Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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A Study on the Prediction of Suitability Change of Forage Crop Italian Ryegrass (Lolium multiflorum L.) using Spatial Distribution Model

공간분포모델을 활용한 사료작물 이탈리안 라이그라스(Lolium multiflorum L.)의 재배적지 변동예측연구

  • Kim, Hyunae (Department of Plant Science, College of Agriculture and Life Science, Seoul National University) ;
  • Hyun, Shinwoo (Department of Plant Science, College of Agriculture and Life Science, Seoul National University) ;
  • Kim, Kwang Soo (Department of Plant Science, College of Agriculture and Life Science, Seoul National University)
  • 김현애 (서울대학교 농업생명과학대학 식물생산과학부) ;
  • 현신우 (서울대학교 농업생명과학대학 식물생산과학부) ;
  • 김광수 (서울대학교 농업생명과학대학 식물생산과학부)
  • Received : 2014.05.29
  • Accepted : 2014.06.21
  • Published : 2014.06.30
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Abstract

Under climate change, it is likely that the suitable area for forage crop cultivation would change in Korea. The potential cultivation areas for italian ryegrass (Lolium multiflorum L.), which has been considered one of an important forage crop in Korea, were identified using the EcoCrop model. To minimize the uncertainty associated with future projection under climate change, an ensemble approach was attempted using five climate change scenarios as inputs to the EcoCrop model. Our results indicated that most districts had relatively high suitability, e.g., >80, for italian ryegrass in South Korea. Still, suitability of the crop was considerably low in mountainous areas because it was assumed that a given variety of italian ryegrass had limited cold tolerance. It was predicted that suitability of italian ryegrass would increase until 2050s but decrease in 2080s in a relatively large number of regions due to high temperature. In North Korea, suitability of italian ryegrass was considerably low, e.g., 28 on average. Under climate change, however, it was projected that suitability of italian ryegrass would increase until 2080s. For example, suitability of italian ryegrass was more than 80 in 10 districts out of 14 by 2080s. Because cold tolerance of italian ryegrass varieties has been improved, it would be preferable to optimize parameters of the EcoCrop model for those varieties. In addition, it would be possible to grow italian ryegrass as the second crop following rice in Korea in the future. Thus, it merits further study to identify suitable areas for italian ryegrass cultivation after rice using new varieties of italian ryegrass.

우리나라에서의 사료작물 생산면적이 제한적이기 때문에 미래의 기후조건에서 최적 재배 가능 지역을 중심으로 이탈리안 라이그라스와 같은 사료작물의 생산체계를 설계하는 것이 필요하다. 특히, 한반도를 대상으로 이탈리안 라이그라스의 재배 가능지역을 파악하는 것이 미래를 대비한 정책 결정에 도움을 줄 수 있다. 이번 연구에서는 기후자료를 기반으로 작물의 재배적합도를 예측하는 EcoCrop 모델을 사용하여 현재(1950~2000), 2020년대(2010~2039), 2050년대(2040~2069), 2080년대(2070~2099)의 이탈리안 라이그라스의 재배 가능지역을 분석하였다. 또한, 전구 기후모델인 CCCMA, CSIRO, UKMO-HadCM3, UKMO-HadGEM1, 그리고 NCAR 모델 등으로부터 얻어진 규모축소 기후자료를 활용한 앙상블 예측기법을 재배적합도 예측에 적용하여 미래 기후변화 조건에서의 불확실도를 낮추는 것을 시도하였다. 2050년대까지 이탈리안 라이그라스의 재배적합도는 남한과 북한 모두 크게 상승할 것으로 예측되었다. 예를 들어, 현재 기후조건에서 충청북도와 강원도에서 평균적인 재배적합도가 76.75와 44.77으로 낮게 예측되었지만 2020년대에 각각 16.2% 및 46.1% 증가하여 2080년대에는 모든 행정구역에서 평균적인 재배적합도가 90이상으로 나타날 것으로 예측되었다. 반면, 2080년대에 16개의 시 도 중 11개의 지역에서 재배적합도가 감소할 것으로 예측되었다. 북한의 경우 현재 기후조건에서 평균적인 재배적합도는 28.40으로 평균적인 재배적합도가 낮았다. 그러나 기후변화가 진행되면서 재배적합도가 크게 증가하여 2080년대에는 14개 행정구역 중 10곳에서 평균적인 재배적합도가 80 이상일 것으로 예측되었다. 특히 나선, 신의주 및 개성 인근 지역의 재배적합도가 크게 증가할 것으로 예측되어 이를 중심으로 수출을 위한 사료 생산단지 및 축산단지 조성이 가능할 것으로 예상되었다. 현재, 내한성 향상을 중심으로 이탈리안 라이그라스의 새로운 품종들이 개발 및 보급되고 있어 이러한 신품종을 대상으로 한 이모작 가능지를 구분하기 위해 품종별로 최적화된 모수를 활용한 재배적합도 예측지도를 작성연구가 연구가 필요할 것으로 사료되었다.

Keywords

References

  1. Choi, G. J., Y. C. Lim, K. Y. Kim, B. R. Sung, J. K. Lee, H. C. Ji, H. S. Park, D. K. Kim, J. S. Moon, and S. Seo, 2007: A ultra cold-tolerant and early-maturing italian ryegrass (Lolium multiflorum Lam.) new variety, 'Kowinearly'. Proceedings of the Korean Society of Grassland and Forage Science conference 45, 192-193. (in Korean with English abstract)
  2. Dzyubenko, N. I., E. A. Dzyubenko, and A. N. Dzyubenko, 2004: Interactive agricultural ecological atlas of Russia and neighboring countries. Economic Plants and Their Diseases, Pests and Weeds [Online]. Available at: http://www.agroatlas.ru/en/content/related/Loniceraedulis/.
  3. Feng, J., D. K. Lee, C. Fu, J. Tang, Y. Sato, H. Kato, J. L. Megregor, and K. Mabuchi, 2011: Comparison of four ensemble methods combining regional climate simulations over Asia. Meteorology and Atmospheric Physics 111, 41-53. https://doi.org/10.1007/s00703-010-0115-7
  4. Hijmans, R. J., L. Guarino, M. Cruz, and E. Rojas, 2001: Computer tools for spatial analysis of plant genetic resources data. 1. DIVA-GIS. Plant Genetic Resources Newsletter 127, 15-19.
  5. Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis, 2005: Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25, 1965-1978. https://doi.org/10.1002/joc.1276
  6. IPCC, 2007: The AR4 Synthesis Report.
  7. MIFAFF, 2011: Forage production and utilization for animal production. Ministry of Food, Agriculture, Forest and Fisheries. (in Korean)
  8. Kim, K. D., K. I. Sung, Y. S. Jung, H. I. Lee, E. J. Kim, J. G. Nejad, M. H. Jo, and Y. C. Lim, 2012: Suitability classes for italian ryegrass (Lolium multiflorum Lam.) using soil and climate digital database in Gangwon Province. The Korean Society of Grassland and Forage Science 32(4), 437-446. (in Korean with English abstract) https://doi.org/10.5333/KGFS.2012.32.4.437
  9. Kim, K. Y., H. C. Ji, S. H. Lee, K. W. Lee, W. H. Kim, M. W. Jung, S. Seo, and G. J. Choi, 2010: Yield and nutritive value of spring-seeded early and late maturity italian ryegrass (Lolium multiflorum Lam.). The Korean Society of Grassland and Forage Science 30(2), 97-102. (in Korean with English abstract) https://doi.org/10.5333/KGFS.2010.30.2.097
  10. Kim, M. J., J. G. Kim, G. S. Ki, G. J. Choi, S. Seo, and S. H. Yoon, 2007: Feeding effect of TMR using italian ryegrass silage on milk yield and composition in dairy cattle. Proceedings of the Korean Society of Grassland and Forage Science conference 45, 254-255. (in Korean with English abstract)
  11. Kim, W. H., K. Y. Kim, M. W. Jung, H. C. Ji, Y. C. Lim, S. Seo, J. D. Kim, B. K. Yoon, and H. W. Lee, 2011: Dry matter yield and forage quality at mixture of annual legumes and italian ryegrass on paddy field. Journal of the Korean society of grassland and forage science 31, 33-38. (in Korean with English abstract) https://doi.org/10.5333/KGFS.2011.31.1.33
  12. Kim, Y. D., S. K. Suh, H. K. Park, J. S. Chae, M. G. Shin, and J. S. Yang, 1991: Multiple fodder cropping systems of upland fodder crops in southern region. Research Reports of the Rural Development Administration 33, 47-53. (in Korean with English abstract)
  13. Krishnamurti, T. N., C. M. Kishtawal, T. E. LaRow, D. R. Bachiochi, Z. Zhang, E. Williford, S. Gadgil, and S. Surendran, 1999: Improved weather and seasonal climate forecasts from multimodel superensemble. Science 285, 1548-1550. https://doi.org/10.1126/science.285.5433.1548
  14. Krishnamurti, T. N., C. M. Kishtawal, Z. Zhang, T. LaRow, D. Bachiochi, and E. Williford, 2000: Multimodel ensemble forecasts for weather and seasonal climate. Journal of Climate 13, 4196-4216. https://doi.org/10.1175/1520-0442(2000)013<4196:MEFFWA>2.0.CO;2
  15. Lee, B. W., J. C. Shin, and J. H. Bong, 1991: Impact of climate change induced by the increasing atmospheric $CO_{2}$ concentration on agroclimatic resources, net primary productivity and rice yield potential in Korea. Korean Journal of Crop Science 36, 112-126. (in Korean with English abstract)
  16. Ramirez-Villegas, J., A. Jarvis, and P. Laderach, 2013: Empirical Approaches for Assessing Impacts of Climate Change on Agriculture: The EcoCrop model and a case study with grain sorghum. Agricultural and Forest Meteorology 170: 67-78. https://doi.org/10.1016/j.agrformet.2011.09.005
  17. Seo, S., W. H. Kim, K. Y. Kim, G. J. Choi, H. C. Ji, S. H. Lee, K. W. Lee, and M. J. Kim, 2011: Forage productivity and quality of domestic italian ryegrass and barley varieties. The Korean Society of Grassland and Forage Science 31, 261-268. (in Korean with English abstract) https://doi.org/10.5333/KGFS.2011.31.3.261
  18. Shin, J. C., C. G. Lee, Y. H. Yoon, and Y. S. Kang, 2000: Impact of climate variability and change on crop productivity. Korean Journal of Crop Science 45, 12-27. (in Korean with English abstract)
  19. Suh, M. S., S. G. Oh, D. K. Lee, D. H. Cha, S. J. Choi, C. S. Jin, and S. Y. Hong, 2012: Development of new ensemble methods based on the performance skills of regional climate models over South Korea. Journal of Climate 25, 7067-7085. https://doi.org/10.1175/JCLI-D-11-00457.1
  20. Yang, J. S., 1992: Cultivation of forage crops after rice on paddy-land. Journal of Korean Grassland Forage Science 31, 261-268. (in Korean with English abstract)
  21. Yun, W. T., L. Stefanova, and T. N. Krishinamurti, 2003: Improvement of the multimodel superensemble technique for seasonal forecasts. Journal of Climate 16, 3834-3840. https://doi.org/10.1175/1520-0442(2003)016<3834:IOTMST>2.0.CO;2
  22. Weigel, A. P., R. Knutti, M. A. Liniger, and C. Appenzeller, 2010: Risks of model weighting in multimodel climate projections. Journal of Climate 23, 4175-4191. https://doi.org/10.1175/2010JCLI3594.1