[目的]新疆属于我国极度干旱缺水的省份,南疆更是以沙漠和戈壁为主,水是限制南疆农业发展的第一因素。筛选出适合于旱地栽培的品种,对于南疆生态恢复和经济发展非常重要。小黑麦(Triticale)属于较抗旱的作物,但不同品种间抗旱性存在着较大差异。本试验旨在探讨南疆小黑麦不同品种(系)的抗旱性能,分析小黑麦抗旱性遗传特征,筛选抗旱性鉴定指标并建立可靠的农艺产量性状评价数学模型,为南疆地区选育抗旱小黑麦新品种以及生产推广奠定基础。[方法]以20个小黑麦品种(系)为试验材料,采用田间鉴定法,在野外自然气候状态下,随机区组设计,干旱处理和有压滴管灌水处理两种方式,可真实反映出小黑麦在大田干旱状态下的实际胁迫环境及真实生长状况。将田间记录和室内考种方式相结合,对2种处理下各材料的株高、生育期、旗叶长、旗叶宽、穗下节长、有效穗数、穗长、主穗粒数、千粒重、容重、籽粒亩产、鲜草产量等共计12个农艺性状进行测定,以各单项性状的抗旱系数作为评价抗旱性的依据,运用主成分分析、聚类分析和逐步回归等方法对其抗旱性进行综合评价及分类,并分析小黑麦品种(系)各抗旱型的产量性状表现特点。[结果]在干旱胁迫下,参试的12个性状与正常灌水对照相比,均有所下降,但所有品种各单项性状的变异幅度均有差异。通过主成分分析,本试验将干旱胁迫处理下小黑麦的12个单项性状浓缩成了5个相互独立的综合性状。通过隶属函数分析,得到不同的小黑麦品种抗旱性综合评价D值,并通过聚类分析,将20个小黑麦品种划分为3种抗旱类型,其中中度抗旱型品种4个,中间型10个,干旱较敏感型6个。进一步运用逐步回归分析法建立了可靠的小黑麦抗旱性评价数学模型D = −0.483 + 0.297X6 + 0.205X7 + 0.387X9 + 0.333X11 + 0.411X12,该方程的决定系数是0.9815,同时筛选出了对小黑麦抗旱性有极显著影响的5个单项性状,分别是有效穗数,穗长,千粒重,籽粒亩产和鲜草产量。对该数学模型的拟合精度进行评价,各品种(系)的拟合精度均在92%以上,说明本试验筛选出来的鉴定性状对小黑麦抗旱性具有显著作用,该模型能用于小黑麦抗旱性综合评价。在此基础上,将综合聚类与逐步回归分析相结果,分析了各个抗旱类型材料的产量性状特点。结果发现相比干旱较敏感类型品种(系),中度抗旱类型的品种能够在有效穗数、穗长、千粒重、籽粒亩产和鲜草产量等性状上保持较高水平。[结论]中度抗旱类型的小黑麦品种(系)能够在有效穗数,穗长,千粒重,籽粒亩产,鲜草产量等这5个产量性状上比干旱较敏感类型品种(系)保持更高的水平,在相同的干旱环境下,可通过测定其它品种的有效穗数,穗长,千粒重,籽粒亩产,鲜草产量这5个产量性状来判定目标品种抗旱性的强弱,可为抗旱栽培、育种及种质资源的鉴定与筛选提供理论依据。 [Objective] Xinjiang is the most extremely water scarcity area in China, Southern Xinjiang is dominated by desert and Gobi, and water is the most limited factor constraining the agriculture development. It is important to screen the suitable varieties for dry farming to the ecological restoration and economic development of Southern Xinjiang. The Triticale is a kind of drought resistant crop, however the drought resistance of different varieties differed wildly. The purpose of this experiment is to explore the drought resistance of different Triticale varieties (lines) in South Xinjiang, analyze the genetic characteristics of the drought resistance of forage type triticale, screen the identification indexes of drought resistance and establish a reliable mathematical model for the evaluation of agronomic yield characters, so as to lay a firm foundation for breeding new varieties of drought resistant forage type triticale and production extension in South Xinjiang. [Methods] Twenty Triticale varieties (lines) as experimental materials, field identification method was used, the field drought as drought treatment and pressure drip irrigation treatment as the irrigation with random block design. Combining the field record with the laboratory test, 12 agronomic characters including plant height, growth period, flag leaf length, flag leaf width, node length under the ear, ef-fective ear number, ear length, grain number of main ear, 1000 grain weight, grain bulk density, grain yield/666.6m2 and fresh grass yield were measured under the two treatments. The drought resistance coefficient of each character was used as the basis for the evaluation the drought re-sistance, the principal component analysis, the cluster analysis and stepwise regression were used to comprehensively evaluate and classify the drought resistance, and to analyze the yield characters of drought resistance Triticale varieties (lines). [Results] Under the drought stress, compared with the irrigation control, the tested 12 characters of decreased, whereas the variation of each character with all varieties was different. Through principal component analysis, the 12 characters under drought stress were condensed into 5 independent comprehensive characters. Through membership function analysis, the comprehensive evaluation D value of drought resistance of different forage type triticale varieties was obtained. Through cluster analysis, 20 triticale varieties were divided into three types of drought resistance, including 4 moderate drought resistance type varieties, 10 intermediate type varieties and 6 drought sensitive types. Furthermore, by stepwise regression analysis, a reliable mathematical model for evaluating the drought resistance of forage type triticale was established: D = −0.483 + 0.297X6 + 0.205X7 + 0.387X9 + 0.333X11 + 0.411X12, with the decisive coefficient of 0.9815. Five single characters that had a significant effect on the drought resistance of forage type triticale were screened, which were the effective ear number, ear length, 1000 grain weight, grain yield per unit area and fresh forage yield. The fitting accuracy of the mathematical model was evaluated, and the fitting accuracy of each variety (line) was more than 92%, which indicated that the identification characters screened in this experiment had significant effect on the drought resistance. The model can be used for the comprehensive evaluation of the drought resistance of forage type Triticale. On this basis, the comprehensive clustering and stepwise regression analysis were used to analyze the yield characteristics of various drought resistant materials. The results showed that compared with the drought sensitive varieties (lines), the middle drought resistant varieties were able to maintain a higher level in effective ear number, ear length, 1000 grain weight, grain yield per unit area and fresh forage yield. [Conclusion] Compared with sensitive drought resistance forage type triticale varieties (lines), the moderate drought resistance can maintain a higher effective ear number, ear length, 1000 grain weight, grain yield per unit area and fresh forage yield. Under the same drought environment, the five characteristics of effective ear number, ear length, 1000 grain weight, grain yield per unit area, fresh forage yield can be measured to determine the drought resistance of the target varieties, which can provide theoretical basis for drought resistance cultivation, breeding and germplasm resources identification and selection.
[目的]新疆属于我国极度干旱缺水的省份,南疆更是以沙漠和戈壁为主,水是限制南疆农业发展的第一因素。筛选出适合于旱地栽培的品种,对于南疆生态恢复和经济发展非常重要。小黑麦(Triticale)属于较抗旱的作物,但不同品种间抗旱性存在着较大差异。本试验旨在探讨南疆小黑麦不同品种(系)的抗旱性能,分析小黑麦抗旱性遗传特征,筛选抗旱性鉴定指标并建立可靠的农艺产量性状评价数学模型,为南疆地区选育抗旱小黑麦新品种以及生产推广奠定基础。[方法]以20个小黑麦品种(系)为试验材料,采用田间鉴定法,在野外自然气候状态下,随机区组设计,干旱处理和有压滴管灌水处理两种方式,可真实反映出小黑麦在大田干旱状态下的实际胁迫环境及真实生长状况。将田间记录和室内考种方式相结合,对2种处理下各材料的株高、生育期、旗叶长、旗叶宽、穗下节长、有效穗数、穗长、主穗粒数、千粒重、容重、籽粒亩产、鲜草产量等共计12个农艺性状进行测定,以各单项性状的抗旱系数作为评价抗旱性的依据,运用主成分分析、聚类分析和逐步回归等方法对其抗旱性进行综合评价及分类,并分析小黑麦品种(系)各抗旱型的产量性状表现特点。[结果]在干旱胁迫下,参试的12个性状与正常灌水对照相比,均有所下降,但所有品种各单项性状的变异幅度均有差异。通过主成分分析,本试验将干旱胁迫处理下小黑麦的12个单项性状浓缩成了5个相互独立的综合性状。通过隶属函数分析,得到不同的小黑麦品种抗旱性综合评价D值,并通过聚类分析,将20个小黑麦品种划分为3种抗旱类型,其中中度抗旱型品种4个,中间型10个,干旱较敏感型6个。进一步运用逐步回归分析法建立了可靠的小黑麦抗旱性评价数学模型D = −0.483 + 0.297X6 + 0.205X7 + 0.387X9 + 0.333X11 + 0.411X12,该方程的决定系数是0.9815,同时筛选出了对小黑麦抗旱性有极显著影响的5个单项性状,分别是有效穗数,穗长,千粒重,籽粒亩产和鲜草产量。对该数学模型的拟合精度进行评价,各品种(系)的拟合精度均在92%以上,说明本试验筛选出来的鉴定性状对小黑麦抗旱性具有显著作用,该模型能用于小黑麦抗旱性综合评价。在此基础上,将综合聚类与逐步回归分析相结果,分析了各个抗旱类型材料的产量性状特点。结果发现相比干旱较敏感类型品种(系),中度抗旱类型的品种能够在有效穗数、穗长、千粒重、籽粒亩产和鲜草产量等性状上保持较高水平。[结论]中度抗旱类型的小黑麦品种(系)能够在有效穗数,穗长,千粒重,籽粒亩产,鲜草产量等这5个产量性状上比干旱较敏感类型品种(系)保持更高的水平,在相同的干旱环境下,可通过测定其它品种的有效穗数,穗长,千粒重,籽粒亩产,鲜草产量这5个产量性状来判定目标品种抗旱性的强弱,可为抗旱栽培、育种及种质资源的鉴定与筛选提供理论依据。
小黑麦,抗旱性,隶属函数分析,综合评价,逐步回归
Shiqi Ten1, Lihong Feng2, Quanzhong Wu1, Shan Gao1, Ruiqing Wang1, Youwu Wang1*
1The Plant Science College, Tarim Univeristy, Alaer Xinjiang
2Xinhe County Populus euphratica Management Station, Xinhe County Xinjiang
Received: Mar. 28th, 2021; accepted: Apr. 21st, 2021; published: Apr. 28th, 2021
[Objective] Xinjiang is the most extremely water scarcity area in China, Southern Xinjiang is dominated by desert and Gobi, and water is the most limited factor constraining the agriculture development. It is important to screen the suitable varieties for dry farming to the ecological restoration and economic development of Southern Xinjiang . The Triticale is a kind of drought resistant crop, however the drought resistance of different varieties differed wildly. The purpose of this experiment is to explore the drought resistance of different Triticale varieties (lines) in South Xinjiang, analyze the genetic characteristics of the drought resistance of forage type triticale, screen the identification indexes of drought resistance and establish a reliable mathematical model for the evaluation of agronomic yield characters, so as to lay a firm foundation for breeding new varieties of drought resistant forage type triticale and production extension in South Xinjiang. [Methods] Twenty Triticale varieties (lines) as experimental materials, field identification method was used, the field drought as drought treatment and pressure drip irrigation treatment as the irrigation with random block design. Combining the field record with the laboratory test, 12 agronomic characters including plant height, growth period, flag leaf length, flag leaf width, node length under the ear, effective ear number, ear length, grain number of main ear, 1000 grain weight, grain bulk density, grain yield/ 666.6m 2 and fresh grass yield were measured under the two treatments. The drought resistance coefficient of each character was used as the basis for the evaluation the drought resistance, the principal component analysis, the cluster analysis and stepwise regression were used to comprehensively evaluate and classify the drought resistance, and to analyze the yield characters of drought resistance Triticale varieties (lines). [Results] Under the drought stress, compared with the irrigation control, the tested 12 characters of decreased, whereas the variation of each character with all varieties was different. Through principal component analysis, the 12 characters under drought stress were condensed into 5 independent comprehensive characters. Through membership function analysis, the comprehensive evaluation D value of drought resistance of different forage type triticale varieties was obtained. Through cluster analysis, 20 triticale varieties were divided into three types of drought resistance, including 4 moderate drought resistance type varieties, 10 intermediate type varieties and 6 drought sensitive types. Furthermore, by stepwise regression analysis, a reliable mathematical model for evaluating the drought resistance of forage type triticale was established: D = −0.483 + 0.297X6 + 0.205X7 + 0.387X9 + 0.333X11 + 0.411X12, with the decisive coefficient of 0.9815. Five single characters that had a significant effect on the drought resistance of forage type triticale were screened, which were the effective ear number, ear length, 1000 grain weight, grain yield per unit area and fresh forage yield. The fitting accuracy of the mathematical model was evaluated, and the fitting accuracy of each variety (line) was more than 92%, which indicated that the identification characters screened in this experiment had significant effect on the drought resistance. The model can be used for the comprehensive evaluation of the drought resistance of forage type Triticale. On this basis, the comprehensive clustering and stepwise regression analysis were used to analyze the yield characteristics of various drought resistant materials. The results showed that compared with the drought sensitive varieties (lines), the middle drought resistant varieties were able to maintain a higher level in effective ear number, ear length, 1000 grain weight, grain yield per unit area and fresh forage yield. [Conclusion] Compared with sensitive drought resistance forage type triticale varieties (lines), the moderate drought resistance can maintain a higher effective ear number, ear length, 1000 grain weight, grain yield per unit area and fresh forage yield. Under the same drought environment, the five characteristics of effective ear number, ear length, 1000 grain weight, grain yield per unit area, fresh forage yield can be measured to determine the drought resistance of the target varieties, which can provide theoretical basis for drought resistance cultivation, breeding and germplasm resources identification and selection.
Keywords:Triticale, Drought Resistance, Membership Function Analysis, Comprehensive Evaluation, Stepwise Regression
Copyright © 2021 by author(s) and Hans Publishers Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY 4.0).
http://creativecommons.org/licenses/by/4.0/
干旱作为农业发展的一大制约因素,造成了全球范围内的作物产量减少,品质降低以及植被退化等严重后果 [
小黑麦(Triticale)是由小麦属(Triticum)和黑麦属(Secale)物种经过属间杂交人工培育而成的一种粮饲兼用型的优良禾本科作物,它既继承了小麦亲本籽粒高产、优质的特点,又继承了黑麦生物学产量高,抗病性及耐逆性强的特点,有作粮用、饲用、制酒精与啤酒及编织等多种用途 [
本研究在南疆极端高温大气干旱和土壤干旱双重胁迫下,采用田间自然状态水旱对照交互式干旱处理法,以新小黑麦3号品种为对照和南疆自育的19份小黑麦品系为参试材料,选用12个农艺和产量性状,进行抗旱性鉴定,分析参试材料的抗旱性及其遗传规律,旨在了解各新品种(系)抗旱能力的大致情况,初步判断这些品种(系)抗旱级别的整体水平状态,为南疆地区选育抗旱小黑麦新品种以及生产推广提供参考依据,同时希望广大育种者对南疆沙漠气候生态环境条件下小黑麦品种(系)的抗旱性给予关注。
试验地点设置在阿克苏地区沙雅县,土壤类型为沙壤土,肥力中等偏差且分布均匀,干旱处理和灌水处理的试验地在入冬前均灌水一次,水量320 m3/亩,供试材料由塔里木大学小黑麦育种课题组提供。参试材料为小黑麦品种(系)共计20个,分别是:H16-5;H12-8;H17-13;H07-5;H8-23;H06-8;草鉴13;草品08;H11-8;H10-2;H11-7;H13-5;H07-2;H14-64;H15-12;H13-9;H09-12;H07-6;H14-3和新小黑麦3号(CK)。
本试验中抗旱性采用田间鉴定法 [
各性状的相对值(抗旱系数)为干旱胁迫下处理值与对照值比例的百分比 [
耐 旱 系 数 ( α ) = 处 理 测 定 值 / 对 照 测 定 值 × 100 % (1)
在模糊数学中,1个评价因素指标实测值隶属于某一级别的程度称为隶属度,为0~1之间的数值。该数值越接近1,隶属于这一级别的程度越大。每个评价因素指标实测值,就对应1个隶属度,这种对应关系称为隶属函数 [
u ( X j ) = ( X j − X min ) / ( X max − X min ) j = 1 , 2 , ⋯ , n ; (2)
参照袁闯 [
式中 X j 表示第j个综合性状; X min 表示第j个综合性状的最小值。 X max 表示第j个综合性状的最大值。
w j = p j / ∑ j = 1 n p j , j = 1 , 2 , ⋯ , n ; (3)
式中, w j 表示第j个综合性状在所有综合性状中的重要程度即权重。 p j 为各个基因型第j个综合性状的贡献率。
D = ∑ j = 1 n [ u ( X j ) × w j ] , j = 1 , 2 , ⋯ , n ; (4)
式中,D值为各品种(系)在干旱胁迫条件下用综合性状评价所得的抗旱性综合评价值。
根据D值将供试品种进行抗旱性分级,0.8 ≤ D ≤ 1为1级抗旱型(高抗旱型),0.6 ≤ D < 0.8为2级抗旱型(中抗旱型),0.4 ≤ D < 0.6为3级抗旱型(中间型),0.2 ≤ D < 0.4为4级抗旱型(干旱较敏感型),0 < D < 0.2为5级抗旱型(干旱敏感型) [
试验数据统计分析采用浙江大学唐启义的DPS7.05版统计软件并结合EXCELL软件对数据进行多元统计分析。
名称 varieties | 株高 (plant height) | 生育期 Growth stage | 旗叶长 Flag leaf length | 旗叶宽 Flag leaf width | 穗下节长 Node length under the ear | 有效穗数 Effective ear |
---|---|---|---|---|---|---|
H16-5 | 0.69 | 0.69 | 0.9 | 0.51 | 0.67 | 0.55 |
H12-8 | 0.69 | 0.76 | 0.83 | 0.57 | 0.74 | 0.5 |
H17-13 | 0.67 | 0.67 | 0.73 | 0.58 | 0.78 | 0.51 |
H012-5 | 0.59 | 0.67 | 0.86 | 0.62 | 0.55 | 0.56 |
H8-23 | 0.62 | 0.56 | 0.77 | 0.62 | 0.68 | 0.54 |
H06-8 | 0.9 | 0.86 | 0.68 | 0.52 | 0.89 | 0.45 |
草鉴13 | 0.83 | 0.87 | 0.77 | 0.62 | 0.86 | 0.53 |
草品08 | 0.52 | 0.53 | 0.81 | 0.56 | 0.49 | 0.5 |
H11-8 | 0.79 | 0.85 | 0.72 | 0.29 | 0.75 | 0.43 |
H10-2 | 0.82 | 0.86 | 0.85 | 0.69 | 0.78 | 0.5 |
H11-7 | 0.83 | 0.86 | 0.52 | 0.37 | 0.87 | 0.49 |
H13-5 | 0.68 | 0.57 | 0.51 | 0.35 | 0.67 | 0.42 |
H07-2 | 0.75 | 0.77 | 0.81 | 0.52 | 0.79 | 0.45 |
H14-64 | 0.61 | 0.66 | 0.91 | 0.63 | 0.62 | 0.46 |
H15-12 | 0.71 | 0.71 | 0.98 | 0.4 | 0.68 | 0.53 |
H13-9 | 0.83 | 0.87 | 0.79 | 0.56 | 0.86 | 0.56 |
H09-12 | 0.8 | 0.82 | 0.57 | 0.94 | 0.77 | 0.66 |
H07-6 | 0.72 | 0.79 | 0.49 | 0.53 | 0.73 | 0.42 |
H14-3 | 0.65 | 0.74 | 0.7 | 0.51 | 0.65 | 0.37 |
新3号 | 0.63 | 0.75 | 0.86 | 0.44 | 0.68 | 0.49 |
名称 | 穗长 Ear length | 主穗粒数 Grain number of the main stem ear | 千粒重 1000 grain weight | 容重 Bulk weight | 籽粒亩产 Grain yield per unit area | 鲜草产量 Fresh forage yield |
H16-5 | 0.55 | 0.58 | 0.59 | 0.64 | 0.52 | 0.69 |
H12-8 | 0.54 | 0.55 | 0.57 | 0.64 | 0.52 | 0.71 |
H17-13 | 0.6 | 0.51 | 0.56 | 0.63 | 0.51 | 0.63 |
H012-5 | 0.54 | 0.48 | 0.57 | 0.53 | 0.46 | 0.63 |
H8-23 | 0.52 | 0.49 | 0.7 | 0.84 | 0.5 | 0.58 |
---|---|---|---|---|---|---|
H06-8 | 0.86 | 0.53 | 0.79 | 0.78 | 0.58 | 0.94 |
草鉴13 | 0.83 | 0.55 | 0.68 | 0.67 | 0.61 | 0.82 |
草品08 | 0.65 | 0.5 | 0.39 | 0.38 | 0.51 | 0.54 |
H11-8 | 0.87 | 0.68 | 0.84 | 0.85 | 0.67 | 0.76 |
H10-2 | 0.53 | 0.49 | 0.57 | 0.56 | 0.5 | 0.88 |
H11-7 | 0.77 | 0.54 | 0.63 | 0.57 | 0.35 | 0.82 |
H13-5 | 0.64 | 0.52 | 0.61 | 0.65 | 0.52 | 0.58 |
H07-2 | 0.58 | 0.51 | 0.6 | 0.69 | 0.57 | 0.79 |
H14-64 | 0.44 | 0.34 | 0.62 | 0.68 | 0.34 | 0.66 |
H15-12 | 0.44 | 0.65 | 0.65 | 0.31 | 0.66 | 0.72 |
H13-9 | 0.63 | 0.59 | 0.45 | 0.52 | 0.61 | 0.88 |
H09-12 | 0.67 | 0.46 | 0.46 | 0.55 | 0.45 | 0.85 |
H07-6 | 0.44 | 0.81 | 0.4 | 0.42 | 0.39 | 0.76 |
H14-3 | 0.64 | 0.69 | 0.51 | 0.52 | 0.52 | 0.63 |
新3号 | 0.9 | 0.5 | 0.87 | 0.89 | 0.95 | 0.72 |
表1. 小黑麦品种干旱胁迫条件下各性状的抗旱系数
各个参试的性状采用相对值可以很好的消除各个基因型间的固有差异,比相对值更能准确清晰的反映出小黑麦的抗旱能力大小。依据公式(1)分别求得每个参试性状的抗旱系数(表1),由表1可知,不同品种经干旱胁迫处理后,参试的性状株高、生育期、旗叶长、旗叶宽、穗下节长、有效穗数、穗长、主穗粒数、千粒重、容重、籽粒亩产和鲜草产量和正常灌水对照相比,均有所下降( 抗 旱 系 数 ( α ) ≤ 100 % ),但是不同品系(种)各性状变化的幅度存在着差异,因而用不同单项性状的抗旱系数来评价小黑麦抗旱性则结果均不相同,这与谢楠等 [
名称 | 株高 Plant height | 生育期 Growth period | 旗叶长 Flag leaf length | 旗叶宽 Flag leaf width | 穗下节长 The node length under the ear | 有效穗数 Effective ear |
---|---|---|---|---|---|---|
生育期 | 0.87** | |||||
旗叶长 | −0.33 | −0.18 | ||||
旗叶宽 | 0.01 | 0.05 | 0.06 | |||
穗下节长 | 0.92** | 0.80** | −0.34 | 0 |
有效穗数 | 0.08 | 0.04 | 0.23 | 0.63** | 0.06 | |
---|---|---|---|---|---|---|
穗长 | 0.4 | 0.42 | −0.26 | −0.27 | 0.4 | −0.09 |
每穗粒数 | 0.2 | 0.29 | −0.3 | −0.44* | 0.14 | −0.39 |
千粒重 | 0.2 | 0.18 | 0.21 | −0.45* | 0.23 | −0.18 |
容重 | 0.13 | 0.09 | 0.01 | −0.15 | 0.26 | −0.14 |
籽粒亩产 | 0.04 | 0.14 | 0.36 | −0.34 | 0.07 | −0.02 |
鲜草产量 | 0.92** | 0.92** | −0.15 | 0.17 | 0.83** | 0.18 |
名称 | 穗长 | 每穗粒数 | 千粒重 | 容重 | 籽粒亩产 | |
每穗粒数 | 0 | |||||
千粒重 | 0.57** | −0.14 | ||||
容重 | 0.54* | −0.3 | 0.78** | |||
籽粒亩产 | 0.54* | 0.13 | 0.59** | 0.39 | ||
鲜草产量 | 0.34 | 0.11 | 0.15 | 0.08 | 0.11 |
表2. 旱胁迫下小黑麦品种性状相关系数矩阵
*和**分别表示打到了0.05显著水平和0.01极显著水平。
主成分分析方法是把多个指标简化为少数几个综合指标的统计分析方法,主要是通过对原始变量相关矩阵或协方差矩阵内部结构关系的研究,利用原始变量的线性组合形成几个综合指标(主成分),在保留原始变量主要信息的前提下起到降维、简化问题与浓缩数据的作用,可以有效的补充单个性状抗旱性评价的很多不足。在实际研究中,为了全面分析问题,往往提出很多与本试验相关的变量(或因素),因为每个变量都在不同程度上反映了该试验的某些信息。对12个单项性状的抗旱系数进行主成分分析,由表3可知,前5个综合评价性状CI1~CI5的贡献率分别为35.33%,22.642%,16.87%,9.477%,5.808%,这几个主成分的综合信息量已经能够代表原来参试性状的90%以上,因而这5个综合性状可以代表参试的12个性状的信息量,剩余综合变量信息可以忽略不计,将原来12个单项性状转换为5个新的相互独立的综合性状。
主成分 | CI1 | CI2 | CI3 | CI4 | CI5 |
---|---|---|---|---|---|
株高 | 0.440 | −0.211 | −0.017 | 0.009 | −0.130 |
生育期 | 0.426 | −0.191 | −0.014 | 0.187 | −0.082 |
旗叶长 | −0.137 | 0.174 | 0.364 | 0.605 | −0.414 |
旗叶宽 | −0.076 | −0.384 | 0.431 | −0.119 | 0.157 |
穗下节长 | 0.431 | −0.167 | 0.004 | −0.079 | −0.155 |
有效穗数 | −0.023 | −0.252 | 0.509 | 0.157 | 0.415 |
穗长 | 0.326 | 0.267 | 0.051 | −0.186 | 0.537 |
每穗粒数 | 0.102 | −0.037 | −0.560 | 0.370 | 0.221 |
千粒重 | 0.236 | 0.462 | 0.160 | −0.040 | −0.220 |
容重 | 0.201 | 0.383 | 0.238 | −0.391 | −0.150 |
籽粒亩产 | 0.164 | 0.399 | 0.124 | 0.453 | 0.397 |
鲜草产量 | 0.417 | −0.240 | 0.098 | 0.152 | −0.138 |
---|---|---|---|---|---|
特征值 | 4.240 | 2.717 | 2.025 | 1.137 | 0.697 |
百分率% | 35.330 | 22.642 | 16.870 | 9.477 | 5.808 |
累计百分率% | 35.330 | 57.971 | 74.842 | 84.319 | 90.127 |
表3. 各综合性状的系数以及累计贡献率
在干旱胁迫下,采用单一性状不能客观有效的评价参试小黑麦各品种的抗旱性,更无法给出相应的抗旱等级。隶属函数法可以将各抗旱指标进行综合定量评价,能够更加全面地综合评价不同品种的抗旱性 [
CI1 | CI2 | CI3 | CI4 | CI5 | |
---|---|---|---|---|---|
H16-5 | −1.034 | 0.186 | 0.499 | 0.814 | −0.213 |
H12-8 | −0.420 | −0.236 | 0.252 | 0.331 | −0.515 |
H17-13 | −0.865 | −0.082 | 0.257 | −0.636 | 0.135 |
H012-5 | −2.711 | −0.159 | 1.093 | 0.246 | 0.200 |
H8-23 | −1.892 | 1.188 | 1.292 | −1.211 | −0.209 |
H06-8 | 4.016 | 0.485 | 0.237 | −0.736 | −0.481 |
草鉴13 | 2.623 | −0.208 | 0.931 | 0.187 | 0.489 |
草品08 | −4.274 | 0.199 | −0.256 | 0.152 | 1.371 |
H11-8 | 2.788 | 2.741 | −1.284 | 0.098 | 0.090 |
H10-2 | 1.075 | −1.709 | 0.994 | 0.632 | −1.117 |
H11-7 | 2.322 | −1.017 | −1.282 | −1.428 | −0.174 |
H13-5 | −1.255 | 1.423 | −1.758 | −1.844 | 0.340 |
H07-2 | 0.707 | 0.157 | 0.108 | 0.089 | −0.990 |
H14-64 | −2.509 | 0.130 | 1.433 | −1.048 | −2.061 |
H15-12 | −1.054 | 0.086 | −0.532 | 3.036 | −0.409 |
H13-9 | 1.806 | −1.875 | 0.262 | 1.364 | 0.360 |
H09-12 | 0.762 | −3.516 | 2.327 | −0.953 | 1.691 |
H07-6 | −0.358 | −2.322 | −3.607 | 0.079 | 0.158 |
H14-3 | −1.045 | 0.337 | −2.430 | 0.136 | 0.182 |
新3号 | 1.318 | 4.191 | 1.463 | 0.693 | 1.155 |
u1 | u2 | u3 | u4 | u5 | D值 | 综合评价 | |
---|---|---|---|---|---|---|---|
H16-5 | 0.391 | 0.480 | 0.692 | 0.545 | 0.492 | 0.492 | 3级抗旱 |
H12-8 | 0.465 | 0.426 | 0.650 | 0.446 | 0.412 | 0.484 | 3级抗旱 |
H17-13 | 0.411 | 0.446 | 0.651 | 0.248 | 0.585 | 0.459 | 3级抗旱 |
H012-5 | 0.189 | 0.436 | 0.792 | 0.428 | 0.603 | 0.415 | 4级抗旱 |
H8-23 | 0.287 | 0.610 | 0.826 | 0.130 | 0.494 | 0.466 | 3级抗旱 |
H06-8 | 1.000 | 0.519 | 0.648 | 0.227 | 0.421 | 0.695 | 2级抗旱 |
草鉴13 | 0.832 | 0.429 | 0.765 | 0.416 | 0.680 | 0.665 | 2级抗旱 |
草品08 | 0.000 | 0.482 | 0.565 | 0.409 | 0.915 | 0.329 | 4级抗旱 |
H11-8 | 0.852 | 0.812 | 0.391 | 0.398 | 0.573 | 0.690 | 2级抗旱 |
H10-2 | 0.645 | 0.234 | 0.775 | 0.507 | 0.252 | 0.527 | 3级抗旱 |
H11-7 | 0.796 | 0.324 | 0.392 | 0.085 | 0.503 | 0.508 | 3级抗旱 |
H13-5 | 0.364 | 0.641 | 0.312 | 0.000 | 0.640 | 0.403 | 4级抗旱 |
H07-2 | 0.601 | 0.477 | 0.626 | 0.396 | 0.286 | 0.532 | 3级抗旱 |
H14-64 | 0.213 | 0.473 | 0.849 | 0.163 | 0.000 | 0.378 | 4级抗旱 |
H15-12 | 0.388 | 0.467 | 0.518 | 1.000 | 0.440 | 0.500 | 3级抗旱 |
H13-9 | 0.733 | 0.213 | 0.652 | 0.657 | 0.645 | 0.574 | 3级抗旱 |
H09-12 | 0.608 | 0.000 | 1.000 | 0.183 | 1.000 | 0.509 | 3级抗旱 |
H07-6 | 0.472 | 0.155 | 0.000 | 0.394 | 0.592 | 0.304 | 4级抗旱 |
H14-3 | 0.390 | 0.500 | 0.198 | 0.406 | 0.598 | 0.397 | 4级抗旱 |
新3号 | 0.674 | 1.000 | 0.854 | 0.520 | 0.857 | 0.785 | 2级抗旱 |
权重 | 0.392 | 0.251 | 0.187 | 0.105 | 0.064 |
表4. 各品种(系)的综合性状得分、权重及综合评价值
用公式(4)计算各个品种(系)综合抗旱评价值D,为了更加科学的评价参试材料的综合抗旱性,采用系统聚类分析最小距离法进行聚类,结果见图1,根据图1可知,参试的20个品种(系)共划分为3大类,其中第一类共计包括4个品种(系),分别是新小黑麦3号、草鉴13、H11-8和H06-8为2级抗旱型,即中度抗旱型。第二类共计包括10个品种(系),分别是H16-5、H15-12、H12-8、H17-13、H8-23、H10-2、H07-2、H11-7、H09-12和H13-9为3级抗旱型,即中间型,第三类共计包括6个品种(系),分别是H012-5、H13-5、H14-3、H14-64草品08和H07-6为4级抗旱型,即干旱较敏感型,本试验参试的材料中无1级抗旱型,即高抗旱型品种(系)。景蕊莲等学者 [
聚类分析是数理统计中研究“物以类聚”的一种多元统计学方法。它将一批样品或变量按照它们在性质上的亲疏程度进行分类。首先将n个样品自成一类,然后每次将具有最小距离的两类合并,合并后重新计算大类之间的距离,将此过程一直继续到所有样品归为一类为止,最后把这个过程做成聚类谱系图。本研究中我们采用此方法评价小黑麦抗旱性。
另外,由上面的聚类分析结果可知,本研究中2级抗旱型和3级抗旱型品种的综合评价D值与传统的分类方法一致,但是4级抗旱型品种中的H13-5的D值为0.403,H012-5的D值为0.415与兰巨生、冯德益和楼世博等 [
图1. 聚类分析结果
为了分析参试品种各个性状与抗旱性之间的关系,筛选出可用于外推的抗旱性状,建立可用于小黑麦抗旱评价的数学模型,本试验中参照戴海芳等 [
利用逐步回归所建立的最优回归方程包含了对因变量有显著影响的自变量,不包含对因变量没有显著影响的自变量 [
品种 variety | 观测值 Observation value | 拟合值 Fitting value | 拟合误差 Fitting error | 拟合精度% Fitting accuracy |
---|---|---|---|---|
H16-5 | 0.492 | 0.478 | 0.014 | 97.13 |
H12-8 | 0.484 | 0.462 | 0.023 | 95.35 |
H17-13 | 0.459 | 0.437 | 0.022 | 95.12 |
---|---|---|---|---|
H012-5 | 0.415 | 0.426 | −0.011 | 97.33 |
H8-23 | 0.466 | 0.459 | 0.007 | 98.58 |
H06-8 | 0.695 | 0.712 | −0.017 | 97.63 |
草鉴13 | 0.665 | 0.648 | 0.017 | 97.38 |
草品08 | 0.329 | 0.341 | −0.012 | 96.45 |
H11-8 | 0.690 | 0.683 | 0.007 | 99.03 |
H10-2 | 0.527 | 0.523 | 0.004 | 99.18 |
H11-7 | 0.508 | 0.517 | −0.009 | 98.18 |
H13-5 | 0.403 | 0.420 | −0.017 | 95.91 |
H07-2 | 0.532 | 0.516 | 0.016 | 96.99 |
H14-64 | 0.378 | 0.368 | 0.010 | 97.33 |
H15-12 | 0.500 | 0.532 | −0.032 | 94.04 |
H13-9 | 0.574 | 0.551 | 0.023 | 96.03 |
H09-12 | 0.509 | 0.527 | −0.018 | 96.53 |
H07-6 | 0.304 | 0.329 | −0.025 | 92.46 |
H14-3 | 0.397 | 0.387 | 0.010 | 97.56 |
新3号 | 0.785 | 0.796 | −0.011 | 98.66 |
表5. 回归方程的拟合精度分析
综合聚类分析以及逐步回归分析结果,比较各鉴定指标,在小黑麦不同抗旱类群间的表现特征,进一步挖掘导致南疆小黑麦品种(系)抗旱性能级别差异的原因。经干旱胁迫后各类群农艺性状的抗旱系数具体表现特征见表6。
类群 group | 有效穗数 Effective ear | 穗长 Ear length | 千粒重 1000 grain weight | 籽粒亩产 Grain yield | 鲜草产量 Fresh forage yield |
---|---|---|---|---|---|
2级抗旱型 | 0.475 | 0.865 | 0.795 | 0.703 | 0.810 |
3级抗旱型 | 0.529 | 0.583 | 0.578 | 0.519 | 0.755 |
4级抗旱型 | 0.455 | 0.558 | 0.517 | 0.457 | 0.633 |
表6. 各类群品种(系)产量性状的抗旱系数表现特征
由表6可以看出,第一类2级抗旱型品种(系):穗长、千粒重、籽粒亩产和鲜草产量明显高于3级抗旱型和4级抗旱型品种,而有效穗数则介于两者之间。第二类群为3级抗旱型品种(系),具体表现为有效穗数明显高于2级抗旱性和4级抗旱型品种(系),而穗长、千粒重、籽粒亩产和鲜草产量则介于前两者之间。第三类群为4级抗旱型品种(系)具体表现特征为有效穗数、穗长、千粒重、籽粒亩产和鲜草产量均低于2级抗旱性和3级抗旱型品种(系),由此可知小黑麦不同品种(系)间抗旱性存在着较大遗传差异,这也与冀天会等 [
干旱随着全球气候的变暖有越来越严重的趋势,造成的作物减产时常发生,在多种逆境因子中,干旱已成为限制农业生产的重要因子 [
抗旱系数指同一品种干旱胁迫与非胁迫的比值。它反映了不同小麦品种对干旱的敏感程度,一个品种的抗旱系数高,则品种的抗旱性强,稳产性好,对于小麦种质抗旱性筛选更直观 [
相关系数表明了各性状间的相关情况,相关系数越大,则性状间的相关程度越高。本试验中采用抗旱系数进行相关分析,结果显示参试性状间均呈现出一定水平上的相关性,且相关系数或大或小,说明各农艺产量性状在不同的品种中所发挥的作用和大小存在着差异。
在植物生长抗旱性研究中,由于植物本身抗旱机理的复杂性和植物对干旱条件适应的多样性,采用单一指标不能全面反映植物抗旱性,因此采用多指标综合评价法才能更加客观地评价植物抗旱性强弱 [
作物的抗旱性是一个复杂的数量性状, 是由多种因素共同作用的结果, 要单从某一性状准确评价作物抗旱性比较困难 [
本试验在借鉴前人研究基础上通过主成分分析,将参试的12个农艺产量性状浓缩成了5个相互独立的综合性状,进一步采用隶属函数法合并各性状的综合信息,得到了20个品种(系)的耐旱性综合评价D值,较为客观全面地反映了各参试品种的抗旱性。结合聚类分析,参试的20个品种(系)共划分为3大类,其中第一类共计包括4个品种(系)为2级抗旱型,即中度抗旱型。第二类共计包括10个品种(系)为3 级抗旱型,即中间型,第三类共计包括6个品种(系)为4 级抗旱型,即干旱较敏感型,本试验参试的材料中无1级抗旱型,即高抗旱型品种(系)。进一步利用逐步回归分析方法建立了小黑麦农艺产量性状回归模型D = −0.483 + 0.297X6 + 0.205X7 + 0.387X9 + 0.333X11 + 0.411X12,筛选出了极显著影响小黑麦抗旱性能的5个单项指标,即有效穗数,穗长,千粒重,籽粒亩产,鲜草产量。在相同的逆境条件下,可通过测定其他品种的这5个抗旱性鉴定性状,并利用该评价模型来判断目标品种抗旱性的强弱,使小黑麦的抗旱性鉴定与利用更有预见性,也可为抗逆栽培、育种及种质资源的鉴定与筛选提供理论依据 [
在干旱胁迫下,小黑麦的生育期、旗叶长、旗叶宽、穗下节长、有效穗数、穗长、主穗粒数、千粒重、容重、籽粒亩产和鲜草产量等性状与正常灌水对照相比,均有所下降,但所有品种各单项性状的变异幅度不同,因而用不同单项性状的抗旱系数来评价小黑麦抗旱性则结果均不同。参试的20个品种(系)共划分为3大类,第一类包括4个品种(系)为2级抗旱型,即中度抗旱型。第二类包括10个品种(系)为3级抗旱型,即中间型。第三类包括6个品种(系)为4级抗旱型,即干旱较敏感型。2级抗旱型品种(系)在有效穗数、穗长、千粒重、籽粒亩产和鲜草产量性状上明显高于4级抗旱型品种(系),主要是通过产量性状来达到增产的目的。在相同的干旱环境下,可通过测定其它品种的有效穗数,穗长,千粒重,籽粒亩产,鲜草产量这5个抗旱性状来判定目标品种抗旱性的强弱,可为抗旱栽培、育种及种质资源的鉴定与筛选提供理论依据。
塔里木大学校长基金,编号TDZKGG201503。
滕仕奇,冯丽红,吴全忠,高 山,王瑞清,王有武. 小黑麦在南疆抗旱性综合评价The Comprehensive Evaluation of the Triticale Drought Resistance in South Xinjiang[J]. 农业科学, 2021, 11(04): 375-388. https://doi.org/10.12677/HJAS.2021.114054