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Industrial Crops and Products 52 (2014) 801– 808

Contents lists available at ScienceDirect

Industrial Crops and Products

journa l h om epage: www.elsev ier .com/ locate / indcrop

nnual growth increment and stability of rubber yield in the tappinghase in rubber tree clones: Implications for early selection

uilherme Augusto Peres Silva ∗, Lígia Regina Lima Gouvêa, Cecília Khusala Verardi,ndré Luis Bombonato de Oliveira, Paulo de Souza Gonc alves

nstituto Agronômico de Campinas, Programa Seringueira, Caixa Postal 28, CEP 13001 970 Campinas, SP, Brazil

r t i c l e i n f o

rticle history:eceived 7 August 2013eceived in revised form7 November 2013ccepted 3 December 2013

eywords:evea brasiliensisemporal stabilityhenotypically and genotypicallyorrelating

a b s t r a c t

The annual girth or diameter growth in the tapping phase is an important trait associated with rubberproduction, resistance to wind breakage and wood production. The main objective of the present studywas to assess the temporal stability of rubber tree genotypes for both natural rubber production andannual girth growth in the post-tapping phase. The phenotypic and genetic correlations of these vari-ables over the years of evaluation were estimated in a rubber tree breeding program. Thirty-two cloneswere assessed along with the control genotype RRIM 600 for two traits, annual production and girthgrowth, 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 accu-mulated rubber yield (rg = −0.58, P < 0.01), and high stability of yield with AMMI statistics explaining 96%

× Y interaction of interactions. The study concluded that early selection in the first year of rubber yield may reduce theevaluation time of clones in a rubber tree breeding program. There was a negative phenotypic correlationbetween annual girth growth and yield. The study allowed differentiation of the genotypes assessed fortemporal stability and overall performance for yield during tapping. Genotype selected for stability ofproduction it is not the same as those selected just for annual growth. The stability of annual girth growth

h the

correlates negatively wit

. Introduction

In Brazil, rubber tree [Hevea brasiliensis (Willd. ex Adr. de Juss.)üell-Arg.] plantations are expanding to areas considered free

rom south American leaf blight disease wilt caused by the fungusicrocyclus ulei (P. Henn) V. Arx., including the southeast where the

ubber tree shows great adaptability to varied ecological conditionsGonc alves and Marques, 2008).

Year-to-year climatic variations, in addition to the diver-ity of sites where rubber tree is cropped, evaluations needo be conducted over several years to fully understand geno-ype × environment interaction, so that a comprehensive pictures obtained of the genotype by environment interactions. Here,

nvironment is represented by “year” (G × Y) allowing for estima-ion of temporal stability of genotypes providing greater safety inecommending clones.

∗ Corresponding author.E-mail addresses: silva.gap@gmail.com (G.A.P. Silva),

gouvea@iac.sp.gov.br (L.R.L. Gouvêa), ckverardi@yahoo.com.br (C.K. Verardi),eh.bombonato@hotmail.com (A.L.B.d. Oliveira), paulog@iac.sp.gov.brP.d.S. Gonc alves).

926-6690/$ – see front matter © 2013 Published by Elsevier B.V.ttp://dx.doi.org/10.1016/j.indcrop.2013.12.010

stability of yield.© 2013 Published by Elsevier B.V.

In rubber tree breeding programs desirable genotypes are suchthat in addition to high yield they should have both vigorous growthand yield stability during the tapping phase. According to Koo et al.(2007), the advantage of selecting superior genotypes by stabilityanalysis is that stable genotypes are reliable across the environ-ments, reducing the genotype-environment interaction.

To improve rubber plantation productivity, basic knowledgeabout the genetic traits of the plant populations of the speciesof interest is necessary for efficient selection and to conductwell-targeted crossings. Quantitative data analyses economicallyimport traits that are useful to estimate genetic variances, typeof genetic action involved, heritability and genetic correlations, sothat the results obtained can be used to predict genetic gains aftersuccessive selection cycles. The quantitative information, besideswidening the understanding of rubber tree genetics and its repro-ductive characteristics, also assists to determine the best selectionstrategy overcoming problems and difficulties in superior genotypeselection. The main objectives of rubber tree breeding is to increaseyield and vigor through methods that can shorten the breeding

cycle of the crop, estimate the genetic parameters and correlatesthese traits (Gonc alves et al., 2006).

Silva et al. (2012), studying open-pollinated progenies, con-cluded that the annual trunk girth increment and virgin bark

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hickness are variables that are genetically correlated and a simul-aneous selection for increase in the two variables. This study alsooncluded that progeny-mean hereditability for the rubber yieldrait and annual trunk girth increment were superior to individual-nd within-progeny heritabilities. This can be the basis of a strategyo increase the genetic gain in the rubber tree.

To make this recommendation as reliable as possible, a detailedtudy is needed on the temporal stability of the genotypes and withespect to their economically important traits (Cruz, 2006). Adapt-bility and stability analyses are, therefore, statistical procedureshat allow identification of the cultivars with more stable perfor-

ance that respond predictably to the environmental variationsSilva and Duarte, 2006).

The objective of the present study was to assess the temporaltability of rubber tree genotypes for natural rubber production,nnual girth growth in the post-tapping phase, phenotypically andenotypically correlating these variables along the years of reviewsn a breeding program for the species.

. Materials and methods

Thirty-two rubber tree genotypes belonging to the Institutogronômico de Campinas (IAC) breeding program were assessedlong with the control genotype (RRIM 600). That is the mostlanted clone in Brazil and around the world (Table 1) to responsesf interest corresponded to five years’ rubber yield and six yearsf annual girth growth. The experiment was conducted at the Jaúxperimental Station, Brazil (22◦17′S latitude, 48◦64′W longitude)ocated at an altitude of 580 m, in moderate A eutrophic, red–yellowlay soil, with sandy/medium texture. The predominant climaten the region is the Aw type (Koppen) with a defined dry season,1.6 ◦C mean annual temperature, mean annual relative humidityf around 70%, with extremes of 77% in February and 59% in August.he annual mean rainfall is 1344 mm with 74% of the rainfall fromctober to March and 26% from April to September (INPE-CPTEC,013).

A randomized complete block design was used with three repli-ates and six trees per plot in a 7 m × 3 m spacing. The trees begano be tapped at 7 years of age. The system used to assess therst annual rubber yield was 1/2S d/4, 5d/7, 11 m/y, ET 2.5% Pa8/y)—tapping in a half spiral (1/2S), performed at four-day inter-als (d/4), for 11 months of the year (11 m/y), using Ethefon (ET)ith 2.5% active ingredient applied on the regenerating recently

apped panel (pa) eight times a year (8/y) (Dijkman 1951). After

apping, latex was collected in plastic cups provided for each tree.his system is widely standardized and documented in the litera-ure (Gouvêa et al., 2011; Gonc alves et al., 2011; Vijayakumar et al.,000).

able 1eans of rubber yield (RY, g.tree−1 tapping) and annual girth growth (AGG, cm y−1) of 33

irth growth.

ID Genotypes RY AGG ID Genotypes

01 IAC 400 95.16* 4.30 12 Pind 060/87

02 IAC 401 79.22* 2.75 13 Pind 141/87

03 IAC 403 63.38 3.37 14 Pind 147/87

04 IAC 404 66.18 3.29 15 Pind 161/88

05 IAC 417 69.64 3.54 16 Pind 218/88

06 IAC 424 42.50 3.38 17 Pind 237/87

07 IAN 873 56.36 3.28 18 Pind 267/88

08 PB 235 74.17 3.67 19 Pind 282/87

09 GU 198 79.94* 3.58 20 Pind 300/87

10 GU 176 45.75 3.13 21 Pind 302/88

11 Pind 14/88 39.12 3.08 22 Pind 373/88

Overall Average 54.22 3.47CV(%) 16.44 43.63

* Significant for P < 0.05 Dunnett test with respect to the control genotype RRIM 600.

d Products 52 (2014) 801– 808

To assess girth growth in the tapping phase, annual measure-ments were taken of plant vigor expressed in girth growth. Sixyears’ growth data were analyzed in the post-tapping period. Thetrunk girth (cm) was measured at 120 cm above the soil, using apiece of tape. Annual girth growth was calculated by subtractingfrom the circumference of one year. Individual-year analyses ofvariance were carried out to assess the genetic variability amongthe clones and the experimental accuracy, followed by joint analy-sis of variance across years.

Joint analysis of variance was carried out using the randomizedcomplete block design with split-plot in time model, consist-ing of fixed effects for genotypes and environments; in this caseenvironment was represented by year. The model fitted this anal-ysis 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 fixedyear in the kth replicate; � is the average mean; gi is a fixed effectof 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 isthe fixed interaction between ith genotype with the jth year; (gb)ikis the interaction between ith genotype with the kth block; (gab)ijkis the interaction between genotype, year and replicate, eijk is theexperimental error.

The analyses of variance were carried out using the ANOVA pro-cedure of the SAS program (SAS Institute, 2002).

Further analyses were carried out using the AMMI methodology.AMMI analysis is a combination of univariate methods (analysisof variance) with multivariate methods (main component anal-ysis and single-value partitioning) (Zobel et al., 1988). The SASmanual (SAS Institute, 2002) was used as described in Duarteand Vencovsky (1999). The proposed model was: Yij = � + gi + ej +

n∑k=1

�k�ik˛jk + �ijwhere: Yij is the mean response of the ith genotype

in jth environment; � is the average mean; gi is the fixed effect ofthe ith genotype (i = 1, 2,. . .,g); ej is the fixed effect of the jth envi-ronment j (j = 1, 2,. . .,a); �k is the square root of the kth eigenvalueof the matrices (GE)(GE)’ and (GE)’(GE) (of non-equal eigenvalues);� ik is the ith term (related to genotype i) of the kth eigenvector ofthe (GE)(GE)’; ˛jk is the jth term (related to environment j) of thekth eigenvector of the (GE)’(GE); �ij is the error term.

Complementing the principal components analysis AMMI alsowas used the analysis-the linear regression Eberhart and Russell(1966). The model used for this methodology was the follow-ing: Yij = mi + bilj + dij + eijwhere: 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 indexj; dij = deviation of the regression of i genotype in environmentj; eij = mean error associated to the average general.

genotypes in five years of assessment rubber production and six years for annual

RY AGG ID Genotypes RY AGG

39.15 3.14 23 Pind 512/88 42.02 4.3866.82 2.74 24 Pind 673/88 53.45 2.7144.35 3.95 25 Vot 056/88 55.70 4.1740.94 4.74* 26 Vot 061/88 62.83 4.0726.61 4.53 27 Vot 171/88 50.54 3.5328.06 4.09 28 Vot 211/88 50.30 2.7350.84 2.94 29 Vot 237/88 53.71 3.8628.23 3.37 30 Vot 272/88 59.15 3.0953.84 3.51 31 Vot 275/88 53.59 3.3758.10 3.52 32 1-2-56-77 43.05 4.3050.56 4.62 33 RRIM 600 66.16

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The environment index was calculated by Ij = Y.j − Y .., withnj=1Ij = 0,is the number of environments.The trait-to-trait correlations were estimated from the expres-

ions shown by Falconer (1964): genotypic (i.e., clonal) correlationetween traits rg = covg(x,y)/

√�2

gx�2gy. The genetic covariance

ovg(x,y) was obtained by using mean cross products from theAS program (SAS Institute, 2002). Approximate standard devi-tions of the genetic correlations within years were calculatedccording to Falconer (1964). The phenotypic correlations rp

etween trait x and y were rp =(

covg(x,y) + cove(x,y)

)/�x�y =

ov(x,y)/�x�y; where covg(x,y)is genetic covariance between theraits x and y; �gxis genetic standard deviation for the trait x;ove(x,y) is environmental covariance between the traits x and; cov(x,y) is phenotypic covariance between the traits x and; �y phenotypic standard deviation for trait y. The signifi-ance of these correlations was evaluated according to Fisher1941). The formula for heritability (or repeatability) of genotype

eans was calculated using (F − 1)/F, or 1 − 1/F. The F is value of-test.

After selection by the AMMI model, temporal stability statis-ics were studied in a biplot graph. Biplot graphs were obtainedy combinations of the orthogonal Interaction Principal Compo-ent Axis (IPCA). In this type of graph it is possible to observehe standard portion of the genotype × year interaction, show-ng the genotypes and environments (year) that contributedeast to the interaction, and were therefore stable, combina-ions of genotypes and environments (year) that are desirable fordaptability.

The term biplot refers to a type of graph containing two cate-ories of points or markers, one axis referring to genotypic meansnd the other to environments (year) in the case of temporal sta-ility (Duarte and Vencovsky, 1999). The stability of the genotypesnd environments was interpreted based on the graphic display ofhe AMMI biplot.

The AMMI biplot was interpreted by approximating the geno-ypes and environments (year) close to the zero score that

ontributed little to the interaction indicating temporal stability.n the AMMI biplot the genotypes around the line of the zero markn the IPCA1 corresponded to the most stable genotypes and envi-onments (year) (Pacheco et al., 2005).

able 2oint analyses of variance (ANOVA), with split plots in time, of rubber yield data (RY, g.treeve and six year of assessment, respectively and partitioning of the G × Y interaction. The to the AMMI methodology, obtained in five and six year of assessment, respectively.

Source of variation RY

DF MS F P value

Replicates 2 838.817 3.120 0.0511

Genotypes 32 3570.493 13.270** <0.0001

Error a 64 269.071 –

Year 4 30708.251 199.770** <0.0001

Error b 8 153.720 –

genotypes × year 128 349.569 4.410** <0.0001

IPCA 1 35 253.440 10.383** 0.0000

Residual 1 93 64.651 2.648** 0.0000

IPCA 2 33 87.256 3.574** 0.0000

Residual 2 60 52.219 2.139** 0.0000

IPCA 3 31 82.653 3.386** 0.0000

Residual 3 29 19.687 0.806 0.7511

IPCA 4 29 19.687 0.806 0.7511

Residual 4 – – – –

IPCA 5 – – – –

Residual 5 – – – –

Error c 256 79.225 – –

Total 494 – – –

P < 0.05.** P < 0.01; (DF) degrees of freedom.

d Products 52 (2014) 801– 808 803

3. Results and discussion

The joint analysis of variance for yield (Table 2) also showedsignificant effects of the genotypes, year, and genotype × year inter-action (P value <0.001). This indicated that there was variabilityamong the genotypes, differences among the years, and interactionbetween genotype and year.

The relative performance of the genotypes varied in relationto the different environmental conditions among the assessmentyears. In this case, complex interaction predominated, in that therankings of the genotypes varied during the assessment period.That is an undesirable for rubber tree breeding program becauseclones do not maintain a constant and predictable productive per-formance over years (Table 2).

The G × Y interaction matrix was partitioned into four compo-nents using the AMMI methodology for the yield variable over allsix years (by the G × Y matrix, where P is the minimum betweeng − 1 and a − 1 {[min (33 − 1) and (5 − 1)] = 4}). Using the Fr testproposed by Cornelius et al. (1992), the first axis was highly signif-icant (P value <0.001) and the residual unexplained portion of theG × Y resulted non-significant (P value >0.005). The selection of theIPCA 3, model accumulating 96.16%, was selected; and thereforethe noise correspondent to 3.84% of the sum of square of the G × Yinteraction (SSG × Y) on Table 2.

Values of G × Y interaction found in this study are compatiblewith the values previously found in other cultures.

In a study developed by Wamatu et al. (2003) in clones of coffeeduring 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 Oliveiraet al. (2003), Rocha et al. (2007) and Melo et al. (2007), the sum ofthe 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 geno-type × year interaction. Gauch (1988) explained that the first AMMIaxis captures the largest “standard” percentage that is the non-attributable part of interaction.

The parameters obtained by the Eberhart and Russell methodbased on rubber yield (RY) are shown in Table 3. Seven of the33 genotypes assessed showed significant deviations from the

−1 tapping) and annual girth growth in the post-tapping (AGG, cm y−1), obtained inogether accumulated variance explained for the main component (MC%) according

AGG

MC% DF MS F P value MC%

2 0.436 0.110 0.896132 6.087 1.530** 0.072964 3.966 –5 69.981 27.600** <0.000110 2.535 –160 3.396 1.400** 0.0062

59.60 36 1.831 2.262** 0.0001 33.68124 1.293 1.293** 0.0381

78.94 34 1.705 2.107** 0.0005 63.2990 0.798 0.986 0.5196

96.16 32 0.853 1.054 0.392158 0.768 0.949 0.583430 0.801 0.989 0.485628 0.733 0.905 0.607128 0.733 0.905 0.6071– – – –320 2.428 – –593 – – –

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 3Estimates 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 Genotype RY AGG

ˆi S2

diˆ

i S2di

01 IAC 400 1.18 ns 155.68 ** 0.46 ns 4.89 **

02 IAC 401 1.31 ns −15.93 ns 1.19 ns −0.47 ns03 IAC 403 0.81 ns −11.57 ns 1.43 ns 0.01 ns04 IAC 404 1.05 ns −0.03 ns −0.87 ** 0.99 ns05 IAC 417 1.71 ** −26.47 ns 1.64 ns −0.11 ns06 IAC 424 0.78 ns −17.31 ns 0.95 ns 0.30 ns07 IAN 873 1.15 ns −46.28 ns 1.31 ns −0.79 ns08 PB 235 1.16 ns −34.01 ns 0.66 ns −0.7 ns09 GU 198 2.43 ** 367.46 ** 0.31 ns 0.38 ns10 GU 176 0.88 ns −21.32 ns 0.76 ns 0.32 ns11 Pind 14/88 0.69 ns 18.76 ns 0.55 ns 1.92 *

12 Pind 060/87 0.79 ns 9.04 ns 1.24 ns 1.21 ns13 Pind 141/87 0.77 ns 81.08 * 0.85 ns −0.54 ns14 Pind 147/87 0.52 * 113.85 ns 1.57 ns −0.2 ns15 Pind 161/88 0.53 * −37.00 ns 2.37 * 0.96 ns16 Pind 218/88 0.32 ** −15.5 ns 1.57 ns −0.45 ns17 Pind 237/87 0.52 * −16.44 ns 1.00 ns −0.64 ns18 Pind 267/88 0.98 ns 36.22 ns 1.38 ns 0.52 ns19 Pind 282/87 0.44 ** −42.98 ns 1.72 ns −0.47 ns20 Pind 300/87 1.10 ns 30.31 ns 1.18 ns −0.28 ns21 Pind 302/88 1.36 ns −19.38 ns 1.48 ns −0.28 ns22 Pind 373/88 0.83 ns 100.67 * 0.78 ns 0.02 ns23 Pind 512/88 0.76 ns −43.71 ns 0.79 ns 1.11 ns24 Pind 673/88 0.85 ns 182.31 ** 0.83 ns 0.49 ns25 Vot 056/88 1.03 ns −24.19 ns 1.35 ns −0.54 ns26 Vot 061/88 1.46 * 12.98 ns −0.60 * 0.04 ns27 Vot 171/88 0.50 * −16.36 ns 0.87 ns −0.67 ns28 Vot 211/88 1.20 ns 14.43 ns 1.65 ns −0.58 ns29 Vot 237/88 0.96 ns 272.33 ** 2.24 * −0.41 ns30 Vot 272/88 1.45 * −35.84 ns 0.60 ns −0.84 ns31 Vot 275/88 1.25 ns −37.87 ns 0.64 ns −0.23 ns32 1-2-56-77 0.62 ns 3.46 ns −0.13 ns 0.09 ns33 RRIM 600 1.59 ** −18.62 ns 1.22 ns −0.50 ns

* P < 0.05.** P < 0.01; ˆ

i is regression coefficient; S2di

is deviation of the regression coefficient; the control genotype RRIM 600.

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egression(

S2di

> 0)

, indicating interaction or lack of stability.he IAC 400, GU 198, Pind 141/87, Pind 147/87, Pind 373/88,ind 673/88 and Vot 237/88 genotypes did not show stability.egarding adaptability, the genotypes IAC 417, GU 198, Vot 061/88nd RRIM 600 showed specific adaptability in favorable environ-

ents(

ˆi > 1.0

), while genotypes Pind 147/88, Pind 161/88, Pind

37/87, Pind 282/87, Vot 171/88 showed specific adaptability for

nfavorable environments(

ˆi > 1.0

).

The estimates of phenotypic stability and adaptabil-ty obtained for AGG showed that two genotypes assessedhowed significant deviations from the regression

(S2

di> 0

),

ndicating interation. Regarding adaptability, the genotypesind 161/88 and Vot 237/88 showed specific adaptability in

avorable environments(

ˆi > 1.0

), while genotypes IAC 404

nd Vot 061/88 showed specific adaptability for unfavorable

nvironments(

ˆi < 1.0

).

There was agreement among the most stable genotypes iden-ified with the different analytical methods; however, the rankingf genotypes was altered. If we observe the progenies that showed

ignificant genotype × years interaction by the F test in the methodberhart and Russel, these were the progenies with the greatestistances point where it intersects the abscissa and the ordi-ate. From the breeder’s point of view, processing data by several

able 4stimates of genotypic and phenotypic correlations of rubber yield, annual girth growth a

RY I RY II RY III RY IV RY V

RY Irg – – – - –

rp – – – - –

hg 0.92 – – - –

RY IIrg 0.88** – – – –

rp 0.83** – – – –

hg – 0.86 – – –

RY IIIrg 0.81** 0.73** – – –

rp 0.76** 0.70** – – –

hg – – 0.88 – –

RY IVrg 0.80** 0.86** 0.74** – –

rp 0.74** 0.82** 0.70** – –

hg – – – 0.88 –

RY Vrg 0.61** 0.81** 0.63** 0.88** –

rp 0.57** 0.75** 0.58** 0.82** –

hg – – – – 0.8AGG I

rg −0.36** −0.38* −0.58 −0.40 −0.6rp −0.29 −0.21 −0.37 −0.27 −0.3hg – – – – -

AGG IIrg 0.05 −0.43** −0.63** −0.62 −0.3rp −0.27 −0.20 −0.30 −0.31 −0.1hg – – – – –

AGG IIIrg −0.51** −0.82** −0.34* −0.75** −0.9rp −0.33 −0.44** −0.21 −0.42** −0.4hg – – – – –

AGG IVrg 0.68** 0.02 0.34* 0.19 −0.1rp 0.30 0.10 0.17 0.16 −0.1hg – – – – –

AGG Vrg −0.10 −0.07 −0.14 −0.18 −0.2rp 0.06 0.01 −0.09 −0.10 −0.0hg – – – – –

* P < 0.05.** P < 0.01; (RY) rubber yield.; (AGG) annual Girth growth; (I) year one; (II) year two; (II

d Products 52 (2014) 801– 808 805

methods of adaptability and stability analysis, while consideringthe peculiarities of each method, is better for decision-makingwhen indicating cultivars. When the genotype × environmentinteraction results variation of unpredictable environmental fac-tors, such as year to year variation, as was the case in the presentstudy, breeders need to select stable genotypes that can performreasonably well in a wide range of conditions. Such breeding strate-gies can help rubber producers to avoid risks.

Table 4 presents the coefficient estimates of genetic and phe-notypic correlation between the traits. All genetic and phenotypiccorrelation estimates (rg and rp respectively) between rubber-yieldvariables were positive and significant. It is observed that the rub-ber yield in the first year had high correlates with the yields ofsubsequent years with values of rg ranking from 0.61 to 0.88, andfor rp 0.53 to 0.83. Emphasizing the results between the correla-tions of rubber yield is observed correlations significant of highmagnitude 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 YieldIV (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 firstyear of production of rubber can substantially reduce the evalua-

tion time of the clones in a breeding program. Silva et al. (2012),assessing the rubber production in open-pollinated progenies ofrubber concluded that the first measurement cycle can be effectivefor an early selection of genotypes for rubber production. In tree

nd heritability.

AGG I AGG II AGG III AGG IV AGG V

– – – – –– – – – –– – – – –

– – – – –– – – – –– – – – –

– – – – –– – – – –– – – – –

– – – – –– – – – –– – – – –

– – – – –– – – – –

8 – – – – –

0 – – – – –5 – – – – –

0.03 – – – –

2 0.33** – – – –7 0.28 – – – –

– 0.31 – – –

0 0.40** 0.55** – – –7 0.34* 0.12 – – –

– – 0.33

9 −0.05 −0.57** 0.29 – –0 0.05 −0.59** 0.07 – –

– – – 0.16 –

7 0.38** 0.55** −0.41* −0.01 –9 0.29 0.37* 0.01 −0.02 –

– – – – 0.29

I) year three; (IV) year four; (V) year five.

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06 G.A.P. Silva et al. / Industrial Cr

reeding, early selection has been shown to have advantages byhortening the generation interval and this reducing the breedingycle (Nanson, 1970; Lambeth, 1980; McKeand, 1988; Mathesont al., 1994). Shortening the breeding cycle of a tree through earlyelection can produce more genetic gains per unit time (year)f there is strong genetic correlation between early and matureraits.

Analyzing the annual girth growth I (AGG I), it shows that thisrait is genetically correlated with annual girth growth of subse-uent years (AGG II, rg = 0.33**), (AGG III, rg = 0.40**) and (AGG V,g = 0.38 **). This fact opens the possibility of an early selection inhe first year of assessment for this trait.

Analyzing the results between annual girth growth (AGG) andubber yield in Table 4, indicate significant and negative geneticorrelations between the first three years of evaluations (AGG Iith 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 geneticorrelations between yields in successive years to understand rela-ionships between years of coffee yield. Their reported correlationsetween yields of the different years were moderately stable, andevealed a major tree effect within clones. The traits involved werearliness, alternation, and the intensity of variations between years.

espite a marked tendency towards biennial cropping, especially in

he early years, the estimated genetic correlations between years,nd between individual years and cumulative yield were generallyigh.

able 5stimates of genotypic and phenotypic correlations of rubber yield cumulative, annual gi

RY I RY II RY III RY IV RY V

RY Irg – – – – –

rp – – – – –

hg 0.92 – – – –

RY IIrg 0.97** – – – –

rp 0.95** – – – –

hg – 0.91 – – –

RY IIIrg 0.95** 0.96** – – –

rp 0.93** 0.96** – – –

hg – – 0.91 – –

RY IVrg 0.93** 0.96** 0.98** – –

rp 0.89** 0.94** 0.97** – –

hg – – – 0.92 –

RY Vrg 0.87** 0.93** 0.94** 0.98** –

rp 0.84** 0.92** 0.93** 0.98** –

hg – – – – 0.9AGG I

rg −0.36** −0.38** −0.49** −0.48** 0.5rp −0.29 −0.26 −0.33 −0.31 −0.3hg – – – – –

AGG IIrg −0.50** −0.51** −0.63** −0.64** −0.6rp −0.35* −0.32 −0.39** −0.39** −0.3hg – – – – –

AGG IIIrg −0.56** −0.64** −0.68** −0.72** −0.7rp −0.42** −0.43** −0.45** −0.47** −0.5hg – – – – –

AGG IVrg −0.32 −0.51** −0.55** −0.60** −0.6rp −0.20 −0.26 −0.28 −0.30 −0.3hg – – – – –

AGG Vrg −0.29 −0.43** −0.47** −0.53** −0.5rp −0.19 −0.23 −0.26 −0.28 −0.3hg – – – – –

* P < 0.05;** P < 0.01; (RY I) rubber yield; (AGG) annual Girth growth; (I) year one; (II) year two; (I

d Products 52 (2014) 801– 808

Table 5 presents the estimates of genotypic and phenotypiccorrelations of cumulative rubber yield, annual girth growth cumu-lative. These are results indicate that all genetic and phenotypiccorrelation estimates between rubber-yield variables are positiveand significant (P < 0.05). Table 5 presents that AGG is geneticallycorrelated with annual girth growth of subsequent years positivelyfrom 0.97 in accumulated AGG V with AGG IV, and annual girthgrowth and rubber yield are negatively correlated from −0.58 inaccumulated AGG V with RY V. The average heritability of rubberyield in the five years evaluated in Table 4 was (hg = 0.88), rangingfrom (hg = 0.86) to (hg = 0.92). The average annual girth growth ofheritability was (hg = 0.24), ranging from (hg = 0.03) to (hg = 0.33).The heritability of accumulated traits shown in Table 5 which hasmore stable under varying over between years. The cumulativeheritability rubber yield was 0.92, ranging from 0.92 to 0.91. Theaccumulated heritability of annual girth growth was 0.35, rangingfrom 0.21 to 0.37.

According to Gonc alves et al. (2006), trunk girth incrementdecreases after tapping starts because the photosynthesis is par-titioned into two competing sources: exploited latex and trunkdiameter growth. Another important aspect relating to vigor is thatwhen latex production declines, after about 25 to 30 years of tree

exploitation, the sources of the rubber tree can be used for a widerange of products, replacing wood from natural forests (Killmannand Hong, 2000). Therefore, vigorous genotypes accumulate higheralternative value as wood producers.

rth growth cumulative and heritability.

AGG I AGG II AGG III AGG IV AGG V

– – – – –– – – – –– – – – –

– – – – –– – – – –– – – – –

– – – – –– – – – –– – – – –

– – – – –– – – – –– – – – –

– – – – –– – – – –

2 – – – – –

3** – – – – –4* – – – – –

0.35 - - - -

5** 0.90** – – – –9** 0.85** – – – –

– 0.37 - - -

7** 0.82** 0.95** – – –0** 0.80** 0.89** – – –

– – 0.30 – –

6** 0.78** 0.82** 0.94** – –3 0.70** 0.76** 0.86** – –

– – – 0.21 –

8** 0.74** 0.96** 0.93** 0.97** –1 0.70** 0.79** 0.84** 0.94** –

– – – – 0.35

II) year three; (IV) year four; (V) year five.

G.A.P. Silva et al. / Industrial Crops and Products 52 (2014) 801– 808 807

f asses

tPvgps

4g0ctiw

g1abc

gtty

vcwcfttu(

i

Fig. 2. Biplot graph for annual girth growth during tapping in six years o

In Fig. 1, the abscissa represents the main effects (mean of geno-ypes across years), which are plotted against the first Interactionrincipal Component Axis. Thus, the genotypes close to IPCA 1 withalues close to zero indicate stability over the test years; a group ofenotypes and the year with scores with the same sign had specificositive interactions, and clones with a combination of oppositeigns presented negative specific interactions.

The genotype that contributed least to the interaction was IAC00 (ID-01) which produced the highest mean yields 95.16 in (RY,.tree−1 tapping). From Table 1 the mean yield of the IAC 400 (ID-1) and IAC 401 (ID-02) clone differed statistically from all the otherlones assessed by the 5% Dunnett means test, surpassing the con-rol genotype RRIM 600 (ID-33). Therefore, these clones can be annteresting alternative to the clone RRIM 600, which is the most

idely planted in Brazil.The genotypes that contributed most to the interaction were the

enotypes GU 198 (ID-09), Pind 147/87 (ID-14), Pind 237/87 (ID-6) and Pind 282/87 (ID-19) because they presented the greatestmplitudes, as shown in Fig. 1. This interaction was also influencedy the AGG as presented in Table 4 where the annual girth growthorrelates negatively with yield.

The joint, across-years analysis of variance for the annual girthrowth (Table 2) showed significant effects by the F test for geno-ypes, year and genotype × year interaction. This indicated thathere was variability among the genotypes, difference among theear and interaction between genotype and year.

Using the AMMI methodology for the annual girth growthariable the interaction matrix could be partitioned into 5 mainomponents. By the Fr test of Cornelius et al. (1992) the first axisas significant at 1% probability and residual 1 was not signifi-

ant, thus leading to selection of the IPCA 2 model which accountedor 63.3% of the SSG × Y, corresponding to the standard portion, andhe rest belonged to the portion called ‘noise’; that was 36.7% ofhe SSG × Y (Table 2). This value is not inconsistent with the val-

es already observed Rea et al. (2011), 43.3% and Rocha and Maia2004), 26%.

The biplot graph was elaborated up to the IPCA 1 model shownn Fig. 2. The first main axis of the interaction captured 33.7% of the

sment. Numbers correspond to IDs of genotypes in according to Table 1.

SSG × Y, where it is the largest “standard” percentage. The genotypeIAC 400 (ID-01) was that contributed most to the interaction of thetrait annual girth growth and that least contributed to interactionof yield and had the highest average production. Even with stronginteraction for annual girth growth the genotype IAC 400 (ID-01)had great growth.

4. Conclusions

Stability methods based on different principles can show agree-ment to identify rubber stable genotypes in rubber yield and annualgirth growth. The study allowed differentiation of the genotypesassessed for temporal stability and their performance for yield dur-ing tapping. The top genotypes selected for stability of yield are notthe same genotypes as would be selected for annual growth. Indeed,the stability of annual girth growth correlates negatively with thestability of yield. The study also showed the best clone, highlightingthe IAC 400, this is recommended for planting, because it has highyield and quick growth.

The genotypic and phenotypic correlations of rubber yieldcumulative, annual girth growth cumulative and heritabilityshowed results more stable and consistent that[n estimates ofgenotypic and phenotypic correlations of rubber yield noncumu-lative,annual girth growth noncumulative and heritability. Theearly selection in the first year for rubber yield may reducethe evaluation time for clones in a breeding program of rubbertree, because there is a good correlation between the first-year yield and the other four years’ yield. There is a negativegenetic and phenotypic correlation between annual girth growthand yield. The annual girth growth is at the expense of rubberyield.

Acknowledgements

The authors thank Fundac ão de Amparo à Pesquisa do Estado deSão Paulo (FAPESP) and Coodenac ão de Aperfeic oamento de Pessoalde Nível Superior (CAPES) for funding.

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eferences

ilas, C., Montagnon, C., Bar-Hen, A., 2011. Yield stability in clones of Coffea canephorain the short and medium term: longitudinal data analyses and measures ofstability over time. Tree Genet. Genomes 7, 421–429.

ornelius, P.L., Seyedsadr, M.S., Crossa, J., 1992. Using the shifted multiplicativemodel search for “separability” in crop cultivar trials. Theor. Appl. Genet. 84,161–172.

ruz, C.D., 2006. Programa Genes: Análise Multivariada e Simulac ão. UFV, Vic osa.uarte, J.B., Vencovsky, R., 1999. Interacão genótipo × ambiente: Uma introduc ão a

análise “AMMI”. Sociedade Brasileira de Genética, Ribeirão Preto.berhart, S.A., Russell, W.W., 1966. Stability parameters for comparing varieties.

Crop. Sci. 6, 36–40.alconer, D.S., 1964. Introduction to Quantitative Genetics. The Ronald Press Co.,

New York.isher, R.A., 1941. Statistical Methods for Research Workers. Oliver and Boyd, Edin-

burgh.auch, H.G., 1988. Model selection and validation for yield trials with interaction.

Biometrics 44, 705–715.onc alves, P.S., Marques, J.R.B., 2008. Melhoramento genético da seringueira: pas-

sado, presente e futuro. In: Alvarenga, A.P., Carmo, C.A.F.S. (Eds.), Seringueira.Epamig, Vic osa, pp. 399–534.

onc alves, P.S., Scaloppi-Júnior, E.J., Martins, M.A., Moreno, R.M.B., Branco, R.B.F.,Gonc alves, E.C.P., 2011. Assessment of growth and yield performance of rubbertree clones of the IAC 500 series. Pesqui. Agropecu. Bras. 46, 1643–1649.

oncalves, P.S., Silva, M.A., Gouvêa, L.R.L., Scaloppi, Junior, E.J., 2006. Genetic vari-ability for girth growth and rubber yield characters in Hevea brasiliensis. Sci.Agric. 63, 246–254.

ouvêa, L.G.L., Silva, G.A.P., Scaloppi-Junior, E.J., Gonc alves, P.S., 2011. Differentmethods to assess yield temporal stability in rubber. Pesqui. Agropecu. Bras.46, 491–498.

NPE-CPTEC—Instituto Nacional de Pesquisas Espaciais-Centro de Previsão deTempo e Estudos Climáticos, 2013. Dados Climatológicos. INPE, Brasil,〈http://www.cptec.inpe.br/〉 (last accessed 11.19.13).

AS Institute, 2002. User’s Guide. Version 9.1.3. S.A.S. Institute, Cary, NC.

illmann, W., Hong, L.T., 2000. Rubberwood—the success of an agricultural by-

product. Unasylva 51, 66–72.oo, Y.B., Yeo, J.K., Woo, K.S., Kim, T.S., 2007. Selection of superior clones by stabil-

ity analysis of growth performance in Populus davidiana dode at age 12. SilvaeGenet. 56, 3–4.

d Products 52 (2014) 801– 808

Lambeth, C.C., 1980. Juvenile-mature correlations in Pinaceae and implications forearly selection. Forensic Sci. 26, 571–580.

Matheson, A., Spencer, D.J., Magnussen, D., 1994. Optimum age for selection in Pinusradiata using basal area under bark for age:age correlations. Silvae Gen. 43,352–357.

McKeand, S.E., 1988. Optimum age for family selection for growth in genetic test ofloblolly pine. Forensic Sci. 34, 400–411.

Melo, L.C., Santos, P.G., Faria, L.C., Diaz, J.L.C., Del-Peloso, M.J., Rava, C.A., Costa, J.G.C.,2007. Interac ão com ambientes e estabilidade de genótipos de feijoeiro-comumna Região Centro-Sul do Brasil. Pesqui. Agropecu. Bras. 42, 715–723.

Nanson, A., 1970. Juvenile and correlated trait selection and its effect on selectionprograms. In: Proceedings of Second Meeting of Working Group on QuantitativeGenetics IUFRO, Louisina, pp. 17–25.

Oliveira, A.B., Duarte, J.B., Pinheiro, J.B., 2003. Emprego da análise AMMI na avaliac ãoda estabilidade produtiva em soja. Pesqui. Agropecu. Bras. 38, 357–364.

Pacheco, R.M., Duarte, J.B., Vencovsky, R., Pinheiro, J.B., Oliveira, A.B., 2005. Useof supplementary genotypes in AMMI analysis. Theor. Appl. Genet. 110,812–818.

Rea, R., Sousa-Vieira, O.S., Ramón, M., Alejos, G., Diaz, A., Briceno, R., 2011. AMMIanalysis and its application to sugarcane regional trials in Venezuela. Sugar Tech13, 108–113.

Rocha, M.M., Freire-Filho, F.R., Ribeiro, V.Q., Carvalho, H.W.L., Belarmino-Filho, J.,Raposo, J.A.A., Alcântara, J.P., Ramos, S.R.R., Machado, C.F., 2007. Adaptabilidadee estabilidade produtiva de genótipos de feijão-caupi de porte semi-ereto naRegião Nordeste do Brasil. Pesqui. Agropecu. Bras. 42, 283–1289.

Rocha, M.M., Maia, M.C.C., 2004. Yield stability of soybean lines using additive maineffects and multiplicative interaction analysis—AMMI. Crop. Breed. Appl. Biot.4, 377–384.

Silva, G.A.P., Golvêa, L.R.L., Verardi, C.K., 2012. Genetic parameters and correlationin early measurement cycles in rubber trees. Euphytica 189, 343–350.

Silva, W.C.J., Duarte, J.B., 2006. Métodos estatísticos para estudo de adaptabilidadee estabilidade fenotípica em soja. Pesqui. Agropecu. Bras. 41, 23–30.

Vijayakumar, K.R., Thomas, K.U., Rajagopal, R., 2000. Tapping. In: George, P.J., Jacob,C.K. (Eds.), Natural Rubber Agromanagement and Crop Processing. Kottayam,Kerala, India.

Wamatu, J.N., Thomas, E., Piepho, H.P., 2003. Responses of different arabica cof-fee (Coffea arabica L.) clones to varied environment conditions. Euphytica 129,175–182.

Zobel, R.W., Wright, M.J., Gauch, H., 1988. Statistical analysis of a yield trial. Agron.J. 80, 388–393.