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对于基因型-环境-管理组合集的时间解析表型的动态建模的概念框架:一个模型库
George A K van Voorn1, Martin P Boer1, Sandra Huynh Truong2
1Biometris, Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands.
Frontiers in plant science
|June 30, 2023
概括
简单的作物生长模型通过模拟基因型和环境相互作用来预测产量. 将这些模型结合起来,可以为各种作物条件和气候变化场景提供灵活的预测工具.
科学领域:
- 农业科学 农业科学
- 计算生物学 计算生物学
- 环境科学 环境科学
背景情况:
- 动态作物生长模型对于在不同的遗传和环境条件下预测作物产量至关重要,包括气候变化.
- 现型特征是遗传学,环境和管理之间的复杂相互作用的结果,需要采用能够随着时间的推移捕捉这些动态的模型.
- 从遥感技术获得的高分辨率作物表型数据的可用性增加,提高了模型开发和验证的潜力.
研究的目的:
- 提出和评估四种低复杂度的现象学过程模型来描述作物生长.
- 在这些模型中,将基因型差异概念化为作物生长参数值的变化.
- 评估这些模型在基因型和环境变化下预测作物生长的有用性.
主要方法:
- 开发了四种现象学模型,使用微分方程来描述作物生长和环境相互作用.
- 模拟后勤增长,隐式或显式限制来自辐射,温度和水的可用性.
- 通过使用APSIM-小麦平台,在31年的时间里,在澳大利亚多样化的环境中为199种基因型模拟小麦生物质数据.
主要成果:
- 每个模型都适合特定的基因型试验组合,突出显示了不同环境驱动因素的影响.
- 没有一个单一的模型能够在所有基因型和试验中提供最佳匹配,这表明了特定环境的局限性.
- 基因型和环境变异显著影响了模型性能,需要量身定制的方法.
结论:
- 解决关键环境限制的低复杂现象学模型的组合可以有效地预测作物生长.
- 这些模型为了解作物对基因型和环境变异的反应提供了有价值的工具.
- 该方法提供了一个灵活的框架,用于在不断变化的农业景观和气候条件下预测作物表现.
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