Coupling individual kernel-filling processes with source-sink interactions into GREENLAB-Maize

文献类型： 外文期刊

第一作者： Ma, Yuntao

作者： Ma, Yuntao;Chen, Youjia;Guo, Yan;Li, Baoguo;Zhu, Jinyu;Meng, Lei;Meng, Lei;Hoogenboom, Gerrit;Hoogenboom, Gerrit

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关键词： Individual kernel filling; model; remobilization; source-sink dynamic; Zea mays

期刊名称：ANNALS OF BOTANY （ 影响因子：4.357； 五年影响因子：5.488 ）

ISSN： 0305-7364

年卷期： 2018 年 121 卷 5 期

页码：

收录情况： SCI

摘要： Background and Aims Failure to account for the variation of kernel growth in a cereal crop simulation model may cause serious deviations in the estimates of crop yield. The goal of this research was to revise the GREENLAB-Maize model to incorporate source-and sink-limited allocation approaches to simulate the dry matter accumulation of individual kernels of an ear (GREENLAB-Maize-Kernel). Methods The model used potential individual kernel growth rates to characterize the individual potential sink demand. The remobilization of non-structural carbohydrates from reserve organs to kernels was also incorporated. Two years of field experiments were conducted to determine the model parameter values and to evaluate the model using two maize hybrids with different plant densities and pollination treatments. Detailed observations were made on the dimensions and dry weights of individual kernels and other above-ground plant organs throughout the seasons. Key Results Three basic traits characterizing an individual kernel were compared on simulated and measured individual kernels: (1) final kernel size; (2) kernel growth rate; and (3) duration of kernel filling. Simulations of individual kernel growth closely corresponded to experimental data. The model was able to reproduce the observed dry weight of plant organs well. Then, the source-sink dynamics and the remobilization of carbohydrates for kernel growth were quantified to show that remobilization processes accompanied source-sink dynamics during the kernel-filling process. Conclusions We conclude that the model may be used to explore options for optimizing plant kernel yield by matching maize management to the environment, taking into account responses at the level of individual kernels.

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