Scientia Agricultura Sinica ›› 2013, Vol. 46 ›› Issue (6): 1136-1148.doi: 10.3864/j.issn.0578-1752.2013.06.006

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Regional Prediction and Evaluation of Wheat Phenology Based on the Wheat Grow and CERES Models

 吕Zun-Fu , LIU  Xiao-Jun, TANG  Liang, LIU  Lei-Lei, CAO  Wei-Xing, ZHU  Yan   

  1. College of Agriculture, Nanjing Agricultural University/National Engineering and Technology Center for Information Agriculture/Jiangsu Key Laboratory for Information Agriculture, Nanjing 210095
  • Received:2012-10-01 Online:2013-03-15 Published:2012-12-05

PDF

1149

Cited

6

Abstract: 【Objective】The objectives of this study are to investigate the key techniques of wheat phenological model for regional application, and to evaluate the validity of regional application techniques through analyzing the simulated phenology results of WheatGrow and CERES-Wheat at regional scale. 【Method】First, the daily meteorological data at different weather stations were interpolated using the method of Thin Plate Spline (TPS) , and the gridded daily meteorological surface data was generated. Then, the historical sowing dates of wheat at different stations were interpolated using TPS method, and the multi-year average values of interpolated results were generated as regional sowing date surface data. In addition, by integrating the Markov Chain Monte Carlo (MCMC) method with the above two wheat models, the region-specific cultivar parameters were estimated with the measured phenology data from 1998 to 2003, and were taken as the typical cultivar parameters of each province. Finally, the regional daily meteorological data, sowing surface data and region-specific cultivar parameters were input into the above two models, which took grid as basic unit to simulate regional wheat phenology. Meanwhile, the simulated results were evaluated with observed data and the uncertainty coming from cultivar parameters was quantified at site and regional scales. 【Result】The estimated values from the above two models agreed well with the observed values at regional scale. R2 between estimated and observed values of jointing date, heading date and maturity date were 0.85, 0.87 and 0.86 (WheatGrow), 0.87, 0.85 and 0.82 (CERES), respectively. RMSE between the estimated and observed values of jointing date, heading date and maturity date were 9.6, 7.2 and 6.3 d (WheatGrow), 9.4, 7.8 and 6.6 d (CERES), respectively. By analyzing the simulated results of the two models at regional scale, the WheatGrow model had higher prediction accuracy than CERES-Wheat, while the uncertainty came from crop parameters were also higher than CERES-Wheat model.【Conclusion】Through combining the TPS interpolation method with MCMC-based parameter estimation techniques, the mechanism-based model could be effectively extended to regional scales to accurately predict the regional wheat phenology, and quantify the uncertainty coming from the parameter estimation. These results will provide a technology support for estimating crop productivity at regional scale in the future.

Key words: wheat , phenology , TPS interpolating method , MCMC , regional application , uncertainty

[1]Chipanshi A C, Ripley E A, Lawford R G. Large-scale simulation of wheat yields in a semi-arid environment using a crop-growth model. Agricultural Systems, 1999, 59(1): 57-66.

[2]Harrison P A, Butterfield R E. Effects of climate change on Europe-wide winter wheat and sunflower productivity. Climate Research, 1996, 7: 225-241.

[3]Rosenzweig C, Parry M L. Potential impact of climate change on world food supply. Nature, 1994, 367: 133-138.

[4]Easterling W E, Crosson P R, Rosenberg N J, McKenney M S, Katz L A, Lemon K M. Agricultural impacts of and responses to climate change in the Missouri-Iowa-Nebraska-Kansas (MINK) region. Climate Change, 1993, 24(1-2): 23-61.

[5]Wolf J. Effects of climate change on wheat and maize production potential in the EC. Oxford: University of Oxford, 1993.

[6]Tao F, Zhang Z. Adaptation of maize production to climate change in North China Plain: Quantify the relative contributions of adaptation options. European Journal of Agronomy, 2010, 33: 103-116.

[7]Cantelaube P, Terres J M. Seasonal weather forecasts for crop yield modelling in Europe. Tellus A, 2005, 57: 476-472.

[8]White J W, Hoogenboom G, Stackhouse Jr. P W, Hoell J M. Evaluation of NASA satellite- and assimilation model-derived long-term daily temperature data over the continental US. Agricultural and Forest Meteorology, 2008, 148: 1574-1584.

[9]van Bussela L G J, Ewert F, Leffelaar P A. Effects of data aggregation on simulations of crop phenology. Agriculture, Ecosystems and Environment, 2011, 142: 75-84.

[10]姜晓剑, 刘小军, 黄芬, 姜海燕, 曹卫星, 朱艳. 逐日气象要素空间插值方法的比较. 应用生态学报, 2010, 21(3): 624-630.

Jiang X J, Liu X J, Huang F, Jiang H Y, Cao W X, Zhu Y. Comparison of spatial interpolation methods for daily meteorological elements. Chinese Journal of Applied Ecology, 2010, 21(3): 624-630. (in Chinese)

[11]Xiong W, Matthews R, Holman I, Lin E, Xu Y. Modelling China’s potential maize production at regional scale under climate change. Climatic Change, 2007, 85(3): 433-451.

[12]马玉平, 王石立, 张黎, 庄立伟. 基于升尺度方法的华北冬小麦区域生长模型初步研究Ⅰ.潜在生产水平. 作物学报, 2005, 31(6): 697-705.

Ma Y P, Wang S L, Zhang L, Zhuang L W. A preliminary study on a regional growth simulation model of winter wheat in north China cased on scaling-up approachⅠ. potential production level. Acta Agronomica Sinica, 2005, 31(6): 697-705. (in Chinese)

[13]Harrison P A, Porter J R, Downing T E. Scaling-up the AFRCWHEAT2 model to assess phenological development for wheat in Europe. Agricultural and Forest Meteorology, 2000, 101: 167-186.

[14]江敏, 金之庆. CERES-Rice模型区域应用中遗传参数升尺度的一种方法. 中国水稻科学, 2009, 23(2): 172-178.

Jiang M, Jin Z Q. A method to upscale the genetic parameters of CERES-Rice in regional applications. Chinese Journal of Rice Science, 2009, 23(2): 172-178. (in Chinese)

[15]Makowski D, Wallach D, Tramblay M. Using a Bayesian approach to parameter estimation; comparison of the GLUE and MCMC methods. Agronomie, 2002, 22: 191-203.

[16]Xu T, White L, Hui D, Luo Y. Probabilistic inversion of a terrestrial ecosystem model: Analysis of uncertainty in parameter estimation and model prediction. Global Biogeochemical Cycles, 2006, 20: 1-15.

[17]Tao F, Yokozawa M, Zhang Z. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new process-based model development, optimization, and uncertainties analysis. Agricultural and Forest Meteorology, 2009, 149(5): 831-850.

[18]Iizumi T, Yokozawa M, Nishimori M. Parameter estimation and uncertainty analysis of a large-scale crop model for paddy rice: Application of a Bayesian approach. Agricultural and Forest Meteorology, 2009, 149(2): 333-348.

[19]Ceglar A, ?repinšek Z, Kajfe?-Bogataj L, Poga?ar T. The simulation of phenological development in dynamic crop model: The Bayesian comparison of different methods. Agricultural and Forest Meteorology, 2011, 151(1): 101-115.

[20]He J, Jones J W, Graham W D, Dukes M D. Influence of likelihood function choice for estimating crop model parameters using the generalized likelihood uncertainty estimation method. Agricultural Systems, 2010, 103(5): 256-264.

[21]石晓燕, 汤亮, 刘小军, 曹卫星, 朱艳. 基于模型和GIS 的小麦空间生产力预测研究. 中国农业科学, 2009,42(11): 3828-3835.

Shi X, Tang L, Liu X, Cao W X, Zhu Y. Predicting spatial productivity in wheat based on model and GIS. Scientia Agricultura Sinica, 2009, 42(11): 3828-3835. (in Chinese)

[22]姚凤梅, 秦鹏程, 张佳华, 林而达, Vijendra B. 基于模型模拟气候变化对农业影响评估的不确定性及处理方法. 科学通报, 2011, 56(8): 547-555.

Yao F M, Qin P C, Zhang J H, Lin E D, Vijendra B. Uncertainties in assessing the effect of climate change on agriculture using model simulation and uncertainty processing methods. Chinese Science Bulletin, 2011, 56: 547-555. (in Chinese)

[23]Jagtap S S, Jones J W. Adaptation and evaluation of the CROPGRO-soybean model to predict regional yield and production. Agriculture, Ecosystems & Environment, 2002, 93(1–3): 73-85.

[24]金善宝. 中国小麦学. 北京:农业出版社, 1996.

Jin S B. China Wheat. Beijing: China Agricultural Press, 1996. (in Chinese)

[25]Yan M C, Cao W X, Luo W H. A model of phasic and phenological development of wheat. I. Assumption and description of the model. Chinese Journal of Applied Ecology, 2000, 11: 1-9.

[26]Liu T, Cao W X, Luo W H. Calculation of physiological development time and prediction of development stages after heading. Journal of Triticeae Crops, 2000, 20: 29-34.

[27]Cao W, Moss D N. Modelling phasic development in wheat: a conceptual integration of physiological components. Journal of Agricultural Science, 1997, 129: 163-172.

[28]Ritchie J T, Otter-Nacke S. Description and performance of CERES-Wheat: use-oriented wheat yield model//ARS Wheat Yield Project. ARS-38 National Technical Information Service: Springfield, 1985.

[29]Jones J W, Hoogenboom G, Porter C H, Boote K J, Batchelor W D, Hunt L A, Wilkens P W, Singh U, Gijsman A J, Ritchie J T. The DSSAT cropping system model. European Journal of Agronomy, 2003, 18: 235-265.

[30]Mcmaster G S, Wilhelm W W. Phenological responses of wheat and barley to water and temperature: improving simulation models. Journal of Agricultural Science, 2003, 141: 129-147.

[31]McMaster G S, Smika D E. Estimation and evaluation of winter wheat phenology in the central Great Plains. Agricultural and Forest Meteorology, 1988, 43(1): 1-18.

[32]姜志伟, 陈仲新, 周清波, 任建强. CERES-Wheat作物模型参数全局敏感性分析. 农业工程学报, 2011, 27(1): 236-242.

Jiang Z W, Chen Z X, Zhou Q B, Ren J Q. Global sensitivity analysis of CERES-Wheat model parameters. Transactions of the Chinese Society of Agriculture Engineering, 2011, 27(1): 236-242. (in Chinese)

[33]Hutchinson M F. Anusplin Version4.36 User Guide. Canberra: Australia National University, 2004.

[34]Hartkamp A D, de Beurs K, Stein A, White J W. Interpolation techniques for climate variables (NRG-GIS paper 99-01). CIMMYT, INT. Mexico DF, Mexico, 1999.

[35]熊伟. CERES-Wheat模型在我国小麦区的应用效果及误差来源. 应用气象学报, 2009, 20(1): 88-94.

Xiong W. The performance of CERES-Wheat model in wheat planting areas and its uncertainties. Journal of Applied Meteorology Science, 2009, 20(1): 88-94. (in Chinese)

[36]Jarvis C H, Stuart A N. A comparison among strategies for interpolating maximum and minimum daily air temperatures. part II: the interaction between number of guiding variables and the type of interpolation method. Journal of Applied Meteorology and Climatology, 2001, 40: 1075-1084.

[37]Hutchinson M F, Gessler P E. Splines-more than just a smooth interpolator. Geoderma, 1994, 45: 45-67.

[38]金之庆, 石春林. 江淮平原小麦渍害预警系统(WWWS). 作物学报,2006,32(10): 1458-1465.

Jin Z Q, Shi C L. An early warning system to predict waterlogging injuries for winter wheat in the Yangtze-Huai Plain (WWWS). Acta Agronomica Sinica, 2006, 32(10): 1458-1465. (in Chinese)

[39]Dai C, Yao M, Xie Z, Chen C, Liu J. Parameter optimization for growth model of greenhouse crop using genetic algorithms. Applied Soft Computing, 2009, 9: 13-19.

[40]Ferreyra R A. A faster algorithm for crop model parameterization by inverse modeling: simulated annealing with data reuse. American Society of Agricultural and Biological Engineers, 2004, 47: 1793-1801.
[1] ZHU Qi, JIA ZhenPeng, Tahir SHAH, XU ChenSheng, LI ZhiQi, LÜ HuiShuai, ZHU PengChao, WEI XiaoMin, HUANG DongLin, SUN YanNi, CAO WeiDong, GAO YaJun, WANG ZhaoHui, ZHANG DaBin. Green Manure Crops Combined with Enhanced-Efficiency Products Reduced Greenhouse Gas Emissions and Carbon Footprints in Dryland Wheat Fields [J]. Scientia Agricultura Sinica, 2026, 59(7): 1507-1522.
[2] LI WenHu, LI HaiFeng, DU YuPeng, DING YuLan, LUO YiNuo, LI YuKe, SHE WenTing, ZHANG Feng, TENG Yu, ZHANG SiQi, HUANG Cui, LI XiaoHan, LIU JinShan, WANG ZhaoHui. Regional Differences in Wheat Zinc Uptake and Translocation Responses to Soil Zinc Fertilization [J]. Scientia Agricultura Sinica, 2026, 59(5): 1034-1047.
[3] JIAO WenJuan, HE WanLong, GENG HongWei, BAI Bin, LI JianFeng, CHENG YuKun. Stripe Rust Resistance Evaluation and Molecular Characterization of Yr Genes for 155 Spring Wheat Varieties (Lines) [J]. Scientia Agricultura Sinica, 2026, 59(5): 937-950.
[4] CUI ShiYou, CHEN PengJun, MIAO YuanQing, HAN JiJun, SHEN JunMing. Development and Field Evaluation of Glyphosate-Resistant Wheat Germplasm Generated Through EMS Mutagenesis [J]. Scientia Agricultura Sinica, 2026, 59(4): 723-733.
[5] QIAN Jin, LI YingXue, WU Fang, ZOU XiaoChen. Improved Leaf Phosphorus Content Estimation of Winter Wheat Using Ensemble Hyperspectral Dimensionality Reduction Method [J]. Scientia Agricultura Sinica, 2026, 59(4): 781-792.
[6] KONG Yuan, CUI ShaSha, LI Mei, LI Jian, YANG SiYu, FANG Feng, LIU ShuaiShuai, LIU MingPing, ZENG Yan, GAO XingXiang, BAI LianYang. Spatiotemporal Distribution Dynamics of Five Grass Weed Species Including Lolium multiflorum in Winter Wheat Fields of the Huang- Huai-Hai Region [J]. Scientia Agricultura Sinica, 2026, 59(4): 807-823.
[7] WANG YongSheng, NIU Li, WANG ChangJie, MA LiHua, LIAN XiaoXiao, MENG YaXiong, MA XiaoLe, YAO LiRong, ZHANG Hong, YANG Ke, LI BaoChun, WANG HuaJun, SI ErJing, WANG JunCheng. Genome-Wide Association Study and Candidate Gene Identification for Thousand Grain Weight in Winter Wheat [J]. Scientia Agricultura Sinica, 2026, 59(3): 499-514.
[8] LI XinYi, LI JiaNing, YANG WenPing, XIA Qing, HUO YingRui, HAO ShiHang, HUANG TingMiao, REN YongKang, CHEN Jie, GAO ZhiQiang, YANG ZhenPing. Effects of Post-Anthesis Foliar Zinc Application on Zinc Nutrition in Colored-Grain Wheat [J]. Scientia Agricultura Sinica, 2026, 59(3): 515-527.
[9] XIAN QingLin, XIAO JianKe, GAO AQing, GAO LiChuang, LIU Yang. Effects of Planting Patterns Combined with Soil Moisture Measurement and Supplementary Irrigation on the Yield and Water Use Efficiency of Winter Wheat [J]. Scientia Agricultura Sinica, 2026, 59(3): 589-601.
[10] ZHANG ZhiYong, TAN ShiChao, XIONG ShuPing, MA XinMing, WEI YiHao, WANG XiaoChun. Effects of Annual Water and Nitrogen Optimization on Yield and Nitrogen Migration of Wheat-Maize Rotation System in Irrigation Area of Northern Henan [J]. Scientia Agricultura Sinica, 2026, 59(2): 336-353.
[11] LÜ XuDong, SUN ShiYuan, LI YaNan, LIU YuLong, WANG YanQun, FU Xin, ZHANG JiaYing, NING Peng, PENG ZhengPing. Effects of Intelligent Mechanized Layered Fertilization on Root-Soil Nutrient Distribution and Yield in Wheat Fields [J]. Scientia Agricultura Sinica, 2026, 59(1): 129-146.
[12] LU Hao, ZHANG MingLong, HAN Mei, YAN QingBiao, LI ZhengPeng, YIN Wen, FAN ZhiLong, HU FaLong, CHAI Qiang. Green Manure Returning via Sheep Digest with Nitrogen Fertilizer Reduction are Beneficial to Improve Wheat Yield and Soil Quality at Qinghai-Tibet Plateau [J]. Scientia Agricultura Sinica, 2026, 59(1): 147-160.
[13] YE MeiJin, CHEN JiaTing, ZHOU JieGuang, YIN Li, HU XinRong, LAN YuXin, CHEN Bin, SU LongXing, LIU JiaJun, LIU TianChao, LI XiaoYu, MA Jian. Identification, Validation and Genetic Effect Analysis of Major QTL for Spike Density in Wheat [J]. Scientia Agricultura Sinica, 2026, 59(1): 17-28.
[14] PU LiXia, ZHANG JiaRui, YE JianPing, HUANG XiuLan, FAN GaoQiong, YANG HongKun. The Combined Effects of 16, 17-Dihydro Gibberellin A5 and Straw Mulching on Tillering and Grain Yield of Dryland Wheat [J]. Scientia Agricultura Sinica, 2025, 58(9): 1735-1748.
[15] LI YunLi, DIAO DengChao, LIU YaRui, SUN YuChen, MENG XiangYu, WU ChenFang, WANG Yu, WU JianHui, LI ChunLian, ZENG QingDong, HAN DeJun, ZHENG WeiJun. Genome-Wide Association Study of Heat Tolerance at Seedling Stage in A Wheat Natural Population [J]. Scientia Agricultura Sinica, 2025, 58(9): 1663-1683.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
京ICP备09089781号-30
Copyright 2018 © Editorial office of Scientia Agricultura Sinica
Tel: 86-010-82106279    E-mail: zgnykx@mail.caas.net.cn
Supported by Beijing Magtech Co.Ltd