Refined Risk Zoning of High Temperature and Heat Damage to Greenhouse Tomato in Southern China
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摘要: 以中国南方设施番茄为研究对象,利用1990—2019年3—9月359个气象站点的气象资料、温室小气候实测资料以及高温控制试验资料,通过BP神经网络模拟南方塑料大棚内日最高气温,结合高温控制试验资料,采用相关性分析和主成分分析方法,构建适用于中国南方设施番茄高温热害等级指标体系,开展设施番茄高温热害风险区划。结果表明:1990—2019年高温热害发生频率增加趋势不显著,轻度高温热害发生频率最高,其次是中度高温热害,各等级高温热害发生频率变化趋势均不显著,且年际变化较大。南方设施番茄高温热害高风险区主要分布在广东西部和东部、广西东部和西部以及云南北部、中部和南部;次高风险区分布在湖南南部、广西大部、广东中北部、江西南部以及福建;中度风险区分布在湖南中北部、江西北部、浙江、安徽、湖北、重庆;其他地区为低风险区。Abstract: With the intensification of global warming, high temperature and heat damage of spring-summer occurs frequently in recent years, which seriously affects the growth of greenhouse tomato and reduces agricultural economic efficiency. The high temperature and heat damage risk zoning of tomato is especially important, but the research is not sufficient. Based on meteorological data of 359 stations from 1990 to 2019 and greenhouse microclimate measured data, the highest daily temperature in the southern greenhouse is simulated by the BP neural network. Combining high temperature control test data using correlation analysis and principal component analysis methods, the main indicators are screened out, and a high temperature stress index model is constructed. The mean-standard deviation method is used to divide three levels of high temperature stress, and then the temperature with duration corresponding to the critical value of different levels of high temperature stress index are determined respectively, and the risk index system is established. Using geographic information system (GIS) software and other mathematical statistical methods, the characteristics of the year-over-year changes in the frequency of high temperature and thermal damage of tomatoes in the research area are analyzed, and a comprehensive risk index is established to assess the trend of risk development of high temperature and heat damage to greenhouse tomato in the past 30 years. The results show that the frequency of mild and severe high temperature and heat damage is increasing from 1990 to 2019, while the frequency of moderate high temperature and heat damage shows a different pattern (insignificant decrease trend). Among them, the frequency of mild high temperature and heat damage is the highest, followed by moderate high temperature and heat damage. The frequency of heat damage of various grades varies greatly from year to year. There are obvious differences in the distribution of the risk of high temperature and heat damage in space. High-risk areas of heat damage in southern greenhouse tomato are mainly distributed in the western and eastern parts of Guangdong, the eastern and western parts of Guangxi, and the northern, central, and southern parts of Yunnan. The second high-risk areas are mainly distributed in southern Hunan, most areas of Guangxi, central and northern Guangdong, southern Jiangxi, and most areas of Fujian. Areas with moderate heat damage are mainly distributed in north-central Hunan, northwestern Jiangxi, Zhejiang, Anhui, Hubei, and Chongqing. And the risk in western region is low.
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表 1 小气候资料来源
Table 1 Sources of microclimate data
地点 采集时段 样本量 江苏省宿迁市 2008-03-19—09-23 191 上海市 2009-03-21—09-30,2010-03-01—09-30,2011-03-01—04-02 474 浙江省慈溪市 2007-03-21—05-09,2008-03-01—06-24,07-01—30,2009-04-26—05-25 224 浙江省温州市 2010-03-01—06-10 103 福建省福清市 2017-05-01—09-30,2018-03-01—09-30 368 福建省连城县 2017-06-08—29,2017-07-06—09-30,2018-03-01—09-22,2019-03-01—03-31,2019-04-23—09-03 449 表 2 高温处理下各指标主成分分析特征向量
Table 2 The first 5 eigenvectors of principal component analysis of each index after high temperature treatments
参数 特征向量 第1主成分 第2主成分 第3主成分 第4主成分 第5主成分 Pn 0.066 0.092 0.006 -0.159 -0.033 Slp 0.065 0.065 -0.037 -0.133 -0.112 Clp -0.065 0.038 -0.011 0.029 0.152 Pmax 0.066 0.069 0.035 -0.178 0.111 Eaq 0.054 0.090 -0.091 -0.237 0.153 Gs 0.056 0.083 -0.186 -0.173 0.116 Ci -0.056 0.101 0.101 -0.253 0.118 Ls 0.056 -0.101 -0.101 0.252 -0.119 Tr 0.044 0.135 -0.256 0.074 -0.166 Ewu 0.010 -0.126 0.384 -0.117 0.184 φ 0.050 -0.145 0.018 0.097 -0.010 Pit 0.067 -0.001 0.145 -0.003 -0.115 Ff 0.067 -0.074 0.032 -0.096 0.015 Pia 0.062 -0.095 0.086 0.139 0.207 Ac 0.023 0.157 0.175 -0.140 -0.447 Tc 0.056 0.084 0.005 -0.027 0.502 Dc -0.043 0.166 -0.042 0.265 0.105 Ec 0.058 0.083 -0.057 0.085 0.343 Cha 0.061 -0.112 0.025 -0.145 -0.023 Chb 0.055 -0.045 -0.054 0.391 0.183 Sod 0.045 0.126 0.203 0.247 -0.161 Pod -0.030 0.153 0.235 0.073 0.289 Cat 0.042 0.135 0.164 0.358 -0.161 Mda -0.069 0.002 -0.065 0.112 0.120 表 3 不同处理下IHS值
Table 3 IHS under different treatments
处理日数/d 高温胁迫 T38℃ T41℃ T44℃ 3 0.157 0.17 0.199 6 0.201 0.251 0.317 9 0.285 0.351 0.468 12 0.316 0.431 0.621 表 4 设施番茄高温热害等级划分
Table 4 Classification of high temperature and heat damage of greenhouse tomato
热害等级 日最高气温(T)/℃ 持续时间(D)/d 轻度 36 < T≤38 3 < D≤6 T>38 2≤D≤3 中度 36 < T≤38 6 < D≤12 38 < T≤41 3 < D≤9 T>41 3 < D≤6 重度 36 < T≤38 D>12 38 < T≤41 D>9 T>41 D>6 -
[1] Rothan C, Diouf I, Causse M. Trait discovery and editing in tomato. Plant Journal, 2019, 97(1): 73-90. doi: 10.1111/tpj.14152 [2] Chittaranjan K. Genomic Designing of Climate-smart Vegetable Crops. Switzerland: Springer International Publishing, 2020. [3] Wang X, Jia Y, Peng S, et al. Root growth, fruit yield and water use efficiency of greenhouse grown tomato under different irrigation regimes and nitrogen levels. J Plant Growth Regul, 2018, 38: 400-415. http://d.wanfangdata.com.cn/periodical/ChlQZXJpb2RpY2FsRW5nTmV3UzIwMjEwMTE1EiBhMTRmMDBlMTg4OGMwYjNmNDQ3N2Q3NGY0NmU2N2Y1NxoINmI5cWo4bWk%3D [4] 李天来. 我国设施蔬菜科技与产业发展现状及趋势. 中国农村科技, 2016(5): 75-77. doi: 10.3969/j.issn.1005-9768.2016.05.024Li T L. The status and trend of facility vegetable technology and industry development in China. Chin Rural Sci Tech, 2016(5): 75-77. doi: 10.3969/j.issn.1005-9768.2016.05.024 [5] 丁一汇, 李霄, 李巧萍. 气候变暖背景下中国地面风速变化研究进展. 应用气象学报, 2020, 31(1): 1-12. doi: 10.11898/1001-7313.20200101Ding Y H, Li X, Li Q P. Advances of surface wind speed change over China under global warming. J Appl Meteor Sci, 2020, 31(1): 1-12. doi: 10.11898/1001-7313.20200101 [6] 董晓峣, 武炳义. 江淮地区夏季高温事件与北极冷异常的动力联系. 应用气象学报, 2019, 30(4): 431-442. doi: 10.11898/1001-7313.20190404Dong X Y, Wu B Y. Dynamic linkages between heat wave events in Jianghuai region and arctic summer cold anomaly. J Appl Meteor Sci, 2019, 30(4): 431-442. doi: 10.11898/1001-7313.20190404 [7] 林爱兰, 谷德军, 彭冬冬, 等. 近60年我国东部区域性持续高温过程变化特征. 应用气象学报, 2021, 32(3): 302-314. doi: 10.11898/1001-7313.20210304Lin A L, Gu D J, Peng D D, et al. Climatic characteristics of regional persistent heat event in the eastern China during recent 60 years. J Appl Meteor Sci, 2021, 32(3): 302-314. doi: 10.11898/1001-7313.20210304 [8] 安晓宇. 气象灾害对农业生产影响的研究. 哈尔滨: 东北农业大学, 2018.An X Y. Research on the Impact of Meteorological Disasters on Agricultural Production. Harbin: Northeast Agricultural University, 2018. [9] Shamshiri R R, Kalantari F, Ting K C, et al. Advances in greenhouse automation and controlled environment agriculture: A transition to plant factories and urban agriculture. Int J Agric Biol Eng, 2018, 11(1): 1-22. [10] 杨世琼, 杨再强, 蔡霞, 等. 高温高湿胁迫下设施番茄光响应曲线的拟合. 生态学杂志, 2018, 37(7): 2003-2012. https://www.cnki.com.cn/Article/CJFDTOTAL-STXZ201807012.htmYang S Q, Yang Z Q, Cai X, et al. Simulation of light response of photosynthesis for greenhouse tomato leaves under high temperature and high humidity stress. Chin J Ecol, 2018, 37(7): 2003-2012. https://www.cnki.com.cn/Article/CJFDTOTAL-STXZ201807012.htm [11] 鲍恩财. 装配式日光温室主动蓄热循环系统传热特性研究. 杨凌: 西北农林科技大学, 2018.Bao E C. Research on Heat Transfer Characteristics of Active Heat Storage Cycle System in Prefabricated Solar Greenhouse. Yangling: Northwest A&F University, 2018. [12] Zheng Y J, Yang Z Q, Xu C, et al. The interactive effects of daytime high temperature and humidity on growth and endogenous hormone concentration of tomato seedlings. Hort Science, 2020, 55(10): 1575-1583. http://www.researchgate.net/publication/347766206_The_Interactive_Effects_of_Daytime_High_Temperature_and_Humidity_on_Growth_and_Endogenous_Hormone_Concentration_of_Tomato_Seedlings [13] 颜彩燕, 张美丽, 黄济, 等. 南方连栋塑料温室环境调控技术. 农业工程技术, 2018, 38(4): 37-41. https://www.cnki.com.cn/Article/CJFDTOTAL-NSGJ201804008.htmYan C Y, Zhang M L, Huang J, et al. Environmental control technology of southern multi-span plastic greenhouse. Agri Eng Tech, 2018, 38(4): 37-41. https://www.cnki.com.cn/Article/CJFDTOTAL-NSGJ201804008.htm [14] Li Z M, Palmer W M, Martin A P, et al. High invertase activity in tomato reproductive organs correlates with enhanced sucrose import into, and heat tolerance of, young fruit. J Exp Bot, 2012, 63(3): 1155-1166. doi: 10.1093/jxb/err329 [15] 黄琴琴, 杨再强, 刘显男, 等. 苗期高温高湿影响番茄花芽分化进程的机理探讨. 中国农业气象, 2021, 42(1): 56-68.Huang Q Q, Yang Z Q, Liu X N, et al. Discussion on the mechanism of effects of high temperature and humidity on tomato flower bud differentiation in seedling stage. Chin J Agrom, 2021, 42(1): 56-68. [16] 韦婷婷, 杨再强, 王明田, 等. 高温与空气湿度交互对花期番茄植株水分生理的影响. 中国农业气象, 2019, 40(5): 317-326. doi: 10.3969/j.issn.1000-6362.2019.05.006Wei T T, Yang Z Q, Wang M T, et al. Effects of high temperature and different air humidity on water physiology of flowering tomato seedlings. Chin J Agrom, 2019, 40(5): 317-326. doi: 10.3969/j.issn.1000-6362.2019.05.006 [17] 赵勇竣, 徐术菁, 王钊, 等. 高温胁迫对3个番茄品种生长和生理指标的影响. 江苏农业科学, 2019, 47(17): 147-149. https://www.cnki.com.cn/Article/CJFDTOTAL-JSNY201917035.htmZhao Y J, Xu S J, Wang Z, et al. Effects of high temperature stress on the growth and physiological indexes of three tomato varieties. Jiangsu Agric Sci, 2019, 47(17): 147-149. https://www.cnki.com.cn/Article/CJFDTOTAL-JSNY201917035.htm [18] 韦婷婷. 高温下提高空气湿度对设施番茄果实有机酸代谢和内在品质的影响. 南京: 南京信息工程大学, 2020.Wei T T. Effects of Elevated Air Humidity at High Temperature on Organic Acid Metabolism and Intrinsic Quality of Facility Tomato Fruits. Nanjing: Nanjing University of Information Science & Technology, 2020. [19] 赵和丽. 高温高湿对设施番茄果实生长及糖、氮代谢的影响. 南京: 南京信息工程大学, 2020.Zhao H L. Effects of High Temperature and High Humidity on Tomato Fruit Growth, Sugar and Nitrogen Metabolism in Greenhouse. Nanjing: Nanjing University of Information Science & Technology, 2020. [20] 喻树龙, 王健, 杨晓光, 等. 新疆加工番茄适生种植气候区划. 中国农业气象, 2005, 26(4): 268-271. doi: 10.3969/j.issn.1000-6362.2005.04.016Yu S L, Wang J, Yang X G, et al. Climatic regionalization of suitable planting of processing tomato in Xinjiang. Chin J Agrom, 2005, 26(4): 268-271. doi: 10.3969/j.issn.1000-6362.2005.04.016 [21] 季芬. 石河子垦区加工番茄精细气候区划分析. 新疆农垦科技, 2016, 39(10): 61-63. doi: 10.3969/j.issn.1001-361X.2016.10.030Ji F. Analysis of fine climatic regionalization of tomato processing in Shihezi reclamation area. Xinjiang Farm Res Sci Tech, 2016, 39(10): 61-63. doi: 10.3969/j.issn.1001-361X.2016.10.030 [22] 张波, 胡家敏, 谷晓平, 等. 基于气候适宜度的贵州番茄精细化农业气候区划. 北方园艺, 2018(2): 193-198. https://www.cnki.com.cn/Article/CJFDTOTAL-BFYY201802039.htmZhang B, Hu J M, Gu X P, et al. Precise comprehensive agricultural climate division for tomato in Guizhou province based on climatic suitability models. North Hort, 2018(2): 193-198. https://www.cnki.com.cn/Article/CJFDTOTAL-BFYY201802039.htm [23] 王尚明, 张文红, 曾凯, 等. 早稻春季低温气象灾害指标研究. 江西农业学报, 2012, 24(6): 176-178. doi: 10.3969/j.issn.1001-8581.2012.06.052Wang S M, Zhang W H, Zeng K, et al. Study on meteorological indexes of spring low temperature injury to early rice. Acta Agri Jiangxi, 2012, 24(6): 176-178. doi: 10.3969/j.issn.1001-8581.2012.06.052 [24] 彭晓丹, 杨再强, 柳笛, 等. 温室黄瓜低温气象灾害指标. 气象科技, 2013, 41(2): 394-400. doi: 10.3969/j.issn.1671-6345.2013.02.034Peng X D, Yang Z Q, Liu D, et al. Study of low-temperature disaster index of greenhouse cucumbers. Meteor Sci Tech, 2013, 41(2): 394-400. doi: 10.3969/j.issn.1671-6345.2013.02.034 [25] 肖芳. 高温胁迫对苗期红提葡萄生理及基因表达特性的影响. 南京: 南京信息工程大学, 2018.Xiao F. Effects of High-temtperature Stress on Physiological and Gene Expression Characteristics of Grapevine (vitis vinifera L. Hongti) during Seedling Stage. Nanjing: Nanjing University of Information Science & Technology, 2018. [26] 韦婷婷, 杨再强, 王琳, 等. 玻璃温室和塑料大棚内逐时气温模拟模型. 中国农业气象, 2018, 39(10): 644-655. doi: 10.3969/j.issn.1000-6362.2018.10.003Wei T T, Yang Z Q, Wang L, et al. Simulation model of hourly air temperature inside glass greenhouse and plastic greenhouse. Chin J Agrom, 2018, 39(10): 644-655. doi: 10.3969/j.issn.1000-6362.2018.10.003 [27] 杨世琼, 杨再强, 王琳, 等. 高温高湿交互对设施番茄叶片光合特性的影响. 生态学杂志, 2018, 37(1): 57-63. https://www.cnki.com.cn/Article/CJFDTOTAL-STXZ201801011.htmYang S Q, Yang Z Q, Wang L, et al. Effect of high humidity and high temperature interaction on photosynthetic characteristics of greenhouse tomato crops. Chin J Ecol, 2018, 37(1): 57-63. https://www.cnki.com.cn/Article/CJFDTOTAL-STXZ201801011.htm [28] 柳芳, 陈思宁, 李春, 等. 天津市日光温室热量资源评价及其茬口搭配标准. 北方园艺, 2018, 48(9): 93-99. https://www.cnki.com.cn/Article/CJFDTOTAL-BFYY201809019.htmLiu F, Chen S Y, Li C, et al. Evaluation of heat resources and crop collocation standard of solar greenhouse in Tianjin. North Hort, 2018, 48(9): 93-99. https://www.cnki.com.cn/Article/CJFDTOTAL-BFYY201809019.htm [29] 郭林茂, 常娟, 徐洪亮, 等. 基于BP神经网络和FEFLOW模型模拟预测多年冻土活动层温度——以青藏高原风火山地区为例. 冰川冻土, 2020, 42(2): 399-411. https://www.cnki.com.cn/Article/CJFDTOTAL-BCDT202002010.htmGuo L M, Chang J, Xu H L, et al. Simulation and prediction of permafrost active layer temperature based on BP neural network and FEFLOW model: Take the Fenghuoshan area on the Tibetan Plateau as an example. J Glaciol Geocryol, 2020, 42(2): 399-411. https://www.cnki.com.cn/Article/CJFDTOTAL-BCDT202002010.htm [30] 高丽娜, 孙擎, 郭翠荣, 等. 山西日光温室逐日极端气温预测模型研究. 中国农学通报, 2015, 31(15): 240-246. doi: 10.11924/j.issn.1000-6850.casb14120160Gao L N, Sun Q, Guo C R, et al. Forecast model of daily extreme temperature in solar greenhouse in Shanxi Province. Chin Agric Sci Bull, 2015, 31(15): 240-246. doi: 10.11924/j.issn.1000-6850.casb14120160 [31] 王春玲, 魏瑞江, 申双和, 等. 基于BP神经网络的冬季日光温室小气候模拟. 中国农学通报, 2014, 30(5): 149-157. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNTB201405027.htmWang C L, Wei R J, Shen S H, et al. Microclimate simulation of sunlight greenhouse in winter based on BP neural network. Chin Agric Sci Bull, 2014, 30(5): 149-157. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNTB201405027.htm [32] 王琳, 杨再强, 王明田, 等. 空气相对湿度对高温下番茄幼苗营养物质含量及干物质分配的影响. 中国农业气象, 2018, 39(5): 304-313. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY201805002.htmWang L, Yang Z Q, Wang M T, et al. Effect of air humidity on nutrient content and dry matter distribution of tomato seedlings under high temperature. Chin J Agrom, 2018, 39(5): 304-313. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGNY201805002.htm [33] 王纯枝, 霍治国, 郭安红, 等. 中国北方冬小麦蚜虫气候风险评估. 应用气象学报, 2021, 32(2): 160-174. doi: 10.11898/1001-7313.20210203Wang C Z, Huo Z G, Guo A H, et al. Climatic risk assessment of winter wheat aphids in northern China. J Appl Meteor Sci, 2021, 32(2): 160-174. doi: 10.11898/1001-7313.20210203 [34] 杨建莹, 霍治国, 王培娟, 等. 中国北方苹果干旱等级指标构建及危险性评价. 应用气象学报, 2021, 32(1): 25-37. doi: 10.11898/1001-7313.20210103Yang J Y, Huo Z H, Wang P J, et al. Evaluation index construction and hazard risk assessment on apple drought in northern China. J Appl Meteor Sci, 2021, 32(1): 25-37. doi: 10.11898/1001-7313.20210103 [35] 程雪, 孙爽, 张镇涛, 等. 我国北方地区苹果不同干旱等级时空特征. 应用气象学报, 2020, 31(4): 405-416. doi: 10.11898/1001-7313.20200403Cheng X, Sun S, Zhang Z T, et al. Spatial-temporal distribution of apples with different drought level in northern China. J Appl Meteor Sci, 2020, 31(4): 405-416. doi: 10.11898/1001-7313.20200403 [36] 刘景鹏. 中国南方夏季降水的年代际变化特征和机理分析. 北京: 中国气象科学研究院, 2018.Liu J P. Interdecadal Variabilities and Mechanisms of Southern China Summer Rainfall. Beijing: Chinese Academy of Meteorological Sciences, 2018. [37] 陈丽娟, 赵俊虎, 顾薇, 等. 汛期我国主要雨季进程成因及预测应用进展. 应用气象学报, 2019, 30(4): 385-400. doi: 10.11898/1001-7313.20190401Chen L J, Zhao J H, Gu W, et al. Advances of research and application on major rainy seasons in China. J Appl Meteor Sci, 2019, 30(4): 385-400. doi: 10.11898/1001-7313.20190401 [38] 刘伯奇, 祝从文. 中国夏季降水预测因子潜在技巧分布图及应用. 应用气象学报, 2020, 31(5): 570-582. doi: 10.11898/1001-7313.20200505Liu B Q, Zhu C W. Potential skill map of predictors applied to the seasonal forecast of summer. J Appl Meteor Sci, 2020, 31(5): 570-582. doi: 10.11898/1001-7313.20200505 [39] 梅海霞, 梁信忠, 曾明剑, 等. 2015-2017年夏季南京雨滴谱特征. 应用气象学报, 2020, 31(1): 117-128. doi: 10.11898/1001-7313.20200111Mei H X, Liang X Z, Zeng M J, et al. Raindrop size distribution characteristics of Nanjing in summer of 2015-2017. J Appl Meteor Sci, 2020, 31(1): 117-128. doi: 10.11898/1001-7313.20200111