Repeatability of vegetative characteristics and yield of banana cultivars

Repetibilidade de características vegetativas e de rendimento de cultivares de bananeiras

Abstract:

The objective of this study was to estimate the repeatability coefficients (r) and the minimum number of measurements (n) required to predict the true values of agronomic traits in banana plants, using pre-established coefficients of determination (R²). Analysis of variance was performed using two factors: 24 cultivars and three production cycles, to estimate r and n for plant height (PH), pseudostem perimeter (PP), hand weight (HW), number of hands (NH), number of fruits per bunch (NFB), number of fruits per hand (NFH), mean hand weight (MHW), mean fruit weight (MFW), external fruit length (EFL), and fruit diameter (FD). The repeatability coefficient (r) was high for all traits except FD. Estimating the genotypic value with R² = 0.95 requires two evaluation cycles for MFW and EFL. With R² = 0.90, two evaluations are necessary for PH, HW, and MHW. For R² = 0.85, two measurements are required for NH and NFB, three for NFH and PP, and five for FD.

Index terms
Musaspp.; analysis of variance; number of measurements; breeding

Resumo:

Objetivou-se estimar coeficientes de repetibilidade (r) e número mínimo de medições (n) para predizer o valor real de características agronômicas de bananeira com coeficientes de determinação (R²) preestabelecidos. Aplicou-se o método da análise de variância, com dois fatores, vinte e quatro cultivares e três ciclos de produção, para estimar r e n para altura da planta (AP), perímetro do pseudocaule (PP), massa das pencas (MP), número de pencas (NP), de frutos por cacho (NFC) e de frutos por penca (NFP), massa média das pencas (MMP), massa média (MMF), comprimento externo (CEF) e diâmetro do fruto (DF). O r foi elevado, exceto para DF. A estimativa do valor genotípico com R² = 0.95, requer dois ciclos de avaliação para MMF e CEF. Com R² = 0.90, demandam duas avaliações para AP, MP e MMP, com R² = 0.85, necessitam-se duas medições para NP e NFC, três para NFP e PP, e cinco para DF.

Termos para indexação
Musa spp.; análise de variância; número de medições; seleção

Introduction

Brazil still has significant potential to expand its share of global banana production.

To achieve this, investments in breeding programs aimed at developing more efficient cultivars adapted to the country’s conditions are essential. The banana production cycle is relatively long, with the period from planting to harvest potentially reaching up to 1.3 years (SALOMÃO et al., 2016). Furthermore, plant height, vigor (as determined by pseudostem perimeter), and bunch weight generally increase until the fourth production cycle, with the most substantial gains occurring between the mother and daughter plant cycles (ARANTES et al., 2017).

This process requires multiple evaluations over time to accurately estimate the true value of these traits, which makes breeding research more challenging. Therefore, studies such as repeatability analyses are essential, as they allow breeders to reduce the time required for evaluation and selection while achieving reliable results at lower costs. They are also valuable for farmers, enabling them to assess, within a few production cycles, the suitability of banana cultivars for their specific production systems.

When breeding a genotype, it is expected that its initial performance will remain consistent throughout its productive lifespan.

Thus, favorable performance expressed in certain plant structures or integral parts of the individual reflects the genotype’s overall potential for use (CRUZ et al., 2012). This consistency can be verified through the repeatability coefficient of the evaluated trait.

Repeatability coefficients are classified as high when r ≥ 0.60, moderate when 0.30 < r < 0.60, and low when r ≤ 0.30 (OLIVEIRA et al., 2025).

When the variance caused by permanent environmental effects is minimized, repeatability approaches the broad-sense heritability estimate, serving as an effective tool for identifying superior genotypes even within a few evaluation cycles (CRUZ et al., 2012). High repeatability coefficient values allow for an accurate estimation of the true value of a trait with a reduced number of measurements (CUNHA et al., 2021).

Conversely, low coefficients indicate the need for additional repetitions to achieve reliable estimates (QUINTAL et al., 2017).

Studies evaluating vegetative and productive characteristics of banana plants through repeatability analysis are still scarce in Brazil. Notable contributions in this field include those by Lessa et al. (2014) and Yokomizo and Dias (2016) in Brazil, and by Tenkouano et al. (2012) in other countries.

Nevertheless, repeatability analyses are more frequently applied to other fruit crops, such as passion fruit (Passiflora edulis) (JESUS et al., 2021) and bacaba (Oenocarpus bacaba) (SOUZA et al., 2023), among others.

Therefore, the objective of this study was to estimate the repeatability coefficients (r) and the minimum number of measurements (n) required to predict the true values of agronomic traits in banana plants, based on pre-established coefficients of determination(R²).

For this purpose, a database from the study of Arantes et al. (2017) was used. The original experiment was conducted in the experimental area of the Instituto Federal Baiano, campus Guanambi, located in the district of Ceraíma, municipality of Guanambi, Bahia, Brazil (14°17’38” S, 42°41’42” W), on a medium- textured Red-Yellow Latosol. The mean annual precipitation and temperature were 678 mm and 26 ºC, respectively. The total precipitation and the maximum and minimum temperatures recorded during the experimental period, corresponding to three years, were, respectively, 602.8 mm, 13.9 °C and 38.7 °C; 621.6 mm, 16.0 °C and 37.7 °C; and 562.9 mm, 14.6 °C and 40.1 °C.

The study by Arantes et al. (2017) used a completely randomized experimental design, following a split-plot-in-time arrangement, with five replications, for a total of 72 treatments. The plots consisted of 24 banana cultivars– Prata-Anã, Maravilha, FHIA-18, BRS FHIA-18, BRS Platina, JV42- 135, Pacovan, Japira, PV79-34, Pacovan- Ken, Preciosa, Garantida, Maçã, Caipira, BRS Tropical, BRS Princesa, YB42-03, YB42-07, YB42-47, Grande-Naine, Calipso, Bucaneiro, FHIA-23, and FHIA-17 – while subplots corresponded to three production cycles. Each experimental unit consisted of four observational plants, spaced at 3.0 m × 2.5 m, established with micropropagated plantlets.

The database (ARANTES et al., 2017) comprised agronomic traits including plant height (PH), corresponding to the length of the pseudostem, and pseudostem perimeter (PP), expressed in centimeters (cm); hand weight (HW) and mean hand weight (MHW), expressed in kilograms (kg); number of hands (NH); number of fruits per hand (NFH) and number of fruits per bunch (NFB); mean fruit weight (MFW), expressed in grams (g); external fruit length (EFL), expressed in centimeters (cm); and fruit diameter (FD), expressed in millimeters (mm).

To estimate the repeatability coefficients (r) and the minimum number of measurements (n) required to predict the true values of banana agronomic traits, with pre-established coefficients of determination (R²), a statistical model with two sources of variation, genotypes and production cycles, was employed. This model was applied to eliminate the effects of temporary environmental variation, following the methodology proposed by Cruz et al. (2012). The model can be expressed as follows:

1 $${\mathrm{Y}}_{\mathrm{i}\mathrm{j}}=\mathrm{\mu }+{\mathrm{g}}_{\mathrm{i}}+{\mathrm{a}}_{\mathrm{j}}+{\mathrm{\epsilon }}_{\mathrm{i}\mathrm{j}}$$

Y_ij: observation regarding the i-th genotype and j-th cycle ;

μ: overall mean;

g_i: effect of the i-th genotype under the influence of the permanent environment (i = 1, 2, …, 24);

a_j: effect of the j-th cycle;

ε_ij: effect of the temporary environment associated with the j-th measurement on the i-th genotype.

Analysis of variance (ANOVA), applied to the adopted statistical model, provided the mean squares associated with genotypes, production cycles, and residual effects, allowing the partitioning of total phenotypical variability into its respective components.

This step is essential for determining the relative contribution of genetic differences among genotypes and environmental or residual factors to the observed variation in traits. Based on these estimates, the repeatability coefficient was calculated, expressing the degree of consistency of repeated measurements of the same trait across evaluation cycles. Statistically, repeatability corresponds to the correlation between successive measurements of the same individual over time or across environments (CRUZ et al., 2012).

Formally, the repeatability coefficient can be expressed as:

2 $$\mathrm{r}=\widehat{\rho }=\frac{\mathrm{C}\widehat{\mathrm{o}}\mathrm{v}({\mathrm{Y}}_{\mathrm{i}\mathrm{j}}\mathrm{},{\mathrm{Y}}_{\mathrm{i}{\mathrm{j}}^{\mathrm{\text{'}}}}\mathrm{})}{\sqrt{\widehat{\mathrm{V}}\left({\mathrm{Y}}_{\mathrm{i}\mathrm{j}}\mathrm{}\right)\widehat{\mathrm{V}}\left({\mathrm{Y}}_{\mathrm{i}{\mathrm{j}}^{\mathrm{\text{'}}}}\mathrm{}\right)}}=\frac{{\widehat{\mathrm{\sigma }}}_{\mathrm{g}}^2}{{\widehat{\mathrm{\sigma }}}_{\mathrm{\gamma }}^2}=\frac{{\widehat{\mathrm{\sigma }}}_{\mathrm{g}}^2}{{\widehat{\mathrm{\sigma }}}_{\mathrm{\epsilon }}^2+{\widehat{\mathrm{\sigma }}}_{\mathrm{g}}^2}$$

where Y_ij and Y_ij’ are the different measurements performed on the same individual, r is the estimated repeatability coefficient, σ̂²_g is the estimated variance of genotypes reflecting the variance among genotypes, σ̂²_ε is the estimated environmental variance that captures the variance within genotypes.

The estimated genetic variance is obtained from the mean squares of the analysis of variance and is calculated using the following relationship:

3 $${\widehat{\mathrm{\sigma }}}_{\mathrm{g}}^2=\frac{\mathrm{M}\mathrm{S}\mathrm{G}-\mathrm{M}\mathrm{S}\mathrm{E}}{\mathrm{n}}$$

where n is the number of production cycles and σ̂²_ε = MSE; MSG is the mean square of genotypes and MSE is the mean squared error. Thus, the repeatability coefficient provides a quantitative measure of the phenotypic stability of a trait across cycles, indicating how much of the observed variation is attributable to genetic differences and, consequently, the degree of reliability of the measurements for breeding purposes.

The calculation of the number of measurements required (n) to predict the true genotypic value of the evaluated traits in the 24 banana cultivars considered pre-established coefficients of determination (R²) of 0.85, 0.90, and 0.95, representing increasing levels of predictive accuracy. This procedure, based on the methodology of Cruz et al. (2012), allows the estimation of the minimum number of evaluations necessary to achieve a desired level of reliability, taking into account the relationship between r and R², as expressed in Equation 4:

4 $$\mathrm{n}=\frac{{\mathrm{R}}^2(1-\mathrm{r})}{\left(1-{\mathrm{R}}^2\right).\mathrm{r}}$$

Applying this expression enables the identification of the point at which increasing the number of measurements no longer results significant gains in accuracy. This approach contributes to more efficient experimental planning and better allocation of resources in repeatability studies and banana breeding programs.

Statistical analyses were performed using the R software (R CORE TEAM, 2024), using the base stats package, specifically the function aov () for analysis of variance.

The cultivars differed significantly (p ≤ 0.01) for all evaluated traits, indicating the presence of substantial genotypic variability (Table 1).

Table 1
Summary of the analysis of variance and estimated variance components between gen¬otypes and environmental variances of the traits: plant height (PH), pseudostem perimeter (PP), hand weight (HW), number of hands (NH), number of fruits per bunch (NFB), number of fruits per hand (NFH), mean hand weight (MHW), mean fruit weight (MFW), external fruit length (EFL), and fruit diame¬ter (FD), measured in 24 banana cultivars over three production cycles.

Source of variation	DF	Mean square
		PH (cm)	PP (cm)	HW (kg)	NH (un)	NFB (un)	NFH (un)	MHW (kg)	MFW (g)	EFL (cm)	FD (mm)
Cultivars	23	8644**	285.6**	194.49**	10.72**	4664**	6.02**	0.91**	4430**	27.55**	11.36**
Production cycle	2	57279**	2642.6**	39.71 ns	31.11**	13948**	15.05**	1.4**	8025**	29.86**	12.31 ns
Residual	46	481	31.5	10.62	1.04	329	0.709	0.05	145	0.71	2.53
Mean		336.76	88.92	19.03	9.49	157.03	16.5	1.96	126.82	18.71	32.15
CV (%)		6.51	6.31	17.12	10.75	11.55	5.10	10.88	9.50	4.50	4.94
		2721	84.7	61.29	3.23	1445	1.77	0.29	1428.33	8.95	2.94
		481	31.5	10.62	1.04	329	0.709	0.0455	145	0.709	2.53

• ^*
  ^ns not significant; **significant at 1% by the F-test; DF: degrees of freedom

This variability is expected, given that the cultivars differ in both type and genomic group (ARANTES et al., 2017): Prata type – Prata-Anã and Pacovan (AAB), Maravilha, FHIA-18, BRS FHIA-18, BRS Platina, Pacovan, Japira, Pacovan-Ken, Preciosa, Garantida, and genotypes PV79-34 and JV42-135 (AAAB); Maçã type – Maçã (AAB), Caipira AAA), BRS Tropical, BRS Princesa, and genotypes YB42-03, YB42-17, and YB42-47 (AAAB); Cavendish type – Grande-Naine (AAA); and Gros Michel type – Calipso, Bucaneiro, FHIA-23, and FHIA-17 (AAAA).

Additionally, genotypic variability may also occur even within the same genomic group, as reported by Lessa et al. (2014) in diploid hybrids (AA).

Analysis of variance revealed significant differences in the expression of most evaluated traits among banana cultivars, influenced by the production cycle factor, which represents variation over time and environmental conditions. Exceptions were observed for HW and FD. The original dataset (ARANTES et al., 2017) showed increases in plant size and vigor across three production cycles for all cultivars. However, for HW, when considering the interaction between cultivars and cycles, the data indicated increases from the mother plant cycle to the daughter plant cycle, followed by stabilization or reduction in the granddaughter plant cycle. This pattern helps explain the absence of significant differences when evaluating the environmental effect of the cycle factor independently, as performed in the present study. In contrast, FD is predominantly influenced by genotype rather than by environmental factors, although some variation across cycles may still occur (DONATO et al., 2008).

Similar findings have been reported in other crops. For example, in a study on açaí palm progenies in the state of Pará found that, even under varying environmental conditions, including fluctuations in relative humidity, temperature, and precipitation, no significant differences were detected in the evaluated traits when considering time as a source of variation (YOKOMIZO et al., 2020).

The coefficients of variation (CV) for the traits PH, PP, NFH, MFW, EFL, and FD were below 10%, which is classified as low, while the remaining traits showed CV values between 10% and 20%, classified as moderate (PIMENTEL-GOMES, 2022). These values indicate low to moderate variability under field conditions. The obtained CVs are consistent with those commonly reported in the literature for these traits (DONATO et al., 2008).

Although, for example, the CVs of PH, PP, NFB, and NH were slightly higher than those reported by Lessa et al. (2014), and the CVs of NFB, CEF, and FD were slightly lower than those found by Donato et al. (2008), all values remain within the variation ranges established by Pimentel-Gomes (2022). It is also important to note that variability is not only inherent to each trait but may also be influenced by environmental conditions, differences among genotypes, crop management practices, and measurement precision.

Genotypic and phenotypic parameters indicate that, for the analyzed traits, the genetic contribution was relatively greater than the residual effect. This suggests that a substantial proportion of the phenotypic variation among plants is attributable to genetic factors, demonstrating a high heritability potential for these traits. Consequently, environmental influence on the evaluated phenotypes was relatively low, indicating that differences between individuals were primarily attributable to genetic variability.

These findings are consistent with the genetic diversity of the 24 evaluated cultivars, which belong to different genomic groups and ploidy levels: AAA (triploid Acuminata), AAB (triploid Acuminata × Balbisiana), AAAA (tetraploid Acuminata), and AAAB (tetraploid Acuminata × Balbisiana). Even within the same genomic group, cultivars differ by phenotype (ARANTES et al., 2017) or by genotypic composition (LESSA et al., 2014). These results reinforce the potential for significant advances in banana breeding programs, since selection based on phenotypic traits tends to reflect direct underlying genetic variability, making the breeding process more efficient and predictable (CRUZ et al., 2012).

Overall, repeatability was high for most analyzed traits, indicating consistency and stability in the expression of these variables across different measurement cycles (Table 2). This pattern suggests that many of the evaluated traits are under substantial genetic control, with limited influence from temporary environmental factors.

Therefore, the high repeatability observed indicates that phenotypic values obtained from a few evaluation cycles can reliably represent the genetic potential of the studied cultivars, reducing the need for repeated measurements and enhancing the efficiency of the breeding process.

Table 2
Repeatability coefficients (r) and num¬ber of measurements required (n) estimat¬ed by the analysis of variance to evaluate the traits: plant height (PH), pseudostem perime¬ter (PP), hand weight (HW), number of hands (NH), number of fruits per bunch (NFB), number of fruits per hand (NFH), mean hand weight (MHW), mean fruit weight (MFW), external fruit length (EFL) and fruit diameter (FD) in 24 banana cultivars, with pre-established coefficients of determina¬tion (R²).

	Estimates
Características	r	n (R2=0.85)	n (R2=0.90)	n (R2=0.95)
PH	0.85	1	2	4
PP	0.73	3	4	8
HW	0.85	1	2	4
NH	0.76	2	3	6
NFB	0.81	2	3	5
NFH	0.71	3	4	8
MHW	0.83	2	2	4
MFW	0.91	1	1	2
EFL	0.93	1	1	2
FD	0.54	5	8	17

• ^*
  n is the number of measurements desired to predict the true value of individuals with 85%, 90% and 95% determination.

However, FD exhibited a moderate repeatability coefficient, indicating greater variability across production cycles and, consequently, lower stability in its phenotypic expression. This trait has been strongly influenced by environmental conditions, management practices, even when evaluated within a single genotype (DONATO et al., 2008). For example, these authors reported a reduction in FD from 40.77 mm to 34.13 mm between the mother and daughter plant cycles in the tetraploid hybrid AAAB, Maça-type BRS Tropical. Therefore, the sensitivity of this trait to variations in management or microenvironmental conditions may have contributed to the observed lower genetic control. As a result, FD appears to be a more complex variable to predict accurately, requiring a greater number of measurements to reliably estimate the true genotypic compared with more stable traits.

Lessa et al. (2014) estimated repeatability coefficients in diploid Acuminata banana hybrids using analysis of variance with a two-factor model and observed high repeatability coefficients for PP, PH, NH, and NFB, results that are consistent with those obtained in the present study. Conversely, Yokomizo and Dias (2016) reported high repeatability coefficients for PP, PH, and MHW, while NH showed a moderate value.

The consistent expression of PP and PH across evaluation cycles suggests strong genetic control of these traits. Therefore, PP and PH can be reliably estimated with only a few measurements, enabling accurate determination of the true value of each cultivar. This information is useful for both breeders and growers, as it allows the evaluation of cultivar performance within a limited number of production cycles. Likewise, Tenkouano et al. (2012) reported high r values for plant height and hand weight in banana genotypes with different genomic groups and ploidy levels; however, for fruit weight, their values were lower than those found in the present study.

For FD, the repeatability estimate of 0.54 indicates that increasing the number of evaluations would have a limited effect on improving the predictive accuracy of true genotypic values. When fixing the coefficient of determination (R²) at 0.85, corresponding to the lowest predefined precision level, it is possible to estimate the true value with five measurements. In contrast, for PH (r = 0.85), HW (r = 0.85), MFW (r = 0.91), and EFL (r = 0.93), results demonstrated greater stability across evaluation cycles, allowing accurate prediction with only a single measurement.

For NH (r = 0.76), NFB (r = 0.81), and MHW (r = 0.83), two measurements are required, whereas for PP (r = 0.73) and NFH (r = 0.71), three measurements are needed.

When aiming for higher reliability in estimating agronomic traits (R2 = 0.90), a greater number of measurements is required to achieve the desired precision. At this level, eight evaluations are necessary for the FD trait, four for PP and NFH, three for NP and NFB, two for PH, HW, and MHW, and only one for MFW and EFL. These results demonstrate a high repeatability for the latter traits, indicating lower variability across production cycles and, therefore, greater stability and reliability for use in breeding programs. This phenotypic stability suggests that small environmental fluctuations or management differences have limited influence on the expression of these traits, allowing accurate evaluations even relatively few measurements. In contrast, Tenkouano et al. (2012), working with trials involving several genotypes across different environments, observed high repeatability for plant height and bunch weight, but low repeatability for fruit weight.

As the required precision increases, aiming for a higher level of reliability (R² = 0.95), the number of necessary measurements rises considerably. Under this scenario, seventeen evaluations are necessary for FD, eight for PP and NFH, six for NH, five for NFB, four for PH, HW, and MHW, and two for MFW and EFL. These results indicate that, although some traits require more measurements to achieve high accuracy, others allow accurate predictions with fewer measurements.

Understanding these differences is essential for optimizing experimental design and resource allocation, as it enables adjusting the number of measurements according to the stability of each trait. Moreover, this information can support banana producers in assessing the performance of new cultivars based on a limited number of growing cycles.

Overall, the repeatability coefficient was high for most evaluated traits, except for FD, which showed a moderate value. This indicates good reliability of phenotypic values as an estimator of genotypic values, as well as a strong capacity for consistent trait expression across production cycles.

Based on the obtained estimates, only two evaluation cycles are sufficient to predict, with 95% reliability, the genotypic values of MFW and EFL. To achieve a reliability level of 90%, two measurements are required for PH, HW, and MHW. For an 85% reliability level, three measurements are recommended for PP and NFH, and five for FD, reflecting the lower stability of this trait.

Acknowledgments

We would like to thank the National Council for Scientific and Technological Development (CNPq), the Coordination for the Improvement of Higher Education Personnel (CAPES, Funding Code 001), and the Federal Institute of Bahia – campus Guanambi for their financial support. We also extend our gratitude to UNIMONTES for providing the necessary infrastructure and for the high-quality education that contributed to the development of this work.

DATA AVAILABILITY

The data that support the findings of this study are available from the corresponding author, Junior, D.M.G., upon reasonable request.

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Edited by

• Scientific Editor
  Alexandre Pio Viana

• Associate Editor
  Alexandre Pio Viana

Data availability

Data citations

R Core Team. R: A language and environment for statistical computing. Vienna: R Foundation for Statistical Computing, 2024. Disponível em: https://www.r-project.org/ Acesso em: 12 abr. 2025.

Publication Dates

• Publication in this collection
  20 July 2026
• Date of issue
  2026

History

• Published
  20 May 2026
• Received
  12 Nov 2025
• Accepted
  27 Apr 2026