DOI QR코드

DOI QR Code

기온, 강수량, 이산화탄소농도 변화에 따른 CERES-Barley 국내품종의 종실수량 반응

Grain Yield Response of CERES-Barley Adjusted for Domestic Cultivars to the Simultaneous Changes in Temperature, Precipitation, and CO2 Concentration

  • 김대준 (경희대학교 식물환경신소재공학과) ;
  • 노재환 (국립식량과학원 벼맥류부) ;
  • 윤진일 (경희대학교 식물환경신소재공학과)
  • Kim, Dae-Jun (College of Life Science, Kyung Hee University) ;
  • Roh, Jae-Hwan (Rice Research Division, National Institute of Crop Science) ;
  • Yun, Jin I. (College of Life Science, Kyung Hee University)
  • 투고 : 2013.12.17
  • 심사 : 2013.12.26
  • 발행 : 2013.12.30

초록

주어진 작물모형이 기후요소 중 어떤 것에 얼마만큼 반응하는지 상대적인 민감도를 모르는 상태에서 계산결과만으로 미래 전망을 내리는 것은 적절치 않을 수 있다. 본 연구에서는 미래기후전망에 근거한 국내 보리 품종의 민감도 변화 실험을 통해 기온, 강수량, 이산화탄소 농도의 변화가 작물의 생산량에 미치는 상대적인 영향을 분석하였다. 주요 보리 품종(올보리-수원, 알보리-밀양, 새쌀보리-익산, 삼도보리-진주)에 맞게 품종모수가 조정된 CERES-Barley를 RCP 8.5 시나리오의 2071-2100 기후변동범위(1981-2010 기준 기온 $-1^{\circ}C{\sim}+8^{\circ}C$, 강수량 -50% ~ +50%, $CO_2$ 농도 330ppm ~ 900ppm)에 근거한 99개 처리조합에 의해 구동하였다. 이들 처리조합에 의해 생성된 11,880개 종실수량 계산결과를 토대로 각 기후요소의 변화에 따른 종실수량의 민감도를 분석한 결과 이산화탄소농도 변화에 가장 민감한 반응을 보인 반면 강수량 변동에 대한 반응이 가장 무디었고, 온도변화에 대해서는 기준온도대비 상승, 하강 모두 감소하는 경향이었지만 품종 별로 차이가 뚜렷하였다. CERES-Barley는 우리나라에서 농업부문 기후변화 영향평가에 널리 사용되므로 실험설계 시 이러한 모형의 민감도를 감안하여야 현실성 있는 생육모의가 가능하다.

Our understanding of the sensitivities of crop responses to changes in carbon dioxide, temperature, and water is limited, which makes it difficult to fully utilize crop models in assessing the impact of climate change on future agricultural production. Genetic coefficients of CERES-Barley model for major domestic cultivars in South Korea (Olbori at Suwon, Albori at Milyang, Saessalbori at Iksan, and Samdobori at Jinju) were estimated from the observed data for daily weather and field trials for more than 10 years by using GenCalc in DSSAT. Data from 1997-2002 annual crop status report (Rural Development Administration, RDA) were used to validate the crop coefficients. The sitecalibrated CERES-Barley model was used to perform crop growth simulation with the 99 treatments of step change combinations in temperature, precipitation and carbon dioxide concentration with respect to the baseline climate (1981-2010) at four sites. The upper boundary corresponds to the 2071-2100 climate outlook from the RCP 8.5 scenario. The response surface of grain yield showed a distinct pattern of model behavior under the combined change in environmental variables. The simulated grain yield was most sensitive to $CO_2$ concentration, least sensitive to precipitation, and showing a variable response to temperature depending on cultivar. The emulated impacts of response surfaces are expected to facilitate assessment of projected climate impacts on a given cultivar in South Korea.

키워드

참고문헌

  1. Jones, J. W., G. Hoogenboom, C. H. Porter, K. J. Boote, W. D. Batchelor, L. A. Hunt, P. W. Wilkens, U. Singh, A. J. Gijsman, and J. T. Ritchie, 2003: DSSAT Cropping System Model. European Journal of Agronomy 18, 235-265. https://doi.org/10.1016/S1161-0301(02)00107-7
  2. Kim, D. J., J. H. Roh, J. G. Kim, and J. I. Yun, 2013: The Influence of Shifting Planting Date on Cereal Grains Production under the Projected Climate Change. Korean Journal of Agricultural and Forest Meteorology 15(1), 26-39. (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2013.15.1.026
  3. Kim, D. J., S. O. Kim, K. H. Moon, and J. I. Yun, 2012: An outlook on cereal grains production in South Korea based on crop growth simulation under the RCP 8.5 climate change scenarios. Korean Journal of Agricultural and Forest Meteorology 14(3), 132-141 (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2012.14.3.132
  4. Kim, S. K., J. S. Park, Y. S. Lee, H. C. Seo, K. S. Kim, and J. I. Yun, 2004: Development and Use of Digital Climate Models in Northern Gyunggi Province - II. Site-specific Performance Evaluation of Soybean Cultivars by DCMbased Growth Simulation. Korean Journal of Agricultural and Forest Meteorology 6(1), 61-69. (in Korean with English abstract)
  5. Kim, Y. H., H. D. Kim, S. W. Han, J. Y. Choi, J. M. Koo, U. Chung, J. Y. Kim, and J. I. Yun, 2002: Using Spatial Data and Crop Growth Modeling to Predict Performance of South Korean Rice Varieties Grown in Western Coastal Plains in North Korea. Korean Journal of Agricultural and Forest Meteorology 4(4), 224-236. (in Korean with English abstract)
  6. Ruane, A. C., S. Mcdermid, and C. Rosenzweig, 2013: C3MP protocols and procedures. C3MP, AgMIP, 1-14.
  7. Shim, K. M., S. H. Min, D. B. Lee, G. Y. Kim, H. C. Jeong, S. B. Lee, and K. K. Kang, 2011: Simulation of the effects of the A1B climate change scenario on the potential yield of winter naked barley in Korea. Korean Journal of Agricultural and Forest Meteorology 13(4), 192-203. (in Korean with English abstract) https://doi.org/10.5532/KJAFM.2011.13.4.192
  8. Yun, J. I., 1990: Analysis of the Climatic Impact on Korean Rice Production under the Carbon Dioxide Scenario. Journal of Korean Meteorological Society 26(4), 263-274. (in Korean with English abstract)
  9. Yun, J. I., J. M. Hwang, and S. G. Lee, 1997a: Simulation of Local Climate and Crop Productivity in Andong after Multi-Purpose Dam Construction. Korean Journal of Crop Science 42(5), 579-596.
  10. Yun, J. I., K. H. Hwang, H. H. Chung, M. Y. Shin, J. T. Lim, and J. C. Shin, 1997b: Effects of an Artificial Lake on the Local Climate and the Crop Production in Suncheon Area. Journal of Korean Meteorological Society 33(3), 409-427. (in Korean with English abstract)
  11. Yun, J. I., and K. H. Lee, 2000: Agroclimatology of North Korea for Paddy Rice Cultivation: Preliminary Results from a Simulation Experiment. Korean Journal of Agricultural and Forest Meteorology 2(2), 47-61. (in Korean with English abstract)
  12. 국립기상연구소, 2011: IPCC 5차 평가보고서 대응을 위한 기후변화 시나리오 보고서 2011. 79-110. 기상청.
  13. 한원식, 1997: 농업생명과학을 위한 통계적 방법. 자유아카데미, 503-529.

피인용 문헌

  1. Yearly Variation in Growth and Yield of Winter Hulless Barley at Iksan vol.61, pp.3, 2016, https://doi.org/10.7740/kjcs.2016.61.3.171
  2. Classifying multi-model wheat yield impact response surfaces showing sensitivity to temperature and precipitation change vol.159, 2018, https://doi.org/10.1016/j.agsy.2017.08.004