International Scholarly Research Notices

International Scholarly Research Notices / 2013 / Article

Research Article | Open Access

Volume 2013 |Article ID 346982 | https://doi.org/10.1155/2013/346982

Edward Missanjo, Gift Kamanga-Thole, Vidah Manda, "Estimation of Genetic and Phenotypic Parameters for Growth Traits in a Clonal Seed Orchard of Pinus kesiya in Malawi", International Scholarly Research Notices, vol. 2013, Article ID 346982, 6 pages, 2013. https://doi.org/10.1155/2013/346982

Estimation of Genetic and Phenotypic Parameters for Growth Traits in a Clonal Seed Orchard of Pinus kesiya in Malawi

Academic Editor: J. Kaitera
Received24 Aug 2013
Accepted07 Oct 2013
Published26 Nov 2013

Abstract

Genetic and phenotypic parameters for height, diameter at breast height (dbh), and volume were estimated for Pinus kesiya Royle ex Gordon clonal seed orchard in Malawi using an ASReml program, fitting an individual tree model. The data were from 88 clones assessed at 18, 23, 30, 35, and 40 years of age. Heritability estimates for height, dbh, and volume were moderate to high ranging from 0.19 to 0.54, from 0.14 to 0.53, and from 0.20 to 0.59, respectively, suggesting a strong genetic control of the traits at the individual level, among families, and within families. The genetic and phenotypic correlations between the growth traits were significantly high and ranged from 0.69 to 0.97 and from 0.60 to 0.95, respectively. This suggests the possibility of indirect selection in trait with direct selection in another trait. The predicted genetic gains showed that the optimal rotational age of the Pinus kesiya clonal seed orchard is 30 years; therefore, it is recommended to establish a new Pinus kesiya clonal seed orchard. However, selective harvest of clones with high breeding values in the old seed orchard should be considered so that the best parents in the old orchard can continue to contribute until the new orchard is well established.

1. Introduction

Pinus kesiya Royle ex Gordon occurs naturally in Himalaya region (Asian): Burma, China, India, Laos, Philippines, Thailand, Tibet, and Vietnam [1]. This species particularly grows well at altitudes from 600 to 1800 m above sea level [2]. The trees can reach heights of 30–35 or 45 m tall with straight, cylindrical trunk [3]. Pinus kesiya is a major exotic plantation species in Malawi and other Southern African countries. Its success as an exotic is due to its fast growth rate and wide adaptability. With the increasing demand for wood products globally [4], maximizing wood production on available land resources is of major importance. The high growth rate of Pinus kesiya, the variation evident in natural stands and plantations in Malawi, and the need to improve timber quality and production led to the establishment of a breeding programme in Malawi in the 1970s [5]. The breeding programme included phenotypic mass selection in Pinus kesiya stands and use of the material for seed production in clonal seed orchards.

Seed orchards are plantations created for production of genetically improved seeds to create commercial forest crops [6]. The genetic quality of the seeds depends on the genetic superiority of the plus trees, their relationships, their combination ability, and the rate of pollen contamination, among other factors [7]. The major constraint to the efficient breeding of Pinus kesiya in Malawi has been the lack of genetic parameter information to guide decisions on the most appropriate breeding strategy and, more generally, to monitor genetic progress.

Genetic parameters estimates available for Pinus kesiya are mainly from studies in Brazil. Heritability estimates for diameter at breast height (dbh) and height from these studies were high [1]. There appear to be no estimates of genetic parameters for height and dbh in Pinus kesiya grown in Malawi, which is an issue of concern as fast growing tree crops are likely to exhibit different genetic parameters than slower ones [8]. According to Díaz et al. [9], genetic parameters may differ among regions. This lack of genetic parameter estimates for these economically important traits has potentially adverse consequences for realizing genetic progress in Pinus kesiya in Malawi.

Many models have been proposed for estimation of genetic parameters of quantitative traits in sort of mixed mating system. The model proposed by [10] is the most complete because it considers use of unbalanced data and allows more accurate prediction of genetic values. The optimum estimation/prediction procedure of genetic values is Reml/Blup, that is, the estimation of the components of variance by restricted maximum likelihood (Reml) and the prediction of genetic values by the best linear unbiased prediction (Blup).

The aim of the study was to estimate genetic parameters: variance components, heritability, genetic and phenotypic correlations, and genetic gains for height, dbh, and volume traits for Pinus kesiya clonal seed orchard in Malawi using Reml and prediction of additive genetic and genotypic values by Blup.

2. Materials and Methods

2.1. Study Site

The study was conducted in Malawi located in Southern Africa in the tropical savannah region at Mapale, Dedza (14°21′ S, 34°19′ E, and 1690 m above sea level). Mapale receives from 1200 mm to 1800 mm rainfall per annum, with annual temperature ranging from 7°C to 25°C. It is situated about 85 km southeast of the capital Lilongwe.

2.2. Plant Material and Seed Orchard

The study was carried out with 88 clones of Pinus kesiya seed orchard, which was established in 1972. The clones were selected phenotypically for growth from plantations in Kenya, Malawi, Zambia, and Zimbabwe. The seed orchard was established in a 10 × 10 triple lattice, five trees per plot, and planted following a randomized complete block design in four replications. The trees were planted at a space of 6 × 6 m. At the ages of 18, 23, 30, 35, and 40 years after planting, data were collected for the following traits: total height (distance along the axis of the stem of the tree from the ground to the uppermost point), dbh, and true volume. Total height was measured using a Suunto clinometer with standard, while dbh was measured at 1.3 m above the ground for each standing tree using a calliper. Tree volume was calculated from the tree dbh and height using a tree volume function [5].

2.3. Statistical Analysis

Data obtained were subjected to Kolmogorov-Smirnov D and normal probability plot tests using Statistical Analysis of Systems software version 9.1.3 [11]. This was done in order to check the normality of the data. The characteristics of the data sets for the traits analysed are given in Table 1. Estimation of variance components, heritability, predicted breeding values (EBVs), genetic and phenotypic correlations, and genetic gains was undertaken with the statistical software ASReml [12] using the following individual tree model: where is vector observation; , , , and are the data vectors of fixed effects (block means), of additive genetic effects (random), of plot effects (random common environment effects of the plots), and of the random errors, respectively; , , and are known matrices of incidences, formed by the values 0 and 1 which associate the incognita , , and , respectively, with the data vector . Approximate standard errors of statistics were obtained by Taylor expansion within the ASReml programme.


Age (years)TraitMeanSDCV%Number of trees

18Height (m) 12.91.239.21869
dbh (cm)24.42.018.61869
Volume (m3) 0.2830.0923.51869

23Height (m) 22.51.958.61840
dbh (cm)28.23.267.31840
Volume (m3) 0.6590.1522.41840

30Height (m) 26.41.938.91743
dbh (cm)36.72.417.51743
Volume (m3) 1.2980.3420.21743

35Height (m) 28.31.569.11731
dbh (cm)37.12.538.01731
Volume (m3) 1.4090.6721.81731

40Height (m) 28.51.628.41619
dbh (cm)37.42.497.81619
Volume (m3) 1.4630.6923.41619

SD is the standard deviation; CV is the coefficients of variation.

3. Results and Discussion

The overall means, standard deviation, and coefficient of variation for height, dbh, and volume and the number of trees at each age are shown in Table 1. The coefficient of variation at all ages was relatively low for height and dbh, ranging from 8.4% to 9.2% and from 7.3% to 8.6%, respectively, indicating that reliable estimates can be obtained from the variance analyses. The coefficient of variation for volume was moderate, ranging from 20.2% to 23.5%. Higher CV value for volume is expected, when comparing to height and dbh parameters, as volume is estimated from these two variables, combining the experimental errors of both of them [13].

3.1. Variance Components and Heritability Estimates

Variance components and heritability values are given in Tables 2, 3, and 4. The results indicate that additive variances for all traits in all the three levels (individual, among familie and within families) peaked at 18 years and continued to increase with age up to 23 years and almost remained constant up to 30 years of age and then decreased with age. A similar trend for heritability was also observed. All heritabilities were relatively high, suggesting a strong genetic control of the traits at the individual level, among families, and within families. These results suggest that important genetic progress can be achieved using a simple individual selection in the orchard or a combined selection among and within families. The values observed in this study at the age of 18 years are in agreement with those reported by [1]. However, they are higher than those reported by [14] working with Pinus caribaea hondurensis from Isla de Guanaja, confirming the promising genetic control of the traits as well as the high potential of the population for selection.


Age (years)Trait (s.e) (%)

18Height (h) 17414812690.48 (0.01)13.5
dbh 258453246270.26 (0.01)15.7
Volume 22211350.46 (0.01)18.9

23Height (h) 17912802650.50 (0.02)18.5
dbh 267443116140.28 (0.03)21.1
Volume 23211350.48 (0.04)23.4

30Height (h) 18012802640.50 (0.02)19.7
dbh 275433096100.29 (0.02)22.3
Volume 24211340.51 (0.03)24.5

35Height (h) 11813712650.34 (0.03)13.3
dbh 191363236240.19 (0.02)15.8
Volume 16210330.36 (0.03)17.6

40Height (h) 6510702550.19 (0.02)10.6
dbh 147353206210.15 (0.01)11.2
Volume 9210320.20 (0.03)11.9

: additive genetic variance, : genetic variance among families, : genetic variance within families, : residual variance, : heritability, : genetic gain.

Age (years)Trait (s.e) (%)

18Height (h) 130251743290.51 (0.01)11.2
dbh 200312074380.51 (0.01)12.5
Volume 1928290.59 (0.01)14.8

23Height (h) 135261683200.53 (0.03)14.3
dbh 206322004310.53 (0.04)16.4
Volume 2028280.59 (0.04)17.6

30Height (h) 137271663180.54 (0.05)15.6
dbh 210322004290.53 (0.06)16.8
Volume 2128270.59 (0.04)17.9

35Height (h) 123211853490.45 (0.03)11.9
dbh 195232164440.43 (0.02)12.1
Volume 13215390.47 (0.05)12.6

40Height (h) 114162413850.33 (0.02)10.1
dbh 172192994920.32 (0.04)10.9
Volume 11224420.38 (0.05)11.3

: additive genetic variance, : genetic variance among families, : genetic variance within families, : residual variance, : heritability, : genetic gain.

Age (years)Trait (s.e) (%)

18Height (h) 156121132810.51 (0.01)17.8
dbh 184483856170.22 (0.01)18.5
Volume 21415400.43 (0.01)22.7

23Height (h) 158101022780.53 (0.03)24.6
dbh 196363766140.26 (0.03)26.7
Volume 22414390.46 (0.04)28.9

30Height (h) 15910992760.54 (0.05)24.9
dbh 210323716100.29 (0.04)28.3
Volume 22413380.47 (0.04)29.6

35Height (h) 140171082980.41 (0.04)20.7
dbh 180533806580.19 (0.03)21.4
Volume 20615410.33 (0.03)23.2

40Height (h) 117231133010.31 (0.03)11.2
dbh 175793816810.14 (0.05)11.6
Volume 16716450.20 (0.03)12.5

: additive genetic variance, : genetic variance among families, : genetic variance within families, : residual variance, : heritability, : genetic gain.

This study and that of [15] differ in the age of maximum heritability; the estimates were maximum at the same mean height, suggesting a possible link between mean height and heritability estimate. This is consistent with findings of [16]. The change in heritability in long rotation crops such as trees is not surprising since genes involved in growth may change with age [17], and these changes may be related to different growth phases [18]. In animals, this change in heritability with age was also attributed to the fact that the trait may change genetically with age [19] and is probably related to different growth phases as reported for trees. These growth phases might be due to changing influences of maternal effects in animals and to a lesser extent in trees and to nursery or competition effects in trees. Changes in heritability with age here may also be attributed to management practices.

3.2. Genetic and Phenotypic Correlations

The genetic and phenotypic correlations for height, dbh, and volume are given in Table 5. Age-age genetic and phenotypic correlations between traits were high, ranging from 0.69 to 0.97 and from 0.60 to 0.95, respectively. As the age interval increased, genetic and phenotypic correlations for all traits decreased. Genetic correlations were generally higher than phenotypic correlations. This suggests the possibility of indirect selection in trait with direct selection in another trait. As previously discussed, volume was the indicative trait for selection, but this trait was associated with a high experimental error. Thus, as dbh and volume presented very high genetic and phenotypic correlations (0.97 and 0.95, resp.) and dbh is an easily measurable trait, the selection can be based on this specific trait, resulting in indirect gains in volume. This means that selection of plus trees, aiming to maximise the genetic gains in volume, must be based on the dbh because of the high additive genetic correlation and low standard deviation between dbh trait and volume. These results are in agreement with those in the literature [1315].


Age (years)TraitHeight (h)dbhVolume

18Height (h) 0.68 (0.05)0.74 (0.04)
dbh 0.79 (0.04)0.95 (0.01)
Volume 0.86 (0.03)0.97 (0.01)

23Height (h) 0.67 (0.05)0.72 (0.04)
dbh 0.76 (0.05)0.93 (0.02)
Volume 0.85 (0.04)0.95 (0.01)

30Height (h) 0.65 (0.05)0.69 (0.05)
dbh 0.75 (0.05)0.92 (0.02)
Volume 0.82 (0.03)0.94 (0.01)

35Height (h) 0.62 (0.06)0.68 (0.05)
dbh 0.71 (0.06)0.91 (0.02)
Volume 0.81 (0.04)0.92 (0.02)

40
Height (h) 0.60 (0.05)0.63 (0.07)
dbh 0.69 (0.07)0.90 (0.03)
Volume 0.78 (0.05)0.91 (0.03)

3.3. Genetic Gains

Estimates of genetic gains for selection of 50% of families and 50% of trees within families for height, dbh, and volume for Pinus kesiya at different ages are given in Tables 2, 3, and 4. The genetic gains were high for all studied traits. These results suggest that the growth improvement through individual selection in the orchard or a combined selection among and within families is possible. The results show that the genetic gains increased with an increased age up to the age of 30 years and decreased with an increased age after the age of 30 years for all the traits. This indicates that the rotational age of the Pinus kesiya clonal seed orchard is 30 years of age. According to Andersson et al. [20], Eriksson et al. [21], and Prescher [22], genetic gain is one of the important factors to consider when considering optimal active life span of a seed orchard. Prescher [22] explained that as long as the genetic gain is increasing, the seed orchard can produce genetically better reproductive material. Therefore, this paper recommends an establishment of a new Pinus kesiya clonal seed orchard. However, selective harvest of clones with high breeding values in the old seed orchard should be considered so that the best parents in the old orchard can continue to contribute until the new orchard is well established.

4. Conclusion

The evaluated Pinus kesiya clonal seed orchard presented potential for improvement in view of high heritability estimates for the traits’ height, dbh, and volume. The correlation among growth traits was significantly high and the accuracy of the predicted genotypic values was also of high magnitude, confirming the reliability of the genetic gain estimates. The predicted genetic gains showed that the optimal rotational age of the Pinus kesiya clonal seed orchard is 30 years of age; therefore, it is recommended to establish a new Pinus kesiya clonal seed orchard. However, selective harvest of clones with high breeding values in the old seed orchard should be considered so that the best parents in the old orchard can continue to contribute until the new orchard is well established.

Acknowledgments

The authors thank Mr. Edward Moyo and his colleagues of Forestry Research Institute of Malawi (FRIM) Centre for providing them with data that were used in this study.

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Copyright © 2013 Edward Missanjo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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