응용통계연구 (The Korean Journal of Applied Statistics)

  • 제27권7호
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  • Pages.1187-1196
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  • 2014
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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구조방정식 모형을 이용한 이탈리안 라이그라스 생산량에 대한 기후요인의 연구

Analysis of Climate Effects on Italian Ryegrass Yield via Structural Equation Model

  • 김문주 (강원대학교 정보통계학과) ;
  • 성경일 (강원대학교 사료생산과학전공) ;
  • 김영주 (강원대학교 정보통계학과)
  • Kim, Moonju (Department of Statistics, Kangwon National University) ;
  • Sung, Kyung-Il (Department of Feed Science and Technology, Kangwon National University) ;
  • Kim, Young-Ju (Department of Statistics, Kangwon National University)
  • 투고 : 2014.10.10
  • 심사 : 2014.12.05
  • 발행 : 2014.12.31
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초록

우리나라 대표적인 동계 사료작물인 이탈리안 라이그라스(Italian Ryegrass: IRG)는 사초의 품질과 수량이 높은 반면 내한성이 낮아 중남부 지방에서 주로 재배되고 있다. 본 연구는 우리나라에서 수행된 IRG 연구 자료(n = 375)와 기상청의 기상자료를 이용하여 IRG 수량과 온도, 강수량 등의 기상 변수들과의 인과관계를 분석하였다. 다변량 정규성가정 하에 계절효과를 지닌 구조방정식모형을 고려하여 분석한 결과, 동계작물인 IRG의 수량은 이듬해 봄의 기온에 직접적인 영향을 받고, 이듬해 봄 강수는 다른 요인을 통하여 영향을 미치는 것으로 나타났다. 즉, 저온으로 월동에 문제가 있는 지역에서 IRG 를 이른 봄에 파종하여도 충분히 생산성이 있다는 것을 의미한다. 이번 연구를 통해서 IRG 수량에 대한 보다 구체적이고 종합적인 인과관계를 고찰하는 계기를 마련하였으며, 앞으로 다른 초종에 대해서도 다양한 구조방정식 모형 연구를 통하여 수량증대에 기여할 것으로 사료된다.

Italian Ryegrass (IRG), which is known as high yielding and the highest quality winter annual forage crop, is grown in mid-south area in Korea. This study aims to analyze the cause-and-effect relationship between IRG yield and climate variables such as temperature and precipitation by using IRG data and climate data of Korea Weather Bureau. From path analysis of structural equation model under multivariate normality, we found that there was a weather effect on IRG yield that the winter grass IRG yield was directly affected by spring temperature and indirectly affected by spring rainfall. These results showed that IRG can be sown in early spring in the area where it is hard to prepare for winter due to low temperature. This paper can contribute to increase IRG yield by showing the cause-and-effect relationship and this study can be extended to various structural equation models for other crops.

키워드

참고문헌

  1. Arbuckle, J. (2005). Amos 6.0 user's guide, Marketing Department, SPSS incorporated.
  2. Bollen, K. A. (1989). A New Incremental Fit Index for General Structural Equation Models, Sociological Methods and Research, 17, 306-316.
  3. Browne, M. W. (1984). Asymptotically distribution-free methods for the analysis of covariance structures, British Journal of Mathematical and Statistical Psychology, 37, 62-83. https://doi.org/10.1111/j.2044-8317.1984.tb00789.x
  4. Gao, S., Mokhtarian, P.L. and Johnston, R.A. (2008). Nonnormality of data in structural equation models, Transportation Research Record: Journal of the Transportation Research Board, 2082, 116-124. https://doi.org/10.3141/2082-14
  5. Goldberger, A. S. (1973). Structural equation models: An overview. In Structural Equation Models in the Social Sciences, Seminar Press, New York.
  6. Hooper D., Coughlan, J. and Mullen, M. R. (2008). Structural Equation Modelling: Guidelines for Determining Model Fit, Electronic Journal of Business Research Methods, 6, 53-60.
  7. Hox, J. J. and Bechger T. M. (1998). An Introduction to Structural Equation Modeling, Family Science Review, 11, 354-373.
  8. Hu, L. T., Bentler, P. M. and Hoyle, R. H. (1995). Structural equation modeling: Concepts, issues, and applications, Evaluating Model Fit, Sage, London.
  9. Kim, M. J., Sung, K. I., Kim, B. W., Peng, J. L., Hyeon, D. H., Lee, B. H., Kim, E. J., Jo, M. H., Lim, Y. C. and Kim, K. D. (2014). Prediction of Italian Ryegrass Yield Affecting Climate and Soil. Proceedings of 2014 Annual Congress of Korean Society of Animal Sciences and Technology, 204.
  10. Lee, J. W., Jang, Y. J., Ko, K. K. and Park, J. K. (2013). Effects of Meteorological Elements in the Production of Food Crops: Focused on Regression Analysis using Panel Data, Journal of Environmental Science International, 22, 1171-1180. https://doi.org/10.5322/JESI.2013.22.9.1171
  11. Mullen, M. R., Milne, G. R. and Doney, P. M. (1995). An International Marketing Application of Outlier Analysis for Structural Equations: A Methodological Note, Journal of International Marketing, 3, 45-62.
  12. Schlenker, W. and Roberts, M. J. (2009). Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change, Proceeding of the National Academy of sciences, 106, 15594-15598. https://doi.org/10.1073/pnas.0906865106
  13. Seo, S. (2009). Development of new varieties and production of forages in Korea, Journal of The Korean Society of Grassland and Forage Science, 29, 1-10. https://doi.org/10.5333/KGFS.2009.29.1.001
  14. Shin, C. N., Ko, K. H. and Kim, J. D. (2012). Agronomic Characteristics and Forage Productivity of Italian Ryegrass(Lolium multiflorum Lam.) Cultivar, Journal of The Korean Society of Grassland and Forage Science, 32, 229-236. https://doi.org/10.5333/KGFS.2012.32.3.229
  15. Sung, K. I., Kim, B. W., Peng, J. L., Hyeon, D. H., Lee, B. H., Kim, E. J., Jo, M. H., Lim, Y. C., Kim, K. D. and Kim, M. J. (2014). Prediction of Italian Ryegrass Yield Affecting Climate Variables. Proceedings of 2014 Annual Congress of Korean Society of Animal Sciences and Technology, 203.
  16. Yuan K. H., Chan W. and Bentler P. M. (2000). Robust transformation with application to structural equation modelling, British Journal of Mathematical and Statistical Psychology, 53, 31-50. https://doi.org/10.1348/000711000159169

피인용 문헌

  1. Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea vol.63, pp.3, 2017, https://doi.org/10.1111/grs.12163
  2. Detecting the Climate Factors related to Dry Matter Yield of Whole Crop Maize vol.17, pp.3, 2015, https://doi.org/10.5532/KJAFM.2015.17.3.261