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Article

Research on Regional Adaptability and Stability of Maize Hybrids in Mid-to-High Altitude Areas of Yunnan Province Based on GGE Biplot Analysis

by
Qingyan Zi
1,2,3,
Zhilan Ye
1,2,3,*,
Chenyu Ma
1,2,3 and
Chaorui Liu
1,4,*
1
College of Agriculture and Biological Science, Dali University, Dali 671003, China
2
Co-Innovation Center for Cangshan Mountain and Erhai Lake Integrated Protection and Green Development of Yunnan Province, Dali University, Dali 671003, China
3
Key Laboratory for Agroecology in Erhai Lake Watershed of the Department of Education of Yunnan Province, Dali University, Dali 671003, China
4
Yunnan Zufeng Seed Industry Co., Ltd., Dali 671003, China
*
Authors to whom correspondence should be addressed.
Agronomy 2026, 16(1), 54; https://doi.org/10.3390/agronomy16010054
Submission received: 17 November 2025 / Revised: 19 December 2025 / Accepted: 20 December 2025 / Published: 24 December 2025
(This article belongs to the Section Crop Breeding and Genetics)
Browse Figures
Versions Notes

Abstract

Identifying superior genotypes in multi-environment trials is crucial for accelerating cultivar improvement and breeding innovation. This study evaluated the yield potential of 29 maize hybrids (including the control) across 10 trial locations in mid-to-high altitude regions of Yunnan Province from two growing seasons (2023–2024), aiming to recommend high-yielding, stable, and widely adapted maize varieties. Analysis of variance indicated that genotype, environment, and their interaction all had highly significant effects (p < 0.001) on maize yield, with environmental factors accounting for the primary source of variation; in 2023 and 2024, 63.79% and 64.15% of the total variation were explained, respectively. The grain yield of the maize hybrids ranged from 8873 kg/ha to 12,089 kg/ha, with the highest yield over the two consecutive years being 11,783 kg/ha (XR-399). Yield mean analysis identified the top-performing hybrids annually: in 2023, these were G28, G13, G22; in 2024, they included G5, G13, G4. In the GGE biplot analysis, E2 (Binchuan), E5 (Lijiang), E7 (Shilin), and E8 (Xuanwei) were the most distinguishable and representative test environments. The “mean vs. stability” GGE biplot indicated that G22 (LS-2305), G9 (LS-2303), and G13 (XR-399) exhibited consistent high yields and stability across years. Based on the “Which-Won-Where” GGE biplot, G27 (SS-2205) and G13 (XR-399) were identified as the optimal hybrids for each mega-environment, with G13 (XR-399) emerging as the most outstanding. Therefore, these findings confirm that the GGE biplot method is effective for screening high-yielding, stable hybrids and identifying representative test environments, thereby providing a scientific foundation for maize breeding work in the region.

1. Introduction

Maize (Zea mays L.) originated in Central America and was introduced to Europe and other continents following Columbus’s voyages in the late 15th century [1]. With an annual global planting area of about 197 million hectares, maize is positioned as the world’s second-most widely planted crop after wheat [2]. China contributes 23% of the global maize supply while accounting for 21% of the world’s maize cultivation area [3]. Maize yield serves as a critical indicator of a country or region’s economic performance and is closely linked to national and regional food security [4]. Given the widespread issues of cropland occupation and fragmentation [5,6], selecting suitable maize varieties and improving yield per unit area are essential for ensuring food security [7].
In multi-site trials for crop breeding, evaluating yield potential and stability plays a decisive role in assessing and promoting new varieties [8]. Variety stability primarily stems from genotype-by-environment (G × E) interaction effects [9]. However, significant G × E interaction limits single-environment variety recommendations, requiring multi-environment testing for yield stability evaluation [10,11,12]. Therefore, G × E analysis can effectively assess the stability and adaptability of genotypes in terms of yield and yield-related traits [13,14]. Currently, the AMMI model and the GGE biplot are widely used for investigating and interpreting G × E interaction [15,16,17]. The AMMI model can estimate the contributions of genotype (G), environment (E), and genotype-by-environment interaction (GEI) effects on yield [18]. Compared to the AMMI model, the GGE biplot offers superior interpretability and is specifically designed to visualize and dissect GEI patterns in multi-environment studies [19]. It is widely considered a preferred method for analyzing large-scale environmental impacts, assessing genotypes, associating traits, and examining hybridization pattern [20,21]. The GGE biplot model has been widely used to analyze high yield and yield stability in crops, such as maize, rice, oats, wheat, and sugarcane in recent years [22,23,24,25,26,27,28]. Hence, in large-scale environmental analyses and genotype evaluations, the GGE biplot offers advantages over the AMMI biplot [28].
Therefore, this study employed the GGE biplot method to evaluate the performance of a set of promising maize hybrids in multi-environment trials conducted across diverse ecological sites in Yunnan. The province’s ecologically diverse environments make yield stability evaluation a critical aspect in assessing and recommending maize hybrids [29]. However, in-depth evaluation of the performance of specific hybrids in such complex environments is still lacking, and the practically significant “Which-Won-Where” question, which provides direct guidance for real-world promotion, remains insufficiently explored. This gap has led to a disconnect between breeding efforts and practical application.
In light of this, the GGE biplot analysis applied in this study aims not only to validate the performance of these hybrids but also to clarify their optimal planting zones through analyses such as “Which-Won-Where” The ultimate goal is to provide a basis for precise variety deployment and the scientific management of cultivation risks.

2. Materials and Methods

2.1. Experimental Materials, Sites, and Design

The trial involved 29 maize genotypes (Table 1) and was conducted over two years (2023–2024) at 10 experimental sites in mid-to-high altitude regions of Yunnan Province (Table 2, Figure 1). The experiment used a randomized complete block design with three replications. Management aligned with local farmers’ practices. Each 20 m2 plot had alternating wide (0.9 m) and narrow (0.4 m) rows, with plant spacing at 0.25 m, giving a density of 61,575 plants per hectare. Maize was seeded in late April to early May. Water supply throughout the entire maize growth cycle was not solely dependent on natural rainfall. During extended dry periods, supplemental irrigation was applied manually to meet crop water requirements. For weed control, a soil-sealing treatment was implemented. A pre-emergence herbicide mixture (Metolachlor, Atrazine, and Acetochlor) was sprayed onto the soil surface before crop emergence to form herbicidal film. Additionally, Jinzhendihu Herbicide was applied through the drip irrigation system to ensure effective weed management. Subsequently, pests were controlled using Chlorfenapyr and Imidacloprid against Spodoptera frugiperda and aphids, respectively. Fertilization included 30 kg of compound fertilizer per mu as base, mixed with 1.4 kg of phoxim for pest control, and 30 kg of urea per mu was top-dressed later.

2.2. Trait Measurement

During the growing seasons of 2023 and 2024, At plant maturity, ten maize plants were randomly selected from each experimental plot for the measurement of agronomic traits. Traits included ear length, ear diameter, bald tip length, kernels per row, hundred-kernel weight, kernel output rate, ear kernel weight, growth duration, plant height, and ear height. For plant height and ear height, healthy and disease-free plants were prioritized. Plant height was measured at full maturity using a tape from the ground to the tassel top, while the ear height was recorded from the ground to the primary ear’s node. Growth duration was counted as the days from emergence to maturity. Traits such as ear length, ear diameter, bald tip length, and kernel per row were manually measured post-examination. Furthermore, after maize maturity, ten ears were selected from the middle three rows of each plot for grain yield determination. The calculated grain yield was uniformly adjusted to a standard moisture content of 14% [30]. The grain output rate and yield were calculated using the following formulas.
kernel output rate = (Grain dry weight/Fresh ear weight) × 100%;
Maize yield (kg/ha) = (Yield per unit area (kg)/Unit area (m2)) × 10,000 (m2/ha)
where 10,000 m2 = area of 1 hectare plot.

2.3. Data Analysis

We managed data and performed descriptive statistics in Microsoft Excel. All analyses were conducted in R version 4.2.3. We utilized the agricolae (R(4.2.3)) package for ANOVA and GGEBiplotGUI (R(4.2.3)) package for GGE biplot analysis. The GGE biplot, generated via PCA, visualized genotypes (G) effects and G × E interactions based on the first two principal components [13]. The analysis utilized environment centering (scale = 1) for data standardization, with singular value partitioning focused on genotype main effects (SVP = 2) to accentuate genotypic differences. The environmental vector scaling factor was set to 1.5 to optimize visualization.

3. Results

3.1. Variance Analysis (ANOVA) for Maize Yield

The combined ANOVA for 29 maize hybrids across 10 locations showed that the genotype (G), environment (E), and their interaction (GEI) had highly significant impacts (p < 0.001) on maize yield in both evaluation years (Table 3 and Table 4). Furthermore, the trial environments explained 63.79% and 64.15% of the total variation in 2023 and 2024, respectively.

3.2. Comprehensive Visualization of Yield Bar Chart and Yield–Environment–Cultivar Relationship Heatmap

Maize yield performance was assessed in Figure 2 and Figure 3. In 2023, the highest-yielding hybrids were G28, G13, G22, G3, G10, G9, and G27 (Figure 2a), while in 2024, G5, G13, G4, G2, G27, G22, and G26 emerged as the top performers (Figure 2b). The heatmap of the two-year average yield revealed significant environmental variations in yield (Figure 3). Among the hybrids, G3, G10, G2, G11, G9, G22, G27, G13, and G28 had the highest yields. Locations E5, E8, E1, and E7 were identified as optimal for cultivating high-yield maize.

3.3. Analysis of Correlation Between Agronomic Traits and Yield

The two-year field trials revealed significant natural variation in both yield and agronomic traits among the 29 maize hybrids, and these traits collectively influenced the overall yield of the hybrids. Correlation analysis demonstrated the interrelationships between yield and yield-related traits. In 2023, yield showed a strong positive correlation with ear kernel weight and a positive correlation with plant height. Plant height was positively correlated with ear height and ear kernel weight (Figure 4a). This indicates that taller plants, with their larger vegetative bodies and higher ear height, optimize light capture and supply more photosynthetic assimilates for grain filling, but this also increases the risk of lodging. In 2024, ear length exhibited positive correlations with ear diameter and kernels per row. Hundred-kernel weight demonstrated positive correlations with plant height and yield, while yield was positively correlated with plant height (Figure 4b). These relationships imply that not only do superior ear traits underpin a larger sink capacity, but optimal plant height and high hundred-kernel weight also facilitate the buildup of assimilates, resulting in higher yield. Therefore, achieving the high-yield goal of large ears with abundant kernels, plump grains, and optimal plant architecture serves as an important theoretical basis for breeding high-yielding and superior-quality maize varieties.

3.4. GGE Biplot Analysis

3.4.1. Relationship Among Test Environments

In Figure 5, AXIS1 and AXIS2 represent the first two principal components, which account for the variation in maize hybrid yield across 10 environments in 2023 (61.98%) and in 2024 (63.62%). The GGE biplot evaluates the relationships among environments. The smaller the angle between two environments, the higher the correlation between them, indicating greater co-occurrence or repeatability of genotype rankings. Conversely, a larger angle between two environments suggests lower correlation or contradictory performance of genotypes; when the angle is 90 degrees, it indicates that there is no correlation between the two environments [31]. Small angles (E4 and E7, E2 and E5, and E3 and E8) indicated strong environmental correlations, suggesting similar rankings of maize genotypes within these paired environments. In contrast, the angle between E6 and E8 was greater than 90°, indicating uncorrelated genotypic performance between these two environments (Figure 5a). Similarly, the smallest angles were observed between E1 and E5, as well as among E7, E6, and E8, reflecting the highest environmental correlations. Conversely, the angle between E2 and E10 was more than 90°, indicating weak genotypic correlation between these environments (Figure 5b).

3.4.2. Selection of Ideal Test Environments

The GGE biplot of “discriminating ability vs. representativeness” view and environmental ranking view across 10 test environments are displayed in Figure 6 and Figure 7, respectively. The discriminative power of each environment can be evaluated by its environmental vector, which is defined as the line segment connecting the origin of the biplot to the environmental point. The discriminative ability of a test environment depends on the length of its vector (longer length indicates stronger discriminative power), while its representativeness is determined by the angle between this vector and the average environment coordinate (AEC) axis (a smaller angle indicates higher representativeness). If the angle between an environmental point and the AEC axis is obtuse, it is considered unsuitable as a test site. In 2023, Environments E6, E2, and E5 had the longest vectors, indicating the strongest discriminative power. Relative to the AEC abscissa, E1, E2, and E5 showed the smallest angles, suggesting their high environment representativeness (Figure 6a). And E1, E2, and E5 were in a smaller concentric circle and could be identified as the top-ranked test environments (Figure 7a). Therefore, in 2023, E2 (Binchuan) and E5 (Lijiang) were identified as ideal test environments with both strong discriminating power and high representativeness due to their unique complementary stress combinations. In 2024, E5 and E7 displayed the longest vectors, indicating the strongest discriminative power, while E8 showed relatively weaker genotype differentiation compared to E5 and E7 but remained a suitable test environment. E6, E7, and E8 had the smallest angles relative to the AEC abscissa, indicating their high representativeness (Figure 6b). And E7 and E8 were the top-ranked test environments because the concentric circles were closest to the center of the circle (Figure 7b). Consequently, in 2024, E7 (Shilin) and E8 (Xuanwei) were identified as ideal test environments due to their effectiveness in selecting for stress and drought-resistant traits.

3.4.3. Screening of Elite Cultivars Under Test Environments

The polygon view of the GGE biplot revealed the “Which-Won-Where” pattern of yield performance among 29 maize hybrids, with the vertices of the polygon representing the winning genotypes in their respective environmental groups (Figure 8). In 2023, G27, G28, and G3 demonstrated optimal performance in their respective test environments. Moreover, the location of G13 was infinitely close to that of G28, so it can also be regarded as the most promising variety (Figure 8a). In 2024, genotypes G27, G5, and G2 exhibited superior performance, with G13 and G22 continuing to show excellent potential in specific sectors (Figure 8b). This indicates that G27 (SS-2205) and G13 (XR-399), distinguished by their outstanding disease resistance and comprehensive stress tolerance, have emerged as key candidate hybrids for further in-depth research and potential commercialization.

3.4.4. Selection of Hybrids with High Stability and Productivity

The stability view reflects the productivity and stability of hybrids. Genotypes located on the right side of the AEC axis exhibit higher mean yields, and a shorter perpendicular distance from a genotype to the AEC axis indicates higher stability. In the ranking view, concentric circles are drawn based on the average environment axis. If a variety falls within a smaller circle, it indicates better performance in terms of both productivity (yield) and stability (interaction) (Figure 9 and Figure 10). In 2023, G28, G13, G22, G9, and G10 demonstrated the optimal balance between productivity and stability (Figure 9a and Figure 10a). In 2024, G5, G13, G22, G4, and G9 exhibited excellent stability and productivity (Figure 9b and Figure 10b). Therefore, G13 (XR-399), G22 (LS-2305), and G9 (LS-2303) demonstrated consistently high and stable productivity over two consecutive years of trials, attributable to their robust abiotic stress tolerance, superior water-use efficiency, and strong physiological drought resistance.

4. Discussion

4.1. Yield Variance Analysis

The environmental factors accounted for a larger proportion of variation compared to genotypic effects and genotype-by-environment interaction effects, highlighting the paramount role of the environmental condition in shaping yield performance. And this also underscored the necessity of evaluating genotypes across diverse environments to ensure the robustness and reliability of results (Table 3 and Table 4). Similar findings have been documented in previous studies by Ma et al. (2024), Taak et al. (2025), Gonçalves et al. (2025), and Liu et al. (2022) [17,32,33,34]. However, the predominant contribution of environmental factors to yield variation is attributable to the fluctuations in key local climatic factors, particularly changes in temperature and precipitation. Furthermore, the effects of environment, genotype, and their interaction on maize yield were all statistically significant at the extremely high levels (p < 0.001). These results aligned with recent research by Zendrato et al. (2025), Neelam et al. (2025), and Nišavić et al. (2025) [35,36,37], confirming that the performance of maize genotypes was strongly modulated by specific environmental conditions. Consequently, the application of the GGE biplot analysis for yield evaluation is methodologically validated.

4.2. Evaluation of Ideal Test Environments

Heatmap analysis identified E5 (Lijiang), E8 (Xuanwei), E1 (Baoshan), and E7 (Shilin) as optimal environments for evaluating high-yielding genotypes (Figure 3). It is worth noting that the three locations (E5 (Lijiang), E8 (Xuanwei), and E7 (Shilin)) were highly consistent with the ideal environment results obtained by the GGE biplot (Figure 6 and Figure 7). This mutual validation of analytical methods has also been confirmed in the studies by Zhang et al. (2016) and Nišavić et al. (2025) [20,38]. Yan et al. (2021) and Li et al. (2023) emphasized that the efficacy and precision of cultivar selection were markedly influenced by the discriminating discriminatory capacity of test environments [8,39], reinforcing the importance of selecting appropriate trial locations. In this study, E2 (Binchuan) and E5 (Lijiang) in 2023, alongside E7 (Shilin) and E8 (Xuanwei) in 2024, were identified as the most suitable ideal test environments for cultivar evaluation (Figure 6 and Figure 7). Furthermore, Ma et al. (2024) and Liu et al. (2022) validated that Binchuan, Shilin, and Xuanwei were relatively ideal environments [17,34], which was highly consistent with the ideal environments identified in our study. As highlighted by Mullualem et al. (2024), Kona et al. (2024) and Tiwari et al. (2025), the delineation of representative environments necessitated multi-year experimental validation [40,41,42]. Among these, E2 (Binchuan) with its high-temperature and drought-stress was screened for heat and drought tolerance; E5 (Lijiang) with its strong radiation was tested for temperature-difference-fluctuating drought-stress tests for light–temperature utilization efficiency and water response plasticity; E7 (Shilin) with its fluctuating extreme drought-stress identifies with extreme stress resistance; and E8 (Xuanwei) with its concentrated rainy seasons, winter–spring droughts, and large daily temperature differences assesses the adaptability and promotion potential of varieties in rain-fed medium-and-low-yield areas.
Therefore, the unique ecological conditions of these sites collectively form an ideal breeding test environment, providing a crucial natural foundation for exploring yield potential and developing new cultivars.

4.3. Evaluation of Ideal Genotypes

The results of high-yielding hybrids obtained from the analysis of yield performance and the polygon view of GGE biplot showed a very high degree of overlap (Figure 2 and Figure 8). The high-yielding varieties with complete overlap were as follows: G3(ZF-2303), G27(SS-2205), G28(SS-2206) and G13(XR-399) in 2023, and G27(SS-2205), G5(XR-18), G2(ZF-2302) and G13(XR-399) in 2024. Meanwhile, Zhao and Kusmec indicated that variations in temperature and precipitation are key climatic factors affecting hybrid maize yields; consequently, under the influence of such environmental fluctuations, yield performance exhibits significant differences across two consecutive years [43,44]. Notably, the mutual validation of analytical approaches has been further confirmed in studies by Liu et al. (2022), Ma et al. (2024), Ruswandi et al. (2022), and BaduApraku et al. (2023) [17,34,45,46]. Otherwise, as a mature tool for genotype-by-environment interaction analysis, the GGE model has demonstrated good applicability in selecting ideal genotypes for various important crops, including rice (Li et al., 2023) [27], wheat (Mullualem et al., 2024) [40], sugarcane (Chaudhary et al., 2025) [47], and oats (Sanadya et al., 2025) [48] in variety adaptation evaluation. However, yield potential alone cannot ensure temporal or spatial stability [49], high and stable yield performance remains a fundamental criterion for determining a variety’s suitability for large-scale commercial promotion [50,51]. In this study, G13 (XR-399), G22 (LS-2305), and G9 (LS-2303) were identified as the most desirable genotypes, owing to their robust abiotic stress tolerance, superior water-use efficiency, and strong physiological drought resistance, which together contributed to their excellent and stable productivity across two consecutive years of trials (Figure 9 and Figure 10). This analysis of high-yield and stable hybrids based on the GGE model has been validated in studies by Gonçalves et al. (2025), Kumar et al. (2024), and Nagesh et al. (2025) [33,52,53].
However, the genotypes screened in this study—G22 (LS-2305), G9 (LS-2303), and G13 (XR-399)—have demonstrated outstanding performance in comprehensive evaluations. This excellence is reflected not only in their high yield and stability but also stems from their strong adaptability to the region’s typical red soil and subtropical plateau monsoon climate, characteristics which confer upon them broad production potential. For the practical application of these superior genotypes, maximizing their yield potential necessitates focused attention on local optimal sowing dates and the management of pests and diseases.

4.4. Study Limitations

Although this study provides valuable insights into genotype-by-environment interactions and the screening of superior genotypes and test environments, it was conducted over only two growing seasons (2023 and 2024) at a limited number of trial sites. While the selected sites (e.g., Binchuan, Shilin, Xuanwei, Lijiang) represent distinct ecological zones within Yunnan Province, the relatively short trial duration and finite number of locations may be insufficient to fully reveal the long-term stability of the genotypes. Furthermore, aspects such as grain quality and water and nutrient use efficiency were not investigated. Future work will involve multi-location trials over more years to validate stability, the inclusion of secondary traits in a comprehensive evaluation index, and the integration of molecular data or crop models to elucidate the genetic and physiological basis of the adaptation identified in this study.

5. Conclusions

A multi-environment maize yield trial, analyzed via the GGE biplot model, revealed significant effects of genotypes, environments, and their interactions on yield (p < 0.001), with environmental factors accounting for the primary source of variation; in 2023 and 2024, 63.79% and 64.15% of total variation were explained, respectively. The grain yield of the maize hybrids ranged from 8873 kg/ha to 12,089 kg/ha, with the highest yield over the two consecutive years being 11,783 kg/ha (XR-399). The optimal testing environments were E2 (Binchuan), E5 (Lijiang), E7 (Shilin), and E8 (Xuanwei). For superior genotypes, G22 (LS-2305), G9 (LS-2303), and G13 (XR-399) exhibited consistently high and stable yields across years, with G13 (XR-399) emerging as the most exceptional, this outstanding hybrid demonstrates not only high yield and stability, but also broad adaptability, along with exceptional performance in specific regions. For farmers, G13 (XR-399) is recommended as a low-risk candidate for direct commercial planting, especially within the target areas identified in this study, to ensure stable, high-quality yields. For breeders, the paternal and maternal lines of this hybrid can serve as valuable parental materials to be incorporated into crossing programs aimed at further enhancing yield and stress resistance, as well as broadening the genetic base of future varieties. Therefore, the GGE biplot method proved highly effective in screening for high-yielding, stable hybrids and in identifying optimal test environments, thereby providing scientific guidance for maize breeding programs in the mid- to high-elevation regions of Yunnan Province.
In the future, more in-depth research into the genetic and physiological mechanisms will facilitate the development of marker-assisted selection (MAS) and provide mechanistic explanations for the genotype-by-environment interactions revealed by the GGE biplot.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/agronomy16010054/s1; Supplementary Table S1. Basic Climatic Information for the Ten Test Environments; Supplementary Table S2. The average yield (kg/ha) of 29 maize hybrids across ten environments in 2023; Supplementary Table S3. The average yield (kg/ha) of 29 maize hybrids across ten environments in 2024; Supplementary Table S4. The average yield (kg/ha) of 29 maize hybrids across ten environments over two years.

Author Contributions

Q.Z.: original draft, writing—review and editing, visualization, validation, methodology, investigation, formal analysis, data curation, project administration. Z.Y.: writing—review and editing, supervision, resources, conceptualization, project administration, funding acquisition. C.M.: writing—review and editing, investigation, project administration. C.L.: writing—review and editing, investigation, supervision, project administration. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China (No. 32401771), Yunnan Fundamental Research Projects (Nos. 202201AU070003, 202301AT070025), Xingdian Talent Support Program of Yunnan Province (No. XDYC-QNRC-2023-0016), Scientific Research Foundation of Education Department of Yunnan Province (Nos. 2025J0813, 2025Y1251), Doctoral Research Start-up Project of Dali University (No. KYBS2021068), and Research Development Fund of Dali University (No. KY2519104040). The funding bodies provided financial support in carrying out the experiments, sample and data analysis, and MS writing.

Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author(s).

Acknowledgments

We would like to express our sincere gratitude to the companies listed in Table 1 for providing germplasm resources, and to Yunnan Zufeng Seed Industry Co., Ltd. for their assistance during the crop growth cycle investigation. Additionally, we are thankful to DeepSeek V3.2 for its support in English translation, grammar proofreading, structural optimization, spelling checking, punctuation correction, and formatting adjustment.

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.

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Figure 1. The location of 10 test sites in mid-to-high altitude areas of Yunnan Province.
Figure 1. The location of 10 test sites in mid-to-high altitude areas of Yunnan Province.
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Figure 2. (a) Maize yield performance in 2023; (b) maize yield performance in 2024. Different letters indicate significant differences at the probability level of 0.05 (p < 0.05), whereas shared letters denote no significant differences between groups.
Figure 2. (a) Maize yield performance in 2023; (b) maize yield performance in 2024. Different letters indicate significant differences at the probability level of 0.05 (p < 0.05), whereas shared letters denote no significant differences between groups.
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Figure 3. Heatmap of two-year average yield from 2023 to 2024. The clustering heatmap of yield: the horizontal coordinate represents the test sites, and the vertical coordinate represents the hybrids. The color gradient in the figure indicates the yield level, with hybrid yields increasing from bottom to top. The specific names and corresponding codes of the hybrids and test sites are provided in Table 1 and Table 2.
Figure 3. Heatmap of two-year average yield from 2023 to 2024. The clustering heatmap of yield: the horizontal coordinate represents the test sites, and the vertical coordinate represents the hybrids. The color gradient in the figure indicates the yield level, with hybrid yields increasing from bottom to top. The specific names and corresponding codes of the hybrids and test sites are provided in Table 1 and Table 2.
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Figure 4. (a) Correlation analysis of agronomic traits in 2023; (b) correlation analysis of agronomic traits in 2024. EL: ear length; ED: ear diameter; BTL: bald tip length; KPR: kernels per row; HKW: hundred-kernel weight; KR: kernel output rate; EKW: ear kernel weight; HD: growth duration; PH: plant height; EH: ear height.
Figure 4. (a) Correlation analysis of agronomic traits in 2023; (b) correlation analysis of agronomic traits in 2024. EL: ear length; ED: ear diameter; BTL: bald tip length; KPR: kernels per row; HKW: hundred-kernel weight; KR: kernel output rate; EKW: ear kernel weight; HD: growth duration; PH: plant height; EH: ear height.
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Figure 5. Relationships among test environments revealed by GGE biplot analysis across 10 trial environments. (a) GGE biplot for 2023; (b) GGE biplot for 2024.
Figure 5. Relationships among test environments revealed by GGE biplot analysis across 10 trial environments. (a) GGE biplot for 2023; (b) GGE biplot for 2024.
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Figure 6. GGE biplot of “discriminating ability vs. representativeness” view across 10 test environments. (a) GGE biplot for 2023; (b) GGE biplot for 2024.
Figure 6. GGE biplot of “discriminating ability vs. representativeness” view across 10 test environments. (a) GGE biplot for 2023; (b) GGE biplot for 2024.
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Figure 7. GGE biplot of environmental ranking view across 10 test environments. (a) GGE biplot for 2023; (b) GGE biplot for 2024.
Figure 7. GGE biplot of environmental ranking view across 10 test environments. (a) GGE biplot for 2023; (b) GGE biplot for 2024.
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Figure 8. GGE biplot of “Which-Won-Where” view among 29 maize hybrids across 10 test environments. (a) GGE biplot for 2023; (b) GGE biplot for 2024. The red lines are the equality lines between adjacent genotypes on the polygon, with each equality line dividing the biplot into a distinct sector.
Figure 8. GGE biplot of “Which-Won-Where” view among 29 maize hybrids across 10 test environments. (a) GGE biplot for 2023; (b) GGE biplot for 2024. The red lines are the equality lines between adjacent genotypes on the polygon, with each equality line dividing the biplot into a distinct sector.
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Figure 9. GGE biplot displaying the “means vs. stability” view for the average yield of 29 maize hybrids across 10 test environments. (a) GGE biplot of “means and stability” view for 2023; (b) GGE biplot of “means and stability” view for 2024.
Figure 9. GGE biplot displaying the “means vs. stability” view for the average yield of 29 maize hybrids across 10 test environments. (a) GGE biplot of “means and stability” view for 2023; (b) GGE biplot of “means and stability” view for 2024.
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Figure 10. GGE biplot displaying the “ranking genotype” view for the average yield of 29 maize hybrids across 10 test environments. (a) GGE biplot of ranking genotype view for 2023; (b) GGE biplot of ranking genotype view for 2024.
Figure 10. GGE biplot displaying the “ranking genotype” view for the average yield of 29 maize hybrids across 10 test environments. (a) GGE biplot of ranking genotype view for 2023; (b) GGE biplot of ranking genotype view for 2024.
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Table 1. The detailed information of 29 maize hybrids.
Table 1. The detailed information of 29 maize hybrids.
HybridsCodeParentalSource
ZHY-103G1ZH808 × ZH194Yunnan Zu Feng Seed Industry Co., Ltd., Dali, China
ZF-2302G2ZF3089 × ZF3375Yunnan Zu Feng Seed Industry Co., Ltd., Dali, China
ZF-2303G3ZF1868 × H974Yunnan Zu Feng Seed Industry Co., Ltd., Dali, China
XR-17G4E168 × JS1196Yunnan Xuanrui Seed Industry Co., Ltd., Qujing, China
XR-18G5JS3051 × N4822Yunnan Xuanrui Seed Industry Co., Ltd., Qujing, China
DY-604G6DY6029 × DY7112Yunnan Di Yu Seed Industry Co., Ltd., Qujing, China
YBY-201G7DY201 × XY9A-1Yunnan Yundan Seed Technology Co., Ltd., Xiangyun, China
LS-2301G8LFM10-31 × LFLB-1Yunnan Linfeng Seed Industry Co., Ltd., Shilin, China
LS-2303G9LFM68-164 × LFD109Yunnan Linfeng Seed Industry Co., Ltd., Shilin, China
MS-2301G10GM3073 × GM901Yunnan Guangmao Seed Industry Co., Ltd., Binchuan, China
ZF-2304G11ZF1410 × ZF3375Yunnan Zu Feng Seed Industry Co., Ltd., Dali, China
ZF-2305G12HK48 × ZF1807Yunnan Zu Feng Seed Industry Co., Ltd., Dali, China
XR-399G13B1196 × JS1196Yunnan Xuanrui Seed Industry Co., Ltd., Qujing, China
DY-801G14DY1156 × DY8119Yunnan Di Yu Seed Industry Co., Ltd., Qujing, China
YBY-202G15DY202 × XY9A-1Yunnan Yundan Seed Technology Co., Ltd., Xiangyun, China
SS-2201G16SFH04 × SFY01Yunnan Shi Feng Seed Industry Co., Ltd., Shilin, China
SS-2202G17SFH05 × SFY01Yunnan Shi Feng Seed Industry Co., Ltd., Shilin, China
JG-1872G18LX751 × LX1845Mile Jin Gu Seed Industry Co., Ltd., Mile, China
JG-1881G19XYD3 × LX2614Mile Jin Gu Seed Industry Co., Ltd., Mile, China
MS-2302G20GM23B × GM1681Yunnan Guangmao Seed Industry Co., Ltd., Binchuan, China
LS-2304G21LFM68-174 × LFJM181804Yunnan Linfeng Seed Industry Co., Ltd., Shilin, China
LS-2305G22LFM10 × LF984Yunnan Linfeng Seed Industry Co., Ltd., Shilin, China
JG-1356G23LX201 × 1F28Mile Jin Gu Seed Industry Co., Ltd., Mile, China
JG-1865G24LX750 × 1F38Mile Jin Gu Seed Industry Co., Ltd., Mile, China
SS-2203G25SFCM03 × SFCM02Yunnan Shi Feng Seed Industry Co., Ltd., Shilin, China
SS-2204G26SFCM03 × SFCQ42Yunnan Shi Feng Seed Industry Co., Ltd., Shilin, China
SS-2205G27SFZY14 × SFZY16Yunnan Shi Feng Seed Industry Co., Ltd., Shilin, China
SS-2206G28SFZY15 × SFZY17Yunnan Shi Feng Seed Industry Co., Ltd., Shilin, China
WG-3861(CK)G29WG6320 × WG646Gansu Wugu Seed Industry Co., Ltd., Lanzhou, China
Table 2. Ten test sites and their codes.
Table 2. Ten test sites and their codes.
LocationCodeLatitude (N)Longitude (E)Altitude (m)
BaoshanE125°09′99°13′1592
BinchuanE225°48′100°35′1430
ChuXiongE325°08′101°18′1767
GengmaE423°21′99°48′1340
LijiangE526°58′100°3′1819
MileE624°27′103°31′1543
ShilinE724°41′103°27′1927
XuanweiE826°15′104°8′1980
YanshanE923°07′104°34′1490
ZhaotongE1027°19′103°42′1920
Table 3. Variance analysis for maize yield (kg/ha) in 2023.
Table 3. Variance analysis for maize yield (kg/ha) in 2023.
Source of
Variation		Degrees of Freedom (DF)Sum of Squares (SS)Mean SquaresF-CalculatedProportion of SS
(%)
Environments (E)92,386,119265,124,355.8576.3844 ***63.79
Genotypes (G)284,921,44717,576,597.0538.2117 ***13.16
G × E Interaction2525,684,3862,255,708.64.9039 ***15.2
Replication22,246,72622,467.260.04880
Residuals6392,939,261459,978.29 7.86
Total9293,740,651 100
Note: ***, p < 0.001.
Table 4. Variance analysis for maize yield (kg/ha) in 2024.
Table 4. Variance analysis for maize yield (kg/ha) in 2024.
Source of
Variation		Degrees of
Freedom (DF)		Sum of Squares (SS)Mean SquaresF-CalculatedProportion of SS
(%)
Environments (E)92,642,420,060293,602,228.9773.4477 ***64.15
Genotypes (G)28605,018,46221,607,802.256.9222 ***14.69
G × E Interaction252625,274,8462,481,249.46.5364 ***15.18
Replication24,503,1612,251,580.45.93140.11
Residuals638242,186,004379,601.9 5.88
Total9294,119,402,533 100
Note: ***, p < 0.001.
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Zi, Q.; Ye, Z.; Ma, C.; Liu, C. Research on Regional Adaptability and Stability of Maize Hybrids in Mid-to-High Altitude Areas of Yunnan Province Based on GGE Biplot Analysis. Agronomy 2026, 16, 54. https://doi.org/10.3390/agronomy16010054

AMA Style

Zi Q, Ye Z, Ma C, Liu C. Research on Regional Adaptability and Stability of Maize Hybrids in Mid-to-High Altitude Areas of Yunnan Province Based on GGE Biplot Analysis. Agronomy. 2026; 16(1):54. https://doi.org/10.3390/agronomy16010054

Chicago/Turabian Style

Zi, Qingyan, Zhilan Ye, Chenyu Ma, and Chaorui Liu. 2026. "Research on Regional Adaptability and Stability of Maize Hybrids in Mid-to-High Altitude Areas of Yunnan Province Based on GGE Biplot Analysis" Agronomy 16, no. 1: 54. https://doi.org/10.3390/agronomy16010054

APA Style

Zi, Q., Ye, Z., Ma, C., & Liu, C. (2026). Research on Regional Adaptability and Stability of Maize Hybrids in Mid-to-High Altitude Areas of Yunnan Province Based on GGE Biplot Analysis. Agronomy, 16(1), 54. https://doi.org/10.3390/agronomy16010054

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