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

doi:10.13305/j.cnki.jts.2024.06.010

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Journal of Tea Science ›› 2024, Vol. 44 ›› Issue (6) : 901-916. DOI: 10.13305/j.cnki.jts.2024.06.010

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

ZHAO Qian^1,2,4, LIU Qian¹, CAI-HE Jiayi², HE Jieqi², FANG Yunya², LIU Yuxin², CHEN Chao², ZHENG Yaodong¹, ZHANG Tianjing², YU Wenjuan³, YANG Guang^1,3,4,*

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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

clustering algorithm / drought and low-temperature stress / photosynthetic physiology characteristics / regression prediction / tea plant

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ZHAO Qian, LIU Qian, CAI-HE Jiayi, HE Jieqi, FANG Yunya, LIU Yuxin, CHEN Chao, ZHENG Yaodong, ZHANG Tianjing, YU Wenjuan, YANG Guang. Effects of Combined Drought and Low-temperature Stress on Photosynthetic Physiological Characteristics of Tea Plants and Simulation Prediction[J]. Journal of Tea Science. 2024, 44(6): 901-916 https://doi.org/10.13305/j.cnki.jts.2024.06.010

References

[1] 李娜娜. 新梢白化茶树生理生化特征及白化分子机理研究[D]. 杭州: 浙江大学, 2015.
Li N N.Physiological, biochemical characteristics and molecular albinism of the albino tea (Camellia sinensis) plant[D]. Hangzhou: Zhejiang University, 2015.
[2] 陈宗懋, 刘仲华, 杨亚军, 等. 2019年中国茶叶科技进展[J]. 中国茶叶, 2020, 42(5): 1-12.
Chen Z M, Liu Z H, Yang Y J, et al.Progress of tea science and technology in China in 2019[J]. China Tea, 2020, 42(5): 1-12.
[3] Shu S, Tang Y, Yuan Y, et al.The role of 24-epibrassinolide in the regulation of photosynthetic characteristics and nitrogen metabolism of tomato seedlings under a combined low temperature and weak light stress[J]. Plant Physiology and Biochemistry, 2016, 107: 344-353.
[4] 刘彦, 汪志威, 张冬莲, 等. 茶树抗旱研究进展[J]. 江西农业, 2018, 14: 56-57.
Liu Y, Wang Z W, Zhang D L, et al.Advancements in research on drought resistance in tea plants[J]. Jiangxi Agriculture, 2018, 14: 56-57.
[5] Zio E, Golea L, Mrs C.Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms[J]. Reliability Engineering and System Safety, 2012, 99: 172-177.
[6] Peeva V, Cornic G.Leaf photosynthesis of Haberlea rhodopensis before and during drought[J]. Environmental and Experimental Botany, 2009, 65(2): 310-318.
[7] Golldack D, Li C, Mohan H, et al.Tolerance to drought and salt stress in plants: unraveling the signaling networks[J]. Frontiers in Plant Science, 2014, 5: 151. doi: 10.3389/FPLS.2014.00151.
[8] 郭春芳. 水分胁迫下茶树的生理响应及其分子基础[D]. 福州: 福建农林大学, 2008.
Guo C F.Physiological response and molecular basis of tea plant (Camellia sinensis) exposed to water stress[D]. Fuzhou: Fujian Agriculture and Forest University, 2008.
[9] 李庆会, 徐辉, 周琳, 等. 低温胁迫对2个茶树品种叶片叶绿素荧光特性的影响[J]. 植物资源与环境学报, 2015, 24(2): 26-31.
Li Q H, Xu H, Zhou L, et al.Effects of low temperature stress on chlorophyll fluorescence characteristics in leaf of two cultivars of Camellia sinensis[J]. Journal of Plant Resources and Environment, 2015, 24(2): 26-31.
[10] Hetherington S E, Smillie R M, Hardacre A K, et al.Using chlorophyll fluorescence in vivo to measure the chilling tolerances of different populations of maize[J]. Functional Plant Biology, 1983, 10(3): 247-256.
[11] Liu Z G, Sun W C, Zhao Y N, et al.Effects of low nocturnal temperature on photosynthetic characteristics and chloroplast ultrastructure of winter rapeseed[J]. Russian Journal of Plant Physiology, 2016, 63(4): 451-460.
[12] 刘海卿, 孙万仓, 刘自刚, 等. 北方寒旱区白菜型冬油菜抗寒性与抗旱性评价及其关系[J]. 中国农业科学, 2015, 48(18): 3743-3756.
Liu H Q, Sun W C, Liu Z G, et al.Evaluation of drought resistance and cold resistance and research of their relationship at seedling stage of winter rapeseed (Brassica campestris L.) in cold and arid regions in north China[J]. Scientia Agricultura Sinica, 2015, 48(18): 3743-3756.
[13] 蒲光兰, 周兰英, 胡学华, 等. 干旱胁迫对金太阳杏叶绿素荧光动力学参数的影响[J]. 干旱地区农业研究, 2005, 23(3): 44-48.
Pu G L, Zhou L Y, Hu X H, et al.Effect of soil drought stress on characteristics of chlorophyll fluorescence in Jintaiyang apricot variety[J]. Agricultural Research in the Arid Areas, 2005, 23(3): 44-48.
[14] Eriksson P G, Reczko B, Merkle R, et al.Early proterozoic black shales of the Timeball Hill Formation, South Africa: volcanogenic and palaeoenvironmental influences[J]. Journal of African Earth Sciences, 1994, 18(4): 325-337.
[15] 王文森. 基于叶绿素荧光动力学的大豆干旱/NaCl胁迫影响分析[D]. 沈阳: 沈阳农业大学, 2018.
Wang W S.Analysis of effect of drought/NaCl stress on Soybean based on chlorophyll fluorescence kinetics[D]. Shenyang: Shenyang Agricultural University, 2018.
[16] Ehlert O, Bücking W, Riegler J, et al.Organometallic synthesis and electrophoretic characterization of high-quality ZnS: Mn/ZnS core/shell nanoparticles for bioanalytical applications[J]. Microchimica Acta, 2008, 160(3): 351-356.
[17] Belyaeva N E, Bulychev A A, Riznichenko G Y, et al.Analyzing both the fast and the slow phases of chlorophyll a fluorescence and P700 absorbance changes in dark-adapted and preilluminated pea leaves using a Thylakoid Membrane model[J]. Photosynthesis Research, 2019, 140(1): 1-19.
[18] Wang Y, Yang F, Gritsenko M A, et al.Reversed-phase chromatography with multiple fraction concatenation strategy for proteome profiling of human MCF10A cells[J]. Proteomics, 2011, 11(10): 2019-2026.
[19] Silva E A, Gouveia-Neto A S, Oliveira R A, et al. Water deficit and salt stress diagnosis through LED induced chlorophyll fluorescence analysis in Jatropha curcas L.[J]. Journal of Fluorescence, 2012, 22(2): 623-630.
[20] 生利霞, 王倩, 孟祥毅, 等. 植物耐涝分子机理研究进展[J]. 分子植物育种, 2017, 15(7): 2823-2828.
Sheng L X, Wang Q, Meng X Y, et al.Research progress on molecular mechanism of waterlogging tolerance in plants[J]. Molecular Plant Breeding, 2017, 15(7): 2823-2828.
[21] Dai F, Zhou M X, Zhang G P.The change of chlorophyll fluorescence parameters in winter barley during recovery after freezing shock and as affected by cold acclimation and irradiance[J]. Plant Physiology & Biochemistry, 2007, 45(12): 915-921.
[22] Shin Y K, Bhandari S R, Lee J G.Monitoring of salinity, temperature, and drought stress in grafted watermelon seedlings using chlorophyll fluorescence[J]. Frontiers in Plant Science, 2021, 12: 2913-2924.
[23] Shin Y K, Bhandari S R, Jo J S, et al.Effect of drought stress on chlorophyll fluorescence parameters, phytochemical contents, and antioxidant activities in lettuce seedlings[J]. Horticulture, 2021, 7(8): 238-254.
[24] 葛君, 刘震. 低温胁迫对拔节期小麦光合色素, 光合参数及叶绿素荧光特性的影响[J]. 山西农业科学, 2021, 49(11): 1253-1256.
Ge J, Liu Z.Effects of low temperature stress on photosynthetic pigments, photosynthetic parameters and chlorophyll fluorescence characteristics of wheat at jointing stage[J]. Journal of Shanxi Agricultural Sciences, 2021, 49(11): 1253-1256.
[25] 林郑和, 钟秋生, 陈常颂, 等. 低温对茶树新品系叶绿素与电导率的影响[J]. 福建茶叶, 2014, 36(5): 10-11.
Lin Z H, Zhong Q S, Chen C S, et al.The effect of low temperature on chlorophyll and electrical conductivity in new tea cultivar[J]. Tea in Fujian, 2014, 36(5): 10-11.
[26] 程国山, 游新才, 武艳, 等. 低温胁迫后抗寒茶树品种‘紫阳圆叶’的基因差异表达分析[J]. 植物资源与环境学报, 2013, 22(4): 38-43.
Cheng G S, You X C, Wu Y, et al.Analysis on gene differential expression of cold-resistance cultivar ‘Ziyangyuanye' of Camellia sinensis after low temperature stress[J]. Journal of Plant Resources and Environment, 2023, 22(4): 38-43.
[27] 孔海云, 张丽霞, 王日为. 低温与光照对茶树叶片叶绿素荧光参数的影响[J]. 茶叶, 2011, 37(2): 75-78.
Kong H Y, Zhang L X, Wang R W.The effects of light and low temperature on chlorophyll fluorescence kinetics parameters of tea leaves[J]. Journal of Tea, 2011, 37(2): 75-78.
[28] Farquhar G D, Von C S, Berry J A.A biochemical model of photosynthetic CO₂ assimilation in leaves of C3 species[J]. Planta, 1980, 149(1): 78-90.
[29] Busch F A, Sage R F.The sensitivity of photosynthesis to O₂ and CO₂ concentration identifies strong Rubisco control above the thermal optimum[J]. New Phytologist, 2016, 213(1): 1036-1051.
[30] Guo L P, Kang H J, Ouyang Z, et al.Photosynthetic parameter estimations by considering interactive effects of light, temperature and CO₂ concentration[J]. International Journal of Plant Production, 2015, 9(3): 321-346.
[31] Retkute R, Smithunna S E, Smith R W, et al.Exploiting heterogeneous environments: does photosynthetic acclimation optimize carbon gain in fluctuating light?[J] Journal of Experimental Botany, 2015, 66(9): 2437-2447.
[32] Sharwood R E, Ghannoum O, Kapralov M V, et al.Temperature responses of Rubisco from Paniceae grasses provide opportunities for improving C₃ photosynthesis[J]. Nature Plants, 2016, 2(12): 1618-1636.
[33] Zhang N Y, Li G, Yu S X, et al.Can the responses of photosynthesis and stomatal conductance to water and nitrogen stress combinations be modeled using a single set of parameters?[J] Frontiers in Plant Science, 2017, 1(8): 328-348.
[34] 唐星林. 基于FvCB模型的植物光合生理生态特性研究[D]. 北京: 中国林业科学研究院, 2017.
Tang X L.Physiological and ecological characteristics of photosynthesis in plants based on FvCB model[D]. Beijing: Chinese Academy of Forestry, 2017.
[35] 王文静, 麻冬梅, 蔡进军, 等. 基于FvCB模型的盐胁迫下紫花苜蓿幼苗光合特性的研究[J]. 中国生态农业学报, 2021, 29(3): 540-548.
Wang W J, Ma D M, Cai J J, et al.Photosynthetic characteristics of alfalfa seedlings under salt stress based on FvCB model[J]. Acta Ecologica Sinica, 2021, 29(3): 540-548.
[36] 刘振洋, 赵家松, 胡仁傑, 等. 基于关联规则与多元线性回归的云南省甘蔗产量预测模型[J]. 广东农业科学, 2022, 49(12): 160-166.
Liu Z Y, Zhao J S, Hu R J, et al.Yield prediction model for sugarcane in Yunnan province based on association rules and multiple linear regression[J]. Guangdong Agricultural Sciences, 2022, 49(12): 160-166.
[37] Breiman L.Random forests, machine learning 45[J]. Journal of Clinical Microbiology, 2001, 45(1): 5-32.
[38] 田惠玲, 朱建华, 何潇, 等. 基于随机森林模型的东北三省乔木林生物质碳储量预测[J]. 林业科学, 2022, 58(4): 40-50.
Tian H L, Zhu J H, He X, et al.Projected biomass carbon stock of arbor forest of three provinces in northeastern China based on random forest model[J]. Scientia Silvae Sinicae, 2022, 58(4): 40-50.
[39] Samui P.Slope stability analysis: a support vector machine approach[J]. Environmental Geology, 2008, 56(2): 255-267.
[40] Samui P, Das S K.Support vector machine and relevance vector machine classifier in analysis of slopes[J]. International Association for Computer Methods and Advances in Geomechanics, 2008, 12(5): 4667-4674.
[41] Tian Y, Yun Z Q, Yang X H.Improving shrub biomass estimations in the Qinghai-Tibet Plateau: age-based Caragana intermedia allometric models[J]. The Forestry Chronicle, 2014, 90(2): 154-160.
[42] 伍丹华, 周礼梅. 基于BP神经网络的粮食产量预测[J]. 农业工程技术, 2020, 40(27): 51-53.
Wu D H, Zhou L M.Grain yield prediction based on BP neural network[J]. Agricultural Engineering Information, 2000, 40(27): 51-53.
[43] Awchi T A.River discharges forecasting in northern Iraq using different ANN techniques[J]. Water Resources Management, 2014, 28(3): 801-814.
[44] 张兴泽, 滕瑞海, 牟振鹏. 北方茶区冻害的发生和防治[J]. 中国茶叶, 2011, 33(7): 24-25.
Zhang X Z, Teng R H, Mou Z P.Occurrence and prevention of frost damage in northern tea-growing regions[J]. China Tea, 2011, 33(7): 24-25.
[45] 雷荣森. 高山茶区寒冻气候对茶树的影响及防御措施[J]. 福建茶叶, 2020, 42(3): 12.
Lei R S.Impact of cold and freezing climates on tea plants in high-mountain tea regions and defensive measures[J]. Tea in Fujian, 2020, 42(3): 12.
[46] 韦英英, 林添水, 张金超, 等. 铁观音示范茶园立体气候特征及影响研究[J]. 海峡科学, 2022(8): 31-39.
Wei Y Y, Lin T S, Zhang J C, et al.Study on the three-dimensional climatic characteristics and impacts in Tieguanyin demonstration tea garden[J]. Staits Science, 2022(8): 31-39.
[47] 张云伟, 李晓东, 徐希斌. 低温干旱对山东省青岛市茶生产的影响及对策[J]. 落叶果树, 2012, 44(1): 32-34.
Zhang Y W, Li X D, Xu X B.Impact of low temperature and drought on tea production in Qingdao, Shandong Province, and countermeasures[J]. Deciduous Fruits, 2012, 44(1): 32-34.
[48] 徐晓莹, 黄文尉, 张丽霞. 茶树响应低温胁迫的分子机制研究进展[J]. 茶叶通讯, 2023, 50(3): 288-294.
Xu X Y, Huang W W, Zhang L X.The research progress on molecular mechanism of tea plants response to cold stress[J]. Journal of Tea Communication, 2023, 50(3): 288-294.
[49] 张波, 罗阳欢, 李浪, 等.贵州茶树春季低温灾害气候风险评估与区划[J/OL]. 中国农业科技导报, 1-9 [2024-12-09]. https://doi.org/10.13304/j.nykjdb.2023.0722.
Zhang B, Luo Y H, Li L, et al.Climate risk assessment and zoning of spring low temperature disasters of tea trees in Guizhou[J/OL]. Journal of Agricultural Science and Technology, 1-9 [2024-12-09]. https://doi.org/10.13304/j.nykjdb.2023.0722.
[50] 陈鑫, 邬晓龙, 刘升锐, 等. 干旱胁迫下AMF对茶树光合特性及其基因表达的影响[J]. 园艺学报, 2024, 51(10): 2358-2370.
Chen X, Wu X L, Liu S R, et al.Effects of AMF on photosynthetic characteristics and gene expressions of tea plants under drought stress[J]. Acta Horticulturae Sinica, 2024, 51(10): 2358-2370.
[51] 陈博雯, 覃子海, 张烨, 等. 干旱胁迫下澳洲茶树生理活性及内源激素动态变化研究[J]. 山东农业科学, 2019, 51(10): 55-59.
Chen B W, Qin Z H, Zhang Y, et al.Dynamic changes of physiological activities and endogenous hormones in melaleuca alternifolia under drought stress[J]. Shandong Agricultural Sciences, 2019, 51(10): 55-59.
[52] 张恒益, 郑惠玲. 利用K均值聚类算法识别遗传疾病致病SNP位点[J]. 家畜生态学报, 2020, 41(12): 25-31.
Zhang H Y, Zheng H L.Recognition of risk SNPs related to genetic diseases based on K-means clustering algorithm[J]. Journal of Domestic Animal Ecology, 2020, 41(12): 25-31.
[53] Bai L, Liang J Y, Sui C, et al.Fast global k-means clustering based on local geometrical information[J]. Information Sciences, 2013, 245(10): 168-180.
[54] 刘伟, 艾希珍, 梁文娟, 等. 低温弱光下水杨酸对黄瓜幼苗光合作用及抗氧化酶活性的影响[J]. 应用生态学报, 2009, 20(2): 441-445.
Liu W, Ai X Z, Liang W J, et al.Effects of salicylic acid on the leaf photosynthesis and antioxidant enzyme activities of cucumber seedlings under low temperature and light intensity[J]. Chinese Journal of Applied Ecology, 2009, 20(2): 441-445.
[55] 张玉翠, 王连翠. 低温对茶树叶片膜透性和保护酶活性的影响[J]. 北方园艺, 2010(9): 38-40.
Zhang Y C, Wang L C.Effects of low temperature on membrane permeability and protective enzyme activities in tea leaves[J]. Northern Horticulture, 2010(9): 38-40.
[56] 秦红艳, 沈育杰, 艾军, 等. 盐胁迫对不同葡萄品种叶片中叶绿素荧光参数的影响[J]. 中外葡萄与葡萄酒, 2010(5): 35-38.
Qin H Y, Shen Y J, Ai J, et al.Effects of salt stress on shlorophyll fluorescence parameters of leaf in different grape varsities[J]. Sino-overseas Grapevine & Wine, 2010(5): 35-38.
[57] 李学孚, 倪智敏, 吴月燕, 等. 盐胁迫对‘鄞红’葡萄光合特性及叶片细胞结构的影响[J]. 生态学报, 2015, 35(13): 4436-4444.
Li X F, Ni Z M, Wu Y Y, et al.Effects of salt stress on photosynthetic characteristics and leaf cell structure of 'Yinhong' grape seedling[J]. Acta Ecologic Sinica, 2015, 35(13): 4436-4444.
[58] Redmond S J, Heneghan C.A method for initialising the K-means clustering algorithm using kd-trees[J]. Pattern Recognition Letters, 2007, 28(8): 965-973.
[59] 魏杰. 基于K-means聚类算法改进算法的研究[J]. 信息通信, 2018(5): 14-15.
Wei J.Research on improved algorithms based on K-means clustering algorithm[J]. Information & Communications, 2018(5): 14-15.
[60] 梁泽, 王玥瑶, 岳远紊, 等. 耦合遗传算法与RBF神经网络的PM_2.5浓度预测模型[J]. 中国环境科学, 2020, 40(2): 523-529.
Liang Z, Wang Y Y, Yue Y W, et al.A coupling model of genetic algorithm and RBF neural network for the prediction of PM2.5 concentration[J]. China Environmental Science. 2020, 40(2): 523-529.
[61] 周佳俊, 龚道新, 蒋紫烟, 等. 基于BP神经网络的柑橘农药残留预测[J]. 湖南农业大学学报(自然科学版), 2022, 48(5): 572-577.
Zhou J J, Gong D X, Jiang Z Y, et al.Prediction of pesticide residues in citrus using BP neural network[J]. Journal of Hunan Agricultural University (Natural Sciences), 2022, 48(5): 572-577.