基于多元统计分析的小麦低温冻害评价及水分效应差异研究

doi:10.3864/j.issn.0578-1752.2022.07.004

Abstract

Abstract:

【Objective】In order to clarify the freezing injury degree of wheat under different water conditions caused by low temperature stress, the identification indexes and quantitative evaluation model of freezing injury were screened and established, which provided the theoretical support for prevention and control of freezing injury in wheat production. 【Method】 Weak spring cultivars of Yanzhan 4110 and Lankao 198, semi-winter cultivars of Zhengmai 366 and Fengdecunmai 21 were used as experimental materials. They were treated with irrigation (W) or no irrigation (D) one week before the freezing injury, respectively. Pot experiments were moved to a low-temperature simulation room during the female and male ear differentiation stages. The temperatures were set as -2℃ (T1), -4℃ (T2), -6℃ (T3), -8℃ (T4), -10℃ (T5) and control (CK is the field temperature on the same day). Physiological and biochemical indexes of wheat were measured on the second day after low temperature stress. The standardized physiological indexes were analyzed by multivariate statistical analysis, such as principal component, membership function, cluster analysis and step wise regression. 【Result】 There were significant correlations among the individual physiological and biochemical indexes under different cultivars, water contents and temperatures. Through principal component analysis, 19 physiological and biochemical indexes were transformed into 6 mutually independent comprehensive indexes, whose contribution degrees were 55.972%, 11.93%, 7.168%, 5.075%, 4.236% and 3.079%, respectively, representing 87.459% information of all original data. According to the membership function algorithm, the comprehensive evaluation value ( F value ) of freezing injury degree of each treatment was calculated. Take F value as the dependent variable, the seven key indexes were selected by stepwise regression analysis, namely chlorophyll a, leaf water content, proline, F_v/F_m, soluble protein, MDA and SOD, and the mathematical model for quantitative estimation of F value was established. At the same time, the correlation between F prediction value and yield loss rate was analyzed, and the linear equation determination coefficient R ²= 0.898, indicating that the F prediction model could well evaluate the freezing injury degree. F predicted value could be divided into five categories by further cluster analysis: non-freezing (D-CK, W-CK), mild frozen (D-T1, W-T1), moderate frozen (D-T2, W-T2, W-T3), severe frozen (D-T3, W-T4), and extremely severe frozen (D-T4, W-T5, D-T5). Corresponding yield loss rate were 0, 0-10%, 10%-30%, 30%-50% and more than 50%, respectively. Under the same temperature and moisture conditions, the freezing injury degree of weak spring varieties was heavier than that of semi-winter varieties, and the freezing injury degree of no irrigation treatment was heavier than that of irrigation treatment under the same varieties and temperature conditions. With the increasing of low temperature stress, chlorophyll a, leaf water content and F_v/F_m showed a decreasing trend, the activities of proline, soluble protein and SOD increased first and then decreased, while MDA showed an opposite trend. According to the clustering results, under the same temperature and water conditions, the freezing injury degree of weak spring cultivars was more serious than that of semi-winter cultivars. Under the same variety and temperature conditions, the freezing damage degree without irrigation was worse than that under irrigation. 【Conclusion】Therefore, the semi-winter varieties should be selected in the areas prone to late frost in production, and the irrigation management should be strengthened before a cold wave according to the weather forecast. When freezing injury happened, the injury degree could be accurately assessed in timely through the evaluation index and quantitative model, which was conducive to prevention and control of late frost injury, and provides technology basis for production recovery and decision management after freezing disaster.

Key words: winter wheat, frost damage degree, irrigation, comprehensive evaluation, estimation model

WANG YangYang,LIU WanDai,HE Li,REN DeChao,DUAN JianZhao,HU Xin,GUO TianCai,WANG YongHua,FENG Wei. Evaluation of Low Temperature Freezing Injury in Winter Wheat and Difference Analysis of Water Effect Based on Multivariate Statistical Analysis[J].Scientia Agricultura Sinica, 2022, 55(7): 1301-1318.

Figures/Tables 11

Fig. 1

Fig. 2

Table 1

Table 2

Correlation coefficient between different physiological and biochemical indexes"

指标 index	Chla	Chlb	Car	Chla+b	a/b	SWC	LWC	SSC	Pro	Y (II)	ETR	qP	Fv/Fm	Fv/Fo	BCA	CAT	MDA	POD	SOD
Chla	1.000
Chlb	0.476**	1.000
Car	0.872**	0.34**	1.000
Chla+b	0.505**	0.237**	0.455**	1.000
Chla/b	0.825**	0.094	0.872**	0.442**	1.000
SWC	0.392**	0.362**	0.352**	0.112	0.257**	1.000
LWC	0.804**	0.361**	0.733**	0.518**	0.666**	0.488**	1.000
SSC	0.527**	0.07	0.524**	0.322**	0.591**	0.047	0.501**	1.000
Pro	0.659**	0.143	0.666**	0.277**	0.739**	0.217*	0.591**	0.702**	1.000
Y (II)	0.751**	0.134	0.813**	0.267**	0.782**	0.304**	0.669**	0.521**	0.71**	1.000
ETR	0.740**	0.12	0.801**	0.264**	0.778**	0.286**	0.657**	0.522**	0.711**	0.995**	1.000
qP	0.829**	0.264**	0.875**	0.411**	0.8**	0.362**	0.759**	0.57**	0.704**	0.917**	0.908**	1.000
Fv/Fm	0.838**	0.248**	0.85**	0.419**	0.826**	0.36**	0.805**	0.557**	0.697**	0.914**	0.904**	0.922**	1.000
Fv/Fo	0.805**	0.235**	0.826**	0.371**	0.784**	0.407**	0.708**	0.423**	0.631**	0.902**	0.887**	0.912**	0.907**	1.000
BCA	0.292**	-0.161	0.338**	0.017	0.422**	0.09	0.201*	0.278**	0.36**	0.407**	0.399**	0.347**	0.431**	0.406**	1.000
CAT	0.251**	0.04	0.243**	0.282**	0.349**	-0.047	0.274**	0.509**	0.415**	0.199*	0.192*	0.294**	0.32**	0.232*	0.367**	1.000
MDA	-0.827**	-0.336**	-0.819**	-0.352**	-0.761**	-0.416**	-0.786**	-0.572**	-0.72**	-0.734**	-0.724**	-0.795**	-0.798**	-0.78**	-0.391**	-0.367**	1.000
POD	0.394**	-0.105	0.378**	0.222*	0.540**	0.044	0.37**	0.362**	0.489**	0.43**	0.417**	0.393**	0.499**	0.472**	0.519**	0.492**	-0.526**	1.000
SOD	0.373**	-0.11	0.33**	0.252**	0.500**	-0.164	0.302**	0.764**	0.612**	0.41**	0.413**	0.462**	0.44**	0.368**	0.333**	0.634**	-0.469**	0.465**	1.000

Table 2

Table 3

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

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指标 Index	不灌水 No irrigation (D)			灌水 Irrigation (W)			曼-惠特尼 U Mann-Whitney U
指标 Index	平均值 Mean	变化范围 Variation range	变异系数 Coefficient of variance	平均值 Mean	变化范围 Variation range	变异系数 Coefficient of variance	U值 U value	P
Chla	1.76	0.8-2.53	29.58	2.05	1.26-2.68	20.24	189.5	0.042
Chlb	0.55	0.32-0.80	24.54	0.58	0.43-0.75	15.77	170.5	0.015
Car	0.36	0.15-0.49	29.3	0.4	0.23-0.49	19.52	232.5	0.252
Chla+b	2.32	1.12-3.33	27.8	2.64	1.69-3.44	18.77	184	0.032
Chla/b	3.15	2.07-4.19	16.11	3.52	2.91-4.24	11.48	259	0.550
SWC	83.67	69.98-87.49	4.09	84.51	79.66-88.98	2.53	259	0.55
LWC	77.46	57.14-85.17	10.08	80.22	69.24-84.90	5.84	187	0.037
SSC	22.65	10.53-34.78	30.76	28.72	14.74-47.63	28	166	0.012
Pro	312.49	190.30-448.28	21.85	344.47	243.53-480.63	18.5	192	0.048
Y (II)	0.34	0.1-0.46	38.56	0.39	0.20-0.51	18.03	264	0.621
ETR	25.92	2.20-36.20	44.5	30.09	10.90-40.60	20.77	260	0.564
qP	0.41	0.11-0.63	42.56	0.49	0.19-0.63	30.02	187.5	0.038
F_v/F_m	0.59	0.10-0.84	46.72	0.65	0.28-0.84	32.72	160	0.008
F_v/F_o	3.38	0.64-5.31	46.06	3.59	1.43-5.42	32.41	190	0.043
SPC	24.65	12.45-33.16	25.63	27.96	18.87-38.76	17.37	187	0.037
CAT	2759.58	1549.16-4386.36	29.25	3147.65	1949.11-4782.55	26.71	191	0.045
MDA	22.43	12.99-47.60	52.12	19.5	11.35-39.77	44.59	190	0.043
POD	624.92	431.41-70.54	13.24	669.88	573.11-801.92	9.35	209	0.103
SOD	382.21	200.91-505.89	21.65	431.19	302.13-582.87	17.84	185	0.034

主要因素Principal factor	第一主成分CI1	第二主成分CI2	第三主成分CI3	第四主成分CI4	第五主成分CI5	第六主成分CI6
特征值 Eigen value	10.635	2.267	1.362	0.964	0.805	0.585
方差百分比 Contributive ratio (%)	55.972	11.93	7.168	5.075	4.236	3.079
累积 Cumulative contributive ratio (%)	55.972	67.902	75.069	80.144	84.38	87.459
隶属函数权重 U (X) weight	0.64	0.14	0.08	0.06	0.05	0.04

Evaluation of Low Temperature Freezing Injury in Winter Wheat and Difference Analysis of Water Effect Based on Multivariate Statistical Analysis

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