Kane et al. (2021). Soil organic matter protects US maize yields and lowers crop insurance payouts under drought

, Mark A Bradford

, Emma Fuller

, Emily E Oldfield

and Stephen A Wood

,

1

School of the Environment, Yale University, New Haven, CT 06511, United States of America

2

Granular, Inc., San Francisco, CA 94103, United States of America

3

The Nature Conservancy, Arlington, VA 22203, United States of America

E-mail:

daniel.kane@yale.edu

Keywords:

agriculture, climate change, resilience, soil organic matter

Supplementary material for this article is available

online

Abstract

Higher levels of soil organic matter improve soil water retention, meaning they could mitigate
agricultural yield losses from drought. Yet evidence to support such claims is mixed and
incomplete. Using data from 12 376 county-years in the United States of America, we show that
counties with higher soil organic matter are associated with greater yields, lower yield losses, and
lower rates of crop insurance payouts under drought. Under severe drought, an increase of 1% soil
organic matter was associated with a yield increase of 2.2

±

0.33 Mg ha

−

1

(32.7 bu ac

−

1

) and a

36

±

4.76% reduction in the mean proportion of liabilities paid. Similar, yet smaller, effects were

found for less severe levels of drought and this effect was reduced as soil clay content increased.
Confirmatory pathway analyses indicate that this positive association of soil organic matter and
yields under drought is partially explained by positive effects of soil organic matter on available
water capacity and cation exchange capacity, but that soil organic matter may be imparting yield
protection via mechanisms not fully captured by those metrics. Overall, our results suggest soil
organic matter predicts yield resilience at regional scales in the United States. We argue that data on
soil organic matter should be used in agricultural policy and financial planning, with our analyses
providing quantitative evidence of the co-benefits of soil organic matter believed fundamental to
advancing soil health and carbon sequestration initiatives.

1. Introduction

Rain-fed agriculture, which made up 75% of global
cropland use as of 2000 (Portmann

et al

2010

), is

highly susceptible to extreme weather conditions,
such as heat and drought. Extreme heat accelerates
plant development, effectively shortening growing
season length and reducing harvest index, and in
extreme instances, extreme heat causes plant repro-
ductive failures, such as kernel abortion in maize,
that drastically reduce yields (Sage and Kubien

,

Craufurd and Wheeler

, Hsiao

et al

). Simil-

arly, drought leads to elevated vapor pressure deficit
which can lead to increased transpiration by plants,
closing of stomata, and ultimately reduced rates of
photosynthesis that slow plant growth and reduce
grain yields (Lobell

et al

2013

,

). Climate change

is predicted to increase the incidence and severity of

droughts and floods (Meehl

et al

, Luber and

McGeehin

2008

), thereby increasing the risk of crop

failures and yield losses (Urban

et al

2012

). Conser-

vative estimates for maize suggest yields could drop
between 20% and 80% in the US under plausible
future climate scenarios (Schlenker and Roberts

,

Lobell

et al

, Hsiao

et al

). Such scenarios

threaten global food security and suggest that resili-
ence planning to mitigate these impacts is necessary.

Increasing soil organic matter can increase soil

water holding capacity on similarly textured soils
(Hudson

1994

, Minasny and McBratney

) and

improve water infiltration (Boyle

et al

1989

, Elliott

and Efetha

1999

, Franzluebbers

2002

) by support-

ing greater aggregate formation and, hence, a greater
volume of pore spaces (Lado

et al

2004

). Researchers

have argued that soils with higher organic matter
can retain more water under vapor pressure deficit,

Environ. Res. Lett.

16

(2021) 044018

D A Kane

et al

protecting crops from losses induced by extreme heat
and drought better than low organic matter soils
(Bot and Benites

2005

, Iizumi and Wagai

, Car-

minati and Javaux

). Yet recent work suggests

the actual effect of soil organic matter on plant avail-
able water is modest (Libohova

et al

, Minasny

and McBratney

), and it is unclear whether these

effects on water retention are great enough to reduce
drought-induced yield losses. Studies have demon-
strated that higher soil organic matter is associated
with lower long-term interannual yield variability at
regional scales (Pan

et al

, Williams

et al

2016

But lower variability is not necessarily indicative of
greater resilience or protection against yield losses
and/or crop failure under adverse conditions. Some
field-level studies have shown that practices known
to increase soil organic matter can protect yields
(Gaudin

et al

2015

, Bowles

et al

), but these do

not explicitly test the relative effect of organic matter
and do not provide information at county or regional
scales which are arguably most relevant to policy ini-
tiatives. Furthermore, these studies did not attempt
to quantify how the effect of soil organic matter on
yields does or does not scale under different drought
conditions.

In light of these evidence gaps, we quantified the

impact of soil organic matter on agronomic risk to
drought in the United States of America. We ana-
lyzed county-level maize (

Zea mays

L.) yield and

crop insurance payouts in the US from 2000 to 2016
in combination with soil characterization data and
county-level Standardized Precipitation Evapotran-
spiration Index (SPEI) data. Data were gathered for
754 counties where maize production was predomin-
antly rain-fed, representing a total of 12 376 county-
years, 5945 of which experienced drought conditions
over the summer growing season (Kane

et al

We hypothesized that counties with higher levels of
soil organic matter in surface soils (0–30 cm depth)
where most of the fine-root biomass is found, would
be less prone to yield losses in drought years given
expected positive effects of soil organic matter on crop
water availability and that, as a result, a lower propor-
tion of crop insurance liabilities would be paid out in
drought years.

2. Methods

2.1. Data collection

2.1.1. Maize yield data

We collected mean maize (

Zea mays

L.) yield

(Mg ha

−

1

) data for all US counties between the years

2000 and 2016 for which maize yield data were avail-
able from the United States Department of Agricul-
ture National Agricultural Statistics Service (USDA
NASS) (USDA NASS Staff

) accessed via the

rnas-

sqs

package (Potter

et al

) in the R v.3.6.3 statist-

ical software environment (R Core Team

). Data

were limited to the years 2000–2016 to minimize the

confounding effect improvements in maize genetics
may have on yield data and because other data used
to estimate drought incidence in each county detailed
below were only available beginning in the year 2000.
We removed data from counties in which corn was
not grown for at least 16 of the 17 years in the study
period. Yield data were then detrended on a county
basis per the method detailed in Lu

et al

(

2017

).

Briefly, we fit a locally weighted regression model
wherein yield was the dependent variable and year
was the independent variable. Models were fit using
the R package

caret

(Kuhn

et al

) using a ten-fold

cross validation approach wherein the span para-
meter was constrained to a range of 5–10 years and the
degree parameter was allowed to be either 1 (linear)
or 2 (polynomial). We then added the yearly residuals
of these models to the long-term county average yield
to estimate detrended yield for each county-year in
the study period, and we also divided the observed
yield by the predicted yield of these models for each
county-year to estimate yield anomaly.

Because soil organic matter might protect yields

by improving soil water dynamics, irrigation could
mask the effects of soil organic matter on agricul-
tural resilience. As such, we restricted the analysis
to primarily non-irrigated acres. We also retrieved
data from USDA NASS (USDA NASS Staff

) via

the

rnassqs

(Potter

et al

) on the total num-

ber of corn acres harvested in each county-year and
the total number of irrigated corn acres harvested
in each county-year for the US Agricultural Census
years of 1997, 2002, 2007, and 2012. The US Agri-
cultural Census is conducted every 5 years, so data
on irrigation within our study period is only avail-
able for those years. We then calculated the percent
of maize-growing acres that were irrigated in each
county for each census year, averaged those figures
across all four census years, and filtered yield data to
those counties in which corn-growing acres were on
average

⩽

5% irrigated during the study period and

in which average acres irrigated had a standard devi-
ation of

⩽

1% across the four census years for which

data were retrieved.

2.1.2. Drought data

We retrieved county-level SPEI figures from the Cen-
ter for Disease Control (National Environmental
Public Health Tracking Network

). SPEI is a

multi-scalar drought index based on the similar
Standardized Precipitation Index (Vicente-Serrano

et al

,

2012

). Differences between cumulat-

ive monthly precipitation and potential evapotran-
spiration are calculated for a chosen time scale
(i.e. 1 month, 3 months, etc). These figures are
then standardized based on a log-logistic distribu-
tion so that they are comparable across locations.
The CDC dataset we used is based on a 1 month
standardization. We summarized SPEI data in each
county-year by calculating the mean of monthly SPEI

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Environ. Res. Lett.

16

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D A Kane

et al

values for the summer growing season months of May
to August.

In addition to SPEI, we also retrieved drought

data from The National Drought Mitigation Cen-
ter (The National Drought Mitigation Center

2019c

),

which reports on a daily basis the percent area of each
county that is at each level of their drought index:
D0 (no drought)—D4 (severe drought). This drought
index is a categorization based on the Palmer Drought
Severity Index, Standardized Precipitation Index, soil
and streamflow models, and local expert assess-
ment/verification by USDA field agents (The National
Drought Mitigation Center

2019b

). We then conver-

ted these coverage statistics to a daily Drought Sever-
ity Classification Index (DSCI), per the instructions
of the US Drought Monitor website (The National
Drought Mitigation Center

2019a

). Briefly, DSCI is

a weighted sum of the percent area of each county
under each drought index level. We then subset daily
DSCI data to the months of May to August, the
months that are most crucial to maize growth and
yield, and averaged them for each county-year across
the study period. DSCI data were not used in our
primary analyses but were instead used in a set of
sensitivity analyses (supplementary information) to
determine the relative importance of how drought is
quantified on estimating the mitigating effect of soil
organic matter on yields under drought.

2.1.3. Crop insurance data

The USDA Risk Management Agency collates a vari-
ety of data on the US crop insurance market on an
annual basis, including total liabilities, total indem-
nities, and cause of loss. These data are available at the
county-level via the USDA Southwest Climate Hub’s
AgRisk Viewer (Southwest Climate Hub

). We

retrieved data on total liabilities and total indemnities
in USD for maize due to loss by drought for the same
set of counties for which we retrieved yield data. We
then used these data to calculate loss cost (Reyes and
Elias

), a unitless index based on the ratio of total

indemnities to total liabilities in a given county-year.
Loss cost implicitly accounts for differences in USD
figures across years due to inflation, as well as differ-
ences across counties due to differences in total out-
put and coverage patterns.

2.1.4. Soil data

To aggregate soil data for each county in our ana-
lysis we first identified those areas in each county
that are primarily used for maize production. To do
so, we used the ‘Corn Frequency’ raster available at
USDA NASS’s CropScape portal, which reports how
many years between 2008 and 2017 each pixel was
used for maize production (Han

et al

). We then

subset this raster to only those pixels in which maize
was produced for two or more years to identify pixels
from any field in which maize was grown with relative
consistency but not opportunistically (e.g. in a high

price year), while also eliminating non-crop areas.
This subset of pixels was then used to generate a mask-
ing layer used in subsequent steps.

The gSSURGO database is spatially organized as

a series of discrete polygons referred to as map units
that are composed of different component soil series.
Associated with each soil series is characterization
data organized by pedological horizon, including soil
texture, soil organic matter, and measures of soil bio-
physical characteristics. As such, we first used the

aqp

(Beaudette

et al

) package in R to convert char-

acterization data for each component soil series to a
fixed depth increment of 0–30 cm to represent the
typical rooting zone of maize. We then calculated a
representative map unit value for each soil character-
istic soil organic matter (%), clay content (%), H

3

O

+

concentration (mol), cation exchange capacity (meq
100 g soil

−

1

), and available water capacity (%) by cal-

culating a map unit mean weighted by the relative
proportions of each component soil series in a map
unit. We then converted the data to a raster format
and used the masking layer described previously to
remove soil data in each county from areas where
maize is not consistently grown. Finally, we used these
masked rasters to calculate county-level means for all
soil properties we then used in our analyses.

2.1.5. Cropping system management

For the purposes of this study we did not include data
on the rates of different cropping system management
strategies or practices, such as mean fertilizer rate by
county or incidence of conservation tillage by county.
Such data are not available at the county level or on
a timestep that would make them sufficiently useful
for our analysis. For example, data on per area fertil-
izer use is available via USDA NASS only at the state
level, and questions on implementation of conserva-
tion agriculture practices were only recently included
in the agricultural census. Given these inconsisten-
cies in data, we could not account for management
effects with a fixed effect for each practice. Instead, we
accounted for them by implementing a random effect
for state (section

2.2

). We argue that since farms in

the same state are generally more likely to implement
fertilizer rates and management practices similar to
other farms in their state than those out of their state,
our model accounts to some degree for broad differ-
ences in management. Whereas direct estimation of
the effect of different management practices would be
preferable, sufficient data simply do not exist and use
of current, sparser data could lead to flawed inference.

2.2. Data analysis

Initial data analysis demonstrated that the yield
response to SPEI begins to saturate above SPEI values
of 0, indicating that when the balance of precipitation
and evapotranspiration is negative, yields drop below
the typical yield potential of a given area. Addition-
ally, we found that when SPEI decreased (i.e. drought

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Environ. Res. Lett.

16

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D A Kane

et al

conditions became more severe) the impact of soil
organic matter was greater on maize yield (Mg ha

−

1

),

yield anomaly, and loss cost (supplementary inform-
ation). As such, we chose to subset our data into
different levels of drought severity based on SPEI
and then analyze each subset to understand how the
effect of soil organic matter on each outcome variable
changed as drought severity changed. Drought sever-
ity thresholds were calculated based on the global
mean and standard deviation of SPEI. Very severe
drought was defined as greater than two standard
deviations from the global mean (SPEI

⩽

−

1.02);

severe drought as between one and two standard devi-
ations from the mean (

−

1.02 < SPEI

⩽

−

0.46); mod-

erate drought as between one standard deviation from
the mean and the mean (

−

0.46 < SPEI

⩽

0.10); and

normal conditions as greater than or equal to the
mean (SPEI > 0.10).

Within each of these drought subsets we then fit

a series of models wherein the dependent variable
in these models was either yield (Mg ha

−

1

), yield

anomaly, or loss cost. Independent variables across
all models included soil organic matter, soil clay con-
tent, and soil H

3

O

+

concentration. These variables

were chosen by fitting a multivariate linear model
with multiple potential independent variables then
assessing variance inflation factors to eliminate spuri-
ous, highly collinear variables. We also included a ran-
dom effect of state to account for possible impacts of
geographic differences in management and produc-
tion environment on model outcomes not accoun-
ted for in the data we collected. For yield and yield
anomaly, linear mixed effects models including all
possible interaction effects were fit using a restricted
maximum likelihood approach in the

lme4

(Bates

et al

) package in R. For loss cost, a mixed effects Tobit

regression model was fit using a Newton–Raphson
maximization approach in the

censReg

(Henningsen

) package in R to account for the fact that loss

cost was left censored at a value of 0. Prior to model
fitting, all observations of each independent variable
were standardized so that coefficient estimates would
also be standardized. Data standardization was done
by subtracting the mean of a given variable from each
observation and then dividing that value by 2

×

the

standard deviation of that variable (Gelman

2008

Finally, we conducted a series of confirmatory

path analyses to evaluate to what extent the impacts
of soil organic matter on maize yields were medi-
ated by their impacts on soil available water capa-
city and cation exchange capacity, a proxy variable for
soil fertility, under both drought and normal condi-
tions. First, we split data into those observations from
normal SPEI years (SPEI > 0.10) and drought SPEI
years (SPEI

⩽

0.10), and then calculated the mean

yield (Mg ha

−

1

) for each county under either set of

conditions. We then employed a piecewise structural
equation modeling approach using the

piecewiseSEM

(Lefcheck

et al

) package in R to fit models in

which the effects of soil organic matter were either
partially mediated or fully mediated by its effects on
available water capacity and cation exchange capa-
city. Briefly, the fully mediated SEM was such that
available water capacity and cation exchange capacity
were modeled as functions of soil organic matter and
yield was modeled as a function of available water
capacity and cation exchange capacity. Whereas, in
the partially mediated SEM, available water capacity
and cation exchange capacity were modeled the same
way, but soil organic matter was included as an addi-
tional independent variable for modeling yield. To
determine the most parsimonious model, we com-
pared models via an analysis of variance and on the
basis of AIC/BIC scores. Coefficients were extrac-
ted from the final model and standardized to then
assess whether or not effects of organic matter on
yields under drought were mediated by its impacts
on available water capacity and cation exchange capa-
city. An initial analysis with all data in either SPEI
category indicated a negative relationship between
cation exchange capacity and yields. When we manu-
ally inspected data we found that this result was the
consequence of outlying cation exchange capacity val-
ues, defined as being two standard deviations greater
than the mean (CEC

⩾

31.9), with extremely high

clay content. As such, we removed these outliers for
the final path analysis to better estimate effects on
typical soils, but include results from a path ana-
lysis including these observations in supplementary
information.

3. Results and discussion

3.1. Maize yields and yield anomalies

We found that across all county-years and pos-
sible weather conditions, soil organic matter con-
tent was a strong positive predictor of yield. Soil
organic matter had a standardized marginal effect
of 0.83 with a standard error of 0.04, meaning an
increase of 1% soil organic matter was associated
with an increase in yields of 0.83

±

0.04 Mg ha

−

1

.

This observed relationship between soil organic
matter and yield is consistent with other studies
which show yield increases are associated with higher
levels of soil organic matter (Oldfield

et al

).

Our analysis extends these observations by show-
ing that as drought conditions became more severe,
the marginal effect of soil organic matter on yields
increased (figure

; table

). For example, under

moderate drought conditions (

−

0.46

⩽

SPEI < 0.12)

an increase of 1% soil organic matter was associated
with an increase in yields of 0.76

±

0.07 Mg ha

−

1

, and

under very severe drought conditions (SPEI <

−

1.02)

a 2.2

±

0.33 Mg ha

−

1

increase. Interaction effects

of other soil properties with soil organic matter also
emerged across the severe, moderate, and normal
drought categories (table

stacks.iop.org/ERL/16/044018/mmedia

). Sensitivity analyses

of these interaction effects revealed that in many

4

Environ. Res. Lett.

16

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D A Kane

et al

Figure 1.

Maize yields increase with soil organic matter content (%), with the effect becoming more pronounced with increasing

drought severity. Drought levels are based on the following ranges of SPEI: Very severe <

−

0.99; Severe

⩾

−

0.99 <

−

0.44;

Moderate

⩾

−

0.44 < 0.12; Normal

⩾

0.12. Numbers on each panel represent the marginal effect of soil organic matter for the

corresponding drought level and trendlines represent predicted yields based on that marginal effect.

Table 1.

Results of linear mixed effects models across multiple levels of drought severity relating yield (Mg ha

−

1

) to soil organic matter

(SOM, %), clay (%), and H

3

O

+

concentration (mol). Coefficients for terms are standardized. Sample numbers for each drought level

are as follows: Very severe,

n

=

410; Severe,

n

=

1537; Moderate,

n

=

3998; Normal,

n

=

6431. Conditional coefficient of determination

for each model is as follows: Very severe,

R

2

=

0.53; Severe,

R

2

=

0.45; Moderate,

R

2

=

0.51; Normal,

R

2

=

0.59.

Drought

Term

Coefficient

Std. error

p

SOM

2

.

1976

0

.

3339

<

0

.

0001

Clay

0

.

4999

0

.

3355

0

.

1371

H

3

O

+

0

.

5665

0

.

6013

0

.

3467

SOM:Clay

0

.

6549

0

.

6312

0

.

3001

SOM:H

3

O

+

0

.

0426

0

.

9302

0

.

9635

Clay:H

3

O

+

1

.

0140

0

.

9089

0

.

2653

Very severe

SOM:Clay:H

3

O

+

0

.

9120

1

.

6866

0

.

5890

SOM

1

.

1820

0

.

1270

<

0

.

0001

Clay

−

0

.

1776

0

.

1340

0

.

1852

H

3

O

+

−

0

.

3637

0

.

1716

0

.

0342

SOM:Clay

0

.

8457

0

.

2340

0

.

0003

SOM:H

3

O

+

−

0

.

5317

0

.

2429

0

.

0288

Clay:H

3

O

+

−

0

.

0274

0

.

2900

0

.

9249

Severe

SOM:Clay:H

3

O

+

−

1

.

6099

0

.

5223

0

.

0021

SOM

0

.

7616

0

.

0687

<

0

.

0001

Clay

0

.

3132

0

.

0718

<

0

.

0001

H

3

O

+

−

0

.

3794

0

.

0949

0

.

0001

SOM:Clay

0

.

6793

0

.

1189

<

0

.

0001

SOM:H

3

O

+

−

0

.

2837

0

.

1264

0

.

0249

Clay:H

3

O

+

−

0

.

2869

0

.

1549

0

.

0640

Moderate

SOM:Clay:H

3

O

+

−

0

.

5372

0

.

2941

0

.

0678

SOM

0

.

7266

0

.

0514

<

0

.

0001

Clay

0

.

2085

0

.

0577

0

.

0003

H

3

O

+

−

0

.

5189

0

.

0856

<

0

.

0001

SOM:Clay

0

.

5992

0

.

0985

<

0

.

0001

SOM:H

3

O

+

−

0

.

1456

0

.

1219

0

.

2322

Clay:H

3

O

+

−

0

.

2103

0

.

1445

0

.

1456

Normal

SOM:Clay:H

3

O

+

0

.

1430

0

.

2600

0

.

5822

cases outlying observations were responsible for
the interaction. When those outliers were removed,
the size and significance of interaction effects
and corresponding main effects were diminished

(supplementary information (available online at

)). However,

interaction effects of soil organic matter and clay
content were robust to outlier observations and

5

Environ. Res. Lett.

16

(2021) 044018

D A Kane

et al

indicated that the effect of soil organic matter on
yields remained positive at higher levels of clay but
was diminished relative to lower clay soils. This result
is consistent with broad-scale studies which demon-
strate the primary importance of clay on the water
holding capacity of soils and the diminished impact
of organic matter when clay is high (Libohova

et al

, Minasny and McBratney

). Nonetheless,

our results show the effect of soil organic matter is
still positive, regardless of clay content, and improves
maize yields, and that soil organic matter was the only
soil property that consistently buffered yields against
drought conditions.

To more fully evaluate whether this greater rel-

ative yield advantage under drought is because soil
organic matter protects against drought-induced
yield losses, we also evaluated the relationship
between soil organic matter and annual yield anom-
aly, the ratio of observed yield to expected yield
estimated from long-term trends (Lu

et al

2017

Under very severe drought conditions (SPEI <

−

1.01)

an increase of 1% soil organic matter content
was associated with a mitigation of yield losses
of 12

±

0.03%, under severe drought conditions

(

−

1.01

⩽

SPEI <

−

0.46) this effect decreased to a

5

±

0.01% mitigation, and under moderate drought

(

−

0.46

⩽

SPEI < 0.10) the effect was non-significant

(table S2).

Further examination of yield anomaly data

revealed that counties with lower soil organic mat-
ter content have high interannual variability, outper-
forming historical yield trends in favorable conditions
but experiencing greater losses relative to historical
trends under adverse conditions. Whereas counties
with high soil organic matter have low interan-
nual variability and consistently yield near expected
yields based on historical trends, even under adverse
drought conditions. For example, in counties with
greater than 2.5% soil organic matter content, the
mean of soil organic matter content across all counties
in this study, the interquartile range for yield anom-
alies was 98%–107% of expected yield under normal
conditions and 91%–104% under all drought condi-
tions. By contrast, counties with lower than 2.5% soil
organic matter ranged from 99% to 114% of expected
yield under normal conditions and 82%–104% under
all drought conditions. This pattern is consistent with
previous studies demonstrating that higher soil car-
bon is associated with lower long-term interannual
yield variability (Pan

et al

, Williams

et al

2016

Our findings offer additional insight by demonstrat-
ing that those reductions in interannual variability
are partly explained by the association of higher soil
organic matter and lower yield losses under drought
conditions.

3.2. Crop insurance

Given the decrease in yield risk associated with
greater levels of soil organic matter, we expected that

Figure 2.

Higher levels of soil organic matter were

associated with lower loss cost, the unitless ratio of crop
insurance indemnities to liabilities, under drought
conditions. Figure

(a) is a map of mean soil organic matter

content across counties (

n

=

730) within a subset of states

(PA, WI, MI, MN, IL, IN, IA, OH, MO, and KY) included
in this study. Figure

(b) is a map of mean loss cost under

all drought conditions in those same counties. The
marginal effect of soil organic matter on loss cost under all
drought conditions is

−

6.47 (

σ

=

0.84,

p

< 0.001).

lower yield risk would be reflected in crop insur-
ance payouts to farmers. Specifically, we expected that
counties with higher soil organic matter would have
lower loss cost (Reyes and Elias

), a metric based

on the ratio of total indemnities to total liabilities.
Our results support this expectation, showing that
soil organic matter is associated with reduced loss cost
under drought conditions and that the marginal effect
of soil organic matter increases as drought severity
increases (figure

). Under very severe drought con-

ditions (SPEI

⩽

−

1.02), an increase in soil organic

matter of 1% was associated with a 36

±

4.76%

reduction in loss cost (table S3). Similar to yield
anomaly, though, this effect decreases sharply as SPEI
approaches normal. Soil organic matter was associ-
ated with an 8.4

±

1.41% reduction in loss cost under

severe drought conditions (

−

1.02 < SPEI

⩽

−

0.46)

and just a 4

±

0.73% reduction under moderate

drought (

−

0.46 < SPEI

⩽

0.10). Nevertheless, given

the expectation of increasing frequency of severe
droughts (Adams

et al

1990

, Meehl

et al

, Hay-

hoe

et al

2010

), our results suggest that it would be

strategic for rain-fed US agriculture to directly incor-
porate differences in soil properties into policy and
insurance planning for yield resilience.

3.3. Path analyses

The fact that we found that soil organic matter
appeared to impart such effective protection against

6

Environ. Res. Lett.

16

(2021) 044018

D A Kane

et al

Figure 3.

Increases in yield under drought associated with

increasing soil organic matter appear only partially
mediated by increases to soil available water capacity
induced by higher soil organic matter. Figure

is a diagram

of a structural equation model of best fit developed during a
series of confirmatory pathway analyses. Numbers on each
arrow represent standardized coefficients of the relationship
between the variables connected, and the direction of
arrows indicate variable dependence. Solid lines indicate
statistically significant (

p

⩽

0.05) relationships and dotted

lines indicate non-significant relationships.

yield losses under severe drought appears inconsistent
with results from recent studies using large soil data-
bases into how soil organic matter influences the plant
available water capacity in soils. Briefly, the ability of
soils to provide water to plants is often estimated as
‘available water capacity’, which typically is the differ-
ence in water content of saturated soil samples dried
on pressure plates at

−

1500 kPa and

−

33 kPa (Soil

Survey Staff

2015

, Cassel and Nielsen

). These

analyses have suggested that the net impact of soil
organic matter on available water capacity is relatively
modest and contingent on soil texture (Libohova

et al

, Minasny and McBratney

). To investigate

potential discrepancies in conclusions between these
past studies and our work, we performed a series of
confirmatory path analyses (Shipley

, Lefcheck

) to investigate the extent to which soil organic

matter associations with yields under drought and
non-drought conditions are related to its influence on
available water capacity and cation exchange capacity,
used here as a proxy measure of soil fertility. We found
that soil organic matter was strongly associated with
cation exchange capacity but only weakly associated
with available water capacity, and under both drought
and non-drought conditions, cation exchange capa-
city and available water capacity were positively asso-
ciated with yields (figure

). However, our confirmat-

ory path analysis also suggested that soil organic mat-
ter had an independent, unmediated positive effect on
yields under both drought and non-drought condi-
tions (table S4).

These results confirm that soil organic matter has

a positive influence on yields via its effects on available
water capacity and soil fertility. But it also suggests
that soil organic matter likely influences plant water

availability and soil fertility in ways not captured
by how those properties are commonly measured.
Although our analyses cannot resolve these additional
influences, we do know that soil organic matter affects
other important soil biophysical properties, such as
porosity, bulk density, and water infiltration (Boyle

et al

1989

, Franzluebbers

2002

, Lado

et al

2004

, Libo-

hova

et al

). Favorable changes in all of these

properties may increase the soil volume from which
plants can draw water and may effectively increase the
supply of water to plants between rain events. In addi-
tion, soil organic matter is also an important source
of key nutrients for plant growth. Under drought con-
ditions, water transpiration and radiation efficiency
in maize plants increase with increasing nitrogen fer-
tilizer use (Teixeira

et al

) and nitrogen fertilizer

can be important for maintaining key metabolic func-
tions and increasing yield (Zhang

et al

). Fur-

ther work is required to ascertain whether soil organic
matter has similar, nutrient-mediated effects under
drought conditions.

3.4. Broader implications

Our analyses are based on subcontinental-scale vari-
ation in soil organic matter and yield outcomes.
As such, they cannot be used to argue directly
that field-scale increases in soil organic matter
achieved through conservation agricultural practices
such as cover-cropping or reduced tillage, will lead
to the same level of meaningful yield protection
under drought. More specifically, the demonstrated
increases in soil organic matter such practices often
achieve is smaller than the relative range of soil
organic matter content represented in this study, and
the magnitude of the effect of a soil organic mat-
ter demonstrated here may not be maintained at the
field scale. Similarly, our results are specific to the
‘corn belt’ region of the US and we only examined
the impact of soil organic matter under drought on
maize. Much of this region comprises relatively high
organic matter soils, and maize is a drought sensit-
ive crop. Last, our results likely mask substantial vari-
ation in management practices (i.e. fertilizer regimes,
tillage, cover crops, etc) that could also impart resili-
ence on rain-fed maize systems. Additional farm-scale
evidence is required to understand whether increases
in soil organic matter over time are associated with
resilience to drought conditions at the farm scale,
whether or not management practices can impart
similar resilience, and whether or not these results are
generalizable to other geographies, agro-ecological
zones, and crops.

Nevertheless, our results do appear to have the

potential to directly inform agricultural financing
programs and policy in the US. At present, knowledge
of risk is incorporated into US Federal Crop Insur-
ance Programs (FCIP) only indirectly. Premiums are
based on the Actual Production History (APH) of a
given area and farm, and current policy dictates that

7

Environ. Res. Lett.

16

(2021) 044018

D A Kane

et al

APH be calculated based on a 10 year trend excluding
years in which yield losses were extreme (Bryant and
O’Connor

2017

). While differences in soil organic

matter and other biophysical limitations to resilience
are arguably endogenous to these yield data, APH
may become less predictive of risk under future cli-
mate scenarios where drought frequency is predicted
to increase. It may therefore be more strategic for
policy planning for agricultural resilience to explicitly
consider differences in soil properties, such as organic
matter levels, across counties. For instance, if maize
yields in counties with low soil organic matter are
particularly vulnerable to drought, it may make more
sense to incentivize a transition to crops that are more
appropriate to soil and predicted climate conditions
in those counties, than to focus exclusively on eco-
nomic protection through insurance.

Furthermore, while more extensive evidence is

needed to understand if the trends we identified at the
aggregate-level scale down to the farm, we argue that
our findings generally support the notion that large-
scale efforts to restore soil organic matter levels at
regional/continental scales should improve the resi-
lience of agricultural systems. Such a notion is key to
soil health initiatives aimed at increasing agricultural
resilience by rebuilding soil organic matter (Bradford

et al

) and initiatives such as 4 ‰ that argue

the co-benefits of increased soil organic matter are an
important additional incentive to advancing soil car-
bon sequestration (Lal

2016

, Chenu

et al

https://doi.org/10.5063/F1M61HN6

4. Conclusions

Our analyses demonstrate that counties with higher
mean soil organic matter content are associated with
lower maize yield loss due to drought, that this
relationship is strongest under severe drought con-
ditions, and this increased yield protection trans-
lates to lower crop insurance payouts under drought
conditions. Furthermore, we demonstrate that these
impacts are not solely mediated through the impact
of soil organic matter on conventional measures of
plant available water, but likely occur through addi-
tional pathways that influence soil water supply and
use by plants, which appear to collectively provide
the yield protection benefits we document here. At
least at the county level then for US rain-fed maize
agriculture, soil organic matter content appears to
be an important predictor of resilience to the type
of drought conditions that are likely to occur more
frequently under future climate scenarios. Further
work should investigate whether similar benefits of
soil organic matter for yield protection are afforded
by agricultural managements that build organic mat-
ter in agricultural soils worldwide. In the interim,
our analyses highlight the potential value of integrat-
ing soil information into resilience planning as agri-
cultural outcomes become more uncertain with the

increasing incidence and severity of extreme weather
events.

Data availability statement

The data that support the findings of this study
are openly available at the following URL/DOI:

.

Acknowledgments

This research was conducted as part of the Man-
aging Soil Carbon working group with the Science For
Nature and People Partnership. DK was supported
by a Yale School of the Environment Fellowship, and
a 2017 Soil Health Institute Literature Review grant
from the Soil Health Institute was important in sup-
porting early work that led to the development of this
article.

Author contributions

D K, E F, and S W conceived the original project idea.
D K and E F developed initial datasets and data pro-
cessing code. D K and S W developed and verified the
analytical methods. All authors contributed to ana-
lysis and interpretation of results and development of
the final manuscript.

References

Adams R M, Rosenzweig C, Peart R M, Ritchie J T, McCarl B A,

Glyer J D, Curry R B, Jones J W, Boote K J and Allen L H
1990 Global climate change and US agriculture

Nature

345

219–24

Bates D

et al

2020 lme4: linear mixed-effects models using “Eigen”

and S4 (available at:

https://CRAN.R-project.org/

Beaudette D, Roudier P and Brown A 2020 aqp: algorithms for

quantitative pedology (available at:

https://CRAN.

R-project.org/package

www.fao.org/3/a0100e/a0100e00.htm#

aqp

)

Bot A and Benites J 2005 Creating drought-resistant soil

The

importance of soil organic matter: Key to drought-resistant soil
and sustained food production

(

FAO Soils Bulletin

(Rome:

Food and Agriculture Organization of the United Nations)
(available at:

Contents

)

Bowles T M

et al

2020 Long-term evidence shows that

crop-rotation diversification increases agricultural resilience
to adverse growing conditions in North America

One Earth

284–93

Boyle M, Frankenberger W T and Stolzy L H 1989 The influence

of organic matter on soil aggregation and water infiltration

J. Prod. Agric.

290–9

Bradford M A

et al

2019 Soil carbon science for policy and

practice

Nat. Sustain.

https://CRAN.R-project.org/package

1070–2

Bryant L and O’Connor C 2017 Creating incentives to improve

soil health through the federal crop insurance program

Global Soil Security

(

Progress in Soil Science

) ed D J Field, C L

S Morgan and A B McBratney (Berlin: Springer) pp

403–9

Carminati A and Javaux M 2020 Soil rather than xylem

vulnerability controls stomatal response to drought

Trends

Plant Sci.

25

868–80

Cassel D K and Nielsen D R 2018 Field capacity and available

water capacity

Methods of Soil Analysis

(New York: Wiley)

pp

901–26

8

Environ. Res. Lett.

16

(2021) 044018

D A Kane

et al

Chenu C, Angers D A, Barré P, Derrien D, Arrouays D and

Balesdent J 2019 Increasing organic stocks in agricultural
soils: knowledge gaps and potential innovations

Soil Tillage

Res.

188

41–52

Craufurd P Q and Wheeler T R 2009 Climate change and the

flowering time of annual crops

J. Exp. Bot.

60

2529–39

Elliott J A and Efetha A A 1999 Influence of tillage and cropping

system on soil organic matter, structure and infiltration in a
rolling landscape

Can. J. Soil Sci.

79

457–63

Franzluebbers A J 2002 Water infiltration and soil structure

related to organic matter and its stratification with depth

Soil Tillage Res.

66

197–205

Gaudin A C M, Tolhurst T N, Ker A P, Janovicek K, Tortora C,

Martin R C and Deen W 2015 Increasing crop diversity
mitigates weather variations and improves yield stability

PLoS One

10

e0113261

Gelman A 2008 Scaling regression inputs by dividing by two

standard deviations

Stat. Med.

27

2865–73

Han W, Yang Z, Di L and Yue P 2014 A geospatial web service

approach for creating on-demand cropland data layer
thematic maps

Trans. ASABE

57

239–47

Hayhoe K, VanDorn J, Croley T, Schlegal N and Wuebbles D 2010

Regional climate change projections for Chicago and the US
Great Lakes

J. Gt. Lakes Res.

36

7–21

Henningsen A 2020 censReg: censored regression (Tobit) models

(available at:

https://doi.org/10.5063/F1M61HN6

censReg

)

Hsiao J, Swann A L S and Kim S-H 2019 Maize yield under a

changing climate: the hidden role of vapor pressure deficit

Agric. For. Meteorol.

279

107692

Hudson B D 1994 Soil organic matter and available water capacity

J. Soil Water Conserv.

49

189–94

Iizumi T and Wagai R 2019 Leveraging drought risk reduction for

sustainable food, soil and climate via soil organic carbon
sequestration

Sci. Rep.

9

1–8

Kane Daniel, Bradford Mark, Fuller Emma, Oldfield Emily and

Wood Stephen KNB 2020 Mean maize yield, soil
characteristics, and drought metrics for US maize growing
counties, 2000–2016 (

https://CRAN.R-project.org/package

Kuhn M

et al

2020 caret: classification and regression training

(available at:

caret

)

Lado M, Paz A and Ben-Hur M 2004 Organic matter and

aggregate size interactions in infiltration, seal formation,
and soil loss

Soil Sci. Soc. Am. J.

68

935–42

Lal R 2016 Beyond COP 21: potential and challenges of the ‘4

per Thousand’ initiative

J. Soil Water Conserv.

71

20A–25A

Lefcheck J 2019 Structural equation modeling in R for ecology

and evolution (available at:

https://jslefche.github.

io/sem_book/index.html

CRAN.R-project.org/package

Lefcheck J, Byrnes J and Grace J 2019 piecewiseSEM: piecewise

structural equation modeling (available at:

https://

piecewiseSEM

Libohova Z, Seybold C, Wysocki D, Wills S, Schoeneberger P,

Williams C, Lindbo D, Stott D and Owens P R 2018
Reevaluating the effects of soil organic matter and other
properties on available water-holding capacity using the
national cooperative soil survey characterization database

J.

Soil Water Conserv.

73

411–21

Lobell D B, Hammer G L, McLean G, Messina C, Roberts M J and

Schlenker W 2013 The critical role of extreme heat for maize
production in the United States

Nat. Clim. Change

https://data.cdc.gov/Environmental-Health-Toxicology/
Standardized-Precipitation-Evapotranspiration-Inde/
6nbv-ifib/data

497–501

Lobell D B, Roberts M J, Schlenker W, Braun N, Little B B, Rejesus

R M and Hammer G L 2014 Greater sensitivity to drought
accompanies maize yield increase in the U.S. midwest

Science

344

516–9

Lu J, Carbone G J and Gao P 2017 Detrending crop yield data for

spatial visualization of drought impacts in the United States,
1895–2014

Agric. For. Meteorol.

237–238

196–208

Luber G and McGeehin M 2008 Climate change and extreme heat

events

Am. J. Prev. Med.

35

429–35

Meehl G A

et al

2007 Global climate projections

Clim. Change

3495

747–845

Minasny B and McBratney A B 2018 Limited effect of organic

matter on soil available water capacity

Eur. J. Soil Sci.

69

39–47

National Environmental Public Health Tracking Network 2018

Standardized precipitation evapotranspiration index,
1895–2016

Data Cent. Dis. Control Prev.

(available at:

)

Oldfield E E, Bradford M A and Wood S A 2019 Global

meta-analysis of the relationship between soil organic
matter and crop yields

Soil

5

15–32

Pan G, Smith P and Pan W 2009 The role of soil organic matter in

maintaining the productivity and yield stability of cereals in
China

Agric. Ecosyst. Environ.

129

344–8

Portmann F T, Siebert S and Döll P 2010 MIRCA2000—global

monthly irrigated and rainfed crop areas around the year
2000: a new high-resolution data set for agricultural and
hydrological modeling

Glob. Biogeochem. Cycles

24

5

Potter N, Adams J, Stachelek J, Piaskowski J, Sparks A and

Richardson N 2019 rnassqs: access the NASS “Quick Stats”
API (available at:

https://CRAN.R-project.org/

package

rnassqs

)

R Core Team 2020 R: a language and environment for statistical

computing (Vienna: R Foundation for Statistical
Computing (available at:

www.R-project.org/

https://gdg.sc.egov.usda.gov/

Reyes J J and Elias E 2019 Spatio-temporal variation of crop loss

in the United States from 2001 to 2016

Environ. Res. Lett.

14

074017

Sage R F and Kubien D S 2007 The temperature response

of C3 and C4 photosynthesis

Plant Cell Environ.

30

1086–106

Schlenker W and Roberts M J 2009 Nonlinear temperature effects

indicate severe damages to U.S. crop yields under climate
change

Proc. Natl Acad. Sci.

106

15594–8

Shipley B 2009 Confirmatory path analysis in a generalized

multilevel context

Ecology

90

363–8

Soil Survey Staff 2015 Gridded soil survey geographic (gSSURGO)

database for the United States of America and the territories,
commonwealths, and Island Nations served by the
USDA-NRCS. United States department of agriculture,
natural resources conservation service

Geospatial Data

Gatew

(available at: