Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

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

DOI QR Code

Analysis of Effect of Environment on Growth and Yield of Autumn Kimchi Cabbage in Jeonnam Province using Big Data

빅데이터를 활용한 재배환경이 전라남도 지방 가을배추의 생육과 수량에 미치는 영향 분석

  • Wi, Seung Hwan (Vegetable Research Division, National Institute of Horticultural and Herbal Science, RDA) ;
  • Lee, Hee Ju (Vegetable Research Division, National Institute of Horticultural and Herbal Science, RDA) ;
  • Yu, In Ho (Vegetable Research Division, National Institute of Horticultural and Herbal Science, RDA) ;
  • Jang, YoonAh (Vegetable Research Division, National Institute of Horticultural and Herbal Science, RDA) ;
  • Yeo, Kyung-Hwan (Vegetable Research Division, National Institute of Horticultural and Herbal Science, RDA) ;
  • An, Sewoong (Vegetable Research Division, National Institute of Horticultural and Herbal Science, RDA) ;
  • Lee, Jin Hyoung (Vegetable Research Division, National Institute of Horticultural and Herbal Science, RDA)
  • 위승환 (국립원예특작과학원 채소과) ;
  • 이희주 (국립원예특작과학원 채소과) ;
  • 유인호 (국립원예특작과학원 채소과) ;
  • 장윤아 (국립원예특작과학원 채소과) ;
  • 여경환 (국립원예특작과학원 채소과) ;
  • 안세웅 (국립원예특작과학원 채소과) ;
  • 이진형 (국립원예특작과학원 채소과)
  • Received : 2020.09.08
  • Accepted : 2020.09.28
  • Published : 2020.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study was conducted to evaluate the effect of environment factors on the growth of autumn season cultivation of Kimchi cabbage using the big data in terms of public open data(weather, soil information, and growth of crop, etc.). The growth data and the environment data such as temperature, daylength, and rainfall from 2010 to 2019 were collected. As a result of composing the correlation matrix, the height and leaf number showed high correlation in growing degree days(GDDs) and daylength, and the yield showed negative correlation in growing degree days and the concentration of clay. GDDs and daylength explained about 89% and 84% of variation in height, respectively. These two environmental factors also explained about 85% and 79% of variation in leaf numbers, respectively. In contrast, the coefficient of determination was low for yield when GDDs and concentration of clay was used. The outcome of regional statistical analysis indicated that relationship between yield and sum of sand and silt were high in Haenam and Jindo areas. Hierarchical cluster analysis, which was performed to verify the association of yield, GDDs, and concentration of clay, showed that Haenam and Jindo were clustered together. Although GDDs and yield vary by year and region, and there are regions with similar concentration of clays, observation data are grouped as the result. These suggests that GDDs and soil texture are expected to be related to yield. The cluster analysis results can be used for further data analysis and agricultural policy establishment.

본 연구는 배추 주산지인 전남지방의 가을배추 생육 빅데이터를 활용하여 재배환경요소가 가을배추의 생육에 미치는 영향을 구명하기 위해 수행되었다. 전남지방의 2010~2019년 가을배추 생육데이터를 수집하여 기온, 일조시간, 강우량, 토성 등 재배환경데이터와의 연관성을 분석하였다. 상관행렬을 작성한 결과 초장과 엽수는 생육도일과 일조시간에서 높은 상관계수를 보였고 수량은 생육도일과 점토함량에서 음의 상관관계를 보였다. 상관계수 분석을 통해 선발된 지표들을 선형회귀분석 하였는데 초장과 생육도일, 초장과 합계 일조시간, 엽수와 생육도일, 엽수와 합계일조시간의 결정계수가 각각 0.79, 0.71, 0.7, 0.62이었으나 수량과 생육도일, 수량과 토성의 경우 결정계수가 낮았다. 수량과 생육도일 토성을 통계분석 한 결과 해남과 진도 지역의 수량이 높았고 생육도일과 토양내 점토 함량이 낮은 것으로 나타났다. 수량, 생육도일, 토성에 대한 연관성 검증을 위하여 계층적 군집분석을 수행한 결과, 통계적으로 유사 지역으로 분류된 해남과 진도가 같은 군집을 이루었고 그 외 지역이 다른 군집을 이루었다. 연도 및 지역에 따라 생육도일과 수량이 다르고 토성이 유사한 지역이 있음에도 불구하고 관측데이터가 지역별로 군집이 이루어진 것으로 보아 생육도일과 점토 함량은 수량과 연관성이 있는 것으로 판단된다. 연관성 분석 외에도 지역별 군집을 통하여 데이터 분석 및 영농정책 수립 등에 군집분석 결과를 활용할 수 있을 것으로 기대된다.

Keywords

References

  1. Ahn, J. H., K. D. Kim, and J. T. Lee, 2014: Growth modeling of Chinese cabbage in an alpine area. Korean Journal of Agricultural and Forest Meteorology 16(4), 309-315. https://doi.org/10.5532/KJAFM.2014.16.4.309
  2. Chang, W., 2012: R graphics cookbook: practical recipes for visualizing data. O'Reilly Media, Inc., CA, USA.
  3. Charrad, M., N, Ghazzali, V. Boiteau, A. Niknafs, and M. Charrad, 2014: Package 'nbclust'. Journal of statistical software 61, 1-36.
  4. Cho, C., G. Hwang, and S. Yoon, 2018: Development of ubiquitous sensor network quality control algorithm for highland cabbage. Korean Journal of Agricultural and Forest Meteorology 20(4), 337-347. https://doi.org/10.5532/KJAFM.2018.20.4.337
  5. Gilmore, E. C., and J. S. Rogers, 1958: Heat units as a method of measuring maturity in corn. Agronomy Journal 50(10), 611-615. https://doi.org/10.2134/agronj1958.00021962005000100014x
  6. Hoogenboom, G., 2000: Contribution of agrometeorology to the simulation of crop production and its application. Agricultural and forest meteorology 103, 137-157. https://doi.org/10.1016/S0168-1923(00)00108-8
  7. Jun, H. K., 2014: Taxonomical Classification of Korean Soils. Rural Development Administration, WanJu, Korea.
  8. Kim, K. D., J. T. Suh, J. N. Lee, D. L. Yoo, M. Kwon, and S. C. Hong, 2015: Evaluation of factors related to productivity and Yield estimation based on growth characteristics and growing degree days in highland Kimchi cabbage. Korean Journal of Horticultural Science & Technology 33, 911-922. https://doi.org/10.7235/hort.2015.15074
  9. Kim, S. W., H. Y. Rho, and S. H. Yun, 2016: Prediction of Chinese cabbage and radish consumption. KREI 18, 5-6.
  10. KOSTAT, 2019: Crop Production Statics 2018. Statistics Korea, Daejeon, Korea.
  11. Lee, J. W., 1996: A study of decision-making factors of production for radish and Chinese cabbage. KREI R346, 39-67.
  12. Lee, S. G., J. H. Moon, Y. A. Jang, W. M. Lee, I. H. Cho, S. Y. Kim, and K. D. Ko, 2009: Photosynthetic characteristics and cellular tissue of Chinese cabbage are affected by temperature and $CO_2$ concentration. Journal of Bio-Environment Control 18(2),148-152.
  13. Lee, S. G., H. J. Lee, S. K. Kim, C. S. Choi, S. T. Park, Y. A. Jang, and K. R. Do, 2015: Effect of vernalization, temperature, and soil drying periods and the growth and yield of Chinese cabbage. Korean Journal of Horticultural Science & Technology 33, 820-828. https://doi.org/10.7235/hort.2015.15076
  14. Lee, S., and K. S. Kim, 2018: Estimation of fresh weight for chinese cabbage using the Kinect sensor. Korean Journal of Agricultural and Forest Meteorology, 20(2), 205-213. https://doi.org/10.5532/KJAFM.2018.20.2.205
  15. Lim, Y. H., 2017: Policy Trends of Agricultural Science and Technology in Major Countries in Response to the Fourth Industrial Revolution. Naju, Korea.
  16. Mcmaster, G. S., and W. W. Wilhelm, 1997: Growing degree-days: one equation, two interpretations. Agricultural and forest meteorology 87, 291-300. https://doi.org/10.1016/S0168-1923(97)00027-0
  17. Moon, K. H., E. Y. Song, S. H. Wi, and S. J. Oh, 2018: Development of a Chinese cabbage model using Microsoft Excel/VBA. Korean Journal of Agricultural and Forest Meteorology 20(2), 228-232. https://doi.org/10.5532/KJAFM.2018.20.2.228
  18. Na, S. I., B. K. Min, C. W. Park, K. H. So, J. M. Park, and K. D. Lee, 2017: Development of field scale model for estimating garlic growth based on UAV NDVI and meteorological factors. Korean Journal of Soil Science and Fertilizer 50(5), 422-433. https://doi.org/10.7745/KJSSF.2017.50.5.422
  19. Oh, S. J., K. H. Moon, I. C. Son, E. Y. Song, Y. E. Moon, and S. C. Koh, 2014: Growth, photosynthesis and chlorophyll fluorescence of Chinese cabbage in response to high temperature effects of differentiated temperature. Korean Journal of Horticultural Science & Technology 32, 318-329. https://doi.org/10.7235/hort.2014.13174
  20. Park, S. H., J. K. Kim, 2013: Cabbage. Rural Development Administration, WanJu, Korea.
  21. Perry, K. B., D. C. Sanders, D. M. Granberry, J. T. Garrett, D. R. Decoteau, R. T. Nagata, and W. J. McLaurin, 1993: Heat units, solar radiation and daylength as pepper harvest predictors. Agricultural and forest meteorology 65(3-4), 197-205. https://doi.org/10.1016/0168-1923(93)90004-2
  22. Schwab, K., 2017: The fourth industrial revolution. Portfolio Penguin, London, U.K.
  23. Son, I. C., K. H. Moon, E. Y. Song, S. J. Oh, H. H. Seo, Y. E. Moon, and J. Y. Yang, 2015: Effects of differentiated temperature based on growing season temperature on growth and physiological response in Chinese cabbage 'Chunkwang'. Korean Journal of Agricultural and Forest Meteorology 17, 254-260. https://doi.org/10.5532/KJAFM.2015.17.3.254
  24. Teetor, P., 2011: R cookbook: Proven recipes for data analysis, statistics, and graphics. O'Reilly Media, Inc., CA, USA.
  25. Wei, T., V. Simko, M. Levy, Y. Xie, Y. Jin, and J. Zemla, 2017: Package 'corrplot'. Statistician 56(316), e24.
  26. Wi, S. H., E. Y. Song, S. J. Oh, I. C. Son, S. G. Lee, H. J. Lee, B. H. Mun, and Y. Y. Cho, 2018: Estimation of optimum period for spring cultivation of 'Chunkwang' Kimchi Cabbage based on growing degree days in Korea. Agricultural and Forest Meteorology 20, 175-182.
  27. Yang, S. J., S. I. Jo., and Y. B. Jo, 2020: Big data analysis and utilization. Rural Development Administration, WanJu, Korea.