Early Realized Genetic Gains for
Coastal Douglas-Fir in the
Northern Oregon Cascades
J. Bradley St. Clair and Nancy L. Mandel, USDA Forest Service, Pacific Northwest
Research Station, 3200 SW Jefferson Way, Corvallis, OR 97331-4401; and
Keith J.S. Jayawickrama, Oregon State University, Department of Forest Science, 321
Richardson Hall, Corvallis, OR 97331-5752.
ABSTRACT: Block-plot realized genetic gain trials were established for coastal Douglas-fir (Pseudotsuga
menziesii var. menziesii) at five sites in the northern Oregon Cascades. The long-term objectives of these
trials are to explore the growth trajectories and productivity of genetically improved stands and their
relationship to predicted genetic gains based on performance in progeny tests. Measurements 5 years after
planting provide an opportunity for an early assessment of realized genetic gains as compared to predicted
gains and provide data for determining the number of replicates needed to detect statistically significant
differences between improved and unimproved populations using large block plots. Results indicate that
progress from selection and breeding of Douglas-fir is readily achievable, and realized genetic gains 5
years after planting are similar to those predicted based on results from progeny tests. Realized genetic
gains were about 6% for height, 8% for diameter, and 28% for stem volume, compared to predicted genetic
gains of about 8% for height, 7% for diameter, and 25% for stem volume. Large numbers of replicates
(30 –50) are required to detect statistically significant differences in height and diameter between improved
and unimproved populations given genetic gains expected in a typical tree improvement program. West. J.
Appl. For. 19(3):195–201.
Key Words: Tree improvement, realized genetic gain, Douglas-fir, sample size.
T
ree improvement has become a common part of the
management of forest lands for wood production, and pre­
dicting the productivity and realized gains from forests
planted with genetically improved trees will become in­
creasingly important. Douglas-fir (Pseudotsuga menziesii)
tree improvement programs began in earnest in the Pacific
Northwest in the 1960s, and seed orchards are now produc­
ing large amounts of improved seed (Silen and Wheat 1979,
Adams et al. 1990, Woods 1993). Companies and govern­
ment agencies have invested millions of dollars on the
genetic improvement of Douglas-fir, with over 4 million
progeny from about 33,000 parent trees having been tested
on more than 1,000 sites (Lipow et. al. 2003). As second-
NOTE:
Brad St. Clair may be reached at (541) 750-7294; Fax: (541)
750-7329; bstclair@fs.fed.us. This article was written by U.S.
Government employees and therefore is in the public domain.
The Northwest Tree Improvement Cooperative provided finan­
cial and technical support for this project. Special thanks go to
Mike Bordelon of the Oregon Department of Forestry for
initiating and helping design the study.
generation breeding gets underway in the Pacific North­
west, fundamental questions about the growth of genetically
improved stands and the interaction of genetics and silvi­
cultural practices remain unanswered. In particular, it has
not yet been shown how predicted genetic gains based on
growth differences measured on individual trees in young
progeny tests translate to gains in stand yield at rotation age.
As a result of these concerns, the Northwest Tree Im­
provement Cooperative (NWTIC) and the USDA Forest
Service Pacific Northwest Research Station (PNWRS) ini­
tiated a study to explore the growth trajectories and produc­
tivity of genetically improved Douglas-fir stands and their
relation to predicted genetic gains as estimated from prog­
eny test data. This is a long-term study designed to answer
questions of genetic differences in stand-level growth and
productivity as a function of increasing competition and
stand development. Results will be important for develop­
ing growth models that account for genetic improvement.
Early measurements of this study provide some insight into
the means and variability of realized genetic gains before
appreciable competition has occurred. The objectives of this
WJAF 19(3) 2004
195
article are to: (1) compare early realized gains under oper­
ational conditions with gains predicted from breeding val­
ues estimated from progeny tests; and (2) determine the
number of replicates needed to find statistically significant
differences between blocks of improved and unimproved
trees. Results from this assessment are important for quan­
tifying early growth and establishment of genetically im­
proved stands and for guiding the experimental design of
additional realized gain trials and other silvicultural trials.
Given the increasing interest in large-plot trials to quantify
gains from silvicultural treatments and the high cost of
establishing such trials, it is important to provide some
indications of how many replicates are needed to show
statistically significant differences of a given magnitude.
Materials and Methods
Parents for the realized genetic gain trials came from the
Molalla breeding zone located in the Cascade foothills
southeast of Portland, OR. The breeding zone encompasses
an area of approximately 70 km north-south by 35 km
east-west and a range of elevations between 275 and 850 m.
The first-generation tree improvement program tested the
wind-pollinated progeny of 372 parents on eight test sites
representative of the breeding zone. Parents used for the
improved populations in the realized genetic gain trials were
selected based on the 15-year-old stem volume index (de­
fined as height X diameter2) of offspring planted at eight
progeny tests (as well as a few other criteria such as the
availability of pollen and female strobili). Breeding values
of parents were calculated by using best linear prediction
procedures as described by White and Hodge (1989). For
comparison of predicted genetic gains to realized genetic
gains, parental breeding values were determined for height,
diameter at breast height (dbh), and stem volume at age 7,
5 years after planting, based on data from six of the eight
progeny test sites (two sites did not have data at age 7). The
predicted genetic gain for a cross was calculated as the
average breeding values of the two parents. Although this
procedure does not allow for specific combining effects, we
assumed that these effects average to zero given enough
parents in an improved population. Assuming that the six
progeny test sites are representative of the breeding zone,
the predicted percentage genetic gain is the mean breeding
value of all crosses relative to the overall mean of the six
sites. The proportion of trees of each cross in the realized
genetic gain trials, however, was unequal. Thus, to compare
predicted and realized genetic gains, the genetic gains pre­
dicted in the realized gain trials were weighted by the
proportion of live trees in each cross.
Two improved populations were chosen to provide mul­
tiple levels of genetic gain for comparing realized and
predicted gains as a function of levels of improvement. An
elite population was created using single pair matings of 20
top parents, and an intermediate population was created
using single pair matings of 20 parents of somewhat lower
ranking than the top parents. The goal for the intermediate
population was to aim for a genetic gain in stem volume of
196
WJAF 19(3) 2004
about half that of the elite population. The unimproved
population was a random selection of 50 trees collected
specifically for this study from naturally regenerated stands
well distributed throughout the breeding zone. The unim­
proved population is assumed to represent both the average
genetic quality of trees that would be used for reforestation
in the absence of tree improvement and the original base
populations from which selections were made. These are
expected to be equal because parent trees in the base pop­
ulation were roadside selections chosen with little emphasis
on growth superiority.
The realized genetic gain trials were planted in 1997 at
five sites within the breeding zone. The five sites are rep­
resentative of environments from which parents were se­
lected and in which improved trees are being planted. Ele­
vations ranged from 400 m at the Mill City and Colton sites
to 760 m at the Silver Falls site. All sites were clearcut
harvested during the year before test establishment. Com­
peting vegetation was operationally treated with herbicide
prior to planting and as needed during the years following
planting.
At each site, the three genetic quality types (elite, inter­
mediate, and unimproved) were planted at each of two
densities. In the low-density treatment, 772 trees/ha were
planted at a spacing of 3.6 X 3.6 m. The low-density
treatment was chosen to represent operational conditions
without early thinning. In the high-density treatment, 3086
trees/ha were planted at a spacing of 1.8 X 1.8 m. The
high-density treatment was chosen to allow consideration of
density effects and the interaction with genetics, as well as
to provide some early results by promoting stand competi­
tion effects at an earlier age. The experimental design at
each site was a split plot design with density treatments
occupying the whole plots, and genetic quality types occu­
pying the split plots. Each split-plot had 100 trees arranged
in a 10 X 10 tree square. An additional outside row of trees
of the same genetic quality type was included as a buffer
between split-plots, and an additional buffer row was in­
cluded between different planting densities and around the
outside of the study area. Seedlings from each family were
randomly assigned to planting spots, genetic quality types to
split plots, and densities to whole plots. Six replications
were planted at each site. Three-year-old seedlings were
planted in Feb. and Mar. 1997. The seedlings were grown
for 1 year in containers and 2 years as transplants in a bare
root nursery. Total height and dbh were measured in fall
2001 after the trees had completed five growing seasons.
Stem volume index was calculated as height X dbh2. Per­
centage of survival was determined for each split-plot.
Statistical analyses were performed using SAS and the
general linear models (GLM) procedure (SAS Institute Inc.
1999). The linear model was:
y ijkl = J + Li + Ri(j) + Sk + LSik + 8ijk
+ Gl + LGil + SGkl + LSGikl + Eijkl
where Li is the effect of the ith test site location, Rij is the
effect of the jth replication within the ith location, Sk is the
effect of the kth spacing treatment, Gl is the effect of the lth
genetic population treatment, 8ijk is the whole-plot error,
and Eijkl is the split-plot error. The other terms are the
corresponding interaction terms. The effects of genetic pop­
ulations and spacings were regarded as fixed, whereas the
effect of test site locations was regarded as random. The
error terms for testing main effects and interactions are
given in Table 1. We also tested two nonorthogonal con­
trasts between specific genetic populations: (1) the elite
population versus the unimproved population; and (2) the
intermediate population versus the unimproved population.
Mean squares for some of the interaction sources of varia­
tion may be pooled into the split-plot error when those
interactions are clearly nonsignificant (e.g., P > 0.25)
(Sokal and Rolf 2001). When this is done, more degrees of
freedom are available for testing the differences between
genetic populations, resulting in smaller P values and a
greater likelihood that differences among genetic popula­
tions are real (e.g., the P value for dbh decreases from 0.053
to 0.016 for differences among the three genetic popula­
tions). To simplify presentation of results, we chose to
present all P values for mean squares not pooled; the inter­
pretation of the results does not change—tests significant at
P < 0.10 after pooling were also significant prior to pool­
ing. We also considered differences between genetic popu­
lations at each site. The linear model in that case is as above
but without the main effect and interaction terms that in­
clude test site locations.
We determined the number of replicates required to
detect statistically significant differences in height and di­
ameter between improved and unimproved populations us­
ing procedures outlined in Odeh and Fox (1991). Because
we are primarily interested in comparing the improved
versus unimproved, we redid the analyses of variance ex­
cluding the intermediate treatment to estimate variance
components. The genetic population X site variance com­
ponent was near zero (F = 1.00, P = 0.41 for height; F =
1.09, P = 0.37 for diameter); thus, it was pooled into the
residual sums of squares, and differences between the two
populations were tested against the pooled residual mean
square. The pooled residual mean square was used to esti­
mate the sample sizes required to detect a range of differ­
ences between improved and unimproved populations with
a power of 0.80 and a = 0.01, 0.05, and 0.10. The results
are presented in graphical form as the number of replicates
required to detect a significant percentage difference be­
tween improved and unimproved populations for a = 0.01,
0.05, and 0.10.
Results
Overall Analyses of Variance
Differences among the three genetic populations were
statistically significant at a level of P < 0.10 (Table 1); trees
in the elite population were larger than those in the inter­
mediate population, which were larger than those in the
unimproved population (Tables 2– 4). Differences among
test sites were large and highly statistically significant (Ta­
ble 1), with differences in height and diameter of about
100% between the fastest and slowest growing sites, and
differences in volume of about 600% (Tables 2– 4). Trees
Table 1. Results from the analyses of variance for differences in height, dbh, and stem volume index for test sites,
spacings, genetic populations, and their interactions.
Source of variation
Error term for F test
Degrees
of
freedomb
Test site location (L)
Spacing (S)
Genetic population (G)
SXL
GXS
GXL
GXSXL
Replicates within locations (R)
SXL
GXL
S X R (whole plot error)
GXSXL
G X R (split plot error)
G X R (split plot error)
4, 24
1, 4
2, 8
4, 24
2, 8
8, 96
8, 96
a
b
a
Height
dbh
Volume index
F
P
F
P
F
P
67.62
8.00
4.67
1.99
2.34
1.51
1.21
<0.0001
0.05
0.05
0.13
0.16
0.16
0.30
52.41
2.52
4.35
2.24
0.95
1.06
0.95
<0.0001
0.19
0.05
0.09
0.43
0.40
0.48
30.93
1.23
3.69
4.12
1.40
1.56
0.70
<0.0001
0.33
0.07
0.01
0.30
0.15
0.69
Effects of genetic populations and spacings were regarded as fixed, and the effect of test site locations was regarded as random.
Degrees of freedom in numerator and denominator for F test.
Table 2. Least square means for height of coastal Douglas-fir 5 years after planting in the northern
Oregon Cascades.
Within sites
Across populations (cm)
Elite population (cm)
Intermediate population (cm)
Unimproved population (cm)
Percentage difference of elite
vs. unimproved
P-value for difference of
elite vs. unimproved
Across
sites
Colton
Estacada
Mill City
Molalla
Silver
Falls
182
191
184
180
5.9
203
209
206
194
7.8
181
187
171
185
1.3
262
270
262
253
6.6
137
138
139
133
4.1
143
150
144
136
10.3
0.02
0.01
0.78
0.08
0.25
0.01
WJAF 19(3) 2004
197
Table 3. Least square means for dbh for coastal Douglas-fir 5 years after planting in the northern
Oregon Cascades.
Within sites
Across populations (cm)
Elite population (cm)
Intermediate population (cm)
Unimproved population (cm)
Percentage difference of elite
vs. unimproved
P-value for difference of
elite vs. unimproved
Across
sites
Colton
Estacada
Mill City
Molalla
Silver
Falls
1.35
1.45
1.36
1.34
7.9
1.57
1.63
1.59
1.47
11.2
1.39
1.47
1.29
1.41
4.5
2.20
2.33
2.15
2.10
10.8
0.93
0.94
0.94
0.90
3.7
0.83
0.85
0.82
0.81
4.9
0.02
0.02
0.54
0.11
0.54
0.43
Table 4. Least square means for stem volume index (height x dbh2) for coastal Douglas-fir 5 years
after planting in the northern Oregon Cascades.
Within sites
Across populations (cm3)
Elite population (cm3)
Intermediate population (cm3)
Unimproved population (cm3)
Percentage difference of elite
vs. unimproved
P-value for difference of elite
vs. unimproved
Across
sites
Colton
Estacada
Mill City
Molalla
Silver
Falls
655
791
659
619
27.8
789
869
810
689
26.1
558
651
462
561
15.9
1694
1987
1619
1475
34.8
238
262
240
212
23.8
170
186
167
157
18.5
0.03
0.03
grown at the tight spacing were 6% taller and 6% larger in
diameter than trees grown at the wide spacing, although
only height was statistically significant (Table 1). Most
interactions were not statistically significant, except for the
interaction of spacing and test site for both volume (P =
0.01) and diameter (P = 0.09).
Percentage of survival was greater for the elite popula­
tion (91%) and the intermediate population (92%) compared
to the unimproved population (86%) (statistically signifi­
cant at P < 0.05). Survival varied among sites. Survival at
Molalla was the lowest at 83%. Survival at the other four
sites ranged from 90 to 93%.
Realized and Predicted Genetic Gains
Realized genetic gains for the elite population (the differ­
ence between elite and unimproved) were 5.9% for height,
7.9% for diameter, and 27.8% for stem volume (Tables 2– 4).
Realized gains for the intermediate population were 2.4% for
height, 1.5% for diameter, and 6.5% for volume. Realized and
predicted genetic gains were similar, particularly for the elite
population (Figure 1). For example, predicted gains for height
after five growing seasons for the elite crosses was 7.5% as
compared to actual realized gains of 5.9%. Realized genetic
gains varied greatly among individual test sites (Tables 2– 4).
Height differences between the elite and unimproved popula­
tions were greatest at the Silver Falls, Colton, and Mill City
sites. Diameter differences were greatest at the Colton and Mill
City sites. The Estacada site was the poorest site for distin­
guishing differences between the elite and unimproved popu­
lations. Compared to the analyses across all sites, detecting
statistically significant differences was more difficult at each
198
WJAF 19(3) 2004
0.41
0.07
0.24
0.34
individual site; the lower sample size probably resulted in a
poorer estimate of the means, and the lower number of degrees
of freedom in the error (20 df) made it more difficult to declare
differences significant.
Sample Size Required to Detect Significant Differences
The sample sizes required to detect a significant differ­
ence between improved and unimproved populations is
quite sensitive to the desired a level and to the expected
difference when those differences are relatively small (Fig­
ures 2 and 3). For example, the number of replicates re­
quired to detect a significant difference of 6% between the
heights of improved and unimproved populations (as ob­
served in this study) at a = 0.05 was 33, whereas the
number of replicates required to detect the same difference
was 26 at a = 0.10 and 52 at a = 0.01 (Figure 2). If height
differences were just 1% greater (7 instead of 6%), 25
replicates would be required instead of 33 at a = 0.05.
Larger numbers of replicates were required to detect signif­
icant differences in diameter and volume (which is largely a
function of diameter) of the magnitudes observed in this
study. At a = 0.05, 52 replicates were required to detect a
significant difference of 8% in diameter, and 46 replicates
were required to detect a significant difference of 28% in
volume.
Discussion
Results from this study indicate that progress from se­
lection and breeding of Douglas-fir is readily achievable.
Realized genetic gains 5 years after planting were close to
those predicted based on results from earlier progeny tests at
Figure 2. Number of 100-tree block-plot replicates required to
detect a statistically significant difference (percentage) in
height 5 years after planting between improved and unim­
proved populations at a = 0.01, 0.05, and 0.10.
Figure 1. Realized and predicted genetic gains (percentage)
for height, diameter, and stem volume for two genetically im­
proved populations of coastal Douglas-fir 5 years after planting
in the northern Oregon Cascades.
six other sites within the breeding zone. Although blockplot genetic gain trials are rare due to the expense of
establishing such large trials, realized genetic gains have
been demonstrated for several species, including radiata
pine (Pinus radiata) (Eldridge 1982, Carson et al. 1999),
loblolly pine (P. taeda) (Martin and Shiver 2002), and
ponderosa pine (P. ponderosa) (McDonald et al. 1999). In a
similar set of trials in British Columbia, Woods et al. (1995)
found that genetically improved Douglas-fir trees were sig­
nificantly taller 3 years after planting compared to an un­
improved wild stand collection. Most realized gain trials are
still young: only the radiata pine trials are beyond
half-rotation.
To extrapolate results from realized genetic gain trials to
other seed lots, the genetic value of the parents must be
known. In most cases, the genetic value of the parents
comprising a seed lot will be estimated as their breeding
values based on progeny tests. For this reason, comparing
Figure 3. Number of 100-tree block-plot replicates required to
detect a statistically significant difference (percentage) in dbh 5
years after planting between improved and unimproved popu­
lations at a = 0.01, 0.05, and 0.10.
realized genetic gains and predicted genetic gains from
progeny tests is important. In New Zealand, a system for
indicating genetic value has been devised in which a seed
lot is given a “GF rating” that combines information on the
growth and form characteristics of parents as determined in
progeny tests. Carson et al. (1999) have proposed using
genetic-gain multipliers to adjust existing radiata pine
growth models to predict the growth of improved seed lots
having different GF ratings. In British Columbia, a “genetic
worth” is assigned to seed orchard seed lots that represents
the expected genetic gain for a trait at rotation age (Xie and
Yanchuk 2003). It uses the average breeding values of the
parental trees and incorporates decreased genetic gains be­
tween rotation and selection ages due to imperfect age-age
genetic correlations. Our results provide confidence that
genetic gains predicted from progeny tests may be used to
predict realized genetic gains in operational plantations, at
least through early stand development.
WJAF 19(3) 2004
199
Realized genetic gains in tests or in operational planta­
tions may differ from predicted genetic gains for several
reasons. Realized and predicted gains may differ simply
because poor experimental design could lead to poor pa­
rameter estimates. Comparing parental means in the first
generation of the Molalla program was complicated by a
design in which parents were allocated to different sets, but
sets were not included within the same replication. Realized
and predicted gains may differ as a result of sampling
effects or random error. Environmental variation within a
site may lead to poor estimates of genetic gains. For this
reason, researchers arrange plots in replicated blocks and
attempt to minimize environmental variation within a block.
Realized gain trials that use large plots and mimic opera­
tional conditions often have greater error variances as a
result of increased variation within replications, increased
browse, and less stringent control of competing vegetation.
In our study, sites with considerable animal damage (Mo­
lalla, Silver Falls) or with patchy competing vegetation,
such as scotch broom (Cytisus scoparius) (Estacada), had
the highest coefficients of variation for the split-plot error.
Environmental variation among sites may also lead to poor
estimates of genetic gains and poor correspondence between
realized and predicted gains if sites are not representative of
the breeding and planting zones. Furthermore, genotype X
environment interaction may contribute to differences in
genetic gains estimated from progeny tests and realized gain
trials. Genotype X environment interaction may involve
differences in environments among locations, among years,
and in different silvicultural regimes. Specific combining
effects may also lead to crosses that differ from the mean of
both parents as predicted in progeny tests. Because specific
combining effects should average out given enough crosses,
we chose to cross at least 20 parents to create a genetic
population in our study. Poor estimates of predicted and
realized gains may also arise as a consequence of age-age
correlations that are much less than one. Realized gains
from seed orchards may differ from those predicted as a
result of pollen contamination or unequal, unknown contri­
butions of parents to the improved population resulting from
differential pollen and flower production. Finally, realized
genetic gains may be less than predicted gains after the
onset of stand competition due to the selection of highly
competitive genotypes in mixed-family progeny tests. Com­
petitive genotypes planted together might be expected to
compete for the same resources in the same space and time,
thus leading to growth that would be less than when in
competition with less competitive genotypes. Selection for
crop ideotypes with attributes that lead to high stand pro­
ductivity has been suggested as an alternative to the unin­
tentional selection for highly competitive genotypes
(Donald 1968, Cannell 1978).
The interaction between genetic population and test site
was small and statistically nonsignificant despite a wide
range of test environments including lower elevation-higher
productivity sites and higher elevation-lower productivity
sites. This finding is not surprising given that first genera­
tion breeding zones were conservative (Johnson 1997);
200
WJAF 19(3) 2004
thus, we would expect little family X site interaction over a
wide range of families, let alone interactions of means of
groups of families with sites. These results indicate that
future gain trials do not necessarily need to sample a wide
range of sites for comparing improved and unimproved
populations, although sites should still be representative of
the environments to be planted. The genetic population X
spacing interaction was also small and statistically nonsig­
nificant, although little competition has occurred between
trees to date. Previous studies did not find a large interaction
between spacings and families (Campbell et al. 1986, St.
Clair and Adams 1991); thus, we might not expect an
interaction for means of groups of families. The findings of
greater growth at tighter spacings has been observed previ­
ously in spacing trials throughout the region (Scott et al.
1998).
Many replicates are required to detect statistically sig­
nificant differences between improved and unimproved
stands of magnitudes that might be expected in a typical tree
improvement program. Results from our study indicate that
20 – 40 replicates are needed to detect significant differences
in early height growth, and 30 –50 replicates are needed to
detect significant differences in early diameter growth. Be­
cause the interaction of genetic population and site was
small and nonsignificant, replicates may be allocated either
among sites or within sites. If one were more interested in
characterizing the mean and variation in productivity of
improved stands across a breeding zone, allocating repli­
cates to more sites would be desirable. If one were more
interested in a good estimate of realized genetic gain at a
site to explore the relation between gain and site character­
istics, allocating more replicates within sites would be de­
sirable. In both cases, practical restrictions due to the num­
ber and size of available sites are likely. It should be noted
that if the interaction of genetic population and site was
large, then it would become the error term, and one would
then want to allocate the number of replicates across more
sites to maximize the probability of finding a significant
difference. A significant and large site X genetic population
interaction, however, would indicate that breeding zones
were poorly defined, which is unlikely for Douglas-fir in the
Pacific Northwest where breeding zones are considered
conservative.
We measured trees at an early age before appreciable
tree-to-tree competition had occurred. At later ages and
stages of stand development, estimates of realized genetic
gains and comparisons to gains predicted from progeny tests
would become more meaningful. Furthermore, the overall
objectives of this study are to explore growth trajectories
and incorporate them into growth models that consider the
genetic value of specific sets of known families to project
the productivity of improved stands at anticipated rotation
ages. These objectives require multiple measurements as the
blocks of trees age and begin to compete.
Conclusions
Achieving realized genetic gains in a study like this that
are very close to genetic gains predicted from progeny tests
are encouraging. It provides a level of faith in first-gener­
ation testing and selection procedures despite statistical
limitations, experimental error, and sampling of a different
set of sites with somewhat different management proce­
dures applied to them. The wide range of realized genetic
gains among individual sites, however, gives cause for
concern over trying to extrapolate results from a single site
or too few replicates. It may also provide impetus for
achieving a high level of control over factors that can
contribute to experimental error. Differential competition
from shrubs, particularly scotch broom, among plots within
a replication probably was the greatest contributor to exper­
imental error. Browsing from elk was also a problem, al­
though it appeared to be more evenly distributed and not as
patchy as problems with competing vegetation. The effects
of competing vegetation and browse should diminish over
time. Nevertheless, a large number of replicates allocated
over at least a few representative sites will contribute
greatly to achieving meaningful results from studies looking
at realized genetic gains.
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