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한국 김제의 벼 경작 시스템의 기후스마트농업 (Climate-Smart Agriculture) 기반의 평가
Climate-Smart Agriculture (CSA)-Based Assessment of a Rice Cultivation System in Gimje, Korea 원문보기

한국농림기상학회지 = Korean Journal of Agricultural and Forest Meteorology, v.23 no.4, 2021년, pp.235 - 250  

모하마드 사미울 아산 탈룩더 (방글라데시 실렛농업대학교 농림업.환경과학과) ,  김준 (서울대학교 협동과정 농림기상학전공) ,  심교문 (농촌진흥청 농업과학기술원)

초록
AI-Helper 아이콘AI-Helper

본 연구에서는 '한국 김제의 전형적인 벼 경작 시스템이 기후스마트농업(CSA)의 삼중 도전에 어떻게 부합하고 있는가?'라는 질문에 답하기 위해, (1) 벼 경작 시스템의 에너지, 물, 탄소 및 정보의 흐름을 직접 관측하였고, (2) 생산성/효율성, 온실가스 방출/흡수 및 회복성을 평가할 수 있는 다양한 측정도구(metrics)를 사용하여 기후스마트농업의 관점에서 평가하였다. 국내 플럭스 관측망인 KoFlux 관측지의 하나인 김제의 대표적인 벼 경작 시스템에서 3년간(2011, 2012, 2014)의 생육기간 동안 에디공분산 기술을 사용하여 에너지, 물, 이산화탄소 및 메탄 플럭스의 흐름을 모니터링하였다. 생산 효율성 평가를 위해서는 총일차생산량(GPP), 생태계 호흡량(RE), 곡물 수확량, 빛사용효율(LUE), 물사용효율(WUE), 및 탄소흡수효율(CUE)을 지표로 사용하였다. 온실가스 정량화를 위해서는, 이산화탄소 플럭스(FCO2)와 메탄 플럭스(FCH4)의 경우 직접 관측한 자료를 사용하였고, 아산화질소 플럭스(FN2O)는 IPCC지침에 따라 간접적으로 산출한 자료를 사용하였다. 회복성 평가를 위해서는 자기-조직화(self-organization, S) 지표를 사용하였으며, 벼 경작 시스템에서 가장 포괄적인 세 과정(총일차생산, 메탄플럭스, 증발산)을 대상으로 정보이론을 사용하여 정량화 하였다. 결과에 따르면, 3년 간의 생육 기간 중 2011년이 상대적으로 CSA 삼중 목표를 모두 성취하였으나, 이어지는 2012년과 2014년에 모두 생산량이 감소하고 온실가스 방출이 크게 증가하여 기후스마트 한 관리가 이루어지지 않은 것으로 보인다. 3년 생육기간을 평균한 CSA 지표의 값과 범위의 경우, 생산성에 관련된 지표들은 문헌에 보고된 다른 연구 결과와 비교할 때 대부분 중-상위의 범위에 속했으나, 온실가스 완화의 경우 평균 이하였고, 회복성은 높았지만 보고된 자료가 없어 비교하지 못했다. 기후스마트한 벼재배를 위해서는, 1) 이해 관계자들이 함께 목적에 맞게 목표의 우선순위를 정하고('거버넌스'), 2) CSA 지표를 분석한 결과로부터 얻어진 되먹임(feedback) ('모니터링') 정보를 기반으로, 3) 상황에 맞는 적절한 개입('관리'), 즉 거버넌스/관리/모니터링의 삼합으로 이루어지는 비저니어링이 필요함을 시사한다.

Abstract AI-Helper 아이콘AI-Helper

The overarching question of this study is how a typical rice cultivation system in Gimje, Korea was keeping up with the triple-win challenge of climate-smart agriculture (CSA). To answer this question, we have employed (1) quantitative data from direct measurement of energy, water, carbon and inform...

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  1. Alberto, M. C. R., R. Wassmann, T. Hirano, A. Miyata, A. Kumar, A. Padre, and M. Amant, 2009: CO 2 /heat fluxes in rice fields: Comparative assessment of flooded and non-flooded fields in the philippines. Agricultural and Forest Meteorology 149(10), 1737-1750. 

  2. Alberto, M. C. R., R. Wassmann, T. Hirano, A. Miyata, R. Hatano, A. Kumar, A. Padre, and M. Amant, 2011: Comparisons of energy balance and evapotranspiration between flooded and aerobic rice fields in the philippines. Agricultural Water Management 98(9), 1417-1430. 

  3. Alberto, M. C. R., T. Hirano, A. Miyata, R. Wassmann, A. Kumar, A. Padre, and M. Amante 2012: Influence of climate variability on seasonal and interannual variations of ecosystem CO 2 exchange in flooded and non-flooded rice fields in the Philippines. Field Crops Research 134, 80-94. 

  4. Aubinet, M., B. Chermanne, M. Vandenhaute, B. Longdoz, M. Yernaux, and E. Laitat, 2001: Long term carbon dioxide exchange above a mixed forest in the Belgian Ardennes. Agricultural and Forest Meteorology 108(4), 293-315. 

  5. Brunsell, N. A., S. J. Schymanski, and A. Kleidon, 2011: Quantifying the thermodynamic budget of the land surface: is this useful? Earth System Dynamics 2(1), 87-103. 

  6. Burba, G., 2013: Eddy Covariance Method for Scientific, Industrial, Agricultural and Regulatory Applications: A Field Book on Measuring Ecosystem Gas Exchange and Areal Emission Rates. Li-Cor Biosciences, Lincoln. 

  7. Choi, S.-W., J. Kim, M. Kang, S. H. Lee, N. Kang, and K.-M. Shim, 2018: Estimation and Mapping of Methane Emissions from Rice Paddies in Korea: Analysis of Regional Differences and Characteristics. Korean Journal of Agricultural and Forest Meteorology 20(1), 88-100. (in Korean with English abstract). 

  8. Chun, J. A., K.-M. Shim, S. H. Min, and Q. Wang, 2015: Methane mitigation for flooded rice paddy systems in South Korea using a process-based model. Paddy and Water Environment DOI 10.1007/s10333-015-0484-0. 

  9. Cochran, F.V., N. A. Brunsell, and A. E. Suyker, 2016: A thermodynamic approach for agroecosystem sustainability. Ecological Indicators 67, 204-214. 

  10. Diaz, M. B., D. R. Roberti, J. V. Carneiro, V. d. A. Souza, and O. L. L. de Moraes, 2019: Dynamics of the superficial fluxes over a flooded rice paddy in southern Brazil. Agricultural and Forest Meteorology 276(277), 107650. doi:10.1016/j.agrformet.2019.107650 

  11. Fernandez, N., C. Maldonado, and C. Gershenson, 2014: Information measures of complexity, emergence, self-organization, homeostasis, and autopoiesis. In Guided self-organization: Inception, Springer, 19-51. 

  12. Forster, P., V. Ramaswamy, P. Artaxo, T. Bernsten, R. Betts, D. W. Fahey, J. Haywood, J. Lean, D. C. Lowe, G. Myhre, J. Nganga, R. Prinn, G. Raga, M. Schulz, and R. Van Dorland, 2007: Changes in atmospheric constituents and in radiative forcing. In: Climate Change. 2007. The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds. Solomon S, D Qin, M Manning et al.), 130-234. Cambridge University Press, Cambridge, UK. 

  13. Fratini, G., A. Ibrom, N. Arriga, G. Burba, and D. Papale, 2012: Relative humidity effects on water vapour fluxes measured with closed-path eddy-covariance systems with short sampling lines. Agricultural and Forest Meteorology 165, 53-63. 

  14. Gershenson, C., and N. Fernandez, 2012: Complexity and information: Measuring emergence, selforganization, and homeostasis at multiple scales. Complexity 18(2), 29-44. 

  15. Gitelson, A. A., and J. A. Gamon, 2015: The need for a common basis for defining light-use efficiency: Implications for productivity estimation. Remote Sensing of Environment 156, 196-201. 

  16. Gitelson, A. A., A. Vina, S. B. Verma, D. C. Rundquist, T. J. Arkebauer, G. G. Burba, and A. E. Suyker, 2006: Relationship between gross primary production and chlorophyll content in crops: Implications for the synoptic monitoring of vegetation productivity. Journal of Geophysical Research: Atmospheres 11(D8). 

  17. Hong, J. K., H. J. Kwon, J.-H. Lim,Y. H. Byun, J. H. Lee, and J. Kim, 2009: Standardization of KoFlux eddy-covariance data processing. Korean Journal of Agricultural and Forest Meteorology 11(1), 19-26. 

  18. Horst, T. W., and D. H. Lenschow, 2009: Attenuation of scalar fluxes measured with spatially-displaced sensors. Boundary Layer Meteorology 130(2), 275-300. 

  19. Hossen, M. S., M. Mano, A. Miyata, A. Baten, and T. Hiyama, 2012: Surface energy partitioning and evapotranspiration over a double-cropping paddy field in bangladesh. Hydrological Processes 26, 1311-1320. 

  20. Hossen, M. S., M. Mano, A. Miyata, M. A. Baten, and T. Hiyama, 2011: Seasonality of ecosystem respiration in a double-cropping paddy field in Bangladesh. Biogeosciences Discussions 8, 8693-8721. 

  21. Hwang, Y., Y. Ryu, Y. Huang, J. Kim, H. Iwata, and M. Kang, 2020: Comprehensive assessments of carbon dynamics in an intermittently-irrigated rice paddy. Agricultural and Forest Meteorology 285,107933. 

  22. Ikawa, H., K. Ono, M. Mano, K. Kobayashi, T. Takimoto, K. Kuwagata, and A. Miyata, 2017: Evapotranspiration in a rice paddy field over 13 crop years. Journal of Agricultural Meteorology 73(3), 109-118. 

  23. Indrawati, Y. M., J. Kim, and M. Kang, 2018: Assessment of Ecosystem Productivity and Efficiency using Flux Measurement over Haenam Farmland Site in Korea (HFK). Korean Journal of Agricultural and Forest Meteorology 20(1), 57-72. doi:10.5532/KJAFM.2018.20.1.57 

  24. IPCC (Intergovernmental Panel on Climate Change), 2007: Nitrous oxide and carbon dioxide in agriculture; OECD/IPCC/IEA phase II development of IPCC guidelines for natural greenhouse gas inventory methodology, Workshop Report, 4-6 December, 1995, OECD, IPCC, IEA (Geneva), 1997. 

  25. Kang, M., J. Kim, S. H. Lee, J. Kim, J. H. Chun, and S. Cho, 2018: Changes and improvements of the standardized eddy covariance data processing in KoFlux. Korean Journal of Agricultural and Forest Meteorology 20(1), 5-17. 

  26. Kang, M., J. Kim, B. Malla Thakuri, J. Chun, and C. Cho, 2018: New gap-filling and partitioning technique for H 2 O eddy fluxes measured over forests. Biogeosciences 15, 631-647. 

  27. Kang, M., J. Kim, H.S. Kim, B. Malla Thakuri, and J.-H. Chun, 2014: On the nighttime correction of CO 2 flux measured by eddy covariance over temperate forests in complex terrain. Korean Journal of Agricultural and Forest Meteorology 16(3), 233-245. 

  28. Kang, N., J. Yun, M. S. A. Talucder, M. Moon, M. Kang, K.-M. Shim, and J. Kim, 2015: Corrections on CH 4 fluxes measured in a rice paddy by Eddy covariance method with an open-path wavelength modulation spectroscopy. Korean Journal of Agricultural and Forest Meteorology 17 (1), 15-24. 

  29. Kim, J., M. Kang, T. Oki, E. W. Park, K. Ichii, Y. M. Indrawati, S. Cho, J. Moon, W. C. Yoo, J. Rhee, H. Rhee, K. Njau, and S. Ahn, 2018: Rural Systems Visioneering: Paradigm Shift from Flux Measurement to Sustainability Science. Korean Journal of Agricultural and Forest Meteorology 20(1), 101-116. doi:10.5532/KJAFM.2018.20.1.101 

  30. 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 & Environment 223, 67-75. doi:10.1016/j.agee.2016.02.032 

  31. Knox, S. H., J. H. Matthes, C. Sturtevant, P. Y. Oikawa, J. Verfaillie, and D. Baldocchi, 2016: Biophysical controls on interannual variability in ecosystem-scale CO 2 and CH 4 exchange in a California rice paddy. Journal of Geophysical Research: Biogeosciences 121, doi:10.1002/2015JG003247. 

  32. LI-COR Inc. 2014: Eddypro-eddy covarience software: Version 5.2.1 user's guide and reference. 

  33. Lin, H., M. Cao, P. C. Stoy, and Y. Zhang, 2009: Assessing self-organization of plant communities a thermodynamic approach. Ecological Modelling 220(6), 784-790. 

  34. Lin, H., M. Cao, and Y Zhang, 2011: Self-organization of tropical seasonal rain forest in southwest China. Ecological Modelling 222(15), 2812-2816. 

  35. Lipper, L., P. Caron, F. Kossam, R. Sessa, P. Thornton, A. Cattaneo, W. Mann, R. Shula, B. M. Campbell, D. Garrity, N. McCarthy, A. Tibu, T. Baedeker, K. Henry, A. Meybeck, R. Hottle, and E. F. Torquebiau, 2014: Climate-smart agriculture for food security. Nature Climate Change 4(12), 1068-1072. 

  36. Lopez-Ruiz, R., H. L. Mancini, and X. Calbet, 1995: A statistical measure of complexity. Physics Letters A 209(5-6), 321-326. 

  37. Mauder, M. and T. Foken, 2011: Documentation and Instruction Manual of the Eddy-covariance Software Package TK3. Univ., Abt. Mikrometeorologie, 46:60. 

  38. McMillen, R. T., 1988: An eddy correlation technique with extended applicability to non-simple terrain. Boundary Layer Meteorology 43(3), 231-245. 

  39. Meijide, A., G. Manca, I. Goded, V. Magliulo, P. di Tommasi, G. Seufert, and A. Cescatti, 2011: Seasonal trends and environmental controls of methane emissions in a rice paddy field in Northern Italy. Biogeosciences 8, 3809-3821. 

  40. Min, S.-H., K.-M. Shim, Y.-S. Kim, M.-P. Jung, S.-C. Kim, K.-H. So, 2013: Seasonal Variation of Carbon Dioxide and Energy Fluxes During the Rice Cropping Season at Rice-barley Double Cropping Paddy Field of Gimje. Korean Journal of Agricultural and Forest Meteorology 15(4), 273-281. doi:10.5532/kjafm.2013.15.4.273 

  41. Miyata, A., R. Leuning, O. T. Denmead, J. Kim, and Y. Harazono, 2000: Carbon dioxide and methane fluxes from an intermittently flooded paddy field. Agricultural and Forest Meteorology 102(4), 287-303. doi:Doi 10.1016/S0168-1923(00)00092-7 

  42. Miyata, A., T. Iwata, H. Nagai, T. Yamada, H. Yoshikoshi, M. Mano, K. Ono, G. Han, Y. Harazono, and E. Ohtaki, 2005: Seasonal variation of carbon dioxide and methane fluxes at single cropping paddy fields in central and western Japan. Phyton 45(4), 89-97. 

  43. Monteith, J. L., and C. Moss, 1977: Climate and the efficiency of crop production in Britain [and discussion]. Philosophical Transactions of the Royal Society of London B: Biological Sciences 281(980), 277-294. 

  44. Mosier, A. R., C. Kroeze, C. Nevison, O. Oenema, S. Seitzinger, and O. Van Cleemput, 1998: Closing the global N 2 O budget: nitrous oxide emissions through the agricultural nitrogen cycle. OECD/IPCC/IEA phase II development of IPCC guideslines for national greenhouse gas inventory methodology. Nutrient Cycling in Agroecosystems 52, 225-248. 

  45. Neufeldt, H., M. Jahn, B. M. Campbell, J. R. Beddington, F. DeClerck, A. De Pinto, J. Gulledge, J. Hellin, M. Herrero, A. Jarvis, and D. LeZaks, 2013: Beyond climate-smart agriculture: toward safe operating spaces for global food systems. Agriculture & Food Security 2(1), 12. 

  46. Nielsen, S. and S. Jorgensen, 2013: Goal functions, orientors and indicators (GoFOrtIt's) in ecology. Application and functional aspects-Strengths and weaknesses. Ecological Indicators 28, 31-47. 

  47. Odum, E., 1969: The strategy of ecosystem development. Science (New York, NY), 164(3877), 262-270. 

  48. Papale, D., M. Reichstein, M. Aubinet, E. Canfora, C. Bernhofer, W. Kutsch, B. Longdoz, S. Rambal, R. Valentini, T. Vesala, and D. Yakir, 2006: Towards a standardized processing of net ecosystme exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3, 571-583. 

  49. Pereira, J., N. Figueiredo, P. Goufo, J. Carneiro, R. Morais, C. Carranca, J. Coutinho, and H. Trindade, 2013: Effects of elevated temperature and atmospheric carbon dioxide concentration on the emissions of methane and nitrous oxide from Portuguese flooded rice fields. Atmospheric Environment 80, 464-471. 

  50. Prokopenko, M., F, Boschetti., and A. J. Ryan, 2009: An information-theoretic primer on complexity, self-organization, and emergence. Complexity 15(1), 11-28. 

  51. Reichstein, M., P. Ciais, D. Papale, R. Valentini, S. Running, N. Viovy, W. Cramer, A. Granier, J. Ogee, V. Allard, and M. Aubinet, 2007: Reduction of ecosystem productivity and respiration during the European summer 2003 climate anomaly: a joint flux tower, remote sensing and modelling analysis. Global Change Biology 13(3), 634-651. 

  52. Ricepedia. Retrieved 08 December 2019, from http://ricepedia.org/rice-as-a-crop 

  53. Rosenstock, T. S., C. Lamanna, S. Chesterman, P. Bell, A. Arslan, M. Richards, J. Rioux, A. O. Akinleye, C. Champalle, Z. Cheng, C. CornerDolloff, J. Dohn, W. English, A. S. Eyrich, E. H. Girvetz, A. Kerr, M. Lizarazo, A. Madalinska, S. McFatridge, K. S. Morris, N. Namoi, N. Poultouchidou, M. Ravina da Silva, S. Rayess, H. Strom, K. L. Tully, and W. Zhou, 2016: The scientific basis of climate-smart agriculture: A systematic review protocol. CCAFS Working Paper no. 138. Copenhagen, Denmark: CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS). 

  54. Santamaria-Bonfil,.G.,.N..Fernandez, and C. Gershenson, 2016: Measuring the complexity of continuous distributions Entropy 18(3), 72. 

  55. Santamaria-Bonfil,.G.,.C..Gershenson,.and.N..Fernandez, 2017: A package for measuring emergence, self-organization, and complexity based on shannon entropy. Frontiers in Robotics and AI 4(10), 1-12. 

  56. Shannon, C. E., 1948: A Mathematical Theory of Communication. The Bell System Technical Journal 27,379-423, 623-656. 

  57. Shindell, D., G. Faluvegi, D. M. Koch, G. A. Schmidt, N. Unger, and S. E. Bauer, 2009: Improved attribution of climate forcing to emissions. Science 326(5953), 716-718. 

  58. Smith, P., D. Martino, Z. Cai, D. Gwary, H. Janzen, P. Kumar, B. McCarl, S. Ogle, F. O'mara, C. Rice, B. Scholes, O. Sirotenko, M. Howden, T. McAllister, G. Pan, V. Romanenkov, and U. Schneider, S. Towprayoon, 2007: Policy and technological constraints to implementation of greenhouse gas mitigation options in agriculture Agriculture, Ecosystems & Environment 118(1), 6-28. 

  59. Sun, H., S. Zhou, Z. Fu, G. Chen, G. Zou, and X. Song, 2016: A two-year field measurement of methane and nitrous oxide fluxes from rice paddies under contrasting climate conditions. Scientific Reports 6, 28255. doi:10.1038/srep28255 

  60. Svirezhev,Y., 2010: Entropy and Entropy Flows the Biosphere. Global Ecology 154. 

  61. Van Dijk, A., A. F. Moene, and H. A. R. De Bruin, 2004: The principles of surface flux physics: theory, practice and description of the ECPACK library. Meteorology and Air Quality Group. Wageningen University, Wageningen, The Netherlands, 99. 

  62. Van Gorsel, E., N. Delpierre, R. Leuning, A. Black, J. W. Munger, S. Wofsy, M. Aubinet, C. Feigenwinter, J. Beringer, D. Bonal, and B. Chen, 2009: Estimating nocturnal ecosystem from the vertical turbulent flux and change in storage of of CO 2 . Agricultural and Forest Meteorology 149 (11), 1919-1930. 

  63. Van Groenigen, K. J., C. Van Kessel, and B. A. Hungate, 2013: Increased greenhouse-gas intensity of rice production under future atmospheric conditions. Nature Climate Change 3(3), 288-291. 

  64. Wang, Y., L. Zhou, Q. Jia, and W. Yu, 2017: Water use efficiency of a rice paddy field in Liaohe Delta, Northeast China. Agricultural Water Management 187, 222-231. 

  65. Webb, E. K., G. Pearman, and R. Leuning, 1980: Correction of flux measurements for density effects due to heat and water vapor transfer. Quarterly Journal of Royal Meteorological Society 106, 85-100. 

  66. Yang, H., Y. M. Indrawati, A. E. Suyker, J. Lee, K.-d. Lee, and J. Kim, 2020: Radiation, Energy, and Entropy Exchange in an Irrigated-Maize Agroecosystem in Nebraska, USA. Korean Journal of Agricultural and Forest Meteorology 22(1), 26-46. doi:10.5532/KJAFM.2020.22.1.26 

  67. Yu, G., X. Song, Q. Wang, Y. Liu, D. Guan, J. Yan, X. Sun, L. Zhang, and X. Wen, 2008: Water-use efficiency of forest ecosystems in eastern China and its relations to climatic variables. New Phytologist 177(4), 927-937. 

  68. Zaccarelli, N., B. L. Li, I. Petrosillo, and G. Zurlini, 2013: Order and disorder in ecological time-series: introducing normalized spectral entropy. Ecological Indicators 28, 22-30. 

  69. Zhang, Y., M. Xu, H. Chen, and J. Adams, 2009: Global pattern of NPP to GPP ratio derived from MODIS data: effects of ecosystem type, geographical location and climate. Global Ecology and Biogeography 18(3), 280-290. 

  70. Zurlini, G., I. Petrosillo, K. B. Jones, and N. Zaccarelli, 2013: Highlighting order and disorder in social-ecological landscapes to foster adaptive capacity and sustainability. Landscape Ecology 28(6), 1161-1173. 

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