Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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The Study of MP-MAS Utilization to Support Decision-Making for Climate-Smart Agriculture in Rice Farming

벼농사의 기후스마트농업을 위한 의사결정지원시스템 MP-MAS 활용 연구

  • Received : 2016.11.07
  • Accepted : 2016.12.01
  • Published : 2016.12.30
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Abstract

International societies are currently working together to achieve the Climate-Smart Agriculture (CSA) initiative which aims the triple wins: (1) sustainably increasing agricultural productivity and incomes; (2) adapting and building resilience to climate change; and (3) mitigating greenhouse gases emissions. In terms of its scope and context, CSA follows the '3Nong (三農)' vision cast about 200 years ago by Dasan Jeong Yak-Yong who emphasized the triad of governance, management and monitoring towards comfortable, profitable and noble agriculture. Yet, the CSA provides the practical aims that facilitate the development of holistic indicators for quantitative evaluation and monitoring, on which decision-making support system is based. In this study, we introduce an agent-based model, i.e. Mathematical Programming Multi-Agent Systems (MP-MAS), as a tool for supporting the decision-making toward CSA. We have established the initial version of MP-MAS adapted for domestic use and present the preliminary results from an application to the rice farming case in Haenam, Korea. MP-MAS can support both farmers and policy-makers to consider diverse management options from multiple perspectives. When the modules for system resilience and carbon footprint are added, MP-MAS will serve as a robust tool that fulfills not only CSA but also Dasan's '3Nong' vision of sustainable agricultural-societal systems.

국제사회는 현재 1) 지속가능한 생산력 및 소득의 증대, 2) 기후변화에 적응하는 탄력 구축, 그리고 3) 온실 기체 방출의 감축을 함께 달성하는 기후스마트농업(Climate-Smart Agriculture, CSA)의 비전을 제시하고 이를 실현하고자 협력하고 있다. 이는 200여 년 전 다산 정약용이 강조한 후농(厚農), 편농(便農), 상농(上農)의 3농의 비전과 맥락을 같이 할 뿐 아니라, 성과를 정량적으로 평가하고 관리할 총체적 지수의 개발과 이를 기반으로 한 의사결정을 지원하는 실용적 목표를 제시하고 있다. 본 연구에서는 기후스마트농업의 의사결정을 지원할 행위자 기반 모형인 MP-MAS를 소개하고, 국내 적용을 위해 구축한 초기 모형을 벼농사에 적용하여 도출한 예비 결과를 제시하였다. MP-MAS는 농민들과 정책 입안자들이 함께 다른 관점에서 다양한 선택 사양을 고려할 수 있도록 지원할 수 있다. 추후 기후변화에 적응하는 탄력 구축과 온실기체 방출의 감축을 동시에 고려할 수 있는 시스템으로 확장될 경우, 국제적인 압박으로 다가오는 기후스마트농업의 목표 달성뿐만 아니라, 다산의 3농 비전인 지속가능한 농업-사회시스템을 구현하는 중요한 도구로 사용될 것으로 기대된다.

Keywords

References

  1. Aune, J.B., and R. Lal, 1995: The Tropical Soil Productivity Calculator : A Model for Assessing Effects of Soil Management on Productivity. CRC Press, Boca Rato, USA, 499-520.
  2. Berger, T., and C. Troost, 2014: Agent-based modelling of climate adaptation and mitigation options in agriculture. Journal of Agricultural Economics 65(2), 323-348. https://doi.org/10.1111/1477-9552.12045
  3. Berger, T., and P. Schreinemachers, 2012: Mathematical Programming-based Multi-Agent Systems (MPMAS) Technical Model Documentation Version 3.0. Hohenhiem university, Germany, 17-46.
  4. Berger, T., and P. Schreinemachers, 2006: Creating agents and landscapes for multiagent systems from random samples. Ecology and Society 11(2), 19pp.
  5. Berger, T., 2001: Agent-based models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agricultural Economics 25(2-3), 245-260. https://doi.org/10.1111/j.1574-0862.2001.tb00205.x
  6. Choi, S., M. Kang, Y.M. Indrawati, H. Kim, Y. Kim, and J. Kim, 2016: Carbon footprint of annual crop using long-term flux measurement in Korea: implication for climate-smart agriculture (CSA). Ecosummit 2016 Ecological Sustainability: Engineering Change Conference. 8.29-30.2016. Montpellier, France.
  7. Clarke, D., M. Smith, and K. El-Askari, 1998: CropWat for Windows: User Guide. Food and Agricultural Organization of the United Nations, Rome, Italy, 3-43.
  8. FAO (Food and Agriculture Organization), 2013: CLIMATE-SMART AGRICULTURE Sourcebook. Food and Agricultural Organization of the United Nations, Rome, Italy, ix pp.
  9. Hazell, P.B.R., and R.D. Norton, 1986: Mathematical Programming for Economic Analysis in Agriculture. Macmillan, New York, USA, 9-53.
  10. Kim, Y., M.S.A. Talucder, M. Kang, K-M. Shim, N. Kang, and J. Kim, 2016: Interannual variations in methane emission from an irrigated rice paddy caused by rainfalls during the aeration period. Agriculture, Ecosystems and Environment 223, 67-75. https://doi.org/10.1016/j.agee.2016.02.032
  11. Kim, D., H. Kim, H. Koo, and E. Kim, 2015a: Development and application of an agent based urban model. The Korea Spatial Planning Review 86, 17-31. (in Korean with English abstract) https://doi.org/10.15793/kspr.2015.86..002
  12. Kim, H., S-W. Choi, and J. Kim, 2015b: Research trends and future direction for sustainable agricultural and forest management. Korean Journal of Agricultural and Forest Meteorology 17(3), 236-247. (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2015.17.3.236
  13. Kim, J., 2010: A proposition on applying agent-based model for analyzing logistics system. Journal of Korea Port Economic Association 26(3), 130-142. (in Korean with English abstract)
  14. Kim, J., 2009: Carbon cycles and green growth. Orbis Sapientiae 6, Acanet, Seoul, South Korea, 204-218.
  15. Kim, J., 2005: An agent-based model for airline evolution, competition and airport congestion. Ph.D. Dissertation, Virginia Tech, Virginia.
  16. NICS (National Institute of Crop Science), 2016: 2016 Wet-hill-direct-seeding Field Workshop to Promote Practical Use of New Rice Technology. Rural Development Administrator, Jeonbuk, South Korea, 127pp.
  17. Park, S., Y. S. An, Y. Shin, S. Lee, W. Sim, J. Moon, G. Y. Jeong, I. Kim, H. Shin, D. Huh, J. H. Sung, and C. R. Park, 2015: A multi-agent system to assess land-use and cover changes caused by forest management policy scenarios. Journal of the Korean Geographical Society 50(3), 255-276. (in Korean with English abstract)
  18. RDA (Rural Development Administrator), 2013: 2012 Major Research Achievements of Agricultural Science and Technology Development Business. Rural Development Administrator, Jeonbuk, South Kore, 63pp.
  19. Schreinemachers, P., and T. Berger, 2011: An agent-based simulation model of human-environment interactions in agricultural systems. Environmental Modelling & Software 26(7), 845-859. https://doi.org/10.1016/j.envsoft.2011.02.004
  20. Schreinemachers, P., T. Berger, A. Sirijinda, and S. Praneetvatakul, 2009: The diffusion of greenhouse agriculture in Northern Thailand: combining econometrics and agent-based modeling. Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie 57(4), 513-536. https://doi.org/10.1111/j.1744-7976.2009.01168.x
  21. Shon, J., C-K. Lee, J. Kim, W. Yang, K-J. Choi, H-K. Park, T-S. Park, C-k. Kim, and Y-H. Yoon, 2013: Changes of weedy rice occurrence in repeated wet direct seeding and alternate transplanting/wet direct seeding of rice. The Korean Society of Weed Science and The Turfgrass Society of Korea 2(4), 348-351. (in Korean with English abstract)
  22. Smith, M., 1992: CROPWAT: A Computer Program for Irrigation Planning and Management. Food and Agriculture Organization of the United Nations, Rome, Italy, 46pp.
  23. Special Economy, 2016: Senator Wanyoung Lee, urging rice production and inventory control measures... six kinds of measures proposed. 13th Oct. Special Economy Press, Korea. http://www.speconomy.com/news/articleView.html?idxno=73848 (Retrieved 18th/Oct/2016).
  24. Tesfatsion, L., 2006: Agent-based computational economics: a constructive approach to economic theory. Handbook of Computational Economics 2, 831-880. https://doi.org/10.1016/S1574-0021(05)02016-2
  25. Weiss, G., 2000: Multiagent Systems: a Modern Approach to Distributed Artificial Intelligence. The MIT Press, Cambridge, USA.
  26. Yang, W., J. Kim, M. Lee, S. Chen, and H. Han, 2015: Status and prospect on rice direct seeding technology of farmers. The Korean Society of International Agriculture 27(3), 342-347. (in Korean with English abstract) https://doi.org/10.12719/KSIA.2015.27.3.342