Estimating short-rotation Eucalyptus saligna production in Hawaii: An integrated yield and economic model

Bioresource Technology 45 ( 1993) 167 - 176

ESTIMATING SHORT-ROTATION EUCALYPTUS SALIGNA PRODUCTION IN HAWAII: A N INTEGRATED YIELD A N D ECONOMIC MODEL Wei Liu, a Robert A. Merriam, b Victor D. Phillips, a,* Devindar S i n g h a aCollege of Tropical Agriculture and Human Resources, Universityof Hawaii at Manoa, Honolulu, Hawaii 96822, USA bForestry Consultant, 616 Pamaele Street, Kailua, Hawaii 96734, USA (Received 3 September 199 2; revised version received 5 March 199 3; accepted 8 March 199 3)

as cut flowers, coffee, and macadamia nuts, to keeping Hawaii green through increased attention to conserving and restoring endemic biota and to creating an ecologically sound and economically profitable forest industry. The latter includes SRIC production of promising tropical hardwoods, e.g. Eucalyptus spp., Leucaena spp., Acacia spp., and others, that would be sustainably managed renewable resources for the manufacture of fiber and fuel products contributing to the local economy (Phillips et al., 1988; 1992a). Marketing analyses are being conducted in Hawaii for SRIC biomass feedstocks in fiberboard, charcoal, and electricity, methanol, ethanol, fuel production locally and wood chips for export for paper manufacture. Previous studies and trials have demonstrated that SRIC forestry production yields and costs are highly site- and management-specific (Perlack et al., 1986; Crabb, 1988; Strauss etal., 1988; Dudley, 1990; Hartsough & Nakamura, 1990; Liu et al., 1992). The authors' work focuses on modeling and optimizing SRIC systems utilizing a variety of species and management strategies on a site-specific basis. The results serve as a first approximation of yield and establishment costs for the production of fiber and fuel products, with associated aesthetic value to residents and tourists, with ecological integrity and economic success. The authors have reported previously on methodologies for determining land availability (Singh et al., 1992), land suitability (Phillips et al., 1990; 1992b), and biomass systems integration and cost modeling (Phillips et al., 1991; Liu et al., 1992) for SRIC forestry applications in Hawaii and elsewhere. Land owners and other decision-makers who are engaged in making choices on alternative land uses need reliable and timely information. To evaluate SRIC biomass production systems, site-specific biomass yield must be estimated, usually from costly and lengthy field experiments. The results of these experiments are limited to specific parcels of land unless they can be extended to areas where no field trials exist. A SRIC biomass yield model that integrates existing data from field trials and uses a geological information system to extend results to land areas where no field

Abstract Growth data of Eucalyptus saligna from plantation experiments in Hawaii and South Africa were analyzed to determined relationships between yield and soil, climatic, and management variables. Detailed investigations were made of growing space, fertilization, and site conditions on tree diameter. The independent variables used were growth age, planting density, nitrogen fertilizer application, temperature, rainfall and solar radiation, soil nitrogen content and p H value, and site elevation. The regression equation developed was used to estimate E. saligna yield of various short-rotation strategies and site-specific climate and soil conditions. By coupling these site-specific results with a geographical information system, yield was then predicted at all locations on the island of Kauai identified as available for biomass plantations. The estimated total land area available and potential biomass are presented. Establishment costs for E. saligna plantations on Kauai ranged from US $1207/ha to US S1655/ha for different planting densities and amounts of fertilizer applied.

Key words: Biomass crops, short rotation forestry, Hawaii, Eucalyptus saligna.

INTRODUCTION

Short-rotation, intensive-culture (SRIC) forestry is being evaluated currently in Hawaii for its potential contribution to diversified agriculture and the greening of the tourist industry and the energy system (Phillips et al., 1993). As traditional crops such as sugarcane and pineapple continue to decline, land owners are exploring alternative land uses. These range from urban and resort development, and production of new crops such

*To whom correspondence should be addressed. Bioresource Technology 0960-8524/93/S06.00 © 1993 Elsevier Science Publishers Ltd, England. Printed in Great Britain 167

168

Wei Liu, Robert A. Merriam, Victor D. Phillips, Devindar Singh yield (BioEnergy Development Corporation, 1980-1986; Yost et aL, 1987; Crabb, 1988; DeBell et aL, 1989; Dudley, 1990; Osgood & Dudley, 1990). Information recorded for each experiment included growth age (t, years), average diameter at breast height (DBH, cm), average height (HT, m), initial planting density (DEN, trees/ha), survival rate (%), amount of nitrogen fertilizer applied (FN, kg/tree), and plot aspect and elevation. Descriptions of weather and soil conditions of the plantation sites were also collected from the literature or from the Hawaii Natural Resources Information System (HNRIS) database and geographical information system (Liang & Khan, 1986). Site variables with sufficient information to allow inclusion in the analysis were elevation (EL, m), mean temperature (TE, °C), mean annual rainfall (RA, cm/year), mean daily solar radiation (SO, cal/cm 2 per day), soil nitrogen content (NI, %), and soil pH value.

trials have been conducted is needed (Phillips et al., 1990). Several biomass yield models have been developed for a wide variety of environments, species, and calibration requirements (Liang et al., 1983; Wisiol, 1984; Shirley, 1987). To estimate yield of selected species at close spacing and short-rotation intervals on potential plantation sites in Hawaii, however, a model that estimates tree size using management and commonly available site weather and soil data is not available. The authors have developed such a model that integrates the effects of growing space with cultural management and site conditions. This paper describes an integrated SRIC biomass yield model to accurately estimate biomass yield and establishment costs of Eucalyptus saligna production in Hawaii. METHODS Data collection

Integrated model development

The conceptual framework for data collection was based on experiments conducted in South Africa designed to study the effects of competition and thinning on Pinus spp. and Eucalyptus spp. production in managed forest stands (Bredenkamp, 1984). In this study, data are used from those same experiments as reported by Burgers (1976) to understand and develop the relationship between tree size and growing space on a site. A procedure is employed developed recently by Merriam (1992) to evaluate the growth of freegrowing trees of a species on a given site as well as the amount of growing space necessary for that tree to reach its potential growth. All available data from field trials in Hawaii on the growth of E. saligna were examined for their utility in this study (Table 1). These field trials, some of which are still in progress, have been implemented to evaluate the yield performance of promising species of tropical hardwoods in different environments (Dudley, 1990; Osgood & Dudley, 1990), the effect of species, provenances, planting densities, fertilization treatments, soil types, topography, and other variables on biomass

The size of a tree is closely related to the growing space available for densely planted and SRIC plantations. A plant first allocates the energy obtained through using its available growing space to maintain its present living cells (Oliver & Larson, 1990). After respiration demand is fulfilled, any extra energy is used for growth. A tree occupying a fixed growing space increases in size at progressively slower rates as competition for light, water, and nutrients increases and greater amounts of energy are allocated to respiration and maintenance of the increasingly larger living system. Size eventually reaches a maximum in a fixed growing space when all photosynthesis is used for respiration. The tree cannot grow larger unless its growing space is increased and no limiting factor comes into play (Oliver & Larson, 1990). When plants have filled all available growing space, they begin competing with other plants to obtain and maintain water, light, nutrients, and growing space (Fig. 1). A tree first undergoes a period of 'free growth' in the open-grown condition before it occupies all growing space (O'Connet, 1935). At this time, growth is identical for trees at

Table 1. Data sources for model development of Eucalyptus saligna plantations in Hawaii

Source BioEnergy Development Corporation & USFS Institute of Pacific Islands Forestry

Hawaiian Sugar Planters' Association

Location

Study type

Years measurement taken

Akaka falls Akaka falls Akaka falls Amauulu Kamae Kamae Kamae Ka'u Onomea Honokaa Hoolehua Mountain View

Spacing Fertilizer Fertilizer Spacing Nitrogen fixation Spacing Species trial Spacing Species trial Species and spacing Species and spacing Provenance test

1-6.1 1-6 1-25-4.75 3-4 2-4 3-6 1-5.5 1.5-5 1-6 1 2 2

Estimating short-rotation E. safigna production in Hawaii

all spacings. Trees at closer spacings fully occupy all growing space sooner and slow in growth. The space needed for a tree's growth, which can be obtained from the points where competition started (Fig. 1), increases as its size expands as indicated in Fig. 2. A tree is identified as 'free-growing' when it is not influenced by restriction in growing space as expressed in its crown or stem (O'Connor, 1935). The free-

100

Free growing

80 trees/ha 124 ,~, 60

7: 40

741 988 1483 2965 4175 6672

20

0

L

~

0

5

10

15

20

2'5

30

35

Age (years) Fig. 1. The development of average stand DBH in Eucalyptus saligna-grandis plantations established at different spacings in South Africa (after Burgers, 1976).

0

8

170 160 150 141? 130 120 110 100 9O

.//

e~

ru

6O 50 417 30 20 10 0

lb

/o

30

Step one -- the growing space model The effect of growing space on diameter growth was evaluated using the concept of a generalized competition index (Merriam, 1987) as subsequently modified following the concepts developed in South Africa by O'Connor (1935) and verified using data from Burgers (1976). T he effects of competition are twofold. Current size reflects all past competition and can be used as a measure of the reduced potential for future growth of a competitively grown tree. The fraction of the potential growth which can be realized during the next growth period is a function of the growing space available to the tree in relation to the growing space it would require to attain its growth potential. Current potential growth of a competitively grown tree (CGTPOT) is estimated from the potential growth of a free-grown tree (FGTPOT) adjusted for the size difference in the two, so that:

CGTPOT = FGTPOT x SIZERATIO

(1)

SIZERATIO = CGTSIZE/FGTSIZE

(2)

where:

CGTGRO = CGTPOT x COMPMOD

(3)

where COMPMOD is the modified negative exponential equation proposed by Merriam ( 1987): COMPMOD = [1 - e -

r---"

0

growth potential of a species varies from site to site because of site-specific, growth-promoting factors (e.g. solar radiation, temperature) and growth-limiting factors (e.g. rainfall, soil type, nitrogen fertilization) (Mohren & Rabbinge, 1990). Therefore, site spatial information must be superimposed on the relationship between tree growth and growing space. The development of our SRIC biomass yield model involved two steps. In the first step, the relationship between tree size (DBH), age, and planting density was derived using the data from South African experiments that were designed to study this relationship. In the second step, the effects of fertilizer, weather, and soil conditions were superimposed on the relationship developed in step one using data from all pertinent field trials in Hawaii. Regression analysis of experimental data was used to derive equations to predict average tree diameter at breast height (DBH).

CGTSIZE is current tree size and FGTSIZE is freegrown size. Expected growth (CGTGRO) is calculated from the equation (Holdaway, 1984):

?.

/

16 9

4b

50

DBH (era) Fig. 2. Growing space needed for a free-grown Eucalyptus saligna-grandis tree versus tree size (after Burgers, 1976).

k(CGTSPAVAIL/(CTS

x S1ZERATIO))] c

(4)

CGTSPAVAIL is the average growing space available to each tree in the stand and CTS defines the growing space required by a free-grown tree. In this equation the exponent c was selected to provide a curve with an inflection point fairly close to the origin and that adequately fits the known behavior of planted stands. Because of the asymptotic approach of COMPMOD to 1 when growing space is adequate, the value of k was

Wei Liu, Robert A. Merriam, Victor D. Phillips, Devindar Singh

170

derived so that C O M P M O D = 0.99 when CGTSPAVAIL= (CTS x SIZERATIO). There is no record of diameter measurements of free-grown E. saligna in Hawaii. Merriam (1992) reported that competitively grown eucalypt trees in Hawaii mimic growth of eucalypts measured by Burgers (1976). Therefore, it was assumed that a growth curve for a free-grown tree in South Africa and the growing space needed for that growth could be used as a surrogate growth curve for free-grown E. saligna in Hawaii. While Merriam (1987) used annual growth, a derivative function can express the competition process dynamically: d (DBH(DEN, t)) _ d(DBHF(t)) x SIZERATIO dt dt X [1 - e -

k(CGTSPAVAIL/(CTS x SIZERATIO))]c

(5)

where: DBH(DEN, t)

= diameter at breast height of a competitively grown treee at time t, (cm)

DBHF(t)

= diameter at breast height of a free-growing tree at time t,

where: A

= asymptotic D B H (cm)

k and m = regression coefficients Regression coefficients in eqn (7) were derived from the experimental data of the flee-grown trees in Fig. 1 using a SAS non-linear regression program (SAS Institute, 1985). The equation of best fit obtained was: DBHF(t) = 89.108( 1 - e - 0096(t- 1.65))

(8)

The growing space needed for a free-growing tree was obtained from the same source as: CTS = 284"593( 1 - e- 0.234,)5

(9)

By incorporating eqns (8) and (9) into eqn (6) and utilizing the Simulation Control Program (SCoP) computer software program (Kootsey, 1989), eqn (6) was solved and was used to predict E. saligna D B H at planting densities of 741, 988, 1483, 2965, 4175 and 6672 trees/ha (Fig. 3). Curves with the same shape characteristics as Fig. 3 were prepared by Burgers (1976) for Pinus spp. and Eucalyptus spp. The same curves were used by Clutter et al. (1983) and Oliver and Larson (1990) to illustrate the effects of competition on growth. No equation for this family of curves was given by any of these authors.

(cm) d(DBH(DEN, t))/dt=DBH growth rate of a competitively grown tree at time t (cm/year)

d(DBHF(t))/dt

= D B H growth rate of a freegrowing tree at time t (cm/ year)

SIZERATIO

= DBH(DEN, t)/DBHF(t)

CGTSPAVAIL

= 10 000/DEN (m 2)

DEN

= tree density (trees/ha)

CTS

=competition threshold space, growing space needed by a free-growing tree at time t (Fig. 2)(m 2)

k

=4"7

C

=1.1

The form of the resulting equation was: d(DBH(DEN, t) dt x ( 1 - e [-

d(DBHF(t)) x D B H ( D E N ' t ) dt DBHv(t)

4.7( 10 000/DEN)fl CTS x DBH(DEN,

t)/DBHF(t))])I. 1

Tree size varies from site to site and by cultural manangement. Therefore, site and management information must be superimposed into the DBH(DEN,t) estimated only from density and age. The model was fitted to the experimental data from Hawaii using multiple linear regression analysis. The variables -- mean daily temperature, mean annual rainfall, mean daily solar radiation, elevation, soil nitrogen content, soil pH value, nitrogen fertilization, and DBH(DEN, t) -- were used. The best fitting equation was determined by selecting the model with minimum error mean square and maximum R 2. Backwards elimination was used to delete insignificant independent variables until only the variables with significant (P< 0-05) partial T-values were included. The residuals were plotted against all the independent variables to make sure that the linear models fitted the responses well, and that no significant lack of fit remained after analysis (Draper & Smith, 1966). The equation to characterize the effects of management, soil, and weather parameters is: D B H = - 23" 13 + 0.42DBH(DEN, t)+ 14.26NI

(6)

The D B H growth curve of a free-grown tree was derived by using the Chapman-Richards growth function: DBHF(t ) = A(1 - e - k(~- to))11(1- m)

Step two -- integration of site and management information

(7)

+ 0-3SO + 32"20FN + 0"24TE

(10)

The F-statistics of the model is 171, and the multiple coefficient of determination value (R 2) is 0"868, of which 81% is attributed to the DBH(DEN, t) variable alone. The sign and significance of all the independent variables are listed in Table 2. Figure 4 depicts the

Estimating short-rotation E. saligna production in Hawaii

difference between experimental data used to construct the model and the predicted D B H by using the above regression equation for E. saligna. After the average D B H was estimated, average tree dry weight was then calculated using the formula by Whitesell et al. (1992) and yield (YI, dry Mg/ha) was calculated by multiplying average tree weight by density as:

60

Free growing

50 t_

40

~

171

for younger trees (t< 4 years): YI = 0"13580 × (DBH22336) x D E N

;2

30

for older trees (t = 4 to 6 years): YI = 0"06594 x (DBH 2"5772)× D E N

1483

2965

20

4175 6672

10

0

2

4

6

8

10

12

YI = f(DEN, t, SITE,FN)

Estimated DBH of Eucalyptus saligna trees versus density and age.

Variables

Prob > I TI

Sign

DBH(DEN, t) NI FN SO TE

0-0001 0"0001 0"0001 0.0001 0"0330

+ + + + +

(13)

where: YI

Table 2. Variables in order of partial T-value in the regression analysis for Eucalyptussaligna average DBH

(12)

The data ranges used to develop the E. saligna yield prediction model are presented in Table 3. The objective of developing a SRIC biomass yield model requires that the independent variables must be commonly available from previous experiments and potential planting sites. The model should reflect tree growth within a stand of a specified planting density (step one) and amount of fertilizer applied as well as the effects of a plantation site's soil and climatic conditions (step two). The result is an integrated model that is expressed as follows:

Age (years) Fig. 3.

(11)

= dry metric tonnes biomass per hectare (dry Mg/ha)

SITE = site-specific variables FN

= nitrogen fertilizer application (kg/tree)

D E N = initial planting density (trees/ha) t

= growth age (years)

Table 3. Data used in the development of Eucalyptus saligna yield prediction model from field trials at eight sites in Hawaii ÷

Variables "

16

+

+÷÷ 4*

÷ ÷~

'~

÷

8 +

7÷\

o

+ ÷ +~÷÷

.

4

8

i'2

PredictedD B H

2'o (cm)

Fig. 4. The diffcrencc between experimental and multivariate regression data for average Eucalyptus saligna DBH in Hawaii.

Site variables Elevation (m) Rainfall (mm) Temperature (°C) Solar radiation (cal/cm 2 per day) Soil nitrogen content (%) Soil pH value Management variables Planting density (trees/ha) Age (months) Nitrogen fertilizer applied (kg/tree)

Range of values 100-550 700-5080 19-9-23.8 310-491 0.063-0.793 4.4-6.8 625-10 000 12-73 0.013-0" 169

Sources: DeBell et al. (1989); Skolmen (1986); Dudley ( 1990); BioEnergy Development Corporation ( 1980-1986).

172

Wei L iu, Robert A. Merriam, Victor D. Phillips, Devindar Singh

DISCUSSION

Table 4. Management strategies used for model application

Not surprisingly, diameter at breast height as affected by density and age, or DBH(DEN, t), was a powerful predictor of tree size. It reflects the effect of growing space on tree size over time, i.e. competition within stands of a SRIC plantation. Soil nitrogen content is another significant variable affecting a tree's size (Whitesell et al., 1992). This factor should be considered when selecting sites for E. saligna plantations and deciding the amount of fertilizer to be applied. Nitrogen fertilizer application was another significant variable. Average tree DBH increases with increased fertilizer application. Also, solar radiation and temperature were positively associated with E. saligna tree size. This model combined the effects of growing space and site-specific variables on a tree's growth. The accuracy of the model is dependent on the precision and adequacy of the tree size measurements and of the soil and weather data. Because some of the soil and weather data on plantation sites were not available, an average value over a large area was used. Therefore, the performance prediction is always an approximation. The modeling approach does incorporate and utilize individual experimental results, however, and offers a step-by-step objective method that can be scrutinized and evaluated. It can be updated readily when new data become available. Further research should attempt to use data ranging over longer rotation periods. The longer rotation period (up to 10 years) will allow examination of the entire juvenile growth process. If new sample plots are contemplated, a wider range of nutrient applications and site conditions should be considered. Also, density-related mortality functions should be developed to improve the predictive properties of the model.

Application no.

Fertilizer application (kg/tree)

Planting density (trees/ha)

Growth age (months)

1 2 3 4

0.10 0.15 0.15 0.15

1000 1000 1000 2000

60 60 72 72

Model application The derived biomass yield equation was applied to predict E. saligna yield and total biomass at all locations on the island of Kauai identified as available for biomass plantations (Singh et al., 1992). Climatic data were obtained from HNRIS (Liang & Khan, 1986). HNRIS was developed for the management and planning of Hawaii agriculture and environment. One of its data files consists of historic weather and soil information for each 8-ha land parcel in Hawaii. Various management strategies (Table 4) were applied to demonstrate the effects of fertilizer application, planting density, and rotation age on yield. Figures 5-8 present the estimated biomass yield on selected sites under different management strategies and the results are summarized in Table 5. For application 1 (1000 trees/ ha, 0"10 kg nitrogen fertilizer/tree and 5 years growth age), E. saligna yield was low for all potentially available land for SRIC plantations on Kauai, with only 1315 ha identified from which 50-75 dry Mg/ha can be produced. In application 2 (same management as application 1 except that fertilizer was increased to

0"15 kg FN/tree), the total area where E. saligna can be produced between 50 and 75 dry Mg/ha increased to 8175 ha. Application 3 is the same as application 2 except the growth age is increased to 6 years. With the increase of one year of growth, 3287 and 10619 ha can produce biomass in the ranges of 75-100 and 50-75 dry Mg/ha, respectively. In application 4 (same management as application 3 except planting density was increased to 2000 trees/ha), E. saligna yield can be improved significantly -- 4793 ha can produce more than 100 dry Mg/ha and 6246 ha can produce in the range of 75-100 dry Mg/ha. Establishment cost estimation Many factors affect establishment cost, such as weather and soil conditions, previous land use, and scale of operation. BioEnergy Development Corporation has planted 700 acres of eucalyptus trees for a research and development biomass energy project on the island of Hawaii since 1978 (Whitesell et al., 1992). Standard field practices have been developed for all phases of operations including nursery, cleating, planting, replanting, fertilizing, weed control, and road-building (BDC, 1982). Estimates of general biomass production costs were provided based on experience in the BDC project (Whitesell et al., 1992). In this study, establishment costs were estimated using BDC data (Whitesell et al., 1992) and adjusted to the cultural management strategies analyzed.

Seedling costs The total seed cost for a plantation was determined from seed price and total seedlings needed. Eucalyptus seedlings were produced at a cost of US $36 per thousand (BDC, 1982). Land clearing costs Because most of the land available for biomass production on the island of Kauai is sugarcane land, the site clearing cost was based on that experienced by the sugar industry. Researchers in BDC suggested that on abandoned sugarcane land, a low ground-pressure D-6 tractor equipped with wide-gage shoes be used to pull a heavy-duty offset cutaway harrow. On very rocky soils, a heavy Krajewski roller needs to be used to cut and crush the cane and other vegetation. If the area is covered with particularly heavy vegetation and brush, a tractor equipped with a bulldozer blade is used. One

Estimating short-rotation E. saligna production in Hawaii

saligna

E.

YIELD . . . . . . . . . . . . . . . . . . .

(drg Mg/ha) l nBu t..,ho 1.1 k9 FN/tree

s~-7s 1~1s ho

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--:-

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ISg'37'38'

i 159"22'38'

Estimated Eucalyptussalignayield on selected areas of the island of Kauai -- application 1.

Fig. 5.

E. soligno YIELD ................... (dry Mg/ha)

i .................. i .................. !............. i .~--~,--~.,.. ~.,~..-ah.~

laua Lr.~,h0

! f "

8.15 kg FM/tree S yeors

~ _

•ii~i Sm- 75 8175 ha I

)

IHB

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:'-",,,

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f~,

!

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:

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/ 21"~2"~'

i 159"45' O'

Fig. 6.

pre-plant clearing.

159"37'30'

!-~ 159"38' O'

-! 159"22'38 '

Estimated Eucalyptussalignayield on selected areas of the island of Kauai -- application 2.

herbicide

application

is

applied

after

Planting costs Planting was assumed to be performed manually by a two-person crew, with one worker opening holes with a

metal dibble bar and the other following behind to place a seedling in each hole and to stamp the soil around the hole to close and finn the soil around the root mass. About 600 seedlings can be planted per person-day under good site conditions. In the rocky soils where an increased effort is required for the dig-

Wei Liu, Robert A. Merriam, Victor D. Phillips, Devindar Singh

174

E. soligno YIELD (dr~j Hg/ho)

................. ~ .................. i .................. :: ............

i

0.1S kg Ffl/Lree - • 50 - 7S 10619 ha

m

>~o,

:

:

¢~.

',

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~_...............

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~..,'-,

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:~i:!!i!1

I

!' .....

~: . . . . . . . . . . .

IB9"37'38'

~ ................

I~9"38' 8'

:ii~ii!~4~; . . . . .

I~9"~'3~ '

Estimated Eucalytpus saligna yield on selected areas of the island of Kauai -- application 3.

Fig. 7.

E. saligno YIELD

~!~! 58- 75

,

,

-i

gTG2

G24G ) 181] 4738

~°~#~ 75-188

.................................................

i t.:: ~

°" °'

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i:yl

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~.~'!iL:::::i~::~:.

i

159"37'38 "

~"~l~it;::i,;i,:~i,i,;:,:.,.i'l?~Jiiiiii%iii '~ 159"38' 8'

.;:1

159"22'30 '

Estimated Eucalyptus saligna yield on selected areas of the island of Kauai -- application 4.

ging and planting operation, only half as many seedlings (about 300) can be planted per person-day.

Fertilizing costs Two applications of 113 kg/ha of N-P-K (14-14-14) were applied at planting and six months after planting based on the experience of BDC (Whitesell et al.,

1992). The subsequent amount of fertilizer required varies according to site conditions, crop species, expected crop yield and economic considerations. Because the effect of N fertilizer on E. saligna was much greater than the effect of P and K fertilizer (Whitesell et al., 1992), only N fertilizer was considered in this study. Urea was used because of high

Estimating short-rotation E. saligna production in Hawaii nitrogen content (analysis 46-0-0) and relatively low price. The fertilizer cost per hectare was calculated by multiplying the total amount by its price.

Post-plant weed control costs Post-planting weed control is done with manual backpack sprayers. Glyphosate at a rate of approximately Table 5. Potential Eucalyptus saligna production under different management strategies on the island of Kauai

Application no.

Yield range (dry Mg/ha)

Total biomass (103 dry Mg)

1

50-75 75-100 > 100

69 0 0

1315 0 0

50-75 75-100 > 100

483 0 0

8175 0 0

50-75 75-100 >100

656 267 0

10619 3287 0

50-75 75-100 > 100

607 551 534

9762 6246 4738

Value in 1990 USS

Land clearing Pre-plant herbicide Planting stock Hand planting Replanting Post-plant herbicide Mowing between rows (spaced 3 m apart) Fertilizer application: 0 and 6 months Subsequent

5 h/ha at $75/h (range 0.7-7'4 h/ha) 0"5 h/ha at S40/h plus S173/lia for chemicals S0-05/tree S0"13/tree on Hamakua coast S0.26/tree on Ka'u District (rocky site) 5% of original planting cost 10 h/ha at $8-33/h + S79/ha for chemicals S47/ha NPK applied by hand to individual trees; 3 h/ha at $8"33/h + S0"04 per tree for fertilizer Urea applied by aircraft at S25/ha + S0"68/kg N

Source: Whitesell et al. (1992). Table 7.

10 liters/ha, with appropriate surfactant and antifoam agents, was used as a directed spray on the weeds at the time of two to three months after planting. Table 6 lists the assumptions for estimating establishment costs by BDC. Table 7 shows the estimated establishment costs of applications 1-4 in this study. The establishment costs varied from US $1207/ha to US $1655/ha according to different planting densities and amounts of fertilizer applied. CONCLUSION

Total area (ha)

Table 6. Assumptions underlying cost estimates for shortrotation Eucalyptus saligna plantations in Hawaii

Cost

175

Given the continuing decline of traditional plantation crops such as sugarcane and pineapple in Hawaii, landowners and other decision-makers actively seek information on alternative land uses. The SRIC biomass yield model developed provides such information for three tropical hardwood species, i.e.E, saligna, E. grandis, and Leucaena leucocephala, that hold promise for potential renewable energy or fiber industries in Hawaii. In this paper, the authors have featured E. saligna on the island of Kauai to illustrate model development and application. The authors are presently working to incorporate the economics of harvesting, post-harvest processing, transport and storage, and conversion of biomass feedstocks to marketable products into the model presented here. The comprehensive model will be capable of generating biomass supply curves to estimate amounts and delivered costs of biomass from potential plantation sites to specific conversion facility locations. This approach is a costand time-efficient means for providing information to landowners and decision-makers contemplating alternative land uses in Hawaii. The methodology can be applied readily to other regions worldwide. ACKNOWLEDGEMENTS This work was supported by the US Department of Energy and administered by the National Renewable Energy Laboratory through subcontract No. XZ-212025-1 to the Hawaii Natural Energy Institute, University of Hawaii at Manoa. Thanks go to the Department of Agricultural Engineering, University of Hawaii at Manoa (Tung Liang), for access to the Hawaii Natural Resource Information System. The authors recognize the pioneering efforts on SRIC forestry of the BioEnergy Development Corporation, Hilo, Hawaii (Thomas Crabb and Thomas Schubert);

Estimati•n•festab•ishmentc•stsforsh•rt-r•tati•nEucalyptussa•ignap•antati•nsinHawaii

Application no.

Site preparation (S/ha)

Planting and replanting (S/ha)

Herbicide (S/ha)

Nitrogen fertilization (S/ha)

Mowing between rows (S/ha)

Total (S/ha)

1 2 3 4

375 375 375 375

189 189 189 378

354 354 354 354

242 276 276 501

47 47 47 47

1207 1241 1241 1655

176

Wei Liu, Robert A. Merriam, Victor D. Phillips, Devindar Singh

the US Forest Service Institute of Pacific Islands Forestry, Honolulu, Hawaii (Craig Whitesell); the Hawaiian Sugar Planters' Association (Robert Osgood and Nick Dudley); and the Department of Horticulture at the University of Hawaii at Manoa (James Brewbaker and Robert Wheeler) from which data were collected for constructing the authors' model.

REFERENCES

BioEnergy Development Corporation (1980-1986). Eucalyptus plantations for energy production in Hawaii. Annual reports to the US Department of Energy. BioEnergy Development Corp., Hilo, HI. Bredenkamp, B. V. (1984). The C.C.T. concept in spacing research: A review. Proc. IUFRO Symp. on Site and Productivity of Fast Growing Plantations, 1, 313-32. Burgers, T. E (1976). Management graphs derived from the correlated curve trend projects. Bulletin 54, Department of Forestry, Pretoria, 60 pp. Clutter, J. L., Fortson, J. C., Pienaar, L. V., Brister, G. H. & Bailey, R. L. (1983). Timber Management: A Quantitative Approach. John Wiley & Sons, New York. Crabb, T. B. (1988). Eucalyptus Plantations for Energy in Hawaii: Ten Years Later -- The BioEnergy Development Corporation Story. BioEnergy Development Corporation, Hilo, HI. DeBell, D. S., Whitesell, C. D. & Schubert, T. H. (1989). Using N2-fixing albizia to increase growth of Eucalyptus plantations in Hawaii. Forest Sci., 35, 64-75. Draper, N. R. & Smith, H. (1966). Applied Regression Analysis. John Wiley, New York. Dudley, N. (1990). Performance and management of fast growing tropical trees in diverse Hawaii environments. MS thesis, Agronomy and Soil Science Department, University of Hawaii at Manoa, HI. Hartsough, B. R. & Nakamura, C. (1990). Harvesting Eucalyptus for fuel chips. California Agriculture, Jan-Feb, 7-8. Holdaway, M. R. (1984). Modeling the effect of competition on tree diameter growth as applied in STEMS. Gen. Tech. Rep. NC-78. USDA For. Serv., N. Central Forest Exp. Sta., St. Paul, MN. Kootsey, J. M. (1989). Introduction to Computer Simulation. National Biomedical Simulation Resource, Duke University Medical Center, Durham, NC. Liang, T. & Khan, M. A. (1986). A natural resource information system for agriculture. Agricultural Systems, 21, 81-105. Liang, T., Wong, W. P. H. & Uehara, G. (1983). Simulation and mapping agricultural land productivity: An application to Macadamia nut. Agricultural Systems, 11,225-53. Liu, W., Merriam, R. A., Phillips, V. D. & Singh, D. (1992). A spatial model for the economic evaluation of biomass production systems. Biomass and Bioenergy, 3,345-56. Merriam, R. A. (1987). A generalized competition index. Paper presented at the IUFRO Forest Growth Modeling and Prediction Conf, Minneapolis, MN, 24-28 Aug. 1987. Me rriam, R. A. (1992). Growth estimation in short rotation intensively cultured forest plantations based on a free grown tree standard. In Hawaii Integrated Biofuels Research Program -- Phase IV Annual Report. Subcontract No. XN-0-19164-1. Hawaii Natural Energy Institute. University of Hawaii at Manoa, HI. Mohren, G. M. C. & R@binge, R. (1990). Growth-influencing factors in dynamic models of forest growth. In Process Modeling of Forest Growth Responses to Environmental

Stress, ed. R. K. Dixon, R. S. Meldahl, G. A. Ruark & W. G. Warren. Timber Press, Portland, OR, USA, pp. 229-40. O'Connor, A. J. (1935). Forest research with special reference to planting distances and thinning. In Proc. British Empire Forestry Conf., South Africa, Government Printers, Pretoria, Republic of South Africa, pp. 1-30. Oliver, C. D. & Larson, B. C. (1990). Forest Stand Dynamics. McGraw-Hill, New York. Osgood, R. V. & Dudley, N. S. (1990). Establishment of biomass-to-energy research facilities. In Annual Report of Hawaii Integrated Biofuels Research Program -- Phase IlL Subcontract No. XN-0-19164-1. Hawaii Natural Energy Institute, University of Hawaii at Manoa, HI. Perlack, R. D., Ranney, J. W., Barron, W. F., Cushman, J. H. & Trimble, J. L. (1986). Short-rotation intensive culture for the production of energy feedstocks in the US: A review of experimental results and remaining obstacles to commercialization. Biomass, 9, 145-59. Phillips, V. D., Neill, D. R. & Takahashi, P. K. (1988). Methanol plantations in Hawaii. Paper presented at VIII Internal Symp. on Alcohol Fuels, Tokyo, Japan, 13-16 Nov. 1988. Phillips, V. D., Singh, D., Khan, M. A. & Takahashi, P. K. (1990). Matching species and sites for biomass plantations in Hawaii. In Proc. IGT Energy from Biomass and Wastes XIV Conf., Paper no. 8. Lake Buena Vista, Florida, 29 Jan.-2 Feb. 1990. Phillips, V. D., Giordano, A. & Alderucci, V. (1991). Methodology for assessing the biomass resource potential of Sicily. In Proc. 6th European Conf. of Biomass for Energy, Industry, and Environment, Athens, Greece, 21-27 April 1991. Phillips, V. D., Singh, D., Merriam, R. A. & Khan, M. A. (1993). Land available for biomass crop production in Hawaii. Agricultural Systems, 43, 1-17. Phillips, V. D., Chuveliov, A. V. & Takahashi, P. K. (1992a). A case study of renewable energy for Hawaii. Energy -The International Journal, 17, 191-200. Phillips, V. D., Singh, D., Khan, M. A. & Takahashi, P. K. (1992b). Preliminary assessment of biomass energy resources in Hawaii. Energy Sources, 14, 381-91. SAS Institute (1985). SAS User's Guide: Statistics, Version 5. SAS Institute, Inc., Cary, NC. Shirley, S. R. (1987). A generalized system of models forecasting central states tree growth. Research Paper NC279. USDA For. Serv., N. Central Forest Exp. Sta., St. Paul, MN. Singh, D., Phillips, V. D., Merriam, R. A., Khan, M. A. & Takahashi, P. K. (1992). Identifying land potentially available for biomass plantations in Hawaii. Agricultural Systems, 41, 1-22. Skolmen, R. G. (1986). Performance of Australian provenances of Eucalyptus grandis and Eucalyptus saligna in Hawaii. Research Paper PSW-181. USDA For. Serv., Pacific SW Forest and Range Exp. Sta., Berkeley, CA. Strauss, C. H., Blankenhorn, P. R., Bowersox, T. W. & Grado, S. C. (1988). Financial and energy costs of supplying woody biomass to conversion sites. Applied Biochemistry and Biotechnology, 18, 217-30. Whitesell, C. D., DeBell, D. S., Schubert, T. H., Strand, R. F. & Crabb, T. B. (1992). Short-rotation management of Eucalyptus: Guidelines for plantations in Hawaii. Gen. Tech. Rep. PSW-GTR-137. USDA For. Serv., Pacific SW Res. Sta., Albany, CA. Wisiol, K. (1984). Estimating grazing land yield from commonly available data. J. Range Management, 37, 471-5. Yost, R. S., DeBell, D. S., Whitesell, C. D. & Miyasaka, S. C. (1987). Early growth and nutrient status of Eucalyptus saligna as affected by nitrogen and phosphorus fertilization. Aust. For. Res., 17, 203-14.