Modelling rice growth and grain yield in rice ratooning production system

文献类型： 外文期刊

第一作者： Ling, Xiaoxia

作者： Ling, Xiaoxia;Deng, Nanyan;Yuan, Shen;Cui, Kehui;Peng, Shaobing;Huang, Jianliang;Zhang, Tianyi;Yuan, Gaohua;He, Wenjie;Nie, Lixiao;Li, Tao;Huang, Jianliang

作者机构：

关键词： Ratoon crop; Crop growth model; Evaluation; Sensitivity analysis; Management practice

期刊名称：FIELD CROPS RESEARCH （ 影响因子：5.224； 五年影响因子：6.19 ）

ISSN： 0378-4290

年卷期： 2019 年 241 卷

页码：

收录情况： SCI

摘要： Ratoon rice (RR) is one of the cropping systems with the potential to increase rice production with high resource use efficiency. So far, there has been limited information in the literature on the simulation of the dynamic biomass production of the ratoon crop (RC) in the RR system. ORYZA series models have been widely applied in rice modelling worldwide. By adding a reserve pool submodule to ORYZA v3, ORYZA_R model has been developed to simulate the growth and development of main crop (MC) and RC. This study made the first attempt to evaluate the prediction performance of ORYZA_R model. The model was calibrated and validated in central China by using field data collected from the experiments with the various heights of stubbles left from the MC (SHMLeft), nitrogen (N) applications, and varieties in 2015 and 2016. The values of normalized root mean square error (NRMSE) of grain yields in calibration and validation were 7% and 9% for the MC, respectively, and 9% and 12% for the RC, respectively. Furthermore, the simulations of the dynamic growth of various organs for both MC and RC were robust. The sensitive analysis implied that the most sensitive parameters affecting the RC simulation were those relating the initial development stage and the dry matter production and allocation in the early growth stage. The scenario simulation indicated that simulated grain yield and biomass of RC demonstrated a strong response to SHMLeft, and N applications. In conclusion, the calibrated ORYZA_R model can properly simulate the rice growth and grain yield of both MC and RC. The exploration of physiological mechanism underlying the response of RC yield to management practices and additional evaluation of this model in diverse environments are needed so as to improve the performance of model prediction.

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