R E V I EW AR T I C L E
Cover crops affect pool specific soil organic carbon in
cropland – A meta-analysis
Julia Fohrafellner1,2 | Katharina M. Keiblinger2 |
Sophie Zechmeister-Boltenstern2 | Rajasekaran Murugan1 |
Heide Spiegel3 | Elena Valkama4
1BIOS Science Austria, Vienna, Austria
2Department of Forest- and Soil Sciences,
Institute of Soil Research, University of
Natural Resources and Life Sciences
Vienna, Vienna, Austria
3Department for Soil Health and Plant
Nutrition, Austrian Agency for Health
and Food Safety, Institute for Sustainable
Plant Production, Vienna, Austria
4Natural Resources Institute Finland
(Luke), Bioeconomy and Environment,
Sustainability Science and Indicators,
Turku, Finland
Correspondence
Julia Fohrafellner, BIOS Science Austria,
Silbergasse 30 1190 Vienna, Austria.
Email: julia.fohrafellner@boku.ac.at
Funding information
European Union's Horizon 2020,
Grant/Award Number: 862695
Abstract
Cover crops (CC) offer numerous benefits to agroecosystems, particularly in
the realm of soil organic carbon (SOC) accrual and loss mitigation. However,
uncertainties persist regarding the extent to which CCs, in co-occurrence with
environmental factors, influence SOC responses and associated C pools. We
therefore performed a weighted meta-analysis on the effects of CCs on the
mineral-associated organic carbon (MAOC), the particulate organic carbon
(POC) and the microbial biomass carbon (MBC) pool compared to no CC culti-
vation in arable cropland. Our study summarized global research of compara-
ble management, with a focus on climatic zones representative of Europe,
such as arid, temperate and boreal climates. In this meta-analysis, we included
71 independent studies from 61 articles published between 1990 and June 2023
in several scientific and grey literature databases. Sensitivity analysis was con-
ducted and did not identify any significant publication bias. The results
revealed that CCs had an overall statistically significant positive effect on SOC
pools, increasing MAOC by 4.8% (95% CI: 0.6%–9.4%, n = 16), POC by 23.2%
(95% CI: 13.9%–34.4%, n = 39) and MBC by 20.2% (95% CI: 11.7%–30.7%,
n = 30) in the top soil, compared to no CC cultivation. Thereby, CCs feed into
the stable as well as the more labile C pools. The effect of CCs on MAOC was
dependent on soil clay content and initial SOC concentration, whereas POC
was influenced by moderators such as CC peak biomass and experiment dura-
tion. For MBC, for example, clay content, crop rotation duration and tillage
depth were identified as important drivers. Based on our results on the effects
of CCs on SOC pools and significant moderators, we identified several research
needs. A pressing need for additional experiments exploring the effects of CCs
on SOC pools was found, with a particular focus on MAOC and POC. Further,
we emphasize the necessity for conducting European studies spanning the
north–south gradient. In conclusion, our results show that CC cultivation is a
Received: 31 October 2023 Revised: 26 February 2024 Accepted: 27 February 2024
DOI: 10.1111/ejss.13472
This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any
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© 2024 The Authors. European Journal of Soil Science published by John Wiley & Sons Ltd on behalf of British Society of Soil Science.
Eur J Soil Sci. 2024;75:e13472. wileyonlinelibrary.com/journal/ejss 1 of 30
https://doi.org/10.1111/ejss.13472
key strategy to promote C accrual in different SOC pools. Additionally, this
meta-analysis provides new insights into the state of knowledge regarding
SOC pool changes influenced by CCs, offering quantitative summary results
and shedding light on the sources of heterogeneity affecting these findings.
KEYWORD S
effect size, EJPSOIL, field experiments, MAOC, MBC, POC, review, SOC, synthesis
1 | INTRODUCTION
Cover crops (CCs), which are generally cultivated
between cash crops, cover agricultural soils that would
otherwise be left bare (Lal, 2015). They are able to
provide multiple benefits to the agroecosystem, for exam-
ple, reducing soil erosion (Kaye & Quemada, 2017) and
nitrogen (N) losses (Ketterings et al., 2015; Valkama
et al., 2015), increasing above- and belowground biodi-
versity (Lal, 2004), favouring soil temperature and heat
flux (Lal, 2015) and improving overall soil quality and
health (Chahal & Van Eerd, 2019). Further, it is evident
that CCs also have a positive impact on soil organic
carbon (SOC) sequestration (Kaye & Quemada, 2017;
Poeplau & Don, 2015) and loss mitigation (Seitz
et al., 2022). Among several agricultural practices, CCs
were identified to be a very effective measure to increase
C inputs (Xu et al., 2020). By now, there are numerous
meta-analyses and reviews that quantitatively synthe-
sized the effects of CCs on SOC globally (Bai et al., 2019;
Crystal-Ornelas et al., 2021; Jian et al., 2020; McClelland
et al., 2021; Poeplau & Don, 2015; Sun et al., 2020) and in
the Mediterranean climate (Aguilera et al., 2013), all
confirming the positive influence on SOC. However, there
are still uncertainties about the magnitude by which CCs
and environmental factors are driving SOC response and
specific soil C pools (McClelland et al., 2021).
Total SOC is often not the most sensitive indicator to
explain SOC accrual mechanisms or estimate SOC stock
changes (Heckman et al., 2022; Rocci et al., 2021). By
separating SOC into fractions, which are more sensitive to
changes, better insights into C dynamics can be provided.
In recent years, the idea of using two well-defined and
operationally delineated fractions has gained increased
acceptance. These are the mineral-associated organic mat-
ter (MAOM) and the particulate organic matter (POM)
pools. Both have different properties when it comes to, for
example, formation pathways, protection mechanisms,
mean residence time and saturation (Lavallee et al., 2020).
MAOM is smaller than POM, at less than 53 μm in size.
Due to the adsorption to minerals and physical separation
from microbes, it is well protected against decomposition
and has a mean residence time between decades and
centuries. Contrary, POM is sized between 53 μm–
2000 μm and can be stabilized in soil from <10 years up to
decades, being mainly protected by occlusion in aggregates
(Lavallee et al., 2020).
Another C fraction, which is tightly linked to MAOM
and POM, is the microbial biomass carbon (MBC) pool
(Liang et al., 2017). It describes the living organisms in
soil, measured by their carbon content (Ramesh
et al., 2019). Soil microbes reduce SOC stocks by mineral-
izing organic matter, but also increase SOC stocks by
transforming plant organic matter into microbial biomass
and extracellular byproducts (e.g., carbohydrates, lipids,
and peptides) and, finally, necromass. These components
often form connections with mineral surfaces, so-called
organo-mineral complexes, which foster stable organic
matter (MAOM) production and therefore persistent SOC
buildup (Cotrufo et al., 2013; Kästner & Miltner, 2018;
Liang et al., 2017; Plaza et al., 2013). This process primarily
takes place in microbial hotspots such as the rhizosphere
through the in vivo microbial turnover pathway (Liang
et al., 2017; Sokol, Sanderman, & Bradford, 2019).
SOC accrual under CCs is further dependent on a
broad range of factors related to CC characteristics, envi-
ronmental conditions and agricultural management
(Lal, 2015; Wiesmeier et al., 2019). First, CC root-to-shoot
Highlights
• First meta-analysis on the effects of cover crops
(CCs) on soil organic carbon (SOC) pools in
cropland relevant to European conditions.
• CCs significantly increased mineral-associated
organic carbon (MAOC), particulate organic
carbon (POC) and microbial biomass carbon
(MBC), but to different extents.
• All pool changes due to CCs were influenced
by moderators, while MBC response was
impacted most.
• We depict unresolved knowledge gaps of CC
effects on SOC pools and long-term research
needs.
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ratio (Rasse et al., 2005) and biomass production
(McClelland et al., 2021; Seitz et al., 2022) are examples
of how SOC accrual success is built upon CC characteris-
tics. These are also influenced by pedoclimatic factors
such as mean annual temperature or soil texture, which
were found to influence SOC change under CCs (Jian
et al., 2020; Moukanni et al., 2022). Lastly, SOC is also
dependent on additional agricultural management prac-
tices applied, for example, tillage, main crop residue
management or crop rotation (Paustian et al., 1997).
Currently, there are several meta-analyses or quanti-
tative synthesis available that study the effects of CCs on
various SOC pools on a global (Hao et al., 2023; Hu
et al., 2023; Kim et al., 2020; Muhammad et al., 2021;
Wooliver & Jagadamma, 2023) and national level (Ma
et al., 2021). However, a meta-analysis quantifying these
effects for European agriculture, whilst following strict
quality standards, is missing. A synthesis of available data
studying the effects of CCs on SOC pools for agricultural
management and climatic zones relevant to Europe
would allow us to estimate the impact of this agricultural
measure for this continent. This assessment holds signifi-
cant importance, as it enables us to gauge the efficacy of
this practice to foster SOC accrual and mitigate SOC
losses. Consequently, it can significantly contribute to
enhancing soil health, a matter of paramount concern in
Europe's mission “A Soil Deal for Europe” (Commission
et al., 2021). We therefore conducted the first global
meta-analysis, investigating the effects of CCs on the
mineral-associated organic carbon (MAOC), particulate
organic carbon (POC) and MBC pool, that only included
experimental studies conducted in climate zones that are
relevant to Europe, whilst also following strict quality
criteria (Borenstein et al., 2009; Fohrafellner, Zechmeister-
Boltenstern, Murugan, & Valkama, 2023; Koricheva
et al., 2013). This approach was chosen as not enough stud-
ies based on European experiments were available to con-
duct a meta-analysis. Throughout the adoption of this
methodology, the relation to the European agricultural con-
text and the comparability of studies was considered.
We hypothesized that the incorporation of CCs into
an arable cropland system would affect MAOC, POC and
MBC differently, with increasing POC and MBC through
enhanced aggregation and assimilation of new C inputs,
respectively, while only having a small but positive
impact on MAOC. These changes would be affected by,
for example, CC root systems and residue incorporation,
hence, differ throughout the soil profile. Moreover, CC
characteristics, agricultural management and other abiotic
environmental factors would moderate these changes.
Based on these hypotheses, the following research ques-
tions were addressed:
1. How does CC cultivation in arable cropland affect
MAOC, POC and MBC?
2. How do CCs impact pool-specific SOC changes
throughout the soil profile?
3. How do CC characteristics (e.g., sowing time, mixture)
affect pool-specific SOC changes?
4. How do agricultural management practices
(e.g., fertilization, residue management) impact pool-
specific SOC changes in the presence of CCs?
5. How do pedo-climatic factors (e.g., climate zone, tem-
perature, precipitation, soil texture) impact pool-
specific SOC changes in the presence of CCs?
2 | MATERIALS AND METHODS
2.1 | Published protocol and information
on primary data
A complete description of our materials and methods
was previously published as a protocol (Fohrafellner,
Zechmeister-Boltenstern, Murugan, Keiblinger, et al., 2023)
in the open access journal MethodsX to make our research
plans publicly available and allow fellow scientists to provide
feedback and suggestions. The protocol contains information
on the identification of the topic and the objective including
description in the form of the PICO framework, where the
population of scope was later extended to not only comprise
crop rotations with mostly cereal crops but also soybean
monocropping to allow the inclusion of additional experi-
ments. Further, the complete literature search strategy and
data management, screening strategy and developed
eligibility criteria including a PRISMA flow diagram of the
literature retrieval. Moreover, a list of moderators we planned
to extract and analyse, as well as a short description of data
extraction and synthesis, moderator and sensitivity analysis
and data presentation, are available. There, we further
describe that, as available data on the MAOC and POC pool
was scarce, we also included organic matter data in our anal-
ysis, namely MAOM and POM. This was possible, as the
effect size of choice (log response ratio) allows to summarize
values with a large variation across studies (Fohrafellner,
Zechmeister-Boltenstern, Murugan, & Valkama, 2023).
Therefore, effect sizes, calculated from organic matter or
organic carbon, can be compared with each other. To
enhance the readability of this paper, we simplified our ter-
minology by referring to both organic carbon and matter
(MAOC, MAOM and POC, POM) as “MAOC” and “POC”.
Moreover, we completed the PRISMA 2020 Checklist (Page
et al., 2021), which is attached to the Supplementary Material
of this article. In the following, we describe the studies which
were included in the meta-analyses.
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The final database (https://doi.org/10.5281/zenodo.
10707812) consisted of 71 independent studies from 61 arti-
cles conducted globally since 1994 (Figure 1), studying the
effects of CCs on the MAOC, POC and MBC pool. From
each study, we extracted the means of response variables
(SOC pools), the number of replicates (plots/blocks) and
corresponding standard deviations for treatments
(CC) and control (no CC). To allow moderator analysis,
we further extracted an extensive number of possible
explanatory variables and also provided descriptive infor-
mation on, for example, CC species cultivated or use of
herbicides or pesticides. In Table 1 the 71 studies including
authors, publication year, country and site of experiment,
soil texture class, Köppen-Geiger climate zone and SOC
pools investigated are shown. All calculations regarding
descriptive statistics (Section 3.1) were conducted in Sig-
maPlot Version 14.5.
2.2 | Moderators
After finishing the extraction of data and starting the moder-
ator analysis, several explanatory variables were changed to
descriptive variables, as the available data was not sufficient
to do moderator analysis. For example, harvest time of CCs
was rarely reported in articles, as most did not harvest CCs
in the first place. Even though these moderators could not
be assessed, descriptive information can be found in the
database. Moreover, if initial moderator description from the
protocol did not fit the available data in articles, we adapted
them. For instance, inorganic N fertilization was changed
from “type” to “applied or not”. In Table 2 an updated list of
moderators and their groups or ranges is presented. Modera-
tor “Clay content class” (High >25%; medium 15%–25%; low
<15%) was removed, as the results were in contradiction
with the moderator “Clay content (%)” and therefore
showed that this classification, based on soil texture classes
as described in the articles, was not suitable to describe clay
content. Further, the subgroups “Continent” and “Method
used to analyze initial SOC content” were added.
2.3 | Meta-analysis and
heterogeneity tests
The meta-analyses were conducted using MetaWin 2.1
software (Rosenberg et al., 2000) and IBM SPSS Statistics
Version 27 and 29. For each SOC pool, we calculated an
effect size (i.e., the magnitude of the treatment effect)
that can be averaged across independent studies. For the
response variables (the SOC pools), the response ratio (R)
was computed as an index of the effect size:
R¼XCC=XC ð1Þ
where XCC and XC represent the means for treatments
(CC cultivation) and for controls (no CC cultivation),
respectively, averaged for experimental replicates.
Since the distribution of R is skewed, performing
statistical analyses in the metric of the natural logarithm of
R is preferable due to its more normal distribution in small
samples compared to that of R (Hedges et al., 1999):
ln Rð Þ¼ ln XCC=XC

 ¼ ln XCC

  ln XC

  ð2Þ
normal distribution for ln(R) for each SOC pool was
tested by Shapiro–Wilk test.
The variance of ln(R) was calculated as follows:
V ln Rð Þ ¼ SDCCð Þ
2
nCC XCC

 2þ
SDCð Þ2
nC XC

 2 ð3Þ
where SDCC and SDC are the corresponding standard devi-
ations, and n is the sample size (number of replicates).
FIGURE 1 Experimental locations
of the 71 included studies.
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TABLE 1 Meta-data describing the 71 included studies.
Nr. ID* Authors and year Country Site
Soil
texture
Köppen-Geiger
climate zone
Pools
studied
1 717 Ali et al. (2023) China Hubei Clay Cfa POC, MAOC
2 303a Amado et al. (2006) Brazil Santa Maria Loam Cfa POC
3 303c Amado et al. (2006) Brazil Cruz Alta Clay Cfa POC
4 610 Anuo et al. (2023) USA Clay Center Loam Dfa POC, MAOM
5 420 Balota et al. (2014) Brazil Pato Branco Clay Cfa MBC
6 217a Beehler et al. (2017) USA Mason Loam Dfb POC
7 183 Beltran et al. (2018) Argentina Balcarce n.s. Cfb POC, MAOC
8 716 Biederbeck et al. (2005) Canada Semiarid Prairie Loam Dfb MBC
9 531 Bloszies et al. (2022) USA Goldsboro Loam Cfa MBC
10 386 Brandan et al. (2017) Argentina Salta Loam Bsh MBC
11 604 Bremer et al. (2008) Canada Brooks Loam Bsk POC
12 219 Cates and Ruark (2017) USA Arlington Loam Dfb POC, MAOC
13 351 Chahal and Van Eerd (2020) Canada Ridgetown Loam Dfb MBC
14 742 Chen et al. (2020) Norway Gjoevik Loam Dfb MBC
15 187 Cordoba et al. (2018) Denmark Foulum Loam Cfb POC
16 73a Crespo et al. (2021) Argentina Balcarce Loam Cfb POC
17 73b Crespo et al. (2021) Argentina Marcos Juarez Loam Cfa POC
18 73c Crespo et al. (2021) Argentina Parana Loam Cfa POC
19 807 Debosz et al. (1999) Denmark Foulum Sand Cfb MBC
20 607 D'Hose (2015) Belgium Bottelare Loam Cfb MBC
21 360 Feng et al. (2020) USA Dickinson Loam Bsk MBC
22 298 Franzluebbers and
Brock (2007)
USA Iredell County Loam Cfa MBC, POC
23 396 Frasier, Quiroga, and
Noellemeyer (2016)
Argentina Anguil Loam Cfa MBC
24 359 Ghimire and Khanal (2020) USA Clovis Loam Bsk MBC
25 309a Griffin and Porter (2004) USA Clover Loam Dfb MBC
26 4a Gyawali et al. (2022) USA Blacksburg Loam Cfa POC, MAOC
27 4b Gyawali et al. (2022) USA Harrisonburg Loam Cfa POC, MAOC
28 4c Gyawali et al. (2022) USA Ferrum Loam Cfa POC, MAOC
29 4e Gyawali et al. (2022) USA Painter Loam Cfa POC, MAOC
30 344 Hamer et al. (2021) Germany Göttingen Loam Cfb MBC
31 371 Hontoria et al. (2019) Spain La Chimenea Loam Bsk MBC
32 125a Jilling et al. (2020) USA Champaign Silt Cfa POM, MAOM
33 125b Jilling et al. (2020) USA Mason Sand Dfa POM, MAOM
34 125c Jilling et al. (2020) USA Rock Spring Silt Cfa POM, MAOM
35 214 King and Hofmockel (2017) USA Boone County Loam Dfa MBC
36 918 Kumar et al. (2023) India New Delhi Loam Bsh POC, MAOC
37 569 Landriscini et al. (2020) Argentina Cordoba Loam Cfa POC, MAOC
38 605 Liebig et al. (2002) USA Mead Loam Dfa POM
39 317a Liebman (2018) USA Grand Rapids Loam Dfb MBC, POC
40 317b Liebman (2018) USA Lamberton Loam Dfa MBC, POC
41 357 Malobane et al. (2020) South Africa Fort Hare Loam Bsk MBC
(Continues)
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It was assumed that studies do not share the same
effect size and consequently, a random effects model
was used to combine estimates across studies. The
random effects model accounts for experimental method
differences between studies which may introduce hetero-
geneity (τ2) among the true effects.
We calculated the weighted mean of ln(R) for all
studies as follows:
ln Rð Þ¼
Pn
i¼1
wi lnRi
Pn
i¼1
wi
ð4Þ
where lnRi is the log response ratio for study i, n is the
number of studies and wi is the weight for study i,
defined as (Borenstein et al., 2009):
TABLE 1 (Continued)
Nr. ID* Authors and year Country Site
Soil
texture
Köppen-Geiger
climate zone
Pools
studied
42 282 Marinari et al. (2010) Italy Viterbo Loam Csa MBC
43 130a Martinez et al. (2020) Argentina Arequito Silt Cfa MAOC
44 606 Martín-Lammerding et al. (2015) Spain Miño de San Esteban Loam Bsk MBC
45 423 Mohammad et al. (2014) Pakistan Peshawar Loam Cfb MBC
46 295 Mtambanengwe and
Mapfumo (2008)
Zimbabwe Zimuto Sand Bsh POM, MAOM
47 319 Mukumbareza (2014) South Africa Alice Jozini n.s. Cfa MBC, POM
48 964 Murphy et al. (2011) Australia Gnowangerup Loam Csb POC
49 226a Novelli et al. (2017) Argentina Oro Verde Loam Cfa POC
50 411 O'Dea et al. (2015) USA Montana Loam Dfb MBC
51 256a Osborne et al. (2014) USA Brookings Loam Dfa POM, MAOM
52 779 Piotrowska-Dlugosz and
Wilczewski (2015)
Poland Bydgoszcz Loam Cfb MBC
53 42 Restovich et al. (2022) Argentina Pergamino n.s. Cfa POC
54 732 Roldan et al. (2003) Mexico Ajuno Loam Csb MBC
55 205a Ruis et al. (2018) USA Lincoln Loam Dfa POM
56 205b Ruis et al. (2018) USA Clay Center Loam Dfa POM
57 967 Sainju and Lenssen (2011) USA Culbertson Loam Bsk MBC
58 276 dos Santos et al. (2011) Brazil Ponta Grossa Clay Cfa POC
59 450 Sapkota et al. (2012) Italy Enrico Avanzi Loam Csa MBC
60 554 Sawchik et al. (2012) Uruguay Colonia del Sacramento Clay Cfa POC
61 693 Semmartin et al. (2023) Argentina Don Eduardo Loam Cfa POC
62 141 Singh et al. (2020) USA Jackson Silt Cfa POC, MAOC
63 968 Somenahally et al. (2018) USA El Reno Loam Bsk MBC
64 673 Tong et al. (2023) Canada Elora Loam Dfb POC, MAOC
65 100 Tyler (2021) USA Stoneville Silt Cfa MBC
66 314 Wander et al. (1994) USA Kutztown Loam Cfa POC
67 302 Wander et al. (2007) USA Williams Bay Loam Dfa POC
68 437 Weyers et al. (2013) USA Morris Loam Dfb MBC
69 3 Williams et al. (2022) Australia Pampas Clay Bsh POC, MAOC
70 525 Zhang et al. (2022) USA Ferguson Loam Cfb POC, MAOC
71 683 Zhang, Ghahramani,
et al. (2023)
Australia Goondiwindi Clay Bsh POC
Note: n.s. stands for “not stated” in studies/articles.
*Article ID (identification) according to article numbers in Database.
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TABLE 2 Updated list of explanatory variables (moderators) and their ranges or groups.
Explanatory variables (moderators) Groups/ranges
Cover crop (CC) characteristics
Type Legumes; grasses; mixed
Species number 1–13
Single grown or in mix Single; mixed
Sowing time (season) Spring/summer; autumn/winter
Seed rate (kg ha1 year1) 13–3600
CC above ground peak biomass (Mg dry matter ha1) 2–12
CC above ground average biomass (Mg dry matter ha1 year1) 1–8
Termination method Herbicides; tillage; cut; none
Termination time (season) Spring/summer; autumn/winter
Years in rotation with CC 1–10
Residue management Left on field; incorporated; harvested/removed
Agricultural management
Cropping system Monocropping; crop rotation
Number of main crop species in rotation 1–6
Presence of leguminous main crops in rotation Yes; no
Crop rotation duration (years) 1–9
Inorganic N fertilizer Yes; no
Amount of inorganic N fertilizer (kg N ha1 year1) 0–700
Other inorganic fertilizer Yes; no
Amount of other inorganic fertilizer (kg fertilizer ha1 year1) 0–130
Residue management of main crop Left on field; incorporated; removed
Rate of residue incorporation of main crop (%) 0–100
Tillage system Conventional tillage; reduced/minimum tillage; no-till
Maximum depth tilled (cm) 0–33
Pedo-climatic factors
Initial SOC concentration (%) 0.5–5.5
Method used for to analyse initial SOC content Wet oxidation, loss on ignition, dry combustion
Soil pH 4.5–8.5
Soil texture class Clay; loam; silt; sanda
Clay (%) 6–72
Silt (%) 6–83
Sand (%) 4–85
Köppen-Geiger climatic zones in Europe B (arid), C (warm temperate), D (boreal)
Mean annual rainfall (mm yr1) 50–1930
Mean annual temperature (C) 3.5–27
Continent Africa, Asia, Australia and Oceania, Europe, North
America, South America
SOC pools
Duration of experiment (years) 1–38
Deepest point of soil sampling (cm) 0–60
Time of soil sampling (season) Spring; summer; autumn; winter; several dates
Organic matter or carbon fraction Organic matter; organic carbon
(Continues)
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wi¼ 1Viþ τ2 ð5Þ
where Vi is the variance of the study i and τ2 denotes the
amount of residual heterogeneity (between-study
variance). As the variance of an effect size is a function of
its sample size, studies with a larger sample size have
lower variances and therefore receive heavier weights.
The bootstrap statistical method to generate bias-
corrected 95% CIs around the log response ratios from
4999 iterations was applied (Efron & Tibshirani, 1986).
To test whether response ratios differed between the
groups of categorical moderators, we used the χ2 test to
examine the between-group heterogeneity (QB) as well
as to check for possible inter-correlation between the
variables. To study the effect of continuous moderators,
we ran weighted meta-regressions. The χ2 test was used
to examine model heterogeneity (QM), which describes
the amount of heterogeneity explained by the regression
models. The significant level of QM indicates that an
independent variable (a moderator) explains a significant
amount of variability in effect sizes ln(R).
Results for the overall effects of CCs on SOC pools
and subgroup analysis were back-transformed and reported
in the text and figures, respectively, as percentage changes
from the controls:
SOCpool change %ð Þ¼ EXP ln Rð Þð Þ1½  100% ð6Þ
The CC cultivation effects on the SOC pools were
considered significantly different from the controls if the
95% confidence interval (CI) did not overlap with zero.
2.4 | Sensitivity analysis
To assess potential publication bias, funnel plot asymmetry
was investigated by plotting the natural logarithm of R
(lnR) against its corresponding standard error, following the
approach outlined by Sterne and Egger (2001). Additionally,
we employed Egger's regression-based test to detect any
signs of funnel plot asymmetry. A non-significant p-value
from Egger's test indicates the absence of publication bias.
To address the potential impact of missing studies and
create a more symmetric funnel plot, we conducted a
Trim-and-Fill analysis (Duval & Tweedie, 2000). This analy-
sis involves adding values for missing studies, enabling us
to estimate a new mean effect size. To gauge the magnitude
of the file-drawer problem, we calculated the Rosenthal
Fail-Safe Number (Nfs). The Nfs represents the number of
unpublished, non-significant, or missing studies that would
need to be included in the meta-analysis to alter the results
from significant to non-significant (Borenstein et al., 2009).
Lastly, rank correlation analysis using Kendall's τ was
conducted to check the relationship between the effect size
and variance. These analyses were done in MetaWin 2.1
and 3 software.
3 | RESULTS
3.1 | Review descriptive statistics
In order to get an overview of the available data studying
the effects of CCs on SOC pools, we analysed the
included studies regarding type of pools studied, publica-
tion year, experiment location, climate zone and
maximum soil sampling depth. Moreover, the normal dis-
tribution of included studies for the pools was assessed.
The number of times each pool was studied in the
71 studies varied greatly (Figure 2a). Best studied was
the POC pool (43 studies), followed by the MBC pool
(32 studies) and the MAOC pool (20 studies). The
number of articles on this topic has been increasing
steadily in the last decades (exponential regression,
y = 1.0598e71  exp(0.0817*x), R2 = 0.577, p < 0.001,
n = 61) (Figure 2b). Starting our search from 1990
onwards, the first article identified was published in 1994
(Wander et al., 1994). Over 36% of the included articles
have been published in the last 3 years (from 2020 until
June 2023, when the search for literature ended). The dura-
tion of the experiments ranged from 1 (minimum require-
ment for study inclusion) to 38 years. The majority of
studies had a duration of less than 10 years. Studies were
conducted mostly in the USA (n = 31), followed by
Argentina (n = 11) (Table 3). All other countries, with
counts of maximum four studies, were less represented.
Regarding counts per continent, North America (36 studies),
South America (16 studies) and Europe (10 studies) were
represented best.
TABLE 2 (Continued)
Explanatory variables (moderators) Groups/ranges
MAOC size (μm) < 20 μm; < 53 μm
Correction factor for MBC None; 0.41–0.45; 0.33–0.38
aTexture classes according to IUSS Working group WRB (2022) and USDA (2019).
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According to Köppen-Geiger (Kottek et al., 2006)
(Figure 2c), the majority of included experiments were
conducted in the warm temperate and a substantial share
in the boreal climate. The maximum soil sampling depth
for SOC pool samples was in the top 20 cm for most
extracted data (Figure 2d). Therefore, the analysed data-
set is representative of the top soil only. When taking a
closer look at the included articles, we found that the
majority studied winter-hardy CCs, which were usually
terminated in spring, before sowing the main crop, and
were rarely harvested. Overall, 116 CCs were investigated
in the 71 studies, of which vetch (Vicia) species (n = 16)
were studied most often, followed by rye (Secale cereale)
(n = 15), clover (Trifolium) species (n = 14), oat (Avena
sativa) (n = 11) and radish (Raphanus) species (n = 9).
The main crops most represented throughout the data-
base were maize (Zea mays) (n = 42), followed by winter
wheat (Triticum aestivum) (n = 17). Besides cereals,
soybean (Glycine max) was frequently part of the rota-
tions (n = 34). The number of main crop species in treat-
ment and control were mostly equal. The predominant
farming system was conventional agriculture, with no
irrigation and no additional organic matter input. Over-
all, the included studies were comparable in regards to
crops and management methods applied. Other variabil-
ities between studies (e.g., climate zones, temperature,
precipitation, tillage type and tillage depth) were
addressed through moderator analysis. Regarding POC
analysis in these studies, most authors chose to investi-
gate total POC (50–2000 μm) compared to smaller sub-
fractions. Over 80% of studies measured MAOC and/or
POC by dry combustion with elemental analysers. MBC
was almost in all cases analysed by chloroform fumiga-
tion extraction. A complete description of each included
study can be found in the database.
In Figure 3, we show the normal distribution of effect
sizes for the MAOC pool, the POC pool and the MBC
pool after exclusion of outliers. For the MAOC and POC
pool we identified four outliers each (ID 141, 918, 219,
256a and 295, 141, 303a, 319, respectively). Each pool
had two outliers where lnR was too small (0.84 [57%],
0.55 [42%] for MAOC and 1.05 [65%], 0.92
[60%] for POC) and two where lnR was too high (0.46
[58%] and 0.86 [136%] for MAOC and 1.00 [172%], 1.21
[235%] for POC). The effect sizes for the MBC pool were
normally distributed after removing two outliers
(ID 968 and 420) with large lnR (0.84 [132%] and 0.86
[136%]). The effect sizes of the outliers and other
extracted data can be found in the database. Finally,
we had 16, 39 and 30 independent studies examining
the effects of CCs cultivation on MAOC, POC and MBC
pool, respectively.
3.2 | Mineral-associated organic carbon
MAOC can persist long-term in soils before turning
over, as the organic compounds within this pool gener-
ally exhibit spatial separation from microbes and
strong physio-chemical sorption to mineral surfaces
FIGURE 2 (a) Number of times
each SOC pool was studied among the
71 studies and (b) publication year of
included articles. The line represents the
exponential regression line. The green-
shaded area highlights studies published
since 2020. Number of publications for
2023 only includes articles published
until June of that year. (c) Percentage of
studies located in climatic zones
according to Köppen-Geiger. (d) Box plot
for maximum soil sampling depth
(cm) for SOC pools extracted from
included studies.
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(Dungait et al., 2012; Sokol, Sanderman, & Bradford, 2019;
von Lützow et al., 2006). To answer how CCs impact
MAOC in arable cropland, influenced by moderating fac-
tors, we calculated an overall effect size and co-variate
impacts.
In our analysis, a total of 11 studies out of 16 investi-
gated the effects of CCs on the mineral-associated frac-
tion in the form of organic carbon, whereas five studies
looked at the whole organic matter. We combined these
parameters and reported them as “MAOC”, as their
response to CCs did not show significant differences
(QB = 0.247, df = 1,15, p = 0.642). All included studies
were conducted outside of Europe. Effect sizes of MAOC
ranged from 10.3% (lnR = 0.11) to +25.5%
(lnR = 0.23) across all studies (Figure 4). The weighted
summary effect size showed that MAOC increased
slightly, by 4.76% (lnR = 0.047), under CC cultivation
compared to no CC cultivation. The result was signifi-
cant, as the 95% CI (0.6%–9.4% or lnR 0.01–0.09) did not
overlap with zero (control).
None of the studied moderators of categories “CC
characteristics”, “Agricultural management” or “SOC
pools” were significant for MAOC (Table S1). With
respect to the category “Pedo-climatic factors”, meta-
regression indicated that MAOC responses to CCs were
significantly dependent on the clay content of soils
(p < 0.005) (Figure 5a). When clay contents were low
(e.g., 10%), CC cultivation reduced MAOC (28% or
lnR = 0.33), while with rising clay contents to, for
example, 30%, it became positive (5% or lnR = 0.05).
Moreover, the effect of CCs on MAOC depended on initial
SOC concentration (Figure 5b). For example, we found that
CCs increased MAOC by about 6% (lnR = 0.06) in soils
TABLE 3 Number of studies per continent and country.
Continents and countries
Number
of studies
North America 36
USA 31
Canada 4
Mexico 1
South America 16
Argentina 11
Brazil 4
Uruguay 1
Europe 10
Denmark 2
Italy 2
Spain 2
Germany 1
Belgium 1
Norway 1
Poland 1
Africa 3
South Africa 2
Zimbabwe 1
Asia 3
China 1
India 1
Pakistan 1
Australia & Oceania 3
Australia 3
FIGURE 3 The distribution of effect sizes examining the effect of CCs on (a) MAOC, (b) POC and (c) MBC after exclusion of outliers.
The dashed line indicates no CC cultivation (control). The W-Statistic (Shapiro–Wilk test), p-values of normal distribution tests and number
of studies (n) for each pool are shown.
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with initial SOC concentrations of 1%, while the effect was
only 3% (lnR = 0.03) in soils with 3% SOC. Nevertheless,
the overall effect was always positive.
All other studied moderators could not explain any
variability in effect sizes for MAOC (Table S1). For some
of these non-significant categorical moderators, imbal-
ances in the number of studies within sub-groups need to
be acknowledged. Moreover, various moderators were
excluded from the heterogeneity analysis due to an insuf-
ficient number of distinct groups (<2) or studies (<5)
(see Supplementary Material Section 1.1.).
3.3 | Particulate organic carbon
Being of predominantly plant origin, POM consists of
many structural C compounds with low N content, and
can be available freely in soil or protected through occlusion
in aggregates (Cotrufo et al., 2019; Golchin et al., 1994;
Lavallee et al., 2020). To answer the research question of
how CCs will impact POC under various conditions, we
synthesized primary research results and calculated moder-
ator effects.
32 studies described CC effects on the particulate
organic fraction as organic carbon (POC), whereas seven
studies investigated the whole organic matter (POM). For
further analysis, we combined both parameters and
reported them as “POC”, since there was no statistically
significant difference between their response to CCs
(QB = 1.85, df = 1,38, p = 0.196). One of the included
studies was conducted in Europe (Cordoba et al., 2018).
The effect sizes ranged from 23.1% (lnR = 0.26) to
+154.3% (lnR = 0.93) across all studies (Figure 6), with
the overall effect of +23.2% (lnR = 0.21) compared to
control. Since its 95% CI did not overlap with zero
(13.9%–34.4% or lnR 0.13–0.30), the results were statisti-
cally significant.
Regarding heterogeneity analysis, moderator “CC
seed rate” demonstrated a strong impact on POC change
(Figure 7a). Meta-regression showed a decline in POC
FIGURE 4 Forest plot showing
effect sizes for 16 independent studies
examining the effect of cover crop
(CC) cultivation on the MAOC pool
compared to no CC cultivation (control).
Black squares are the effect estimates for
each study with lower and upper 95%
CIs. Square size corresponds to study
weight. White square indicates weighted
average with 95% CIs across all studies.
The dashed vertical line indicates the
control. When a number is shown after
the publication year, this indicates that
several independent studies (different
sites) have been extracted from this
article.
0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5
y = 0.0792 - 0.0180 * x
QM = 19.04
p =  0.00001, n = 9
Clay content (%)
10 20 30 40 50
C
ha
ng
e 
in
 M
A
O
C
 d
ue
 to
 C
C
 (l
nR
)
-0.10
-0.05
0.00
0.05
0.10
0.15
0.20
y = - 0.0738 + 0.0041 * x
QM = 9.88
p = 0.002, n = 7
Initial SOC concentration (%)
(a) (b)FIGURE 5 Weighted linear
regression between changes in mineral-
associated organic carbon (MAOC) due
to cover crops (lnR) and (a) clay content
(%) and (b) initial soil organic carbon
(SOC) concentration (%). The solid line
shows the linear regression, the striped
line the control and the size of the dots
indicates the study weight. In the top
corner, the equation for the linear
regression and values for QM (model
heterogeneity), p-value and number of
independent studies are stated.
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with increasing seed rate, for example, a seed rate of
20 kg ha1 showed stronger effects on POC (lnR = 0.35
or 42%) compared to 120 kg ha1 (lnR = 0.11 or 12%).
Further, moderator “CC above ground peak biomass”,
where “peak” describes the highest biomass (Mg ha1)
measured during the experiment, also was highly signifi-
cant (Figure 7b). A low peak CC biomass production up to
3.4 Mg ha1 led to a decrease in POC compared to control,
while with increasing peak biomass from 3.4 Mg ha1
upwards, a positive impact was observed. Further, no signif-
icant inter-correlation was found between seed rates and
peak biomass (R2 = 0.102, p = 0.37, n = 10) or average bio-
mass (R2 = 0.012, p = 0.77, n = 10).
In the category “SOC pools”, experiment duration
was an important factor influencing POC change
(Figure 7c). With increasing experiment duration, an
increase in POC due to CCs can be observed. For exam-
ple, after 5 years of experiment establishment, a POC
change of +20% (lnR = 0.18) compared to the control
was found, whereas after 20 years a change of +46%
(lnR = 0.38) was visible. It is worth noting that only two
studies out of 39 evaluated CC effects over 20 years.
Lastly, the time of soil sampling had a significant
influence on the effect size (Figure 7d). Early sampling of
soil (i.e., in spring) under CC cultivation resulted in the
highest POC response levels (46% or lnR = 0.38). Soil
sampling in the autumn months still indicated signifi-
cantly higher POC contents (20% or lnR = 0.19), whereas
sampling in summer showed a tendency for POC reduc-
tion (3% or lnR = 0.03). When soil was sampled on
several dates (e.g., three times in 1 year), a change in
POC of 28% (lnR = 0.25) was observed.
None of the other examined moderators, including all
moderators in category “Agricultural management”, did
account for any variations in effect sizes for POC
(p > 0.05, Table S2). As for MAOC, several moderators
had to be excluded from the heterogeneity analysis
because there were imbalances within sub-groups or
insufficient studies available (n < 5) (see Supplementary
Material Section 1.2.).
3.4 | Microbial biomass carbon pool
Soil microbes, responsible for the formation and turnover
of SOC, convert organic matter into microbial biomass
Effect size (lnR)
-1.5 -1.0 -0.5 0.0 0.5 1.0 1.5
Liebman (2018) - 2
Wander (2007)
Jilling (2020) - 1
Murphy (2011)
Gyawali (2022) - 3
Santos (2011)
Gyawali (2022) - 4
Williams (2022)
Jilling (2020) - 2
Liebman (2018) - 1
Novelli (2017)
Gyawali (2022) - 1
Ruis (2018) - 1
Semmartin (2023)
Jilling (2020) - 3
Cordoba (2018)
Ruis (2018) - 2
Beehler (2017)
Osborne (2014)
Crespo (2021) - 1
Franzluebbers (2007)
Wander (1994)
Tong (2023)
Liebig (2002)
Amado (2006)
Bremer (2008)
Kumar (2023)
Crespo (2021) - 3
Crespo (2021) - 2
Zhang (2023)
Ali (2023)
Gyawali (2022) - 2
Zhang (2022)
Restovich (2022)
Landriscini (2020)
Anuo (2023)
Beltran (2018)
Sawchik (2012)
Cates (2017)
All studies (n=39)
×
FIGURE 6 Forest plot showing the
effect sizes for 39 independent studies
examining the effect of cover crop
(CC) cultivation on the particulate
organic carbon (POC) pool compared to
no CC cultivation (control). Black
squares are effect estimates for each
study with lower and upper 95%
confidence intervals (CIs). Square size
corresponds to study weight. White
square indicates weighted average with
95% CIs across all studies. The dashed
vertical line indicates control. When a
number is shown after the publication
year, this indicates that several
independent studies (different sites)
have been extracted from this article.
Cross indicates European study.
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and byproducts (Liang et al., 2011, 2015), making MBC a
rapidly reacting C pool. In regards to the research ques-
tion on how CCs affect these processes and therefore
MBC, we included 30 studies in this meta-analysis and
calculated effect sizes. They ranged from 15.1%
(lnR = 0.16) to +100% (lnR = 0.69) across all studies
(Figure 8). The overall effect estimate was +20.2%
(lnR = 0.184) and statistically significant, with its 95%
CIs not crossing zero (11.7%–30.7 or lnR 0.11–0.27). For
this pool, 9 of the 30 studies were conducted in Europe.
We ran an additional analysis for the European
studies and found that the summary effect of +22.40%
(lnR = 0.20) and 95% CIs (4.82%–31.77% or lnR
0.05–0.28) were close to the one of all 30 studies
(Figure 8). The effect sizes for European studies ranged
from 15.1% (lnR = 0.16) to 38.3% (lnR = 0.32), and
were well distributed throughout the full dataset.
Heterogeneity analysis identified several moderators
that significantly affected MBC under CC cultivation
(Figure 9), others showed non-significant trends. First,
regarding category “CC characteristics”, changes in MBC
were significantly influenced by the sowing time of CCs
(p < 0.05). When CCs were cultivated in spring and sum-
mer, MBC was 36% (lnR = 0.31) higher compared to con-
trol, while autumn and winter cultivation resulted in a
9% increase in MBC response under CCs (lnR = 0.09)
(Figure 9a). The number of years in a rotation cultivated
with CC had a positive, but non-significant impact
(p < 0.1, Figure 9b). Moreover, concerning category
“Pedo-climatic factors”, MBC change due to CCs was
highly dependent on clay content in soil (p < 0.01) and
decreased with increasing percentages of clay (Figure 9c).
For example, in soil with 5% clay, a 26% (lnR = 0.23)
increase in MBC response was found, whereas this
change declined to 5% (lnR = 0.05) in soils with 30% clay
content.
For the category “Agricultural management”, two
moderators showed significant impacts on MBC. First,
the duration of the crop rotation was linked to MBC
change. Rotations with longer durations had a positive
impact on MBC change due to CCs (p < 0.05, Figure 9d).
Second, responses of MBC due to CCs were significantly
influenced by maximum tillage depth (p < 0.05,
Figure 9e). When no-till was applied, effects of CCs on
MBC were stronger (24% or lnR = 0.22) than with maxi-
mum tillage depths of, for example, 20 cm (11% or
lnR = 0.10). As a great variation of effect sizes for no-till
studies was found (2% to +88% or lnR = 0.02 to 0.63),
we did a separate heterogeneity analysis for no-till studies
only. This analysis, however, did not find a significant
(a) (b)
(c) (d)
Change in POC due to CC (%)
-20 0 20 40 60 80
Ti
m
e 
of
 s
oi
l s
am
pl
in
g
several dates
spring
autumn
summer (7)
(9)
(10)
(7)
QB = 16.61
p = 0.011, n = 33
CC peak biomass (Mg ha-1)
0 2 4 6 8 10 12 14
y = -0.2084 + 0.0620 * x
QM =  6.92
p = 0.009, n = 10
Experiment duration (years)
0 10 20 30 40
C
ha
ng
e 
in
 P
O
C
 d
ue
 to
 C
C
 (l
nR
)
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
y = 0.1190 + 0.0128 * x
QM =  3.98
p = 0.046, n = 39
CC seed rate (kg ha-1)
0 20 40 60 80 100 120 140 160
C
ha
ng
e 
in
 P
O
C
 d
ue
 to
 C
C
 (l
nR
)
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
y = 0.4008 - 0.0024 * x
QM = 7.54
p = 0.006, n = 16
FIGURE 7 Weighted linear
regression between changes in
particulate organic carbon (POC) due to
cover crops (CCs) (lnR) and (a) CC seed
rate (kg ha1), (b) CC peak biomass
(Mg ha1), (c) experiment duration
(years) and (d) sub-group analysis (%)
for time of soil sampling. The striped
line indicates the control. In the top
corner, the values for QM (model
heterogeneity) or QB (between-group
heterogeneity), p-value and number of
independent studies are stated. For the
regression analysis, the solid line shows
the linear regression and the size of the
dots indicates the study weight. In the
top corner, the equation for the linear
regression is shown.
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moderator or trend impacting the effect size for MBC under
no-till. Lastly, with increased numbers of main crop species
used in the rotation, hence increased diversity, MBC change
showed a positive trend (p < 0.1, Figure 9f). Interestingly,
monocropping (growing a single crop year after year on the
same land) showed a large variation of effect sizes across
studies (48% to +99% or lnR = 0.65 to 0.69). Therefore,
as done for studies under no-till, we analysed whether other
moderators impacted CC effects on MBC under monocrop-
ping. We found that the type of CC (grass, legume, mixed)
showed a trend (QB = 8.64, df = 2,14, p = 0.063) with
grasses having the most positive impact (68% or lnR = 0.52)
followed by legumes (19% or lnR = 0.17) and mixes (7% or
lnR = 0.07). Moreover, increased experiment duration had
a significant and positive impact on MBC under CC cultiva-
tion in monocropping systems (QM = 5.09, df = 1,14,
p = 0.024).
All other considered moderators could not explain
any variability in effect sizes for MBC (p > 0.05), includ-
ing all moderators in category “SOC pools” (Table S3).
Regarding experiment duration, it is crucial to acknowl-
edge that only two studies exceeded durations beyond
12 years. As for the other pools, several moderators had
to be excluded from heterogeneity analysis because
there were imbalances within sub-groups or insufficient
studies available (n < 5) (see Supplementary Material
Section 1.3.).
3.4.1 | Sensitivity analysis
Sensitivity analysis, comprising of several tests, is necessary
to determine the robustness of meta-analytical results.
When testing included studies on MAOC for publication
bias, we did not observe any evidence of funnel plot asym-
metry. Furthermore, Trim-and-fill analysis did not identify
any missing studies, as depicted in Figure S1. Additionally,
examination using Egger's regression did not reveal any
indication of publication bias (p = 0.51). The rank correla-
tion analysis using Kendall's τ yielded non-significant
results (τ = 0.20, p = 0.31), indicating no relationship
between effect sizes and variances. However, the Rosenthal
Fail-safe-N (Nfs) value of 6 indicates the moderate robust-
ness of our findings. Adding six unpublished, non-
significant, or missing studies would need to be included in
the meta-analysis to alter the results for MAOC from signifi-
cant to non-significant.
Regarding the investigation of POC, the sensitivity
analysis similarly indicated the absence of publication
bias. Funnel plot asymmetry was not detected, and Trim-
and-fill analysis could not identify any missing studies, as
illustrated in Figure S2. Moreover, Egger's regression
results were not statistically significant (p = 0.51), further
supporting the absence of publication bias. The high
Rosenthal Nfs of 343 provides strong evidence of the
robustness of our findings, suggesting that they are
Effect size (lnR)
-0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0 1.2
Marinari (2010)
Martin-Lammerding (2015)
Hamer (2021)
Liebman (2018) - 2
Mohammad (2014)
Sainju (2011)
Malobane (2020)
Murphy (2011)
Weyers (2013)
Tyler (2021)
Bloszies (2022)
D'Hose (2015)
Ghimire (2020)
Sapkota (2012)
Franzluebbers (2007)
O'Dea (2015)
Griffin (2004)
King (2017)
Hontoria (2019)
Frasier (2016)
Debosz (1999)
Liebman (2018) - 1
Chen (2020)
Piotrowska-Dlugosz (2015)
Roldan (2003)
Feng (2020)
Biederbeck (2005)
Chahal (2020)
Mukumbareza (2014)
Brandan (2017)
European studies (n=9)
All studies (n=30)
×
×
×
×
×
×
×
×
×
FIGURE 8 Forest plot showing
effect sizes for 30 independent studies
examining the effect of cover crop
(CC) cultivation on the MBC pool
compared to no CC cultivation (control).
Black squares are effect estimates for
each study with lower and upper 95%
confidence intervals (CIs). Square size
corresponds to study weight. White
square indicates weighted average for all
studies with 95% CIs across all studies.
Grey square indicates weighted average
for European studies only. The dashed
vertical line indicates control. When a
number is shown after the publication
year, this indicates that several
independent studies (different sites)
have been extracted from this article.
Crosses indicate European studies.
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unlikely to be influenced by unpublished studies. Kendall's
τ also showed non-significant results (τ = 0.20, p = 0.07).
In the case of MBC, our Trim-and-fill analysis
detected two missing studies, as shown in Figure S3.
These two imputed studies caused a slight shift in the
lnR value, from 0.184 (20%) to 0.194 (21%). However,
the adjusted estimate remains very close to the origi-
nal. Egger's regression results were not statistically sig-
nificant (p = 0.34), supporting the absence of
publication bias. The high Nfs of 226 indicates the
robustness of our findings and suggests that they are
unlikely to be significantly influenced by unpublished
studies. Additionally, Kendall's τ yielded non-
significant results (τ = 0.01, p = 0.96).
4 | DISCUSSION
This meta-analysis is the first of its kind summarizing
effects of CCs on SOC pools in cropland under conditions
relevant to European arable farming. It considers three
different soil C pools differing largely in their turnover times.
The underlying studies cover a publication period of almost
30 years and focus on the top layer of arable cropland soils.
Moreover, a wide range of moderators was considered to
explain the heterogeneity of the outcomes across these stud-
ies. There are several published meta-analyses and quantita-
tive syntheses, which studied the impact of CC cultivation on
several SOC pools on a global level (Hao et al., 2023; Hu
et al., 2023; Kim et al., 2020; Muhammad et al., 2021;
Number of main crop species in rotation
0 1 2 3 4 5 6 7
(a) (b)
(c) (d)
(e) (f)
Crop rotation duration (years)
0 2 4 6 8 10
y = 0.0463 + 0.0372 * x, QM = 4.78
p = 0.029, n = 28
Change in MBC due to CC (%)
0 10 20 30 40 50 60 70
C
C
 s
ow
in
g 
tim
e
autumn + winter
spring + summer
QB = 8.48
p =  0.004, n = 18
(8)
(10)
Clay (%)
0 10 20 30
C
ha
ng
e 
in
 M
B
C
 d
ue
 to
 C
C
 (l
nR
)
-0.2
0.0
0.2
0.4
0.6
0.8
y = 0.2656 - 0.0072 * x, QM = 10.36
p = 0.001, n = 19
Years in rotation with CC
0 1 2 3 4 5 6 7
C
ha
ng
e 
in
 M
B
C
 d
ue
 to
 C
C
 (l
nR
)
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
y = 0.1080 + 0.0513 * x, QM = 3.76
 p = 0.053, n = 29
Maximum depth tilled (cm)
0 10 20 30
C
ha
ng
e 
im
 M
B
C
 d
ue
 to
 C
C
 (l
nR
)
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
y = 0.2236 - 0.0062 * x, QM = 3.94
p = 0.047, n = 18
y = 0.1206 + 0.0307 * x, QM = 2.91
p = 0.088, n = 30
FIGURE 9 Sub-group analysis
between changes in microbial biomass
carbon (MBC) due to cover crops (CCs)
(%) and (a) CC sowing time and
weighted linear regression (lnR) for
(b) years in rotation with CC (outlier: ID
282), (c) clay content (outlier: ID 386),
(d) crop rotation duration (outliers: ID
319, 386), (e) maximum depth tilled
(outliers: ID 319, 716) and (f) number of
main crop species in rotation. The
striped line indicates the control. In the
top corner, the values for QM (model
heterogeneity) or QB (between-group
heterogeneity), p-value and number of
independent studies are stated. For the
regression analysis, the solid line shows
the linear regression and the size of the
dots indicates the study weight. In the
top corner, the equation for the linear
regression is shown.
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Wooliver & Jagadamma, 2023). Our meta-analysis synthe-
sized global data on CC effects on SOC pools, focusing on
agricultural management and climatic zones which are
also found in Europe. At the same time, we followed
strict quality criteria for conducting agricultural meta-
analyses (Borenstein et al., 2009; Fohrafellner,
Zechmeister-Boltenstern, Murugan, & Valkama, 2023;
Koricheva et al., 2013). Therefore, our meta-analysis pro-
vides novel information and high-quality data on the
responses of SOC pools to CCs, cultivated under condi-
tions representative of Europe.
4.1 | Overall effects of CCs on SOC pools
Our analysis reveals a significant positive impact of CCs
on MAOC, POC and MBC when compared to non-CC
cultivation. The most pronounced response was observed
in POC with an increase of 23.2%, followed by MBC with
a 20.2% increase and MAOC with a 4.8% increase. These
changes can be attributed to various soil processes. First,
living microbial organisms play a fundamental role in
SOC storage, as they transform plant residues, as pro-
vided by CCs, through the ex vivo and in vivo microbial
pathways. In the ex vivo pathway, plant residues are
enzymatically converted into plant-derived carbon
deposits, which are not readily assimilated by microbes,
whereas in the in vivo pathway, microbes incorporate
plant-derived carbon into their biomass, forming
microbial-derived carbon (Liang et al., 2017; Sokol, San-
derman, & Bradford, 2019). Although microbial biomass
makes up less than 5% of SOM (Dalal, 1998), the struc-
tures generated by microbial activities can become associ-
ated with mineral surfaces, incorporated into organo-
mineral complexes, and occluded to aggregates, therefore
forming primarily MAOC (Cotrufo et al., 2013; Liang
et al., 2017; Sollins et al., 1996; von Lützow et al., 2007).
This highlights the indispensable role of microbes in sta-
ble SOC, hence MAOC formation. POC on the other
hand is mainly fed by physical transfer of fragmented
and depolymerized plant litter to the mineral soil. There,
it facilitates aggregation, providing protection to the C in
the litter by spatial inaccessibility and formation of
occluded POC (Cotrufo et al., 2015; Lavallee et al., 2020).
All these processes are reliant on plants, their biomass
and root exudates. Introducing CCs into agricultural sys-
tems can accelerate these processes and enhance C inputs
into soil where it can be readily used or stored short- and
long-term.
When comparing our findings with other meta-
analyses on this topic, both similarities and differences
emerge. A meta-analysis by Hu et al. (2023) reported a
15% increase in POC (CI: 12%–17%, n = 255), a 33%
increase in MBC (CI: 28%–39%, n = 141) and a 7%
increase in MAOC (CI: 5%–9%, n = 120). Their conclu-
sions differed from ours, as they found CCs to most
strongly affect MBC, while we observed a greater impact
on POC. Despite following most meta-analytical quality
criteria, the authors extracted SD only for some studies
(Fohrafellner, Zechmeister-Boltenstern, Murugan, &
Valkama, 2023) and did not extract studies indepen-
dently, thereby causing overrepresentation of these obser-
vations (Hungate et al., 2009). According to another
meta-analysis by Wooliver and Jagadamma (2023), who
examined CC effects on several agricultural systems
(e.g., cereals, vineyards and cotton) and reported com-
bined effects, POC increased by 15% (CI: 9%–22%,
n = 404), which is approximately 8% lower than our
results. For MAOC they reported an increase by 6% (CI:
2%–9%, n = 178) which is similar to our findings. It is
important to note that they included effect sizes
for MAOC in their meta-analysis which were estimated
by subtracting POC from total SOC and not directly mea-
sured (Fohrafellner, Zechmeister-Boltenstern, Murugan, &
Valkama, 2023).
Lastly, the research question on how CCs affect SOC
pools throughout the soil profile could not be answered,
as most extracted data was representative for the upper
20 cm (Figure 2d). Thus, future experimental studies and
quantitative reviews should aim to sample deeper soil
layers, up to 100 cm, and analyse these results in a meta-
analytical manner to generate knowledge on how C
accrual is distributed in soil.
To summarize, our research suggests that CCs posi-
tively impact MAOC, POC, and MBC through a set of
fundamental processes involved in SOC formation. How-
ever, the strength of this impact varies among the pools,
as supported by other meta-analyses. While the effect
sizes for MAOC are consistent throughout meta-analyses,
differences in POC and MBC effects can be attributed to
variations in statistical methodologies and the inclusion
of studies from diverse pedo-climatic zones and agricul-
tural systems. It is essential to consider these factors
when comparing and interpreting the results of different
meta-analyses. Table 4 provides a comprehensive sum-
mary of the findings concerning the impact of CCs on
SOC pools, major moderators and supporting evidence
gathered through our meta-analysis and pertinent litera-
ture. These conclusions are further elaborated in Sec-
tions 4.2 and 4.3 of this study.
4.2 | Sources of variation across studies
The distribution of effect sizes in this study described a
wide range of SOC pool changes in response to CCs.
For MAOC, effect sizes ranged from 10.3% to +25.5%,
for POC from 23.1% to +154.3% and for MBC from
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+15.1% to +100%. Therefore, moderator analysis was
conducted to assess where these differences in effects
arise from. In the following sections, we will discuss
these results and possible underlying mechanisms of
the observed effects, structured according to the pre-
defined moderator categories (i.e., “CC characteris-
tics”, “Agricultural management”, “Pedo-climatic fac-
tors” and “SOC pools”).
4.2.1 | Impact of CC characteristics
We hypothesized that CC characteristics would impact
the response of SOC pools to CCs. After conducting mod-
erator analysis and discussing the results, several conclu-
sions could be drawn. Starting with POC, a significant
negative relation between CC seed rate and response
ratio was observed (Figure 7a). As no significant
TABLE 4 Summary table on the effects of cover crops (CCs) on soil organic carbon (SOC) pools and major moderators impacting
outcomes.
Conclusions SOC pools
Confirmed by other
meta-analyses, reviews
and original articles
Supported/not supported
by this meta-analysis
CCs have an overall positive effect MAOC Hu et al. (2023); Wooliver and
Jagadamma (2023)
Supported, Figure 4
POC Hao et al. (2023); Hu et al.
(2023); Wooliver and
Jagadamma (2023)
Supported, Figure 6
MBC Hao et al. (2023); Hu et al. (2023) Supported, Figure 8
Important CC characteristics
CC type MAOC Wooliver and Jagadamma (2023) Not supported, Table S1
CC seed rate POC – Supported, Figure 7a
CC peak above ground biomass POC Liang et al. (2023) Supported, Figure 7b
CC sowing time MBC McClelland et al. (2021);
Moukanni et al. (2022)
Supported, Figure 9a
Number of years in rotation with CCs MBC Brennan and Acosta-Martinez
(2017); White et al. (2020)
Supported, Figure 9b
Important agricultural management practices
Tillage type POC Wooliver and Jagadamma (2023) Not supported, Table S2
MBC Kim et al. (2020) Not supported, Table S3
Maximum tillage depth MBC Kandeler et al. (1999); Zuber and
Villamil (2016)
Supported, Figure 9e
Crop rotation duration MBC Feng et al. (2020); Liu et al.
(2023)
Supported, Figure 9d
Number of main crop species in rotation MBC Motta et al. (2007) Supported, Figure 9f
Important pedo-climatic factors
Mean annual temperature POC Hu et al. (2023); Wooliver and
Jagadamma (2023)
Not supported, Table S2
Clay content MAOC – Supported, Figure 5a
MBC Franzluebbers et al. (1996);
Muhammad et al. (2021)
Supported, Figure 9c
Initial total SOC concentrations MAOC Cotrufo et al. (2019) Supported, Figure 5b
Important SOC pool-related factors
Experiment duration POC Moukanni et al. (2022); Wu et al.
(2023)
Supported, Figure 7c
MAOC Hu et al. (2023); Wooliver and
Jagadamma (2023)
Not supported, Table S1
Soil sampling time POC – Supported, Figure 7d
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inter-correlation between seed rate and CC above-ground
biomass (both average and peak) was found, potential
effects of root biomass were considered. Due to a lack of
root data in the studies, no statistical testing was possible.
Despite the acknowledged significance of roots in SOC
accrual, their measurement is often neglected in experi-
mental studies due to labour-intensive efforts. Neverthe-
less, it is evident that both the rhizodeposits of living
roots and the decomposition of dead roots contribute sub-
stantially to both MAOC and POC (Huang et al., 2021;
Sokol, Kuebbing, et al., 2019; Yang et al., 2023). The pre-
dominant impact of living root inputs on the net forma-
tion of MAOC aligns with expectations from the
‘dissolved organic C (DOC)-microbial pathway’ theory
(Cotrufo et al., 2015). This theory posits that labile DOC
compounds are efficiently anabolized by microbes,
undergo turnover, and are subsequently deposited into
the MAOC pool (Sokol, Kuebbing, et al., 2019). In con-
trast, structural litter inputs are believed to preferentially
contribute to POC (Cotrufo et al., 2015). These underly-
ing processes are species-dependent, influenced by fac-
tors such as the root-to-shoot ratio (Huang et al., 2021)
and root morphology, which exerts a stronger impact on
the accrual of root carbon into the MAOC and POC pools
than root quality (C:N ratio) (Engedal et al., 2023). This
influence extends beyond the described C allocated in the
form of rhizodeposition to include the provision of root
surface area for in vivo and ex vivo transformation of
fresh C. Additionally, CC functional groups exhibit vary-
ing qualitative traits, such as root length, that signifi-
cantly impact SOC pool accrual (Engedal et al., 2023).
These findings underscore the indispensable role of CC
roots and associated plant traits when studying the effects
of CCs on SOC.
When connecting these findings with the significant
results for moderator “Peak CC biomass”, we can observe
that aboveground (and potentially belowground) biomass
production is an important driver in POC accrual under
CCs. Interestingly, a negative impact on POC is seen
when peak biomass is below 3.4 Mg ha1 which
increases with rising peak biomass (Figure 7b). This
could be explained by rhizosphere C inputs which can
cause a rhizosphere priming effect of native C
(Kuzyakov, 2010), especially when C pulses are large and
infrequent (Moukanni et al., 2022). C priming can be
defined as “an extra decomposition of organic C after
addition of easily-decomposable organic substances to
the soil” (Dalenberg & Jager, 1989). This is dependent on
the increased activity and/or amount of microbial bio-
mass causing either acceleration or retardation of SOC
turnover (Kuzyakov et al., 2000). Our results confirm the
findings by Liang et al. (2023), who concluded that SOC
buildup is constrained by low CC biomass production
(hence C input rates) and positive priming effects. In
order to achieve net SOC accrual, C inputs need to exceed
losses from priming. Common factors contributing to low
CC biomass production in temperate and cold climates
include late seeding within the cropping season. This
delay, often accompanied by reduced daylight hours and
cold temperatures, hampers the optimal growth conditions
for CCs. On the other hand, arid climates pose challenges
to CC growth due to water limitations, which can signifi-
cantly stress their development. An alternative strategy to
address these challenges involves undersowing CCs in the
main crop. However, this practice is not yet widely estab-
lished across Europe (Smit et al., 2019). While undersowing
offers potential solutions to the issues of late seeding and
related environmental constraints, it comes with its own set
of limitations. For instance, it may not be compatible with
all main crops, and special machinery may be required to
effectively implement this practice (Smit et al., 2019).
In the case of MBC, our heterogeneity analysis identi-
fied a significant influence of sowing time of CCs on the
outcomes. Specifically, CCs sown during the spring and
summer seasons exhibited a more pronounced positive
effect on MBC change compared to CCs sown in the
autumn and winter seasons (Figure 9a). This suggests a
season-dependent variation in the impact of CCs on MBC
levels in the soil. Nevertheless, it needs to be acknowl-
edged that more than half of the included studies in this
meta-analysis were conducted in warm temperate cli-
mates, possibly causing a bias towards this climate zone.
CC growing period throughout Europe differs greatly,
from short durations in Romania to the Netherlands with
long periods, keeping CCs until the early spring (Smit
et al., 2019). Overall, CC growth duration is an important
predictor of SOC responses to CC cultivation
(McClelland et al., 2021; Moukanni et al., 2022) along
with plant establishment, biomass and residue quality.
Therefore, adjusting CC growth duration by selecting CC
planting and termination times is an intrinsic consider-
ation when managing CCs (Alonso-Ayuso et al., 2014).
Regarding the impact of the number of years in a crop
rotation cultivated with CCs, our analysis detected a posi-
tive trend on MBC change (Figure 9b). Continued incor-
poration of CCs promotes plant-derived C inputs, for
example, litter (Lal, 2004), root biomass and exudates
(Dijkstra et al., 2021; Schmidt et al., 2011), which support
growth and accumulation of microbial biomass
(Brennan & Acosta-Martinez, 2017; White et al., 2020).
Other experimental studies revealed a rapid and substan-
tial response in soil microbial biomass size and commu-
nity composition in response to the introduction of CCs.
This change is attributed to ongoing residue inputs and a
consistently active rhizosphere throughout the year
(Frasier, Noellemeyer, et al., 2016; Lehman et al., 2012).
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Lastly, for MAOC, our analysis did not reveal any
significant moderators or trends within the category “CC
characteristics” (Table S1). This might be because MAOC
is a slow cycling pool and its C accrual is most dependent
on prevalent soil characteristics (see Section 4.2.3) rather
than CC features. These findings align with the observa-
tions made by Hu et al. (2023), who likewise found no
significant influence of CC type or termination method
on MAOC change under CCs. In contrast, Wooliver and
Jagadamma (2023) reported that MAOC change was sig-
nificantly related to CC type and growing season.
In regards to our research question on how CC char-
acteristics impact SOC pool responses to CCs, we can
conclude that POC change was significantly negative and
positive affected by CC seed rate and peak biomass,
respectively, whereas MBC change showed a significant
relation to CC sowing time and a positive trend regarding
years in rotation with CCs (Table 4). Effects on MAOC
responses could not be identified, which is not consistent
with the findings of Wooliver and Jagadamma (2023).
4.2.2 | Impact of agricultural management
As initially hypothesized that other additional agricul-
tural management practices will impact SOC pool
changes under CCs, we obtained a significant negative
impact of maximum tillage depth on MBC in response to
CCs in our study (Figure 9e). A deeper tillage depth cor-
responds to a higher degree of soil disruption (Zhang,
Wang, et al., 2023), which, in turn, has the potential to
interfere with microorganisms and associated processes
(Babujia et al., 2010; Sae-Tun et al., 2022). However, our
findings, in contrast to Kim et al. (2020), did not reveal
any apparent impact of tillage type on changes in MBC
under CCs.
Moreover, our analysis found that the duration of
studied crop rotations was significantly and positively
correlated with MBC change. Longer rotation durations
allow the inclusion of more CCs (number as well as spe-
cies) and provide the necessary time for effective CC
establishment, which as mentioned before, is crucial
regarding above- and belowground biomass production.
Further, the moderator “number of main crop species in
the rotation”, which can be seen as a proxy for main crop
diversity, followed a positive trend (Figure 9d,f, respec-
tively). Diverse crop rotations, in comparison to mono-
cropping, provide abundant plant biomass inputs and
rhizodeposits, which are known to stimulate microbial
growth and, hence can promote MBC accrual (Feng
et al., 2020; Motta et al., 2007). Additionally, crop rota-
tions are recognized for their capacity to enhance soil
structure, providing an environment favourable to
microbial development (Kennedy, 1999) and hence MBC
accrual.
No significant results or trends were observed for the
MAOC or POC change under CCs (Tables S1 and S2) for
moderators in this category. When comparing these find-
ings with the agricultural management moderators stud-
ied in other meta-analyses, outcomes are inconsistent.
For instance, Hu et al. (2023) also found no significant
impact of tillage effects on the POC or MAOC response
to CCs, whereas Wooliver and Jagadamma (2023)
reported that tillage type affected POC, but not MAOC
response.
Concerning the research question on how agricultural
management practices influence SOC pools under CCs,
we can conclude that MBC response was significantly
positively related to crop rotation duration and signifi-
cantly negatively related to maximum tillage depth
(Table 4). For the number of main crop species in rota-
tion on MBC change a positive trend was observed.
Contrary, MAOC and POC responses under CCs were
not impacted by other agricultural management practices
in our study, which is not consistent with the results of
others (Wooliver & Jagadamma, 2023). Hence, the
research question on how agricultural management prac-
tices impact SOC pool change under CCs is not clearly
answered and supplementary research on the effects of
this management practice on MAOC and POC is
advisable.
4.2.3 | Impact of pedo-climatic factors
Pedo-climatic factors were hypothesized to significantly
impact SOC pool change under CC cultivation. Starting
with the moderator clay content, interesting observations
could be drawn from heterogeneity analysis. For both
MAOC and MBC, highly significant impacts were found,
but calculated meta-regressions followed opposite direc-
tions, being positive for MAOC and negative for MBC
(Figure 5a and 9c, respectively). Attempting to first
explain the results for MAOC, we followed the prevailing
hypothesis that higher clay contents mean higher surface
areas and therefore higher binding capacities for C
(Cotrufo et al., 2019; Lavallee et al., 2020). Nevertheless,
MAOC under CCs was negatively impacted by clay to a
content of about 20%. In an experiment by Jilling et al.
(2021), the authors observed that common root exudates
(i.e., glucose and oxalic acid) caused a significant increase
in turnover and potential release of C from MAOM
through direct (e.g., mobilization of metal oxides) and
indirect (e.g., enzyme induction) mechanisms. Similar
effects were found in another field experiment by Huang
et al. (2021), who showed that about 70% of rhizosphere
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priming occurred in MAOC, which differed between spe-
cies. The interspecific variations in the priming effects
were explained by the differences in specific root length
and root N concentration. Considering this, in soils with
low clay contents and therefore less binding site capaci-
ties, CCs may cause a net decline in MAOC through
priming, desorption and ultimately, mineralization.
Regarding the negative relationship between clay and
CC effects on MBC (Figure 9c), it can be hypothesized
that the abundance and distribution of bacterial and fun-
gal communities are highly influenced by soil texture and
pores (Bodner et al., 2023). When clay contents are low
(as in coarse-textured soils), it appears that more C from
CCs is stored in the MBC rather than the MAOC pool.
This might be caused by enhanced anaerobic conditions
in fine-textured soils and limitation of aerobic microor-
ganisms (Drury et al., 1991). Medium-textured soils were
found to increase MBC, phospholipid fatty acid (PLFA),
fungi-to-bacteria ratio and actinomycetes (Muhammad
et al., 2021). Moreover, in a lab experiment by Franzlueb-
bers et al. (1996) the authors reported that the amount of
mineralizable C per unit MBC decreased with increasing
clay content, thereby indicating that MBC was more
active in coarse-textured soils compared to fine-textured
soils. At the same time, we found that low clay contents
influenced C stabilization negatively in the MAOC pool
under CCs, possibly by causing priming of initial C. To
conclude, when clay contents are higher, it seems that
more C is stabilized in MAOC, and MBC is less influ-
enced by CCs.
Moreover, we found a highly significant negative rela-
tion between the response of MAOC under CCs and ini-
tial total SOC concentrations, where the positive impact
of CCs on MAOC decreased with greater initial SOC
(Figure 5b). This result can be explained mathematically
by considering the diminished likelihood of C increase
when the total SOC content is already high. For instance,
soils with a coarse texture and low initial SOC levels may
undergo more pronounced changes as a result of sustain-
able soil management practices, in contrast to fine-
textured soils with already abundant SOC content
(Schweizer et al., 2019). Contrary to our findings, which
partially correspond to statements by Cotrufo et al.
(2019), Liang et al. (2023) observed opposite effects. They
studied the C sequestration potential of CCs based on a
long-term field experiment and found that C storage in
MAOC showed a strong positive correlation to total SOC.
Moreover, the meta-analysis by Li et al. (2023), who stud-
ied the effects of legume incorporation on SOC pools,
summarized that initial SOC concentrations did not sig-
nificantly influence MAOC. Also, a recent paper by Begill
et al. (2023) challenged the prevalent hypothesis of
MAOC saturation by Cotrufo et al. (2019). They report no
upper limit of MAOC was observed within 189 samples
from the German Agricultural Soil Inventory. In conclu-
sion, there is an ongoing debate on the relationship
between total SOC concentrations and MAOC, and possi-
ble saturation effects. Opposing results are reported in
different syntheses efforts and the underlying mecha-
nisms are still under discussion. Another aspect that
might impact the response of MAOC under CCs, influ-
enced by initial total SOC concentrations, is the method
with which initial SOC was measured. As only 2 out of
9 studies provided information on the method applied for
analysing initial SOC in MAOC studies, we were not able
to study this moderator. Nevertheless, methods like wet
oxidation or loss on ignition are dependent on correction
factors, which can potentially introduce bias. This and
other limitations of analytical methods applied to mea-
sure SOC in total soil or fractions are discussed below in
Section 4.2.4.
Lastly, regarding POC, heterogeneity analysis did not
determine any significant moderators or trends for this
category (Table S2). Contrary, both Hu et al. (2023) and
Wooliver and Jagadamma (2023) observed a significant
impact of mean annual temperature on POC changes
under CCs in their global studies without limitations to
certain climatic zones. Our meta-analysis only collected
studies from arid, temperate and cold climates and more
than half of the studies included belong to the warm tem-
perate climate (Figure 2c), causing an overrepresentation
of this zone. This overrepresentation may be partially
attributed to the challenges posed by arid and boreal cli-
mates when it comes to implementing CC cultivation.
These regions are characterized by water limitations
(Mitchell et al., 2015) and a scarcity of warm days suit-
able for crop growth (Mela, 1996), respectively, which
can make the implementation of CCs more challenging.
Moreover, as climate change continues to unfold and cli-
matic zones shift across Europe, these challenges are
expected to become more pronounced in the southern
regions (Lavalle et al., 2009), while boreal areas may
experience an increase in warm days hence prolonged
growing season (Peltonen-Sainio et al., 2018). To con-
clude, more experimental results conducted in arid and
boreal climates, preferably set up in Europe, are needed
to provide a more balanced analysis. Lastly, our modera-
tor analysis regarding continents, climate zones, precipi-
tation and temperature did not reveal any significant
impacts on the studied effects of CCs on SOC pools,
thereby indicating that the results of the meta-analysis
are applicable globally for the investigated climatic zones,
such as arid, temperate and boreal climates.
In addressing our research question regarding the
impact of pedo-climatic factors on SOC pools under CCs
(Table 4), our analysis revealed that the initial SOC
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concentration had a significant negative effect on the
response of MAOC, which contrasts with the results
reported by others (Li et al., 2023; Liang et al., 2023).
Moreover, clay content in soils exhibited a significant
positive influence on MAOC responses, while MBC dem-
onstrated a significant but negative change. Lastly, our
research did not identify a significant impact on POC
responses, in contrast to findings from Wooliver and
Jagadamma (2023) and Hu et al. (2023), who suggested a
relationship with mean annual temperature.
4.2.4 | Impact of SOC pool-related factors
Lastly, for moderator category “SOC pool-related factors”,
we hypothesized to see a significant effect on SOC pool
change. Nonetheless, concerning MAOC and MBC, no
significant moderators or trends were found for this cate-
gory (Tables S1 and S3). Against initial expectations
based on the hypothesis that the mean-residence time of
MAOC reaches from decades to centuries (Lavallee
et al., 2020), we did not observe a significant impact of
experiment duration on MAOC response to CCs. A possi-
ble explanation is that only one included study investi-
gated CC effects on MAOC for more than 10 years
(ID 673, 37 years), which made it difficult to analyse long
experiment duration effects for this pool. On the other
hand, we found that experiment duration was positively
related to POC response to CCs. CCs promote aggregate
formation and physical protection and therefore stabiliza-
tion of occluded POM (Moukanni et al., 2022), which can
lead to an accumulation of POC over time. The meta-
analysis by Wu et al. (2023), studying nitrogen fertiliza-
tion on SOC pools, found that POC change was also posi-
tively and significantly impacted by experiment length.
Contrary to these results, Hu et al. (2023) and Wooliver
and Jagadamma (2023) did not observe a significant
impact on the duration of CC cultivation on POC, but on
MAOC in their meta-analyses. It is noteworthy that the
studies included by Wooliver and Jagadamma (2023)
were mostly less than 15 years long.
In regards to methods applied to measure SOC pools
in the primary studies and their potential impact on CC
effects on SOC pool, we considered the analysis method
and correction factor for MBC and sieve size, density sep-
aration and analysis method for MAOC and POC. Firstly,
for MBC, the majority of studies used chloroform fumiga-
tion extraction, hence, no moderator analysis of the anal-
ysis method applied was possible. Further, the sub-group
analysis revealed that correction factors did not impact
CC effects on MBC significantly. Regarding MAOC, we
found that the impact of sieve size on CC effects on
MAOC was not significant. As the majority of studies
applied the sieve size separation method for MAOC and
POC (in contrast to density separation) and sieve sizes
used for POC were mostly uniform (50–2000 μm for total
POC), we were not able to perform the complete modera-
tor analysis as initially planned. Nevertheless, this illus-
trates that the dataset was relatively homogeneous, and
no strong effects of SOC pool analysis methodologies
were expected. Moreover, the analytical method used to
measure organic matter or carbon content of MAOC and
POC fractions may introduce additional bias, as they dif-
fer in methodology. Methods used frequently are wet oxi-
dation, weight loss on ignition and dry combustion. Wet
oxidation, a method that requires little laboratory infra-
structure, was applied to measure MAOC and/or POC
only in 3 of 42 studies. The recovery of C with this
method varies, but is typically ranging from 60%–95%,
and therefore requires a correction factor (Nelson &
Sommers, 1996), which might induce bias. Moreover, wet
oxidation is believed to recover the most active SOC pools
(Kamara et al., 2007) and is texture-dependent (Lettens
et al., 2007) and hence could overestimate POC and
underestimate MAOC. Loss on ignition (used in 2 of
42 studies) also requires the application of a correction
factor or regression analysis to convert weight loss into
SOC contents. It was found that the standard conversion
factor of 0.58 overestimated SOC, particularly with
increasing contents of clay and fine particles <20 μm.
Also, the application of regression models under- and
overestimated SOC stocks, which was dependent on clay
contents (Jensen et al., 2018). Lastly, of the 42 studies,
3 expressed MAOM and POM dry weight as a percentage
of the initial soil mass, which we named “mass fraction”
(see database). This method can only yield an estimated
value of organic matter, as C/N ratios differ in various
mass fractions (Mikha & Marake, 2023), therefore each
mass fraction is not directly proportional to its organic C
content, and inorganic C is not accounted for. As the
majority of included studies in our analysis used dry com-
bustion to measure SOC contents in MAOC and POC
(over 80%), we were not able to study the impact of frac-
tion analysis on MAOC and POC change under CCs.
Overall, differences in sampling procedures, sample prep-
aration and analysis of MAOC and POC between studies
are to be expected, as there is a multitude of methods
available to conduct fractionation (Just et al., 2021;
Poeplau et al., 2018) as well as organic carbon and matter
measurement (Johns et al., 2015; Kögel-Knabner &-
Rumpel, 2018; Nelson & Sommers, 1996). Nevertheless,
similarly to fractionation sizes used, the dataset is mostly
homogeneous in this regard and no strong effects of this
moderator were expected. To conclude, these differences
and pitfalls in fractionation and carbon analysis method-
ology might introduce additional bias into syntheses,
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which, compared to the impacts of other moderators are
small, but still should be acknowledged.
Lastly, soil sampling time was found to impact POC
change due to CCs significantly in the present study.
When sampled in spring, changes in POC were largest
under CCs, followed by sampling on several dates
throughout the year, in autumn and in summer months.
An explanation could be that most included studies that
reported sampling in spring and summer were investigat-
ing winter-hardy CCs that were cultivated in autumn and
terminated in spring, thereby maximizing their growing
period, C inputs and potential to support aggregate
formation (Blanco-Canqui et al., 2011; Calonego et al., 2017),
hence POC acceleration (Moukanni et al., 2022). When
sampled in summer, growth period of most studied CCs was
already over and beneficial effects might be less pronounced
than during CC cultivation.
In regards to our research question on how SOC pool-
related factors influence SOC pool change under CCs, we
can summarize that experiment duration and time of soil
sampling had a significant impact on POC response,
whereas for MAOC and MBC change no significant mod-
erators were identified (Table 4).
4.3 | Implications and perspectives
Our findings show that CC cultivation has positive effects
on SOC pools in arable cropland under European condi-
tions. Nevertheless, cultivation of CCs throughout
Europe is still limited. A 2019 survey collecting data from
over 600 farmers in Spain, France, Netherlands and
Romania, found that the average adoption rate of CC
across arable farms was 11.6 %, varying greatly between
countries. Based on these adoption rates, potential adop-
tion area and C-sequestration were calculated which ran-
ged from 2,592,700 t1 CO2 equivalent to 2,399,490 ha in
Spain to 79,300 t1 CO2 equivalent to 47,170 ha in the
Netherlands (Smit et al., 2019). A modelling study found
that the recent CC area in Germany could be tripled to
30% of arable cropland, thereby enhancing total C inputs
by 12% and facilitating an annual increase of 2.5 Tg CO2
in the top 30 cm (Seitz et al., 2022). It is evident that poli-
cies are the strongest external determinant of adoption
rates of CC (Kathage et al., 2022) and that they are shap-
ing CC application in the European Union to different
extents. For example, Switzerland's agriculture is highly
regulated and offers substantial financial incentives to
implement CC cultivation (Garland et al., 2021). The
Nitrates Directive and the Common Agricultural Policy's
greening requirements impact CC adoption patterns
strongest (Kathage et al., 2022). These findings stress not
only the diversity of European agricultural systems but
also varying potentials within countries and their depen-
dency on policies and incentives.
Besides the potential of CC cultivation for Europe, we
identified five crucial research needs regarding CC effects
on SOC pools relevant to European agricultural condi-
tions. First, a necessity for additional experiments study-
ing the effects of CCs on SOC pools was found,
specifically regarding MAOC. After screening almost
1000 articles, we were able to retrieve 20 studies that
investigated MAOC under CCs, showing that this param-
eter is measured rarely in experimental studies, especially
when compared to POC (n = 43). Often, as each addi-
tional fraction analysed increases the workload signifi-
cantly (Poeplau et al., 2018), measurement of MAOC is
neglected. Researchers also tend to calculate MAOC by
subtracting POC from total SOC, thereby estimating
parameters that cannot be used in high-quality meta-
analysis, which only include measured response variables
(Fohrafellner, Zechmeister-Boltenstern, Murugan, &
Valkama, 2023). We therefore encourage scientists to
assess the stable fraction analytically in their experi-
ments, as there are still uncertainties on how CCs and
related moderators impact MAOC. This can also be done
in ongoing experiments, where SOC pools have not been
analysed so far, in order to increase the amount of infor-
mation on this topic.
This brings us to the second point, which is about
contradictory and missing results regarding moderator
analysis. First, differences between our and other avail-
able meta-analyses were encountered. Experiment dura-
tion (>10 years) was found to impact MAOC and POC
contrastingly in different meta-analyses. Similarly, con-
flicting results for the moderators “mean annual temper-
ature” on POC responses, “tillage type” on POC and
MBC responses and “CC type” on MAOC responses
under CCs were observed. Second, the impact of irriga-
tion, organic agriculture and organic matter input under
CCs on MAOC, POC and MBC changes are unknown, as
a lack of experimental studies including these manage-
ment practices was identified. Hence, moderator analysis
was not possible for these parameters. Therefore, more
experiments, preferably longer than 10 years, that
address comprehensive sets of parameters regarding soil
and agricultural management, are needed (Chaplot &
Smith, 2023). This is specifically true for organic farming,
as these systems are dependent on nutrient inputs from
CCs and organic matter. These new studies also should
provide a detailed description of all agricultural practices
applied (e.g., fertilization types and rates, irrigation
amounts, crop residue management) so meta-analytical
analysis is possible.
In relation to these research needs, we found that the
number of European experiments studying the response of
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SOC pools to CCs is low. Only 10 articles, of which 9 stud-
ied MBC, 1 POC and none MAOC were identified. This
shows why a meta-analysis only including European
experiments is, up to date, not possible. By communicating
this knowledge gap, we hope to inspire fellow researchers
to tackle this issue. Increasing not only the number but
also the spatial distribution of European studies would
allow the analysis of European pedoclimatic impacts in
more detail. The planned development of numerous living
labs, as implemented by the EU Mission “A Soil Deal for
Europe”, constitutes a key opportunity to address these
knowledge gaps (European Commission, 2021).
Fourth, our meta-analysis encountered limitations in
addressing the research question regarding the impact of
CCs on SOC pools throughout the soil profile, as the
majority of extracted effect sizes from included studies were
sampled within the first 20 cm of the soil. We anticipate that
future experimental studies should expand their sampling to
encompass SOC pools in deeper soil layers. Additionally, we
encourage future meta-analyses to place a particular empha-
sis on including data from sampling depths beyond the top
layer to provide a more comprehensive understanding of
SOC dynamics throughout the soil profile.
Lastly, meta-analysts are dependent on primary litera-
ture to produce synthesis results. Unfortunately, we often
find promising articles which fit our scope perfectly, but
then encounter hurdles when it comes to including these
articles in our meta-analysis. The reporting of standard
deviation (SD) or standard error (SE) of means for SOC
pools is crucial to allow the calculation of weights (see
Equation 3). Nevertheless, many authors fail to provide
this basic information, thereby forcing meta-analysts to
either neglect their articles or search for possible ways
to obtain SD another way. Often, this leads to estimating
SD by various measures, which are highly imprecise, if
not fabricated. A recently developed tool by Acutis et al.
(2022) allows to compute SD from ANOVA and multiple
comparison test outcomes, thereby offering a highly useful
way to combat this issue. Nevertheless, we encourage
authors to provide information on SD in their article, as no
tool can exceed complete statistical reporting. Moreover, a
definite lack in presentation of basic soil parameters, such
as soil texture, pH or initial SOC concentrations was
observed, causing difficulties in moderator analysis. Also, in
this regard, we urge authors to improve their reporting of
valuable information, preferably in the form of databases
uploaded to online repositories.
5 | CONCLUSION
The present meta-analysis evaluated the response of
MAOC, POC and MBC to CC cultivation. By synthesizing
the results of 71 independent studies whilst following
meta-analytical quality criteria, we were able to generate
high-quality outputs, relevant to European conditions.
Therefore, we provided a novel contribution to the
understanding on how CCs affect SOC on a pool level.
Our findings demonstrate that CCs had a positive and
significant effect on all three studied pools, with POC
and MBC presenting the highest sensitivity (+23.2% and
+ 20.2%, respectively) whereas MAOC exhibited a mod-
est increase (+4.8%). Among these pools, it was MBC
change that was most influenced by moderators. Specifi-
cally, CC characteristics and other agricultural practices
demonstrated substantial impacts on MBC responses.
This highlights the considerable role that agricultural
management choices play in shaping the positive effects
of CCs on MBC accrual.
Apart from this, analysis was not possible for the
moderators “irrigation”, “organic agriculture” and
“organic matter input”, as a lack of experimental studies
including these management practices was identified.
Further, more studies on the dynamics of SOC pools
throughout the soil profile are necessary. Lastly, a pressing
need for additional experiments exploring the effects of CCs
on SOC pools, specifically for Europe, was identified, with a
particular focus on MAOC, POC and long-term experi-
ments. The establishment of living labs, as integral compo-
nents of the European Soil Mission, presents a crucial
opportunity to address these research needs.
AUTHOR CONTRIBUTIONS
Julia Fohrafellner: Methodology; investigation; data
curation; visualization; writing – original draft;
writing – review and editing; software; formal analysis;
validation; resources. Katharina Keiblinger: Writing –
review and editing; supervision; conceptualization; meth-
odology. Sophie Zechmeister-Boltenstern: Writing –
review and editing; supervision; funding acquisition;
methodology. Rajasekaran Murugan: Writing – review
and editing; supervision; methodology. Heide Spiegel:
Methodology; writing – review and editing. Elena Valk-
ama: Conceptualization; methodology; writing – review
and editing; project administration; funding acquisition;
supervision; software; formal analysis; validation.
ACKNOWLEDGEMENTS
We want to thank Michael Schwarz (AGES) for the crea-
tion of initial maps in ArcGIS and for the extraction of
pedoclimatic zones for each site with the ArcGIS software.
FUNDING INFORMATION
This project has received funding from the European
Union's Horizon 2020 research and innovation program
under grant agreement No. 862695 EJP SOIL and was
FOHRAFELLNER ET AL. 23 of 30
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iley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
conducted as a part of the EJP SOIL project MIXROOT-C
and CarboSeq.
CONFLICT OF INTEREST STATEMENT
The authors declare that they have no known competing
financial interests or personal relationships that could
have appeared to influence the work reported in this
paper.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are
openly available in Zenodo.org at https://doi.org/10.
5281/zenodo.10707812.
PROTOCOL AND REGISTRATION
This meta-analysis was not registered. The corresponding
protocol to this meta-analysis can be accessed online:
Fohrafellner, J., Zechmeister-Boltenstern, S., Murugan,
R., Keiblinger, K., Spiegel, H. & Valkama, E. 2023. Meta-
analysis protocol on the effects of cover crops on pool-
specific soil organic carbon. MethodsX, 11, 102,411,
https://doi.org/10.1016/j.mex.2023.102411.
ORCID
Julia Fohrafellner https://orcid.org/0000-0001-5734-
7353
Katharina M. Keiblinger https://orcid.org/0000-0003-
4668-3866
Sophie Zechmeister-Boltenstern https://orcid.org/0000-
0001-5839-5904
Rajasekaran Murugan https://orcid.org/0000-0001-
6931-2756
Heide Spiegel https://orcid.org/0000-0003-1285-8509
Elena Valkama https://orcid.org/0000-0002-8337-8070
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How to cite this article: Fohrafellner, J.,
Keiblinger, K. M., Zechmeister-Boltenstern, S.,
Murugan, R., Spiegel, H., & Valkama, E. (2024).
Cover crops affect pool specific soil organic carbon
in cropland – A meta-analysis. European Journal of
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