Phenotypic variation and diversity assessment in maize landraces

Longxue Wei; Lianghai Guo; Dongbo Zhao; Jianjun Guo; Yirong Jin; Jiansheng Gao; Huini Cui; Liang Zhang; Peng Liu; Zhihui Guo

doi:10.1371/journal.pone.0355741

Abstract

Maize landraces are invaluable reservoirs of genetic diversity for crop improvement, yet systematic evaluation of their quantitative traits remains insufficient. In this study, 119 maize landraces were evaluated for nine quantitative traits related to yield and tassel morphology. Genetic diversity indices, principal component analysis(PCA), cluster analysis, and stepwise regression were applied to characterize phenotypic variation and identify core evaluation indicators. The landraces exhibited substantial genetic variation, with coefficients of variation ranging from 20.73% to 60.96% and Shannon-Winner index from 0.88 to 0.98. PCA extracted four principal components explaining 68.81% of the total variation, with the first component (24.17%) primarily driven by the number of secondary tassel branches. Based on comprehensive D values (0.033–0.960), ten superior landraces were identified, including Z102, Z7, and Z89. Cluster analysis classified the 119 landraces into three groups differing in tassel morphology, grain characteristics, and agronomic performance. Stepwise regression further identified four key indicators—number of secondary tassel branches, angle between primary tassel branches and main axis, tassel diameter, and number of spike rows—as the most informative traits for breeding selection. These results provide a practical framework for the efficient exploitation of maize landrace resources and the identification of elite breeding materials.

Citation: Wei L, Guo L, Zhao D, Guo J, Jin Y, Gao J, et al. (2026) Phenotypic variation and diversity assessment in maize landraces. PLoS One 21(8): e0355741. https://doi.org/10.1371/journal.pone.0355741

Editor: Mehdi Rahimi, KGUT: Graduate University of Advanced Technology, IRAN, ISLAMIC REPUBLIC OF

Received: March 31, 2026; Accepted: July 26, 2026; Published: August 14, 2026

Copyright: © 2026 Wei et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This work was supported by the Dezhou Comprehensive Experimental Station of National Corn Industry Technology System (Grant No. CARS-2) and the Key Technological Innovation and Demonstration for Year-Round Water-Saving and Yield-Increasing of Wheat and Corn in the Yellow River Basin (Grant No. 2023TZXD011) The Dezhou Comprehensive Experimental Station of National Corn Industry Technology System (CARS-2) supported the sowing management of germplasm resources and data collection. The Key Technological Innovation and Demonstration for Year-Round Water-Saving and Yield-Increasing of Wheat and Corn in the Yellow River Basin (2023TZXD011) supported the field management and data collection.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Maize, cultivated on approximately 200 million hectares worldwide, is a critical crop for global food security [1]. The foundation of maize genetic improvement lies in the effective utilization of diverse germplasm resources, which provide the raw materials necessary for breeding innovations [2]. Among these resources, maize landraces hold particular importance due to their strong adaptability to local environments, stress resistance, and rich genetic variation. Historically, they have not only contributed directly to maize production in regions such as China but also established the genetic basis for modern hybrid breeding [3]. Therefore, a comprehensive and systematic study of these resources is a prerequisite and key to managing and utilizing them effectively. The current narrow genetic base of hybrid maize varieties underscores the urgency of systematically characterizing and utilizing maize landraces to broaden the genetic diversity available for breeding programs [4].

Maize landraces represent the most diverse genetic resource within maize germplasm, making them indispensable for overcoming challenges such as germplasm bottlenecks, variety homogenization, and reduced breeding efficiency [5–8]. Global studies have documented the extensive genetic diversity and population structure of maize landraces across diverse agro-ecological zones, including Peru [9], Mexico [10], Tunisia [11], northern Argentina [12], Europe [13]), and central China [14]. These findings highlight the valuable variation present in local maize germplasm and reinforce the need for targeted evaluation. Phenotypic traits, as the observable morphological and agronomic characteristics, provide a direct measure of germplasm diversity shaped by the interaction of genetics and environment. Evaluating phenotypic diversity is fundamental for understanding germplasm resources, mining beneficial genes, and guiding the development of new varieties [15,16]. For instance, research on Chinese maize landraces revealed high phenotypic diversity, especially in Southwest China, and identified favorable alleles such as TU1 linked to high yield potential [17,18]. Additionally, the rhizosphere microorganisms associated with maize landraces have been shown to promote plant growth and disease resistance, further emphasizing their breeding value [19].

Their research still has following limitations: Firstly, it is based on the molecular level population genetic analysis, without the support of the systematic phenotypic quantification data for the local variety resources, which leads to the disconnection of “genotype” and “phenotype”. Secondly, even if phenotypic identification is conducted, it is mostly limited to the broad trait description, without the in-depth analysis on the correlation between the key traits, nor the establishment of the efficient quantitative evaluation criteria for the selection of excellent germplasm. For Chinese maize landraces, phenotypic variation has been reported, but there is rare attention to tassel configuration, an inflorescence trait controlled by high heritability, and resource classification and utilization potential evaluation based on statistical models.

This study focuses on the phenotypic traits of local maize landraces, employing statistical analyses to assess their diversity and comprehensively evaluate their performance. The objectives are to develop efficient evaluation criteria, explore trait correlations, and identify superior germplasm resources. These efforts aim to provide a robust foundation for the conservation, utilization, and breeding of maize landraces, thereby accelerating the innovative application of these valuable genetic resources.

Materials and methods

Test materials

In this study, a total of 119 maize landraces were collected, hailing from the Huang-Huai production regions, including Shandong, Shanxi, Shanxi, Anhui, and Henan provinces. These varieties were obtained from the National Maize Germplasm Resources Bank and supplied by the Institute of Crop Sciences at the Chinese Academy of Agricultural Sciences (S1 Table). Comprehensive details regarding these varieties are presented in Table 1.

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Table 1. Codes, D values of comprehensive evaluation and ranking of 119 maize landraces.

https://doi.org/10.1371/journal.pone.0355741.t001

Test method

Overview of test site.

The experiment was conducted in the modern agricultural science and Technology Industrial Park in Huangheya Town, Dezhou City, Shandong Province in 2024. Sown on June 13, the average sunshine duration of maize growth period was about 7.4 hours, the average temperature was about 24.2 °C, and the total precipitation was about 397 mm. It was harvested on October 10 of the same year. There was no abnormal phenomenon in the average temperature of maize growth period. The 119 maize landraces used in this experiment had consistent environmental variables at the test site.

Test design.

The trial was designed as a completely randomized block trial with three replicates. Each resource was planted in 3 rows, with a length of 3 m, a row spacing of 0.6 m, a plant spacing of 22.2 cm, and a plot planting density of 75000 plants hm^-2. The experiment site has sufficient light, the cultivation management measures and water and fertilizer conditions were the same as the local field management measures, and the pest control was carried out according to the conventional conditions.

9 quantitative traits.

Number of spike rows (SRN): the number of rows of kernels in the middle of the spike at grain maturity;

Number of grains per row (GPR): number of grains per row of spike at grain maturity;

1000-grain weight (TGW): the weight of 1000 seeds at grain maturity;

Tassel stalk length (TSL): tassel and stem node to tassel base branch point;

Tassel principal axis length (TPAL): the length from the branch point of tassel base to the top, measured with a ruler, expressed in cm;

Diameter of tassel stalk (TSD): measured with vernier caliper, expressed in cm;

Number of tassel branches(TBN): investigate the number of branches disposed by tassel principal axis at silking stage;

Secondary branch number of tassel(TSBN): the number of re branches at the primary branch of tassel was investigated at silking stage;

Angle between tassel primary branch and tassel principal axis(AFBMA): investigate the angle between tassel primary branch and tassel principal axis at silking stage, and measure it with protractor in degrees.

Data analysis

Membership function analysis.

[1]

In the formula, u (X_i) is the membership function value of the ith character of each material, X_i is the value of the ith character of each material, and x_imax and X_imin are the maximum and minimum values of the ith character of all tested materials respectively.

Genetic diversity index.

The relative frequency of each grade of each trait was obtained by the membership function, and then the Shannon Wiener diversity index (H ′) was used to evaluate the genetic diversity.

[2]

In the formula, Pi is the percentage of the number of materials in grade i of a certain character in the total number of materials, ln is the natural logarithm [20].

Principal component analysis weight coefficient of principal component:

[3]

Where Ci is the contribution rate of the ith principal component.

Comprehensive score of maize landraces in each:

[4]

Where Pi is the weight coefficient of the ith principal component, and Zi is the score of the ith principal component of the corresponding maize germplasm.

Comprehensive evaluation.

The formula for calculating the comprehensive score:

[5]

Stepwise regression analysis.

Using the comprehensive score D value as the dependent variable and 9 trait indicators as independent variables, a stepwise regression analysis was conducted to screen key indicators.

Data processing.

Excel 2018 was used to calculate the mean and standard deviation of each trait. The quantitative traits were measured repeatedly for three times, and the average of the three measured values was taken as the result SPSS 26.0 software (IBM company, New York, USA) was used to calculate the coefficient of variation and analyze the frequency distribution of different character types. The Shannon-Wiener index was calculated using Excel 2018. Circular visualization, including enhancements and implementations, was performed in the Microbioinformatics cloud platform (www.bioinformatics.com.cn), which also facilitated z-score transformation and Euclidean distance-based complete clustering. Origin 2021 was used to draw the grouped violin diagram, IBM SPSS statistics 26 conducted analysis of variance (one-way ANOVA, Duncan method), correlation analysis (Pearson, Perform statistical analysis on the mean and standard deviation, conduct a two-tailed test of significance, and exclude paired cases. P = 0.05, P = 0.01), stepwise regression analysis (a linear regression approach using the stepwise method, where normal distribution is confirmed by the Durbin-Watson test; default settings include: a significance level of 0.05 for introducing variables, ANOVA with an F-to-enter value of <0.05 and an F-to-remove value of >0.10, and output of tolerance and VIF for collinearity diagnosis) and constructed the optimal regression equation, and conducted principal component analysis, calculated the scores of each principal component and the D value of the comprehensive score for comprehensive evaluation, and screened excellent germplasm.

Results and analysis

Phenotypic diversity analysis of maize germplasm resources.

Descriptive statistical analysis was carried out on 9 phenotypic quantitative traits of 119 maize landraces (S2 File). The coefficient of variation of 9 phenotypic traits ranged from 20.73% to 60.96%, and the genetic diversity index calculated across traits ranged from 0.88 to 0.98. Among the 9 quantitative traits of the tested materials, 1000 grain weight, the angle between the first branch of tassel and the main axis, etc., had a larger variation range; The coefficient of variation of the number of secondary branches of tassels was the largest (60.96%), followed by the coefficient of variation of the angle between the first branches and the main axis of tassels (52.72%); The average coefficient of variation for the seven other quantitative traits was 25.74%. The phenotypic diversity index, ranked from highest to lowest, was: 1000-grain weight (0.98), number of grains per row (0.97), number of secondary tassel branches (0.94), tassel stalk diameter (0.93), tassel stalk length (0.92), tassel principal axis length (0.91), tassel branches and number of rows per spike (both 0.89), and angle between the first tassel branches and principal axis (0.88).The results showed that the genetic variation of phenotypic traits of the 119 maize landraces were rich, and the genetic diversity was high(Table 2).

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Table 2. Phenotypic diversity and variation analysis of 9 quantitative traits in tested materials.

https://doi.org/10.1371/journal.pone.0355741.t002

Correlation analysis.

In order to understand the correlation between the phenotypic traits of the tested resources, the correlation analysis was carried out on the 9 traits, and the Pearson correlation coefficient was used to express the strength of the correlation. There were extensive correlations among 21 pairs of traits among 9 phenotypic traits, reaching significant and extremely significant correlation levels. Specific analysis showed that:

The correlation coefficients between the number of rows per spike and the number of grains per row, the number of rows per spike and 1000-grain weight, and the number of rows per spike and tassel stalk diameter were −0.16, −0.40, and 0.16, respectively, all significant at the 0.01 level (S3 Table). The correlations between grains per row and 1000 grain weight, grains per row and:tassel principal axis length; grains per row and tassel stalk diameter; as well as grains per row and tassel secondary, all reached significance at the 0.01 level. The results showed that there were significant positive correlations:between 1000 grain weight and tassel stalk length, between 1000 grain weight and tassel principal axis length, between 1000 grain weight and tassel secondary branch number; There was a significant negative correlation between tassel stalk length and tassel branch number (p < 0.01, −1 < r < 0). The correlations between tassel principal axis length and tassel stalk diameter, tassel principal axis length and:tassel secondary branch number, all reached significance at the 0.01 level. The correlations between tassel stalk diameter and tassel stalk diameter,tassel stalk diameter and:tassel secondary branch number, all reached significance at the 0.01 level.The correlations between tassel branch number and tassel stalk diametertassel branch number tassel branch number and the angle between the first branch of tassel and the main axis of tassel, all reached significance at the 0.01 level. In addition to the number of tassel branches, It can be seen that there is a certain correlation between the three factors of maize yield (row number per spike, grain number per row, 1000 grain weight) and the tassel traits of maize, and the tassel of maize plays an important role in the formation of yield(Table 3).

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Table 3. Pearson correlation analysis of 9 quantitative traits in tested materials.

https://doi.org/10.1371/journal.pone.0355741.t003

Principal component analysis.

By conducting principal component analysis (PCA) on nine phenotypic traits, we extracted four principal component factors. The eigenvalues of the four principal components were 2.18, 1.63, 1.27 and 1.12, which were greater than 1, and the cumulative variance interpretation rate was 68.81%, which could explain 68.81% of the information of the nine investigated quantitative traits (S4 File). The contribution rate of the first principal component was the largest (24.17%), which was closely related to the number of secondary branches of tassel. The contribution rate of the second principal component was 18.07%, which was closely related to the number of tassel branches. The contribution rates of the third principal component and the fourth principal component were 14.07% and 12.49%, respectively, which were closely related to the number of rows per spike and the angle between the first branch and the main axis of tassel, respectively. It can be seen that the original nine trait indicators can be transformed into four independent comprehensive indicators through principal component analysis, which can be used to build a linear model of four principal components and carry out comprehensive evaluation and analysis (Table 4).

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Table 4. Eigen values and eigenvectors of first four principal components.

https://doi.org/10.1371/journal.pone.0355741.t004

Comprehensive evaluation.

The principal component score was used for comprehensive evaluation, and the “linear combination coefficient matrix” was used to establish the relationship equation between the principal component and the research item (the relationship expression is established based on the standardized data), as follows:

In the formula, X1 ~ X9 are respectively: X1:spike row number; X2:grains per row; X3:1000 grain weight; X4:tassel stalk length; X5:tassel principal axis length; X6:tassel stalk diameter; X7:tassel branch number; X8:tassel secondary branch number; X9:The angle between the first branch of tassel and the main axis of tassel.

The scores of the four principal components are normalized by using the fuzzy membership function to calculate the comprehensive score D value: D value = 0.351W1+0.263W2 + 0.204W3+0.182W4.

The D values of 119 maize landraces ranged from 0.033 to 0.960 (Table 1), and the top ten maize landraces with D values were Z102, Z102 and Z102, respectively Z7, Z89, Z74, Z113, Z98, Z94, Z110, Z69, Z116.

Cluster analysis and characteristics of various clusters.

Based on 9 quantitative traits for systematic clustering analysis, 119 maize landraces were divided into 3 categories (S5 File). Cluster Ⅰ includes 17 maize landraces, accounting for 14.29% of all maize landraces, with a D value range of 0.033–0.960; Cluster Ⅱ includes 49 maize landraces, accounting for 41.18% of all maize landraces, with a D value range of 0.148–0.751; Cluster Ⅲ includes 53 maize landraces, accounting for 44.54% of all maize landraces, with a D value range of 0.050–0.677 (Fig 1).

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Fig 1. Cluster analysis of comprehensive scores of 119 maize landraces.

Note:SRN: spike row number; GPR: grains per row; TGW: 1000 grain weight; TSL: tassel stalk length; TPAL: tassel principal axis length; TSD: tassel stalk diameter; TBN: tassel branch number; TSBN: tassel secondary branch number; AFBMA: The angle between the first branch of tassel and the main axis of tassel; Same as bellow. Cluster tree color: Black represents Group Ⅰ; Red represents Group Ⅱ; Green represents Group Ⅲ.

https://doi.org/10.1371/journal.pone.0355741.g001

Further comparison of the three yield factors and tassel correlation traits among the three clusters showed that there were no significant differences among the three clusters for the four traits of tassel stalk length, tassel principal axis length, tassel stalk diameter, and tassel secondary branch number. However, there were significant differences among the three clusters for the two traits of thousand grain weight and the angle between the first branch of tassel and the main axis of tassel (Fig 2).

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Fig 2. Comparison of the phenotypic differences of three cluster groups of maize landraces.

Note: A: spike row number; B: grains per row; C: 1000 grain weight; D: tassel stalk length; E: tassel principal axis length; F: tassel stalk diameter; G: tassel branch number; H: tassel secondary branch number; I: The angle between the first branch of tassel and the main axis of tassel. NS: The difference is not significant; *: There is a significant difference at the 0.05 level. C1: Cluster Ⅰ (n = 17); C2: Cluster Ⅱ (n = 49); C3: Cluster Ⅲ (n = 53).

https://doi.org/10.1371/journal.pone.0355741.g002

There were significant differences (p < 0.05) in the three essential traits of yield between cluster I and other clusters, with lower number of grains per row and thousand grain weight compared to other clusters. The angle between the first branch of tassel and the main axis of tassel of cluster I was higher than that of other clusters, and the difference between them was significant. The number of spike rows was 15.29, the number of grains per row was 26.47, the thousand grain weight was 193.45 g, the length of tassel stalk was 19.21 cm, the length of tassel principal axis was 34.70 cm, the diameter of tassel stalk was 0.63 cm, the number of tassel branches was 20.67, the number of secondary branches per tassel was 4.69, and the angle between the first branch of tassel and the main axis was 67.51 °. The main characteristics of this cluster were slightly more rows per spike, but slightly lower grain number and thousand grain weight per row. The angle between the first branch and the main axis of the tassel was large, and the overall performance was long bald tips, low grain density, and flat branches of the tassel.

Compared with the other two clusters, Cluster II has the fewest number of rows per spike, with higher row grain count, thousand grain weight, tassel stalk length, and secondary branch count. However, only the thousand grain weight and the angle between the first branch and the main axis of the tassel show significant differences compared to other clusters. The main characteristics of this clusters were slightly higher number of grains per row, slightly higher thousand grain weight, overall slender spikes, slightly longer tassel stalks, slightly more secondary branches of per tassel, and moderate other traits.

Comparison between Cluster I and Cluster II showed that Cluster III has the shortest tassel stem length, the fewest number of tassel branches, the smallest angle between the first branch and the main axis of tassel, the longest principal axis length, and the largest tassel stalk diameter. The main characteristics of this cluster were that the tassel stalk was thick and short, the main axis is slightly long, the tassel branches were few and clustered, and the other traits were moderate.

Stepwise regression analysis.

To screen the evaluation and identification indicators for the three factors of yield and tassel traits in maize landraces, 9 trait values were used as independent variables, and the comprehensive evaluation D value was used as the dependent variable. Through stepwise regression analysis, the optimal regression equation was constructed as follows:

D = −0.402 + 0.024 X8 + 0.004 X9 + 0.515 X6 + 0.014 X1, In the formula, X8, X9, X6, and X1 respectively represent the number of secondary branches of the tassel, the angle between the first branches and the main axis of of the tassel, the diameter of the tassel stalk, and the number of spikes. The coefficient of determination for this equation was R² = 0.952, P < 0.0001, This indicated that the constructed regression equation has a highly significant linear relationship. Therefore, the number of secondary branches of the tassel, the angle between the first branches and the main axis of the tassel, the diameter of the tassel stalk, and the number of spike rows could be used as evaluation indicators for the excellent traits of maize landraces. these traits may be utilized for the selection of traits in maize breeding.

Discussion

Phenotypic traits encompass both qualitative and quantitative characteristics and remain one of the most direct and foundational approaches for assessing plant genetic diversity [21]. In diversity analyses, the coefficient of variation (CV) primarily reflects the magnitude of phenotypic variation within a trait—essentially the relative spread of trait values across accessions [22–23]. By contrast, the Shannon diversity index (H′) captures the distribution pattern of phenotypic classes or categories, integrating both richness (the number of distinct phenotypic states) and evenness (their relative abundance) [24]. It is therefore important to distinguish these metrics conceptually: CV emphasizes the intensity of variation, whereas H′ emphasizes the structural complexity of diversity.

Crop improvement fundamentally depends on the presence and accessibility of genetic diversity [25]. As the bridge between genotype and phenotype, phenotypic traits provide essential information for germplasm evaluation, utilization, and the dissection of complex agronomic traits [26]. In this study, nine quantitative phenotypic traits were measured across 119 maize landraces. The coefficients of variation ranged from 20.73% to 60.96%, while genetic diversity indices (H′) ranged from 0.88 to 0.98. These values indicate not only substantial phenotypic variation but also relatively high evenness in trait-state distribution, suggesting that the studied landraces harbor considerable genetic diversity with potential utility in breeding.

These findings align with previous reports emphasizing the richness of genetic variation in traditional maize germplasm. Using combined phenotypic and SSR data, Wei et al. [27] demonstrated that 102 local maize varieties from southwestern China possess abundant genetic variability and unique alleles. Similarly, Nelimor et al. [28] and Aleksandar et al. [29] highlighted the high degree of phenotypic and genetic variation characteristic of farmer-managed maize landraces. The present results are consistent with these conclusions, reinforcing the view that landraces represent reservoirs of adaptive and functional diversity. Beyond general diversity, several studies have pointed to their practical breeding value: Nelimor et al. [30] suggested that maize landraces can serve as sources of drought tolerance [31]; Gaikpa et al. [32] showed that high genetic variation in European maize cultivars could be effectively exploited through integrated genomic tools to improve resistance to Fusarium head blight; and Mayer et al. [33] reported that landrace-derived haplotypes often outperform those from modern breeding lines in key agronomic traits.

Among the nine quantitative traits analyzed here, three were yield-related (rows per ear, grains per row, thousand-grain weight), and six described tassel architecture. Traits exhibiting particularly pronounced variation included thousand-grain weight and the angle between the first tassel branch and the main axis. Given that yield-related traits directly influence overall productivity [34–35], such variation implies tangible potential for yield improvement. Furthermore, maize flowering traits—tasseling, silking, and pollen shedding—represent a critical window for grain set and are highly sensitive to high-temperature stress during reproductive development [36–38]. The observed phenotypic variation among the 119 landraces may thus encompass valuable alleles or trait combinations relevant to both yield stability and climate resilience.

Yield in maize is determined by a suite of interrelated traits, including effective spike number, rows per ear, grains per row, and thousand-grain weight. While spike traits generally show positive associations with yield [39], the strength and direction of these relationships can vary depending on genetic background and environmental conditions [40]. In this study, pairwise correlation analysis among nine phenotypic traits revealed 21 significant associations, many reaching p < 0.05. Yield components—rows per ear, grains per row, and thousand-grain weight—were strongly intercorrelated; however, the relationships were not uniformly positive. Thousand-grain weight was positively associated with grains per row but negatively correlated with rows per ear. Such counterintuitive patterns likely reflect compensation effects, whereby increased sink capacity (more rows) limits resource allocation per grain, reducing individual grain mass under fixed genetic or environmental constraints. These findings partially contrast with Pallabi et al. [41], who reported uniformly positive correlations between yield and rows per ear, grains per spike, and thousand-grain weight. The discrepancy may stem from differences in germplasm structure, population size, or growing environments, underscoring the context-dependency of trait–yield relationships in maize. Tassel architecture represents another key determinant of yield potential. As the primary male reproductive organ, the tassel influences plant resource allocation and photosynthetic efficiency [42]. In this study, the number of tassel branches was negatively correlated with both rows per ear and grains per row, aligning with previous evidence that excessive tassel branching reduces yield [43]. Biologically, this trade-off can be explained by Lambert et al. [44], who demonstrated that tassels develop apically and precede ear formation. Excessive branching increases vegetative investment, intensifying nutrient competition with developing ears and generating surplus pollen that shades leaves, thereby impairing photosynthesis. Consequently, tassel branch number emerges as a pivotal trait linking plant architecture with yield efficiency.

Maize tassel branch number is quantitatively inherited, governed by multiple genetic loci with high heritability (>80%) and primarily additive and dominant gene action, showing little epistasis [45–46]. This genetic architecture makes it amenable to selection, particularly in breeding programs targeting compact plant types and improved resource-use efficiency. Collectively, these results highlight that maize yield is shaped not merely by the magnitude of individual traits but by their balance and coordination. Negative correlations among yield components emphasize the importance of optimizing trait combinations rather than maximizing single traits. Compared with earlier studies, our findings reinforce the view that maize landraces maintain complex trait networks balancing reproductive allocation and resource efficiency. Leveraging this diversity could enable breeders to fine-tune yield architectures suited to both high-input systems and stress-prone environments.

Principal Component Analysis (PCA) was employed to reduce the dimensionality of the nine phenotypic traits, transforming them into a smaller set of uncorrelated synthetic variables that capture the majority of the phenotypic variation [47]. Four principal components (PCs) were extracted, explaining a cumulative variance of 68.81% among the 119 maize landraces. A deeper interpretation of the loadings reveals distinct biological themes. PC1 was heavily weighted by the number of tassel branches and secondary branches, representing vegetative allocation to the male inflorescence; this axis essentially contrasts excessive tassel proliferation against compactness. PC2, dominated by rows per ear and grains per row, represents yield sink capacity. PC3 and PC4 were defined by the tassel branch angle and thousand-grain weight, respectively, reflecting plant architecture and grain-filling efficiency. These components illustrate that phenotypic diversity in these landraces is structured around the trade-off between resource allocation to reproduction and vegetative growth. By integrating PCA scores with membership function analysis, we derived a comprehensive evaluation index (D-value, range: 0.033–0.960) to rank the germplasm. Landraces such as Z102, Z7, and Z89 emerged as superior resources. This multivariate approach refines traditional classification methods [48] and translates complex trait data into actionable breeding metrics. Crucially, since PC1 and PC2 capture traits known to be controlled by additive and dominant genes with high heritability [45–46], the identified elite materials provide a robust foundation for marker-assisted selection (MAS) and genomic selection (GS) [49].

Our findings align with previous reports of rich diversity in rural maize varieties [27–28] but extend them by highlighting the antagonistic relationship between tassel proliferation (PC1) and yield components (PC2). This supports the hypothesis that excessive tassel growth intensifies competition for resources, thereby limiting yield potential [44]. Compared to studies focusing solely on genetic distances, our phenotypic component analysis offers a clearer target for breeders: selecting for reduced tassel branching while maintaining high sink capacity. While the integration of phenomics [50] and genome-wide association studies (GWAS) [51] promises further advances, this study provides a practical framework for utilizing landraces. However, we acknowledge that the performance of these landraces is subject to genotype-by-environment interactions [49,52]. Future work should integrate SSR markers [53] or GWAS to map the quantitative trait loci (QTL) underlying these principal components, validating whether the D-value is a reliable predictor of genetic merit across environments [54]. In conclusion, this study demonstrates that maize landraces harbor complex trait architectures that balance reproductive output with resource allocation. By deconvoluting these traits into independent principal components, we provide breeders with not just a list of elite materials, but a strategic blueprint for optimizing yield potential through specific morphological adjustments.

Conclusion

In summary, this study demonstrates that the 119 maize landraces evaluated herein harbor substantial phenotypic variability and high genetic diversity, confirming their status as vital reservoirs of adaptive alleles. Beyond merely quantifying diversity, our multivariate analysis elucidated the intrinsic trade-offs among key agronomic traits. The principal component analysis clarified that the divergence among these landraces is primarily structured along axes representing the balance between tassel proliferation and yield sink capacity, as well as grain-filling efficiency. This finding provides a biological explanation for the negative correlations observed between certain yield components and tassel traits, highlighting that optimizing resource allocation is crucial for yield improvement.

The identification of elite germplasm (e.g., Z102, Z7, Z89) through comprehensive scoring offers immediate practical value. These materials, characterized by favorable trait combinations identified via PCA, serve as ideal parental candidates for doubled haploid (DH) breeding programs [55] aimed at enhancing both architectural efficiency and productivity. Furthermore, the high heritability of the traits driving these principal components suggests that the phenotypic patterns observed here can be translated into genetic gains through marker-assisted selection.

Future perspectives should focus on bridging the gap between phenotype and genotype. While this study provides a robust framework for phenotypic evaluation, subsequent efforts should employ high-density molecular markers to validate the quantitative trait loci (QTL) underlying these key principal components. Integrating genomic selection models with the diverse genetic backgrounds identified in this study will facilitate the precise introgression of desirable alleles into elite cultivars, ultimately contributing to the development of maize varieties that are both high-yielding and resilient to environmental stresses.

Supporting information

S1 Table. The germplasm names of 119 maize landraces.

https://doi.org/10.1371/journal.pone.0355741.s001

(DOC)

S2 File. Dataset of phenotypic quantitative traits.

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S3 Table. P value of Pearson correlation analysis among 9 personality traits.

Note: * represents significant correlation at P < 0.05; **represents significant correlation at P < 0.01.

https://doi.org/10.1371/journal.pone.0355741.s003

(DOC)

S4 File. Data from principal component analysis and comprehensive score analysis of 119 maize landraces.

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(XLS)

S5 File. Dataset with 3 clusters.

https://doi.org/10.1371/journal.pone.0355741.s005

(XLS)

Acknowledgments

The authors want to thank the staff of the Corn Research Team for their valuable guidance and suggestions during the research process. We also appreciate the Institute of Crop Sciences, Chinese Academy of Agricultural Sciences for providing the germplasm resources that supported this study.

References

1. Ranum P, Peña-Rosas JP, Garcia-Casal MN. Global maize production, utilization, and consumption. Ann N Y Acad Sci. 2014;1312:105–12. pmid:24650320
- View Article
- PubMed/NCBI
- Google Scholar
2. Prasanna BM. Diversity in global maize germplasm: characterization and utilization. J Biosci. 2012;37(5):843–55. pmid:23107920
- View Article
- PubMed/NCBI
- Google Scholar
3. Wei K, Zhang H, Xu X, Du H, Huang Y, Zhang Z. Evaluation of phenotype and genetic diversity of maize landraces from Hubei Province, Southwest China. Front Agric China. 2009;3(4):374–82.
- View Article
- Google Scholar
4. Ruiz de Galarreta JI, Alvarez A. Morphological classification of maize landraces from northern Spain. Genetic Resources and Crop Evolution. 2001;48(4):391–400.
- View Article
- Google Scholar
5. Tamang A, Macharia MW, Caproni L, Miculan M, Mager S, Ahmed JS, et al. Genomic, climatic, and cultural diversity of maize landraces from the Himalayan Kingdom of Bhutan. Plants People Planet. 2024;6(4):965–78.
- View Article
- Google Scholar
6. Costa FM, de Almeida Silva NC, Vidal R, Clement CR, Veasey EA. A New Methodological Approach to Detect Microcenters and Regions of Maize Genetic Diversity in Different Areas of Lowland South America. Econ Bot. 2023;77(4):345–71.
- View Article
- Google Scholar
7. Troyer AF. Adaptedness and Heterosis in Corn and Mule Hybrids. Crop Science. 2006;46(2):528–43.
- View Article
- Google Scholar
8. Normile D. Variety Spices Up Chinese Rice Yields. Science. 2000;289(5482):1122–3.
- View Article
- Google Scholar
9. Mamani-Huarcaya BM, González-Fontes A, Navarro-Gochicoa MT, Camacho-Cristóbal JJ, Ceacero CJ, Herrera-Rodríguez MB, et al. Characterization of two Peruvian maize landraces differing in boron toxicity tolerance. Plant Physiol Biochem. 2022;185:167–77. pmid:35696891
- View Article
- PubMed/NCBI
- Google Scholar
10. Connolly LN, Lorenz N, Maleki K, Kayafas N, Dick RP, Mercer KL. Nitrogen fixation rates and aerial root production among maize landraces. Front Plant Sci. 2025;16:1502884. pmid:39935953
- View Article
- PubMed/NCBI
- Google Scholar
11. Hammami MDE, Madur D, Kthiri Z, Galaretto A, Nicolas SD, Charcosset A, et al. Phylogenetic relationships and genetic diversity of Tunisian maize landraces. PLoS One. 2025;20(1):e0316185. pmid:39841663
- View Article
- PubMed/NCBI
- Google Scholar
12. Dominguez PG, Gutierrez AV, Fass MI, Filippi CV, Vera P, Puebla A, et al. Genome-Wide Diversity in Lowland and Highland Maize Landraces From Southern South America: Population Genetics Insights to Assist Conservation. Evol Appl. 2024;17(12):e70047. pmid:39628628
- View Article
- PubMed/NCBI
- Google Scholar
13. Balconi C, Galaretto A, Malvar RA, Nicolas SD, Redaelli R, Andjelkovic V, et al. Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity. Biology (Basel). 2024;13(6):454. pmid:38927334
- View Article
- PubMed/NCBI
- Google Scholar
14. Guo R, Li T, Zhang Q, Wang J, Guo J, Wang L, et al. Genetic and molecular characterization of maize landraces from central China. Agronomy. 2024;14(6):1278.
- View Article
- Google Scholar
15. Babic V, Stanisavljevic D, Zoric M, Mikic S, Mitrovic B, Andjelkovic V, et al. Identification of New Sources for Earliness and Low Grain Moisture at Harvest through Maize Landraces’ Test-Cross Performance. Agronomy. 2022;12(8):1939.
- View Article
- Google Scholar
16. Yang M, Yang Z, Yang W, Yang E. Genetic Diversity Assessment of the International Maize and Wheat Improvement Center and Chinese Wheat Core Germplasms by Non-Denaturing Fluorescence In Situ Hybridization. Plants (Basel). 2022;11(11):1403. pmid:35684176
- View Article
- PubMed/NCBI
- Google Scholar
17. Li Y, Shi Y, Cao Y, Wang T. Establishment of a core collection for maize germplasm preserved in Chinese National Genebank using geographic distribution and characterization data. Genet Resour Crop Evol. 2005;51(8):845–52.
- View Article
- Google Scholar
18. Li Y, Wang J, Zhong S, Huo Q, Wang Q, Shi Y, et al. MADS-box encoding gene Tunicate1 positively controls maize yield by increasing leaf number above the ear. Nat Commun. 2024;15(1):9799. pmid:39532880
- View Article
- PubMed/NCBI
- Google Scholar
19. Rivera-Hernández G, Tijerina-Castro GD, Cortés-Pérez S, Ferrera-Cerrato R, Alarcón A. Evaluation of functional plant growth-promoting activities of culturable rhizobacteria associated to tunicate maize (Zea mays var. tunicata A. St. Hil), a Mexican exotic landrace grown in traditional agroecosystems. Front Microbiol. 2024;15:1478807. pmid:39417083
- View Article
- PubMed/NCBI
- Google Scholar
20. Shannon CE. A Mathematical Theory of Communication. Bell System Technical Journal. 1948;27(3):379–423.
- View Article
- Google Scholar
21. Stepansky A, Kovalski I, Perl-Treves R. Intraspecific classification of melons (Cucumis melo L.) in view of their phenotypic and molecular variation. Pl Syst Evol. 1999;217(3–4):313–32.
- View Article
- Google Scholar
22. Xin Y-H, Wu Y-X, Qiao B, Su L, Xie S-Q, Ling P. Evaluation on the phenotypic diversity of Calamansi (Citrus microcarpa) germplasm in Hainan island. Sci Rep. 2022;12(1):371. pmid:35013363
- View Article
- PubMed/NCBI
- Google Scholar
23. Zhang Y, Cao Y, Huo H, Xu J, Tian L, Dong X, et al. An assessment of the genetic diversity of pear (Pyrus L.) germplasm resources based on the fruit phenotypic traits. Journal of Integrative Agriculture. 2022;21(8):2275–90.
- View Article
- Google Scholar
24. Reed DH, Frankham R. Correlation between Fitness and Genetic Diversity. Conservation Biology. 2003;17(1):230–7.
- View Article
- Google Scholar
25. Bhanu AN. Assessment of Genetic Diversity in Crop Plants - An Overview. APAR. 2017;7(3).
- View Article
- Google Scholar
26. Campbell DR. Using phenotypic manipulations to study multivariate selection of floral trait associations. Ann Bot. 2009;103(9):1557–66. pmid:19218579
- View Article
- PubMed/NCBI
- Google Scholar
27. Wei K, Zhang H, Xu X, Du H, Huang Y, Zhang Z. Evaluation of phenotype and genetic diversity of maize landraces from Hubei Province, Southwest China. Front Agric China. 2009;3(4):374–82.
- View Article
- Google Scholar
28. Nelimor C, Badu-Apraku B, Garcia-Oliveira AL, Tetteh A, Paterne A, N’guetta AS-P, et al. Genomic Analysis of Selected Maize Landraces from Sahel and Coastal West Africa Reveals Their Variability and Potential for Genetic Enhancement. Genes (Basel). 2020;11(9):1054. pmid:32906687
- View Article
- PubMed/NCBI
- Google Scholar
29. Popović A, Kravić N, Babić M, Prodanović S, Sečanski M, Babić V. Breeding potential of maize landraces evaluated by their testcross performance. Zemdirbyste-Agriculture. 2020;107(2):153–60.
- View Article
- Google Scholar
30. Nelimor C, Badu-Apraku B, Tetteh AY, N’guetta ASP. Assessment of Genetic Diversity for Drought, Heat and Combined Drought and Heat Stress Tolerance in Early Maturing Maize Landraces. Plants (Basel). 2019;8(11):518. pmid:31744251
- View Article
- PubMed/NCBI
- Google Scholar
31. Barbosa PAM, Fritsche-Neto R, Andrade MC, Petroli CD, Burgueño J, Galli G, et al. Introgression of Maize Diversity for Drought Tolerance: Subtropical Maize Landraces as Source of New Positive Variants. Front Plant Sci. 2021;12:691211. pmid:34630452
- View Article
- PubMed/NCBI
- Google Scholar
32. Gaikpa DS, Kessel B, Presterl T, Ouzunova M, Galiano-Carneiro AL, Mayer M, et al. Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools. Theor Appl Genet. 2021;134(3):793–805. pmid:33274402
- View Article
- PubMed/NCBI
- Google Scholar
33. Mayer M, Hölker AC, González-Segovia E, Bauer E, Presterl T, Ouzunova M, et al. Discovery of beneficial haplotypes for complex traits in maize landraces. Nat Commun. 2020;11(1):4954. pmid:33009396
- View Article
- PubMed/NCBI
- Google Scholar
34. Mohammadi SA, Prasanna BM, Singh NN. Sequential Path Model for Determining Interrelationships among Grain Yield and Related Characters in Maize. Crop Science. 2003;43(5):1690–7.
- View Article
- Google Scholar
35. M MK, S B, F S, S JH, M M. Path analysis and multivariate factorial analyses for determining interrelationships between grain yield and related characters in maize hybrids. Afr J Agric Res. 2012;7(48):6437–46.
- View Article
- Google Scholar
36. Wang Y, Tao H, Tian B, Sheng D, Xu C, Zhou H, et al. Flowering dynamics, pollen, and pistil contribution to grain yield in response to high temperature during maize flowering. Environmental and Experimental Botany. 2019;158:80–8.
- View Article
- Google Scholar
37. Wang Y, Liu X, Hou X, Sheng D, Dong X, Gao Y, et al. Maximum lethal temperature for flowering and seed set in maize with contrasting male and female flower sensitivities. J Agronomy Crop Science. 2021;207(4):679–89.
- View Article
- Google Scholar
38. Wang Y, Sheng D, Zhang P, Dong X, Yan Y, Hou X, et al. High temperature sensitivity of kernel formation in different short periods around silking in maize. Environmental and Experimental Botany. 2021;183:104343.
- View Article
- Google Scholar
39. Olawamide DO, Fayeun LS. Correlation and Path Coefficient Analysis for Yield and Yield Components in Late Maturing Pro-vitamin A Synthetic Maize (Zea mays L.) Breeding Lines. JEAI. 2020;:64–72.
- View Article
- Google Scholar
40. Wei K, Zhang H, Xu X, Du H, Huang Y, Zhang Z. Evaluation of phenotype and genetic diversity of maize landraces from Hubei Province, Southwest China. Front Agric China. 2009;3(4):374–82.
- View Article
- Google Scholar
41. Roy PR, Haque MdA, Ferdausi A, Al Bari MdA. Genetic variability, correlation and path co-efficients analyses of selected maize (Zea mays L.) genotypes. Fundamental and Applied Agriculture. 2018;3(1):382–9.
- View Article
- Google Scholar
42. Brown PJ, Upadyayula N, Mahone GS, Tian F, Bradbury PJ, Myles S, et al. Distinct genetic architectures for male and female inflorescence traits of maize. PLoS Genet. 2011;7(11):e1002383. pmid:22125498
- View Article
- PubMed/NCBI
- Google Scholar
43. Geraldi I, Filho JBM, Vencovsky R. Estimates of genetic parameters for tosses characters in maize (Zea mays L.) and breeding perspectives. Maydica. 1985;30:1–14.
- View Article
- Google Scholar
44. Lambert RJ, Johnson RR. Leaf Angle, Tassel Morphology, and the Performance of Maize Hybrids1. Crop Science. 1978;18(3):499–502.
- View Article
- Google Scholar
45. Mock JJ, Schuetz SH. Inheritance of Tassel Branch Number in Maize1. Crop Science. 1974;14(6):885–8.
- View Article
- Google Scholar
46. Schuetz SH, Mock JJ. Genetics of tassel branch number in maize and its implications for a selection program for small tassel size. Theor Appl Genet. 1978;53(6):265–71. pmid:24309903
- View Article
- PubMed/NCBI
- Google Scholar
47. Ben Salem K, Ben Abdelaziz A. Principal Component Analysis (PCA). Tunis Med. 2021;99(4):383–9. pmid:35244921
- View Article
- PubMed/NCBI
- Google Scholar
48. Yao Y, Yue J, Liu Y, Yang H, Feng H, Shen J. Classification of maize growth stages based on phenotypic traits and UAV remote sensing. Agriculture. 2024;14(7):1175.
- View Article
- Google Scholar
49. Yu HQ, Nie LX, Zhang WJ, Zheng HB, Liu M, Lin J. Effects of environment variables on maize yield and ear characters. Advanced Materials Research. 2013;2480(726–731):106–13.
- View Article
- Google Scholar
50. Zhou G, Li S, Ma L, Wang F, Jiang F, Sun Y, et al. Mapping and Validation of a Stable Quantitative Trait Locus Conferring Maize Resistance to Gibberella Ear Rot. Plant Dis. 2021;105(7):1984–91. pmid:33616427
- View Article
- PubMed/NCBI
- Google Scholar
51. Dube SP, Sibiya J, Kutu F. Genetic diversity and population structure of maize inbred lines using phenotypic traits and single nucleotide polymorphism (SNP) markers. Sci Rep. 2023;13(1):17851. pmid:37857752
- View Article
- PubMed/NCBI
- Google Scholar
52. Pace BA, Perales HR, Gonzalez-Maldonado N, Mercer KL. Physiological traits contribute to growth and adaptation of Mexican maize landraces. PLoS One. 2024;19(2):e0290815. pmid:38300909
- View Article
- PubMed/NCBI
- Google Scholar
53. Yuan LX, Fu JH, Warburton M, Li XH, Zhang SH, Khairallah M, et al. Comparison of genetic diversity among maize inbred lines based on RFLPs, SSRs, AFLPs and RAPDs. Yi Chuan Xue Bao. 2000;27(8):725–33. pmid:11055126
- View Article
- PubMed/NCBI
- Google Scholar
54. Roland P, Eschholz TW, Stamp P, Liedgens M. Early growth of flint maize landraces under cool conditions. Crop Science. 2009;49(1):169–78.
- View Article
- Google Scholar
55. Strigens A, Schipprack W, Reif JC, Melchinger AE. Unlocking the genetic diversity of maize landraces with doubled haploids opens new avenues for breeding. PLoS One. 2013;8(2):e57234. pmid:23451190
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Ranum P, Peña-Rosas JP, Garcia-Casal MN. Global maize production, utilization, and consumption. Ann N Y Acad Sci. 2014;1312:105–12. pmid:24650320
View Article
PubMed/NCBI
Google Scholar

[2] View Article

[3] PubMed/NCBI

[4] Google Scholar

[ref2] 2. Prasanna BM. Diversity in global maize germplasm: characterization and utilization. J Biosci. 2012;37(5):843–55. pmid:23107920
View Article
PubMed/NCBI
Google Scholar

[6] View Article

[7] PubMed/NCBI

[8] Google Scholar

[ref3] 3. Wei K, Zhang H, Xu X, Du H, Huang Y, Zhang Z. Evaluation of phenotype and genetic diversity of maize landraces from Hubei Province, Southwest China. Front Agric China. 2009;3(4):374–82.
View Article
Google Scholar

[10] View Article

[11] Google Scholar

[ref4] 4. Ruiz de Galarreta JI, Alvarez A. Morphological classification of maize landraces from northern Spain. Genetic Resources and Crop Evolution. 2001;48(4):391–400.
View Article
Google Scholar

[13] View Article

[14] Google Scholar

[ref5] 5. Tamang A, Macharia MW, Caproni L, Miculan M, Mager S, Ahmed JS, et al. Genomic, climatic, and cultural diversity of maize landraces from the Himalayan Kingdom of Bhutan. Plants People Planet. 2024;6(4):965–78.
View Article
Google Scholar

[16] View Article

[17] Google Scholar

[ref6] 6. Costa FM, de Almeida Silva NC, Vidal R, Clement CR, Veasey EA. A New Methodological Approach to Detect Microcenters and Regions of Maize Genetic Diversity in Different Areas of Lowland South America. Econ Bot. 2023;77(4):345–71.
View Article
Google Scholar

[19] View Article

[20] Google Scholar

[ref7] 7. Troyer AF. Adaptedness and Heterosis in Corn and Mule Hybrids. Crop Science. 2006;46(2):528–43.
View Article
Google Scholar

[22] View Article

[23] Google Scholar

[ref8] 8. Normile D. Variety Spices Up Chinese Rice Yields. Science. 2000;289(5482):1122–3.
View Article
Google Scholar

[25] View Article

[26] Google Scholar

[ref9] 9. Mamani-Huarcaya BM, González-Fontes A, Navarro-Gochicoa MT, Camacho-Cristóbal JJ, Ceacero CJ, Herrera-Rodríguez MB, et al. Characterization of two Peruvian maize landraces differing in boron toxicity tolerance. Plant Physiol Biochem. 2022;185:167–77. pmid:35696891
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref10] 10. Connolly LN, Lorenz N, Maleki K, Kayafas N, Dick RP, Mercer KL. Nitrogen fixation rates and aerial root production among maize landraces. Front Plant Sci. 2025;16:1502884. pmid:39935953
View Article
PubMed/NCBI
Google Scholar

[32] View Article

[33] PubMed/NCBI

[34] Google Scholar

[ref11] 11. Hammami MDE, Madur D, Kthiri Z, Galaretto A, Nicolas SD, Charcosset A, et al. Phylogenetic relationships and genetic diversity of Tunisian maize landraces. PLoS One. 2025;20(1):e0316185. pmid:39841663
View Article
PubMed/NCBI
Google Scholar

[36] View Article

[37] PubMed/NCBI

[38] Google Scholar

[ref12] 12. Dominguez PG, Gutierrez AV, Fass MI, Filippi CV, Vera P, Puebla A, et al. Genome-Wide Diversity in Lowland and Highland Maize Landraces From Southern South America: Population Genetics Insights to Assist Conservation. Evol Appl. 2024;17(12):e70047. pmid:39628628
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref13] 13. Balconi C, Galaretto A, Malvar RA, Nicolas SD, Redaelli R, Andjelkovic V, et al. Genetic and Phenotypic Evaluation of European Maize Landraces as a Tool for Conservation and Valorization of Agrobiodiversity. Biology (Basel). 2024;13(6):454. pmid:38927334
View Article
PubMed/NCBI
Google Scholar

[44] View Article

[45] PubMed/NCBI

[46] Google Scholar

[ref14] 14. Guo R, Li T, Zhang Q, Wang J, Guo J, Wang L, et al. Genetic and molecular characterization of maize landraces from central China. Agronomy. 2024;14(6):1278.
View Article
Google Scholar

[48] View Article

[49] Google Scholar

[ref15] 15. Babic V, Stanisavljevic D, Zoric M, Mikic S, Mitrovic B, Andjelkovic V, et al. Identification of New Sources for Earliness and Low Grain Moisture at Harvest through Maize Landraces’ Test-Cross Performance. Agronomy. 2022;12(8):1939.
View Article
Google Scholar

[51] View Article

[52] Google Scholar

[ref16] 16. Yang M, Yang Z, Yang W, Yang E. Genetic Diversity Assessment of the International Maize and Wheat Improvement Center and Chinese Wheat Core Germplasms by Non-Denaturing Fluorescence In Situ Hybridization. Plants (Basel). 2022;11(11):1403. pmid:35684176
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref17] 17. Li Y, Shi Y, Cao Y, Wang T. Establishment of a core collection for maize germplasm preserved in Chinese National Genebank using geographic distribution and characterization data. Genet Resour Crop Evol. 2005;51(8):845–52.
View Article
Google Scholar

[58] View Article

[59] Google Scholar

[ref18] 18. Li Y, Wang J, Zhong S, Huo Q, Wang Q, Shi Y, et al. MADS-box encoding gene Tunicate1 positively controls maize yield by increasing leaf number above the ear. Nat Commun. 2024;15(1):9799. pmid:39532880
View Article
PubMed/NCBI
Google Scholar

[61] View Article

[62] PubMed/NCBI

[63] Google Scholar

[ref19] 19. Rivera-Hernández G, Tijerina-Castro GD, Cortés-Pérez S, Ferrera-Cerrato R, Alarcón A. Evaluation of functional plant growth-promoting activities of culturable rhizobacteria associated to tunicate maize (Zea mays var. tunicata A. St. Hil), a Mexican exotic landrace grown in traditional agroecosystems. Front Microbiol. 2024;15:1478807. pmid:39417083
View Article
PubMed/NCBI
Google Scholar

[65] View Article

[66] PubMed/NCBI

[67] Google Scholar

[ref20] 20. Shannon CE. A Mathematical Theory of Communication. Bell System Technical Journal. 1948;27(3):379–423.
View Article
Google Scholar

[69] View Article

[70] Google Scholar

[ref21] 21. Stepansky A, Kovalski I, Perl-Treves R. Intraspecific classification of melons (Cucumis melo L.) in view of their phenotypic and molecular variation. Pl Syst Evol. 1999;217(3–4):313–32.
View Article
Google Scholar

[72] View Article

[73] Google Scholar

[ref22] 22. Xin Y-H, Wu Y-X, Qiao B, Su L, Xie S-Q, Ling P. Evaluation on the phenotypic diversity of Calamansi (Citrus microcarpa) germplasm in Hainan island. Sci Rep. 2022;12(1):371. pmid:35013363
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref23] 23. Zhang Y, Cao Y, Huo H, Xu J, Tian L, Dong X, et al. An assessment of the genetic diversity of pear (Pyrus L.) germplasm resources based on the fruit phenotypic traits. Journal of Integrative Agriculture. 2022;21(8):2275–90.
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref24] 24. Reed DH, Frankham R. Correlation between Fitness and Genetic Diversity. Conservation Biology. 2003;17(1):230–7.
View Article
Google Scholar

[82] View Article

[83] Google Scholar

[ref25] 25. Bhanu AN. Assessment of Genetic Diversity in Crop Plants - An Overview. APAR. 2017;7(3).
View Article
Google Scholar

[85] View Article

[86] Google Scholar

[ref26] 26. Campbell DR. Using phenotypic manipulations to study multivariate selection of floral trait associations. Ann Bot. 2009;103(9):1557–66. pmid:19218579
View Article
PubMed/NCBI
Google Scholar

[88] View Article

[89] PubMed/NCBI

[90] Google Scholar

[ref27] 27. Wei K, Zhang H, Xu X, Du H, Huang Y, Zhang Z. Evaluation of phenotype and genetic diversity of maize landraces from Hubei Province, Southwest China. Front Agric China. 2009;3(4):374–82.
View Article
Google Scholar

[92] View Article

[93] Google Scholar

[ref28] 28. Nelimor C, Badu-Apraku B, Garcia-Oliveira AL, Tetteh A, Paterne A, N’guetta AS-P, et al. Genomic Analysis of Selected Maize Landraces from Sahel and Coastal West Africa Reveals Their Variability and Potential for Genetic Enhancement. Genes (Basel). 2020;11(9):1054. pmid:32906687
View Article
PubMed/NCBI
Google Scholar

[95] View Article

[96] PubMed/NCBI

[97] Google Scholar

[ref29] 29. Popović A, Kravić N, Babić M, Prodanović S, Sečanski M, Babić V. Breeding potential of maize landraces evaluated by their testcross performance. Zemdirbyste-Agriculture. 2020;107(2):153–60.
View Article
Google Scholar

[99] View Article

[100] Google Scholar

[ref30] 30. Nelimor C, Badu-Apraku B, Tetteh AY, N’guetta ASP. Assessment of Genetic Diversity for Drought, Heat and Combined Drought and Heat Stress Tolerance in Early Maturing Maize Landraces. Plants (Basel). 2019;8(11):518. pmid:31744251
View Article
PubMed/NCBI
Google Scholar

[102] View Article

[103] PubMed/NCBI

[104] Google Scholar

[ref31] 31. Barbosa PAM, Fritsche-Neto R, Andrade MC, Petroli CD, Burgueño J, Galli G, et al. Introgression of Maize Diversity for Drought Tolerance: Subtropical Maize Landraces as Source of New Positive Variants. Front Plant Sci. 2021;12:691211. pmid:34630452
View Article
PubMed/NCBI
Google Scholar

[106] View Article

[107] PubMed/NCBI

[108] Google Scholar

[ref32] 32. Gaikpa DS, Kessel B, Presterl T, Ouzunova M, Galiano-Carneiro AL, Mayer M, et al. Exploiting genetic diversity in two European maize landraces for improving Gibberella ear rot resistance using genomic tools. Theor Appl Genet. 2021;134(3):793–805. pmid:33274402
View Article
PubMed/NCBI
Google Scholar

[110] View Article

[111] PubMed/NCBI

[112] Google Scholar

[ref33] 33. Mayer M, Hölker AC, González-Segovia E, Bauer E, Presterl T, Ouzunova M, et al. Discovery of beneficial haplotypes for complex traits in maize landraces. Nat Commun. 2020;11(1):4954. pmid:33009396
View Article
PubMed/NCBI
Google Scholar

[114] View Article

[115] PubMed/NCBI

[116] Google Scholar

[ref34] 34. Mohammadi SA, Prasanna BM, Singh NN. Sequential Path Model for Determining Interrelationships among Grain Yield and Related Characters in Maize. Crop Science. 2003;43(5):1690–7.
View Article
Google Scholar

[118] View Article

[119] Google Scholar

[ref35] 35. M MK, S B, F S, S JH, M M. Path analysis and multivariate factorial analyses for determining interrelationships between grain yield and related characters in maize hybrids. Afr J Agric Res. 2012;7(48):6437–46.
View Article
Google Scholar

[121] View Article

[122] Google Scholar

[ref36] 36. Wang Y, Tao H, Tian B, Sheng D, Xu C, Zhou H, et al. Flowering dynamics, pollen, and pistil contribution to grain yield in response to high temperature during maize flowering. Environmental and Experimental Botany. 2019;158:80–8.
View Article
Google Scholar

[124] View Article

[125] Google Scholar

[ref37] 37. Wang Y, Liu X, Hou X, Sheng D, Dong X, Gao Y, et al. Maximum lethal temperature for flowering and seed set in maize with contrasting male and female flower sensitivities. J Agronomy Crop Science. 2021;207(4):679–89.
View Article
Google Scholar

[127] View Article

[128] Google Scholar

[ref38] 38. Wang Y, Sheng D, Zhang P, Dong X, Yan Y, Hou X, et al. High temperature sensitivity of kernel formation in different short periods around silking in maize. Environmental and Experimental Botany. 2021;183:104343.
View Article
Google Scholar

[130] View Article

[131] Google Scholar

[ref39] 39. Olawamide DO, Fayeun LS. Correlation and Path Coefficient Analysis for Yield and Yield Components in Late Maturing Pro-vitamin A Synthetic Maize (Zea mays L.) Breeding Lines. JEAI. 2020;:64–72.
View Article
Google Scholar

[133] View Article

[134] Google Scholar

[ref40] 40. Wei K, Zhang H, Xu X, Du H, Huang Y, Zhang Z. Evaluation of phenotype and genetic diversity of maize landraces from Hubei Province, Southwest China. Front Agric China. 2009;3(4):374–82.
View Article
Google Scholar

[136] View Article

[137] Google Scholar

[ref41] 41. Roy PR, Haque MdA, Ferdausi A, Al Bari MdA. Genetic variability, correlation and path co-efficients analyses of selected maize (Zea mays L.) genotypes. Fundamental and Applied Agriculture. 2018;3(1):382–9.
View Article
Google Scholar

[139] View Article

[140] Google Scholar

[ref42] 42. Brown PJ, Upadyayula N, Mahone GS, Tian F, Bradbury PJ, Myles S, et al. Distinct genetic architectures for male and female inflorescence traits of maize. PLoS Genet. 2011;7(11):e1002383. pmid:22125498
View Article
PubMed/NCBI
Google Scholar

[142] View Article

[143] PubMed/NCBI

[144] Google Scholar

[ref43] 43. Geraldi I, Filho JBM, Vencovsky R. Estimates of genetic parameters for tosses characters in maize (Zea mays L.) and breeding perspectives. Maydica. 1985;30:1–14.
View Article
Google Scholar

[146] View Article

[147] Google Scholar

[ref44] 44. Lambert RJ, Johnson RR. Leaf Angle, Tassel Morphology, and the Performance of Maize Hybrids1. Crop Science. 1978;18(3):499–502.
View Article
Google Scholar

[149] View Article

[150] Google Scholar

[ref45] 45. Mock JJ, Schuetz SH. Inheritance of Tassel Branch Number in Maize1. Crop Science. 1974;14(6):885–8.
View Article
Google Scholar

[152] View Article

[153] Google Scholar

[ref46] 46. Schuetz SH, Mock JJ. Genetics of tassel branch number in maize and its implications for a selection program for small tassel size. Theor Appl Genet. 1978;53(6):265–71. pmid:24309903
View Article
PubMed/NCBI
Google Scholar

[155] View Article

[156] PubMed/NCBI

[157] Google Scholar

[ref47] 47. Ben Salem K, Ben Abdelaziz A. Principal Component Analysis (PCA). Tunis Med. 2021;99(4):383–9. pmid:35244921
View Article
PubMed/NCBI
Google Scholar

[159] View Article

[160] PubMed/NCBI

[161] Google Scholar

[ref48] 48. Yao Y, Yue J, Liu Y, Yang H, Feng H, Shen J. Classification of maize growth stages based on phenotypic traits and UAV remote sensing. Agriculture. 2024;14(7):1175.
View Article
Google Scholar

[163] View Article

[164] Google Scholar

[ref49] 49. Yu HQ, Nie LX, Zhang WJ, Zheng HB, Liu M, Lin J. Effects of environment variables on maize yield and ear characters. Advanced Materials Research. 2013;2480(726–731):106–13.
View Article
Google Scholar

[166] View Article

[167] Google Scholar

[ref50] 50. Zhou G, Li S, Ma L, Wang F, Jiang F, Sun Y, et al. Mapping and Validation of a Stable Quantitative Trait Locus Conferring Maize Resistance to Gibberella Ear Rot. Plant Dis. 2021;105(7):1984–91. pmid:33616427
View Article
PubMed/NCBI
Google Scholar

[169] View Article

[170] PubMed/NCBI

[171] Google Scholar

[ref51] 51. Dube SP, Sibiya J, Kutu F. Genetic diversity and population structure of maize inbred lines using phenotypic traits and single nucleotide polymorphism (SNP) markers. Sci Rep. 2023;13(1):17851. pmid:37857752
View Article
PubMed/NCBI
Google Scholar

[173] View Article

[174] PubMed/NCBI

[175] Google Scholar

[ref52] 52. Pace BA, Perales HR, Gonzalez-Maldonado N, Mercer KL. Physiological traits contribute to growth and adaptation of Mexican maize landraces. PLoS One. 2024;19(2):e0290815. pmid:38300909
View Article
PubMed/NCBI
Google Scholar

[177] View Article

[178] PubMed/NCBI

[179] Google Scholar

[ref53] 53. Yuan LX, Fu JH, Warburton M, Li XH, Zhang SH, Khairallah M, et al. Comparison of genetic diversity among maize inbred lines based on RFLPs, SSRs, AFLPs and RAPDs. Yi Chuan Xue Bao. 2000;27(8):725–33. pmid:11055126
View Article
PubMed/NCBI
Google Scholar

[181] View Article

[182] PubMed/NCBI

[183] Google Scholar

[ref54] 54. Roland P, Eschholz TW, Stamp P, Liedgens M. Early growth of flint maize landraces under cool conditions. Crop Science. 2009;49(1):169–78.
View Article
Google Scholar

[185] View Article

[186] Google Scholar

[ref55] 55. Strigens A, Schipprack W, Reif JC, Melchinger AE. Unlocking the genetic diversity of maize landraces with doubled haploids opens new avenues for breeding. PLoS One. 2013;8(2):e57234. pmid:23451190
View Article
PubMed/NCBI
Google Scholar

[188] View Article

[189] PubMed/NCBI

[190] Google Scholar

Figures

Abstract

Introduction

Materials and methods

Test materials

Test method

Overview of test site.

Test design.

9 quantitative traits.

Data analysis

Membership function analysis.

Genetic diversity index.

Comprehensive score of maize landraces in each:

Comprehensive evaluation.

Stepwise regression analysis.

Data processing.

Results and analysis

Phenotypic diversity analysis of maize germplasm resources.

Correlation analysis.

Principal component analysis.

Comprehensive evaluation.

Cluster analysis and characteristics of various clusters.

Stepwise regression analysis.

Discussion

Conclusion

Supporting information

S1 Table. The germplasm names of 119 maize landraces.

S2 File. Dataset of phenotypic quantitative traits.

S3 Table. P value of Pearson correlation analysis among 9 personality traits.

S4 File. Data from principal component analysis and comprehensive score analysis of 119 maize landraces.

S5 File. Dataset with 3 clusters.

Acknowledgments

References