Introduction

Eucalyptus is a genus of the Myrtaceae family endemic to Australia, Tasmania, and nearby islands. Being endemic to Australia, South East Asia and the pacific, Eucalyptus is grown mainly as exotic species (Stape, 2010). It is widely planted throughout the world including in India to provide various products. It is the longitudinally widely cultivated forest tree in the world (Nichols et al., 2010). The members of genus Eucalyptus are mostly planted in tropical and subtropical regions with the total plantation area in the world are reported to be more than 21 million ha. (Midgley, 2013). Eucalyptus plantations occupy more than 20 million hectares globally (Ribeiro et al., 2015). It is one of the most intensively managed commercial forest species in the world leading to a doubling of growth rates in many areas (Binkleyet al., 2017). Probably more than one million hectares area in India is under Eucalyptus plantations which will increase in the coming decades as demand for the wood is increasing highly (Tumbull, 1999). It occurs over a good range of climate and topography like river flats or hill slopes with alluvial or sandy to gravelly soils. It has been the most successful in summer rainfall conditions with a moderate to fairly severe dry season. It is considered drought-resistant but is susceptible to frost. It is a fast-growing, short-rotation tree and has a high wood density. Eucalyptus species are used in bio energy. Eucalyptus has become one of the world’s major sources of woody biomass for energy production (Shepherd et al., 2011) and is leading hardwood sawn wood, pulpwood, fuelwood and timber feedstock (Luo et al., 2014). In India, Eucalyptus is one of the most prime species in an agroforestry model and farmers prefer clonal material for high return in short rotation. In India, agroforestry could be a major land use activity after agriculture and forestry (Dhyani et al., 2013).

In the clonal improvement and mass propagation of Eucalyptus, individual trees are identified, and then selected (candidate plus trees or CPTs) from the existing plantations based on different morphological performance, mobilize and rejuvenate of their first vegetative propagules; production of rooted plants from juvenile shoots; planting and screening in the field testing or in clonal orchards; again rejuvenate the chosen individuals, and begin their mass multiplication by rooting the cuttings. The advantages of clonal plantations could be attributable to fixation of a non-additive component of genetic variance, maintenance of hybrid vigour (Ginwal, 2010) and exploitation of genotypic environmental interactions for the development of site-specific genotypes (White et al., 2007). Commercial Eucalyptus clonal planting materials perform better in volume and productivity than rooted seedlings (Sharda and Verma, 2008). Clonal variation studies will be helpful for a selection of suitable clones for different sites. Genetic variations have been reported for growth and wood traits in a number of Eucalyptus populations such as diameter, wood density, lignin content and S/G ratio in Eucalyptus globulus (Stackpole et al., 2011) and Eucalyptus europhylla (Hein et al., 2012). Kumar et al. (2010) reported that significant variations were recorded for height and diameter at breast height (DBH) and clear bole length (CBH) for eighteen clones of Eucalyptus tereticornis for various growth parameters. Hybridisation and selection programmes have been implemented for various Eucalyptuses in a number of countries resulting in improvement of growth and adaptability traits (Hardener et al., 2011). The future thrust for genetic improvement will be for higher growth, productivity, bio energy, abiotic stress, diseases and insect resistance, etc. Taking the above facts into consideration the present research was undertaken with objectives to study the performance of different clones of Eucalyptus tereticornis Sm., to study parameters of variation in different characters of Eucalyptus tereticornis Sm., and to identify superior clones based on their performance suitable for the test region.

Materials and Methods

The present investigation was carried out at the instructional farm, College of Forestry, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, India from August, 2018 to June, 2019. The location’s latitude is 20°26’9.58” N and the longitude is 85°78’4.45” E. It comes under the agro-climatic zone of the east and south-eastern coastal plain. Its climate is hot and humid. The mean annual rainfall is 1577 mm. The mean maximum summer temperature is 39°C and the mean minimum winter temperature is 11.5°C. Eight clones (treatments) namely T₁ (ITC 526), T₂ (ITC 288), T₃ (ITC 7), T₄ (ITC 316), T₅ (ITC 413), T₆ (ITC 286), T₇ (ITC 71), and T₈ (ITC 136) of Eucalyptus tereticornis Sm. each with 20 rametes were collected from the nursery of “Avantha Agritech Ltd”, Jaypur, Koraput, Odisha. The planting materials were of 25 days old at the time of collection. The experiment was conducted in a randomized complete block design (RCBD) with 4 replications (blocks) and 8 treatments (clones). The size of each block is 24 meters in length and 7.5 meters in breadth that it having an area of 180 square meters. Each replication (block) contains 8 treatments each with five rametes planted using the random table given by Gomez and Gomez (1984). All recommended cultural practices were carried out while raising the clonal seedlings. For different morphological characters, observations were recorded based on randomly selected plants in each clone in each replication after 300 days of planting. The nine important characters considered were plant height, leaf area and collar diameter, number of branches plant^-1, number of leaves plant^-1, leaf length, leaf width, L/W ratio, and dry biomass.

2.1.  Statistical analysis

The data recorded on different characters were subjected to different statistical analyses for estimation of different parameters following the standard procedures given by Singh and Chaudhary (1985).

2.1.1.  Mean

The arithmetic mean is defined as the value arrived at by dividing the sum of the observations by the total number of observations. It was calculated from the following formula:

X= (Σ Xi) / n

Where ` X’ represents Arithmetic mean; `n’ total number of observations;

‘Σ Xi’ is the sum of `n’ observations.

2.1.2.  Analysis of variance (ANOVA)

ANOVA for each character was carried out for partitioning total variance into components attributed to replication, treatment, and error.

Test for significance was done by `F’ test. `F’ value for treatment was calculated by dividing the treatment mean sum of squares with the error means the sum of squares. `F’ value for replication was calculated by dividing replication mean sum of squares with error means sum of squares. This calculated `F’ value was compared against the tabulated `F’ value at error degrees of freedom to test the hypothesis. When the calculated `F’ value was greater than the tabulated `F’ value, the null hypothesis was rejected and variation was considered significant.

The test of significance, for the difference between means of two treatments, was done by the `t’ test for which critical difference (CD) was calculated as follows

CD (at p=0.05) = (2MSe/r) ½ x t0.05

Where CD implies to Critical Difference, MSe to error mean sum of squares, `r’ to a number of replications, and `t’ value at the chosen level of significance (5%) at error degrees of freedom.

2.1.3.  Estimation variance components

The phenotypic, genotypic, and environmental variance components for different characters were estimated from the mean sum of squares in ANOVA according to the standard procedure given by Al-Jibouriet al. (1958).

Environmental variance (σ²e)=MSe

Genotypic variance (σ²g) = (MSt-MSe)/r

Phenotypic variance (σ²p) = (σ²g)+(σ²e) 

Where σ²g represents genotypic variance, σ²p is phenotypic variance and σ2e is variance due to the environment.

2.1.4.  Coefficient of variation

The coefficient of variation is the ratio of the standard deviation to the mean of the observations. It was calculated as a percentage from the following formula.

CV=(SD/X)×100

Where CV represents the coefficient of variation and SD represents standard deviation.

Estimation of components of the coefficient of variation

Phenotypic coefficient of variation (PCV), genotypic coefficient of variation (GCV), and environmental coefficient of variation was calculated from the following formula,

PCV=(σ²p) 1/2 /X)×100

GCV=(σ²g) 1/2 /X)×100

ECV=(σ²e) 1/2 /X)×100

Where, σ2p, σ2g, and σ2e are the square root of the phenotypic, genotypic, and environmental variance respectively.

2.1.5.  Estimation of heritability

Heritability (h2) in the broad sense of characters was estimated by the formula suggested by Hanson et al. (1956), using the components of variance, as follows.

h2=σ²g/σ² p

Where, σ²g and σ²p represent genotypic and phenotypic variance respectively.

2.1.6.  Estimation of genetic advance

The expected genetic advance (GA) from selection among treatments for different characters was calculated according to Johnson et al. (1955) as follows.

GA=K×h²×σp

Where k represents standardized selection differential at 5% selection intensity.

2.1.7.  Genetic advance as % of mean

The genetic advance as percentage of mean (GAM) was expressed as a % of the mean for comparison among characters.

GA (as % of mean) or GAM=(GA/Mean)×100

Results and Discussion

The variations that exist among the tested clones for different morphological characters were assessed through various statistical tools like analysis of variance, estimation of components of variation, and heritability. The analysis of variance revealed significant differences among the treatments for different characters like biomass, plant height, collar diameter, and number of branches plant^-1, number of leaves plant^-1, leaf width, leaf area, leaf length, and leaf length to leaf width ratio.

3.1.  Performances of the different quantitative characters

The collected data in respect of all 9 quantitative characters were subjected to analysis of variance (ANOVA) following the standard procedure. The mean values, the coefficient of variation (C.V.), along critical difference (CD) at a 5% level of significance are presented in table-1, which revealed a wide range of variation for all traits studied.

3.2.  Plant height

The performances of the tested clones had shown significant variation with respect to plant height. The mean performance of plant height ranged from 168.64 cm to 230.71 cm with an overall mean of 200.94 cm (Table 1). The maximum height (247.9 cm) was observed in T₁ followed by T₅ (221.02 cm) and T₈ (218.79 cm). The minimum height (151.45 cm) was observed in T-2 which was statistically at par with T₇ (167.79 cm).

Table 1: Mean value of growth parameters in different clones of Eucalyptus tereticornis Sm.

3.3.  Collar diameter

Significant variation was observed among all clones for the collar diameter. The overall mean was 21.17 mm (Table 1). The maximum collar diameter (23.25 mm) was observed in T₁ which was statistically at par with T₈ (22.92 mm), T₄ (22.16 mm), and T₆ (21.86 mm). The minimum collar diameter (18.35 mm) was observed in T₇ which was statistically at par with T₂ (18.79 mm).

3.4.  Number of branches

All the treatments differed significantly for number of branches. The overall mean was observed at 23.65. T₁ recorded the highest number of branches (27.5 number), which was statistically at par with T₅ (26.25 number).

3.5.   Leaf area

The performance of the leaf area ranged from 30.12 cm² to 42.70 cm² with an overall mean of 36.10 cm² (Table 1). T₈ recorded the highest leaf area (42.70 cm²) followed by T₅ (39.77 cm²) and T₁ (37.72 cm²). The lowest leaf area was observed in T₇ (30.12 cm²) which was statistically at par with T₂ (32.6 cm²) and T₃ (32.9 cm²).

3.6.  Leaf length (L)

The leaf length varied from 12.65 cm to 15.0 cm with an overall mean of 14.13 cm (Table 1). T₈ recorded the highest length (15 cm) which was statistically at par with T₅ (14.75 cm) and T₁ (14.3 cm). The lowest leaf length was recorded in T₇ (12.65 cm) followed by T₂ (13.87 cm).

3.7.  Leaf width (W)

The performance of the tested clones had shown significant variation with respect to leaf width. The leaf width was found to range from 3.95 cm to 5.75 cm with an overall mean of 4.66 cm. The maximum leaf width ((5.75 cm) was recorded in T-8 followed by T₁ (4.85 cm) and T-6 (4.77 cm). The minimum leaf width (3.95 cm) was recorded both in T₂ and T₇ (Table 1).

3.8.  L/W ratio

Significant variation was observed among all clones for the trait L/W ratio. The overall mean was found to be 3.06 (Table 1). The maximum L/W ratio (3.51) was observed in T₂ which was followed by T-3 (3.21). The minimum L/W ratio (2.61) was observed in T₈ which was followed by both T₅ and T₁ (2.95).

3.9.  Number of leaves

A perusal of the data in Table 1 showed the number of leaves ranged from 269.5 to 463.25 number with an overall mean of 364.53 number  T₁ recorded the highest number of leaves (463.25 number) which was statistically at par with T₅ (429.75 number). The lowest number of leaves was recorded in T₂ (269.5 number) which was statistically at par with T₇ (288 number).

3.10.  Total biomass (dry)

Based on the data appended in Table 1, it was found that the tested clones had shown significant variation with respect to total biomass. The biomass ranged from 1124.17 g to 356.18 g with an overall mean of 645.07 g. The maximum biomass (1124.17 g) was recorded in T₁ followed by T₅ (757.06 g) and T₈ (735.74 g). The minimum total biomass (356.18 g) was observed in T₂ which was statistically at par with T₇ (403.76 g). Dhilon and Singh (2010) reported significant differences among seed sources of Eucalyptus tereticornis Sm. with respect to growth traits i.e. height and diameter. Different quantitative traits like biomass, plant height, collar diameter, no of branches plant^-1, leaf length, leaf width, etc. can be considered as desirable attributes as they contribute to timber yield (Kumar and Dhillon, 2016; Marron et al., 2005). Kulkarni (2002) studied the performance of 86 clones and identified 19 clones having clear bole, 16 high productive clones, 19 adaptable to refractory sites, and 6 having disease resistance. In tree improvement programs clones possessing desirable attributes can be used as parents for specific traits. Saravanan (2020) reported clonal variation in growth, biomass production and components in a study of Eucalyptus clones. Srivatav et al. (2020) reported variation in height, girth at breast height(GBH), basal area and volume of trees ha^-1. in clones of Eucalyptus. In the present study, we have found certain clones performed better with respect to growth characters and could be selected for the tree improvement program.

3.11.  Assessment of genetic parameters

The tested clones of Eucalyptus tereticornis Sm. had shown wide genetic variation among themselves for traits studied. Genetic parameters like variance, coefficient of variation, heritability, genetic advance and genetic advance as a percentage of mean were calculated from the mean sum of squares and are presented in Table 2.

Table 2: Genetic Parameters of different growth characters in clones of Eucalyptus tereticornis Sm.

3.12.  Variance

The total phenotypic variance was partitioned into genotypic and environmental components of variance according to their contribution to the total variance. In all nine quantitative characters for which significant variation among clones was found, all have higher genotypic variance than the environmental variance. The maximum phenotypic variance (65477.63) was observed in biomass and the least (0.07) in the L/W ratio. In the case of genotypic variance, biomass had the highest (57016.59) value followed by number of leaves (4269.97) and plant height (3775.97). The lowest genotypic variance (0.06) was observed in the L/W ratio. The environmental variance was also showed a similar trend that is maximum (8461.04) in biomass, and minimum (0.01) in L/W ratio. The higher contribution of genotypic variance to total phenotypic variance signifies high heritability of characters, and this can be evidenced from the heritability estimates of characters. This is considered desirable from a breeding point of view. Similar significant differences among clones of different Eucalyptus species have been reported by various workers Gomes and Correia (1995), Lal (2005), Kumar and Bangawa (2006), and Pima et al. (2016).

3.13.  Coefficient of variation

The extent of the coefficient of variation among the studied clones for different characters was estimated in terms of coefficient of phenotypic variation (PCV), coefficient of genotypic variation (GCV), and coefficient of environmental variation (ECV). Among the 9 quantitative traits, the PCV ranged from 5.75% in leaf length to 39.66% in biomass. Similarly, the GCV of the characters varied between 4.66% in leaf length to 37.01% in biomass, which represents the presence of significant variability among the clones. Other characters showed low PCV and GCV value. Environmental coefficient of variation (ECV) was highest for biomass (14.25%), while lowest (3.32%) for L/W ratio. High PCV and GCV value and small difference between them in biomass indicate the character is under the control of strong genetic factors and the environment has little contribution towards the variability expression of the character. Biomass could be improved by the selection of superior phenotypes independent of genotypes. It also suggests genotypes with high biomass could be selected for hybridisation and selection programme. It is assumed that high GCV gives rise to variable offspring in segregating generation.

The low environmental coefficient of variation suggests all the characters under study are under genetic control.  Huse et al. (2018) reported high PCV and GCV value for biomass and low PCV and GCV value for plant height and diameter in a study of Eucalypt clones which was similar to the findings of the present study.

3.14.   Heritability

The broad-sense heritability was observed high (>60%) for all the quantitative characters under study. It ranged from 64.27% in collar diameter to 87.35% in plant height. High heritability was also found in biomass (87.07%), number of leaves (86.91%), L/W ratio (86.49%), leaf width (84.88%) and number of branches (84.69%). High heritability in all the characters under study suggests the genetic control over the variability in character expression and reliability of the above phenotypic characters for selection. It also indicates a very low influence of the environment in the expression of these characters, so these characters should be chosen for improvement of Eucalyptus. Dlamini et al. (2017) found heritability in both height and diameter at breast height was high at family level in a study of 2^nd generation progeny test of Eucalyptus urophylla suggesting that selection of the best families would be possible at an early stage.

3.15.   Genetic advance

In this study, the value of genetic advance (GA) ranges from 0.49 to 459.01. The maximum GA value was recorded in biomass (459.01) followed by number of leaves plant^-1 (125.49), plant height (59.15), leaf area (7.42). The character L/W ratio recorded the lowest (0.49) GA value. The genetic advance should be given priority along with the heritability estimate in the process of selection (Johnson et al., 1955).

3.16.   Genetic advance as % of the mean (GAM)

Expected genetic advance (GA) under selection (5% selection intensity) among the clones was estimated for each character and was expressed as % of the mean. The estimated GA % under selection for characters varied from 7.79% to 71.15%. The biomass recorded the highest (71.15) GA as % of the mean. Besides high genetic advance as % of the mean (>20%) was observed in number of leaves plant^-1 (34.42%), plant height (29.43%), leaf width (23.46%), number of branches plant^-1 (21.44%), leaf area (20.55%) and the lowest (7.79%) was observed in leaf length. Characters with higher genetic advance as % of the mean signify expected higher genetic gain in response to selection at a given selection intensity and vice versa. Silva et al. (2004) also found in Eucalyptus globules that for genetic gains selection for additive genetic merit and clonal testing are equally important.

In this experiment biomass recorded high GCV and genetic advance % of the mean, so this trait is controlled by additive gene action and could be selected through direct selection.

Conclusion

Treatment-1 (Clone-526) had recorded maximum mean value with respect to five important growth traits such as biomass, plant height, collar diameter, number of branches plant^-1, and number of leaves plant^-1. Biomass had high GCV (%) and genetic advance (% of the mean) which under additive gene action and could be selected for the improvement programme.

Acknowledgement

The authors are highly thankful to the Dean, College of Forestry, OUAT, Bhubaneswar for providing a land facility for the trial and also equally thankful to the Director, Institute of Forest Productivity, Ranchi for providing a laboratory facility for both carrying out research and analysis.

Reference

Al-Jibouri, H.A., Miller, P.A., Robinson, H.F., 1958. Genotypic and environmental variances and covariances in an upland cotton cross of interspecific origin. Agronomy Journal 50, 633–636.

Arumugasundaram, S., Ghosh, M., Veerasamy, S., Ramasamy, Y., 2011. Species discrimination, population structure and linkage disequilibrium in Eucalyptus camaldulensis and Eucalyptus tereticornis using SSR markers. Plos One 6(12). https://doi.org/10.1371/journal.pone.0028252

Binkley, D., Campoe, O.C., Alvares, C., Carneiro, R.L., Cegatta, I., Stape, J.L., 2017. The interactions of climate, specing and genetics of clonal Eucalyptus plantations across Brazil and Uruguay. Forest Ecology and Management 405, 271–283.

Dhillon, G.P.S., Singh, A., 2010. Variation in growth traits among progenies of Eucalyptus tereticornisSm under flood plain conditions of Punjab. Indian Journal of Agroforestry 12(1), 91–94.

Dlamini, L.N., Pipatwattanakul, D., Maelin, S., 2017. Growth variation and heritability in a second generation Eucalyptus urophylla progeny test at Lad Krating plantation, Chachoengsao province, Thailand. Agriculture and Natural Resources 51(3), 158–162.

Dhyani, S.K., Handa, A.K., Uma, 2013. Area under agroforestry in India: An assessment for present status and future perspective. Indian Journal of Agroforestry 15(1), 1–11.

FSI, 2015. Indian State of Forest Report. Forest Survey of India, Ministry of Environment, Forest, and Climate Change, Govt. of India. Dehradun.

Ginwal, H.S., 2010. Inbreeding depression in Eucalyptus tereticornis Sm. due to cleistogamous flowering. New Forests 40(2), 205–212.

Gomez, K.A., Gomez, A.A., 1984. Statistical procedures for agricultural research. John Wiley and Sons, USA.

Gomes, F., Correia, C., 1995. Clonal selection and evaluation of genotype x environment interactions of Eucalyptus globulasclones.Silva – Lusitana3,145–162.

Hanson, C.H., Robinson, H.F., Comstock, R.E., 1956. Biometrical studies of yield in segregating populations of Korean lespedeza.Agronomy Journal 48(6), 268–272.

Hardner, C., Dieters, M., DeLacy, I., Neal, J., Fletcher, S., Dale, G., Basford, K., 2011. Identifying deployment zone for Eucalyptuscamadulensis×Eucalyptus globulusand×Eucalyptusgrandis hybrids using factor analytic modelling of genotype by environment interaction. Australian Forestry 74(1), 30–35

Hein, P.R.G., Bouvet, J.M., Mandrou, E., Vigneron, P., Clair, B., Chaix, G., 2012. Age trends of microfibril angle inheritance and their genetic and environmental correlation with growth, density and chemical properties in Eucalyptus europhylla S.T. Blake wood. Annals of Forest Science 69, 681–689.

Huse, S.K., Gunaga, R.P., Sinha, S.K., 2018. Genetic estimates of growth and wood anatomical properties in Eucalyptus clones. International Journal of Genetics 10, 495–497.

Johnson, H.W., Robinson, H.F., Comstock, R.E., 1955. Estimates of genetic and environmental variability in Soybeans. Agronomy Journal 47, 314–318.

Kulkarni, H.D., 2002. Bhadrachalam clones of eucalyptus - An Achievement of ITC, IUFRO Science/Policy Interface Task Force regional meeting. M.S.Swaminathan Research Foundation, Chennai, India, 1-11.

Kulkarni, H.D., 2014. Industrial agroforestry. An ITC initiative. Indian Farming 63(11), 36–53

Kumar, R., Bangarwa, K.S., 2006. Clonal evaluation in Eucalyptus tereticornisSm. Environment and Ecology 24(4), 1188–1191.

Kumar, A.,Dhillon, G.P.S., 2016. Clonal testing of Eucalyptus clones and estimation of their genetic parameters. The Indian Forester 142(2), 127–132.

Kumar, A., Luna, R.K., Praveen, Kumar, V., 2010. Variability in growth characteristics for different genotypes of Eucalyptus tereticornis (Sm.). Journal of Forestry Research 21(4), 487–491.

Lal, P., 2005. Performance of Eucalyptus clones in Punjab. Proc; National Symposium on Exotics in Indian Forestry, Department of Forestry and Natural Resources, PAU, Ludhiana, 15-18.

Luo, J.Z., Arnold, R.J., LU, W.H., Liny, Y., 2014. Genetic variation in Eucalyptus camadulensis and Eucalyptustereticornis for early growth and susceptibility to the gall wasp Leptocybeinvasa in China. Euphytica 196, 397–411.

Marron, N., Villar, M., Dreyer, E., Delay, D., Boudouresque, E., Petit, J.M., Delmotte, F.M., Guehl, J.M., Brignolas, F., 2005. Diversity of leaf traits related to productivity in 31 Populus deltoides×Populus nigra clones. Tree Physiology 25(4), 425–435. 

Midgley, S.J., 2013. Making a difference: celebrating success in Asia. Australian Forestry 76(2), 73–75.

Nichols, J.D., Smith, R.G.B., Grant, J., Glencross, K., 2010. Subtropical Eucalyptus plantations in eastern Australia. Journal of Australian Forest 73, 53–62.

Pima, N.E., Chamshama, S.A.O., Iddi, S., Maguzu, J., 2016. Growth performance of Eucalyptus clones in Tanzania.Environment and Ecology Research 4(3), 146–154.

Ribeiro, S.C., Soares, C.P.B., Fehrmann, L., Jacovine, L.A.G., Gadow, K.V., 2015. Aboveground and belowground biomass and carbon estimates for clonal Eucalyptus trees in southeast Brazil. Revista Arvore 39(2), 353–363.

Saravanan, S., 2020. Growth, biomass production and components variation in Eucalyptus clones. Journal of Tree Sciences 39(1), 17–25

Sepherd, M., Bartle, J., Lee, D.J., Brawner, J., Bush, D., Turnbull, P., Macdonnel, Brown, T.R., Simmons, B.,  Henry, R., 2011. Eucalyptus as biofuel feedstock. Biofuels 2(6), 639–657

Sharda, A.K., Verma, S.K., 2008. Commercial clonal propagation of Eucalyptus through rooting of cuttings-an achievement of forest organization, JK Paper Limited. The Indian Forester 134(5), 703–710.

Silva, J.C., Borralho, N.M.G., Potts, B.M., 2004. Additive and non-additive genetic parameters from clonally replicated and seedling progenies of Eucalyptus globules.Theoretical and Applied Genetics 108, 1113–1119.

Singh, R.K., Chaudhary, B.D., 1985. Biometrical methods in quantitative genetic analysis. Kalyani Publishers, New Delhi.39–229.

Srivastav, A., Tomar, A., Agarwal, Y.K., 2020. Performance of Eucalyptus clones in trans Ganga region of Uttar Pradesh, India. Indian Journal of Agroforestry 22(1), 43–47

Stackpole, D.J., Vaillancourt, R.E., Alves, A., Rodrigues, J., Potts, B.M., 2011. Genetic variation in the chemical components of Eucalyptus globulus wood. G3Genes, Genomes, Genetics 1(2), 151–159. 

Stape, J.L., Binkley, D., Ryan, M.G., Fonesca, S., Loos, R.A., Takahashi, E.N., Silva, C.R., Silva, S.R., Hakamada, R.E., Ferreira, J.M.A., Lima, A.M.N., Gava, J.L., Leite, F.P., Andrade, H.B., Alves, J.M., Silva, G.G.C., Azevedo, M.R., 2010. The Brazil Eucalyptus potential productivity project:influence of water, nutrients and stand uniformity on wood production. Forest Ecology and Management 259, 1684–1694.

Tumbull, J.W., 1999. Eucalyptus plantations. New Forests 17, 37–52.

White, T.L., Adams, W.T., Neale, D.B., 2007. Tree improvement programs: structure, concepts and importance, Forest Genetics. CAB International, Oxfordshire, UK, 465–477.