干旱低温复合胁迫对茶树光合生理特性的影响及模拟预测

赵茜; 刘倩; 蔡何佳奕; 何婕绮; 方筠雅; 刘雨欣; 陈超; 郑曜东; 张天经; 余文娟; 杨广

doi:10.13305/j.cnki.jts.2024.06.010

茶叶科学 >

2024 , Vol. 44 >Issue 6: 901 - 916

DOI: https://doi.org/10.13305/j.cnki.jts.2024.06.010

研究报告

干旱低温复合胁迫对茶树光合生理特性的影响及模拟预测

赵茜 ,
刘倩 ,
蔡何佳奕 ,
何婕绮 ,
方筠雅 ,
刘雨欣 ,
陈超 ,
郑曜东 ,
张天经 ,
余文娟 ,
杨广

展开

1.农业农村部闽台作物有害生物综合治理重点实验室,福建福州 350002;
2.福建农林大学植物保护学院,福建福州 350002;
3.中国农业科学院深圳农业基因组研究所,广东深圳 518124;
4.害虫绿色防控福建省高校重点实验室,福建福州 350002

赵茜,女,副研究员,从事茶树抗性育种及分子生物学方面研究。

收稿日期: 2024-05-23

修回日期: 2024-11-22

网络出版日期: 2025-01-08

基金资助

福建省自然科学基金项目（2024J01372）

收起

Effects of Combined Drought and Low-temperature Stress on Photosynthetic Physiological Characteristics of Tea Plants and Simulation Prediction

ZHAO Qian ,
LIU Qian ,
CAI-HE Jiayi ,
HE Jieqi ,
FANG Yunya ,
LIU Yuxin ,
CHEN Chao ,
ZHENG Yaodong ,
ZHANG Tianjing ,
YU Wenjuan ,
YANG Guang

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1. Key Laboratory of Integrated Pest Management for Fujian-Taiwan Crops, Ministry of Agriculture, Fuzhou 350002, China;
2. College of Plant Protection, Fujian Agriculture and Forest University, Fuzhou 350002, China;
3. Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen 518000, China;
4. Key Laboratory of Green Pest Control, Fujian Province University, Fuzhou 350002, China

Received date: 2024-05-23

Revised date: 2024-11-22

Online published: 2025-01-08

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摘要

为明确多重气候胁迫对茶树光合效率的影响,开发了一套高效、精准的胁迫分级体系,以实现对茶树胁迫的即时监测。以福建省主栽茶树品种为研究对象,系统监测了茶树在干旱低温复合胁迫条件下的光合生理响应;基于监测数据,构建了基于光合生理特性的胁迫快速分级方法及光合作用预测预警模型。研究结果显示,在干旱低温复合胁迫下所有参试茶树品种叶片的光合效率均显著下降,且随胁迫强度增大光合效率的下降幅度增大。参试茶树品种中,铁观音的光合效率下降幅度显著低于其他品种,具有较强的耐胁迫能力;而福鼎大白茶的耐胁迫能力最弱。筛选了茶树对复合胁迫高度敏感的光合生理参数,利用K-ansme聚类算法对该参数进行聚类,构建了胁迫快速分级方法,聚类精确度在80%以上。利用不同模型预测并验证光合生理指标对环境胁迫的响应,结果表明随机森林模型的精度最高。本研究构建的胁迫分级方法实现了对茶树复合胁迫的快速分级,构建的随机森林模型实现了对光合生理的无损伤监测与预警,研究结果可为多重气候条件下茶树品种的选育提供参考,对茶叶生产具有较高的实用价值。

关键词： 茶树; 干旱低温胁迫; 光合生理; 聚类分析; 回归预测

本文引用格式

赵茜 , 刘倩 , 蔡何佳奕 , 何婕绮 , 方筠雅 , 刘雨欣 , 陈超 , 郑曜东 , 张天经 , 余文娟 , 杨广 . 干旱低温复合胁迫对茶树光合生理特性的影响及模拟预测[J]. 茶叶科学, 2024 , 44(6) : 901 -916 . DOI: 10.13305/j.cnki.jts.2024.06.010

Abstract

This study aimed to investigate the effects of multiple climatic stresses on the photosynthetic efficiency of tea plants and to devise an efficient, precise stress classification system for real-time monitoring. We focused on the typical tea cultivars grown extensively in Fujian Province and systematically monitored their photosynthetic physiological responses under combined drought and low-temperature stress. Utilizing the collected data, we established a rapid stress classification method based on photosynthetic physiological characteristics and constructed a photosynthesis prediction and early warning model. The results reveal that all tested tea cultivars exhibited a significant decline in leaf photosynthetic efficiency under combined stress, with the decreasing trend displaying a clear linear relationship with increasing stress intensity. Notably, ‘Tieguanyin’ demonstrated a significantly lesser decline in photosynthetic efficiency compared to other cultivars, suggesting its robust stress tolerance. In contrast, ‘Fuding Dabaicha’ showed the least stress tolerance. By selecting photosynthetic physiological parameters highly sensitive to combined stress and employing the K-means clustering algorithm, we developed a rapid stress classification method with an accuracy exceeding 80%. Various models were then used to predict and validate the response of photosynthetic physiological indicators to environmental stress, with the Random Forest (RF) model yielding the highest accuracy. This study provided a reference for the selection and breeding of tea cultivars under diverse climatic conditions. The stress classification method enables swift categorization of combined stress in tea plants, while the RF model facilitates non-destructive monitoring and early warning of photosynthetic physiology, offering significant practical value in tea production.

Key words： tea plant; drought and low-temperature stress; photosynthetic physiology characteristics; clustering algorithm; regression prediction

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