA were less for FSAI than for AI using the liquid semen (LSAI; $10.9{\pm}0.3$ vs. $13.4{\pm}0.1$ and $10.0{\pm}0.3$ vs. $12.0{\pm}0.1$ piglets, respectively, for FSAI vs. LSAI in TNB and NBA, respectively; p<0.01). In conclusion, these results suggest that the reproduction efficiency for FSAI, which is lower than that for LSAI, could be improved by selecting an optimal period of the year for the use of the former.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.12 no.3
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• pp.201-210
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• 2019

This paper proposes the AMCS(Agricultural Machine Control System that distinguishes farms using satellite photos or drone photos of farms and controls the self-driving and operation of farm drones and tractors. The AMCS consists of the LSM(Local Server Module) which separates farm boundaries from sensor data and video image of drones and tractors, reads remote control commands from the main server, and then delivers remote control commands within the management area through the link with drones and tractor sprinklers and the PSM that sets a path for drones and tractors to move from the farm to the farm and to handle work at low cost and high efficiency inside the farm. As a result of AMCS performance analysis proposed in this paper, the PSM showed a performance improvement of about 100% over Dijkstra algorithm when setting the path from external starting point to the farm and a higher working efficiency about 13% than the existing path when setting the path inside the farm. Therefore, the PSM can control tractors and drones more efficiently than conventional methods.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.50 no.3
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• pp.250-256
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• 2017

This study investigated the removal efficiency of organic matter and nitrogen from fish farm effluent by hydraulic retention time (HRT) using an upflow biological filter (ANR system) reactor. The recycling time and influent flow in the reactor were controlled to 14.8, 7.4, 5.5 and 3.2 h to evaluate HRT. In addition, each reactor was coupled to a fixed bed upflow filter charged with media. The results showed that removal efficiency was ${\geq}95%%$ with an HRT of 5.5 h, and nitrification efficiency was reduced to 81% with an HRT of 3.2 h, although nitrification efficiency temporarily decreased due to the shock load as HRT decreased. Total nitrogen removal rate was also reduced to about 65% with an HRT of 3.2 h, which was considered a washout effect of nitrifying and denitrifying microorganisms by increasing the shearing force to the filter media, which decreased organic matter and nitrogen removal efficiency.

• Journal of Advanced Technology Convergence
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• v.1 no.1
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• pp.1-6
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• 2022

Since 2015, the government has been making efforts to distribute Korean smart farms. However, the supply is limited to large-scale facility vegetable farms due to the limitations of technology and current cultivation research data. In addition, the efficiency and reliability compared to the introduction cost are low due to the simple application of IT technology that does not consider the crop growth and cultivation environment. Therefore, in this paper, data analysis services was performed based on public and external data. To this end, a data-based target smart farm system was designed that is suitable for the situation of farms growing in facilities. To this end, a farm risk information notification service was developed. In addition, light environment maps were provided for proper fertilization. Finally, a disease prediction model for each cultivation crop was designed using temperature and humidity information of facility farms. Through this, it was possible to implement a smart farm data service by linking and utilizing existing smart farm sensor data. In addition, economic efficiency and data reliability can be secured for data utilization.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.198-205
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• 2003

This paper proposes the adaptive relaying of protective devices applied in the neighboring distribution feeders for reliable and efficient operations of a wind farm interconnected with distribution networks by dedicated lines. A wind farm connected to an electric power network is one of the greatest alternative energy sources. However, the wind turbine generators are influenced by abnormal grid conditions such as disturbances occurring in the neighboring distribution feeders as well as the dedicated power. Particularly, in cases of a fault happening in the neighboring distribution feeders, a wind farm might be accelerated until protective devices clear the fault. Therefore, the delayed operation time of protective devices for satisfying the coordination might overly expose the interconnected wind turbine generators to the fault and cause damage to them. This paper describes the proper delayed operation time of protective relay satisfying the coordination of the distribution networks as well as reducing damage on the interconnected wind farm. The simulation results for the Hoenggye substation model composed of five feeders and one dedicated line using PSCAD/EMTDC showed that the proper delayed time of protective devices reflecting the fault condition and the power output of the wind farm could improve the operational reliability, efficiency, and stability of the wind farm.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.130-133
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• 2020

Smart pig farm which is based on IoT has been widely adopted by many pig farmers. In order to achieve optimal control of smart pig farm, the relation between environmental conditions and performance metric should be characterized. In this study, the relation between multiple environmental conditions including temperature, humidity and various performance metrics, which are daily gain, feed intake, and MSY, is analyzed based on data obtained from 55 real pig farm. Especially, based on preprocessing of data, various regression based machine learning algorithms are considered. Through performance evaluation, we show that the performance can be predicted with high precision, which can improve the efficiency of management.