A global meta‐analysis of soil organic carbon response to corn stover removal

Abstract

Corn (zea mays L.) stover is a global resource used for livestock, fuel, and bioenergy feedstock but excessive stover removal can decrease soil organic C (SOC) stocks and deteriorate soil health. Many site‐specific stover removal experiments report accrual rates and SOC stock effects, but a quantitative, global synthesis is needed to provide a scientific base for long‐term energy policy decisions. We used 409 data points from 74 stover harvest experiments conducted around the world for a meta‐analysis and meta‐regression to quantify removal rate, tillage, soil texture, and soil sampling depth effects on SOC. Changes were quantified by: (1) comparing final SOC stock differences after at least three years with and without stover removal, and (2) calculating SOC accrual rates for both treatments. Stover removal generally reduced final SOC stocks by 8% in the upper 0 to 15‐ or 0 to 30‐cm, compared to stover retained, irrespective of soil properties and tillage practices. A more sensitive meta‐regression analysis showed that retention increased SOC stocks within the 30‐ to 150‐cm depth by another 5%. Compared to baseline values, stover retention increased average SOC stocks temporally at a rate of 0.41 Mg C ha⁻¹yr⁻¹ (statistically significant at p<0.01 when averaged across all soil layers). Although SOC sequestration rates were lower with stover removal, with moderate (<50%) removal they can be positive, thus emphasizing the importance of site‐specific management. Our results also showed that tillage effects on SOC stocks were inconsistent due to the high variability in practices used among the experimental sites. Finally, we conclude that research and technological efforts should continue to be given high priority because of the importance in providing science‐based policy recommendations for long‐term global carbon management.
This article is protected by copyright. All rights reserved.

Full text

GCB Bioenergy. 2019;11:1215–1233.
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1215
wileyonlinelibrary.com/journal/gcbb
Received: 26 February 2019
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Revised: 11 April 2019
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Accepted: 9 May 2019
DOI: 10.1111/gcbb.12631
ORIGINAL RESEARCH
A global meta‐analysis of soil organic carbon response to corn
stover removal
HuiXu1
|
HeidiSieverding2
|
HoyoungKwon1
|
DavidClay3
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CatherineStewart4
|
Jane M. F.Johnson5
|
ZhangcaiQin1,7
|
Douglas L.Karlen6
|
MichaelWang1
This is an open access article under the terms of the Creat ive Commo ns Attri bution License, which permits use, distribution and reproduction in any medium, provided the original
work is properly cited.
© 2019 The Authors. GCB Bioenergy Published by John Wiley & Sons Ltd
This article has been contributed to by US Government employees and their work is in the public domain in the USA
1Systems Assessment Group, Energy
Systems Division,Argonne National
Laboratory, Lemont, Illinois
2South Dakota School of Mines &
Technology, Rapid City, South Dakota
3South Dakota State University, Brookings,
South Dakota
4Agricultural Research Service,United
States Department of Agriculture, Fort
Collins, Colorado
5Agricultural Research Service,United
States Department of Agriculture, Morris,
Minnesota
6Agricultural Research Service,United
States Department of Agriculture, Ames,
Iowa
7School of Atmospheric Sciences, and
Guangdong Province Key Laboratory
for Climate Change and Natural Disaster
Studies,Sun Yat‐sen University,
Guangzhou, China
Correspondence
Hui Xu, Systems Assessment Group,
Energy Systems Division, Argonne
National Laboratory, Lemont, IL, USA.
Email: hui.xu@anl.gov
Funding information
Bioenergy Technologies Office, Grant/
Award Number: DE-AC02-06CH11357;
USDA Agricultural Research Service;
Greenhouse gas Reduction through
Agricultural Carbon Enhancement network
Abstract
Corn (Zea mays L.) stover is a global resource used for livestock, fuel, and bioenergy
feedstock, but excessive stover removal can decrease soil organic C (SOC) stocks
and deteriorate soil health. Many site‐specific stover removal experiments report ac-
crual rates and SOC stock effects, but a quantitative, global synthesis is needed to
provide a scientific base for long‐term energy policy decisions. We used 409 data
points from 74 stover harvest experiments conducted around the world for a meta‐
analysis and meta‐regression to quantify removal rate, tillage, soil texture, and soil
sampling depth effects on SOC. Changes were quantified by: (a) comparing final
SOC stock differences after at least 3years with and without stover removal and (b)
calculating SOC accrual rates for both treatments. Stover removal generally reduced
final SOC stocks by 8% in the upper 0–15 or 0–30cm, compared to stover retained,
irrespective of soil properties and tillage practices. A more sensitive meta‐regression
analysis showed that retention increased SOC stocks within the 30–150cm depth
by another 5%. Compared to baseline values, stover retention increased average
SOC stocks temporally at a rate of 0.41Mg C ha−1year−1 (statistically significant
at p<0.01 when averaged across all soil layers). Although SOC sequestration rates
were lower with stover removal, with moderate (<50%) removal they can be posi-
tive, thus emphasizing the importance of site‐specific management. Our results also
showed that tillage effects on SOC stocks were inconsistent due to the high variabil-
ity in practices used among the experimental sites. Finally, we conclude that research
and technological efforts should continue to be given high priority because of the
importance in providing science‐based policy recommendations for long‐term global
carbon management.
KEYWORDS
carbon sequestration, corn, meta‐analysis, soil organic carbon, stover removal, tillage

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1
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INTRODUCTION
Globally, corn (Zea Mays L.) comprises roughly 13% of
the world's arable land (Food and Agriculture Organization
(FAO) of the United Nations, 2016; OECD (Organization for
Economic Co‐operation and Development) & FAO, 2018)
and is expected to increase to over 190megahectares (Mha)
with yields surpassing 1.2 billion megagrams (Mg) per year
by 2027 (OECD & FAO, 2018). Traditionally, residue from
grain production has been harvested and used for animal for-
age, bedding, or household heat. Depending on the region,
unharvested crop residues are sometimes burned, grazed, or
left in fields to aid in soil fertility or health. In North America,
corn became the dominant crop resource that supported the
rise of precolonial civilizations, modern agriculture, and
more recently the biofuel industry.
Bioenergy mandates have created a large global demand
for crop residue. As food and feed demands for corn grain
continue to rise, the proportion of corn grain used for bio-
fuel production (17%) is projected to decrease (OECD &
FAO, 2018), while the use of other perennial feedstocks,
various wastes, and crop residues expands by more than
60%. The United States (US) Environmental Protection
Agency (EPA) has proposed a 2019 cellulosic biofuel
production target of 1.4 billion liters (EPA, 2018), which
will likely be fulfilled predominantly using corn residue
or corn stover (herein defined as the aboveground parts of
the corn plant remaining after the corn grain is harvested;
Wilhelm, Johnson, Hatfield, Voorhees, & Linden, 2004),
simply because of the established stover harvest techniques
and the volume of available feedstock (Moriarty et al.,
2018). According to the US Department of Energy's (DOE)
2016 Billion Ton Report (DOE, 2016), corn residue was
determined to be a near‐term, cost‐effective, and widely
available biomass source, which does not require addi-
tional cultivation or dedicated land. The European Union
(EU) has set a binding renewable energy target of 20% by
2020; ~27millionMg of corn residue is expected to fulfill
two‐thirds of this mandate (Monforti et al., 2015; Scarlat,
Martinov, & Dallemand, 2010).
While corn stover is considered to be a readily available
biofuel feedstock, there is concern about the long‐term via-
bility of removal (Karlen et al., 2011). Residue (a) protects
the soil surface, (b) feeds biological and microbiological
processes essential in soil aggregate formation, (c) regu-
lates soil temperature and moisture, and (d) provides C and
biological material for soil aggradation (Blanco‐Canqui &
Lal, 2009; Johnson, Allmaras, & Reicosky, 2006). Through
these processes, residue can minimize soil erosion and deg-
radation and assist in improving the productivity of future
crops. However, under very high biomass production, wet
soil, or ecologically damaged areas, surface residue can
negatively affect subsequent crops due to poor seed–soil
contact or soil water saturation or increase the use of ag-
rochemicals for weed and pest management. Residue
management is essential to balancing soil health and with
long‐term cropland productivity. In cropping systems, sto-
ver removal for biofuel production or other uses needs to be
managed carefully to preserve the soil resource including
SOC stocks (Wilhelm et al., 2004).
Many global policies use greenhouse gas (GHG) account-
ing for biofuel production and feedstocks to quantify the ef-
fects of production and to allocate renewable fuel credits,
such as California's low carbon fuel standard (LCFS; CARB,
2009), the US renewable fuel standard (RFS; EPA, 2010), and
the European Union's renewable energy directive (European
Parliament, 2009). However, the life‐cycle impact of crop
residues and their relationship to SOC change has not been
consistently addressed (EPA, 2010). In many accounting
schemas, crop residues have a minimal allocated production
burden (Kim et al., 2019). However, crop residue utilization
may change SOC stocks and therefore alter life‐cycle GHG
emissions of residue‐based biofuels. Using SOC and life‐cycle
analysis (LCA) models, Qin et al. (2018) estimated that 30%
stover removal under conventional tillage (CT) in the US can
decrease SOC by 0.04Mg Cha−1year−1. Based on this value,
stover‐based biofuel will not meet the 60% life‐cycle GHG
emission reduction target set by the US RFS mandate unless
the tillage intensity is reduced, or additional organic matter
sources are amended. Still, this analysis relied on modeling
having limited field validation. Globally, momentum for ac-
tion on sustaining or building SOC stocks is growing, but the
lack of robust measurement is often identified as a barrier to
investment in sustainable management practices (Vermeulen
et al., 2019). To inform producers, industry, and other stake-
holders, empirical data are needed to calibrate, validate, and
refine process‐based models so that SOC impacts of residue
removal can be properly accounted for (Johnson et al., 2014).
In recent years, substantial research funded by a vari-
ety of sources, including the DOE, commodity groups, US
Department of Agriculture (USDA), and EPA, was initiated
to evaluate how corn stover harvests affect soil properties
including SOC. Many of these studies reported contradict-
ing and variable results (Johnson et al., 2014; Wilhelm et al.,
2004). For instance, Blanco‐Canqui, Lal, Post, Izaurralde,
and Owens (2006) found that SOC decreased by more than
30% within 1 year after complete stover removal for two
sites in Ohio. Wilts, Reicosky, Allmaras, and Clapp (2004)
reported that stover removal decreased SOC by more than
20MgCha−1year−1, about 20% of the initial SOC stock. In
contrast, a 5year study in Pennsylvania showed that there
was no significant change in SOC due to stover removal
treatment (Adler, Rau, & Roth, 2015). Similarly, Clapp,
Allmaras, Layese, Linden, and Dowdy (2000) reported
that 13years of stover removal did not affect SOC in the
surface soil layer. These varying results occur because soil

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XU et al.
processes are responsive to abiotic and biotic changes, ag-
ronomic management, and spatial and temporal variability.
Furthermore, where, when, how, and why soil tests were
conducted can affect observations and scientific conclu-
sions (Davis et al., 2017).
Many meta‐analyses show that management practices such
as residue removal, fertilization, and rotational diversification
and study duration will affect SOC sequestration (Anderson‐
Teixeira, Davis, Masters, & Delucia, 2009; Chivenge, Vanlauwe,
& Six, 2011; Haddaway et al., 2017; Han, Zhang, Wang, Sun,
& Huang, 2016; Liu, Lu, Cui, Li, & Fang, 2014; Qin, Dunn,
Kwon, Mueller, and Wander (2016b); West & Post, 2002), but
there is a lack of robust quantitative synthesis of the empirical
evidence (Schmer, Stewart, & Jin, 2017; Wilhelm et al., 2004).
Furthermore, previous meta‐analyses have evaluated the effect
of different management practices (e.g., tillage) on SOC for
crop production studies, few focused on evaluating corn residue
impacts. For example, Manley, Kooten, Moeltner, and Johnson
(2005) reported in a global meta‐analysis that no‐till (NT) corn
production could sequester 0.2–0.3MgCha−1year−1 depend-
ing on location and NT duration; however, this study did not
address residue removal. In another case, Lehtinen et al. (2014)
reviewed responses of SOC to crop residue incorporation, in-
cluding crop stover, in European agricultural soils and found
that SOC increased by 7% on average following crop residue
incorporation. However, corn was not a major crop in their da-
tabase. By selecting corn residue retention and removal as key
criteria, Anderson‐Teixeira et al. (2009) and Qin et al. (2016b)
also conducted meta‐analysis primarily focused on land use
change and included croplands previously cultivated with crops
other than corn.
This study was undertaken to provide more empirical ev-
idence regarding how to balance the use of corn residue to
maintain SOC, promote soil health, and provide feedstock
for the bioenergy industry (Johnson et al., 2014; Qin et al.,
2016b). Our objectives were to: (a) assess the overall ef-
fects (direction and magnitude) of stover removal on global
final experimental SOC stocks and accrual rates over time
and (b) identify factors driving the observed SOC changes.
Meta‐analysis was used to quantitatively determine mean
SOC responses to different crop residue management prac-
tices and to compute confidence limits around those means.
Heterogeneity tests and meta‐regression were also employed
to identify the magnitude and sources of variations in the
SOC responses.
2
|
MATERIALS AND METHODS
2.1
|
Global database
Data were collected from archived publicly available data-
sets or published peer‐reviewed literature on corn stover
management and SOC changes. To minimize publication
bias, a literature review and database were compiled follow-
ing four major steps: (a) collecting literature records using
selected keywords, (b) screening and extracting data from
qualified studies, (c) assessing data quality based on meth-
ods used and emphasis on detail provided by the authors, and
(d) conducting statistical analysis (Charles et al., 2017). The
review was limited to 3,380 peer‐reviewed publications in-
cluded in the Web of Science as of June 2018. Keywords for
the review (Table 1) were developed by combining typical
phrases associated with Web of Science literature searches.
First, abstracts of all papers were read to determine rel-
evancy in a standardized manner, using a set of six criteria
(Table 1). This resulted in 2,758 papers being rejected. For
the remaining 622 papers, full‐text screening was carried
out using the same screening criteria. Eligible but dupli-
cated studies were identified and removed from the database
(Figure S1). The net result was 52 eligible studies, including
two studies identified by co‐authors but not returned from a
Web of Science search. Multiple data sources (e.g., related
publications, personal communications) were referenced to
gather sufficient information for a meaningful evaluation of
each study. Both corn grain (48) and corn silage studies (4)
were included in the database, because the purpose of this
meta‐analysis was to evaluate the effect of aboveground bio-
mass removal on SOC and the amount of dry matter of corn
silage (~100% removal) is comparable to that of corn grain
plus 100% stover harvest. Partial corn silage removal studies
and sweet corn cultivars were excluded from this analysis.
Most studies were published between 2011 and 2018 (35)
or 2001 and 2010 (12), with five published prior to 2000.
Some studies reported from multiple locations, so the final
database used for statistical analysis contained 74 unique
experiment sites and 409 paired observations (non‐removal
vs. removal treatment). Unfortunately, many studies only re-
ported SOC at the end of the experiment (hereafter, “final”
SOC), so assessment of temporal SOC change was limited to
272 comparisons. All 409 observations were used for assess-
ing the differences in final SOC stocks between removal and
non‐removal groups.
The 74 experiment sites included in our analysis were pri-
marily located in the United States (40 sites) and China (17
sites) that contribute about 50% of global corn production
(OECD & FAO, 2018). Furthermore, the site distribution was
consistent with the spatial patterns of corn production. Most
US sites were within the Corn Belt, while in China, sites were
concentrated in the Northern provinces (Figure 1).
Among the 74 sites, 40 short‐term (3–5years), 26 medium‐
term (6–15 years), and eight long‐term (>15 years) experi-
ments were included (Figure 2a). We set a minimum duration
of studies to 3years to include as many experiments as possi-
ble and because significant SOC changes may be detected even
within such a short timeframe (Blanco‐Canqui et al., 2006;

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Sindelar, Lamb, & Coulter, 2015). In terms of soil texture, me-
dium‐fine and medium soils were dominant (Figure 2b). For
the 409 paired (control vs. treatment) data points, SOC sam-
pling depth mostly fell within the range of 0–15cm (n=180)
or 0–30cm (n=156) (Figure 2c), with only 73 data points
sampled in a 30–150 cm soil depth. Median and maximum
SOC sampling depth was 20 and 150cm, respectively.
The most common tillage practice (Figure 2d) associated
with the corn stover removal experiments was NT (208 data
points), followed by conventional tillage (CT: 167) and re-
duced tillage (RT: 32). Tillage classification was defined
based on implement type but not the number of passes. Two
sites did not report tillage‐specific results. Conventional till-
age included moldboard plow, chisel plow, disk plowing, and
rotary‐till. Some studies reported CT without specifying the
system. RT mainly included strip tillage and offset disking.
2.2
|
Meta‐analysis
Quantitative meta‐analysis can help assess the overall ef-
fects and identify the sources of variation in outcomes
(Gurevitch, Koricheva, Nakagawa, & Stewart, 2018),
which may not be evident in individual studies because of
conflicting results. Therefore, a common estimator used in
meta‐analysis is the magnitude of the experimental treat-
ment mean (
XTR
) relative to the control or reference mean
(
XCK
). Following Don, Schumacher, and Freibauer (2011)
and Han et al. (2016), a logarithmic response ratio (RR) was
calculated as the main effect size estimator (Equation 1):
In this study,
XTR
and
XCK
refer to SOC stock (MgC/ha)
of experimental treatment with and without corn stover re-
moval practices, respectively, holding the other parameters
constant. This estimator only considers SOC stocks at the
end of the studies.
Differences in annualized SOC change rate
(ΔSOC_R)
between removal and non‐removal cases were calculated be-
cause the absolute magnitude of changes in SOC stock over
time is of interest:
SOCTR,T0
and ~
SOCTR,T1
refer to the initial and final SOC
stock for fields with stover removal.
SOCCK,T0
and
SOCCK,T1
represent the initial and final SOC stock under control (non‐re-
moval), and T is the duration of a study (years).
SOC change can be quantified in multiple ways, depend-
ing on the definition of baseline SOC stocks (Figure 3). Both
RR and
ΔSOC_R
calculate SOC change relative to SOC
stock with stover retention treatment as the baseline scenario
(Figure 3). The two metrics are complementary to each other:
RR evaluates percentage changes in final SOC stocks and
ΔSOC_R
assesses differences in annual SOC change rates.
Note that if
ΔSOC_R
or RR is negative, it can be viewed as C
loss relative to the baseline.
Because neither RR nor
ΔSOC_R
evaluated whether
SOC stock in a given plot has increased or depleted
since experiments initialized, average SOC accrual rates
(i.e., changes in SOC relative to initial SOC levels,
Mg C ha−1 year−1) along with 95% confidence intervals
(1)
RR
=Ln
(
XTR
X
CK )
(2)
ΔSOC_R
=
(
SOCTR,T1 −SOCTR,T0
)
−
(
SOCCK,T1 −SOCCK,T0
)
T
TABLE 1 Keywords and criteria used in literature search
Crop keywords
Logical
operator SOC keywords
Logical
operator
Farm management
keywords
 “Corn” AND  “soil carbon” AND  “residue”
OR “Maize”  OR “soil C”  OR “stover”
OR “Zea mays”  OR “organic carbon”  OR “straw”
   OR “organic C”  OR “tillage”
   OR “soil organic matter”  OR “manure”
   OR “carbon sequestration”  OR “irrigation”
   OR “soil health”  OR “irrigated”
   OR “soil quality”  OR “cover crop”
Criteria
• Corn was the predominant rotational crop (e.g., continuous corn, corn–soybean, corn–wheat, and corn–millet).
• Measurements or experiments were conducted 3years or longer.
• The study had to be field experiments. Modeling, simulation studies, and laboratory incubations were excluded.
• Soil organic carbon was measured.
• Defined corn stover (residue) removal treatments were included in the experiment.
• The experimental design included both control (non‐removal) and treatment (removal) groups.

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XU et al.
were also calculated for various stover removal rates and
tillage types. However, these descriptive statistics were
not used in pairwise heterogeneity test or meta‐regression
analysis.
Using the two estimators defined above, a weighted
meta‐analysis was conducted using the Open Meta‐analysis
for Ecology and Evolution software (OpenMEE) (Wallace
et al., 2017). In each experiment, mean, standard deviation
(SD), and sample size (replicates) of SOC measurement
were extracted to weight each study by variation (SD) and
sample size (n). The SD of SOC stock was extracted for
62% of the studies. When it was not possible to extract
SD or standard error (SE) information from a study, a SD
equal to one‐tenth of the mean was assigned, as was done
FIGURE 1 Location of experiment sites included in meta‐analysis. Shaded color map shows distribution of major corn acreages worldwide.
SPAM, Spatial Production Allocation Model (You et al., 2014)
FIGURE 2 Distribution of studies by (a) duration of field experiment measurement, (b) soil texture of fields, (c) soil organic C (SOC)
sampling depth, and (d) tillage system

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by others (Gattinger et al., 2012; Han et al., 2016; Luo, Hui,
& Zhang, 2006).
To identify predominant drivers associated with the vari-
ations among observations, subgroup analysis was conducted
by grouping paired observations into different groups based
on biophysical (e.g., SOC sampling depth) and management
practices (e.g., tillage system). The mean effect size of each
group along with 95% confidence intervals (CIs) was re-
ported. Meta‐analysis generally uses either fixed‐effect or
random‐effect models. A fixed‐effect model only considers
sampling variance and it assumes that effect size is the same
in all studies, which is not plausible for independent corn
stover experiments as site condition varies across studies. A
random‐effect model considers both sampling variance and
heterogeneity as it assumes that the true effects are normally
distributed (Gurevitch et al., 2018). “Random” here means
studies can share similar but not identical true effects. In this
study, the random‐effect model was used because it is more
general than a fixed‐effect model.
Multiple factors can affect SOC dynamics simultaneously.
To investigate potential drivers associated with the observed
SOC differences, a random‐effects meta‐regression model was
used. This was also run in the OpenMEE software. In addition
to stover removal, a range of other factors including long‐term
average annual precipitation and temperature, which are known
to influence SOC, were also tested.
To facilitate the interpretation of effect size, the response
ratio was transformed as a percentage change ([
eRR
−1]×100),
with zero suggesting no SOC difference between the removal
and non‐removal groups, and positive and negative values indi-
cating lower and higher SOC stock in the removal groups than
the no‐removal groups, respectively. Differences in annualized
SOC change rates (
ΔSOC_R
) between control and treatment
groups were presented in physical units (MgC ha−1year−1),
with positive and negative values indicating that annual SOC
sequestration rates with stover removal were higher or lower
than corresponding non‐removal sites.
2.3
|
Data processing
For RR estimation, it was implicitly assumed that initial SOC
stock was the same for both non‐removal and removal plots,
so that differences in final SOC stock between plots can be
attributed to differences in farm management practices. We
subsequently found that differences in measured initial SOC
stocks of removal and non‐removal sites were more than 5%
for 70 comparisons (16% of total data points). To address this
issue, for RR estimation, the final SOC stock for the removal
group was adjusted based on differences between initial SOC
stock reference values and treatment fields. For instance, if
the initial SOC stock within a control plot was 5Mg C/ha
lower than the plot with stover removal treatment, 5Mg C/
ha was added to the final control plot value, so that the dif-
ferences between removal and non‐removal plots would not
be overestimated. For subgroup analysis, data points were
grouped into three intervals (0–15, 0–30, and 0–150 cm)
based on each data point's sampling depth.
For
ΔSOC_R
, soil depth adjustment is necessary because
it directly affects the absolute amount of SOC change. Among
the 272 comparisons, most studies reported SOC stock for
the 0–15 (n=83), 0–30 (n=79), or 0–60cm (n=43) depth
increments. Measurements from studies with different sam-
pling depths were adjusted to one of the three depths, which
were used as standard profile segments. For observations with
a sampling depth <30cm and CT, it was assumed that SOC
was uniformly distributed. For studies with NT, SOC measure-
ments were converted to standard profiles based on vertical
SOC distribution patterns. For instance, a conversion factor of
1.35, instead of 1.5, was used to convert 0–20cm SOC stock to
0–30cm values (Puget & Lal, 2005; Yang & Wander, 1999).
This was done because C concentration in the surface layer is
generally higher within NT systems. Because previous studies
rarely reported vertical SOC distribution beyond the 0–100cm
interval, seven data points with a depth >100cm were excluded
from
ΔSOC_R
analysis to minimize uncertainties caused by
FIGURE 3 Illustration of soil organic
C (SOC) change quantification based on
dynamic baseline (SOC change relative to
stover retention or non‐removal) versus
steady baseline (SOC change relative to
initial SOC stock). Modified from Qin et al.
(2018)

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depth adjustment. With that, maximum soil profile used in an-
nual SOC change analysis was 60cm rather than 150cm as
used in the RR analysis. More details on SOC stock adjustment
can be found in the Supplementary Information.
For studies reporting fixed‐depth SOC, the original SOC
measurement was transformed to ESM (Ellert & Bettany, 1995)
when possible. This was necessary because soil bulk density may
change substantially over a monitoring period, due to differences
in management practices and other factors. Among the 74 exper-
iment sites, 23 reported SOC based on ESM. Using initial and
end SOC concentration (%) and bulk density data, we adjusted
the end SOC based on ESM for another 11 sites. The remaining
40 sites did not have sufficient information for ESM adjustment.
For meta‐analysis, the dataset used in all calculations in-
cludes both ESM and fixed‐depth measurements. This is neces-
sary to include as many eligible studies as possible, and to test
the impact of SOC calculation method on meta‐analysis results.
3
|
RESULTS
3.1
|
Effects of corn stover removal on SOC
stock
3.1.1
|
Response ratios grouped by
management practices and soil properties
Compared to control plots (stover retention), final SOC
stocks were lower (Figure 4a) if stover was removed and RR
varied by the soil profile intervals. For data points in the 0–15
and 0–30cm profiles, plots with stover removal had an aver-
age of 7.4% and 8.4% less stored SOC than stover retention
plots, respectively. Furthermore, for studies that reported
SOC change over the 0–150 cm profile, there was no sig-
nificant difference in SOC stock due to stover removal treat-
ment (p>0.91). Note that 150cm is the maximum sampling
depth, and all data points with a SOC sampling depth greater
than 30cm are included in the 0–150cm profile. Note that
many studies reported SOC for only one of the three profile
intervals (i.e., 0–15, 0–30, 0–150 cm), so that the number
of data points varied by SOC sampling depth. The database
includes only 73 observations with a sampling depth >30cm,
which thus represents a relatively small fraction of the entire
database. Among the 74 sites, 29 sites reported SOC for both
0–15 and 0–30cm intervals, and 12 sites reported SOC for
all three intervals. When studies reported SOC at multiple
depths (e.g., 0–15 and 0–30cm), both surface and subsurface
layers were included in our analysis.
In addition to soil profile intervals, RR also varied by
soil texture (Figure 4b), tillage system (Figure 4c), and sto-
ver removal rate (Figure 4d), though differences between
various soil texture and tillage systems were not statisti-
cally significant. Response ratios of medium‐fine (−5.6%)
and medium (−5.8%) soils were close to each other. RR for
fine and coarse soils was larger than 10%, but sample size
of both groups was small (Figure 4b), so the results may be
less robust. In terms of tillage, magnitudes of SOC change
under CT (−7%) were slightly larger than NT (−6.4%),
suggesting that removal may have a relatively smaller im-
pact on SOC when NT is adopted. However, the difference
in mean values was not significant (t=−0.522, p=0.602).
Results for RT were determined to be unreliable due to the
small sample size (n=32). RR was sensitive to the intensity
of stover removal (Figure 4d). While high removal (>75%)
FIGURE 4 Changes in final soil
organic C (SOC) stock (%) (mean with 95%
confidence intervals [CIs]) by different
(a) soil profile intervals, (b) soil texture,
(c) tillage system, and (d) corn stover
removal rate. Positive and negative values
correspond to stover removal stored more
and less SOC than stover retention (control)
plots, respectively. Error bar represents 95%
CIs, and numbers in parentheses represent
number of paired comparisons in each
category

1222
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XU et al.
reduced SOC stock by 8.7% on average, compared to sto-
ver retention, reduction in SOC was only about 1.4% for
fields with moderate (<50%) removal rates. The difference
between RR of moderate (<50%) and medium (50%–75%)
removal groups was significant (t= −2.913, p= 0.004),
but the difference between high and medium removal RR
was not significant (t=−1.532, p=0.126).
Given that NT may affect surface soil carbon concentration
more than sub‐surface layers, RR was also calculated by different
tillage systems and SOC sampling depths (Figure 5). The results
suggested that SOC change in the 0–15cm profile was margin-
ally smaller for fields with a NT (mean=−7.4%, SE=1.11) than
CT (−8.6%, SE=2.94) (Figure 5), but the difference between
CT and NT was not statistically significant (t=0.425, df=157,
p=0.671) because CT fields have a large variance. The response
ratio calculated for the 0–30 cm profile suggested that SOC
change under NT and CT was close to each other. When SOC
change was reported for the 0–150cm profile, the mean RR for
NT and CT was 1.82% (SE= 1.51) and −1.69% (SE = 1.41),
respectively, and the difference between NT and CT was signif-
icant at a 0.1 level (t=−1.671, df=69, p= 0.099). However,
due to the relatively small sample size, more research is needed.
The response of SOC to stover removal rate also varied
by sampling depth (Figure 6). When SOC stock was mea-
sured for the 0–15 or 0–30cm soil profiles, the mean SOC
change was much smaller under moderate removal (<50%)
than medium (50%–75%) or high (>75%) removal rates. At
the 0–15cm profile, the RR of the moderate removal (<50%)
group was close to zero (mean=−1.4%, SE=1.82) and not
significantly different from zero, suggesting small differ-
ences in SOC stock between the removal and non‐removal
sites. In contrast, a much stronger SOC response (mean
RR=−9.3%, SE=2.33) was associated with high stover re-
moval treatment. A t test showed that differences between the
high and moderate removal groups were statistically different
(t=1.915, df =117, p=0.058). Differences between SOC
change rates of the moderate (mean= −2.8%, SE = 1.82)
and high removal groups (mean=−9.6%, SE= 1.92) were
smaller in the 0–30cm profile and marginally significant at
a 0.1 level (t= 1.642, df = 97, p=0.102). For SOC mea-
surements based on the 0–150cm profile, SOC stocks under
moderate removal were higher than under high removal, but
the differences were not statistically significant (p=0.384).
3.1.2
|
Fixed‐depth versus equivalent
soil mass
Because both fixed‐depth and ESM results were included in
the meta‐analysis, effect size and variance for each method
were also calculated separately to evaluate the impact of
SOC calculation method on RR. The dataset was first di-
vided into two parts based on SOC calculation method
(fixed‐depth or ESM), and then, a subgroup analysis based
on tillage (Figure 7a) or stover removal intensity (Figure 7b)
was conducted for each part of the database. Results sug-
gest that differences due to ESM can overwhelm variations
caused by tillage and removal rate (Figure 7). With respect to
FIGURE 5 Changes in final soil organic carbon stock (%) (mean
with 95% confidence intervals [CIs]) by different tillage systems and
soil sampling depth. Error bar represents 95% CIs, and numbers in
parentheses represent number of paired comparisons in each category.
“Mean” category measures mean (with 95% CIs) response ratios for
each soil organic C (SOC) sampling depth. NT and CT refer to no-till
and conventional tillage
FIGURE 6 Changes in final soil organic carbon stock (%) (mean
with 95% confidence intervals [CIs]) based on removal rate and soil
organic C (SOC) sampling depth, compared to stover retention. Error
bars represent 95% CIs. Numbers in parentheses refer to number of
paired data points. For each SOC sampling depth category, mean affect
size (with 95% CI) averaged across residual removal intensity was also
presented (labeled as “Mean” in legend)

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XU et al.
the high removal, studies reported based on ESM suggested
that stover removal lowered SOC stock by 2.6%. However,
observations with fixed‐depth measurements indicated that
stover lowered SOC stock by 11.4%. Still, one of the caveats
with heterogeneity test is that it focuses on one variable at a
time so that variations caused by multiple factors (e.g., crop
rotation, precipitation) were not considered in the test. In
this case, the impacts of ESM on RR may be overestimated
in subgroup analysis. Meta‐regression presented in the fol-
lowing section was designed to address this issue.
3.1.3
|
Meta‐regression analysis
Meta‐regression analysis suggested that end SOC stock, SOC
reporting method (ESM or fixed‐depth), sampling depth, du-
ration of studies, and crop rotation were the five most sig-
nificant variables explaining the variation in response ratios
(Table 2). Tillage, N fertilizer rate, irrigation status (rainfed
or irrigated), and climate (mean annual precipitation and
mean temperature) were not significant variables. Variance
analysis suggested that 70.8% of the total variance was be-
tween studies (I2= 70.7%), and the covariates were able to
explain 44.6% of the between‐study variance (R2=44.7%).
Consistent with subgroup analysis results, meta‐regression
analysis confirmed that SOC changes were sensitive to corn
stover removal rate, but not tillage type. Compared to mod-
erate removal (<50%), medium and high removal treatments
may further reduce SOC stock by about 3%. Estimates of CT
and RT were not statistically different from zero (p> 0.1),
suggesting that tillage was not a significant predictor.
Estimated coefficients for end SOC stock (0.002), ESM
(0.068), and corn–soybean rotation (0.077) were positive.
Holding other parameters constant, a positive coefficient
suggested a smaller difference in end SOC stock between re-
moval and non‐removal treatments. These results indicated
that stover removal had a smaller impact on plots with higher
SOC content, if other conditions were equivalent. Note that
the SOC stock variable refers to the final SOC stock rather
than the initial SOC stock, because approximately half of the
studies did not report initial SOC values. The ESM estimated
SOC losses were about 7% less than the fixed‐depth measure-
ments. Similarly, reductions in SOC stock were 7.5% smaller
for plots with corn–soybean rotations than continuous corn.
Negative coefficients for duration (years) and SOC sam-
pling depth suggested that differences in SOC stock between
removal and non‐removal sites increased with study dura-
tion and sampling depth. It is reasonable that differences
in SOC stock tend to be larger for longer term studies than
short‐term studies because changes in SOC stock due to stover
removal may accumulate over years. Similarly, compared to
studies focusing on the 0–15cm profile, reductions in final
SOC stock were 4.2% or 9.7% larger on average, in the 0–30
and 0–150cm profile, respectively. While subgroup analysis
suggested that stover removal only affects SOC stock in the
0–30cm profile, meta‐regression results suggested that stover
removal influenced SOC stock beyond the 0–30cm profile.
3.2
|
Effects of corn stover removal on SOC
change rate
3.2.1
|
SOC accrual rates
For plots with stover returned rather than removed, SOC ac-
crual rate calculated from experimental baseline (initial SOC
FIGURE 7 Changes in soil organic carbon stocks (%) (mean with 95% confidence intervals [CIs]) for observations with (a) different ESM
status and tillage systems, and (b) different ESM status and stover removal rates. Error bars represent 95% CIs. Numbers in parentheses refer to
number of paired data points. NT and CT refer to no-till and conventional tillage.

1224
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XU et al.
level) tended to be positive (mean=0.41MgCha−1year−1,
CI = 0.25, 0.58) (Figure 8). While the magnitude of SOC
accrual rate is relatively small, t test shows that it is signifi-
cantly different from zero (t=4.796, df=271, p<0.01).
Calculation of SOC change induced by stover removal
depends on the definition of baseline and management
practices. When reference fields (stover retention) were
used as the baseline, treatments in which the stover was
removed generally had lower SOC accrual rates. This dif-
ference in SOC sequestration rates can be viewed as “fore-
gone carbon sequestration.” If the baseline scenario is
pretreatment or initial SOC stock, final SOC stock tends to
increase over time under NT and lower removal intensity
treatment, and to decrease over time under CT and high re-
moval intensity treatment (Figure 8). For instance, for NT
fields, mean SOC accrual rates under medium (50%–75%)
(mean=0.32MgCha−1year−1, SD=1.08) and low were
positive and significantly greater than 0, suggesting that
fields with stover removal treatment may still accumu-
late SOC over time, although only marginally. Wilhelm,
Johnson, Karlen, and Lightle (2007) had similar results,
and reported that less stover biomass input was needed to
maintain SOC under NT than CT, and that stover produced
beyond the amount needed to maintain SOC and address
other environmental issues could be removed for other
uses. For fields under CT, the mean SOC accrual rates were
TABLE 2 Meta‐regression results for soil organic C (SOC) change response ratio (SOC stocks)
Variable Point estimate
Standard
error p‐value
95%
lower 95% upper
Significance
level
Intercept −0.139 0.038 <0.001 −0.213 −0.065 ***
Duration (years) −0.003 0.001 0.005 −0.006 −0.001 **
SOC stock of treatment plot (Mg C/ha) 0.002 0 <0.001 0.001 0.002 ***
N fertilizer ratea−0.001 0.001 0.387 −0.002 0.001 
Mean annual precipitationb0.002 0.002 0.311 −0.002 0.007 
Mean temperature (°C) 0.001 0.002 0.444 −0.002 0.004 
Irrigation applied 0.02 0.02 0.314 −0.019 0.059 
Based on ESM 0.068 0.014 <0.001 0.042 0.095 ***
Soil, fine texture −0.058 0.028 0.041 −0.113 −0.002 *
Soil, coarse texture −0.037 0.031 0.239 −0.099 0.025 
Soil, medium texture −0.04 0.014 0.005 −0.068 −0.012 **
Corn–millet rotation 0.028 0.054 0.607 −0.078 0.133 
Corn–soybean rotation 0.077 0.02 <0.001 0.039 0.116 ***
Corn–wheat rotation −0.027 0.026 0.305 −0.079 0.025 
SOC sampling depth (>30cm) −0.097 0.027 <0.001 −0.149 −0.044 ***
SOC sampling depth (0–30cm) −0.042 0.014 0.003 −0.069 −0.014 **
Stover removal rate (>75%) −0.033 0.017 0.047 −0.066 0 *
Stover removal rate (50%–75%) −0.035 0.017 0.034 −0.068 −0.003 *
Conventional tillage 0.019 0.012 0.113 −0.005 0.043 
Reduced tillage 0.02 0.021 0.343 −0.021 0.062 
Test of the model
T2=0.006 (SE=0.001), I2=70.81%, R2=44.62%
Test for residual heterogeneity
QE=1,546.525, df=387, p<0.001
Test for moderators
QM=208.211, df=19, p<0.001
Note: Positive and negative estimates suggest smaller and larger differences between removal and non‐removal groups, respectively. ESM status, soil texture, crop
rotation, SOC sampling depth, stover removal rate, and tillage are categorical covariates. Their reference values were fixed‐depth method, medium fine texture, con-
tinuous corn, 0–15cm sampling depth, moderate removal group (<50%), and no‐till, respectively.
Significant levels: 0 “***” 0.001 “**” 0.01 “*” 0.05.
aRaw N rate data were divided by 10, so one unit of change is 10kgN/ha/year.
bRaw precipitation data were divided by 100, so one unit of change is 100mm/year.

|
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XU et al.
negative even under the moderate stover removal treatment
(Figure 8). A t test showed that differences between SOC
accrual rates of NT and CT plots were statistically signif-
icant under moderate (t=3.053, df=36, p=0.004), me-
dium (t=3.155, df=108, p=0.002), and high removals
(t=−2.177, df=115, p=0.0315).
It is necessary to point out that data presented in
Figure 8 represent SOC accrual rates grouped by differ-
ent tillage and removal treatments. These descriptive sta-
tistics are different from meta‐analysis statistics because
they were not pairwise heterogeneity tests. With that, al-
though mean SOC change rate for NT with <50% treat-
ment (0.98MgCha−1year−1, SD=1.72) was higher than
0.41Mg Cha−1 year−1, it does not mean that NT (<50%)
treatment has the highest SOC accrual rate. This is because
mean value for the retention treatment group was computed
based on all 272 control data points, whereas the 0.98 value
was computed for the group of data points (n= 20) with
NT and <50% removal rates only. If mean value of stover
retention treatment was calculated based on the 20 corre-
sponding control (stover retention) data points, then there
were no statistical differences between retention and NT
(<50%) treatment (t=0.133, df=38, p=0.895). Results
based on pairwise meta‐analysis (∆SOC_R) were pre-
sented in the next section.
Note that mean SOC change rates were calculated from
data points with varying sampling depths (0–15, 0–30, and
0–60cm). Averaged across all crop systems, the SOC ac-
crual rates with stover retention treatment in the 0–15 and
0–30cm intervals were 0.39 and 0.49Mg C ha−1year−1,
respectively (Figure 9). The mean SOC change rate
(0.29Mg Cha−1year−1, SD=0.17) was smaller for data
points with a sampling depth >30cm. However, most stud-
ies did not compare SOC change at both 0–30 and 0–60cm
depths, and the sample size for the 0–60cm profile was rel-
atively small (57). In this case, the results for the 0–60cm
profile may not be representative. Furthermore, differences
in mean SOC change rates among the three groups were not
statistically significant (p>0.5).
For biofuel policy‐making in the United States, whether
domestic SOC response is consistent with the interna-
tional database is relevant. Overall, SOC accrual rates in
US Midwestern states (Figure S2) were similar to values
in the global database (Figure 8), partly because most
FIGURE 8 Soil organic C (SOC) accrual rate from experimental baseline (initial SOC level) (MgCha−1year−1, mean with 95% confidence
intervals [CIs]) for control (stover retention, averaged for conventional tillage [CT] and no‐till [NT]) and treatment cases grouped by different
tillage types and removal rates from descriptive statistics. Positive values mean that SOC stock increased over time, and negative values mean that
SOC stock decreased over time. Stover retention group was calculated based on all 272 control data points. Error bar represents 95% CIs. Values in
parentheses represent number of data points in each treatment group
FIGURE 9 Soil organic C (SOC) accrual rate
(MgCha−1year−1) (mean with 95% confidence intervals [CIs]) by
SOC sampling depth for plots with stover retained in fields. Positive
values mean that SOC stock increased over time, and negative values
mean that SOC stock decreased over time. Error bars represent 95% CIs

1226
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XU et al.
comparisons (214, or 78%) were from this highly pro-
ductive, corn‐producing region. Most studies outside the
United States did not report initial SOC content. SOC se-
questration under retention treatment was relatively smaller
than the group database: Midwest SOC accrual rate for
the non‐removal group (mean=0.19MgC‐1ha−1year−1,
SD = 1.175) was lower than that in the global database
(mean=0.41MgCha−1year−1). Although the magnitude
of C sequestration is relatively small, this positive trend
was significantly different from zero (t=2.43, df = 214,
p=0.02).
3.2.2
|
Differences in annualized
change rates
Differences in annualized SOC change rates (∆SOC_R)
between pairwise removal and non‐removal plots were
calculated for both tillage systems and sampling depths
in the meta‐analysis. The mean ∆SOC_R of NT was
smaller than that of CT plots, but the difference between
the two was not statistically significant for the 0–15 and
0–60cm profiles (Figure 10). For the 0–30cm profile, the
∆SOC_R of NT plots (mean=−0.196MgCha−1 year−
1, SD= 0.353) were marginally smaller than those of CT
plots (mean=−0.42MgCha−1year−1, SD=0.763), and
the differences were statistically significant at a level of 0.1
(t=−1.872, df=103, p=0.064).
The ∆SOC_R calculated based on SOC sampling depth
and stover removal rate (Figure 11) indicated that higher
stover removal rates were associated with larger differences
in annual SOC change rates. Compared to non‐removal
treatments, high removal lowered SOC accumulation rates by
0.15, 0.44, and 0.77MgCha−1year−1 on average for the 0–15,
0–30, and 0–60cm profile intervals, respectively (Figure 11).
Nonetheless, the differences between the ∆SOC_R of high
and medium removal groups were not statistically significant
(p>0.1) at all three sampling depths. The moderate removal
group was excluded from this analysis because sample size
was less than 10 in most sampling depths.
Differences in annualized SOC change rates between re-
moval and non‐removal groups depended on the field ex-
periment duration. Overall, the magnitudes of ∆SOC_R
were larger for short‐term (<5 years) than longer term stud-
ies (Figure 12). On average, ∆SOC_R tended to be negative
(mean=−0.63MgCha−1year−1, SD=1.63) for short‐term stud-
ies, but ∆SOC_R approached zero for longer term (>10years)
studies (mean=−0.08 Mg C ha−1 year−1, SD = 0.94). Even
though annualized SOC change tended to decrease with time,
accumulated differences in SOC stock were larger for longer
term studies, as indicated by meta‐regression analysis (Table 2).
4
|
DISCUSSION
4.1
|
Stover removal reduced SOC stocks
compared to retention
Understanding how agricultural management, site charac-
teristics, and residue removal intensity impact SOC stock
changes is critical in maintaining soil fertility and health
as well as determining science‐based policy recommenda-
tions for carbon management. Our global synthesis of 409
data points found that stover removal generally reduced
SOC stocks by about 8% in the 0–15 or 0–30cm profile,
FIGURE 10 Weighted differences in annualized soil organic
C change rates (ΔSOC_R) (MgCha−1year−1) for data points with
different sampling depths and tillage systems (mean with 95%
confidence intervals [CIs]). Error bars represent 95% CIs. Numbers in
parentheses are the number of data points in each group. NT and CT
refer to no-till and conventional tillage.
FIGURE 11 Weighted differences in annualized soil organic
C change rates (ΔSOC_R) (MgCha−1year−1) for different soil
depths and stover removal rates (mean with 95% confidence intervals
[CIs]). Error bars represent 95% CIs. Numbers in parentheses are the
number of data points in each group. 50%–75% and 75% refer to stover
removal rate

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XU et al.
compared to when stover was returned, irrespective of
soil properties and tillage system. However, the magni-
tude of SOC stock reduction depended on the intensity of
stover removal, the soil depth considered, and crop rota-
tion with tillage having a relatively little effect. The SOC
stock decreased with increasing stover removal rate, which
was consistent with previous findings (Anderson‐Teixeira
et al., 2009; Johnson et al., 2006, 2014; Larson, Clapp,
Pierre, & Morachan, 1972). On average, SOC stocks under
moderate removal rate (<50%) were 1.4% lower in SOC
(RR = −1.4%) than corresponding non‐removal plots,
and plots with high removal (>75%) had 8.7% lower SOC
stocks. Note that this reduction refers to “foregone C se-
questration” (Figure 3), rather than soil C change over
time. A previous meta‐analysis (Qin et al., 2016b) reported
that stover removal did not reduce SOC stocks. In fact,
SOC stocks increased by 15%–23% when stover removal
rate is less than 70%. A key difference between Qin et al.
(2016b) and this analysis is that response ratios were calcu-
lated differently: Qin et al. (2016b) evaluated SOC change
over time in the same field, whereas RR in this study was
calculated based on final SOC stocks of fields with stover
removal and stover retention treatments. In addition, Qin
et al. (2016b) did not evaluate additional variables (e.g.,
soil texture, precipitation) other than removal intensity.
When additional important variables were included, meta‐
regression results confirm that stover removal rate was still
a significant variable, but the magnitude of SOC change
was relatively smaller. Holding other parameters constant,
removing 50%–75% stover can further reduce SOC by
about 3% when compared to moderate removal. This 3%
reduction found by meta‐regression was less than half the
7% difference identified in subgroup meta‐analysis, due to
normalization across site variables (i.e., variations caused
by soil properties, tillage, and SOC stock calculation
methods). These results indicated that limiting stover re-
moval to a low level (e.g., 30%–40%) could minimize the
adverse impacts of stover removal on SOC, as is currently
the recommended practice. Owen et al. (2016) suggested
that up to 50% of stover may be removed sustainably, if
the grain yield was higher than 11Mgha−1year−1 based
on a synthesis of field data from the Sun Grant Regional
Feedstock Partnership. It is important to remember that
higher yields produce more stover and, for corn, the har-
vest index (grain/(grain + stover)) is approximately 0.5.
Harvesting 50% of 11Mg/ha leaves 5.5Mg/ha of residue,
which some have suggested is an appropriate maintenance
requirement (Johnson et al., 2014; Owens et al., 2016).
While field experiments often included both low and
high removal treatments, intensive removal is unlikely to be
widely adopted because farmers recognize the need for corn
residues to protect the soil and replenish soil organic matter
(Obrycki & Karlen, 2018; Schmer, Brown, Jin, Mitchell, &
Redfearn, 2017). In the Corn Belt, stover removal can also
be limited by conservation guidelines. For instance, con-
servation tillage systems require 30% groundcover to meet
policy requirements (e.g., commodity program conservation
compliance; Tyndall, Berg, & Colletti, 2011). Because RR
was only about 1.4% on average, a low removal (e.g., 30%)
rate may be feasible, especially when there are additional C
inputs and conservation practices (e.g., manure, cover crop;
Wegner et al., 2015). On a national (United States) average
basis, Qin et al. (2018) projected that SOC stock could still
increase with 30% stover removed, if cover crop and manure
application practices were also adopted.
Residue removal reduced SOC stocks in both the 0–15
and 0–30 cm profiles, which was consistent with observa-
tions from previous field experiments (Clay et al., 2015;
Schmer, Jin, Wienhold, Varvel, & Follett, 2014). However,
according to subgroup analysis (Figure 3a), this reduction
FIGURE 12 Differences in
annualized soil organic C change rates
(ΔSOC_R) plotted against the duration of
field experiment measurements

1228
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XU et al.
in SOC stock by stover removal was close to zero when the
entire 0–150cm profile was considered. Some studies sug-
gest that stover removal primarily influences near‐surface
SOC dynamics (Blanco‐Canqui & Lal, 2007; Halvorson &
Stewart, 2015; Stewart et al., 2015). For instance, a 5‐year
study in South Dakota found that residue removal influenced
C cycling in the 0–15 and 15–30cm depths, but did not affect
SOC turnover in the 30–60cm depth (Clay et al., 2015). A
10‐year study in Nebraska also found that stover removal did
not affect SOC beyond the 0–30cm profile (Schmer et al.,
2014). However, most studies (82% of the total data points)
did not compare SOC change beyond 30cm; more data are
needed to validate whether there are stover removal effects at
depths greater than 30cm.
Agricultural management effects, such as stover re-
moval, could be difficult to detect when SOC change was
integrated over a large profile interval (Kravchenko &
Robertson, 2011), but studies do detect deep SOC changes.
While subgroup analysis suggested that residual removal
did not affect SOC beyond the 0–30cm profile, meta‐re-
gression analysis indicated that reduction in SOC stock was
larger in the 0–150cm interval than the 0–15 or 0–30cm
interval. In fact, some field experiments (Huang, Yang,
Huang, & Ju, 2018; Stewart, Halvorson, & Delgado, 2017)
found that changes in tillage and C input can affect deep
SOC, probably because deep SOC can be susceptible to
decomposition and priming from the addition of new labile
organic C. For instance, a 13year study found that adoption
of NT reduced corn‐derived C in layers deeper than 30cm,
which in turn reduced SOC (Stewart et al., 2017). Follett,
Vogel, Varvel, Mitchell, and Kimble (2012) reported that
more than 50% of the increase in SOC was below the 30cm
depth. Other studies also found that changes in SOC stock
due to crop management practices can be more significant
at the 30–100 cm depth than at the 0–30 cm depth (Fan
et al., 2014; Huang et al., 2018; López‐Bellido, Fontán,
López‐Bellido, & López‐Bellido, 2010). The mechanisms
of deep soil change are not well measured or understood.
Possible explanations include leaching of dissolved organic
carbon, changes in C input from upper soil, bioturbation
caused by earthworms, and changes in root distribution
(Kinoshita, Schindelbeck, & Es, 2017; Stewart et al., 2017).
Effects of tillage on response of SOC to stover removal
were not consistent and small compared to other agricultural
management practices. On the one hand, mean SOC accrual
rates grouped by tillage (Figure 8) suggested that, averaged
across studies, NT fields sequestered more C than CT fields,
given similar stover removal intensity. On the other hand, till-
age was not a significant predictor in the meta‐analysis het-
erogeneity test and meta‐regression, largely due to the large
variability of response ratio under CT (Figure 5). Unlike NT,
CT is a general term that includes many different types of till-
age systems (Figure 2d) with varying tillage depths, and some
sites were tilled multiple times. For this reason, although NT
fields presented higher mean SOC accrual rates, benefits of
NT relative to CT were highly variable at the site level. A
key difference between meta‐analysis and descriptive sta-
tistics (Figure 8) is that meta‐analysis evaluates differences
in paired‐site experiments, but descriptive statistics simply
compare SOC change rates among different treatment groups
without considering site‐level differences. These results sug-
gest that NT fields may store slightly more SOC than CT
fields (averaged values for all studies), but the benefit of NT
relative to CT is highly variable in paired‐site experiments.
Residue removal effects on SOC stocks are a function
of the interaction of stover removal intensity and tillage
(Figure 8). For instance, differences between NT and CT
SOC change rates tend to diminish with higher stover re-
moval rates (Figure 8). This is because the amount of crop
residues strongly affects SOC sequestration; therefore, stover
removal will reduce surface biomass input and diminish the
benefits of NT (Zhang, Lal, Zhao, Xue, & Chen, 2014). A re-
cent meta‐analysis on NT and SOC (Du, Angers, Ren, Zhang,
& Li, 2017) also found that the response of SOC stock to
NT system was greater when residue was returned; response
was not significant when residue was removed (p= 0.099).
Because stover removal intensity appears to be a robust pre-
dictor across studies, whereas the effect of tillage on SOC
is more variable, future efforts aiming to utilizing stover re-
source sustainably should probably focus more on identifying
proper stover removal intensity.
The greater sensitivity of meta‐regression allowed us to de-
tect crop rotation effects on SOC stocks, when the subgroup
analysis did not. Using meta‐regression, we found that reduc-
tions in final SOC stocks due to stover removal were 7.7%
lower under corn–soybean rotation compared to continuous
corn fields. In other words, corn–soybean rotation was able to
preserve SOC better than continuous corn. Under multicrop ro-
tations, corn stover removal would not happen every year, so
that its impact on SOC would be smaller compared to continu-
ous corn. By synthesizing 55 studies, Ugarte, Kwon, Andrews,
and Wander (2014) also found that a multicrop rotation (3
years) can increase SOC by 7%–25% compared to a continuous
corn system, though crop residue removal was not considered.
4.2
|
SOC change rate and
baseline definition
SOC change is an important component in biofuel LCA and
bioenergy policy discussions (Qin, Dunn, Kwon, Mueller, &
Wander, 2016a). SOC change should be evaluated using a
baseline because conclusions on SOC change can differ de-
pending on the definition of the baseline (Qin et al., 2016a,
2016b). Two general themes: SOC change overtime and
“foregone” C sequestration were included in this analysis.

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XU et al.
Meta‐analysis based on response ratio (RR) and differences
in annual SOC change rate (∆SOC_R) use SOC stocks under
retention treatment as the reference or baseline scenario.
With this baseline definition, both RR and ∆SOC_R‐based
analysis found stover removal SOC stocks were lower than
non‐removal plots. Because stover retention can store more
C than removal, difference between retention and removal
treatment SOC stocks can be viewed as “foregone” C seques-
tration or C debit for stover removal. If net SOC change was
assessed by comparing initial and final SOC stocks overtime,
this study found that stover retention generally increased
SOC stock overtime, but moderate stover removal may also
maintain or even increase SOC stock over time. Nonetheless,
meta‐analysis based on ∆SOC_R indicated that SOC accrual
rates with medium and high stover removal rates were lower
than non‐removal plots (negative ∆SOC_R values, Figure
11). Depending on the definition of baseline scenario, corn
stover as a bioenergy feedstock may either receive a C credit
because moderate removal treatment may still increase ini-
tial SOC stock or carries C debit because it lowered SOC se-
questration rate compared to residue retention. These results
suggest that it is important to clarify the baseline used for
SOC change assessment and to distinguish between absolute
changes versus relative changes when evaluating the impact
of stover removal on SOC.
Stover removal lowered SOC accrual rates overall, but differ-
ences in annual SOC change rates tended to decrease with time.
These results are consistent with first‐order kinetics (Guzman
& Al‐Kaisi, 2010; Janzen et al., 1998; Lal, 2004; Lugato, Berti,
& Giardini, 2006; West & Post, 2002). As a result, ∆SOC_R,
which was calculated by dividing the SOC change by the num-
ber of years, will be smaller for long‐term studies, as changes in
SOC were concentrated in the first few years.
Our findings on SOC change rate are consistent with Qin
et al. (2016a) but different from Anderson‐Teixeira et al.
(2009). While Anderson‐Teixeira et al. (2009) found that corn
stover removal consistently resulted in SOC losses (3–8Mg/
ha) in the top 30cm, Qin et al. (2016a) suggested that stover
removal did not reduce SOC. Differences between Anderson‐
Teixeira et al. (2009) and our analysis can be attributed to
multiple reasons, but the main factor might be the number of
sites included in each study. Anderson‐Teixeira et al. (2009)
included 15 data points from five sites in their analysis, and
12 of them were from a single study (Blanco‐Canqui & Lal,
2007). With a small data sample, influence of a specific study
can be significant. The same study was also included in our
analysis, but analysis based on a larger database (409 data
points) suggested that responses of SOC could be positive or
negative, depending on removal intensity and other factors.
These different results confirmed that SOC responses varied
by site. To estimate the overall trend, constructing a com-
prehensive database that covers multiple regions and farming
systems is important.
4.3
|
Equivalent soil mass balance
Our analysis indicated that reporting SOC change based on
ESM or fixed‐depth has a significant impact on the SOC
change evaluation (Figure 7 and Table 2), which was con-
sistent with previous meta‐analysis studies (Du et al., 2017;
Meurer, Haddaway, Bolinder, & Kätterer, 2018). Historically,
SOC stock was most commonly calculated to a fixed‐depth as
the product of bulk density and SOC concentration and depth
(Wendt & Hauser, 2013). However, the fixed‐depth method
may introduce substantial errors, because changes in man-
agement practices can increase or decrease soil bulk density
and therefore soil volumes over time (Ellert & Bettany, 1995;
Meurer et al., 2018). To address this issue, a binary variable
(ESM or fixed‐depth) was included in the meta‐regression
analysis as a control variable (Table 2). If ESM results were
assumed to be closer to actual changes, regression results in-
dicated that fixed‐depth overestimated the SOC reduction by
about 6.8%, after variations in other variables (e.g., tillage,
crop rotation, sampling depth) were considered. Although
the 6.8% difference was smaller than the 10% difference ob-
tained via subgroup analysis, it is still significant considering
the difference between moderate and medium removal inten-
sity was only about 4% (Table 2).
4.4
|
US versus international SOC responses
Overall, US Midwestern (Figure S2) SOC accrual rates
were similar to the global database (Figure 8), but the mag-
nitudes of SOC change were relatively smaller than those
in the global database. By analyzing long‐term samplings
(n=81,391) from fields in South Dakota, Clay et al. (2012)
found that long‐term (1985–2010) surface (0–15 cm), non‐
ESM SOC increases of 0.37MgCha−1year−1 were in align-
ment with global means. The difference between Midwest
versus international SOC change rates could largely be attrib-
uted to the fact that most Midwestern studies (83%) used in
the meta‐analysis reported SOC based on ESM, whereas only
59% of the studies included outside the region used ESM. As
mentioned above, fixed‐depth measurements did not consider
changes in soil volume overtime. For this reason, meta‐regres-
sion results suggested SOC change reported based on ESM
method would be 6.8% smaller than fixed‐depth method.
In addition, initial SOC content also impacted the results.
On average, SOC content (Figure S3) of the Midwestern plots
was noticeably higher than those at international experimen-
tal sites, largely because of their rich soils (e.g., Mollisols;
Russell, Laird, Parkin, & Mallarino, 2005). Responses of
SOC to additional biomass input can be less noticeable in fer-
tile soils than in regions where soils are damaged or less pro-
ductive. Studies have indicated that C sequestration rates can
decrease if a soil is approaching saturation (Stewart, Paustian,
Conant, Plante, & Six, 2007). These results indicated that the

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XU et al.
net impact of potential stover removal on SOC will vary by
local soil conditions and management practices. In soils ap-
proaching C saturation, addition of stover may have lower
gains. If moderate stover removal (<50%) did not reduce
SOC level, then utilization of stover resource may be plausi-
ble. In fields with high C sequestration capacity, stover return
may be preferred to increase SOC accrual. Still, if the overall
objective of stover utilization is reducing GHG emission in-
tensity of energy production, then a life‐cycle assessment is
needed to evaluate whether stover should be used as biofuel
feedstock to reduce fossil fuel consumption or be returned to
fields to increase SOC accrual rates.
4.5
|
Limitations and future study
Since many studies considered here rarely reported how stover
removal affected belowground C inputs (e.g., roots), which
are critical in building and maintaining soil (Stewart et al.,
2016), and rarely measured SOC changes beyond the 0–30cm
profile, it was difficult to distinguish treatment‐induced SOC
changes. Previous studies (Du et al., 2017; Syswerda, Corbin,
Mokma, Kravchenko, & Robertson, 2011; Ugarte et al., 2014)
also found similar issues related to large variations and limited
sample sizes in deep depths. For a more accurate and confi-
dent SOC change assessment, more observations measuring
deep (>30cm) soil profiles are clearly needed.
To include as many eligible studies as possible, both
ESM and fixed soil sampling depths were included in this
analysis. In addition, how bulk density was measured also
needs to be assessed. For future studies, reporting SOC
changes based on a standard calculation protocol would
assist in distinguishing SOC changes from soil volume
changes, which would be particularly important for long‐
term studies. At a minimum, future experimental studies
should report initial SOC and bulk density measurements.
In addition, soil sampling protocols need to be designed
for specific question. If the purpose is to chemically deter-
mine the impact of a treatment on the amount of organic C
contained in belowground carbon pools, then grinding and
sieving the samples can underestimate the reported values
(Clay et al., 2015).
Once the initial SOC baseline was accounted for the anal-
yses demonstrated that stover removal tended to slow the ac-
crual rate, but not necessarily deplete SOC stocks. Among
the biophysical and management variables examined, this
study found that changes in SOC were most sensitive to the
intensity of stover removal. Further analysis on the effects
of stover removal along with key conservation practices like
cover crop or manure addition would deepen our under-
standing of SOC changes in current farming practices (Palm,
Blanco‐Canqui, DeClerck, Gatere, & Grace, 2014; Ugarte et
al., 2014). Finally, our analysis evaluated the overall impact
of stover removal on SOC, but responses of soil to stover
removal varied by regions and management practices. To
guide sustainable utilization of crop residue resources at a
relevant scale, a spatially explicit database regarding SOC
and farm management practices, including stover removal, is
necessary.
ACKNOWLEDGEMENTS
This research effort was supported by the Bioenergy
Technologies Office (BETO) of Energy Efficiency and
Renewable Energy of the US Department of Energy under
contract DE‐AC02‐06CH11357. We are grateful to Kristen
Johnson and Alicia Lindauer of BETO for their support and
guidance.
This publication is based on research supported by the
USDA Agricultural Research Service's Resilient Economic
Agricultural Practices (REAP) and Greenhouse gas
Reduction through Agricultural Carbon Enhancement net-
work (GRACEnet) projects. The USDA is an equal opportu-
nity provider and employer.
The authors are grateful to Chang Jiyul, Deepak R. Joshi,
and Damaris Roosendaal for their assistance with literature
screening and data collection. The authors thank two anon-
ymous reviewers whose insightful comments have greatly
improved this manuscript.
The submitted manuscript has been created by UChicago
Argonne, LLC, Operator of Argonne National Laboratory
(“Argonne”). Argonne, a U.S. Department of Energy Office
of Science laboratory, is operated under Contract No. DE-
AC02- 06CH11357. The U.S. Government retains for itself,
and others acting on its behalf, a paid- up nonexclusive, ir-
revocable worldwide license in said article to reproduce,
prepare derivative works, distribute copies to the public, and
perform publicly and display publicly, by or on behalf of the
Government. The Department of Energy will provide pub-
lic access to these results of federally sponsored research in
accordance with the DOE Public Access Plan. http://energy.
gov/downl oads/doe- publi c- acces s- plan
DATA AVAILABILITY STATEMENT
Database constructed in this study can be downloaded from
https ://greet.es.anl.gov
ORCID
Hui Xu https://orcid.org/0000-0003-2994-4892
Catherine Stewart https://orcid.org/0000-0003-1216-0450
Zhangcai Qin https://orcid.org/0000-0001-9414-4854

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SUPPORTING INFORMATION
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How to cite this article: Xu H, Sieverding H, Kwon H,
et al. A global meta‐analysis of soil organic carbon
response to corn stover removal. GCB Bioenergy.
2019;11:1215–1233. https ://doi.org/10.1111/gcbb.12631