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A mass participatory experiment provides a rich temporal profile of temperature response in spring onions

机译:大规模参与实验提供了丰富的洋葱温度响应的时间分布图

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摘要

Plants modulate their growth rates based on the environmental signals; however, it is difficult to experimentally test how natural temperature and light fluctuations affect growth, since realistic outdoor environments are difficult to replicate in controlled laboratory conditions, and it is expensive to conduct experiments in many environmentally diverse regions. In partnership with BBC Terrific Scientific, over 50 primary schools from around the UK grew spring onions outside of hydroponic growth chambers that they constructed. Over 2 weeks, students measured the height of the spring onions daily, while the hourly temperature and visibility data were determined for each school based on the UK Meteorological Office data. This rich time series data allowed us to model how plants integrate temperature and light signals to determine how much to grow, using techniques from functional data analysis. We determined that under nutrient‐poor hydroponic conditions, growth of spring onion is sensitive to even a few degrees change in temperature, and is most correlated with warm nighttime temperatures, high temperatures at the start of the experiment, and light exposure near the end of the experiment. We show that scientists can leverage schools to conduct experiments that leverage natural environmental variability to develop complex models of plant‐environment interactions.
机译:植物根据环境信号调节生长速度;但是,由于很难在受控的实验室条件下复制真实的室外环境,因此很难通过实验测试自然温度和光的波动如何影响生长,并且在许多环境不同的地区进行实验非常昂贵。与BBC Terrific Sc​​ientific合作,来自英国各地的50多家小学在他们建造的水培生长室外面种植了葱。在2周的时间内,学生每天测量葱的高度,同时根据英国气象局的数据确定每所学校的每小时温度和能见度数据。这些丰富的时间序列数据使我们能够使用功能数据分析中的技术对植物如何整合温度和光信号进行建模,以决定要生长多少。我们确定,在营养不良的水耕条件下,葱的生长即使对温度的几度变化也很敏感,并且与夜间温暖的温度,实验开始时的高温以及实验结束时的光照最相关。本实验。我们表明,科学家可以利用学校进行利用自然环境可变性的实验,以开发复杂的植物-环境相互作用模型。

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