Journal of Forest Research

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An examination on the site—family interaction oftree height of open-pollinated families of hinoki(Chamaecyparis obtusa) plus trees in the Kansai
Forest tree breeding region
Hiroo Yamada & Tomiyasu Miyaura
To cite this article: Hiroo Yamada & Tomiyasu Miyaura (2004) An examination on the site—familyinteraction of tree height of open-pollinated families of hinoki (Chamaecyparis�obtusa) plus trees
in the Kansai Forest tree breeding region, Journal of Forest Research, 9:1, 81-84, DOI: 10.1007/
s10310-003-0048-9
To link to this article: https://doi.org/10.1007/s10310-003-0048-9

Published online: 01 Feb 2004.

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J For Res (2004) 9:81–84
DOI 10.1007/s10310-003-0048-9© The Japanese Forestry Society and Springer-Verlag Tokyo 2004

SHORT COMMUNICATION

Hiroo Yamada · Tomiyasu Miyaura

An examination on the site–family interaction of tree height of openpollinated families of hinoki (Chamaecyparis obtusa) plus trees in the
Kansai Forest tree breeding region

Received: July 22, 2002 / Accepted: May 30, 2003

Abstract Stability analyses to evaluate site–family interaction for tree height at 15 years old were conducted intrials of open-pollinated families of hinoki (Chamaecyparis
obtusa Endl.) plus trees established in the Honshu andShikoku area of the Kansai Forest tree breeding region,
Japan. The Honshu area included 103 sites (226 families),
and the Shikoku area included 27 sites (62 families). Analyses of variance indicated that the interaction between site
and family was not significant in the Honshu area, whereas
in the Shikoku area the interaction was significant. The
stability analyses regressed the family–site means againstsite means. The linear regression analysis was carried out
using the data for 164 families that were tested at five or
more test sites from the 130 test sites. Approximately 93%
of families had values of linear regression coefficient thatwere not significantly different from unity. These results
suggest that almost every open-pollinated family of hinoki
plus trees have average stability and are equally well
adapted to good and poor sites. The differences in the reaction of a family to site productivity are not the main causesof the interaction.
Key words Genotype–environment interaction · Height ·Hinoki plus tree · Stability

Introduction
Genotype–environment interaction is the differential response of genotypes to different environments. Such inter-

H. Yamada (*)
Forest Tree Breeding Center, 3809-1 Ishi, Juo-cho, Taga-gun, Ibaraki
319-1301, Japan
Tel. ⫹81-293-32-7000; Fax ⫹81-293-32-7306
e-mail: hirooy@affrc.go.jp
T. Miyaura
Ryukoku University, Ohtsu, Japanactions complicate testing and selection in tree improvement programs and reduce overall genetic gains. The genotype–environment interaction can be diminished in two
ways (Shelbourne 1972): (1) by creating groups of essentially homogeneous environments and selecting cultivars
suited to each environment (Shelbourne and Low 1980;
Kurinobu 1984; Clair and Kleinschmit 1986; Nishimura andTajima 1993; Miyaura 1994) and (2) by developing stable
cultivars which perform consistently over a range ofenvironments (Finlay and Wilkinson 1963; Owino 1977;
La Farge and Kraus 1981; Yeiser et al. 1981; Clair andKleinschmit 1986; Li and McKeand 1988; McKeand et al.
1990; Yamada and Miyaura 2002).
Many studies in Japan were conducted based on thefirst method of diminishing interaction (Kurinobu 1984;
Nishimura and Tajima 1993; Miyaura 1994). Finlay andWilkinson (1963) reported that a linear regression coefficient (aj) of growth traits against site productivity indicatesstability and relative adaptability of a tree variety to a site.
They considered a variety with a coefficient value near unity
to be of average stability and equally adapted to good andpoor sites. A variety with a value greater than unity is
unstable and better adapted to good sites. A variety with avalue less than unity is stable and better adapted to poorsites. They expected that varieties having similar values of aj
have less fluctuation in the ranking of height among forestsites. Yamada and Miyaura (2002) used the linear regression coefficient of growth traits against site productivity to
examine the stability and relative adaptability to different
environments within a breeding district by clonal lines
and open-pollinated families of sugi (Cryptomeria japonica
D.Don) plus trees. However, the examination of genotype–environment interactions of the growth traits of hinoki plus
trees (Chamaecyparis obtusa Endl.) has not been conductedin the Forest Tree Improvement programs in Japan.
The objective of this study was to examine the interaction between family and site concerning to the tree height ofopen-pollinated families of hinoki plus trees in the Kansai
Forest tree breeding region. We used the techniques ofFinlay and Wilkinson (1963) and Yamada and Miyaura(2002).

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Fig. 2. Distribution of test site productivity in the six breeding districts

Fig. 1. Locations of progeny test sites of hinoki plus trees

Materials and methods
Test sites and families
The analysis was carried out using the height data at 15
years old of 287 open-pollinated families. The number of
the analyzed test sites was 130 (Fig. 1). The tests were
established in randomized complete block designs of mostly
three (up to nine) replications. The number of families per
test site ranged from 5 to 45. Overall, 257 families out of 287
were tested at two or more test sites.
The Kansai Forest tree breeding region is administratively divided into six breeding districts: Kinki, Setouchi,
East Nihonkai, West Nihonkai, North Shikoku, and South
Shikoku. A total of 160 families were measured only in thebreeding district from which they had been selected, 102
families were measured in both the selection district and aneighboring district, and 25 families were measured in threeor more districts. Although 127 families out of 287 were
tested in two or more districts, only one family was tested inboth the Honshu area (Kinki, Setouchi, East Nihonkai, andWest Nihonkai breeding districts) and the Shikoku area
(North Shikoku and South Shikoku districts). Therefore,
each area was analyzed separately.

Henderson’s method 3 (Henderson 1953; Kurinobu et al.
1984).
Regression analysis
We examined the relationships between site productivity
and height of the families using the height data for 164
families tested at five or more test sites from 287 families.
Linear regression equations of height against site productivity were estimated with the ordinary least squares method:yij ⫽ ajXi ⫹ bj
where yij is the mean height of the jth family at the ith test
site, Xi is the site productivity of the ith test site, aj is thelinear regression coefficient of the jth family, and bj is aconstant specific to the jth family. Site productivity wasevaluated as the mean height in each of the 130 test sites.
We assumed that the site productivity shows the total effectof environment factors on the tree growth, such as climate,soil type, and management (Finlay and Wilkinson 1963).
The confidence interval at the 95% significance level of aj
for each family was calculated to examine whether or not
the value of aj is significantly different from unity.

Results
Difference of site productivities

Analysis of variance
SPSS (2001) was used to conduct an analysis of variance ofthe plot mean of tree height. A linear model for the analysisis:

The test site productivity of each breeding district is shownin Fig. 2. There was a considerable range in productivities
among the 130 test sites: from 2.37 m to 9.78 m. Each breeding district had test sites with productivities ranging fromhigh to low.

yijk ⫽ µ ⫹ αi ⫹ αβij ⫹ γk ⫹ αγik ⫹ εijk
where µ is the population mean across the tests, αi is a fixed
effect of the ith test, αβij is a fixed effect of the jth replicationin the ith test, γk is a random effect of the kth family, αγik isa random effect of the interaction between the ith site andthe kth family, and εijk is a random plot error of yijk.
We adopted typeII sum of squares of SPSS (Searle 1987).
That is equivalent to the sum of squares derived by the

Analysis of variance
The results of analysis of variance are shown in Table 1. The
height was significantly different among families in bothareas. There was no significant interaction between test site
and family in the Honshu area, whereas in the Shikoku area
the interaction was significant.

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Fig. 3. Linear regression coefficients and their confidence interval at
the 95% significance level plotted in ascending order of the value of theregression coefficient within each area. Black dots and vertical bars
represent the linear regression coefficients and their confidence intervals, respectively

Table 1. Analyses of variance for the plot mean of tree height forfamilies at the 103 sites in the Honshu area and the 27 sites in the
Shikoku area
Honshu area

Shikoku area

Sourcedf

Mean squaredf

Mean square

Site
Replicate (site)
FamilySite ⫻ Family
Error

102
198
225
1322
3040

75.65***
5.01***
0.67***
0.52NS
0.49

26
67
61
279
980

59.41***
5.17***
0.79*
0.50*
0.42

*** Significant at P ⬍ 0.001, * Significant at P ⬍ 0.05, NS nonsignificant

Regression analysis
Linear regression analysis showed a significant correlation(P ⬍ 0.05) between height and site productivity for 160
families out of 164 (r ⫽ 0.740 to 0.999). The values of thelinear regression coefficient aj of these 160 families ranged
from 0.643 to 1.355 (Fig. 3). Eleven families out of 160 had
values of aj that were significantly different from unity at the
5% significance level (the Honshu area: 9 out of 130, theShikoku area: 2 out of 30). The value of aj of seven of thesefamilies was significantly lower than unity, and that of fourfamilies was significantly greater than unity.
Figure 4 shows the relationship between the confidence
interval (95% significance level) of the linear regression
coefficient and the range of productivity of the test sites
within which each family was tested. The confidence interval tends to decrease as the range of site productivities
increases.

Discussion
The growth traits of each plus tree are evaluated by leastsquare estimates from the data of multiple progeny tests ineach breeding district, and the plus trees are ranked in thegrowth order in the Forest Tree Improvement programs ofJapan. This ranking is used for the purpose of roguing seed

Fig. 4. Relationship between confidence interval at the 95% significance level and the range of productivity of the test sites within which
each family was tested

orchards and choosing parents for the second generation
breeding population. In the present study, each breeding
district had test sites with considerable variations in productivity (Fig. 2). Each breeding district will include
heterogeneous environments. This fact suggests that theinvestigation of genotype–environment interaction is required in order to apply the ranking to the Forest Tree
Improvement programs. If there is strong genotype–environment interaction within a breeding district, the ranking
may show great fluctuation among forest sites. In this case,the stability and relative adaptability to different environments must be examined for each plus tree together withthe ranking.
We obtained several important facts concerning the existence and magnitude of any genotype–environment interactions of open-pollinated families of hinoki plus trees.
There was no significant interaction between test site andfamily in the Honshu area, whereas in the Shikoku area theinteraction was significant (Table 1). Only 11 families out of
160 had values of the linear regression coefficient aj thatwere significantly different from unity at the 5% significance level (Fig. 3). Approximately 93% of families hadvalues of aj that were not significantly different from unity.
These results suggest that almost all families had averagestability and were equally well adapted to good and poorsites (Finlay and Wilkinson 1963) in spite of the fact that
there was significant interaction in the Shikoku area. We
expect that the open-pollinated families of hinoki plus trees
show little fluctuation in the ranking of height among forestsites.
The confidence interval at the 95% significance level ofthe linear regression coefficient tended to decrease as
the range of site productivities increased (Fig. 4). The accumulation of data from many test sites with different
productivities is now in progress. The analysis using theaccumulated data in the future may reveal any other families having a value of aj significantly different from unity.
Moreover, the analyzed data in this study included considerable differences in testing frequencies among the families.
Further study is required to examine the existence and magnitude of any genotype–environment interaction.

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The 11 families showing values significantly different
from unity must be cautiously treated. Since these families
are either too sensitive or fairly stable to different site
productivities, these families may have great fluctuations in
the ranking of height among forest sites (Finlay andWilkinson 1963). The value of regression coefficient aj, as anindex of stability and relative adaptability of tree varieties
to sites, suggests an important indicator to prevent the reduction of overall genetic gains.
Acknowledgments We are grateful to the staff of the Prefectures andForest Agency for their kind help in collecting data, and to Y. Ando, K.
Nishimura, and M. Kubota for their kind help with analyzing the data.
We also would like to express our thanks to the two anonymous
reviewers for their many invaluable comments which helped us to
improve the manuscript.

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