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随机回归用于模拟大豆植物对灌变化的反应,使用时间序列多谱数据.

Kengo Sakurai1, Yusuke Toda1, Kosuke Hamazaki1

  • 1Graduate School of Agricultural and Life Sciences, University of Tokyo, Tokyo, Japan.

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概括

这项研究开发了一种新方法,使用时间序列多谱数据和随机回归模型来测量和建模植物对干旱的反应. 这种方法有效地捕捉了大豆干旱耐受性的遗传变异,提高了预测准确度.

关键词:
甘氨酸最大 (L.) 均.干旱造成的压力是干旱.灌变化 灌变化多光谱 (MS) 是指多光谱的.植物对植物反应的反应随机回归模型 (RRM) 是一种随机回归模型.单一的环境试验时间序列时间序列.

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科学领域:

  • 农业科学 农业科学
  • 植物生理学 植物生理学
  • 遗传学和基因组学 在

背景情况:

  • 耐旱性是非生物压力下的作物产量的一个关键特征.
  • 目前用于测量和建模植物对干旱的反应的现有方法尚未完全确立.
  • 开发准确的方法对于培育性作物品种至关重要.

研究的目的:

  • 开发一种新的方法来测量和建模植物对灌变化的反应,使用时间序列多光谱 (MS) 数据.
  • 评估随机回归模型 (RRMs) 在捕获大豆干旱应对基因变异方面的有效性.
  • 评估基因组预测模型对预测干旱耐受性特征的准确性.

主要方法:

  • 在三年 (2019-2021) 中,根据各种灌处理 (W5,W10,D10,D) 评估了178个大豆加入.
  • 在灌/非灌周期中使用无人驾驶飞行器收集的时间序列MS数据.
  • 开发了随机回归模型 (RRMs) 和使用遗传RRM系数作为次要特征的基因预测模型.

主要成果:

  • RRMs有效地捕捉了植物对灌变化的反应.
  • 基因组预测模型建立在改变灌治疗的基础上,显示在2020-2021年持续干旱 (r=0.34-0.44) 的基因组预测模型比在持续干旱 (r=0.34-0.44) 的基因组预测模型准确度更高 (r=0.44-0.49).
  • 与简单的基因组预测 (r=0.32) 相比,在改变灌治疗的几年内预测的准确性提高了42% (r=0.46).

结论:

  • 使用时间序列MS数据开发的RRM方法有效地测量和建模植物对干旱的反应.
  • 这种方法成功地捕获了与大豆干旱耐受性相关的遗传变异.
  • 这些发现为改善抗旱大豆品种的育种策略提供了基础.