[논문]오차 계산 방식에 따른 사료용 벼 품종의 품종모수 추정치 불확도 비교

조영상; 현신우; 김광수

doi:10.5532/kjafm.2023.25.3.129

오차 계산 방식에 따른 사료용 벼 품종의 품종모수 추정치 불확도 비교
Comparison between Uncertainties of Cultivar Parameter Estimates Obtained Using Error Calculation Methods for Forage Rice Cultivars 원문보기

한국농림기상학회지 = Korean Journal of Agricultural and Forest Meteorology, v.25 no.3, 2023년, pp.129 - 141

조영상 (서울대학교 식물생산과학부) , 현신우 (서울대학교 농림생물자원학부) , 김광수 (서울대학교 식물생산과학부)

초록
AI-Helper

작물 모형은 작물의 유전적 특성을 나타내는 품종모수를 요구하며, 품종모수는 작물의 개별 품종별로 추정되어야 한다. 품종모수의 추정에는 고품질의 많은 생육 자료가 요구되지만, 자료의 생산에 상당한 비용이 필요하다. 비교적 낮은 품질의 가용성이 높은 자료를 활용하는 대신, 대량의 랜덤 모수를 생성하고 이를 평가하여 품종모수를 추정할 수 있다. 본 연구에서는 SIMPLE 작물 모델의 불확도를 최소화하기 위해 품종모수 추정 방식을 비교하고, 두 앙상블 방식과 대한 비교를 하였다. 모수 추정을 위한 Metropolis-Hastings (MH) 알고리즘에 대한 목적함수로 로그 가능도(log-likelihood: LL)와 generic composite similarity measure (GCSM)를 사용하였다. 또한 품종모수의 평균값을 사용한 예측(E_pm)과 개별 모수들로부터 얻어진 추정값의 평균값(E_em)의 일치도를 분석하여 앙상블 방식에 따른 불확도 변화를 파악하였다. 국내에서 재배되는 사료용 벼 품종인 조우 벼와 영우 벼를 대상으로 품종모수를 추정하였다. 2013년, 2014년, 2016년에 대한 수원, 전주, 나주, 익산에 위치한 실험포장에서 얻은 수량 관측 자료를 사용하였다. 또한 2016년부터 2018년까지 수원에서 보고된 별도의 수량 관측 자료를 사용하였다. 목적함수에 따라 추정된 품종모수의 분포에 차이가 있었다. LL을 통해 얻은 품종모수는 GCSM으로 얻은 품종모수보다 좁은 범위에 분포하였다. 두 가지 앙상블 접근법은 통계적으로 유의한 차이가 나타나지 않음을 확인하였다. GCSM의 상대적으로 높은 불확도는 수용확률을 조정하여 낮출 수 있다고 사료되고, E_pm의 결과는 기존과 다른 앙상블 방식을 통해 적은 연산을 통해 불확도를 낮출 수 있음을 보인다.

Abstract ▼ AI-Helper

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (Eem), using individual parameters. The second ensemble approach was model output (Epm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (Epm) indicates that the uncertainty can be reduced with less computation using ensemble approach.

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표/그림 (9)

표 Table 1. Definitions and default ranges of parameters for SIMPLE crop model (Zhao et al., 2019)
표 Table 2. Cultivation method of whole-crop silage rice by RDA(2013; 2014; 2016)
표 Table 3. Cultivation method of whole-crop silage rice used for validation by Ahn et al. (2019)
그림 Fig. 1. Box plot of parameter for cultivar 'Jowoo' including (A) Tsum, (B) I_50A, and (C) I_50B.
그림 Fig. 2. Box plot of parameter for cultivar 'Yeongwoo' including (A) T_sum, (B) I_50A, and (C) I_50B.
그림 Fig. 3. Distribution of error rate between E_pm and E_em for calibration data of cultivar 'Yeongwoo'.
그림 Fig. 4. NRMSE Comparison of E_pm and E_em (A: cultivar 'Yeongwoo' B: cultivar 'Jowoo').
그림 Fig. 5. Distribution of Error rate between observation and estimation yield for calibration data of cultivar 'Jowoo'.
표 Table 4. Parameter values of SIMPLE crop model for cultivar 'Jowoo' and 'Yeongwoo'

참고문헌 (45)

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, 1699-1712.

상세보기
Ahn, E. K., E. G. Jeong, H. M. Park, K. H. Jung,？U. J. Hyun, and J. H. Ku, 2019: Double cropping？productivity of main whole-crop silage rice and？winter feed crops in the central plains of Korea.？Korean Journal of Crop Science 64(4), 311-322.？(in Korean with English abstract)
Ahn, J. B., J. N. Hur, and K. M. Shim, 2010: A？simulation of agro-climate index over the Korean？peninsula using dynamical downscaling with a numerical？weather prediction model. Korean Journal of？Agricultural and Forest Meteorology 12(1), 1-10.？(in Korean with English abstract)
Andrieu, C., N. De Freitas, A. Doucet, and M. I.？Jordan, 2003: An introduction to MCMC for？machine learning. Machine Learning 50, 5-43.
Bian, J., J. Wu, S. Nie, Y. Wang, and X. Lin,？2023: The parametric uncertainty estimation of？water and nitrogen transport simulation in a paddy？field experiment using HYDRUS-1D. Irrigation and？Drainage 1-14.
Breiman, L., 1996: Bagging predictors. Machine learning？24, 123-140.
Bouman, B. A. M., 2001: ORYZA2000: modeling？lowland rice. IRRI.
Caviglia, O. P., V. O. Sadras, and F. H. Andrade,？2011: Yield and quality of wheat and soybean in？sole-and double-cropping. Agronomy Journal 103(4),？1081-1089.

상세보기
Egli, D. B., and W. P. Bruening, 2000: Potential of？early-maturing soybean cultivars in late plantings.？Agronomy Journal 92(3), 532-537.

상세보기
Fukui, S., Y. Ishigooka, T. Kuwagata, and T. Hasegawa,？2015: A methodology for estimating phenological？parameters of rice cultivars utilizing data from？common variety trials. Journal of Agricultural？Meteorology 71(2), 77-89.

상세보기
Garthwaite, P. H., Y. Fan, and S. A. Sisson, 2016:？Adaptive optimal scaling of Metropolis-Hastings？algorithms using the Robbins-Monro process.？Communications in Statistics-Theory and Methods？45(17), 5098-5111.

상세보기
Gelman, A., W. R. Gilks, and G. O. Roberts, 1997:？Weak convergence and optimal scaling of random？walk Metropolis algorithms. The Annals of Applied？Probability 7(1), 110-120.
Hansen, J. W., and J. W. Jones, 2000: Scaling-up？crop models for climate variability applications.？Agricultural Systems 65(1), 43-72.

상세보기
He, D., E. Wang, J. Wang, and M. J. Robertson,？2017: Data requirement for effective calibration of？process-based crop models. Agricultural and Forest？Meteorology 234, 136-148.

상세보기
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.

상세보기
Heggenstaller, A. H., M. Liebman, and R. P. Anex,？2009: Growth analysis of biomass production in？sole-crop and double-crop corn systems. Crop Science？49(6), 2215-2224.

상세보기
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. (in Korean with English abstract)
Hyun, S., J. Y. Park, J. Kim, D. H. Fleisher, and？K. S. Kim, 2022: GLUEOS: A high performance？computing system based on the orchestration of？containers for the GLUE parameter calibration of？a crop growth model. Computers and Electronics？in Agriculture 197, 106906. (in Korean with English？abstract)

상세보기
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.

상세보기
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.
Kang, M., S. Hyun, and K. S. Kim, 2022: Spatial？Assessment of Climate Suitability for Summer？Cultivation of Potato in North Korea. Korean？Journal of Agricultural and Forest Meteorology？24(1), 35-47.
Kellner, J., T. Houska, R. Manderscheid, H. J.？Weigel, L. Breuer, and P. Kraft, 2019: Response？of maize biomass and soil water fluxes on elevated？CO2 and drought-From field experiments to process-based simulations. Global Change Biology 25(9),？2947-2957.

상세보기
Kim, D. J, S. Kim, K. H. Moon, and J. I. Yun,？2012: An Outlook on Cereal Grains Production in？South Korea Based on Crop Growth Simulation？under the RCP8.5 Climate Change Scenarios. Korean？Journal of Agricultural and Forest Meteorology？14(3), 132-141. (in Korean with English abstract)
Kim, J. G., S. N. Wei, Y. F. Li, H. J. Kim, M. J.？Kim, and E. C. Cheong, 2020: Agronomic？Characteristics and Productivity of Winter Forage？Crop in Sihwa Reclaimed Field. Journal of The？Korean Society of Grassland and Forage Science？40(1), 19-28. (in Korean with English abstract)
Kim Y. J., W. H. Kim, S. H Lee, H. S. Park, K.？Y. Kim, H. C. Ji, K. C. Choi, S. H. Lee, J. S.？Jung, J. H. Kim, T. Y. Hwang, K. W. Lee, H. S.？Kim, and G. J. Choi, 2015: Crop Analysis through？Growth Survey after Wintering of Winter Annual？Forages Grown from 2014 to 2015. Journal of？The Korean Society of Grassland and Forage Science？35, 309-15. (in Korean with English abstract)
Kim, K. S., S. O. Kim, J. H. Kim, K. H. Moon, J.？H. Shin, and J. Cho, 2018: Development and？application of crop models in Korea. Korean？Journal of Agricultural and Forest Meteorology？20(2), 145-148. (in Korean with English abstract)
Kim, M. J., G. J. Choi, W. B. Yook, Y. C. Lim, S.？H. Yoon, J. G. Kim, H. S. Park, and S. S. Seo,？2007: Effects of Seeding Method on the Winter？Survival, Dry Matter Yield and Nutrient Value of？Italian Ryegrass in Paddy Field. Journal of The？Korean Society of Grassland and Forage Science？27(4), 269-274 (in Korean with English abstract)
Kim W. H., J. S. Shin, Y. C. Lim, S. Seo, K. Y.？Kim, and J. K. Lee, 2005: Study on the？Promising Double Cropping System of Summer？and Winter Forage Crop in Paddy Field. Journal？of The Korean Society of Grassland Science 25(4),？233-238. (in Korean with English abstract)
Liu, Y., K. S. Kim, R. M. Beresford, and D. H.？Fleisher, 2020: A generic composite measure of？similarity between geospatial variables. Ecological？Informatics 60, 101169.

상세보기
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, e0195841.

상세보기
Lu, H., Q. Shen, J. Chen, X. Wu, and X. Fu, 2019:？Parallel multiple-chain DRAM MCMC for large-scale？geosteering inversion and uncertainty quantification.？Journal of Petroleum Science and Engineering 174,？189-200.

상세보기
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.

상세보기
Minoli, S., J. Jagermeyr, S. Asseng, A. Urfels, and？C. Muller, 2022: Global crop yields can be lifted？by timely adaptation of growing periods to climate？change. Nature Communications 13(1), 7079.

상세보기
Palubinskas, G. 2017: Image similarity/distance？measures: what is really behind MSE and SSIM?.？International Journal of Image and Data Fusion？8(1), 32-53.

상세보기
Pinheiro, J. C., and D. M. Bates, 1995: Approximations？to the log-likelihood function in the nonlinear mixed-effects model. Journal of Computational and Graphical？Statistics 4(1), 12-35.

상세보기
Rural Development Administration. 2013: Project report？for collaborative research program to develop new？variety of summer crop.
Rural Development Administration. 2014: Project report？for collaborative research program to develop new？variety of summer crop.
Rural Development Administration. 2016: Project？report for collaborative research program to develop？new variety of summer crop.
Seo, J. H., C. D. Hwang, and S. H. Oh, 2021:？Compatibility of double cropping of winter wheat -？summer grain crops in paddy field of southern？Korea, Korean Journal of Crop Science 66, 18-28.？(in Korean with English abstract)
Shcherbakov, M. V., A. Brebels, N. L. Shcherbakova,？A. P. Tyukov, T. A. Janovsky, and V. A. E.？Kamaev, 2013: A survey of forecast error measures.？World Applied Sciences Journal 24(24), 171-176.
Tan, J., J. Cao, Y. Cui, Q. Duan, and W. Gong,？2019: Comparison of the generalized likelihood？uncertainty estimation and Markov chain Monte？Carlo methods for uncertainty analysis of the？ORYZA_V3 model. Agronomy Journal 111(2),？555-564.

상세보기
Wilkerson, G. G., J. W. Jones, K. J. Boote, K. T.？Ingram, and J. W. Mishoe, 1983: Modeling？soybean growth for crop management. Transactions？of the ASAE 26(1), 63-0073.
Wang, Z., A. C. Bovik, H. R. Sheikh, and E. P.？Simoncelli, 2004: Image quality assessment: from？error visibility to structural similarity. IEEE？Transactions on Image Processing 13(4), 600-612.
Zhao, C., B. Liu, L. Xiao, G. Hoogenboom, K. J.？Boote, B. T. Kassie, P. Willingthon, S. Vakhtang,？K. S. Kim, M H. O. Ixchel, W. Daniel, H. P.？Cheryl, O. S. Claudio, Z. Yan, and A. Senthold,？2019: A SIMPLE crop model. European Journal？of Agronomy 104, 97-106.

상세보기
Zhao, Z., X. Qin, E. Wang, P. Carberry, Y. Zhang,？S. Zhou, X. Zhang, C. Hu, and Z. Wang, 2015:？Modelling to increase the eco-efficiency of a？wheat-maize double cropping system Agriculture,？Ecosystems & Environment 210, 36-46.

상세보기

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