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Annual growth increment and stability of rubber yield in the tapping phase in rubber tree clones: Implications for early selection
Industrial Crops and Products 52 (2014) 801–808
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Annual growth increment and stability of rubber yield in the tapping phase in rubber tree clones: Implications for early selection Guilherme Augusto Peres Silva ∗ , Lígia Regina Lima Gouvêa, Cecília Khusala Verardi, André Luis Bombonato de Oliveira, Paulo de Souza Gonc¸alves Instituto Agronômico de Campinas, Programa Seringueira, Caixa Postal 28, CEP 13001 970 Campinas, SP, Brazil
a r t i c l e
i n f o
Article history: Received 7 August 2013 Received in revised form 27 November 2013 Accepted 3 December 2013 Keywords: Hevea brasiliensis Temporal stability Phenotypically and genotypically correlating G × Y interaction
a b s t r a c t The annual girth or diameter growth in the tapping phase is an important trait associated with rubber production, resistance to wind breakage and wood production. The main objective of the present study was to assess the temporal stability of rubber tree genotypes for both natural rubber production and annual girth growth in the post-tapping phase. The phenotypic and genetic correlations of these variables over the years of evaluation were estimated in a rubber tree breeding program. Thirty-two clones were assessed along with the control genotype RRIM 600 for two traits, annual production and girth growth, which were evaluated for five and six years, respectively. A randomized complete block design, with effectively split-plots in time, was used with three replicates, six trees per plot, spaced at 7 m × 3 m. We observed that negative genetic correlations of the accumulated annual girth growth with the accumulated rubber yield (rg = −0.58, P < 0.01), and high stability of yield with AMMI statistics explaining 96% of interactions. The study concluded that early selection in the first year of rubber yield may reduce the evaluation time of clones in a rubber tree breeding program. There was a negative phenotypic correlation between annual girth growth and yield. The study allowed differentiation of the genotypes assessed for temporal stability and overall performance for yield during tapping. Genotype selected for stability of production it is not the same as those selected just for annual growth. The stability of annual girth growth correlates negatively with the stability of yield. © 2013 Published by Elsevier B.V.
1. Introduction In Brazil, rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Müell-Arg.] plantations are expanding to areas considered free from south American leaf blight disease wilt caused by the fungus Microcyclus ulei (P. Henn) V. Arx., including the southeast where the rubber tree shows great adaptability to varied ecological conditions (Gonc¸alves and Marques, 2008). Year-to-year climatic variations, in addition to the diversity of sites where rubber tree is cropped, evaluations need to be conducted over several years to fully understand genotype × environment interaction, so that a comprehensive picture is obtained of the genotype by environment interactions. Here, environment is represented by “year” (G × Y) allowing for estimation of temporal stability of genotypes providing greater safety in recommending clones.
∗ Corresponding author. E-mail addresses: [email protected] (G.A.P. Silva), [email protected] (L.R.L. Gouvêa), [email protected] (C.K. Verardi), [email protected] (A.L.B.d. Oliveira), [email protected] (P.d.S. Gonc¸alves). 0926-6690/$ – see front matter © 2013 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.indcrop.2013.12.010
In rubber tree breeding programs desirable genotypes are such that in addition to high yield they should have both vigorous growth and yield stability during the tapping phase. According to Koo et al. (2007), the advantage of selecting superior genotypes by stability analysis is that stable genotypes are reliable across the environments, reducing the genotype-environment interaction. To improve rubber plantation productivity, basic knowledge about the genetic traits of the plant populations of the species of interest is necessary for efficient selection and to conduct well-targeted crossings. Quantitative data analyses economically import traits that are useful to estimate genetic variances, type of genetic action involved, heritability and genetic correlations, so that the results obtained can be used to predict genetic gains after successive selection cycles. The quantitative information, besides widening the understanding of rubber tree genetics and its reproductive characteristics, also assists to determine the best selection strategy overcoming problems and difficulties in superior genotype selection. The main objectives of rubber tree breeding is to increase yield and vigor through methods that can shorten the breeding cycle of the crop, estimate the genetic parameters and correlates these traits (Gonc¸alves et al., 2006). Silva et al. (2012), studying open-pollinated progenies, concluded that the annual trunk girth increment and virgin bark
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thickness are variables that are genetically correlated and a simultaneous selection for increase in the two variables. This study also concluded that progeny-mean hereditability for the rubber yield trait and annual trunk girth increment were superior to individualand within-progeny heritabilities. This can be the basis of a strategy to increase the genetic gain in the rubber tree. To make this recommendation as reliable as possible, a detailed study is needed on the temporal stability of the genotypes and with respect to their economically important traits (Cruz, 2006). Adaptability and stability analyses are, therefore, statistical procedures that allow identification of the cultivars with more stable performance that respond predictably to the environmental variations (Silva and Duarte, 2006). The objective of the present study was to assess the temporal stability of rubber tree genotypes for natural rubber production, annual girth growth in the post-tapping phase, phenotypically and genotypically correlating these variables along the years of reviews in a breeding program for the species. 2. Materials and methods Thirty-two rubber tree genotypes belonging to the Instituto Agronômico de Campinas (IAC) breeding program were assessed along with the control genotype (RRIM 600). That is the most planted clone in Brazil and around the world (Table 1) to responses of interest corresponded to five years’ rubber yield and six years of annual girth growth. The experiment was conducted at the Jaú Experimental Station, Brazil (22◦ 17 S latitude, 48◦ 64 W longitude) located at an altitude of 580 m, in moderate A eutrophic, red–yellow clay soil, with sandy/medium texture. The predominant climate in the region is the Aw type (Koppen) with a defined dry season, 21.6 ◦ C mean annual temperature, mean annual relative humidity of around 70%, with extremes of 77% in February and 59% in August. The annual mean rainfall is 1344 mm with 74% of the rainfall from October to March and 26% from April to September (INPE-CPTEC, 2013). A randomized complete block design was used with three replicates and six trees per plot in a 7 m × 3 m spacing. The trees began to be tapped at 7 years of age. The system used to assess the first annual rubber yield was 1/2S d/4, 5d/7, 11 m/y, ET 2.5% Pa (8/y)—tapping in a half spiral (1/2S), performed at four-day intervals (d/4), for 11 months of the year (11 m/y), using Ethefon (ET) with 2.5% active ingredient applied on the regenerating recently tapped panel (pa) eight times a year (8/y) (Dijkman 1951). After tapping, latex was collected in plastic cups provided for each tree. This system is widely standardized and documented in the literature (Gouvêa et al., 2011; Gonc¸alves et al., 2011; Vijayakumar et al., 2000).
To assess girth growth in the tapping phase, annual measurements were taken of plant vigor expressed in girth growth. Six years’ growth data were analyzed in the post-tapping period. The trunk girth (cm) was measured at 120 cm above the soil, using a piece of tape. Annual girth growth was calculated by subtracting from the circumference of one year. Individual-year analyses of variance were carried out to assess the genetic variability among the clones and the experimental accuracy, followed by joint analysis of variance across years. Joint analysis of variance was carried out using the randomized complete block design with split-plot in time model, consisting of fixed effects for genotypes and environments; in this case environment was represented by year. The model fitted this analysis was: Yijk = + gi + aj + bk + (ga)ij + (gb)ik + (ab)jk + (gab)ijk + eijk where: Yijk : is the observed value of the ith genotype in the fixed year in the kth replicate; is the average mean; gi is a fixed effect of the ith genotype (i = 1, 2,. . .g); aj is a effects of the jth year (j = 1, 2,. . .,a); bk is a fixed effect of the kth block (k = 1, 2,. . .,b); (ga)ij is the fixed interaction between ith genotype with the jth year; (gb)ik is the interaction between ith genotype with the kth block; (gab)ijk is the interaction between genotype, year and replicate, eijk is the experimental error. The analyses of variance were carried out using the ANOVA procedure of the SAS program (SAS Institute, 2002). Further analyses were carried out using the AMMI methodology. AMMI analysis is a combination of univariate methods (analysis of variance) with multivariate methods (main component analysis and single-value partitioning) (Zobel et al., 1988). The SAS manual (SAS Institute, 2002) was used as described in Duarte and Vencovsky (1999). The proposed model was: Yij = + gi + ej + n
k ik ˛jk + ij where: Yij is the mean response of the ith genotype
k=1
in jth environment; is the average mean; gi is the fixed effect of the ith genotype (i = 1, 2,. . .,g); ej is the fixed effect of the jth environment j (j = 1, 2,. . .,a); k is the square root of the kth eigenvalue of the matrices (GE)(GE)’ and (GE)’(GE) (of non-equal eigenvalues); ik is the ith term (related to genotype i) of the kth eigenvector of the (GE)(GE)’; ˛jk is the jth term (related to environment j) of the kth eigenvector of the (GE)’(GE); ij is the error term. Complementing the principal components analysis AMMI also was used the analysis-the linear regression Eberhart and Russell (1966). The model used for this methodology was the following: Yij = mi + bi lj + dij + e¯ ij where: Yij =is the observed mean of genotype i in environment j; mi = general mean of genotype i; bi = coefficient of regression of genotypic i; lj = environmental index j; dij = deviation of the regression of i genotype in environment j; e¯ ij = mean error associated to the average general.
Table 1 Means of rubber yield (RY, g.tree−1 tapping) and annual girth growth (AGG, cm y−1 ) of 33 genotypes in five years of assessment rubber production and six years for annual girth growth. ID
Genotypes
RY
AGG
ID
Genotypes
RY
AGG
ID
Genotypes
RY
AGG
01 02 03 04 05 06 07 08 09 10 11
IAC 400 IAC 401 IAC 403 IAC 404 IAC 417 IAC 424 IAN 873 PB 235 GU 198 GU 176 Pind 14/88 Overall Average CV(%)
95.16* 79.22* 63.38 66.18 69.64 42.50 56.36 74.17 79.94* 45.75 39.12 54.22 16.44
4.30 2.75 3.37 3.29 3.54 3.38 3.28 3.67 3.58 3.13 3.08 3.47 43.63
12 13 14 15 16 17 18 19 20 21 22
Pind 060/87 Pind 141/87 Pind 147/87 Pind 161/88 Pind 218/88 Pind 237/87 Pind 267/88 Pind 282/87 Pind 300/87 Pind 302/88 Pind 373/88
39.15 66.82 44.35 40.94 26.61 28.06 50.84 28.23 53.84 58.10 50.56
3.14 2.74 3.95 4.74* 4.53 4.09 2.94 3.37 3.51 3.52 4.62
23 24 25 26 27 28 29 30 31 32 33
Pind 512/88 Pind 673/88 Vot 056/88 Vot 061/88 Vot 171/88 Vot 211/88 Vot 237/88 Vot 272/88 Vot 275/88 1-2-56-77 RRIM 600
42.02 53.45 55.70 62.83 50.54 50.30 53.71 59.15 53.59 43.05 66.16
4.38 2.71 4.17 4.07 3.53 2.73 3.86 3.09 3.37 4.30
*
Significant for P < 0.05 Dunnett test with respect to the control genotype RRIM 600.
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The environment index was calculated by Ij = Y¯ .j − Y¯ .., with
n
I = 0,is the number of environments. j=1 j The trait-to-trait correlations were estimated from the expressions shown by Falconer (1964): genotypic (i.e., clonal) correlation 2 2 . The genetic covariance between traits rg = covg(x,y) / gx gy covg(x,y) was obtained by using mean cross products from the SAS program (SAS Institute, 2002). Approximate standard deviations of the genetic correlations within years were calculated according to Falconer (1964). The phenotypic correlations rp between trait x and y were rp = covg(x,y) + cove(x,y) /x y = cov(x,y) /x y ; where covg(x,y) is genetic covariance between the traits x and y; gx is genetic standard deviation for the trait x; cove(x,y) is environmental covariance between the traits x and y; cov(x,y) is phenotypic covariance between the traits x and y; y phenotypic standard deviation for trait y. The significance of these correlations was evaluated according to Fisher (1941). The formula for heritability (or repeatability) of genotype means was calculated using (F − 1)/F, or 1 − 1/F. The F is value of F-test. After selection by the AMMI model, temporal stability statistics were studied in a biplot graph. Biplot graphs were obtained by combinations of the orthogonal Interaction Principal Component Axis (IPCA). In this type of graph it is possible to observe the standard portion of the genotype × year interaction, showing the genotypes and environments (year) that contributed least to the interaction, and were therefore stable, combinations of genotypes and environments (year) that are desirable for adaptability. The term biplot refers to a type of graph containing two categories of points or markers, one axis referring to genotypic means and the other to environments (year) in the case of temporal stability (Duarte and Vencovsky, 1999). The stability of the genotypes and environments was interpreted based on the graphic display of the AMMI biplot. The AMMI biplot was interpreted by approximating the genotypes and environments (year) close to the zero score that contributed little to the interaction indicating temporal stability. In the AMMI biplot the genotypes around the line of the zero mark on the IPCA1 corresponded to the most stable genotypes and environments (year) (Pacheco et al., 2005).
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3. Results and discussion The joint analysis of variance for yield (Table 2) also showed significant effects of the genotypes, year, and genotype × year interaction (P value <0.001). This indicated that there was variability among the genotypes, differences among the years, and interaction between genotype and year. The relative performance of the genotypes varied in relation to the different environmental conditions among the assessment years. In this case, complex interaction predominated, in that the rankings of the genotypes varied during the assessment period. That is an undesirable for rubber tree breeding program because clones do not maintain a constant and predictable productive performance over years (Table 2). The G × Y interaction matrix was partitioned into four components using the AMMI methodology for the yield variable over all six years (by the G × Y matrix, where P is the minimum between g − 1 and a − 1 {[min (33 − 1) and (5 − 1)] = 4}). Using the Fr test proposed by Cornelius et al. (1992), the first axis was highly significant (P value <0.001) and the residual unexplained portion of the G × Y resulted non-significant (P value >0.005). The selection of the IPCA 3, model accumulating 96.16%, was selected; and therefore the noise correspondent to 3.84% of the sum of square of the G × Y interaction (SSG × Y ) on Table 2. Values of G × Y interaction found in this study are compatible with the values previously found in other cultures. In a study developed by Wamatu et al. (2003) in clones of coffee during six years of yield the average sum of squares explained 61% of the interaction G × Y. In studies on annual crops (non-perennial) like soybean, cowpea and beans respectively performed by Oliveira et al. (2003), Rocha et al. (2007) and Melo et al. (2007), the sum of the squares explained 60% of the interaction G × Y. Fig. 1 is showing the biplot graph of average latex yield IPCA1 (mean × IPCA1). IPCA1 explained 59.6% of sum of squares of genotype × year interaction. Gauch (1988) explained that the first AMMI axis captures the largest “standard” percentage that is the nonattributable part of interaction. The parameters obtained by the Eberhart and Russell method based on rubber yield (RY) are shown in Table 3. Seven of the 33 genotypes assessed showed significant deviations from the
Table 2 Joint analyses of variance (ANOVA), with split plots in time, of rubber yield data (RY, g.tree−1 tapping) and annual girth growth in the post-tapping (AGG, cm y−1 ), obtained in five and six year of assessment, respectively and partitioning of the G × Y interaction. The together accumulated variance explained for the main component (MC%) according to the AMMI methodology, obtained in five and six year of assessment, respectively. Source of variation
Replicates Genotypes Error a Year Error b genotypes × year IPCA 1 Residual 1 IPCA 2 Residual 2 IPCA 3 Residual 3 IPCA 4 Residual 4 IPCA 5 Residual 5 Error c Total *
RY
AGG
DF
MS
F
P value
2 32 64 4 8 128 35 93 33 60 31 29 29 – – – 256 494
838.817 3570.493 269.071 30708.251 153.720 349.569 253.440 64.651 87.256 52.219 82.653 19.687 19.687 – – – 79.225 –
3.120 13.270** – 199.770** – 4.410** 10.383** 2.648** 3.574** 2.139** 3.386** 0.806 0.806 – – – – –
0.0511 <0.0001
P < 0.05. ** P < 0.01; (DF) degrees of freedom.
MC%
<0.0001 <0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.7511 0.7511 – – – – –
59.60 78.94 96.16
DF
MS
F
P value
2 32 64 5 10 160 36 124 34 90 32 58 30 28 28 – 320 593
0.436 6.087 3.966 69.981 2.535 3.396 1.831 1.293 1.705 0.798 0.853 0.768 0.801 0.733 0.733 – 2.428 –
0.110 1.530** – 27.600** – 1.400** 2.262** 1.293** 2.107** 0.986 1.054 0.949 0.989 0.905 0.905 – – –
0.8961 0.0729
MC%
<0.0001 0.0062 0.0001 0.0381 0.0005 0.5196 0.3921 0.5834 0.4856 0.6071 0.6071 – – –
33.68 63.29
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Fig. 1. Biplot graph for rubber yield production in five years of assessments. Numbers correspond to IDs of genotypes in according to Table 1.
Table 3 Estimates of phenotypic stability and adaptability obtained by the Eberhart & Russell method, and mean rubber yield (RY, g.tree−1 tapping) and annual girth growth (AGG, cm y−1 ) of 33 Hevea brasiliensis genotypes assessed, during six years and five years. ID
RY
Genotype ˆi ˇ
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 * **
IAC 400 IAC 401 IAC 403 IAC 404 IAC 417 IAC 424 IAN 873 PB 235 GU 198 GU 176 Pind 14/88 Pind 060/87 Pind 141/87 Pind 147/87 Pind 161/88 Pind 218/88 Pind 237/87 Pind 267/88 Pind 282/87 Pind 300/87 Pind 302/88 Pind 373/88 Pind 512/88 Pind 673/88 Vot 056/88 Vot 061/88 Vot 171/88 Vot 211/88 Vot 237/88 Vot 272/88 Vot 275/88 1-2-56-77 RRIM 600
1.18 1.31 0.81 1.05 1.71 0.78 1.15 1.16 2.43 0.88 0.69 0.79 0.77 0.52 0.53 0.32 0.52 0.98 0.44 1.10 1.36 0.83 0.76 0.85 1.03 1.46 0.50 1.20 0.96 1.45 1.25 0.62 1.59
AGG ˆi ˇ
2 Sˆ di
ns ns ns ns **
ns ns ns **
ns ns ns ns * * ** *
ns **
ns ns ns ns ns ns * *
ns ns *
ns ns **
155.68 −15.93 −11.57 −0.03 −26.47 −17.31 −46.28 −34.01 367.46 −21.32 18.76 9.04 81.08 113.85 −37.00 −15.5 −16.44 36.22 −42.98 30.31 −19.38 100.67 −43.71 182.31 −24.19 12.98 −16.36 14.43 272.33 −35.84 −37.87 3.46 −18.62
**
ns ns ns ns ns ns ns **
ns ns ns *
ns ns ns ns ns ns ns ns *
ns **
ns ns ns ns **
ns ns ns ns
0.46 1.19 1.43 −0.87 1.64 0.95 1.31 0.66 0.31 0.76 0.55 1.24 0.85 1.57 2.37 1.57 1.00 1.38 1.72 1.18 1.48 0.78 0.79 0.83 1.35 −0.60 0.87 1.65 2.24 0.60 0.64 −0.13 1.22
P < 0.05. ˆ i is regression coefficient; Sˆ 2 is deviation of the regression coefficient; the control genotype RRIM 600. P < 0.01; ˇ di
2 Sˆ di
ns ns ns **
ns ns ns ns ns ns ns ns ns ns *
ns ns ns ns ns ns ns ns ns ns *
ns ns *
ns ns ns ns
4.89 −0.47 0.01 0.99 −0.11 0.30 −0.79 −0.7 0.38 0.32 1.92 1.21 −0.54 −0.2 0.96 −0.45 −0.64 0.52 −0.47 −0.28 −0.28 0.02 1.11 0.49 −0.54 0.04 −0.67 −0.58 −0.41 −0.84 −0.23 0.09 −0.50
**
ns ns ns ns ns ns ns ns ns *
ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns
G.A.P. Silva et al. / Industrial Crops and Products 52 (2014) 801–808
2 > 0 , indicating interaction or lack of stability. regression Sˆ di The IAC 400, GU 198, Pind 141/87, Pind 147/87, Pind 373/88, Pind 673/88 and Vot 237/88 genotypes did not show stability. Regarding adaptability, the genotypes IAC 417, GU 198, Vot 061/88 and RRIM specific adaptability in favorable environ 600 showed ˆ i > 1.0 , while genotypes Pind 147/88, Pind 161/88, Pind ments ˇ
805
methods of adaptability and stability analysis, while considering the peculiarities of each method, is better for decision-making when indicating cultivars. When the genotype × environment interaction results variation of unpredictable environmental factors, such as year to year variation, as was the case in the present study, breeders need to select stable genotypes that can perform reasonably well in a wide range of conditions. Such breeding strategies can help rubber producers to avoid risks. Table 4 presents the coefficient estimates of genetic and phenotypic correlation between the traits. All genetic and phenotypic correlation estimates (rg and rp respectively) between rubber-yield variables were positive and significant. It is observed that the rubber yield in the first year had high correlates with the yields of subsequent years with values of rg ranking from 0.61 to 0.88, and for rp 0.53 to 0.83. Emphasizing the results between the correlations of rubber yield is observed correlations significant of high magnitude between Yield I with Yield II (rg = 0.88** and rp = 0.83**), Yield II with Yield III (rg = 0.73** and rp = 0.70**), Yield III with Yield IV (rg = 0.74** and rp = 0.70**), and Yield IV with Yield V (rg = 0.88** and rp = 0.82**). Based on the above it is possible that the selection in the first year of production of rubber can substantially reduce the evaluation time of the clones in a breeding program. Silva et al. (2012), assessing the rubber production in open-pollinated progenies of rubber concluded that the first measurement cycle can be effective for an early selection of genotypes for rubber production. In tree
237/87, Pind 282/87, Vot 171/88 showed specific adaptability for ˆ i > 1.0 . unfavorable environments ˇ The estimates of phenotypic stability and adaptability obtained for AGG showed that two genotypes assessed 2 >0 , showed significant deviations from the regression Sˆ di indicating interation. Regarding adaptability, the genotypes showed specific adaptability in Pind 161/88 and Vot 237/88 ˆ favorable environments ˇi > 1.0 , while genotypes IAC 404 and Vot 061/88 showed specific adaptability for unfavorable ˆ environments ˇi < 1.0 . There was agreement among the most stable genotypes identified with the different analytical methods; however, the ranking of genotypes was altered. If we observe the progenies that showed significant genotype × years interaction by the F test in the method Eberhart and Russel, these were the progenies with the greatest distances point where it intersects the abscissa and the ordinate. From the breeder’s point of view, processing data by several
Table 4 Estimates of genotypic and phenotypic correlations of rubber yield, annual girth growth and heritability. RY I RY I rg rp hg RY II rg rp hg RY III rg rp hg RY IV rg rp hg RY V rg rp hg AGG I rg rp hg AGG II rg rp hg AGG III rg rp hg AGG IV rg rp hg AGG V rg rp hg * **
RY II
RY III
RY IV
RY V
AGG I
AGG II
AGG III
AGG IV
AGG V
– – 0.92
– – –
– – –
-
– – –
– – –
– – –
– – –
– – –
– – –
0.88** 0.83** –
– – 0.86
– – –
– – –
– – –
– – –
– – –
– – –
– – –
– – –
0.81** 0.76** –
0.73** 0.70** –
– – 0.88
– – –
– – –
– – –
– – –
– – –
– – –
– – –
0.80** 0.74** –
0.86** 0.82** –
0.74** 0.70** –
– – 0.88
– – –
– – –
– – –
– – –
– – –
– – –
0.61** 0.57** –
0.81** 0.75** –
0.63** 0.58** –
0.88** 0.82** –
– – 0.88
– – –
– – –
– – –
– – –
– – –
−0.36** −0.29 –
−0.38* −0.21 –
−0.58 −0.37 –
−0.40 −0.27 –
−0.60 −0.35 -
– – 0.03
– – –
– – –
– – –
– – –
0.05 −0.27 –
−0.43** −0.20 –
−0.63** −0.30 –
−0.62 −0.31 –
−0.32 −0.17 –
0.33** 0.28 –
– – 0.31
– – –
– – –
– – –
−0.51** −0.33 –
−0.82** −0.44** –
−0.34* −0.21 –
−0.75** −0.42** –
−0.90 −0.47 –
0.40** 0.34* –
0.55** 0.12 –
– – 0.33
– –
– –
0.19 0.16 –
−0.19 −0.10 –
−0.57** −0.59** –
0.29 0.07 –
– – 0.16
– – –
−0.18 −0.10 –
−0.27 −0.09 –
0.68** 0.30 – −0.10 0.06 –
0.02 0.10 – −0.07 0.01 –
0.34* 0.17 – −0.14 −0.09 –
−0.05 0.05 – 0.38** 0.29 –
0.55** 0.37* –
P < 0.05. P < 0.01; (RY) rubber yield.; (AGG) annual Girth growth; (I) year one; (II) year two; (III) year three; (IV) year four; (V) year five.
−0.41* 0.01 –
−0.01 −0.02 –
– – 0.29
806
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breeding, early selection has been shown to have advantages by shortening the generation interval and this reducing the breeding cycle (Nanson, 1970; Lambeth, 1980; McKeand, 1988; Matheson et al., 1994). Shortening the breeding cycle of a tree through early selection can produce more genetic gains per unit time (year) if there is strong genetic correlation between early and mature traits. Analyzing the annual girth growth I (AGG I), it shows that this trait is genetically correlated with annual girth growth of subsequent years (AGG II, rg = 0.33**), (AGG III, rg = 0.40**) and (AGG V, rg = 0.38 **). This fact opens the possibility of an early selection in the first year of assessment for this trait. Analyzing the results between annual girth growth (AGG) and rubber yield in Table 4, indicate significant and negative genetic correlations between the first three years of evaluations (AGG I with Yield I, rg = −0.36**) (AGG II with Yield II, rg = −0.43**) and (AGG III with Yield III, rg = −0.34*). Cilas et al. (2011) studied genetic correlations between yields in successive years to understand relationships between years of coffee yield. Their reported correlations between yields of the different years were moderately stable, and revealed a major tree effect within clones. The traits involved were earliness, alternation, and the intensity of variations between years. Despite a marked tendency towards biennial cropping, especially in the early years, the estimated genetic correlations between years, and between individual years and cumulative yield were generally high.
Table 5 presents the estimates of genotypic and phenotypic correlations of cumulative rubber yield, annual girth growth cumulative. These are results indicate that all genetic and phenotypic correlation estimates between rubber-yield variables are positive and significant (P < 0.05). Table 5 presents that AGG is genetically correlated with annual girth growth of subsequent years positively from 0.97 in accumulated AGG V with AGG IV, and annual girth growth and rubber yield are negatively correlated from −0.58 in accumulated AGG V with RY V. The average heritability of rubber yield in the five years evaluated in Table 4 was (hg = 0.88), ranging from (hg = 0.86) to (hg = 0.92). The average annual girth growth of heritability was (hg = 0.24), ranging from (hg = 0.03) to (hg = 0.33). The heritability of accumulated traits shown in Table 5 which has more stable under varying over between years. The cumulative heritability rubber yield was 0.92, ranging from 0.92 to 0.91. The accumulated heritability of annual girth growth was 0.35, ranging from 0.21 to 0.37. According to Gonc¸alves et al. (2006), trunk girth increment decreases after tapping starts because the photosynthesis is partitioned into two competing sources: exploited latex and trunk diameter growth. Another important aspect relating to vigor is that when latex production declines, after about 25 to 30 years of tree exploitation, the sources of the rubber tree can be used for a wide range of products, replacing wood from natural forests (Killmann and Hong, 2000). Therefore, vigorous genotypes accumulate higher alternative value as wood producers.
Table 5 Estimates of genotypic and phenotypic correlations of rubber yield cumulative, annual girth growth cumulative and heritability. RY I RY I rg rp hg RY II rg rp hg RY III rg rp hg RY IV rg rp hg RY V rg rp hg AGG I rg rp hg AGG II rg rp hg AGG III rg rp hg AGG IV rg rp hg AGG V rg rp hg * **
RY II
RY III
RY IV
RY V
AGG I
AGG II
AGG III
AGG IV
AGG V
– – 0.92
– – –
– – –
– – –
– – –
– – –
– – –
– – –
– – –
– – –
0.97** 0.95** –
– – 0.91
– – –
– – –
– – –
– – –
– – –
– – –
– – –
– – –
0.95** 0.93** –
0.96** 0.96** –
– – 0.91
– – –
– – –
– – –
– – –
– – –
– – –
– – –
0.93** 0.89** –
0.96** 0.94** –
0.98** 0.97** –
– – 0.92
– – –
– – –
– – –
– – –
– – –
– – –
0.87** 0.84** –
0.93** 0.92** –
0.94** 0.93** –
0.98** 0.98** –
– – 0.92
– – –
– – –
– – –
– – –
– – –
−0.36** −0.29 –
−0.38** −0.26 –
−0.49** −0.33 –
−0.48** −0.31 –
0.53** −0.34* –
– – 0.35
– – -
– – -
– – -
– – -
−0.50** −0.35* –
−0.51** −0.32 –
−0.63** −0.39** –
−0.64** −0.39** –
−0.65** −0.39** –
0.90** 0.85** –
– – 0.37
– – -
– – -
– – -
−0.56** −0.42** –
−0.64** −0.43** –
−0.68** −0.45** –
−0.72** −0.47** –
−0.77** −0.50** –
0.82** 0.80** –
0.95** 0.89** –
– – 0.30
– – –
– – –
−0.32 −0.20 –
−0.51** −0.26 –
−0.55** −0.28 –
−0.60** −0.30 –
−0.66** −0.33 –
0.78** 0.70** –
0.82** 0.76** –
0.94** 0.86** –
– – 0.21
– – –
−0.29 −0.19 –
−0.43** −0.23 –
−0.47** −0.26 –
−0.53** −0.28 –
−0.58** −0.31 –
0.74** 0.70** –
0.96** 0.79** –
0.93** 0.84** –
0.97** 0.94** –
– – 0.35
P < 0.05; P < 0.01; (RY I) rubber yield; (AGG) annual Girth growth; (I) year one; (II) year two; (III) year three; (IV) year four; (V) year five.
G.A.P. Silva et al. / Industrial Crops and Products 52 (2014) 801–808
807
Fig. 2. Biplot graph for annual girth growth during tapping in six years of assessment. Numbers correspond to IDs of genotypes in according to Table 1.
In Fig. 1, the abscissa represents the main effects (mean of genotypes across years), which are plotted against the first Interaction Principal Component Axis. Thus, the genotypes close to IPCA 1 with values close to zero indicate stability over the test years; a group of genotypes and the year with scores with the same sign had specific positive interactions, and clones with a combination of opposite signs presented negative specific interactions. The genotype that contributed least to the interaction was IAC 400 (ID-01) which produced the highest mean yields 95.16 in (RY, g.tree−1 tapping). From Table 1 the mean yield of the IAC 400 (ID01) and IAC 401 (ID-02) clone differed statistically from all the other clones assessed by the 5% Dunnett means test, surpassing the control genotype RRIM 600 (ID-33). Therefore, these clones can be an interesting alternative to the clone RRIM 600, which is the most widely planted in Brazil. The genotypes that contributed most to the interaction were the genotypes GU 198 (ID-09), Pind 147/87 (ID-14), Pind 237/87 (ID16) and Pind 282/87 (ID-19) because they presented the greatest amplitudes, as shown in Fig. 1. This interaction was also influenced by the AGG as presented in Table 4 where the annual girth growth correlates negatively with yield. The joint, across-years analysis of variance for the annual girth growth (Table 2) showed significant effects by the F test for genotypes, year and genotype × year interaction. This indicated that there was variability among the genotypes, difference among the year and interaction between genotype and year. Using the AMMI methodology for the annual girth growth variable the interaction matrix could be partitioned into 5 main components. By the Fr test of Cornelius et al. (1992) the first axis was significant at 1% probability and residual 1 was not significant, thus leading to selection of the IPCA 2 model which accounted for 63.3% of the SSG × Y , corresponding to the standard portion, and the rest belonged to the portion called ‘noise’; that was 36.7% of the SSG × Y (Table 2). This value is not inconsistent with the values already observed Rea et al. (2011), 43.3% and Rocha and Maia (2004), 26%. The biplot graph was elaborated up to the IPCA 1 model shown in Fig. 2. The first main axis of the interaction captured 33.7% of the
SSG × Y, where it is the largest “standard” percentage. The genotype IAC 400 (ID-01) was that contributed most to the interaction of the trait annual girth growth and that least contributed to interaction of yield and had the highest average production. Even with strong interaction for annual girth growth the genotype IAC 400 (ID-01) had great growth. 4. Conclusions Stability methods based on different principles can show agreement to identify rubber stable genotypes in rubber yield and annual girth growth. The study allowed differentiation of the genotypes assessed for temporal stability and their performance for yield during tapping. The top genotypes selected for stability of yield are not the same genotypes as would be selected for annual growth. Indeed, the stability of annual girth growth correlates negatively with the stability of yield. The study also showed the best clone, highlighting the IAC 400, this is recommended for planting, because it has high yield and quick growth. The genotypic and phenotypic correlations of rubber yield cumulative, annual girth growth cumulative and heritability showed results more stable and consistent that[n estimates of genotypic and phenotypic correlations of rubber yield noncumulative,annual girth growth noncumulative and heritability. The early selection in the first year for rubber yield may reduce the evaluation time for clones in a breeding program of rubber tree, because there is a good correlation between the firstyear yield and the other four years’ yield. There is a negative genetic and phenotypic correlation between annual girth growth and yield. The annual girth growth is at the expense of rubber yield. Acknowledgements The authors thank Fundac¸ão de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Coodenac¸ão de Aperfeic¸oamento de Pessoal de Nível Superior (CAPES) for funding.
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Annual growth increment and stability of rubber yield in the tapping phase in rubber tree clones: Implications for early selection Guilherme Augusto Peres Silva ∗ , Lígia Regina Lima Gouvêa, Cecília Khusala Verardi, André Luis Bombonato de Oliveira, Paulo de Souza Gonc¸alves Instituto Agronômico de Campinas, Programa Seringueira, Caixa Postal 28, CEP 13001 970 Campinas, SP, Brazil
a r t i c l e
i n f o
Article history: Received 7 August 2013 Received in revised form 27 November 2013 Accepted 3 December 2013 Keywords: Hevea brasiliensis Temporal stability Phenotypically and genotypically correlating G × Y interaction
a b s t r a c t The annual girth or diameter growth in the tapping phase is an important trait associated with rubber production, resistance to wind breakage and wood production. The main objective of the present study was to assess the temporal stability of rubber tree genotypes for both natural rubber production and annual girth growth in the post-tapping phase. The phenotypic and genetic correlations of these variables over the years of evaluation were estimated in a rubber tree breeding program. Thirty-two clones were assessed along with the control genotype RRIM 600 for two traits, annual production and girth growth, which were evaluated for five and six years, respectively. A randomized complete block design, with effectively split-plots in time, was used with three replicates, six trees per plot, spaced at 7 m × 3 m. We observed that negative genetic correlations of the accumulated annual girth growth with the accumulated rubber yield (rg = −0.58, P < 0.01), and high stability of yield with AMMI statistics explaining 96% of interactions. The study concluded that early selection in the first year of rubber yield may reduce the evaluation time of clones in a rubber tree breeding program. There was a negative phenotypic correlation between annual girth growth and yield. The study allowed differentiation of the genotypes assessed for temporal stability and overall performance for yield during tapping. Genotype selected for stability of production it is not the same as those selected just for annual growth. The stability of annual girth growth correlates negatively with the stability of yield. © 2013 Published by Elsevier B.V.
1. Introduction In Brazil, rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.) Müell-Arg.] plantations are expanding to areas considered free from south American leaf blight disease wilt caused by the fungus Microcyclus ulei (P. Henn) V. Arx., including the southeast where the rubber tree shows great adaptability to varied ecological conditions (Gonc¸alves and Marques, 2008). Year-to-year climatic variations, in addition to the diversity of sites where rubber tree is cropped, evaluations need to be conducted over several years to fully understand genotype × environment interaction, so that a comprehensive picture is obtained of the genotype by environment interactions. Here, environment is represented by “year” (G × Y) allowing for estimation of temporal stability of genotypes providing greater safety in recommending clones.
∗ Corresponding author. E-mail addresses: [email protected] (G.A.P. Silva), [email protected] (L.R.L. Gouvêa), [email protected] (C.K. Verardi), [email protected] (A.L.B.d. Oliveira), [email protected] (P.d.S. Gonc¸alves). 0926-6690/$ – see front matter © 2013 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.indcrop.2013.12.010
In rubber tree breeding programs desirable genotypes are such that in addition to high yield they should have both vigorous growth and yield stability during the tapping phase. According to Koo et al. (2007), the advantage of selecting superior genotypes by stability analysis is that stable genotypes are reliable across the environments, reducing the genotype-environment interaction. To improve rubber plantation productivity, basic knowledge about the genetic traits of the plant populations of the species of interest is necessary for efficient selection and to conduct well-targeted crossings. Quantitative data analyses economically import traits that are useful to estimate genetic variances, type of genetic action involved, heritability and genetic correlations, so that the results obtained can be used to predict genetic gains after successive selection cycles. The quantitative information, besides widening the understanding of rubber tree genetics and its reproductive characteristics, also assists to determine the best selection strategy overcoming problems and difficulties in superior genotype selection. The main objectives of rubber tree breeding is to increase yield and vigor through methods that can shorten the breeding cycle of the crop, estimate the genetic parameters and correlates these traits (Gonc¸alves et al., 2006). Silva et al. (2012), studying open-pollinated progenies, concluded that the annual trunk girth increment and virgin bark
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thickness are variables that are genetically correlated and a simultaneous selection for increase in the two variables. This study also concluded that progeny-mean hereditability for the rubber yield trait and annual trunk girth increment were superior to individualand within-progeny heritabilities. This can be the basis of a strategy to increase the genetic gain in the rubber tree. To make this recommendation as reliable as possible, a detailed study is needed on the temporal stability of the genotypes and with respect to their economically important traits (Cruz, 2006). Adaptability and stability analyses are, therefore, statistical procedures that allow identification of the cultivars with more stable performance that respond predictably to the environmental variations (Silva and Duarte, 2006). The objective of the present study was to assess the temporal stability of rubber tree genotypes for natural rubber production, annual girth growth in the post-tapping phase, phenotypically and genotypically correlating these variables along the years of reviews in a breeding program for the species. 2. Materials and methods Thirty-two rubber tree genotypes belonging to the Instituto Agronômico de Campinas (IAC) breeding program were assessed along with the control genotype (RRIM 600). That is the most planted clone in Brazil and around the world (Table 1) to responses of interest corresponded to five years’ rubber yield and six years of annual girth growth. The experiment was conducted at the Jaú Experimental Station, Brazil (22◦ 17 S latitude, 48◦ 64 W longitude) located at an altitude of 580 m, in moderate A eutrophic, red–yellow clay soil, with sandy/medium texture. The predominant climate in the region is the Aw type (Koppen) with a defined dry season, 21.6 ◦ C mean annual temperature, mean annual relative humidity of around 70%, with extremes of 77% in February and 59% in August. The annual mean rainfall is 1344 mm with 74% of the rainfall from October to March and 26% from April to September (INPE-CPTEC, 2013). A randomized complete block design was used with three replicates and six trees per plot in a 7 m × 3 m spacing. The trees began to be tapped at 7 years of age. The system used to assess the first annual rubber yield was 1/2S d/4, 5d/7, 11 m/y, ET 2.5% Pa (8/y)—tapping in a half spiral (1/2S), performed at four-day intervals (d/4), for 11 months of the year (11 m/y), using Ethefon (ET) with 2.5% active ingredient applied on the regenerating recently tapped panel (pa) eight times a year (8/y) (Dijkman 1951). After tapping, latex was collected in plastic cups provided for each tree. This system is widely standardized and documented in the literature (Gouvêa et al., 2011; Gonc¸alves et al., 2011; Vijayakumar et al., 2000).
To assess girth growth in the tapping phase, annual measurements were taken of plant vigor expressed in girth growth. Six years’ growth data were analyzed in the post-tapping period. The trunk girth (cm) was measured at 120 cm above the soil, using a piece of tape. Annual girth growth was calculated by subtracting from the circumference of one year. Individual-year analyses of variance were carried out to assess the genetic variability among the clones and the experimental accuracy, followed by joint analysis of variance across years. Joint analysis of variance was carried out using the randomized complete block design with split-plot in time model, consisting of fixed effects for genotypes and environments; in this case environment was represented by year. The model fitted this analysis was: Yijk = + gi + aj + bk + (ga)ij + (gb)ik + (ab)jk + (gab)ijk + eijk where: Yijk : is the observed value of the ith genotype in the fixed year in the kth replicate; is the average mean; gi is a fixed effect of the ith genotype (i = 1, 2,. . .g); aj is a effects of the jth year (j = 1, 2,. . .,a); bk is a fixed effect of the kth block (k = 1, 2,. . .,b); (ga)ij is the fixed interaction between ith genotype with the jth year; (gb)ik is the interaction between ith genotype with the kth block; (gab)ijk is the interaction between genotype, year and replicate, eijk is the experimental error. The analyses of variance were carried out using the ANOVA procedure of the SAS program (SAS Institute, 2002). Further analyses were carried out using the AMMI methodology. AMMI analysis is a combination of univariate methods (analysis of variance) with multivariate methods (main component analysis and single-value partitioning) (Zobel et al., 1988). The SAS manual (SAS Institute, 2002) was used as described in Duarte and Vencovsky (1999). The proposed model was: Yij = + gi + ej + n
k ik ˛jk + ij where: Yij is the mean response of the ith genotype
k=1
in jth environment; is the average mean; gi is the fixed effect of the ith genotype (i = 1, 2,. . .,g); ej is the fixed effect of the jth environment j (j = 1, 2,. . .,a); k is the square root of the kth eigenvalue of the matrices (GE)(GE)’ and (GE)’(GE) (of non-equal eigenvalues); ik is the ith term (related to genotype i) of the kth eigenvector of the (GE)(GE)’; ˛jk is the jth term (related to environment j) of the kth eigenvector of the (GE)’(GE); ij is the error term. Complementing the principal components analysis AMMI also was used the analysis-the linear regression Eberhart and Russell (1966). The model used for this methodology was the following: Yij = mi + bi lj + dij + e¯ ij where: Yij =is the observed mean of genotype i in environment j; mi = general mean of genotype i; bi = coefficient of regression of genotypic i; lj = environmental index j; dij = deviation of the regression of i genotype in environment j; e¯ ij = mean error associated to the average general.
Table 1 Means of rubber yield (RY, g.tree−1 tapping) and annual girth growth (AGG, cm y−1 ) of 33 genotypes in five years of assessment rubber production and six years for annual girth growth. ID
Genotypes
RY
AGG
ID
Genotypes
RY
AGG
ID
Genotypes
RY
AGG
01 02 03 04 05 06 07 08 09 10 11
IAC 400 IAC 401 IAC 403 IAC 404 IAC 417 IAC 424 IAN 873 PB 235 GU 198 GU 176 Pind 14/88 Overall Average CV(%)
95.16* 79.22* 63.38 66.18 69.64 42.50 56.36 74.17 79.94* 45.75 39.12 54.22 16.44
4.30 2.75 3.37 3.29 3.54 3.38 3.28 3.67 3.58 3.13 3.08 3.47 43.63
12 13 14 15 16 17 18 19 20 21 22
Pind 060/87 Pind 141/87 Pind 147/87 Pind 161/88 Pind 218/88 Pind 237/87 Pind 267/88 Pind 282/87 Pind 300/87 Pind 302/88 Pind 373/88
39.15 66.82 44.35 40.94 26.61 28.06 50.84 28.23 53.84 58.10 50.56
3.14 2.74 3.95 4.74* 4.53 4.09 2.94 3.37 3.51 3.52 4.62
23 24 25 26 27 28 29 30 31 32 33
Pind 512/88 Pind 673/88 Vot 056/88 Vot 061/88 Vot 171/88 Vot 211/88 Vot 237/88 Vot 272/88 Vot 275/88 1-2-56-77 RRIM 600
42.02 53.45 55.70 62.83 50.54 50.30 53.71 59.15 53.59 43.05 66.16
4.38 2.71 4.17 4.07 3.53 2.73 3.86 3.09 3.37 4.30
*
Significant for P < 0.05 Dunnett test with respect to the control genotype RRIM 600.
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The environment index was calculated by Ij = Y¯ .j − Y¯ .., with
n
I = 0,is the number of environments. j=1 j The trait-to-trait correlations were estimated from the expressions shown by Falconer (1964): genotypic (i.e., clonal) correlation 2 2 . The genetic covariance between traits rg = covg(x,y) / gx gy covg(x,y) was obtained by using mean cross products from the SAS program (SAS Institute, 2002). Approximate standard deviations of the genetic correlations within years were calculated according to Falconer (1964). The phenotypic correlations rp between trait x and y were rp = covg(x,y) + cove(x,y) /x y = cov(x,y) /x y ; where covg(x,y) is genetic covariance between the traits x and y; gx is genetic standard deviation for the trait x; cove(x,y) is environmental covariance between the traits x and y; cov(x,y) is phenotypic covariance between the traits x and y; y phenotypic standard deviation for trait y. The significance of these correlations was evaluated according to Fisher (1941). The formula for heritability (or repeatability) of genotype means was calculated using (F − 1)/F, or 1 − 1/F. The F is value of F-test. After selection by the AMMI model, temporal stability statistics were studied in a biplot graph. Biplot graphs were obtained by combinations of the orthogonal Interaction Principal Component Axis (IPCA). In this type of graph it is possible to observe the standard portion of the genotype × year interaction, showing the genotypes and environments (year) that contributed least to the interaction, and were therefore stable, combinations of genotypes and environments (year) that are desirable for adaptability. The term biplot refers to a type of graph containing two categories of points or markers, one axis referring to genotypic means and the other to environments (year) in the case of temporal stability (Duarte and Vencovsky, 1999). The stability of the genotypes and environments was interpreted based on the graphic display of the AMMI biplot. The AMMI biplot was interpreted by approximating the genotypes and environments (year) close to the zero score that contributed little to the interaction indicating temporal stability. In the AMMI biplot the genotypes around the line of the zero mark on the IPCA1 corresponded to the most stable genotypes and environments (year) (Pacheco et al., 2005).
803
3. Results and discussion The joint analysis of variance for yield (Table 2) also showed significant effects of the genotypes, year, and genotype × year interaction (P value <0.001). This indicated that there was variability among the genotypes, differences among the years, and interaction between genotype and year. The relative performance of the genotypes varied in relation to the different environmental conditions among the assessment years. In this case, complex interaction predominated, in that the rankings of the genotypes varied during the assessment period. That is an undesirable for rubber tree breeding program because clones do not maintain a constant and predictable productive performance over years (Table 2). The G × Y interaction matrix was partitioned into four components using the AMMI methodology for the yield variable over all six years (by the G × Y matrix, where P is the minimum between g − 1 and a − 1 {[min (33 − 1) and (5 − 1)] = 4}). Using the Fr test proposed by Cornelius et al. (1992), the first axis was highly significant (P value <0.001) and the residual unexplained portion of the G × Y resulted non-significant (P value >0.005). The selection of the IPCA 3, model accumulating 96.16%, was selected; and therefore the noise correspondent to 3.84% of the sum of square of the G × Y interaction (SSG × Y ) on Table 2. Values of G × Y interaction found in this study are compatible with the values previously found in other cultures. In a study developed by Wamatu et al. (2003) in clones of coffee during six years of yield the average sum of squares explained 61% of the interaction G × Y. In studies on annual crops (non-perennial) like soybean, cowpea and beans respectively performed by Oliveira et al. (2003), Rocha et al. (2007) and Melo et al. (2007), the sum of the squares explained 60% of the interaction G × Y. Fig. 1 is showing the biplot graph of average latex yield IPCA1 (mean × IPCA1). IPCA1 explained 59.6% of sum of squares of genotype × year interaction. Gauch (1988) explained that the first AMMI axis captures the largest “standard” percentage that is the nonattributable part of interaction. The parameters obtained by the Eberhart and Russell method based on rubber yield (RY) are shown in Table 3. Seven of the 33 genotypes assessed showed significant deviations from the
Table 2 Joint analyses of variance (ANOVA), with split plots in time, of rubber yield data (RY, g.tree−1 tapping) and annual girth growth in the post-tapping (AGG, cm y−1 ), obtained in five and six year of assessment, respectively and partitioning of the G × Y interaction. The together accumulated variance explained for the main component (MC%) according to the AMMI methodology, obtained in five and six year of assessment, respectively. Source of variation
Replicates Genotypes Error a Year Error b genotypes × year IPCA 1 Residual 1 IPCA 2 Residual 2 IPCA 3 Residual 3 IPCA 4 Residual 4 IPCA 5 Residual 5 Error c Total *
RY
AGG
DF
MS
F
P value
2 32 64 4 8 128 35 93 33 60 31 29 29 – – – 256 494
838.817 3570.493 269.071 30708.251 153.720 349.569 253.440 64.651 87.256 52.219 82.653 19.687 19.687 – – – 79.225 –
3.120 13.270** – 199.770** – 4.410** 10.383** 2.648** 3.574** 2.139** 3.386** 0.806 0.806 – – – – –
0.0511 <0.0001
P < 0.05. ** P < 0.01; (DF) degrees of freedom.
MC%
<0.0001 <0.0001 0.0000 0.0000 0.0000 0.0000 0.0000 0.7511 0.7511 – – – – –
59.60 78.94 96.16
DF
MS
F
P value
2 32 64 5 10 160 36 124 34 90 32 58 30 28 28 – 320 593
0.436 6.087 3.966 69.981 2.535 3.396 1.831 1.293 1.705 0.798 0.853 0.768 0.801 0.733 0.733 – 2.428 –
0.110 1.530** – 27.600** – 1.400** 2.262** 1.293** 2.107** 0.986 1.054 0.949 0.989 0.905 0.905 – – –
0.8961 0.0729
MC%
<0.0001 0.0062 0.0001 0.0381 0.0005 0.5196 0.3921 0.5834 0.4856 0.6071 0.6071 – – –
33.68 63.29
804
G.A.P. Silva et al. / Industrial Crops and Products 52 (2014) 801–808
Fig. 1. Biplot graph for rubber yield production in five years of assessments. Numbers correspond to IDs of genotypes in according to Table 1.
Table 3 Estimates of phenotypic stability and adaptability obtained by the Eberhart & Russell method, and mean rubber yield (RY, g.tree−1 tapping) and annual girth growth (AGG, cm y−1 ) of 33 Hevea brasiliensis genotypes assessed, during six years and five years. ID
RY
Genotype ˆi ˇ
01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 * **
IAC 400 IAC 401 IAC 403 IAC 404 IAC 417 IAC 424 IAN 873 PB 235 GU 198 GU 176 Pind 14/88 Pind 060/87 Pind 141/87 Pind 147/87 Pind 161/88 Pind 218/88 Pind 237/87 Pind 267/88 Pind 282/87 Pind 300/87 Pind 302/88 Pind 373/88 Pind 512/88 Pind 673/88 Vot 056/88 Vot 061/88 Vot 171/88 Vot 211/88 Vot 237/88 Vot 272/88 Vot 275/88 1-2-56-77 RRIM 600
1.18 1.31 0.81 1.05 1.71 0.78 1.15 1.16 2.43 0.88 0.69 0.79 0.77 0.52 0.53 0.32 0.52 0.98 0.44 1.10 1.36 0.83 0.76 0.85 1.03 1.46 0.50 1.20 0.96 1.45 1.25 0.62 1.59
AGG ˆi ˇ
2 Sˆ di
ns ns ns ns **
ns ns ns **
ns ns ns ns * * ** *
ns **
ns ns ns ns ns ns * *
ns ns *
ns ns **
155.68 −15.93 −11.57 −0.03 −26.47 −17.31 −46.28 −34.01 367.46 −21.32 18.76 9.04 81.08 113.85 −37.00 −15.5 −16.44 36.22 −42.98 30.31 −19.38 100.67 −43.71 182.31 −24.19 12.98 −16.36 14.43 272.33 −35.84 −37.87 3.46 −18.62
**
ns ns ns ns ns ns ns **
ns ns ns *
ns ns ns ns ns ns ns ns *
ns **
ns ns ns ns **
ns ns ns ns
0.46 1.19 1.43 −0.87 1.64 0.95 1.31 0.66 0.31 0.76 0.55 1.24 0.85 1.57 2.37 1.57 1.00 1.38 1.72 1.18 1.48 0.78 0.79 0.83 1.35 −0.60 0.87 1.65 2.24 0.60 0.64 −0.13 1.22
P < 0.05. ˆ i is regression coefficient; Sˆ 2 is deviation of the regression coefficient; the control genotype RRIM 600. P < 0.01; ˇ di
2 Sˆ di
ns ns ns **
ns ns ns ns ns ns ns ns ns ns *
ns ns ns ns ns ns ns ns ns ns *
ns ns *
ns ns ns ns
4.89 −0.47 0.01 0.99 −0.11 0.30 −0.79 −0.7 0.38 0.32 1.92 1.21 −0.54 −0.2 0.96 −0.45 −0.64 0.52 −0.47 −0.28 −0.28 0.02 1.11 0.49 −0.54 0.04 −0.67 −0.58 −0.41 −0.84 −0.23 0.09 −0.50
**
ns ns ns ns ns ns ns ns ns *
ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns ns
G.A.P. Silva et al. / Industrial Crops and Products 52 (2014) 801–808
2 > 0 , indicating interaction or lack of stability. regression Sˆ di The IAC 400, GU 198, Pind 141/87, Pind 147/87, Pind 373/88, Pind 673/88 and Vot 237/88 genotypes did not show stability. Regarding adaptability, the genotypes IAC 417, GU 198, Vot 061/88 and RRIM specific adaptability in favorable environ 600 showed ˆ i > 1.0 , while genotypes Pind 147/88, Pind 161/88, Pind ments ˇ
805
methods of adaptability and stability analysis, while considering the peculiarities of each method, is better for decision-making when indicating cultivars. When the genotype × environment interaction results variation of unpredictable environmental factors, such as year to year variation, as was the case in the present study, breeders need to select stable genotypes that can perform reasonably well in a wide range of conditions. Such breeding strategies can help rubber producers to avoid risks. Table 4 presents the coefficient estimates of genetic and phenotypic correlation between the traits. All genetic and phenotypic correlation estimates (rg and rp respectively) between rubber-yield variables were positive and significant. It is observed that the rubber yield in the first year had high correlates with the yields of subsequent years with values of rg ranking from 0.61 to 0.88, and for rp 0.53 to 0.83. Emphasizing the results between the correlations of rubber yield is observed correlations significant of high magnitude between Yield I with Yield II (rg = 0.88** and rp = 0.83**), Yield II with Yield III (rg = 0.73** and rp = 0.70**), Yield III with Yield IV (rg = 0.74** and rp = 0.70**), and Yield IV with Yield V (rg = 0.88** and rp = 0.82**). Based on the above it is possible that the selection in the first year of production of rubber can substantially reduce the evaluation time of the clones in a breeding program. Silva et al. (2012), assessing the rubber production in open-pollinated progenies of rubber concluded that the first measurement cycle can be effective for an early selection of genotypes for rubber production. In tree
237/87, Pind 282/87, Vot 171/88 showed specific adaptability for ˆ i > 1.0 . unfavorable environments ˇ The estimates of phenotypic stability and adaptability obtained for AGG showed that two genotypes assessed 2 >0 , showed significant deviations from the regression Sˆ di indicating interation. Regarding adaptability, the genotypes showed specific adaptability in Pind 161/88 and Vot 237/88 ˆ favorable environments ˇi > 1.0 , while genotypes IAC 404 and Vot 061/88 showed specific adaptability for unfavorable ˆ environments ˇi < 1.0 . There was agreement among the most stable genotypes identified with the different analytical methods; however, the ranking of genotypes was altered. If we observe the progenies that showed significant genotype × years interaction by the F test in the method Eberhart and Russel, these were the progenies with the greatest distances point where it intersects the abscissa and the ordinate. From the breeder’s point of view, processing data by several
Table 4 Estimates of genotypic and phenotypic correlations of rubber yield, annual girth growth and heritability. RY I RY I rg rp hg RY II rg rp hg RY III rg rp hg RY IV rg rp hg RY V rg rp hg AGG I rg rp hg AGG II rg rp hg AGG III rg rp hg AGG IV rg rp hg AGG V rg rp hg * **
RY II
RY III
RY IV
RY V
AGG I
AGG II
AGG III
AGG IV
AGG V
– – 0.92
– – –
– – –
-
– – –
– – –
– – –
– – –
– – –
– – –
0.88** 0.83** –
– – 0.86
– – –
– – –
– – –
– – –
– – –
– – –
– – –
– – –
0.81** 0.76** –
0.73** 0.70** –
– – 0.88
– – –
– – –
– – –
– – –
– – –
– – –
– – –
0.80** 0.74** –
0.86** 0.82** –
0.74** 0.70** –
– – 0.88
– – –
– – –
– – –
– – –
– – –
– – –
0.61** 0.57** –
0.81** 0.75** –
0.63** 0.58** –
0.88** 0.82** –
– – 0.88
– – –
– – –
– – –
– – –
– – –
−0.36** −0.29 –
−0.38* −0.21 –
−0.58 −0.37 –
−0.40 −0.27 –
−0.60 −0.35 -
– – 0.03
– – –
– – –
– – –
– – –
0.05 −0.27 –
−0.43** −0.20 –
−0.63** −0.30 –
−0.62 −0.31 –
−0.32 −0.17 –
0.33** 0.28 –
– – 0.31
– – –
– – –
– – –
−0.51** −0.33 –
−0.82** −0.44** –
−0.34* −0.21 –
−0.75** −0.42** –
−0.90 −0.47 –
0.40** 0.34* –
0.55** 0.12 –
– – 0.33
– –
– –
0.19 0.16 –
−0.19 −0.10 –
−0.57** −0.59** –
0.29 0.07 –
– – 0.16
– – –
−0.18 −0.10 –
−0.27 −0.09 –
0.68** 0.30 – −0.10 0.06 –
0.02 0.10 – −0.07 0.01 –
0.34* 0.17 – −0.14 −0.09 –
−0.05 0.05 – 0.38** 0.29 –
0.55** 0.37* –
P < 0.05. P < 0.01; (RY) rubber yield.; (AGG) annual Girth growth; (I) year one; (II) year two; (III) year three; (IV) year four; (V) year five.
−0.41* 0.01 –
−0.01 −0.02 –
– – 0.29
806
G.A.P. Silva et al. / Industrial Crops and Products 52 (2014) 801–808
breeding, early selection has been shown to have advantages by shortening the generation interval and this reducing the breeding cycle (Nanson, 1970; Lambeth, 1980; McKeand, 1988; Matheson et al., 1994). Shortening the breeding cycle of a tree through early selection can produce more genetic gains per unit time (year) if there is strong genetic correlation between early and mature traits. Analyzing the annual girth growth I (AGG I), it shows that this trait is genetically correlated with annual girth growth of subsequent years (AGG II, rg = 0.33**), (AGG III, rg = 0.40**) and (AGG V, rg = 0.38 **). This fact opens the possibility of an early selection in the first year of assessment for this trait. Analyzing the results between annual girth growth (AGG) and rubber yield in Table 4, indicate significant and negative genetic correlations between the first three years of evaluations (AGG I with Yield I, rg = −0.36**) (AGG II with Yield II, rg = −0.43**) and (AGG III with Yield III, rg = −0.34*). Cilas et al. (2011) studied genetic correlations between yields in successive years to understand relationships between years of coffee yield. Their reported correlations between yields of the different years were moderately stable, and revealed a major tree effect within clones. The traits involved were earliness, alternation, and the intensity of variations between years. Despite a marked tendency towards biennial cropping, especially in the early years, the estimated genetic correlations between years, and between individual years and cumulative yield were generally high.
Table 5 presents the estimates of genotypic and phenotypic correlations of cumulative rubber yield, annual girth growth cumulative. These are results indicate that all genetic and phenotypic correlation estimates between rubber-yield variables are positive and significant (P < 0.05). Table 5 presents that AGG is genetically correlated with annual girth growth of subsequent years positively from 0.97 in accumulated AGG V with AGG IV, and annual girth growth and rubber yield are negatively correlated from −0.58 in accumulated AGG V with RY V. The average heritability of rubber yield in the five years evaluated in Table 4 was (hg = 0.88), ranging from (hg = 0.86) to (hg = 0.92). The average annual girth growth of heritability was (hg = 0.24), ranging from (hg = 0.03) to (hg = 0.33). The heritability of accumulated traits shown in Table 5 which has more stable under varying over between years. The cumulative heritability rubber yield was 0.92, ranging from 0.92 to 0.91. The accumulated heritability of annual girth growth was 0.35, ranging from 0.21 to 0.37. According to Gonc¸alves et al. (2006), trunk girth increment decreases after tapping starts because the photosynthesis is partitioned into two competing sources: exploited latex and trunk diameter growth. Another important aspect relating to vigor is that when latex production declines, after about 25 to 30 years of tree exploitation, the sources of the rubber tree can be used for a wide range of products, replacing wood from natural forests (Killmann and Hong, 2000). Therefore, vigorous genotypes accumulate higher alternative value as wood producers.
Table 5 Estimates of genotypic and phenotypic correlations of rubber yield cumulative, annual girth growth cumulative and heritability. RY I RY I rg rp hg RY II rg rp hg RY III rg rp hg RY IV rg rp hg RY V rg rp hg AGG I rg rp hg AGG II rg rp hg AGG III rg rp hg AGG IV rg rp hg AGG V rg rp hg * **
RY II
RY III
RY IV
RY V
AGG I
AGG II
AGG III
AGG IV
AGG V
– – 0.92
– – –
– – –
– – –
– – –
– – –
– – –
– – –
– – –
– – –
0.97** 0.95** –
– – 0.91
– – –
– – –
– – –
– – –
– – –
– – –
– – –
– – –
0.95** 0.93** –
0.96** 0.96** –
– – 0.91
– – –
– – –
– – –
– – –
– – –
– – –
– – –
0.93** 0.89** –
0.96** 0.94** –
0.98** 0.97** –
– – 0.92
– – –
– – –
– – –
– – –
– – –
– – –
0.87** 0.84** –
0.93** 0.92** –
0.94** 0.93** –
0.98** 0.98** –
– – 0.92
– – –
– – –
– – –
– – –
– – –
−0.36** −0.29 –
−0.38** −0.26 –
−0.49** −0.33 –
−0.48** −0.31 –
0.53** −0.34* –
– – 0.35
– – -
– – -
– – -
– – -
−0.50** −0.35* –
−0.51** −0.32 –
−0.63** −0.39** –
−0.64** −0.39** –
−0.65** −0.39** –
0.90** 0.85** –
– – 0.37
– – -
– – -
– – -
−0.56** −0.42** –
−0.64** −0.43** –
−0.68** −0.45** –
−0.72** −0.47** –
−0.77** −0.50** –
0.82** 0.80** –
0.95** 0.89** –
– – 0.30
– – –
– – –
−0.32 −0.20 –
−0.51** −0.26 –
−0.55** −0.28 –
−0.60** −0.30 –
−0.66** −0.33 –
0.78** 0.70** –
0.82** 0.76** –
0.94** 0.86** –
– – 0.21
– – –
−0.29 −0.19 –
−0.43** −0.23 –
−0.47** −0.26 –
−0.53** −0.28 –
−0.58** −0.31 –
0.74** 0.70** –
0.96** 0.79** –
0.93** 0.84** –
0.97** 0.94** –
– – 0.35
P < 0.05; P < 0.01; (RY I) rubber yield; (AGG) annual Girth growth; (I) year one; (II) year two; (III) year three; (IV) year four; (V) year five.
G.A.P. Silva et al. / Industrial Crops and Products 52 (2014) 801–808
807
Fig. 2. Biplot graph for annual girth growth during tapping in six years of assessment. Numbers correspond to IDs of genotypes in according to Table 1.
In Fig. 1, the abscissa represents the main effects (mean of genotypes across years), which are plotted against the first Interaction Principal Component Axis. Thus, the genotypes close to IPCA 1 with values close to zero indicate stability over the test years; a group of genotypes and the year with scores with the same sign had specific positive interactions, and clones with a combination of opposite signs presented negative specific interactions. The genotype that contributed least to the interaction was IAC 400 (ID-01) which produced the highest mean yields 95.16 in (RY, g.tree−1 tapping). From Table 1 the mean yield of the IAC 400 (ID01) and IAC 401 (ID-02) clone differed statistically from all the other clones assessed by the 5% Dunnett means test, surpassing the control genotype RRIM 600 (ID-33). Therefore, these clones can be an interesting alternative to the clone RRIM 600, which is the most widely planted in Brazil. The genotypes that contributed most to the interaction were the genotypes GU 198 (ID-09), Pind 147/87 (ID-14), Pind 237/87 (ID16) and Pind 282/87 (ID-19) because they presented the greatest amplitudes, as shown in Fig. 1. This interaction was also influenced by the AGG as presented in Table 4 where the annual girth growth correlates negatively with yield. The joint, across-years analysis of variance for the annual girth growth (Table 2) showed significant effects by the F test for genotypes, year and genotype × year interaction. This indicated that there was variability among the genotypes, difference among the year and interaction between genotype and year. Using the AMMI methodology for the annual girth growth variable the interaction matrix could be partitioned into 5 main components. By the Fr test of Cornelius et al. (1992) the first axis was significant at 1% probability and residual 1 was not significant, thus leading to selection of the IPCA 2 model which accounted for 63.3% of the SSG × Y , corresponding to the standard portion, and the rest belonged to the portion called ‘noise’; that was 36.7% of the SSG × Y (Table 2). This value is not inconsistent with the values already observed Rea et al. (2011), 43.3% and Rocha and Maia (2004), 26%. The biplot graph was elaborated up to the IPCA 1 model shown in Fig. 2. The first main axis of the interaction captured 33.7% of the
SSG × Y, where it is the largest “standard” percentage. The genotype IAC 400 (ID-01) was that contributed most to the interaction of the trait annual girth growth and that least contributed to interaction of yield and had the highest average production. Even with strong interaction for annual girth growth the genotype IAC 400 (ID-01) had great growth. 4. Conclusions Stability methods based on different principles can show agreement to identify rubber stable genotypes in rubber yield and annual girth growth. The study allowed differentiation of the genotypes assessed for temporal stability and their performance for yield during tapping. The top genotypes selected for stability of yield are not the same genotypes as would be selected for annual growth. Indeed, the stability of annual girth growth correlates negatively with the stability of yield. The study also showed the best clone, highlighting the IAC 400, this is recommended for planting, because it has high yield and quick growth. The genotypic and phenotypic correlations of rubber yield cumulative, annual girth growth cumulative and heritability showed results more stable and consistent that[n estimates of genotypic and phenotypic correlations of rubber yield noncumulative,annual girth growth noncumulative and heritability. The early selection in the first year for rubber yield may reduce the evaluation time for clones in a breeding program of rubber tree, because there is a good correlation between the firstyear yield and the other four years’ yield. There is a negative genetic and phenotypic correlation between annual girth growth and yield. The annual girth growth is at the expense of rubber yield. Acknowledgements The authors thank Fundac¸ão de Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Coodenac¸ão de Aperfeic¸oamento de Pessoal de Nível Superior (CAPES) for funding.
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