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Acta Agronomica Sinica ›› 2024, Vol. 50 ›› Issue (4): 836-856.doi: 10.3724/SP.J.1006.2024.33034

• CROP GENETICS & BREEDING·GERMPLASM RESOURCES·MOLECULAR GENETICS • Previous Articles     Next Articles

Comprehensive evaluation of maize hybrids in the mega-environments of Huanghuaihai plain based on GYT biplot analysis

YUE Hai-Wang(), WEI Jian-Wei, LIU Peng-Cheng, CHEN Shu-Ping, BU Jun-Zhou()   

  1. Dryland Farming Institute, Hebei Academy of Agriculture and Forestry Sciences / Hebei Provincial Key Laboratory of Crops Drought Resistance Research, Hengshui 053000, Hebei, China
  • Received:2023-05-22 Accepted:2023-10-23 Online:2024-04-12 Published:2023-11-29
  • Contact: * E-mail: bujunzhou@126.com
  • Supported by:
    “Three-Three-Three Talent Project” Funded Project in Hebei Province(A202101056);Key Research and Development Projects of Hebei Province(20326305D);China Agriculture Research System of MOF and MARA (Maize, CARS-02);Hebei Provincial Maize Modern Seed Industry Science and Technology Innovation Team(21326319D);HAAFS Science and Technology Innovation Special Project(2023KJCXZX-HZS-1);HAAFS Science and Technology Innovation Special Project(2023KJCXZX-HZS-12)

Abstract:

The selection of superior maize hybrids under different climatic types and multiple traits has been a difficult problem for crop breeders. It is necessary to explore the combination of envirotyping techniques (ET) and multi-trait selection for the comprehensive evaluation of the participating hybrids in the summer maize regional trials in the Huanghuaihai plain, which can provide a theoretical basis for the rational layout of hybrids. In this study, based on the data of the Huanghuaihai summer maize group from 2016 to 2017 were used, 40 sites were divided into different mega-environments (ME) by envirotyping techniques based on 19 environmental covariates in the same year. The combination performance of agronomic traits such as growth period, plant height, ear height, lodging rate, empty ears rate, ear length, bare tip length, kernels row number, grains weight per ear, hundred-seed weight, stalk rot, and common smut in the different mega-environments was comprehensively evaluated by using the GYT biplot technique. The ANOVA of the Additive Main effects and Multiplicative Interaction (AMMI) model showed that genotype (G), environment (E), and GE effects for the evaluated agronomic traits reached highly significant level (P<0.01) in 2016 and that genotype, environment, and GE effects for the evaluated agronomic traits reached highly significant level in 2017, except for the GE effect of ear height, which was not significant. The 40 sites located in eight provinces were divided into four MEs based on the meteorological factor information of the current year, and meteorological factors deficit by precipitation (dbp), maximum temperature (Tmax), vapor pressure deficit (vpd), and relative humidity (rh) showed a large trend during the five phenological periods. Among the hybrids evaluated in 2016, Hengyu 321 and Jifeng 118 both showed outstanding productivity and good stability in the four mega-environments, which belonged to the productive and stable genotypes. Among the genotypes evaluated in 2017, DK 56 showed a more harmonious yield-trait combination in ME2 and ME4, while DK205 and Hengyu 6105 performed better in ME1 and ME3, respectively. The control hybrid Zhengdan 958 had a good stability but an average productivity in the two-year regional trials. In conclusion, based on the combination of environtyping techniques to divide mega-environments and GYT biplot, we evaluated the high-yielding, the adaptability and stability of the evaluated hybrids, which could realize the fine positioning of genotype promotion, and provide a theoretical basis for the comprehensive evaluation of multiple traits of genotypes in the summer maize area of the Huanghuaihai plain.

Key words: summer maize hybrid, mega-environment, genotype-environment interaction, climatic variables, genotype × yield × trait biplot (GYT biplot)

Table 1

Description of the hybrids used in this study"

品种名称
Hybrid name
品种缩写
Hybrid abbreviation
试验年份
Year
品种名称
Hybrid name
品种缩写
Hybrid abbreviation
试验年份
Year
衡玉321 Hengyu 321 HY321 2016-2017 宿单617 Sudan 617 SD617 2017
DK56 DK56 2016-2017 DK205 DK205 2017
潞研1502 Luyan 1502 LY1502 2016 冀玉902 Jiyu 902 JY902 2017
潞玉36 Luyu 36 LY36 2016 冀玉757 Jiyu 757 JY757 2017
冀丰118 Jifeng 118 JF118 2016-2017 衡玉6105 Hengyu 6105 HY6105 2017
邯玉9112 Hanyu 9112 HY9112 2016 中单111 Zhongdan 111 ZD111 2017
宿单510 Sudan 510 SD510 2016 潞研1611 Luyan 1611 LY1611 2017
冀玉518 Jiyu 518 JY518 2016 唐玉6461 Tangyu 6461 TY6461 2017
中农大626 Zhongnongda 626 ZND626 2016 邯玉5177 Hanyu 5177 HY5177 2017
中农大696 Zhongnongda 696 ZND696 2016 PT1212 PT1212 2017
唐玉6678 Tangyu 6678 TY6678 2016 郑单958 Zhengdan 958 ZD958 2016-2017
冀玉196 Jiyu 196 JY196 2016

Table 2

Characteristics of the tested locations used in this study"

省份
Province
试点
Testing site
经度
Longitude
纬度
Latitude
海拔
Altitude (m)
年份
Year
河北Hebei 武强Wuqiang 115°98′ 38°06′ 18 2016-2017
河北Hebei 藁城Gaocheng 114°85′ 38°02′ 59 2016-2017
河北Hebei 邯郸Handan 114°54′ 36°63′ 55 2016-2017
河北Hebei 深州Shenzhou 115°56′ 38°01′ 28 2016-2017
河北Hebei 赵县Zhaoxian 114°78′ 37°76′ 44 2016-2017
河北Hebei 高阳Gaoyang 115°79′ 38°68′ 12 2016
河北Hebei 泊头Botou 116°54′ 37°94′ 16 2016
河北Hebei 永年Yongnian 114°50′ 36°78′ 65 2017
河北Hebei 行唐Xingtang 114°55′ 38°43′ 102 2017
河南Henan 原阳Yuanyang 113°97′ 35°05′ 78 2016-2017
河南Henan 南阳Nanyang 112°52′ 32°99′ 131 2016-2017
河南Henan 荥阳Xingyang 113°38′ 34°79′ 144 2016-2017
河南Henan 鹤壁Hebi 114°28′ 35°74′ 88 2016-2017
河南Henan 新乡Xinxiang 113°92′ 35°30′ 70 2016-2017
河南Henan 焦作Jiaozuo 113°24′ 35°22′ 100 2016-2017
河南Henan 滑县Huaxian 114°45′ 35°63′ 72 2016-2017
河南Henan 洛阳Luoyang 112°46′ 34°61′ 140 2016-2017
河南Henan 周口Zhoukou 114°69′ 33°62′ 49 2016
河南Henan 巩义Gongyi 113°01′ 34°63′ 670 2016
河南Henan 驻马店Zhumadian 114°02′ 33°01′ 84 2016
河南Henan 商丘Shangqiu 115°65′ 34°41′ 50 2016
河南Henan 小冀Xiaoji 113°77′ 35°18′ 80 2016
河南Henan 临颍Linying 113°93′ 33°83′ 62 2017
河南Henan 西华Xihua 114°53′ 33°76′ 52 2017
河南Henan 西平Xiping 114°02′ 33°39′ 64 2017
河南Henan 辉县Huixian 113°80′ 35°46′ 95 2017
河南Henan 长葛Changge 113°82′ 34°19′ 88 2017
湖北Hubei 襄阳Xiangyang 112°12′ 32°01′ 70 2017
江苏Jiangsu 徐州Xuzhou 117°95′ 33°85′ 25 2016-2017
江苏Jiangsu 宿迁Suqian 118°82′ 34°04′ 10 2016
山东Shandong 聊城Liaocheng 115°99′ 36°46′ 37 2016-2017
山东Shandong 济宁Jining 116°54′ 35°37′ 38 2016-2017
山东Shandong 莱州Laizhou 119°95′ 37°17′ 56 2016-2017
山东Shandong 潍坊Weifang 119°19′ 36°60′ 82 2016-2017
山东Shandong 德州Dezhou 116°36′ 37°43′ 23 2016-2017
山东Shandong 嘉祥Jiaxiang 116°40′ 35°36′ 36 2016-2017
山东Shandong 平度Pingdu 120°02′ 37°10′ 18 2016-2017
山东Shandong 临朐Linqu 118°48′ 36°50′ 407 2016-2017
山东Shandong 临沂Linyi 117°61′ 35°57′ 174 2016-2017
山东Shandong 泰安Tai’an 117°09′ 36°20′ 167 2020-2021
山东Shandong 菏泽Heze 115°58′ 35°04′ 45 2016
山东Shandong 淄博Zibo 117°89′ 36°45′ 77 2017
山东Shandong 东营Dongying 118°54′ 37°50′ 7 2017
山东Shandong 茌平Chiping 116°30′ 36°50′ 35 2017
山东Shandong 章丘Zhangqiu 117°52′ 36°68′ 143 2016
安徽Anhui 界首Jieshou 115°35′ 32°96′ 35 2016-2017
安徽Anhui 埇桥Yongqiao 117°06′ 33°61′ 23 2016-2017
安徽Anhui 泗县Sixian 117°91′ 33°41′ 19 2016
山西Shanxi 运城Yuncheng 110°89′ 35°01′ 369 2016-2017
陕西Shaanxi 杨凌Yangling 108°08′ 34°27′ 465 2016-2017
陕西Shaanxi 泾阳Jingyang 108°75′ 34°46′ 388 2016-2017

Table 3

List of environmental covariables (EC) used in this study"

来源 Source 协变量名称 Environmental factor 单位 Unit
Nasa POWERa 水平表面上的日射Insolation incident on a horizontal surface MJ m-2 d-1
向下热红外(长波)辐射通量Downward thermal infrared (longwave) radiative flux MJ m-2 d-1
地外辐射Extraterrestrial radiation MJ m-2 d-1
离地2 m处的风速Wind speed at 2 m above the surface of the earth m s-1
离地2 m处的最低温度Minimum air temperature at 2 above the surface of the earth ℃ d -1
离地2 m处的平均温度Average air temperature at 2 above the surface of the earth ℃ d -1
离地2 m处的最高温度Maximum air temperature at 2 above the surface of the earth ℃ d -1
离地2 m处的露点温度Dew-point temperature at 2 m above the surface of the earth ℃ d -1
离地2 m处的相对湿度Relative air humidity at 2 above the surface of the earth %
降水量Rainfall precipitation mm d -1
Calculated b 温度区间Temperature range ℃ d-1
潜在蒸散量Potential evapotranspiration mm d-1
降水亏缺Deficit by precipitation mm d-1
饱和水汽压差Vapor pressure deficit kPa d-1
饱和蒸汽压曲线斜率Slope of saturation vapor pressure curve kPa ℃ d-1
温度对辐射利用效率的影响Effect of temperature on radiation-use efficiency from 0 to 1
生长度日Growing degree day ℃ d-1
实际日照时间Actual duration of sunshine h
白昼时间Daylight hours h

Table 4

AMMI model for various agronomic traits of evaluated maize genotypes in 2016"

变异来源
Source of
variations
籽粒产量
Grain yield (kg hm-2)
生育期
Growth period (d)
株高
Plant height (cm)
穗长
Ear length (cm)
平方和
Sum of
square
占比
Percentage (%)
平方和
Sum of
square
占比
Percentage (%)
平方和
Sum of
square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
基因型G 104,297,302.00** 11.22a 556.98** 3.75a 16.29** 50.31a 449.91** 26.23a
环境 E 514,296,691.09** 55.31a 11,295.23** 75.99a 9.06** 27.97a 638.61** 37.24a
互作G×E 311,236,339.89** 33.47a 3011.95** 20.26a 7.03** 21.72a 626.52** 36.53a
第一主成分PC1 88,415,976.66** 28.41b 2190.35** 72.72b 2.18** 31.08b 153.29** 24.47b
第二主成分PC2 53,807,079.58** 17.29b 224.13** 7.44b 1.11** 15.77b 97.99** 15.64b
变异来源
Source of
Variance
茎腐病
Stalk rot (%)
秃尖
Bare tip length (cm)
百粒重
Hundred-seed weight (g)
倒伏率
Lodging rate (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
基因型 G 657.54** 5.06a 97.06** 23.74a 2144.93** 23.88a 3,715.86** 9.57a
环境 E 2358.43** 18.16a 144.60** 35.38a 4179.72** 46.53a 11,521.50** 29.66a
互作G×E 9974.26** 76.78a 167.09* 40.88a 2658.61* 29.60a 23,606.78** 60.77a
第一主成分PC1 6505.80** 65.23b 38.32** 22.93b 552.15** 20.77b 16,678.64** 70.65b
第二种成分PC2 2221.05** 22.27b 33.33** 19.95b 467.16** 17.57b 2,829.18** 11.98b
变异来源
Source of
variations
穗位高
Ear height (cm)
空秆率
Empty ears rate (%)
穗粒重
Grains weight per ear (g)
穗行数
Kernel row number
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
基因型G 2.23** 21.75a 617.39** 6.58a 10,890.70** 3.02a 242.21** 34.44a
环境 E 4.55** 44.44a 4754.40** 50.64a 238,390.87** 66.19a 193.68** 27.54a
互作G×E 3.46** 33.80a 4017.41** 42.79a 110,899.41* 30.79a 267.48* 38.03a
第一主成分PC1 0.85** 24.63b 2106.61** 52.44b 26,190.91** 23.62b 64.58** 24.14b
第二主成分PC2 0.60** 17.29b 686.58** 17.09b 17,841.66** 16.09b 37.44** 14.00b
变异来源
Source of
variations
黑粉病
Common smut (%)
平方和
Sum of square
占比
Percentage (%)
基因型 G 33.28** 6.48a
环境 E 209.12** 40.73a
互作G×E 270.97** 52.78a
第一主成分PC1 124.87** 46.08b
第二主成分PC2 65.31** 24.10b

Table 5

AMMI model for various agronomic traits of evaluated maize genotypes in 2017"

变异来源
Source of
variations
籽粒产量
Grain yield (kg hm-2)
生育期
Growth period (d)
株高
Plant height (cm)
穗长
Ear length (cm)
平方和
Sum of
square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
基因型 G 126,699,091.28** 11.30a 310.33** 2.74a 24.60** 45.24a 254.59** 16.81a
环境 E 727,983,372.76** 64.94a 9669.41** 85.46a 22.87** 42.05a 639.58** 42.23a
互作G×E 266,401,328.41** 23.76a 1334.31** 11.79a 6.91** 12.71a 620.22** 40.96a
第一主成分PC1 64,270,653.80** 24.13b 469.35** 35.18b 2.05** 29.69b 152.08** 24.52b
第二主成分PC2 44,125,936.33** 16.56b 261.33** 19.59b 1.01** 14.56b 99.95** 16.12b
变异来源
Source of
Variance
茎腐病
Stalk rot (%)
秃尖
Bare tip length (cm)
百粒重
Hundred-seed weight (g)
倒伏率
Lodging rate (%)
平方和
Sum of
square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
基因型 G 4,851.65** 9.34a 102.16** 16.02a 1858.69** 16.04a 437.02** 6.38a
环境 E 22,552.31** 43.44a 149.08** 23.38a 6456.73** 55.71a 1,999.51** 29.21a
互作G×E 24,514.94** 47.22a 386.49** 60.60a 3274.52** 28.25a 4,408.72** 64.41a
第一主成分PC1 12,850.15** 52.42b 228.95** 59.24b 625.46** 19.10b 2,439.29** 55.33b
第二种成分PC2 5,868.29** 23.94b 37.83** 9.79b 452.38* 13.82b 506.29** 11.48b
变异来源
Source of
variations
穗位高
Ear height (cm)
空秆率
Empty ears rate (%)
穗粒重
Grains weight per ear (g)
穗行数
Kernel row number
平方和
Sum of
square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
平方和
Sum of square
占比
Percentage (%)
基因型 G 4.83** 31.77a 61.48** 0.75a 38,332.15** 8.61a 334.43** 1.33a
环境 E 6.95** 45.70a 6417.13** 78.75a 298,178.23** 66.94a 1,830.65** 7.27a
互作G×E 3.43ns 22.56a 1670.48** 20.50a 108,901.75** 24.45a 23,030.67** 91.41a
第一主成分PC1 0.64** 18.67b 819.82** 49.08b 22,716.35** 20.86b 13,771.85** 59.80b
第二主成分PC2 0.56** 16.21b 271.84** 16.27b 16,310.55** 14.98b 8,857.66** 38.46b
变异来源
Source of
variations
黑粉病
Common smut (%)
平方和
Sum of square
占比
Percentage (%)
基因型 G 55.68** 4.63a
环境 E 372.02** 30.94a
互作G×E 774.78** 64.43a
第一主成分PC1 294.99** 38.07b
第二主成分PC2 166.60** 21.50b

Fig. 1

Heat map showing the four delineated mega-environments considering the similarity based on information for 19 environmental covariables in 2016 (A) and 2017 (B) ME1, ME2, ME3, and ME4 indicate mega-environments 1, mega-environments 2, mega-environments 3, and mega-environments 4, respectively."

Fig. S1

Distribution of climate variables and grain yield in the different mega-environments in 2016 (A) and 2017 (B) etp: potential evapotranspiration; dbp: deficit by precipitation; GY: grain yield; rh: relative humidity; sihs: all sky insolation incident on a horizontal surface; svpc: slope of saturation vapor pressure curve; Tmax: maximum temperature; Tmin: minimum temperature; Tmean: mean temperature; Trange: daily temperature range;vpd: vapor pressure deficit; The horizontal coordinates of the graph showed the units of each meteorological factor and grain yield, and the vertical coordinates showed the four mega-environments."

Fig. 2

Correlation analysis between meteorological factors in 2016 (A) and 2017 (B) etp: potential evapotranspiration; dbp: deficit by precipitation; rh: relative humidity; sihs: all sky insolation incident on a horizontal surface; svpc: slope of saturation vapor pressure curve; Tmax: maximum temperature; Tmin: minimum temperature; Tmean: mean temperature; Trange: daily temperature range; vpd: vapor pressure deficit; prec: rainfall precipitation."

Fig. 3

Biplots for the principal component analysis between environmental variables in 2016 (A) and 2017 (B) me: mega-environments. Abbreviations of the meteorological factors are the same as those given in Fig. 2."

Fig. S2

Quantiles for deficit by precipitation observed in the studied environments (A) and mega-environments (B) across distinct crop stages in 2016"

Fig. S3

Quantiles for deficit by precipitation observed in the studied environments (a) and mega-environments (b) across distinct crop stages in 2017"

Fig. S4

Quantiles for the maximum temperature observed in the studied environments (A) and mega-environments (B) across distinct crop stages in 2016"

Fig. S5

Quantiles for maximum temperature observed in the studied environments (a) and mega-environments (b) across distinct crop stages in 2017"

Fig. 4

Genotype by trait (GT) biplot of tested maize hybrids in 2016 (A) and 2017 (B) HY321: Hanyu 321; JF118: Jifeng 118; LY1502: Luyan 1502; HY9112: Hanyu 9112; SD510: Sudan 510; JY518: Jiyu 518; ZND626: Zhongnongda 626; ZND696: Zhongnongda 696; TY6678: Tangyu 6678; JY196: Jiyu 196; SD617: Sudan 617; JY902: Jiyu 902; JY757: Jiyu 757; HY6105: Hengyu 6105; ZD111: Zhongdan 111; LY1611: Luyan 1611; TY6461: Tangyu 6461; HY5177: Hanyu 5177; ZD958: Zhengdan 958. GY: grain yield; CS: common smut; SR: stalk rot; EER: empty ears rate; PH: plant height; EH: ear height; GP: growth period; LR: lodging rate; EL: ear length; KRN: kernel row number; BTL: bare tip length; GWE: grains weight per ear; HSW: hundred-seed weight."

Fig. 5

Tester vector view of the genotypes by yield × trait (GYT) biplot in 2016 (A) and 2017 (B) HY321: Hanyu 321; JF118: Jifeng 118; LY1502: Luyan 1502; HY9112: Hanyu 9112; SD510: Sudan 510; JY518: Jiyu 518; ZND626: Zhongnongda 626; ZND696: Zhongnongda 696; TY6678: Tangyu 6678; JY196: Jiyu 196; SD617: Sudan 617; JY902: Jiyu 902; JY757: Jiyu 757; HY6105: Hengyu 6105; ZD111: Zhongdan 111; LY1611: Luyan 1611; TY6461: Tangyu 6461; HY5177: Hanyu 5177; ZD958: Zhengdan 958. Y×BTL (-1): grain yield×bare tip length; Y×CS (-1): grain yield×common smut; Y× EER (-1): grain yield×empty ears rate; Y×EH: grains yield×ear height; Y×EL: grains yield×ear length; Y×GP: grains yield×growth period; Y×GWE: grains yield×grains weight per ear; Y× HSW: grains yield×hundred-seed weight; Y×KRN: grains yield×kernel row number; Y×LR (-1): grains yield×lodging rate; Y×PH: grains yield×plant height; Y× SR (-1): grains yield×common smut."

Table 6

Pearson correlations between yield and trait in 2016"

产量×性状
Yield by trait
Y×BTL(-1) Y×CS(-1) Y×EER(-1) Y×EH Y×EL Y×GP Y×GWE Y×HSW Y×KRN Y×LR(-1) Y×PH Y×SR(-1)
产量×秃尖长度Y×BTL(-1) 1
产量×黑粉病Y×CS(-1) 0.348ns 1
产量×空秆率Y×EER(-1) 0.580* 0.278ns 1
产量×穗位高
Y×EH
0.749** 0.356ns 0.774** 1
产量×穗长
Y×EL
0.759** 0.349ns 0.811** 0.961** 1
产量×生育期
Y×GP
0.776** 0.343ns 0.837** 0.960** 0.987** 1
产量×穗粒重Y×GWE 0.804** 0.362ns 0.826** 0.961** 0.950** 0.950** 1
产量×百粒重Y×HSW 0.716** 0.318ns 0.817** 0.970** 0.975** 0.975** 0.957** 1
产量×穗行数Y×KRN 0.686** 0.338ns 0.759** 0.971** 0.978** 0.966** 0.933** 0.988** 1
产量×倒伏率Y×LR(-1) 0.306ns 0.139ns 0.375ns 0.394ns 0.298ns 0.375ns 0.354ns 0.325ns 0.269ns 1
产量×株高
Y×PH
0.760** 0.289ns 0.813ns 0.969** 0.986ns 0.987** 0.939ns 0.974** 0.976** 0.346ns 1
产量×茎腐病Y×SR(-1) 0.085ns -0.206ns 0.574* 0.525* 0.438ns 0.424ns 0.467ns 0.495ns 0.486ns 0.332ns 0.464ns 1

Table S1

Standardized genotype by yield×trait (GYT) data and superiority index (SI) for the evaluated maize genotypes in 2016"

品种名称
Genotype name
产量×秃尖
Y× BTL (-1)
产量×瘤黑粉病
Y*CS (-1)
产量×空秆率
Y× EER (-1)
产量×穗位高
Y×EH
产量×穗长
Y×EL
产量×生育期
Y×GP
产量×穗粒重
Y× GWE
产量×百粒重
Y× HSW
产量×穗行数
Y×KRN
产量×倒伏率
Y×LR (-1)
产量×株高
Y×PH
产量×茎腐病
Y× SR (-1)
理想指数
Superiority index
DK56 0.38 1.19 0.79 0.96 1.3 1.11 0.5 1.06 1.33 -0.99 1.23 0.25 0.8
衡玉321 HY321 0.94 1.27 1.08 1.6 1.12 1.23 1.33 1.39 1.28 1.67 1.29 0.63 1.24
邯玉9112 HY9112 -0.8 -0.09 0.72 0.47 0.52 0.57 0.42 0.5 0.53 1.54 0.56 0.84 0.48
冀丰118 JF118 2.02 1.13 1.31 0.94 1.29 1.26 1.54 0.91 0.88 -0.02 1.16 -0.46 1.01
冀玉196 JY196 0.28 -0.47 0.75 0.55 0.45 0.24 0.9 0.81 0.62 -0.65 0.36 1.32 0.42
冀玉518 JY518 0.64 -1.98 0.23 -0.13 -0.21 -0.04 -0.3 -0.31 -0.44 0.75 0.14 0.48 -0.1
潞研1502 LY1502 -0.63 -0.11 -1.8 -1.17 -1.04 -1.18 -1.11 -1.15 -1.07 -1.51 -1.11 -2.46 -1.19
潞玉36 LY36 -2.09 -0.11 -0.96 -1.87 -2.01 -2.14 -1.88 -2.12 -2.04 -0.84 -2.05 0.28 -1.53
宿单510 SD510 -0.4 -0.98 0.8 0.13 0.39 0.49 0.28 0.46 0.32 -0.64 0.23 0.5 0.15
唐玉6678 TY6678 -0.11 1.04 -0.28 -1.17 -1.03 -0.79 -0.93 -0.91 -1.18 0.78 -1.12 -1.45 -0.62
郑单958 ZD958 0.65 0.43 -0.8 0.56 0.19 0.13 0.34 0.05 0.2 0.58 0.01 0.36 0.21
中农大626 ZND626 -0.34 -0.23 -0.62 -0.16 -0.51 -0.27 -0.29 -0.18 -0.06 -0.72 -0.21 -0.17 -0.3
中农大696 ZND696 -0.56 -1.12 -1.22 -0.72 -0.47 -0.62 -0.8 -0.49 -0.37 0.05 -0.49 -0.12 -0.57
平均 Mean 0 0 0 0 0 0 0 0 0 0 0 0 0
标准差
Standard Deviation
1 1 1 1 1 1 1 1 1 1 1 1 1

Table 7

Pearson correlations between yield and trait in 2017"

产量×性状
Yield by trait
Y×BTL(-1) Y×CS(-1) Y×EER(-1) Y×EH Y×EL Y×GP Y×GWE Y×HSW Y×KRN Y×LR(-1) Y×PH Y×SR(-1)
产量×秃尖长度Y×BTL(-1) 1
产量×黑粉病Y×CS(-1) -0.120ns 1
产量×空秆率Y×EER(-1) 0.209ns -0.435ns 1
产量×穗位高
Y×EH
0.170ns -0.337ns 0.788** 1
产量×穗长
Y×EL
0.210ns -0.222ns 0.819** 0.865** 1
产量×生育期
Y×GP
0.247ns -0.247ns 0.828** 0.912** 0.973** 1
产量×穗粒重Y×GWE 0.236ns -0.085ns 0.744** 0.878** 0.914** 0.953** 1
产量×百粒重Y×HSW 0.197ns -0.087ns 0.683** 0.861** 0.881** 0.888** 0.951** 1
产量×穗行数Y×KRN -0.473ns -0.013ns -0.106ns 0.154ns 0.079ns 0.156ns 0.139ns 0.110ns 1
产量×倒伏率Y×LR(-1) 0.141ns -0.260ns 0.761** 0.739** 0.875ns 0.798** 0.710** 0.647* -0.129ns 1
产量×株高
Y×PH
0.240ns -0.333ns 0.781** 0.960** 0.884** 0.951** 0.925** 0.851** 0.169ns 0.739** 1
产量×茎腐病Y×SR(-1) 0.308ns -0.106ns 0.269ns 0.284ns 0.058ns 0.153ns 0.050ns 0.055ns -0.087ns -0.087ns 0.159ns 1

Table S2

Standardized genotype by yield × trait (GYT) data and superiority index for the evaluated maize genotypes in 2017"

品种名称
Genotype name
产量×秃尖
Y× BTL (-1)
产量×瘤黑粉病
Y×CS (-1)
产量×空秆
Y× EER (-1)
产量×穗位高
Y×EH
产量×穗长
Y×EL
产量×生育期
Y×GP
产量×穗粒重
Y× GWE
产量×百粒重
Y× HSW
产量×穗行数
Y×KRN
产量×倒伏率
Y×LR (-1)
产量×株高
Y×PH
产量×茎腐病
Y× SR (-1)
理想指数
Superiority index
DK205 0.65 1.24 0.5 -0.38 1.02 0.82 0.65 0.5 -0.12 0.46 0.07 -0.25 0.45
DK56 1.72 -0.65 0.5 1.04 0.93 1.12 0.61 0.25 0.06 0.78 1.02 1.78 0.78
衡玉321 HY321 -0.08 -1.64 0.01 -0.24 -0.84 -0.5 -0.78 -0.86 -0.42 -1.09 0.02 0.63 -0.48
邯玉5177 HY5177 0.2 -0.51 -0.81 -1.82 -1.73 -2.05 -2.45 -2.26 -0.99 -0.83 -2.18 0.58 -1.31
衡玉6105 HY6105 1.06 0.2 -1.03 -0.45 -0.36 -0.5 -0.23 -0.08 -0.29 -0.1 -0.19 -1.99 -0.32
冀丰118 JF118 -0.81 -0.62 1.06 1.04 1.22 0.87 0.91 1.35 -0.17 0.96 0.69 -0.15 0.56
冀玉757 JY757 -1.98 0.17 -1.23 -0.48 -0.73 -0.53 -0.58 -0.57 3.3 -1.28 -0.49 -0.42 -0.4
冀玉902 JY902 0.9 0.63 0.6 1.46 0.6 0.86 1.19 1.24 0.14 0.17 1.28 1.4 0.88
潞研1611 LY1611 -0.2 -0.82 0.34 0.98 0.93 0.74 0.6 1.15 -0.12 1.1 0.69 -0.09 0.46
PT1212 -0.83 0.03 1.73 1.02 0.8 0.83 0.64 0.41 0.06 1.21 0.8 0.29 0.59
宿单617 SD617 -0.21 0.73 -0.9 -0.96 -0.71 -0.81 -0.58 -0.33 -0.47 -1.08 -1.11 -0.21 -0.57
唐玉6461 TY6461 0.51 -1.5 1.29 0.54 0.86 0.99 1.11 0.59 0.1 1.06 1.02 -1.14 0.49
中单111 ZD111 0.43 1.28 -0.82 -0.98 -1.3 -1.03 -0.43 -0.3 -0.59 -1.63 -1.01 0.63 -0.53
郑单958 ZD958 -1.36 1.46 -1.23 -0.77 -0.69 -0.82 -0.67 -1.06 -0.49 0.27 -0.63 -1.05 -0.6
平均 Mean 0 0 0 0 0 0 0 0 0 0 0 0 0
标准差SD 1 1 1 1 1 1 1 1 1 1 1 1 1

Fig. 6

Mean performance and stability of tested genotypes based on GGE biplot in 2016 ME1: mega-environments 1; ME2: mega-environments 2; ME3: mega-environments 3; ME4: mega-environments 4."

Fig. 7

Mean performance and stability of tested genotypes based on GGE biplot in 2017 ME1: mega-environments 1; ME2: mega-environments 2; ME3: mega-environments 3; ME4: mega-environments 4."

Fig. S6

Quantiles for the vapor pressure deficit observed in the studied environments (A) and mega-environments (B) across distinct crop stages in 2016"

Fig. S7

Quantiles for the relative humidity observed in the studied environments (a) and mega-environments (b) across distinct crop stages in 2016"

Fig. S8

Quantiles for the vapor pressure deficit observed in the studied environments (A) and mega-environments (B) across distinct crop stages in 2017"

Fig. S9

Quantiles for the relative humidity observed in the studied environments (a) and mega-environments (b) across distinct crop stages in 2017"

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