Agricultural Systems 125 (2014) 12–22

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Agricultural Systems

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A framework for evaluating ecosystem services provided by covercrops in agroecosystems

0308-521X/$ - see front matter � 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.agsy.2013.11.004

⇑ Corresponding author. Address: Department of Soil and Crop Sciences, ColoradoState University, 1170 Campus Delivery, Fort Collins, CO 80523, USA.Tel.: 1 970 4916517.

E-mail address: meagan.schipanski@colostate.edu (M.E. Schipanski).

Meagan E. Schipanski a,c,⇑, Mary Barbercheck b, Margaret R. Douglas b, Denise M. Finney c,e, Kristin Haider a,Jason P. Kaye c, Armen R. Kemanian a, David A. Mortensen a,c, Matthew R. Ryan d, John Tooker b,Charlie White c

a Dept of Plant Sciences, The Pennsylvania State University, University Park, PA 16802, USAb Dept of Entomology, The Pennsylvania State University, University Park, PA 16802, USAc Dept of Ecosystem Science and Management, The Pennsylvania State University, University Park, PA 16802, USAd Dept of Crop and Soil Sciences, Cornell University, Ithaca, NY 14853, USAe Intercollege Graduate Degree Program in Ecology, The Pennsylvania State University, University Park, PA 16802, USA

a r t i c l e i n f o

Article history:Received 18 November 2012Received in revised form 7 November 2013Accepted 25 November 2013Available online 25 December 2013

Keywords:Green manuresEcosystem servicesMultifunctionalityTrade-offsCropping systemsTemporal dynamics

a b s t r a c t

Cropping systems that provide ecosystem services beyond crop production are gaining interest fromfarmers, policy makers and society at large, yet we lack frameworks to evaluate and manage for multipleecosystem services. Using the example of integrating cover crops into annual crop rotations, we presentan assessment framework that: (1) estimates the temporal dynamics of a suite of ecosystem services; (2)illustrates ecosystem multifunctionality using spider plots; and (3) identifies key time points for optimiz-ing ecosystem service benefits and minimizing trade-offs. Using quantitative models and semi-quantita-tive estimates, we applied the framework to analyze the temporal dynamics of 11 ecosystem services andtwo economic metrics when cover crops are introduced into a 3-year soybean (Glycine max)–wheat (Trit-icum aestivum)–corn (Zea mays) rotation in a typical Mid-Atlantic climate. We estimated that cover cropscould increase 8 of 11 ecosystem services without negatively influencing crop yields. We demonstratethat when we measure ecosystem services matters and cumulative assessments can be misleading dueto the episodic nature of some services and the time sensitivity of management windows. For example,nutrient retention benefits occurred primarily during cover crop growth, weed suppression benefitsoccurred during cash crop growth through a cover crop legacy effect, and soil carbon benefits accruedslowly over decades. Uncertainties exist in estimating cover crop effects on several services, such as pestdynamics. Trade-offs occurred between cover crop ecosystem benefits, production costs, and manage-ment risks. Differences in production costs with and without cover crops varied 3-fold over 10 years, lar-gely due to changes in fertilizer prices, and thus cover crop use will become more economical withincreasing fertilizer prices or if modest cost-sharing programs are established. Frameworks such as thatpresented here provide the means to quantify ecosystem services and facilitate the transition to moremultifunctional agricultural systems.

� 2013 Elsevier Ltd. All rights reserved.

1. Introduction

The most common metrics for evaluating cropping systems aregrain and forage yields and short-term profitability. Within thiscontext, cover crops are treated as a tool to be used only if theydo not interfere with cash crop production. However, there isgrowing interest among farmers, policy makers and society at largeto establish cropping systems that provide ecosystem benefits be-yond maximizing crop yield due to rising or variable costs of

agricultural inputs and concerns about the environmental impactsof farming (OECD, 2001; Boody et al., 2005; Bennett and Balvanera,2007; Pilgrim et al., 2010; Lovell et al., 2010).

Integration of cover crops into annual crop rotations presentsan opportunity to increase the ecosystem services provided byagricultural systems. Ecosystem services are functions providedby the environment that benefit humans and they can be classifiedas provisioning, regulating, supporting, or cultural services (Mil-lennium Ecosystem Assessment, 2005). Agriculture, which is prac-ticed on 40% of the Earth’s land surface, both provides and dependson ecosystem services. For example, crop production, a provision-ing ecosystem service, depends on supporting services, such asnutrient and water cycling, pest regulation, and maintenance ofsoil quality and biodiversity (Power, 2010). In addition, production

M.E. Schipanski et al. / Agricultural Systems 125 (2014) 12–22 13

practices influence regulating services that provide benefits exter-nal to the farm, including regulating water and air quality, storingsoil carbon (C), and supporting biodiversity (Power, 2010).

Due to trade-offs and dynamic interactions among services,there are substantial challenges to managing agricultural systemsto optimize economic returns and multiple ecosystem servicessimultaneously (Snapp et al., 2005; Power, 2010). These trade-offscan be straightforward, such as potential water quality degradationdue to increased use of fertilizer to maximize crop yields (Jayneset al., 2001). Other trade-offs, however, are more subtle or indirect.For example in natural systems, individual plant species may opti-mize different services such that increasing plant diversity in-creases multifunctionality (Gamfeldt and Hillebrand, 2008);however, species that maximize stress tolerance may pay a penaltyin biomass production (Zavaleta et al., 2010). Temporal scales alsocontribute to indirect trade-offs, such as management practicesaimed at maximizing current crop production that degrade soilquality over time (Rodriguez et al., 2006).

One possible solution for maximizing benefits is to diversifyprovisioning of ecosystem services in time. In particular, integrat-ing cover crops into annual grain cropping systems adds importanttemporal, taxonomic, and functional biodiversity. Cover crops canprovide numerous ecosystem services, including improving soilquality, nutrient cycling, pest regulation, and crop productivity(Tonitto et al., 2006; Lundgren and Fergen, 2011; Ryan et al.,2011; Schipanski and Drinkwater, 2011). Most cover crop assess-ments, however, have focused on individual or subsets of servicesdefined along disciplinary lines (e.g., Tonitto et al., 2006). Recentreviews and extension resources that synthesize single functionstudies to discuss cover crop multifunctionality highlight currentresearch gaps (Snapp et al., 2005; Cherr et al., 2006; SAN, 2007).Consideration of time series data are currently lacking in covercrop studies, despite the importance of temporal dynamics in covercrop based systems (Cherr et al., 2006). In addition, improved esti-mates of the off-farm environmental benefits of cover crops areneeded for a complete analysis of cover crop costs and benefits(Snapp et al., 2005).

We see opportunities for extracting a broader suite of ecosystemservices from agricultural lands that both support crop productionand reduce the impact of agriculture on the environment. Evaluatingthese services requires an approach to quantify and manage multi-ple ecosystem services. Here, our goal is to advance the understand-ing, visualization, and management of the temporally dynamicinteractions across ecosystem services in agricultural systems. Wedeveloped and applied an assessment framework within the contextof a typical grain crop rotation in a temperate climate that: (1) esti-mates the temporal dynamics of a suite of ecosystem services withindifferent management scenarios; (2) uses spider plots to normalizeservice estimates and illustrate the multifunctionality of each sys-tem; and (3) evaluates spider plots both cumulatively over an entirerotation cycle and for points in time to identify key time points formanaging ecosystem service benefits and trade-offs. We utilizedcropping systems models, literature reviews, and expert opinion toestimate ecosystem services and identify key knowledge gaps. Dueto this integrated approach using quantitative and semi-quantita-tive methods, this paper represents a cross between a traditional re-search paper and a literature review.

2. Methods

We simulated a 3-year soybean (Glycine max)–wheat (Triticumaestivum)–corn (Zea mays) rotation with (CC) and without covercrops (NoCC) in central Pennsylvania (Centre County). The agro-ecological conditions are broadly representative of the Northeastand Mid-Atlantic regions of the United States though the assess-

ment framework is not bounded by region or specific crops. TheCC rotation included red clover (Trifolium pratense) frost-seededinto winter wheat in March and winter rye (Secale cereale) plantedafter corn grain harvest. Our simulated management practices in-cluded tillage, synthetic fertilizer use, and mechanical weed con-trol (Table 1). Because one of our objectives was to understandthe impact of cover crops on pest regulation services, we assumedthat herbicides, insecticides, and transgenic crops were not used.Fertilizer applications were the same across both systems withone exception. In the CC system, the red clover cover crop was as-sumed to be the only source of nitrogen (N) for corn.

For each cropping system, we developed estimates for 11 eco-system services and two economic metrics over the 3-year rotation(Table 2). Where possible, we used existing models to generatequantitative estimates of ecosystem services; however, croppingsystem-based models do not exist for several services of interest.In particular, our ability to estimate the temporal dynamics of soilorganisms and pests (insects and weeds) lags far behind our abilityto simulate soil C, nutrient cycling, and soil erosion. For these ser-vices, we used information from literature and expert opinion togenerate semi-quantitative time series estimates of the impact ofcover crops on each service relative to our system without covercrops. Below, we summarize the rationale for including each ser-vice and the approach used to estimate services.

2.1. Services related to crop and soil C and N

Increased C storage in soils, a supporting ecosystem service, in-creases soil quality and crop yield while reducing soil erodibilityand CO2 emissions into the atmosphere (Lal, 2010). Ecosystem ser-vices related to N are more complex. Fixation of N by legumes andN mineralization from cover crop residue provide supporting eco-system services because they reflect the potential to support cropproduction through internal nutrient cycling, thereby reducing useof synthetic fertilizers, and their associated fossil fuel emissions. Inaddition, excess N inputs can increase nitrate (NO3) pollution instreams and groundwater, and nitrous oxide (N2O) emissions tothe atmosphere, thereby impacting air and water quality regulation.

We estimated ecosystem services related to crop C fluxes (bio-mass production and crop yields), soil C storage, N supply, N reten-tion, and N2O emissions using the Cycles cropping systems model.Cycles is a multi-year, multi-crop, daily time step cropping systemssimulation model that shares modules with C-Farm (Kemanian andStöckle, 2010) and CropSyst (Stöckle et al., 2003). The model has asimple and robust crop growth algorithm with standard parame-ters for many annual and perennial agricultural plants, modulesto simulate the coupled C and N cycles that require no calibration,and modules representing the effect of management practices onsoil C turnover and other processes.

We ran Cycles model simulations for 40 years and used the out-put of the final 30 years for analysis (10 rotation cycles). We usedweather and radiation data for Centre County, Pennsylvania, USA(NOAA, 2011; Thornton et al., 2011) for the years 1984–2010 andthe ClimGen weather generator (Stöckle et al., 2003) to extendthe weather data series to a 40-year period. Soil properties werebased on a Hagerstown silt loam soil typical of central Pennsylva-nia. We averaged daily values for each of the three rotation yearsacross the 10 rotation cycles for above and belowground biomassproduction, net N mineralization, NO3 leaching, and N2O emissionsfrom both nitrification and denitrification. We selected net N min-eralization as a proxy for N supply because it integrated biologicalN fixation inputs and other processes that contribute to plant Navailability. We used mean grain yields for each cash crop in therotation (n = 10 for each crop). To estimate differences in soil Cstorage between cropping systems we used final soil C estimates

Table 1Management practices and general timing for two simulated cropping systems: (1) cover crops and no N fertilizer applied to corn (CC); and (2) without cover crops and with168 kg N fertilizer for corn (NoCC). No insecticides or herbicides were used in either system and both relied on tillage and cultivation for weed control.

Year in rotation Month CC NoCC

1 May Plow in rye, plant soybeans Plow, plant soybeans1 June Cultivation (4 passes) Cultivation (4 passes)1 October Harvest soybeans Harvest soybeans1 October Plow and plant wheat Plow and plant wheat1 October 22 kg N fertilizer ha�1 22 kg N fertilizer ha�1

2 March Frost-seed red clover2 March 90 kg N fertilizer ha�1 90 kg N fertilizer ha�1

2 July Harvest wheat Harvest wheat

3 May Plow in clover and plant corn Plow and plant corn3 May 56 kg N fertilizer ha�1

3 May/June Cultivation (4 passes) Cultivation (4 passes)3 June 112 kg N fertilizer ha�1

3 October Harvest corn Harvest corn3 October Disk and plant rye

Table 2The 11 ecosystem services and two economic metrics estimated based on either direct estimates or proxies using existing models (Cycles or RUSLE) or literature-based estimates.

Proxy ES categorya Cycles model RUSLE model Literature-based estimates

Ecosystem serviceFood production Crop yield Provisioning xBiomass production Biomass production Supporting xN supply N mineralization Supporting xN2O reduction N2O production (–)b Regulating xNO3 retention NO�3 leaching (–) Regulating xSoil C storage Soil C Supporting xErosion control Soil loss (–) Regulating xAMF colonization AMF colonization Supporting xWeed suppression Weed pressure (–) Regulating x xPest suppression Lepidopteran activity (–) Regulating xBeneficial insect conservation Carabid activity Regulating x

Socio-economic metricsManagement ease Management risk (–) xProfitability Partial enterprise budget x

a Ecosystem service category as defined by the Millennium Assessment (MEA, 2005).b (–) Indicates the scale for this service was reversed to maintain a ‘more is better’ scale for use in spider plots.

14 M.E. Schipanski et al. / Agricultural Systems 125 (2014) 12–22

for the full soil profile (1.8 m depth) at the end of the 40-year sim-ulation period.

2.2. Erosion prevention

Soil erosion leads to aquatic ecosystem degradation, increasedmaintenance costs due to lake and waterway sedimentation, andrepresents a relatively irreversible form of agricultural land degra-dation (Pimentel and Kounang, 1998). Thus, erosion prevention isanother important regulating ecosystem service. Our assessmentof soil erosion prevention is based on the Revised Universal SoilLoss Equation (RUSLE), a widely used predictor of average annualsoil loss (Renard et al., 1991). The RUSLE equation is:

A ¼ R � K � LS � C � P

where A is the mean soil erosion (kg ha�1 yr�1), R is the rainfall–runoff factor, which depends on precipitation intensity(MJ mm ha�1 h�1 yr�1); K is the soil erodibility factor, which repre-sents both susceptibility of soil to erosion and the rate of runoff(kg h MJ�1 mm�1); LS is the slope length and steepness factor; C isa factor that integrates residue management practice effects on ero-sion rates; P is the practices factor that integrates the effects of spe-cific support management practices such as contouring and strip-cropping on erosion losses computed using values for each factorrepresenting site-specific conditions. We used RUSLE2, a software

program used to predict average soil loss using RUSLE (Fosteret al., 2000), to determine the daily soil loss rate (kg ha�1 d�1) forthe 3-year rotation for each cropping system. We ran the simulationfor Centre County, Pennsylvania, on a Hagerstown silt loam with aslope of 3–8%, average slope steepness of 6.0%, and average slopelength of 50 m and using historical average daily weather data forRock Springs, Pennsylvania (NOAA).

2.3. Mycorrhizal colonization

Arbuscular mycorrhizal fungi (AMF) colonization represents anintegrated measure of supporting ecosystem services. AMF play animportant role in plant acquisition of phosphorous and may in-crease plant uptake of N and zinc, pest resistance, and drought tol-erance (Smith and Read, 2008; Porcel et al., 2006; Vannette andHunter, 2009). Additionally, AMF can have a stabilizing effect onsoil by increasing aggregate stability (Wilson et al., 2009).

We reviewed literature on cover crop effects on AMF coloniza-tion in the subsequent cash crop to develop a hypothesized timesequence. The percent colonization of roots of a bioassay plant isoften used as a proxy for AMF inoculum potential. However, it isdifficult to directly compare percent colonization across crop spe-cies because plant species and genotypes vary in their ability tosupport AMF colonization (Smith and Read, 2008). To standardizevalues across crops, we estimated percent colonization of cash

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crops and cover crops relative to the maximum potential coloniza-tion for each crop to describe AMF activity over the 3-year soy-bean–wheat–corn rotation in the CC and NoCC systems. For thisreason, the estimated percent colonization was zero during fallowperiods.

The overall rate and extent of colonization with and withoutcover crops in the rotation is based on studies that explored theimpact of winter cover crops on the colonization of corn by AMF(Kabir and Koide, 2002; Deguchi et al., 2007; White and Weil,2010). These studies showed that corn planted after a winter covercrop was colonized by AMF more rapidly and had greater coloniza-tion than corn planted after a winter fallow (Kabir and Koide, 2002;Deguchi et al., 2007). In addition, long fallow periods and tillagedecrease the ability of AMF to colonize crops (Kabir et al., 1997;Boswell et al., 1998). Based on the most conservative reportedvalues, we assumed that crops following a cover crop reached theirmaximum colonization potential, 100%, and crops following afallow were colonized to a lesser degree, 85%. We define theseestimates as semi-quantitative because we recognize that theshape and peak of the curves for percent of maximum colonizationwould be influenced by many site, environmental, and manage-ment factors such as the AMF species at the site, spring and falltemperature, crop species in the rotation, weed populations, andtillage regime (Hetrick and Bloom, 1984; Kabir et al., 1997; Kliron-omos, 2000). Currently, quantitative models that integrate all ofthese factors do not exist in the scientific literature.

2.4. Weed suppression

The regulating ecosystem service of weed suppression repre-sents the potential of the cropping system to manage weeds with-out reliance on external inputs, thereby reducing the need forsynthetic herbicides, fuel for tillage and cultivation, and soil distur-bance that can have potentially deleterious effects on environmen-tal and human health. We reviewed the literature to predict howour cover cropping scenarios would influence weed biomassthrough time. Such estimates are complicated because weed emer-gence and growth in agricultural fields is a community dynamicwhere suites of winter annual species are influenced by fall covercrop growth and suites of early summer annual species are influ-enced in the spring (Mortensen et al., 2000). Soil cover, allelopathy,and N availability were mechanisms integrated into our estimatesof cover crop effects on weed suppression. Cover crop biomass andweed suppression are often positively correlated (Liebman and Da-vis, 2000; Teasdale and Mohler, 2000; Ryan et al., 2011). Cover cropresidues can release allelopathic compounds that inhibit germina-tion and growth of weed seedlings. Rye, in particular, producessecondary metabolites, e.g., benzoxazinoid 2,4-dihydroxy-1,4-benzoxazin-3-one (DIBOA), that are known to inhibit weed seed-ling germination and growth (Reberg-Horton et al., 2005). Covercrops can also differentially affect soil N availability, giving cropsa competitive advantage over weeds. Incorporated cereal ryeimmobilizes soil N, which favors legume crops, such as soybean(Wells et al., 2010). Incorporated red clover provides N that is moretemporally synchronized with corn crop demand than mineralfertilizer (Dyck and Liebman, 1995).

We estimated cover crop weed suppression effects relative to amaximum weed pressure potential that followed a distributionsimilar to seasonal biomass production patterns. We used soil cov-er estimates from RUSLE2 to estimate spring weed suppression ofwinter and early summer annual weeds during cover crop phasesin the rotation. We estimated that rye and red clover cover cropsin the spring prior to soybean and corn, respectively, suppressedweeds by an average of 24% relative to the NoCC system. In latesummer and early fall following wheat harvest, we estimated an

average weed suppression effect of 42% from red clover relativeto the fallow period in the NoCC system.

During soybean and corn establishment, weed suppression po-tential was set back to near zero with each cultivation event. Dataare lacking on the extent to which weed suppression during covercrop growth reduces weed pressure in the following cash cropwithin tilled systems because most studies on the weed suppres-sion effects of cover crops are conducted under no-tillage manage-ment conditions (e.g., Mirsky et al., 2011; Ryan et al., 2011;Williams et al., 2000). Based on expert opinion and field observa-tions, we estimated that the reduction in weed abundance duringcover crop growth periods would translate into decreased weedabundance in cash crops. We, therefore, estimated that the weedsuppression effect during cover crop growth periods resulted in alegacy weed suppression effect of 14% during soybean and corngrowth periods.

2.5. Insect-related services

In agroecosystems, the degree of naturally occurring insect–pest regulation reflects both bottom-up (i.e., plant-based) andtop-down (natural enemy-based) factors (Letourneau, 1997). How-ever, agroecologists generally lack quantitative models that relateinsect dynamics to crop management. Therefore, we used litera-ture reviews to predict the response of two focal taxonomicgroups, one pest and one beneficial, to our cover-cropping scenar-ios as proxies for the regulating ecosystem services of pest sup-pression and beneficial insect conservation, respectively.

For pest insects, we chose caterpillars (Lepidoptera) as a proxybecause they are the most problematic insect pest group commonto all three cash crops in our analysis. Given that many lepidopter-an populations are highly variable (e.g. Willson and Eisley, 1992;Fleischer and Hutchinson, 2011), we used the number of key cater-pillar species active at a given time for central Pennsylvania as anindex of pest risk (key species: true armyworm, Mythimna uni-puncta; black cutworm, Agrotis ipsilon: European corn borer, Ostri-nia nubilalis; and corn earworm/soybean podworm, Helicoverpazea). We found no relevant studies to inform our estimates in soy-beans or wheat, or for some of our focal pests in corn. Black cut-worm and true armyworm, both early-season pests of corn, havein some cases been associated with cover crops (Willson and Stin-ner, 1994; Showers, 1997). For true armyworm, this risk is usuallylimited to grass cover crops (Willson and Eisley, 1992; Willson andStinner, 1994), and so does not appear relevant to our red clover/corn scenario. In addition, both black cutworm and true armywormwould likely be reduced by tillage (Showers et al., 1985; Willsonand Eisley, 1992). We therefore predicted that caterpillar riskwould be similar in our two scenarios and that general pest activitywould follow a unimodal distribution over the season. Caterpillaractivity is plotted on a reverse scale to represent an estimate ofpest suppression, with low values representing high potential cat-erpillar activity and vice versa.

For beneficial insects, we focused our analysis on activity–den-sity of ground beetles (Coleoptera: Carabidae) because they play azmajor role in agroecosystems by contributing to the mortality ofweed seeds, insects, and slugs (Luff, 1987; Bohan et al., 2011). Bio-logical control from ground beetles and other natural enemies canincrease yields and decrease the need for pesticides (Losey andVaughan, 2006).

We assumed that a unimodal distribution approximates thecarabid activity–density over the season in our climate (e.g. Car-mona and Landis, 1999), with most activity occurring betweenApril and October (M. Douglas, personal observation). Becausefew studies address our particular combinations of crops and man-agement practices, we conservatively extrapolated from the cara-bid literature. We estimated that winter cover (wheat, rye, or red

Table 3Average grain yields over a simulated 30-year period for soybean–wheat–cornrotations with (CC) and without cover crops (NoCC). Standard errors presented inparentheses.

CC (Mg ha�1) NoCC (Mg ha�1)

Soybean 3.1 (0.1) 3.1 (0.1)Wheat 5.8 (0.3) 5.9 (0.3)Corn 8.1 (0.4) 8.2 (0.5)

16 M.E. Schipanski et al. / Agricultural Systems 125 (2014) 12–22

clover) would increase carabid activity–density relative to fieldswithout cover by 30% in early spring (Hance et al., 1990; Halajet al., 2000; O’Rourke et al., 2008) and that red clover interseededin wheat would enhance carabid activity–density throughout theseason by 20% relative to wheat alone (Luff, 1987; Carmona andLandis, 1999). In corn and soybeans, soil disruption from plowingand cultivation is likely to reduce carabid activity–density in Mayand June (Brust et al., 1986; Shearin et al., 2007). After tillage oper-ations, we predicted that carry-over effects of winter cover (Lovei,1984) and persistence of some cover crop residue would result in amodest 10% increase in carabid activity–density in the cover cropscenario.

2.6. Management risk

The limited time windows for cover crop establishment andmanagement around corn and soybean planting and harvest datesis the most common constraint to cover crop adoption identifiedby grain farmers in the U.S. (CTIC, 2010). To integrate this key man-agement constraint into our analysis, we estimated managementrisk narrowly defined as a relative measure of the risk of crop yieldloss or cover crop establishment failure due to the time-sensitivityof field management activities. We defined timing and duration ofmanagement windows using USDA planting and harvesting datesfor Pennsylvania (USDA-NASS, 1997) and the Pennsylvania StateAgronomy Guide (PSU, 2011); the specific management practicesconsidered were planting, cultivation for weed control, and cropharvest. We assumed that management risk ranged from 0% to100%, increasing as the end of the management window ap-proaches and then returning to zero again once the managementwindow passes. The cumulative management risk for each activitywas assumed to be equal such that management activities with alonger management window had a lower maximum daily risk va-lue than activities with a shorter window. For example, the typicalmanagement window for corn grain harvest in Pennsylvania isSeptember 25 to December 10. Seeding rye following corn shortensthe potential corn harvest window to September 25 to November5, to provide sufficient time for rye establishment before the onsetof winter. As a result, the maximum daily management risk forcorn harvest in the system without rye was estimated as 54%and the risk with rye was estimated as 99%. When managementwindows overlapped for different activities, such as planting andcultivating, risk values were summed for each activity.

2.7. Profitability

Profitability of each cropping system scenario was estimatedusing partial enterprise budget analysis. Partial budgeting is a com-mon decision support tool for estimating differences in costs andrevenues between management scenarios (PSU, 2002). A compari-son of costs and revenues between CC and NoCC systems were cal-culated for 2012 and for a 10-year period from 2003 to 2012. Costswere estimated for all practices that differed between the twocropping systems. For the CC scenario, additional input costs in-cluded disk tillage and drilling the rye cover crop, broadcastingthe red clover cover crop, and cover crop seed costs. Equipmentoperation costs were estimated based on Pennsylvania customrates (USDA-NASS, 2012). Seed prices from a single, regional sup-plier were only available for the last 5 years (King’s AgriSeeds, Ron-ks, PA). For the 10-year cost analysis, we assumed 2008 prices forthe previous 5 years (2003–2007). For the NoCC-crop scenario,additional input costs included N fertilizer applied to corn. UreaN fertilizer prices were based on USDA-ERS (2012). Revenues rep-resented prices paid for harvested soybean, wheat and corn grainadjusted for moisture content of 13%, 13%, and 15.5%, respectively(USDA-ERS, 2012). Prices were relativized based on 2003 values.

2.8. Analysis

We used spider plots, or radar plots, to visualize the differencesin multifunctionality through time for the CC and NoCC croppingsystems. Ecosystem services are presented in spider plots on a con-sistent ‘‘more is better’’ scale. To do this, we subtracted daily esti-mates from the maximum daily value to reverse the scale ofseveral functions (Table 2). For the cumulative spider plot, whichrepresents multifunctionality integrated over the entire rotation,data were normalized to a scale from 0 to 1 using the followingequation:

Ynew ¼Ysys

Ytot � 2

where Ysys is the average daily value across all three rotation yearsfor a given system (CC or NoCC), Ytot the average daily value acrossboth systems, and Ynew is the normalized value. For crop yield,which was not estimated on a daily time step, Ynew was calculatedusing the average annual crop yields for each system (Ysys) relativeto the average across both systems (Ytot). Similarly, normalized val-ues for profitability were calculated using the cumulative, 3-yearprofitability for each system relative to the average profitabilityacross both systems.

We then normalized data to analyze the temporal dynamicsacross ecosystem services over the 3-year rotation. We calculatedmonthly averages in each year (1, 2, and 3) of each cropping sys-tem (CC or NoCC) from daily estimates. To maintain a constantscale across time, monthly averages were normalized to a uniformscale from 0 to 1 for all services estimated on a daily time step,using the following equation:

Xnew ¼X � Xmin

Xmax � Xmin

where X is the monthly average value for a given month within agiven cropping system, Xmin and Xmax values represented the mini-mum and maximum monthly average value across both croppingsystems and across all 3 years, and Xnew is the normalized value.

3. Results and discussion

3.1. Ecosystem services provided by cover crops

We estimated that cover crops can provide a broad suite of eco-system services beyond nutrient retention and erosion control.Cumulatively across the 3-year crop rotation, cover crops increasedthe provisioning of 8 of the 11 ecosystem services relative to thesystem without cover crops (Fig. 1). Cover crops increased almostall supporting and regulating services, including biomass produc-tion, N supply, soil C storage, NO3 retention, erosion control, weedsuppression, AMF colonization, and beneficial insect conservation.The exceptions were insect pest suppression and N2O reduction,which were not different or decreased, respectively, in the CC sys-tem. In our simulations, the red clover cover crop provided suffi-cient N to the following corn crop to achieve equivalent yields toa corn crop fertilized with 168 kg N ha�1. Crop yields for all three

Fig. 1. Normalized values for 11 ecosystem services and two economic metricsaveraged across the 3-year rotation of cropping systems with (CC) and without(NoCC) cover crops.

Fig. 2. Relative changes in soil profile carbon over 40 years for soybean–wheat–corn rotations with (CC) and without cover crops (NoCC) simulated with the Cyclesmodel.

Fig. 3. Relative prices paid over the past 10 years for cover crop managementactivities (CC mgmt costs), additional N fertilizer required in system without covercrops (NoCC fert costs), crop revenues (Revenues), and the relative difference inprofitability (Profitability difference) between systems with and without covercrops.

M.E. Schipanski et al. / Agricultural Systems 125 (2014) 12–22 17

cash crops, a key metric of agronomic success, were equivalent be-tween the CC and NoCC cropping systems (Table 3).

The metrics of profitability and management ease, which con-tribute directly and indirectly to the economic success of agricul-tural systems, were lower in the CC system. Based on 2012prices, the increased input costs of tillage and planting operationsand cover crop seed costs exceeded the benefits of reduced N fer-tilizer inputs in the CC system by 64 USD ha�1 compared withNoCC system over the 3-year rotation (Table 4). This estimated costdifference included costs associated with cover crop seeding andmanagement, but did not include potential reductions in herbicideand pesticide costs due to cover crop pest suppression benefits inthe CC system. Fertilizer prices have increased at a faster rate thancustom labor rates over the past decade (Fig. 3). If fossil fuel pricescontinue to rise, the N fertilizer replacement cost savings of legumecover crops will likely exceed cover crop management costs.

Management risk can encompass a much broader suite offactors than included in our analysis, which focused exclusivelyon production risk. In addition, we did not attempt to integrate

Table 4Partial enterprise budgets for soybean–wheat–corn rotations with (CC) and without cover crops (NoCC) based on 2012 prices and 10-year range of prices paid and received(2003–2012).

System Item Qty 2012 Price (USD) Units USD ha�1 10-yr price range (USD ha�1)

CC CostsBroadcast clover seed 1 28.10 ha $28.10 17.30–29.10Disking for rye seedbed 1 46.70 ha $46.70 30.10–46.70Drilling rye seed 1 42.00 ha $42.00 30.40–42.00Clover seed 11.2 5.81 kg $65.13 3.85–5.81a

Rye seed 125 0.88 kg $110.52 0.59–0.88a

Total costs $292.45

ReturnsCorn 8000 0.30 kg $2400.00 0.09–0.30Soybean 3000 0.52 kg $1560.00 0.20–0.52Wheat 6000 0.29 kg $1740.00 0.12–0.29

Total returns $5700.00

NoCC CostsUrea N corn fertilizer 168 1.36 kg $228.48 0.64–1.36

Total costs $228.48

ReturnsCorn 8000 0.30 kg $2400.00 0.09–0.30Soybean 3000 0.52 kg $1560.00 0.20–0.52Wheat 6000 0.29 kg $1740.00 0.12–0.29

Total returns $5700.00

Profitability difference (CC–NoCC) �$63.97 �137 to �38

a Only 5-year record of cover crop seed prices available.

18 M.E. Schipanski et al. / Agricultural Systems 125 (2014) 12–22

the economic value of ecosystem services such as soil C storage inthe budget. We selected economic metrics and management con-straints to highlight the current constraints to cover crop adoption.The economic constraints may continue to decrease in the comingyears due to increasing fertilizer costs while management con-straints will likely continue to constrain adoption. In particular,any real or perceived effects of cover crop management systemson crop yield will be an important driver of adoption because rev-enues were approximately 100-fold greater than the differences ininput costs between CC and NoCC systems (Table 4).

Most assessments of multifunctionality compare agricultural orother land uses with cumulative measures or integrated indicatorsat a single time point (Kelly, 1996; Liebig et al., 2001; Lovell et al.,2010). Our cumulative assessment provided a useful summary ofservices that integrate over annual or longer time periods, suchas crop yields and profitability, and services that accumulate grad-ually. Soil C differences accumulated over time slowly and consis-tently when cover crops were used in the crop sequence (Fig. 2).

3.2. Temporal gaps filled by cover crops

While the cumulative assessment synthesized estimates ofmultiple ecosystem services across the 3-year rotation, it maskedimportant temporal dynamics. In particular, the cumulative assess-ment failed to highlight that ecosystem services provided by covercrops occurred at specific points during the crop rotation, or cer-tain periods within each year, often when there was a greater needto support crop production and/or reduce environmental impacts.In particular, cover crops extended the time periods of C assimila-tion by approximately 8 months and soil cover by approximately15 months over the 36-month rotation, with cascading effects on

Fig. 4. Temporal dynamics of ecosystem services and economic metrics estimated in dailyyear soybean–wheat–corn rotation. Units are listed for estimates generated using quandifferences between the systems based on literature reviews and expert opinion. Shadedyield and profitability are not included because they were integrated, annual measurem

all other services. Nitrate leaching tended to be greatest in betweengrowing seasons (Fig. 4) when biomass production was absent inthe system without cover crops. The system with cover crops re-duced the leaching potential by 32% during the red clover andrye cover crop windows relative to the system without cover crops.The effect was strongest during the rye cover crop phase followingcorn despite relatively low rye biomass production (0.9 Mg/ha).This supports estimates that the majority of annual NO3 leachinglosses in a soybean–wheat–corn rotation occur during the cornproduction year, with most of these losses occurring following cornharvest (Syswerda et al., 2012). In addition, the extended windowof biomass production and ground cover contributed to other ser-vices, such as weed suppression and beneficial insect conservation.

Other service provisioning during cash crop production periodsrepresented ‘‘legacy’’ benefits of the cover crops. For example, theincreased organic matter residues in the CC system contributed todecreased erosion following tillage events during the growing sea-son (Fig. 4). Weed suppression during cover crop growth was esti-mated to result in a carry-over effect through reduced weedpressure during the following cash crop phase (Fig. 4). Servicesmediated by soil microbes, such as N supply, and denitrificationpotential, followed seasonal increases in temperature and lightavailability and showed the greatest differences between systemsduring the growing season. Similarly, increased beneficial insectactivity and AMF colonization during the cash crop phase repre-sented cover crop legacy effects. As a result, the legacy of the redclover cover crop was still apparent in mid-season of the followingcorn crop (Fig. 5E). Similar legacy effects were found in soybeansfollowing the rye cover crop (Fig. 5A).

Due to these seasonal dynamics, the relative effect of a covercrop-based rotation on overall multifunctionality was strongly

time series in cropping systems with (CC) and without (NoCC) cover crops over a 3-titative models. No units are listed for semi-quantitative estimates of the relative

regions indicate monthly slices in time used for spider plots presented in Fig. 4. Cropents.

Fig. 5. Spider plots of normalized monthly averages at the time points shaded in Fig. 3 for 11 ecosystem services and economic metrics across a 3-year soybean–wheat–corncropping system with (CC) and without (NoCC) cover crops. Time points were selected to demonstrate the temporal dynamics of services provided in CC and NoCC systemsduring cover crop growth (A and D) and cover crop legacy effects during cash crop growth (B, C, and E). Crop yield and profitability are not included because they wereintegrated, annual measurements.

M.E. Schipanski et al. / Agricultural Systems 125 (2014) 12–22 19

influenced by the point in time analyzed. We selected key timepoints to illustrate these temporal differences (Fig. 5). During thered clover phase, particularly in spring prior to incorporation, largedifferences between systems occurred when the overwinteringperennial legume increased weed suppression, nitrate retention,N supply, beneficial insect conservation, and AMF colonization rel-ative to the system without cover crops (Fig. 5D). There were fewersystem differences in services provided during the wheat phase ofthe rotation, which was managed similarly in both systems, com-pared with other time points (Fig. 5C). The large difference inAMF colonization at the time of wheat harvest between CC andNoCC systems (Fig. 5C) reflects the lack of a living plant host inthe NoCC system relative to the CC system where the interseededred clover provided host continuity.

Managing for multiple ecosystem services requires an under-standing of temporal dynamics both for individual services andthe interactions between services and their environmental context.For example, this temporally informed framework could be appliedto predict and illustrate the potential impacts of increased springand fall extreme rainfall events predicted for the Mid-Atlantic re-gion due to climate change (Hayhoe et al., 2006). Our analysis heresuggests that cover crops provide multiple services during thesekey time periods, such as erosion prevention, nutrient retention,and soil C effects on water infiltration.

3.3. Trade-offs between ecosystem services and economic metrics

Our temporal analysis was also important for identifying trade-offs between ecosystem services and key economic metrics. Similar

to a recent review (Pilgrim et al., 2010), we found trade-offs pri-marily between economic metrics and ecosystem services andfew trade-offs among ecosystem services. While our simulationsdid not indicate crop yield trade-offs with cover crop production,this result is likely system and region dependent. For example,studies in drier climates have reported that soil moisture depletionby cover crops decreased the following cash crop yields in someyears (Unger and Vigil, 1998). An indirect trade-off occurred be-tween management risk related to terminating the cover cropwithout delaying cash crop planting and the multiple ecosystemservices provided by allowing sufficient spring cover crop growth.Similarly, planting the cover crop early enough in fall to allow suf-ficient biomass production to provide ecosystem services is anadditional management constraint that competes with crop har-vest and favors the use of shorter season crop varieties that mayhave lower yield potential. The relatively small window of oppor-tunity for seeding winter cover crops after corn and soybean areharvested and the additional labor, fuel, and seed expenses aretop farmer-identified constraints to cover crop adoption in the ma-jor grain growing regions of the U.S. (CTIC, 2010). Interseeding cov-er crops into standing corn or soybean crops using ground (Rothet al., 2011) and aerial methods can help overcome these con-straints by extending the cover crop growth window.

A potential trade-off that we identified between ecosystem ser-vices was between N supply benefits and denitrification risks. TheN fixation inputs of red clover eliminated the need for synthetic Nfertilizer in corn, but simulated denitrification losses were higherfollowing red clover incorporation than in the fertilized systemwithout cover crops. Cropping systems using legume-based N

20 M.E. Schipanski et al. / Agricultural Systems 125 (2014) 12–22

inputs generally have lower net greenhouse gas (GHG) emissionsthan cropping systems reliant on synthetic N fertilizer inputs dueto the GHG emissions associated with N fertilizer production (Jen-sen et al., 2011); however, high N2O emissions following the incor-poration of monoculture legume cover crops have been reportedby others (reviewed by Jensen et al., 2011). While losses of N2Oby nitrification from ammonia based fertilizers are low in well aer-ated soils (Engel et al., 2009), these losses can be as high or higherthan those by denitrification in wet conditions (Panek et al., 2000;Zhu et al., 2013), for instance in early spring when N is applied tocorn. Assessing this potential tradeoff requires a better under-standing of N2O emission dynamics.

Other trade-offs were linked to our management choices. Forexample, use of mechanical cultivation provided weed controland reduced the risk of insect pest damage, but increased erosionpotential and decreased beneficial insect activity and AMF coloni-zation potential. Alternatively, the use of herbicides rather thantillage for weed control could reduce erosion potential, but hasassociated environmental risks. For example, use of cover cropswould reduce selection pressure for herbicide resistant weedssince the frequency and amount of herbicides (or specific activeingredients) would be reduced (Winchester et al., 2009; Mortensenet al., 2012).

Trade-offs also occur when comparing functions provided byindividual cover crop species. We designed our crop-cover croprotation based on the perceived need for ecosystem services bythe following crop, reflecting realistic management scenarios.Therefore, our analysis did not show trade-offs in N retentionand N supply that would occur when comparing grass and legumecover crops (Tonitto et al., 2006). Trade-offs between plant speciesfunctions may be more relevant when investigating cover cropmixtures.

3.4. Limitations and knowledge gaps

One challenge in multifunctionality assessments is determininghow to weight or standardize variables for cross-function compar-isons. Our data normalization approaches helped visualize whetherservice estimates were greater or less for a given system using aconsistent scale across all services, but the magnitude of the differ-ences between systems is difficult to interpret. The relative differ-ences are dependent on the variability within each service and donot effectively reflect their functional significance. For example,the cumulative soil C differences are likely functionally importantfor drought tolerance and soil quality (Lotter et al., 2003). However,due to the normalization calculation used, the relative differencesbetween the CC and NoCC systems appear small (Fig. 1). In con-trast, the large differences estimated for AMF colonization wouldlikely have a modest impact on crop yields and nutrient cyclingdynamics.

In addition, we have a higher degree of uncertainty surroundingthe semi-quantitative estimates than the modeled quantitativeestimates. We are confident in the directionality of the relativecover crop effects (greater vs. less), but not the magnitude or itscascading effects on other services. These uncertainties highlightseveral knowledge gaps that limit our ability to manage for multi-ple ecosystem services and an unexpected benefit of this study wasthe degree to which it helped identify knowledge gaps in a numberof the ecosystem services considered herein. In particular, there isa lack of data or simulation models to estimate cover crop effectson services such as weed suppression, pest and beneficial insectactivity, and AMF inoculation potential. In part, this is due to thechallenging nature of generalizing population and communitydynamics of weeds or insects from individual studies. More re-search and synthesis is needed in these areas to inform generaliz-able theories of the impacts of management practices on

population dynamics and ecosystem services. In the case of insectdynamics, one promising approach is to directly measure the influ-ence of management practices on the provisioning of insect-med-iated services such as pollination (Kremen et al., 2002) andpredation of weed seeds (Davis and Liebman, 2003) or insect pests(Lundgren and Fergen, 2011). In the case of beneficial insectdynamics, research has focused on identifying key indicator spe-cies, such as carabid beetles, that are sensitive to managementpractices. However, as shown by the absence of a link betweenour estimated beneficial insect conservation and pest suppressionestimates, we still lack an ability to quantify the ecosystem func-tions of beneficial insect conservation (Letourneau et al., 2011).For some crops, it may be a key pest that drives the crop response.Corn rootworm mortality, for instance, increased by 80% whenslender wheatgrass (Elymus trachycaulus) was used as a cover cropin a corn production system (Lundgren and Fergen, 2011).

It is difficult to account for interactions among services, high-lighting another knowledge gap. Additional research is needed toimprove our understanding particularly of feedback mechanismsbetween services that may strengthen both short-term and long-er-term legacy effects of cover crops. For example, our estimatedyields did not account for differences in weed suppression orAMF colonization between systems. In addition, while cover cropsincreased provisioning of ecosystem services related to soil quality,there is a lack of long-term studies that link soil quality benefits tooverall cropping system productivity and resilience (Bennett et al.,2010).

3.5. Implications for management, research, and policy

There are currently few economic incentives for U.S. farmers toprioritize the multiple benefits of using cover crops. The few cost-sharing programs that have been developed in the U.S. focus onone or two ecosystem services—erosion control and nutrient reten-tion (MACS, 2009). Our approach provides a tool for quantitativelyand visually communicating the multiple benefits of cover crops tofarmers and policy makers.

While we applied our assessment framework to evaluate covercrop effects, the approach is system neutral and provides tool forvisualizing and integrating knowledge about complex systems.Our approach synthesized multiple services across disciplinesand could be applied to other systems and services. It could serveas an important educational tool that complements decision sup-port tools, which typically present a series of outcome options orscenarios based on scientific knowledge about a specific set of cov-er crop species, management practices, or cropping systems (McC-own, 2012). In particular, use of spider plots is an effectiveextension education tool for understanding complex systems (Gar-eau et al., 2010).

Our more quantitative approach could be adapted to analyze re-sults from field experiments or to identify priorities for manage-ment among land managers and policy makers. The commontradeoffs between economic metrics and ecosystem services high-light the need for more integrated ecological and economic model-ing to enable the analysis of ecosystem service impacts (Wätzoldet al., 2006). In addition, we selected two relatively simple man-agement systems for comparison. Integrated modeling approachescould be used to analyze ecosystem service impacts for a broaderrange of management scenarios.

While our approach was more quantitative, a less quantitativeassessment informed by farmer or other stakeholder knowledgeand experience could improve understanding and managementof system complexity. A less quantitative approach relying on moreinformal knowledge and experience could populate estimates for asuite of ecosystem services defined through dialogue with commu-nities of practitioners. For example, differences in farmer and

M.E. Schipanski et al. / Agricultural Systems 125 (2014) 12–22 21

researcher mental models of weed management practices high-light the importance of developing tools for understanding howland managers view and manage for complexity (Jabbour et al.,2013). Participatory processes could also be particularly helpfulin weighting the different services. While our analysis gave eachecosystem service equal weight, different stakeholder groups couldadapt this framework and weight services of interest as part of amulti-criteria assessment process.

4. Conclusion

By integrating a suite of ecosystem services into a unified ana-lytical framework, we highlighted the potential for cover crops toinfluence a wide array ecosystem services. We estimated that cov-er crops increased 8 of 11 ecosystem services. In addition, we dem-onstrated the importance of considering temporal dynamics whenassessing management system effects on ecosystem services.Trade-offs occurred between economic metrics and environmentalbenefits. Near-term economic hurdles to widespread cover cropadoption could be reduced if fertilizer prices increase or throughthe implementation of cost-sharing or incentive programs. In thelong-term, many of the ecosystem services that cover crops pro-vide may improve resilience with positive feedbacks to yield sta-bility, reduced external input requirements, and profitability.Assessment frameworks like the one presented here can improveunderstanding of the temporal dynamics of different services,guide future research, and inform policy to realize agricultural sys-tems that are economically, environmentally, and sociallysustainable.

Acknowledgements

We would like to thank J. Franklin Egan for helpful comments,Stephanie Bailey for her contributions, and anonymous reviewersfor their suggestions. This work was supported by funding fromUSDA National Institute of Food and Agriculture (#2011-51300-30638 and #2012-67012-19908).

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