National Agriculture and Food Research Organization (NARO), Institute of Agro-Environmental Sciences, Kannondai, Tsukuba, Ibaraki, Japan.
Kyushu University, Faculty of Agriculture, Fukuoka, Japan.
Ann Bot. 2021 Feb 9;127(3):317-326. doi: 10.1093/aob/mcaa197.
Most perennial plants memorize cold stress for a certain period and retrieve the memories for cold acclimation and deacclimation, which leads to seasonal changes in cold-hardiness. Therefore, a model for evaluating cold stress memories is required for predicting cold-hardiness and for future frost risk assessments under warming climates. In this study we develop a new dynamic model of cold-hardiness by introducing a function imitating past temperature memory in the processes of cold acclimation and deacclimation.
We formulated the past temperature memory for plants using thermal time weighted by a forgetting function, and thereby proposed a dynamic model of cold-hardiness. We used the buds of tea plants (Camellia sinensis) from two cultivars, 'Yabukita' and 'Yutakamidori', to calibrate and validate this model based on 10 years of observed cold-hardiness data.
The model captured more than 90 % of the observed variation in cold-hardiness and predicted accurate values for both cultivars, with root mean square errors of ~1.0 °C. The optimized forgetting function indicated that the tea buds memorized both short-term (recent days) and long-term (previous months) temperatures. The memories can drive short-term processes such as increasing/decreasing the content of carbohydrates, proteins and antioxidants in the buds, as well as long-term processes such as determining the bud phenological stage, both of which vary with cold-hardiness.
The use of a forgetting function is an effective means of understanding temperature memories in plants and will aid in developing reliable predictions of cold-hardiness for various plant species under global climate warming.
大多数多年生植物会对冷胁迫记忆一定时间,并在冷驯化和解驯化过程中检索这些记忆,从而导致抗寒性的季节性变化。因此,需要一种评估冷胁迫记忆的模型,以预测在气候变暖条件下的抗寒性和未来的霜害风险。在本研究中,我们通过在冷驯化和解驯化过程中引入模仿过去温度记忆的函数,开发了一种新的抗寒力动态模型。
我们使用热时间作为植物过去温度记忆的指标,并通过遗忘函数进行加权,从而提出了一种抗寒力动态模型。我们使用来自两个品种的茶树(Camellia sinensis)的芽,即‘Yabukita’和‘Yutakamidori’,根据 10 年的抗寒性观测数据对该模型进行校准和验证。
该模型捕获了抗寒性观测值的 90%以上,并且对两个品种都能准确预测,根均方误差约为 1.0°C。优化后的遗忘函数表明,茶树芽能记忆短期(最近几天)和长期(前几个月)的温度。这些记忆可以驱动短期过程,如增加/减少芽中的碳水化合物、蛋白质和抗氧化剂含量,以及长期过程,如确定芽的物候阶段,这些过程都随抗寒性而变化。
使用遗忘函数是理解植物中温度记忆的有效方法,将有助于在全球气候变暖的情况下,为各种植物物种开发可靠的抗寒性预测。