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Introduction to Empirical Approach to Estimate Rice Yield and Comparison with Remote Sensing Approach

경험적 벼 작황예측 방법에 대한 소개와 원격탐사를 이용한 예측과의 비교

  • Kim, Junhwan (Crop physiology and production, National Institute of Crop Science, Rural Development Administration) ;
  • Lee, Chung-Kuen (Planning and Coordination, National Institute of Crop Science, Rural Development Administration) ;
  • Sang, Wangyu (Crop physiology and production, National Institute of Crop Science, Rural Development Administration) ;
  • Shin, Pyeong (Crop physiology and production, National Institute of Crop Science, Rural Development Administration) ;
  • Cho, Hyeounsuk (Crop physiology and production, National Institute of Crop Science, Rural Development Administration) ;
  • Seo, Myungchul (Crop physiology and production, National Institute of Crop Science, Rural Development Administration)
  • 김준환 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 이충근 (농촌진흥청 국립식량과학원 기획조정과) ;
  • 상완규 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 신평 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 조현숙 (농촌진흥청 국립식량과학원 작물재배생리과) ;
  • 서명철 (농촌진흥청 국립식량과학원 작물재배생리과)
  • Received : 2017.09.11
  • Accepted : 2017.09.28
  • Published : 2017.10.30

Abstract

This review introduces the empirical approach of rice yield forecasting and compares it with remote sensing approach. The empirical approach, was based on the results of the rice growth and yield monitoring experiment in 17 sites, estimated rice yield by recombination of yield components. The number of spikelet per unit area was from results of experiment sites and grain filling rate was estimated from linear regression with sunshine hours. The estimation results were relatively accurate from 2010 to 2016. The smallest error was 1 kg / 10a and the largest error was 19 kg / 10a. The largest error was caused by the typhoon. The empirical approach did not fully reflect the spatial variation caused by disasters such as typhoon or pest. On the other hand, remote sensing could explain spatial variation caused by disasters. Therefore, if there are not any disaster in rice field, both approaches are valid and remote sensing will be more accurate when any local disaster occurs.

본 총설에서는 작황조사 시험을 활용한 통계적 작황예측 방법에 대해 소개하고 이를 원격탐사를 이용한 방법과 비교하였다. 17개 지역에서 이루어지는 작황조사시험 기반으로 작황조사시험의 수량구성요소 중 등숙률을 일사량과 선형회귀식으로 예측하고 면적당 영화수는 작황조사의 실측값을 활용하여 수량을 재구성하는 방법으로 예측 결과를 얻어진다. 예측 결과는 비교적 정확하였는데 지난 2010년부터 2016년까지 가장 적은 오차는 1 kg/10a였으며 가장 큰 편차는 19 kg/10a 이었다. 크게 편차가 발생한 이유는 태풍에 의해 피해 때문이었다. 즉 작황조사를 이용한 통계적 방법은 재해에 의한 공간변이를 충분히 반영하지 못하는 약점이 있다. 반면 원격탐사는 이러한 재해에 의한 공간적 변이를 보다 잘 설명할 수 있는 장점이 있다. 따라서, 벼의 생육상황에 큰 문제가 없는 경우에는 두가지 접근법 모두 유효하고 재해가 발생하였을 때는 원격탐사가 더 정확할 수 있을 것으로 보인다.

Keywords

References

  1. Baruth, M. and G. Genovese, 2008. The use of remote sensing within the MARS crop yield monitoring system of the European commission, The international archives of the photogrammetry, Remote Sensing and Spatial Information Science, XXXVII(B8): 935-939.
  2. Basso, B., D. Cammarano, and E. Carfagna, 2012. Review of Crop Yield Forecasting Methods and Early Warning Systems. In: GS SAC -Improving methods for crops estimates, FAO Publication, Rome, UK.
  3. Bounman, B. M.J. Kropff, T.P. Tung, M. Woperesis, H. ten Berge, and H.H. van Laar, 2001. Oryza2000: Modeling lowland rice, IRRI, Manila, Philippines.
  4. Han, S., B. Lee, M. Park, J. Seung, H. Yang, and S. Shin, 2011. A study of building crop yield forecasting model considering meteorological elements, KREI Research report, KREI (in Korean with English abstract)
  5. Hong S.Y., Hur J., J. Ahn, J. Lee, B. Min, C. Lee, Y. Kim, K. Lee, S. Kim, G.Y. Kim, and K. M. Shim, 2012. Estimating rice yield using MODIS NDVI and meteorological data in Korea, Korean Journal of Remote Sensing, 28:509-520 (in Korean with English abstract). https://doi.org/10.7780/kjrs.2012.28.5.4
  6. Jeong, J.H., J.P. Resop, N.D. Mueller, D.H. Fleisher, K. Yun, E. E. Butler, D.J. Timlin, K.M. Shim, J.S. Gerber, V.R. Reddy, and S.H.Kim, 2016. Random Forests for Global and Regional Crop Yield Predictions, PLoS One, 11(6): e0156571, doi:10.1371/journal.pone.0156571.
  7. Kim J., J. Shon, C.K. Lee, W. Yang, Y. Yoon, W.H. Yang, Y.G. Kim, and B.W. Lee, 2011. Relationship between grain filling duration and leaf senescence of temperate rice under high temperature, Field Crops Research, 122: 207-213. https://doi.org/10.1016/j.fcr.2011.03.014
  8. Kim, J. W. Sang, H. Shin, H. Cho, and M. Seo, 2017. A Meteorological Analysis on High Rice Yield in 2015 in South Korea, Korean Journal of Agriculture and forest Meteorology, 19: 54-61
  9. Lee, E.W., 1994. Rice production, pp. 89-91. Hiangmunsa, Seoul, Republic of Korea (In Korean).
  10. Loague, K. and R.E. Green, 1991. Statistical and graphical methods for evaluating solute transports models: Overview and application, Journal of Contaminant Hydrology, 7:51-73. https://doi.org/10.1016/0169-7722(91)90038-3
  11. Nguyen, D., K. Lee, D. Kim, N. T. Anh, and B. Lee, 2014. Modeling and validation of hightemperature induced spikelets sterility in rice, Field Crop Research, 156: 293-302. https://doi.org/10.1016/j.fcr.2013.11.009
  12. Park, D., R. Cui, and B. Lee, 2002. Relationship of spikelet number with nitrogen content, biomass and nonstructural carbohydrate accumulation during reproductive stage of rice. Korean Journal of Crop Science, 47: 486-492 (in Korean with English abstract).
  13. RDA, 2016. 2015 Annual report of Summer crop breeding program, RDA, Republic of Korea (In Korean).
  14. Yoshida, S., 1981. Fundamentals of rice crop science. p 63. IRRI. Manila, Philippines.