Genetic divergence in maize regarding grain yield and tassel traitsABSTRACT
Maize tassels with a longer length and higher number of branches prevent the passage of solar radiation into the upper plant canopy and act as a drain for photoassimilates that could be used for grain production. This study aimed to verify if there is genetic divergence regarding grain yield and tassel traits among maize cultivars in three agricultural years. Twenty maize cultivars were evaluated and 11 tassel traits and grain yield were measured. Individual and joint analyses of variance were performed for each trait. Principal component analysis was performed, the generalized Mahalanobis distance matrix between cultivars was determined, and the genetic divergence analysis was performed, using the unweighted pair group method with arithmetic mean (UPGMA), which allowed constructing the dendrogram. The cophenetic correlation coefficient was calculated to validate the cluster. There is genetic divergence among maize cultivars and six groups of cultivars were formed. Tassel length, total number of branches, central spike dry matter, and grain yield are the traits that most contribute to the genetic divergence among maize cultivars.
Keywords: Principal components; Genetic dissimilarity; Zea mays L
RESUMO
Pendões de milho, com maior comprimento e maior número de ramificações acabam impedindo a passagem da radiação solar para o dossel superior da planta e atuam como dreno de fotoassimilados, sendo que, poderiam ser destinados a produção de grãos. O objetivo deste trabalho foi verificar se há divergência genética, em relação à produtividade de grãos e os caracteres de pendão entre os cultivares de milho, em três anos agrícolas. Foram avaliados 20 cultivares de milho e mensurados 11 caracteres de pendão e a produtividade de grãos. Para cada caractere, foram realizadas as análises de variância individual e conjunta. Foi realizada a análise de componentes principais, determinada a matriz da distância generalizada de Mahalanobis entre os cultivares e realizada a análise de divergência genética, por meio do método de agrupamento da ligação média entre grupo (UPGMA) e, a partir disso, foi construído o dendrograma. Para validação do agrupamento calculou-se o coeficiente de correlação cofenética. Existe divergência genética entre os cultivares de milho e foram formados seis grupos de cultivares. O comprimento do pendão, o número total de ramificações, a massa de matéria seca da espiga central e a produtividade de grãos são os caracteres que mais contribuem para a divergência genética entre os cultivares de milho.
Palavras-chave: Componentes principais; Dissimilaridade genética; Zea mays L
INTRODUCTION
Maize (Zea mays L.) is a monoecious species, that is, it presents female and male inflorescences on the same plant. The female inflorescence, represented by the ear, produces egg cells that turn into grains after fertilization. Tassel is the male reproductive organ and is responsible for producing pollen (BORÉM; GALVÃO; PIMENTEL, 2015). The development of productive lines and hybrids is important in maize breeding programs. Plants with smaller tassels (length) and fewer branches, but with sufficient pollen production for fertilization (DUVICK, 2005; FISCHER; EDMEADES, 2010) are targeted. According to Edwards (2011), longer tassels prevent the passage of solar radiation into the plant canopy and act as a drain for photoassimilates that could be used for grain production.
The genetic divergence is a strategy to obtain selection gains in crosses of divergent groups that have traits of interest. It can be estimated by multivariate analysis techniques, including principal component analysis and dissimilarity measures. The principal component analysis allows discarding traits that contribute little to the discrimination of the evaluated material, thus reducing time, cost, and labor. On the contrary, dissimilarity measures quantify the distances between genotypes, with the generalized Mahalanobis distance being used for data obtained with repetitions, considering the correlations between traits (CRUZ; REGAZZI; CARNEIRO, 2012).
There are several clustering methods, and hierarchical and optimization methods are the most used. In hierarchical methods, parents are grouped by a process repeated at various levels until the dendrogram establishment (CRUZ; CARNEIRO; REGAZZI, 2014). The unweighted pair group method with arithmetic mean (UPGMA) is among the hierarchical methods for clustering analysis and has been used in genetic divergence studies in maize (SIMON et al., 2012), being considered the most efficient method to group maize cultivars (CARGNELUTTI FILHO; GUADAGNIN, 2011).
Studies on genetic divergence through multivariate statistics was carried out in maize by Alves et al. (2015), Brewbaker (2015), Cargnelutti Filho and Guadagnin (2011), Chandel and Guleria (2019), Iqbal, Shinwari and Rabbani (2015), Nardino et al. (2016, 2017), Oliboni et al. (2012), Öner (2018), Silva et al. (2016), and Simon et al. (2012). However, studies on the genetic divergence between maize cultivars as a function of grain yield and with a higher number of tassel traits, that is, a higher detail between the constituent parts of the tassel, were not found in the literature. This information is of paramount importance, as it allows evaluating the behavior of cultivars to indicate genotypes that present divergent behavior. Thus, this study aimed to verify if there is genetic divergence regarding grain yield and tassel traits among maize cultivars in three agricultural years.
MATERIAL AND METHODS
Twenty maize cultivars (20A55, 30F53, AG8780, BM3066, DKB290, MS2010, MS2013, MS3022, StatusVIP, SX7331, 30A68, AG9025, AM9724, AS1666, AS1677, Celeron, DKB230, P1630, P2530, and SHS7915) were evaluated in three experiments conducted at the experimental area of the Department of Plant Science of the Federal University of Santa Maria, Rio Grande do Sul, Brazil, in the 2015/2016 (experiment 1), 2016/2017 (experiment 2), and 2017/2018 agricultural years (experiment 3) (Table 1). According to the Köppen classification, the regional climate is Cfa, that is, a humid subtropical climate with hot summers and no defined dry season (ALVARES et al., 2013). The soil of the experimental area is classified as an arenic dystrophic Red Argisol (SANTOS et al., 2018).
Descriptors of 20 maize cultivars regarding technology, company, type, cycle, use, grain, color, and investment.
The experiments were conducted in a randomized complete block design with three replications. The plots consisted of two rows of 5 m in length, spaced 0.80 m between rows and 0.20 m between plants in the row. Plant density was adjusted through manual thinning to reach five plants per meter of row, totaling 62,500 plants ha−1. The cultural practices were carried out according to the recommendations for maize cultivation, keeping the experiment free of weeds, pests, and diseases (FANCELLI; DOURADO NETO, 2009).
A total of 20, 11, and 20 tassels were randomly collected per plot in experiments 1, 2, and 3, respectively, at the end of the reproductive stage. These tassels were identified, stored in paper packaging, and taken to a forced-air oven at 60 °C until reaching a constant weight.
The following traits were measured in each tassel: peduncle length (PL, considering the distance between the collar of the flag leaf and the first branch, in cm), branch space length (BSL, cm), central spike length (CLS, cm), tassel length (TL= PL+BSL+CSL, cm), number of primary branches (NPB), number of secondary branches (NSB), total number of branches (TBN=NPB+NSB), peduncle dry matter (PDM, considering the region between the flag leaf collar and the first branch, in g), branch space dry matter (BSDM, g), central ear dry matter (CSDM, g), and total tassel dry matter (TDM=PDM+BSDM+CSDM, g) (Figure 1). Grain yield (GY, Mg ha−1 at 13% moisture) was evaluated from all plants in the plot.
The data from the 12 traits were subjected to individual and joint analyses of variance. The cultivar effect was considered fixed, while the agricultural year was considered random in the joint analysis. The differences between the means of the individual analyses of variance were tested using the Scott-Knott test at a 5% significance level for each experiment.
The overall mean obtained in the joint analysis of variance was used to perform the principal component analysis, a statistical technique of multivariate analysis that transforms a set of variables correlated with each other into a smaller set in such a way that their similarities and differences are highlighted (CRUZ; CARNEIRO; REGAZZI, 2014).
Subsequently, the traits were standardized, and the generalized Mahalanobis distance (D2) matrix was determined. The clustering analysis of the cultivars was performed from D2 using the hierarchical method unweighted pair group method with arithmetic mean (UPGMA), followed by the construction of the dendrogram (CRUZ; CARNEIRO; REGAZZI, 2014), considering a 50% cutoff point for group formation.
Representation of the traits evaluated in maize tassels, PL: peduncle length (considering the distance between the collar of the flag leaf and the first branch), in cm; BSL: branching space length, in cm; CSL: central spike length, in cm; TL: tassel length, in cm; NPB: number of primary branches; NSB: number of secondary branches; PDM: peduncle dry matter (considering the region between the flag leaf collar and the first branch), in g; BSDM: branching space dry matter, in g; CSDM: central spike dry matter, in g. Adapted from Wartha et al. (2016).
The cophenetic correlation coefficient (CCC) was calculated to evaluate the cluster consistency (FERREIRA, 2018). The CCC allowed verifying the adjustment capacity of the dendrogram in reproducing the dissimilarity matrix. CCC values close to the unit indicate a better representation of the dendrogram (CRUZ; CARNEIRO; REGAZZI, 2014). The cluster validation was complemented with the analysis of variance to compare the groups of cultivars, and the Scott-Knott test was carried out to compare the means of groups. Statistical analyses were performed using the software Genes (CRUZ, 2016) and the application Office Excel®.
RESULTS AND DISCUSSION
Individual analyses of variance showed that the cultivar effect was significant for the 12 traits in the three agricultural years. The joint analysis of variance enabled to observe significant cultivar effects for all traits, whereas only BSL showed no significant difference for agricultural year. These results show the presence of genetic variability, enabling the identification of superior cultivars. In addition, the interaction cultivar × agricultural year was significant for ten out of the 12 traits, showing the importance of studies on genetic variability in different years of cultivation, as different responses of cultivars can be obtained with changes in the environment (Table 2). According to Cargnelutti Filho et al. (2008), clusters of cultivars based on only one growing season may provide misleading information because the environmental variability between years and growing seasons is not considered within the same location. Thus, we can infer that there is genetic variability among maize cultivars, and the results are consistent because they come from three agricultural years, enabling the study of genetic divergence through the UPGMA hierarchical method.
In the joint analysis of variance, the mean tassel length was 46.874 cm, the number of primary branches was 10.161, the total number of branches was 12.545, the tassel dry matter was 2.701 g, and the grain yield was 9.184 Mg ha−1 (Table 2). Similar results were obtained by Brewbaker (2015), Nardino et al. (2016), Simon et al. (2012), and Yi et al. (2018), respectively, demonstrating an adequate development of maize plants in the three experiments.
Significance of the F-test for cultivar (C), agricultural year (Y), interaction C×Y, and mean and coefficient of variation (CV) of the individual and joint analyses of variance of 12 traits evaluated in 20 maize cultivars in the 2015/2016, 2016/2017, and 2017/2018 agricultural years.
The coefficients of variation (CV) ranged from 2.560% for tassel length (experiment 1) to 21.730% for the number of secondary branches (experiment 1) (Table 2). According to the classes established by Pimentel-Gomes (2009) for field experiments with crops, the coefficient of variation is classified as low when less than 10%, medium from 10 to 20%, high from 20 to 30%, and very high when higher than 30%. Thus, the coefficient of variation was classified as low for 18 cases, medium for 17 cases, and high for only one case. Overall, the high experimental precision lends credibility to the genetic divergence study.
The cultivars were separated into groups with different numbers of groups formed for each trait in each experiment based on the Scott-Knott test. The trait with the lowest number of groups was grain yield, with two groups in experiment 1 (Table 3) and three groups in experiments 2 (Table 4) and 3 (Table 5). Simon et al. (2012) studied the grain yield of 19 single maize hybrids, evaluated in the 2007/2008 growing season and 2008/2008 off-season, and verified the formation of two and four groups, respectively. Alves et al. (2015) evaluated the nutritional quality and grain yield in 18 maize cultivars and observed the formation of two groups for grain yield. Moreover, the number of groups formed for GY was similar to that obtained in this study, which can be explained by the high yield potential of maize hybrids when grown using appropriate cultivation techniques and favorable environments.
Means followed by the same letter in the column do not differ from each other by the Scott-Knott test at a 5% probability.
Means followed by the same letter in the column do not differ from each other by the Scott-Knott test at a 5% probability.
The principal component analysis considering the joint analysis of the three agricultural years showed that the first and second principal components represented 55.169 and 19.472% of the total variation, while the first four components accumulated 94.418% of the total variance (Table 6). PL, BSL, CLS, NPB, NSB, PDM, BSDM, and TDM were discarded because they were the traits that most contributed to genetic divergence in the last principal components. Thus, the traits that remained for the clustering analysis of cultivars were TL, TBN, CSDM, and GY. GY remained in the analysis for being the fourth principal component that contributed the most to genetic divergence among the cultivars, also showing the highest agronomic interest. Öner (2018) carried out an experiment with ear and tassel traits and observed that tassel length is one of the preferable traits for the study of genetic divergence in maize.
Percentage of the total variation explained by the principal components (PC) and the set of eigenvalues (λ) associated for 20 maize cultivars evaluated in three agricultural years.
The dissimilarity measures estimated from the generalized Mahalanobis distance (D2) presented magnitudes that ranged from 1.75 to 172.36, and the relationship between the highest and lowest observed D2 value was 98.49, indicating the presence of genetic variability among cultivars. The lowest D2 values were verified among the cultivars AG8780 and DKB290 (1.75), followed by AS1666 and AS1677 (3.12), and DKB290 and Celeron (5.24). The highest distances were found between the cultivars StatusVIP and P2530 (172.36), SX7331 and P2530 (144.13), and StatusVIP and P1630 (124.18) (Table 7). The amplitudes of the estimates scores (D2) suggest the existence of dissimilarity between these cultivars, which can be recommended for crosses aiming at maximizing hybrid combinations with higher heterotic effect, increasing the possibility of recovering superior genotypes (CRUZ; CARNEIRO; REGAZZI, 2014).
Studies on genetic diversity using generalized Mahalanobis distance as a dissimilarity measure for cluster analysis were carried out in maize cultivars by Alves et al. (2015), resulting in four groups, Chandel and Guleria (2019), with the formation of nine groups, Nardino et al. (2017), with the formation of eight groups, and Silva et al. (2016), who observed the formation of 11 groups.
Dissimilarity between maize cultivars for tassel length, total number of branches, central ear dry matter, and grain yield relative to three agricultural years and based on the generalized Mahalanobis distance (D2).
The dendrogram obtained by the unweighted pair group method with arithmetic mean (UPGMA), using 50% as dissimilarity criterion, showed the formation of six groups of cultivars (Figure 2). Group I was formed by five cultivars (AG8780, DKB290, Celeron, MS2010, and SX7331), group II by one cultivar (30A68), group III by five cultivars (MS3022, AM9724, 20A55, MS2013, and BM3066), group IV by one cultivar (StatusVIP), group V by seven cultivars (30F53, P2530, AG9025, AS1666, AS1677, P1630, and SHS7915), and group VI by one cultivar (DKB230). The UPGMA clustering method was considered the most efficient method for grouping maize cultivars, according to Cargnelutti Filho and Guadagnin (2011). Silva et al. (2016) observed that the UPGMA hierarchical method was efficient in identifying groups of superior and contrasting cultivars for traits of highest interest for sweetcorn production.
Dendrogram obtained by the cultivar clustering method utilizing the hierarchical method of unweighted pair group method with arithmetic mean (UPGMA) from the generalized Mahalanobis distance (D2) among 20 maize cultivars grouped based on the tassel length, total number of branches, central spike dry matter and grain yield. Cophenetic correlation coefficient = 0.6017 and significant at a 5% probability error.
A significant cophenetic correlation coefficient (CCC) of 0.6017 was obtained from the UPGMA clustering method (p-value ≤ 0.05), showing a reliable representation of the genetic distances of the cultivars in the dendrogram. Alves et al. (2015) studied the nutritional quality and yield of maize grains and observed a significant CCC value equal to 0.5788 using the UPGMA clustering method. Nardino et al. (2017) studied the genetic divergence between different environments and determined a CCC value of 0.60 by the UPGMA method. Silva et al. (2016) studied the dissimilarity in progenies of sweetcorn and observed a significant CCC value of 0.65 by the UPGMA method. These results are similar to those observed in the present study, indicating that the matrix data had a satisfactory fit in the graphical representation shown by the dendrogram.
The comparison of the means of groups using the Scott-Knott test showed that the four traits used to group the cultivars had a significant difference. The longest and smallest tassel lengths were observed for groups II and IV, respectively, the total number of branches had the highest mean in group IV and lowest mean in group V, the central spike dry matter showed the highest means in groups II, III, and V and the lowest mean in group VI, and groups I, II, III, and IV stood out for having the highest grain yield (Table 8).
Means of the traits tassel length (TL, cm), total number of branches (TBN), central spike dry matter (CSDM, g), and grain yield (GY, Mg ha−1) for 20 maize cultivars allocated into six groups using the UPGMA method and represented in the dendrogram (Figure 2).
This study proved that tassel traits should be considered in studies of genetic divergence in maize. Tassel length, total number of branches, central ear dry matter, and grain yield were the traits that most contributed to genetic divergence and should be considered in maize breeding programs.
CONCLUSIONS
1 – There is a genetic divergence between maize cultivars.
2 – Tassel length, total number of branches, central spike dry matter, and grain yield are the traits that most contribute to the genetic divergence among maize cultivars.
ACKNOWLEDGMENTS
To the National Council for Scientific and Technological Development (CNPq – Process No. 146258/2019-3 and 304652/2017-2) and the Coordination for the Improvement of Higher Education Personnel (CAPES), for granting scholarships to the authors.
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Editor-in-Chief: Prof. Salvador Barros Torres - sbtorres@ufersa.edu.br
Publication Dates
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Publication in this collection
15 Oct 2021 -
Date of issue
2021
History
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Received
24 July 2020 -
Accepted
02 Feb 2021
