The RUSLE (Eq. 1) is used to estimate the quantities of residue that must remain on the field to keep rainfall-induced erosion at or below T.

A = R x K x S x L x C x P (1)

in which A is the average annual soil loss (metric t/[ha/yr]), R is the rain­fall-runoff erosivity factor (location/county specific), K is the soil erodibil — ity factor, S is the slope steepness factor, L is the slope-length factor, C is the cover-management factor, and P is the support-practices factor. The A in RUSLE can be replaced by T (tolerable soil loss limit) to give

T = R x K x L x S x C x P (2)

in which K and S are as described for Eq. 1 and are specific to each soil type examined. P is assumed to be 1.0, which provides the most conservative estimate for residue removal. All factors except C are independent of crop grain yield or crop-management practices and can be combined into a single value termed ASTAR (A*), which is specific to each particular soil type. ASTAR was calculated for each LCC I-VIII soil type in each of the 10 states.

C is a function of the yield at harvest and is directly influenced by field operations that affect field surface cover throughout the year (i. e., tillage). To estimate the annual erosion and quantities of removable crop residues attributable to specific field operations and harvest yields, the C-factor must be determined in relation to these conditions. Equation 2 can be re­written as

C = (R x K x L x S x P)/T (3)

in which C is now the only unknown parameter. To solve for C, the RUSLE C-Batch Program (developed by USDA National Soil Survey Center) is used. C-Batch estimates C-factors for various crop rotations, crop grain yield variations, and tillage operations and timing combinations. For this analysis, crop grain yields of 124, 198, 247, 309, and 371 bu/ha for corn; 62, 74, 99, 124, and 148 bu/ha for winter wheat; and 49, 62, 86, 111, and 124 bu/ha for spring wheat are assumed. Soybean yields were 37, 62, 74, 86, and 111 bu/ha. These yields reflect typical ranges for these crops in most states considered in the study.

Table 4 shows variation in the C-factor with respect to a continuous corn and continuous winter wheat rotation for each of the three tillage

scenarios and three grain yield levels in Brown County, Kansas. The C — factors vary between the two crops owing to different protective cover for corn stover vs wheat straw, with lower C-factors associated with greater protective cover. They also vary across the three tillage scenarios for each crop (on average, the C-factor decreases as the tillage scenario becomes less aggressive, going from conventional till to reduced/mulch to no-till). This is logical because as residue burial increases (such as with a mold­board plow and/or heavy disking representative of conventional till), less protective cover is present on the field and, therefore, it is more likely for soil erosion to occur. From the standpoint of the RUSLE equation, erosion increases when the cover-management factor increases, because ASTAR is constant for a single soil type and erosion is the product of ASTAR and the C-factor. In practical terms, for the same soil type and cropping rota­tion, more residue is potentially available for removal under no-till field management vs mulch till or conventional till field-management prac­tices because less residue is buried and more residue stays on the field surface to protect against the impact of rainfall and wind forces.

Estimation of Minimum Retainable Residue Levels

for Continuous (Single)-Crop Rotation—Rainfall Erosion The estimated C-factors corresponding to each crop rotation, tillage, and grain yield combination are multiplied by the soil-specific ASTAR values to obtain expected erosion rates (Mg/[ha-yr]) for each soil type. To determine crop residue levels (Mg/[ha-yr]) for which expected erosion rates are at or below T, a regression curve is fitted to the data, with the variables of the independent variable, the natural logarithm of the residue produced (quantity of stover and/or straw present in the field at the time of harvest), and of the dependent variable, the erosion rate. The level of soil erosion varies depending on the quantities of residue left on the field at the time of harvest and throughout the year. Given that expected erosion (for each soil type, crop rotation, and tillage practice combination) is estimated for five grain crop yields (bu/ha), the regression is fitted to five data pairs.

A natural logarithmic function provides the best fit. For a single/continu — ous-crop rotation (continuous corn or continuous wheat), the minimum quantities of residue (Rmin) that must remain on the field throughout the year to keep erosion at or below T are estimated by rearranging the fitted regression equation (Eq. 4). The quantities of residues that can be removed (Rrem) are estimated as the quantity of residue produced (Rprod) minus the minimum quantity that must remain (Rmin) (Eq. 5). If Rprod is less than Rmin, no residue can be removed.

Table 5 presents the regression analysis and estimated quantities of residues that must remain on the field subject to a reduced/mulch till, continuous corn rotation on a Shidler-Catoosa silt-loam soil in Allen County, Kansas. In this example, the regression equation is fitted to the following five pairs of erosion and dry residue-equivalent yield data (20.79 and 1.145, 15.19 and 1.615, 11.70 and 1.839, 9.16 and 2.062, and 7.68 and 2.244). This provides an estimated intercept of 34.755 and a slope of -12.269. Using Eq. 5 and a T value of 11.2 Mg/(ha-yr), the quantity of residue that must remain on the field is estimated as 6.82 Mg/(ha-yr).

Estimation of Minimum Retainable Residue Levels for Multiple-Crop Rotation—Rainfall Erosion

Estimated residues that can be removed for a 2-yr, multiple-crop rotation differ from the continuous-crop, single-year analysis in that removal rates must remain at or below T for each year of the rotation.

The average annual residue present at harvest over the 2-yr period is calculated at each of the five yield pairs from the C-batch program (e. g., for a corn-soybean rotation, the yield pairs of 124/37, 198/62, 247/74, 309/87, and 371/111 bu/ha equate to average residue levels of 2.33, 3.78, 4.66, 5.7, and 7.0 dry Mg/(ha-yr), respectively, over the 2-yr period). As with continuous-crop rotations, the C-factors vary between rotations (i. e., soybean residue provides less protective cover than corn stover, resulting in higher C-factors for a corn-soybean rotation than a continuous-corn rotation), and across all tillage practices (i. e., the C-factor decreases as tillage becomes less intensive). (Note that the total residue produced during the two-year rotation is twice the 2-yr average). Table 6 illustrates how rotational C-factors vary with respect to tillage for a corn-soybean rotation in the Midwest.

For a multiple-crop rotation, residues that must remain (Rmin) are cal­culated by the same equation used for a continuous-crop rotation (Eq. 4), except the intercept and slope are functions of the 2-yr average residue levels of each residue pair. Rmin represents the amount of residue that must be left in the field each year of the rotation to ensure that rainfall erosion does not exceed T. Note that Rmin is the same for both cropping years. This follows because the C-factor was calculated on the basis of a rotation, not two independent crops. Unlike the continuous-crop rotation, however, three potential situations can arise that will affect residue quantities that can be removed.

Situation No. 1

Both crops produce more residue each year than Rmin. If the residue — equivalent production yields of both crops are greater than Rmin, then the residues from each crop can be removed and are estimated according to Eqs. 6 and 7.

ARR1 = R1prod — Rmin

ARR2 = R2prod — Rmin

in which ARR1 and ARR2 are the average annual removable residue from crops one and two, Rlprod and R2prod are the gross residues produced for crops one and two (based on the average production yield of crops one and two in the county), and Rmin is the average minimum residue over the 2-yr period.

Situation No. 2

The average residue produced by the two crops is less than Rmin. If the residue quantity produced for either crop (R1prod or R2prod) is less than the average minimum residue, Rmin, then a test is conducted to determine whether the average annual residue produced by the rotation (the sum of the gross residue produced by each crop divided by two), ARR, is less than Rmin. If it is, then no residue can be removed in either year. If the average is greater than Rmin, situation #3 arises.

Situation No. 3

The average residue produced by the two crops is greater than Rmin, but one crop, Aos residue is less than Rmin. This situation also involves the position that one of the crops produces an amount of residue less than Rmin (the average minimum residue), but the difference between ARR and Rmin is greater than zero. In this situation, it is acceptable to remove residue from only the crop that produces more residue than Rmin, provided that enough residue from that crop is left to ensure that the average amount of residue left on the field over the 2 yr is at least as great as Rmin. No residue can be removed from the crop that produces less residue than Rmin. Math­ematically, in this situation, the amount of residue removed from the crop that produces "excess" residue is equal to twice the average annual resi­due — Rmin. For example, if Rmin = 2.2 Mg/(ha-yr), R1prod = 3.36, and R2prod = 1.8 Mg/(ha-yr), respectively, then no residue could be removed from crop two and 0.76 Mg/(ha-yr) could be removed from crop one the year it was grown.

Although the tests were performed in an exploratory manner and thus neither a complete factorial design nor a complete fraction of a factorial design was completed, a significant amount of revealing information was collected in the tests. Since the goal was to bracket allowable moisture and inoculum ranges, statistical analyses of the xylan and glucan degradation data were conducted by regression analysis and used to explore system sensitivity to initial moisture and inoculum contents.

Regression Analyses

The conversion data for all tests were combined into a single data set represented by 234 data points varying in inoculum amount (I, mg of P. ostreatus/g of stems), gravimetric moisture content (M, g of H2O/g of stems), and treatment time (t, d). A power series expansion of the three variables through the second-order terms was fitted using linear regres­sion; the expansion included the terms I, M, t, IM, It, Mt, I2, M2, and t2, with an intercept of zero. Note that this equation has no basis in theory and was chosen simply because its shape was appropriate. The primary goal of the regression analyses was to obtain statistically valid equations for both AX and AG, and to use these relationships to estimate the sensitivity of the system to inoculum, moisture, and treatment time.

The xylan conversion (AX) and glucan conversion (AG) data were fitted separately to the power series expansion, resulting in r2 values of 0.925 and 0.910, respectively. However, an analysis of variance indicated that the terms M, IM, M2 in both analyses were statistically insignificant and thus unnecessary to fit the data. The data were refitted after dropping those terms, resulting in statistically valid fits with r2 values of 0.924 and 0.909. The results of the regression analyses are presented for both fits in Table 5, and comparisons of the measured and predicted values of AX and AG are shown in Figs. 3 and 4, respectively. Relatively good fits to the data were obtained, indicating that the data were internally consistent and that the system behaved in a predictable manner. The fits were more accurate at higher values of inoculum and moisture, caused by the higher

amount of variability in degradation data at lower inoculum levels and moisture contents. The predicted conversions of xylan (AX), glucan (AG), and the ratio AX/AG with time are shown for the ultimately selected treatment conditions (40 mg of P. ostreatus /g of stems, 1.60 g of H2O/g of stems) in Fig. 5. The percentages of degradation for the nearest experi­mentally observed combination (44.0 mg of P. ostreatus/g of stems, 1.60 g of H2O/g of stems) were under-predicted by 5-10% at later treat­ment times (not shown). It is clear from Fig. 5 that the time-rate of degra­dation had decreased substantially by 12 wk and thus harvesting at 10 or 14 wk would make little difference in the final composition. In addition, the selectivity for xylan degradation over glucan degradation is predicted to be initially about 2.0 and then to decrease with time to about 1.2. This suggests that shorter treatment times would be preferred with this organ­ism if a more selective degradation is desired, although the initially high rate of decline of AX/AG is most likely an artifact of higher measurement uncertainties in the data at lower moisture and inoculum.

Table 4 presents fractions of sunk, suspended, and floating material after a single separation. The residual grain represents grain that was hand sorted from either the suspended or floating material. The DM in the efflu­ent was obtained by mass balance after other components had been dried and separated.

Table 4 Grain and Stover Proportions After One Water Separation and Various Pretreatments Pretreatment Silage used (%) MC (%) Sunk grain DM after one Sunk Suspended stover stover separation (%) Floating Residual stover grain DM in effluent Grain concentration in sunk material (%) 1. Untreated silage 100 64.6 21.3 c 8.4 a 7.1 b 44.1 ad 1.5 b 17.6 a 71.8 d 2. Partially dried to lose 100 54.8 22.9 c 9.5 a 5.2 c 45.4 c 0.5 c 16.5 ab 70.6 d 10% units of MC 3. Partially dried to lose 100 45.3 25.8 b 2.2 c 2.1 d 54.6 b 1.0 bc 14.3 bc 92.3 b 20% units of MC 4. Oven-dried 100 0.0 23.8 bc 0.1 c 0.6 d 62.3 a 2.5 a 10.8 d 99.4 a 5. Sieved and fresh 50.3 64.6 37.0 a 9.8 a 9.5 a 29.5 e 0.6 c 13.6 cd 79.1 c 6. Sieved, partially dried 50.3 55.5 34.6 a 4.7 b 5.1 c 41.1 d 1.7 ab 12.8 cd 88.0 b to lose 10% units of MC SEM 1.4 1.0 0.8 1.8 0.4 1.3 2.7 LSD 3.1 2.2 1.7 3.9 0.9 2.8 5.8 a Experiment 3 with silage from Manthe Farm: 64.6% MC, unprocressed and 10.3mm MPL; average of three replications. Values with the same superscript letter in a given column indicate no significant difference (p < 0.05). SEM, standard error of means; LSD, least significant difference.

Sieving increased the proportion of grain collected (37 vs 21%, with­out drying), and improved the grain concentration in the sunk material (79 vs 72%). However, the total amount of grain obtained in the sunk material was lower after sieving (18.6 vs 21.3%) because about half the silage remained in the other sieves that were not used for the water sepa­ration. The smaller-size sieves would contain a significant amount of broken grain.

The proportion of sunk stover decreased significantly with partial drying (20 percentage units of moisture loss) and with complete drying in the oven. The grain concentration was enhanced as high as 99.4% for bone-dry material. The proportion of suspended stover was highest for sieved and fresh pretreatment. This material was of relatively uniform length (between 9 and 18 mm), so grain could sink rapidly in the absence of long stover pieces that tended to float and hinder the descent of smaller grain. The proportion of floating material was significantly higher for dry material. Residual grain was highest also in dry material. The proportion of DM in the effluent decreased as the silage was dried. The reduction in soluble and fine particles after drying might be owing to a loss of volatile organic acids.

In experiment 3, effluent contained an average of 17.6% of fresh silage DM after one separation. In experiment 2, in which eight successive water separations occurred, the effluent water contained between 20.7 and 26.0% of the fresh silage DM. A large proportion of the soluble and fine particles mixed rapidly in the effluent water. These values suggest a range between 68 and 85% for the ratio between DM in the effluent after one separation and DM in the effluent after eight separations. A ratio of 70% was assumed in Figs. 1 and 2 to illustrate initial DM in effluent and is within the experimental range of 68 to 85%.

The estimated variable costs of operating a 40 million-gal ethanol plant excluding the cost of feedstock materials are shown in Table 3. These costs were compiled using data from the California Energy Commission (10), Pacific Gas and Electric (11), Yancey (9), and Northwestern Corpora­tion (12). The largest components of variable cost, excluding the feedstock, are the costs of natural gas and processing materials. The sum of all com­ponents is $0.479/gal of ethanol (Table 3). The estimated variable cost is $0.599/gal of ethanol if the DG is dried, when using corn as the feedstock.

These estimates are somewhat higher than the estimated variable cost of $0.392/gal for a 40 million-gal dry-mill plant provided by Whims (13) and the average cash operating cost of $0.417/gal for dry mills reported in a survey of ethanol producers for 1998 (14). The difference is owing primarily to the higher cost of natural gas in California at the time we prepared our estimates. The prices of natural gas we use are explained in Table 3.

The estimated feedstock costs for the materials we examine range from $0.92/gal for Midwestern corn to $6.79/gal for grapes (Table 4). We adjust these costs for the value of coproducts generated when using grapes, rai­sins, or corn, and we express the adjustments on a per-gallon-of-ethanol basis. The coproduct values include $0.26/gal of ethanol for grape residue sold to the concentrate market, $0.06/gal of ethanol for raisin pomace, and $0.26/gal of ethanol for DDG [5]. The adjusted, net feedstock costs range from $0.67/gal of ethanol for Midwestern corn to $6.53/gal for grapes (Table 4). The total variable cost of ethanol production, which includes the net feedstock cost and the nonfeedstock variable costs, ranges from $1.15/ gal for Midwestern corn to $7.01/gal for grapes (Table 4). The total cost includes an additional $0.11/gal for all feedstocks. The costs shown for corn in Table 4 pertain to the case in which DG is not dried. An additional $0.12/gal of ethanol must be added to the total variable cost and total cost if the DG is dried.

Tracy P. Houghton,1 David N. Thompson,*1
J. Richard Hess,1 Jeffrey A. Lacey,1
Michael P. Wolcott,2 Anke Schirp,2 Karl Englund,2
David Dostal,2 and Frank Loge2

11dahn Natinnal Engineecing and Envicnnmental Labncatncy,
Idahn Falls, ID 83415-2203,

E-mail: thnmdn@inel. gnv; and
2Washingtnn State Univecsity,

Pullman, WA 99164-1806

Abstract

Combining biologic pretreatment with storage is an innovative approach for improving feedstock characteristics and cost, but the magnitude of responses of such systems to upsets is unknown. Unsterile wheat straw stems were upgraded for 12 wk with Pleurotus ostreatus at constant temperature to estimate the variation in final compositions with variations in initial mois­ture and inoculum. Degradation rates and conversions increased with both moisture and inoculum. A regression analysis indicated that system perfor­mance was quite stable with respect to inoculum and moisture content after 6 wk of treatment. Scale-up by 150x indicated that system stability and final straw composition are sensitive to inoculum source, history, and inoculation method. Comparative testing of straw-thermoplastic composites produced from upgraded stems is under way.

Index Entries: Fungal upgrading; engineered storage; biological prepro­cessing; Pleurotus ostreatus; straw composite.

Introduction

Agricultural crop residues are a valuable renewable biomass resource. In 1999, American farmers harvested 53,909,000 acres of wheat (1). The straw from this acreage of wheat represents >50 million t annually. Cur­rently, some of the straw is harvested (baled) for use as livestock bedding or low-grade animal feed. However, these low-value uses provide only a

*Author to whom all correspondence and reprint requests should be addressed. Applied Binchemistcy and Bintechnnlngy 71 Vnl. 113-116, 2004

minimal return. Nationally, only about 3.2% of the economic return on wheat is from straw (1). Producers have long recognized the potential eco­nomic and environmental benefits of producing forage, bioenergy, and bioproducts from excess wheat straw residue. However, because of the low bulk density of straw and the loss of fermentable sugars to microbial activ­ity during storage, there are harvest, transportation, storage, and prepro­cessing methods and logistics issues that must be worked out before the excess straw can be economically utilized on a national scale.

The U. S. Department of Energy and U. S. Department of Agriculture recently began a concentrated national effort under the Biomass Research and Development Act of 2000 to develop and demonstrate working biorefineries in the near term. The "vision" and "roadmap" documents for near-term utilization of agricultural residues to produce fuels, chemicals, and bioproducts have recently been completed and focus primarily on corn stover and cereal straws as the feedstocks (2,3). Objectives and research pathways identified in the roadmap document for stover and straw pre­processing and storage issues include the following (3):

1. Cost-Effective Pre-Delivery Treatment Processes—The develop­ment and testing of cost-effective pre-conversion treatment pro­cesses to increase energy — and chemical-density of raw materials at the point of harvest.

2. Best Practices for Harvesting and Storage—The biomass/agricul — tural communities must identify, develop, test, and implement best practices for cost-effective and environmentally sound pre-treat­ment, collection, storage, and transport of plant and animal resi­due-based feedstocks. This should lead to improved plant and animal residue recovery, more effective separation, improved han­dling and storage technologies/procedures, and reduced environ­mental impacts.

Thus, several issues related to preprocessing and storage have been identified as important research and development priorities for the near term. An innovative and potentially useful approach to addressing these issues would be to combine preprocessing and storage into a single sys­tem. In this way, energy use and infrastructure could be reduced by modifying the feedstock while it is waiting to be utilized. These modifi­cations could be biological or chemical in nature. In the case of biological treatments, for such a system to be workable, it would be necessary for microbes carrying out the desired modifications to outcompete indig­enous microorganisms vying for the same resources.

Straw utilization for composites is limited by poor resin and polymer penetration, and excessive resin consumption owing to the straw cuticle, fines, and the lignin-hemicellulose matrix (4). Some white-rot fungi, in­cluding Pleurotus ostreatus, degrade the cuticle and selectively degrade lig­nin and hemicellulose, leaving behind relatively more cellulose (4). Thus, treatments by these fungi could potentially be used to improve resin pen­etration and resin binding without the use of physical or chemical pretreat­ments. Although long treatment times and large footprints limit the use of fungal treatments on a large scale, distributed fungal pretreatments could alleviate land requirements.

In a previous study (4), we presented the results of a preliminary inves­tigation to determine whether P. ostreatus could be competitive with indig­enous organisms in unsterilized wheat straw stems. A detailed description of the potential benefits of preparing straw-thermoplastic composites from wheat straw stems upgraded by selective degradation by a white-rot fun­gus was provided in that study (4). In general, the potential benefits focus primarily on the reduction of fines (reduced external surface area) via a selective harvest method (5), and removal of amorphous matrix compo­nents by P. ostreatus to increase internal surface area and allow better pen­etration of composite formulation components into the lignocellulose matrix (4). Our previous study was conducted with the aim of moving toward the development of a passive, potentially distributed fungal upgrading system to improve feedstock characteristics for production of straw-thermoplastic composites (4). As envisioned, the system would be constructed and oper­ated similarly to passive composting systems and could be operated for 12 wk or longer in a distributed or centralized manner, depending on land use requirements. Such a system fits within the frameworks of both engineered storage systems and pre-conversion processing.

In the preliminary study it was found that above about 11 mg of P. ostreatus/g of stems and 0.77 g of H2O/g of stems, the inoculated P. ostreatus was generally competitive with indigenous microbes (4), which is consistent with a previous report showing good competitiveness of Pleurotus sp. with soil microorganisms (6). In the present article, we describe completed laboratory studies conducted at the Idaho National Engineering and Environmental Laboratory (INEEL) that were tasked with determin­ing acceptable moisture and inoculum ranges for pilot-scale fungal upgrad­ing tests. Inoculated P. ostreatus was found to more completely dominate degradation of the straw stems as inoculum size and moisture content increased, but to be less selective with respect to polysaccharide degrada­tion. Inoculum and moisture levels of 40 mg of P. ostreatus /g of stems and

1.6 g of H2O/g of stems, respectively, allowed successful competition of the inoculum with indigenous organisms and gave acceptable amounts of deg­radation of xylan and glucan (on a total degradation basis). Statistical analy­sis of the data was conducted to predict the variability of final compositions in response to ±30% variations in initial moisture and inoculum levels. Mini­mal variations in final composition would be desirable to ensure consistent product composition in outdoor systems having few environmental con­trols. In addition, we present the experimental design for the composite formulation/extrusion testing, as well as initial results from several extru­sion tests conducted at the Wood Materials & Engineering Laboratory at Washington State University (WSU). In the near term, these data will be used to devise and test a pilot-scale fungal upgrading windrow system at WSU for demonstration of larger-scale operation and extrusion.

A sterile syringe was used to collect samples via the sampling tube driven to the very bottom of the fermentor. Prior to sample withdrawal, 3 mL of culture broth was taken and discharged in order to wash the remains away from the sampling tube. When taking the sample a few mil­liliters of fermentation broth was taken aseptically from the vessel and with the exception of an aliquot used for optical density (OD) measurement, the sample was immediately subjected to phase separation (4000 rpm, 10 min). The supernatant collected was assayed for reducing sugar content, glucose concentration, and cellulase activity.

Optical Density

OD was read at 660 nm (OD660) against distilled water (14).

Determination of Reducing Sugar Content

Total reducing sugar was determined by the dinitrosalicylic acid (DNS) method (15) using D-glucose as a standard. Appropriately diluted samples were made up to 1.5 mL with distilled water, and 3 mL of DNS reagent was added. The color obtained after boiling the mixture for 5 min and then diluting with 16 mL of distilled water was evaluated by reading the absorbance at 550 nm. Total reducing sugar generated during the assay was estimated as glucose equivalents.

Estimation of Minimum Retainable Residue Levels

for Continuous-Crop Rotation

In general, crop residue removal was affected by wind erosion more than rainfall erosion in the western two-thirds of Kansas, Nebraska, and South Dakota. Rainfall erosion was the dominant erosive force in the east­ern one-third of these three states, as well as all the other seven states considered. Equation 8 presents WEQ:

E = f (WI, WK, WC, WL, WV) (8)

in which E is the average annual soil loss (Mg/[ha-yr]); WI is the wind erod — ibility index (a measure of soil susceptibility to detach and be transported by wind) and varies by individual soil type; WK is the soil ridge-roughness factor and describes the condition of the field surface at a particular time;

WC is the climate factor and represents the amount of erosive wind energy present at a particular (county-level) location; W L is a function of wind direction, field length, and width and is the unsheltered median travel dis­tance of wind across a field; and WV is the vegetative factor. The relationship between E and the other variables is highly nonlinear.

The amount of residue potentially available for removal with respect to applying WEQ was determined by analyzing total soil loss attributable to wind forces in each field-management period (time between each field operation) for all individual soil types and then summing across all field — management periods including crop growth. These values were then com­pared to erosion values obtained in the rainfall erosion analysis, and the greater required minimum residue level at harvest was chosen. A detailed discussion of the application of WEQ to agricultural crop residue removal is provided in an article by Nelson (9).

Results

Tables 7 and 8 present data concerning maximum quantities of corn stover and wheat residue, respectively, that could potentially be removed from agricultural cropland in each of the 10 largest corn-producing states in the United States subject to the constraints of the tillage scenarios, pro­duction yields, soil types, and field topologies considered in this analysis. The removable residue quantities presented in this article reflect remov­able residue with respect to only soil erosion and no accounting/method — ology was performed with respect to the impact removing residue would have on, e. g., soil tilth and nutrients. These are amounts that could be removed if all agricultural cropland (not just those in corn, wheat, and soybeans) in each state were planted to a particular rotation and subject to conventional, reduced/mulch, and no-till field-management practices (till­age scenarios), and the counties achieved crop yields equivalent to the 1997-2001 5-yr average. The quantities are the lesser of the two quantities that can be removed under the rain and wind erosion analyses.

For example, if all agricultural cropland in Iowa were managed in a continuous-corn, no-till rotation, 84.4 million dry Mg of corn stover could be harvested annually. If all the agricultural cropland were managed in a corn-winter wheat rotation using mulch till practices, 30.2 million dry Mg of corn stover and 13.4 million dry Mg of wheat residue could be harvested annually. (Note, that if a county did not produce a specific crop during 1997-2001, then it is assumed that crop is not produced in that county and thus any rotation with that crop is also not produced in that county; the same constraint is not applied regarding to tillage practices.) An assump­tion was made that all tillage practices are possible in all counties.

In addition, it should be noted that not all estimated residue quantities will actually be removed owing to the potential of some farmers being unwilling to remove residues from their fields, as well as weather condi­tions that may prohibit collection. Other factors may come into play as well.

Maximum Removable Corn Stover Quantities (Million Metric Tons at Harvest) by Rotation
and Tillage Practice When All Cropland Acres in Each State Are Planted to the Rotation"

Maximum Removable Wheat Straw Quantities (Million Metric Tons at Harvest) by Rotation
and Tillage Practice When All Cropland Acres in Each State Are Planted to the Rotation"

Quantities can be adjusted for these factors by assuming that only a certain percentage of the estimated quantities is actually removed.

The estimated quantities of removable corn stover and wheat straw presented in Tables 7 and 8 conform to intuitive expectations in that as tillage operations become less intensive (i. e., go from conventional to no-till), the amounts of removable residue increase across all rotations in all states. Differences in estimated removable quantities among states is a function of several factors including production location (whether the majority of production occurs in areas that have highly erodible soils and field topology nonconducive to removal), climatic/erosive conditions at the locations of production, and actual yields at these specific locations among others. These factors must be considered before residues can be removed at any specific location.

Conclusion

A methodology was developed to assess the amount of agricultural crop residue that can be removed without exceeding the tolerable soil-loss limit in both single and multicrop (2-yr) rotations. Application of this methodology to select corn — and wheat-based cropping rotations on land capability class I-VIII soils subject to conventional, reduced/mulch, and no-till field-management practices in Iowa, Illinois, Nebraska, Minnesota, Indiana, Ohio, Kansas, South Dakota, Missouri, and Wisconsin indicates that significant removable quantities of corn stover and wheat straw exist, but there is considerable variation in the amounts of removable residue with respect to each tillage scenario across all states analyzed. These amounts only consider the need to keep erosion to a tolerable level and do not encompass soil carbon considerations.

References

1. Energy Information Administration. (2003), in Annual Energy Outlook 2003 with Pro­jections to 2025, US Department of Energy, Washington, DC, p. 18.

2. Energy Information Administration. (2003), Annual Energy Outlook 2003 with Projec­tions to 2025, US Department of Energy, Washington, DC.

3. Energy Information Administration. (2003), in Annual Energy Outlook 2003 with Pro­jections to 2025, U S Department of Energy, Washington, DC, p. 4.

4. Energy Information Administration. (2003), in Annual Energy Outlook 2003 with Pro­jections to 2025, US Department of Energy, Washington, DC, p. 3.

5. Nelson, R. G., Enersol Resources. (2001), Resource Assessment, Removal Analysis, Edge — of-Field Cost Analysis, and Supply Curves for Corn Stover and Wheat Straw in the Eastern and Midwestern United States, National Renewable Energy Laboratory, Golden, CO.

6. Larson et al. (1979), in Journal of Soil and Water Conservation, Special Publication No. 25, Soil Conservation Society of America, Ankeny, IA.

7. US Department of Agriculture. (1997), Predicting Soil Erosion by Water: A Guide to Conservation Planning with the Revised Universal Soil Loss Equation (RUSLE), Agricul­tural Handbook Number 703, US Department of Agriculture, Agricultural Research Service.

8. Skidmore, E. L. (1988), in Soil Erosion Research Methods, Soil and Water Conservation Society of America, Ankeny, IA.

9. Nelson, R. G. (2002), Biomass Bioenergy 22, 349-363.

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Sensitivity analyses were conducted using the regression models to test the sensitivity of the treatment method at 21 ± 2 °C. First, the inoculum was varied ±30% in the regression model (28.0-52.0 mg of P. ostreatus/g of stems) at constant moisture. Second, the moisture was separately varied ±30% (1.12-2.08 g of H2O/g of stems) at constant inoculum. The upper and lower bounds chosen represent very large variations in both inoculum and moisture. The predictions are plotted vs time in Fig. 6 for xylan deg­radation. The xylan degradation ranges at 12 wk for varied inoculum and moisture were 34.7-39.3 and 32.5-41.6% degraded, respectively. Similarly, the glucan degradation ranges at 12 wk for varied inoculum and moisture were 29.2-32.8 and 27.7-34.4% degraded, respectively (not shown). When varying only one parameter, the final compositions are predicted to be relatively insensitive to inoculum size, with the largest deviation of at most ±5% degradation at 12 wk. For moisture the system was predicted to be more sensitive, but it was less sensitive at shorter degradation times.

This indicates that initial moisture is the more critical parameter to control, and also that the system is less sensitive to initial moisture at shorter treat­ment times. Shorter treatment times could be used without compromising final compositions by increasing initial inoculum size, depending on final costs.

Additional sensitivity analyses were conducted by simultaneously varying both inoculum amount and moisture content (±30% for each). This analysis predicted maximum ranges at 6 and 12 wk of 24.5-31.9 and 30.1­43.8% xylan degraded, and 19.4-24.7 and 25.9-36.1% glucan degraded, respectively. This corresponds to ЛХ/AG ranges of 1.27-1.29 and 1.16-1.21 at 6 and 12 wk, respectively, and indicates that the expected AX/AG does not vary as widely as would be suggested by combining the individual sensitivity analyses. In addition, shorter treatment times were again fa­vored for minimum variation in the treated straw stem compositions.

In terms of selectivity and reduced sensitivity to initial moisture and inoculum, the results indicate that shorter treatment times are preferred, especially if moisture is either not controlled or poorly controlled. The regression models were next used to generate topographic plots of AX, AG, and AX/AG at the various combinations of inoculum and moisture.

The results are plotted in Figs. 7-9 for the regression model predictions after 6 wk of treatment. For locations of the parameter combinations used in this study on these plots, refer to Fig. 1 (which uses the same axes). Note that the parameter combinations (21.0, 0.77), (34.0, 0.90), and (41.0, 1.20) were not included in the statistical analyses because of poor distribution of the fungal inoculum onto the straw stems. The topographic plot of percentage xylan degraded after 6 wk of treatment is shown in Fig. 7. The diamond represents the conditions chosen for preparation of treated stems for the extrusion testing. The region of only 15-20% xylan degrada­tion roughly corresponds to the region in which the inoculated P. ostreatus was observed to be unable to outcompete the indigenous microbes, since about 15% xylan degradation was observed to occur without inoculum. Increased xylan removal is predicted as both moisture and inoculum increase. There are, however, wide ranges of parameter combinations that will give the same amount of xylan degradation, indicating a fairly insensitive system in terms of overall xylan degradation after 6 wk of treatment. The curvature of the dividing curve between 25-30 and 30­35% xylan degradation, and above 100 mg of P. ostreatus/g of stems, seems odd in that it curves back toward the inoculum axis. However, this was experimentally observed by comparing the results of the (149, 1.67) and (105, 2.24) parameter combinations (see Fig. 1). Since these experi­ments were independently replicated, the behavior appears to be real.

The topographic plot of percentage glucan degraded after 6 wk of treatment is shown in Fig. 8. Again, the diamond represents the condi­tions chosen for preparation of treated stems for the extrusion testing. The region of only 10-15% glucan degradation closely corresponds to the experimentally observed region in which the inoculated P. ostreatus was unable to outcompete the indigenous microbes. Increased glucan removal is predicted as both moisture and inoculum increase. As shown for xylan, there are again wide ranges of parameter combinations that give the same amount of glucan degradation, indicating that the system is also fairly insensitive in terms of overall glucan degradation after 6 wk of treatment.

Finally, the topographic plot of AX/AG after 6 wk of treatment is shown in Fig. 9. The diamond again represents the conditions chosen for preparation of treated stems for the extrusion testing. The region of AX/AG of 1.20-1.25 encompasses the region in which it was experimen­tally observed that AX/AG was about 1.0. A ratio of AX/AG of 1.0 indi­cates nonselective polysaccharide degradation and was taken as an indication of poor competition of the inoculated fungus with the indig­enous microbes. This reinforces the observations that at low moisture and inoculum, the regression model predictions are less accurate. Figure 9 also shows that AX/AG of 1.25-1.30 is predicted after 6 wk of treatment over a very large percentage of the possible moisture and inoculum com­binations. Thus, the system is very stable with respect to selectivity of polysaccharide degradation within the parameter ranges tested.