ISRN Forestry

Volume 2013 (2013), Article ID 346982, 6 pages

http://dx.doi.org/10.1155/2013/346982

Research Article

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

Edward Missanjo, Gift Kamanga-Thole, and Vidah Manda

Malawi College of Forestry and Wildlife, Private Bag 6, Dedza, Malawi

Received 24 August 2013; Accepted 7 October 2013

Academic Editors: N. Frascaria-Lacoste, J. Kaitera, and Z. Kaya

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.

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.

Table 1: Characteristics of the data sets.

Age (years)
Trait
Mean
SD
CV%
Number of trees

18
Height (m)
12.9
1.23
9.2
1869

dbh (cm)
24.4
2.01
8.6
1869

Volume (m³)
0.283
0.09
23.5
1869

23
Height (m)
22.5
1.95
8.6
1840

dbh (cm)
28.2
3.26
7.3
1840

Volume (m³)
0.659
0.15
22.4
1840

30
Height (m)
26.4
1.93
8.9
1743

dbh (cm)
36.7
2.41
7.5
1743

Volume (m³)
1.298
0.34
20.2
1743

35
Height (m)
28.3
1.56
9.1
1731

dbh (cm)
37.1
2.53
8.0
1731

Volume (m³)
1.409
0.67
21.8
1731

40
Height (m)
28.5
1.62
8.4
1619

dbh (cm)
37.4
2.49
7.8
1619

Volume (m³)
1.463
0.69
23.4
1619

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.

Table 2: Variance components and heritability (standard errors) for individual height, dbh and volume traits at different ages.

Age (years)
Trait

(s.e)
(%)

18
Height (h)
174
14
81
269
0.48 (0.01)
13.5

dbh
258
45
324
627
0.26 (0.01)
15.7

Volume
22
2
11
35
0.46 (0.01)
18.9

23
Height (h)
179
12
80
265
0.50 (0.02)
18.5

dbh
267
44
311
614
0.28 (0.03)
21.1

Volume
23
2
11
35
0.48 (0.04)
23.4

30
Height (h)
180
12
80
264
0.50 (0.02)
19.7

dbh
275
43
309
610
0.29 (0.02)
22.3

Volume
24
2
11
34
0.51 (0.03)
24.5

35
Height (h)
118
13
71
265
0.34 (0.03)
13.3

dbh
191
36
323
624
0.19 (0.02)
15.8

Volume
16
2
10
33
0.36 (0.03)
17.6

40
Height (h)
65
10
70
255
0.19 (0.02)
10.6

dbh
147
35
320
621
0.15 (0.01)
11.2

Volume
9
2
10
32
0.20 (0.03)
11.9

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

Table 3: Variance components and heritability (standard errors) among families for height, dbh and volume traits at different ages.

Age (years)
Trait

(s.e)
(%)

18
Height (h)
130
25
174
329
0.51 (0.01)
11.2

dbh
200
31
207
438
0.51 (0.01)
12.5

Volume
19
2
8
29
0.59 (0.01)
14.8

23
Height (h)
135
26
168
320
0.53 (0.03)
14.3

dbh
206
32
200
431
0.53 (0.04)
16.4

Volume
20
2
8
28
0.59 (0.04)
17.6

30
Height (h)
137
27
166
318
0.54 (0.05)
15.6

dbh
210
32
200
429
0.53 (0.06)
16.8

Volume
21
2
8
27
0.59 (0.04)
17.9

35
Height (h)
123
21
185
349
0.45 (0.03)
11.9

dbh
195
23
216
444
0.43 (0.02)
12.1

Volume
13
2
15
39
0.47 (0.05)
12.6

40
Height (h)
114
16
241
385
0.33 (0.02)
10.1

dbh
172
19
299
492
0.32 (0.04)
10.9

Volume
11
2
24
42
0.38 (0.05)
11.3

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

Table 4: Variance components and heritability (standard errors) within families for height, dbh and volume traits at different ages.

Age (years)
Trait

(s.e)
(%)

18
Height (h)
156
12
113
281
0.51 (0.01)
17.8

dbh
184
48
385
617
0.22 (0.01)
18.5

Volume
21
4
15
40
0.43 (0.01)
22.7

23
Height (h)
158
10
102
278
0.53 (0.03)
24.6

dbh
196
36
376
614
0.26 (0.03)
26.7

Volume
22
4
14
39
0.46 (0.04)
28.9

30
Height (h)
159
10
99
276
0.54 (0.05)
24.9

dbh
210
32
371
610
0.29 (0.04)
28.3

Volume
22
4
13
38
0.47 (0.04)
29.6

35
Height (h)
140
17
108
298
0.41 (0.04)
20.7

dbh
180
53
380
658
0.19 (0.03)
21.4

Volume
20
6
15
41
0.33 (0.03)
23.2

40
Height (h)
117
23
113
301
0.31 (0.03)
11.2

dbh
175
79
381
681
0.14 (0.05)
11.6

Volume
16
7
16
45
0.20 (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 [13–15].

Table 5: Genetic (below diagonal) and phenotypic (above diagonal) correlations and their standard errors in parenthesis for height, dbh and volume traits at different ages.

Age (years)
Trait
Height (h)
dbh
Volume

18
Height (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)

23
Height (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)

30
Height (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)

35
Height (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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