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

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

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GENETIC CONTROL ON GROWTH AND WOOD DENSITY OF EUCALYPTS HYBRIDS UNDER TWO NUTRIENT CONDITIONS

Mulawarman1), Mohammad Na’iem 2), and Setyono Sastrosumarto2)

1)R&D Riaufiber, APRIL Indonesia 2) University of Gadjah Mada Yogyakarta, Indonesia

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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 m3, 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

Introduction

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E. grandisxpellita

Why E. pellita and its hybrid? - disease resistance

E. grandis x pellita
E. grandis

E.urophylla

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

E. pellita
E. grandis x pellita
E. urophylla
E. grandis x urophylla

Why E. pellita and its hybrid? - better growth

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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 P205, 50 kg K2O 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 σf

2 = ¼ σaf 2

σm

2 = ¼ σam 2

σfm

2 = ¼ σd 2

σa

2 = 2 (σf 2 + σm 2)

A/D = σa

2 / σd 2

AF/A = σaf

2 / σa 2

A/G = σaf

2 / σg 2

σg

2 = σa 2 + σd 2

Measurements

Growth – diameter, height at 6, 12 and 66 months Wood density at 66 months

E. pellita x urophylla development and field trial

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Additive 0.30 1.00 NE 0.36 0.36 51 529 Wood density (kg m-3) Additive 0.29 1.00 NE 0.19 0.19 0.073 0.093 Volume (m3) Dominance 0.00 0.17 0.20 0.08 0.47 3.9 11.8 Diameter (cm) Dominance NE 0.00 0.00 0.00 0.62 4.2 16.9 Height (m) 66 months Additive 1.00 0.61 1.54 0.33 0.54 0.65 1.54 Diameter (cm) Additive 1.00 0.54 1.18 0.24 0.44 101.2 134.1 Height (cm) 12 months Additive 0.37 0.59 1.47 0.28 0.46 0.2 0.53 Diameter (cm) Dominance 0.46 0.47 0.90 0.25 0.53 20.3 75.9 Height (cm) 6 months Genetic effect AF/A A/G A/D h2 G/P SE Mean Traits

Genetic control under low nutrient condition

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Genetic control under high nutrient condition

66 months 12 months 6 months Additive 0.24 1.00 NE 0.27 0.27 49 536 Wood density (kg m-3) Additive 0.20 1.00 NE 0.29 0.29 0.085 0.125 Volume (m3) Additive 0.24 1.00 NE 0.29 0.29 4.2 13.4 Diameter (cm) Additive 0.33 1.00 NE 0.13 0.13 3.9 18.4 Height (m) Dominance 1.00 0.27 0.36 0.16 0.61 1.12 2.73 Diameter (cm) Dominance 1.00 0.28 0.38 0.16 0.60 74.5 206.4 Height (cm) Dominance 1.00 0.18 0.22 0.11 0.59 0.36 1.02 Diameter (cm) Dominance 1.00 0.25 0.34 0.16 0.62 37.5 118.7 Height (cm) Genetic effect AF/A A/G A/D h2 G/P SE Mean Traits

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.

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0.000 0.020 0.040 0.060 0.080 0.100 0.120 0.140 0.160 Low 0.040 0.060 0.080 0.100 0.120 0.140 0.160 0.180 0.200 0.220 High

1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 47 46

440 460 480 500 520 540 560 580 600 Low 480 500 520 540 560 580 600 620 High

1 2 3 4 5 6 7 8 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47

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)

Adaptive genotypes High nutrient demanding genotypes Low nutrient demanding genotypes Always inferior genotypes Rs = 0.43 (p = 0.003) Adaptive genotypes High nutrient demanding genotypes Low nutrient demanding genotypes Always inferior genotypes Rs = 0.11 (p = 0.484)

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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

nly 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.

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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.