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Estimation of Crop Yield and Evapotranspiration in Paddy Rice with Climate Change Using APEX-Paddy Model

APEX-Paddy 모델을 이용한 기후변화에 따른 논벼 생산량 및 증발산량 변화 예측

  • Choi, Soon-Kun (Climate Change and Agroecology Division, National Institute of Agricultural Science) ;
  • Kim, Min-Kyeong (Climate Change and Agroecology Division, National Institute of Agricultural Science) ;
  • Jeong, Jaehak (Texas A&M AgriLife Research, Texas A&M University) ;
  • Choi, Dongho (Climate Change and Agroecology Division, National Institute of Agricultural Science) ;
  • Hur, Seung-Oh (Climate Change and Agroecology Division, National Institute of Agricultural Science)
  • Received : 2016.08.23
  • Accepted : 2017.06.19
  • Published : 2017.07.31

Abstract

The global rise in atmospheric $CO_2$ concentration and its associated climate change have significant effects on agricultural productivity and hydrological cycle. For food security and agricultural water resources planning, it is critical to investigate the impact of climate change on changes in agricultural productivity and water consumption. APEX-Paddy model, which is the modified version of APEX (Agricultural Policy/Environmental eXtender) model for paddy ecosystem, was used to evaluate rice productivity and evapotranspiration based on climate change scenario. Two study areas (Gimjae, Icheon) were selected and the input dataset was obtained from the literature. RCP (Representitive Concentration Pathways) based climate change scenarios were provided by KMA (Korean Meteorological Administration). Rice yield data from 1997 to 2015 were used to validate APEX-Paddy model. The effects of climate change were evaluated at a 30-year interval, such as the 1990s (historical, 1976~2005), the 2025s (2011~2040), the 2055s (2041~2070), and the 2085s (2071~2100). Climate change scenarios showed that the overall evapotranspiration in the 2085s reduced from 10.5 % to 16.3 %. The evaporations were reduced from 15.6 % to 21.7 % due to shortend growth period, the transpirations were reduced from 0.0% to 24.2 % due to increased $CO_2$ concentration and shortend growth period. In case of rice yield, in the 2085s were reduced from 6.0% to 25.0 % compared with the ones in the 1990s. The findings of this study would play a significant role as the basics for evaluating the vulnerability of paddy rice productivity and water management plan against climate change.

Keywords

References

  1. Allen, L. H., D. Pan, K. J. Boote, N. B. Pickering, and J. W. Jones, 2003. Carbon dioxide and temperature effects on evapotranspiration and water use efficiency of soybean. Agronomy Journal 95(4): 1071-1081. https://doi.org/10.2134/agronj2003.1071
  2. Baier, W. and G. W. Robertson, 1965. Estimation of latent evaporation from simple weather observations. Canadian Journal of Plant Science 45(3): 276-284. https://doi.org/10.4141/cjps65-051
  3. Chung, S.O., 2010. Simulating Evapotranspiration and Yield Response of Rice to Climate Change using FAO-AquaCrop. Journal of the Korean Society of Agricultural Engineers 52(3): 57-64 (in Korean). https://doi.org/10.5389/KSAE.2010.52.3.057
  4. Hargreaves, G. H. and Z. A. Samani, 1985. Reference crop evapotranspiration from temperature. American Society of Agricultural Engineers 1(2):96-99.
  5. IPCC, 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. IPCC, Geneva, Switzerland.
  6. Jang, S. S., S. R. Ahn, H. K. Joh, and S. J. Kim, 2015. Assessment of climate change impact on Imha-dam watershed hydrologic cycle under RCP scenarios. Journal of the Korean Association of Geographic Information Studies 18(1): 156-169 (in Korean). https://doi.org/10.11108/kagis.2015.18.1.156
  7. Jones, C. A. and J. R. Kiniry, 1986. CERES-Maize: a simulation model of maize growth and development. Texas A&M Univirsity Press, Collage Station, Texax, USA.
  8. Jones, R. J. and T. A. Mansfield. 1970. Increases in the diffusion resistances of leaves in a carbon dioxide-enriched atmosphere. J. Expt. Bot. 21: 951-958. https://doi.org/10.1093/jxb/21.4.951
  9. Kim D. J., J. H. Roh, and J. I. Yun, 2013a. Grain yield response of CERES-Barley adjusted for domestic cultivars to the simultaneous changes in temperature, precipitation, and CO2 concentration. Korean Society of Agricultural and Forest Meteorology 15(4): 312-319 (in Korean). https://doi.org/10.5532/KJAFM.2013.15.4.312
  10. Kim, M. K., M. S. Han, D. H. Jang, S. G. Baek, W. S. Lee, Y. H. Kim, and S. Kim, 2012. Production technique of Observation grid data of 1km resolution. Journal of Climate 7(1): 55-68.
  11. Kim, M. K., D. H. Lee, and J. U. Kim, 2013b. Production and validation of daily grid data with 1km resolution in South Korea. Journal of Climate 8(1): 13-25.
  12. Kim, W. T., D. R. Lee, and C. Yoo, 2004. Effects of climate change on the streamflow for the daechung dam watershed. Journal of Korean Water Resources Association 37(4): 305-314 (in Korean). https://doi.org/10.3741/JKWRA.2004.37.4.305
  13. Kimball, B. A., 1983. Carbon dioxide and agricultural yield: An assemblage and analysis of 430 prior observations. Agronomy 75: 779-788. https://doi.org/10.2134/agronj1983.00021962007500050014x
  14. Kimball, B. A. and S.B. Idso, 1983. Increasing atmospheric CO2: Effects on crop yield, water use, and climate. Agri. Water Manage. 7: 55-72. https://doi.org/10.1016/0378-3774(83)90075-6
  15. Lai, M., K. K. Singh, L. S. Rathore, G. Srinivasan, S. A. Saseendran, 1998. Vulnerability of rice and wheat yield in NW India to future changes in climate. Agricultural and Forest Meteorology 89: 101-114. https://doi.org/10.1016/S0168-1923(97)00064-6
  16. Lee, T. S., J. Y. Choi, S. H. Yoo, S. H. Lee, and Y. G. Oh, 2012. Analyzing consumptive use of water and yields of paddy rice by climate change. Journal of the Korean Society of Agricultural Engineers 54(1): 47-54 (in Korean). https://doi.org/10.5389/KSAE.2012.54.1.047
  17. Matthews, R. B., M. J. Kropff, T. Horie, and D. Bachelet, 1997. Simulation the impact of climate change on rice production in Asia and evaluating options for adaptation. Agricultural Systems 54(3): 399-425. https://doi.org/10.1016/S0308-521X(95)00060-I
  18. Miyazaki, N., K. Kamewada, and S. Iwasaki, 2005. Quality changes of agricultural water passing through paddy fields. Bulletin of the Tochigi Prefectural Agricultural Experiment Station 55, 45-55.
  19. Monteith, J. L., 1964. Evaporation and environment. Symposia of the society for experimental biology 19: 205-234.
  20. Monteith, J. L. and C. J. 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
  21. National Institute of Agricultural Sciences, Http://soil.rda.go.kr. Accessed 7 Jun. 2016.
  22. Neitsch, S. L., J. G. Arnold, J. R. Kiniry, and J. R. Williams, 2011. Soil and water assessment tool theoretical documentation version 2009. Texas Water Resources Institute.
  23. Penman, H. L., 1948. Natural evaporation from open, bare soil and grass. Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences 193(1032): 120-145. https://doi.org/10.1098/rspa.1948.0037
  24. Priestley, C. H. B. and R. J. Taylor, 1972. On the assessment of surface heat flux and evaporation using large-scale parameters. Monthly weather review 100(2): 81-92. https://doi.org/10.1175/1520-0493(1972)100<0081:OTAOSH>2.3.CO;2
  25. Ruane, A. C., N. I. Hudson, S. Asseng, D. Camarrano, F. Ewert, P. Martre, and B. Basso, 2016. Multi-wheat-model ensemble responses to interannual climate variability. Environmental Modelling & Software 81: 86-101. https://doi.org/10.1016/j.envsoft.2016.03.008
  26. Rural Development Administrator, 2009. 2008 crop test report. 33-34.
  27. Sharpley, A. N. and J. R. Williams, 1990. EPIC, Erosion/productivity impact calculator, I. Model documentation. Technical Bulletin - United States Department of Agriculture 1768.
  28. Shim, K. M., K. A. Roh, K. H. So, G. Y. Kim, H. C. Jeong, and D. B. Lee, 2010. Assessing impacts of global warming on rice growth and production in Korea. Journal of Climate Change Research 1(2): 121-131 (in Korean).
  29. Statistics Korea, Http://kosis.kr. Accessed 11 May 2016.
  30. Steglich, E. M. and J. W. Williams, 2013. Agricultural Policy/Environmental Extender Model User's Manual Version 0806. BREC Report.
  31. Stockle, C. O., J. R. Williams, N. J. Rosenberg, and C. A. Jones, 1992. A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part I - Modification of the EPIC model for climate change analysis. Agricultural Systems 38(3): 225-238. https://doi.org/10.1016/0308-521X(92)90067-X
  32. Stooksbury, D., 2003. Evaluating CROPGRO-soybean performance for use in climate impact studies. Agronomy Journal 95: 537-544. https://doi.org/10.2134/agronj2003.0537
  33. Vaghefi, N., M. N. Shamsudin, A. Makmom, and M. Bagheri, 2011. The economic impacts of climate change on the rice production in Malaysia. International Journal of Agricultural Research 6(1): 67-74. https://doi.org/10.3923/ijar.2011.67.74
  34. Williams, J. R. and R. C. Izaurralde, 2006. The APEX model. Watershed models 437-482.
  35. Williams, J. R., R. C. Izaurralde, and E. M. Steglich, 2012. Agricultural policy/environmental extender model theoretical documentation version 0806. Texas A&M Blackland Research Center Temple.
  36. Williams, J. R., C. A. Jones, and P. Dyke, 1984. A modeling approach to determining the relationship between erosion and soil productivity. Transactions of the ASAE 27(1): 129-144. https://doi.org/10.13031/2013.32748