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  1. 1. Synergies between cover crops and corn stover removal Michelle R. Pratt a , Wallace E. Tyner a,⇑ , David J. Muth Jr. b , Eileen J. Kladivko c a Department of Agricultural Economics, Purdue University, 403 West State Street, West Lafayette, IN 47907-2054, USA b Praxik, Inc., 2701 Kent Ave, Suite 130, Ames, IA 50010, USA c Department of Agronomy, Purdue University, 915 West State Street, West Lafayette, IN 47907-2054, USA a r t i c l e i n f o Article history: Received 2 December 2013 Received in revised form 26 June 2014 Accepted 28 June 2014 Available online 25 July 2014 Keywords: Cover crops Corn stover Removable biomass Benefit-cost analysis a b s t r a c t The potential harvest of corn stover as a feedstock for biofuels to meet government mandates has raised concerns about the agronomic impacts of its removal from fields. Furthermore, in order to meet these mandates, larger quantities of stover will be required. As a result, increased attention has been placed on sustainable agronomic practices, such as cover crops. While cover crops may offer desirable benefits, adoption comes at a cost. The objective of this study was to determine the extent to which cover crop costs could be compensated by additional stover removal and additional agronomic benefits from the use of cover crops. To meet the objective, we took three distinct approaches: (1) benefit-cost analysis, (2) integrated model analysis, and (3) representative farm analysis using the linear programming model PC-LP. Each approach was a different take on the same issue; however, each provided different information. First, we estimated cover crop costs and agronomic benefits and employed benefit-cost analyses, including stochastic anal- ysis in @RISK. Second, we tested cover crops with stover removal for 24 Indiana farms using PC-LP. Cover crop costs ranged from $81.76/ha to $172.50/ha, with variability being driven by differences in the seed- ing rate and seed cost. Agronomic benefits included reduced erosion, which was calculated using a newly created integrated modeling system. The mean estimated reduced soil erosion with a cover crop and no residue removal was 0.72 metric tons/ha. An analysis of cover crop agronomic benefits resulted in private benefits (on-site) ranging from $91.45/ha to $192.07/ha, and $97.63/ha to $198.27/ha from society’s perspective. These benefits were highly influenced by added or scavenged nitrogen (N) from the cover crop. For sensitivity we eliminated the benefit from added N and reevaluated the results. Without the N credit, benefits ranged from $74.72/ha to $134.62/ha. Benefit-cost analyses when considering the agronomic benefits of cover crops resulted in a range of a net loss of $11.09/ha to a net benefit of $87.32/ha for the private perspective. The integrated modeling system results indicated that, on average, while holding soil erosion constant, an additional 4.01 metric tons/ha of stover could be removed if a cover crop were used. Accounting for cover crop costs and stover removal, a benefit-cost analysis suggested that at a farm-gate stover price of $66.14/metric ton, net benefits ranged from a loss of $3.78/ha to a net benefit of $86.93/ha. At a farm-gate stover price of $88.18/metric ton, mean net benefit ranged from $158.81/ha to $249.52/ha. Results from the farm model (PC-LP) indicated that cover crops, along with increased stover removal, impacted crop rotations, increased the total amount of stover harvested, and had the potential to increase farm profits. Ó 2014 Elsevier Ltd. All rights reserved. 1. Introduction As concerns with global warming increase, alternative energy sources are continually being sought. The Energy Independence and Security Act (EISA, 2007) established that by 2022, 36 billion gallons (144 billion liters) of biofuel are to come from renewable fuel sources. More specifically, the mandate requires the produc- tion of cellulosic biofuels to increase to 16 billion gallons (64 bil- lion liters) ethanol equivalent annually by 2022 (EISA, 2007). Cellulosic biofuels are derived from several sources including corn (Zea mays L.) residue, or corn stover. Of the 16 billion gallons man- dated by 2022, 7.8 billion gallons (31.2 billion liters) are estimated to come from corn stover (EPA, 2009). It is also favored due to the fact that it is readily available and has a high cellulosic content (Blanco-Canqui and Lal, 2009). 0308-521X/Ó 2014 Elsevier Ltd. All rights reserved. ⇑ Corresponding author. Tel.: +1 (756) 494 0199; fax: +1 (756) 494 9176. E-mail address: (W.E. Tyner). Agricultural Systems 130 (2014) 67–76 Contents lists available at ScienceDirect Agricultural Systems journal homepage:
  2. 2. Corn stover (also interchangeably referred to as ‘‘residue’’, ‘‘sto- ver’’, and ‘‘biomass’’ throughout this study) is a crop residue which is identified as the ‘‘above ground material left in fields after corn grain harvest’’ (Karlen et al., 2011). The components of corn stover were found to be 30% husks, shanks, silks, and cobs, and the rest stalks, tassels, leaf blades, and leaf sheaths (Hoskinson et al., 2007). Crop residues, such as corn stover, which typically remain on the field, are responsible in numerous ways for preserving the soil (Huggins et al., 2011). While corn stover could be a promising source of biofuels, several concerns have risen about its removal from the fields. This has brought new attention to conservation practices, such as planting cover crops. While it has been observed that moderate removal of stover may actually be beneficial (Swan et al., 1994), increased removal can have many adverse effects (Blanco-Canqui and Lal, 2009; Blanco-Canqui and Lal, 2007; Meki et al., 2011; Wilhelm et al., 2004). Acceptable removal rates of corn stover vary across studies, but there is evidence to suggest that rates of removal may be limited to around 33% of total available stover due to the potential negative effects on soil quality and pro- ductivity (Blanco-Canqui and Lal, 2009; Graham et al., 2007; Kim and Dale, 2004; McAloon et al., 2000; Nelson, 2002; Petrolia, 2006; Thompson and Tyner, 2011; Thompson and Tyner, 2014). Over the years, much research has been conducted to show the agronomic advantages and disadvantages of using various cover crops (Frye and Blevins, 1989; Dapaah and Vyn, 1998; Stivers- Young and Tucker, 1999; Kinyangi et al., 2001; Andraski and Bundy, 2005; Snapp et al., 2005). More recent research has shown that in addition to agronomic benefits associated with cover crops, there may also be an opportunity for economic gains if cover crop residue could reduce subsequent fertilizer application and even more so if it can be sold as forage (Gabriel et al., 2013). Given the known benefits of cover crops, it is believed that these benefits could mitigate the potentially adverse impacts of stover removal. Furthermore, the use of cover crops may allow corn stover to be removed at higher rates, which could potentially increase farm revenues. While the Midwest Corn Belt region is seen as a major supplier of corn stover, cover crops have not been widely adopted. The aim of this study is to analyze the economic and agronomic impacts of stover removal when done in combination with cover crops in the Midwest. Specifically, to what extent would it pay for famers to establish a cover crop if it were possible to increase stover removal rates from 33% to 50% or higher. This analysis considers data from several sources in order to quantify the benefits and costs of cover crops. Additionally, we evaluate the extent to which cover crops allow for increased stover removal without adverse agronomic consequences. Ultimately, the combination of information on sto- ver removal and cover crops is used to determine if the additional revenue from stover removal will compensate farmers for the costs of establishing cover crops. 2. Materials and methods The data used (or applied) in this study comes from several sources including the Midwest Cover Crop Council (MCCC) Cover Crop Decision Tool, farmer interviews, and anecdotal evidence. We consider six pure cover crops and two cover crop mixes for our analysis: (1) annual ryegrass (lolium multiflorum), (2) cereal rye (secale cereal), (3) crimson clover (trifdium incarnatum), (4) hairy vetch (vicia villosa), (5) oats (avena sativa), (6) oilseed radish (raphanus sativus), (7) annual ryegrass/crimson clover mix, (8) annual ryegrass/oilseed radish mix. Because of paucity of data, and in some cases poor understanding of how management prac- tices affect soils, we employ several methods and models to ana- lyze the costs and benefits of cover crops coupled with corn stover removal. Each method allows us to approach our objective from a different angle. In doing so, each approach brings something to the overall picture and helps us confirm our results. First we estimate the cost of cover crops. Next we quantify the benefits of cover crops. Quantifying cover crop benefits involves two separate cases, both of which involve the use of an integrated model: one for agronomic benefits, and another for additional sto- ver removal. Once costs and benefits are quantified and estimated, a benefit-cost analysis with risk distributions is conducted. Finally, cover crop costs are used in a linear programming model to simu- late the impacts of cover crops and corn stover removal at the farm level based on real data for 24 Midwest farmers. 2.1. Cover crop cost estimates We develop a method of cover crop cost estimation which breaks down costs into three components: (1) establishment, (2) termination, and (3) unexpected costs. Establishment costs assumed in this analysis are those costs that are required to aeri- ally inter-seed the cover crop in the fall into the standing cash crop. The components of the establishment cost therefore include the recommended cover crop seeding rate, seed cost, and the cost of aerial application. Recommended aerial seeding rates for each cover crop come from the MCCC Cover Crop Selector (MCCC, 2012) (available at as a range and are measured as pounds of pure live seed (PLS) per acre. This measure is adjusted to account for the percent purity and the percent germination of a cover crop. The percent purity for seeds is usually about 98–99% and percent germination ranges from 85% to 90% (E. Kladivko, personal com- munication, September 4, 2012). Since an actual plot is not being tested, we increase the recommended PLS rates for each cover crop provided by the MCCC by 10%. Given the recommended aerial seeding rate for each cover crop and taking into account the cost of seed, it is possible to estimate the seed costs of cover crops. Using quotes from seed suppliers listed in Clark (2007) and prices stated by several farmers, a range of seed costs for each cover crop or cover crop mix is generated. The final component of the estab- lishment cost is the cost of aerial application, which is often done at custom rates. Data for this component comes from anecdotal evidence (Vollmer, 2011) and farmer interviews. The mean esti- mated aerial application cost is $30.39/ha, the minimum is $24.71/ha and the maximum is $37.06/ha. Termination costs assumed in this analysis are those costs asso- ciated with chemically killing the cover crop in the spring before planting a cash crop, which very often is also done at a custom rate. Using estimated custom rate costs from anecdotal evidence (USDA, 2011a) and farmer interviews, the estimated mean termination cost is $15.76/ha. The minimum is $11.12/ha and the maximum termination cost is $22.24/ha. It should be noted that in some cases, this chemical application would occur regardless of the pres- ence of cover crops or not. Therefore, since we cannot differentiate the proportion of this cost that could be attributed to the use of cover crops or standard field operations, the full cost is used in our analysis. Due to the inherent risk that planting cover crops carries to a farmer, a cost item has been included to account for an unexpected negative event, such as needing more than one pass of cover crop termination (chemical or mechanical) if it does not kill initially, untimely termination, the cover crop becoming a weed issue in the following cash crop and/or the need to disc an area twice in the spring. Snapp et al. (2005) reports on similar events as ‘‘indirect on-farm costs’’ where the establishment of a cover crop may inter- fere with the following cash crop or where the cover crop has excessive growth or becomes a weed. For this analysis, the total unexpected cost is the probability that the unexpected cost will be incurred multiplied by the associated cost per acre. Based on 68 M.R. Pratt et al. / Agricultural Systems 130 (2014) 67–76
  3. 3. two probability estimates of 10% (Ostendorf, 2010) and 15% (A. & C. Ault, personal communication) at an estimated cost of $32.12/ha (A. & C. Ault, personal communication, 2012), the mean unexpected cost is $4.03/ha, the minimum is $3.21/ha, and the maximum is $4.82/ha. The total cost of a cover crop is then the summation of establishment, termination, and unexpected costs. 2.2. Cover crop benefits In this analysis we consider the agronomic benefits that cover crops offer, as well as the additional benefits that may be associ- ated with the ability to sustainably harvest higher levels of residue. Cover crop agronomic benefits are estimated for four benefit cate- gories: (1) added nutrient content, (2) increase soil organic matter (SOM), (3) reduced compaction, and (4) reduced soil erosion. Added nutrient content accounts for the ability of legume cover crops to add N to the soil, as well as the ability of non-legume cover crops to scavenge N and make it available for the subsequent cash crop. Data on added N comes from the MCCC Cover Crop Decision Tool as a range of values. Adjustments were then made to this data. For annual ryegrass/oilseed radish mix and the oilseed radish cover crop, the values for added N are quite high and would most likely only be possible if the soil has manure applied to it as well. Therefore, the annual ryegrass/oilseed radish mix is adjusted to 11.21–44.82 kg/ha, and oilseed radish cover crop is adjusted to 22.41–56.03 kg/ha (E. Kladivko, personal communication, Septem- ber 4, 2012). Furthermore, data is adjusted to account for the fact that some of the N contributes to building SOM, while some will be available for the next crop. This avoids double counting when the increase in SOM is considered. The assumption is that 50% of the N could be available for the next crop (E. Kladivko, personal communication, September 4, 2012). Added N is then valued. The value of N comes from United States Department of Agriculture (USDA) historical US average farm prices of N fertilizers (USDA, 2012). Average prices for three N fertilizers, anhydrous ammonia, nitrogen solutions (30%), and urea 44–46% are considered for 2008–2012. By accounting for the percentage of N in each of the fertilizers, the price of the fertilizer in dollars per ton is converted to the price of N in dollars per kilogram. Combining prices for all N fertilizers, the mean cost is $1.15/kg, the minimum is $0.66/kg, and the maximum is $1.48/kg. Increased SOM is the percentage increase in SOM, which is a proxy for soil health, soil carbon and nutrient content, and thereby linked to soil productivity and crop yields. SOM is converted from the dry matter produced by each cover crop (Hoorman, 2012), and the assumption here is that 25% of the dry matter produced from the cover crop becomes decomposed organic matter. The percent- age increase in SOM is that quantity (in tons) divided by the base SOM. That % increase in SOM can be converted to a value per acre using Eq. (1): Increased SOM ð$=acreÞ ¼ SOM Increase ð%Þ Ã Value of SOM ð$=ð1%ÞÞ ð1Þ Reduced compaction accounts for the benefit of not having to deep rip fields, as well as enhanced root growth of the following cash crop. This is the cost of deep tilling, assuming that the use of cover crops reduces the need for deep tillage of a field due to root growth, which alleviates and/or prevents compaction of the soil. The value of this is estimated to be between $74.13 and $86.48/ha (Hoorman, 2010). However, it is unlikely that a farmer would deep rip more than once in every five years if there is a com- paction problem (E. Kladivko, personal communication, September 4, 2012). Therefore, the estimates provided by Hoorman are adjusted to reflect the probability of deep ripping once in five years, resulting in a reduced compaction value range of $14.38–$17.30 per hectare per year. The reduced erosion is the difference between soil erosion (wind and water) with and without a cover crop. This estimate comes from an integrated model, which will be discussed in more detail below. The value of reduced soil erosion comes from the USDA-NRCS (2011b) and is the cost to replace soil function and remediate off-site damage. There are on-site and off-site values of soil erosion. The on-site value represents the cost to the farmer of soil erosion while the off-site value represents the cost of soil erosion to society. The on-site value of soil erosion is $11.21/metric ton, which accounts for reduced yields and water and nutrient loss. The off-site value of soil erosion is $19.83/metric ton, which includes impacts on air quality (health and property) and water quality (USDA, 2011b). Although there may be other benefits, they are not considered to avoid overlap and due to lack of data. Once the benefits of each category listed above have been quantified, a range of values, based on anecdotal evidence, is assigned to each benefit. The second set of benefits we consider are those associated with stover removal. Cover crops alone appear to offer many benefits. We hypothesize that cover crops will allow for additional stover to be removed. Provided there is an existing and viable market for corn stover, there will be an economic benefit associated with corn stover removal. This benefit is the value of stover beyond the cost of removal. The benefit from corn stover removal is defined by the value of stover multiplied by the total stover removed, where the value of stover is equal to a farm-gate stover price less the on-farm harvest costs associated with stover removal. We test two farm-gate prices of stover: $66.14/metric ton and $88.18/metric ton. These prices were selected based on results from Thompson and Tyner (2014) which indicate that significant stover harvest begins around $60/Mg, and substantial harvest at $80/Mg. On-farm harvest costs are those estimated by Thompson and Tyner (2014) and Fiegel (2012). In order to estimate the value of reduced erosion and estimate the additional amount of corn stover that can be removed with a cover crop, we utilize an integrated modeling system that com- bines the Revised Universal Soil Loss Equation, Version 2 (RUSLE2), the Wind Erosion Prediction System (WEPS), and the Soil Condi- tioning Index (SCI). RUSLE2 simulates daily changes in field condi- tions based on soil aggregation, surface wetness, field management practices, and residue status, and is driven by daily weather parameters. RUSLE2 is used to guide conservation planning activi- ties and previous studies have shown the model to accurately rep- resent trends in field data (Ismail, 2008; Dabney et al., 2006; Foster et al., 2006; Schmitt, 2009). RUSLE2 has also been applied to simu- late water erosion processes within broader analysis efforts rang- ing from watershed scale soil quality assessments (Karlen et al., 2008), assessing risks at abandoned mining sites (Vaszita et al., 2009), and socio-economic impacts of biophysical processes (Halim et al., 2007). WEPS uses a process-based daily time-step model to simulate soil erosion due to wind forces considering both direction and magnitude (Wagner and Tatarko, 2001). WEPS mod- els a three-dimensional simulation region requiring a set of param- eters describing climate, soil aggregation, surface wetness, field scale, field management practices (including crop rotation and growth) and residue status, and is driven by daily weather projec- tions. WEPS has been evaluated for erosion predictions on cropland fields (Hagen, 2004) and has been used previously for case studies in corn stover harvest (Wilhelm et al., 2007). RUSLE2 and WEPS each calculate components of an NRCS-developed metric for estab- lishing management practice impacts on overall soil health, named the Soil Conditioning Index (SCI). The SCI provides qualitative pre- dictions of the impact of cropping and tillage practices on soil M.R. Pratt et al. / Agricultural Systems 130 (2014) 67–76 69
  4. 4. organic carbon, which is an important factor in sustainable agricul- tural residue removal. The SCI has been used to support watershed scale soil quality assessments (Karlen et al., 2008), evaluate crop- ping systems in northern Colorado (Zobeck et al., 2008), and inves- tigate southern high plains agroecosystems (Zobeck et al., 2007). The model, developed by Muth and Bryden (2013) utilizes a data and software integration framework that tightly couples the RUSLE2, WEPS, and SCI scenarios for fully automated high perfor- mance computing applications. The integration framework has been named the Landscape Environmental Assessment Framework (LEAF) (LEAF, 2014; Moore and Karlen, 2013; Karlen and Muth, 2013). The LEAF integrated model has been used for a broad range of studies investigating sustainable residue removal at the national scale (Muth et al., 2012a), at the regional scale (English et al., 2013; Bonner et al., 2014), at the subfield scale (Muth et al., 2012b; Muth and Bryden, 2012), and additionally for developing integrated bio- energy landscape designs (Karlen and Muth, 2013; Koch et al., 2012; Abodeely et al., 2012). The data management and model inputs for the LEAF integrated model are described in detail in Muth and Bryden, 2013. The meth- odologies using publicly available data resources such as the NRCS SSURGO soils database, the NASS Cropland Data Layer, NRCS devel- oped climate databases, and Conservation Information Technology tillage databases are detailed in Muth et al., 2012a. Extensive valida- tion of the individual models is documented through the previously mentioned studies. The integrated model has been verified to deli- ver analyses consistent with validation studies (Muth and Bryden, 2013). Using the LEAF integrated model, we have defined the user inputs as follows. The spatial area to be analyzed is the state of Indiana, which has high corn production and large potential for stover removal. The management practices specified include: cover crops and cover crop combinations, residue removal, crop rota- tions, tillage practices, vegetative barriers, and yield drag with con- tinuous corn. Outputs from the integrated modeling system are the SCI and its three sub-factors, wind erosion, water erosion, and the amount of residue removed. Using various combinations of the management practices, we will be able to extract two pieces of information to be used in the benefit-cost analyses: (1) the mean avoided wind and water erosion with a cover crop; and (2) the additional biomass that is available for removal with a cover crop while holding total soil erosion constant. These values were esti- mated econometrically from the nearly two million data points that were obtained from all the combinations of soil types, slope, management practices, etc. First we sum the wind and water erosion values to obtain total soil erosion. Next, a cover crop dummy variable was created, where the value equals one if any cover crop is present and zero if no cover crop is present. The mean avoided soil erosion is thus calcu- lated as the difference between the mean erosion values with and without a cover crop. In order to estimate the additional total removable biomass with a cover crop holding soil erosion constant, several steps were taken. First, we wanted to separate the impacts of no-till cultivation and cover crop. This is done by first estimating the following equation for observations under each crop rotation with a cover crop and again for observations under each crop rotation with no cover crop. y ¼ b0b1X1 þ b2X2 ð2Þ where y is the total soil erosion, X1 is a tillage dummy variable which equals 1 if no-till and 0 otherwise, X2 is the annual biomass removed. The additional removable biomass from no-till, holding soil ero- sion constant is then: Àb1=b2 ð3Þ Therefore, the contribution of a cover crop to the amount of additional biomass removable with no-till and cover crops is the difference between additional removable biomass by no-till with a cover crop and additional removable biomass by no-till with no cover crop. 2.3. Benefit-cost analyses After the costs and benefits of cover crops have been estimated, a benefit-cost analysis is conducted to calculate the mean net ben- efit of a cover crop as well as the probability of a loss using Monte Carlo simulation. To account for variability in the costs and bene- fits, the Palisades risk and decision analysis software @RISK (2001), was used to perform the risk analysis. All of the uncertain variables had a minimum, most likely (mode), and maximum. The distributions that are normally used in this situation are the PERT and triangular. We actually tested both distributions, but report here only the results from the triangular distribution, as they were quite similar. There are two different perspectives for evaluating benefits: (1) there is no stover removal, and the benefits of the cover crop are agronomic (increased N, SOM, reduced compaction, and reduced erosion), and (2) there is stover removal where the benefit is the value of stover removed, and there are no agronomic benefits con- sidered. Furthermore, in the case for which there is no stover removal and cover crop benefits are agronomic, we will assume two cases of reduced erosion: (1) on-site value of reduced erosion and (2) off-site value of reduced erosion. That is, we consider the private gain for the farmer of reduced erosion as well and the societal gain from reduced downstream erosion. 2.4. Analyzing costs and benefits at the farm level After the cover crop costs and benefits are estimated, various scenarios are analyzed using farm level data in PC-LP. PC-LP is a linear programming model that was developed within the Agricul- tural Economics department at Purdue University (West Lafayette, IN). Users specify input data including land, labor, machinery, crop yields, crop prices, and costs. Given these inputs, which are farm specific, the PC-LP model determines the profit-maximizing crop mix (Doster et al., 2009a, 2009b). Input information for the pro- gram comes from farmers participating in the Top Farmer Crop Workshop at Purdue University. PC-LP is used to combine cover crops and corn stover removal. In 2011, Thompson added stover harvest options into the PC-LP model by creating two new crops: BC + Stover (soybean corn rota- tion) and CC + Stover (continuous corn). Thompson (2011, 2014) assumed the stover-to-grain ratio to be 0.95 and the removal rate to be 33%. The corn harvest index is the ratio of corn grain to the sum of corn grain and stover, and is generally between 0.50 and 0.55 (Michigan State University Extension, 2013). Thus, our assumption is consistent with the normal values for corn harvest index. The addition of stover removal into PC-LP involved account- ing for the harvest and storage costs associated with stover harvest. Expanding upon the methodology developed by Thompson (2011, 2014) cover crops can be added to the PC-LP model to esti- mate the impact of corn stover removal and crop mix at a farm level. We consider two cases: (1) a stover removal rate of 33% with no cover crop and (2) a stover removal rate of 75% with a cover crop. The difference between the two cases is the impact of cover crops at the farm level. Three cases of cover crop costs are analyzed as a means of conducting sensitivity analysis on the cover crop cost. Additionally, calculations are done at varying levels of stover price, beginning at $44.09/metric ton and increasing in $22.05/ metric ton increments up to $132.28/metric ton. 70 M.R. Pratt et al. / Agricultural Systems 130 (2014) 67–76
  5. 5. There are three ways in which the addition of a cover crop affects the current methodology in PC-LP. First, the addition of the cover crop is an extra cost to the farm. To account for the costs incurred by a cover crop, the cost will be added to the harvest cost; doing so allows us to adjust the crop price to reflect the use of cover crops without changing the existing model. Second, it is assumed that a cover crop will increase the acceptable rate of sto- ver removal due to beneficial agronomic properties. Third, we assume the increase in stover removal rate will impact the harvest and storage costs associated with stover removal. Given the exist- ing methodology in PC-LP for the incorporation of stover removal, costs for all cases are estimated by ton and by hectare. Costs used for the base case (no cover crop) in this analysis are those esti- mated by Fiegel (2012) for stover that is harvested at 15% moisture. Costs used for the cover crop cases are those used for the base case adjusted to reflect the increase in stover removal. Cost components associated with stover removal include: stor- age, net wrap, labor, equipment, nutrients, and fuel. Harvest cost is the sum of net wrap, fuel, labor and equipment used in harvesting stover per hectare. We assume that per ton, net wrap and nutrient costs will remain the same for all cases, while storage, labor, equip- ment, and fuel costs will exhibit some economies of scale. Per hect- are we assume that storage, net wrap, and nutrient costs will increase to match the full increase in the stover removal rate, while labor, equipment, and fuel costs will increase, however by a per- centage of the increase in the stover removal rate. Given the bales per hectare assumption, 15% moisture, and tons per bale assump- tion estimated by Thompson (2011) and Fiegel (2012), and our estimated increase in the stover removal rate, we estimate these costs to increase by 64%. For BC + Stover, the harvest cost was estimated at $87.47/ha for the base case and $159.70/ha for the cover crop cases. A cost-sav- ing of $61.77/ha (Karlen et al., 2011) is assumed by Thompson (2014) for CC + Stover from reduced tillage, making the harvest cost for CC + Stover $25.70/ha for the base case and $97.92/ha for the cover crop cases. Since tillage practice was not indicated in the PC-LP model data, we assume reduced or conventional tillage, so the $61.77/ha savings will apply to the CC + Stover crop for all farms in our analysis. A summary of the cost components for all cases is presented in Table 1. Given the estimated values of stover harvest, and the assumed farm-gate prices for stover ($/ton), the mean net value of stover harvest is estimated. Using a stover farm-gate price of $66.14/met- ric ton, the average value of stover harvest (mean between BC + Stover and CC + Stover for the cover crop cases) is $22.87/met- ric ton. At a stover farm-gate price of $88.18/metric ton, the aver- age value of stover harvest is $41.92/metric ton. These mean values are used in the benefit-cost analysis as the value of stover removal per ton of stover removed. 3. Results and discussion 3.1. Cover crop costs Estimation of the cover crop costs involved the use of Monte Carlo simulation in @RISK using the triangular distributions. There is a wide range of variability in cover crop costs. This is derived from the differences in cover crop seeding rates and seed costs (Table 2). Annual rye had the lowest cost. While oats have the sec- ond highest average seeding rate, the seed costs is the second low- est on average. Similarly, annual ryegrass has a relatively moderate seeding rate and seed cost. Hairy vetch was the most expensive cover crop and appears to be a bit of an outlier since its mean cost is more than $49.42/ha higher than any other cover crop. While hairy vetch does not have the highest seeding rate, it does have the second highest seed cost. Oilseed radish has the highest seed cost, but its seeding rate is two to three times less than hairy vetch. As expected, the cover crop mixes have a mean cost that lies between the mean costs for the two individual crops that make up the mix. This may indicate that cover crop mixes provide an opportunity for farmers to combine cover crops for maximum ben- efits at a lower cost. 3.2. Cover crop benefits The integrated modeling system yields two results that are con- sidered as benefits of cover crops. The first is reduced soil erosion and the second is the potential for additional stover removal. We analyze the mean reduced soil erosion overall with and without a cover crop. The mean difference in soil erosion with and without a cover crop is 0.72 metric tons/ha. This value is used for the reduced erosion cover crop benefit category. Second, using Ordinary Least Squares (OLS) regression analysis and holding soil erosion constant, we estimate the additional sto- ver that can be removed with a cover crop. Regression results are shown in Table 3 for our two sets of observations (those with cover crops and those without cover crops). Soil erosion is the dependent variable, and total biomass removed and a dummy variable (NT) indicating no till or conventional till are the independent variables. By dividing the negative of the coefficient of NT by the coefficient of totBioRem_1 and converting to tons/ha we estimate the addi- tional removable biomass holding erosion constant. Comparing the two results yields the benefit of a cover crop. Based upon the results for all rotations combined, a cover crop appears to provide a 4.01 metric tons/ha gain over no-till alone, with a larger gain for corn-soybean than continuous corn. This value for additional removable stover with a cover crop is used for the benefit cost analysis case with stover removal. Table 1 Costs associated with stover harvest for the base case (no cover crop and 33% stover removal) and scaled for the cover crop cases (cover crop and 75% stover removal. Cost component Base case Cover crop cases $/ha $/metric ton $/ha $/metric ton Storage 74.87 18.15 151.52 15.80 Net wrap 26.02 6.17 59.15 6.17 Labor 14.31 3.40 23.40 2.44 Equipment 30.44 7.21 49.81 5.19 Nutrients 59.20 14.03 134.57 14.03 Fuel 16.70 3.96 27.33 2.85 BC + stover total 221.55 52.50 445.81 46.48 Tillage savings À61.77 À14.64 À61.77 À6.44 CC + stover total 159.77 37.86 384.04 40.05 Table 2 Total costs of each cover crop/cover crop mix as estimated by the mean of a triangular probability distribution based on the cost of establishment, termination, and unexpected cost. Cover crop/mix Seed ($/ha) Total ($/ha) 60% Annual ryegrass/40% Oilseed radish 43.38 94.87 60% Crimson clover/40% Annual ryegrass 48.41 99.90 Annual ryegrass 36.93 88.41 Cereal rye 52.07 103.56 Crimson clover 55.98 107.47 Hairy vetch 121.01 172.50 Oats 59.18 93.92 Oilseed radish 53.91 105.40 Note: While uncertainty is included in all the cost components, the mean values are the same for aerial application (30.72), termination (16.75), and unexpected costs (4.02). There is no termination cost for oats. The mean values for seed costs do vary considerably, so seed costs and total costs are included here. M.R. Pratt et al. / Agricultural Systems 130 (2014) 67–76 71
  6. 6. Cover crop benefits were estimated for two cases. The first case assumes that no stover is harvested. Therefore, the agronomic ben- efits of cover crops are accrued by the farmer. The second case assumes that some stover is harvested. Cover crop benefits in the case where no stover is harvested are shown in Table 4. Results for the second case, where stover is harvested, are shown in Table 5 along with results from the farmer perspective. The estimated cover crop benefits suggest that a crimson clover cover crop provides the greatest benefit, while oilseed radish pro- vides the least benefit. Crimson clover has the second highest con- tribution of N and the largest SOM percent accumulation. Therefore, we can expect a large benefit overall. Oilseed radish on the other hand has low added N and low SOM percent accumu- lation. Although hairy vetch has the highest mean cost, it does not have the highest mean benefit. The crimson clover/annual ryegrass cover crop mix offers larger benefits than the two individual cover crops. While the annual ryegrass/oilseed radish mix offers higher benefits than the oilseed radish cover crop, the mean benefit is slightly lower than the annual ryegrass benefit, which is due to the low mean benefit of the oilseed radish cover crop. Cover crop benefits from the societal perspective are uniformly higher by $6.20/ha. In both of these cases, the benefit from crimson clover is signif- icantly larger than other cover crops. This is largely due to the N credit associated with crimson clover. Hairy vetch also has a high N credit. The standard assumption behind the N credit is that if a cover crop is adding N, a farmer will reduce N application. How- ever, in our farmer interviews, many farmers do not abide by this assumption. In other words, regardless of the N credit provided by a cover crop, they assume it to be zero and continue with their nor- mal regimen of N application. While we consider added N to be a cover crop benefit, as there is added value into the soil, if farmers assume this value to be zero, it can impact benefit-cost analysis results, specifically for legume cover crops such as crimson clover and hairy vetch. Therefore, an additional case was tested to dem- onstrate the impact of the N credit becoming zero for all cover crops and it is also shown in Table 4. While only legumes can add N to the soil, the other crops orig- inally had a value associated with the scavenged N, which is why there is a decrease in net benefit for all cover crops. However, removing the added N benefit provides more balanced results. While crimson clover still has the highest benefit, hairy vetch no longer has second highest benefit; cereal rye is higher. Further- more, crimson clover now has a benefit closer to cereal rye and annual ryegrass. The most commonly used cover crops from the farmers we interviewed were annual ryegrass and cereal rye. These results seem to confirm that farmers do not at present place a value on added or scavenged N from cover crops. The second case for which we estimate cover crop benefits is when there is corn stover harvest. The benefit of a cover crop with stover removal is the profit that can be made from stover once sto- ver harvest costs have been accounted for. Assuming that a Table 3 Ordinary Least Squares (OLS) regression results used to estimate additional remov- able biomass when a cover crop is present. These regressions are the result of observations from the integrated modeling system developed by Muth and Bryden (2013). Variable No cover crop Cover crop Constant 1.80771* 1.75204* (0.02774) (0.02774) totBioRem_1 0.00048861* 0.000173250* (0.00000527) (0.00000527) NT À1.35454* À1.10069* (0.02761) (0.02761) R-squared 0.237 0.1035 No. Observations 35679 115692 Standard errors are reported in parentheses. Ã Significant at the 99% level. Table 4 The benefit of a cover crop measured in $/ha from the private perspective for the case where no N credit is assumed from the use of the cover crop versus the case where an N credit is accounted for. Cover crop/mix Increased SOM No N Credit With N Credit 60% Annual ryegrass/40% Oilseed radish 69.87 93.99 106.29 60% Crimson clover/40% Annual ryegrass 84.32 108.44 143.33 Annual ryegrass 84.32 108.44 108.44 Cereal rye 102.39 126.51 126.51 Crimson clover 108.42 132.54 192.07 Hairy vetch 69.87 93.99 167.89 Oats 96.37 120.49 120.49 Oilseed radish 50.59 74.71 93.17 Note: The benefits for reduced soil compaction (16.06) and reduced soil erosion from the private perspective (8.06) are the same for all cover crops. The increased soil organic matter and N credit are the major differences among cover crops. Table 5 Summary of cover crop benefit-cost analysis for (i) the private perspective, where a cover crop is present and no stover is harvested, and (ii) the case where a cover crop is presented and there is 75% stover removal. The net benefit for each case is the mean of a triangular probability distribution. Cover crop/mix Private perspective With stover removala Net benefit ($/ha/year) Standard deviation Probability of net benefit < 0 Net benefit ($/ha/year) Standard deviation Probability of net benefit < 0 60% Annual ryegrass/40% Oilseed radish 11.44 21.28 0.311 73.83 10.97 0 60% Crimson clover/40% Annual ryegrass 43.44 17.1 0.003 68.82 12.06 0 Annual ryegrass 20.04 26.59 0.239 80.28 11.02 0 Cereal rye 22.96 21.52 0.148 65.16 12.8 0 Crimson clover 84.61 23.25 0 61.23 14.9 0 Hairy vetch À4.6 24.51 0.588 À3.78 15.12 0.582 Oats 26.56 29.26 0.184 74.8 22.14 0.002 Oilseed radish À12.21 15.34 0.773 63.31 13.69 0 a The assumed stover price is $66.14/mt. 72 M.R. Pratt et al. / Agricultural Systems 130 (2014) 67–76
  7. 7. removal rate of 33% allows for about 3.36 metric tons/ha of stover to be sustainably removed, and that an additional 4.01 metric tons/ ha of stover can be removed with a cover crop, the total amount of stover removed is 7.38 metric tons/ha. Accounting for the on-farm harvest costs, including nutrient replacement, the benefit of stover removal at a stover price of 66.14/metric ton is $168.69/ha and at a stover price of $88.18/metric ton the benefit is $331.26/ha. 3.3. Benefit-cost analyses We now combine the benefits and costs to obtain net benefits. In the cases with the use of a cover crop and no stover removal, the cost of the cover crop is incurred, and the agronomic benefits of the cover crop are accrued. The results from the private perspective benefit cost analysis are presented in Table 5. All cover crops except hairy vetch and oilseed radish yield a net benefit. Although hairy vetch has large benefits, the seed costs are high enough that the mean net benefit is negative. Crimson clover has the highest net benefit. While crimson clover had the second highest mean cost, it had the largest mean benefit. As for the cover crop mixes, while crimson clover/annual ryegrass and annual ryegrass/oilseed radish had similar mean costs, crimson clover/annual ryegrass has much higher mean benefits, yielding a net benefit about four times larger than annual ryegrass/oilseed radish. The probability of a loss (probability that net benefit is less than zero) is simply the probability in the stochastic analysis that net benefit is negative. Most cover crops incur some probability of a loss, with the exception of crimson clover. Hairy vetch and oilseed radish are the two cover crops with a probability of a loss greater than 50% (58.8% and 77.3%, respectively). Although oilseed radish has a negative net benefit, and the probability of a loss is high, mix- ing it with annual ryegrass yields a positive net benefit and reduces the probability of a loss by about 50%. Furthermore, combining annual ryegrass with crimson clover yields the second highest mean net benefit and a probability of loss very close to zero. From the social perspective, the net benefit for each cover crop is uni- formly higher by $6.20/ha while the standard deviations remain the same. The probability of a loss for each cover crop is slightly less for the societal case. The second benefit-cost analysis scenario is where a cover crop is present, and instead of accumulating additional agronomic ben- efits from the cover crop, benefits from stover removal, given a via- ble stover market, are included (also Table 5). The stover removed for sale is the additional removable biomass holding erosion con- stant with a cover crop on top of a base of 3.36 metric tons/ha. This stover then is assigned a value. Since the market for stover has not been commercially established, we use a range of values based on prior research (Thompson and Tyner, 2014). In this case we analyze two farm-gate stover prices. The cover crop acts to hold agronom- ics constant (in other words, there should be no adverse effects from stover removal). However, stover removal means that nutri- ents are removed from the ground. These removed nutrients are accounted for by subtracting the cost of nutrient replacement from the value of stover. Although we account for nutrient replacement, it should be noted that other agronomic costs, such as increased compaction due to harvest machinery, are not accounted for. Har- vest costs associated with collecting the stover are included. The first stover price tested is $66.14/metric ton with results in Table 5. Hairy vetch is the only cover crop with a negative mean net benefit (under a triangular distribution). For hairy vetch the probability of a loss is 58%. The probability of a loss for all other cover crops is essentially zero, and the mean net benefit is between $61 and $80/ha. The standard deviation is about $11–$22/ha across all cover crops. Comparing this case to the cases with no stover removal and agronomic benefit, we can see that stover removal with a cover crop offers significantly increased benefits. Since actual stover prices are unknown, the sensitivity of stover value is tested but detailed results are not included here. The sec- ond case tests a stover value of $88.18/metric ton. Although the value of stover has increased by $22.05/metric ton, the net benefit for each cover crop increases by $162.59/ha. This is because once the harvest cost and nutrient replacement costs have been accounted for, the remaining value of the stover for $88.18/metric ton is much higher than $66.14/metric ton, yielding higher mean net benefits in the benefit-cost analysis. Furthermore, the probabil- ity that the net benefit is less than zero is essentially zero with a stover price of $88.18/metric ton. 3.4. Costs and benefits at the farm level (PC-LP) The 24 PC-LP farms have a total of 62,632 acres available. Results are for a base case with no cover crop and three cases of cover crops at varying corn stover prices (Table 7). The base case assumes no cover crop and 33% stover removal, while the two cover crop scenarios are estimated using a stover removal rate of 75%. Cover crop costs used include those associated with annual ryegrass and crimson clover. Furthermore, since our benefit-cost analyses suggest crimson clover as significantly outperforming annual ryegrass due to added N, we test a sub-case of the crimson clover cover crop. In this sub-case, we assume that the farmers rec- ognize the added N from crimson clover and adjust their usual N inputs accordingly. Therefore, this sub-case considers the per acre cost of a crimson clover cover crop less the value of added N per acre. Farms will not begin to harvest stover until the benefit of stover harvest exceeds the costs. Results from PC-LP indicate that at a sto- ver price lower than $44.09/metric ton, no farms will participate in stover harvest, while at prices of $88.18/metric ton and greater, all 24 farms will participate in some stover harvest. At $44.09/metric ton, 8 farms harvest some stover for the base case and 0 farms har- vest some stover for all three cover crop cases. At $66.14/metric ton, 24 farms harvest some stover for the base case, 21 for annual ryegrass, 19 for crimson clover, and all 24 farms harvest some sto- ver for crimson clover adjusted for N. PC-LP also determines the profit-maximizing crop mix for farms. For the base case, at stover price of $0 and $22.05/metric ton no acres are allocated to stover acres. However, beginning at a stover price of $41.51/metric ton, there is a shift from continuous corn with no stover removal (CCorn), corn-soybean with no stover removal (BCorn), soybean acres, and other (such as wheat or milo) acres to include continuous corn with stover removal (CC + Stover) and corn-soybean with stover removal (BC + Stover) acres. Stover is first harvested from all CCorn acres, then from BCorn acres. As stover price increases, more acres are assigned to stover acres, and increasingly to CC + Stover acres. As a result, there is a decline in the assignment of acres to other crops. This is an indication that as stover prices increase, there will be more incentive for farms to not only harvest corn stover, but to also allot more acres to corn production. This pattern of acreage assignments also holds true for the three cover crop cases. However, the shift to more corn acres with stover removal is rapid. For example, at a stover price of $66.14/metric ton the percentage of acres assigned to CC + Sto- ver removal for all cases is as follows: 21% for the base case, 22% for annual ryegrass, 20% for crimson clover, and 24% for crimson clover adjusted for N. Figs. 1 and 2 illustrate the acreage allocation by stover price for the base case and annual ryegrass, respectively. From the PC-LP results we also analyze the total amount of sto- ver harvested at each stover price. Fig. 3 illustrates these results. Since for cases involving cover crops the stover removal rate is increased to 75%, we expect to see greater quantities of stover har- vested in the cover crop cases. We observe that crimson clover with the N reduction allows for the greatest amount of stover to M.R. Pratt et al. / Agricultural Systems 130 (2014) 67–76 73
  8. 8. be harvested. At stover prices of $88.18/metric ton and greater, the amount of stover harvested levels off and is almost indistinguish- able among the three cover crop cases; this is due to the limit placed on the amount of stover that can be harvested. Since we allow for 75% removal in the PC-LP model, once this limit is reached, so long as the yield and stover-to-grain ratio remain the same, the amount of total stover harvested will eventually flatten. Finally, PC-LP allows us to analyze total farm profit. Given that cover crops allow for increased stover removal, and greater amount of stover to be harvested, we expect that as stover price increases and more farms harvest stover, profits will also increase. Fig. 4 illustrates the results for farm profit for all cases. As shown, the farm profit with crimson clover after adjusting N, yields the largest profit after a stover price of $44.09/metric ton. While the lowest cover crop cost yields the highest profit, all three cover crop cases are relatively similar, especially after a stover price of $66.14/ metric ton. Furthermore, the cover crop cases offer significantly higher profit than the base case. The results from the four PC-LP simulations provide an insight to the activities of profit-maximizing farms in Indiana. This is key because farms such as those whose data are in PC-LP are targeted for stover removal in the Midwest to meet biofuel standards. It tests how farms will react to added costs associated with cover cropping if the practice will allow them to increase their stover removal without concern for farm agronomics. Furthermore, results confirm the findings from our benefit cost analysis; based on cost alone, annual ryegrass offers greater farm benefits than crimson clover, but when the cost of crimson clover is adjusted to reflect the value of added N, crimson clover provides greater benefits than annual ryegrass. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 44 66 88 110 132 AssignmentofArea Stover Price ($/Mg) CCorn CC+Stover BCorn BC+Stover Soybeans Other Fig. 1. Assignment of total farm area in the base case, where no cover crop is used and stover removal is at 33%. 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 44 66 88 110 132 AssignmentofArea Stover Price ($/Mg) CCorn CC+Stover BCorn BC+Stover Soybeans Other Fig. 2. Assignment of total farm area where an annual ryegrass cover crop is assumed and stover removal is increased to 75%. 0 20 40 60 80 100 120 140 160 180 200 40 60 80 100 120 Stover(Mgx1000) Stover Price ($/Mg) No Cover Crop Annual Ryegrass Crimson Clover Crimson Clover N Adjusted Fig. 3. Tons of total stover harvested for each case run in PC-LP, where the no cover crop case is the base case. 15 17 19 21 23 25 27 29 40 60 80 100 120 TotalProfit($106) Stover Price ($/Mg) No Cover Crop Annual Ryegrass Crimson Clover Crimson Clover N Adjusted Fig. 4. Total farm profit for each case run in PC-LP, where the no cover crop case is the base case. Table 6 Comparison of benefit-cost analysis results for annual ryegrass and crimson clover cover crop. Results include cost estimates, private and societal benefits and net benefits without stover removal, and net benefit with stover removal for two prices of stover. Analysis Annual ryegrass Crimson clover Cost ($/ha) 88.41 107.46 Private benefit ($/ha) 108.45 192.07 Society benefit ($/ha) 114.63 198.27 Private benefit ($/ha); N credit = $0 108.45 132.54 Private agronomic net benefit ($/ha) 20.04 84.61 Probability of net benefit < 0 0.239 0 Societal agronomic net benefit ($/ha) 26.22 90.81 Probability of net benefit < 0 0.177 0 Net benefit at stover price = $66.14/metric ton 35.81 27.31 Probability of net benefit < 0 0 0 Net benefit at stover price = $88.18/metric ton 108.34 99.85 Probability of net benefit < 0 0 0 Note: The benefit and cost values are means of the triangular probability distribution. 74 M.R. Pratt et al. / Agricultural Systems 130 (2014) 67–76
  9. 9. 4. Conclusions The results from the two cases, which involved cover crops and stover removal, tell us several important things: (1) from the agronomic benefit analysis, in most cases cover crop alone offer potential net benefits to farmers, (2) the net benefit of cover crop with stover removal is sensitive to the value of stover (farm-gate price), (3) cover crops with stover removal appear to have the abil- ity to substantially increase farm profit over cover crops alone, and (4) adding a cover crop to stover removal can, in many cases, pay the cost of the cover crop while allaying fears of increased erosion and SOM loss from corn stover removal. These cases are fairly gen- eralized and contain a certain amount of risk. Overall, we can draw several key conclusions: (1) cover crop costs and benefits vary by the selected cover crop, (2) the use of a cover crop allows stover removal to sustainably increase by about 4.0 metric tons/ha, and (3) the increase in stover removal, along with increases in stover price, changes farm acreage alloca- tions, increases the total amount of stover available, and increases farm profit. However, based on farmer interviews, and the results for cover crop benefits when the value of added N is eliminated (Table 6), the benefits of cover crops perceived by farmers may be lower than those estimated in this analysis. Acknowledgements Monsanto Corporation provided funding for this research. Addi- tionally, several Monsanto personnel were consulted during the study. Several farmers from Indiana were also consulted for infor- mation on cover crop usage and corn stover harvest. References Abodeely, J., Muth, D., Bryden, K.M., 2012. Integration of the DAYCENT Biogeochemical Model within a Multi-Model Framework. In: 2012 International Congress on Environmental Modelling and Software. Managing Resources of a Limited Planet: Pathways and Visions under Uncertainty, Sixth Biennial Meeting, Leipzig, Germany. Andraski, T.W., Bundy, L.G., 2005. Cover crop effects on corn yield response to nitrogen on an irrigated sandy soil. Agronomy J. 97, 1239–1244. Blanco-Canqui, H., Lal, R., 2007. Soil and crop response to harvesting corn residues for biofuel production. Geoderma 141, 355–362. Blanco-Canqui, H., Lal, R., 2009. Corn stover removal for expanded uses reduces soil fertility and structural stability. Soil Sci. Soc. Am. J. 73 (2), 418–426. Bonner, I., Muth, D., Koch, J., Karlen, D., 2014. Modeled impacts of cover crops and vegetative barriers on corn stover availability and soil quality. BioEnergy Res. 7, 576–589. Clark, A. (Ed.), 2007. Managing Cover Crops Profitability, 3rd ed. Sustainable Agricultural Network (SAN), Beltsville, MD. Dabney, S.M., Yoder, D.C., Foster, G.R., Nearing, M.A., 2006. Application of RUSLE2 to pasturelands. In: Proceedings of the 14th International Soil Conservation Organization Conference, Marrakesh, Morocco. Dapaah, H.K., Vyn, T.J., 1998. Nitrogen fertilization and cover crop effects on soil structural stability and corn performance. Commun. Soil Sci. Plant Anal. 29 (17&18), 2557–2569. Doster, D.H., Dobbins, C.L., Griffin, T.W., 2009. B-21 Input Form Guide Book. Department of Agricultural Economics, Purdue University, June 2009. Doster, D.H., Dobbins, C.L., Patrick, G.F., Miller, W.A., Preckel, P.V., 2009. Purdue PC- LP Farm Plan. Department of Agricultural Economics, Purdue University, July 2009. Energy Independence Secutiry Act [EISA], 2007. Energy Independence and Security Act (EISA) of 2007. H.R. 6 (110th). English, A., Tyner, W., Sesmero, J., Phillip, O., Muth, D., 2013. Environmental tradeoffs of stover removal and erosion in Indiana. Biofuels, Bioprod. Biorefin. 7 (1), 78–88. Environmental Protection Agency [EPA], 2009. Draft Regulatory Impact Analysis: Changes to Renewable Fuel Standard Program. United States Environmental Protection Agency, May 2009. Fiegel, J., 2012. Development of a Viable Corn Stover Market: Impacts on Corn and Soybean Markets. MS thesis, Purdue University. Foster, G.R., Romkens, M.J., Dabney, S.M., 2006. Soil erosion predictions from upland areas: A discussion of selected RUSLE2 advances and needs. In: Proceedings of the Sino-American Workshop on Advanced Computational Modeling in Hydroscience and Engineering, Beijing, China. Frye, W.W., Blevins, R.L., 1989. Economically sustainable crop production with legume cover crops and conservation tillage. J. Soil Water Conserv. 44 (1), 57– 60. Gabriel, J.L., Garrido, A., Quemada, M., 2013. Cover crops effect on farm benefits and nitrate leaching: linking economic and environmental analysis. Agric. Syst. 121, 23–32. Graham, R.L., Nelson, R., Sheehan, J., Perlack, R.D., Wright, L.L., 2007. Current and potential U.S. corn stover supplies. Agronomy J. 99 (1), 1–11. Hagen, L.J., 2004. Evaluation of the Wind Erosion Prediction System (WEPS) erosion submodel on cropland fields. Environ. Model. Softw. 19 (2), 171–176. Halim, R., Clemente, R.S., Routray, J.K., Shrestha, R.P., 2007. Integration of biophysical and socio-economic factors to assess soil erosion hazard in the Upper Kaligarang Watershed, Indonesia. Land Degrad. Dev. 18 (4), 453–469. Hoorman, J.J., 2010. Cover Crop Economics. Ohio Ag Manager, June. Ohio State University Extension. Hoorman, J.J., 2012. Will that Cover Crop Check Bounce? [PowerPoint slides]. In: 8th Annual Adams County No-till Meeting, Gettysburg, PA. < topics/agriculture-and-natural-resources>. Hoskinson, R.L., Karlen, D.L., Birrell, S.J., Radtke, C.W., Wilhelm, W.W., 2007. Engineering, nutrient removal, and feedstock conversion evaluations of four corn stover harvest scenarios. Biomass Bioenergy 31, 126–136. Huggins, D.R., Karow, R.S., Collins, H.P., Ransom, J.K., 2011. Introduction: evaluating long-term impacts of harvesting crop residues on soil quality. Agronomy J. 103 (1), 230–233. Ismail, J., 2008. RUSLE2 model application for soil erosion assessment using remote sensing and GIS. Water Resour. Manage. 22 (1), 83. Karlen, D.L., Muth, D.J., 2013. Landscape management for sustainable supplies of bioenergy feedstock and enhanced soil quality. Agrocencia 17 (2), 121–130. Karlen, D.L., Tomer, M.D., Neppel, J., Cambardella, C.A., 2008. A preliminary watershed scale soil quality assessment in north central Iowa, USA. Soil Till. Res. 99 (2), 291–299. Karlen, D.L., Varvel, G.E., Johnson, J.M.F., Baker, J.M., Osoborne, S.L., Novak, J.M., Adler, P.R., Roth, G.W., Birrell, S.J., 2011. Monitoring soil quality to assess the sustainability of harvesting corn stover. Agronomy J. 103 (1), 288–295. Kim, S., Dale, B.E., 2004. Global potential bioethanol production from wasted crops and crop residues. Biomass Bioenergy 26, 361–375. Kinyangi, J.M., Smucker, A.J.M., Mutch, D.R., Harwood, R.R., 2001. Managing Cover Crops to Recycle Nitrogen and Protect Groundwater. Extension Bulletin E-2763. East Lansing, MI, Michigan State University Extension. Koch, J., Muth, D., Bryden, K.M., 2012. An Integrated Modeling and Data Management Strategy for Cellulosic Biomass Production Decisions. In: 2012 International Congress on Environmental Modelling and Software. Managing Resources of a Limited Planet: Pathways and Visions under Uncertainty, Sixth Biennial Meeting, Leipzig, Germany. LEAF, 2014. Landscape Environmental Assessment Framework project code repository. <> (accessed 13.02.14). McAloon, A., Taylor, F., Yee, W., 2000. Determining the Cost of Producing Ethanol from Corn Starch and Lignocellulosic Feedstocks. Technical Report. NREL/TP- 580-28893. National Renewable Energy Lab. Meki, M.N., Marcos, J.P., Atwood, J.D., Norfleet, L.M., Steglich, E.M., Williams, J.R., Gerick, T.J., 2011. Effects of site-specific factors on corn stover removal Table 7 Comparison of PC-LP results at a stover price of $66.14/metric ton of the base case, where no cover crop is assumed and stover removal is 33%, to three different cases of cover crops where stover removal is at 75%. The three cover crop cases are annual ryegrass, crimson clover, and crimson clover when the N credit is accounted for. Stover price = $66.14/metric ton Base case (no cover crop) Annual ryegrass Crimson clover Crimson clover (Adjusted) Removal rate 33% 75% 75% 75% Farms participating 24 21 19 24 % BC + stover acres 34.67% 32.46% 25.67% 33.66% % CC + stover acres 21.04% 21.60% 20.29% 23.99% Tons of stover harvested 50,352 111,152 95,887 117,736 Harvest rate (metric tons/ha) 1.93 4.31 3.70 4.55 Total farm profit ($) 17,677,745 17,663,419 17,370,976 18,179,671 Farm profit ($/ha) 679.57 685.37 674.03 705.09 M.R. Pratt et al. / Agricultural Systems 130 (2014) 67–76 75
  10. 10. thresholds and subsequent environmental impacts in the Upper Mississippi River Basin. J. Soil Water Conserv. 66 (6), 386–399. Michigan State University Extension, 2013. Harvest index: A predictor of corn stover yield. < corn_stover_yield>. Midwest Cover Crop Council [MCCC] Cover Crop Decision Tool, 2012. <http://>. Moore, K.J., Karlen, D.L., 2013. Double cropping opportunities for biomass crops in the north central USA. Biofuels 4 (6), 605–615. Muth, D., Bryden, K.M., 2012. An investigation of sustainable variable rate residue removal. J. Environ. Qual. 41 (6), 1796–1805. Muth, D., Bryden, K.M., 2013. An integrated model for assessment of sustainable agricultural residue removal limits for bioenergy systems. Environ. Model. Softw. 39, 50–59. Muth, D., Nelson, R., Bryden, K.M., 2012a. Sustainable agricultural residue removal for bioenergy: a spatially comprehensive national assessment. Appl. Energy 102, 403–417. Muth, D., McCorkle, D., Koch, J., Bryden, K.M., 2012b. Modeling the impact of variability at the sub-field scale on sustainable agricultural residue removal. Agronomy J. 104 (4), 970–981. Nelson, R.G., 2002. Resource assessment and removal analysis for corn stover and wheat straw in the Eastern and Midwestern United States—rainfall and wind- induced soil erosion methodology. Biomass Bioenergy 22 (5), 349–363. Ostendorf, M., 2010. Annual Ryegrass. No-Till Farmer, pp. 3. Palisade Corporation, 2001. @Risk – Advanced Risk Analsysis for Spreadsheets, Newfield, NY. Petrolia, D.R., 2006. Ethanol from Biomass: Economic and Environmental Potential of Converting Corn Stover and Hardwood Forest Residue in Minnesota. Paper presented at American Agricultural Economics Association Annual Meeting. Long Beach, CA, July 23–26, 2006. Schmitt, L.K., 2009. Developing and applying a soil erosion model in a data-poor context to an island in the rural Philippines. Environ. Dev. Sust. 11 (1), 19–42. Snapp, S.S., Swinton, S.M., Labarta, R., Mutch, D., Black, J.R., Nyiraneza, J., O’Neil, K., 2005. Evaluating cover crops for benefits, costs and performance within cropping system niches. Agronomy J. 97, 322–332. Stivers-Young, L.J., Tucker, F.A., 1999. Cover-cropping practices of vegetable producers in Western New York. HortTechnology 9 (3). Swan, J.B., Higgs, R.L., Bailey, T.B., Wollenhaupt, N.C., Paulson, W.H., Peterson, A.E., 1994. Surface residue and in-row treatment effects on long-term no tillage continuous corn. Agronomy J. 86 (4), 711–718. Thompson, J.L., Tyner, W.E., 2011 Corn Stover for Bioenergy Production: Cost Estimates and Farmer Supply Response. Purdue Extension RE-3-W. West Lafayette, IN, Purdue University. Thompson, J.L., Tyner, W.E., 2014. Corn stover for bioenergy production: cost estimates and farmer supply response. Biomass Bioenergy 62, 166–173. U.S. Department of Agriculture, 2011a. Cover Crop Costs. Conservation Systems Fact Sheet No. 04o. Agricultural Research Services, Auburn AL. U.S. Department of Agriculture, 2011b. Soil Organic Matter: What is topsoil worth? Natural Resources Conservation Services. < soil_organic_matter/som_value.html>. U.S. Department of Agriculture, 2012. Average U.S. farm prices of selected fertilizers, 1960–2012. [Dataset]. < and-price.aspx#26727>. Vaszita, E., Siki, Z., Gruiz, K., 2009. GIS-based quantitative hazard and risk assessment of an abandoned mining site. Land Contam. Reclamation 17 (3–4), 513–529. Vollmer, L., 2011. Cropping Decisions Survey & Focus Group. Partners, January 2011. <>. Wagner, L.E., Tatarko, J., 2001. Demonstration of the WEP 1.0 wind erosion model. In: Ascough II, J.C., Flanagan, D.C. (Eds.). Proceedings of the Soil Erosion Research for the 21st Century. Honolulu, HI, 3–5 January 2001. ASAE Proc. International Symposium Report No. 701P0007, St. Joseph, MI, pp. 372–375. Wilhelm, W.W., Johnson, J.M.F., Hatfield, J.L., Voorhees, W.B., Linden, D.R., 2004. Crop and soil productivity response to corn residue removal: a literature review. Agronomy J. 96 (1), 1–17. Wilhelm, W.W., Johnson, J.M.F., Karlen, D.L., Lightle, D.T., 2007. Corn stover to sustain soil organic carbon further constrains biomass supply. Agronomy J. 99, 1665–1667. Zobeck, T.M., Crownover, J., Dollar, M., Van Pelt, R.S., Acosta-Martinez, V., Bronson, K.F., et al., 2007. Investigation of soil conditioning index values for southern high plains agroecosystems. J. Soil Water Conserv. 62 (6), 433–442. Zobeck, T.M., Halvorson, A.D., Wienhold, B., Acosta-Martinez, V., Karlen, D.L., 2008. Comparison of two soil quality indexes to evaluate cropping systems in northern Colorado. J. Soil Water Conserv. 63 (5), 329–338. 76 M.R. Pratt et al. / Agricultural Systems 130 (2014) 67–76