Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
권호 표지
DOI QR코드

DOI QR Code

Long Range Forecast of Garlic Productivity over S. Korea Based on Genetic Algorithm and Global Climate Reanalysis Data

전지구 기후 재분석자료 및 인공지능을 활용한 남한의 마늘 생산량 장기예측

  • Jo, Sera (Climate change Assessment Division, National Institute of Agricultural Sciences) ;
  • Lee, Joonlee (School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology) ;
  • Shim, Kyo Moon (Climate change Assessment Division, National Institute of Agricultural Sciences) ;
  • Kim, Yong Seok (Climate change Assessment Division, National Institute of Agricultural Sciences) ;
  • Hur, Jina (Climate change Assessment Division, National Institute of Agricultural Sciences) ;
  • Kang, Mingu (Climate change Assessment Division, National Institute of Agricultural Sciences) ;
  • Choi, Won Jun (Climate change Assessment Division, National Institute of Agricultural Sciences)
  • 조세라 (국립농업과학원 기후변화평가과) ;
  • 이준리 (울산과학기술원 도시환경공학부) ;
  • 심교문 (국립농업과학원 기후변화평가과) ;
  • 김용석 (국립농업과학원 기후변화평가과) ;
  • 허지나 (국립농업과학원 기후변화평가과) ;
  • 강민구 (국립농업과학원 기후변화평가과) ;
  • 최원준 (국립농업과학원 기후변화평가과)
  • Received : 2021.10.07
  • Accepted : 2021.11.15
  • Published : 2021.12.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study developed a long-term prediction model for the potential yield of garlic based on a genetic algorithm (GA) by utilizing global climate reanalysis data. The GA is used for digging the inherent signals from global climate reanalysis data which are both directly and indirectly connected with the garlic yield potential. Our results indicate that both deterministic and probabilistic forecasts reasonably capture the inter-annual variability of crop yields with temporal correlation coefficients significant at 99% confidence level and superior categorical forecast skill with a hit rate of 93.3% for 2 × 2 and 73.3% for 3 × 3 contingency tables. Furthermore, the GA method, which considers linear and non-linear relationships between predictors and predictands, shows superiority of forecast skill in terms of both stability and skill scores compared with linear method. Since our result can predict the potential yield before the start of farming, it is expected to help establish a long-term plan to stabilize the demand and price of agricultural products and prepare countermeasures for possible problems in advance.

본 연구에서는 최신의 연구 트렌드인 빅데이터와 인공지능을 농업분야에 접목하여 유전자 알고리즘(GA)과 전지구 기후 재분석 자료를 활용한 마늘 생산량의 장기 예측 모형을 개발하고 그 예측성능을 평가해 보았다. 해당 모형은 마늘의 파종량을 수정할 수 있는 11월에 예측 자료를 생산하므로, 마늘의 생산 시기와 시간공간적으로 떨어진 전지구 기후 재분석 자료로부터 마늘생산량의 예측 인자로 활용할 수 있는 시그널을 찾아 장기적 마늘 생산량 예측에 활용하였다. 그 결과 결정론적 예측과 확률론적 예측 모두 마늘 생산량의 경년변동성을 통계적으로 99% 신뢰수준에서 관측과 유사하게 모의하였으며, 범주형 예측에서도 이분위 예측에서 93.3%, 삼분위 예측에서 73.3%의 적중률을 보이며 우수한 예측 성능을 나타내었다. 또한, 예측인자들 사이의 선형 및 비선형적 관계를 모두 고려하는 GA방법을 사용하였을 때, 선형적 앙상블 방법을 적용하였을 때 보다 높은 예측성능과 안정적인 예측결과를 보이는 것을 알 수 있다. 본 연구에서 개발된 마늘 생산량 예측 모형은 기존의 단기예측 위주의 농산물 생산량 예측의 한계를 극복하고 한 해의 농사가 시작되기 전 잠재 생산량을 전망 정보를 생산하여 농산물의 수요·공급 및 가격안정화를 위한 장기적 계획을 수립하는 것에 도움이 될 것으로 생각된다.

Keywords

Acknowledgement

이 연구는 농촌진흥청 국립농업과학원 농업과학기술 연구개발사업(과제번호: PJ01493701)의 지원으로 수행되었습니다.

References

  1. Ahn, J. B., and J. L. Lee, 2016: A new multimodel ensemble method using nonlineargenetic algorithm: An application to boreal winter surface air temperature and precipitation prediction. Journal of Geophysical Research: Atmospheres 121(16), 9263-9277. https://doi.org/10.1002/2016JD025151
  2. Ahn, J. B., J. L. Lee, and S. Jo, 2018: Evaluation of PNU CGCM ensemble forecast system for boreal winter temperature over South Korea. Atmosphere 28(4), 509-520. (in Korean with English abstract) https://doi.org/10.14191/ATMOS.2018.28.4.509
  3. Barnston, A. G., 1994: Linear statistical short-term climate predictive skill in the Northern Hemisphere. Journal of Climate 7(10), 1513-1564. https://doi.org/10.1175/1520-0442(1994)007<1513:LSSTCP>2.0.CO;2
  4. Cheung, H. N., W. Zhou, H. Y. Mok, and M. C. Wu, 2012: Relationship between Ural-Siberian blocking and the East Asian winter monsoon in relation to the Arctic Oscillation and the El Nino-Southern Oscillation. Journal of Climate 25(12), 4242-4257. https://doi.org/10.1175/JCLI-D-11-00225.1
  5. Choi, S. C., and J. S. Baek, 2016: Garlic yields estimation using climate data. Journal of the Korean Data & Information Science Society 27(4), 969-977. (in Korean with English abstract) https://doi.org/10.7465/jkdi.2016.27.4.969
  6. Cohen, J., J. A. Screen, J. C. Furtado, M. Barlow, D. Whittleston, D. Coumou, J. Francis, K. Dethloff, D. Entekhabi, J. Overland, and J. Jones, 2014: Recent Arctic amplification and extreme mid-latitude weather. Nature geoscience 7(9), 627-637. https://doi.org/10.1038/ngeo2234
  7. Ding, S., B. Wu, and W. Chen, 2021: Dominant characteristics of early autumn Arctic Sea ice variability and its impact on winter Eurasian climate, Journal of Climate 34(5), 1825-1846. https://doi.org/10.1175/JCLI-D-19-0834.1
  8. Gong, D. Y., S. W. Wang, and J. H. Zhu, 2001: East Asian winter monsoon and Arctic Oscillation. Geophysical Research Letters 28(10), 2073-2076. https://doi.org/10.1029/2000GL012311
  9. Honda, M, J. Inoue, and S. Yamane, 2009: Influence of low Arctic sea-ice minima on anomalously cold Eurasian winters. Geophysical Research Letters 36(8), L08707. https://doi.org/10.1029/2008GL037079
  10. Im, E.-S., and J.-B. Ahn, 2004: Analysis of relationship between Korean winter temperature variability and global circulation indices. Atmosphere 40(4), 441-452. (in Korean with English abstract)
  11. Jeong, J. H., and C. H. Ho, 2005: Changes in occurrence of cold surges over east Asia in association with Arctic Oscillation. Geophysical Research Letters 32(14), L14704. https://doi.org/10.1029/2005GL023024
  12. Jo, S., and J.-B. Ahn, 2015: Improvement of CGCM prediction for wet season precipitation over Maritime Continent using a bias correction method. International Journal of Climatology 35(13), 3721-3732. https://doi.org/10.1002/joc.4232
  13. Jo, S., and J.-B. Ahn, 2017: Statistical forecast of early spring precipitation over South Korea using multiple linear regression. Journal of Climate Research 12(1), 53-71. (in Korean with English abstract) https://doi.org/10.14383/cri.2017.12.1.53
  14. Kim, H.-J., and J.-B. Ahn, 2010: The large-scale atmospheric circulation related to Korea winter temperature variability. Journal of Climate Research 5(2), 118-1303. (in Korean with English abstract)
  15. Kim, Y.-H., M.-K. Kim, and W.-S. Lee, 2008: An investigation of large-scale climate indices with the influence on temperature and precipitation variation in Korea. Atmosphere 18(2), 83-95. (in Korean with English abstract)
  16. Kug, J. S., J. Y. Lee, and I. S. Kang, 2008: Systematic error correction of dynamical seasonal prediction of sea surface temperature using a stepwise pattern project method. Monthly Weather Review 136(9), 3501-3512. https://doi.org/10.1175/2008mwr2272.1
  17. Kwon, M. H., 2013: Diagnosis of Northeast Asian summer precipitation using the Western North Pacific subtropical high index. Journal of the Korean Earth Science Society 34(1), 102-106. (in Korean with English abstract) https://doi.org/10.5467/JKESS.2013.34.1.102
  18. Lee, H., W. Lee, J. A. Chun, and H. W. Lee, 2020: Probabilistic heat wave forecast based on a large-scale circulation pattern using the TIGGE data. Weather and Forecasting 35(2), 367-377. https://doi.org/10.1175/waf-d-19-0188.1
  19. Lee, J.-L., and J. B. Ahn, 2019: A new statistical correction strategy to improve long-term dynamical prediction International Journal of Climatology 39(4), 2173-2185. https://doi.org/10.1002/joc.5943
  20. Lee, K.-K., K.-K. Ko, and J.-W. Lee, 2012: Correlation analysis between meteorological factors and crop products. Journal of the Environmental Sciences 21(4), 461-470 (in Korean with English abstract) https://doi.org/10.5322/JES.2012.21.4.461
  21. Lee, S. E., and K. H. Seo, 2013: The development of a statistical forecast model for Changma. Weather Forecasting 28(6), 1304-1321. https://doi.org/10.1175/WAF-D-13-00003.1
  22. Li, F., and H. Wang, 2013: Relationship between Bering Sea ice cover and East Asian winter monsoon year-to-year variations. Advances in Atmospheric Sciences 30(1), 48-56. https://doi.org/10.1007/s00376-012-2071-2
  23. Li, X., G. Gollan, R. J. Greatbatch, and R. Lu, 2019: Impact of the MJO on the interannual variation of the Pacific-Japan mode of the East Asian summer monsoon. Climate Dynamics 52(5), 3489-3501. https://doi.org/10.1007/s00382-018-4328-7
  24. Li, Y., F. Liu, and P. C. Hsu, 2020: Modulation of the intraseasonal variability of Pacific-Japan pattern by ENSO. Journal of Meteorological Research 34, 546-558. https://doi.org/10.1007/s13351-020-9182-y
  25. Liu, J., W. Zhang, R. M. Horton, C. Wang, and X. Ren, 2007: Variability of North Pacific sea ice and East Asia-North Pacific winter climate. Journal of Climate 20(10), 1991-2001. https://doi.org/10.1175/JCLI4105.1
  26. Miao, J., and T. Wang, 2020: Decadal variations of the East Asian winter monsoon in recent decades. Atmospheric Science Letters 21(4), e960. https://doi.org/10.1002/asl.960
  27. Michaelsen, J., 1987: Cross-validation in statistical climate forecast models. Journal of Applied Meteorology and Climatology 26(11), 1589-1600. https://doi.org/10.1175/1520-0450(1987)026<1589:CVISCF>2.0.CO;2
  28. Nitta, T., 1987: Convective activities in the tropical western Pacific and their impact on the Northern Hemisphere summer circulation. Journal of the Meteorological Society of Japan 65(3), 373-390.
  29. Overland, J. E., J. M. Adams, and N. A. Bond, 1999: Decadal variability of the Aleutian low and its relation to high-latitude circulation. Journal of Climate 12(5), 1542-1548. https://doi.org/10.1175/1520-0442(1999)012<1542:DVOTAL>2.0.CO;2
  30. Park, S. H., Y. S. Moon, O. J. Jeong, W. K. Kang, and D. B. Kim, 2018: Analysis of influence on garlic crops and its economical value by meteorological and climatological information. Journal of the Korean Earth Science Society 39(5), 419-435. (in Korean with English abstract) https://doi.org/10.5467/JKESS.2018.39.5.419
  31. Rigor, I. G., R. L. Colony, and S. Martin, 2000: Variations in surface air temperature observations in the Arctic, 1979-97. Journal of Climate 13(5), 896-914. https://doi.org/10.1175/1520-0442(2000)013<0896:VISATO>2.0.CO;2
  32. Takaya, K, and H. Nakamura, 2005: Mechanisms of intraseasonal amplification of the cold Siberian high. Journal of the Atmospheric Sciences 62(12), 4423-4440. https://doi.org/10.1175/JAS3629.1
  33. Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the wintertime geopotential height and temperature fields. Geophysical Research Letters 25(9), 1297-1300. https://doi.org/10.1029/98GL00950
  34. Wang, B., and X. Fu, 2000: Pacific-East Asian teleconnection: How does ENSO affect East Asian climate?. Journal of Climate 13(9), 1517-1536. https://doi.org/10.1175/1520-0442(2000)013<1517:PEATHD>2.0.CO;2
  35. Wang, B., R. Wu, and K.-M. Lau, 2001: Interannual variability of the Asian summer monsoon: Contrasts between the Indian and the western North Pacific-East Asian monsoons. Journal of Climate 14(20), 4073-4090. https://doi.org/10.1175/1520-0442(2001)014<4073:IVOTAS>2.0.CO;2
  36. Wang, B., B. Xiang, and J.-Y., Lee, 2012: Subtropical high predictability establishes a promising way for monsoon and tropical storm predictions. Proceedings of the National Academy of Sciences 110(8), 2718-2722. https://doi.org/10.1073/pnas.1214626110
  37. Wang, L., and W. Chen, 2010: How well do existing indices measure the strength of the East Asian winter monsoon?. Advances Atmospheric Sciences 27(4), 855-870. https://doi.org/10.1007/s00376-009-9094-3
  38. Weigel, A. P., M. A. Liniger, and C. Appenzeller, 2007: The Discrete Brier and Ranked Probability Skill Scores. Monthly Weather Review 135(1), 118-124. https://doi.org/10.1175/mwr3280.1
  39. Wilks, D. S., 2006: Statistical Methods in the Atmospheric Sciences. Academic Press.
  40. Xu, M., W. Tian, J. Zhang, T. Wang, and K. Qie, 2021a: Impact of sea ice reduction in the Barents and Kara Seas on the variation of the East Asian trough in late winter. Journal of Climate 34(3), 1081-1097. https://doi.org/10.1175/JCLI-D-20-0205.1
  41. Xu, X., S. He, Y. Gao, B. Zhou, and H. Wang, 2021b: Contributors to linkage between Arctic warming and East Asian winter climate. Climate Dynamics, 1-13. https://doi.org/10.1007/s00382-021-05820-x
  42. Yoon, D. H., S. Y. Oh, K.-W. Nam, K.-C. Eom, and P.-K. Jung, 2013: Changes of cultivation areas and major disease for spicy vegetables by the change of meteorological factors. Climate Change Research 4(1), 47-59. (in Korean with English abstract)
  43. Zhou, W., 2017: Impact of Arctic amplification on East Asian winter climate. Atmospheric and Oceanic Science Letters 10(5), 385-388. https://doi.org/10.1080/16742834.2017.1350093