黄淮海麦区小麦耐热性分析及其鉴定指标的筛选

【目的】分析不同基因型小麦的耐热性,筛选耐热鉴定指标,建立可靠的耐热评价模型,为耐热小麦品种的选育提供理论支撑。【方法】以黄淮海麦区大面积推广的20个小麦品种为试验材料,采用田间试验,设置高温（花后第14—20天,连续7 d高温处理）和自然条件2种处理,在灌浆后期测定小麦穗部冠层温度、旗叶叶绿素相对含量（SPAD）、丙二醛（MDA）含量以及超氧化物歧化酶（SOD）、过氧化物酶（POD）和过氧化氢酶（CAT）活性;收获晾干后测定单穗粒重、千粒重和产量。根据高温处理和自然条件生长下各项指标的耐热系数,采用主成分分析、隶属函数法、聚类分析和逐步回归分析方法对小麦耐热性进行综合评价。【结果】高温处理下各性状变异幅度为-14.89%—15.09%。通过对9个性状指标的相关分析,发现各指标之间存在显著或极显著相关性。通过主成分分析将9个单项指标转换为3个相互独立的综合指标,其贡献率分别为55.970%、15.530%和12.171%,代表了全部数据83.670%的信息量。利用隶属函数法计算综合耐热评价值（D）,并对其进行聚类分析,按照耐热性强弱将20个小麦品种划分为3类,第一类耐热型8个品种;第二类中等耐热型7个品种;第三类高温敏感型5个品种。通过逐步回归方程建立了小麦耐热性的评价数学模型：D=-4.801+0.834X₄+2.913X₇+0.303X₆+2.937X₈- 1.409X₁-0.524X₃+0.876X₉（R²=0.986）,利用建立的最优回归方程预测供试材料的耐热性,预测值（VP）与D值基本一致,表明SOD活性（X₄）、单穗粒重（X₇）、CAT活性（X₆）、千粒重（X₈）、冠层温度（X₁）、MDA含量（X₃）和产量（X₉）这7个指标可用于小麦耐热性品种的鉴定。【结论】采用多元统计分析方法对小麦耐热性评价是可行的;20个小麦品种被分为3类（耐热型、中等耐热型和高温敏感型）;高温处理下,SOD活性、单穗粒重、CAT活性、千粒重、冠层温度、MDA含量和产量可以作为小麦耐热性的鉴定指标。

Analysis of Heat Tolerance of Wheat with Different Genotypes and Screening of Identification Indexes in Huang-Huai-Hai Region

【Objective】 The main objectives of this study were to (i) explore the methods of evaluating heat tolerance, (ii) screen suitable identification indexes of heat tolerance, and (iii) establish an evaluation model of heat tolerance, for screening of heat-tolerance resources and variety breeding of wheat.【Method】 20 wheat cultivars from Huang-Huai-Hai area were used in this study. High temperature treatment was applied by covering a plastic tunnel and the plants were subjected to heat from 14 to 20 days after flowering, during the heat treatment, normal field growth as the control. Canopy temperature, chlorophyll content (SPAD), malondialdehyde content (MDA) superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD) activity were measured at the filling stage; single spike weight, thousand-grain weight and yield of wheat were measured after harvesting, heat tolerance was analyzed and evaluated by using principal component analysis, membership function method, hierarchical cluster analysis and stepwise regression analysis.【Result】After high temperature treatment, the variation range of heat tolerance of each single index was between -14.89% and 15.09%. Correlation analysis of the heat tolerance coefficients about 20 varieties indicated that there existed significant (P<0.05 and 0.01) correlations between nine traits. Three independent comprehensive components were obtained from nine single traits by a principal component analysis, and their contribution rates respectively were 55.970%, 15.530% and 12.171%, which reflected 83.67% information of all traits information. The comprehensive heat tolerance values were calculated from the membership function methods, and 20 wheat materials were divided into three heat-tolerance types by the cluster analysis. The first category is heat tolerance, and the second category is medium heat tolerance, followed by the third category belonging to high temperature sensitive type. A mathematical evaluation model from heat tolerance of wheat was established using the stepwise regression equation: D=-4.801+0.834X₄+2.913X₇+0.303X₆+2.937X₈-1.409X₁-0.524X₃+0.876X₉ (R²=0.986). The predictive values (VP) were basically consistent with the comprehensive heat tolerance value, the results showed that SOD activity (X₄), single spike weight (X₇), CAT activity (X₆), Thousand-grain weight (X₈), canopy temperature (X₁), MDA content (X₃) and yield (X₉) could be used to identify wheat heat-resistant varieties. 【Conclusion】 A comprehensive evaluation system established by multivariate statistics was used to identify heat tolerance of wheat is feasible. Twenty wheat varieties were divided into three heat tolerance types, i.e., heat tolerance type, medium heat tolerance type and heat sensitive type, and SOD activity, single spike weight, CAT activity, Thousand-grain weight, canopy temperature, MDA content and grain yield can be used for identification of heat tolerance of wheat.