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Simulating grain mass and nitrogen concentration in wheat.

机译:模拟小麦的籽粒质量和氮含量。

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Crop simulation models are important tools to enhance our knowledge of plant processes and responses to the environment. The objective of this effort was to re-evaluate the algorithms in CERES-Wheat that simulate grain mass and grain nitrogen concentration, two primary determinants of grain end-use quality. Data from two contrasting wheat (Triticum aestivum L.) cultivars collected over three growing seasons with varying sowing dates and plant population densities were used in this study. Grain mass was simulated using a source-sink approach. The source was current dry matter production and translocation of stem reserves. The sink was the number of grains and a surrogate for cell division and cell expansion within each grain. Cell division and expansion were functions of temperature stress term and water stress during anthesis. The translocated stem reserves were a function of temperature, stage of plant development, and a measure of the sink demand. The nitrogen concentration algorithm was modified by making the minimum stover (above ground organs, not including the spike) nitrogen a cultivar dependent parameter, which would allow more or less nitrogen available for translocation depending upon the cultivar. It was assumed that there was a linear relationship between the number of aborted grains at the beginning of the linear grain filling period and the increase in grain nitrogen concentration. It was further assumed that an initial grain nitrogen mass at the beginning of the linear grain filling period was equal to the initial grain mass times the simulated actual nitrogen concentration of the above ground biomass. The root mean square error (RMSE) for grain mass for both cultivars was 2.9 and 3.0 mg for CERES-Wheat and the modified model, respectively. The advantage of the modified model is that it does not require a genetic coefficient to simulate grain mass as does CERES-Wheat. The RMSE for grain nitrogen concentration for both cultivars was 4.5 and 3.0 mg N g-1 DM for CERES-Wheat and the modified model, respectively. However, when the mean square error was divided into systematic and unsystematic errors, there were inconsistent results for grain mass and grain nitrogen concentration for the same model with different cultivars indicating a lack of plasticity of response. Incorporating this plasticity into future algorithms of key plant processes and further evaluations of the modified model in other wheat growing regions should be carried out to determine the robustness of these responses..
机译:作物模拟模型是增强我们对植物过程和对环境的反应的知识的重要工具。这项工作的目的是重新评估CERES-Wheat中模拟谷物质量和谷物氮浓度的算法,这两个谷物最终使用质量的主要决定因素。在这项研究中,使用了来自三个不同生长季节,播种日期和植物种群密度不同的两个对比小麦(Triticum aestivum L.)栽培品种的数据。使用源汇方法模拟了谷物质量。来源是当前的干物质生产和茎储备的易位。汇是谷物的数目,是每个谷物中细胞分裂和细胞膨胀的替代物。花期中细胞分裂和扩增是温度胁迫项和水分胁迫的函数。易位的茎储备是温度,植物发育阶段和汇需求量度的函数。通过将最小秸秆(地上器官,不包括穗)氮作为栽培品种的相关参数来修改氮浓度算法,这将允许根据栽培品种将更多或更少的氮用于转运。假设在线性籽粒灌浆期开始时流产的籽粒数量与籽粒氮浓度的增加之间存在线性关系。进一步假设线性谷粒充填期开始时的初始谷粒氮质量等于初始谷粒质量乘以上述地上生物量的模拟实际氮浓度。 CERES-Wheat和改良模型的两个品种的籽粒质量的均方根误差(RMSE)分别为2.9和3.0 mg。修改后的模型的优势在于,它不需要像CERES-Wheat一样可以利用遗传系数来模拟谷物质量。 CERES-Wheat和改良模型的两个品种的籽粒氮浓度的RMSE分别为4.5和3.0 mg N g-1 DM。但是,当均方误差分为系统误差和非系统误差时,对于具有不同品种的同一模型,其谷物质量和籽粒氮浓度的结果不一致,表明缺乏响应可塑性。应将这种可塑性纳入未来关键植物过程的算法中,并在其他小麦种植地区对改良模型进行进一步评估,以确定这些响应的稳健性。

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