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Relationships between yield and some anatomical and morphological traits in rubber tree progenies
Industrial Crops & Products 147 (2020) 112221
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Relationships between yield and some anatomical and morphological traits in rubber tree progenies
T
Isabela de Castro Sant’Annaa, Ligia Regina Lima Gouvêab, Acácia Mecejana Diniz Souza Spittic, Antônio Lucio Mello Martinsd, Paulo de Souza Gonçalvesb,* a
Centro de Seringueira e Sistemas agroflorestais, Instituto Agronômico (IAC), Brazil Instituto Agronômico, Programa Seringueira, Caixa Postal 28, Campinas, SP CEP 13012-970, Brazil c Centro de Grãos e Fibras, Instituto Agronômico, Brazil d Apta Regional Centro Norte, Pindorama, Brazil b
A R T I C LE I N FO
A B S T R A C T
Keywords: Canonical correlation Path analysis Rubber yield Correlation network Early selection
The present work evaluated the relationship of rubber yield with morphological and structural traits of the laticiferous system in rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg] open-pollinated progenies to understand the cause and effect relationships between traits in order to enable future early selection. The progenies were installed in Jaú, Pindorama and Votuporanga in a randomized block design, with five replications and 10 plants per plot at a spacing of 2.0 × 2.0 m. The traits evaluated at three years of age were rubber yield (Yld); girth (Grt), expressed by the stem perimeter; bark thickness (BT); number of latex vessel rings (RG); length (PL), diameter (PD) and petiole index (PI); leaflet length(LFL) and width (LFW); leaf width (LW), area (LA) and index (LI); and number of leaf storeys (NLS). Coefficients of the genotypic correlations were used for a graphical representation of the correlation network created in the Genes program. Subsequently, the diagnosis of multicollinearity, as well as the analysis of the direct and indirect genotypic correlations of bark anatomical traits, leaf morphological traits, girth, and rubber yield, were performed. The traits that showed significant genotypic correlations with rubber yield at the three evaluated sites were girth, bark thickness, and number of leaf storeys. The girth character had the largest direct effect on rubber yield. Canonical correlation analysis showed that selection for the number of leaf storeys may increase at least three of the traits (yield, girth, bark thickness, and number of latex vessel rings) for most locations studied. There is an association between the yield traits (yield, girth, and the laticiferous system) and the morphological traits.
1. Introduction
In addition, knowledge of genetic correlations among secondary traits can aid in the identification of traits at young ages that have correlated responses with yield at maturity in rubber trees, and some studies have been conducted to estimate the correlations between different agronomic and anatomical traits (Silva et al., 2014; Oliveira et al., 2015; Souza et al., 2017). Despite the importance of the correlation analysis in the selection of genotypes, it does not identify the direct or indirect influences or provide information on cause and effect. This information can be provided by performing path analysis, which studies the direct and indirect effects of traits on a main variable, or canonical correlation analysis (CCA), which considers the probable associations of two or more data groups and the selection of the most important traits for each data group to (Cruz et al., 2012) The information generated in this study will allow a better understanding of the relationship of rubber yield to morphological and
The main challenge of rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.)] breeding is the development of early selection methods that help to shorten the breeding cycle through early selection and increase productivity (Priyadarshan, 2017a). The rubber tree is a crop with a long generative and testing cycle. Usually, three selection stages are involved, and it takes 25–30 years to release a clone (Goncalves and Marques, 2014). This long breeding process has led to considerable investment in studies of early selection methods to optimize and shorten the cycle as much as possible (Goncalves and Marques, 2014). One component of early selection is the use of secondary traits that show high correlations between traits and predict the effects of direct and/or indirect selection (Priyadarshan, 2017b).
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Corresponding author. E-mail address: [email protected] (P.d.S. Gonçalves).
https://doi.org/10.1016/j.indcrop.2020.112221 Received 17 October 2019; Received in revised form 5 February 2020; Accepted 7 February 2020 0926-6690/ © 2020 Published by Elsevier B.V.
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performed in R (R Development Core Team, 2020). Individual analysis To estimate the genotypic correlations, the genotypic estimate parameters were obtained according to Resende (2007), using the linear mixed model method of Reml/Blup (restricted maximum likelihood/best linear unbiased prediction) that evaluates individuals in progeny with mixed breeding plants, with multiple observations per plot and evaluation at site and at harvest in a complete block. The statistical model is presented below:
Table 1 Geografical characteristics of the experimental sites of the progenies tests. Characteristic
Local Votuporanga
Pindorama
Jaú
Height (m) Latitude Longitude Precipitationb (mm) Temperatureb (°C) Soil types
450 20°20′S 49°58′W 1437 22.8 Acrisol
562 21°13′S 48°56′W 1538 22.2 Acrisol
580 22°17′S 48°34′W 1421 21.8 Lixisol
y = Xr + Za + Wp + e
Data obtained from the Agricultural Climatology Departament of the Instituto Agronômico (IAC).
(1)
where y is a data vector; r is the vector replication effect (assumed to be fixed) added to the general average; a is a vector of the individual genetic additive effects (assumed to be randomized); p is a vector of the effects of plots (random); and e is an error vector (random). The capital letters represent the incidence matrices for these effects.
laticiferous system traits in rubber tree breeding programs, with the goal of enabling future early selection using path analysis, canonical correlation analysis, and correlation networks.
2.2.2.1. Estimates of genetic correlation. The genotypic correlation coefficients are expressed by the following formula:
2. Materials and methods 2.1. Material
ˆrg (ij) =
The data set included open-pollinated progeny obtained from seeds of 22 clones phenotypically selected for yield and girth from a population of rubber tree clones of Asian and African origin established at the Central Experimental Center (CEC) of the Instituto Agronômico de Campinas (IAC), São Paulo state, Brazil in 1952. The seeds corresponding to each progeny were germinated and transplanted to polyethylene bags. After six months of development, they were transplanted to sites at the experimental stations of Jaú, Pindorama and Votuporanga, which represent São Paulo State production areas in Brazil. The experimental design at the three sites was a randomized block design with five replications. The plots were linear, consisting of 10 useful plants spaced at 2.0 × 2.0 m. Table 1 shows edaphoclimatic characteristics and geographical coordinates.
ˆ g (ij) cov σˆ 2 gi. σˆ 2 gj.
(2)
rg (ij) is the genetic correlation coefficient between traits i and j; where ˆ ˆ g (ij) is the genetic covariance between traits i and j ; and σˆ 2 (gi) , σˆ 2 (gj) cov are the genetic variance of the progeny for traits i and j. 2.2.2.2. Correlation network. A correlation network was used to graphically display the functional relationship between the estimates of phenotypic correlation coefficients between the traits, in which the proximity between the nodes (traces) is proportional to the absolute value of the correlation between these nodes. The thickness of the edges was controlled by applying a cut-off value of 0.42; only nodes that passed that cut-off value have their edges highlighted to represent only significant correlations. Positive correlations are highlighted in green, while negative correlations are represented in red. The significance of the correlations was obtained according to Snedecor and Cochran (1955).
2.2. Methods 2.2.1. Analyzed traits In the study of progeny trials, morphological and structural laticiferous system traits were evaluated in addition to dry rubber yield (Yld) (g t−1 t−1) and girth (Grt) (cm), expressed by the stem perimeter. For structural laticiferous system traits such as bark thickness (BT) (mm) and number of latex vessel rings (RG), three samples of bark per useful plant were collected in the third year of planting 15 cm from the ground with the aid of an extractor. The morphological traits were represented by the average of three samples per useful plant at three years of age when the leaves were with four months age. The traits were number of leaf storeys (NLS), petiole diameter (PD) (cm), petiole length (PL) (cm), petiole index (PI) (%), leaflet length (LFL) (cm), leaflet width (LFW) (cm), leaf width (LW) (cm), leaf area (LA) (cm2) and leaf index (LI)(%). The studied trait of rubber yield was obtained by the Hamaker Morris-Mann test (HMMm) modified for three-year-old plants. The results were expressed in grams of dry rubber yield per tapping per tree (g t−1 t−1). The yield was estimated based on the average of the three tests. Girth was obtained when the plants were three years old, 50 cm from the ground with the aid of a tape measure and was expressed in centimeters (cm). More information about those data set traits was detailed by Souza et al. (2017).
2.2.2.3. Path analysis. For the path analysis, the genotypic correlation matrix of the explanatory variables was verified in relation to multicollinearity. The test used was the assessment of the condition number (CN), as proposed by Montgomery et al., (2012) which examines the ratio between the highest and lowest eigenvalues of the correlation matrix. Multicollinearity is considered low when CN < 100 and severe if CN > 1000. When multicollinearity was identified, it removed some variables to obtain a condition number smaller than 100. Path analysis was performed as described by Dewey and Lu (1959). 2.2.2.4. Canonical correlation analysis. In the canonical correlation analysis, the traits identified in group 1 were yield, girth and the laticiferous system, and the morphological traits were in group 2. This analysis assesses the degree of association between two sets of random quantitative traits, summarizing the information of each set in linear combinations. The analysis was performed as described by Thompson (2005). 3. Results and discussion 3.1. Correlation network
2.2.2. Statistical analyses Individual analyses were performed for the traits studied at each site. The computer software used for individual analysis and estimates of genotypic correlations was Selegen-Reml/Blup (Resende, 2016). For correlation, path analysis, and canonical correlation analysis, the Genes program (Cruz, 2013) was used. The correlation network was
The genotypic correlations obtained in Jaú, Pindorama and Votuporanga are represented in Fig. 1, and only significant correlations are shown. It can be observed that rubber yield (Yld) is positively correlated with girth (Grt), the number of latex vessel rings (RG), bark thickness (BT), and number of leaf storeys (NLS). Yld is negatively 2
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Fig. 1. Network of genotypic correlations between yield and the laticiferous and morphological systems of the 22 progenies evaluated in Jaú, Pindorama, and Votuporanga (from left to rigth). Green lines represent significant positive correlations between traits. The thickness of the lines and the distance between the traits are proportional to the intensity of the correlations. Yld: rubber yield; Grt: girth; BT: bark thickness; RG: number of latex vessel rings; NLS: number of leaf storeys; PD: petiole diameter; PL: petiole length; PI: petiole index; LFL: leaflet length; LFW: leaflet width; LW: leaf width; LA: leaf area; LI: leaf index.
and PL for the three sites evaluated. The traits petiole diameter (PD), petiole index (PI), leaflet length (LFL), and leaf area (LA) were removed from the analysis due to the diagnosis of multicollinearity. In Jaú, the results obtained by the path analysis corroborate the results obtained by the correlation network analysis, since the traits with the greatest direct effect on the character Yld were Grt, BT, RG, and NLS. Although the direct effect of BT was negative, its genetic correlation was significant and positive (0.477) and can be explained by its positive indirect effects on Yld via Grt (0.411), RG (0.212) and NLS (0.100). According to Cruz et al. (2012) traits that show favorable correlation but have direct effects in the opposite direction indicate the absence of cause and effect. Thus, there are other traits that determine the changes in the variable of interest that will be more useful for selection. This result was also found by Aguiar et al. (2010), who evaluated six rubber tree clones by path analysis and found the greatest direct effects on rubber yield from girth and the number of latex vessel rings. The authors also point out that the bark thickness had a direct, high-magnitude negative effect on rubber yield. In Pindorama, the traits with the greatest direct effect on Yld were Grt, BT, RG, PL, and LI. Although the direct effect of LI was high and negative, its genetic correlation was not significant (0.108); this can be explained by the positive indirect effects that other traits have on LI that affect Yld. In addition, the significant positive genotypic correlation between BT and Yld (0.432) was caused by the direct effect of BT (0.208) and by the indirect effects of NLS on BT (0.108) and Grt (0.133), which compensated for the negative indirect effects of various morphological traits. According to Cruz et al. (2012), the existence of significant correlations is indicative of the viability of indirect selection for obtaining gains in the character of interest. In this case, the traits to be considered are Grt, BT, and RG. However, the authors also note that when the auxiliary character has a low correlation with the main character, but its direct effect is of high magnitude, this variable should be disregarded for use in indirect selection, since simultaneous selection may provide good results. The traits PL, BT, and LW presented the greatest direct effects on girth in Votuporanga. Although Grt had a high direct effect on Yld, the genetic correlation between the two traits is not significant, which can be explained by the indirect and negative effect that girth has on the other traits that then affect yield, such as BT (-0.228). According to Cruz et al. (2012), although the genetic correlation is not significant when the direct effect is of high magnitude, genetic correlation should be considered in indirect selection. Moreover, although the direct effect of BT is negative, its genetic correlation with yield is positive, which
correlated with the other morphological traits in the three locations, except at Votuporanga, where Yld has a positive correlation with leaf index (LI) and leaf width (LW). The traits of the laticiferous system and of rubber yield and girth are positively correlated with each other and are negatively correlated with most morphological traits. It is shown by the thickness and color of the lines (green and thick) that most morphological traits are primarily correlated with each other. At the three studied sites (Fig. 1), the main traits correlated with Yld were Grt, BT and NLS. The correlation between Grt, BT and Yld was also reported by numerous studies in the literature, such as Silva et al. (2013; 2014), Mesquita and de Oliveira (2010) and Oliveira et al. (2015). In addition, Silva et al. (2013), studying rubber tree clones, also reported significant correlations between the traits bark thickness and girth. These results reveal a favorable situation, since vigorous rubber tree clones with virgin bark thickness are desirable for selection in breeding programs (Goncalves and Marques, 2014). At Jaú and Pindorama, the RG was also correlated with Yld, but this correlation was not found at Votuporanga. This can be explained by the fact that this character is highly influenced by the environment as described by Silva et al. (2014) who evaluated 51 open-pollinated progenies in six environments and found that the number of latex vessel rings showed a significant genotype-environment interaction. The low correlation between Yld and RG was also reported by Mesquita and de Oliveira (2010), who described that selection by the number of latex vessel rings of young plants showed no increase in selection efficiency. However, Cardoso et al. (2014) observed low production associated with a low number of latex vessel rings. The existence of significant correlations is indicative of the viability of this trait for indirect selection. However, these correlations may be caused by environmental interactions, gene linkages, or pleiotropic effects (Falconer and Mackay, 1996). Thus, from a practical, decision-making point of view, it is more useful to infer the direct genotypic effects using the most appropriate selection criterion. The correlation analysis is only a measurement of the association between traits; on the other hand, path analysis provides detailed knowledge of the relative importance of the traits involved, including their direct and indirect effects (Cruz et al., 2012). 3.2. Path analysis The coefficients of determination ( r2) obtained by the path analysis at Jaú, Pindorama and Votuporanga (Table 2) indicate that the traits used explain 54 %, 58 % and 40 %, respectively, of the variation obtained in the rubber yield trait. Yield is directly determined by Grt, BT, 3
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Table 2 Path coefficient estimates of direct (diagonal) and indirect effects (off – diagonal) among the studied traits (Grt: girth (cm); BT: bark thickness (mm); RG: number of latex vessel rings; NLS: number of leaf ; PL: petiole length (cm); LFW: leaflet width (cm); LW: leaf width (cm); LI: leaf index(%)) on yield at genotypic level in 22 progenies of rubber tree in Jáu, Pindorama and Votuporanga -SP with. Sites
Characters
Jaú
Grt
BT
RG
NLS
PL
LFW
LW
LI
Correlation
Grt BT RG NLS PL LFW LW LI r2= 0.54 Pindorama Grt BT RG NLS PL LFW LW LI r2= 0.58 Votuporanga Grt BT RG NLS PL LFW LF LI r2= 0.40
0.551 −0.227 0.146 0.139 −0.034 0.012 0.013 0.007
0.411 −0.305 0.212 0.100 −0.034 0.018 0.104 −0.030
0.222 −0.178 0.362 0.056 −0.025 0.015 0.075 −0.019
0.357 −0.142 0.095 0.215 −0.039 −0.018 0.038 −0.016
−0.153 0.083 −0.073 −0.068 0.123 −0.072 −0.038 0.034
−0.036 0.029 −0.028 0.020 0.047 −0.189 −0.099 0.041
−0.029 0.135 −0.116 −0.035 0.202 −0.008 −0.234 0.057
−0.054 0.134 −0.105 −0.052 0.062 −0.115 −0.195 0.069
0.594* 0.477* 0.509* 0.490* −0.164 −0.215 −0.284 −0.256
0.481 0.133 0.087 0.011 −0.107 −0.028 −0.001 −0.108
0.308 0.208 0.005 0.009 −0.037 −0.011 −0.001 −0.041
0.114 0.003 0.367 0.001 −0.155 −0.085 0.004 0.195
0.288 0.108 0.025 0.184 −0.161 0.003 0.006 0.049
−0.117 −0.017 −0.129 −0.007 0.438 0.015 −0.005 −0.507
−0.106 −0.018 −0.241 0.000 0.051 0.130 −0.002 −0.043
0.082 0.005 −0.159 −0.001 0.269 0.029 −0.009 −0.602
0.075 0.015 −0.104 −0.001 0.322 0.008 −0.008 −0.692
0.467* 0.432* 0.444* 0.333 −0.330 −0.229 −0.384 −0.384
0.596 −0.228 −0.007 0.056 −0.019 0.005 0.121 −0.069
0.362 −0.464 −0.007 0.054 0.085 0.003 0.146 0.030
0.146 −0.112 −0.031 0.009 −0.139 0.015 0.202 −0.070
0.384 −0.285 −0.003 0.088 −0.070 0.013 −0.010 0.028
−0.020 −0.068 0.007 −0.011 0.584 −0.030 −0.446 0.166
−0.065 0.031 0.011 −0.026 0.395 −0.045 −0.181 0.043
−0.096 0.089 0.008 0.001 0.344 −0.011 −0.757 0.195
−0.154 −0.051 0.008 0.009 0.358 −0.007 −0.548 0.270
0.401 0.208 0.021 0.145 0.183 0.163 −0.225 −0.115
* p ≤ 0.05, significance verified according to Table A11 of Snedecor and Cochran (1974). r2 = coefficient of determination provide by the analyses.
can be explained by the positive indirect effects that BT has on yield, such as Grt (0.362). In this case, Grt and PL are the traits that can contribute to the simultaneous selection for this location. In general, due to the importance of yield and its complex biological regulation related to the cell metabolism of the laticiferous and morphological systems (Kanpanon et al., 2015) as well as the environmental factors that can influence the expression of the traits, these relationships need to be thoroughly studied. According to Priyadarshan (2017b) relationships between traits including Grt, height, BT, RG, have shown inconsistent results over the years because these simple correlations are not adequate to explain the functioning of an entire rubber tree. Although path analysis is able to decompose linear relationships into their direct and indirect effects, their analysis is restricted to understanding how a group of traits affect a single variable of interest, yield. Therefore, in this work, it was chosen to investigate how one set of morphological traits can affect the laticiferous system, girth, and yield traits through canonical correlations that are able to explain how one set of traits can affect another.
Table 3 Canonical correlation between the studied traits in two groups separated as yield, girth and laticiferous system (Group 1) and morphological traits (Group 2) at genotypic level in 22 progenies of rubber tree in Jáu-SP. Group 1
Loads canonical of the group traits I
Yield Girth Bark thickness Number of latex vessel rings Group 2
Number of leaf Petiole diameter Leaflet width Leaf width Leaf index Correlation pvalue
3.3. Canonical correlations
U1
U2
U3
U4
0.732 0.933 0.893 0.559
0.085 0.315 −0.397 −0.298
0.650 −0.167 −0.205 −0.031
0.188 −0.039 −0.052 0.773
Loads canonical of the group traits II V1
V2
V3
V4
0.771 −0.525 −0.155 −0.352 −0.368 0.811 2.20*
0.276 0.378 0.077 0.726 0.640 0.789 13.14
0.092 −0.299 −0.606 −0.587 −0.407 0.327 93.01
−0.431 0.267 0.083 0.040 0.459 0.067 96.45
Ui and Vi represent the canonical pair. (*) = Significant difference at p ≤ 0.05.
The results obtained by the canonical correlation analysis in Jaú can be seen in Table 3. In this analysis, the relationships between the laticiferous system, girth, and yield (group 1) and morphological system (group 2) traits were investigated. The significant canonical correlation found between the groups of traits was considered for biological interpretation (canonical pair u1 / v1) and indicates that the groups are not independent. In this analysis, traits with higher canonical loadings contributed more to the multivariate relationships between plant traits. The NLS component was found to be more influential than the other morphological traits in V1 formation. Grt, Yld, BT were more influential
than the RG in U1 formation. The result of the analysis indicates that the increase in the NLS is a determinant of rubber yield. Thus the selection of this character leads to an improvement in all the traits that have a direct effect on the yield trait in this location. This result is in accordance with Chandrasekhar (2015) that studied the leaflet elongation pattern and growth performance in rubber tree and said that the growth of stem is characterized by rapid elongation of the bud along with the production of leaves in storeys. 4
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by (Omokhafe and Alika, 2003), who studied several traits in ten rubber tree clones at two years of age and used path analysis to evaluate the direct and indirect effects of these traits on rubber yield. The authors also found significant genetic correlations between NLS and BT and between Grt and Yld. However, the authors used phenotypic correlations for the path analysis and detected a direct negative effect of the number of leaf storeys on rubber yield. Determining the relationship between the traits that affect rubber yield is very important for increasing the efficiency of early selection and indirect selection. To this end, this study revealed the relationships among the components of the laticiferous system, girth, and yield and the morphological traits of plants. In general, selection for NLS was able to improve the traits of interest Yld, Grt, RG, and BT at most of the studied sites. Girth and NLS were the most influential traits in this relationship. Therefore, the results obtained in this work can optimize the early selection process; girth and the number of leaf storeys are easy to observe in young plants, thus, selecting for these traits will increase the efficiency of early selection for rubber yield.
Table 4 Canonical correlation between the studied traits in two groups separated as yield, girth and laticiferous system (Group 1) and morphological traits (Group 2) at genotypic level in 22 progenies of rubber tree in Pindorama-SP. Loads canonical of the group traits I Group 1
U1
U2
U3
U4
Yield Girth Bark thickness Number of latex vessel rings
0.209 0.903 0.515 0.435
0.429 −0.163 −0.091 0.886
0.809 0.363 0.673 −0.091
−0.345 −0.160 0.523 −0.134
Group 2
Number of leaf Petiole diameter Leaflet width Leaf width Leaf index Correlation pvalue
Loads canonical of the group traits II V1
V2
V3
V4
0.644 −0.303 −0.495 0.215 0.282 0.776 3.058*
−0.173 −0.363 −0.736 −0.792 −0.579 0.734 9.793
0.719 −0.252 0.201 −0.388 −0.480 0.499 42.98
0.071 0.647 −0.039 0.114 0.538 0.284 50.99
4. Conclusion
Ui and Vi represent the canonical pair. (*)=Significant difference at p ≤ 0.05.
- To increase rubber yield, select for girth, bark thickness, and the number of leaf storeys, which have indirect effects on and significant genetic correlations to rubber yield. - Girth stands out as the trait with the greatest direct effect on rubber tree yield. Among the available traits, it is possible to prioritize those that are easier to evaluate, are correlated with rubber yield, and have considerable indirect or direct effects on the final value of the desired trait. The traits with high and significant correlations and direct effects on rubber yield at most locations are girth, bark thickness, and number of latex vessel rings. - There is an association between the yield traits (yield, girth, and the laticiferous system) and the morphological traits. - Selection for the number of leaf storeys may increase at least three of the traits (yield, girth, bark thickness, and number of latex vessel rings) for most sites studied. It is possible to make early selection using this trait.
Table 5 Canonical correlation between the studied traits in two groups separated as yield, girth and laticiferous system (Group 1) and morphological traits (Group 2) at genotypic level in 22 progenies of rubber tree in Votuporanga-SP. Loads canonical of the group traits I Group I
U1
U2
U3
U4
Yield Girth Bark Thickness Number of vessel rings
0.487 0.839 0.912 0.198
−0.139 0.537 −0.241 0.232
0.273 −0.018 0.002 0.886
0.817 0.088 −0.332 −0.349
Group II
Loads canonical of the group traits II
Number of leaf storeys Petiole diameter Leaflet width Leaf width Leaf index Correlation pvalue
V1
V2
V3
V4
0.795 0.160 −0.038 −0.259 −0.039 0.839 1.459*
0.246 −0.417 −0.283 0.053 −0.581 0.721 16.157
−0.311 −0.379 −0.534 −0.658 −0.520 0.426 54.445
−0.450 0.449 0.796 −0.140 −0.362 0.325 40.849
CRediT authorship contribution statement Isabela de Castro Sant’Anna: Writing - review & editing, Software, Methodology, Formal analysis. Ligia Regina Lima Gouvêa: Writing review & editing, Methodology. Acácia Mecejana Diniz Souza Spitti: Visualization, Investigation, Methodology. Antônio Lucio Mello Martins: Data curation, Methodology. Paulo de Souza Gonçalves: Supervision.
Ui and Vi represent the canonical pair. (*)=Significant difference at p≤0.05.
In Pindorama (Table 4), the loadings for the morphological traits suggested that NLS and LW were the most influential factors in V1 formation. According to the canonical loadings, girth, RG and BT contributed more to the canonical variable U1. Although there are no studies that prove the relationship between LW and NLS and Grt, RG, and BT, the results indicate that these traits have a direct effect on each other. That is, increasing the NLS and decreasing the LW increases Grt, RG and BT (Table 3). According to Righi and Bernardes (2008), leaves are stronger drains in carbon allocation than rubber production, which may be an indication that plants with very large leaflets are not the most productive. Canonical correlation analysis performed in Votuporanga (Table 5) indicates that the increase in NLS is a determinant of the increase in Grt, BT, and Yld. According to the canonical loadings, the pairs NLS and PD and Grt and Yld contributed strongly to the canonical pair V1 and U1, respectively. Although there is no relationship between NLS and Yld, the results indicated that the selection of this trait leads to an improvement in all traits that have a direct effect on production. The results obtained in this work are in agreement with the results obtained
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors gratefully acknowledge the the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (2018/18300-4) for financial supports and researcher fellowship to ICS (2018/26408-0) and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the financial support (400130/2016-5). References Aguiar, A.T.E., Martins, A.L.M., Gonçalves, E.C.P., Júnior, E.J.S., Branco, R.B.F., 2010. Correlações e análise de trilha em clones de seringueira. Rev. Ceres 57, 602–607 (abstract in English).
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characters with respect to latex yield in Hevea brasiliensis Muell. Arg. Tropicultura 21, 173–178. Priyadarshan, P.M., 2017a. Genetics of traits. Biology of Hevea Rubber. Springer, pp. 127–129. Priyadarshan, P.M., 2017b. Refinements to Hevea rubber breeding. Tree Genet. Genomes 13. https://doi.org/10.1007/s11295-017-1101-8. R Development Core Team, 2020. R: a Language and Environment for Statistical Computing. URL. R Foundation for Statistical Computing, Vienna, Austria. https:// www.R-project.org/. Resende, M.D.V. de, 2007. Matemática e Estatística na Análise de Experimentos e no Melhoramento Genético. Embrapa Florestas, Colombo. Resende, M.D.V. de, 2016. Software Selegen-REML/BLUP: a useful tool for plant breeding. Crop Breed. Appl. Biotechnol. 16, 330–339. Righi, C.A., Bernardes, M.S., 2008. The potential for increasing rubber production by matching tapping intensity to leaf area index. Agrofor. Syst. 72, 1–13. https://doi. org/10.1007/s10457-007-9092-3. Silva, G.A.P., Gouvêa, L.R.L., Verardi, C.K., de Resende, M.D.V., Junior, E.J.S., de Souza Gonçalves, P., 2013. Genetic parameters and correlation in early measurement cycles in rubber trees. Euphytica 189, 343–350. https://doi.org/10.1007/s106810120751-8. Silva, G.A.P., Gezan, S.A., de Carvalho, M.P., Gouvêa, L.R.L., Verardi, C.K., de Oliveira, A.L.B., Gonçalves, P., de, S., 2014. Genetic parameters in a rubber tree population: heritabilities, genotype-by-environment interactions and multi-trait correlations. Tree Genet. Genomes 10, 1511–1518. https://doi.org/10.1007/s11295-014-0766-5. Snedecor, G.W., Cochran, W.G., 1955. Statistical Methods. The Iowa State University Press, Iowa. Souza, A.M.D., Gouvea, L.R.L., Bombonato de Oliveira, A.L., Peres Silva, G.A., de S Goncalves, P., 2017. Associations among Rubber Yield and Secondary Traits in Juvenile Rubber Trees Progeny. EUPHYTICA, pp. 213. https://doi.org/10.1007/ s10681-016-1804-1. Thompson, B., 2005. Canonical correlation analysis. Encycl. Stat. Behav. Sci. https://doi. org/10.1002/0470013192.bsa068. Major Reference Works.
Cardoso, S.E.A., Freitas, T.A., Silva, D., da, C., Gouvêa, L.R.L., Gonçalves, P., de, S., Mattos, C.R.R., Garcia, D., 2014. Comparison of growth, yield and related traits of resistant Hevea genotypes under high South American leaf blight pressure. Ind. Crops Prod. 53, 337–349. https://doi.org/10.1016/j.indcrop.2013.12.033. Chandrasekhar, T.R., 2015. Dynamics of leaflet elongation and its relationship with plant performance in rubber (Hevea brasiliensis) trees under dry sub-humid climatic conditions. J. Rubber Res. 18, 99–108. Cruz, C.D., 2013. Genes: a software package for analysis in experimental statistics and quantitative genetics. Acta Sci. Agron. 35, 271–276. Cruz, C.D., Regazzi, A.J., Carneiro, P.C.S., 2012. Modelos biométricos aplicados ao melhoramento genético. 5, editor. Viçosa UFV. Dewey, D.R., Lu, K., 1959. A correlation and path-coefficient analysis of components of crested wheatgrass seed production 1. Agron. J. 51, 515–518. https://doi.org/10. 2134/agronj1959.00021962005100090002x. Falconer, D.S., Mackay, T.F.C., 1996. Introduction to Quantitative Genetics. Longman Group, Harlow, UK. Introd. To Quant. Genet, 4th ed. Longman Group, Harlow, UK. Goncalves, P. de S., Marques, J., 2014. In: Alvarenga, A.P., Carmo, C.A.F.S. do. (Eds.), Melhoramento genético da seringueira: passado, presente e futuro. EPAMIG, ViçosaMG, pp. 401–407. Kanpanon, N., Kasemsap, P., Thaler, P., Kositsup, B., Gay, F., Lacote, R., Epron, D., 2015. Carbon isotope composition of latex does not reflect temporal variations of photosynthetic carbon isotope discrimination in rubber trees (Hevea brasiliensis). Tree Physiol. 35, 1166–1175. https://doi.org/10.1093/treephys/tpv070. Mesquita, A.C., de Oliveira, L.E.M., 2010. Caracteristicas anatômicas da casca e produção de látex em plantas de seringueira não enxertadas. Acta Amaz. 40, 241–246. https:// doi.org/10.1590/S0044-59672010000200001. Montgomery, D.C., Peck, E.A., Vining, G.G., 2012. Introduction to Linear Regression Analysis. John Wiley & Sons. Oliveira, A.L.B., Gouvêa, L.R.L., Verardi, C.K., Silva, G.A.P., de Gonçalves, P.S., 2015. Genetic variability and predicted genetic gains for yield and laticifer system traits of rubber tree families. Euphytica 203, 285–293. https://doi.org/10.1007/s10681-0141256-4. Omokhafe, K.O., Alika, J.E., 2003. Correlation and path coefficients of seed and juvenile
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Relationships between yield and some anatomical and morphological traits in rubber tree progenies
T
Isabela de Castro Sant’Annaa, Ligia Regina Lima Gouvêab, Acácia Mecejana Diniz Souza Spittic, Antônio Lucio Mello Martinsd, Paulo de Souza Gonçalvesb,* a
Centro de Seringueira e Sistemas agroflorestais, Instituto Agronômico (IAC), Brazil Instituto Agronômico, Programa Seringueira, Caixa Postal 28, Campinas, SP CEP 13012-970, Brazil c Centro de Grãos e Fibras, Instituto Agronômico, Brazil d Apta Regional Centro Norte, Pindorama, Brazil b
A R T I C LE I N FO
A B S T R A C T
Keywords: Canonical correlation Path analysis Rubber yield Correlation network Early selection
The present work evaluated the relationship of rubber yield with morphological and structural traits of the laticiferous system in rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg] open-pollinated progenies to understand the cause and effect relationships between traits in order to enable future early selection. The progenies were installed in Jaú, Pindorama and Votuporanga in a randomized block design, with five replications and 10 plants per plot at a spacing of 2.0 × 2.0 m. The traits evaluated at three years of age were rubber yield (Yld); girth (Grt), expressed by the stem perimeter; bark thickness (BT); number of latex vessel rings (RG); length (PL), diameter (PD) and petiole index (PI); leaflet length(LFL) and width (LFW); leaf width (LW), area (LA) and index (LI); and number of leaf storeys (NLS). Coefficients of the genotypic correlations were used for a graphical representation of the correlation network created in the Genes program. Subsequently, the diagnosis of multicollinearity, as well as the analysis of the direct and indirect genotypic correlations of bark anatomical traits, leaf morphological traits, girth, and rubber yield, were performed. The traits that showed significant genotypic correlations with rubber yield at the three evaluated sites were girth, bark thickness, and number of leaf storeys. The girth character had the largest direct effect on rubber yield. Canonical correlation analysis showed that selection for the number of leaf storeys may increase at least three of the traits (yield, girth, bark thickness, and number of latex vessel rings) for most locations studied. There is an association between the yield traits (yield, girth, and the laticiferous system) and the morphological traits.
1. Introduction
In addition, knowledge of genetic correlations among secondary traits can aid in the identification of traits at young ages that have correlated responses with yield at maturity in rubber trees, and some studies have been conducted to estimate the correlations between different agronomic and anatomical traits (Silva et al., 2014; Oliveira et al., 2015; Souza et al., 2017). Despite the importance of the correlation analysis in the selection of genotypes, it does not identify the direct or indirect influences or provide information on cause and effect. This information can be provided by performing path analysis, which studies the direct and indirect effects of traits on a main variable, or canonical correlation analysis (CCA), which considers the probable associations of two or more data groups and the selection of the most important traits for each data group to (Cruz et al., 2012) The information generated in this study will allow a better understanding of the relationship of rubber yield to morphological and
The main challenge of rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.)] breeding is the development of early selection methods that help to shorten the breeding cycle through early selection and increase productivity (Priyadarshan, 2017a). The rubber tree is a crop with a long generative and testing cycle. Usually, three selection stages are involved, and it takes 25–30 years to release a clone (Goncalves and Marques, 2014). This long breeding process has led to considerable investment in studies of early selection methods to optimize and shorten the cycle as much as possible (Goncalves and Marques, 2014). One component of early selection is the use of secondary traits that show high correlations between traits and predict the effects of direct and/or indirect selection (Priyadarshan, 2017b).
⁎
Corresponding author. E-mail address: [email protected] (P.d.S. Gonçalves).
https://doi.org/10.1016/j.indcrop.2020.112221 Received 17 October 2019; Received in revised form 5 February 2020; Accepted 7 February 2020 0926-6690/ © 2020 Published by Elsevier B.V.
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performed in R (R Development Core Team, 2020). Individual analysis To estimate the genotypic correlations, the genotypic estimate parameters were obtained according to Resende (2007), using the linear mixed model method of Reml/Blup (restricted maximum likelihood/best linear unbiased prediction) that evaluates individuals in progeny with mixed breeding plants, with multiple observations per plot and evaluation at site and at harvest in a complete block. The statistical model is presented below:
Table 1 Geografical characteristics of the experimental sites of the progenies tests. Characteristic
Local Votuporanga
Pindorama
Jaú
Height (m) Latitude Longitude Precipitationb (mm) Temperatureb (°C) Soil types
450 20°20′S 49°58′W 1437 22.8 Acrisol
562 21°13′S 48°56′W 1538 22.2 Acrisol
580 22°17′S 48°34′W 1421 21.8 Lixisol
y = Xr + Za + Wp + e
Data obtained from the Agricultural Climatology Departament of the Instituto Agronômico (IAC).
(1)
where y is a data vector; r is the vector replication effect (assumed to be fixed) added to the general average; a is a vector of the individual genetic additive effects (assumed to be randomized); p is a vector of the effects of plots (random); and e is an error vector (random). The capital letters represent the incidence matrices for these effects.
laticiferous system traits in rubber tree breeding programs, with the goal of enabling future early selection using path analysis, canonical correlation analysis, and correlation networks.
2.2.2.1. Estimates of genetic correlation. The genotypic correlation coefficients are expressed by the following formula:
2. Materials and methods 2.1. Material
ˆrg (ij) =
The data set included open-pollinated progeny obtained from seeds of 22 clones phenotypically selected for yield and girth from a population of rubber tree clones of Asian and African origin established at the Central Experimental Center (CEC) of the Instituto Agronômico de Campinas (IAC), São Paulo state, Brazil in 1952. The seeds corresponding to each progeny were germinated and transplanted to polyethylene bags. After six months of development, they were transplanted to sites at the experimental stations of Jaú, Pindorama and Votuporanga, which represent São Paulo State production areas in Brazil. The experimental design at the three sites was a randomized block design with five replications. The plots were linear, consisting of 10 useful plants spaced at 2.0 × 2.0 m. Table 1 shows edaphoclimatic characteristics and geographical coordinates.
ˆ g (ij) cov σˆ 2 gi. σˆ 2 gj.
(2)
rg (ij) is the genetic correlation coefficient between traits i and j; where ˆ ˆ g (ij) is the genetic covariance between traits i and j ; and σˆ 2 (gi) , σˆ 2 (gj) cov are the genetic variance of the progeny for traits i and j. 2.2.2.2. Correlation network. A correlation network was used to graphically display the functional relationship between the estimates of phenotypic correlation coefficients between the traits, in which the proximity between the nodes (traces) is proportional to the absolute value of the correlation between these nodes. The thickness of the edges was controlled by applying a cut-off value of 0.42; only nodes that passed that cut-off value have their edges highlighted to represent only significant correlations. Positive correlations are highlighted in green, while negative correlations are represented in red. The significance of the correlations was obtained according to Snedecor and Cochran (1955).
2.2. Methods 2.2.1. Analyzed traits In the study of progeny trials, morphological and structural laticiferous system traits were evaluated in addition to dry rubber yield (Yld) (g t−1 t−1) and girth (Grt) (cm), expressed by the stem perimeter. For structural laticiferous system traits such as bark thickness (BT) (mm) and number of latex vessel rings (RG), three samples of bark per useful plant were collected in the third year of planting 15 cm from the ground with the aid of an extractor. The morphological traits were represented by the average of three samples per useful plant at three years of age when the leaves were with four months age. The traits were number of leaf storeys (NLS), petiole diameter (PD) (cm), petiole length (PL) (cm), petiole index (PI) (%), leaflet length (LFL) (cm), leaflet width (LFW) (cm), leaf width (LW) (cm), leaf area (LA) (cm2) and leaf index (LI)(%). The studied trait of rubber yield was obtained by the Hamaker Morris-Mann test (HMMm) modified for three-year-old plants. The results were expressed in grams of dry rubber yield per tapping per tree (g t−1 t−1). The yield was estimated based on the average of the three tests. Girth was obtained when the plants were three years old, 50 cm from the ground with the aid of a tape measure and was expressed in centimeters (cm). More information about those data set traits was detailed by Souza et al. (2017).
2.2.2.3. Path analysis. For the path analysis, the genotypic correlation matrix of the explanatory variables was verified in relation to multicollinearity. The test used was the assessment of the condition number (CN), as proposed by Montgomery et al., (2012) which examines the ratio between the highest and lowest eigenvalues of the correlation matrix. Multicollinearity is considered low when CN < 100 and severe if CN > 1000. When multicollinearity was identified, it removed some variables to obtain a condition number smaller than 100. Path analysis was performed as described by Dewey and Lu (1959). 2.2.2.4. Canonical correlation analysis. In the canonical correlation analysis, the traits identified in group 1 were yield, girth and the laticiferous system, and the morphological traits were in group 2. This analysis assesses the degree of association between two sets of random quantitative traits, summarizing the information of each set in linear combinations. The analysis was performed as described by Thompson (2005). 3. Results and discussion 3.1. Correlation network
2.2.2. Statistical analyses Individual analyses were performed for the traits studied at each site. The computer software used for individual analysis and estimates of genotypic correlations was Selegen-Reml/Blup (Resende, 2016). For correlation, path analysis, and canonical correlation analysis, the Genes program (Cruz, 2013) was used. The correlation network was
The genotypic correlations obtained in Jaú, Pindorama and Votuporanga are represented in Fig. 1, and only significant correlations are shown. It can be observed that rubber yield (Yld) is positively correlated with girth (Grt), the number of latex vessel rings (RG), bark thickness (BT), and number of leaf storeys (NLS). Yld is negatively 2
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Fig. 1. Network of genotypic correlations between yield and the laticiferous and morphological systems of the 22 progenies evaluated in Jaú, Pindorama, and Votuporanga (from left to rigth). Green lines represent significant positive correlations between traits. The thickness of the lines and the distance between the traits are proportional to the intensity of the correlations. Yld: rubber yield; Grt: girth; BT: bark thickness; RG: number of latex vessel rings; NLS: number of leaf storeys; PD: petiole diameter; PL: petiole length; PI: petiole index; LFL: leaflet length; LFW: leaflet width; LW: leaf width; LA: leaf area; LI: leaf index.
and PL for the three sites evaluated. The traits petiole diameter (PD), petiole index (PI), leaflet length (LFL), and leaf area (LA) were removed from the analysis due to the diagnosis of multicollinearity. In Jaú, the results obtained by the path analysis corroborate the results obtained by the correlation network analysis, since the traits with the greatest direct effect on the character Yld were Grt, BT, RG, and NLS. Although the direct effect of BT was negative, its genetic correlation was significant and positive (0.477) and can be explained by its positive indirect effects on Yld via Grt (0.411), RG (0.212) and NLS (0.100). According to Cruz et al. (2012) traits that show favorable correlation but have direct effects in the opposite direction indicate the absence of cause and effect. Thus, there are other traits that determine the changes in the variable of interest that will be more useful for selection. This result was also found by Aguiar et al. (2010), who evaluated six rubber tree clones by path analysis and found the greatest direct effects on rubber yield from girth and the number of latex vessel rings. The authors also point out that the bark thickness had a direct, high-magnitude negative effect on rubber yield. In Pindorama, the traits with the greatest direct effect on Yld were Grt, BT, RG, PL, and LI. Although the direct effect of LI was high and negative, its genetic correlation was not significant (0.108); this can be explained by the positive indirect effects that other traits have on LI that affect Yld. In addition, the significant positive genotypic correlation between BT and Yld (0.432) was caused by the direct effect of BT (0.208) and by the indirect effects of NLS on BT (0.108) and Grt (0.133), which compensated for the negative indirect effects of various morphological traits. According to Cruz et al. (2012), the existence of significant correlations is indicative of the viability of indirect selection for obtaining gains in the character of interest. In this case, the traits to be considered are Grt, BT, and RG. However, the authors also note that when the auxiliary character has a low correlation with the main character, but its direct effect is of high magnitude, this variable should be disregarded for use in indirect selection, since simultaneous selection may provide good results. The traits PL, BT, and LW presented the greatest direct effects on girth in Votuporanga. Although Grt had a high direct effect on Yld, the genetic correlation between the two traits is not significant, which can be explained by the indirect and negative effect that girth has on the other traits that then affect yield, such as BT (-0.228). According to Cruz et al. (2012), although the genetic correlation is not significant when the direct effect is of high magnitude, genetic correlation should be considered in indirect selection. Moreover, although the direct effect of BT is negative, its genetic correlation with yield is positive, which
correlated with the other morphological traits in the three locations, except at Votuporanga, where Yld has a positive correlation with leaf index (LI) and leaf width (LW). The traits of the laticiferous system and of rubber yield and girth are positively correlated with each other and are negatively correlated with most morphological traits. It is shown by the thickness and color of the lines (green and thick) that most morphological traits are primarily correlated with each other. At the three studied sites (Fig. 1), the main traits correlated with Yld were Grt, BT and NLS. The correlation between Grt, BT and Yld was also reported by numerous studies in the literature, such as Silva et al. (2013; 2014), Mesquita and de Oliveira (2010) and Oliveira et al. (2015). In addition, Silva et al. (2013), studying rubber tree clones, also reported significant correlations between the traits bark thickness and girth. These results reveal a favorable situation, since vigorous rubber tree clones with virgin bark thickness are desirable for selection in breeding programs (Goncalves and Marques, 2014). At Jaú and Pindorama, the RG was also correlated with Yld, but this correlation was not found at Votuporanga. This can be explained by the fact that this character is highly influenced by the environment as described by Silva et al. (2014) who evaluated 51 open-pollinated progenies in six environments and found that the number of latex vessel rings showed a significant genotype-environment interaction. The low correlation between Yld and RG was also reported by Mesquita and de Oliveira (2010), who described that selection by the number of latex vessel rings of young plants showed no increase in selection efficiency. However, Cardoso et al. (2014) observed low production associated with a low number of latex vessel rings. The existence of significant correlations is indicative of the viability of this trait for indirect selection. However, these correlations may be caused by environmental interactions, gene linkages, or pleiotropic effects (Falconer and Mackay, 1996). Thus, from a practical, decision-making point of view, it is more useful to infer the direct genotypic effects using the most appropriate selection criterion. The correlation analysis is only a measurement of the association between traits; on the other hand, path analysis provides detailed knowledge of the relative importance of the traits involved, including their direct and indirect effects (Cruz et al., 2012). 3.2. Path analysis The coefficients of determination ( r2) obtained by the path analysis at Jaú, Pindorama and Votuporanga (Table 2) indicate that the traits used explain 54 %, 58 % and 40 %, respectively, of the variation obtained in the rubber yield trait. Yield is directly determined by Grt, BT, 3
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Table 2 Path coefficient estimates of direct (diagonal) and indirect effects (off – diagonal) among the studied traits (Grt: girth (cm); BT: bark thickness (mm); RG: number of latex vessel rings; NLS: number of leaf ; PL: petiole length (cm); LFW: leaflet width (cm); LW: leaf width (cm); LI: leaf index(%)) on yield at genotypic level in 22 progenies of rubber tree in Jáu, Pindorama and Votuporanga -SP with. Sites
Characters
Jaú
Grt
BT
RG
NLS
PL
LFW
LW
LI
Correlation
Grt BT RG NLS PL LFW LW LI r2= 0.54 Pindorama Grt BT RG NLS PL LFW LW LI r2= 0.58 Votuporanga Grt BT RG NLS PL LFW LF LI r2= 0.40
0.551 −0.227 0.146 0.139 −0.034 0.012 0.013 0.007
0.411 −0.305 0.212 0.100 −0.034 0.018 0.104 −0.030
0.222 −0.178 0.362 0.056 −0.025 0.015 0.075 −0.019
0.357 −0.142 0.095 0.215 −0.039 −0.018 0.038 −0.016
−0.153 0.083 −0.073 −0.068 0.123 −0.072 −0.038 0.034
−0.036 0.029 −0.028 0.020 0.047 −0.189 −0.099 0.041
−0.029 0.135 −0.116 −0.035 0.202 −0.008 −0.234 0.057
−0.054 0.134 −0.105 −0.052 0.062 −0.115 −0.195 0.069
0.594* 0.477* 0.509* 0.490* −0.164 −0.215 −0.284 −0.256
0.481 0.133 0.087 0.011 −0.107 −0.028 −0.001 −0.108
0.308 0.208 0.005 0.009 −0.037 −0.011 −0.001 −0.041
0.114 0.003 0.367 0.001 −0.155 −0.085 0.004 0.195
0.288 0.108 0.025 0.184 −0.161 0.003 0.006 0.049
−0.117 −0.017 −0.129 −0.007 0.438 0.015 −0.005 −0.507
−0.106 −0.018 −0.241 0.000 0.051 0.130 −0.002 −0.043
0.082 0.005 −0.159 −0.001 0.269 0.029 −0.009 −0.602
0.075 0.015 −0.104 −0.001 0.322 0.008 −0.008 −0.692
0.467* 0.432* 0.444* 0.333 −0.330 −0.229 −0.384 −0.384
0.596 −0.228 −0.007 0.056 −0.019 0.005 0.121 −0.069
0.362 −0.464 −0.007 0.054 0.085 0.003 0.146 0.030
0.146 −0.112 −0.031 0.009 −0.139 0.015 0.202 −0.070
0.384 −0.285 −0.003 0.088 −0.070 0.013 −0.010 0.028
−0.020 −0.068 0.007 −0.011 0.584 −0.030 −0.446 0.166
−0.065 0.031 0.011 −0.026 0.395 −0.045 −0.181 0.043
−0.096 0.089 0.008 0.001 0.344 −0.011 −0.757 0.195
−0.154 −0.051 0.008 0.009 0.358 −0.007 −0.548 0.270
0.401 0.208 0.021 0.145 0.183 0.163 −0.225 −0.115
* p ≤ 0.05, significance verified according to Table A11 of Snedecor and Cochran (1974). r2 = coefficient of determination provide by the analyses.
can be explained by the positive indirect effects that BT has on yield, such as Grt (0.362). In this case, Grt and PL are the traits that can contribute to the simultaneous selection for this location. In general, due to the importance of yield and its complex biological regulation related to the cell metabolism of the laticiferous and morphological systems (Kanpanon et al., 2015) as well as the environmental factors that can influence the expression of the traits, these relationships need to be thoroughly studied. According to Priyadarshan (2017b) relationships between traits including Grt, height, BT, RG, have shown inconsistent results over the years because these simple correlations are not adequate to explain the functioning of an entire rubber tree. Although path analysis is able to decompose linear relationships into their direct and indirect effects, their analysis is restricted to understanding how a group of traits affect a single variable of interest, yield. Therefore, in this work, it was chosen to investigate how one set of morphological traits can affect the laticiferous system, girth, and yield traits through canonical correlations that are able to explain how one set of traits can affect another.
Table 3 Canonical correlation between the studied traits in two groups separated as yield, girth and laticiferous system (Group 1) and morphological traits (Group 2) at genotypic level in 22 progenies of rubber tree in Jáu-SP. Group 1
Loads canonical of the group traits I
Yield Girth Bark thickness Number of latex vessel rings Group 2
Number of leaf Petiole diameter Leaflet width Leaf width Leaf index Correlation pvalue
3.3. Canonical correlations
U1
U2
U3
U4
0.732 0.933 0.893 0.559
0.085 0.315 −0.397 −0.298
0.650 −0.167 −0.205 −0.031
0.188 −0.039 −0.052 0.773
Loads canonical of the group traits II V1
V2
V3
V4
0.771 −0.525 −0.155 −0.352 −0.368 0.811 2.20*
0.276 0.378 0.077 0.726 0.640 0.789 13.14
0.092 −0.299 −0.606 −0.587 −0.407 0.327 93.01
−0.431 0.267 0.083 0.040 0.459 0.067 96.45
Ui and Vi represent the canonical pair. (*) = Significant difference at p ≤ 0.05.
The results obtained by the canonical correlation analysis in Jaú can be seen in Table 3. In this analysis, the relationships between the laticiferous system, girth, and yield (group 1) and morphological system (group 2) traits were investigated. The significant canonical correlation found between the groups of traits was considered for biological interpretation (canonical pair u1 / v1) and indicates that the groups are not independent. In this analysis, traits with higher canonical loadings contributed more to the multivariate relationships between plant traits. The NLS component was found to be more influential than the other morphological traits in V1 formation. Grt, Yld, BT were more influential
than the RG in U1 formation. The result of the analysis indicates that the increase in the NLS is a determinant of rubber yield. Thus the selection of this character leads to an improvement in all the traits that have a direct effect on the yield trait in this location. This result is in accordance with Chandrasekhar (2015) that studied the leaflet elongation pattern and growth performance in rubber tree and said that the growth of stem is characterized by rapid elongation of the bud along with the production of leaves in storeys. 4
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by (Omokhafe and Alika, 2003), who studied several traits in ten rubber tree clones at two years of age and used path analysis to evaluate the direct and indirect effects of these traits on rubber yield. The authors also found significant genetic correlations between NLS and BT and between Grt and Yld. However, the authors used phenotypic correlations for the path analysis and detected a direct negative effect of the number of leaf storeys on rubber yield. Determining the relationship between the traits that affect rubber yield is very important for increasing the efficiency of early selection and indirect selection. To this end, this study revealed the relationships among the components of the laticiferous system, girth, and yield and the morphological traits of plants. In general, selection for NLS was able to improve the traits of interest Yld, Grt, RG, and BT at most of the studied sites. Girth and NLS were the most influential traits in this relationship. Therefore, the results obtained in this work can optimize the early selection process; girth and the number of leaf storeys are easy to observe in young plants, thus, selecting for these traits will increase the efficiency of early selection for rubber yield.
Table 4 Canonical correlation between the studied traits in two groups separated as yield, girth and laticiferous system (Group 1) and morphological traits (Group 2) at genotypic level in 22 progenies of rubber tree in Pindorama-SP. Loads canonical of the group traits I Group 1
U1
U2
U3
U4
Yield Girth Bark thickness Number of latex vessel rings
0.209 0.903 0.515 0.435
0.429 −0.163 −0.091 0.886
0.809 0.363 0.673 −0.091
−0.345 −0.160 0.523 −0.134
Group 2
Number of leaf Petiole diameter Leaflet width Leaf width Leaf index Correlation pvalue
Loads canonical of the group traits II V1
V2
V3
V4
0.644 −0.303 −0.495 0.215 0.282 0.776 3.058*
−0.173 −0.363 −0.736 −0.792 −0.579 0.734 9.793
0.719 −0.252 0.201 −0.388 −0.480 0.499 42.98
0.071 0.647 −0.039 0.114 0.538 0.284 50.99
4. Conclusion
Ui and Vi represent the canonical pair. (*)=Significant difference at p ≤ 0.05.
- To increase rubber yield, select for girth, bark thickness, and the number of leaf storeys, which have indirect effects on and significant genetic correlations to rubber yield. - Girth stands out as the trait with the greatest direct effect on rubber tree yield. Among the available traits, it is possible to prioritize those that are easier to evaluate, are correlated with rubber yield, and have considerable indirect or direct effects on the final value of the desired trait. The traits with high and significant correlations and direct effects on rubber yield at most locations are girth, bark thickness, and number of latex vessel rings. - There is an association between the yield traits (yield, girth, and the laticiferous system) and the morphological traits. - Selection for the number of leaf storeys may increase at least three of the traits (yield, girth, bark thickness, and number of latex vessel rings) for most sites studied. It is possible to make early selection using this trait.
Table 5 Canonical correlation between the studied traits in two groups separated as yield, girth and laticiferous system (Group 1) and morphological traits (Group 2) at genotypic level in 22 progenies of rubber tree in Votuporanga-SP. Loads canonical of the group traits I Group I
U1
U2
U3
U4
Yield Girth Bark Thickness Number of vessel rings
0.487 0.839 0.912 0.198
−0.139 0.537 −0.241 0.232
0.273 −0.018 0.002 0.886
0.817 0.088 −0.332 −0.349
Group II
Loads canonical of the group traits II
Number of leaf storeys Petiole diameter Leaflet width Leaf width Leaf index Correlation pvalue
V1
V2
V3
V4
0.795 0.160 −0.038 −0.259 −0.039 0.839 1.459*
0.246 −0.417 −0.283 0.053 −0.581 0.721 16.157
−0.311 −0.379 −0.534 −0.658 −0.520 0.426 54.445
−0.450 0.449 0.796 −0.140 −0.362 0.325 40.849
CRediT authorship contribution statement Isabela de Castro Sant’Anna: Writing - review & editing, Software, Methodology, Formal analysis. Ligia Regina Lima Gouvêa: Writing review & editing, Methodology. Acácia Mecejana Diniz Souza Spitti: Visualization, Investigation, Methodology. Antônio Lucio Mello Martins: Data curation, Methodology. Paulo de Souza Gonçalves: Supervision.
Ui and Vi represent the canonical pair. (*)=Significant difference at p≤0.05.
In Pindorama (Table 4), the loadings for the morphological traits suggested that NLS and LW were the most influential factors in V1 formation. According to the canonical loadings, girth, RG and BT contributed more to the canonical variable U1. Although there are no studies that prove the relationship between LW and NLS and Grt, RG, and BT, the results indicate that these traits have a direct effect on each other. That is, increasing the NLS and decreasing the LW increases Grt, RG and BT (Table 3). According to Righi and Bernardes (2008), leaves are stronger drains in carbon allocation than rubber production, which may be an indication that plants with very large leaflets are not the most productive. Canonical correlation analysis performed in Votuporanga (Table 5) indicates that the increase in NLS is a determinant of the increase in Grt, BT, and Yld. According to the canonical loadings, the pairs NLS and PD and Grt and Yld contributed strongly to the canonical pair V1 and U1, respectively. Although there is no relationship between NLS and Yld, the results indicated that the selection of this trait leads to an improvement in all traits that have a direct effect on production. The results obtained in this work are in agreement with the results obtained
Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgments The authors gratefully acknowledge the the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) (2018/18300-4) for financial supports and researcher fellowship to ICS (2018/26408-0) and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) for the financial support (400130/2016-5). References Aguiar, A.T.E., Martins, A.L.M., Gonçalves, E.C.P., Júnior, E.J.S., Branco, R.B.F., 2010. Correlações e análise de trilha em clones de seringueira. Rev. Ceres 57, 602–607 (abstract in English).
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