Korean Journal of Agricultural and Forest Meteorology (한국농림기상학회지)

Korean Society of Agricultural and Forest Meteorology (한국농림기상학회)
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Comparison of the Weather Station Networks Used for the Estimation of the Cultivar Parameters of the CERES-Rice Model in Korea

CERES-Rice 모형의 품종 모수 추정을 위한 국내 기상관측망 비교

  • Hyun, Shinwoo (Department of Agriculture, Forestry and Bioresources, Seoul National University) ;
  • Kim, Tae Kyung (Department of Agriculture, Forestry and Bioresources, Seoul National University) ;
  • Kim, Kwang Soo (Department of Agriculture, Forestry and Bioresources, Seoul National University)
  • 현신우 (서울대학교 농림생물자원학부) ;
  • 김태경 (서울대학교 농림생물자원학부) ;
  • 김광수 (서울대학교 농림생물자원학부)
  • Received : 2021.05.31
  • Accepted : 2021.06.28
  • Published : 2021.06.30
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Abstract

Cultivar parameter calibration can be affected by the reliability of the input data to a crop growth model. In South Korea, two sets of weather stations, which are included in the automated synoptic observing system (ASOS) or the automatic weather system (AWS), are available for preparation of the weather input data. The objectives of this study were to estimate the cultivar parameter using those sets of weather data and to compare the uncertainty of these parameters. The cultivar parameters of CERES-Rice model for Shindongjin cultivar was calibrated using the weather data measured at the weather stations included in either ASO S or AWS. The observation data of crop growth and management at the experiment farms were retrieved from the report of new cultivar development and research published by Rural Development Administration. The weather stations were chosen to be the nearest neighbor to the experiment farms where crop data were collected. The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to calibrate the cultivar parameters for 100 times, which resulted in the distribution of parameter values. O n average, the errors of the heading date decreased by one day when the weather input data were obtained from the weather stations included in AWS compared with ASO S. In particular, reduction of the estimation error was observed even when the distance between the experiment farm and the ASOS stations was about 15 km. These results suggest that the use of the AWS stations would improve the reliability and applicability of the crop growth models for decision support as well as parameter calibration.

작물 모형의 품종모수를 추정하기 위한 기상자료는 일반적으로 생육 관측 자료가 수집된 시험지의 인근에 위치한 종관기상 관측자료가 사용되어왔으나, 지형적인 원인이나 시험지와 기상관측소 사이의 거리로 인해 실제 시험지의 기상과 차이가 발생할 수 있다. 반면, 비교적 높은 밀도로 분포하는 방재기상 관측자료를 활용할 경우 이러한 문제점을 보완할 수 있을 것이다. 본 연구에서는 종관기상 관측자료와 방재기상 관측자료를 각각 사용하여 출수기에 영향을 미치는 DSSAT 모형의 모수들을 추정하고, 추정된 모수들의 신뢰도를 비교하고자 하였다. 모수 추정을 위해 사용한 재배관리 및 생육 관측값은 지역장려품종 선발시험과 작황시험으로부터 수집하였다. 모수 추정은 Generalized Likelihood Uncertainty Estimation (GLUE) 방법을 사용하였으며, 불확실성을 고려하여 100번의 반복 추정을 통해 100개의 모수 집합을 생성하였다. 모수 추정에 소요되는 시간을 단축하기 위해 도커 컨테이너를 기반으로 병렬적으로 GLUE를 구동하였다. 추정된 모수들을 사용하여 모의된 출수기의 평균은, 방재기상자료를 사용하였을 때 최대 4일로, 종관기상자료를 사용하였을 때 최대 오차가 7일이었던 것에 비하여 크게 개선되었다. 그러나, 방재기상자료의 원활한 활용을 위해서는 해당 자료에 대한 접근성이 향상되어야 할 것으로 예상되었다.

Keywords

Acknowledgement

본 연구는 농촌진흥청 공동연구사업(과제번호: PJ013837032021)의 지원에 의해 수행되었습니다.

References

  1. Acharya, S., M. Correll, J. W. Jones, K. J. Boote, P. D. Alderman, Z. Hu, and C. E. Vallejos, 2017: Reliability of genotype-specific parameter estimation for crop models: Insights from a markov chain monte-carlo estimation approach. Transactions of the ASABE 60(5), 1699-1712. https://doi.org/10.13031/trans.12183
  2. Beven, K., and A. Binley, 2014: GLUE: 20 years on. Hydrological processes 28(24), 5897-5918. https://doi.org/10.1002/hyp.10082
  3. Chung, M. T., N. Quang-Hung, M.-T. Nguyen, and N. Thoai, 2016: Using docker in high performance computing applications, 2016 IEEE Sixth International Conference on Communications and Electronics (ICCE), IEEE, 52-57.
  4. Gao, Y., D. Wallach, B. Liu, M. Dingkuhn, K. J. Boote, U. Singh, S. Asseng, T. Kahveci, J. He, and R. Zhang, 2020: Comparison of three calibration methods for modeling rice phenology. Agricultural and Forest Meteorology 280, 107785. https://doi.org/10.1016/j.agrformet.2019.107785
  5. He, J., M. D. Dukes, J. W. Jones, W. D. Graham, and J. Judge, 2009: Applying GLUE for estimating CERES-Maize genetic and soil parameters for sweet corn production. Transactions of the ASABE 52(6), 1907-1921. https://doi.org/10.13031/2013.29218
  6. He, J., J. W. Jones, W. D. Graham, and M. D. Dukes, 2010: Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agricultural Systems 103(5), 256-264. https://doi.org/10.1016/j.agsy.2010.01.006
  7. He, J., C. Porter, P. Wilkens, F. Marin, H. Hu, and J. Jones, 2010: Guidelines for installing and running GLUE program. Decision support system for agrotechnology transfer (DSSAT) version 4.
  8. Houska, T., S. Multsch, P. Kraft, H.-G. Frede, and L. Breuer, 2014: Monte Carlo-based calibration and uncertainty analysis of a coupled plant growth and hydrological model. Biogeosciences 11(7), 2069-2082. https://doi.org/10.5194/bg-11-2069-2014
  9. Hyun, S., and K. S. Kim, 2017: Estimation of Heading Date for Rice Cultivars Using ORYZA (v3). Korean Journal of Agricultural and Forest Meteorology 19(4), 246-251. https://doi.org/10.5532/KJAFM.2017.19.4.246
  10. Hyun, S., and K. S. Kim, 2019: Calibration of cultivar parameters for cv. Shindongjin for a rice growth model using the observation data in a low quality. Korean Journal of Agricultural and Forest Meteorology 21(1), 42-54. https://doi.org/10.5532/KJAFM.2019.21.1.42
  11. Hyun, S., B. H. Seo, S. Lee, and K. S. Kim, 2020: Quantitative assessment of the quality of regional adaptation trial data for crop model improvement. Korean Journal of Agricultural and Forest Meteorology 22(3), 194-204. https://doi.org/10.5532/KJAFM.2020.22.3.194
  12. Iizumi, T., M. Yokozawa, and M. Nishimori, 2009: Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach. Agricultural and forest meteorology 149(2), 333-348. https://doi.org/10.1016/j.agrformet.2008.08.015
  13. Jha, P. K., P. Athanasiadis, S. Gualdi, A. Trabucco, V. Mereu, V. Shelia, and G. Hoogenboom, 2019: Using daily data from seasonal forecasts in dynamic crop models for yield prediction: A case study for rice in Nepal's Terai. Agricultural and forest meteorology 265, 349-358. https://doi.org/10.1016/j.agrformet.2018.11.029
  14. Jones, J. W., J. He, K. J. Boote, P. Wilkens, C. H. Porter, and Z. Hu, 2011: Estimating DSSAT Cropping System Cultivar-Specific Parameters Using Bayesian Techniques, In: Ahuja, L. R. and L. Ma (eds.) Methods of Introducing System Models into Agricultural Research, Madison, WI, American Society of Agronomy, Crop Science Society of America, Soil Science Society of America, 365-394.
  15. Jones, J. W., G. Hoogenboom, C. H. Porter, K. J. Boote, W. D. Batchelor, L. A. Hunt, P. W. Wilkens, U. Singh, A. J. Gijsman, and J. T. Ritchie, 2003: The DSSAT cropping system model. European Journal of Agronomy 18(3-4), 235-265. https://doi.org/10.1016/S1161-0301(02)00107-7
  16. Kersebaum, K. C., K. J. Boote, J. Jorgenson, C. Nendel, M. Bindi, C. Fruhauf, T. Gaiser, G. Hoogenboom, C. Kollas, and J. E. Olesen, 2015: Analysis and classification of data sets for calibration and validation of agro-ecosystem models. Environmental Modelling & Software 72, 402-417. https://doi.org/10.1016/j.envsoft.2015.05.009
  17. Kim, D. J., J. H. Roh, J. G. Kim, and J. I. Yun, 2013: The Influence of shifting planting date on cereal grains production under the projected climate change. Korean Journal of Agricultural and Forest Meteorology 15(1), 26-39. https://doi.org/10.5532/KJAFM.2013.15.1.026
  18. Kim, D.-J., J.-H. Park, S.-O. Kim, J.-H. Kim, Y. Kim, and K.-M. Shim, 2020a: A system displaying real-time meteorological data obtained from the automated observation network for verifying the early warning system for agrometeorological hazard. Korean Journal of Agricultural and Forest Meteorology 22(3), 117-127. https://doi.org/10.5532/KJAFM.2020.22.3.117
  19. Kim, J., C. K. Lee, H. Kim, B. W. Lee, and K. S. Kim, 2015: Requirement analysis of a system to predict crop yield under climate change. Korean Journal of Agricultural and Forest Meteorology 17(1), 1-14. https://doi.org/10.5532/KJAFM.2015.17.1.1
  20. Kim, J., J. Park, S. Hyun, D. H. Fleisher, and K. S. Kim, 2020b: Development of an automated gridded crop growth simulation support system for distributed computing with virtual machines. Computers and Electronics in Agriculture 169, 105196. https://doi.org/10.1016/j.compag.2019.105196
  21. Kim, J., W. Sang, P. Shin, J. Baek, C. Cho, and M. Seo, 2019: History and future direction for the development of rice growth models in Korea. Korean Journal of Agricultural and Forest Meteorology 21(3), 167-174. https://doi.org/10.5532/KJAFM.2019.21.3.167
  22. Kim, J., W. Sang, P. Shin, J. Baek, D. Kwon, Y. Lee, J.-I. Cho, and M. Seo, 2020c: Long-term monitoring data for growth and yield of local rice varieties in South Korea. Korean Journal of Agricultural and Forest Meteorology 22(3), 176-182. https://doi.org/10.5532/KJAFM.2020.22.3.176
  23. Kim, J., W. Sang, P. Shin, H. Cho, and M. Seo, 2018a: Calibration of crop growth model CERES-MAIZE with yield trial data. Korean Journal of Agricultural and Forest Meteorology 20(4), 277-283. https://doi.org/10.5532/KJAFM.2018.20.4.277
  24. Kim, K. S., B. H. Yoo, S. Hyun, B.-S. Seo, H.-Y. Ban, J. Park, and B.-W. Lee, 2018b: Simulation of crop growth under an intercropping condition using an object oriented crop model. Korean Journal of Agricultural and Forest Meteorology 20(2), 214-227. https://doi.org/10.5532/KJAFM.2018.20.2.214
  25. Lamsal, A., 2017: Crop model parameter estimation and sensitivity analysis for large scale data using supercomputers. Kansas State University.
  26. Lamsal, A., S. M. Welch, J. W. White, K. R. Thorp, and N. M. Bello, 2018: Estimating parametric phenotypes that determine anthesis date in Zea mays: Challenges in combining ecophysiological models with genetics. PloS one 13(4), e0195841. https://doi.org/10.1371/journal.pone.0195841
  27. Lee, C. K., J. Kim, and K. S. Kim, 2015: Development and application of a weather data service client for preparation of weather input files to a crop model. Computers and Electronics in Agriculture 114, 237-246. https://doi.org/10.1016/j.compag.2015.03.021
  28. Lee, J.-S., and M.-K. Oh, 2019: Distribution analysis of land surface temperature about Seoul using landsat 8 satellite images and AWS data. Journal of the Korea Academia-Industrial cooperation Society 20(1), 434-439. https://doi.org/10.5762/KAIS.2019.20.1.434
  29. Makowski, D., D. Wallach, and M. Tremblay, 2002: Using a Bayesian approach to parameter estimation; comparison of the GLUE and MCMC methods. Agronomie 22(2), 191-203. https://doi.org/10.1051/agro:2002007
  30. Moon, K. H., H. H. Seo, M. J. Shin, E. Y. Song, and S. Oh, 2019: Production of farm-level agro-information for adaptation to climate change. Korean Journal of Agricultural and Forest Meteorology 21(3), 158-166. https://doi.org/10.5532/KJAFM.2019.21.3.158
  31. Park, J., Y. Shin, S. Kim, W. Kang, Y. Han, J. Kim, D. Kim, S. Kim, K. Shim, and E. Park, 2017: Speed-up techniques for high-resolution grid data processing in the early warning system for agrometeorological disaster. Korean Journal of Agricultural and Forest Meteorology 19(3), 153-163. https://doi.org/10.5532/KJAFM.2017.19.3.153
  32. Ramirez-Villegas, J., A. Molero Milan, N. Alexandrov, S. Asseng, A. J. Challinor, J. Crossa, F. van Eeuwijk, M. E. Ghanem, C. Grenier, and A. B. Heinemann, 2020: CGIAR modeling approaches for resource-constrained scenarios: I. Accelerating crop breeding for a changing climate. Crop Science 60(2), 547-567. https://doi.org/10.1002/csc2.20048
  33. Shin, J. H., K. Y. Lee, and J. T. Lee, 2001: Agrometeorological Information Service. Korean Journal of Agricultural and Forest Meteorology 3(2), 121-125.
  34. Shoarinezhad, V., S. Wieprecht, and S. Haun, 2020: Comparison of local and global optimization methods for calibration of a 3D morphodynamic model of a curved channel. Water 12(5), 1333. https://doi.org/10.3390/w12051333
  35. Si, Z., M. Zain, S. Li, J. Liu, Y. Liang, Y. Gao, and A. Duan, 2021: Optimizing nitrogen application for drip-irrigated winter wheat using the DSSAT-CERES-Wheat model. Agricultural Water Management 244, 106592. https://doi.org/10.1016/j.agwat.2020.106592
  36. R Core Team, 2020: R: A language and environment for statistical computing. Version 4.0.3
  37. Yuan, S., S. Peng, and T. Li, 2017: Evaluation and application of the ORYZA rice model under different crop managements with high-yielding rice cultivars in central China. Field Crops Research 212, 115-125. https://doi.org/10.1016/j.fcr.2017.07.010