Page 1
SOIL, 5, 15–32, 2019
https://doi.org/10.5194/soil-5-15-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
SOIL
Global meta-analysis of the relationship between soil
organic matter and crop yields
Emily E. Oldfield
1
, Mark A. Bradford
1
, and Stephen A. Wood
1,2
1
School of Forestry and Environmental Studies, Yale University,
370 Prospect Street, New Haven, CT 06511, USA
2
The Nature Conservancy, Arlington, VA 22201, USA
Correspondence:
Emily E. Oldfield (emily.oldfield@yale.edu)
Received: 28 June 2018 – Discussion started: 6 August 2018
Revised: 13 November 2018 – Accepted: 9 December 2018 – Published: 15 January 2019
Abstract.
Resilient, productive soils are necessary to sustainably intensify agriculture to increase yields while
minimizing environmental harm. To conserve and regenerate productive soils, the need to maintain and build
soil organic matter (SOM) has received considerable attention. Although SOM is considered key to soil health,
its relationship with yield is contested because of local-scale differences in soils, climate, and farming systems.
There is a need to quantify this relationship to set a general framework for how soil management could potentially
contribute to the goals of sustainable intensification. We developed a quantitative model exploring how SOM
relates to crop yield potential of maize and wheat in light of co-varying factors of management, soil type, and
climate. We found that yields of these two crops are on average greater with higher concentrations of SOC (soil
organic carbon). However, yield increases level off at
∼
2 % SOC. Nevertheless, approximately two-thirds of
the world’s cultivated maize and wheat lands currently have SOC contents of less than 2 %. Using this regression
relationship developed from published empirical data, we then estimated how an increase in SOC concentrations
up to regionally specific targets could potentially help reduce reliance on nitrogen (N) fertilizer and help close
global yield gaps. Potential N fertilizer reductions associated with increasing SOC amount to 7 % and 5 % of
global N fertilizer inputs across maize and wheat fields, respectively. Potential yield increases of 10
±
11 %
(mean
±
SD) for maize and 23
±
37 % for wheat amount to 32 % of the projected yield gap for maize and
60 % of that for wheat. Our analysis provides a global-level prediction for relating SOC to crop yields. Further
work employing similar approaches to regional and local data, coupled with experimental work to disentangle
causative effects of SOC on yield and vice versa, is needed to provide practical prescriptions to incentivize soil
management for sustainable intensification.
1
Introduction
The pressure to increase crop production has resulted in the
expansion of land area dedicated to agriculture and the inten-
sification of cropland management through practices such as
irrigation and fertilization. These practices have led to degra-
dation of land and waters, prompting sustainable intensifica-
tion initiatives to increase yields on existing farmland while
decreasing the environmental impact of agriculture (Foley et
al., 2011; Godfray et al., 2010; Mueller et al., 2012). One sign
of land degradation is the loss of soil organic matter (SOM)
(Reeves, 1997). Rebuilding SOM in agricultural lands holds
the promise of improving soil fertility, as SOM affects many
properties of soils, including their ability to retain water and
nutrients, to provide structure promoting efficient drainage
and aeration, and to minimize loss of topsoil via erosion
(Reeves et al., 1997; Robertson et al., 2014). As such, manag-
ing SOM to ensure stable and long-lasting crop productivity
and to decrease reliance on external inputs such as mineral
fertilizers and irrigation has been identified as a critical com-
ponent of sustainable intensification (Foley et al., 2011). Yet
the emphasis on soil management has remained qualitative,
meaning that the potential contribution of building SOM as a
Published by Copernicus Publications on behalf of the European Geosciences Union.
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16
E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
means to increase crop production and minimize the environ-
mental impact of agriculture has not yet been broadly quan-
tified (Adhikari and Hartemink, 2016; Chabbi et al., 2017;
Hatfield et al., 2017).
A primary hurdle to managing SOM for sustainable in-
tensification is the lack of predictive, quantitative targets of
SOM for specific agricultural and environmental objectives
(Herrick, 2000; NRC, 2010). While several studies show cor-
relations between SOM and yield (Culman et al., 2013; de
Moraes Sa et al., 2014; Lucas and Weil, 2012; Stine and Weil,
2002), it remains unclear how much yield could be expected
to increase per unit change in organic matter (Herrick, 2000;
NRC, 2010). Establishing these quantitative metrics is chal-
lenging because research shows increases (Bauer and Black,
1992), decreases (Bhardwaj et al., 2011), and no change (Hi-
jbeek et al., 2017) in yields with increased SOM. This lack of
a general relationship is likely the result of a number of inter-
acting factors related to management, climate, and soil type
that can confound the SOM–yield relationship. This confu-
sion has led some to claim that the amount of SOM is unnec-
essary for crop yields, so long as there is sufficient N fertil-
izer (Hijbeek et al., 2017; Loveland and Webb, 2003; Oelofse
et al., 2015), whereas others highlight the need to build SOM
to increase crop yields while minimizing environmental harm
(Lal, 2004). The growing momentum to launch global-scale
initiatives to manage SOM (Banwart et al., 2014; Lal, 2004;
Minasny et al., 2017; Zomer et al., 2017) suggests the need
to test competing claims about the effects of SOM on these
agricultural and environmental outcomes.
One could critique the effort to establish a global-level un-
derstanding of the SOM–yield relationship on the grounds
that farm-level responses are necessarily heterogeneous and
poorly predicted by global assessments. Yet, global initia-
tives for managing SOM could create policy environments
that stimulate regional and local prescriptions for SOM lev-
els that inform practice (Chabbi et al., 2017; Minasny et al.,
2017; Zomer et al., 2017). Whereas it is difficult to disentan-
gle the extent to which SOM–yield relationships are driven
by SOM effects on yield, as opposed to yield (i.e., higher
plant carbon inputs) effects on SOM, there is nevertheless
experimental evidence showing that building SOM positively
affects yield (Bauer and Black, 1994; Majumder et al., 2008;
Oldfield et al., 2017). In addition, numerous soil properties
that relate to soil fertility, such as water holding capacity, re-
spond positively to increasing SOM and in turn are expected
to increase yields (Williams et al., 2016). As such, correlative
SOM–yield relationships suggest the potential – but likely
not the true – effect of SOM on yield.
We developed a quantitative model exploring how SOM
relates to crop yield potential in light of co-varying factors
of management, soil type, and climate. The aim is that this
model can then be used to establish relationships at broad
scales between SOM and yield to provide better quantifica-
tion of this relationship for policy initiatives. We quantified
the relationship between SOM (measured as soil organic car-
bon, SOC, which is a common proxy for SOM) and yield at
a global level using data from published studies. We focused
our analyses on wheat and maize, two common staple crops
that (along with rice) constitute two-thirds of the energy in
human diets (Cassman, 1999). Along with SOC, we modeled
the effects on crop yields of several factors widely reported
in yield studies: N input rate, irrigation, pH, soil texture (%
clay), aridity, crop type (i.e., wheat or maize), and latitude (as
a proxy for growing-season day length). The data informing
our model came from empirical studies that capture local-
scale variation in these variables, and hence we interpret our
results in light of the correlative nature of the database we
assembled. Using the resulting multiple-regression relation-
ship, we then estimated how an increase in SOC concentra-
tions up to regionally specific target thresholds might affect
global yields. Our overarching aim was to estimate the po-
tential extent to which restoring SOC in global agricultural
lands could help close global yield gaps and potentially help
reduce reliance on – and the negative effects of – N fertilizer.
2
Results and discussion
2.1
The relationship between SOC and yield
At the global level and focusing specifically on the potential
effect size of SOC on yield, we found that the largest gains
in yield occur between SOC concentrations of 0.1 % and
2.0 %. For instance, yields are 1.2 times higher at 1.0 % SOC
than 0.5 % SOC (Fig. 1). Gains in yield leveled off at a con-
centration of approximately 2 % SOC (Fig. 1). Two percent
SOC has previously been suggested as a critical threshold,
with values below this concentration threatening the struc-
ture and, ultimately, the ability of a soil to function (Kemper
and Koch, 1966). Importantly, the asymptotic relationship
between SOC and yield lends support to the idea that build-
ing SOC will increase yields – at least to a certain extent – as
opposed to simply being an outcome of higher yields. That is,
if yield was an explanatory variable for SOC, we would ex-
pect greater yields to keep driving greater levels of SOC (i.e.,
the relationship would appear more linear) since we know
that soils can accumulate concentrations much greater than
2 % (Castellano et al., 2015). However, our data do not dis-
play a linear pattern, suggesting that higher yields are not
driving higher levels of SOC.
It has been suggested that there is no evidence for 2 %
SOC being a critical threshold for productivity, as long as
there is sufficient mineral fertilizer to support crop produc-
tion (Edmeades, 2003; Loveland and Webb, 2003; Oelofse
et al., 2015). Such conclusions deem the amount of SOM as
substitutable by mineral fertilizers (at least for crop growth),
but are inconsistent with the motivation for sustainable inten-
sification to minimize environmental harm caused by min-
eral fertilizers in relation to emissions of greenhouse gases
and eutrophication of waters (Vitousek et al., 2009). The
same logic about substitutability also does not account for
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E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
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Figure 1.
Relationship between SOC and yield of maize for published studies. The regression lines are modeled yields (i.e., effect sizes)
for rain-fed (i.e., non-irrigated) maize using observed means of our meta-dataset for aridity, pH, texture, and latitude at different N in-
put rates. We varied SOC (
x
axis) across the range of values extracted from the literature. The red line represents the mean N input rate
(118 kg N ha
−
1
yr
−
1
) across all studies, with the bottom line representing 0 inputs of N and the top line representing 200 kg N ha
−
1
yr
−
1
.
For the raw data points, N input is mapped as a continuous variable across its range from 0 (smallest circles) to 500 kg N ha
−
1
yr
−
1
(largest
circles). Note that the observed scatter of the individual observations is an outcome of the fact that yield is controlled by multiple factors
(Table 1), and therefore the regression lines isolate just the potential effect of SOC with all other factors held constant.
the other co-benefits associated with building SOM in agri-
cultural lands, such as reductions in nutrient runoff, drought
resistance, and yield stability (Robertson et al., 2014). Field-
and regional-scale studies have shown a similar pattern as
that observed from our global analysis: there exists a positive
relationship between SOC and yield that starts to level off
at
∼
2 % SOC (Kravchenko and Bullock, 2000; Pan et al.,
2009; Zvomuya et al., 2008). Our analysis suggests that this
relationship holds on average at the global scale and when N
fertilization is controlled for.
Ninety-one percent of the published studies used for our
analysis were carried out in fields with less than 2 % SOC,
with a mean of 1.1 %. To see whether these observations in
SOC distribution reflected global patterns, we used globally
gridded data on crop yield and SOC (to a depth of 15 cm)
(Hengl et al., 2014; Monfreda et al., 2008). We found that, by
both area and production, two-thirds of maize and wheat cul-
tivation takes place on soils with less than 2 % SOC (Fig. 2).
Indeed, a recent analysis estimates that agricultural land uses
(including cropland and grazing) have resulted in a loss of
133 Pg of carbon over the past 12 000 years of human land
use (Sanderman et al., 2017). There appears to be, therefore,
significant opportunity to increase SOC on maize and wheat
lands to improve crop yields.
2.2
The interaction between SOC and N fertilizer on
yield
One of the key goals of sustainable intensification is to re-
duce the environmental impacts of agriculture (Foley et al.,
2011; Mueller et al., 2012). Nitrogen fertilization, while a
boon to yields, can cause environmental damages, such as
eutrophication of waters and increased soil emissions of ni-
trous oxide, a potent greenhouse gas (Vitousek et al., 2009).
Using our regression model, we asked whether there might
be target N fertilizer addition rates that suggest the possibil-
ity of maximizing yield per unit N applied by building SOC
and reducing inorganic N inputs. We wanted to see if yields
converge at higher levels of SOC, suggesting that crops are
obtaining sufficient nutrients through SOM and excess min-
eral N is not necessary. Our analysis suggests that SOC is not
directly substitutable for mineral fertilizer (Fig. 1); however,
at lower rates of N input (
≤
50 kg N ha
−
1
), we found that in-
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E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
Figure 2.
Global maize and wheat lands with less than 2 % SOC. Cultivated
(a)
maize lands and
(b)
wheat lands on soils with SOC contents
less than 2 %. Approximately two-thirds of all maize (61 %) and of all wheat (64 %) producing areas are on soils with less than 2 % SOC.
Black areas on the maps are cultivated maize and wheat lands that have concentrations over 2 % SOC. Yield data are taken from EarthStat
and SOC data are taken from ISRIC SoilGrids.
Table 1.
Modeled regression coefficients with standard errors, standardized coefficients, and
P
values for our regression model.
Variable
Unstandardized coefficients
Standardized coefficients
P
value
Intercept
−
1
.
61
±
1
.
71
5
.
59
±
0
.
18
0.35
SOC
1
.
79
±
0
.
59
1
.
44
±
0
.
30
0.003
SOC
2
−
0
.
46
±
0
.
18
−
0
.
66
±
0
.
27
0.012
N input
0
.
018
±
0
.
0014
2
.
71
±
0
.
15
<
0
.
00001
N input
2
−
0
.
000039
±
0
.
0000036
−
1
.
64
±
0
.
15
<
0
.
00001
Irrigation
0
.
75
±
0
.
35
0
.
77
±
0
.
34
0.032
pH
0
.
053
±
0
.
18
0
.
12
±
0
.
42
0.76
Aridity
0
.
16
±
0
.
51
0
.
12
±
0
.
41
0.76
Crop type
1
.
54
±
0
.
15
1
.
54
±
0
.
15
<
0
.
00001
Clay (%)
0
.
013
±
0
.
014
0
.
29
±
0
.
31
0.37
Latitude
0
.
054
±
0
.
016
1
.
40
±
0
.
41
0.001
SOC
×
N input
0
.
0039
±
0
.
00099
0
.
96
±
0
.
25
0.00010
The output of our linear mixed effect model (
n
=
834
). The full model explained 83 % of observed variability within
the dataset with fixed effects (included in the table) accounting for 42 % of the variability. Standardized coefficients
allow for direct comparison of the relative effect size of each modeled variable despite different scales on which the
variables are measured. For example, crop type’s effect on yield is 2 times greater than that of irrigation. Crop type
was coded as a binary variable with 0 for wheat and 1 for maize. Irrigation was also coded as a binary variable with 0
for no irrigation and 1 for irrigation.
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E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
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Figure 3.
Potential reductions in nitrogen fertilizer with an increase in SOC concentration. The lines on the graph represent varying SOC
concentrations – 2.0 %, 1.0 %, and 0.5 % SOC – from top to bottom for rain-fed maize. These lines are plotted on top of the observations from
our dataset with SOC mapped as a continuous variable across its range from 0.1 % (smallest circles) to 3.0 % (largest circles). Our model
shows that keeping yield constant by increasing SOC contents allows for potentially significant reductions in N input (e.g., the same yield is
achievable with 0 N input and 2 % SOC as with 50 kg N ha
−
1
yr
−
1
and 0.5 % SOC). Recognizing that the 0 N input values may influence the
modeled relationship, we analyzed data excluding these values. The qualitative patterns remain the same if the 0 N input values are excluded
from the analysis; and while the absolute quantitative patterns shift slightly, the general trends remain intact.
creasing SOC from 0.5 % to 1.0 % could potentially maintain
current yields and reduce fertilizer inputs by approximately
half (50 %). At higher rates of N input (
≥
200 kg N ha
−
1
), an
increase from 0.5 % to 2.0 % SOC could potentially reduce
synthetic N inputs by up to 70 % per hectare (Fig. 3). Build-
ing SOC from 0.5 % to 2.0 % represents a very large increase,
which would require a significant amount of inputs that may
not be feasible due to inherent and logistical difficulties re-
lated to soil properties, climate, and farmer access to inputs.
Furthermore, such an increase could take several years or
decades to accomplish. For example, results from long-term
field trials show a range of annual increases in SOC for tem-
perate agricultural soils, which were as low as 0.3 % and as
high as 18 % (Poulton et al., 2018). At the low end of this
range, and starting at 0.5% SOC, it would take
∼
47 years to
build to 2 % SOC if the annual relative rate of increase was
constant, and
∼
9 years at the high end of the range. Admit-
tedly, the range emerged as a result of a number of different
inputs ranging from farmyard manure to sewage sludge to
mineral fertilization, some of which may not be available to
farmers given cost and/or access (Poulton et al., 2018). Feasi-
bility aside, however, our results suggest that building SOM
in agricultural lands may supply enough plant available nu-
trients to sustain crop yields while drastically cutting back on
N fertilizer inputs.
There was an interaction between SOC and N input, where
at higher SOC concentrations N input had a greater impact on
yield (Fig. 1, Table 1). This may be because higher SOC im-
proves soil structure and water holding properties, resulting
in improved crop growth at a given level of N input (Powlson
et al., 2011). Higher levels of SOM could also provide more
essential macro- and micronutrients that are limiting in soils
with lower SOC concentrations. Additionally, soils receiv-
ing more N may have greater SOC because N increases crop
yields, which can increase the return of plant residues into
the soil and potentially build SOC (Powlson et al., 2011).
However, if the relationship was simply an effect of greater
inputs building SOC, we should not have seen an interac-
tion between SOC and N on yields (because SOC should
then just have been additively related to yield). Whatever
the specific explanation, the SOC by N interaction we detect
suggests that a combination of both building SOM and us-
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E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
Table 2.
Scenarios for increases in yield and reductions in N input with an increase in SOC concentration to target values.
Scenario
Crop
Global yield average
Increase in production
Nitrogen input
(t ha
−
1
)
(Mt)
(Mt N ha
−
1
)
Current condition
Maize
3
.
34
±
2
.
62
n/a
17.24
Wheat
2
.
43
±
1
.
58
n/a
33.07
Increase SOC to
Maize
3
.
93
±
3
.
08
29.96 (5 %)
15.96
target concentrations
Wheat
3
.
17
±
2
.
06
55.41 (10 %)
32.04
Values (mean
±
SD) represent current EarthStat yields and projected gains in yield and production (with % increase in
parentheses) resulting from an increase in SOC concentration to target values for each agroecological zone (AEZ; targets ranged
from 1.0 % to 2.0 %). We used our regression model to determine potential gains in EarthStat yield and reductions in EarthStat N
input. Global yield averages represent tonnes produced per unit land area, whereas production represents tonnes of maize and
wheat produced globally.
ing targeted N applications could lead to potential increases
in yield (Fig. 3). Practices such as cover cropping represent
a strategy that can both increase N supply and build SOM
through biological N fixation and the return of high-quality
residues (narrow C
:
N ratios) to the soil (Drinkwater et al.,
1998). Building SOM and reducing fertilizer N input would
require a balance where SOM N mineralization accounts for
any limitations in N supply that arise from reducing mineral
fertilizer applications. The balance required will depend on
the amount and C
:
N ratios of inputs used in specific agricul-
tural systems and could prove challenging to achieve in some
smallholder systems where low SOC concentrations might
be compounded by a lack of access to and insufficient qual-
ity of organic inputs (Giller et al., 2009; Palm et al., 2001).
As such, the combination of both SOM improvement and tar-
geted fertilizer input will likely be especially important for
degraded soils, which require a suite of organic and inorganic
nutrients to help build SOM and improve crop yields (Palm
et al., 1997).
Gains in yield from fertilizer input leveled off at about
200 kg N ha
−
1
yr
−
1
(Fig. 3), meaning that optimum yields
appear achievable, at least on average, with this fertilizer
input level and an SOC target concentration of 2 %. Us-
ing this target N input rate, we explored potential fer-
tilizer reductions on agricultural lands using more than
200 kg N ha
−
1
yr
−
1
. We found that for lands receiving more
than 200 kg N ha
−
1
yr
−
1
, current yields could be maintained
while decreasing global N fertilizer inputs by 7 % for maize
and 5 % for wheat. It is estimated that 25 % to 30 % of fer-
tilizer N is exported to streams and rivers, resulting in eu-
trophication (Raymond et al., 2012). Targeted reductions in
the application of fertilizer N on the order of magnitude our
analysis suggests could then prevent the annual export of as
much as 3.73 million tonnes of N into inland waters, which
amounts to 10 % of mineral fertilizer applied to maize and
wheat lands (see Methods for an explanation of how this per-
centage was obtained).
2.3
Exploring potential reductions in global yield gaps of
maize and wheat
With a majority of cultivated lands containing less than 2 %
SOC and a growing imperative to build, restore, and protect
SOC in agricultural soils (NSTC, 2016; FAO, 2008; NRCS,
2012), we used global gridded datasets coupled with our re-
gression model (Table 1) to examine the potential gains in
yield and production if opportunities to increase SOC are
realized (Table 2). We then calculated how these gains in
production would impact global yield gaps of maize and
wheat, the difference between observed and attainable yields
(Mueller et al., 2012). Although our model identified 2 % as
a global target for SOC, we created regionally specific SOC
targets given the fact that achieving 2 % SOC in some soils
(e.g., those of drylands) may be unachievable due to inherent
constraints of physical soil properties and climate (see Meth-
ods). We found that increasing SOC concentrations to the
defined targets has the potential capacity to increase average
yields on a per hectare basis by 10
±
11 % (mean
±
SD) for
maize and 23
±
37 % for wheat. These gains in yield trans-
late to a 5 % and 10 % increase in the global annual tonnes
produced of maize and wheat, respectively (Table 2). These
increases in production would close 32 % of the global yield
gap for maize and 60 % of the gap for wheat (Fig. 4a, b).
These yield gap results represent an exploration of po-
tential best-case impacts of increasing SOC concentrations.
We recognize there are inherent and logistical challenges to
building SOM in agricultural soils; and when managing for
and building SOM, it is important to account for its dynamic
nature. For instance, to derive some of the nutrient benefits
of SOM, it must be mineralized and used (Janzen, 2006), and
so frequent additions of organic inputs may be necessary to
sustain SOM levels. Furthermore, soil characteristics such
as texture can have a large effect on SOC content because
sandier (rather than more clay rich) soils have less surface
area to stabilize SOC (Rasmussen et al., 2018) and so hold
much less water and nutrients than clay-rich soils (Johnston
et al., 2009). Maintaining SOC contents in sandy soils may
require more frequent additions of organic amendments be-
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E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
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Figure 4.
Proportion closure of yield gap for
(a)
maize and
(b)
wheat given an increase in SOC concentration to target values for each AEZ
(ranging from 1 % to 2 %). Modeled gains come from our regression relationship between SOC and yield and applying it to EarthStat yield
gap data. Doing so determines the potential increase in yield and therefore projected reductions in yield gaps for maize and wheat.
cause these soils do not have the surface area to retain nutri-
ents, moisture, and to stabilize SOC (Lehmann and Kleber,
2015).
Different regions and climate types also face different im-
peratives for building SOM. In the midwestern United States,
for instance, building SOM may be a good strategy to re-
duce fertilizer inputs and irrigation needs, whereas in sub-
Saharan Africa, building SOM may be critical for drought
protection and nutrient provision. Notably, high SOM values
are not common in dryland environments (for our dataset,
mean SOC
=
0.9 % for dryland climates versus 1.4 % SOC
for mesic soils), and building and maintaining SOM in arid
zones is typically hindered by the lack of organic matter to
return to soils (Rasmussen et al., 1980). On a positive note,
however, our analysis suggests that increases in SOC in dry-
lands, for example, from 0.5 % to 0.8 %, could potentially
increase yields by 10 %, likely due to impacts on water re-
tention as well as improved nutrient supply.
The goal of our analysis was to establish a global, aver-
age relationship between SOC and yield. Whereas we did use
lower SOC targets (ranging from 1.0 % to 1.5 %) for the arid
agroecological zones (AEZs) in our analysis, the majority of
data used for our analysis are from the more temperate and
tropical humid zones (Fig. S2 in the Supplement) and a large
proportion of our data comes from China (Fig. 5). We recog-
nize that the distribution of our data could potentially bias our
results. As such, we explored the SOM–yield relationship in
the absence of data from China and also for Chinese observa-
tions only. While the effect size of SOC changes depending
on the subset of data analyzed (Table S2 in the Supplement),
the qualitative patterns of this relationship remain the same.
That is, SOC leads to gains in yield that are most pronounced
at lower SOC concentrations and decline in their magnitude
as
∼
2 % SOC is reached (Fig. S3). Notably, when explor-
ing the subset of data from China, the effect size of SOC
was higher than that from the entire dataset (Table S2). How-
ever, China only had 10 observations above 2 % SOC, and so
the modeled relationship for China captures the part of the
SOM–yield relationship where an increase in SOC leads to
the largest gains in yield (i.e., where the modeled slope is the
steepest). Our analysis then highlights both the need for stud-
ies to come evenly from systems where maize and wheat are
grown and also the importance of analyzing regional datasets
that capture the observed range of SOC values in order to
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E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
quantify a regionally specific relationship between SOC and
yield to more directly inform practice.
Moving from the global relationship presented in our pa-
per to bolstering and/or refining SOC targets, our correla-
tive analysis needs to be supplemented with well-replicated
experimental studies incorporating different management
strategies across multiple soil and climate types to develop
SOC–yield relationships that can be applied to the specific
set of local farm conditions. Further, these studies should
ideally report data related to soil texture and mineralogy, nu-
trient management, and paired SOC–yield observations with
SOC taken to meaningful depths, such as those that rep-
resent plant-rooting depth. These experimental studies will
help generate information that practitioners can use to inform
management by taking into account the potential benefits of
SOC, compared against the inherent and logistical challenges
to building SOC to target levels.
3
Conclusions
Despite uncertainties and calls for further research into how
SOM affects agricultural performance (Cassman, 1999; Her-
rick, 2000; Oldfield et al., 2015), policy for sustainable in-
tensification already widely supports the merits of increas-
ing SOM in agricultural lands (FAO, 2008; NRCS, 2012).
The purported benefits include improved yields, increased
resilience, and decreased inputs of fertilizer and irrigation
water. However, although consensus exists around the im-
portance of SOM to soil health, translating SOM policy to
practice is hindered by the lack of a predictive capacity for
SOM target setting to inform management efforts focused
on yield and reducing fertilizer and irrigation (Chabbi et al.,
2017; Herrick, 2000; NRC, 2010). Our analysis helps estab-
lish a quantitative framework for SOC targets that achieve
measurable agricultural outcomes as part of sustainable in-
tensification efforts. It quantifies the potential effect size of
SOC on yield while also accounting for climate, soil, and
management variables that influence crop yield. We find that
greater concentrations of SOC are associated with greater
yields up to an SOC concentration of 2 %. With two-thirds
of global maize and wheat lands having SOC concentrations
of less than 2%, there seems to be significant opportunity to
increase SOC to reduce N inputs and potentially help close
global yield gaps.
4
Methods
Our approach consisted of a two-stage process. In the first
stage, we assembled published empirical data from studies
that reported both SOC and yield data for maize and wheat.
From this meta-dataset, we then quantified how both SOC
concentrations and N input rates are related to yields, in the
context of spatial variation in climatic, management, and soil
co-variables. In the second stage, we used globally gridded
datasets to extract values for the factors we investigated in
the first stage for global lands where maize and wheat is pro-
duced. Using the regression relationship developed from the
published empirical data compiled under the first stage, we
then estimated how an increase in SOC concentrations up
to target thresholds we identified (ranging from 1 % to 2 %
depending on agroecological zoning) affected global yield
potentials. Finally, we used an N input threshold identified
through our regression analysis (200 kg N ha
−
1
yr
−
1
) to cal-
culate potential N reductions on global maize and wheat
lands.
4.1
Data collection
In the first stage of our approach, we searched the database
Web of Science (Thomson Reuters) in January 2016 and
again in October 2016 using the following topic search terms:
soil organic matter, soil organic carbon, soil carbon, or soil
c; and yield, crop yield, productivity, and agricult*. We re-
stricted the initial search to articles published in English be-
tween 1980 and December 2015 and excluded conference
proceedings; the second search captured articles published
in 2016. The initial search resulted in 1384 articles and the
second in 169 articles (Fig. S1). For each citation, we re-
viewed titles and abstracts to select articles that met the fol-
lowing criteria: experimental field studies whose abstract in-
cluded information on yield and SOC for systems growing
wheat and/or maize. This initial screening resulted in 523
records for which we assessed the full text. We assessed these
records for eligibility based on inclusion of data on crop
yield, SOC, and N fertilizer rates for each observation. For
inclusion within our analysis, it was essential that studies re-
ported paired SOC and yield data. Furthermore, we required
SOC concentrations (as opposed to stocks). Studies did not
meet our criteria for inclusion if they reported SOC stocks
with no corresponding data on bulk density to convert into
concentrations and also if they reported baseline SOC con-
centrations as opposed to experimental SOC concentrations
that we could pair with yield data. In addition to our liter-
ature search, we also contacted authors to see if they were
willing to include raw data within our database. This resulted
in three datasets (Adiku et al., 2009; Birkhofer et al., 2008;
Kautz et al., 2010). Finally, we consulted the recently pub-
lished database by the Swedish Board of Agriculture that is
a key repository of peer-reviewed literature focusing specif-
ically on studies (735 in total) related to the effects of agri-
cultural management on soil organic carbon (Haddaway et
al., 2015). We explored this database to find studies from re-
gions that were underrepresented within our literature search
(e.g., the Southern Hemisphere). This resulted in a search of
55 studies to see if they met our criteria for inclusion. We
scanned each paper to see if they included SOC data paired
with matching yield data. From these papers, we extracted
data from 12 studies, which resulted in an additional 52
data points. We encountered limitations similar to our initial
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E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
23
Figure 5.
Distribution of data points by country. Countries are ordered by gross domestic product (GDP) in order from largest (top) to
smallest (bottom). The dataset used for this study contains a total of 840 individual observations from 29 different countries.
search: namely, SOC and yield data were not paired, studies
included only baseline SOC concentrations, or SOC stocks
were reported without any corresponding bulk density data
to convert into concentrations. Overall, our dataset included
840 individual observations from 90 articles covering sites
across the globe (Adiku et al., 2009; Agegnehu et al., 2016;
Albizua et al., 2015; Alijani et al., 2012; Araya et al., 2012;
Atreya et al., 2006; Bai et al., 2009; Bedada et al., 2014;
Bhardwaj et al., 2011; Bhattacharyya et al., 2015; Birkhofer
et al., 2008; Boddey et al., 2010; Boulal et al., 2012; Bre-
mer et al., 1994; Calegari et al., 2008; Campbell et al., 2007;
Castellanos-Navarrete et al., 2012; Celik et al., 2010; Chen et
al., 2015; Chirinda et al., 2010; Cid et al., 2014; Costa et al.,
2010; D’Hose et al., 2014; Datta et al., 2010; DeMaria et al.,
1999; Diacono et al., 2012; Grandy et al., 2006; Guo et al.,
2012, 2009; He et al., 2011; Hossain et al., 2016; Hu et al.,
2015, 2014; Kaihura et al., 1999; Karbozova Saljnikov et al.,
2004; Kautz et al., 2010; Kazemeini et al., 2014; Kucharik et
al., 2001; Larsen et al., 2014; Lebbink et al., 1994; Leogrande
et al., 2016; Li et al., 2015; E. K. Liu et al., 2014; X. E. Liu
et al., 2014; X. Y. Liu et al., 2014; Liu et al., 2016; López-
Garrido et al., 2014; Lu et al., 2016; Ma et al., 2012, 2016;
Madejón et al., 2001; Mandal et al., 2013; Masto et al., 2007;
Mikanová et al., 2012; Mishra et al., 2015; Mupangwa et
al., 2013; N’Dayegamiye, 2006; Niu et al., 2011; Njoku and
Mbah, 2012; Paul et al., 2013; Qin et al., 2015; Quiroga et al.,
2009; Sadeghi and Bahrani, 2009; Saikia et al., 2015; Scalise
et al., 2015; Seremesic et al., 2011; Singh and Dwivedi, 2006;
Singh et al., 2016; Sisti et al., 2004; Soldevilla-Martinez et
al., 2013; Spargo et al., 2011; Šimon et al., 2015; Tejada et
al., 2016; Tiecher et al., 2012; van Groenigen et al., 2011;
Vieira et al., 2007, 2009; Wang et al., 2015; Q. J. Wang et al.,
2014; Z. G. Wang et al., 2014; Wortman et al., 2012; Wu et
al., 2015; Yang et al., 2013; J. Yang et al., 2015; Z. C. Yang et
al., 2015; Yeboah et al., 2016; Zhang et al., 2015, 2009, 2016;
Zhao et al., 2016). Where necessary, we extracted data from
manuscript figures using GraphClick software (version 3.0.3,
http://www.arizona-software.ch/graphclick/, last access: Jan-
uary, 2018).
Studies that presented individual data points recorded over
multiple years were included as well as studies that averaged
both yield and SOC data over multiple years. To avoid over-
representation of studies that included data points recorded
for both yield and SOC over multiple years (
>
10 years), we
took observations from the beginning, middle, and last year
of the study.
4.2
Data compilation
For each extracted observation, we compiled the following
information: latitude, longitude, year of data collection, crop
type, yield, SOC or SOM, depth of SOC or SOM measure-
ment, N fertilization rate, P fertilization rate, soil pH, texture,
and whether or not crops were irrigated. We used SOC (as
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E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
opposed to SOM) for our analysis given that SOC is a com-
mon proxy for SOM. Carbon, as an element that is easily
identified and measured within soil, is thought to comprise
∼
50 %–60 % of SOM and is commonly reported in the liter-
ature (Pribyl, 2010). When SOM was reported, we converted
it to SOC by dividing the value by 1.724 (Cambardella et al.,
2001). Different studies reported SOC concentrations to dif-
ferent depths, which ranged from 0–5 to 0–30 cm, with the
majority of studies reporting SOC to 0–20 cm. When stud-
ies reported SOC to multiple depths, we averaged SOC val-
ues across depths to 30 cm. If no information on irrigation
was provided, we scored the observation as rain fed. Soil
texture and pH were not reported for every study; 79 % of
included studies reported pH, and so we used the study’s lat-
itude and longitude to extract these data using ISRIC Soil-
Grids (Hengl et al., 2014) to fill in the missing pH values.
Texture was reported for about half (49 %) of included stud-
ies, and so we used coordinates to pull these data from Soil-
Grids as well (Hengl et al., 2014). We also used latitude and
longitude to obtain an aridity index through the CGIAR-CSI
database (Zomer et al., 2008). We chose to use aridity as our
primary climatic variable since it is expressed as a function
of precipitation, temperature, and potential evapotranspira-
tion (Trabucco, 2009).
4.3
Data analysis
We used a linear mixed model (LMM) to analyze the obser-
vations we extracted from the literature. Our model included
SOC, N fertilizer rate, crop type (maize or wheat, coded as a
binary variable), irrigation (coded as a binary variable), arid-
ity index, latitude, pH, and texture (% clay) as fixed effects.
The differences in soil carbon observed in our dataset are
from experimental plots capturing long-term differences in
SOC within a given site. Specifically, our data capture differ-
ences within SOC largely driven by management interven-
tions related to inputs (e.g., compost, fertilizer, manure, crop
residues) and tillage (e.g., no till versus till). Site-specific
differences in management as well as spatial and temporal
correlation among the studies were accounted for by nest-
ing year within study as random effects (Bolker et al., 2009).
The LMMs were fit with a Gaussian error distribution in the
“lme4” package for the R statistical program (version 3.3.1),
using the “lmer” function. The first stage of our data analy-
sis was to test the data distributions. We removed data points
with N fertilization rates
>
600 kg N ha
−
1
(four data points)
and yields
>
18 t ha
−
1
(two data points) since these repre-
sented outliers for our dataset (being beyond 3 times the in-
terquartile range of the meta-dataset) and are not represen-
tative of on-farm management practices or outcomes. Our
final model was based on 834 observations across 90 stud-
ies. We added quadratic terms for both SOC and N input
rate since these variables exhibited a nonlinear relationship
with yield. The square root of the variance inflation factors
(vif) was
<
2 for all factors when included as main effects,
indicating that collinearity was low among all variables. As
would be expected, there was a correlation between SOC and
its quadratic term and between N input rate and its quadratic
term. We reran our regression after removing four seemingly
influential data points (those that had high SOC concentra-
tions with low yields; see Fig. 1) and model coefficients re-
mained essentially the same. We calculated the
r
2
values for
our model following Nakagawa and Schielzeth (Nakagawa
and Schielzeth, 2013) to retain the random effects structure.
The
r
2
of our model was 83 % for the full model, with the
fixed effects explaining 42 % of observed variance within our
dataset.
We based the choice of factors for inclusion in our model
on the approach of Hobbs and Hilborn (2006), by only inves-
tigating factors where biological mechanism as to their influ-
ence on yield is firmly established and where we were inter-
ested in their effect sizes relative to one another. Also follow-
ing Hobbs and Hilborn (2006), we did not carry out model
selection. Operationally, there is substantial subjectivity and
lack of agreement in model selection approaches, with dif-
ferent decisions leading to markedly different conclusions as
to the influence of different factors. Instead, coefficients are
generally most robust when all terms are retained in a model,
assuming that the inclusion of each is biologically justified.
We decided to include an SOC by N interaction to explore
potential reductions in N fertilizer with increased SOC con-
centrations. This was an effort to see if there is a level of
SOC that can compensate for N input. We acknowledge that
there are a number of interactions we could have included
within our statistical model, and we did run our regression
model with additional interactions to include SOC by irri-
gation, SOC by clay, and SOC by aridity. Including these
interactions, however, did not offer any additional explana-
tory power and our main results between SOC, N inputs, and
yield were essentially unchanged with these additional inter-
actions (Table S3). As such, we chose to present our analysis
including only the SOC
×
N interaction.
To examine the effect sizes of the factors on yield, we took
two approaches. First, we compared the size of the standard-
ized coefficients, where standardizing involved subtracting
the mean of the factor from each observed value and divid-
ing by 2 standard deviations (Gelman, 2008). Dividing by
2 standard deviations is useful when binary predictors are in-
cluded within regression models (in our case, crop type and
irrigation are coded as binary predictors). This way, contin-
uous and binary variables all have a mean of 0 and a stan-
dard deviation of 0.5 (Gelman, 2008). This accounts for the
fact that the factors were measured on different unit scales
(Table 1). Second, we examined the influence of changing
SOC concentration or N fertilization rates on yield. To do
this, we used the regression relationship derived from our
statistical model, held all other factors at a constant value
(e.g., the mean of all observations for that factor), and sys-
tematically varied SOC or N fertilization across the range of
values we extracted from the literature. For SOC, this meant
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E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
25
varying SOC values from 0.1 % to 3.5 % to estimate chang-
ing yield of rain-fed maize or wheat as SOC concentrations
were increased (Fig. 1). For N fertilization, we varied N in-
put rates from 0 to 300 kg N ha
−
1
for rain-fed maize or wheat
at different SOC concentrations (Fig. 3). When these factor–
yield relationships were plotted, we identified threshold val-
ues where yield became minimally responsive to SOC or N
fertilization as the point where the slope of the relationship
became
<
0
.
25 (for SOC) and
<
0
.
002 (for N fertilization).
4.4
Global extrapolations
We used the regression relationship developed in the first
stage of our approach to predict how building SOC concen-
trations would potentially affect global crop yield averages.
To obtain values for each of the factors in our regression
model at a global scale, we used globally gridded data prod-
ucts. Global SOC, pH, and texture data were taken from IS-
RIC SoilGrids (Hengl et al., 2014) at a 10 km grid cell reso-
lution to match the spatial grain for maize and wheat yields
and N fertilization data, which we obtained from the Earth-
Stat product (Monfreda et al., 2008; Mueller et al., 2012).
SoilGrids has multiple layers for SOC concentrations, so we
used the 0–15 cm layer because the average depth to which
SOC was reported for our dataset was 0–20 cm. The aridity
index was obtained from the CGIAR-CSI database (Zomer et
al., 2008). We used the resulting global dataset to explore the
potential impact of increasing SOC (up to regionally identi-
fied threshold levels ranging from 1 % to 2 %) on yield for
lands across the globe where maize and wheat are produced.
To establish regionally appropriate SOC targets, we clas-
sified maize- and wheat-producing areas by their agroeco-
logical zones. The Food and Agricultural Organization has
18 zones defined on the basis of combinations of soil, land-
form, and climatic characteristics (Ramankutty et al., 2007).
For each AEZ, we examined the distribution of SOC in areas
classified as naturally vegetated (e.g., not in urban or agricul-
tural land uses). We did this by stacking two GIS raster layers
of SOC (SoilGrids) and land use (Friedl et al., 2010), exclud-
ing agricultural and urban land use classifications. We then
extracted SOC data for each AEZ using a shape file outlin-
ing the geographical extent of each AEZ (Ramankutty et al.,
2007). Examining the distribution of SOC across each AEZ,
we identified targets based on the mean SOC value within
each zone. All but four zones had means greater than 2 %
SOC, so we set target values for those zones at 2 %. Mean
SOC concentrations were lower for the more arid zones and
so we set those targets to 1 % for AEZ 1 and 1.5 % for AEZ
zones 2, 3, and 7. These targets were in line with recent
quantitative assessments based on similar climatic classifica-
tions. For instance, recent analysis of global SOC concentra-
tions across globally defined ecoregions shows mean values
of SOC at or greater than 2 % for all regions except land clas-
sified as desert and xeric shrubland (Stockmann et al., 2015).
Prior to our global extrapolations, we performed a suite
of data checks. We wanted to ensure that global yields pre-
dicted using our regression model were comparable to those
from EarthStat. These checks helped validate the strength of
our extrapolations. Firstly, we explored the range of varia-
tion in variables from experimental data used to generate our
model as well as the range of global variation in variables we
project across. The range of our regressors encompasses the
range of global variation, except for aridity, in which case
4.6 % percent of our projections fall in grids that have axis
conditions outside of our range of measurements. These val-
ues fall in extremely arid systems, with aridity values of less
than 0.1. In these extremely arid zones, we do make a point
to use lower target SOC values, recognizing that achieving
2 % SOC in these very arid areas is not very likely. Sec-
ondly, using our regression model to predict global yields for
both maize and wheat (separately), we first removed all val-
ues from the analysis that had predicted yields of less than
0 because negative yields are not possible. This amounted
to 0.004 % of the total predictions for maize and 0.15 % for
wheat. For clarification, we refer to predictions from our re-
gression model as predicted or model predicted. We then cal-
culated the proportional difference between model-predicted
and globally gridded yield data from EarthStat. We dropped
all cells for which the proportional difference between pre-
dicted and gridded data was
>
3 times. This threshold repre-
sents the mean
±
half of the standard deviation for the dis-
tribution of the proportional difference between predicted
and EarthStat yield data. This amounted to 14 % of cells for
maize and 7 % for wheat. The mean proportional difference
between predicted and gridded data was 0
.
85
±
0
.
91 for maize
(Fig. S4b) and 0
.
45
±
0
.
87 for wheat (Fig. S5b). The corre-
lation between predicted and gridded data was
r
=
0
.
73 for
maize (Fig. S4c) and
r
=
0
.
38 for wheat (Fig. S5c). We also
visualized an overlap in the distribution of model-predicted
and gridded data. Model-predicted maize yield had a global
mean of 4
.
66
±
1
.
84 t ha
−
1
and EarthStat had a global mean
of 3
.
34
±
2
.
62 t ha
−
1
(Fig. S4a). Model-predicted wheat yield
had a global mean of 3
.
18
±
1
.
66 t ha
−
1
and EarthStat had a
global mean of 2
.
43
±
1
.
58 t ha
−
1
(Fig. S5a).
We also compared the distribution of EarthStat yield data
with observed yield data from the studies included in our
analysis. We found that the correlation (
r
values) between the
gridded and collected data was 0.56 for maize and 0.39 for
wheat. Average observed maize yield was 5
.
61
±
3
.
32 t ha
−
1
and wheat yield was 4
.
02
±
2
.
11 t ha
−
1
(mean
±
SD). Earth-
Stat maize yield, again, was 3
.
34
±
2
.
62 t ha
−
1
and wheat
yield was 2
.
43
±
1
.
58 t ha
−
1
. These differences between pre-
dicted and EarthStat yield averages are likely due to the fact
that EarthStat data are based on regional census data, in-
corporating much more variability in terms of management
practices and skill than experimental field studies.
After the data checks, we then used our model to extrap-
olate global yield potentials of maize and wheat given in-
creases in SOC. We masked EarthStat production and culti-
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E. E. Oldfield et al.: Global meta-analysis of the relationship between soil organic matter and crop yields
vated area data layers for maize and wheat for cells that had
SoilGrids SOC concentrations of
>
2 %. We compared the
subsetted data (i.e., cultivated lands with
<
2 % SOC) with
the original data layers to determine the fraction of global
maize and wheat production and cropland that is on soils
with less than 2 % SOC. We used this subsetted data along
with our regression model to predict yields at current SOC
levels. As stated above, we used EarthStat, ISRIC SoilGrids,
and CGIAR-CSI data layers to fill in the values for each of
the factors in our regression model. This new data layer was
used as a baseline with which to compare to potential gains
in yield with an increase to SOC target values. This created
a second data layer with model-predicted yields given an in-
crease in SOC. We calculated the percentage increase in yield
between these two layers (the baseline and the improved-
SOC layer) and multiplied this by EarthStat yield and pro-
duction data to determine potential gains in maize and wheat
yields and production (Table 2). We then used EarthStat yield
gap data to see how such an increase in SOC would reduce
projected yields gaps. Using the new yield data layer (with
yields at SOC target values), we calculated the proportion
of EarthStat yield gaps that was reduced for both maize and
wheat.
Finally, we used data on global N use (EarthStat) to ex-
plore potential reductions in fertilizer use for both maize and
wheat, separately. We used a value of 200 kg N ha
−
1
yr
−
1
as our N input threshold, as this is the value from our re-
gression model at which gains in yields level off. We cre-
ated a new data layer for those areas that have N input rates
greater than 200 kg N ha
−
1
yr
−
1
. We then calculated the po-
tential N reductions, in tonnes, by multiplying this new data
layer by EarthStat cultivated maize and wheat lands, sepa-
rately. Finally, we divided the potential reduction in N input
(in tonnes) by total N input (in tonnes) as provided through
the EarthStat data product.
Data availability.
The dataset generated and analyzed during
the current study is available through the KNB repository:
https://doi.org/10.5063/F19W0CQ5 (Oldfield et al., 2018).
Supplement.
The supplement related to this article is available
online at: https://doi.org/10.5194/soil-5-15-2019-supplement.
Author contributions.
EEO, MAB, and SAW conceived the
study. EEO and SAW performed data analysis. EEO wrote the first
draft of the manuscript. All authors contributed to data interpreta-
tion and paper writing.
Competing interests.
The authors declare that they have no con-
flict of interest.
Acknowledgements.
Thanks to Samuel Adiku, Klaus Birhofer,
and Tim Kautz for their contributions of data. Thanks also to
the SNAPP working group on “Managing Soil Carbon” for their
support, as well as Deborah Bossio, Indy Burke, Jon Fisher,
Cheryl Palm, Pete Raymond, and the Bradford Lab Group for
comments on earlier drafts.
Edited by: Cornelia Rumpel
Reviewed by: two anonymous referees
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