Optimizing Parameters of CSM-CERES-Maize Model to Improve Simulation Performance of Maize Growth and Nitrogen Uptake in Northeast China

文献类型： 外文期刊

第一作者： Liu Hai-long

作者： Liu Hai-long;Yang Jing-yi;Drury, Craig F.;Yang Xue-ming;Liu Hai-long;Zhu Ye-ping;Liu Hai-long;He Ping;Bai You-lu;Jin Ji-yun;Li Wen-juan;Xie Jia-gui;Yang Jing-min;Hoogenboom, Gerrit

作者机构：

关键词： DSSAT;CERES-Maize model;maize growth simulation;model evaluation;fertilizer N experiment

期刊名称：JOURNAL OF INTEGRATIVE AGRICULTURE （ 影响因子：2.848； 五年影响因子：2.979 ）

ISSN： 2095-3119

年卷期： 2012 年 11 卷 11 期

页码：

收录情况： SCI

摘要： Crop models can be useful tools for optimizing fertilizer management for a targeted crop yield while minimizing nutrient losses. In this paper, the parameters of the decision support system for agrotechnology transfer (DSSAT)-CERES-Maize were optimized using a new method to provide a better simulation of maize (Zea mays L.) growth and N uptake in response to different nitrogen application rates. Field data were collected from a 5 yr field experiment (2006-2010) on a Black soil (Typic hapludoll) in Gongzhuling, Jilin Province, Northeast China. After cultivar calibration, the CERES-Maize model was able to simulate aboveground biomass and crop yield of in the evaluation data set (n-RMSE=5.0-14.6%), but the model still over-estimated aboveground N uptake (i.e., with E values from -4.4 to -21.3 kg N ha(-1)). By analyzing DSSAT equation, N stress coefficient for changes in concentration with growth stage (CTCNP2) is related to N uptake. Further sensitivity analysis of the CTCNP2 showed that the DSSAT model simulated maize nitrogen uptake more precisely after the CTCNP2 coefficient was adjusted to the field site condition. The results indicated that in addition to calibrating 6 coefficients of maize cultivars, radiation use efficiency (RUE), growing degree days for emergence (GDDE), N stress coefficient, CTCNP2, and soil fertility factor (SLPF) also need to be calibrated in order to simulate aboveground biomass, yield and N uptake correctly. Independent validation was conducted using 2008-2010 experiments and the good agreement between the simulated and the measured results indicates that the DSSAT CERES-Maize model could be a useful tool for predicting maize production in Northeast China.

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