Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (7): 1301-1318.doi: 10.3864/j.issn.0578-1752.2022.07.004


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

WANG YangYang1,2(),LIU WanDai1,2,HE Li1,2(),REN DeChao3,DUAN JianZhao1,2,HU Xin3,GUO TianCai1,2,WANG YongHua1,2,FENG Wei1,2()   

  1. 1College of Agronomy, Henan Agriculture University/Key Laboratory of Regulating and Controlling Crop Growth and Development, Ministry of Education, Zhengzhou 450046
    2National Engineering Research Center for Wheat, Zhengzhou 450046
    3Wheat Research Institute, Shangqiu Academy of Agriculture and Forestry Sciences, Shangqiu 476000, Henan
  • Received:2021-06-09 Accepted:2021-10-08 Online:2022-04-01 Published:2022-04-18
  • Contact: Li HE,Wei FENG;;


【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, Fv/Fm, 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 Fv/Fm 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

Fig. 1

Effects of freezing injury on young spike morphology of winter wheat YZ4110 under different irrigation conditions D, non-irrigated treatments; W, irrigated treatments. Low temperatures for the five treatments were -2℃ (T1), -4℃ (T2), -6℃ (T3), -8℃ (T4), and -10℃ (T5). The same as below"

Fig. 2

Effects of freezing injury on wheat yield under different irrigation conditions A: 2019, B: 2020. YZ4110, LK198, ZM366 and FDC21 were Yanzhan4110, Lankao198, Zhengmai366 and Fengdecunmai21. Different letters above error bars indicated significant difference among treatments at P<0.05. The same as below"

Table 1

Comparison of plant growth traits under different water treatments affected by low temperature stress"

不灌水 No irrigation (D) 灌水 Irrigation (W) 曼-惠特尼 U Mann-Whitney U
Variation range
Coefficient of variance
Variation range
Coefficient of variance
U value
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
Fv/Fm 0.59 0.10-0.84 46.72 0.65 0.28-0.84 32.72 160 0.008
Fv/Fo 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

Table 2

Correlation coefficient between different physiological and biochemical indexes"

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 3

Eigenvalue, contribution rate and membership function weight of comprehensive indexes"

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

Fig. 3

Change in the comprehensive evaluation of freezing injury degree (F-value) for four wheat varieties under different irrigation conditions"

Fig. 4

F-value clustering under different moisture, varieties and temperature treatments"

Fig. 5

Relationship between relative F value and yield loss rate"

Fig. 6

Effects of freezing injury on leaf water content, chlorophyll a content and Fv/Fm under different irrigation treatments"

Fig. 7

Effects of freezing injury on proline and soluble protein under different irrigation treatments"

Fig. 8

Effects of freezing injury on SOD activity and MDA content under different irrigation treatments"

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