Assessment of three-year DBH and height data and genetic gain prediction for five Acacia mearnsii (black wattle) subpopulations in South Africa using REML/BLUP

Southern Hemisphere Forestry Journal 2007, 69(2): 111–116Printed in South Africa — All rights reserved

Copyright © NISC Pty LtdSOUTHERN HEMISPHERE

FORESTRY JOURNALEISSN 1991–9328

doi: 10.2989/SHFJ.2007.69.2.6.292

Assessment of three-year DBH and height data and genetic gain prediction for five Acacia mearnsii (black wattle) subpopulations

in South Africa using REML/BLUP

SL Beck1*, J de Guisti1, AK Louw1 and MDV Resende2

1 Institute for Commercial Forestry Research, PO Box 100281, Scottsville 3209, South Africa2 Embrapa Florestas, Ministry of Agriculture — National Center for Forestry Research, Caixa Postal 319, CEP 83411-000,

Colombo, PR, Brazil* Corresponding author, e-mail: [email protected]

Introduction

Black wattle (Acacia mearnsii) was historically grown for the

tannins present in the bark, which were primarily used for

tanning leather. As time passed, these vegetable tannins

were replaced with more reliable chemically synthesised

tans. Although there is still a small demand for vegetable

tans from the bark, there is an increasing demand for the

thermosetting industrial adhesives (sold under the brand

name Bondtite) which are used to manufacture a number of

by-products (Dobson and Feely, 2002). In the mid-1980s,

black wattle timber was identified as a source of high-

quality pulp. It was thus decided that the breeding of A.mearnsii (black wattle), within the Tree Improvement

Programme at the Institute for Commercial Forestry

Research (ICFR), Pietermaritzburg, KwaZulu-Natal, change

its emphasis from breeding for improved bark yield and

quality, to improving timber yield and quality while still

maintaining an acceptable bark quality. In 2002, a diversi-

f ied Multiple Population Breeding Strategy was

implemented to cater for these changes (Barnes, 1995;

Dunlop et al., 2003). The main breeding population is held

in six subpopulations, established across different sites in

KwaZulu-Natal, and each subpopulation is determined by

origin of seed. The decision to use subpopulations was

made to keep the untested material separate from the

advanced genetic material. This will prevent dilution of the

genetic gain and ensure that there is sufficient genetic

variation for future advancements (Dunlop et al., 2003). The

differences between the subpopulations will be exploited

through potential crossing between the subpopulations,

when the second generation of subpopulations is estab-

lished. This is the first time that a trial series such as this

has been implemented for black wattle, and for this reason

measurements are being done annually. Age–age correla-

tions can therefore be established and used to see if growth

predictions can be made at an early age, and in this way

increase generation turnover. Restricted Maximum Like-

lihood (REML) and Best Linear Unbiased Prediction (BLUP)

are being used to analyse the data to ensure that superior

individuals are accurately identified.

This paper summarises the three-year growth measure-

ments taken from the first five subpopulations and looks at

the genetic gain that can be made through various selection

strategies in order to provide any indication of predicted

improvement at full rotation.

Recent research has shown Acacia mearnsii (black wattle) to be a source of high-quality pulp. This led to a change in the

emphasis in the breeding programme at the Institute for Commercial Forestry Research, from improving bark yield and

quality, to improving timber yield and quality while maintaining an acceptable bark quality. A Multiple Population Breeding

Strategy was implemented to cater for these changes. Six subpopulations were established across different sites in

KwaZulu-Natal and were determined by origin of seed. Five of these subpopulations were established in 2002 and the sixth

one in 2004. Each subpopulation was established as a progeny trial with a breeding seed orchard adjacent to it. The

management of the seed orchards will be determined according to the performance of the families within the progeny trials.

This paper is a summary of the three-year growth measurements taken from the first five subpopulations. The results

showed promising heritability values with respect to genetic gain. These data were then projected to estimate results at full

rotation using appropriate growth models. Results showed that if one selects the top 20 individuals, mean annual increments

(MAIs) of 13.20–19.60m3 ha–1 y–1 can be expected. If one selects the top 20 families, MAIs of 10.60–19.10m3 ha–1 y–1 can be

expected. Growth data will continue to be collected each year. This will allow for continuous assessment of age–age correla-

tions for the various traits being assessed, and will provide an appropriate decision-making tool for selecting individuals for

future generations.

Keywords: heritability, mean annual increment, Multiple Population Breeding Strategy

Materials and methods

Six subpopulations were established across different sites in

KwaZulu-Natal (Table 1). Each are determined by origin of

seed (Table 1), i.e. from open-pollinated historical and current

selections as well as from controlled crosses. The sixth

subpopulation was established two years later than the other

five subpopulations, due to availability of the seed. Each

subpopulation was established as a progeny trial with a

breeding seed orchard (BSO) adjacent to it. The progeny trial

of Subpopulation 1 is a full-sib progeny trial, whereas the

progeny trials of the other five subpopulations are half-sib

progeny trials. The management of the BSOs will be

determined according to the performance of the families

within the progeny trials. The progeny trials were planted as

five-tree line plots, replicated five times, and the BSOs were

planted as single-tree plots, replicated 25 times. Having two

different trial designs at each site will also allow for the

testing of the impact of each design (specifically the aspect of

plot size) on selection and variance estimation. Each

subpopulation progeny test includes nine genetic checks as

entries. This will allow for any genotype by environment

interaction to be identified across the sites. The genetic

checks are not included in the single-tree plot BSOs, in order

to keep the subpopulations unrelated for future crosses.

Growth measurements were taken annually from both the

progeny test and BSO component of each subpopulation.

Restricted Maximum Likelihood (REML) was used to

determine the variance components and the heritabilities of

the five subpopulations.

In the individual analyses, the linear model applied to the

trial data was:

y = Xb + Za + Wc + e

where y, b, a, c and e are the data vector, block effects

(fixed), additive genetic effects (random), plot effects

(random) and random error effects, respectively.

The narrow-sense individual heritability was calculated

as:

where:

σ 2a = additive genetic variance

σ 2c = variance among plots

σ 2e = residual variance (environmental within plot + non-

additive)

Note that for Subpopulation 1 (full-sib families), the above

equation was amended and the coefficient of relationship

was adjusted in accordance with the increased relatedness

of sibs, i.e. the ‘¼’s were replaced by ‘½’.

Best Linear Unbiased Prediction (BLUP) was used to

determine the breeding values and, hence, the genetic

value of each of the individuals in the five subpopulations,

as well as the predicted genetic gain if selection was

applied. Estimation and prediction by the REML/BLUP

procedure were performed using the computer program

Selegen (Resende, 2002).

To date, three-year diameter at breast height (DBH)

measurements have been taken for the BSO components.

For the progeny trials, three-year DBH measurements and

three-year height measurements have been taken, from

which three-year wood volumes have been estimated using

the following Schumacher and Hall equation, developed by

Schönau (1972):

ln volume = 1.95322(ln DBH) + 1.2315(ln height) –

10.9156

Results from the five subpopulations were compared with

predictions for black wattle on good, average and poor

sites, using models developed by the Mensuration

Modelling Research Co-operative (MMRC) (MMRC, 2005).

Using these same models, 10-year volume predictions

were made for each subpopulation (using entire trial data

and various selection intensities) and once again compared

with predictions made by the MMRC for good, average and

poor black wattle sites. This was done as an exercise to

verify whether the Multiple Population Breeding Strategy

could provide improvement in the future and is by no

means to be used as an accurate prediction.

Beck, de Guisti, Louw and Resende112

Subpopulation Location Longitude Latitude Mean annual Mean annual Altitude (m) Origin of seed

rainfall (mm) temperature (°C)

1 Enon 30°14’E 29°48’S 1 054 15.7 1 360 Advanced generation of

controlled crosses

2 Bloemendal 30°28’E 29°32’S 897 17.9 850 Backward-selected,

open-pollinated parents

3 Liff 30°24’E 29°16’S 1 134 16.1 1 290 Cold, frost-tolerant seed

4 Bloemendal 30°28’E 29°32’S 897 17.9 850 High-production

Australian provenances

5 Mistley 30°39’E 29°11’S 708 18.6 740 Piet Retief land races

6 Baynesfield 30°15’E 29°45’S 950 17.0 945 Top 10% of families in

old BSOs

Note: In addition to the number of families planted in each subpopulation, there were also nine genetic checks or control families planted in

the progeny trial at each site

Table 1: Details of the six subpopulations of Acacia mearnsii and sites chosen for the establishment thereof (amended from Dunlop et al.,2003)

σ

σ σ σ+ +

=

2

2 2 2

2

14

14

a

a c e

h

Southern Hemisphere Forestry Journal 2007, 69(2): 111–116 113

Results and discussion

Three-year growth measurements Subpopulation 3 showed the lowest trait average for all

traits (DBH = 7.23cm; height = 7.00m) (Tables 2 and 3).

This was to be expected, as this subpopulation is selected

for cold and frost tolerance rather than volume. Subpopu-

lations 1, 4 and 5 were the best performers with respect to

trait averages, with Subpopulation 5 displaying particularly

high trait averages (DBH = 10.48cm; height = 13.51m)

(Tables 2 and 3). In general, the trait averages are greater

in the BSO than progeny test components: Subpopulation 1

(DBH): 9.64cm vs 10.90cm; Subpopulation 4 (DBH):

9.13cm vs 10.35cm; Subpopulation 5 (DBH): 10.48cm vs

10.97cm (Table 2).

The results show that significant gains are possible (in

comparison to the trial mean) by selecting either the top 20

individuals, according to breeding values predicted by

BLUP: DBH (progeny): 9.02 to 30.51%; DBH (BSO): 11.00

to 28.13%; height (progeny): 5.85 to 28.43%, or top 20

families, according to genetic values predicted by BLUP:

DBH (progeny): 0.93 to 5.73%; DBH (BSO): 0.21 to 3.97%;

height (progeny): 0.93 to 6.57%, from each of the subpopu-

lations (Tables 2 and 3). In both the BSO and progeny

trials, the greatest gain comes from selecting the top 20

individuals, compared with individuals from the top 20

families, as this increases the selection intensity.

In this study, heritability is a measure of how much of the

observed variation can be attributed to additive genetic

effects. A high heritability indicates a high amount of genetic

variation in the population. In Subpopulation 1, the individ-

ual narrow-sense heritability (h2a) estimates are low — h2

a

Subpopulations

1 2 3 4 5

Progeny Top 20 individuals Trait average (cm) 9.64 8.38 7.23 9.13 10.48

Trait average after selection (cm) 10.51 10.51 8.82 11.92 11.59

Gain (%) 9.02 25.43 21.94 30.51 10.58

Top 20 families Trait average (cm) 9.64 8.38 7.23 9.13 10.48

Trait average after selection (cm) 9.73 8.86 7.44 9.59 10.80

Gain (%) 0.93 5.73 2.90 5.04 3.05

h2a 0.09 0.54 0.50 0.65 0.17

σ 2a 0.57 2.14 0.87 9.13 0.78

BSO Top 20 individuals Trait average (cm) 10.09 10.35 10.97

Trait average after selection (cm) 12.39 13.27 12.18

Gain (%) 13.65 28.13 11.00

Top 20 families Trait average (cm) 10.09 10.35 10.97

Trait average after selection (cm) 11.02 10.38 11.41

Gain (%) 1.06 0.21 3.97

h2a 0.16 0.00 0.00 0.63 0.21

σ 2a 0.97 0.07 0.01 3.26 1.10

h2a is the individual narrow-sense heritability

σ 2a is the additive genetic variance

Note: The percentage genetic gain was calculated as a percentage of the original value of that trait. For example, if the top 20 individuals are

selected in Subpopulation 1, then a 9.02% gain (of 9.64cm) is made

Table 2: The average DBH, the possible percentage gain from selection in DBH and the heritabilities for DBH, for the progeny trials and

BSOs of the first five subpopulations

. Subpopulations

1 2 3 4 5

Progeny Top 20 individuals Trait average (m) 11.82 11.23 7.00 11.45 13.51

Trait average after selection (m) 14.30 12.48 8.99 13.12 14.30

Gain (%) 20.98 11.13 28.43 14.59 5.85

Top 20 families Trait average (m) 11.82 11.23 7.00 11.45 13.51

Trait average after selection (m) 11.93 11.57 7.46 11.86 13.76

Gain (%) 0.93 3.03 6.57 3.58 1.85

h2a 0.13 0.41 0.39 0.50 0.19

σ 2a 0.80 1.21 1.44 1.21 0.53

h2a is the individual narrow-sense heritability

σ 2a is the additive genetic variance

Table 3: The average height, the possible percentage gain from selection for height and the heritabilities for height, of the progeny trials of

the first five subpopulations (height was not measured in the BSOs)


(DBH): 0.09 to 0.16; h2a (height): 0.13) (Tables 2 and 3) —

and consequently selection of the top 20 individuals is not

recommended. This is confirmed by the fact that in both the

BSO and progeny trials, almost all of the top 20 individuals

came from a single family. Thus, for this subpopulation, it is

recommended that a restriction should be applied to the

number of individuals selected per family and that a number

of top individuals across a range of families should be

selected to ensure sufficient genetic gain and variability.

Subpopulation 5 also had relatively low individual heritability

estimates — h 2a (DBH): 0.17 to 0.21; h 2

a (height): 0.19

(Tables 2 and 3) — and it is recommended that this popula-

tion be managed in a similar way to Subpopulation 1.

Subpopulation 4 had very good trait averages and

showed the highest heritabilities and, hence, the greatest

potential for improvement — h2a (DBH): 0.63 to 0.65; h2

a

(height): 0.49 (Tables 2 and 3). Subpopulations 2 and 3

showed promising individual heritability estimates in the

progeny trial — h2a (DBH): 0.54 and 0.50 respectively; h2

a

(height): 0.41 and 0.39 respectively, but there was no

genetic variation detected in the BSO components of these

subpopulations, as evidenced by the near-zero heritability

values (Tables 2 and 3). The reason for these low heritabili-

ties is unclear at present; however, all selections will be

made using the progeny trial for each subpopulation.

ImprovementsThe three-year data from the five subpopulations were

compared against the base population, control Family 86

(unimproved Natal Tanning Extracts Ltd (NTE) material), as

well as with MMRC predicted three-year data across three

black wattle growing sites (good, average and poor)

(MMRC, 2005), to determine whether there was any

improvement. Family 86 performed below the trial mean for

DBH and height across all sites except Subpopulation 3

(Table 4). Furthermore, all the subpopulations (except

Subpopulation 3, which is aimed more at selecting trees for

cold and frost tolerance rather than volume) performed well

above the good site quality predictions from the MMRC

models (average three-year DBH for subpopulations:

7.29–10.43cm; average three-year height for subpopula-

tions: 7.11–13.56m; MMRC three-year DBH ranges from

6.60cm to 7.60cm; MMRC three-year height ranges from

7.10m to 9.70m) (Table 5). Another interesting comparison

can be made, as the MMRC model for Site Quality 2 (SQ2)

is based on data taken from the same areas as that of

Subpopulation 5, and similarly for SQ3 and Subpopulations

2 and 4. In this comparison, the impact of site or environ-

mental effects is removed, and in all cases the subpopula-

tion data outperform the predictions for the average (SQ2)

and poor (SQ3) site qualities. After three years the material

in the subpopulations is performing well and genetic gains

are real, relative to the MMRC predictions.

Future improvementsA further investigation was conducted to see whether these

genetic gains will provide any improvement in volume at full

rotation. MMRC models which had been recently updated

to accommodate data from young material were applied.

The comparisons were applied to the progeny trial data

only, as three-year height measurements were not

measured in the BSOs and hence volumes could not be

predicted. At the predicted full rotation, using entire trial

Family Subpopulations

1 2 3 4 5

DBH Height DBH Height DBH Height DBH Height DBH Height

(cm) (m) (cm) (m) (cm) (m) (cm) (m) (cm) (m)

83 (natural regeneration) 9.26 12.13 9.24 11.99 7.92 7.87 8.81 11.49 10.35 13.74



86 (NTE: completely unimproved seed) 8.44 10.89 7.09 10.36 7.83 7.52 8.11 11.05 9.74 12.96

87 (PSO 5: improved material) 8.42 11.19 8.65 11.26 7.92 7.12 8.89 11.81 10.94 14.45

88 (controlled crosses) 10.89 12.96 9.18 11.54 7.76 7.62 9.85 12.17 11.00 13.79




Trial mean 9.64 11.82 8.38 11.23 7.23 7.00 9.13 11.45 10.48 13.51

Trial mean after selection of top 20 individuals 10.51 14.30 10.51 12.48 8.82 8.99 11.92 13.12 11.59 14.30

Trial mean after selection of top 20 families 9.73 11.93 8.86 11.57 7.44 7.46 9.59 11.86 10.80 13.76

Note: for DBH (p = 0.05, LSD = 1.08); height (p = 0.05, LSD = 0.98)

Table 4: Mean DBH (cm) and height (m) of control families across the five subpopulations and the trial mean for DBH at each subpopulation

Subpopulations Progeny trial average at three years

Av. DBH (cm) Av. height (m) Stem ha–1

1 9.64 11.85 1 331

2 (SQ3) 8.38 11.47 1 411

3 7.23 7.11 1 161

4 (SQ3) 9.13 11.56 1 475

5 (SQ2) 10.48 13.56 1 221

SQ1 (good) 7.60 9.70 1 391

SQ2 (average) 7.20 8.50 1 391

SQ3 (poor) 6.60 7.10 1 391

Table 5: A comparison of three-year growth measurements (height

and DBH) from the five subpopulations and three typical sites —

good (SQ1), average (SQ2) and poor (SQ3), as predicted by the

MMRC — for growing Acacia mearnsii

Southern Hemisphere Forestry Journal 2007, 69(2): 111–116 115

data (i.e. no selection applied), MAIs of 10.40–15.80m3 ha–1

y–1 or 9.10–13.90t ha–1 y–1 can be expected (Table 6). If one

selects the top 20 individuals (based on BLUP’s predicted

genetic worth), MAIs of 13.20–19.60m3 ha–1 y–1 or 11.60–

16.00t ha–1 y–1 can be expected. If one selects the top 20

families (based on predicted genetic worth as estimated by

BLUP), MAIs of 10.60–19.10m3 ha–1 y–1 or 9.30–16.80t ha–1

y–1 can be expected. These predictions are, in most cases

(except for Subpopulation 3), all well above those predicted

from MMRC models for a good site (13.00m3 ha–1 y–1 1 or

11.40t ha–1 y–1). It is important to note that these predictions

were made using data from young material, and that this

exercise was done only to determine whether there was

any indication that the Multiple Population Breeding Stra-

tegy can provide the industry with significant long-term

gains in volume.

Results show that the potential for genetic gain in the

future is real, which is encouraging for the black wattle

breeding programme at the ICFR and the black wattle

industry.

Site effectsWhen comparing the genetic controls in the progeny trials

within each subpopulation, Subpopulation 3 generally

produced significantly lower values (Table 4), this being due

to the fact that it was planted on a temperate site. It must

be noted that Subpopulations 1 (Enon) and 5 (Mistley) were

established on better-quality sites than Subpopulations 2

and 4 (both at Bloemendal) and Subpopulation 3 (Liff). In

addition, Subpopulation 5 showed significantly higher trait

averages than the other four subpopulations; however,

Subpopulation 1 was not signif icantly greater than

Subpopulations 2 and 4 (the Bloemendal sites) (Table 4).

When comparing controls across the five subpopulations,

it can be seen that Families 88 (a controlled cross from the

existing breeding programme), 85 (seed collected from a

natural regenerated stand) and 90 (advanced-generation

controlled cross) were consistently the best-performing

families. Family 85 performed very poorly (in comparison to

some of the other control families) in Subpopulations 2 and

4 at Bloemendal, particularly with respect to DBH (Table 4).

This family, however, was among the top-performing

families in Subpopulations 1 and 5 (the good-quality sites at

Enon and Mistley, respectively). This indicates a degree of

genotype by environment interaction, as it appears that

Family 85 outperformed the other control families on good-

quality sites and was outperformed by most of the other

control families on poor-quality sites. Control Family 90

performed favourably on the two Bloemendal sites

(medium- to poor-quality sites), but poorly (in comparison to

the other control families) on the worst site at Liff (Sub-

population 3, the temperate site) and on the best site at

Mistley (Subpopulation 5), indicating that this family per-

forms optimally on medium-quality sites (Table 4). Family

86 (unimproved material from NTE) performed consistently

poorly across all the sites, as expected; however, this family

was not always significantly poorer than other controls.

Although Family 87 (material from Production Seed

Orchard 5, which currently supplies the industry with

improved seed), performed on average across all sites, it

did not perform much better than Family 86.

Family 86 (unimproved NTE material) performed below

the trial mean for DBH and height across all sites, except

for Subpopulation 3. Family 87 (PSO 5: improved material)

performed above the trial mean for DBH and height at all

sites, except at Sites 1 and 4, where the trial means ranked

higher for DBH. The trial mean, in comparison to the

controls, improved considerably after selection of the top 20

individuals.

Only in Subpopulations 1 (DBH and height) and 4 (DBH)

did the trial mean rank higher than Family 87 (PSO 5:

improved material).

The controls used in this study were from different

genetic compositions, i.e. not all open-pollinated families,

whereas Burdon (1977) noted that for an accurate indica-

tion of genotype-by-environment interaction (GEI), the

common families should have the same genetic make-up

and this is why GEI was not discussed here.

Conclusion

This is the first time that a series of trials of this nature has

been planted as part of a breeding strategy for black wattle

in South Africa. The results to date show potential for signif-

icant genetic gain in this species. The three-year results

showed promising heritability values and prospective

genetic gain, particularly from many of the BSOs. It is

proposed that in future height as well as DBH be measured

Subpopulation Progeny trial 10-year volume predictions

Entire trial Top 20 individuals Top 20 families

m3 ha–1 t ha–1 m3 ha–1 t ha–1 m3 ha–1 t ha–1

1 122.30 107.50 269.00 236.50 162.20 142.60

2 (SQ3) 133.40 117.30 255.60 224.70 143.60 126.20

3 104.00 91.40 132.10 116.10 106.70 93.80

4 (SQ3) 158.10 139.00 290.10 255.00 180.10 158.30

5 (SQ2) 151.20 132.90 296.30 260.40 191.20 168.10

SQ1 (good) 130.00 114.30

SQ2 (average) 100.00 87.90

SQ3 (poor) 70.00 61.50

Table 6: A comparison between predicted 10-year volume of the five subpopulations and actual 10-year volume from three Acacia mearnsiisites. The three Acacia mearnsii sites are SQ1 (good-quality site), SQ2 (average-quality site) and SQ3 (poor-quality site)


in the BSOs, as this will allow one to compare if more

accurate estimates, for families and individuals, can be

gained from the BSOs rather than the progeny trials.

Subsequently, the effect of trial design, specifically plot size,

appears to be noteworthy and these differences will be

monitored with future annual measurements, with the aim of

making more precise selections of superior individuals.

Annual measurements of these trials allow for any changes

in heritabilities and rankings of individuals to be taken into

account, as well as any potential effect of genotype by

environmental interaction to be considered prior to selecting

individuals and families for advanced generations.

The authors believe that this strategy and the analytical

techniques employed will provide the best method of identi-

fying top individuals and families to be used in future

nucleus breeding populations. Ultimately, this strategy will

benefit the growers who choose to use the seed coming out

of this breeding programme.

Acknowledgements — The authors would like to thank all the ICFR

staff involved in the measuring of the trials as well as Sappi,

MondiBP, NCT, Graeme Pope-Ellis and their foresters for their co-

operation and maintenance of these trials.

References

Barnes RD (1995) The breeding seedling orchard in the multiple

population breeding strategy. Silvae Genetica 44(2&3): 81–88

Burdon RD (1977) Genetic correlation as a concept for studying

genotype-environment interaction in forest tree breeding. SilvaeGenetica 26(5&6): 168–175

Dobson D and Feely J (2002) The wattle forest products industry.

In: Dunlop RW and MacLennan LA (eds) Black wattle: The SouthAfrican research experience. Institute for Commercial Forestry

Research, Pietermaritzburg. pp 3–9

Dunlop RW, Barnes RD, Nixon KM and Hagedorn SF (2003) A

breeding strategy for Acacia mearnsii (black wattle) in South

Africa. Southern African Forestry Journal 199: 55–63

MMRC (2005) Acacia mearnsii modelling workshop, October 2005

(interim unpublished report). Mensuration and Modelling

Research Consortium Technical Group, Institute of Commercial

Forestry Research, Pietermaritzburg

Resende MDV de (2002) Software Selegen-Reml/Blup. Embrapa

Florestas, Colombo. 67pp

Schönau APG (1972) Metric timber volume, percentage ultilisation

and stump and kerf wastage tables for black wattle, Acaciamearnsii. In: Wattle Research Institute Report for 1971–1972.

Wattle Research Institute, Pietermaritzburg. pp 39–47

Documents

Assessment of three-year DBH and height data and genetic gain prediction for five Acacia mearnsii (black wattle) subpopulations in South Africa using REML/BLUP