Evaluating the Potential of Marginal Land for Cellulosic Feedstock

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Evaluating the potential of marginal land for cellulosic feedstock production and carbon sequestration in the United States Isaac Emery, Steffen Mueller, Zhangcai Qin, and Jennifer B. Dunn Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b04189 • Publication Date (Web): 01 Dec 2016 Downloaded from http://pubs.acs.org on December 6, 2016

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Environmental Science & Technology

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Evaluating the potential of marginal land for cellulosic feedstock production and carbon sequestration in the United States

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Isaac Emery†, Steffen Mueller‡, Zhangcai Qin§, and Jennifer B. Dunn*

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†

Department of Systems Engineering and Management, Air Force Institute of Technology, WrightPatterson Air Force Base, Ohio 45433, United States

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‡

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§

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* Energy Systems Division, Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, Illinois 60439, United States. Phone: 630-252-4667; Fax 630-252-3443; [email protected]

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Energy Resources Center, University of Illinois at Chicago, Chicago, Illinois 60607, United States Energy Systems Division, Argonne National Laboratory, Lemont, Illinois 60439, United States

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Abstract

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TOC/Abstract Art

Land availability for growing feedstocks at scale is a crucial concern for the bioenergy industry. Feedstock production on land not well-suited to growing conventional crops, or marginal land, is often promoted as ideal although there is a poor understanding of the qualities, quantity, and distribution of marginal lands in the United States. We examine the spatial distribution of land complying with several key marginal land definitions at the United States county, agro-ecological zone, and national scales, and compare the ability of both marginal land and land cover data sets to identify regions for feedstock production. We conclude that very few land parcels comply with multiple definitions of marginal land. Furthermore, to examine possible carbon flow implications of feedstock production on land that could be considered marginal per multiple definitions, we model soil carbon changes upon transitions from marginal cropland, grassland, and cropland-pastureland to switchgrass production for three marginal land-rich counties. Our findings suggest that total soil organic carbon (SOC) changes per county are small, and generally positive, and can influence life-cycle greenhouse gas (GHG) emissions of switchgrass ethanol.

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ACS Paragon Plus Environment


1. Introduction:

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Substantial increases in cellulosic energy crop production to fuel second generation biofuel production requires dedicated land that could be used for other purposes, including production of food, feed, and fiber. Many different types of non-conventional agricultural lands, however, could be used for cellulosic biomass crops, including abandoned cropland, grasslands, pasture or rangeland, and others.1 These types of lands are sometimes called “marginal” lands. Areas of nutrient-poor or erosion-prone soil that are not financially or ecologically sound under row crops could be quite capable of producing perennial bioenergy grasses. Furthermore, there is potential for energy crop production on these types of lands to bring about ecosystem services such as increasing soil organic carbon (SOC), which should be accounted for in biofuel life cycle analysis (LCA).

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Understanding the potential of so-called marginal lands’ availability for cellulosic energy crop production requires knowledge of several factors including an estimate of the area of land that could be classified as marginal (a term that should be more strictly defined as discussed in this article). Moreover, evaluation of soil organic carbon effects of growing energy crops on these lands, and, consequently, greenhouse gas (GHG) emissions associated with biofuels, is an important element of the ongoing discussion of biofuel life-cycle GHG emissions. These two points of insight are the focus of this article, while a discussion of total biomass yield on these lands and the efficiency of their conversion to liquid fuels are outside the scope of this discussion. Two challenges, however, have limited understanding of the two points of insight that we discuss herein.

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First, estimates of the amount of marginal land in the US vary greatly among studies (e.g., 250 to 1,500 Mha),2 because of differences among studies in the underlying data sets, assumptions about the factors determining land availability, and the authors’ choice of definition of marginal land. Gopalakirshanan et al. find nearly 10-fold variability in their estimates of marginal land in Nebraska depending on analysis methodology.3 Surveying available studies of global bioenergy potential on marginal lands, Wicke reports a range of 8 to 147 EJ/y, depending on the definition of marginal land and type of biomass considered.1 Examining abandoned agricultural areas using global historical land use data sets, Campbell et al. identify between 385 – 472 Mha of potential land for bioenergy crops based on global abandoned agricultural land,4 of which 179 Mha may be available in the US. For comparison, recent USDA ERS publications report 13.6 Mha of land enrolled in the conservation reserve program (CRP) and 14.9 Mha ‘cropland pasture’ in 2007.5

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Even when clearly defined, ‘marginal’ can refer to a wide range of factors, including land cover, current, anticipated, or intended land use, soil type and other biophysical parameters (table S1).1, 6, 7 Some authors simplify the term by using it as a synonym for degraded or abandoned land,8 poor quality soils,9 or low quality land in general.10, 11 Troublingly, roughly half of authors mentioning marginal land or soil in peer-reviewed literature between 2008-2012 did not define the term.7 Approaches to estimating marginal land quantities take many approaches including using recent land use as a proxy for determining marginal land areas.1, 3, 4, 8, 12-15 Other studies use data based on biophysical parameters, such as USDA NRCS or Canadian Land Inventory (CLI) soils maps.3, 16-19 A minority of studies describe seasonally available or ecologically sensitive areas as marginal.10


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Second, there are few soil carbon emission factors or data available for use in estimates of soil organic carbon (SOC) change upon transitioning marginal (e.g., low productivity, poor soil quality) land to producing cellulosic feedstocks. Existing SOC data and emissions factors (EFs) for land conversion to bioenergy crops have mainly been previously calculated at a coarse scale without significant consideration of land-use history and spatially explicit factors like soil type, climate, and local crop yield. (See SI Section S.1 for a discussion of treatment of yields on low-yielding, “marginal” lands that influence SOC EF estimation.) Empirical SOC studies of cellulosic feedstock production, on the other hand, often examine farm management or land use scenarios at small spatial scales. The results of these studies are difficult to generalize due to differences in land management practices (e.g., tillage, crop rotations), soil depths, and site-to-site variability in local soil and climate parameters. It is important to characterize potential carbon emissions or sequestration during these land transitions because the LCAs required by the U.S. Environmental Protection Agency and the California Air Resources Board for their renewable fuels programs (the Renewable Fuel Standard and the Low Carbon Fuel Standard, respectively) must take into account land use change (LUC) GHG emissions.20, 21 Many economic models that predict LUC associated with increased biofuel production draw upon marginal lands as key areas to grow second generation feedstocks; understanding carbon stock changes, including SOC changes, upon them is therefore important.22

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In this paper, we undertake two main objectives. First, we assess the extent of agreement between land cover and land quality data sets. Using a unique data-based approach to identify marginal land in the U.S. based on several land cover, land use, and land quality data sets, we compare marginal land estimates at national and agro-ecological zone (AEZ) scales using five spatially-explicit land cover data sets and four definitions of marginal land. Second, we examine GHG emissions (or sequestration) that would occur in three case study U.S. counties if marginal land or land that alters between cropland and pastureland therein were converted to switchgrass production, investigating the influence of key SOC modeling parameters such as the land use history. This latter type of land, cropland-pasture, figures prominently as de facto marginal land in models that aim to capture land-use change (LUC) associated with expanded biofuels production. To produce these estimated emissions, we developed spatially explicit soil carbon emission factors at the county-level with the Surrogate CENTURY Soil Organic Carbon (SCSOC) model, that takes into account location-specific factors such as soil type, climate, and crop yield.23 Given that so-called marginal land is often proposed as a primary land resource for the growth of cellulosic feedstocks and that researchers and policy makers seek to understand the carbon implications of using this land as part of estimating biofuel life-cycle GHG emissions, the insights this study contributes are needed.



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2. Data and Methods:

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Selection of land cover data sets

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Five data sets covering the continental U.S. were selected for comparison based on their data quality, resolution, and relevance to US land use policy: the U.S. Department of Agriculture (USDA) Cropland Data Layer (CDL),24, 25 USGS National Land Cover Data Set (NLCD),26 MODIS,27 GlobCover,28 and the Global Land Cover-SHARE project (GLC-SHARE)29 (Table S2). Land cover categories for each raster were simplified to provide a basis for comparison, resulting in nine categories: cropland, hay & grass cropland, idle cropland, pasture, grassland, shrubland, savanna, herbaceous vegetation, and other (Figure S1). 'Other' land includes forest, woodland, urban areas, barren land, and all other categories. We consider all categories except cropland and other to be available for conversion to perennial bioenergy crops. Note that all acronyms are defined in Table S10 in the supporting information.

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Selection of data sets for assessment of variations in marginal land availability estimates

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To complete the first objective of the paper, we consider marginal land availability under four definitions of marginal land. The first, moderate ecosystem productivity, is based on the Cai et al. modeled marginal ecosystem productivity data set (CMMEP).18 The CMMEP is one component of a highresolution, spatially explicit data set that categorizes land as regular (R), marginal (M, CMMEP), or low (L) productivity based on aggregation of soil quality, regional climate, and other key components of ecosystem productivity.18 The second, abandoned agricultural land, is based on the abandoned agricultural lands assessment by Zumkehr and Campbell, who modeled the spatial distribution of U.S. farmland at 5 min resolution based on historical survey data and satellite mapping from 1850 to 2000.30 The third definition is that of CRP land. To address shifting rates of land enrollment, CRP area was defined as the largest area enrolled by county in the 1997-2012 censuses of agriculture.31 The fourth definition of marginal land was based on selected land cover data sets overlaid with land capability classifications (LCC) 5-8.32 (We discuss our selection of LCC 5-8 in SI section S.2.) We analyzed the amount of marginal land in the U.S. based on each of these definitions when they were applied to the CDL, a data set that is calibrated to differentiate among a wide range of agricultural land uses, including production of many individual crops. It is one of the highest-resolution land cover data sets of the U.S.25

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The Cai et al. ecosystem productivity raster was overlaid with each of the five land cover data sets to compare the differences in estimates of available marginal (CMMEP) land between data sets at the AEZ and county scales within the United States. The common factor among these analyses was the definition of marginal land (CMMEP). The high resolution of the CDL required us to decompose the larger Cai et al. ecosystem productivity pixels. Because Cai et al. developed their data set using algorithms to minimize overlap between cropland and marginal or low productivity regions and maximize overlap between marginal productivity and pasture18, we used a similar method to assign CMMEP area from pixels with mixed productivity to underlying CDL land cover area (shown in detail in figure S2). Next, an analogous


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process was used to match each CMMEP × CDL sub-pixel fraction with corresponding area from LCC category groups 1 & 2, 3 & 4, and 5-8 (figure S2). We assumed that abandoned agricultural lands were distributed evenly among underlying CDL, CMMEP, and LCC pixels.

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All raster calculations were done using ArcGIS (version 10.2, Esri, Redlands CA).

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County selection and land categorization for SOC changes upon bioenergy grass production

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One county each from Kansas, Missouri, and Iowa were selected for further analysis based on 1) high proportion of CMMEP area18 and 2) high proportion of grassland or shrubland from at least two of three land cover data sets from independent data sources (CDL, MODIS, GlobCover).

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We used available CMMEP land according to each of the five land cover data sets to estimate annual carbon storage potential under bioenergy crops in three counties with significant amounts of CMMEP land. This estimation is dependent upon land use history and current land state, which can be difficult to ascertain for cropland-pastureland. Cropland-pasture area in cropland and pasture phases were estimated from USDA NASS data for 1997-2012. Because reported cropland-pasture in each of the selected counties declined during recent decades (figure S3), cropland-pasture in 2012 was counted as pasture-phase cropland pasture, while the difference in reported cropland-pasture between 2007 and 2012 was counted as cropland-phase cropland-pasture under the assumption that those areas had been converted to cropland.31 We subtracted pasture phase cropland-pasture from available marginal grassland (the sum of hay, pasture, grassland, savanna, and shrubland in each county) to avoid doublecounting while generating an estimate of total marginal land availability. A sixth scenario in Table 1, LCC × CDL, used USGS LCC data in lieu of the CMMEP as the definition of marginal land.18 We defined available land in this scenario as overlap between cropland and LCC(5-8) (marginal cropland), hay cropland and pasture overlap with LCC(1-4) (grassland), and hay cropland and pasture overlap with LCC(5-8) (marginal grassland).

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Development of SOC change estimates for switchgrass production on potential marginal land in three U.S. counties

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A well-documented model SCSOC,33 was used to simulate the soil carbon dynamics (0-100 cm) in the three counties from 2011 to 2040 and generate SOC emission factors (EF). One challenge in using a soil carbon model to estimate SOC EFs is that, as a rule, the model should allow SOC to come to equilibrium after the land transition for a period of 20-30 years.22 With marginal lands such as cropland-pasture, the cycling of land among different states renders this approach impossible. The approach we adopted to overcome this challenge was to run a pair of analyses for each cropland-pasture scenario. For each county, cropland-pasture models were run with conversion to switchgrass at the end of a pasture phase and at the end of a cropland phase. See sections S.1 and S.3 for more details about SOC modeling,



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including how we treated yield of conventional crops and switchgrass on non-marginal and marginal lands.

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The contribution of SOC changes to life-cycle greenhouse gas emissions of switchgrass ethanol produced with switchgrass from each county were calculated using the formula:

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GHG (gCO2e/MJ) = ΔSOC (g/ha/yr) × [YH (t/ha/yr) × 375 L/t × 21.3 MJ/L]-1

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Where ΔSOC and YH are the change in SOC and harvested biomass yield for each county land conversion scenario, biomass is converted to ethanol at an efficiency of 375 L ethanol / t biomass, and the LHV of ethanol is 21.3 MJ/L.34 Previous analyses reported that SCSOC estimated soil carbon sequestration rates generally consistent with field experiments and other modeling work.33


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Figure 1 Distribution of likely marginal lands in the United States by county per multiple definitions. Colors indicate the 18 number of land cover data sets (out of the 5 described above) showing at least 20% of county area in available CMMEP land (A) and the number of marginal land definitions (out of the 4 described above) when applied to the CDL that identify over 20% of county land as available marginal land (B). 'Negligible CMMEP land' indicates counties without substantial CMMEP area, which therefore could not contain 20% land area both CMMEP and available according to any data set.



3. Results & Discussion:

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Figure 1A summarizes the extent of disagreement of the five land cover data sets regarding marginal land availability for bioenergy crops. All five data sets characterize only a small portion of counties, 5.3%, as having more than 20% of their area as CMMEP land, primarily in the Western Great Plains. No other region of the country shows this level of harmonization among the data sets. In fact, most of the 31% of US counties that have 20% or more available CMMEP land meet this criteria according to only one or two data sets (9.5% and 9.9% of counties, respectively). Most of these counties are in Appalachia and the Southeastern U.S. The large differences in the number and distribution of counties identified by each land cover data set highlights the influence of data source on analyses of marginal land and bioenergy land availability. While the data sets generally disagree regarding which counties have marginal land, they all characterize wide swaths of the U.S. in the Southwest and Great Lakes regions as having little marginal land.

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Distribution of marginal land types in the U.S. Several western and southeastern states that show available CMMEP land per at least one data set (figure 1A) could be categorized as marginal per one or two other criteria (figure 1B). Conversely, areas in the eastern Dakotas, Nebraska, and Kansas meet multiple marginal definitions though they are not identified as CMMEP land. The most prevalent marginal land type is abandoned cropland, with 26%

U.S. domestic marginal land availability as predicted by five high-resolution data sets For this analysis, we have defined “marginal land” as CMMEP area that overlaps with "available land," grassland, pasture, shrubland, and mixed herbaceous vegetation in AEZs 7-12. We term this land CMMEP land rather than marginal land for clarity and to indicate the uniqueness of these results to the analysis technique we used. By this definition, the area of available CMMEP land in the US ranges from 58.9 to 82.5 Mha depending on the land cover data set used (Table 1). Using LCC (5-8) as an alternative marginal land metric combined with CDL available land (sixth scenario in Table 1) gives a larger estimate of 126.7 Mha. Although several data sets show close agreement at the national level, results vary widely at the AEZ and county scales, highlighting the importance of spatial specificity when examining land availability and characteristics (further discussed in Section S.4 and Figure S4). (Results are reported at an AEZ level because this is the level at which commonly-used economic models that estimate biofuelinduced LUC report results.) As described earlier, the five high-resolution land cover data sets we examined in this study use different data sources and calibration methods to identify land cover. These differences become apparent when we map how each data set categorizes land across the U.S. (figure S1). While most maps agree in some regions, there are few state-level regions where all maps clearly agree – for example, Iowa and Illinois are primarily cropland in four of the five maps, but GlobCover reports a large proportion of grassland in these states. Data sets also differ in the overall balance of available land cover categories. This difference is most obvious in western and southeastern states.


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(814) of counties containing over 20% land area which may once have been cropland.30 21% of counties contain at least 20% available CMMEP land, while cropland on poor soils is the most restrictive marginal lands definition with only 179 counties containing over 20% area.

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These marginal lands are variably distributed among AEZs (figure S4). CMMEP lands are the most widespread type of marginal land, with over 35 Mha in AEZ 7 and 10-12 (when restricted to land available for conversion to bioenergy grasses according to the CDL, these areas decrease to 10 to 12 Mha in AEZ 10-12, figure S4). Even when restricted to available land cover types, CMMEP and abandoned agricultural lands are similar in area to current cropland in several AEZs. CRP enrollment and cropland-pasture area are an order of magnitude lower than available CMMEP and abandoned lands, 0.30-4.50 Mha per AEZ. Such a stark difference between cropland-pasture, CRP enrollment and marginal lands identified based on broader assessments of land use or productivity could be a symptom of the difficulty in defining and categorizing marginal agricultural land and may indicate that 1) results of these broader studies do not fit with land operator decision making; 2) a larger pool of land could potentially transition to and from more intensive agricultural production than has been recently utilized; 3) while a relatively small quantity of land remains which can easily transition to conventional row crop production, there is a much larger potential land area for perennial agricultural systems; or some combination of these. See Section S.4 for more information about our analysis of marginal land area and Table S9 for an overview of areas of agriculture, pasture, cropland-pasture, grassland and shrubland per major data sets employed herein and other recent publications with reported marginal land areas also specified when possible.

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One technique to hone in on marginal, available lands would be to do a more detailed, local investigation of land biophysical characteristics and land use history in counties with substantial ‘marginal and available’ area according to two or more data sets. For example, across the Midwest and Great Plains states there are several regions where most or all of the land cover maps agree that counties may be prime targets for bioenergy crop production according to our criteria (figure 1).

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Table 1 Available marginal land area in the United States (by AEZ) and selected counties, including each of five land cover data sets overlaid with CMMEP as well as the LCC × CDL scenario. US Marginal Land by AEZ (Mha)

Marginal Land by County (ha)

Data sources 7

8

9

10

11

12

Total

Allamakee, IA

Butler, KS

Vernon, MO

CMMEP × GLC-SHARE

27.8

18.0

1.1

6.4

1.8

6.5

61.7

1,310

285,493

1,966

CMMEP × GlobCover

17.0

13.8

1.2

9.7

11.4

7.9

61.2

63,977

288,834

124,096

CMMEP × MODIS

31.6

16.9

0.7

5.8

3.3

0.7

58.9

85

299,858

33,478

CMMEP × NLCD

26.3

16.2

1.1

9.8

13.6

13.4

80.5

51,788

237,625

92,518

CMMEP × CDL

28.3

17.3

1.2

10.1

13.3

12.4

82.5

48,804

233,910

95,834

LCC(5-8) × CDL

50.9

34.7

6.1

15.7

11.6

7.7

126.7

28,531

117,696

35,530

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256 Figure 2 Categorization of all land in Butler, Vernon, and Allamakee counties by marginal land metric and land cover. The color of the columns indicate land cover category 18 (cropland, hay, pasture, or other). Marginally productive lands (un-shaded) are based upon Cai et al. metrics (CMMEP). Patterned columns denote lands exhibiting nonmarginal productivity. Within each column, rows divide land area according to land capability classification (LCC) groups and cropland abandonment metrics for marginal land. Land that could be characterized as least marginal (LCC 1-4, not abandoned) has the lightest shading and is at the bottom of the figure. Land that meets both marginal land criteria (LCC 5-8, abandoned) has the darkest shading and is at the top.


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Distribution of marginal land types in three counties

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To meet the second objective of the paper, which is to examine SOC changes that would occur in three case study U.S. counties if marginal land therein were converted to switchgrass production, we selected three counties (Allamakee County, IA, Butler County, KS, and Vernon County, MO) for further analysis. In this subsection, we describe how different data sets characterize the amount of available CMMEP land in these counties differently. The next subsection presents SOC modeling results.

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The estimates of marginal land in two of these counties (Allamakee, Vernon) varied significantly with data set (Table 1). Estimates for Butler County, KS were relatively consistent, although the LCC(5-8) × CDL technique estimates significantly less marginal land than the other data sets. Small differences between the NLCD and CDL may be due to the year of data collection (2011 and 2013, respectively). Larger differences between GLC-SHARE and the CDL, a locally validated source, suggests that although GLC-SHARE is the most accurate and up-to-date global land cover data set available, it may not be suitable for county-level analysis in some regions.

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Figure 2 reveals general consistency in the distribution of CMMEP, modeled abandoned lands, and sampling-based soil quality categories across land use categories in each of the three counties, though there are some important differences. In Butler and Allamakee Counties, cropland contains a higher proportion of LCC(1-4) and non-CMMEP land than other land cover types. Non-marginal cropland, hay, and pasture lands also overlap with higher-quality soils (LCC 1-4), indicating agreement between multiple marginal land metrics. In all three counties, most non-marginal land is high quality land with the notable exception of "other land” Allamakee, IA, which is primarily low modeled ecosystem productivity18 as well as LCC(5-8) (areas for each land cover and marginal definition in Figure 2 are available in Table S6, more details in Section S.6).

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The relatively large overlap between abandoned agricultural lands and current cropland could be due to changes in actual land use between 2000 (the year of data collection for inputs to the Zumkehr & Campbell abandoned land model)30 and 2013 (the year of CDL data collection), differences in the land cover data sets used, or an artifact of our algorithm for assigning abandoned lands to overlapping land cover pixels. Although the order in which we assigned land and soil quality measures to CDL land cover pixels could affect these results, altering the order of pixel categorization had very minor effects on the resulting proportions of marginal land for each land cover (data not shown). Similarly, a sensitivity analysis of abandoned land classification showed that reassigning 33% of abandoned land area between land cover and marginal land classifications within a county did not dramatically alter our results. The sensitivity tests affected only 1% of the land area in most available land cover categories (data not shown).

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Across all three counties, we found little variation in the LCC of CMMEP land across land use categories. This could indicate that despite the apparent corroboration of soil quality and CMMEP classifications of marginal land, these data sets are not sufficiently accurate for assessing marginality between land use categories at this scale. Alternatively, there may be other biophysical or socioeconomic factors driving these local land use patterns. Taken together, these observation point towards the complexity of



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understanding land quality and land use at the county scale from existing data sets, which highlights the role of local information in decision-making.

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SOC changes in the three case study counties

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In each county, we modeled SOC changes when land of varying initial states (i.e., cropland, low-yielding (marginal) cropland, grassland, and cropland-pasture land with different land use histories) was converted to producing switchgrass. In this portion of the paper, the term “marginal cropland” means that yield and root-to-shoot ratio differ from non-marginal cropland (see sections S1 and S3) to reflect poor quality land. We provide details of SOC modeling including yield assumptions for regular and “marginal” productivity in Sections S1 and S3. In the SOC modeling, we devised a land use history for “marginal” cropland that reflects low soil quality and productivity. Local information including soil type and weather, however, are based on data for these counties. Marginal and regular productivity cropland transitions to switchgrass resulted in similar net increases in soil carbon (Table 2) although gains on marginal lands were significantly greater than on cropland-pasturelands, which are often treated as marginal lands (see next subsection). SOC losses were incurred in all three counties when grassland was used to grow switchgrass, with the largest SOC loss in Allamakee County. The county that saw the greatest SOC gains upon land transition to switchgrass production was Vernon County. Switchgrass yields in Kansas and Missouri are over 40% higher than in Iowa, which is the underlying reason that switchgrass production shows SOC benefits in these states whereas the Iowa county experiences SOC losses more often regardless of scenario. Other factors that influence results include weather and soil type. These differences highlight the importance of spatially-specific considerations in examining SOC changes.

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Table 2 Modeled changes in SOC during land of varying initial states’ transition to switchgrass and associated GHG emissions assigned to cellulosic ethanol produced from switchgrass. 'Marginal' refers to crop yield as discussed in sections S.1 and S.3.

321

Land use transition From To Cropland Switchgrass

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Marginal Cropland

Switchgrass

Marginal Cropland

Marginal Switchgrass

Grassland

Switchgrass

Marginal Grassland

Marginal Switchgrass

C-P-Crop C-P-Pasture C-P-Crop C-P-Pasture C-P-Crop C-P-Pasture

Switchgrass

County Allamakee, IA Butler, KS Vernon, MO Allamakee, IA Butler, KS Vernon, MO Allamakee, IA Butler, KS Vernon, MO Allamakee, IA Butler, KS Vernon, MO Allamakee, IA Butler, KS Vernon, MO Allamakee, IA Butler, KS Vernon, MO

ΔSOC 1 (tC /ha /yr)

SOC EF 2 (gCO2e/MJ)

0.13 0.19 0.29 0.13 0.20 0.28 0.14 0.21 0.27 -0.06 -0.02 -0.02 -0.02 0.05 0.06 -0.01 -0.04 0.06 0.02 0.06 0.04

-5.7 -6.0 -8.5 -5.7 -6.3 -8.2 -6.9 -7.5 -8.9 2.6 0.6 0.6 1.0 -1.8 -2.0 0.4 1.8 -1.9 -0.6 -1.8 -1.2

1

Positive values indicate carbon accumulation, negative values indicate carbon loss. Positive values indicate increased carbon emissions, negative values indicate decreased carbon emissions.

2

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SOC change after conversion of cropland-pasture to switchgrass production

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Cropland-pasture is a focus of this analysis because economic models widely used to predict land-use change (LUC) upon expanded biofuel feedstock production often, generally implicitly, adopt land falling in the USDA census category “cropland-pasture” as de facto marginal land.35 For example, Taheripour and Tyner found that in most bioethanol production scenarios, the majority of LUC due to bioenergy production occurred on cropland-pastureland – up to 8.3 Mha in a switchgrass ethanol scenario,35 an area much larger than is currently available in that land use category according to the USDA. The characteristics of cropland-pastureland in economic models are not well-defined, complicating assessments of the SOC implications of its use for feedstock production which feed into estimates of LUC GHG emissions.20, 21 For example, the frequency of switches between cropland and pastureland phases influences SOC levels, but this frequency is not well understood.



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In fact, despite often-cited definitions from the USDA Economic Research Service of cropland-pasture as current pasture land in “long-term rotation” with cropland, the land use history of cropland-pasture is not tracked by the USDA. The USDA Census of Agriculture relies on land managers to report farm area qualifying as “other pasture and grazing land that could have been used for crops without additional improvements.”36 Because the Census does not track the history of individual farm parcels, and highresolution agricultural land cover data for the United States is available for less than a decade in most states, it may be impossible to determine the actual frequency of cropland-to-pasture conversions and reversions. Perhaps just as significantly, the area reported as cropland-pasture is declining rapidly. In 1997, cropland-pasture (27.4 Mha) exceeded CRP enrollment (13.6 Mha). While CRP enrollment declined to 10.5 Mha by 2014, cropland-pasture dropped dramatically to 5.3 Mha in 2012 (Figure S3). Though one report from ERS cites a change in wording of the USDA Census Survey as the reason for this decline,5 after examining the recent history of survey terminology we find this explanation unlikely (Table S5). A key conclusion is that improved land use history data and updated cropland-pasture availability could have a large influence on LUC estimates, and subsequent LUC GHG emissions, for bioenergy crops.

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Our SOC modeling results (Table 2) show that regular transitions between long pasture phases and short cropland phases generate similar SOC changes whether conversion to switchgrass occurs after a pasture phase or a cropland phase, with small influence on SOC change-related GHG emissions. These GHG emissions values are most similar to those from the marginal grassland conversion scenario. Even when switchgrass is established on land that had been under cropland for five years, soil C sequestration rates were low (

Evaluating the Potential of Marginal Land for Cellulosic Feedstock

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