GENETIC CONTROL ON GROWTH AND WOOD DENSITY OF EUCALYPTS HYBRIDS - PowerPoint PPT Presentation

GENETIC CONTROL ON GROWTH AND WOOD DENSITY OF EUCALYPTS HYBRIDS UNDER TWO NUTRIENT CONDITIONS Mulawarman 1) , Mohammad Na’iem 2), and Setyono Sastrosumarto 2) 1) R&D Riaufiber, APRIL Indonesia 2) University of Gadjah Mada Yogyakarta, Indonesia

Introduction • Area of natural forest in Indonesia is declining rapidly , Government of Indonesia has stated that planted forest should supply all wood for forest industry by 2010 and targeted the establishment of 5 millions of planted forest by 2009 • Current wood demand for pulp mills in Indonesia is about 25 million m 3 , mainly Acacia wood. • Eucalypts is increasingly important as alternative species. E. pellita and hybrid derived from E. pellita is promising in low elevation area. • Although hybrid has been used extensively, no sufficient information on genetic basis of hybrid superiority and hybrid breeding strategy. • Since fiber plantations are established in wide range of site and silvicultural conditions, developing genotypes capable in rapid growth under specific site and silvicultural condition is increasingly important

Why E. pellita and its hybrid? - disease resistance E. grandisxpellita E. grandis E.urophylla E. grandis x pellita

3/27/6 A2787 A1974 Why E. pellita and its hybrid? - better growth A1975 E. grandis x urophylla TPL7 E. grandis x pellita 2/38/2 A1980 E. urophylla A2613 4/32/6 E. pellita TPL6 A2441 3/24/7 A2515 4/31/6 A1976 6/28/8 TPL17 6/9/1 TPL9 A2534 2/32/3 Clone A1990 A2559 TPL2 3/40/1 6/7/7 2/14/1 1/17/5 1/9/1 5/8/2 1/18/6 3/26/5 TPL10 5/15/8 2/21/6 2/19/1 TPL3 6/15/8 1/41/3 TPL11 IND32 IND34 5/12/3 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0 3/ha/yr) Predicted MAI at 1.5 yr (m

E. pellita x urophylla development and field trial • Genetic material 37 hybrid families derived from controlled pollination using factorial mating design – 9 E. pellita as female parent and 5 E. urophylla as male parents. • Progeny testing Row-column design, 6 reps, 2 trees per plot, established in year 2000 in two nutrient condition – no fertilization and 100 kg N, 50 kg P 2 0 5 , 50 kg K 2 O per hectare • Statistical model y = µ + rep + row + col + fem + male + fem.male +err Variance component estimation by using REML, from which genetic parameters were derived σ f 2 = ¼ σ af 2 σ m 2 = ¼ σ am 2 σ fm 2 = ¼ σ d 2 σ a 2 = 2 ( σ f 2 + σ m 2 ) A/D = σ a 2 / σ d 2 AF/A = σ af 2 / σ a 2 A/G = σ af 2 / σ g 2 σ g 2 = σ a 2 + σ d 2 • Measurements Growth – diameter, height at 6, 12 and 66 months Wood density at 66 months

Genetic control under low nutrient condition h 2 Mean SE Traits G/P A/D A/G A F /A Genetic effect 6 months Height (cm) 75.9 20.3 0.53 0.25 0.90 0.47 0.46 Dominance Diameter (cm) 0.53 0.2 0.46 0.28 1.47 0.59 0.37 Additive 12 months Height (cm) 134.1 101.2 0.44 0.24 1.18 0.54 1.00 Additive Diameter (cm) 1.54 0.65 0.54 0.33 1.54 0.61 1.00 Additive 66 months 16.9 4.2 Height (m) 0.62 0.00 0.00 0.00 NE Dominance Diameter (cm) 11.8 3.9 0.47 0.08 0.20 0.17 0.00 Dominance Volume (m 3 ) 0.093 0.073 0.19 0.19 NE 1.00 0.29 Additive Wood density (kg m -3 ) 529 51 0.36 0.36 NE 1.00 0.30 Additive

Genetic control under high nutrient condition h 2 Mean SE Traits G/P A/D A/G A F /A Genetic effect 6 months Height (cm) 118.7 37.5 0.62 0.16 0.34 0.25 1.00 Dominance Diameter (cm) 1.02 0.36 0.59 0.11 0.22 0.18 1.00 Dominance 12 months Height (cm) 206.4 74.5 0.60 0.16 0.38 0.28 1.00 Dominance Diameter (cm) 2.73 1.12 0.61 0.16 0.36 0.27 1.00 Dominance 66 months Height (m) 18.4 3.9 0.13 0.13 NE 1.00 0.33 Additive Diameter (cm) 13.4 4.2 0.29 0.29 NE 1.00 0.24 Additive Volume (m 3 ) 0.125 0.085 0.29 0.29 NE 1.00 0.20 Additive Wood density (kg m -3 ) 0.27 0.27 NE 1.00 0.24 Additive 536 49 • Relative contribution of the additive and the dominance genetic effect on growth is affected by growth stages and nutrient condition • There was a significant change in variance structure as affected by nutrient condition.

41 0.220 620 High nutrient demanding Adaptive 20 Adaptive High nutrient genotypes genotypes genotypes demanding genotypes 10 0.200 42 600 39 45 0.180 25 13 11 40 26 44 580 41 8 30 0.160 16 2 21 18 14 32 22 560 0.140 40 36 High 21 High 45 38 16 2 33 25 31 15 33 11 44 23 38 10 0.120 37 4 35 540 42 12 46 3 29 26 6 30 6 18 17 28 5 Low 37 31 43 1 27 0.100 43 Always inferior Low nutrient nutrient 4 15 32 19 23 22 24 520 genotypes demanding demanding 29 14 47 46 27 34 genotypes genotypes 0.080 28 20 24 7 8 7 17 3 35 34 47 1 5 500 39 0.060 19 Always inferior 36 13 genotypes 12 Rs = 0.11 (p = 0.484) 480 Rs = 0.43 (p = 0.003) 0.040 440 460 480 500 520 540 560 580 600 0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160 Low Low • Genotypes differ in response to nutrient condition • This response is related to their internal capacity to be adaptive both in ‘good’ and ‘poor’ sites • Some genotype always perform well under high and low nutrient condition (adaptive genotypes), some genotypes need specific nutrient condition (high nutrient demanding and low nutrient demanding genotypes) and genotypes that are always inferior both under high and low nutrient condition)

Conclusion & practical implication • Hybrids performance is not predictable from their parental performance. It is worth spending more effort on producing the hybrid rather than selecting the parents to be hybridized desired crosses. • The difference in response to nutrient condition as shown in this study indicates the benefit of selecting genotype for specific nutrient regime. • Nutrient problems in planted forest should not be solved exclusively by soil amendment. Screening genotypes that are match with particular fertilization regime is important to optimize site productivity and maximize economic return of fertilization. Selection should not focus only on general performing genotypes. • The underlying processes that contribute to the differences in response to nutrient condition should be well understood. It is worth studying whether the genetic difference in response to nutrient condition is also expressed in genetic variation in nutrient use efficiency.

Acknowledgement • Field staffs of Wanagama Forest Research Station for their assistance in establishing, maintaining the trials and carrying out measurements and taking core sample, • Agus Kurnia and his staffs in Riaufiber Wood Technology Laboratory for determination of wood density. • Director of Riaufiber R&D and the management of Riaufiber for supporting the main author to attend the “Australasian Forest Genetic Conference (AFGC)” held on 11-14 April 2007 in Hobart, Tasmania. • AFGC organizing committee for allowing the authors to make presentation.