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      KCI등재후보

      Cost analysis of outdoor mandarin and smart farms : Using Monte Carlo simulation

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      https://www.riss.kr/link?id=A107936245

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      다국어 초록 (Multilingual Abstract)

      Purpose: This study is to investigate the difference in management costs between smart farms and outdoor mandarin through Monte Carlo simulations. Agriculture is threatened with sustainability due to a decrease in the labor force. In particular, the a...

      Purpose: This study is to investigate the difference in management costs between smart farms and outdoor mandarin through Monte Carlo simulations. Agriculture is threatened with sustainability due to a decrease in the labor force. In particular, the aging of agriculture in Korea is at a serious level compared to other countries. Smart farms are being distributed to solve the aging problem of agriculture. In particular, IoT, blockchain, machine learning, artificial intelligence, augmented reality, and big data are becoming more common, and such digital agriculture enables sustainable agriculture. such digital agriculture enables sustainable agriculture. So, these technologies often involve uncertainty between technology and technology users. This uncertainty is to be resolved through simulation. Data and methodology: Monte Carlo simulation was performed through Oracle Ball. The outdoor mandarin and the mandarin of the smart farm were set to compare. And the collected data were tornado analysis, spider chart, sensitivity analysis, predictor analysis, and prediction comparison analysis using Oracle balls. Results: As a result of the analysis, In Tornado, pesticide costs, fertilizer costs, and site development cost have a great influence on intermediate goods. however, in sensitivity analysis, pesticide costs, redemption cost for farming facilities, and fertilizer costs have a great influence on intermediate goods. Through the overall analysis, it was analyzed that pesticide and fertilizer costs commonly affect intermediate goods, and pesticide costs affect intermediate goods the most. In the predictive simulation analysis, it was analyzed that there was no significant difference between smart farms and outdoor mandarin of intermediate goods. Conclusion: There is no significant difference between smart farms and outdoor mandarin in terms of management costs. So, Considering the initial facility investment cost of smart farms, it will be difficult to obtain profitability in a short period of time. Pesticide costs have a significant impact on intermediate goods between outdoor mandarin and smart farms, because it was analyzed that there is no significant difference in pesticide costs between outdoor mandarin and smart farm. Currently, smart farms are mainly focused on reducing production costs and reducing labor. But in the future, smart farms need to develop technologies that reduce pesticide costs

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      목차 (Table of Contents)

      • 1. 서론 2. 이론적 배경 3. 연구설계 및 방법 4. 분석 결과 5. 결론 Acknowledgement References
      • 1. 서론 2. 이론적 배경 3. 연구설계 및 방법 4. 분석 결과 5. 결론 Acknowledgement References
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      참고문헌 (Reference)

      1 이재경, "지능형 스마트 팜 활용과 생산성에 관한 연구: 토마토 농가 사례를 중심으로" The Korean Society of Business Venturing 14 (14): 185-199, 2019

      2 이동훈, "스마트 팜 투자 사업의 실물옵션 분석: - 딸기・토마토 농가를 중심으로 -" 국회입법조사처 10 (10): 275-303, 2018

      3 Regan, Á., "‘Smart farming’in Ireland : A risk perception study with key governance actors" 90 : 100292-, 2019

      4 Kutter, T., "The role of communication and co-operation in the adoption of precision farming" 12 (12): 2-17, 2011

      5 Charania, I., "Smart farming : Agriculture's shift from a labor intensive to technology native industry" 9 : 100142-, 2020

      6 Korea Agricultural Research Institute, "Smart farm status survey and performance analysis summary"

      7 Choi, Y. C., "Smart farm in the 4th industrial revolution" 36 (36): 9-16, 2019

      8 Kim, Y. J., "Smart Farm Status and Success Factor Analysis" Korea Rural Economic Institute 1-74, 2016

      9 Finger, R., "Precision farming at the nexus of agricultural production and the environment" 11 : 313-335, 2019

      10 Malik, A. W., "Leveraging fog computing for sustainable smart farming using distributed simulation" 7 (7): 3300-3309, 2020

      1 이재경, "지능형 스마트 팜 활용과 생산성에 관한 연구: 토마토 농가 사례를 중심으로" The Korean Society of Business Venturing 14 (14): 185-199, 2019

      2 이동훈, "스마트 팜 투자 사업의 실물옵션 분석: - 딸기・토마토 농가를 중심으로 -" 국회입법조사처 10 (10): 275-303, 2018

      3 Regan, Á., "‘Smart farming’in Ireland : A risk perception study with key governance actors" 90 : 100292-, 2019

      4 Kutter, T., "The role of communication and co-operation in the adoption of precision farming" 12 (12): 2-17, 2011

      5 Charania, I., "Smart farming : Agriculture's shift from a labor intensive to technology native industry" 9 : 100142-, 2020

      6 Korea Agricultural Research Institute, "Smart farm status survey and performance analysis summary"

      7 Choi, Y. C., "Smart farm in the 4th industrial revolution" 36 (36): 9-16, 2019

      8 Kim, Y. J., "Smart Farm Status and Success Factor Analysis" Korea Rural Economic Institute 1-74, 2016

      9 Finger, R., "Precision farming at the nexus of agricultural production and the environment" 11 : 313-335, 2019

      10 Malik, A. W., "Leveraging fog computing for sustainable smart farming using distributed simulation" 7 (7): 3300-3309, 2020

      11 Yun, N. G., "Korean smart farm policy and technology development" 59 (59): 19-27, 2017

      12 Tzounis, A., "Internet of Things in agriculture, recent advances and future challenges" 164 : 31-48, 2017

      13 Kim, Y. K., "Internet of Things Communication Technology Research Trend in Smart Farm Environment" 38 (38): 11-18, 2021

      14 신봉희, "ICT 기반의 스마트팜 설계" 중소기업융합학회 10 (10): 15-20, 2020

      15 Ma S. J., "How to Advance a Yiung Beginning Farmers a Fostering System" Korea Rural Economic Institute 1-185, 2017

      16 Proagrica, "How Big Data Will Change Agriculture"

      17 Díez, C., "Hacia una agricultura inteligente" 60 : 4-11, 2017

      18 Kim, G. H., "Forecasting Methods for Monthly Expenditures by Monte-Carlo Simulation : Focused on Apartment Housing Projec" 28 (28): 149-156, 2012

      19 Yoon, C., "Factors affecting adoption of smart farms : The case of Korea" 108 : 106309-, 2020

      20 Van der Burg, S., "Ethics of smart farming : Current questions and directions for responsible innovation towards the future" 90 : 100289-, 2019

      21 Alm, E., "Digitizing the Netherlands:How the Netherlands can drive and benefit from an accelerated digitized economy in Europe" Boston Consulting Group 2016

      22 Himesh, S., "Digital revolution and Big Data : A new revolution in agriculture" 13 (13): 1-7, 2018

      23 Ramcharan, A., "Deep learning for image-based cassava disease detection" 8 : 1852-, 2017

      24 Colezea, M., "CLUeFARM : Integrated web-service platform for smart farms" 154 : 134-154, 2018

      25 Wolfert, S., "Big data in smart farming–a review" 153 : 69-80, 2017

      26 Manyika, J., "Big data : The next frontier for innovation, competition, and productivity" 12 (12): 33-45, 2011

      27 Kunisch, M., "Big Data in agriculture–perspectives for a service organization" 71 (71): 1-3, 2016

      28 Rehman A-u, "Application of modern high performance networks" Bentham Science Pub 120-129, 2009

      29 Blok, V., "Agricultural technologies as living machines: toward a biomimetic conceptualization of smart farming technologies" 21 (21): 246-263, 2018

      30 Rural Development Administration, "Agricultural and Livestock Income Databook for Agricultural Management Improvement (Agricultural Management Research Report)" 2019

      31 Marin, E., "Advanced method of managing soil conservation works in Smart Farms" EDP Sciences 286 : 03016-, 2021

      32 Kamilaris, A., "A review on the practice of big data analysis in agriculture" 143 : 23-37, 2017

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      학술지 이력

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