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Provenance by Site Interaction of Pinus Densiflora in Korea

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Provenance by Site Interaction of Pinus Densiflora in Korea Kim et. al.·Silvae Genetica (2008) 57-3, 131-139 Provenance by Site Interaction of Pinus densiflora in Korea By IN-SIK KIM1),*), HAE-YUN KWON2), KEUN-OK RYU2) and WAN YONG CHOI2) (Received 26th October 2006) Abstract also reported that there are remarkable genetic varia- Thirty-six provenances of Pinus densiflora were evalu- tion in inter and intra populations of P. densiflora using ated for stability and adaptability for height growth at isozyme and DNA markers (KIM et al., 1995; LEE et al., 11 test sites in Korea. The data were obtained from 1997). measurements at age 6 and analyzed using linear A range wide provenance test for P. densiflora was regression model and AMMI (additive main effect and established in 1996 by the Korea Forest Research Insti- multiplicative interaction) model. There was significant tute to address seed transfer zoning and to determine provenance by site interaction effect (p < 0.011). The interaction term explained 7.1% of total variation. the suitable seed sources for reforestation programs. While the regression model accounted for 15.8% of GxE KIM et al. (2005) reported that temperature, humidity interaction term, the AMMI model accounted for 74.9% and annual mean growing days of test sites were posi- with four PCA values. Most of the provenances were not tively correlated with survival rate and height growth. A significantly different from the unity (b =1.0), except for considerable amount of variation in survival rate and Inje (1), Jungsun (4), Bongwha (5), Koryung (26), height growth was explained by latitude, annual mean Hamyang (30) and Seoguipo (36). Adaptability of prove- growing days, extreme low temperature (Dec. ~ Feb.) nances to the test sites was estimated with mean height and extreme high temperature (Nov. ~ Feb.) of prove- growth and first AMMI component scores (IPCA 1). Inje nances. These results were obtained from four test sites (1), Bongwha (5), Taean (20) and Seoguipo (36) were specifically adapted to the high yielding environments. representing the cool-temperate, mid-temperate, warm- Considering the first and second AMMI components temperate and sub-tropical zones in Korea, respectively. (IPCA 1 and IPCA 2, respectively) scores, Whachun (2), However, the magnitude and pattern of genotype by Samchuk (10), Joongwon (14) and Buan (29) prove- environment interaction for P. densiflora has not yet nances were more stable than others. The implication of been reported. GxE interaction was discussed in view of seed transfer In tree improvement, the GxE interaction is an and delineation of seed zones. important consideration, as both its magnitude and its Key words: linear regression model, AMMI model, provenance pattern have profound implications for breeding, test- test. ing, and seed deployment (JOHNSON and BURDON, 1989; CARSON, 1991). The existence of GxE interactions calls Introduction for the evaluation of genotypes in many environments to determine their true genetic potential (CHAHAL and Pinus densiflora is naturally distributed in temperate GOSAL, 2002). Thus, an assessment of stability (or regions of Korea, Japan and eastern China (RICHARDSON adaptability) and GxE interaction is fundamental to the and RUNDEL, 1998). In Korea, P. densiflora is an impor- development of a sound seed movement policy (YEISER et tant timber species and the most widely distributed al., 2001). conifer species (LEE and CHO, 2001). Its range extends from Hambuk province in North Korea to Jeju province The GxE interaction may be defined as the inconsis- in South Korea. Although it is observed at alpine region, tent relative performance of two or more genotypes over their main distribution range is low to moderate eleva- two or more environments (YEISER et al., 1981). Geno- tion (KOREA FOREST RESEARCH INSTITUTE, 1999). It often type by environment interaction may be due to hetero- forms pure stands along the mountain ridges and slopes geneity of variance measured at each of the sites, where but sometimes forms mixed stands with hardwood ranking of genotypes in various environments is unaf- species in Korea. fected or due to both heterogeneity of variances and There are much variation of growth characteristics rank changes (DICKERSON, 1962). among P. densiflora provenances and great potential for Analysis of stability and adaptability is a biometrical selection and breeding, i.e., tree form (UYEKI, 1928), bio- method with great potential for characterization of the mass production (PARK and LEE, 1990) and wood quality relative performance of a group of population (families, (YIM and LEE, 1979; KIM et al., 2002). Other researchers varieties, hybrids, lines, clones, etc.) under different environmental conditions (VIANA and CRUZ, 2002). The detection and quantification of GxE interaction has 1) Forest Seed Research Center, Korea Forest Research Institute, 670-4 Suheori, Suanbomyun, Chungju, Chungbuk 380-941, been attempted through four different statistical Republic of Korea. approaches, i.e., partitioning of variance, regression 2) Tree Breeding Division, Korea Forest Research Institute, 44-3 analysis, non-parametric statistics and multivariate Omokchun, Kwonsun, Suwon, Kyonggi 441-847, Republic of technique (CHAHAL and GOSAL, 2002). Korea. In analysis of variance, the interaction between geno- *) Author to whom all correspondence should be addresses: IN-SIK KIM. Phone: +82 43 850 3332, fax: +82 43 848 2902. types and environments contribute to the total variance E-mail: [email protected] which is estimated and tested for statistical significance. Silvae Genetica 57, 3 (2008) 131 DOI:10.1515/sg-2008-0020 edited by Thünen Institute of Forest Genetics Kim et. al.·Silvae Genetica (2008) 57-3, 131-139 Each of variance components not only reflects the analysis is based on the concept that the component of a nature of genotype by environment interaction but helps genotype by environment interactions are linearly relat- to devise suitable selection and testing strategy to avoid ed to environmental effects measured as the average or utilize these interactions for the development of suit- performance of all test genotypes for the character able types of provenances (ISIK et al., 2000). Regression under consideration (FINLAY and WILKINSON, 1963). In Table 1. – The location of 36 provenances of Pinus densiflora. 132 DOI:10.1515/sg-2008-0020 edited by Thünen Institute of Forest Genetics Kim et. al.·Silvae Genetica (2008) 57-3, 131-139 this approach, the interpretation of the genotype pat- domized complete block design with five replications. tern obtained when genotype regression coefficients are Each provenance was planted in 10-tree row plot in each plotted against genotype mean values. The non-para- block and at a spacing of 1.8 m x 1.8 m. The data of metric approaches of genotype by environment interac- height growth was obtained from measurement at age 6. tions are based on the concept of analyzing relative Data set was analyzed with a linear regression model ranks of genotypes in different environments. In non- (FINLAY and WILKINSON, 1963) and AMMI (additive main parametric approaches, no assumption is required about effect and multiplicative interaction) models (GAUCH and mathematical distribution of analyzed values, homo- ZOBEL, 1988) to evaluate adaptability and stability of geneity of variances and additivity of effects (HÜHN, P. densiflora provenances at different environments. 1966). The basic concept of multivariate analysis is to Simple linear regression provides a conceptual model explain the multidimensional variation by a reduced for genotype stability and is the most widely used statis- number of dimensions has been used to characterize and tical technique in plant breeding (HAYWARD et al., 1993). understand genotype by environment interactions in the In this approach, the components of GxE interactions form of pattern of relationship among genotypes which are linearly related to environmental effects measured perform uniformly across environments (CHAHAL and as the average performance of all test genotypes for the GOSAL, 2002). There are some statistical and biological character under consideration (FINLAY and WILKINSON, criticisms and limitations in these four approaches 1963). The estimates of linear regression and the geno- above mentioned (PSWARAYI et al., 1997; CHAHAL and typic means are then used to indicate the adaptive prop- GOSAL, 2002; ALBERTS, 2004). Thus, it is considered that erty of each genotype (CHAHAL and GOSAL, 2002). The GxE interaction was detected and quantified using two linear regression model is: or more approaches together. This study was conducted to examine 1) the magni- tude of provenance by environment interaction, 2) the where Y is the mean of provenance i in environment j; response pattern of provenances to environments and 3) ij µ is the general mean; g is the mean of provenance i to select the suitable seed sources for reforestation of i over all environment; E is the environmental index for P. densiflora. j environment j (Y.j – Y..); bi is the slope of regression spe- cific for provenance i; and eij is the residual variation Materials and Methods which is assumed to be zero for the values averaged over To examine GxE interaction and stability of P. densi- replications. The provenances and test sites were flora provenances, data were collected from eleven regarded as random effect because the provenances and provenance trials established by Korea Forest Research test sites were sampled at random as mentioned above. Institute in 1996 with 36 provenances (Table 1). The So the provenance and site effect were tested against seed sources were systematically selected to cover whole the interaction mean square. geographic range of P. densiflora, i.e., a point of intersec- AMMI model is a multivariate approach to analyze tion between latitudinal and longitudinal line was GxE interaction. AMMI extracts genotype and environ- selected as a sampling site (HYUN and HAN, 1994). The ment main effects, then uses principal component analy- planting sites were also selected in similar manner sis to explain patterns in the GxE or residual matrix (Table 2). The field trials were established with a ran- (HAYWARD et al., 1993).
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