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Dec 1, 2016 - AND WILLIAM PARTON. 2. 1The Nature Conservancy ...... America Journal 53:800–805. Copper, C., L. Larson, A. Dayer, R. Stedman, and.

Potential carbon dioxide emission reductions from avoided grassland conversion in the northern Great Plains MARISSA AHLERING,1,  JOSEPH FARGIONE,1 AND WILLIAM PARTON2 1

The Nature Conservancy, 1101 West River Parkway, Suite 200, Minneapolis, Minnesota 55415 USA Natural Resource Ecology Laboratory, Colorado State University, NESB B233, Fort Collins, Colorado 80523 USA

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Citation: Ahlering, M., J. Fargione, and W. Parton. 2016. Potential carbon dioxide emission reductions from avoided grassland conversion in the northern Great Plains. Ecosphere 7(12):e01625. 10.1002/ecs2.1625

Abstract. Protection of lands threatened with conversion to agriculture can reduce carbon emissions. Until recently, most climate change mitigation incentive programs for avoided conversion have focused on forested ecosystems. We applied the Avoided Conversion of Grasslands and Shrublands v.1.0 (ACoGS) methodology now available through the American Carbon Registry to a threatened region of grasslands in the northern Great Plains. For all soil types across 14 counties in North and South Dakota, we used the DAYCENT model calibrated to the study area to quantify the difference in CO2 and N2O emissions under a cropping and a protection scenario, and we used formulas in the ACoGS methodology to calculate CH4 emissions from enteric fermentation under the protection scenario. We mapped the resulting GHG emissions across the entire project area. Emissions averaged 51.6 tCO2e/ha over 20 years, and with a 31% reduction for leakage and uncertainty from the ACoGS methodology, carbon offsets averaged 35.6 tCO2e/ ha over 20 years. Protection of 10% of the 2.1 million unprotected ha in the project area with the highest emissions would reduce emissions by 11.7 million tCO2e over 20 years (11% of the total emissions from all unprotected grassland) and avoid a social cost of $430 million worth of CO2 emissions. These results suggest that carbon offsets generated from avoided conversion of grasslands can meaningfully contribute to climate mitigation and grassland conservation objectives. Key words: carbon offsets; DAYCENT; grassland conversion; greenhouse gas emissions; Prairie Pothole Region. Received 4 October 2016; accepted 26 October 2016. Corresponding Editor: Nichole N. Barger. Copyright: © 2016 Ahlering et al. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.   E-mail: [email protected]

INTRODUCTION

Yahdjian et al. 2015). For example, it is estimated that agriculture as a whole contributes 30–35% of the global greenhouse gas (GHG) emissions (Foley et al. 2011). Globally, rangeland habitat covers over 50% of the land surface with just under half in grassland or savannah (Estell et al. 2012). Because of the productivity of grassland soils, approximately 70% of the world’s grasslands have already been converted to agriculture (Foley et al. 2011). North American grasslands are no exception (Samson et al. 2004), where conversion is still continuing at a rapid pace. In particular, in the Prairie Pothole Region (PPR) of North America, conversion of grassland to row-crop agriculture

The demand for food, feed, and energy is expected to increase in response to a projected global population of 9.15 billion by 2050 (Alexandratos and Bruinsma 2012) and biofuels policy (Searchinger et al. 2009, Lark et al. 2015). This demand can be met by both agricultural intensification through higher yields and extensification through conversion of grasslands and forests to cropland (Johnson et al. 2014). The conversion of natural systems to cropland, however, may degrade other ecosystem services provided by these habitats, such as carbon storage, soil retention, and recreation (Gascoigne et al. 2011, ❖ www.esajournals.org

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This study focuses exclusively on upland grassland habitats, incorporates other sources of GHG emissions such as N2O and CH4, and maps emissions at a regional scale.

in recent years has occurred at rates similar to the highest observed rates of tropical deforestation in the Brazilian Amazon (Wright and Wimberly 2013). Grassland protection and restoration are key conservation strategies, and an important policy mechanism to restore grassland has been the Conservation Reserve Program (CRP). Funding for this program has been reduced, and CRP enrollment has declined by over 10 million acres since its peak of 37 million acres in 2007 (Stubbs 2014). Further economic incentives may be necessary to maintain grasslands. One potential option is a carbon offset program. Carbon offsets have been available for reduced deforestation and degradation (REDD) projects in forested ecosystems for a few years (Gibbs et al. 2007, Olander et al. 2008). REDD methodologies have been published by certifiers, and carbon credits for protection of forested systems are traded on the voluntary market both within the United States and abroad. Until recently, an approved methodology did not exist to certify carbon offsets from avoided conversion of grassland systems, but with the publication of a methodology by the American Carbon Registry (Dell et al. 2013), the menu of potential economic incentives for private landowners to maintain their rangelands for ranching instead of row-crop agriculture has increased. This is the first study to quantify the potential GHG emission reductions for the avoided conversion of both restored and native grassland at a large scale. We chose a particularly at-risk region in the northern Great Plains, the central PPR, because of its significance to grassland biodiversity (Doherty et al. 2015) and the high threat of conversion (Wright and Wimberly 2013, Lark et al. 2015). We applied the existing Avoided Conversion of Grassland and Shrublands to Row Crop Agriculture v1.0 methodology (Dell et al. 2013) to the soil types across the study area to quantify the climate benefits of grassland protection in this region. We used the existing and well-tested DAYCENT model calibrated for the study area to quantify potential emissions (Hartman et al. 2011, Del Grosso et al. 2016). In the PPR, extensive work has been done to quantify carbon sequestration rates for restored wetlands (Gleason et al. 2005, 2008) and others have quantified sequestration of restored grasslands at a local scale (Phillips et al. 2015). ❖ www.esajournals.org

METHODS Study area We focused on 14 counties in central North Dakota and South Dakota: Hyde, Hand, Faulk, Edmunds, and McPherson in South Dakota and McIntosh, Emmons, Logan, Stutsman, Kidder, Burleigh, Sheridan, Mclean, and McHenry in North Dakota. These counties align with the Missouri Coteau landform, a prominent feature in the PPR that is important habitat for waterfowl, waterbird, shorebird, and grassland songbird populations (Doherty et al. 2015). We targeted this area because of its biodiversity significance and high rate of grassland to cropland conversion (Wright and Wimberly 2013, Lark et al. 2015).

DAYCENT modeling To calculate potential carbon emissions, we included the following greenhouse gas (GHG) pools: above- and belowground live biomass and soil organic carbon. We also included net nitrous oxide (N2O) flux and methane (CH4) flux from enteric fermentation by cattle. We used the DAYCENT model to calculate the potential avoided CO2 and N2O emissions for each soil type in the 14 county regions (Hartman et al. 2011). We calibrated the model output using data from the region. The soil carbon output was validated using long-term datasets from North and South Dakota (Haas and Evans 1957) and data from cultivated and uncultivated sites in the Great Plains (Burke et al. 1989). The N2O flux output was calibrated with data from long-term datasets (Haas and Evans 1957), and the DAYCENT model has been shown to correctly simulate N2O fluxes for different types of agricultural systems (Del Grosso et al. 2008a, b). Finally, aboveground plant productivity was calibrated using a dataset from North and South Dakota (Sala et al. 1988). Because soil carbon can vary dramatically by soil type, we ran DAYCENT for each soil type across the project area, parameterizing the model with county-level weather and latitude and longitude values. For climate, we used repeated VEMAP and DayMet interpolated daily weather 2

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files from 1895 to 2003 (Kittel et al. 1997, Thornton et al. 1997). For each county, we extracted every unique combination of percentage sand, silt, and clay values from the soil taxa occurring in that county according to the SSURGO dataset, which resulted in 888 unique combinations. The soil data from SSURGO are mapped as polygons across the study area, and DAYCENT uses the soil data to model carbon in gC/m2 and nitrogen in gN/m2. To avoid outliers, errors, and missing data in the SSURGO dataset, bulk density was derived from the SSURGO texture values (Saxton et al. 1986). For each soil type, we ran three DAYCENT simulations: one to establish an historic baseline and equilibrium, a second to simulate the most likely cropping scenario had it been converted, and a third to simulate the continuation of the current native condition. For all simulations of the native grassland, we used a mixed grass prairie that included nitrogen fixers and where the grass component was a 50/50 breakdown of warm and cool season grasses. As a starting point for further simulations, we ran the mixed grass prairie through the DAYCENT model for 5000 years with a moderate impact of grazing. This provided a historic baseline with carbon pools in equilibrium. We used the output of this simulation as the starting point for the second two simulations. We obtained information on the dominant crop rotation, conversion practices, and subsequent tillage practices for recently converted land from Natural Resources Conservation Service (NRCS) District Conservation officers for both South Dakota and North Dakota. We used this information to parameterize our simulations for the conversion to cropping scenario. Using the starting values from the baseline simulation that brought the carbon pools to equilibrium, we ran the cropping scenario for 150 years. The first 50 years was a continuation of the native mixed grass system with moderate grazing pressure. In year 51, an end of growing season haying followed by an herbicide treatment was applied. The remaining years alternated a corn and soybean rotation with conventional plowing occurring every year prior to planting, as our NRCS consultations indicated were still the dominant practices. Fertilizer was applied to the corn rotations twice, once at the time of first cultivation at a rate of 5 gN/m2 and once at the time of ❖ www.esajournals.org

planting at a rate of 6.6 gN/m2. Fertilizer was not added to the soybean plantings because soybeans are nitrogen fixers and do not require nitrogen additions. The same cultivation methods were used for both corn and soybean, and the grain was harvested at the end of every growing season for both crops. To simulate the continuation of the native condition under the avoided conversion scenario, we used the starting values from the historic baseline scenario and extended the native condition scenario of moderate grazing for 150 years. For both of the extend simulations (i.e., native conditions and the cropping scenario), we used repeated weather data for each county from 1973 to 2003 (Thornton et al. 2014). From each scenario, we used the annual output variables for total system carbon, aboveground production, and N2O flux to calculate the CO2 and N2O emissions. Belowground and aboveground CO2 emissions were calculated separately for each scenario. Belowground carbon was calculated as the difference between total system carbon and aboveground carbon. The DAYCENT model is sensitive to the weather data used to run the simulations. Large fluctuations in temperature and precipitation can cause large annual variation in the amount of carbon lost or gained. Therefore, we fit an emission curve to the modeled data to reduce the annual variability in emissions due to weather inputs in any given year (Appendix S1). Emissions are based on changes in the belowground carbon pool. For the year after conversion, the relevant change in the belowground carbon pool is the change from unconverted grassland. To avoid undue influence of climate variables on our estimate, we averaged our estimate of carbon in unconverted grassland over a 100-year timeframe to capture the majority of the climate variability in our weather file, rather than the carbon level from the single year prior to conversion. For subsequent years, the relevant change in the belowground carbon pool is the year-on-year temporal change in the cropped scenario. As the belowground carbon pool in the cropland reaches a new equilibrium, the pool stops changing and emissions decline to zero. An exponential loss model best fit the loss of belowground CO2 from the cropping simulations. This fitted model for each soil type in each county was then 3

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used to calculate emissions on an annual basis for the 100 years following conversion. Nitrous oxide emissions are reported from the simulations as an annual flux for both the cropped and grassland scenarios. The offsets are based on the difference between these annual fluxes. Therefore, beginning with the first year of cultivation, we subtracted each year of the grassland scenario from the same year of the cropping scenario to obtain the annual N2O emissions for avoided conversion. We used the difference in N2O flux over time to fit a linear model that averaged over temporal fluctuations due to weather variability. The aboveground biomass pool changes only once, immediately upon conversion to annual cropland. We calculated the average daily aboveground live carbon for the 100-year project period for both the grassland and cropland scenarios and subtracted the cropping production from the grassland production. The result was added to the first year of emissions from the total system carbon and the nitrous oxide. The expected land use for grasslands not converted to cropland in this region is cattle production. Therefore, methane emissions from the cattle were deducted from the emissions accrued from the avoided conversion. We calculated the methane emissions produced through enteric fermentation from cattle using the equations in the American Carbon Registry’s Methodology for the Avoided Conversion of Grasslands and Shrublands (ACoGS; Dell et al. 2013). Methane emissions are based on the number and type of livestock, the number of days spent grazing in each year, the enteric fermentation emission factor of methane per head per day, and the global warming potential for methane. We used the recommended values from the ACoGS methodology for the enteric fermentation emission factor of non-dairy cattle (53 kg CH4 head 1yr 1) and for the global warming potential of methane (Hongmin et al. 2006). To estimate the methane emissions for the five focal counties in north central South Dakota, we used a publication distributed by South Dakota State University’s Extension office to obtain recommended stocking rates (Mousel 2013:35). Cattle are the dominant livestock type in the focal area, and Mousel (2013) provides stocking recommendations for 1400 lb cattle across the state. For ❖ www.esajournals.org

Table 1. Stocking rate recommendations for 1400 lb cows in cowsha 1month 1 by county for the 14 focal counties in central North Dakota and South Dakota (Manske 2004, Mousel 2013) and associated enteric methane emissions, expressed as metric tons of carbon dioxide equivalents. County

Stocking rate (cowsha 1month 1)

Methane emissions (tCO2eha 1yr 1)

McPherson, SD Edmunds, SD Faulk, SD Hyde, SD Hand, SD Burleigh, ND Emmons, ND Kidder, ND Logan, ND McHenry, ND McIntosh, ND McLean, ND Sheridan, ND Stutsman, ND

1.33 1.01 1.33 1.01 1.33 1.09 1.09 1.09 1.09 1.28 1.09 1.09 1.09 1.09

0.123 0.094 0.123 0.094 0.123 0.101 0.101 0.101 0.101 0.119 0.101 0.101 0.101 0.101

1400 lb cows, two different recommended stocking rate categories occur across the five focal counties, 1.33 and 1.01 cowsha 1month 1. Because stocking rate recommendations incorporate both number of cows and length of time spent grazing, these numbers can be used to calculate emissions. Using the above recommendations, we calculated the methane emissions from enteric fermentation to be 0.123 or 0.094 tCO2eha 1yr 1, depending on the county (Table 1). The same procedure for calculating methane emissions from enteric fermentation was used for North Dakota. Manske (2004) reports recommended stocking rates across North Dakota based on three ecological divisions: drift prairie, Missouri Coteau, and West River. All of the North Dakota counties in this project fall primarily within the Missouri Coteau, with the exception of McHenry County, which is primarily drift prairie. For each county, we used the recommended stocking rates for the appropriate landform on good condition upland prairies. To be consistent with the recommendations made for South Dakota, we used the stocking rates given for 1400 lb cows. For the Missouri Coteau counties, methane emissions from enteric fermentation are 0.101 tCO2eha 1yr 1, and for the drift prairie, methane emissions are 0.119 tCO2eha 1yr 1 (Table 1). 4

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Data analysis

(2012–2014), the value of cropland was at least double the value of pastureland for all counties except McPherson, SD. The value of cropland in McPherson county was 93% greater than pastureland; avoided conversion from this county could still be certified, but with a discount applied to the estimated avoided conversion. Within each county, some lands are unsuitable for cropping and so are not at risk of conversion. We identified and excluded these lands from our analyses. To identify these lands, we used the Natural Resource Conservation Service’s Land Capability Classes (LCCs) in the SSURGO database to characterize cropping suitability. The LCC is an index with integer values that range from 1 to 8. To determine which LCCs were at risk of conversion, we used empirical data on recent conversion in our project area. We used the CropScape data (USDA NASS CDL 2014) to evaluate how much grassland was lost across the project area between 2010 and 2014 for each LCC. We also converted the tCO2e emissions to a social cost of carbon from published values (EPA 2013c). We used the Environmental Protection Agency’s (EPA) Interagency Working Group on Social Cost of Carbon recommended value of $37 per tCO2 under a discount rate of 3.0% in 2015. We report all emissions for the first 20-year period of the DAYCENT modeling because this is the minimum project term for ACoGS projects and the period over which the majority of the carbon emissions accrue (Dell et al. 2013).

Once the total emissions were calculated for each soil type in each county, we cross-walked the emissions back to the soil types in the spatial SSURGO data layer to map the emissions across the 14 county regions. Because carbon offsets are only available for unprotected lands at risk of conversion, we created an unprotected grassland layer for the project area using ArcGIS Desktop 10.1 (ESRI, Redlands, California, USA). We created a current grassland and shrubland layer from the 2014 CropScape data layer (USDA National Agricultural Statistics Service Cropland Data Layer 2014) including the following landcover classes: grassland/pasture, herbaceous wetlands, other hay/non-alfalfa, and shrubland. Grasslands in this region are commonly interspersed with wetlands, but wetland soils are not eligible for carbon credits under the ACoGS methodology. We used the National Wetlands Inventory data layer to remove any remaining wetlands from the grassland layer (U.S. Fish and Wildlife Service 2010). Finally, we used the national Protected Areas Database (USGS GAP 2012) and the National Conservation Easement Database (NCED 2011) to remove already protected lands from the current grassland layer. We summarized total emissions and emissions from the 10% of unprotected hectares with the highest and lowest emissions. In reality, individual pastures will include a range of soil types and emission values for a given pasture would be calculated for each soil type and weighted by the percentage of that soil type in the pasture. Because we do not have access to parcel boundaries, we calculate emissions based on soil types in the highest and lowest categories to report the variability in the dataset across the project area. ACoGS projects are focused on grasslands under imminent risk of conversion to cropland. Risk of conversion is driven by the value of the land as cropland vs. pastureland. To evaluate lands at risk based on cash value, we used survey data from the U.S. Department of Agriculture (USDA) to compare rental rates of cropland to pastureland by county across our study area (http://quic kstats.nass.usda.gov/results/58B27A06-F574-315B -A854-9BF568F17652#7878272B-A9F3-3BC2-960D -5F03B7DF4826). Lands with a value as cropland at least twice that of pastureland were considered to be under imminent risk. Using a 3-year average ❖ www.esajournals.org

RESULTS The calibration datasets were well simulated by the model. The model showed a 30% loss of soil carbon after 40 years, which is consistent with research in this region (Haas and Evans 1957) and meta-analyses (Davidson and Ackerman 1993), and results showed that soils high in clay content had lower loss of carbon than sandy soils, which is consistent with empirical data reviewed by Burke et al. (1989). The modeled N2O flux rates also matched empirical data well, with less than a 10% difference between simulated and observed values (Haas and Evans 1957). Finally, the model predicted annual grass production in the range of 100–110 gC/m2, which is within the range reported by Sala et al. (1988), and the simulated mean corn and wheat yields 5

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to corn and soybeans (although some grazing of residue is possible, we conservatively do not estimate emissions from enteric fermentation on crop residue). Therefore, the methane emissions from enteric fermentation of the cattle in the avoided conversion scenario must also be subtracted from the belowground and N2O offsets (Table 2). The 14 county project area covers 5,130,527 ha that includes 2,080,095 ha of unprotected grassland (Fig. 1). If all of the unprotected grassland was converted to annual row crop, it would release 106,879,912 metric tons of CO2e into the atmosphere. The variability in per ha CO2e emissions is largely stratified by latitude and longitude across the project area with the counties in South Dakota and the southeastern portion of the North Dakota project area having the highest per ha emissions (Fig. 1). In the DAYCENT model, county-level weather data and latitude and longitude were used and probably account for most of the countylevel boundary differences that appear across the project area. Regardless, the variability in emissions between soil types across the entire project area is not dramatic. Compared to the 10% of ha with the highest emissions (11,741,194 tCO2e), the 10% of ha with the lowest emissions is only 20% lower (9,387,896 tCO2e). Within the project area, there was a 14% loss of unprotected grassland between 2010 and 2014. Of this unprotected grassland, 80% of it was in LCCs 1–4 and 84% of the conversion occurred on this land; 15% of the unprotected grassland was in LCCs 5–6 and 15% of the conversion occurred on this land; 5% of the unprotected grassland was in LCCs 7–8, but only 1% of the conversion occurred on this land. Because they were converted in proportion to their occurrence on the landscape, this indicates that LCCs 1–6 are considered suitable for cropping and are at risk of conversion, while LCCs 7–8 are generally unsuitable for cropping. Protection of all land in LCCs 1 through 4 would reduce emissions by 85,167,883 tCO2e over 20 years, and protection of all land in LCCs 5 and 6 would reduce emissions by an additional 16,148,568 tCO2e over 20 years.

Table 2. Difference between the conversion and protection scenario in metric tons of carbon dioxide equivalents (CO2e) per ha over the 20-year project period by source averaged across all soil types for each county. County North Dakota Burleigh Emmons Kidder Logan McHenry McIntosh McLean Sheridan Stutsman South Dakota Edmunds Faulk Hand Hyde McPherson

Belowground† Aboveground‡ N2O CH4§ 47.64 42.30 44.97 47.94 44.75 48.06 44.43 46.21 46.26

1.024 1.086 0.983 1.150 0.839 1.164 0.986 0.979 1.242

7.969 6.437 7.366 8.226 6.835 7.944 8.033 7.811 8.932

2.03 2.03 2.03 2.03 2.37 2.03 2.03 2.03 2.03

49.42 50.28 49.27 48.11 47.15

1.357 1.281 1.282 1.297 1.204

9.936 8.792 8.265 8.599 8.451

1.88 2.47 2.47 1.88 2.47

† Includes belowground biomass and soil organic carbon. ‡ This value is a one-time loss over the 20-year project period. Values are negative because the annual aboveground crop biomass production is greater than the annual native mixed grass prairie production. § Values are negative because cattle are only present in the avoided conversion scenario and the methane emissions must be subtracted from the carbon emission savings.

also matched observed values from 1940 to 2003 (Hartman et al. 2011). Across the project area, emissions on different soil types ranged between 36.8 and 63.3 tCO2e/ha over 20 years assuming constant tillage and cropping scenarios (mean = 51.6 tCO2e/ha, SD = 2.9, median = 51.7 tCO2e/ha). Belowground carbon, including both plant biomass and soil carbon, is by far the greatest predicted source of emissions with emissions from N2O a distant second (Table 2). Model results for aboveground biomass production for corn and soybean crops are quite high and on average greater than the aboveground biomass production for native mixed grass prairie (Table 2). This difference in productivity must be subtracted from the offsets accrued from the belowground and N2O emissions. Furthermore, when grasslands in this project are protected from conversion to cropland, the most common use of these grasslands is as rangeland or pasture for cattle grazing. Cattle are not generally present on the site once it has been converted ❖ www.esajournals.org

DISCUSSION Our model results demonstrate that protecting the carbon stored in grasslands can meaningfully 6

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Fig. 1. Potential greenhouse gas emissions in metric tons of CO2e per hectare over a 20-year project period for all unprotected grassland across The Nature Conservancy (TNC) project area.

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contribute to reducing global GHG emissions. This is important because the carbon stored in these grasslands has a high risk of loss to the atmosphere under current conversion rates (Wright and Wimberly 2013, Lark et al. 2015). By targeting areas with the greatest potential for GHG emissions if converted, protecting only 10% of the currently unprotected grasslands would avoid 11.7 tCO2e emissions, which is equivalent to taking 2.5 million passenger cars off the road for a year (EPA 2013a, b, FHWA 2013) and would avoid a societal cost of $430 million (EPA 2013c). Protection of grasslands in this project area would also provide numerous other ecosystem services, regionally and continentally. Specifically, the PPR supports the highest population densities of breeding waterfowl in all of North America (Zimpfer et al. 2013), which is crucial for the over 90 million people in the United States that engage in wildlife recreation, including hunting (U.S. Department of the Interior 2011, Copper et al. 2015), and supports an annual industry of $145 billion (U.S. Department of the Interior 2011). The CO2e emissions reported here reflect total avoided GHG emissions if protection of at-risk grassland occurs. However, as in all systems, there is leakage and uncertainty of protection that needs to be accounted for. To ensure that the carbon offsets traded in the markets are not overestimated, the ACoGS methodology applies deductions for leakage and a buffer pool deduction for uncertainty of protection permanence. Under the ACoGS methodology, the offsets in this project area would be subject to a 20% market leakage deduction (Dell et al. 2013). The ACoGS methodology also requires 11% of the credits to be placed in a buffer pool to mitigate the risk of “reversals” in which sequestered carbon ends up being released, for example, if a protected grassland was illegally plowed (VCS 2010). Although ten percentage of offsets previously contributed to the buffer pool are returned every five years if there are no reversals, the offset numbers presented here conservatively ignore any potential recovery of credits in the buffer pool. Combining these two adjustments, we arrive at a 31% deduction to our model output, which yields average per ha offsets of 35.6 tCO2e over 20 years and a total of 9.4 million tCO2e over 20 years for the 10% of hectares with the highest emissions. ❖ www.esajournals.org

Emission quantification is an essential step to support the development of carbon offset projects as an economic incentive to avoid grassland conversion. Currently, the primary tool for grassland protection in the PPR is perpetual easements. The rising price of corn in recent years has increased land values, cropland rental rates, grassland conversion rates, and subsequently the cost of perpetual easements from $482/ha in 1998 to $1,922/ha in 2012 (Walker et al. 2013). This rising cost of easements highlights the need for additional funding sources, such as carbon offsets. Financing carbon projects also requires the use of capital that is donated or provided with no or low interest rates because the payments for perpetual easements must be made up front, while the carbon offsets are issued over ensuing decades (periodically, after each verification). Once certified, carbon offsets can be sold on the voluntary market. The average offset in 2014 sold for about $3.8/t (Forest Trends Ecosystem Marketplace 2015), although avoided conversion projects with significant co-benefits can fetch substantially higher prices. Using the average carbon offsets modeled here brings this to $137/ha for a 20-year period, which is not enough to cover easement costs that as of 2012 are over $1,235/ha in both North and South Dakota (Walker et al. 2013). Carbon offsets need not be enough on their own to secure grasslands, but can be supplemental. Additional lands are protected from conversion when carbon offset funding partners with existing conservation easement efforts by providing supplementary funding that (1) targets easements to those properties most at risk of conversion and highest in carbon offset potential and (2) generates new funding that is used for additional grassland protection. Such an approach could bring in millions of dollars of new funding for conservation easements, targeted to those grasslands most at risk of conversion and with the greatest potential for reducing GHG emissions. The spatially mapped and modeled carbon emissions reported here provide the information necessary to embark on such a supplemental funding program for grassland easements. Carbon offsets from avoided conversion can help protect at-risk grasslands, reduce GHG emissions, and produce positive outcomes for biodiversity and ranchers in a manner similar to REDD+ projects in forested ecosystems. 8

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ACKNOWLEDGMENTS

Version 1.0. American Carbon Registry, Arlington, Virginia, USA. Doherty, K. E., J. S. Evans, J. Walker, J. H. Devries, and D. W. Howerter. 2015. Building the foundation for international conservation planning for breeding ducks across the U.S. and Canadian border. PLoS ONE 10:e0116735. Environmental Protection Agency (EPA). 2013a. Inventory of U.S. greenhouse gas emissions and sinks: 1990–2011. Annex 6 (Additional Information), Table A-275. U.S. EPA #430-R-13-001. U.S. Environmental Protection Agency, Washington, D.C., USA. Environmental Protection Agency (EPA). 2013b. Inventory of U.S. greenhouse gas emissions and sinks: 1990–2011. Chapter 3 (Energy), Tables 3-12, 3-13, and 3-14. U.S. EPA #430-R-13-001. U.S. Environmental Protection Agency, Washington, D.C., USA. Environmental Protection Agency (EPA). 2013c. Technical update of the social cost of carbon for regulatory impact analysis. United States Government, Washington, D.C., USA. Estell, R. E., K. M. Havstad, A. F. Cibils, E. L. Fredrickson, D. M. Anderson, T. S. Schrader, and D. K. James. 2012. Increasing shrub use by livestock in a world with less grass. Rangeland Ecology & Management 65:553–562. Federal Highway Administration (FHWA). 2013. Highway statistics 2011. Table VM-1. Office of Highway Policy Information, Federal Highway Administration, Washington, D.C., USA. Foley, J. A., et al. 2011. Solutions for a cultivated planet. Nature 478:337–342. Forest Trends Ecosystem Marketplace. 2015. State of the voluntary carbon market 2015. Forest Trends Ecosystem Marketplace, Washington, D.C., USA. Gascoigne, W. R., D. Hoag, L. Koontz, B. A. Tangen, T. L. Shaffer, and R. A. Gleason. 2011. Valuing ecosystem and economic services across land-use scenarios in the Prairie Pothole Region of the Dakotas, USA. Ecological Economics 70:1715–1725. Gibbs, H. K., S. Brown, J. O. Niles, and J. A. Foley. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters 2:045023. Gleason, R. A., N. H. Euliss Jr., R. L. McDougal, K. E. Kermes, and E. N. Steadman. 2005. Potential of restored prairie wetlands in the glaciated North American prairie to sequester atmospheric carbon. Paper 92. USGS Northern Prairie Wildlife Research Center, Jamestown, North Dakota, USA. Gleason, R. A., M. K. Laubhan, and N. H. Euliss Jr. 2008. Ecosystem services derived from wetland conservation practices in the United States Prairie Pothole Region with an emphasis on the U.S. Department of Agriculture Conservation Reserve

We thank the Paulson Family Fund for providing funding to complete this work and the USDA Conservation Innovation Grant titled “Ducks Unlimited Avoided Grassland Conversion Carbon Project” for funding the development of the methodology that directed the GHG emission quantification. We thank Ducks Unlimited for providing joint funding along with The Nature Conservancy for the DAYCENT model validation. Finally, we thank Robin Kelly for assistance throughout the analysis process and William Gascoigne for constructive comments on the manuscript.

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AHLERING ET AL. of REDD: a review of data and methods. Environmental Research Letters 3:025011. Phillips, R. L., M. R. Eken, and M. S. West. 2015. Soil organic carbon beneath croplands and re-established grasslands in the North Dakota Prairie Pothole Region. Environmental Management 55:1191–1199. Sala, O. E., W. J. Parton, L. A. Joyce, and W. K. Lauenroth. 1988. Primary production of the central grassland region of the United States. Ecology 69:40–45. Samson, F. B., F. L. Knopf, and W. Ostlie. 2004. Great Plains ecosystems: past, present and future. Wildlife Society Bulletin 32:6–15. Saxton, K. E., W. J. Rawls, J. S. Romberger, and R. I. Papendick. 1986. Estimating generalized soil-water characteristics from texture. Soil Science Society of America Journal 50:1031–1036. Searchinger, T., et al. 2009. Fixing a critical climate accounting error. Science 326:527–528. Stubbs, M. 2014. Conservation Reserve Program (CRP): status and issues. 7-5700. R42783. Congressional Research Service, Washington, D.C., USA. Thornton, P. E., S. W. Running, and M. A. White. 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology 190:214–251. Thornton, P. E., M. M. Thornton, B. W. Mayer, N. Wilhelmi, Y. Wei, R. Devarakonda, and R. B. Cook. 2014. Daymet: daily surface weather data on a 1-km grid for North America, Version 2. Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. http://daac.ornl.gov United State Geological Survey Gap Analysis Program (USGS GAP). November 2012. Protected areas database of the United States (PADUS). Version 1.3. Combined Feature Class. United States Department of Agriculture National Agricultural Statistics Service Cropland Data Layer (USDA NASS CDL). 2014. Published crop-specific data layer. USDA-NASS, Washington, D.C., USA. http://nassgeodata.gmu.edu/CropScape/ U.S. Department of the Interior, U.S. Fish and Wildlife Service, U.S. Department of Commerce, and U.S. Census Bureau. 2011. National survey of fishing, hunting and wildlife-associated recreation. FHW/ 11-NAT (RV). Washington, D.C., USA. U.S. Fish and Wildlife Service. 2010. National Wetlands Inventory. U.S. Department of the Interior, Fish and Wildlife Service, Washington, D.C., USA. http://www.fws.gov/wetlands/ Verified Carbon Standard (VCS). 2010. AFOLU NonPermanence Risk Tool: VCS Version 3. http://www. v-c-s.org/sites/v-c-s.org/files/AFOLU%20Non-Per manence%20Risk%20Tool,%20v3.2.pdf

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AHLERING ET AL. Yahdjian, L., O. E. Sala, and K. M. Havstad. 2015. Rangeland ecosystem services: shifting focus from supply to reconciling supply and demand. Frontiers in Ecology and the Environment 13: 44–51. Zimpfer, N. L., W. E. Rhodes, E. D. Silverman, G. S. Zimmerman, and K. D. Richkus. 2013. Trends in duck breeding populations, 1955–2013. U.S. Fish & Wildlife Service, Division of Migratory Bird Management, Laurel, Maryland, USA.

Walker, J., J. J. Rotella, C. R. Loesch, R. W. Renner, J. K. Ringelman, M. S. Lindberg, R. Dell, and K. E. Doherty. 2013. An integrated strategy for grassland easement acquisition in the Prairie Pothole Region, USA. Journal of Fish and Wildlife Management 4:267–279. Wright, C. K., and M. C. Wimberly. 2013. Recent land use change in the western corn belt threatens grasslands and wetlands. Proceedings of the National Academy of Sciences USA 110:4134–4139.

SUPPORTING INFORMATION Additional Supporting Information may be found online at: http://onlinelibrary.wiley.com/doi/10.1002/ecs2. 1625/full

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