DOI QR코드

DOI QR Code

Assessment of Ecosystem Productivity and Efficiency using Flux Measurement over Haenam Farmland Site in Korea (HFK)

플럭스 관측 기반의 생태계 생산성과 효율성 평가: 해남 농경지 연구 사례

  • Indrawati, Yohana Maria (Interdisciplinary Program in Agricultural & Forest Meteorology, Seoul National University) ;
  • Kim, Joon (Interdisciplinary Program in Agricultural & Forest Meteorology, Seoul National University) ;
  • Kang, Minseok (National Center for AgroMeteorology)
  • Received : 2018.01.25
  • Accepted : 2018.03.26
  • Published : 2018.03.30

Abstract

Time series analysis of tower flux measurement can be used to build quantitative evidence for the achievement of climate-smart agriculture (CSA). In this study, we have assessed the first objective of CSA (regarding ecosystem productivity and efficiency) for rice paddy-dominated heterogeneous farmland. A set of quantitative indicators were evaluated by analysing the time series data of carbon, water and energy fluxes over the Haenam farmland site in Korea (HFK) during the rice growing seasons from 2003 to 2015. Four different varieties of rice were cultivated during the study period in chronological order of Dongjin No. 1 (2003-2008), Nampyung (2009), Onnuri (2010-2011), and Saenuri (2012-2015). Overall at HFK, gross primary productivity (GPP) ranged from 800 to $944g\;C\;m^{-2}$, water use efficiency (WUE) ranged from 1.91 to $2.80g\;C\;kg\;H_2O^{-1}$, carbon uptake efficiency (CUE) ranged from 1.06 to 1.34, and light use efficiency (LUE) ranged from 0.99 to $1.55g\;C\;MJ^{-1}$. Among the four rice varieties, Dongjin No. 1-dominated HFK showed the highest productivity with higher WUE and LUE, but comparable CUE. Considering the heterogeneous vegetation cover at HFK, a rule of thumb comparison suggested that the productivity of Dongjin No1-dominated HFK was comparable to those of monoculture rice paddies in Asia, whereas HFK was more efficient in water use and less efficient in carbon uptake. Saenuri-dominated HFK also produced high productivity but with the growing season length longer than Dongjin No.1. Although the latter showed better traits for CSA, farmers cultivate Saenuri because of higher pest resistance (associated with adaptability and resilience). This emphasizes the need for the evaluation of other two objectives of CSA (i.e. system resilience and greenhouse gas mitigation) for complete assessment at HFK, which is currently in progress.

기후스마트농업(Climate-Smart Agriculture, CSA)이 성취되고 있는지에 대한 정량적인 평가방법을 구축하기 위해 타워 기반의 플럭스 관측 시계열 자료를 활용할 수 있다. 이 연구에서는 벼농사가 지배적인 전형적인 비균질 농경지를 대상으로 CSA의 첫 번째 목표와 관련된 생산성과 효율성 평가를 시도하였다. 이를 위해 해남 농경지에 위치한 KoFlux 사이트(HFK)에서 2003년부터 2015년까지 벼의 생장기간 동안에 관측된 탄소, 물 및 에너지 플럭스의 시계열 자료를 분석하여 일련의 정량적인 지표들을 평가하였다. 이 연구기간 동안에 HFK에서는 네 가지의 다른 품종(동진 1호; 2003-2008, 남평; 2009, 온누리; 2010-2011, 새누리; 2012-2015)의 벼가 경작되었다. 전반적으로 품종을 구분하지 않을 경우, 연구기간 동안의 HFK의 총일차생산(GPP)은 800 - 944 g C m-2, 물사용효율(WUE)은 1.91 - 2.80 g C kg H2O-1, 탄소사용효율(CUE)은 1.06 - 1.34, 그리고 광사용효율(LUE)은 0.99 - 1.55 g C MJ-1이었다. 벼 이외의 다른 식생이 포함된 HFK의 비균질성을 고려하여 어림 잡아 비교해 보면, 네 품종 중에서 동진1호를 재배했을 때에 HFK의 생산성이 아시아의 단일 벼논의 생산성과 비슷했고 WUE도 높았던 반면에 CUE는 상대적으로 낮았다. 또한, 새누리를 재배했을 때에도 HFK가 비슷하게 높은 생산성을 보였으나 동진1호보다 생장기간이 상대적으로 길었다. 따라서 동진1호가 지배적인 HFK가 CSA의 관점에서 더 좋은 특성을 보여 준다. 그러나 현실적으로는 농부들이 해충 저항성이 동진1호보다 높은 새 누리를 재배하고 있다. 이는 CSA의 나머지 두 목표의 하나인 탄력(resilience) 향상을 통한 적응력 강화와 관련된 것으로 온실가스 방출 저감을 포함한 총체적인 평가가 이루어져야 함을 시사하며, 이에 대한 평가와 분석이 현재 진행 중에 있다.

Keywords

References

  1. Alberto, M. C. R., R. Wassmann, T. Hirano, A. Miyata, R. Hatano, A. Kumar, A. Padre, and M. Amante, 2011: Comparisons of energy balance and evapotranspiration between flooded and aerobic rice fields in the Philippines. Agricultural Water Management 98(9), 1417-1430. https://doi.org/10.1016/j.agwat.2011.04.011
  2. 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. https://doi.org/10.1016/S0168-1923(01)00244-1
  3. Baldocchi, D., E. Falge, L. Gu, and R. Olson, 2001: FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bulletin of the American Meteorological Society 82(11), 2415. https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2
  4. Baldocchi, D. D., S. B. Verma, and N. J. Rosenberg, 1985: Water use efficiency in a soybean field: influence of plant water stress. Agricultural and forest meteorology 34(1), 53-65. https://doi.org/10.1016/0168-1923(85)90054-1
  5. Beer, C., P. Ciais, M. Reichstein, D. Baldocchi, B. E. Law, D. Papale, J. F. Soussana, C. Ammann, N. Buchmann, D. Frank, and D. Gianelle, 2009: Temporal and among-site variability of inherent water use efficiency at the ecosystem level. Global biogeochemical cycles 23(2).
  6. Beer, C., M. Reichstein, P. Ciais, G. Farquhar, and D. Papale, 2007: Mean annual GPP of Europe derived from its water balance. Geophysical Research Letters 34(5).
  7. Brunsell, N. A., S. J. Schymanski, and A. Kleidon, 2011: Quantifying the thermodynamic entropy budget of the land surface: is this useful? Earth System Dynamics 2(1), 87-103. https://doi.org/10.5194/esd-2-87-2011
  8. Choi, S.-W., J. Kim, M. Kang, S. H. Lee, N. Kang, Y. Ryu, 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. (in Korean with English abstract)
  9. Churkina, G., D. Schimel, B. H. Braswell, and X. Xiao, 2005: Spatial analysis of growing season length control over net ecosystem exchange. Global Change Biology 11(10), 1777-1787. https://doi.org/10.1111/j.1365-2486.2005.001012.x
  10. Ciais, P., M. Reichstein, N. Viovy, A. Granier, J. Ogee, V. Allard, M. Aubinet, N. Buchmann, C. Bernhofer, A. Carrara, and F. Chevallier, 2005: Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437(7058), 529. https://doi.org/10.1038/nature03972
  11. Cochran, F. V., N. A. Brunsell, and A. E. Suyker, 2016: A thermodynamic approach for assessing agroecosystem sustainability. Ecological Indicators 67, 204-214.
  12. Falge, E., D. Baldocchi, J. Tenhunen, M. Aubinet, P. Bakwin, P. Berbigier, C. Bernhofer, G. Burba, R. Clement, K. J. Davis, and J. A. Elbers, 2002: Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements. Agricultural and Forest Meteorology 113(1), 53-74. https://doi.org/10.1016/S0168-1923(02)00102-8
  13. Farquhar, G., and R. Richards, 1984: Isotopic composition of plant carbon correlates with water-use efficiency of wheat genotypes. Functional Plant Biology 11(6), 539-552.
  14. 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.
  15. Gitelson, A. A., A. Vina, S. B. Verma, D. C. Rundquist, T. J. Arkebauer, G. Keydan, B. Leavitt, V. Ciganda, 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, 111(D8).
  16. 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. (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2009.11.1.019
  17. Hsieh, C. I., G. Katul, and T. W. Chi, 2000: An approximate analytical model for footprint estimation of scalar fluxes in thermally stratified atmospheric flows. Advances in Water Resources 23, 765-772. https://doi.org/10.1016/S0309-1708(99)00042-1
  18. http://asiaflux.net/index.php?page_id=60 (2017.11.1).
  19. https://egis.me.go.kr/main.do (2007.12.10).
  20. http://www.haenam.go.kr/planweb/board/list.9is?boardUid=4a94e38a4830deca0148e83ce61d14d2&contentUid=18e3368f5281ef400152b037a5fe4007&layoutUid=4a94e38a478f462d01479f3dd2820253 (2017.08.23).
  21. Ikawa, H., K. Ono, M. Mano, K. Kobayashi, T. Takimoto, T. Kuwagata, and A. Miyata, 2017: Evapotranspiration in a rice paddy field over 13 crop years. Journal of Agricultural Meteorology 73(3), 109-118. https://doi.org/10.2480/agrmet.D-16-00011
  22. IRRI, 2013: Rice Knowledge Bank, Step-by-step production: Crop calendar. IRRI.
  23. Jang, T., S.-B. Lee, C.-H. Sung, H.-P. Lee, and S.-W. Park, 2010: Safe application of reclaimed water reuse for agriculture in Korea. Paddy and Water Environment 8(3), 227-233. https://doi.org/10.1007/s10333-010-0203-9
  24. Kang, M., 2013: Understanding the evapotranspiration dynamics in East Asian forest ecosystems for resilient water management. dissertation Thesis, Yonsei University, Seoul Korea, 263.
  25. Kang, M., J. Kim, H.-S. Kim, B. M. 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. https://doi.org/10.5532/KJAFM.2014.16.3.233
  26. Kang, M., B. L. Ruddell, C. Cho, J. Chun, and J. Kim, 2017: Identifying CO 2 advection on a hill slope using information flow. Agricultural and Forest Meteorology 232, 265-278. https://doi.org/10.1016/j.agrformet.2016.08.003
  27. Keenan, T. F., D. Y. Hollinger, G. Bohrer, D. Dragoni, J. W. Munger, H. P. Schmid, and A. D. Richardson, 2013: Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499(7458), 324-327. https://doi.org/10.1038/nature12291
  28. Kim, Y., and J. Kim, 2018: Application of spectrum and wavelet analyses for the fields of agriculture, forestry and ecohydrology. (in preparation)
  29. 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. https://doi.org/10.1016/j.agee.2016.02.032
  30. KOSIS, 2015: Korean Statistical database 2015, Korea.
  31. Kuglitsch, F. G., M. Reichstein, C. Beer, A. Carrara, R. Ceulemans, A. Granier, I. A. Janssens, B. Koestner, A. Lindroth, D. Loustau, and G. Matteucci, 2008: Characterisation of ecosystem water-use efficiency of european forests from eddy covariance measurements. Biogeosciences Discussions 5(6), 4481-4519. https://doi.org/10.5194/bgd-5-4481-2008
  32. Kwon, H., J. Kim, J. Hong, and J. Lim, 2010: Influence of the Asian monsoon on net ecosystem carbon exchange in two major ecosystems in Korea. Biogeosciences 7(5), 1493-1504. https://doi.org/10.5194/bg-7-1493-2010
  33. Kwon, H., T.-Y. Park, J. Hong, J.-H. Lim, and J. Kim, 2009: Seasonality of Net Ecosystem Carbon Exchange in Two Major Plant Functional Types in Korea. Asia-Pacific Journal of Atmospheric Sciences 45(2), 149-163.
  34. Law, B. E., E. Falge, L. V. Gu, D. D. Baldocchi, P. Bakwin, P. Berbigier, K. Davis, A. J. Dolman, M. Falk, J. D. Fuentes, and A. Goldstein, 2002: Environmental controls over carbon dioxide and water vapor exchange of terrestrial vegetation. Agricultural and Forest Meteorology 113(1), 97-120. https://doi.org/10.1016/S0168-1923(02)00104-1
  35. Lee, C. K., J. Kim, J. Shon, W. H. Yang, Y. H. Yoon, K. J. Choi, and K. S. Kim, 2012: Impacts of climate change on rice production and adaptation method in Korea as evaluated by simulation study. Korean Journal of Agricultural and Forest Meteorology 14(4), 207-221. (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2012.14.4.207
  36. Lee, H. C., J. Hong, C.-H. Cho, B.-C. Choi, S.-N. Oh, and J. Kim, 2003: Surface exchange of energy and carbon dioxide between the atmosphere and a farmland in Haenam, Korea. Korean Journal of Agricultural and Forest Meteorology 5(2), 61-69.
  37. Lee, Y.-H., J. Kim, and J. Hong, 2008: The Simulation of Water Vapor and Carbon Dioxide Fluxes over a Rice Paddy Field by Modified Soil-Plant-Atmosphere Model (mSPA). Asia-Pacific Journal of Atmospheric Sciences 44(1), 69-83.
  38. 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. https://doi.org/10.1016/j.ecolmodel.2009.01.003
  39. Lin, H., M. Cao, and Y. Zhang, 2011: Self-organization of tropical seasonal rain forest in southwest China. Ecological Modelling 222(15), 2812-2816. https://doi.org/10.1016/j.ecolmodel.2010.07.006
  40. Lipper, L., P. Thornton, B. M. Campbell, T. Baedeker, A. Braimoh, M. Bwalya, P. Caron, A. Cattaneo, D. Garrity, K. Henry, and R. Hottle, 2014: Climate-smart agriculture for food security. Nature Climate Change 4(12), 1068-1072. https://doi.org/10.1038/nclimate2437
  41. McMillen, R. T., 1988: An eddy correlation technique with extended applicability to non-simple terrain. Boundary-Layer Meteorology 43(3), 231-245. https://doi.org/10.1007/BF00128405
  42. Mizoguchi, Y., A. Miyata, Y. Ohtani, R. Hirata, and S. Yuta, 2009: A review of tower flux observation sites in Asia. Journal of forest research, 14(1), 1-9. https://doi.org/10.1007/s10310-008-0101-9
  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. https://doi.org/10.1098/rstb.1977.0140
  44. NCAM, 2013: Advancement in urban, agricultural and forest land surface model (II): Designing land surface model for agricultural management using long-term flux data, Seoul, South Korea, 69pp.
  45. NCIS., 2017: 2017 Explanatory Notes on Major Crops (Rice) Varieties (2017주요 식랑작물(벼) 품종해설서), National Institute of Crop Science, Korea.
  46. 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. https://doi.org/10.1186/2048-7010-2-12
  47. Nielsen, S., and S. Jorgensen, 2013: Goal functions, orientors and indicators (GoFOrIt's) in ecology. Application and functional aspects-Strengths and weaknesses. Ecological Indicators, 28, 31-47.
  48. Odum, E., 1969: The strategy of ecosystem development. Science (New York, NY), 164(3877), 262. https://doi.org/10.1126/science.164.3877.262
  49. Palombi, L., and R. Sessa, 2013: Climate-smart agriculture: sourcebook. Food and Agriculture Organization of the United Nations(FAO).
  50. 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 Ecosystem Exchange measured with eddy covariance technique: algorithms and uncertainty estimation. Biogeosciences 3(4), 571-583. https://doi.org/10.5194/bg-3-571-2006
  51. Ponton, S., L. B. Flanagan, K. P. Alstad, B. G. Johnson, K. A. I. Morgenstern, N. Kljun, T. A. Black, and A. G. Barr, 2006: Comparison of ecosystem water-use efficiency among Douglas-fir forest, aspen forest and grassland using eddy covariance and carbon isotope techniques. Global Change Biology 12(2), 294-310. https://doi.org/10.1111/j.1365-2486.2005.01103.x
  52. Prokopenko, M., F. Boschetti, and A. J. Ryan, 2009: An information-theoretic primer on complexity, self-organization, and emergence. Complexity 15(1), 11-28. https://doi.org/10.1002/cplx.20249
  53. 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. https://doi.org/10.1111/j.1365-2486.2006.01224.x
  54. Reichstein, M., E. Falge, D. Baldocchi, D. Papale, M. Aubinet, P. Berbigier, . . ., and A. Granier, 2005: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm. Global Change Biology 11(9), 1424-1439. https://doi.org/10.1111/j.1365-2486.2005.001002.x
  55. Rosenstock, T. S., C. Lamanna, S. Chesterman, P. Bell, A. Arslan, M. Richards, J. Rioux, A. O. Akinleye, C. Champalle, Z. Cheng, and C. Corner-Dolloff, 2016: The scientific basis of climate-smart agriculture: A systematic review protocol.
  56. Saito, M., A. Miyata, H. Nagai, and T. Yamada, 2005: Seasonal variation of carbon dioxide exchange in rice paddy field in Japan. Agricultural and forest meteorology 135(1-4), 93-109. https://doi.org/10.1016/j.agrformet.2005.10.007
  57. Schneider, E. D., and J. J. Kay, 1994: Life as a manifestation of the second law of thermodynamics. Mathematical and computer modelling 19(6), 25-48. https://doi.org/10.1016/0895-7177(94)90188-0
  58. Svirezhev, Y., 2010: Entropy and Entropy Flows in the Biosphere. Global Ecology 154.
  59. 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 respiration from the vertical turbulent flux and change in storage of CO 2. Agricultural and forest meteorology 149(11), 1919-1930. https://doi.org/10.1016/j.agrformet.2009.06.020
  60. Wang, E., C. J. Smith, W. J. Bond, and K. Verburg, 2005: Estimations of vapor pressure deficit and crop water demand in APSIM and their implications for prediction of crop yield, water use, and deep drainage. Crop and Pasture Science 55(12), 1227-1240.
  61. 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. https://doi.org/10.1016/j.agwat.2017.03.029
  62. Webb, E. K., G. I. Pearman, and R. Leuning, 1980: Correction of flux measurements for density effects due to heat and water vapor transfer. Quarterly Journal of the Royal Meteorological Society 106(447), 85-100. https://doi.org/10.1002/qj.49710644707
  63. Xin, F., X. Xiao, B. Zhao, A. Miyata, D. Baldocchi, S. Knox, M. Kang, K. M. Shim, S. Min, B. Chen, and X. Li, 2017: Modeling gross primary production of paddy rice cropland through analyses of data from $CO_2$ eddy flux tower sites and MODIS images. Remote sensing of environment 190, 42-55.
  64. Yoo, G., and J. Kim, 2007: Development of a methodology assessing rice production vulnerabilities to climate change. RE-14. Korea Environment Institute.
  65. 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. https://doi.org/10.1111/j.1469-8137.2007.02316.x
  66. Yun, J., M. Kang, S. Kim, J. H. Chun, C. H. Cho, and J. Kim, 2014: How is the Process Network Organized and When Does it Show Emergent Properties in a Forest Ecosystem? In ISCS 2013: Interdisciplinary Symposium on Complex Systems, 307-317. Springer Berlin Heidelberg.
  67. 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. https://doi.org/10.1016/j.ecolind.2011.07.008
  68. 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. https://doi.org/10.1111/j.1466-8238.2008.00442.x
  69. 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. https://doi.org/10.1007/s10980-012-9763-y