Bioenerg. Res. (2011) 4:258–275DOI 10.1007/s12155-011-9139-1

Agent-Based Analysis of Biomass FeedstockProduction Dynamics

Yogendra Shastri · Luis Rodríguez ·Alan Hansen · K. C. Ting

Published online: 3 August 2011© Springer Science+Business Media, LLC. 2011

Abstract The success of the bioenergy sector based onlignocellulosic feedstock will require a sustainable andresilient transition from the current agricultural systemfocused on food crops to one also producing energycrops. The dynamics of this transition are not wellunderstood. It will be driven significantly by the collec-tive participation, behavior, and interaction of variousstakeholders such as farmers within the productionsystem. The objective of this work is to study the systemdynamics through the development and application ofan agent-based model using the theory of complexadaptive systems. Farmers and biorefinery, two keystakeholders in the system, are modeled as independentagents. The decision making of each agent as well asits interaction with other agents is modeled using a setof rules reflecting the economic, social, and personalattributes of the agent. These rules and model para-meters are adapted from literature. Regulatory mech-anisms such as Biomass Crop Assistance Program areembedded in the decision-making process. The modelis then used to simulate the production of Miscanthusas an energy crop in Illinois. Particular focus has beengiven on understanding the dynamics of Miscanthusadaptation as an agricultural crop and its impact on bio-

Y. Shastri (B)Energy Biosciences Institute, University of Illinoisat Urbana-Champaign, 1206 W. Gregory Drive,Urbana, IL 61801, USAe-mail: yshast1@illinois.edu

Y. Shastri · L. Rodríguez · A. Hansen · K. C. TingDepartment of Agricultural and Biological Engineering,University of Illinois at Urbana-Champaign,1304 W. Pennsylvania Avenue, Urbana,IL 61801, USA

refinery capacity and contractual agreements. Resultsshowed that only 60% of the maximum regional produc-tion capacity could be reached, and it took up to 15 yearsto establish that capacity. A 25% reduction in the landopportunity cost led to a 63% increase in the steady-state productivity. Sensitivity analysis showed that higherinitial conversion of land by farmers to grow energycrop led to faster growth in regional productivity.

Keywords Agent-based model · Bioenergy feedstock ·Dynamics · Miscanthus · Stakeholder

Abbreviations

BCAP Biomass Crop Assistance ProgramNPV Net present value

Introduction

Biomass-based renewable energy is an important com-ponent of the future US renewable energy targets [1–3].However, an important challenge for achieving thesetargets is the establishment of a sustainable, reliable,and cost-effective biomass feedstock production andprovision system [4]. Agriculture will play a key rolein this by providing the necessary material input forthe production of power, liquid biofuels, and/or bio-materials [1, 5]. This will require the adaptation andcultivation of new dedicated energy crops in additionto the conventional crops currently grown in the USA.Consequently, a new agricultural system that integratesenergy crop production with the production of food andfeed will have to be developed.

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Significant progress is being made in science andengineering to ensure cost-effective and sustainableproduction of energy crops. Simultaneously, regulatoryinitiatives such as the Biomass Crop Assistance Pro-gram (BCAP) are being proposed to stimulate the feed-stock sector; however, in addition to these scientific,engineering, economic, and policy initiatives, the devel-opment of this sector will also be significantly affectedby the collective participation, behavior, and interac-tion of various independent stakeholders within theagricultural production system [6]. This includes farm-ers, consultants, storage and transportation agencies,and the biorefinery, each of whom will make indepen-dent decisions that can lead to a collective impact on thedevelopment and functioning of the system. Farmers inparticular will play a critical role in the developmentof this system. This has implications on two differentissues related to the bioenergy feedstock sector.

First, the development of the proposed integratedagricultural sector is difficult to predict given the com-plex interactions of these independent stakeholders.It is acknowledged that this development will hap-pen gradually over time. However, there is currentlya significant lack of understanding about the possiblepath and dynamics of this transition. Economists haveworked rigorously on understanding the developmentof such a system using tools from macro-economics [7–10]. Although, it provides an excellent framework toidentify or evaluate policy alternatives, these analysesoften make simplifying assumptions that ignore thepractical aspects of feedstock production system. Ex-amples include the assumption of a rational farmer,competitive market or market equilibrium. It cannot beguaranteed that the stakeholders involved will followthe decision-making process that is optimum from apolicy perspective. Moreover, such analyses also ignorethe impact of technology development and feedstockproduction management.

Second, the functioning of a production system ata given time is also difficult to predict. Engineeringresearch has focused significantly on improving tech-nology, operations, and system design for feedstockproduction [11–18]. This research is extremely impor-tant to identify better technologies, system bottlenecks,and operating practices. However, each of the stake-holders in a real production system, such as farmers,biorefinery, transportation companies, storage eleva-tors, farm consultants, and equipment renters, mustparticipate and yet will compete for a fraction of theprofit. This introduces inefficiencies and uncertaintiesthat cannot typically be analyzed using traditional en-gineering models. Moreover, these studies do not con-sider the transitional phase of the feedstock production

sector. Instead, the analysis is conducted assuming analready established feedstock production sector.

As a result of these shortcomings, there has been anemphasis on understanding and quantifying the stake-holder behavior within the bioenergy feedstock pro-duction system in recent times [6, 19–24]. These studiesare typically based on surveys and therefore providevery important information. However, this informationhas not yet been systematically incorporated in thebroader systems analysis framework that can juxtaposethese issues with the economic and engineering aspectsof feedstock production that have been previously an-alyzed. Therefore, a framework that integrates thesethree dimensions is needed to better understand thesystem development, dynamics, and its performance.

The broad objective of this work is to develop sucha framework through the development of an agent-based model using the theory of complex adaptivesystems. The important stakeholders such as farmersand biorefinery within the feedstock production sys-tem are modeled using an object-oriented approach.The important decision-making steps for each class aremodeled using a set of rules, which includes economicas well as social aspects of decision making. BioFeedmodel has been used to model and optimize crop pro-duction operations by farmers [11, 12]. The regula-tory aspects are also factored into the decision-makingprocess. The general framework is parameterized tostudy the development of Miscanthus production inIllinois as an energy crop.

The article is organized as follows. The next sectiondescribes the approach taken in this work to study theaforementioned research questions. This is followed bya comprehensive description of the agent-based modeldevelopment and its application to a case study prob-lem. The final section summarizes the work, draws im-portant conclusions, and presents future research plans.

Approach

It is evident that the feedstock production system ishighly complex involving multiple stakeholders. Thedecision making and behavior of these individual andindependent stakeholders will impact the overall prop-erties of the system, and these properties cannot be un-derstood by studying these stakeholders independently.This represents a system with multiple scales, nonlin-ear interactions, and emergent properties. This work,therefore, takes the complex systems theory approach.

Guckenheimer and Ottino [25] define a complexsystem by identifying four important characteristicsof a system that make it complex: internal structure

260 Bioenerg. Res. (2011) 4:258–275

consisting of networks of interacting components possi-bly spanning over multiple spatial and temporal scales,emergent properties that cannot be analyzed by study-ing individual components of the system, adaptationand evolution by the components of the systems, andthe presence of uncertainty. Similar definitions butfocusing on one of these aspects have also been pro-posed [26–28]. Agent-based modeling is one of themathematical tools to model and study complex sys-tems [28]. It is based on the assumption that somesystems are modeled more effectively in terms of acomputer program (algorithm) rather than in terms ofequations [28]. In this approach, each stakeholder in asystem is modeled as an independent agent. The deci-sion making and the behavior of each agent as well asthe interactions between multiple agents are describedusing a set of rules [29]. The important feature of theagent-based models is that these rules by themselvesare not very complex. In fact, they are often simplifiedas much as possible. However, the interactions of alarge number of agents over multiple spatial and tem-poral scales lead to the simulation results that are non-intuitive and cannot be predicted from the individualagent rules. This is called the emergent behavior ofthe complex systems. These rules can be modified asa function of space or time over the course of the simu-lation to model evolution, adaptation, and uncertainty.Given their unique capabilities, agent-based modelshave been used in diverse fields such as energy [30, 31],environment and policy [32], agriculture [33, 34], sup-ply chain management [35], bioenergy assessment [36],electricity markets [37], and software engineering [38].

The features of the bioenergy feedstock productionsystem are similar to those of a complex system. Itinvolves multiple agents such as farmers, biorefinery,storage, and transportation companies that interactwith each other. Each of these agents behaves basedon specific decision-making rules that can be relativelysimple or highly complex. These rules evolve over timereflecting learning and adaptation. Decision makingcan cover multiple scales of time, including participa-tion decisions over multiple years and management de-cisions over hours or days. Regional factors such as dis-tance and weather introduce spatial aspects. Moreover,each agent is unique which means that the dynamics ofthe complete system cannot be predicted from the studyof an individual agent. It is therefore logical to use acomplex systems theory approach and more specificallythe agent-based modeling approach to study the dy-namics of the bioenergy feedstock production system.

It must be understood that it is impossible to de-velop a mathematical model that can completely andaccurately capture the real-world functioning of a sys-

tem like this. Highly complex models developed forgreater accuracy are often computationally intractableor difficult to parameterize. The objective of this workis therefore to develop an agent-based model as an ex-ploratory tool to understand the development and func-tioning of the feedstock production system by capturingthe most critical components and relationships withinthe system. The scenario simulation results presentedlater should not be taken as predictions. Rather, thoseresults represent some of the many possible outputsfor the real system, and should support more informeddecision making. From the analysis perspective, thiswork lays the modeling foundation. The model struc-ture, rules and data will be updated as more informa-tion becomes available. The next section describes theagent-based model in detail.

Agent-Based Model

The important model development objectives are:

– Model and study the dynamics of the feedstock pro-duction system in the presence of multiple agentsand markets, compare the results with an optimizedsystem, and quantify the impact of engineering andpolicy interventions on system dynamics.

– Model and study the impact of uncertainties andunexpected perturbations on the system dynam-ics, which leads to the quantification of systemresiliency and sustainability.

In order to achieve these objectives, the first step isto model the important agents representing the dif-ferent stakeholders within the system and the model forthose agents must capture their important economic,social and behavioral aspects that can potentially affectthe system dynamics. While doing so, it must also beensured that the model is not excessively complex soas to impede simulation studies and the identificationof causal relationships. The proposed model, there-fore, has been developed to strike this balance. Theimportant stakeholders and their role in the feedstockproduction system are:

– Farmers: provide the necessary material inputthrough crop establishment, cultivation, harvestingand post-harvest processing and management.

– Farm consultants: provide expert knowledge andconsulting service to farmers for crop-establishmentand management.

– Custom harvesters: provide (rent/lease) farmmachinery.

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– Storage elevators: provide biomass storage facilityand a buffer between the seasonal supply and year-round demand of biomass.

– Transportation companies: provide the transporta-tion infrastructure and accomplish the biomasstransportation activities.

– Biorefineries/bioprocessing facilities: convert thebiomass feedstock into power, fuel, and/or bioprod-ucts. These will be referred to as biorefinery in thesubsequent text.

These different stakeholders are identified as theagents in the agent-based model. In addition to this,the regulatory authority, such as the state or nationalgovernment, plays an important role through policies,incentives and regulations that affect some or all of thestakeholders mentioned above. In the model presentedhere, the focus is primarily on modeling the farmer andbiorefinery agents that represent the supplier and con-sumer of the feedstock. The services provided by otheragents are either incorporated in these two agents orare simplified for the purpose of this model. Moreover,the role of the regulatory authority has been consideredthrough the incorporation of incentives such as theBCAP. Future enhancements of the model will includeother agents in greater detail.

The model has been developed using an object-oriented approach. Accordingly, each agent type suchas farmer is declared as an object class with a set ofattributes that are common to each member of the class.The decision-making rules pertaining to that agent classare represented using methods for that object class.Each individual realization of the agent class, such asan independent farmer, is represented as an instance(realization or object) of that class. Each object mayhave a unique set of values for each attribute. A com-prehensive discussion of the object-oriented approachis not included here for the sake of brevity, but canbe found in textbooks on object-oriented programming(e.g., see Booch [39]).

An important objective of the agent-based modelis to incorporate the effect of competition on prices,contracts, and production capacity. The model as-sumes, based primarily on the mechanism of bio-mass production and delivery proposed as part of theBCAP [40], that farmers participate in a competitivebidding process in order to secure contracts for thedelivery of biomass. The contract mechanism, includingthe frequency and validity period of the contracts, willalso impact the functioning of this market. It is expectedthat long-term contracts will be signed between produc-ers and consumers during the initial years to mitigate

the risk for both stakeholders since the production ofenergy crops is not yet well established. The model,therefore, assumes that long-term delivery contractsvalid over multiple years are negotiated between farm-ers and the biorefinery. The frequency of contracts ismonthly, i.e., each year consists of 12 contract slots.The contract negotiations begin after the farmers havedecided to participate in energy crop farming. The yearis assumed to begin at the beginning of the harvestingseason of the energy crop and lasts till the beginningof the next harvesting season. The duration (valid-ity) of each contract in terms of the number of yearscan be varied. The model incorporates various incen-tives and subsidies such as the BCAP in the decision-making rules by the agents. Consequently, the modelcan be used to study the impact of those on the systemdynamics.

Hybrid Miscanthus (Miscanthus × giganteus) is aperennial grass which has been proposed as a potentialenergy crop due to its relatively high productivity evenon marginal lands without fertilization and irrigation[41]. More information about Miscanthus is presentedlater. Illinois is one of the candidate regions in theUSA to grow energy crops due to its productivityand favorable climate. Hence, the model was used tostudy the production of Miscanthus as an energy cropin Illinois and this case study is presented later. Themodel description therefore utilizes this case study asexample at selected places for the illustration of modeldetails. The following sections present a comprehensivedescription of the different agents.

Farmer

The farmer class is defined using attributes such aslocation, farm area (size), age, education, available,capital and distance from the biorefinery. Various in-stantiations of the farmer class are modeled as inde-pendent objects with unique values of these attributesthat depend on the specific case study being modeled.Each farmer is assumed to be an independent operatorand the sole owner of the farm. The farmer class isinitialized by assuming that all farmers are practicingconventional row crop agriculture. The important deci-sions modeled for the farmer agent are:

– Participation in production of an energy crop.– Determination of the fraction of the total farm area

to be used for energy crop production.– Bidding for biomass delivery contract to the biore-

finery. This includes the delivery month, quantity,and the sales price.

262 Bioenerg. Res. (2011) 4:258–275

– Selection of farm machinery for energy crop pro-duction during the first year of production.

– Farm production management and operations dur-ing the harvesting and post-harvest season. Themodel currently assumes that all farmers followstandard pre-harvest crop management practicessuch as fertilization and weed control, leading to aspecific crop establishment cost that is common toall farms. Future versions of the model will incor-porate farm specific crop management practices.

– Modification of farm area allocated to energy cropat the beginning of each growing season.

In economic literature, such decisions are oftenmade using a cost-benefit analysis, such as the cal-culation of the net present value of an investment.However, recent literature acknowledges that decisionsby farmers will depend significantly on the social andcultural attributes of the farmer in addition to the eco-nomic factors [6]. As a result, interest in understandingthe farmer perspective on energy crop production hasincreased [20, 22–24]. Kempener et al. [42] used whatthey referred to as the “mental model” to incorporatethese factors in decision making. The mental modelconsisted of the mental representation, which reflectedthe way information is interpreted by the agent, and thecognitive processes, which reflected the approach usedfor making a decision. Different combinations of men-tal representations and cognitive processes capturedthe social and behavioral aspects of the agents as well astheir diversity [42]. This work, therefore, uses a similarbut simplified approach to derive the farmer decision-making rules. The farmers in this model are classifiedinto two categories:

– Factual farmers who base their decisions only onthe economic data such as production cost, salesprice, storage and equipment costs, and expectedprofit margins and do not consider the actions oftheir peers.

– Social farmers who base their decisions on the de-cisions by their peers in addition to the economicdata mentioned above.

Each farmer has a network of peers, called the socialnetwork of the farmer, that he/she relies on for gather-ing information and making decisions. For action-baseddecisions such as deciding to grow energy crops, thesocial farmer needs a minimum number of peers withinthe social network to also conform to that decision.For quantitative decisions such as estimating the pro-duction cost or bidding price, the social farmer givesimportance to the information available through the

peers within the social network. The size of the socialnetwork, its members as well as the critical numberof peer conformance needed for decision making isunique for each farmer and will depend on the par-ticular case study being modeled. Most of the analysesbased on macro-economics implicitly assume all farm-ers to be factual as per the convention used in thiswork. The modeling of the social farmer, therefore, isa unique feature of the agent-based model. The risk-taking behavior by farmers is also modeled through anattribute α for the farmer class. α > 1 represents a risk-tolerant farmer; α < 1 represents a risk averse farmer;while α = 1 represents a risk neutral farmer. The meanand distribution of α will depend on the particular casestudy being modeled. The use of this parameter indecision making will be explained later in the section.

The energy crop production activities require thefarmers to estimate a number of parameters such as theproduction cost or the sales price for the next year inorder to manage their operations (e.g., calculating bidprice). These estimates are subsequently updated andrefined by each agent based on experience and avail-able information to improve its predictive capability.This work uses a weighted geometric average to modelthis learning behavior and calculate the new estimatesas shown below [33, 43]:

cet+1 = ca

t × (cet )

(1−a) 0 ≤ a ≤ 1 (1)

where, cet is the estimate for the current year, ct is

the actual value from the current year, and cet+1 is the

updated estimate for the next year. The value of agoverns the importance given to the actual value of thecurrent year as compared to the estimate.

The decision-making process associated with vari-ous decisions listed previously for farmers is describedbelow:

– Participation in the production of an energy crop:Each farmer calculates the net present value (NPV)of the conversion of the farm land from conven-tional agricultural crops to an energy crop suchas Miscanthus. The time horizon for the NPV cal-culation is equal to the crop rotation period forthe energy crop. The NPV calculation incorporatesthe establishment cost, expected yield, expectedproduction and sales cost, land opportunity cost,and any regulatory incentives such as the BCAP.If the NPV is negative, the farmer does not plantthe energy crop. If the NPV is positive, a factualfarmer plants the energy crop, while a social farmeradditionally needs a minimum number of his/her

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peers to plant the energy crop in order to make thatdecision.

– Determination of energy crop land allocation:Farmers participating in energy crop farming cal-culate the area allocated to energy crop usingvarious socioeconomic and behavioral factors. Themodel assumes certain base land conversion frac-tion which is then modified based on the specificattributes of the individual farmer such as farmerage, farm area, education and available capital. Thisapproach is based on the survey conducted by Wenet al. [22] and Jensen et al. [24] and, therefore,might be modified in the future as more accuratemethods of estimation become available. Pleasenote that the NPV value is calculated assumingthe conversion of the complete farm. However, thefarmer might allocate only a fraction of the land toenergy crops. This will not change the NPV calcu-lations since it is a linear function of the farm area.Future versions of the model will include a morerigorous approach where the participation and landallocation decisions will be taken simultaneously bythe optimization of a nonlinear function.

– Bidding for biomass delivery contracts: Once thetotal production capacity is known, farmers submitbids to the biorefinery for biomass delivery con-tracts for the next year. For example, land conver-sion decisions for Miscanthus in the MidwesternUSA must be made before March in a year sothat plantation activities can begin in April andbiomass harvesting can begin in the January of thenext year. Therefore, biomass bidding begins inApril for delivery contracts during the followingyear (from January to December). The bid price iscalculated using the estimated production cost andassuming a base expected profit of 25%. The actualprice is modified based on the risk attribute of thefarmer such that a risk tolerant farmer increases thebid price while a risk averse farmer reduces the bidprice. From the second year onwards, the modeluses the learning function illustrated in Eq. 1 to esti-mate the next year’s production costs. The expectedproduction cost is a function of delivery time sincedelivery during the non-harvesting period requireson-farm biomass storage. Therefore, the farmer cal-culates the total profit for each delivery month andthen decides to bid for the month that produces thehighest profit. The farmer bids all available biomassfor a particular contract duration. The biorefinery,however, may accept only a fraction of the biomassbased on its requirement. The farmer then submitsa fresh bid during the next round of bidding foranother contract for the remaining biomass. The

bidding process is continued until either all thefarmers have managed to secure contracts or thebiorefinery demand has been met.During the simulation of the bidding process, thepossibility of getting a delivery contract decreasesas the biorefinery secures biomass or as the har-vesting period approaches. In practice, a farmermight forego profit or even accept a loss in order toensure that biomass harvested in a given year is soldbefore the beginning of the next harvest season. Asimple bid submission process as described abovecannot model such a behavior. Hence, the modelincorporates what is referred to as the “urgencyfactor” (ν). This factor varies between zero andone and is defined such that it approaches oneas contract possibilities reduce, either because thedemand is being met or because the harvestingperiod is approaching. Let N be the total numberof contract slots and let n be the number of contractslots for which biorefinery demand has been met.Let t be the current time step and let T be thefinal time step for contract negotiations. Then ν isdefined as:

ν = Maximum(

nN

,tT

)(2)

The value of ν is then used to determine the magni-tude by which the farmer is willing to compromiseon profit or bid price. This function is modeled as:

C f = ν3 (3)

where, C f represents the level of compromise thefarmer is willing to make. Thus, as ν increases, thevalue of C f approaches 1 in an exponential manner.Figure 1 shows the nature of the variation of C f as afunction of ν. The exact magnitude of this compro-mise can be case or even farmer specific, and can bedefined as a percentage of the production cost.

– Selection of farm machinery: The farmer selects themachinery for the production of the energy cropwhich may be crop specific. This work uses theBioFeed model to optimize these decisions [11, 12]and its integration with the agent-based model isdescribed later.

– Farm production management and operation: Themanagement of farm production activities such asharvesting and post-harvest processing of biomassdetermines the actual production cost for a farm.This work again uses BioFeed to model these activ-ities and determine the total actual biomass outputand production cost. This is described later.

– Modification of energy crop land allocation: At thebeginning of each growing season, every farmer

264 Bioenerg. Res. (2011) 4:258–275

Fig. 1 Compromise level forthe farmer (C f ) andbiorefinery (Cr) in sales andpurchase prices, respectively

growing energy crop reviews the energy crop landallocation decision and revises it for the followingseason. This decision is based primarily on theeconomic performance where profit encourages anincrease in land allocated to energy crop while lossencourages a decrease in land allocated to energycrop. The decision also depends on whether theparticular farmer is factual or social. For a factualfarmer, profit during two consecutive years leadsto an increase in the energy crop land while lossduring two consecutive years leads to a decreasein the energy crop land. However, a social farmerconsiders the behavior of farmers in his/her socialnetwork in addition to these economic factors formaking that decision. Thus, a social farmer in-creases or decreases the energy crop land only ifa certain minimum number of farmers in his/hersocial network also make the same decision.

Biorefinery

The biorefinery is assumed to rely on the cellulosicfeedstock produced in the region for the productionof fuel, power and/or chemicals. The objective of thebiorefinery is to increase the production capacity rep-resented here in terms of Mg of biomass processedper day. This can be achieved by using as much of theavailable biomass as possible. However, the biorefineryalso aims to minimize the production cost by tryingto buy the biomass from the farmers at the lowestpossible price. The biorefinery therefore negotiates the

contracts with farmers based on the bids submitted bythe farmers. The model currently assumes that there isonly one biorefinery in the region. Future versions ofthe model will consider multiple biorefineries that mayutilize the biomass for different purposes such as powerproduction or biobased material production. This willenable the model to quantify the impact of competitionfor biomass on contract prices.

The model assumes that the biorefinery determinesthe capacity (in Mg/day) at the beginning of each year,which governs the total biomass to be contracted foreach month. Although a real biorefinery will not mod-ify its capacity every year, this assumption has beenincluded for the sake of simplicity. It ensures sufficientmarket demand for the biomass produced and henceenables the quantification of farmer characteristics onsimulation results. Future versions of the model willincorporate strategic and step-wise capacity develop-ment as a biorefinery decision. When the bidding fordelivery contract begins, the bids for each contract slotare evaluated by the biorefinery using the followingrules:

– The bid with the lowest bid price is selected.– If two or more bids have the same price, the bid

with higher biomass quantity to sell is selected.– If the bid prices as well as the biomass quantities

of two or more bids are the same, the bids arerandomly selected.

Other contract evaluation schemes based on estab-lished auction mechanisms will be included in the future

Bioenerg. Res. (2011) 4:258–275 265

versions of the model. The model assumes that shortterm feedstock shortfall is handled by the biorefineryby accessing other sources such as agricultural residue.The long term shortfall can be managed by increasingthe collection area.

The biorefinery has an upper bound on the purchaseprice, and bid prices higher than the maximum pur-chase price are not accepted. However, it is also nec-essary to model the possibility of price compromise bythe biorefinery if enough bids are not being accepted.The model therefore again uses the urgency factor ν,defined earlier. The key difference, however, is in thenature of variation of the compromise function Cr forthe biorefinery. Given the large capital investment fora typical biorefinery, it will increase the purchase priceto secure a contract when demand for a large numberof contract slots has not been met. However, as moreand more biomass is secured, the biorefinery will beless willing to compromise on purchase price since ashortfall of biomass becomes less important. Hence, Cr

is modeled as follows:

Cr = 1 − ν5 (4)

It must be noted that as ν increases, the value of Cr

reduces exponentially, as shown in Fig. 1. The exactmagnitude of this compromise is case specific.

Storage Elevator

The delivery contracts are negotiated based on theproduction estimates by farmers; however, it is possiblethat the actual production of biomass from a farm ismore or less than the estimate leading to excess biomassor biomass shortfall. The agent-based model thereforeincorporates a simplified storage elevator in the pro-duction system that acts as a buffer to account for suchsituations. If a farmer has excess biomass which he/shedoes not want to retain for the next year’s bidding, it issold to the storage elevator (cash sale) at the prevailingmarket price (cash sale price). The cash sale price isequal to 75% of the average contract price for thelast three months. The biomass retained for next year’sbidding by a farmer increases the total biomass outputfor the next year after accounting for the storage losses.The total biomass retained however must not be greaterthan 25% of the farm’s production capacity. If there isa biomass shortfall, the biorefinery procures biomassfrom the storage elevator, if available, to fulfill the re-quirements. The model currently does not incorporatea cost-benefit analysis for the storage elevator. In futureversions of the model, a more extensive sub-model ofthe storage elevator as an independent agent will beincluded.

Crop Production Optimization

Once the delivery contracts have been negotiated be-tween farmers and the biorefinery, the farmers thenneed to manage their production activities. This de-pends significantly on equipment availability at thefarms as well as on the operating decisions such as fer-tilization, weed control, irrigation, harvest scheduling,and biomass storage selection and distribution.

BioFeed is an optimization model that has beendeveloped to model and optimize selected harvest andpost-harvest farm production activities on a daily basison an individual farm [11, 12, 44]. The model opti-mizes not only the selection of farm machinery butalso determines the optimal operating schedule of thoseequipment. This work, therefore, uses BioFeed to esti-mate the productivity and costs of energy crop farming.The energy crop area, contract delivery amount, andcontract prices are passed on to BioFeed by the agent-based model. The BioFeed model then optimizes thefarm operations as a function of these decisions so asto maximize farm profit. If there is biomass shortfall,the information is returned to the agent-based modeland the farmer must purchase the required quantityof biomass from the storage elevator. Since the farmmachinery selection decisions are taken once in a fewyears, the BioFeed model optimizes machinery selec-tion only during the first year of the simulation. Themodel assumes that the necessary machinery can eitherbe purchased by the farmer or rented/leased for aspecific duration. The availability of equipment rent-ing as an option is subject to the availability of data.The purchase cost for the equipment is amortized overthe life time of the equipment which is typically 10–15 years. During the subsequent years, the model usesthe same machinery and optimizes only the operationof those machines. Future versions of the model willinclude the option of selling and replacing machin-ery during the course of the simulation. The BioFeedmodel provides the total equipment cost, the operatingcosts, and the total biomass produced to the agent-based model. The storage and handling losses are alsomodeled by BioFeed and depend on the method ofstorage as well as the particular equipment being used.These losses affect the total biomass production fromthe farm. These data are used to perform the cost-benefit analysis of the farm at the end of the produc-tion year. A comprehensive discussion of the BioFeedmodel is not included here for the sake of brevity.Interested readers are referred to Shastri et al. [11, 12].It must be noted that the use of BioFeed is limited bythe type of crops it can model. Currently, this is limitedto switchgrass, Miscanthus, and energy cane.

266 Bioenerg. Res. (2011) 4:258–275

Fig. 2 Simulation sequencefor the agent-based modelconsidering Miscanthus as theenergy crop

Year 1

Jan Feb Mar Apr May Dec

Jan Feb Mar Apr May Dec

Year 2

Energy crop farming and land allocation decision by

farmers

Beginning of bidding and contractual negotiations for biomass to be

delivered in the next year

Contracts negotiated during each month

Beginning of harvesting operations and delivery of biomass: BioFeed simulation on a

monthly basis

Farmers use estimates as well as actual values (if available) for

decision making

Participation decisions for current year and bidding for next year’s

contracts:Use of estimates and actual values (if

available) for decision making

Farmers update mental models based on the observed

economic performance

Simulation Sequence

The simulation sequence for the agent-based model issummarized below and is illustrated schematically inFig. 2 assuming Miscanthus as the energy crop. ForMiscanthus, the growing season begins in April andthe harvesting season begins in January. The simulationsteps are:

1. During the first year of the simulation, the farmersmake participation and land allocation decisionsfor the energy crop.

2. The biorefinery determines the total size (inMg/day) based on the biomass production estimate.

3. The farmers and the biorefinery begin contract ne-gotiations that take place during each month beforethe harvesting period begins.

4. The contract details are used to simulate crop pro-duction using the BioFeed model, which providesthe total biomass quantity and cost.

5. Crop land allocation decisions are modified in thesecond year of production based on the profitabilityinformation available before the beginning of thegrowing season.

6. Contract negotiations for the next year (year 3)begin after the land allocation decisions have beenmade.

7. At the end of year 2, each farmer calculates theprofit or loss associated with energy crop produc-

tion. This information is then used to change theland allocation decisions in the subsequent years.

8. These steps are repeated for each year for thesimulation horizon.

Model Application and Results

Miscanthus as an Energy Crop

Miscanthus is a perennial grass of east Asian origin ex-hibiting C4 photosynthesis mechanism [45]. Its hybrid,namely Miscanthus × giganteus has been proposed asa candidate crop for lignocellulosic feedstock due toits various desirable traits [45–48]. This particular hy-brid does not produce seeds and therefore must betransplanted either vegetatively or through rhizomes.Although this eliminates the threat of invasiveness, italso increases the establishment cost. Miscanthus ×giganteus grows up to 3–4 m in height and has a rela-tively high yield of up to 35 Mg/ha [41, 45]. These yieldsare not significantly affected even in the absence of fer-tilization and on marginal lands. Since it is a perennialgrass, it does not need to be planted every year whichreduces soil erosion. Moreover, it shows the propertyof recycling nutrients back to the rhizome upon senes-cence which reduces the fertilization requirement forthe subsequent year. All these traits make it a highlydesirable energy crop. As a result, it is being used as an

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Table 1 Case study data for Miscanthus production in Illinois(USA): these data are common to all the farmers considered inthe case study and the references have been provided whereverapplicable

Model parameter Value

Land opportunity cost $192/ha [8]BCAP payment amount $45/Mg [40]BCAP payment duration 2 years [40]On-farm open storage cost $1.7645/m2 [12]

(gravel pad)On-farm covered storage cost $10.529/m2 [12]

(shed with no walls)Establishment cost $3,750/ha [8]Miscanthus yield—year 1 10 Mg/ha [41]Miscanthus yield—year 2 15 Mg/ha [41]Miscanthus yield—year 3 20 Mg/ha [41]Crop establishment period 3 years [41]Miscanthus crop rotation duration 15 yearsContract duration 3 yearsTransportation cost $0.25/Mg kmPercentage of factual farmers 40%Base land conversion to 10%

MiscanthusMinimum percentage of 10% of total farm area

Miscanthus areaMagnitude of land 10% of total farm area

allocation changeBase profit margin 25% of the production costFarmer social network size 10

(number of farmers)

energy crop in Europe for direct combustion [46, 49].Illinois has been proposed as a candidate region to growthese crops in the USA where yields up to 35 Mg/hahave been achieved [41]. In Illinois, the crop will beplanted in March/April with peak yields achieved inearly October. However, in order to take the advantageof nutrient recycling, the harvesting season is proposedto begin in January when the harvestable yield hasdropped to about 20 Mg/ha. The typical harvesting andcollection system is expected to consists of mowing,pre-processing (baling, grinding, pelletization, or chop-ping), handling and roadsiding, storage and transporta-

tion to the biorefinery [12]. Given the potential role ofMiscanthus as an energy crop, this work considered itas the energy crop to simulate the agent-based modelcase study. The future versions of the model will alsoconsider other energy crops, such as switchgrass andsweet sorghum, and the competition for the adaptationamong these crops, in addition to the conventionalagricultural crops.

Case Study Details

The model considered Illinois as the geographical re-gion to study Miscanthus production. Since commercialproduction and market for energy crops does not exist,the case study considered 100 farmers in Illinois. Thesefarmers were assumed to be farming conventional rowcrops—in Illinois this is often a corn-soybean rotation.Table 1 shows the data used for the case study simula-tion, while Table 2 reports the farmer attribute data aswell as the method of data generation.

An important parameter in the model is the fractionof factual and social farmers, and this work estimatedthat fraction using data published in literature based onfarmer surveys [6, 19–24]. Jensen et al. [24] found thateven if switchgrass farming is profitable, only 38.9% ofTennessee farmers with prior knowledge of switchgrassagreed to participate in switchgrass farming. A similarstudy in southern Virginia by Wen et al. [22] found that43% farmers were interested in growing switchgrassif it was profitable. Based on these studies, this workassumed that 40% farmers were factual while the re-maining 60% farmers were social. The members withinthe social network of a farmer were randomly selected.For farmers willing to grow Miscanthus, the base landconversion to Miscanthus was 25% of the total farmarea based on data published in Wen et al. [22]. Theactual land conversion for an individual farmer de-pended on his/her attributes. Paulrud and Laitila [20],Wen et al. [22] and Jensen et al. [24] identified farmerage, farm area and available capital as the most im-portant factors affecting this decision. Jensen et al. [24]

Table 2 Farm attributes forthe case study that wererandomly samples: the datageneration method forattributes is provided usingthe type of distribution andthe properties of thatdistribution

Model parameter Value/range Distribution Mean (standarddeviation)

Farm size (ha) 20–1,000 Uniform –Farm-biorefinery transportation 10–60 Uniform –

distance (km)Farmer age (years) – Normal 60 (10)Available capital ($) 1,000–10,000 Uniform –Risk attribute (α) – Normal 1 (0.1)Number of minimum farmers required 3–6 Uniform –

to support a decision by social farmers

268 Bioenerg. Res. (2011) 4:258–275

Table 3 Initialization data for the agent-based model: costs andprice estimates used by farmers during the first year of simulation

Model parameter Value ($/Mg)

Production cost 50On-farm open storage cost 3On-farm covered storage cost 10Expected biorefinery purchase price 85

also identified the marginal impact coefficients of thesefactors on the decision. The coefficients for farm areaand farmer age were −0.001 and −0.002, respectively,which were used in this work. These coefficients de-cide the magnitude of change in land allocation perunit deviation of the farm area and farmer age fromthe mean value. Higher available capital increased thetotal land allocated to Miscanthus. The decision tomodify farm land allocated to Miscanthus dependedon economic performance of the previous 2 years,and the land allocation changed in the magnitude of±10% of the total farm area. A farm could have amix of Miscanthus stands with different ages that wereplanted during different years. In such cases, the totalharvestable yield of that farm was calculated using theweighted average of the farm area with a Miscanthusstand and the yield of that particular Miscanthus stand.It must be noted that although some of these data werenot specific to Miscanthus in Illinois, the case studyhad to rely on these data sources in the absence ofspecific data. Simulation studies conducted in this workand presented later identified the important parametersimpacting the simulation results. The model therefore

served as a guideline to identify important parametersthat must be determined through case specific surveys.

Table 3 shows the initialization values of the rele-vant model parameters. Please note that all farmersbegan with the same estimates during the first year.However, as the simulation progressed, each farmerupdated these estimates every year using Eq. 1 basedon the information available, either through productionexperience or through information gathered from thesocial network. In the equation, factual farmers useda = 0.75 while social farmers used a = 0.25. Therefore,factual farmers gave greater importance to actual val-ues of costs and prices as compared with their pastestimates. On the contrary, the social farmers gavegreater importance to their past estimates than theactual cost and price values. A simulation horizon of15 years was considered since Miscanthus is a perennialcrop with a rotation cycle of about 15 years [41]. For theBioFeed model simulation, only balers could be rentedby the farmers while all other equipment needed to bepurchased.

Base Case Results

Figure 3 shows the results for the base case simulationincluding the total number of farmers out of 100 thatgrew Miscanthus (bars) and the biorefinery capacitythat can be supported based on the regional productioncapacity (line). Since the collection area was hypothet-ical and consisted of only 100 farmers, the biorefinerycapacity in Fig. 3 is plotted in terms of the percentageof the maximum possible biorefinery capacity. This

Fig. 3 Miscanthus productionsystem development inIllinois: base case resultshowing the total number offarmers participating inMiscanthus farming (bars)and total biorefinery capacityas a percentage of maximumcapacity based on thecumulative production by thefarms (line)

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maximum capacity is calculated as the capacity reachedif all the 100 farmers allocated all of their farm land toMiscanthus. Please note that since there was no harvest-ing during the first year of Miscanthus farming (harvest-ing began in January of the second year), the capacityvalues are plotted from the second year onwards. Themost important observation of this result was that ittook about 15 years for the system to reach stableproductivity due to the slow acceptance of Miscanthusas a crop. This was not a preferred solution from abiorefinery perspective, which would want the capacityto increase rapidly and stabilize as soon as possible.During the first year, only 28 farmers, all of whom werefactual farmers, grew Miscanthus. None of the socialfarmers participated since they required a fraction oftheir peers to have grown Miscanthus in the previousyear. The nonacceptance by some of the factual farmerswas due to the longer transportation distances, whichincurred higher transportation costs that were borneby the farmer. By year 15, 67 farmers participated inMiscanthus farming out of which 31 were factual while35 were social farmers. Thus, about 77% of factualand 58% of social farmers grew Miscanthus. The finalbiorefinery capacity of 1,173 Mg/day was only about58% of the maximum possible (2,000 Mg/day). Anotherimportant observation was that the farmer participa-tion stabilized during the seventh year. However, thebiorefinery capacity continued to increase till the end ofthe simulation horizon. This was because most farmersbegan Miscanthus farming on a small fraction of thefarm area. This area increased gradually for most farmsafter the fifth year and hence, the total biorefinery

capacity kept increasing even though very few newfarmers started Miscanthus farming.

Figure 4 shows the average annual contract price forthe simulation horizon and compares it with the opti-mized Miscanthus production cost reported by Shastriet al. [12] using the BioFeed model. The error bars inFig. 4 show the variation in contract price for differentmonths of a particular year. The figure shows that thecontract price was on an average 40% higher than theminimum production cost. This difference was mainlyattributed to the profit margins demanded by the farm-ers. It was also owing to the inefficiencies in crop pro-duction due to suboptimal equipment selection as wellas due to inaccurate cost and price estimates by farmersin the initial years of production. The contract pricesincreased during the first four years after which theprices showed a downward trend. The reason was thatmost farmers were starting Miscanthus farming duringthe first few years which led to lower productivity perunit hectare. This caused the farmers to seek highercontract prices. However, increased productivity as wellas greater competition for securing a contract ensuredthat the average contract prices started decreasing. Itwas also observed that contract prices for the months ofJune till December during most years were higher thanthose for the months before June. This was becausea delivery contract after June necessitated coveredstorage of biomass which increased the production costfor the farmers.

It is important to note that these results should notbe used as predictions. Instead, they represent one ofthe several possibilities that could be realized in the

Fig. 4 Annual averagecontract price during thesimulation horizon and itscomparison with theoptimized Miscanthusproduction cost; the errorbars represent the variabilityin the monthly contract pricesfor a particular year

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Fig. 5 Impact of reducedland opportunity cost onbiorefinery capacity profile

future. It is, therefore, necessary to model and simulatevarious possible scenarios of the agent-based model toget a comprehensive understanding of the system. Suchscenario studies are especially important for the systembeing analyzed here since there are a number of un-certainties. The following sections report the results forsuch scenario studies. This also quantifies the impact ofspecific parameters in the system which can be helpfulin future modeling efforts.

Impact of Reduced Land Opportunity Cost

The base case assumed Illinois as the geographicalregion with the land opportunity cost of $192/ha [8],

which represents the next best alternative for the useof the land with respect to energy crop farming. Pro-duction of conventional crops such as corn and soybeanis very profitable in Illinois due to high productivityand favorable weather conditions. Hence, energy cropsmight find it difficult to gain acceptance among farm-ers in Illinois. A new scenario was therefore modeledwhere the land opportunity cost was reduced by 25%to $144/ha. This effectively models a scenario for adifferent geographical region of the USA.

Figures 5 and 6 show the biorefinery capacity profileand the farmer participation, respectively, for this sce-nario and compare those with the base case scenario.These results illustrated a significant impact of the landopportunity cost on the system development. The final

Fig. 6 Impact of reducedland opportunity cost onfarmer participation inMiscanthus farming

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biorefinery capacity was 1,911 Mg/day which was 63%greater than the base case and was approaching the100% capacity; 99 out of 100 farmers grew Miscanthusduring the 15th year. It must, however, be noted thatthe nature of the biorefinery capacity increase curvedid not change, which meant that it still took morethan 15 years for the production capacity to stabilize.This is an important result that needs to be consideredby the regulators while stimulating the biomass basedeconomy.

Impact of BCAP Incentive

The base case considered the BCAP payment of$45/Mg. This meant that farmers were provided a sin-gle rate of $1 for each $1/Mg (dry basis) paid by thebiorefinery, up to $45/Mg (dry basis) [40]. Different sce-narios were simulated where the BCAP payment levelswere varied, and Fig. 7 shows the biorefinery capacityprofiles for those BCAP values. As expected, higherBCAP payment levels led to higher final biorefinerycapacity. However, it did not significantly change thecapacity increase profile. This meant that even withhigher BCAP payment levels, the system took morethan 15 years to stabilize Miscanthus production.

Impact of Higher Base Land Conversion

The base case scenario assumed that farmers who de-cided to grow Miscanthus used the base level conver-sion of 25% of their farm land into Miscanthus [22].

It is, however, important to understand the impactof this assumption on the model simulation results.Therefore, different scenarios were simulated wherethe base land conversion level was increased up to 75%in units of 10%. Figure 8 shows the results for thesesimulations. It is clearly evident from the plots thathigher base land conversion had a significant impacton the production capacity profile. For the base landconversion of 75%, the production capacity increasedquite rapidly. Such a capacity increase profile would bepreferable for the biorefinery since the capital invest-ments can be better structured. It should however benoted that for all base land conversion levels simulatedhere, the final production capacity during year 15 wasquite similar and within 8% of each other. This meantthat the role of the base land conversion level wasprimarily to change the production growth profile. Itwas observed that beyond the tenth year of production,the capacity increase profiles did not necessarily followthe base land conversion fraction order. For example,the capacity for 65% was higher than that for 75%.This was because contract prices for each scenario wereindependently determined during the simulation andwere governed by a number of different factors. Thisintroduced nonlinearity within the model which wasreflected in such trends. This highlighted the abilityof the agent-based model to capture the complexityand nonlinearity within the system. The results empha-sized that efforts should be made to ensure farmersconvert higher fraction of their farm land to energycrop during the first year itself. This can be achievedthrough better incentives, policies as well as knowledgedissemination.

Fig. 7 Impact of BCAPpayment level on biorefinerycapacity profile

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Fig. 8 Impact of base landconversion level toMiscanthus on biorefinerycapacity profile

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Impact of the Rate of Land Conversion

The base case assumed that the magnitude of landallocation change for a farm was ±10% of the totalfarm area. This assumption was not based on a rigoroussurvey. Hence, multiple scenarios were modeled wherethe magnitude of this step change was increased up to25% in the unit of 5%, and Fig. 9 shows its impacton the biorefinery capacity profile. It was evident thathigher magnitude led to a faster increase in the pro-duction capacity. Hence, this must be encouraged infarmers involved in Miscanthus production. Similar tothe impact of base land conversion level, the final

biorefinery capacity was similar for all these scenariosand was within 12% of each other.

Impact of a Higher Fraction of Factual Farmers

The base case assumed 40% farmers to be factual. Thevalidity of this assumption will depend on a numberof different factors such as the geographical region,farmer attitude, and education. Hence, it is importantto understand the impact of a different fraction offactual farmers on the simulation results. Additionalscenarios were, therefore, simulated with the factualfarmer fraction of 50% and 60%, and Fig. 10 shows the

Fig. 9 Impact of magnitudeof Miscanthus land allocationchange on biorefinerycapacity profile

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Fig. 10 Impact of fraction offactual farmers on biorefinerycapacity profile

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impact on the biorefinery capacity profile. It was seenthat the higher fraction of factual farmers increasedthe production capacity during the simulation horizon.However, the nature of the capacity increase did notchange significantly. This illustrated that the fraction offactual farmers will not increase the rate of capacity riseof the biorefinery.

Conclusions

The development of a sustainable and resilient biomassfeedstock production and provision system depends onthe technological developments and economic driversas well as the socioeconomic and behavioral aspectsof various stakeholders within the system. This workdeveloped an agent-based model based framework thatintegrated these different dimensions using the the-ory of complex adaptive systems. Important agentswithin the feedstock production system such as farmers,biorefinery and storage elevator were modeled usingan object-oriented approach. The important decision-making rules of the agents and the interaction amongthese agents were modeled based on data in the litera-ture. The model was applied to study the developmentof the Miscanthus production system using a hypothet-ical case of 100 farmers in Illinois. Various scenarioswere modeled over a simulation horizon of 15 years tounderstand the impact of different model parameterson the system dynamics. The results emphasized thatproduction systems can take up to 15 years to reachstable productivity which will still be only 60% of themaximum capacity. The average contract prices were

40% higher than the optimized production cost andincreased for the first 4 years but decreased thereafter.A 25% reduction in the land opportunity cost led toa 63% increase in the steady state productivity. Theproduction capacity can be increased rapidly by en-couraging farmers to convert a greater fraction of theirfarm land to grow Miscanthus during the first year.Higher BCAP payment level as well as greater fractionof factual farmers also lead to greater productivity.The model will be extended significantly in the futureby incorporating other agents within the system. Thedecision-making rules for these agents will be updatedas more data become available. The model will then beused to study the resiliency of the feedstock productionsystem in the presence of unforeseen disturbances andvarious uncertainties. This study will eventually help inquantifying the impact of technological development,engineering infrastructure, and policy incentives on thedynamics of the system.

Acknowledgment This work has been funded by the EnergyBiosciences Institute through the program titled “EngineeringSolutions for Biomass Feedstock Production.”

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