Weyerhaeuser Company
Timberland Division
Forestry Research Center Centralia, Washington Number 57
January 1964
RECOMMENDED TRAITS TO BE IMPROVED IN A BREEDING PROGRAM FOR DOUGLAS FIR by
Robert
K.
Campbell1
SUMMARY
Cost of selection limits the number of traits that can be
genetically improved in Do11glas fir, Bree<;l:J_ng mu;3t be :re­
stricted to the very :tew t rait s in which fli].pi>ove1TI :fth!1
give the greatest earnings r 1l'ltive to CO$tS of'''{triprove
men t
By c!on$idering t:raits in relation t& the $everEti
factoPs that influerice earnings, the isk
bf MakiW 'a wrong
'
choice is mihirnized,
_'
.
·
this No te , basic information has been gathered from
ublished studies of trait heritability and v riabi ity to
makE( preqict1qns d:tl geribtid ga1±;1 r6 . 6 Br fts' H1' 1D60g1!a;:J.
fit>; ··va:rHibilities f6r1?-ny giveri tra t r sirh:i1a:r,'f':b m',:i
s, atid to 'stand' c;tnd spec:Les te 'speb ¥:J'.'' f\1th.otigl\11 1J 1 s d
h$ri tal;J'tlities 'are of qu StibniWie vliiue th. pr dictin$1
genetic 'gatri in Dbugl,as '.fir,' they do :ind f ¢t:itE! 'li' l$:t e· '
differences among'', ttlc\ti
Bs.
' ' '
:, J. i
Fo:r
I
•
'
-,
,
I
' '
I
•
'
•
c
'
I
1'
'','1
, i:
·.
·
'· : ,. ',
I
, 1'
,':
Tentative pr fdii6n bf genetic gain per breeding gener­
ation indicate a low (1-3 per cent) gain in height growth
and hollocellulose per cent, a medium (4-7 per cent) gain
in the majority of wood-quality traits, and a high (7-15
per cent) gain in branch angle, stem diameter and stem
volume, when 1 per cent of a stand are selected as superior
parents.
Stem volume growth per unit growing space and whole-wood
specific gravity are recommended for improvement breeding
1
Forest geneticist, Weyerhaeuser Company, Forestry Research
Center, Centralia, Washington
Better Timber Crops Through Industrial Forest Management
2
for Douglas fir. Recommendations are based on a comparison of
traits for genetic gain, selection costs, and length of breed­
ing generation, all of which influence earnings from gain re­
lative to improvement costs. Both traits promise above aver­
age response to selection, and satisfy the requirement that
any trait chosen must retain its relative value over several
rotations.
INTRODUCTION
Most organizations cannot afford the expense of genetically
improving a large number of traits simultaneously. This is
because many desirable traits are either negatively cor­
related one with another or are inherited independently of
one another ( no correlation ) . If two traits are negatively
correlated, selection for one will entail concomitant se­
lection against the other. If the traits are not correlated,
selection for one carries with it no selection for the other.
The problem connected with the simultaneous improvement of
negatively correlated traits is clear. On the other hand, it
may not be so obvious why lack of correlation between traits
affects breeding costs. For example, if a given level of
genetic improvement in trait A requires selection of the best
individual out of 100, the same level of improvement can be
obtained for traits A and B, simultaneously, only by selecting
for both traits the one best individual out of 10,000. If a
third non-correlated trait were added, the best individual
out of 1,000,000 would be required.
Cost of selection, therefore, restricts the number of traits
to be improved, The choice must be narrowed down to one or
a very few traits in which genetic improvement will give the
greatest economic benefit.
Economic benefits from tree-improvement breeding depend, in
(1) The genetic gain per unit
large part on three elements:
time ( year ) that can be obtained by pract ical breeding methods;
(2) The increased economic return directly attributable to
genetic gain--this return will vary from trait to trait de­
pending on the monetary value of the separate traits at the end
of unit time; (3) The cost of improvement breeding over unit
time.
By examining each trait in the light of these three elements,
the likelihood of making the best cholce of traits should be
greatly increased.
Tentative predictions of expected genetic gain, as well as
estimates of relative costs of improvement, can be made for
several traits. This paper includes estimates of genetic gain
per breeding generation for each of twenty-six traits in Douglas
fir. From these, recommendations are made for the initial
3
phases of a Douglas fir improvement-breeding program for
Weyerhaeuser Company, Most of the basic information has been
gathered from published studies on heritability or variability.
This Note also discusses several items other than genetic gain
that affected the recommendations.
There remains the difficult task of predicting relative mone­
tary values for each trait at those specified future dates
when genetically improved trees are to be harvested. Such
predictions require a rather detailed knowledge of organiza­
tional goals, and consequently, they can be made only by
Company management.
ANALYSIS OF BREEDING POTENTIALS
To predict genetic gain three major assumptions are necessary:
First, that we are dealing only with metric traits, i.e.,
traits which vary continuously from one extreme to the other,
so that any division into classes is purely arbitrary. Metric
traits are amenable to measurement, and are generally con­
trolled by many genes. Secondly, that genetic gain in the
initial breeding generation will result primarily from mass
selection within naturally regenerated stands. Thirdly, that
only the additive component of genetic variation will be used
in improvement breeding. This condition follows from the
second assumption, but it may not hold true for Douglas fir
after the initial breeding generation.
Granting these assumptions, the prediction equation described
below estimates the genetic gain to be expected per generation.
Genetic gain ( R ) of a trait ( t ) results as a response to se­
lection amd is defined as the difference between the trait
mean of offspring from selected parents and the trait mean of
offspring from the non-selected population.
Rt
2
ht S
.
2
where ht is trait heritability-in-the-narrow-sense, defined
as the proportion of the measurable population variance, in
trait t, that can be atttibuted to additi e genetic causes.
For exampie, in a hypothetical stand in which the standard
deviation ( s ) of tree height has been computed to be 5.0 feet,
the measuraple population variance equals s2 or 25. In this
stand, each' tree has a set of genes whose average effect on
tree height is slightly different from that of the comparable
sets of genes in the remaining trees. Assume that the stand­
ard deviation of tree heights caused by additive genetic dif­
ferences between trees has been computed to be 1.6 feet,
Variance attributable to additive genetic effects therefore
equals ( 1.6 ) or 2.56. Consequently, for tree height of this
stand, h2 equals 2.56/25 or 0, 10, i. e., 10 per cent of the
measurable variation in tree heigh is due to genetic
2
=
4
differences between trees. The variable conditions of soil,
light, competition, etc, that exist within a stand also affect
height growth and are responsible for a portion of the vari­
ation in tree heights--in this example, 90 per cent.
Selection differential ( S ) is the difference between the trait
mean of the selected individuals and the trait mean of the un­
selected population, For example, assume that the average tree
height is 50 feet at age 20 in the above stand. By selecting
the tallest 5 per cent of trees, the av rage height of the
selected individuals is 60 feet, and the resulting selection
differential is 10 feet,
Under
5 per
would
stand
the conditions hypothesized above, using the tallest
cent of trees as parents, their offspring at age 20
be expected to excel average tree he1ght of the original
by 0, 10 x 10 feet or 1 foot,
Although the above equation clearly demonstrates the reasoning
behind genetic-gain prediction, a slight modification of the
equation is helpful when comparing relative genetic gains in
separate traits or in separate populations. This is because
the selection differential ( S ) for any particular trait de­
pends on (1) the proportion of the population selected and (2)
the variability of the trait. Thus, genetic gain depends on
trait heritability, trait variability and selection intensity.
A generalized equation, which takes all factors into account,
becomes R= i 0 p h2 where:
crp
i
=
=
·
the standard deviation of the trait, a
measure of trait variability.
the intensity of selection defined as the
selection differential ( S) expressed in
standard deviations of the trait, or
i
S/crp·
An estimate of genetic gain results when the appropriate sta­
tistics are substituted in this prediction equation. For the
following treatment, heritabilities and standard deviations
have.been taken from the literature to provide likely esti­
mates for Douglas fir, Data reliability is discussed, and
following this, predicted genetic gains are presented.
Heritability
For any one trait within a given population it is possible to
calculate two general types of heritability--heritability-in­
the-narrow-sense and heritability-in-the-broad-sense. Of the
two, heritability-in-the-narrow-sense is the more useful since
it can be used most directly to predict response to mass selec­
tion, It has been defined above as
5
2
s
=
additive genetic variance in population
total variance observable in population.
For many traits) however) the only available estimates of
heritability are reported as heritability-in-the-broad-sense)
defined as
2
additive genetic variance + variance due to domi­
hbs
nance and interaction
total variance observable in population.
Heritability-in-the-broad-sense contains non-additive genetic
variance components and cannot be used directly to predict
response to selection. Nevertheless) broad-sense herit­
abilities are not without value. The numerator of the broad­
sense heritability fraction contains both additive and non­
additive components of genetic variation and henceJ provides
an estimate of the upper limit of narrow-sense heritability
for a given trait in a given population.
The equations indicate that heritability is a ratio of the
genetic variability in a population to the total observed
variation in that population, Neither the numerator nor the
denominator of this ratio is necessarily constant from popu­
lation to population. Genetic variations may differ between
populations) affecting the ratio numerator; or environmental
variability may differ) affecting the denominator. For
example) heritability for growth traits should be higher in
plantations where initial stand density and spacing is less
variable) than ih wild stands) where density and spacing have
not been artificially cbntrolled. From thisJ it is clear that
heritability is a property of each stand or population and
that heritability estimates have limited utility in pre­
dicting response in one stand from measurements made in an­
other.
Since he itability studies for Douglas fir are virtually lack­
ing) the estimates for traits in Douglas fir are based almost
completely on those made for other species. Even though such
estimates are of questionable value in predicting genetic
gain in Douglas firJ they do indicate relative differences
among traits in degree of heritability. Published herit­
ability values are presented in Table 1.
Variability of traits
Variability of a trait in a population is extremely important:
it sets practical limits to the magnitude of the selection
differential) and consequently) to the genetic gain per gene­
ration. Unfortunately) trait variability in a stand is sub­
ject to little useful control by the breeder) who generally
6
Table 1,
Heritability estimates for twenty-six traits from several species as obtained
from published liteTature.
. Trait
Species
Total height
II
II
Annual ht,
II
increment
II
II
Juvenile ht,
II
II
II
II
II
growth
II
II
II
Stem volume
Diameter breast high
II
II
II
Juvenile stem diameter
Stem-diameter increment
II
II
II
Number branches/whorl
Knottiness ratio
Branch length
Branch angle
Blister rust resistance
Number female flowers
Wood specific gravity
II
II
II
II
II
II
II
II
II
II
II
II
II
II
II
II
II
II
Summerwood specific
gravity
II
II
Summerwood per cent
II
II
II
II
Tracheid length
II
II
Slash pine
Cryptomeria:
Scots pine
Douglas fir
Larix x eurolepis
Western white pine
II
II
II
II
Age
II
II
Douglas fir
Slash pine
II
II
Cryptomeria
Larix x eurolepis
Scots pine
Monterey pine
Douglas fir
II
II
Slash pine
Scots pine
Western white pine
Scots pine
Monterey pine
II
II
Slash pine
Loblolly pine
II
II
II
II
Monterey pine
II
II
Loblolly pine
II
II
Monterey pine
Slash pine
Monterey pine
Slash pine
Summerwood tracheid:
Double wall thickness
Loblolly pine
11
11
Radial lumen diameter
11
11
Radial width
11
11
Tangential width
11
11
Tangential length
11
11
Ratio of double wall
11
11
Thickness t.o radial width
Monterey pine
Spiral grain
11
11
Longitudinal shrinkage
11
11
Holocellulose per cent
9-14
20
6- 9
20-35
2
2+
1
1
14
14
20
2
6- 9
8+
20-35
20-35
9
6- 9
3- 4
6.L 9
13-1 9
·6
12-14
2
6
7- 8
8+
8+
7- 8
7- 8
12+
12-14
8+
6
77777­
7-
Heritability per cent
Narrow
Broad
sense
sense
52*
2-30*
28*
50-63
1-39*
16-36* 71"*:
38*
50-70
73
45-75
45-54
45-72
48
73-86
56
8-14
6-14
8-20
0-10
18-35
29-58
26
0-10
12-48
69
20
21-56
37-49
64-100
76-87
50-76
86-92
8-26
0-84
31-A4
0-81
27-59
85-97
0-72
8
8
8
8
8
8
8+
8+
12+
5-16
26
Refer­
ence
18)
20)
i
17
10
24
18
18
20
14
1
5
'3
1
i )
1
2
1
8
8
19
21
21
l
§ 5
9
9
5
19 )
5)
27)
9
9
9
9
9
9
66
54-74
29
*Repeatability, which is measured by intraclass correlation, was determined from data
presented by the cited author.
Repeatability is the upper limit of heritability-in­
the-broad-sense for populations sampled by the above asterisked s.tudies.
7
wishes a large amount of genetic variation in the stands from
which he makes selections. Greater genetic variation tends to
give rise to greater heritability and variability. This, in
turn, promises greater response to selection. Variability can
be easily decreased in traits such as height or stem diameter
by cutting procedures which eliminate extremes of the population.
On the other hand, variability can be increased only at con­
siderable expense, by hybridization or by special mating sys­
tems, in long-term improvement programs. For these reasons,
trait variation may be considered as being capable of only
slight change.
Coefficients of variation are reported in Table 2 for many
traits. For a few of these, variability values are reported
for several species. Variabilities for any given trait are sur­
prisingly similar from stand to stand and species to species.
Intensity of sel·ect-ion
Intensity of selection remains the only factor in the genetic­
gain equation over which the breeder has direct control. He
will generally attempt to select as intensively as possible with­
in limits set by: (1) the percentage of the population needed
to reproduce the populationj (2) the number of families or clones
needed to minimize inbreeding in orchardsj and (3) the diffi­
culty and consequently expense of trait measurement.
Fortunately, most coniferous forest trees have relatively high
reproduct ve potentials, each tree being capable of producing
many offspring per year, In Douglas fir less than 0.1 per cent
of trees cut yearly from a given acreage could produce numbers
of seedlings sufficient to replant the resulting cutover acre­
age. ( Kozak, et al. 1962). Thus reproductive potential is
not likely to restrict selection intensity in this or similar
species, Since large populations from which selections can be
made are usually available, the second restriction is also re­
latively unimportant. On the other hand, selection intensity
probably will be limited by the difficulty of measuring or
scoring traits. For some traits selection intensity will be
much more limited than for others.
For this paper a selection intensity (i) of 2.67 has been
chosen, meaning that the selected parents are superior to the
original population by 2.67 standard deviations from the mean
when one per cent of the original population are selected as
superior parents. This will generally approximate the upper
limit of selection intensity practicable for most traits. It
is also assumed that the trait values are distributed normally
within the populations, and that population size is infinite.
Neither of these assumptions is strictly true, but the result­
ing error will be negligible for purposes of this Note.
8
Table 2,
Trait variability estimates derived from published and unpublished data for several
traits for several species,
Trait
Total height
Annual height increment
Juvenile ht, growth
Diameter breast high
Stem volume
Stem diameter increment
Number branches/whorl
Knottiness ratio
Branch length
Branch angle
Number of female flowers
Wood specific gravity
Species
Age of
stand
Slash pine
Cryptomeria
Douglas fir
a,Dominants
b,Dominants &
codominants
Douglas fir
a,Dominants
b,Dominants &
codominants
Scots pine
Douglas fir
8
20
II
b,Dominant &
codominant
c,Dominants
d,Dominants &
codominant
e,Dominants
f,Dominant &
codominant
Douglas fir
a,Dominants
b,Dominant &
codominant
c,Dominant
d,Dominant &
codominant
e,Dominants
f,Dominant &
codominant
-
- - - ---
Scots pine
Western hemlock
Douglas fir
II
Tracheid length
II
Scots pine
II
II
Douglas fir
Scots pine
Slash .pine
Longleaf pine
Loblolly pine
Shortleaf pine
Douglas fir
II
Summerwood specific
gravity
Summerwood per cent
II
Slash pine
Cryptomeria
Douglas fir
a.Dominants
II
Western hemlock
II
II
Loblolly pine
II
II
Douglas fir
Loblolly pine
Douglas fir
Western hemlock
Summerwood tracheid:
a.Double wall thickness
Loblolly pine
b,Radial lumen diameter
c,Radial width
d,Tangential width
e,Length
f.Ratio of double wall
thickness to radial width
Loblolly pine
Holocellulose per cent
Western hemlock
Douglas fir
59-78
Coefficient of
variation (CV=
s/
within stand
x
Type of stand
)
Plantation
9.2
23.2
II
Natural
II
59-78
l
14
20
33
Plantation
Natural
Nursery
Plantation
II
Natural-medium
site
II
33
59-78
Natural-low site
59-78
56
Natural-high site
II
(11)
11.0
(11)
2,9
(12)
9.8
11.6
8,4
30.0
38.6
32.7
12)
1
20
15.3
(12)
21,2
15.8
gn
g
24.7
19.6
II
56
(12)
(12)
Natural
32.5
33
45
II
46.0
'
5.4
45
56
56
6- 9
80+
80+
20-35
20-35
20-35
6
6- 9
20-35
6- 9
30+
II
Plantation
Natural
II
II
II
Plantation
II
Natural
Plantation
II
Natural
II
II
Second
growth­
77777-
8
8
8
8
8
7- 8
15-35
80+
80+
40,2
34.5
?g
g
48,0
9.6'
----- i)
-- --
11.8
13.5
22,2
20.7
11.1
21.1
24.7
11.5
53.1
11.3
11,1
10.4
10.4
9.9
18.3
4.8
80+
80+
7- 8
7- 8
80+
25
80+
80+
--
Plantation
II
II
II
Natural
II
Plantation
II
II
II
II
Natural
II
{)
22,6
33
II
?6
8,0
56
56
6- 9
20-35
Refer­
ence
5.0
20,0
5.5
9.3
5.5
8,8
(12)
23)
23)
3
3
3
1
1
3
1
16
15
15
15
6)
? §j
j
23
26
23
23
5
5
6
2
6
9
9
9
9
9
6
1.4
1.5
1.2
9)
§{)
23
-
9
Predicted genetic gain
Reliability of predicted genetic gain depends, of course, upon
the reliability of the statistics used in the prediction equa­
tion. For present purposes, a single selection intensity of
2.67 has been used for all traits even though this is not com­
pletely realistic for allj selection intensity for the more
readily measured traits such as stem diameter and stem height
will likely approximate 2. 67, whereas selection intensity for
traits requiring more costly measurement will undoubtedly be
considerably lower.
Another source of predictive error lies in the heritability
figures extracted from the literature. For most of the traits
considered here, heritability figures are not available for
Douglas fir, or even for other species having stand conditions
similar to those common to Douglas fir, Furthermore, reported
heritability figures are generally based on few individuals,
or upon family averages from young plantation-grown progeny
testsj such conditions, respectively, tend to make estimates
unreliable, or too high to be applied under conditions of
mass selection in natural stands, In this Note, therefore,
for purposes of prediction, an arbitrary range of herit­
abilities has been selected based on the data given in Table
l. In general, the heritability used is conservative when
compared to reported heritabilities and the bias will likely
show up as underestimation of response.
Means and standard deviations used to compute the coefficients
of variation of Table 2 also vary in reliability. For many
of the traits considered here, standard deviations and means
were not reported, as such, by the authors cited in Table 2,
and it was necessary to work with data originally presented
otherwise, For example, standard deviations for a trait
were often der i ved from reported ranges for the trait, or
from variance analysis tables, Data for variability estimates
were taken from stands of diverse origin and condition. In
spite of these approximations, coefficients of variation for
individual traits do not vary greatly between species and
stand conditions and it seems unlikely that variability esti­
mates will unduly bias the estimates of genetic gain.
Gain to be expected per breeding generation is presented in
Tables 3 and 4. In Table 3, gains are based upon herit­
abilities, chosen as discussed above, combined with estimates
of variability for traits in Douglas fir, as given in Table 2.
In Table 4, provisional predictions of gain have been made for
traits for which estimates are lacking, either for herit­
ability in any species, or for variability in Douglas fir.
Estimates are given in two ways,
In column 6 of Tables 3 and
10
Table
3. Estimated genetic gain following one·generation of selection for the bet i er
cent of the population from naturally regenerated stands of Douglas fir·,
Selection
under stand
conditions
Trait
-
Total· height
·
Annual ht, increment
Juvenile ht, growth
Diameter breast ht,
Age
Low site Dominant only
Dominant &
codominant
High site-­
Dominant only
Dominant &
codominant
Dominant &
codominant
Nursery
Low site -­
Dominant only
Dominant &
codominant
High site
Dominant only
Dominant &
co dominant
Medium site -Dominant only
Dominant &
codominant
High site
Dominant only
Dominant &
codominant
Medium site
Dominant only
Dominant &
codominant
Dominant only
Dominant &
codominant
Dominant &
codominant
Dominant &
codominant
Dominant &
codominant
Dominant &
codominant
Dominant &
codominant
Dominant &
codominant
Dominant &
codominant
Dominant &
codominant
Dominant &
codominant
Dominant &
codominant
--
stem.volume
--
-
stem diameter
increment Number branches
per whorl Branch length
Branch angle
Knottiness ratio
Wood specific gravity
Summerwood per cent
Tracheid length
Holocellulose
Estimated
heritability
per cent
Mean for.
trait
Genetic
gain
1
per
Percentage
genetic
gain
59-78
10
100
2,1
ft.
2
59-78
10
89
2,6
ft,
3
56
10
148
1.2
ft.
1
56
10
137
3.6
ft.
3
20-35
1
10 10
2.5
6.5
ft.
em
0.06
.52
ft.
em
2
8
59-78
20
13.2
in.
1.12
in,
8
59-78
20
10.5
in,
1.38
in,
13
56
20
21.8
in,
2,29
in.
10
56
20
18.4
in,
2,24
in.
12
33
20
12,7
in,
1.04
in,
8
33
20
10,8
in,
1. 22
in,
11
56
20 145
cu,ft.
27
cu,ft,
18
56
20
102
cu,ft.
11
cu.ft.
26
45
20 48
cu,ft,
1.4
cu,ft.
3
45
33
20 20 34
27
cu,ft.
cu,ft,
33
20
20
cu,ft.
80+
10
3.3
20-35
10
8.1
20...:35
5
9.5
20-35
30
71°
20-35
10
Second­
growth
mm
5
7 cu,ft,
cu,ft,
22 17
5
cu,ft,
25
0,134
mm
,48
ft.
106
4
6
.14
ft.
2
6,6°
9
5.9
6
20
.413
,022
5
80+
20
.448
.o44
8
80+
30
44%
80+
30
3.64
80+
10
80.3%
2%
mm
.16
4
mm
,26%
4
<1
Table 4,
Estimated genetic gain following one generation of selection for the better 1
per cent of the population from naturally regenerated stands of Douglas fir.
Selection
Estimated
Mean
Percentage
under stand
Trait
heritability
for
Genetic
genetic
conditions
trait
per cent
gain
gain
Surnmerwood
Specific gravity
Surnmerwood tracheid:
a.Double wall thickness
b.Radial lumen diameter
c.Radial width
d.Tangential width
e.Length
f.Ratio of double wall
thickness to radial
width
Wood:
Modulus of rupture
11
11
Modulus of elasticity
11
11
Compression perpendicular
to grain
11
11
Compression parallel
to grain
11
Shear
11
11
Plantation
30
y
1/
2
Plantation
30
30
30
30
30
1/
I/
I/
1/
I/
I/
I/
1/
4
4
4
2
5
30
y
y
5
11
11
11
11
11
1/
1/
Dominant &
codominant
Slow grown
Rapid grown
Slow grown
Rapid grown
10
10
10
10
2/
"2/
"2/
"2/
7473 psi
6950 psi
l503Mpsi
1387 psi
71 psi
326 psi
60Mpsi
78 psi
3
5
4
6
Slow grown
Rapid grown
10 2/
10 "Z/
367 psi
375 psi
28 psi
30 psi
8
8
Slow grown
Rapid grown
Slow grown
Rapid grown
10
10
10
10
2/
2/
"Z/
2/
3717
3480
931
905
psi
psi
psi
psi
138
190
26
33
psi
psi
psi
psi
4
5
3
3
Variability
y
No estimate of variability available for these traits in Douglas fir.
estimates for loblolly pine were used in genetic gain predictions.
2/
No heritability estimates based on experimental evidence are available for these traits.
I-'
I-'
4J response to selection is presented in actual units of gain.
For exampleJ the first line of Table 3 indicates that height
growth of Douglas fir stands 60-80 years old can be genetically
improved by about 2 feet in one breeding generation. It is
very likelyJ however, that one may wish also to compare the re­
lative response to selection of two or more traits. This can
best be done by comparing percentage genetic gain per gener­
ation. This value is presented in column 7 of Tables 3 and 4.
The prediction formula here is: Percentage genetic gain per
h2 x i x CV where h2 is heritability in
breeding generation
the narrow senseJ i is the selection intensityJ and CV is the
coefficient of variation of a trait.
=
In Table 5 the traits have been placed in percentage gain
classes. It can be seen that a majority of the estimates fall
in the 4-6 per cent class.
Factors to consider other than genetic gain per generation
The most realistic criterion for choosing traits for improve­
ment-breeding is earnings from genetic gain relative to costs
of improvement. Since expenditures are typically levied from
year to yearJ it is clear that calculation of earnings must
also be adjusted to a yearly basis. This in turn requires an
estimate of genetic gain per yearJ which can be easily com­
puted by dividing genetic gain per generation by years per
breeding-generation interval.
From the definition of genetic gain per year it is apparent
that traits requiring shorter breeding-generation intervals
will provide more genetic gain per year than will comparable
traits with longer breeding generations. A generation in­
terval is the interval between corresponding stages of the
life-cycle in successive generations ( FalconerJ 1961). A
breeding-generation interval may be similarly definedJ but it
must include the stage of the life cycle at which a given trait
may be evaluated. In agricultural crop breedingJ trait evalu­
ation and sexual maturity usually occur at about the same ageJ
but in forest trees, trait evaluation may follow sexual ma­
turity by several decades for some traits. ThereforeJ breeding­
generation interval is not strictly controlled by the length
of the sexual cycle, It depends to a large extent on the age
at which a trait may be evaluated. This means that in any one
species of forest treesJ different traits may have widely
different breeding-generation intervals. Choice of traits to
improve should consequently consider breeding-generation in­
terval. This interval will greatly affect genetic gain per
year.
The importance of genetic gain per year brings up another
point which should be examined: the importance of choosing
13 Table 5.
Estimated percentage-genetic-gain per generation for
twenty-six traits for Douglas fir.
Estimated percentage­
genetic-gain per
generation
Trait
1- 3 Total height, annual height increment,
branch length, holocellulose percehtage,
shear strength, springwood specific
gravity, summerwood specific gravity,
tangential width of tracheids.
4- 6 Diameter increment per year, number
branches per whorl, whole-wood specific
gravity, per cent summerwood, knotti­
ness ratio, modulus of rupture, modulus
of elasticity, compression parallel to
grain, tracheid double-wall thickness,
tracheid radial lumen-diameter, tracheid
radial-width, tracheid length, ratio of
tracheid double-wall thickness to
tracheid width.
7-10 Diameter breast height of individual
trees, branch angle, compression perpen­
dicular to grain.
ll-15 Stem volume of individual trees.
16-20 Spiral grain.
14 traits having a high probability of retaining their relative
value over more than one economic rotation. This interval will
also include several breeding generations. With every added
breeding generation, within limits, genetic gain per generation
is likely to increase, because more sophisticated breeding
methods will likely be used in each succeeding generation. For
example, in a long-term breeding program, heritabilities can
be increased by reducing environmental variability through
better stand management, Additionally, it will be possible to
employ selection methods more accurate and more appropriate to
forest trees than is now possible, including selection for
general and specific combining ability, Inbreeding to collect
favorable genes will also be possible, Each improvement in
technique will tend to increase genetic gain per year.
Since improvement in every generation is added to improvement
in the previous generations, a long-term breeding program per­
mits truly noteworthy advances in genetic improvement. Hence,
every effort must be made to insure that the relative economic
weights now placed on traits are likely to be valid over
several generations.
RECOMMENDATIONS CONCERNING CHOICE OF TRAITS
Branching traits
Although number of branches per whorl, knottiness ratio, and
branch angle all promise a fairly rapid response to selection
( Table 5), none of these traits can be recommended for first­
generation selection from native stands. There are three
reasons for this conclusion: First, the traits are expensive
to measure in large trees, with the result that selection
differentials are likely to be low and improvement is likely
to be less rapid than predicted in Table 3. Secondly, the
effect of these traits on wood quality has not yet been pro­
perly evaluated for Douglas fir. Thirdly, preliminary
evidence indicates a positive genetic correlation between high
stem volume and greater knottiness, or between high stem
volume and greater numbers of branches per whorl ( Campbell,
1963). Selection for desirable branching traits may, there­
fore, entail a concomitant selection for lower stem volume.
Wood-quality traits
Choice of wood-quality traits to improve may well be made on
some basis other than genetic-gain per generation, since there
appears to be little basis for differentiating between traits
in respect to their relative response to selection. Most wood­
quality traits show moderately rapid response to selection
( Table 5).
For several reasons wood-quality improvement should be re­
stricted to whole wood specific gravity during the initial
breeding generation.
First, in an operative breeding program,
specific gravity probably can be improved more rapidly than
other wood traits within the gain category of 4-6 per cent.
Specific gravity is somewhat more easily and more cheaply
measured than other traits.
Consequently, selection intensity
can be greater than for other wood-quality traits. Secondly,
specific gravity may be evaluated early in a tree's life and
Van Buijtenan
the breeding-generation interval shortened
Although the same may be true for some of the other
wood-quality traits, such has not yet been dem onstrated.
(
1962).
Whole-wood specific gravity is chosen for two reasons.
First,
it is important in all phases of wood utilization.
In lumber,
in poles and piling, and in veneer manufacture, it affects
strength, workability, preservative intake, etc.
It affects
pulp yield because it is a measure of actual wood substance
in a given volume of wood, and it also strongly affects the
quality of paper produced from pulp.
S econdly, by choosing
whole-wood specific gravity over other wood-quality traits
the risk of making a wrong choice is minimized.
Specific
gravity is known to be phenotypically closely correlated with
other wood-quality traits
Wangaard
Goggans
Fairly strong genetic correlations between specific gravity and
Goggans
We can
other wood traits also appear to exist
expect, therefore, that selection for high or low specific
gravity will affect the other associated wood-quality traits.
Hence, if two separate strains are developed, one for high and
the other for low specific-gravity wood, progress will also be
made in one or the other toward improving the associated and
more fundamental properties such as tracheid length, diameter,
etc.
Once the value of these fundamental traits in wood uti­
lization has been determined, they can be easily introduced
into selection indexes for succeeding breeding generations.
Since previous selectioh for specific gravity would have brought
with it some improvement in these other properties, there ap­
pears to be little risk in using specific gravity as the in­
itial wood-quality criterion.
(
1950,
(
1962).
1962).
Growth traits
It
Height growth is not a desirable trait for improvement.
shows relatively poor response to selection whereas, on the
basis of available information, both stem diameter breast-high
and stem volume growth of individual trees may respond well.
Since stem diameter is a component of stem volume, stem volume
might appear to be the logical choice for improvement--yet
stem volume may be prohibitively difficult to measure. Then
16'
selection for greater stem diameter would probably result in
gain in stem volume almost equivalent to that obtainable by
direct selection for volume, By so doing, however, the breeder
would risk a change in stem form brought about by improvement
in stem diameter breast high at the expense of stem form and,
eventually at the expense of volume,
If the breeder defines the trait "stem volume" as volume growth
per unit growing space per year, the choice of stem volume, as
a trait to improve, also satisfies the requirement that any
trait chosen must retain its relative value over several gener­
ations, By selecting for volume growth, as defined above, the
breeder is essentially attempting to improve land productivity
by increasing the efficiency with which a tree utilizes grow­
ing space, It is difficult to visualize a situation where de­
creased productivity of land would be desired by a forest
manager,
Unfortunately the information that permits us to predict re..,.
sponse to selection for volume growth, permits us to predict
only the genetic gain in volume growth for individual trees.
This may be a trait completely separate from volume growth per
unit growing space per year. If so, there would be no basis
for extrapolating genetic-gain predictions from one trait to
the other,
Disease, insect and animal-damage resistance
In this Note, no previous mention has -been made concerning
selection for pest resistance, Although little has been pub­
lished in this aspect of tree-improvement breeding, the avail­
able information indicates the possibility of moderate to
rapid response to selection, Nevertheless, it is question­
able whether first-generation selection should include pest
resistance unless pests are the major limitation to growth in
the species concerned,
17
LITERATURE CITED
I,, ArnborgJ T. and G. Radder. 1957. Studies of some forestry
qualities in clones of Pinus silvestris. Meddel. nr, 87 f.
s&llskapet f r praktisk skogsf8r#dling. Uppsala. pp 125­
157.
2, BinghamJ R. T.J A. E. SquillaceJ and J, W. Wright. 1960.
Breeding blister rust resistant western white pine II.
First results of progeny tests including preliminary esti­
mates of heritability and rate of improvement. Silvae
Genetica 9(4): 33-41.
3. CampbellJ R. K, 1961. Phenotypic variation and some esti­
mates of repeatability in branching characteristics of
Douglas-fir. Silvae Genetica 10(4): 109-118.
4. CampbellJ R. K. 1963. Phenotypic correlation among branch
and upper-crown stem attributes in Douglas-fir. Forest
Science 9(4): 444-451.
5. DadswellJ H. E.J J. M. FieldingJ J. W. P. NichollsJ and
A. G. Brown. 1962. Tree to tree variations and the gross
heritability of wood characteristics of Pinus radiata.
Tappi 44(3): 174-179.
6. DrowJ J. T. 1957. Relationship of locality and rate of
growth to density and strength of Douglas fir. u. s. D. A.
Forest Products Laboratory Rep. No. 2078. 56 pp.
7. FalconerJ D. S. 1961. Introduction to quantitative ge­
netics, Oliver and BoydJ Edinburgh and London. 365 pp. 8. FieldingJ J. M. and A. G. Brown. 1960. Variations in the density of the wood of Monter y pine from tree to tree
Forestry and Timber Bureau. Comm. of Australia. Leaflet No. 77. ,I
9. Goggans J J. F. 1962', The correlationJ variationJ and inheritance of wood properties in loblolly pine (Pinus taeda L.). N. C. State CollegeJ School of ForestryJ Technical Report No. l4J 155 pp. 10. HanoverJ J. W. and B. V. Barnes. 1962. Heritability of
height growth in year-old western white pine, Southern
Forest Tree Imp. Comm,J Sponsored Publ. No. 22J pp 71-75.
11. KerJ J. W. 1952. An evaluation of' several methods of
estimating site index of immature stands. Forestry
Chronicle, 28(3): 63-74.
19
24. WilsonJ B. C. 1962. Methods of selection in juvenile
populations of Douglas-fir (Pseudotsuga menziesii (Mirb.)
Franco.) of known parentage, Master's ThesisJ Univ. of
Wash. :73 pp.
25.
ZobelJ B. J. and R. L. McElwee
lulose in loblolly pine. Tappi
1958. Variation of cel­
41(4): 167-170.
26. ZobelJ B. J.J F. GoggansJ T. E. Maki and F. Henson. 1961.
Some effects of fertilizers on wood properties of loblolly
pine, Tappi 44(3): 186-192.
27. ZobelJ B. J.J D. Cole and R. Stonecypher. 1962. Wood
properties of clones of slash pine. Southern Forest Tree
Imp. Comm.J Sponsored Publ, No. 22J pp 32-39.
l-22-64 rcc