Herbaceous plant biomass harvest and delivery cost with harvest segmented by month and number of harvest machines endogenously determined

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32 (2008) 1016 – 1027

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Herbaceous plant biomass harvest and delivery cost with harvest segmented by month and number of harvest machines endogenously determined Lawrence D. Mapembaa, Francis M. Epplinb,, Raymond L. Huhnkec, Charles M. Taliaferrod a

Department of Agriculture and Applied Economics, University of Malawi, Lilongwe, Malawi Department of Agricultural Economics, Oklahoma State University, Stillwater, OK 74078-6026, USA c Department of Biosystems and Agricultural Engineering, Oklahoma State University, Stillwater, OK 74078, USA d Department of Plant and Soil Sciences, Oklahoma State University, Stillwater, OK 74078, USA b

ar t ic l e i n f o

abs tra ct

Article history:

Infrastructure for production, harvest, storage, and transportation of lignocellulosic

Received 27 June 2006

biomass (LCB) is not developed. While some farmers have forage harvest machines and

Received in revised form

equipment that might be used to harvest LCB, it is unlikely that most regions would have

1 February 2008

machine harvest capacity necessary to provide massive quantities of LCB in a consistent

Accepted 5 February 2008

package and to provide an orderly flow of LCB to a biorefinery throughout the year.

Available online 18 March 2008

Conventional models assume a fixed harvest charge per unit of LCB, and do not recognize

Keywords: Lignocellulosic biomass Harvest

that harvest machines may not be readily available and that harvest days are limited by weather. The objective of this research is to determine how the method of modeling harvest cost changes the estimate of the number of LCB harvest machines necessary to support a biorefinery and the estimated cost to deliver LCB. A mathematical programming

Cost Biorefinery Mathematical programming Economics Oklahoma Corn (Zea mays) stover Wheat (Triticum aestivum) straw Switchgrass (Panicum virgatum) Native prairie grasses Old world bluestem

model is developed that includes integer decision variables enabling investment in harvest machines that provide monthly harvest capacity based on expected harvest days per month. Results from a conventional model are compared to those of the alternative model that endogenously determines the number of harvest machines. To provide 3.628 dry kt daily to a biorefinery, the endogenous harvest cost model selected 26 harvest units. Alternatively, the solution provided by the conventional model that assumes a fixed cost per unit harvested and ignores weather constraints and machine investment provides a solution that in some months would require 55 harvest units and costs substantially more than the exogenously assumed fixed cost per unit.

(Bothrichloa bladhii)

& 2008 Elsevier Ltd. All rights reserved.

Bermudagrass (Cynodon dactylon) Tall fescue (Festuca arundinacea)

1.

Introduction

Alternative methods for producing biobased products including ethanol have been developed that are based on the use of low-valued lignocellulosic biomass (LCB) such as crop residue Corresponding author. Tel.: +1 405 744 6156; fax: +1 405 744 8210.

E-mail address: [email protected] (F.M. Epplin). 0961-9534/$ - see front matter & 2008 Elsevier Ltd. All rights reserved. doi:10.1016/j.biombioe.2008.02.003

and perennial grasses [1–12]. Agricultural residues (e.g. corn (Zea mays) stover, wheat (Triticum aestivum) straw, sugarcane (Saccharum officinarum) bagasse), herbaceous crops (e.g. switchgrass (Panicum virgatum), perennial grasses), forest residues, and other woody biomass, wastepaper, urban

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wastes, and other wastes could be used as LCB feedstock. Theoretically, an LCB-based system could be much more efficient than a starch-based (corn, sorghum (Sorghum bicolor)) system for several reasons. An LCB system could use lowvalue crop residues. Alternatively, if agriculture is used to produce feedstock, LCB could be obtained from perennials that require relatively fewer cultural practices to produce than the annuals (corn, sorghum) currently used as grainethanol feedstock. Additionally, an LCB-based system may use a greater proportion of the harvested plant material and result in less biorefinery waste. Research and development is ongoing in an attempt to develop economically competitive methods to produce ethanol from LCB [7–12]. Examples include enzymatic hydrolysis, acid hydrolysis, gasification, gasification–biofermentation, liquefaction, and mixalco. The optimal feedstock characteristics may depend on whether the processing system that proves to be most economical requires dry versus wet and loose versus dense biomass. Several private and public research entities are attempting to develop gasification–biofermentation technology [8,10,12]. The present model is based on the assumption that an economically competitive gasification–biofermentation system will be developed and that each of the potential feedstocks is of equal value to the biorefinery. This assumption is reasonable if the feedstocks are limited to corn stover, wheat straw, native prairie grasses, old world bluestem (Bothrichloa bladhii), bermudagrass (Cynodon dactylon), tall fescue (Festuca arundinacea), and switchgrass, and if the biorefinery uses the gasification–biofermentation technology. However, this assumption would not be appropriate for all potential conversion technologies. For some of the technologies under development, the potential product yield and level of byproduct will differ across feedstock. Infrastructure for production, harvest, storage, and transportation of LCB is not developed. While some farmers have forage harvest machines and equipment that might be used to harvest LCB, it is unlikely that most regions would have a sufficient investment in harvesting machinery that could provide massive quantities of LCB in a consistent package and provide an orderly flow of LCB to a biorefinery throughout the year. Ultimately, the economic viability of an LCB biorefinery will depend in part on the cost to produce, harvest, and deliver LCB to a conversion facility. LCB harvest and transportation cost will be important components of the cost to produce products from LCB. Previous models of LCB production, harvest, and transportation have assumed a single-point estimate of the harvest cost per mg or per ha or have assumed some form of custom harvesting [13–16]. While this may be a reasonable approach if the feedstock is corn grain, it may be less so for a feedstock such as LCB for which a harvesting infrastructure does not exist. It is important to effectively estimate the procurement, harvesting, and transportation costs of LCB in the project appraisal of an LCB biorefinery system. A number of studies have provided estimates of LCB production costs [13,17–22]. Walsh [22] reported that LCB production cost estimates ranged from 27 $ mg1 to more than 133 $ mg1 depending on crop, region, yield, and method of analysis. Based on a survey of custom harvest charges,

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Kletke and Doye [23] reported an average charge of 25 $ mg1 for cutting, raking, and baling forage. Comparisons across studies are difficult because of differences in assumptions and methods. However, two consistent patterns across studies are that (i) a single-point estimate is reported independent of the assumption about machine size or quantity of material harvested per year by the assumed set of machines and (ii) the lack of an existing harvesting infrastructure has received little attention. This research attempts to provide additional insight regarding the cost to harvest dedicated LCB feedstocks. A coordinated harvest system such as that used by timber processing firms is anticipated to be more cost efficient than systems analogous to harvesting forage for livestock. Previous studies of the cost to deliver LCB to a biorefinery have not considered coordinated sets of harvest machines and harvest crews. Studies that have assumed per unit harvest cost exogenous to the model may have failed to capture the true harvest cost and the investment in harvest machines necessary to support a biorefinery. The objective of the research reported in this paper is to determine how the method of modeling harvest cost changes the estimate of the number of LCB harvest machines necessary to support a biorefinery, and the estimated cost to deliver an mg of LCB to a 3.628 dry kt of LCB per day biorefinery. Results from a conventional model that includes a fixed harvest charge per mg of LCB are compared to those of an alternative model that includes an integer investment activity such that the number of harvest machines is endogenously determined. In this alternative configuration of the model, monthly harvest capacity constraints are included to restrict the number of mg of LCB harvested per month to not exceed the available capacity that depends on the endogenously determined number of harvest machines and the number of harvest days in each month.

2.

Procedure

A multi-region, multi-period, mixed integer mathematical programming model was constructed for the case study region that includes the USA state of Oklahoma. Biorefinery locations are included in the model as binary variables. The number of harvest units is included as an integer variable. The model was designed and solved to determine the area and quantity of LCB harvested by county, number of harvest units, cost to procure, harvest, store, and transport a flow of LCB to a biorefinery. A description of the model is included in Appendix A. The indices, parameters, and variables are described in Tables A2–A4. Thorsell et al. [24] designed a coordinated harvest unit that provides the capacity to harvest a given quantity of LCB per time period that results in substantial size economies. Thorsell [25] assumed that LCB harvest and field storage would require machines that could mow, rake, and bale LCB and a machine that could collect, transport, and stack bales at an in-field location near an all-weather road. The search was limited to established technology and available agricultural equipment and to machines that can travel quickly and legally on section line county roads and highways.

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A three-step procedure was used to estimate the cost to harvest LCB from cropland and rangeland in Oklahoma. First, the agricultural machinery cost computer program AGMACH$ [26] was used to determine which specific type of mower, rake, and baler would result in the lowest costs at intensive levels of use. Second, another machinery complement and cost estimator, MACHSEL [27], was used to design a coordinated set of machines and estimate costs for owning and operating the machines. Third, the area to be harvested was parameterized to enable determination of an estimate of the long-run average cost curve for alternative LCB yields. Both AGMACH$ and MACHSEL use the machinery cost equations published in the American Society of Agricultural Engineering Standards [28] and the American Agricultural Economics Association Costs and Returns Handbook [29]. These programs were used to estimate ownership costs including depreciation, interest on average investment, insurance and taxes, and operating costs including fuel, oil, lubricants, and repairs. Nominal, 2003 prices were used. Interest rate on machinery investment was set at 9%. Harvest machine prices, estimated hours of life, budgeted operating speeds, widths, field efficiency, repair cost parameters, salvage value parameters, and other details are reported in Thorsell et al. [24] and Thorsell [25]. The designed harvest unit includes three 71 kW tractors each to power two 3-m rotary mowers (mower conditioners); three 71 kW tractors each to power two 3-m rakes; three 112 kW tractors each to power a baler that produces large rectangular solid (approximately 1.2 m  1.2 m  2.4 m) bales; and a single self-propelled bale transporter that can travel in a field and collect as many as eight large rectangular solid bales, transport them, and stack them adjacent to an allweather road. The nine tractors and one transporter require 10 workers. With an extended harvest system enabled by the variety of potential feedstocks, it was assumed that salaried laborers could be employed full time and work as a harvest crew during 9 months of the year. The cost of a harvest unit includes estimated wage and benefits cost of $25,000 per worker yr1, or $250,000 for the harvest unit that includes 10 workers. This study differs from prior studies in several respects. First, in the present study the harvest unit as designed by Thorsell et al. [24] is incorporated as an integer and endogenously chosen activity. Monthly harvest capacity depends on the number of harvest days per month and the number of endogenously determined harvest units. Second, an estimate of the expected number of harvest days per month based on historical weather patterns is incorporated. Third, the model breaks the year into 12 discrete periods (months) enabling a flow of feedstock to a biorefinery and recognizes that the expected dry matter yield of LCB depends on the time (month) of harvest and that storage losses will occur and depend on the location of storage and time of storage. Two mathematical programming models were constructed and solved. The first model, the endogenous harvest cost model, includes the coordinated set of harvest machines and harvest crew (called the harvest unit) as an endogenous integer variable. Each harvest unit provides a daily throughput capacity. Total monthly harvest capacity depends on the

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number of harvest days per month and the number of endogenously determined harvest units. The model solves for the optimal investment in harvest machines. For the conventional (second) model the harvest cost is included as an exogenous variable with a fixed cost per mg harvested. The models were solved using GAMS/CPLEX [30].

3.

Data

The mathematical programming model is designed to maximize the net present worth of an LCB biorefinery over a 15-year period [6]. However, estimates of investment and operating costs for a 3.628 dry kt day1 lignocellulosic biorefinery were not available. The model was solved with pseudo estimates of biorefinery costs. In the process of maximizing net present worth, the model determines the least-cost feedstock production, harvest, storage, and transportation system. Mathematically, the cost minimization component is nested in the model. The model includes each of Oklahoma’s 77 counties as potential LCB production sources. The state of Oklahoma has a variety of potential LCB feedstock, including plant residues, indigenous native prairies, and improved pastures [6]. Besides these sources of LCB, cropland could be used to produce dedicated feedstock crops such as switchgrass [31]. Oklahoma has 63,000 km2 that are in native prairie grass, 20,000 km2 in improved pasture, 4000 km2 in the federal government’s Conservation Reserve Program (CRP), and 31,000 km2 of harvested cropland [32]. Nine potential LCB feedstocks were included in the model: corn stover, wheat straw, tall native prairie grasses, short native prairie grasses, mixed native prairie grasses, old world bluestem, bermudagrass, tall fescue, and switchgrass. In Oklahoma wheat straw may be harvested in June and July, and corn stover in September and October. Harvest of perennial grasses could begin in July and continue for an extended period to as late as February the following year [6]. Use of a variety of feedstock enables an extended harvest system from June through February of the following year. Data from the Census of Agriculture [32] were used to determine existing hectares of wheat, corn, native prairies, and improved pastures. CRP hectares were based on 2004 enrollment [33]. A survey of professional forage specialists was conducted to disaggregate native prairie hectares into hectares of tall native grass, mixed native grass, and short native grass prairies by county. Hectares of improved pastures were disaggregated into hectares of tall fescue, old world bluestem, and bermudagrass. Data from Oklahoma Agricultural Statistics [34] were used to estimate the average area and yields of corn and wheat for each of the Oklahoma counties. Harvestable quantities of wheat straw and corn stover were based on estimated relationships between residue yield and grain yield with consideration given to fulfill conservation compliance requirements. Yield estimates for all other LCB feedstock types were based on county estimates by professional agronomists in the respective production counties. Monthly expected yields differ depending on harvest month due to in-field losses. Yield adjustment factors obtained from professional

ARTICLE IN PRESS BIOMASS AND BIOENERGY

agronomists were used to account for relative differences in the monthly expected yields. Harvest costs were based on the coordinated harvest unit as described by Thorsell et al. [24]. The annual ownership and operating cost of one harvest unit (10 laborers, nine tractors, three mowers, three rakes, three balers, and a field transporter) was estimated to be $580,000 [24]. A single harvest unit provides a throughput capacity of 309 mg per harvest day. Reinschmiedt [35] determined, from a survey of Oklahoma farmers, field days lost as a result of alternative amounts of rainfall for several soil types and several levels of soil moisture prior to the rain. Reinschmiedt [35] combined this information with historical rainfall data to produce cumulative distribution functions of the number of days available for fieldwork for each month for each region of the state. For the current study, the 95% level on the cumulative distribution functions was chosen. For example, for some counties, for the month of June, based on Reinschmiedt’s [35] research, at least 17.50 days are expected to be available for field work in 95% of the years (i.e. 19 out of 20 years). The number of LCB harvest days for each county was assumed to be equal to the number of fieldwork days taken from the 95% level of Reinschmiedt’s distributions. Based on the estimated number of harvest days per month and a harvest season that extends from June through February, a harvest unit as defined provides a total annual capacity of 49.739 kt. For the exogenous harvest cost model, it was assumed that harvest machines would be used at capacity. To obtain the harvest cost per mg, the total annual cost of a harvest unit ($580,000) was divided by the total annual capacity of a harvest unit (49.739 kt). This resulted in a harvest cost estimate of 11.66 $ mg1 for the conventional model. This cost estimate is based on the assumption that all harvest machines are used at capacity, ignores the machinery investment requirement and machine capacity constraints, and does not consider the number of harvestable days. Harvested LCB may be stored in the field prior to transport to a biorefinery. LCB stored in-field is assumed to be stacked and covered with a plastic tarp. The cost of storing LCB in field was estimated to be 2.21 $ mg1, regardless of how long the LCB material is stored. It was also assumed that a 0.5% loss in quantity is incurred every month the LCB remains in storage [36]. The USA Farm Security and Rural Investment Act of 2002 (the 2002 Farm Bill) limited harvest on CRP land to once in 3 years. Thus, it was assumed that no more than 25% of the CRP land in a county could be harvested in a representative year. Harvesting of all other land was limited to no more than 10% of the available hectares by county in each year. The average rental rate for CRP land in the region studied was 86.50 $ ha1 [33]. By policy, the CRP payment would be reduced on land harvested for LCB feedstock by 25%; an average of 22 $ ha1. Consequently, CRP land harvested for biorefinery use was assessed at a land rent of 25 $ha1 to compensate landowners for the reduction in CRP payment and removal of LCB. A charge of 11 $ mg1 was assessed to compensate landowners for removal of all feedstock other than feedstock produced on CRP land. Eleven counties were selected as possible biorefinery locations. LCB feedstock requirements were set at 3.628 dry

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kt day1. Storage capacity at the biorefinery was assumed equivalent to the quantity of LCB that could be processed in 3 weeks (i.e. 76.188 kt). Minimum inventory at the biorefinery facility was assumed to be equal to zero. The biorefinery was expected to operate 350 days per year. Estimates of field to biorefinery transportation distances were based on map-kilometers from cities located near the center of the two counties. Total cost of transporting LCB from the LCB-supplying counties to the biorefinery plant location was computed using the LCB transportation cost equation reported by Bhat et al. [37].

4.

Results

Table 1 presents results for both the endogenous and exogenous harvest cost models. For the conventional exogenous harvest cost model, a harvest fixed charge of 11.66 $ mg1 was assessed. For the endogenous harvest cost model, an integer investment activity was included such that the number of harvest units was endogenously determined. In this alternative configuration of the model, monthly harvest capacity constraints were included to restrict the quantity harvested per month to not exceed the available capacity that depends on the endogenously determined number of harvest units and the number of harvest days. With the exception of LCB acquisition cost and in-field storage cost, all other costs are slightly lower for the conventional exogenous harvest cost model than for the endogenous harvest cost model. This is the case because the endogenous model has restrictions on the number of harvest days and the harvest capacity of the harvest unit. These restrictions result in higher costs per unit harvested. The conventional model assumes that all machines are used at capacity, which is not the case with the alternative model. The endogenous harvest cost model solves for a harvest cost of 11.82 $ mg1 compared to the 11.66 $ mg1 assumed for the conventional model. Similar harvest cost results were reported by Cundiff [14], Epplin [15], English et al. [16], Cundiff and Marsh [38], Sokhansanj and Turhollow [39], and Ho [40]. Restrictions in the endogenous harvest cost model regarding harvest capacity of machinery and available harvest days result in higher harvest cost estimates. By not accounting for the investment required in harvest machines and weatherimposed harvest constraints, the conventional approach underestimates harvest cost and underestimates the total cost to deliver feedstock to a biorefinery. The results in Table 1 show that the optimal number of harvest units for the endogenous harvest cost model is 26, which will require an average investment of about $15.3 million. The biorefinery would optimally process 1270 kt of LCB annually, harvested from 3830 km2. Due to storage losses more LCB must be harvested than is required to meet the capacity of the biorefinery. The model finds it optimal to harvest 1275 kt of LCB to fulfill the requirements of the biorefinery. LCB is optimally harvested from each of the nine potential feedstocks except for tall fescue and switchgrass. The harvested material is transported to provide a continuous flow of feedstock throughout the year to a biorefinery located in Canadian county. The plant usage is at 100%.

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Table 1 – Results of models solved to determine how the method of modeling harvest and procurement cost changes the cost to deliver an mg of LCB to a biorefinery that can process 3.628 dry kt day1 Item

Units

Model comparisons Endogenous harvest cost

Exogenous harvest cost

10.43 11.82 0.43 16.00 39.00 26 15.34 3830 1275 1270 7 171 Canadian 100%

10.45 11.66 0.43 15.67 38.53 55 32.45 3675 1280 1270 7 167 Canadian 100%

$ mg1 $ mg1 $ mg1 $ mg1 $ mg1 Number $000,000 km2 kt kt Number km

Biomass acquisition cost Harvest cost Field storage cost Transportation cost Total cost of delivered feedstock Harvest unitsa Average investment in harvest machinesb Area harvested Total biomass harvested Biomass processed Feedstock species harvested Average distance hauled Plant location Capacity usagec

%

a

A harvest unit includes 10 laborers, three mowers, three rakes, three balers, nine tractors, and one transport stacker. The exogenous model harvests most of the feedstock in the months of July, August, and September. To harvest the September LCB would require 55 harvest units compared to 26 harvest units required by the endogenous harvest cost model. b The average capital investment in harvest machines is calculated as half the price of the machine plus the salvage value summed across all machines. The average capital investment for 55 harvest units as required by the exogenous harvest model is $32.45 million compared to $15.34 required by the endogenous harvest cost model. c The biorefinery is expected to operate 350 days per year. The model was restricted to choose only one plant location and size.

Harvested Biomass (Mg)

300,000 Endogenous Cost 250,000

Exogenous Cost

200,000 150,000 100,000 50,000 0 Jan

Feb

Mar

Apr

May

Jun Jul Month

Aug

Sep

Oct

Nov

Dec

Fig. 1 – Total biomass (LCB) harvested from all supplying counties in each month for both the endogenous and exogenous harvest cost models.

Table 1 also includes results selected by the conventional exogenous harvest cost model. The optimal biorefinery would process 1270 kt annually harvested from 3675 km2. The exogenous harvest cost model finds it optimal to harvest 1280 kt of feedstock to suffice the requirements of the biorefinery. The conventional exogenous harvest cost model does not endogenously determine the number of harvest machines to be used and places no restrictions in terms of harvest capacity of machines. Consequently, it indicates the harvest of more LCB than the alternative model. As in the endogenous harvest cost model the plant usage is at 100%.

Fig. 1 presents quantity of LCB harvested in each month for both models. When monthly harvest capacities are not imposed, harvest is concentrated in July, August, and September. And to harvest the estimated September LCB quantity, a total of 55 harvest units would be required. Whereas, when harvest capacities are endogenously determined, the integer harvest unit model finds that it is optimal to have 26 harvest units and to use them at near capacity to harvest a variety of feedstock throughout the nine-month harvest season. Thorsell et al. [18] estimated that a harvest unit would require an average capital investment of approximately $590,000. Average investment is defined to be half of the

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sum of the purchase price plus salvage value for each machine summed across all 19 machines in the defined harvest unit. Based on this estimate, 26 harvest units would require an average investment of $15.34 million. Whereas 55 harvest units would require an average investment of $32.45 million. As noted the annual ownership and operating cost of one harvest unit is $580,000. For 55 harvest units this annual cost would be $31,900,000. Since the exogenous harvest cost model harvests 1280 kt (Table 1), therefore, the true harvest cost per mg would be 24.93$ not the 11.66 $ mg1 that was included in the conventional exogenous model. Ignoring harvest machinery investment requirements and weather constraints has substantial economic consequences. The conventional exogenous harvest cost model indicates optimal harvest of most of the required feedstock (more than 50%) in July, August, and September with 20% harvested in September. This is consistent with the peak LCB yields for all feedstocks except for fescue. If not harvested, harvestable yields start declining in the month of October and continue to decline until February. Harvest is not permitted in March, April, and May; consequently, all shipments to the biorefinery in these months are drawn from storage. Crop residues, however, can only be harvested during the months in which the main crop is being harvested, wheat straw in June and July and corn stover in September and October. When the cost of investing in harvest machines is directly accounted for within the model, the machines would be used at near capacity over as many months as possible (Fig. 1). The endogenous harvest cost model includes monthly capacity constraints. The capacity constraints can be increased at a cost by investing in additional harvest units. Each harvest unit provides daily capacity. However, the number of harvest days per month is limited by historical weather patterns. Thus, the quantity of LCB that can be optimally harvested depends not only on the capacity of the harvest unit but also on the available field workdays in that particular month.

Harvested Biomass (Mg)

500,000

1021

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By harvesting most of the required LCB in the first few months of the harvest season, the conventional exogenous harvest cost model incurs more storage losses than the endogenous harvest cost model. The exogenous harvest cost model harvests more LCB than the endogenous harvest cost model. The additional LCB is required to replace the expected storage losses. Fig. 2 presents the quantity of LCB harvested from each feedstock source. A large proportion of the LCB feedstock shipments would be native prairie grasses (Fig. 2). Wheat straw is the other LCB feedstock that is harvested in large quantities. Feedstock can either be shipped directly from harvested LCB or from LCB kept in field storage. Fig. 3 includes a chart of the estimated quantity of feedstock stored per month at field sites for both models. Harvest begins in June with availability of wheat straw. For the exogenous harvest cost model, replenishment of storage reserves begins in July. Increase in field storage inventory continues from July through September. From September field storage remains almost at the same level until February when the harvesting is completed. At the end of February the combined field and biorefinery storage inventory must be sufficient to provide feedstock until harvest may be resumed in June. In-field storage reserves are drawn down until the end of May when field storage is reduced to zero. For the endogenous harvest cost model, field inventory storage increases gradually from August through February (Fig. 3). At the end of February the combined field and biorefinery storage inventory must be sufficient to provide feedstock until harvest may be resumed in June. In-field storage reserves are drawn down until the end of May when inventory in field storage is reduced to zero. In both models all feedstock harvested in the month of June is shipped directly to the biorefinery. Optimal harvest, storage, and shipment patterns are determined in part by harvest capacity, feedstock

Endogenous Cost Exogenous Cost

400,000

300,000

200,000

100,000

0 WHS

CNM

NAS

CST NAT IBE Feedstock Type

COW

NAM

IOW

Fig. 2 – Total harvested biomass feedstock from all supplying counties by biomass feedstock type for both the endogenous and exogenous harvest cost models. WHS ¼ wheat straw, CNM ¼ mixed native grass grown on CRP land, NAS ¼ short native grass, CST ¼ corn stover, NAT ¼ tall native grass, IBE ¼ bermuda grass, COW ¼ old world bluestem grown on CRP land, NAM ¼ mixed native grass grown on grassland, and IOW ¼ old world bluestem grown on pastureland.

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300,000 Endogenous Cost Exogenous Cost

Stored Biomass (Mg)

250,000 200,000 150,000 100,000 50,000 0 Jan

Feb

Mar

Apr

May

Jun Jul Month

Aug

Sep

Oct

Nov

Dec

Fig. 3 – Total stored biomass feedstock from all in-field storage facilities in each month for both the endogenous and exogenous harvest cost models.

availability, relative feedstock yields by month, storage costs, field losses, and stored feedstock deterioration.

5.

Sensitivity to number of harvest days

The number of harvest days available in each month was reduced by 50% and the endogenous harvest cost model was resolved to determine the sensitivity of results to the number of available harvest days per month. A summary of results associated with the base model in comparison to this third model is presented in Table 2. Harvest costs almost double when the available harvest days in each month are reduced by 50%. This is because more harvest machines (51 harvest units versus 26 harvest units in the base model) would be required to complete the harvest when there are fewer days to harvest. Reducing the number of harvest days by 50% resulted in an increase in the estimated cost of delivered LCB feedstock by 11.57 $ mg1. The results are sensitive to the number of days available for field harvesting. Cundiff [17] found that an increase in harvest days reduced harvest costs by 10% per mg.

6.

Conclusion

The lack of an established infrastructure for LCB feedstock harvest and storage has received little attention in prior studies of the economics of an LCB biorefinery. The specific objective of the research was to determine how the method of modeling harvest cost changes the estimate of the number of LCB harvest machines necessary to support a biorefinery, and the estimated cost to deliver an mg of LCB to a biorefinery. Two methods were used, in one method, timing of harvest was ignored and a fixed charge per mg was assessed; in the second method, harvest machinery investment integer activities were included. The machinery investment activities provided varying levels of harvest capacity per month depending on estimates of expected harvest days per month. Results from the conventional model that included a fixed

harvest charge per mg were compared to those of the alternative model that included an integer investment activity such that the number of harvest machines was endogenously determined. In this alternative configuration of the model, monthly harvest capacity constraints were included to restrict the quantity of feedstock harvested to not exceed the available capacity that depends on the endogenously determined number of harvest machines and the number of harvest days. Assumptions about the harvest structure of LCB feedstock in the LCB biorefinery economic analysis could greatly affect the results and conclusions drawn from the study. The model that assumes a coordinated harvest structure with machinery and harvest crews and operating on time constraints due to differences in monthly field workdays could more nearly capture the true harvest cost and give more reliable results than a conventional model that assumes a fixed cost per mg. The model that endogenously incorporated harvest units reported a harvest cost of 11.82 $ mg1 using 26 harvest units. On the other hand, the model with an assumed exogenously determined fixed harvest cost of 11.66 $ mg1 would require about 55 harvest units. The 55 harvest units would result in an actual harvest cost of 24.93 $ mg1, which is more than double the harvest cost reported by the endogenous harvest cost model. With this higher harvest cost the total cost to deliver an mg of LCB increases to 51.79 $ mg1. This is 12.79 $ mg1 greater than the total cost of 39.00 $ mg1 estimated by the endogenous harvest cost model. The modeling effort suggests that it is appropriate to incorporate harvest machinery investment endogenously in models designed to determine the economics of an LCB biorefinery. The multi-period, multi-region mixed integer mathematical programming model used in this study is deterministic. Therefore, the estimated expected cost to deliver LCB feedstock to a biorefinery of 39.00 $ mg1 should be considered a lower bound and is provided with several caveats. First, the cost estimate depends critically on the assumption that a variety of feedstocks could be processed by a single biorefinery and that each feedstock is of equal value to the biorefinery. If this assumption is not correct and if

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Table 2 – Comparison of results of the base model for endogenous harvest cost with the sensitivity analyses for harvest days for a biorefinery that can process 3.628 dry kt per day (large plant) Item

Units

Base endogenous harvest cost model

Endogenous harvest cost model with 50% reduction in harvest days

Biomass acquisition cost Harvest cost Field storage cost Transportation cost Total cost of delivered feedstock Harvest unitsa Average investment in harvest machinesb Area harvested Total biomass harvested Biomass processed Feedstock species harvested Average distance hauled Plant location Capacity usagec

$ mg1 $ mg1 $ mg1 $ mg1 $ mg1

10.43 11.82 0.43 16.00 39.00

10.50 23.20 0.43 16.06 50.57

Number $000,000

26 15.34

51 30.09

km2 kt kt Number km

3830 1275 1270 7 171 Canadian 100%

3889 1275 1270 7 172 Canadian 100%

%

a

A harvest unit includes 10 laborers, three mowers, three rakes, three balers, nine tractors, and one transport stacker. The average investment in harvest machines is calculated as half the price of the machine plus the salvage value summed across all machines. c The biorefinery is expected to operate 350 days per year. The model was restricted to choose only one plant location and size. b

a biorefinery is limited to processing a single feedstock, cost to delivery LCB would be greater than 39.00 $ mg1. Second, the estimates of available harvest days were adapted from a study designed to estimate fieldwork days. Fieldwork days may be considered an upper bound on the number of days during which LCB could be harvested. As shown in Table 2, results are sensitive to the number of harvest days. Additional research is warranted to obtain more precise estimates of the number of harvest days.

Acknowledgments The authors thank personnel of the Biobased Products and Energy Center at Oklahoma State University, Stillwater, and Rodney R. Wanger, USDA, Farm Service Agency for assistance. This material is based on work supported in part by Aventine Renewable Energy, Inc., USDA-CSREES IFAFS Competitive Grants Program award 00-52104-9662, and USDA-CSREES Special Research Grant award 01-34447-10302. The project was also supported by the USDA Cooperative State Research, Education and Extension Service, Hatch Grant no. H-2574. Support does not constitute an endorsement of the views expressed in the paper by Aventine Renewable Energy or by the USDA. Journal paper AEJ-265 of the Oklahoma Agricultural Experiment Station.

Appendix A.

Model equations

Table A1 includes the details of the model equations. The objective function of the multi-region, multi-period, mixed integer mathematical programming model is given in

Eq. (A.1). The objective function is maximized subject to constraints (Eqs. (A.2)–(A.19)). Model parameters, variables, and indices are defined in Tables A2–A4, respectively.

A.1.

Constraints

A.1.1.

Land resources constraint: Eq. (A.2)

Eq. (A.2) requires that the harvested area may not exceed the area in each county that can be harvested for feedstock.

A.1.2. In-field feedstock resources balance equations: Eqs. (A.3)–(A.7) Eq. (A.3) insures that LCB harvested is equal to the available LCB in the field less any field losses. The yield adjustment factor, YAD, is based on the assumption that LCB yields depend on harvest month. In this model formulation it is assumed that all LCB feedstock is harvested from the proportion of harvestable ha. Eq. (A.4) insures that no land may be harvested during months in which the yield adjustment factor is equal to zero. YAD ranges from zero to one. YAD is based on the assumption that LCB yields are highest if harvested at certain times of the year and decline thereafter. Eq. (A.5) insures that in each month and at each source, the sum of LCB shipped to plants and LCB put in storage of each LCB type, k, should equal the sum of current production and usable portion of stored LCB. Eq. (A.6) requires that quantity of LCB shipped out plus that lost in in-field storage balance with total LCB harvested. Eq. (A.7) requires that, in each month, the quantity of LCB harvested plus that quantity removed from storage must equal the quantity of LCB transported from LCB-producing counties to the biorefinery plus the quantity placed in storage.

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Table A1 – Model equations

32 (2008) 1016 – 1027

Table A1. (continued ) QðJ; S; G; MÞp

Objective function for the endogenous harvest cost model Max NPW q;x;xt;xs;A ( " X X ¼ RHOðGÞ  QðJ; S; G; MÞ M



X

J;S;G





Constraint on number of biorefineries X BETAðJ; SÞp1.

PSIðKÞ

XTðI; J; S; K; MÞ; XPðJ; S; K; MÞ; QðJ; S; G; MÞX0.

3

XðI; KF; MÞ5

KF if KKFðK;KFÞ

X

TAFCðS; FTÞ  BETAðJ; SÞOMEGA  HUg  PVAF.

LandX resources constraint X AðI; KF; MÞp BPðI; K; LÞ  LANDðI; K; LÞ;

8I; K.

(A.2)

L

In-field X feedstock resources balance equations XðI; KF; MÞ ¼ YADðK; MÞ KF if KKFðK;KFÞ



X

AðI; KF; MÞ  BYLDðI; K; FÞ

(A.18)

Integer variable constraint HU is an integer variable.

(A.19)

Objective function for the exogenous harvest cost model Max NPW q;x;xt;xs;A ( " X X RHOðGÞ  QðJ; S; G; MÞ ¼ M

8I; K; M. (A.3)



if YADðK; MÞ ¼ 0;

8I; K; M.

ALPHAðKÞ 

X

AðI; KF; MÞ

KF if KKFðK;KFÞ

X

GAMMAðKÞ  XSPðI; K; MÞ

I;K

(A.4) 

KF if KKFðK;KFÞ

J;S;G

X I;K

 AðI; KF; MÞ ¼ 0

(A.17)

Binary variable constraint BETAðJ; SÞ 2 f0; 1g.

F

X

(A.16)

(A.1)

J;S;FT

KF if KKFðK;KFÞ;M

(A.15)

Non-negativity constraints AðI; KF; MÞ; XðI; KF; MÞ; XSðI; K; MÞ; XSðJ; K; MÞ,

TAUðI; JÞ  XTðI; J; S; K; MÞ

I;J;S;K

I;K



X

GAMMAðKÞ  XSPðI; K; MÞ  X

8G; J; M; S.

AðI; KF; MÞ

KF if KKFðK;KFÞ

I;K

X

LAMBDAðK; GÞ  XPðJ; S; K; MÞ;

K

J;S

X

ALPHAðKÞ 

I;K

X

X

X

TAUðI; JÞ  XTðI; J; S; K; MÞ

I;J;S;K

X



XðI; KF; MÞ þ THETAðI; KÞ  XSðI; K; M  1Þ

X

XTðI; J; S; K; MÞ þ XSðI; K; MÞ;

8I; K; M.

(A.5)

J;S

XTðI; J; S; K; MÞ þ ½1  THETAðI; KÞ

X

XTðI; J; S; K; MÞ 

X

XSPðI; K; MÞ;

8M.

Capacity constraint equations X XHUðI; MÞpHU; 8M.

8I; K. (A.6)



X

KAPPA  XðI; KF; MÞ

)  PVAF

(A.20)

(A.7)

(A.8)

XðI; K; MÞpXHUðI; MÞ  CAPHUðI; MÞ;

8I; M.

(A.9)

K

QðJ; S; E; MÞpCAPPðSÞ  BETAðJ; SÞ;

8J; S; M.

XSðJ; K; MÞpCAPSðSÞ  BETAðJ; SÞ;

8J; S; M.

(A.10) (A.11)

K

On-site feedstock resources balance equations X XTðI; J; S; K; MÞ þ PHIðJ; KÞ  XSðJ; K; M  1Þ ¼ XSðJ; K; MÞ  XPðJ; S; K; MÞ;

8J; K; M; S.

XTðI; J; S; K; MÞ ¼ ½1  PHIðJ; KÞ 

I;M

þ

X

X

(A.12)

XSðJ; S; K; MÞ

M

XPðJ; S; K; MÞ;

8J; K; S.

(A.13)

8J; M; S.

(A.14)

M

XSðJ; K; MÞXMBINVðSÞ  BETAðJ; SÞ;

K

Leontief production function

A.1.3.

Capacity constraint equations: Eqs. (A.8)–(A.11)

Eq. (A.8) requires that the sum of harvest units used in each month may not exceed the total number of harvest units endogenously determined by the model. Eq. (A.9) insures that, in each LCB-producing county and month, the quantity of LCB harvested may not exceed the combined harvesting capacity of the number of harvest units determined by the model. Capacity constraint (Eq. (A.10)) links LCB-processing capacity at the biorefinery to the binary variable. If BETAðJ; SÞ ¼ 1; CAPPðSÞ  BETAðJ; SÞ ¼ CAPPðSÞ,

I

X

TAFCðS; FTÞ  BETAðJ; SÞ

I;KF

I

X

X

I;K

I;J;S;K

X

XðI; KF; MÞ5

XðI; KF; MÞ þ XSNðI; K; MÞ

I;KF if KKFðK;KFÞ

X

XSðI; K; MÞ;

M

X

X

3

J;S;FT

J;S;M

¼

 

XðI; KF; MÞ

KF if KKFðK;KFÞ;M

X

X KF if KKFðK;KFÞ

X ¼

PSIðKÞ

I;K

KF if KKFðK;KFÞ

¼

X

the processing capacity upper bound in units of bio-products and the total production at each plant in that month will be bounded by 0pQðJ; S; E; MÞpCAPPðSÞ. If BETAðJ; SÞ ¼ 0, expression CAPPðSÞ  BETAðJ; SÞ will also equal to zero and since QðJ; S; E; MÞ cannot assume negative value, then it must also equal zero. Eq. (A.11) links LCB storage capacity at the plant to the binary variable. If BETAðJ; SÞ ¼ 1; CAPSðSÞ  BETAðJ; SÞ ¼ CAPSðSÞ,

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32 (2008) 1016 – 1027

Table A2 – Description of model parameters

Table A3 – Description of variables

Parameter

Variable

RHOðGÞ

ALPHAðKÞ GAMMAðKÞ TAUðI; JÞ

PSIðKÞ

Description Price per unit of output g, may be positive for biorefinery outputs such as ethanol or a positive externality, or negative for negative externality or output with disposal cost Cost of producing an mg of LCB type k Cost of storing an mg of LCB k in the field Round-trip cost of transporting an mg of LCB from source county i to plant location j

Annual cost of a harvest unit

KAPPA

Cost of harvesting an mg of LCB

PHIðJ; KÞ

LAMBDAðK; GÞ

LANDðI; K; LÞ

Overall net present worth of the industry

QðJ; S; G; MÞ

Quantity of output g produced in month m by a plant of size s at location j

AðI; KF; MÞ

Hectares of LCB k harvested at source i in month m, where k is under fertility regime f

XSPðI; K; MÞ

mg of LCB k harvested in month m and stored in county i

XTðI; J; S; K; MÞ

mg of LCB k transported in month m from county i to a plant of size s at location j

Cost of procuring an mg of LCB of species k

OMEGA

THETAðI; KÞ

NPW

Proportion of LCB k stored in county i that is usable a month later Proportion of LCB k stored at plant j that is usable a month later Quantity of output g produced from an mg of LCB k at the plant Total hectares of land producing LCB k in county i (hectares)

Description

XðI; KF; MÞ

mg of LCB k harvested in month m at source i, where k is under fertility regime f

XSðI; K; MÞ

mg of LCB k stored at source county i in month m

XSNðI; K; MÞ

mg of LCB k removed from storage at source i in month m

XHUðI; MÞ

Proportion of a harvest unit used in county i in month m

HU

Integer variable representing the total number of harvest units used

XSðJ; K; MÞ

mg of LCB k stored at plant location j in month m

BPðI; K; LÞ

Proportion of land of category l in county i with LCB k available for harvesting for biorefinery use

XPðJ; S; K; MÞ

mg of LCB k processed by a plant of size s at plant location j in month m

YADðK; MÞ

Yield adjustment factor for LCB k if harvested in month m

BETAðJ; SÞ

A binary variable associated with plant size s at location j

BYLDðI; K; FÞ

Yield (mg ha1 yr1) of LCB k if under fertility regime f at county i

TAFCðS; FTÞ

Amortized fixed cost of constructing and operating facility ft of plant size s

CAPHUðI; MÞ

Capacity of a harvest unit in county i month m

CAPPðSÞ

Processing facility capacity associated with plant size s (liters of ethanol per month)

CAPSðSÞ

LCB storage facility capacity associated with plant size s (mg of LCB)

MBINVðSÞ

Minimum LCB inventory for plant size s (mg per month)

PVAF

Present value of annuity factor, where the annuity factor is the annual net benefit for the ethanol production industry

r

Market discount rate, used in the computation of PVAF

t

Plant useful life used in the computation of PVAF

A.1.4. On-site feedstock resources balance equations: Eqs. (A.12)–(A.14) Eq. (A.12) imposes the constraint that total LCB processed or stored at the biorefinery may not exceed the total LCB supply. Eq. (A.13) balances total LCB delivered to the biorefinery with the sum of processed LCB and on-site storage losses. Eq. (A.14) is a constraint that imposes minimum LCB inventory at the biorefinery.

A.1.5.

Leontief production function: Eq. (A.15)

Eq. (A.15) allows the model to assume a Leontief production function at the biorefinery.

A.1.6.

Constraint on number of biorefineries: Eq. (A.16)

Constraint Eq. (A.16) represents an upper bound on the total number of biorefineries, assumed here to be equal to one.

A.1.7. the total LCB storage at any plant will be bounded by 0pXSðJ; K; MÞpCAPSðSÞ. If BETAðJ; SÞ ¼ 0, expression CAPSðSÞ  BETAðJ; SÞ will also be equal to zero, and since XSðJ; K; MÞ cannot assume negative value then it must also equal zero. No storage upper-bounds are assumed for in-field storage.

Non-negativity constraints: Eq. (A.17)

Constraint Eq. (A.17) includes the non-negativity conditions.

A.1.8.

Binary variable constraint: Eq. (A.18)

Eq. (A.18) restricts values of the binary variable to the set of zero and one.

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Table A4 – Model indices and descriptions Index

Description and member elements

Main sets M Month: m ¼ {Mar, Apr, May, Jun, Jul, Aug, Sep, Oct, Nov, Dec, Jan, Feb} J

Set of prospective plant locations: j ¼ {Canadian, Comanche, Custer, Garfield, Jackson, Okmulgee, Payne, Pontotoc, Texas, Washington, Woodward}

S

Set of plant sizes: s ¼ {small, medium, large}

G

Vector of products (e.g. ethanol and other) and byproducts (e.g. CO2, N2, and ash)

I

Set of LCB supply centers (or source counties): i ¼ {All counties in Oklahoma, Panhandle counties in Texas, and counties in southern Kansas}

K

Set of feedstock species: k ¼ {corn stover, wheat straw, old world bluestem, tall fescue, bermudagrass, native tall, native medium, native short, switchgrass}

F

Level of nitrogen application (in kg ha1)

Ft

Set of facilities. In this case, ft ¼ {processing facility, storage facility}

L

Land categories: l ¼ {cropland, improved pasture, pastureland, CRP}

Subsets b(g)

Set of process byproducts or externalities: b ¼ {e.g. CO2, N2, ash}

cr(k)

Set of crop residues: cr ¼ {wheat straw, corn stover}

e(g)

Set of process main products(s): e ¼ {bioproduct, e.g. ethanol}

[5]

[6]

[7]

[8]

[9]

[10]

[11] [12]

[13] [14] [15]

[16]

[17]

A.1.9.

Integer variable constraint: Eq. (A.19)

Eq. (A.19) restricts the number of harvest units to nonnegative integer values.

A.1.10. model

Objective function for the exogenous harvest cost

Eq. (A.20) is the objective function for the model with a fixed harvest charge per mg harvested. Eq. (A.20) is maximized subject to Eqs. (A.2)–(A.18) minus Eqs. (A.8) and (A.9).

[18]

[19]

[20]

R E F E R E N C E S

[21] [1] National Research Council. Review of the research strategy of biomass-derived transportation fuels. Washington, DC: National Academy Press; 1999 accessed 12/09/2005. [2] McKendry P. Energy production from biomass (part 2): conversion technologies. Bioresource Technology 2002;83:47–54. [3] Lynd LR, Cushman JH, Nichols RJ, Wyman CE. Ethanol from cellulosic biomass. Science 1991;251:1318–23. [4] Wyman CE. Ethanol production from lignocellulosic biomass: overview. In: Wyman CE, editor. Handbook on bioethanol:

[22] [23]

[24]

production and utilization. Washington, DC: Taylor and Francis; 1996. p. 1–18. Hertzmark D, Flaim S, Ray D, Parvin G. Economic feasibility of agricultural alcohol production within a biomass system. American Journal of Agricultural Economics 1980;62:967–71. Tembo G, Epplin FM, Huhnke RL. Integrative investment appraisal of a lignocellulosic biomass-to-ethanol industry. Journal of Agriculture and Resource Economics 2003;28:611–33. Aden A, Ruth M, Ibsen K, Jechura J, Neeves K, Sheehan J, et al. Lignocellulosic biomass to ethanol process design and economics utilizing co-current dilute acid prehydrolysis and enzymatic hydrolysis for corn stover. Golden, CO: US Department of Energy, National Renewable Energy Laboratory; 2002 [NREL/TP-510-32438]. Klasson KT, Elmore BB, Vega JL, Ackerson MD, Clausen EC, Gaddy JL. Biological production of liquid and gaseous fuels from synthesis gas. Applied Biochemistry and Bioengineering 1990;24/25:857–73. Mosier N, Wyman C, Dale B, Elander R, Lee YY, Holtzapple M, et al. Features of promising technologies for pretreatment of lignocellulosic biomass. Bioresource Technology 2005;96:673–86. Rajagopalan S, Datar RP, Lewis RS. Formation of ethanol from carbon monoxide via a new microbial catalyst. Biomass and Bioenergy 2002;23:487–93. Service RF. Cellulosic ethanol: biofuel researchers prepare to reap a new harvest. Science 2007;315:1488–91. Bullis K. Cheap ethanol from tires and trash. Technology review. January 14, 2008. /http://www.technologyreview. com/Biztech/20056/S (accessed 1/30/2008). Cundiff JS, Harris WL. Maximizing output-maximizing profits. Resource 1995;2:8–9. Cundiff JS. Simulation of five large round bale harvesting system for biomass. Bioresource Technology 1996;56:77–82. Epplin FM. Cost to produce and deliver switchgrass biomass to an ethanol-conversion facility in the southern plains of the United States. Biomass and Bioenergy 1996;11:459–67. English BC, Short CC, Heady EO, Johnson SK. Economic feasibility of using crop residues to generate electricity in Iowa. Iowa State University, The Center for Agricultural and Rural Development, CARD report 88, January 1980. Kaylen M, Van Dyne DL, Choi Y, Blase´ M. Economic feasibility of producing ethanol from lignocellulosic feedstock. Bioresource Technology 2000;72:19–32. Nienow S, McNamara KT, Gillespie AR, Preckel PV. A model for the economic evaluation of plantation biomass production for co-firing with coal in electricity production. Agricultural and Resource Economic Review 1999;28:106–18. English BC, Short CC, Heady EO. The economic feasibility of crop residues as auxiliary fuel in coal-fired power plants. American Journal of Agricultural Economics 1981;63(4):636–44. Glassner DA, Hettenhaus JR, Schechinger TM. Corn stover collection project. Great lakes regional biomass conference, BioEnergy ‘98, Madison, WI; October 4–8, 1998. /http:// www.afdc.doe.gov/pdfs/5149.pdfS (accessed 9/23/2004). Gallagher P, Johnson D. Some new ethanol technology: cost competition and adoption effects in the petroleum market. The Energy Journal 1999;20(2):89–120. Walsh ME. US bioenergy crop economic analyses: status and needs. Biomass and Bioenergy 1998;14:341–50. Kletke D, Doye DG. Oklahoma farm and ranch custom rates, 2001–2002. Oklahoma Cooperative Extension Service 2002;CR-205:1–9. Thorsell SR, Epplin FM, Huhnke RL, Taliaferro CM. Economics of a coordinated biorefinery feedstock harvest system: lignocellulosic biomass harvest cost. Biomass and Bioenergy 2004;27:327–37.

ARTICLE IN PRESS BIOMASS AND BIOENERGY

[25] Thorsell SR. Economies of size of a coordinated biorefinery feedstock harvest system. MS Thesis, Oklahoma State University, Stillwater, Ok, USA, 2003. [26] Huhnke R. Agricultural field machinery cost estimation software. Department of Biosystems and Agricultural Engineering, Oklahoma State University. Stillwater, Ok, USA, 1999. [27] Kletke D, Sestak R. The operation and use of MACHSEL: a farm machinery selection template. Computer Software Series CSS-53. Stillwater, OK, USA: Department of Agricultural Economics, Oklahoma State University; 1991. [28] American Society of Agricultural Engineers. ASAE standards: standards, engineering practices and data adopted by the American Society of Agricultural Engineers. St. Joseph, MI, USA, 1999. p. 353–66. [29] American Agricultural Economics Association. Commodity costs and returns estimation handbook. Ames, IA, USA: American Agricultural Economics Association; 2000. [30] Brooke A, Kendrick D, Meeras A, Raman R. General algebraic modeling system. GAMS Development Corporation; 1998 (accessed 1/30/2008) /http://www.gams.comS. [31] Epplin FM, Mapemba LD, Tembo G. Economic modeling of a lignocellulosic biomass biorefining industry. In: Outlaw J, Collins KJ, Duffield JA, editors. Agriculture as a producer and consumer of energy. Massachusetts: CABI Publishing; 2005. p. 205–17. [32] Bureau of the Census. 2002 census of agriculture: national, Oklahoma state and county data. US Department of

32 (2008) 1016 – 1027

[33]

[34]

[35]

[36]

[37]

[38]

[39] [40]

1027

Commerce, Economics and Statistics Administration; 2004. USDA, Farm Services Agency. Conservation reserve program, September 2004. /http://www.fsa.usda.gov/dafp/cepd/29th/ county.htmS (accessed 02/16/05). Oklahoma Department of Agriculture. Oklahoma agricultural statistics. US Department of Agriculture, National Agricultural Statistics Service, various issues (1999–2003). Reinschmiedt LL. Study of the relationship between rainfall and fieldwork time available and its effect on optimal machinery selection. MS thesis, Oklahoma State University, Stillwater, OK, USA, 1973. Mapemba LD. Cost to deliver lignocellulosic biomass to a biorefinery. PhD dissertation, Oklahoma State University, Stillwater, OK, USA, 2005. Bhat MG, English B, Ojo M. Regional costs of transporting biomass feedstock. In: Cundiff JS, editor. Liquid fuels from renewable resources: Proceedings of an alternative energy conference, 14–15 December 1992. St. Joseph, MI: American Society of Agricultural Engineers; 1992. Cundiff JS, Marsh LS. Harvest and storage costs for bales of switchgrass in the southeastern United States. Bioresource Technology 1996;56:95–101. Sokhansanj S, Turhollow AF. Baseline cost for corn stover collection. Applied Engineering in Agriculture 2002;18:525–30. Ho GE. Crop residues—how much can be economically harvested? Biomass 1985;7:199–214.