Strategies for recurrent selection programs aiming upright common bean plants

doi:10.21203/rs.3.rs-4706064/v1

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Strategies for recurrent selection programs aiming upright common bean plants

https://doi.org/10.21203/rs.3.rs-4706064/v1

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In common beans (Phaseolus vulgaris L.), upright plant architecture is highly valued for agriculture. As plant architecture is controlled by many genes, recurrent selection is the optimal strategy for breeding programs. Additionally, conducting mass selection before flowering to intercross selected plants in the field increases efficiency by reducing the number of harvest cycles needed to complete a selective breeding cycle. For this to be effective, the plant's architecture at flowering must accurately predicts its trait at harvest. Based on that, the objective of this study was to verify whether there is an association between plant architecture at flowering and at harvest and to study the relationship between traits of plant architecture and grain yield. The traits assessed included plant height and plant architecture scores at flowering and harvest, stem diameter, grain yield, and the mass of one hundred grains. Sixty-two progenies from each S_0:2, S_0:3 and S_0:4 generation, with two commercial cultivars as controls, were evaluated across three environments in a triple lattice experimental design. Genetic and phenotypic parameters and correlation coefficient were estimated. Progenies selection was carried out using index Z, and subsequently, the correlated response in the traits was determined. It was found that the correlation between heights and architecture scores at flowering and harvest were of high magnitude. There was also a low correlation between plant architecture traits and grain yield. The results of this study suggest that selecting upright plants during the flowering stage is feasible, and that plant architecture and grain yield are expressed independently.

Phaseolus vulgaris. Plant architecture. Early selection. Mass selection

The demand for more upright common bean (Phaseolus vulgaris L.) cultivars in Brazil is long-standing, since this is an important feature both for family farming and for large rural entrepreneurs. In the first case, the contact of the pods with the ground decreases when plants are more upright. Thus, even if there is rainfall at harvest, there are less rotten pods as well as less grain germination within the pods. Regarding the rural entrepreneurs, the upright plants facilitate crop management because they allow machines to move between the lines just causing little damage to the plants. Harvest may be mechanized with lower losses. In addition, upright plants enable better air circulation in the crop and reduce the incidence of pathogens (Lemos et al. 2022; Ribeiro et al. 2019; Ribeiro et al. 2024).

Considering the number of agronomic traits involved in bean plant architecture—such as branch insertion angles, guide length, stem height, lodging, insertion of the first pod and insertion of the last pod, stem and hypocotyl diameter—it can be inferred that breeders must manage a large number of genes to develop upright plants (Anjos et al. 2018; Pereira et al. 2017; Resende at al. 2018; Ribeiro et al. 2020). Therefore, the best strategy consists of gradually accumulating favorable alleles in the population over time, i.e., successive recurrent selection cycles (Bernardo 2020). The recurrent selection in common bean crops has been held since 2001 at Federal University of Lavras (Menezes Júnior et al. 2008; Pires et al. 2014). Selection was carried out through the evaluation of the S_0:1 and S_0:2 progenies in the first three cycles. Since the process was slow, selection became massal after the third cycle and it was visually performed among plants in the S₀ = F₂ generation, i.e., in the field, and the most upright plants before flowering were intercrossed. This procedure was adopted up to cycle XII.

Although the recurrent massal selection has been efficient for bean plant architecture (Pereira et al. 2017; Pires et al. 2014), the question is whether the assessment carried out before flowering affects that performed at harvest. Considering the foregoing, the current study aims to investigate whether selection for plant architecture during flowering coincides with the selection for the same trait at harvest. It also aims to estimate the association between these traits and grain yield.

Sixty-two (62) progenies from Federal University of Lavras (UFLA) were evaluated in the S_0:2, S_0:3 and S_0:4 generations from Cycle CXII. Details regarding the recurrent selection program that originated the progenies were presented by Pires et al. (2014). The progenies were evaluated in three growing seasons: S_0:2-generation progenies in winter season (sowing in July), S_0:3 in wet season (sowing in November) and the S_0:4 in dry season (sowing in March). The experiments were conducted at three different sites in Brazil: the three generations in Lavras (21º14’ S; 45º59’ W; 919 m) and Lambari (21º 58’ S; 45º 21’ W; 887 m), and S_0:1 and S_0:2 generations in Patos de Minas (18° 34' S; 46° 31' W; 8815m).

In addition to the 62 progenies, two controls were included in all experiments: the BRSMG Majestoso cultivar, hereinafter called "Majestoso", was chosen as the productivity standard of carioca-type cultivars (Abreu et al. 2007). The other control was "BRS Supremo"; this black-grain cultivar was chosen because it is an upright plant pattern (Costa et al. 2004).

The 8x8 triple lattice experimental design was used in the current study. Each plot consisted of two 2m rows with 60 cm spacing. The number of seeds/m as well as the management followed the agronomic recommendations for beans. Three plants were identified in each plot at flowering and their height in cm (Height 1) and plant architecture (Score 1) were recorded. The same plants marked at flowering had the following data recorded at harvest: plant height (Height 2), stem diameter in cm, mass of 100 grains in g, plant architecture scores (Score 2) and grain yield in kg ha^− 1. Grain yield was obtained by taking under consideration all plants in the plot, with grain moisture standardized to 13%. The plant architecture scores were attributed using the rating scale suggested by Collicchio et al. (1997), where 1 represents non-upright plants and 9 represents fully upright plants.

All data were subjected to variance analysis (ANOVA) presupposition tests to assess normality, independence, and homogeneity of residual variances. Once these assumptions were met, the data were subjected to individual ANOVA. Subsequently, a joint analysis involved all locations and generations (environments) using the individual analyses adjusted means. The progenies and error effects were considered to be random. The selective accuracy (r_gg) was estimated in each experiment (Resende and Duarte 2007; Resende and Alves 2022).

The traits’ adjusted means were standardized in each environment and the Z variable was obtained. After standardization, the Z sum (\(\:\varSigma\:Z)\:\)was obtained by taking under consideration the following traits: height at flowering, mass of 100 grains, stem diameter, plant architecture score at flowering, and grain yield. The \(\:\varSigma\:Z\) was subjected to variance analysis. The heritability (h²) was estimated from the ANOVAs.

The h² of each trait was estimated to select the mean progenies involving all the environments, by using the following estimator:

h² = \(\:\frac{{QM}_{p}-{QM}_{i}}{{QM}_{p}}\) wherein QM_P is the mean square among progenies from ANOVA and QM_i is the mean square of the progenies x environments interaction. Subsequently, the heritability confidence interval was calculated using the expressions presented by Knapp, Stroup and Ross (1985), with α = 0.05.

The mean of the environments was used to estimate the expected gain with the selection (SG%). The estimator used to obtain these estimates was presented by (Ramalho et al. 2024): SG (%) = \(\:\frac{ds\:\times\:{h}^{2}}{{M}_{o}}\) wherein SG (%) is the gain expected with selection expressed in percentage, M_o is the original population mean and ds is the selection differential obtained by M_s-M_o, in which M_s is the mean of the six selected progenies.

In addition to the genetic and phenotypic parameters, the phenotypic and genetic correlations were estimated between traits based on joint analysis.

The selection-correlated response was estimated by considering the index Z (Y) for all traits (X), using the RC_x(y)= \(\:\frac{{M}_{sx}-\:{M}_{o}}{{M}_{o}}\times\:{h}_{x}^{2}\times\:100\) estimator, wherein: RC_x(y) is the X trait correlated response to Y, M_sx is the mean of selected individuals in the X trait based on the ranking performed in the Y character, M_o is the original population mean, and \(\:{h}_{x}^{2}\) : X heritability.

It was found that the progenies variation source was significant (p ≤ 0.01) for all traits, indicating the presence of genetic variability (Table 1). The environments (locations-generations) were very contrasting as it can be seen by the significance of the environment’s variation source.

The progenies x environments interactions were significant (p ≤ 0.01) for all traits, except for the diameter. It shows that the progenies behavior did not match in the different analyzed environments.

Table 1

Significance of P-values obtained in evaluating 62 common bean progenies across three generations (S_0:1, S_0:2 and S_0:3) and three sites of the joint variance analysis, for the following traits: height at flowering (Height1, cm), height at harvest (Height 2, cm), stem diameter (Diameter, cm), mass of 100 grains (M100, g), plant architecture score at flowering (Score 1), plant architecture score at harvest (Score 2) and grain yield (Yield, kg ha^− 1).
Source of variation	DF	Mean Square
Source of variation	DF	Height1	Height2	Diameter	M100	Score1	Score2	Yield
Environment (E)	7	11449.67^**	29080.47^**	96.28^**	1828.53^**	52.51^**	72.07^**	57641338.50^**
Treatment (T)	63	2245.95^**	1560.77^**	5.34^**	108.15^**	18.33^**	22.99^**	863988.60^**
Among progenies (P)	61	1895.07^**	1459.57^**	4.39^**	97.33^**	13.81^**	17.70^**	873074.10^**
Controls (C)	1	465.16	279.50	10.45^**	82.37	42.72^*	102.67^**	884277.42
(P) x (C)	1	25430.62^**	9015.63^*	58.16^**	793.70^**	269.51^**	265.62^**	289482.18
T x E	441	302.17^**	266.54^**	1.31	38.91^*	1.41^**	1.65^**	324757.20^**
P x E	427	276.55^**	246.84^**	1.31	39.65^**	1.33^**	1.58^**	321356.10^**
C x E	7	336.15^**	409.72^**	0.26	19.40	3.56^**	1.60^**	351530.96^**
P x Wi x E	7	1830.96^**	1324.88^**	2.67^*	13.41	4.09^**	5.96^**	505435.91^*
Mean Error	840	153.24	131.27	1.30	33.69	0.75	1.13	239892.59

**, *: significant at 1% and 5% probability, respectively, according to the F test.

An alternative to providing a guideline for the recurrent selection program is to try to identify the traits, to be subjected to selection, that have greater heritability. The higher the h² estimate of the trait, the more likely it is to predict which trait or traits are most likely to gain from recurrent selection. It can be seen in Table 2 that when considering selection based on the average of the progenies, the estimate of this parameter varied between traits. The lowest value was 59.2% for the mass of 100 grains. Initially, the h² estimates were obtained in the broadest sense. However, the contribution of dominance variance (\(\:{\sigma\:}_{D}^{2}\)) to the total genetic variance in autogamous plants is small (Ramalho et al. 2024). Additionally, as inbreeding progresses, there is a reduction in the frequency of heterozygous loci, and evidently the participation of \(\:{\sigma\:}_{D}^{2}\) decreases. Thus, the h² estimates, among the average progenies, in this situation can be considered in a strict sense, that is, with a practically integral response to selection.

Comparing the h² estimates available in the literature is not easy because it depends on the variability between the progenies evaluated, the number of replications and the environments, and experimental precision. The literature presents numerous reports of h² estimates for grain yield in bean crops. Ramalho et al. (2024) presents a compilation of results from the literature. Estimates ranged from 10.6 to 88%. Most grain yield estimates were lower than those obtained in the present study. Pires et al. (2014) used eighth cycle progenies from the same recurrent selection program and found h² estimates for grain yield in the average progenies very similar to those obtained in the present study. It indicates that recurrent selection of plant architecture did not affect grain yield variability.

Height, stem diameter and plant architecture estimates from h² estimates were greater than 70%. Some estimates of h², referring to traits linked to plant architecture, are relatively frequent in the literature. The values found varied from 51.7–87% (Paula et al. 2020; Pereira et al. 2017). Therefore, the h² obtained here are above or within the range of variation reported in the literature.

Table 2

Estimate of heritability, gain with selection based on index Z (\(\:\varSigma\:\)Z) and expected gain with (SG) for the following traits: height at flowering (Height1, cm), height at harvest (Height 2, cm), stem diameter (Diameter, cm), mass of 100 grains (M100, g), plant architecture score at flowering (Score1), plant architecture score at harvest (Score2) and grain yield (Yield, kg ha^− 1). Data obtained from the 62 progenies from S_0:2, S_0:3 and S_0:4 generations assessment in Lavras-MG, Lambari-MG and in Patos de Minas-MG.
Parameters	\(\:\varSigma\:\)Z	Height1	Height2	Diameter	M100	Score1	Score2	Yield
h²(%)	74.61	85.41	83.09	70.24	59.26	90.34	91.09	63.19
LL	63.80	77.29	73.99	57.44	43.05	85.42	86.74	47.02
UL	83.15	89.41	87.87	80.16	73.45	93.20	93.82	75.30
SG(%)	5.47	23.81	5.82	7.25	8.07	9.85	11.54	7.54
R_X(Z)(%)	-	9.5	-	4.63	2.3	8.74	-	2.44

LL; UL: lower and upper limit, respectively, of the confidence intervals, at 5% probability of the heritability (h²) in the mean of progenies; RC_X(Z): response correlated to the traits according to the selection done in the index Z (\(\:\varSigma\:Z).\)

The main focus of this research was to verify whether early selection, that is, carried out at the time of plant flowering, was efficient in relation to traits related to plant architecture. The main alternative to evaluate what is found is through estimates of correlations between traits at different times.

The estimates of phenotypic and genetic correlations showed behavior that was in principle coincident at different times (Table 3). It is worth highlighting the phenotypic and genetic correlation between the different heights in the two evaluation periods. It was of great magnitude (r_F =0.90 and r_G= 0.97). High r_G for height at both ages (j and j') is expected, as one of the causes of genetic correlation is the pleiotropic effect of genes (Ramalho et al. 2024) which occurs in this case as this correlation involves the same trait at two ages. Obviously, the genes are the same, r_Gjj’ must be 1.0. As it was not exactly one, the most likely explanation would be the error associated with the estimate. Another explanation is that the genes are the same and they express at different times, which would contribute to the progenies x evaluations age interaction. Which doesn't seem to be the case at the moment.

As an early selection strategy, what matters is the correlation of the phenotype of the same trait at age j and the genotype at age j' (r_(FjGj')). Bernardo (2020), comments that it is obtained by the expression r_(FjGj') = h²_j r_aj’. Where, h²_j is the heritability of the trait at the time of evaluation j and r_Gjj' has already been defined, r_Gjj' must be 1.0 or close to this value, will be the heritability of this trait obtained at the flowering of the plant. The estimate of h², for this trait, was of great magnitude (Table 2).

The architecture of the plant, in reality, is the trait of greatest interest for carrying out selection and at the same time recombination during flowering. In this condition, the recurrent selection gain per unit of time would be double that achieved at the end of the cycle. The architecture is an index that involves some traits among them, the height, the number of branches and the angle of insertion of the branches, among others. This assessment is subjective and carried out visually. In this case, the genes that are expressed at flowering for architecture are also expected to be the same ones that are expressed closer to harvest. Estimates of genetic correlation for architectural leakage confirm this fact (r_GjGj’ = 0.97) (Table 3). In this case, even though the evaluation was visual, the estimate of h²_j was high. Therefore, early assessment is expected to work and the selection of individuals to be recombined can be carried out at the beginning of flowering at the site itself.

The estimates of the phenotypic correlation between stem diameter and plant architecture scores showed medium magnitude. They were significant and near 60% in both cases.

Table 3

Estimates of phenotypic (above the diagonal) and genetic (below the diagonal) correlations obtained from the mean of the progenies in all evaluated environments, for the following traits: height at flowering (Height1, cm), height at harvest (Height 2, cm), stem diameter (Diameter, cm), mass of 100 grains (M100, g), plant architecture score at flowering (Score1) and at harvest (Score 2) and grain yield (Yield, kg ha^− 1).
	Height1	Height2	Diameter	M100	Score1	Score2	Yield
Height 1	1	0.9^**	-0.23	0.12	-0.51^**	-0.47^**	-0.28^*
Height 2	0.93	1	0.03	0.16	-0.27^*	-0.28^*	-0.27^*
Diameter	-0.31	0.00	1	0.27^*	0.62^**	0.57^**	-0.16
M100	0.15	0.24	0.37	1	0.17	0.13	-0.12
Score1	-0.58	-0.32	0.73	0.23	1	0.93^**	0.03
Score2	-0.53	-0.32	0.68	0.18	0.97	1	0.03
Yield	-0.45	-0.45	-0.25	-0.26	0.04	0.03	1

**, *: Significant at 1% and 5% probability, respectively, according to t-test.

The two controls used are cultivars recommended for the region. Majestoso cultivar shows carioca-type grains, and it is well adapted to the region. However, this cultivar shows growth habit type III, and it is quite prostate. On the other hand, Supremo is an upright black bean cultivar with good performance in the region. This behavior of the two controls may be seen when the contrast between them is assessed. It is possible to verify that the aforementioned behavior was not significant (p > 0.05) for grain yield, both heights and mass of 100 grains. Therefore, controls differ in the traits linked to plant architecture, but not in grain yield. The same was observed for the contrast between progenies and controls, that was not significant for grain yield (p > 0.05).

Breeders' great challenge is to identify the best lines for future seasons as well as the several management systems adopted by farmers (Gauch and Zobel 1988). Since the progeny x environment interaction was significant, it shows that the progenies behavior did not coincide in the different locations and generations (Ramalho et al. 2024). Therefore, the best option is to consider the progenies’ mean performance in as many environments as possible (Lima et al. 2014).

Trying to identify one or more progenies for each location or crop is impracticable since the environmental conditions will certainly not be the same when the progenies will be further recommended. Thus, breeders’ challenge is to find progenies that are more adapted to most environments. Troyer (1996) says that the only way to select progenies to obtain increased adaptation is by conducting the experiments in as many environments as possible. Therefore, the emphasis on choosing the best progenies should be directed to the mean performance in several environments, even when there is interaction. Therefore, this was the procedure used in the current research.

The estimates of both phenotypic and genetic grain yield correlations were negative in most of the cases; however, they showed small magnitude (Table 4). The literature reports similar results (Moura et al. 2013; Ribeiro et al. 2019). These results allow inferring the possibility of selecting progenies to obtain grain yield, despite the other evaluated traits.

The plant architecture recurrent selection program was held by recombining the S_0:3 progenies in the first three cycles (Cunha et al. 2005; Menezes Júnior et al. 2008). From the fourth cycle on, since no significant association between yield and plant architecture score was found, it was decided that the progenies would be selected according to the plant architecture score alone. In this case, since this trait showed greater h², the selection became phenotypic, and it was held in the F₂ generation. However, in order for the process to be efficient, the plant architecture at flowering - when the recombination is performed - must well represent that found at harvest. Table 3 shows that the correlation between the plant architecture scores in the two stages was very high and positive. Thus, the adopted strategy in the phenotypic recurrent selection, which was focused on the most upright plant architecture expressed at flowering in F₂, was appropriate because the most upright plants at flowering kept expressing this phenotype until harvest. Genetic progress estimates by Pires et al. (2014) corroborate this finding.

Another issue that has been questioned for some time is whether the mass of grains is linked to the plant architecture score. It is known that plants with larger leaves have large grains. This association is ontogenetic, i.e., grains and leaves originate from the same tissues. There is evidence that plants with large leaves tend to lodge more than plants with small leaves. It was also questioned whether it is possible to select progenies for upright plants with no grain size restriction. The current study found small magnitude estimates of correlations between the mass of 100 grains and the plant height.

Similar results were reported by Collicchio et al. (1997). It appears that within a light amplitude variation in the mass of grains - as it happens among recurrent selection program plants - it is possible to select upright plants with grains that meet the commercial standard.

The literature reports several selection indices to be used (Baker 1986; Bernardo 2020). The current study made the option for using the sum of the standardized variables (\(\:\varSigma\:Z\)). This index has been used in several bean crop situations in the region (Mendes et al. 2009; Menezes Júnior et al. 2008). The gain estimate was 5.5% when progenies with higher \(\:\varSigma\:Z\) were selected (Table 2). The selection-correlated response was always positive in the traits involved in the index. It indicates that the herein used index was a good strategy in terms of selection, i.e., the chosen progenies resulted in greater height, plant architecture score, diameter stem, mass of 100 grains, and grain yield.

The bean plant ideotype has been suggested for some time (Adams 1973; Adams 1982). According to Kelly (2000), specific models are needed according to the management system and to the environmental conditions. In the specific case of the bean crop in Brazil, the management system is very diverse. It happens because this legume cultivation is simultaneously performed by family farmers - with little or no technology – and by big landowners who use all the available technologies. Thus, according to the herein obtained results, the bean plant ideotype that meets all these farmers’ demands should have type II indeterminate growth habit and main stem diameter of at least 1 cm, to reduce the lodging of plants; strong central stem with many lateral branches, like a bush. The branches should be distributed on the stem in such a way to allow good aeration and to prevent the occurrence of pathogens; tendril emission should be as high as possible after flowering in order to allow increased production of flowers and pods as well as longer flowering. Thus, since the plants will have many branches at the top, this structure will allow the easier collection of the windrowed plants or of those that will be cut by the harvester reel in the mechanized harvesting, according to the crop type. The first pods should be inserted at a sufficient height so that they make no contact with the ground. As far as possible, the pods' structure should be able to reduce grain germination within them - while they are still attached to the plants - when the harvest coincides with intermittent rains.

Phenotypic recurrent selection related to plant architecture may be carried out before flowering, as the plant architecture score phenotyping just before flowering is highly correlated with the score obtained near harvest.

Plant architecture score and grain yield are independent traits in terms of expression. Therefore, selecting one does not necessarily affect the performance of the other.

Competing Interests

The authors have no relevant financial or non-financial interests to disclose.

Funding

Author Indalécio Cunha Vieira Júnior has received research support from National Council for Scientific and Technological Development (CNPq).

Author Contribution

Indalécio Vieira Júnior: Fieldwork, Data collection, Data analysis, Conceptualization, Methodology, Investigation, Writing- original draft. Yasmin Berchembrock and José Maria Pádua: Writing review and editing. All authors read and approved the final manuscript.

Acknowledgement

The authors thank the National Council for Scientific and Technological Development (CNPq) for all support for carrying out the experiments.

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Submission checks completed at journal
14 Jul, 2024
First submitted to journal
08 Jul, 2024

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Strategies for recurrent selection programs aiming upright common bean plants

Status:

Version 1

Abstract

Introduction

Material and Methods

Results

Discussion

Conclusion

Declarations

Competing Interests

Funding

Author Contribution

Acknowledgement

References

Additional Declarations

Status:

Version 1