MATERIAL AND METHOD 1 Management of breeding population

Bogor, 21-22 October 2015 763 disturbances or reducing area of the forest through illegal logging, fire, resettlement and other uses. Acacia mangium is one of major tropical species for plantation in South East Asia, particularly in Indonesia. Tree improvement program of A. mangium has being practiced comprehensively which is recently proceeding into the third cycles of breeding. Sub-line system with the procedures of a recurrent selection was adopted for the management of breeding population. Up to the second generation, infusion of some new collections of plus tree families from natural forest was involved into the structure of advanced generation breeding population. However, in the third generation the available number of the infused families from natural population was much reduced which might be due to the disturbance of natural forest. Therefore, in order to maintain genetic diversity discovering other alternative methods is necessary for optimizing genetic resources. Re-uses of the original founder families of first generation grandparents into advanced generation breeding population called τRecycled Genetic Resource” would become one of the alternative methods. In this paper the optimizing genetic resource following the advanced generation breeding strategy of tropical tree species was studied in third-generation progeny trial of Acacia mangium. The trial was established comprising three types of family representing the families from successive generations of breeding, infusion from natural forest and recycled genetic resources. The genetic variation and status of family ranking of all the tested families was calculated and then the result in optimizing of the genetic resources for advanced generation breeding program was discussed. 2. MATERIAL AND METHOD 2.1 Management of breeding population The breeding strategy for A. mangium was accomplished by using open-pollinated progenies from plus trees which were then tested in a progeny trial. This strategy was adopted due to some characters of the species, such as early flowering habit and difficulty on vegetative propagation as well as the limitation of available techniques of breeding using control pollination Wright, 1976. In order to the simplicity of pedigree control, the breeding population was managed by using sub –line system in which the single large population was split into several sub-populations van Buijitenen Lowe, 1979; McKeand Beineke, 1980; Barnes, 1984; Hodge et al., 1989. The sub-lining system is accomplished by establishing small breeding groups and mating trees is only within a group with allowing for some degree of inbreeding McKeand Beineke, 1980. However, completely outbreed offspring in seed production orchards can be maintained more precise by using only a few best seed parents selected from each sub-line. In order to avoid relatedness among the sub-lines and to adjust the timing of flowering, there are four sub-lines of A. mangium breeding population which were established separately based on their provenances: namely Sub-line A, B, C and D. The successive advanced generation breeding program was then practiced based on each sub-line independently. 2.2 Third-generation of progeny trial The third-generation progeny trial of A. mangium used in this study is sub-line B which composed of the families from Western Province provenance of Papua New Guinea Oriomo, Kini, and Wipim Districts. The trial was established in January 2012 in Central Java, Indonesia which was initially laid-out in randomized complete block design with four line tree-plot of 6 replications and a spacing of 4×2 m. Family tested in the third-generation progeny trial consists of 57 open-pollinated families of selected plus trees from three type of Bogor, 21-22 October 2015 764 resources: 1 plus tree selected in second-generation derived from first-generation 27 families, 2 plus tree selected in second-generation derived from infusion population 17 families, and 3 original founder families of first-generation grandparents 13 families. In this purpose study, the three types of family were then referred to as the three types of different genetic resources. 2.3 Measurement and data analysis 2.3.1 Traits, analysis of variance and genetic parameter Periodical measurements were conducted at one, two and three years of age involving traits of tree height, diameter at breast height dbh and stem straightness in which at one year of age the measured trait is only tree height. The growth traits tree height and dbh were measured using metric scale, while the form trait stem straightness was assessed using scoring of 1 worst to 5 best. Analysis of variance was made using individual tree data y ijkl with the following linear model: λλλλλλλλλλλλλ..1 where, µ, B i , P j , FP jk , FB ik , e ijkl are population mean, the effect the i-th replication, the effect of the j-th type of genetic resource, the k-th family effect within the j-th type of genetic resource, the effect of interaction between the k-th family and the i-th replication, the error associated with y ijkl , respectively. Additive genetic variances for each trait were calculated as four times the family variance component 4 , with assumption that open pollinated families analyzed here were half sibs Falconer, 1981. Phenotypic variances were calculated as the sum of family variance component and that of interaction family x block and error variance . Individual tree heritability h 2 i and family mean heritability h 2 f were then estimated by: λλλλλλ..λλλλλλλλλλλ.λλλλλλλλ2 .........................................................................................................................3 where, N = number of trees per plot and B = number of replications. While, genetic correlations between the two different measurements r gi,j were calculated as follows Falconer 1981: √ .λλ...................................λλλ..............................................λλλ.....4 where, cov fi,j , fi 2 , fj 2 are estimate of family covariance component between the i-th measurement and the j-th measurement, those of family variance component at the i-th measurement and at the j-th measurement, respectively. 2.3.2 Selection index for family ranking In order to observe the field performance of tested families, the selection index was calculated by combining information of several different traits into single index value which was then used to determine the family ranking. In this purpose of study, the three different traits used for selection index are tree height, dbh and stem straightness. Selection index I i for the i-th family was calculated according to the procedures as described by Kurinobu et al. 1994, by the following formula: Bogor, 21-22 October 2015 765 ………λλλλλλλλλλλλλλ. 5 where, b j is the coefficient of weight for the j-th trait, and x ij is the adjusted least square estimate of the i-th family on the j-th trait. The coefficient of weight b was calculated as a form vector by the following matrix formula: λλλλλλλλλλλλλλλ..λλ..λλλλλλλλλ6 where, P, G and a are phenotypic variance covariance matrix, that of family and vector of relative economic weight for the traits, respectively. The adjusted least square estimate was calculated by the following formula: [ ⁄ ] ⁄ ........................................................................................7 where, a ij , σ 2 fj , σ 2 ej and n i. are the lest square estimate of the i-th family on the j-th trait, the family variance component of the j-th trait, that of error variance and number of plots of the i-th family, respectively. For the convenience of analysis, expected genetic gain brought by family selection based on the index value is calculated by assuming one unit of selection intensity with the formula as follows: [ ] λλλλλλλλλλ.λλλ..λλλλλλ..λλλ8 Comparisons for variances, genetic parameters and genetic gain between the two groups of family: type-1+2 and type-1+2+3 were made to observe the impact of “recycled genetic resources” in the third-generation progeny trial of A. mangium. 3. RESULT AND DISCUSSION 3.1 Growth and genetic parameters