The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

A study on the estimation of onion's bulb weight using multi-level model

다층모형을 활용한 양파 구중 추정 연구

  • Kim, Junki (Department of Agricultural Outlook, Korea Rural Economic Institute) ;
  • Choi, Seung-cheon (Department of Agricultural Outlook, Korea Rural Economic Institute) ;
  • Kim, Jaehwi (Department of Agricultural Outlook, Korea Rural Economic Institute) ;
  • Seo, Hong-Seok (Department of Agricultural Outlook, Korea Rural Economic Institute)
  • 김준기 (한국농촌경제연구원 농업관측본부 모형연구팀) ;
  • 최성천 (한국농촌경제연구원 농업관측본부 모형연구팀) ;
  • 김재휘 (한국농촌경제연구원 농업관측본부 모형연구팀) ;
  • 서홍석 (한국농촌경제연구원 농업관측본부 모형연구팀)
  • Received : 2020.08.24
  • Accepted : 2020.10.11
  • Published : 2020.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Onions show severe volatility in production and price because crop conditions highly depend on the weather. The government has designated onions as a sensitive agricultural product, and prepared various measures to stabilize the supply and demand. First of all, preemptive and reliable information on predicting onion production is essential to implement appropriate and effective measures. This study aims to contribute to improving the accuracy of production forecasting by developing a model to estimate the final weight of onions bulb. For the analysis, multi-level model is used to reflect the hierarchical data characteristics consisting of above-ground growth data in individual units and meteorological data in parcel units. The result shows that as the number of leaf, stem diameter, and plant height in early May increase, the bulb weight increases. The amount of precipitation as well as the number of days beyond a certain temperature inhibiting carbon assimilation have negative effects on bulb weight, However, the daily range of temperature and more precipitation near the harvest season are statistically significant as positive effects. Also, it is confirmed that the fitness and explanatory power of the model is improved by considering the interaction terms between level-1 and level-2 variables.

양파는 기상여건에 따른 작황의 변동성이 커 생산량 및 가격 변화가 크다. 정부는 양파를 수급 민감 품목으로 지정하여 다양한 수급 안정대책을 마련하고 시행하는데 이를 위해서는 선제적이고 신뢰도 높은 양파 생산량 예측 정보가 필수적이다. 본 연구에서는 양파의 5월 초 지상부 생육정보와 5월 초부터 수확기까지의 기상정보를 이용하여 최종 생구 무게에 미치는 영향을 추정함으로써 생산량 예측의 정확도 개선에 기여하고자 한다. 위계적 특성을 갖고 있는 자료를 통해 개체별 생육요인인 1-수준 자료와 필지별 기상요인인 2-수준 자료, 그리고 두 수준 간 상호작용을 고려한 다층모형을 도입하여 분석하였다. 분석 결과, 5월 초에 엽수, 엽초경, 초장의 생육이 좋을수록 최종 생구 무게는 증가하는 것으로 추정되었다. 5월 초부터 수확기까지의 기상요인에서는 강수량, 고온일수, 탄소동화저해일수가 생구 무게에 음의 효과가 나타났으며, 일교차와 수확전강수량은 양의 효과로 통계적으로 유의하였다. 또한 1-수준과 2-수준의 교호작용항을 고려하여 모형의 적합도와 설명력을 향상시켰다.

Keywords

References

  1. Ahn, S. R. and Suh, J. K. (2013). Effect of Different Day Length on the Production of Onion, Protected horticulture and Plant Factory, 22, 43-47. https://doi.org/10.12791/KSBEC.2013.22.1.043
  2. Burstein, L. (1980). Chapter 4: The analysis of multilevel data in educational research and evaluation, Review of Research in Education, 8, 158-233. https://doi.org/10.3102/0091732X008001158
  3. Brewster, J. L. (1990). The influence of cultural and environmental factors on the time of maturity of bulb onion crops, Acta Horticulturae, 267, 289-296. https://doi.org/10.17660/actahortic.1990.267.36
  4. Choi, S. and Baek, J. (2016). Onion yield estimation using spatial panel regression model, The Korea Journal of Applied Statistics, 29, 873-885. https://doi.org/10.5351/KJAS.2016.29.5.873
  5. Cooley, W. W., Bond, L., and Mao, B. B. (1981). Analyzing multilevel data. In R A. Berk (Ed.), Educational Evaluation Methodology, Baltimore, MD: Johns Hopkins University Press.
  6. Cronbach, L. J. (1976). Research on classrooms and schools: Formulation of questions, design, and analysis,Occasional paper of the Stanford Evaluation Consortium.
  7. Cronbach, L. J. and Webb, N. (1975). Between-class and within-class effects in a reported aptitude * treatment interaction: Reanalysis of a study by G. L. Anderson, Journal of Educational Psychology, 67, 717-724. https://doi.org/10.1037//0022-0663.67.6.717
  8. De Ruiter, J. M. (1986). The effects of temperature and photoperiod on onion bulb growth and development. Proceedings Agronomy Society of NZ, 16, 93-100.
  9. Finch, W. H., Bolin, J. H., and Kelley, K. (2014). Multilevel Modeling Using R, CRC Press, Fl.
  10. Henriksin, K. (1987). Effect of N- and P-fertilization on yield and harvest time in bulb onions (Allium cepaL.), Acta Horticulturae, 208, 207-215. https://doi.org/10.17660/ActaHortic.1987.198.26
  11. Huh, E. J., Cho, K. S., Kwon, Y. S., and Woo, J. G. (2002). Effects of temperature and photoperiod on bulbing and maturity of spring sown onions in highland, The Journal of Horticultural Science and Biotechnology , 43, 587-590.
  12. Jung, I. (2019). Comparison of the prediction model of the production unit and the area of the onion-comparison of stepwise, LASSO, PCA and PLS methods (Master Thesis), Chonnam National University.
  13. Kang, S. (2016). Multilevel Model, Hakjisa, 290-296.
  14. Kim, D. J., Kim, S. O., Kim, J. H. and Yun, E. J. (2019). Establishment of Geospatial Schemes Based on Topo-Climatology for Farm-Specific Agrometeorological Information, Korean Journal of Agricultural and Forest Meteorology, 21, 146-157. https://doi.org/10.5532/KJAFM.2019.21.3.146
  15. Lee, C. and Yang, S. B. (2017). Development of yield forecast models for onion and garlic using artificial neural network, Horticultural Science and Technology, 35, 128-129.
  16. Lee, H. J., Lee, S. G., Kim, S. K., An, S., Lee, J. H., Lee, H. S., Kim, C. W., Kwon, Y. S., and Han, J. W. (2019). Effects of High-Temperature and Soil Moisture Conditions on the Physiological Response of Onion, Horticultural Science and Technology, 37, 571-578. https://doi.org/10.7235/HORT.20190057
  17. Lee, J. (2015). Onion: From one year old to organic and no-till cultivation method, deulnyeok.
  18. Lee, S. I., Son, S., and Lee, H. (2020). An analysis of changes onion yields in Korea using panel regression analysis and Bayesian network model, Journal of Rural Development, 43, 1-28. https://doi.org/10.36464/JRD.2020.43.2.001
  19. Lim, C. H., Kim, G. S., Lee, E. J., Heo, S., Kim, T., Kim, Y., and Lee, W. K. (2016). Development on crop yield forecasting model for major vegetable crops using meteorological information of main production area, Journal of Climate Change Research, 7, 199-203.
  20. Riekels, J. W. (1972). The influence of nitrogen on the growth and maturity of onions grown on organic soil, Journal of the American Socoety for Horticultural Science, 97, 37-40.
  21. Robinson, W. S. (2009). Ecological Correlations and the Behavior of Individuals, International Journal of Epidemiology, 38, 337-341. https://doi.org/10.1093/ije/dyn357
  22. Snijders, T. B. S. and Bosker, R. J. (2011). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling, Sage Publishers, London.
  23. Song, E. Y., Moon, K. H., Wi, S. H., Kim, C. H., Lim, C. K., Oh, S. J., and Son, I. C. (2017). Impact of Elevated Temperature in Growing Season on Growth and Bulb Development of Extremely Early-Maturing Onion, Korean Journal of Agricultural and Forest Meteorology, 19, 223-231. https://doi.org/10.5532/KJAFM.2017.19.4.223
  24. Steer, B. T. (1980). The bulbing response to day-length and temperature of some Australasian cultivars of onion(Allium cepa L.), Australian Journal of Agricultural Research, 31, 511-518. https://doi.org/10.1071/AR9800511