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Factors affecting nonindustrial private forest landowners’willingness to supply woody biomass for bioenergy

Omkar Joshi a, Sayeed R. Mehmood b,*aArkansas Forest Resource Center, P.O. Box 3468, Monticello, AR 71656, United Statesb School of Forest Resources and Arkansas Forest Resource Center, P.O. Box 3468, Monticello, AR 71656, United States

a r t i c l e i n f o

Article history:

Received 11 August 2009

Received in revised form

14 June 2010

Accepted 4 August 2010

Available online 15 September 2010

Keywords:

Cellulosic biomass

Wood-based bioenergy

Logit model

* Corresponding author. Tel.: þ1 (870) 460 18E-mail address: Mehmood@uamont.edu (

0961-9534/$ e see front matter ª 2010 Elsevdoi:10.1016/j.biombioe.2010.08.016

a b s t r a c t

Bioenergy is a renewable form of potential alternative to traditional fossil fuels that has

come to the forefront as a result of recent concerns over high price of fuels, national

security, and climate change. Nonindustrial private forest (NIPF) landowners form the

dominant forest ownership group in the southern United States. These forests often tend

to have large quantities of small diameter trees. Use of logging residues and non-

marketable small diameter trees for bioenergy production can create economic opportu-

nities for NIPF landowners. The results demonstrated that landowners’ willingness to

harvest woody biomass was influenced by their ownership objectives, size of the forest,

structure and composition of tree species, and demographic characteristics. The model

found that relatively younger landowners who owned large acres of forestland with pine

plantations or mix forests had the potential to become a preferable choice for contractors,

extension foresters and bioenergy industries as they were more likely to supply woody

biomass for bioenergy. Findings of this study will be useful to bioenergy industries,

extension foresters, nonindustrial private forest landowners and policy makers.

ª 2010 Elsevier Ltd. All rights reserved.

1. Introduction and carbon-neutral energy, contribution to nation’s energy

In the United States, bio-fuels generation from biomass has

taken center stage in recent years with rapidly depleting

domestic oil supply and increasing dependence on politically

unstable foreign oil suppliers [1]. Forest and agriculture are the

two largest potential sources of biomass in the United States

with capacities of producing 368 and 998 million tons of

biomass respectively [2]. The forest-based biomass can be

obtained from different woody residues that are produced

during timber harvesting and other silvicultural operations,

the fuelwood collected from forests, urban wood waste, the

residues from mill operations,[2] woody and perennial

herbaceous crops and agricultural residues [3].

Several environmental and socioeeconomic benefits such

as reduction of greenhouse gas emissions through renewable

94; fax: þ1 870 460 1092.S.R. Mehmood).ier Ltd. All rights reserve

supply, enhancement of forest health, and generation of

income and employment for rural communities [4e6] are

some of the reasons behind the recent interest in renewable

energy sources in the United States.

About 48% of the forestland in the United States and 71% in

the South are owned by nonindustrial private forest (NIPF) or

family forest landowners [7]. NIPF growing stocks, from soft-

woods and hardwoods in the year 2002, were 491,800 million

cubic feet and 364,357million cubic feet, respectively [7]. Since

NIPF owners control a large part of the nation’s forest

resources, their forest management decisions are critical for

the supply of timber and other forest products in the United

States [8].

Though most of the current bio-fuel production in the

United States involves agricultural inputs, continuous use of

d.

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 8 6e1 9 2 187

food for ethanol and biodiesel production may have an

adverse effect on sustainable food supply in the long run

[9,10]. Conversion of 30% of the current corn productions to

ethanol shows a competition between feed and bio-fuel,

which indicates that an ambitious goal of energy security

cannot be achieved without utilizing other types of biomass

such as woodymaterials [11]. Thus, use of woody biomass can

help meet the energy demands of the nation without

compromising food demands of both people and domestic

animals [11]. Annual availability of 36.2 million dry tonnes of

recoverable logging residues for electricity production and

CO2 displacement [5] further justify that woody biomass is

a viable option for sustainable production of alternative

source of energy in United States.

In the southern United States, acreage of pine plantation

has increased considerably in the past few years [12]. Now,

many of these plantations are near maturity with declining

pulpwood markets [1]. Given the limited markets for small

diameter wood, large quantities of unthinned small diameter

trees, and damaged timber from natural disaster and pest

outbreaks have increased the risk of wildfire hazards and

negatively impacted forest health [4,12]. On the other hand,

demand for southern stumpage has declined considerably in

the last decade [12]. In the South, bioenergy can be produced

from a variety of woody materials including those obtained

as a byproduct of silvicultural operations and do not have

other attractive economic use [1]. Therefore, bioenergy brings

more economic opportunities in comparison to conventional

timber harvesting as silvicultural byproducts become addi-

tional source of income in bioenergy markets [1]. Past studies

have shown that timber harvesting decisions of landowners

are mostly dependent upon market price, landowner type,

bequest motives, size of forest tract, parcelization, environ-

mental preferences, management objectives, and a number

of demographic and forest management characteristics etc

[13e16]. Given the environmental and socioeeconomic

benefits associated with wood-based energy, it is possible

that influence of these factors on landowners’ decisions to

harvest woody biomass for bioenergy may be different than

conventional timber harvesting decisions. Similarly, the

feedstock for bioenergy can be obtained from the forest

without necessarily conflicting with the wildlife management

or recreational objectives of the landowners. Therefore, these

studies cannot be generalized for understanding landowners’

willingness for supplying wood-based biomass for bioenergy

industry. This study aims to understand different biophysical

and forest management characteristics like size of forest

parcels, length of ownership, size, structure and composition

of tree species, forest management objectives, mode of

harvest and demographic characteristics (like age, education,

income) which may be instrumental in predicting land-

owners’ willingness to supply woody biomass for bioenergy

production.

2. Landowner decision on timber harvesting

Willingness of forest landowners to harvest or supply wood

fiber from their land has been the focus of a number of

studies. Stordal et al. applied two step sample selection

estimation procedure to analyze the factors affecting land-

owner decisions and level of harvesting [17]. The result

showed that the interest in harvesting and harvesting levels

was influenced by factors such as forest management plan,

size and location of property, education and income from

agriculture [17]. Similarly, Favada analyzed empirical timber

supply models from Finnish nonindustrial private forests in

which age of landowner was found to be the important

factor in timber stocking decisions [18]. Likewise, Vokoun

et al. applied multiple bounded discrete choice (MBDC)

approach in which landowners were offered a range of

possible prices and multiple response options that allowed

them to express a level of certainty with their choices [19].

The authors identified size of forest ownership, length of

ownership, presence of existing structures and residence of

landowners as major determinants for their intensity

of harvesting [19]. Similarly, other studies have shown that

timber harvesting decisions of landowners are mostly

dependent upon market price, landowner type, bequest

motives, size of tract, parcelization, environmental prefer-

ences, management objectives, and demographic and forest

management characteristics [13e16,20,21]. But, none of

these studies have focused on understanding landowners

harvesting decisions to supply woody biomass for an

emerging market such as bioenergy.

There have been studies to understand NIPF landowner

attitude toward bioenergy. Munsell and Germain studied

some of the challenges and potential benefits associated with

woody biomass energy with an examination of current har-

vesting practices of private forest landowners [22]. The

authors highlighted that woody biomass, if harvested wisely,

can be used for bioenergy production without negatively

influencing the other forest management goals such as

sustainable harvesting of sawtimber, keeping considerable

amount of residual stock, and avoiding premature harvesting

[22]. Similarly, Foster et al. found logging residues, timber

from silvicultural operations like thinning and stand damaged

by natural disturbances as some of the easily available woody

biomass residueswhich had little or nomarket value [1]. Thus,

an emerging bioenergy market could be an alternative

opportunity for selling woody residues and small diameter

trees [1]. In short, these studies focus primarily on the

potential benefits of bioenergy for NIPF landowners. Though

earlier studies covered some general aspects of opportunities

associatedwith bioenergy, this study explicitly explores forest

management and biophysical forest characteristics that

potentially contribute to NIPF landowners’ participation in

biomass supply.

3. Methods

3.1. Data

The study area includes three southern states namely

Arkansas, Florida and Virginia. The list of nonindustrial

private forestland owners in Arkansas was obtained from the

Arkansas Forestry Commission and a commercial vendor.

Similarly, the lists of nonindustrial private forest landowners

of Florida and Virginia were obtained from the respective

b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 8 6e1 9 2188

county tax assessor’s offices. Sixteen hundred landowners

from each state were randomly selected for the final survey.

Landowners that owned less than twenty acres of forestland

were removed from the database because theywere less likely

to engage in forest management activities [13,20]. The survey

instrument contained a variety of questions within four

sections related to general information about the respon-

dents, information about the land, forest management activ-

ities, and demographic characteristics. The survey was

conducted from September 2007 to August 2008, and generally

followed the recommendations of Dillman’s Tailored Design

Method [23]. The socio-demographic results from this survey

were consistent with previous studies conducted in similar

areas [24,25], which reduced some concerns for possibility of

non-response bias in sampling. Also, statistically insignificant

differences among early and late respondents based on two

sample t-test further reduced the possibility of non-response

error in this survey.

3.2. Statistical analysis

Since the dependent variable in the studywas binary, we used

binomial logistic regression for estimating the model param-

eters. The relationship between dependent and independent

variables was modeled using following equation [26,27].

EðY=xÞ ¼ pðxÞ ¼ eb0þb1

1þ eb0þb1

The likelihood function equals product of individual

contributions of each ith observation.

lðbÞ ¼Yni¼1

pðxiÞyi ½1� pðxiÞ�1�yi

Likelihood equations were used to estimate the value of

b that maximize the log likelihood with respect to each

parameter. (i.e. b0, b1, b2.. bj)

Xi¼1

�yi � pðxiÞ

� ¼ 0 for b0

Xi¼1

xi1

�yi � pðxiÞ

� ¼ 0 for b1

Xi¼1

xi2

�yi � pðxiÞ

� ¼ 0 for b2

and so on through the j different independent variables.

This provided values for estimating logit.

bgðxÞ ¼ b0 þ b1x1 þ b2x2 þ.þ bjxj

This was then be used to calculate an estimated value for

conditional probability.

bpðxÞ ¼ ebgðxÞ1þ ebgðxÞ

Unlike in ordinary least square model, coefficients of the

binary logistic model do not provide direct intuitive interpre-

tation [26,27]. Therefore, marginal effects of the independent

variables on the probability of occurrence of dependent vari-

able were estimated and reported accordingly.

3.3. Model and variable definitions

Previous studies have shown that different forest manage-

ment characteristics, such as: acreage of ownership, structure

and composition, forest management objectives, and demo-

graphic characteristics (age, education, income) affect land-

owner decisions [13,14,16]. Therefore, it can be assumed that

determinants of landowner decision to supplywoody biomass

for bioenergy industry will likely be a vector of forest char-

acteristics (size of forestland, species composition), demo-

graphic characteristics, and forest management objectives.

This can be expressed as,

WILLINGNESS ¼ fðBIOPH;FORMG;DEMO;MANAGEOBJÞwhere,

BIOPH is a vector of biophysical characteristics of the forest.

FORMG is a vector of forest management factors.

MANAGEOBJ is the landowner’s forest management

objectives.

DEMO is a vector of demographic characteristics of the

landowners.

Assuming rational behavior, landowners will maximize

utility from forestland by exercising their subjective prefer-

ence for either supplying woody biomass to bioenergy

industry or doing something else, given resource and budget

constraint. Hence, this situation can be explained with

random utility theory, which permits discrete choices within

a utility maximizing framework [28]. Given these conditions,

landowner’s indirect utility function can be expressed as Vi

(xi, q), where xi is a vector of measureable forest character-

istics, demographic characters and forest management

objectives and q denotes the choice of landowners to supply

woody biomass for bioenergy such that q ¼ 0 if landowner is

not willing to supply and q ¼ 1 if landowner is willing to

supply [28]. According to the Utility maximization concept,

a landowner is not willing to supply woody biomass for bio-

energy if that will not increase his indirect utility, Si (yi,

0) > Si (yi, 1) [28]. Similarly, a landowner will be willing to

supply woody biomass for bioenergy, if in doing so, yields

higher utility, Si (yi, 1) > Si (yi, 0) [28].

Random utility concept is based upon the relationship

between observed data and the utility maximization model

[29] with an assumption that the decision maker has

a perfect discrimination capability [30,31]. While landowners

are assumed to know their willingness to supply woody

biomass for bioenergy, these values can be revealed through

observed choices [30,31]. Therefore, this situation having

incomplete information about landowners’ willingness

reflects uncertainty in the model [30,31]. Earlier studies have

identified different sources of uncertainties such as unob-

servable individual attributes, observable withmeasurement

errors, and proxy measurements pertaining to such a situa-

tion [29e31]. Therefore, utility is modeled as a random vari-

able in order to reflect this uncertainty. More specifically, the

utility associated with landowners’ willingness or unwill-

ingness is given by

Uij ¼ ß1Aþ ß2Bþ ß3Cþ 3ij

Table 1 e Definition and descriptive statistics of variables used in the model for willingness to supply woody biomass.

Variable Definition Units Mean Std. Deviation

WILLING The decision to supply woody biomass or not, 1 if willingness, 0 otherwise 0.85 0.35

LACRE Logarithm of Total land of the landowner 2.05 0.53

PLANTEDPINE The percentage of the land occupied by pine plantation 22.17 34.26

MIXFOREST The percentage of the land occupied by mix forest 20.11 34.67

TIMBERPRODa Timber production objective, 1 if management objective is timber production,

0 otherwise

0.81 0.39

WILDa Wildlife management objective, 1 if management objective is wildlife management,

0 otherwise

0.86 0.35

PRICETIMB Importance of price of timber in harvesting decision, 1 if important, 0 otherwise 0.97 0.16

AGE The age of landowner, 1 if respondent is older than 60 years, 0 otherwise 0.52 0.50

EDUCATION The highest level of landowner’s education, 1 if respondent has Bachelors degree or higher,

0 otherwise

0.44 0.50

a Forest management objectives are not mutually exclusive.

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 8 6e1 9 2 189

where Uij is the utility associated with landowners’ willing-

ness or unwillingness in supplying wood biomass for bio-

energy; A is a vector of biophysical and forest management

characteristics of nonindustrial private forest; B is a vector of

demographic characteristics of landowners; C represents

a vector of forest management objectives, b1, b2, b3 are the

model parameters, and 3ij is random error. Therefore, in this

equation, b1Aþ b2Bþ b3C is the deterministic part of the utility

and 3ij is the stochastic part, capturing the uncertainty. This

model can be estimated using a binary logistic procedure, if

the error terms are assumed to be independent and identically

distributed [32,33]. Hence, the empirical model in this study

can be specified as:

WILLING ¼ f (LACRE, PLANTEDPINE, MIXFOREST, HARD,

TIMBERPROD, WILD, PRICETIMB, INCOME, AGE, EDUCATION,

SMALLD, KNOWB, FHEALTH, RESIDENCE)

The dependent variable (WILLING) was measured by the

observable choice of whether landowners will supply their

woody biomass for bioenergy. It was assumed that the woody

materials used in the bioenergy industry would likely to be in

the form of younger trees, deformed or salvaged trees, and

logging residues. Given that market for feedstock used in

wood-basedbioenergywasstill incipient, existingmarketprice

of the pulpwood in the respective states was used to under-

stand themarket entry decisions of landowners. The response

for this variable was binary, taking the value of “1” if the

landowner indicated positivewillingness (definitelywill sell or

probably will sell) to supply woody biomass at the bid price

offered or “0” otherwise. The independent variables included:

total number of acres owned by landowner (LACRE, expressed

as a logarithm), percentage of their land occupied by planted

pine (PLANTEDPINE), percentage of their land occupied by

mixed forest (MIXFOREST), timber production objective (TIM-

BERPROD), wildlifemanagement objective (WILD), importance

of timber price in harvesting decision (PRICETIMB), age of

landowner (AGE), and education of landowner (EDUCATION).

Other variables tested but not selected for final logisticmodels

were: landowners having small diameter tree (SMALLD),

percentage of their land occupied by Hardwood (HARD),

knowledge about bioenergy (KNOWB), forest health

(FHEALTH), and residence (RESIDENCE). Table 1 reports the

definition and descriptive statistics of the variables that were

finally selected in the logistic regression model.

The explanatory variables LACRE, PLANTEDPINE, and

MIXFOREST were continuous. It was hypothesized that land-

owners owning large amounts of land were more likely to

supply woody biomass for bioenergy since these landowners

were more likely to actively manage their forestland for

financial returns to their investment [34]. Therefore, the

variable LACRE was expected to be positive. Similarly, the

variable PLANTEDPINE was expected to be positive due to

the likelihood that the landowners who planted pine would

have timber harvesting motives due to their substantial

investment in pine plantation and silvicultural operations.

The landowners having mixed forests would have large

amount of understory residues, which could be used aswoody

biomass. Therefore, the variable MIXFOREST was also expec-

ted to be positive.

The management objectives of the landowners have

previously been identified as important factors in land-

owners harvesting decisions [14]. The variable TIMBERPROD

was a likert scale variable that was coded as 1 if the land-

owner indicated that timber production as a management

objective was very important or somewhat important, or 0 if

the landowner indicated that timber production as

a management objective was neutral, somewhat unimpor-

tant or not at all important. It was difficult to predict the

sign of the variable TIMBERPROD as some studies have

shown that timber management behavior of nonindustrial

private landowners is far less predictable than forest

product industries due to multi-objective nature of their

ownership [13,15,35]. Therefore, the sign for this variable

must be found empirically. Similar to TIMBERPROD, the

variable WILD was coded as 1 for landowner who indicated

wildlife management objective was very important or

important, or 0 otherwise. Generally, the landowners having

wildlife management objectives tend to value amenities

higher than timber [24]. On the other hand, majority of such

landowners tend to be sensitive toward environmental

issues [36,37]. Given the environmental and socioeeconomic

benefits of bioenergy, the sign of this variable was difficult

to predict in this model. Likewise, the variable PRICETIMB

was coded as 1 for those landowners who indicated timber

price was very important or somewhat important in their

harvesting decision, or 0 otherwise. The sign of the variable

PRICETIMB was also difficult to predict due to multi-

b i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 8 6e1 9 2190

objective nature of nonindustrial private forest landowners

[13,15,35]. The variable AGE was coded as 1 for those land-

owners who were older than 60 years and 0 for others.

Earlier research have indicated that older landowners were

less interested in any kind of active management activities

[38]. Therefore, AGE was expected to be negative as majority

of the older landowners are likely to enjoy recreational

benefits of forest than harvesting woody biomass from their

forestland. Similarly, the variable EDUCATION was coded as

1 for those landowners who had at least Bachelor’s degree

or equivalent as their highest level of education, 0 other-

wise. The variable EDUCATION was expected to have

a positive coefficient because advanced education would

make people more aware about environmental issues.

Therefore, they were expected to be more likely to supply

their woody biomass for wood-based energy.

Table 2 e Results of logistic regression on willingness tosupply woody biomass model.

Variable Coefficient(chi square-value)

Marginal Effects(Std. Error)

LACRES 0.71b

(5.04)

13.72

(0.32)

PLANTEDPINE 0.01b

(5.72)

0.26

(0.01)

MIXFOREST 0.01b

(4.76)

0.16

(0.01)

TIMBERPROD �0.66a

(17.80)

�12.80

(0.158)

WILD 0.42c

(3.12)

8.13

(0.24)

PRICETIMB �0.45

(2.21)

�8.61

(0.30)

AGE �0.41a

(8.26)

�7.88

(0.14)

EDUCATION 0.24c

(2.97)

4.57

(0.14)

Intercept �0.25

(0.14)

�4.70

(0.68)

Log-likelihood ratio 65.69

Wald Chi-square 54.01

No. of observations 548.00

a Significant at 1% level.

b Significant at 5% level.

c Significant at 10% level.

4. Results and discussion

4.1. Descriptive statistics

Among three age groups (<45, 45e65, >65), the largest

percentage of respondents (About 57% of the landowners in

Arkansas, 61% in Florida and 50% in Virginia) belonged to

the group of 45e65 years. Similarly, 26% of respondents in

Arkansas, 20% in Florida and 31% in Virginia were above 65

years of age, whereas the smallest percentage of respon-

dents in each state (17% in Arkansas, 19% in Florida and 18%

in Virginia) were less than 45 years old. Among three cate-

gories of education levels (did not complete high school,

high school or equivalent vocational/technical training, and

bachelors’ degree or higher), 50% of respondents in Arkan-

sas, 42% in Florida and 43% in Virginia had a high school or

equivalent degree. Similarly, 45% of the respondents in

Arkansas, 54% in Florida and 54% in Virginia had at least

a bachelor’s degree. On the other hand, 5% of the respon-

dents in Arkansas, 4% in Florida and 3% in Virginia did not

complete high school. We also had three categories of

income levels (less than 25K, between 25Ke75K, >75K), 12%

of respondents in Arkansas, 10% in Florida and 6% in Vir-

ginia had an annual income less than 25 thousand dollars

per year. Similarly, 47% of respondents in Arkansas, 34% in

Florida and 40% in Virginia had an annual income in the

range of 25e75 thousand dollars per year. Likewise, 41% of

respondents in Arkansas, 55% in Florida and 55% in Virginia

had an annual income higher than 75 thousand dollars per

year.

About half of the landowners in each state were retirees.

The results also revealed that 78% of the landowners in

Arkansas, 83% in Virginia and 86% in Florida believed energy

dependence on fossil fuels to be crucial for national security

as well as healthy economy. However, 58% in Arkansas, 57%

in Virginia and 43% of the landowners in Florida were not

aware of energy generation from cellulosic biomass. This

shows that while most of the landowners in three states

realized the need of alternative sources of energy, a consid-

erable number were not aware of using woody biomass for

bioenergy.

4.2. Results from logistic regression analysis

Estimates for the willingness to supply woody biomass model

are presented in Table 2. The overall model was significant as

the chi-squared test on the log-likelihood ratio was significant

at 99% confidence level. None of the independent variables

were highly correlated with each other, which reduced the

concern of multicollinearity in our model. As expected, the

significant (alpha ¼ 0.05) and positive coefficient on the loga-

rithm of total area owned (LACRE) showed that landowners

were more likely to supply woody biomass for bioenergy as

the size of their ownership increased. Consistent with the

findings of Beach et al, this result indicated that the land-

owners owning larger forest acres would have economies of

scale in biomass harvesting [39]. In term of marginal effect,

(calculated at themean, LACRE¼ 2.05) a unit increase in log of

acreage owned was associated with 13.71% increase in the

likelihood that the landowner will supply woody biomass for

bioenergy. Similarly, species composition of the landowners’

forest influenced their decision to supply woody biomass for

bioenergy. Landowners who owned pine plantations and

mixed forests were more likely to supply woody biomass.

The variable TIMBERPROD was negative and significant at

99%. This indicated that the landowners who valued timber

production (growing and selling timber) as an important

management objective were less likely to supply woody

biomass for bioenergy. Similarly, the variable PRICETIMB was

also negative but not significant. Negative sign of the variable

TIMBERPRODmay be an indication of the profit maximization

motive of the landowners with strong timber management

b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 1 8 6e1 9 2 191

objectives. The forest biomass that can be used in the

production of wood-based energy would probably be obtained

from young trees, species not in high demand, deformed or

diseased trees, salvaged trees or logging residues. Therefore, it

is probably unlikely that biomass for bioenergy will compete

with traditional lumber industries (and other secondary

producers that use lumber as input), unless the price of

biomass is high enough. Nonetheless, their negative prefer-

ences indicated some degree of skepticism among NIPF

landowners about the feedstock used in wood-based bio-

energy industry. Given that considerable numbers of land-

owners in all the states were either uncertain or not familiar

about wood-based bioenergy, they might believe that their

interest in supplying timber to lumber industry may conflict

with the concept of producing alternative energy from cellu-

losic biomass.

On the other hand, the variable WILD was positively

significant, albeit marginally, at 90%, which meant that

landowners having wildlife management objectives were

more likely to supply their woody biomass for energy.

Generally, the landowners having wildlife management

objectives do not like to clear-cut their timber for commercial

sales. However, woody materials used in wood-based bio-

energy can also be obtained from partial-cutting methods,

which do not necessarily conflict with wildlife management

objectives of the landowners and may even aid in habitat

enhancement. Given that small diameter trees and logging

residues (small branch, thinning, tops of trees and other stem

wood fromharvested trees) are commonly used as a feedstock

in bioenergy industries, it is likely that NIPF landownermay be

interested in maintaining forest health by harvesting such

wood products with a minimal disturbance on existing

species diversity. Among demographic variables, AGE was

negatively significant at 99%, which indicated that environ-

mental benefits of bioenergy did not appeal to older land-

owners. This was consistent with findings of earlier studies on

cost share, harvesting and reforestation behavior of land-

owners [17,33]. Similarly, the variable EDUCATION was

positive and significant at 90%. This was consistent with the

hypothesis that higher level of education would directly

influence the perception of landowners toward environ-

mental and energy security issues.

Table 3 gives the predicted and actual outcomes for the

model which measures the performance of the model in

predicting a landowner’s willingness to supply woody

biomass. The model correctly predicted 86.8% of the obser-

vations (Table 3). It is noteworthy that such a large majority of

the landowners were willing to supply woody biomass, if they

were offered with bids equivalent to existing pulpwood price.

Table 3 e Actual and predicted values for willingness tosupply model.

Actual Predicted Total % Correct

0 1

0 65 7 72 86.8

1 8 472 480

Total 552

These results are not surprising as landowners had little to

lose if they supplied woody materials in a price equivalent to

the existing pulpwood prices. Moreover, several

socioeeconomic and environmental benefits might have

attracted landowners to be willing to supply woody biomass

for energy. This study, however, refrained from covering the

issue of economic viability of wood-based bioenergy. There-

fore, cost-effectiveness of biomass used inwood-based energy

would be an important topic for future research.

5. Conclusions

Sustainable development of bioenergy and bio-based

production system requires a reliable biomass supply at

a reasonable cost which to a large extent depends upon

landowner decisions on whether or not to supply woody

biomass. This study showed that the majority of the land-

owners in the study area regarded nation’s dependence on

imported fuel as critical for both national security and healthy

economy. Such opinions of the landowners reflect their

concern about energy security. However, significant number

of landowners were either uncertain or not familiar about the

concept of producing alternative energy from cellulosic

biomass. Similarly, low motivation of older landowners

toward wood-based energy suggests that they would first

need to be educated and informed about this emerging

market.

This willingness to supply model showed that landowners

having wildlife management objectives were likely to supply

woody biomass. This result may have long term implications

for the future of bioenergy in United States as wildlife and/or

aesthetics are the prime management objectives for the

majority of the landowners [25,36,37]. Similarly, our study

suggests that young landowners owning large pine planta-

tions or mix forests could become preferable choice for the

contractors, extension foresters and bioenergy industries as

they are more likely to supply woody biomass for bioenergy.

Given general landowner skepticism regarding biomass

supply for energy, there is need for a full range of communi-

cation in the form of education and outreach programs. It is

also important to note that excessive and unsustainable har-

vesting of woody biomass could result in loss of biodiversity,

nutrient leaching, soil erosion, infiltration reduction and

increase in sedimentation. Therefore, outreach programs

about current market price of woody biomass, significance of

environmental benefits, best management practices,

employment and other opportunities related to bioenergy can

motivate landowners toward supplying biomass for energy

production.

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