Crops ›› 2023, Vol. 39 ›› Issue (5): 212-218.doi: 10.16035/j.issn.1001-7283.2023.05.030

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Simulation Model of Relative Meteorological 1000-Grain Weight of Wheat of Luohe Based on Principal Component Regression

Huang Jie(), Ge Changbin(), Wang Jun, Cao Yanyan, Qiao Jiliang, Liao Pingʼan(), Song Danyang, Lu Wenying   

  1. Luohe Academy of Agricultural Sciences, Luohe 462300, Henan, China
  • Received:2022-04-24 Revised:2022-09-19 Online:2023-10-15 Published:2023-10-16

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Abstract:

The correlation between meteorological factors and 1000-grain weight of wheat during different filling stages of wheat in Luohe was studied based on the meteorological data and 1000-grain weight data of three stations from 2010 to 2021, then an empirical model with key meteorological factors used to simulate relative meteorological 1000-grain weight (Yr) of wheat was established by using of the principal component regression model and stepwise regression model. The results showed that thermal factors such as average temperature and maximum temperature in rapid-increasing stage and maximum temperature in slight-increasing stage had the greatest impaction on the Yr of wheat in Luohe. The first three components of principal component analysis were “the maximum temperature factor in rapid-increasing stage and slight-increasing stage”, “the average temperature factor in rapid-increasing stage” and “the sunshine factor in rapid-increasing stage”, which could explain 89.44% of the Yr changes of wheat in Luohe. Compared with the stepwise regression model, the predicted and actual values of Yr and 1000-grain weight of wheat by using of the principal component regression model in three stations were better, especially in Wuligang. Therefore, the principal component regression model was more reasonable and accurate in estimating the Yr of Luohe. At present, it is an effective way to increase the 1000-grain weight of wheat in Luohe by breeding and screening high-temperature resistant varieties in late stage.

Key words: Luohe, Wheat, Relative meteorological 1000-grain weight, The principal component regression model

Table 1

Duration of different filling stages of wheat in Luohe during 2010-2021 d"

年份Year T1 T2 T3
2010-2011 13.84 15.49 6.66
2011-2012 16.21 16.46 7.33
2012-2013 16.32 13.17 8.52
2013-2014 14.23 13.86 12.91
2014-2015 12.68 14.63 8.68
2015-2016 9.66 15.49 11.84
2016-2017 18.25 15.49 4.25
2017-2018 12.01 12.54 13.44
2018-2019 13.46 14.63 7.91
2019-2020 12.89 16.46 11.64
2020-2021 15.96 15.49 9.55
平均Average 14.14 14.89 9.34

Table 2

Correlation factors of 1000-grain weight in Luohe during 2010-2021"

年份Year F (g) Yt (g) Yw (g) Yr (%)
2010-2011 42.05 41.36 0.69 1.67
2011-2012 41.60 41.46 0.14 0.35
2012-2013 43.20 42.15 1.05 2.48
2013-2014 44.19 42.97 1.22 2.84
2014-2015 44.78 43.69 1.09 2.49
2015-2016 44.20 43.90 0.30 0.69
2016-2017 47.74 45.43 2.31 5.08
2017-2018 46.73 45.95 0.78 1.69
2018-2019 46.71 46.26 0.45 0.98
2019-2020 46.88 46.51 0.37 0.81
2020-2021 47.98 47.10 0.88 1.88

Table 3

Correlation coefficients between the relative meteorological 1000-grain weight of wheat and meteorological factors of different filling stages"

灌浆阶段
Filling stage		 总降雨量
Precipitation		 相对湿度
Relative
humidity		 日照时数
Sunshine
hour		 平均气温
Average
temperature		 最高气温
The highest
temperature		 最低气温
The lowest
temperature		 温差
Diurnal		 风速
Wind
speed		 大型蒸发量
Large
evaporation
渐增期
Increasing stage		 0.5226 0.3797 0.2339 0.6113* 0.3601 0.1348 0.0999 0.0494 0.0599
快增期
Rapid-increasing stage		 -0.7628** 0.3007 0.7376** 0.7820** -0.7709** 0.1283 0.7600** 0.0493 0.6515*
缓增期
Slight-increasing stage		 -0.8204** -0.8250** 0.7658** 0.3804 -0.8368** 0.3940 0.4948 -0.6281* -0.6032*

Table 4

Diagnosis results of collinearity among key meteorological factors"

项目Item X1 X2 X3 X4 X5 X6 X7 X8 X9
膨胀系数Coefficient of expansion 30.3009 10.7406 23.1545 37.5181 67.7000 19.0305 19.5088 59.5709 134.0738

Table 5

Principal component analysis between the relative meteorological 1000-grain weight of wheat and key meteorological factors in Luohe city"

主成分
Principal
component		 特征值
Eigenvalue		 累计贡献率
Cumulative
contribution (%)		 X1 X2 X3 X4 X5 X6 X7 X8 X9
PC1 6.51 72.37 0.2971 0.3167 0.3537 -0.2933 -0.3532 -0.2979 0.3626 0.3647 -0.3498
PC2 0.81 81.33 -0.6458 0.3133 0.3351 -0.1693 0.3252 0.3519 0.2308 -0.0631 -0.2387
PC3 0.73 89.44 0.1399 0.3425 -0.0132 0.7006 -0.2529 0.4877 0.1768 0.1909 0.0506

Fig.1

The predicted and actual values of Yr of wheat of different models in different area during 2010-2021 Correlation coefficients of the principal component regression model of Wuligang, Wuyang county, Linying county are 0.7897, 0.6767 and 0.7305, respectively; Correlation coefficients of the stepwise regression model of Wuligang, Wuyang county, Linying county are 0.6658, 0.6123 and 0.6760, respectively"

Fig.2

The root mean square error of Yr of wheat under different models in different regions"

Fig.3

The predicted and actual values of the 1000-grain weight of wheat of different models in different areas during 2010-2021 Correlation coefficients of the principal component regression model of Wuligang, Wuyang county, Linying county are 0.9878, 0.9696 and 0.9818, respectively; Correlation coefficients of the stepwise regression model of Wuligang, Wuyang county, Linying county are 0.9791, 0.9584 and 0.9750, respectively"

Fig.4

The root mean square error of the 1000-grain weight of wheat under different models in different regions"

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