Land use change in a biofuels hotspot: The case of Iowa, USA
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hbDepartment of Economics and Finance, EnerNC 27411, USAcCenter for Agricultural and Rural DevelopmdDepartment of Economics, Iowa State Univeof time has generated a relatively large and fast growing bodyof literature. The debate has mostly focused on the overallcarbon footprint of biofuels [1e5], because one of the ratio-nales of policies promoting biofuels is that they reduceincluded other environmental indicators, such as effects onspatially explicit analyses of the environmental impacts,mostly because of the complexity and the integrated nature ofthe modeling efforts necessary. Such an analysis, however, isvital if we want to understand whether there are* Corresponding author. Tel.: 1 618 453 1714.E-mail addresses: email@example.com (S. Secchi), firstname.lastname@example.org (L. Kurkalova), email@example.com (P.W. Gassman),Avai lab le a t www.sc iencedi rec t .com.cob i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 2 3 9 1e2 4 0 firstname.lastname@example.org (C. Hart).The environmental impacts of the large scale production ofbiofuels are a topic of vigorous debate, which in a short periodnitrogen, phosphorus, and pesticide use (see for example[1,2,4]). Until recently, most of the literature did not includemargin, while nitrogen losses increase less. Returning CRP land into production hasa vastly disproportionate environmental impact, as non-cropped land shows much highernegative marginal environmental effects when brought back to row crop production. Thisillustrates the importance of differentiating between the intensive and extensive marginwhen assessing the expansion of biofuel production. 2010 Elsevier Ltd. All rights reserved.1. Introduction greenhouse gas emissions. However, several studies have alsoa r t i c l e i n f oArticle history:Received 11 July 2008Received in revised form12 August 2010Accepted 18 August 2010Available online 26 September 2010Keywords:Land use changeEconomic analysisEnvironmental ImpactEnergy crop productionCorn-soybean rotationLand set-aside0961-9534/$ e see front matter 2010 Elsevdoi:10.1016/j.biombioe.2010.08.047gy and Environmental Systems program, North Carolina A&T State University, Greensboro,ent, Iowa State University, 578 Heady Hall, Ames, IA 50011-1070, USArsity, 260 Heady Hall, Ames, IA 50011-1070, USAa b s t r a c tThis study looks at the land use impact of the biofuels expansion on both the intensive andextensive margin, and its environmental consequences. We link economic, geographicaland environmental models by using spatially explicit common units of analysis and useremote sensing crop cover maps and digitized soils data as inputs. Land use changes arepredicted via economic analysis of crop rotation choice and tillage under alternative cropprices, and the Environmental Policy Integrated Climate (EPIC) model is used to predictcorresponding environmental impacts. The study focuses on Iowa, which is the leadingbiofuels hotspot in the U.S. due to intensive corn production and the high concentration ofethanol plants that comprise 28% of total U.S. production. We consider the impact of thebiofuels industry both on current cropland and on land in the Conservation ReserveProgram (CRP), a land set-aside program. We find that substantial shifts in rotationsfavoring continuous corn rotations are likely if high corn prices are sustained. This isconsistent with larger scale analyses which show a shift of the current soybean productionout of the Corn Belt. We find that sediment losses increase substantially on the intensiveaDepartment of Agribusiness EconomiCarbondale, IL 62901, USAthern Illinois University, Agriculture Building e Mailcode 4410, 1205 Lincoln Drive,cs, SouSilvia Secchi a,*, Lyubov Kurkalova b, Philip W. Gassman c, Chad Hart dLand use change in a biofuelsht tp : / /www.e lsev ierier Ltd. All rights reservedotspot: The case of Iowa, USAm/loca te /b iombioe.induced by the bioenergy expansion are likely to affect cropb i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 2 3 9 1e2 4 0 02392disproportional effects in some areas, and to help directpolicy. Some recent papers have incorporated carbon effectsat a global scale [6,7] by linking large scale economic andenvironmental models, again with a carbon focus. This papertakes a regional scale approach, closer to ones used by geog-raphers and land use scholars (see for example [8e10]). Ourmodeling approach consists of a field-level discrete economicmodel of crop rotation and land use management thatpredicts the impacts of alternative policy scenarios on landuse shifts, which in turn are input into a field-level environ-mental model to estimate environmental implications, suchas erosion, nutrient losses and carbon sequestration in thesoil. The economic and environmental models are interfacedusing common geographical units of analysis. Because of theregional scale at which the models operate, the price of cropsand energy sources are given. This bottom-up approach iscomplementary to large, top down modeling efforts thatpredict global land use and environmental impacts at a coarseresolution.The study focuses on the Midwestern U.S. state of Iowa,which is the leading biofuel hotspot in the U.S. due to inten-sive corn production and the high concentration of ethanolplants that comprise over 25% of total U.S. production. Rapidshifts in crop production are occurring in the state due toincreasing demand for ethanol production. Between 2006 and2007, corn production in Iowa increased by 26%, bringing thecurrent Iowa corn crop to 19% of total U.S. production.Iowa is a very intensively managed agricultural state. Thegreatmajority of the land is in the private sector, and it is usedfor row crop production. Nevertheless, the state has aroundthree quarter of a million hectares in the ConservationReserve Program (CRP), a land set aside program instituted bythe U.S. federal government. We consider two types of landuse changes: 1) on currently cropped land, because croplandcould be farmedmore intensively to produce more feedstocksfor biofuel production e specifically, in the case of the U.S.,corn; and 2) on land currently out of agricultural production(CRP). We refer to the first type of change as a change on theintensivemargin, because increases in corn production in thiscase would not increase the land allocated to crop production,but would just increase the production of corn per acre, and tothe second as the extensivemargin, because CRP land is in setaside and using it for crop production again would increasethe area used for agricultural production.It is important to include both types of effects separatelybecause, while the initial goal of the CRP program in 1985 wasto reduce crop supply, in more recent years land has beentargeted by the program for its environmental vulnerability.Indeed, the analysis on the beneficial effects of CRP on theenvironment has been wide-ranging, including studies thathave calculated the effects of CRP on sediment losses ,carbon sequestration , and bird populations [13e15]. Whileprevious studies have relied on the Natural Resources Inven-tory database  and remote sensing imagery [16,17] todetermine the location of CRP land, we have used the actualConservation Reserve Program parcel data obtained from theFarm Service Agency.Previous work  has also detailed the likely impact of thebiofuel industrye via high crop pricese on thismarginal land,and the impact on the CRP program. These estimates suggestand rotation choices and, indirectly, tillage choices. Somerecent studies have started to assess the impacts that anincrease in corn production could have on surface waterquality in the U.S. Donner and Kucharik  assess suchimpacts for the whole MississippieAtchafalaya River Basinusing spatially explicit modeling at a county-level scale. Themodels are not driven by economics, and there are no differ-ential impacts of CRP and current cropland. Simpson et al. differentiate between CRP and current cropland, but do notuse economic analysis to assess how production will respondto prices, and their study does not include a spatially explicitanalysis. However, all these elements e the responsiveness toprices, the differentiation of types of land use, and thespatially explicit analysis, are important to policymakers whoneed to assess the environmental impacts of biofuels andwhodevise agricultural, conservation and energy policy. Ourcontribution to the literature is to simultaneously include allthese considerations in the analysis.The paper is structured as followed. First, in the materialsand method section, we detail the datasets used to constructa baseline land use for Iowa and baseline environmentalindicators. We then construct an economic model that isbased on production costs by tillage and crop rotation and useforecast prices to predict future land use scenarios. We look atthree price-based scenarios, representing a range of futuremarket conditions. The land use change maps of thesesimulations and their environmental impacts are presented inthe results section. We conclude with a discussion on thesignificance of our findings, both for Iowa agriculture andwithin a larger context.2. Materials and methods2.1. Land useTo construct the baseline land use for Iowa, we use U.S.Department of Agriculture (USDA) National AgriculturalStatistics Service (NASS) remote sensing crop cover maps .These cropland data layers are published yearly to estimatecrop areas and yields.We combine five years of data, 2002e06,to construct historical rotations. The remote sensing croplanddata was used to estimate rotations, using a slightly differentmethodology, in , for the years 2001e07, and comparingthat the impact of the ethanol industry could mean that halfthe CRP land in the state is returned to row crop production inthe near future. This land was enrolled in the programbecause of factors such as high erodibility and impact onwater quality and wildlife. Thus, returning it to agriculturalproduction has disproportionately high per hectare effects onsoil erosion and nutrient losses. However, because the greatmajority of the land in Iowa is already used for crop produc-tion, the largest impact of the biofuel expansion in absolutenumbers is likely to be on the intensive margin, on alreadycultivated cropland. In particular, in Iowa, farmers havehistorically tended to plant corn after soybeans in two yearrotations as detailed by Fig. 1. The changes in crop pricestwo consecutive years at the time. The data contains someerrors in the coverage and cloud cover obstructed view ina..9%crob i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 2 3 9 1e2 4 0 0 2393some years. In particular, the GIS cover tends to overestimatethe amount of continuous soybeans. This is due to the factthat the coverage contains a 5e15% error on cropped fields. The same error is likely to occur for corn. However, in thatcase, it is not as easy to determinewhether an error is present,since continuous corn is a viable crop choice. There isa widespread consensus that continuous soybeans are nota common occurrence in Iowa  and the literature confirmsthis assessment, as two or more years of continuous soybeancreate serious problemswith soybean nematodese HeteroderaFig. 1 e Location of 2002e06 crop rotations and CRP land in Iowshow its location. CRP areas in Iowa correspond to around 7hectares of CRP land versus almost 92,000 km2of cultivatedglycines . Therefore, we created algorithms to re-constructsome of the rotations. Specifically, a three year soybeansequence is converted to soybean-corn-soybean. As notedbefore, the CRP land coverage was obtained from the USDAFarm Service Agency. Fig. 1 illustrates the rotations and thelocation of the CRP land. Tillage choices are an importantdeterminant of environmental quality in cropped land, andtillage choice, because of its impact on yields, is linked to thechoice of rotation [25,26]. Unfortunately, there are no recenttillage data by crop rotation. The Conservation TechnologyInformation Center (CTIC) maintains a county-level databaseof tillage practices, the Crop ResidueManagement Survey ,but the database does not identify the previous crop. TheUSDA Agricultural Resource Management Survey (ARMS)includes information on tillage and crop rotations. However,the survey has a very small sample size and is of limited valueto our analysis . For example, in 2005, information ontillage practices was not available for about half of the cornarea, and the estimate of no till acres is statistically unreliabledue to the combination of a low sample size and highsampling error.Since 1985, farmers wanting to plant crops on highlyerodible land have been required to engage in conservationactivities to participate in farm programs. In Iowa, the mostcommon conservation practice on highly erodible land (HEL)is the use of no-till . Therefore, for simplicity, we assumethat in our baseline all highly erodible land is under no-till,while the rest of the cropped acres are under mulch till. Thismeans that around 23% of the area planted in corn in 2006 -corresponding to the HEL area e will be simulated as being inno-till. A similar percentage area, 25%, is simulated as no-tillsoybean. These compare to 14% and 33%, respectively, fromthe CTIC survey in the same year, the last one available .Note that the CTIC data estimate that almost 60% of the cornand 19% of the soybean are not in any reduced tillagemanagement. Thus, our baseline is likely to be quite conser-Note: the CRP area is enlarged and not true to scale to betterof cultivated cropland (almost three quarters of a millionpland).vative in terms of the environmental impact of current landuse. The location of the highly erodible land was determinedby overlaying the rotationmap on the Iowa Soil Properties AndInterpretations Database (ISPAID) map .2.2. Land management practicesFertilizer application rates are another important determinantof the environmental impact of agriculture. Fertilizer ratesvary across crop rotations and spatially. In fact, the produc-tion technologies that farmers use are best seen as bundles. In particular, there is ample evidence that rotated corngenerates yield benefits over continuous corn, because rota-tions offer a good defense against some pests  and croprotations that include legumes improve soil nitrogen levels. Specifically, there is field-level evidence that corn yieldsunder a corn-soybean rotation exceed yields under contin-uous corn . Similar results have been found for corn-soybean and corn-corn-soybeans rotations . However,some of these yield effects can be at least partially counteredby increasing nitrogen applications . Therefore, contin-uous corn tends to be more heavily fertilized than corn ina two-year rotation with soybeans. Because the dominantrotation has historically been corn-soybean, there are con-flicting data on the size of the increase in nitrogen applicationrates necessary to maintain yields for continuous corn. Theedb i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 2 3 9 1e2 4 0 02394latest ARMS survey, conducted in 2005, reports nitrogen ratesof 165.9 kg ha1 for continuous corn and 156.9 kg ha1 for cornafter soybeans in Iowa. The latest fertilizer sales data for thestate, going back to 2003, which of course cannot be used toFig. 2 e Potential corn yields Note: the CRP area is enlargdifferentiate across rotations, indicates an average nitrogenapplication rate of 145.7 kg ha1 on corn . Agronomic fieldtrials suggest application rates of 202.9 kg ha1 for continuouscorn and 138.9 kg ha1 for corn after soybeans in Iowa .Finally, a recent Iowa extension publication indicates a rate of207.4 kg ha1 for continuous corn and 179.3 kg ha1 for cornafter soybeans in Iowa . In our analysis, we will assumethat corn after soybeans receives 156.9 kg ha1 and corn aftercorn is fertilized with 213 kg ha1. This translates intoconservative estimates of the impact of the added nitrogen,because the baseline nitrogen level is higher than the agro-nomically necessary rate and reflects the reality of someinsurance nitrogen use, while the corn on corn nitrogenlevel is close to the agronomical optimum. The empiricalevidence of this behavior is supported by theoretical work.Uncertainty on the nitrogen soil concentration at the time offertilizer application and uncertainty on the amount of rain-fall expected may increase nitrogen applications over theeconomically optimal level of nitrogen applied .As we noted above, the literature is unanimous on thepositiveyieldbenefitsof rotatedcornover continuous cornandon the differential impact of tillage. Long-term trends on cornand soybean yields have been studied for plow, chisel, ridgeand no-till systems, and continuous corn and corn-soybeanrotations.Bothcornandsoybeanyieldswere10e11%higheronaverage in the non-monoculture rotations across all tillageregimes, but there were differences across tillage regimes .In particular, corn yield reductions in continuous corn weremuchhigher in no-till corn (17%) than in plowed corn (5%). Thereason for this is that corn residue from no-till regimes cancreate problems for germinating and emerging plants. Othershave estimated a 14%decrease in continuous corn yields goingfrom a plow to a no-till system, while in a corn-soybean rota-and not true to scale to better show the yield potential.tion the reduction in corn yields going from a plow to a no-tillsystemwas 5% . Moreover, corn yields are not significantlyaffected by the tillage system of the previous years crop if thecrop is soybean. If the crop is corn, however, yields are highestunder conventional tillage and decrease as the tillage typebecome less intensive . Finally, there is also someevidence,based on field work, that two years of corn between soybeancrops boost the soybean crop yield .On the basis of the literature reviewed above, we haveassumed in our calculation of the optimal rotation and tillagesystem that yields decrease from their maximum potentialunder certain combinations of crop, previous crop, and tillage.Computationally, this reduction in yields means that wemultiply current year yields by a multiplier less than one, e.g.,a 10% reduction means multiplication by 0.9. In particular, weassume that corn after no-till corn will see a reduction of 15%,corn after mulch till corn will yield 6% less and corn afterconventional till corn will yield 5% less than the potentialyield. Soybeans after no-till corn suffer a 6% yield drag, andsoybeans after two years of corn receive a 4% boost. Thepotential yield information is derived from an index of suit-ability for crop production, the Corn Suitability Rating (CSR),with a methodology described in  (Fig. 2).2.3. Economic modelWe use data from Iowa extension budget publications  toconstruct costs of production budgets. The budgets include allthe costs of producing crops, from seed to fertilizer applicationand harvesting, and are differentiated by crop and previouscrops. The creation of these budgets, togetherwith the scenarioprices and the yields, allows us to identify the most profitablecrop and rotation choices for a given set of crop prices.The net returns are used to construct a crop productioncost model. We assume in our analysis that profit maximi-zation is the driving force in farmers behavior  and forsimplicity abstract from the impact that the choice ofproduction technology has on the variance of production.Farmers optimize by choosing the crop rotation and tillagesystem that maximizes their net returns. The crop rotationbudgets by tillage practice take into account some of thebundled nature of the choices farmers make. Specifically, weconsider three crop rotations, continuous corn, corn-soybean and corn-corn-soybean, which account for the greatmajority of crop production in the state (Fig. 1). We alsoconsider three tillage systems: conventional till, mulch till(grouped together with ridge till), and no-till , for a totalof 42 possible choices of combined crop choice, rotations andtillage. The model is static, as we solve for the combinationof rotation and tillage that gives the maximum net returnsfor a given set of prices. We abstract in this analysis fromconservation compliance considerations because the futureof such measures is unclear at this point. On the intensive2.4. Environmental modelThe land use data and the land use choice and managementscenarios are combined with digitized soil layer maps toconduct the environmental modeling. We use the USDANatural Resources Conservation Service (NRCS) Soil SurveySpatial and Tabular Data (SSURGO 2.2). SSURGOs mappingscales generally range from 1:12,000 to 1:63,360. SSURGO is themost detailed level of soil mapping done by NRCS. TheSSURGO database has 10,637 unique soils for Iowa . Thisfine grained information allows us to produce very spatiallydetailed maps.The SSURGO soil database is used as the input to theEnvironmental Policy Integrated Climate (EPIC) model [46,47],which was originally called the Erosion Productivity ImpactCalculator and has been applied for a wide range of conditionsworldwide . EPIC is a field-scale model that is designed tosimulate edge-of-field environmental impacts for drainageareas that are characterized by homogeneous weather, soil,landscape, crop rotation, and management system parame-ters. The model operates on a continuous basis using a dailytime step and can perform long-term simulations of hundredsof years. A wide range of crop rotations, tillage systems, andother management practices can be simulated with EPIC. Theb i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 2 3 9 1e2 4 0 0 2395margin, we assume that farmers will keep the land inproduction and will choose the rotation and tillage systemthat maximize their profits. For the extensive margin, we useUSDA Soil Rental Rates as the opportunity cost of landretirement as described in . Thus, returns from farmingmust be at least as high as the rental rates for farmers toreturn the CRP land to production. This is in line withprevious work that has tied CRP enrollment choices to landquality and anticipated economic returns to alternative usessuch as cropping .Fig. 3 e Projected rotations on the intensive and emost recent versions of EPIC feature improved soil carboncycling routines that are based on routines used in the Centurymodel [47,49]. EPIC provides edge-of-field estimates of soilerosion, nutrient loss, carbon sequestration, and other envi-ronmental indicators. Geographically appropriate manage-ment practices such as irrigation, fertilizer rates and tillageregimes can be fed into the EPIC model. The EPIC model is runusing historical weather data for 30 years, and the results arereported as the 30 year average for the sediment and nutrientlosses, and as the final carbon pool for carbon.xtensive margin at corn prices of 167 $ MgL1.2.5. Scenario developmentWe focus the discussion on three price-based scenarios forour analysis, with corn prices of 108 $ Mg1, 142 $ Mg1, and167 $ Mg1. Soybean prices tend to move in unison with cornprices, both historically and in projected large scale analysis, so we assume a constant difference of $3.50 per bushel, oraround $120 per Mg between the two prices. Themarket placedifference is based on volumes, and since corn is 25.4 kg per1996e2006 historical prices, the profit maximizing rotationwas corn-soybean undermulch till. Thus, the budgets and ourb i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 2 3 9 1e2 4 0 02396baseline (Fig. 3) tillage assumptions represent historicaltrends well.We estimate that, in 2006, cropland in Iowa was farmedaccording to the rotations quantified in Table 1. This corre-sponds to a corn crop area for 2006 of 50,152 km2, which wasvery close to the corresponding NASS estimate of 50,990 km2,of which 49,979 km2 were harvested for grain . Our esti-mate of corn production for 2006 is 49.886 Mt, which is 4%lower than the NASS estimate for 2006. With crop prices of142 $ Mg1 for corn and 254 $ Mg1 for soybeans, whichcorrespond roughly to the crop prices in the spring of 2007, ourmodel predicts corn production of 59.963Mt. This is 94% of theNASS estimate for 2007. Thus, it appears that our frameworkmay somewhat underestimate the production of corn.The changes in rotations described in Table 2 and shown inFig. 3 are directly linked to land productivity. For the mostTable 1 e Percentage of cropped land in Iowa by2002e2006 rotations.Percent area Rotation69.94 Corn-soybean2.78 Continuous corn2.12 2 years of continuous soybean14.06 Corn-corn-soybeanbushel, and soybeans are 27.2 kg per bushel, the actualdifference will change with increasing corn prices.The three corn prices are derived from large scale partialequilibrium trade models, and they represent a spectrumranging from lower than expected ethanol industry expan-sion, a current baseline long term price projection, and an oilindustry price shock . More in general, however, ourframework can be used to assess the land use impacts andcosts of price-based policies, such as subsidies, energy pricechanges, and changes in technology platform profitability.Such chances in relative pricesmodify the farmers net returnsand consequently the profitmaximizing crop choices and landuse maps. The framework can also be used to assess theimpacts and costs of other types of policies, such as regulationon crops to be grown in floodplains, within a particulardistance from waterways or along migratory bird routes.3. ResultsAccording to our analysis, for corn prices of 85 $ Mg1, andsoybean prices of 208 $ Mg1, which correspond to average11.10 3 years of continuous cornproductive land it becomes increasingly profitable to movetowards more corn production as corn prices increase. Thisholds true even with increasing yield losses and the addedcost of fertilizer, because corn becomes relatively more prof-itable than soybeans for highly productive land. As corn pricesincrease, the break-even level of potential corn yields thatinsure higher profits from more corn-intensive rotationsdecreases. Thus, even less productive land shifts first to corn-corn-soybean rotations and, as corn prices increase, more andmore of that marginal cropland is planted with continuouscorn. The shifts in rotations are tied to the shifts in tillage.As corn prices exceed 118 $Mg1, themore productive landstarts to shift into three year rotations. The tillage regimes areconventional tillage on the first year of corn, tomaximize cornyields, and mulch till on the second year of corn and onsoybeans, because there are no yield penalties. When cornprices reach 136 $ Mg1, the most productive land startsshifting into continuous corn production. Because theconservation tillage regimes have higher yields penalties,continuous corn profits are always maximized usingconventional tillage. Once corn prices climb above 142 $ Mg1,marginal land begins shifting from a two-year rotation intothree-year rotations with conventional tillage on the first yearof corn, again to maximize corn yields, and no till on thesecond year of corn to capture the cost savings of no-till.Continuing corn price increases result in an initial shift ofmarginal land into a three-year rotation with conventionaltillage on the first year of corn and mulch till on the secondyear of corn, and then ultimately into continuous corn. TheCRP land follows a similar pattern except that the three yearrotation that is most profitable is always coupled withconventional tillage on the first year of corn and mulch till onthe second year of corn. Thus, the tillage implications of theshifts towardsmore corn production are not as clear cut as thenitrogen implications described next.The changes in crop rotations as a function of corn pricesare directly reflected in changes in nitrogen fertilizer appli-cation. Combining our historical rotations and the nitrogenrates discussed above, we estimate an historical use of around814 kt of nitrogen per year, which is 17% higher than 2003estimates of nitrogen use for corn in Iowa . Our lowest cornprice scenario results in lower nitrogen applied, totaling over743 kt. However, as three year rotations become more preva-lent, nitrogen use increases. For the intermediate corn pricescenario, the increase is approximately 35% over our baseline,and nitrogen use more than doubles in comparison with thebaseline for the very high corn price scenario.Table 3 details the impact of the land use changes on fourimportant indicators: sediment losses, nitrogen losses, phos-phorous and carbon. For sediment losses, we report the sedi-ment loss fromwater erosionusing theModifiedUniversal SoilLoss Equation (MUSLE) option provided in EPIC [46,48]. Fornitrogen losses, we report the sum of nitrate losses in runoff,subsurface flow, and leaching, and the loss of nitrogen insediment. We also report total phosphorous losses and thefinal carbon pool at the end of 30-year EPIC runs. Five countiesare omitted from the tabulated totals, because SSURGO datahas not yet been compiled for them. This likely causes anunderestimate of the impact since these counties are in thesouthernpart of the statewheremore fragile lands are located.For example, general and partial equilibrium trade modelsTable 2 e Historical and projected land use on the basis of corn prices.Rotation area Historic baseline Corn price 108 $ Mg1 Corn price 142 $ Mg1 Corn price 167 $ Mg1Intensive margin e current cropland (km2)Corn-soybean 64,389 92,066 38,618 10,717Corn-corn-soybean 12,944 0 42,784 13,974Continuous corn 2556 0 10,664 67,375Extensive margin e current CRP land (km2)CRP 7087 4189 2492 2027Corn-soybean 0 2898 2952 1050Corn-corn-soybean 0 0 1501 1561Continuous corn 0 0 142 2449b i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 2 3 9 1e2 4 0 0 2397All edge-of-field indicators worsen. Sediment lossesincrease substantially both on the intensive and extensivemargins because of the higher incidence of conventionaltillage and continuous corn as corn prices increase. Nitrogenlosses increase, becausemore continuous corn and three yearrotations including two corn years require higher levels ofnitrogen fertilizer. Phosphorous losses increase as well. Thereason for this is the higher tillage intensity associated withmore corn production e at corn prices of 108 $ Mg1, 85.30% ofthe phosphorus losses are attached to sediment, and at cornprices of 167 $ Mg1, that percentage has increased to 93.29%.Carbon sequestration levels in the soil also decrease as cornprices increase. On currently cropped land, the decrease is lessthan 5% on a per hectare basis from the baseline to the highestcorn price simulated. On the CRP land, on the other hand,there is a 13% decrease in per hectare carbon from the base-line to the highest corn price simulated.This much higher marginal carbon sequestration impacton CRP illustrates a general point. As cropland in Iowa isalready intensively farmed, the impact of increased cornplanting on environmental burdens is more obvious on theextensive margin. Table 4 shows the per unit area impact offarming on both CRP and current cropland, and how returningCRP land into production has a vastly disproportionate envi-ronmental impact, as non-cropped land shows much highernegative marginal environmental effects when brought backto row crop production. This illustrates the importance ofdifferentiating between the intensive and extensive marginwhen assessing the expansion of biofuel production.At high corn prices, forcing highly erodible land to no-tillregimes through programs like conservation compliancewould have a sizable impact on the way land currently in CRPTable 3 e Historical and projected environmental indicators onHistoric baseline Corn price 108 $Intensive margin e current croplandSediment losses (Mg) 20,041,671 36,413,102Nitrogen losses (Mg) 545,136 609,732Phosphorus losses (Mg) 19,120 28,020Soil carbon (Mg) 2,020,321,455 1,978,707,457Extensive margin e current CRP landSediment losses (Mg) 1,023,826 1,646,552Nitrogen losses (Mg) 6794 20,861Phosphorus losses (Mg) 533 1167Soil carbon (Mg) 120,400,225 113,303,996only give a large scale geographical determination of the landuse change impacts of biofuels (see for example [7,50,51]). Ourintegrated approach uses as inputs price projections obtainedfrom world-level models, and determines spatially explicitenvironmental impacts by soil type at a very fine geographicalscale. Our state level land use projections can also be recon-ciled with those of world-level models. The fine detail of theanalysis serves several purposes: Because we have a very rich set of data and distributions,this work can help bracket some of the estimates of envi-ronmental impacts and can provide estimates of theirwould be cropped, rather than on whether it stays out ofproduction. The reason is that for high crop prices, the returnsfrom farming e even if the types of tillage and consequentlyrotations are somewhat restricted e are higher than the soilrental rates paid by the CRP program. Intuitively, the samegeneral observation for CRP land holds if there are relativechanges in the price of soybeans. On both the intensive andextensive margin there is a high sensitivity of the rotations tothe relative profitability of soybeans. Table 5 shows theacreage responses to increases in soybean prices (eachequivalent to 9.19 $ Mg1) for given high corn prices.4. DiscussionThe analysis we conduct is very data intensive, but it isessential in order to assess the economic and environmentalramifications of biofuel production in hotspots such as Iowaand to ground truth and complement larger scale modeling.the basis of corn prices.Mg1 Corn price 142 $ Mg1 Corn price 167 $ Mg145,601,316 64,457,979741,057 934,46431,680 34,5461,956,628,896 1,935,990,1153,579,939 6,633,44839,096 57,4282175 2862108,297,841 104,843,115Table 4 e Historical and projected environmental indicators.Historic baseline Corn price 108 $ Mg1 Corn price 142 $ Mg1 Corn price 167 $ Mg1Intensive margin e current croplandHectares cropped (ha) 8,845,375 8,845,375 8,845,375 8,845,375Sediment losses (Mg ha1) 2.27 4.12 5.16 7.29Nitrogen losses (kg ha1) 61.63 68.93 83.78 105.64Phosphorus losses (kg ha1) 2.16 3.17 3.58 3.91Soil carbon (Mg ha1) 228.40 223.70 221.20 218.87Extensive margin e current CRP landHectares cropped (ha) 0 263,703 421,103 462,026Sediment losses (Mg ha1) 1.65 2.66 5.78 10.71Nitrogen losses (kg ha1) 10.97 33.68 63.13 92.73Phosphorus losses (kg ha1) 0.86 1.88 3.51 4.621 5ssfub i om a s s an d b i o e n e r g y 3 5 ( 2 0 1 1 ) 2 3 9 1e2 4 0 02398distributions, which can reduce uncertainty and can be usedto enrich Life Cycle Analysis (LCAs), for example by illus-trating the variability of marginal environmental impacts. Itis difficult to identify the additional crops grown for energyproduction, and where they are grown, which determinestheir additional (or marginal) environmental impact. Esti-mating such marginal impacts is important in correctlyquantifying the real impact of the biofuels expansion, but italso requires data not available at a very large scale . Ourapproach allows the estimation of some of the impacts,such as changes in carbon in the soil. Moreover, as biofuelLCAs have tended to focus on energy and carbon assess-ment, a standard methodology to study natural resourcedepletion is still lacking . Our framework can providevaluable information in this area. Our research quantifiessoil erosion, can help assess long term soil fertility, and canbe used as a input for biodiversity analysis, which have allbeen identified as good indicators for the environmentalassessment of land use in LCAs . As the analysis is spatially explicit and GIS based, it canidentify sub-regions of particular interest which can bemissed by world-level models. This can be helpful forpurposes such as planning in the hotspot regions where theanalysis is performed. In particular, the projected land useSoil carbon (Mg ha ) 194.41 182.9The CRP results apply to all CRP soils for which EPIC runs were succechanges can be used to identify likely sites where theconstruction or expansion of biorefineries will occur. Theinformation on biomass availability could also be used, forTable 5 e Sensitivity to soybean prices with corn prices equalRotation area285 $ Mg1 294 $ Mg1 303Intensive margin e current cropland (km2)Corn-soybean 10,717 14,253 17Corn-corn-soybean 13,974 19,133 22Continuous corn 67,375 58,680 51Extensive margin e current CRP land (km2)CRP 2931 2906 28Corn-soybean 233 462 57Corn-corn-soybean 3167 3325 36Continuous corn 1337 973 59example, to plan for second generation lignocellulosic bio-fuel plants thatmight use corn stover as a feedstock. Finally,our analysis identifies the opportunity cost that would haveto be met by second generation biofuel crops. For example,planting switchgrass under a certain market conditionscenario for corn and soybeans would require returns atleast equal to those obtained from planting these crops or,for land currently retired from production, it would requirereturns at least as high as those received from land retire-ment programs. Besides the information directly derivedfrom the analysis presented above, our analysis can be usedas input in other studies of land use change assessments.The maps can be overlaid with information on wildlifehabitat to estimate some of the impacts of the industry onbiodiversity. They can also be used as inputs for bothgroundwater and surface water modeling to analyze likelywater quality and quantity impacts. This is going to be ofparticular importance in hotspots where the production ofbiofuel crops requires irrigation, such as the Great Plainsarea in the US.The main limitation of our work is that, because of itsbottom up approach, it takes world prices as given. However,given the amount of corn produced in Iowa, it is possible that174.87 169.29l, or 619,320 ha.world prices could indeed show some responsiveness to largechanges in Iowa corn acreage. We are currently extending theanalysis by linking ourmodel to a large crop productionmodelto 168 $ MgL1.Soybean price$ Mg1 312 $ Mg1 322 $ Mg1 331 $ Mg1,737 20,270 24,691 29,748,553 32,115 41,257 51,654,777 39,681 26,119 10,66448 2828 2809 27457 885 1146 133246 3663 3642 35866 291 70 3a production capacity of 5.73 Mt according to the Renewablewetlands restored through the conservation reserveenhancement program. J Wildl Manage 2008;72(3):654e64. Park S, Egbert SL. Remote sensing-measured impacts of theb i om a s s a n d b i o e n e r g y 3 5 ( 2 0 1 1 ) 2 3 9 1e2 4 0 0 2399Fuels Association (RFA) . Another 5.22 Mt are plannedaccording to the latest RFA estimates. Given an ethanolconversion factor of 0.33 g kg1 of corn, the current productionrequires around 17.8 Mt of corn. An additional 16.1 Mt of cornwould be required to meet currently planned additionalethanol production capacity. The requirement of corn to meetcurrent production demand is equivalent to more than 35% ofthe 2006 estimated production from our model, and over 27%of Iowas 2007 production estimate of 64Mt fromNASS. To putthese numbers in perspective, the Iowa Corn Growers Asso-ciation estimated that for the 2005/2006 Iowa corn crop 11 Mtwere used for ethanol production and 14 Mt were fed to Iowalivestock.At current yields, we estimate that if all the currentlycropped area (almost 92,000 km2) were converted intocontinuous corn, Iowa could produce 88 Mt of corn. We esti-mate that the conversion of all cropland to continuous cornwould require sustained corn prices of over 197 $ Mg1. Weestimate that, on the extensive margin, these corn priceswould bring into production over half a million hectares ofCRP land, with corn production levels of 4.6 Mt on the exten-sive margin. Thus, even for very high corn prices theproduction on the extensive margin would equal only 5% ofthe total. This type of result is likely to be replicated in otherintensely cropped areas, while the extensive margin is likelyto play a much more important role in providing biofuelfeedstock for more marginal, less productive lands or in areasof the world where less intensely managed landscapes exist.These are stark results, showing massive scale changes inthe production of corn in Iowa. They illustrate why Iowa issuch a hotspot for biofuel production. These results are in linewith large scale trade models which forecast that high cornprices will severely impact soybean acreage in the US, andthat Iowa is likely to remain a net corn exporter, even with itslarge livestock industry . The results are also consistentwith a previous study on the environmental impact of cornexpansion in the whole Mississippi River Basin .AcknowledgementsWe would like to thank all the participants to the BiofuelAssessment Conference in Copenhagen, Denmark, June 4e5,2007, and particularly Henrik Wenzel, for helpful discus-sions on the role of intensive and extensive margin in landuse impacts. This research was made possible in part byUSDA-CSREES grants 2005-51130-02366 and 2009-10002-to assess the size of this effect. The environmental impactsare based on past climate data. Structural changes in precip-itation patterns, for example, could affect the results. Since allthese cropping systems are well known, the lack of consid-eration of risk factors is unlikely to bias the results. 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