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RESEARCH ARTICLE
Soil Water Holding Capacity Mitigates
Downside Risk and Volatility in US Rainfed
Maize: Time to Invest in Soil Organic Matter?
Alwyn Williams
1
, Mitchell C. Hunter
2
, Melanie Kammerer
2
, Daniel A. Kane
3
, Nicholas
R. Jordan
1
, David A. Mortensen
2
, Richard G. Smith
4
, Sieglinde Snapp
3
, Adam S. Davis
5
*
1
Department of Agronomy and Plant Genetics, University of Minnesota, St. Paul, Minnesota, United States
of America,
2
Department of Plant Science, Pennsylvania State University, University Park, Pennsylvania,
United States of America,
3
Department of Plant, Soil and Microbial Sciences, Michigan State University,
East Lansing, Michigan, United States of America,
4
Department of Natural Resources and the Environment,
University of New Hampshire, Durham, New Hampshire, United States of America,
5
United States
Department of Agriculture-Agricultural Research Service, Global Change and Photosynthesis Research Unit,
Urbana, Illinois, United States of America
*
asdavis1@illinois.edu
Abstract
Yield stability is fundamental to global food security in the face of climate change, and better
strategies are needed for buffering crop yields against increased weather variability.
Regional- scale analyses of yield stability can support robust inferences about buffering
strategies for widely-grown staple crops, but have not been accomplished. We present a
novel analytical approach, synthesizing 2000
–
2014 data on weather and soil factors to
quantify their impact on county-level maize yield stability in four US states that vary widely
in these factors (Illinois, Michigan, Minnesota and Pennsylvania). Yield stability is quantified
as both
‘
downside risk
’
(minimum yield potential, MYP) and
‘
volatility
’
(temporal yield vari-
ability). We show that excessive heat and drought decreased mean yields and yield stabil-
ity, while higher precipitation increased stability. Soil water holding capacity strongly
affected yield volatility in all four states, either directly (Minnesota and Pennsylvania) or indi-
rectly, via its effects on MYP (Illinois and Michigan). We infer that factors contributing to soil
water holding capacity can help buffer maize yields against variable weather. Given that soil
water holding capacity responds (within limits) to agronomic management, our analysis
highlights broadly relevant management strategies for buffering crop yields against climate
variability, and informs region-specific strategies.
Introduction
Climate change models predict increases in extreme weather over the coming decades (e.g., ris-
ing incidence of excessive heat, more intense droughts, and greater frequency of severe rainfall
events), with negative consequences for agricultural productivity and yield stability [
1
–
8
].
Expected changes in annual rainfall patterns and evapotranspirative demand are forecast to
PLOS ONE | DOI:10.1371/journal.pone.0160974
August 25, 2016
1 / 11
a11111
OPEN ACCESS
Citation:
Williams A, Hunter MC, Kammerer M, Kane
DA, Jordan NR, Mortensen DA, et al. (2016) Soil
Water Holding Capacity Mitigates Downside Risk and
Volatility in US Rainfed Maize: Time to Invest in Soil
Organic Matter? PLoS ONE 11(8): e0160974.
doi:10.1371/journal.pone.0160974
Editor:
Jose Luis Gonzalez-Andujar, Instituto
Agricultura Sostenible, SPAIN
Received:
June 5, 2016
Accepted:
July 27, 2016
Published:
August 25, 2016
Copyright:
This is an open access article, free of all
copyright, and may be freely reproduced, distributed,
transmitted, modified, built upon, or otherwise used
by anyone for any lawful purpose. The work is made
available under the
Creative Commons CC0
public
domain dedication.
Data Availability Statement:
The data have now
been published in a stable archive at Ag Data
Commons. The data are all aggregated from third-
party public data. The correct URL for the data set is
https://data.nal.usda.gov/dataset/data-soil-water-
holding-capacity-decreases-maize-production-
downside-risk-and-volatility
.
Funding:
This work was supported by Agriculture
and Food Research Initiative of the United States
Department of Agriculture, National Institute for Food
and Agriculture (grant number 2011-67003-30343;
https://nifa.usda.gov/program/agriculture-and-food-
Page 2
increase drought frequency in many regions, further exacerbating heat stress from warming.
This highlights the importance of soil water supply to successful crop production [
9
,
10
],
reflected in calls for agronomic management to improve soil function (e.g., by increasing
organic matter to increase soil water holding capacity, WHC). It is widely assumed that
improvements in these attributes will help buffer crop yields against climate variability [
3
,
4
].
However, few empirical studies have tested this assumption [
11
]. While improvements to soil
attributes have been associated with beneficial effects on crop yields and yield stability in
regions where drought and soil degradation are prevalent [
12
,
13
], underlying mechanisms
remain unclear, and the scope of inference remains highly limited. Evidence that permits broad
inferences is urgently needed to understand the role of soils and their management in agricul-
tural climate adaptation.
Here, we provide such evidence, analyzing joint effects of climate and edaphic factors on
yield stability at spatial and temporal scales that subsume finer-grained variation in these fac-
tors [
14
]. Borrowing concepts from finance, namely
‘
downside risk
’
and
‘
volatility
’
, we quanti-
fied yield stability along these two dimensions. Downside risk provides an estimate of the
potential for maize yields to decline with adverse environmental conditions, e.g., drought. Vol-
atility provides a measure of maize yield variability over a period of time. Our results highlight
how soil properties contribute to regional-scale variation in agricultural climate adaptation.
We compiled publicly available data covering the period 2000
–
2014 to assess the relative con-
tributions of climate and edaphic factors to county-level maize (
Zea mays
L.) yields in four US
states with contrasting climates and soil types: Illinois, Michigan, Minnesota and Pennsylvania.
Long-term and spatially distributed data on climate, soil and crop yield enable analysis of the
interactions among these factors across a range of conditions [
15
]. Data sources and aggrega-
tion procedures are described in Methods. Annual growing season weather data (March
through October) included county-level means of daily minimum, maximum and average air
temperatures, and cumulative precipitation.
Results and Discussion
To quantify downside risk, we used adaptability analysis, in which statewide annual yield aver-
ages are used to create an environmental index (EI) that can rank counties and years from
‘
poor
’
to
‘
good
’
[
16
]. County-level maize yield responses to variation in EI were constructed for
each state using linear mixed effects models (LMEs) (
Fig 1
). Variability in EI was driven by
fluctuations in maximum temperature and geographic extent of drought (
S1
and
S2
Figs). In
Pennsylvania, the lowest yielding, high elevation counties were also the least drought prone (
S1
Table
), thus constraining yield variation in drought years. Conversely, low yielding counties in
southern Illinois were also the most drought-prone, increasing yield variation in drought years.
County-level yields thus converged at maximum EI in Illinois and at minimum EI in Pennsyl-
vania (
Fig 1
).
LME model predictions at minimum EI values were used to generate minimum yield
potential (MYP) values for all counties in each state, which provide a measure of downside
risk. A high MYP implies that a county maintained high maize yields under the poorest
state-wide environmental conditions (small downside risk); a low MYP indicates that yields
were poor during unfavorable environmental conditions (large downside risk). Variation in
MYP differed among the four states (
S3 Fig
). Illinois had a wide range of MYPs, indicating
downside risk was large in some counties and small in others. In contrast, counties in Penn-
sylvania, which generally had the lowest yields under even the most favorable environmental
conditions (
Fig 1
), had a much narrower range of MYPs, i.e. all counties had a similar level of
downside risk.
Soil Mitigation of Maize Yield Stability
PLOS ONE | DOI:10.1371/journal.pone.0160974
August 25, 2016
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research-initiative-afri
). The funders had no role in
study design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests:
The authors have declared
that no competing interests exist.
Page 3
To quantify volatility, we assessed three different measures of county-level temporal yield
variability: 1) LME regression slopes [
17
]; 2) coefficient of variation (CV) [
18
]; and 3) Power
Law Residuals (POLAR) [
19
]. POLAR are the residuals of the log-log relationship of county-
level yield variance against EI, and have been demonstrated to be a useful metric of crop yield
stability in some cases [
19
]. LME regression slopes and POLAR both proved unreliable estima-
tors of volatility, showing inconsistent relationships with MYP, climate, or edaphic variables.
CV proved most reliable, and correlated positively with POLAR (
S4 Fig
). County-level volatil-
ity differed greatly among and within states (
Fig 2
). Overall, maize yields in Minnesota were
least volatile, especially in southern counties, while those in Pennsylvania were the most vola-
tile. Within Illinois, there was a strong north-south gradient of increasing yield volatility.
County-level MYPs and CVs were then incorporated into state-wide structural equation
models (SEM, Methods) to assess the relative contributions of climate and edaphic factors to
maize production downside risk and yield volatility. The same conceptual framework was
applied to each state (
Fig 3
), and models were simplified using maximum likelihood to find the
most parsimonious state-specific model (
Fig 4
). This conceptual framework hypothesizes that
relatively stable soil properties related to moisture retention (percent clay, plasticity index, and
water holding capacity) and soil fertility (cation exchange capacity, pH and soil organic matter)
interact with weather conditions to affect yield stability.
For Illinois, MYP was positively associated with soil WHC, and negatively associated with
average daily maximum growing-season temperatures. In contrast, MYP in Pennsylvania was
positively associated with average daily maximum growing-season temperatures. Semi-partial
correlations among MYP, maximum temperature and elevation indicated that this association
was a signature of better yields in low-lying compared to higher elevation counties in Pennsyl-
vania (r
MYP,maxT| elevation
= 0.02, P = 0.86; r
MYP, elevation| maxT
= -0.28, P = 0.03). In both Michi-
gan and Minnesota, increasing levels of precipitation and soil WHC were positively associated
Fig 1. Linear mixed effects models of county-level maize yields (2000
–
2014) against statewide annual mean of maize grain yield (environmental
index).
Thick black lines show state mean fixed effect. Gray lines show individual county relationships (best linear unbiased predictors, BLUPs), as
estimated from the random effects structure. Number of observations, by state: N
IL
= 1215, N
MI
= 705, N
MN
= 780, N
PA
= 840.
doi:10.1371/journal.pone.0160974.g001
Soil Mitigation of Maize Yield Stability
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Page 4
with MYP. Higher MYP was associated with reduced yield volatility (temporal yield variability)
in Illinois, Michigan and Minnesota, constraining the range within which yield variation could
occur. In Minnesota and Pennsylvania, yield volatility also declined directly with increasing
WHC. No relationship was observed between MYP and yield volatility in Pennsylvania, likely
due to low variation in MYP (
Fig 1
and
S3 Fig
). Although SOM, CEC, percent clay and pH
were not retained in the best supported SEM models, they showed positive associations with
maize yield stability in IL, MI and MN and negative associations with maximum drought
extent in all four states (
S1 Table
).
The results show strong regional influences of climate and edaphic factors on maize yield
stability across 2000
–
2014. Our findings extend insights from previous studies that showed
negative impacts of extreme temperatures and drought on maize yields [
14
,
15
,
20
]. Our find-
ings also reveal substantial regional variation in climate and edaphic factors on maize yield sta-
bility. Recent studies indicate that the negative effect of high summer temperatures is due less
to effects on reproductive growth (e.g., heat damage between anthesis and silking reducing pol-
len and grain set) and more to increased moisture stress driven by vapor pressure deficit
(VPD) [
15
,
21
]. Rising VPD increases evapotranspiration, which has a two-fold impact on
crop moisture stress: 1) photosynthesis declines as crops that are unable to meet transpirative
demand reduce their stomatal conductance and 2) soil water supply to the crop declines due to
increased evaporation from the soil surface [
20
].
Soil WHC had a positive effect on yield stability in all states (
Fig 4
), either through direct
reductions in yield volatility (Minnesota, Pennsylvania) or by limiting downside risk (Illinois,
Michigan, Minnesota). In addition, precipitation, which a previous study found to have a weak
effect on crop yields in Iowa [
21
], correlated positively with MYP in both Michigan and Minne-
sota (
Fig 4
). These results, in conjunction with maximum summer temperature data, all point
Fig 2. County-level maize yield volatility (coefficient of variation) in each state over 2000
–
2014.
Minnesota (top
left state) had the least yield volatility and Pennsylvania (far right) the most. Illinois (second from left) had the greatest
variation in yield volatility.
doi:10.1371/journal.pone.0160974.g002
Soil Mitigation of Maize Yield Stability
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Page 5
to strong maize yield sensitivity to increases in VPD [
15
], and demonstrate the importance of
maintaining adequate soil moisture to meet crop water demand. WHC in Illinois increased
with latitude (r = 0.43, P
<
0.0001), underlying the negative association between maximum
temperature and WHC. Across states, semi-partial correlations among soil WHC, clay content
and SOM (r
WHC,clay|SOM
= 0.92, P
<
0.0001; r
WHC,SOM|clay
= 0.58, P
<
0.0001) showed that
Fig 3. Conceptual framework underpinning structural equation modeling approach to investigate effects of climate and edaphic factors on
maize yield stability.
Climate data was restricted to growing season periods. Edaphic variables were separated into two components representing
complementary soil properties: soil fertility and soil moisture retention.
doi:10.1371/journal.pone.0160974.g003
Soil Mitigation of Maize Yield Stability
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although soil texture was strongly associated with variation in WHC, SOM made substantial
contributions to WHC variation that were distinct from those of soil clay content.
In totality, these results suggest a novel hypothesis that requires testing at landscape to
regional scales: US maize producers should manage soils to increase SOM and thereby improve
yield stability. Within pedogenic constraints on SOM accrual and aggregate formation, agricul-
tural management that increases SOM may increase WHC and thereby the soil
’
s capacity to
meet crop water demand. Indeed, previous work in China has shown that cereal productivity
and yield stability are strongly correlated with SOM [
11
]. SOM holds water directly and also
helps bind soil aggregates, which improves soil structure and WHC while also enhancing rain-
fall infiltration [
22
,
23
]. Thus, changes to agronomic management that encourage SOM accu-
mulation may increase soil WHC to counter rising VPD, and thereby increase yield stability.
Such management changes include reducing tillage, managing residues, and increasing organic
matter inputs through use of compost, manure or cover crops [
24
,
25
,
26
].
Our analysis quantifies the interacting effects of soil properties and weather variability on
rainfed maize yield stability in the US over 2000
–
2014. The results portend substantial yield
losses and an increase in yield instability as summer temperatures and precipitation shortfalls
increase in frequency due to climate change [
1
,
2
]. The consistent stabilizing impact of soil
WHC found across all four States indicates that soil attributes responsive to agronomic man-
agement may strongly mediate the effects of climate change on maize yield stability, due to the
importance of meeting crop water demand [
15
,
21
]. This suggests that relatively simple
changes to agricultural soil management aimed at enhancing SOM can contribute meaning-
fully to climate adaptation by increasing WHC [
3
,
25
]. However, the scope for improving SOM
and WHC is constrained by soil type [
27
,
28
], so some regions may have limited opportunities
Fig 4. Best supported structural equation models showing effects of climate and edaphic factors on county-level maize minimum yield potential
(downside risk) and temporal variability (volatility) for each state.
(A) Illinois (
χ
2
= 5.2, df = 2,
P
= 0.07, N = 81), (B) Michigan (
χ
2
= 2.8, df = 2,
P
= 0.24,
N = 47), (C) Minnesota (
χ
2
= 0.68, df = 1,
P
= 0.41, N = 52), (D) Pennsylvania (
χ
2
= 0.75, df = 2,
P
= 0.69, N = 56). Model fit values indicate good support for
each model. Double-headed arrows denote covariances among variables, whereas single-headed arrows denote standardized regression coefficients.
Black arrows show positive relationships; grey arrows show negative relationships.
P
<
0.05 (
*
),
P
<
0.01 (
**
),
P
<
0.001 (
***
).
doi:10.1371/journal.pone.0160974.g004
Soil Mitigation of Maize Yield Stability
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to mitigate climate change impacts through improved soil management. Other strategies will
be required to complement WHC increases, such as crop genetic improvement, cropping sys-
tem design, and irrigation technologies, among others [
29
]. The analytical approach developed
here can be used to better inform agricultural climate adaptation strategies, and help guide the
selective deployment of a broad suite of regionally-specific climate adaptation strategies.
Methods
Database aggregation
County-level data for US maize yields, soil chemical and physical properties, drought status
and weather were downloaded from public databases for the 2000 through 2014 period. Maize
annual yield data were from the US Department of Agriculture (USDA) National Agricultural
Statistics Service [
30
]; soils data from the USDA Web Soil Survey [
31
]; drought status from the
USDA National Drought Mitigation Center [
32
] and weather data from the National Oceanic
and Atmospheric Administration (NOAA) National Centers for Environmental Information
[
33
]. Counties with fewer than 10-years of complete data over 2000
–
2014 were discarded.
Soil chemical properties from the Web Soil Survey included cation exchange capacity (CEC;
meq 100 g soil
-1
at pH 7), pH (1:1 water:soil method), soil organic matter (SOM; %), and soil
physical properties included clay content (%), plasticity index (%; the range of soil moisture
contents in which a soil exhibits the properties of a plastic solid) and water holding capacity at
15 bar (WHC; % v/v). Only soils being used for agricultural purposes (
‘
cultivated
’
land as
defined by the USDA National Agricultural Statistics Service 2014 Cultivated Layer [
34
]) were
included in the dataset. Soil properties were calculated as an area-weighted average across soil
types, over 0 to 30 cm depth (the
‘
plow layer
’
) [
31
].
County-level drought data were obtained as within-year season-long (April through Sep-
tember) averages of the weekly percentage of crop land within a county classified as experienc-
ing moderate (D1) to exceptional (D4) drought intensity [
32
]. To examine the relationship
between county-level soil and site variables and drought intensity within each state, we com-
piled a variable
‘
D1.D4.worst
’
, which represented the percentage of area in each county
experiencing drought intensity of classes D1 (moderate) to D4 (exceptional) in the worst
drought year for each state (highest overall percentage of state in classes D1 to D4) during the
study period. During 2000
–
2014, Illinois experienced its most severe level of drought in 2012,
Michigan and Minnesota in 2007, and Pennsylvania in 2002.
County-level annual weather data were obtained as a within-year average of data from all
weather stations present in the NOAA data set for a given county. Weather data included daily
minimum and maximum air temperatures and precipitation for the period from March
through October. From these data, we calculated season-long county averages for maximum,
minimum and mean temperatures as well as cumulative growing degree days, which were cal-
culated as follows:
GDD
i
¼
P
n
j
¼
1
T
max
þ
T
min
2
T
b
½
1
where
T
max
and
T
min
are maximum and minimum air temperatures (°C), respectively, and
T
b
is the base temperature for calculating thermal time for maize (10°C), with the summation per-
formed for county
i
for the
j
th
day from March through October, summing only those daily
GDD values
>
0.
Soil Mitigation of Maize Yield Stability
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Data analysis
Traditional adaptability analysis, or Finlay-Wilkinson regression, quantifies treatment-specific
variation in yield response to variation in environmental suitability, represented as the mean
yield across treatments across a range of environments (EI, the Environmental Index) [
16
,
17
].
In our analysis, we utilized the adaptability analysis approach to analyze annual county-level
maize yields for each of our four states over 2000
–
2014 in relation to variation in each state
’
s
annual mean yield over 2000
–
2014; for each state, annual mean state-wide yields were used as
our EI. We implemented adaptability analysis by fitting linear mixed effects models (LMEs) for
each state, using the
nlme
package of R 3.2.2 [
35
]. Each model contained a county-level random
intercept and slope structure, and EI was fitted as the fixed effect. Within R, this gave the fol-
lowing model structure: county-level maize yield ~ EI, random = ~ EI | county. County-level
minimum yield potential (MYP;
‘
downside risk
’
) values were calculated from LME predictions
of maize yield at the minimum EI for each state. Coefficient of variation (CV;
‘
yield volatility
’
)
was calculated for each county within a state as the quotient of maize yield standard deviation
over mean maize yield.
Global state-wide structural equation models were organized according to
Fig 3
, with indi-
vidual climate, soil fertility and soil moisture retention variables grouped into corresponding
latent variables [
36
]. SEM models allowed us to quantify the contributions of these variables to
maize production downside risk and yield volatility, while accounting for spatiotemporal
covariances among them. A candidate pool of SEM models containing the global model and
more parsimonious subsets of this model, with soil properties and climate represented by either
latent variables, manifest variables, or both was fit using the
lavaan
package of R 3.2.2 [
35
].
These candidate models were discarded or retained using maximum likelihood estimations
and Akaike (AIC) weights to find the most parsimonious models. In addition, models were
assessed for
‘
goodness of fit
’
using
χ
2
-values, comparative fit index (CFI), and root mean square
error of approximation (RMSEA) [
36
].
Supporting Information
S1 Fig. BLUPs (best linear unbiased predictors) from linear mixed effects model quantify-
ing state-level associations between Environmental Index (annual mean of maize yield in
Mg ha
-1
) and statewide mean of maximum daily temperature (°C).
Fixed effects: inter-
cept = 19.56 (t
1, 55
= 4.2, P
<
0.001), maximum temperature = -0.44 (t
1, 55
= -2.49, P = 0.016).
Random effects: standard deviation (maximum temperature) = 0.19, standard deviation (resid-
ual) = 1.04. N = 15 for all states.
(EPS)
S2 Fig. BLUPs (best linear unbiased predictors) from linear mixed effects model quantify-
ing state-level associations between Environmental Index (annual mean of maize yield in
Mg ha
-1
) and statewide percent area in drought classes D1 to D4 (%)
31
.
Fixed effects: inter-
cept = 9.6 (t
1, 55
= 19.6, P
<
0.0001), % area in drought class = -0.06 (t
1, 232
= -2.1, P = 0.04).
Random effects: standard deviation (% area in drought class) = 0.05, standard deviation (resid-
ual) = 0.88. N = 15 for all states.
(EPS)
S3 Fig. Box-plots showing distributions of MYP (minimum yield potential), represented by
county-level predictions at the lowest value Environmental Index (EI: statewide annual
mean of maize grain yield) from linear mixed effects models of county-level maize yield
response to variation in EI.
Number of observations, by state: N
IL
= 84, N
MI
= 47, N
MN
= 52,
Soil Mitigation of Maize Yield Stability
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Page 9
N
PA
= 56.
(EPS)
S4 Fig. BLUPs (best linear unbiased predictors) from linear mixed effects model quantify-
ing state-level associations between two measures of maize yield stability: CV (coefficient
of variation) and POLAR (Power Law Residuals).
Fixed effects: intercept = 0.17 (t
1, 232
=
10.3, P
<
0.0001), POLAR = 0.18 (t
1, 232
= 8.2, P
<
0.0001). Random effects: standard deviation
(POLAR) = 0.023, standard deviation (residual) = 0.04. Number of observations, by state:
N
IL
= 84, N
MI
= 47, N
MN
= 52, N
PA
= 56.
(EPS)
S1 Table. Pearson correlations between county-level indices of maize yield stability and
drought and selected soil and site properties in four maize-producing US states.
(PDF)
Acknowledgments
Many thanks to A. S. Grandy and C. Pittelkow for reviewing earlier drafts of the manuscript.
Author Contributions
Conceptualization:
AW MK RGS DAK ASD.
Data curation:
ASD.
Formal analysis:
AW MK RGS DAK ASD.
Funding acquisition:
ASD.
Methodology:
AW MK RGS DAK ASD.
Project administration:
ASD.
Supervision:
ASD.
Visualization:
AW MK ASD.
Writing
–
original draft:
AW MCH MK DAK NRJ DAM RGS SS ASD.
Writing
–
review & editing:
AW MCH MK DAK NRJ DAM RGS SS ASD.
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