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Article

Influence of Genotype × Environment Interaction on Yield Stability of Maize Hybrids with AMMI Model and GGE Biplot

by
Chenyu Ma
1,†,
Chaorui Liu
1,2,† and
Zhilan Ye
1,*
1
College of Agriculture & Biological Sciences, Dali University, Dali 671003, China
2
Yunnan ZuFeng Seed Industry Co., Ltd., Dali 671003, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2024, 14(5), 1000; https://doi.org/10.3390/agronomy14051000
Submission received: 25 March 2024 / Revised: 20 April 2024 / Accepted: 3 May 2024 / Published: 9 May 2024
(This article belongs to the Section Crop Breeding and Genetics)
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Abstract

:
Maize yields perform differently in different environments, so the selection of suitable genotypes in diverse environments is essential for variety selection to enable better site-specific planting. Hence, the objective of the study was to estimate the productivity of 11 maize hybrids (G) in 10 different environments (E) and select high-yield and stable varieties for adaptive cultivation in 2022 and 2023. The combined analysis of variance showed that G (4%), E (50%), and their interaction (31%) had a significant effect (p < 0.01) on maize yield, with E factors contributing the most. In addition, the average yield ranged from 9398 kg/ha to 10,574 kg/ha, and ZF-2208 and DY-519 performed relatively well in both years. The AMMI model showed that the varieties DY-213, DY-605, and DY-519 had high and stable production in 2022, whereas it was ZF-2209 and LX-24 in 2023. The “W-W-W” biplot showed that DY-519 and JG-18 were the optimal varieties in 2022, and ZF-2208 and ZF-2210 were optimal in 2023. The “mean vs. stability” biplot indicated that JG-18, DY-605, and DY-213 (in 2022) and ZF-2208, LX-24, and ZF-2209 (in 2023) were the optimal varieties. Additionally, both the discrimination and representative biplot and the ranking biplot reflected that BinChuan and ShiDian (in 2022) and GengMa and YongSheng (in 2023) were the ideal test environments. In conclusion, DY-519, DY-605, ZF-2208, and LX-24 hybrids could be used for variety promotion. Moreover, BinChuan, ShiDian, GengMa, and YongSheng were the ideal test environments for selecting varieties. Therefore, the AMMI model and GGE biplot can be used to complement each other for a comprehensive evaluation of maize yield. In this way, excellent maize hybrids with high yield and stability can be selected, which could promote the selection and popularization of varieties and shorten the breeding process.

1. Introduction

Maize (Zea mays L.) is the most crucial cereal globally, providing 42% of the world’s human food calorie consumption. Stable production ensures global food security [1]. By 2050, global food production will increase by 60% or even double to meet people’s food needs. Maize is a major food crop in China, using 21% of the world’s maize area and producing 260 million tons, which is 23% of the world’s supply. However, with temperatures increasing, most areas are experiencing significant yield losses [2]. Food security remains critical as the population grows, and fertilizer use in a changing climate will reduce yields by close to 20% by 2099 [3]. In order to increase production, Chang et al. and Huang et al. have predicted corn yield through a data-driven crop model and Bayesian model [4,5].
GEIs can reveal the contribution of new varieties to performance and influence breeding programs and resource allocation [6]. Crops are affected by genotype–environment interactions (GEIs), which are caused by variations in the environmental conditions [7]. In multi-site trials, often due to GEIs, variety selection is inefficient and the relative ranking of varieties varies according to the environment. So, it is necessary to analyze the interaction caused by GE effects on yield [8].
GEIs are often thought of as the differential ordering of genotypes in different environments. They can be used to predict the potential and effect of genotypes in different environments [9]. Genotypes exhibit different behaviors in different environments, and this interaction is critical for genotypes adapting to diverse environments [8]. Meanwhile, GEIs are a key issue affecting thousand kernel weight, yield, and breeding for quality traits in maize [10,11]. Currently, the AMMI model and the GGE biplot are statistical tools which are commonly used in the analysis of multi-environmental trials [8,12]. Compared to the AMMI model, the GGE biplot has a stronger regional genotype evaluation, but it is slightly lacking in breeding programmers, and it is an improved version of AMMI [13,14]. Additionally, it can combine the gene main effect with the GE interaction at the same time, so as to cluster the environments and divide the varietal eco-regions [8,15]. Moreover, this methodology combines the mean values of yield and stability and transforms them into a formula for graphical evaluation [16]. The GGE biplot can also be used for breeding-specific combining ability and general combining ability evaluation [17], disease evaluations [18], crop qualitative characteristics [19,20], and regional trial evaluations [21]. And the AMMI model GGE biplot has now been applied to cotton [21], oat [22], pigeonpea [23], cowpea [24], bambara groundnut [25], grain sorghum [26], as well as maize [27,28]. In previous studies, the yield of maize in one year was mostly analyzed by the AMMI model and GGE biplot or by a single method, and few were analyzed by combining the two methods for many years [11,29,30,31,32]. Combining the AMMI model with the GGE biplot can help us to better understand the effects of gene–environment interaction, optimal genotypes, and suitable environments on improving genotype yield [7,33].
Therefore, AMMI and the GGE biplot were used to analyze the yield of 11 maize hybrids in 10 environments across two seasons to comprehensively evaluate the productivity and stability of the participating varieties, as well as the discriminatory power and representativeness of the environment, which can guide the planting layout and the safe production of maize, and then increase social and economic benefits.

2. Materials and Methods

2.1. Environments and Hybrids

The 11 maize hybrids have been grown at 10 locations in 2 seasons (2022 and 2023). All of these 10 environments are located at mid-to-low altitudes in Yunnan, China, and the detailed information of these sites is given in Table 1.
The experimental materials were ZF-2208 (G1), ZF-2209 (G2), ZF-2210 (G3), DY-213 (G4), DY-605 (G5), JG-18 (G6), JG-812 (G7), DY-519 (G8), LX-24 (G9), and SS-2107 (G10), with ZD-808 (G11) as a control. G1, G2, and G3 have high ear height, resistance, and quality; G4 is neat and has a good yield; G5 is neat and has good resistance; G6 and G7 have high plant height, ear height, and resistance; and G8, G9, and G10 have high plant height, ear height, resistance, and yield. The detailed information of 11 hybrids is shown in Table 2.

2.2. Experimental Design

The experiment was conducted by a randomized block design with three replications. Five rows were planted in each plot, the row spacing was 0.8 m, and the area was 20 m2; there were 60,000 plants per ha. Field management and fertilization were implemented according to the Maize Regional Trial Scheme. Maize was sown directly, and chlorfenapyr and imidacloprid were used to control spodoptera frugiperda and aphids, respectively. The base fertilizers were (NH4)2SO4 and K2SO4 (450 kg/ha), and the topdressing fertilizer was CH4N2O, of which the first topdressing fertilizer was 300 kg/ha and the second was 225 kg/ha. Farming was entirely and extensively dependent on natural rainfall. After the maize matured, three rows of each plot were selected for grain yield measurement, and the yield was calculated by the following formula:
Yield ( kg / ha ) = plot   yield plot   area × 666.7 × 15
where 1 mu = 666.7 square meters and 1 ha = 15 mu.

2.3. Data Analysis

Excel was used for basic data organization and calculation. The aov () function in the R package “agricolae” version 4.3.3 was used to perform the analysis of variance (ANOVA). For multiple comparisons, the LSD test was used, an AMMI analysis was conducted using the R package “agricolae”, the “GGEBiplotGUI” package (R (4.3.3)) was used for the GGE biplot [34], the “pheatmap” package (R (4.3.3)) was used for clustering heatmaps, and the “psych” (R (4.3.3)) package was used for Pearson correlation analysis. The GGE biplot and AMMI methods based on giant environmental assessment were used to draw the model diagram to visually show the existence of G × E interactions.

3. Results

3.1. Analysis of Variance (ANOVA) for Yield

The multivariate analysis of variance of 11 varieties across 10 environments showed that all factors significantly influenced the maize yield in two growing seasons (2022 and 2023). Moreover, environmental factors contributed the most, up to 49.96% (Table 3). Therefore, the further analysis of genotypes and environments is necessary.

3.2. Maize Yield Performance in Different Locations

The mean yield of 11 maize hybrids under 10 locations was assessed (Figure 1 and Figure 2, Table 4 and Table 5). The results showed that, in terms of the environment, whether it was in 2022, 2023, or over the two years, these locations (E4, E1, E8, E9, and E3) had higher maize yields (Table 4 and Table 5). For the varieties, the yield ranged from 9398 kg/ha to 11,194 kg/ha, where G8, G6, G4, G5, and G9 were the five most productive varieties in 2022, while G1 performed the best, followed by G3, G9, G2, and G8 in 2023 (Table 5). Moreover, the two years of data showed that G8, G1, G6, G9, and G5 were the top five performers, which was in great agreement with the yield results of the two years alone (Table 4). Moreover, the 10 tested hybrids had higher average yields than the control (Figure 1, Table 4). In order to reflect the yield fluctuation more clearly, a clustering heat map of the yield was made (Figure 2). All these results showed that G8, G1, G9, G6, and G5 were more productive, and E4, E8, E3, E1, and E9 were suitable for breeding high-yielding varieties.
The correlation analysis of yield and other traits showed (Figure 3) that yield and hundred seed weight had a highly significant positive correlation; hundred seed weight and plant height had a significant positive correlation in 2022; yield and ear tip were significantly negatively correlated; and ear tip and kernel ratio were significantly negatively correlated in 2023.

3.3. AMMI Model for Analyzing Variety Yield and Stability

The AMMI biplot is shown in Figure 4, where the average yield is the x-axis and the GE decomposition of IPCA1 is the y-axis. And when the coordinate is farther to the right and closer to the x-axis, the variety yield is higher and more stable; when the environment is farther from the x-axis, the more discriminative it is. In this study, the stable varieties were G7, G10, G8, G5, and G4 in 2022 (Figure 4A), while they were G4, G2, G5, G6, and G9 in 2023 (Figure 4B). Additionally, sites E8, E9, E7, and E6 are well-discriminated environments in both 2022 and 2023 (Figure 4). Therefore, combined with the yield results, we found that G8, G4, and G5 (in 2022) and G2 and G9 (in 2023) were the ideal varieties (with both high yield and stability).
The AMMI1 biplot represented only 51.3% and 62.9% of the varietal–environmental variance information, which was not sufficiently comprehensive to infer varietal stability and environmental discrimination. But, the AMMI2 biplot showed the scores of the varieties of IPCA1 and IPCA2, which can explain most of the intercropping variance (Figure 5). In the AMMI2 biplot, the closer to the origin of the coordinates, the more stable the species is, and the worse the ambient discriminatory power is, so G4 had the best performance in terms of stability, and E1 and E8 had the highest discriminatory power.

3.4. GGE Biplot Analysis

The results of the GGE analysis were displayed by different biplot patterns, where the horizontal coordinate is the PC1 score and the vertical coordinate is the PC2 score, explaining 80.58% of the variance in the environment and 86.04% of the variance in the GEI, respectively.

3.4.1. “Which Won Where” Biplot

In the “which won where” GGE biplot (W-W-W), varieties in the top corners of the polygon were the most productive. Specifically, hybrids were distributed into five sections, with G8 (DY-519) and G6 (JG-18) being the best hybrids in multiple environments in 2022 (Figure 6A). Moreover, G1(ZF-2208) and G3 (ZF-2210) were the most productive in 2023 (Figure 6B). Furthermore, hybrids located at vertices exhibited greater responsiveness compared to those located within polygons. Additionally, varieties outside the polygon performed poorly in some or all of the environments.

3.4.2. “Mean vs. Stability” Biplot

The analysis of “mean vs. stability” was conducted by the GGE biplot with an average environmental correction (AEC). The shorter the vertical segment between the variety and the AEC axis, the more stable the variety is. In the study, G6, G8, G5, G4, and G9 were more productive, while G11, G5, G6, G2, and G4 and were more stable in 2022 (Figure 7A); additionally, G1, G3, G9, G2, and G8 had high yields, while G9, G11, G1, G2, and G4 were more stable in 2023 (Figure 7B). Hence, these results showed that G5 (DY-519), G6 (JG-18), and G4 (DY-213) were the best varieties with excellent yield performance and wonderful stability in 2022, while they were G1 (ZF-2208), G9 (LX-24), and G2 (ZF-2209) in 2023.

3.4.3. “Discriminativeness vs. Representativeness” Biplot

The discriminative power and representativeness of the environment were assessed by the GGE biplot in Figure 8. A site with a long vector length and a small angle to the ACE axis can be considered as an ideal environment for variety selection. In this study, E10, E1, E8, and E5 were highly discriminative, while E6, E2, E3, and E4 were more representative in 2022 (Figure 8A); E8, E9, E3, and E2 were highly discriminative, while E3, E1, E9, and E4 were highly representative in 2023 (Figure 8B). Therefore, these results indicated that E3 (Gengma) and E9 (Yongsheng) were the best environments with good discrimination and representation in 2023.

3.4.4. “Ranking Genotypes” and “Ranking Environments” Biplot

In the ranking GGE biplot, if the variety or site was located closer to the first concentric circle, they were considered for selection. From Figure 9, G6 and G1 were in the first concentric circle of their respective biplot; followed by G8, G5, G4, and G7 in 2022 (Figure 9A); and G3, G9, G2, and G8 in 2023 (Figure 9B). Thus, the results declared that G6, G1, G8, G3, G9, and G5 were the pretty satisfying genotypes.
Additionally, E1, E8, E5, and E10 in 2022 and E3, E9, E8, and E2 in 2023 could be considered as the ideal environments (Figure 10). Therefore, the above sites can be considered ideal environments for their respective years, which were more conducive to screening excellent maize hybrids. Interestingly, this result is consistent with the result found in the discriminativeness and representativeness section (Figure 8).

4. Discussion

4.1. Yield Performance Evaluation

Genotype and environment interaction is a complex question involved in preparing high-yielding and stable genotypes for breeding [16]. In this study, the effects of environment, genotype, and GEIs on maize yield were significant (p < 0.01), and the environmental factor contributed more variation (49.96%) than genotypes and GEIs, reflecting that there was a greater variability in the environment (Table 3). Similar results were found in Adham et al., Alizadeh et al., and Ansarifard et al. that the environment accounted for most of the variance [16,35,36]. Additionally, the GEI effect is largely a response to environmental factors, not genotypic factors [37]; thus, the significant GEI factors demonstrated that the use of AMMI and the GGE biplot was appropriate in yield evaluation [38].

4.2. Yield and Stability of 11 Maize Hybrid Varieties

The AMMI1 biplot was mainly used to identify high-yielding varieties with stable performance potential [7,39]. Firstly, in the AMMI model, varieties G4, G5, and G8 had high yield and stability in 2022, whereas G2 and G9 had the same in 2023 (Figure 4). Secondly, the “mean vs. stability” biplot indicated that G6, G5, and G4 were the ideal varieties in 2022, while G1, G9, and G2 were the optimal varieties in 2023 (Figure 7). And the above results were consistent, which is similar to the findings of Esan et al. and Memon et al. [7,40]. In addition, G1 shows a better yield and stability in the GGE biplot but a better yield and worse stability in the AMMI model. The slight difference between the two methods may be related to the fact that GGE considered GEI in addition to G [39,41].

4.3. Evaluation of Hybrids in Mega-Environments

The GGE biplot model elucidated a substantial proportion of the variance, enabling meaningful inferences to be made. The “W-W-W” biplot can be visualized using a polygon view. To identify the optimal performance in a particular environment, Mehareb et al., Kona et al., and Silva et al. employed a similar method to divide mega-environments [42,43,44]. In this study, the GGE biplot was divided into five or six parts with two large environments (Figure 6), and the apex hybrid was the highest-yielding hybrid in its region. Genotypes G3 and G11 (2022) and G5, G10, G8, and G11 (2023) did not fall into sectors, meaning that these varieties performed poorly in some/all environments [15,45,46]. To improve the accuracy of the test, it is recommended to increase the test period or test environment to better evaluate the varieties [27]. And in previous studies, the GGE biplot has been used to screen ideal varieties, such as in potato [19], sweet maize [47], baby corn maize [48], and sugar beet [45]. Thus, in the study, G8 and G6 (in 2022) and G1 and G3 (in 2023) performed well in general environments.

4.4. Ideal Genotypes and Ideal Environments

Genotype assessment is only relevant in a particular environment. Ideal genotypes should have high yield and stability in that environment, and the ideal environment should be both strongly discriminating and representative [49,50]. Accordingly, in this study, G6, G8, G5, G4, and G7 were closer to the ideal genotypes in 2022, while they were G1, G3, G9, G2, and G8 in 2023; and E1, E8, E5, and E10 in 2022 and E3, E9, E8, and E2 in 2023 could be considered as the ideal environments (Figure 10), which was similar to the studies by Yousaf et al., Kona et al., and Kendal et al. [15,43,51]. Liu et al. found that GengMa was an ideal environment for selecting varieties, which agreed with this study [52]. Additionally, the results of the ranking biplot were in good accordance with the results of the yield performance (Table 4 and Table 5), AMMI model (Figure 4), W-W-W biplot (Figure 6), “Mean vs. Stability” biplot (Figure 7), and “Discriminativeness vs. Representativeness” biplot (Figure 8). There were differences in yield performance between the two years due to the environmental conditions (rainfall and temperature were the main influences), and Ureta also found that rising temperatures and changes in rainfall can affect maize yields [53]. Therefore, G6, G8, G1, and G9 are the varieties with good comprehensive performance (high yield and stability), and E1, E3, E8, and E9 are perfect environments for variety breeding.

5. Conclusions

Genotypes, environments, and GEI had significant effects on maize yield, and the combined ANOVA results showed that environment contributed the most to yield. The AMMI model showed high yield and stability for G8, G4, and G5 in 2022 and G2 and G9 in 2023. The W-W-W biplot showed the existence of optimal varieties (G8 and G6 in 2022; G1 and G3 in 2023). The mean vs. stability biplot indicated that G6, G5, and G4 (in 2022) and G1, G9, and G2 (in 2023) were the optimal varieties with relatively high yield and stability. Therefore, the G8 (DY-519), G6 (DY-605), G1 (ZF-2208), and G9 (LX-24) hybrids could be used for variety promotion. Moreover, E1 (BinChuan), E8 (ShiDian), E3 (GengMa), and E9 (YongSheng) were the perfect environments to choose varieties based on the discrimination and representative biplot and the ranking biplot. The combination of the two analyses provided a comprehensive and reliable approach for evaluating the yield and stability of maize hybrids, and the selected hybrids and environments were conducive to guiding the production in southwest China and bringing economic and social benefits.

Author Contributions

Conceptualization, Z.Y.; formal analysis, C.L. and C.M.; funding acquisition, Z.Y.; investigation, Z.Y., C.L. and C.M.; project administration, C.M. and Z.Y.; resources, Z.Y.; software, C.L. and C.M.; supervision, C.L. and Z.Y.; visualization, C.M.; writing—original draft, C.M. and C.L.; writing—review and editing, C.M., C.L. and Z.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Yunnan Fundamental Research Projects (grant No. 202201AU070003 and grant No. 202301AT070025), and Doctoral Research Start-up Project of Dali University (No. KYBS2021068). The funding bodies provided the financial support in carrying out the experiments, sample and data analysis, and MS writing.

Data Availability Statement

Data will be made available from the corresponding author upon request.

Acknowledgments

We would like to thank the Yunnan Zu Feng Seed Industry Co., Ltd., for their assistance in the investigation during the cropping periods.

Conflicts of Interest

Author Chaorui Liu was employed by the company Yunnan Zu Feng Seed Industry Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors declare that they have no conflicts of interest.

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Figure 1. Mean yield (kg/ha) of 11 maize hybrids over two years. Different letters indicate significant differences (p < 0.05).
Figure 1. Mean yield (kg/ha) of 11 maize hybrids over two years. Different letters indicate significant differences (p < 0.05).
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Figure 2. Clustering heat map of yield: horizontal coordinate is the hybrid, vertical coordinate is the test site, (left) is 2022, (right) is 2023. Environmental and genotypic codes are given in Table 1 and Table 2.
Figure 2. Clustering heat map of yield: horizontal coordinate is the hybrid, vertical coordinate is the test site, (left) is 2022, (right) is 2023. Environmental and genotypic codes are given in Table 1 and Table 2.
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Figure 3. Description and correlation analysis of each trait. Note: (A), 2022; (B), 2023. ***, ** and * represent p < 0.00, p < 0.01 and < 0.05 in the upper panel, respectively. The lower panel shows scatter plots for each pair of traits. The distribution of each phenotype is shown along the diagonal.
Figure 3. Description and correlation analysis of each trait. Note: (A), 2022; (B), 2023. ***, ** and * represent p < 0.00, p < 0.01 and < 0.05 in the upper panel, respectively. The lower panel shows scatter plots for each pair of traits. The distribution of each phenotype is shown along the diagonal.
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Figure 4. AMMI1 biplot. (A) AMMI1 biplot in 2022; (B) AMMI1 biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
Figure 4. AMMI1 biplot. (A) AMMI1 biplot in 2022; (B) AMMI1 biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
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Figure 5. AMMI2 biplot. (A) AMMI2 biplot in 2022; (B) AMMI2 biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
Figure 5. AMMI2 biplot. (A) AMMI2 biplot in 2022; (B) AMMI2 biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
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Figure 6. “Which won where” model of GGE biplot for 11 maize hybrids (G1–G11) evaluated in 10 environments. (A) GGE biplot in 2022; (B) GGE biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
Figure 6. “Which won where” model of GGE biplot for 11 maize hybrids (G1–G11) evaluated in 10 environments. (A) GGE biplot in 2022; (B) GGE biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
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Figure 7. The productivity and stability of 11 maize hybrids were assessed in 10 environments using GGE biplot analysis. (A) GGE biplot in 2022; (B) GGE biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
Figure 7. The productivity and stability of 11 maize hybrids were assessed in 10 environments using GGE biplot analysis. (A) GGE biplot in 2022; (B) GGE biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
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Figure 8. GGE biplot for the evaluation of the differentiating power and representativeness of 10 environments. (A) GGE biplot in 2022; (B) GGE biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
Figure 8. GGE biplot for the evaluation of the differentiating power and representativeness of 10 environments. (A) GGE biplot in 2022; (B) GGE biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
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Figure 9. Ranking of genotypes by the GGE biplot. (A) GGE biplot in 2022; (B) GGE biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
Figure 9. Ranking of genotypes by the GGE biplot. (A) GGE biplot in 2022; (B) GGE biplot in 2023. Environmental codes are in Table 1 and genotypic codes are given in Table 2.
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Figure 10. Ranking of environments under the GGE biplot. (A) GGE biplot in 2022; (B) GGE biplot in 2023. Refer to Table 1 for the environment codes.
Figure 10. Ranking of environments under the GGE biplot. (A) GGE biplot in 2022; (B) GGE biplot in 2023. Refer to Table 1 for the environment codes.
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Table 1. Basic information of the 10 test environments.
Table 1. Basic information of the 10 test environments.
LocationsTime20222023
ParametersMarchAprilMayJuneJulyAugustSeptemberOctoberMarchAprilMayJuneJulyAugustSeptemberOctober
BinchuanTEMP (°C)15.815.918.020.021.421.319.216.515.318.219.620.421.420.619.817.0
E1Min (°C)7.09.112.816.216.716.414.711.57.19.911.915.417.017.114.511.8
Max (°C)23.523.124.325.527.527.625.322.922.425.526.626.627.426.026.923.5
PRCP (mm)0.21.13.44.23.94.33.32.10.30.72.59.73.99.86.84.0
RH (%)47.763.872.576.373.774.978.077.342.843.852.872.473.382.074.673.0
GejiuTEMP (°C)20.319.921.223.224.623.821.919.618.823.624.925.024.723.523.120.9
E2Min (°C)14.814.617.319.820.419.918.115.513.417.619.720.820.820.419.617.0
Max (°C)27.226.227.228.431.030.027.625.126.330.831.431.530.529.128.726.7
PRCP (mm)0.90.92.14.71.93.82.40.40.70.40.92.73.66.01.11.1
RH (%)61.862.270.373.169.673.574.770.162.451.457.766.670.579.772.173.4
GengmaTEMP (°C)20.820.522.522.924.824.123.321.419.623.124.624.724.323.824.021.3
E3Min (°C)11.914.318.120.120.220.319.216.811.915.317.620.720.721.119.717.1
Max (°C)30.029.529.028.432.331.230.728.228.332.132.232.230.629.631.428.4
PRCP (mm)1.01.83.48.13.06.95.63.01.00.63.75.45.47.55.01.6
RH (%)51.265.774.784.976.081.578.778.050.945.756.374.980.086.277.980.6
GuangnanTEMP (°C)19.118.619.223.924.923.821.418.317.122.224.223.825.323.522.319.3
E4Min (°C)12.512.315.619.920.619.717.514.211.515.718.619.720.820.118.514.7
Max (°C)27.325.925.229.131.730.427.123.324.230.431.530.431.329.128.025.5
PRCP (mm)1.12.013.41.81.64.53.62.00.60.21.23.55.48.82.31.5
RH (%)64.364.978.971.369.675.877.974.566.557.061.669.569.079.876.375.7
JingdongTEMP (°C)21.221.923.623.625.324.623.922.220.023.924.624.924.924.124.422.9
E5Min (°C)13.315.219.620.921.121.219.817.912.215.218.321.221.421.520.419.1
Max (°C)30.730.330.228.731.831.130.328.429.333.332.431.230.929.330.929.1
PRCP (mm)1.12.17.310.45.06.07.73.30.10.14.29.67.79.43.13.4
RH (%)57.765.576.687.078.583.079.880.558.247.564.778.876.383.375.277.0
JinghongTEMP (°C)24.724.525.526.227.626.426.124.523.927.627.427.427.626.527.025.7
E6Min (°C)17.518.121.722.923.322.722.020.116.519.821.423.723.623.422.821.7
Max (°C)33.833.132.732.134.833.432.531.033.238.036.234.334.432.233.831.9
PRCP (mm)1.52.25.76.01.812.84.43.11.00.82.22.82.54.32.53.1
RH (%)60.364.375.980.273.078.976.474.155.944.862.275.374.282.374.876.2
MileTEMP (°C)17.116.517.721.022.021.919.616.616.020.422.221.822.521.620.417.4
E7Min (°C)9.49.313.817.417.317.915.712.48.912.315.617.718.118.015.412.9
Max (°C)25.324.223.726.228.828.225.422.124.229.629.829.028.227.226.523.6
PRCP (mm)1.61.77.34.55.03.410.00.60.40.10.37.61.86.50.93.0
RH (%)62.166.480.778.178.279.380.378.461.049.358.171.573.481.574.578.7
ShidianTEMP (°C)16.217.019.221.022.122.421.018.815.218.520.021.722.021.821.518.6
E8Min (°C)7.110.514.718.417.918.116.813.86.610.012.717.618.418.816.413.4
Max (°C)25.024.325.525.728.528.827.325.423.626.927.427.727.127.128.325.4
PRCP (mm)0.62.73.12.55.83.72.32.50.80.62.37.64.29.55.44.9
RH (%)53.267.473.775.976.175.475.074.149.147.056.971.073.178.871.070.4
YongshengTEMP (°C)14.713.716.618.619.619.217.014.012.716.518.419.020.018.617.814.6
E9Min (°C)7.07.411.114.714.714.813.68.86.210.512.214.315.015.012.58.9
Max (°C)22.221.123.024.226.026.022.520.820.123.425.425.226.124.324.421.4
PRCP (mm)0.01.22.48.95.33.84.71.20.10.01.05.73.99.62.62.2
RH (%)33.356.067.473.572.977.282.477.139.136.943.367.471.284.077.171.0
ZhenyuanTEMP (°C)24.624.826.628.330.229.227.424.724.229.130.930.229.328.828.526.4
E10Min (°C)18.318.821.924.224.824.422.620.117.921.724.325.124.524.723.822.1
Max (°C)33.132.133.334.237.536.534.030.732.238.338.037.235.935.135.132.7
PRCP (mm)2.53.15.12.73.33.16.01.10.70.32.28.04.43.20.40.8
RH (%)59.660.265.269.561.866.868.165.852.642.648.262.969.875.465.866.6
Note: TEMP is temperature; Min is minimum temperature; Max is maximum temperature; PRCP is precipitation; RH is relative humidity.
Table 2. The detailed information of 11 maize hybrids.
Table 2. The detailed information of 11 maize hybrids.
HybridsCodeParentalSource
ZF-2208G1DF-2 × ZF739Yunnan Zu Feng Seed Industry Co., Ltd., Dali, China
ZF-2209G2ZF2749 × ZF895Yunnan Zu Feng Seed Industry Co., Ltd., Dali, China
ZF-2210G3DF-2 × ZF824Yunnan Zu Feng Seed Industry Co., Ltd., Dali, China
DY-213G4DY2071 × 3279Yunnan Di Yu Seed Industry Co., Ltd., Qujing, China
DY-605G5DY1237 × 108BYunnan Di Yu Seed Industry Co., Ltd., Qujing, China
JG-18G6LX1849 × LX28Mile Jin Gu Seed Industry Co., Ltd., Mile, China
JG-812G7LX890 × LX1847Mile Jin Gu Seed Industry Co., Ltd., Mile, China
DY-519G8LX1849 × S5392Yunnan Nong Zhi Ben Seed Industry Co., Ltd., Kunming, China
LX-24G9LFCD-9 × TA-1-3Yunnan Lin Feng Seed Industry Co., Ltd., Shilin, China
SS-2107G10SFCB05 × SFCB03Yunnan Shi Feng Seed Industry Co., Ltd., Shilin, China
ZD-808G11Y708M × F880Xiangyang Zheng Da Seed Industry Co., Ltd., Xiangyang, China
Table 3. Combined variance analysis of maize yield under GEIs in two years.
Table 3. Combined variance analysis of maize yield under GEIs in two years.
Source of
Variation		Degrees of
Freedom (DFs)		Sum of Squares
(SS)		Mean SquaresF-ValueProportion of SS (%)
year165,040,00065,041,806157.37 **2.85%
gen1088,230,0008,822,69321.35 **3.87%
env91,140,000,000126,721,432306.61 **49.96%
Year: gen1038,100,0003,810,3379.22 **1.67%
Year: env9579,900,00064,438,849155.91 **25.41%
Gen: env9093,220,0001,035,7932.51 **4.09%
Year: gen: env9095,530,0001,061,4532.57 **4.19%
Residuals440181,900,000413,301 7.97%
Note: ** highly significant at p ≤ 0.01.
Table 4. Average yield performance of 11 varieties in 10 sites over two years.
Table 4. Average yield performance of 11 varieties in 10 sites over two years.
HybridsE1E2E3E4E5E6E7E8E9E10MeanRank
G111,017966411,15211,74210,1397556958612,63012,425958110,549 a2
G210,946935910,50811,81095447594966511,22311,984866210,130 ab8
G311,502912210,83211,94197677513995511,64412,436776810,248 ab7
G411,469902510,33111,81296457567970012,05511,790956710,296 ab6
G511,693926210,33911,82410,2167490957011,15411,951980610,331 a5
G612,263910010,05912,00510,3197533966311,70711,959979710,441 a3
G711,730882610,09111,89610,2677443948010,36011,454939310,094 ab9
G811,992867410,87611,61910,720746910,07912,38112,440975110,600 a1
G910,675888010,94711,67510,0797642975612,43112,263967510,402 a4
G1010,7569118998211,96895997650957910,64411,29993329993 ab10
G1110,1228201918010,738900370678983986110,79581679212 b11
mean11,288 ab9021 d10,391 bc11,730 a9936 cd7502 e9638 cd11,463 ab11,891 a9227 cd
rank49526107318
Note: Different letters indicate significant differences (p < 0.05).
Table 5. Average yield performance of 11 hybrids across 10 sites in 2022 and2023.
Table 5. Average yield performance of 11 hybrids across 10 sites in 2022 and2023.
Locations/
Hybrids		YearG1G2G3G4G5G6G7G8G9G10G11
E1202211,636 ± 22011,865 ± 29212,781 ± 40313,105 ± 28813,559 ± 13914,653 ± 69513,447 ± 66913,908 ± 54911,455 ± 55411,520 ± 17910,896 ± 108
202310,397 ± 82810,028 ± 49210,222 ± 1429833 ± 2979828 ± 3059873 ± 22710,013 ± 40610,075 ± 4539894 ± 14699993 ± 3129349 ± 87
E220228678 ± 3008531 ± 1038478 ± 5968631 ± 2178194 ± 3148314 ± 5478550 ± 1778281 ± 4498217 ± 3328911 ± 2307772 ± 162
202310,650 ± 161110,188 ± 14489766 ± 4529419 ± 21110,330 ± 12509885 ± 7989102 ± 4739068 ± 5059544 ± 4289324 ± 8458629 ± 399
E3202211,244 ± 26110,214 ± 7910,444 ± 25810,350 ± 23810,489 ± 47010,372 ± 16210,403 ± 9111,908 ± 52410,639 ± 23910,667 ± 3129692 ± 198
202311,060 ± 73510,801 ± 70111,219 ± 74510,312 ± 67310,189 ± 6729745 ± 6479779 ± 6329844 ± 65511,256 ± 7379297 ± 5998669 ± 301
E4202212,751 ± 26812,713 ± 20112,691 ± 9013,332 ± 39013,024 ± 20113,285 ± 31713,643 ± 42913,003 ± 27212,501 ± 12213,221 ± 22011,855 ± 241
202311,060 ± 73510,801 ± 70111,219 ± 74510,312 ± 67310,189 ± 6729745 ± 6479779 ± 6329844 ± 65511,256 ± 7379297 ± 5998669 ± 301
E5202211,329 ± 19011,167 ± 4611,996 ± 11211,423 ± 112712,267 ± 21712,727 ± 49412,331 ± 15413,385 ± 44811,711 ± 49311,117 ± 33710,536 ± 220
20238949 ± 4387921 ± 3207538 ± 7917866 ± 2478164 ± 3767911 ± 1178203 ± 2078054 ± 7078446 ± 4328081 ± 2367470 ± 324
E620227422 ± 2467308 ± 2117425 ± 417525 ± 547331 ± 2057317 ± 727258 ± 807289 ± 1717250 ± 1987592 ± 656900 ± 56
20237689 ± 467881 ± 2077600 ± 1437608 ± 897650 ± 1057750 ± 1167628 ± 967650 ± 1148033 ± 1607708 ± 4227233 ± 105
E720229806 ± 7039814 ± 6710,689 ± 95810,061 ± 8679828 ± 869850 ± 3039803 ± 42910,969 ± 76610,361 ± 8639765 ± 559269 ± 363
20239366 ± 2909516 ± 4639221 ± 3889338 ± 10009313 ± 4069477 ± 4729157 ± 1969188 ± 1079152 ± 4529394 ± 4718696 ± 317
E8202212,050 ± 51210,242 ± 66010,226 ± 13812,447 ± 102611,084 ± 47912,424 ± 15510,177 ± 52212,146 ± 22112,933 ± 24610,854 ± 2749558 ± 141
202313,210 ± 118912,204 ± 58413,063 ± 146711,662 ± 62111,223 ± 67910,990 ± 65710,543 ± 120912,617 ± 145611,928 ± 31210,433 ± 47110,165 ± 156
E9202210,208 ± 20510,778 ± 35010,689 ± 95810,253 ± 79210,750 ± 76710,375 ± 27010,433 ± 64710,969 ± 76610,361 ± 86310,225 ± 1169706 ± 309
202314,641 ± 42613,191 ± 22014,184 ± 13113,328 ± 99913,151 ± 66113,543 ± 28612,474 ± 20113,911 ± 178514,164 ± 34412,374 ± 51311,883 ± 252
E10202210,123 ± 2618102 ± 3326025 ± 1409879 ± 50210,386 ± 26910,661 ± 2559625 ± 60710,080 ± 65210,379 ± 1729703 ± 1387799 ± 163
20239039 ± 5059223 ± 9579511 ± 1769255 ± 6749225 ± 3608934 ± 5769161 ± 4989422 ± 1208971 ± 7708962 ± 1638535 ± 251
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Ma, C.; Liu, C.; Ye, Z. Influence of Genotype × Environment Interaction on Yield Stability of Maize Hybrids with AMMI Model and GGE Biplot. Agronomy 2024, 14, 1000. https://doi.org/10.3390/agronomy14051000

AMA Style

Ma C, Liu C, Ye Z. Influence of Genotype × Environment Interaction on Yield Stability of Maize Hybrids with AMMI Model and GGE Biplot. Agronomy. 2024; 14(5):1000. https://doi.org/10.3390/agronomy14051000

Chicago/Turabian Style

Ma, Chenyu, Chaorui Liu, and Zhilan Ye. 2024. "Influence of Genotype × Environment Interaction on Yield Stability of Maize Hybrids with AMMI Model and GGE Biplot" Agronomy 14, no. 5: 1000. https://doi.org/10.3390/agronomy14051000

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