Page 1
RESEARCH ARTICLE
Soil properties resulting in superior maize yields
upon climate warming
Puyu Feng
1
&
Bin Wang
2
&
Matthew Tom Harrison
3
&
Jing Wang
4
&
Ke Liu
3
&
Mingxia Huang
4
&
De Li Liu
2,5
&
Qiang Yu
6,7
&
Kelin Hu
1
Accepted: 1 August 2022 / Published online: 23 August 2022
Abstract
The impacts of global climate warming on maize yield vary regionally. However, less is known about how soil modulates
regionally specific impacts and soil properties that are able to alleviate adverse impacts of climate warming on maize produc-
tivity. In this study, we investigated the impacts of multiple soil inherent properties on the sensitivity of maize yield (S
Y,T
) to
growing season temperature across China. Our results show that a 1°C warming resulted in the largest yield decline (11.2 ± 6.1%)
in the mid-eastern region, but the moderate yield increase (1.5 ± 2.9%) in the north-eastern region. Spatial variability in soil
properties explained around 72% of the variation in S
Y,T
. Soil organic carbon (SOC) content positively contributed the greatest
extent (28.9%) to spatial variation of S
Y,T
, followed by field capacity (9.7%). Beneficial impacts of increasing SOC content were
pronounced in the north-eastern region where SOC content (11.9 ± 4.3 g kg
−
1
) was much higher than other regions. Other soil
properties (e.g., plant wilting point, sand content, bulk density, and saturated water content) were generally negatively correlated
with S
Y,T
. This study is the first one to answer how soil inherent properties can modulate the negative impacts of climate warming
on maize yield in China. Our findings highlight the importance of SOC in alleviating adverse global warming impacts on maize
productivity. To ensure food security for a rapidly increasing population under a changing climate, appropriate farming man-
agement practices that improve SOC content could reduce risk of adverse effects of global climate warming through a gain in
yield stability and more resilient production in China
’
s maize belt.
Keywords
Climate crisis . Sensitivity analysis . Soil inherent properties . Maize yield . China cropping belt . Soil organic carbon
1 Introduction
With a rapidly increasing global population and growing food
demand, farmers are facing a dilemma of producing crops
with higher yield in the same (or even less) cultivated areas
(Cammarano and Tian,
2018
; Harrison et al.,
2012
). More
specifically, the mean growth rate of global crop yield must
exceed 2.4% per year to feed 10 billion people by 2050s (Ray
et al.,
2013
), without degrading natural resources (water, air,
biodiversity, etc.) or producing additional greenhouse gas
emissions (Alcock et al.,
2015
; Harrison et al.,
2014a
).
However, the ongoing climate change and increasingly severe
extreme climatic events are preventing farmers from fulfilling
this goal. As current farming systems have evolved to fit
*
Puyu Feng
fengpuyu@cau.edu.cn
*
Kelin Hu
hukel@cau.edu.cn
1
College of Land Science and Technology, China Agricultural
University, Beijing 100193, China
2
NSW Department of Primary Industries, Wagga Wagga Agricultural
Institute, Wagga Wagga, NSW 2650, Australia
3
Tasmanian Institute of Agriculture, University of Tasmania,
Burnie, Tasmania 7320, Australia
4
College of Resources and Environmental Sciences, China
Agricultural University, Beijing 100193, China
5
Climate Change Research Centre, University of New South Wales,
Sydney 2052, Australia
6
State Key Laboratory of Soil Erosion and Dryland Farming on the
Loess Plateau, Northwest A&F University, Yangling 712100, China
7
College of Resources and Environment, University of Chinese
Academy of Sciences, Beijing 100049, China
Agronomy for Sustainable Development
(2022) 42:85
https://doi.org/10.1007/s13593-022-00818-z
#
The Author(s) 2022
Page 2
within historical climate conditions, climate change-induced
changes of meteorological factors, in particular rising temper-
ature, are expected to pose significant risks for future farming
outputs (Chang-Fung-Martel et al.,
2017
; Zhao et al.,
2017
).
Understanding the impacts of shifting meteorological factors
can provide invaluable information to improve farming
’
s re-
silience to climate change, thereby enhancing food security
while preserving the natural resource base (Harrison et al.,
2021
).
Temperature is a major determinant of crop productivity
and crop phenological responses to climate warming have
been well studied from local through to global scales.
Asseng et al. (
2015
) estimated that global wheat production
is likely to fall by 6% when annual temperature increases by
1°C, based on simulations from process-based crop models.
Lobell and Field (
2007
) demonstrated a negative response of
global maize yield to increased temperature through an ana-
lysis of global recorded maize yield for 1961-2002.
Nevertheless, the actual impacts of increased temperature on
crop yield are usually not uniform across regions. For exam-
ple, maize crops had heterogonous sensitivities in different
regions, e.g., positive in South American yet negative in
northwest Africa during 1961-2014 (Liu et al.,
2020
). Even
within a country, the impacts can also vary greatly. For exam-
ple, positive impacts were mainly distributed in northeast
China from 1980 to 2010, while negative impacts occurred
in most areas of central China (Chen et al.,
2011
; Deng et al.,
2020
). It has been reported that regional disparities in crop
yield impacts are related to latitudes, which present different
initial meteorological conditions (Deryng et al.,
2014
).
Nevertheless, different regions also share varied soil proper-
ties that also likely contribute to spatial variations. However,
the extent to which soil properties modulate the impact of
climate warming on crop yields is yet unknown.
In any cropping system, the suitability of a region for crop
cultivation is determined by climate, yet the yield level is
subject to soil characteristics as well (Bodner et al.,
2015
;
Pinheiro et al.,
2019
). Soil plays a fundamental role in crop
growth by providing physical support and more importantly,
acting as the source of water and nutrients (Bonfante and
Bouma,
2015
). Such capabilities are based on a suite of phys-
ical, hydraulic, and chemical properties, which can show sig-
nificant spatial variation (Ara et al.,
2021
; Harrison et al.,
2011
). Given the interacting nature of the soil-plant system
in response to atmospheric drivers, crop response to climate
warming is expected to vary spatially with different soil prop-
erties. Most soil properties are relatively stable and change
slightly under short-term farming practices. Some of them,
such as texture, water retention, and soil organic carbon
(SOC) concentration, have been demonstrated to account for
the spatial variability of crop responses to increased tempera-
ture (Farina et al.,
2021
; Sándor et al.,
2020
). For example,
yields of seven major crops between 1958 and 2019 in the
USA were generally more sensitive to temperature variability
in coarse-textured soils and less responsive in medium-
textured and fine-textured soils (Huang et al.,
2021a
). Rezaei
et al. (
2018
) also reported that wheat yield on sandy soils
decreased significantly by 24% with increased air temperature
at anthesis, compared to loamy soils or soils consisting of clay
in a controlled environment. In addition, SOC is an important
indicator of soil quality and soils with higher SOC tend to
show better water and nutrient retention (Karhu et al.,
2011
),
which can then help crops buffer the impacts of increased
temperature and even exploit positive effects (Droste et al.,
2020
; Song et al.,
2015
). However, the quantitative impacts
of various soil properties on crop yields at a regional scale
remain uncertain.
With the largest cropping area in the world, China is one of
the world
’
s leading maize producers (FAOSTAT,
2020
).
However, China is also the world
’
s most populous country.
Against a background of global warming, sustainable intensi-
fication of maize production without adverse environmental
trade-offs (Harrison et al.,
2021
) is of great importance for
Chinese both domestic food supply and global food security.
Here for the first time, we used the Agricultural Production
Systems sIMulator (APSIM), to investigate impacts of multi-
ple soil properties on the responses of maize yield to growing
season temperature in China
’
s Maize Belt (CMB). Our objec-
tives were to address the following questions: (I) How does
maize yield respond to climate warming in different zones of
CMB? (II) How do various soil physical, hydraulic, and
chemical properties modulate the impacts of climate warming
on maize yield? By answering these questions, we provide
insights into the development of adaptive strategies for global
warming from the perspective of soil amelioration (Figure
1
).
2 Materials and methods
2.1 Study area
The study area is CMB (Fig.
2
), accounting for over 70% of
national maize production and more than 15% of global pro-
duction (Meng et al.,
2016
). CMB is confined to a relatively
narrow band of land, spreading from the southwest to the
northeast (97.6-134.9° E, 21.4-50.9° N). The characteristics
of topography and climate vary greatly across CMB.
Topographically, the western and south-western parts of
CMB are occupied by plateaus with elevation more than
1000 m, but the eastern and north-eastern parts are mostly
plains of less than 500 m. Climatically, CMB is characterized
by warm and wet conditions in the south-western part, and
cold and dry conditions in the north-eastern part. Varied en-
vironmental conditions result in varied cropping systems, e.g.,
single cropping system with maize in the northeast and north-
west but double cropping system with winter wheat and
85
Page 2 of 13
P. Feng et al.
Page 3
summer maize in the middle of CMB. In addition, there is a
mixed cropping system in the southwest, with both single and
double cropping systems distributed. Thus, to consider the
impacts of regional variations in climate and soils, we divided
CMB into six maize planting regions (Fig.
2
) according to a
previous study (Huang et al.,
2020
). The regions were divided
based on geographic location and different cropping systems,
which were derived from agrometeorological observational
data. Basic information of the six regions is given in Table
1
.
The SPAM (spatial production allocation model) global
synergy cropland map was used to distinguish the maize crop
land (Lu et al.,
2020
). This map was developed by the Chinese
Academy of Agricultural Sciences based on a self-adapting
statistics model with multiple existing maps and national and
subnational statistics fused. It shows higher accuracy and bet-
ter consistency (99%) with statistics than many previous crop-
land maps. This map was originally at a resolution of 5 arc-
minute, but we upscaled to 15 arc-minute to make it match
with climate data. As shown in Fig.
2
, there were 4283 grids in
total (Table
1
) which illustrated cropland pixels over CMB.
Our subsequent data analysis and result visualization were
both performed on these maize cropland pixels.
2.2 Climate data
Historical gridded climate data were obtained from the
Terrestrial Hydrology Research Group at Princeton
University (Sheffield et al.,
2006
). This dataset was developed
Fig. 1
Maize cultivation in northeast China. Images show the farming practices of straw mulching and no tillage, as strategies to improve soil quality.
Beijing
20°N
30°N
40°N
50°N
80°E
90°E
100°E
110°E
120°E
130°E
Longitude
Latitude
Alfisols
Ultisols
Entisols
Inceptisols
Vertisols
Aridisols
Mollisols
Fig. 2
Six major maize
cultivating regions across China
’
s
Maize Belt. Grid colors denote
soil types based on the
classification of the FAO-
UNESCO (Food and Agriculture
Organization of the United
Nations-United Nations
Educational, Scientific and
Cultural Organization) Soil Map
of the world (
https://www.fao.
org/soils-portal/en/
).
Soil properties resulting in superior maize yields upon climate warming
Page 3 of 13
85
Page 4
by blending the NCEP
–
NCAR (National Centers for
Environmental Prediction
–
National Center for Atmospheric
Research) reanalysis data with multiple observation-based
datasets. Known biases in the reanalysis data have been
corrected using observed data. The final product provides a
globally consistent dataset of near-surface meteorological fac-
tors at 15 arc-minute spatial resolution, which are designed for
the purpose of long-term and broad scale terrestrial modeling
studies (Parkes et al.,
2019
; Ruane et al.,
2021
). This dataset
has also been implemented for many modeling studies in
China and given satisfactory simulation results (Li et al.,
2019
; Yao et al.,
2018
). We derived daily series of maximum
and minimum air temperatures, precipitation, and solar radia-
tion (1961
–
2016) for all of the grids located in CMB.
2.3 Soil data
Gridded soil profiles for crop model simulation were derived
from the Global High-Resolution Soil Profile Database of the
Harvard Dataverse (Hengl et al.,
2014
). This dataset is an
improved version of the SoilGrids dataset released by ISRIC
(International Soil Reference and Information Centre) in
2014, with more soil hydraulic properties (e.g., soil water
content at saturation, wilting point, and field capacity) includ-
ed, making it readily available for simulating crop growth.
Other soil physical and chemical properties, such as bulk den-
sity, texture, and organic carbon content, are also available
and have been frequently used as inputs for crop modeling
studies, including China (Wang et al.,
2020
; Zhang et al.,
2018
). In all grids, the soil profiles have six layers, namely
0-5 cm, 5-15 cm, 15-30 cm, 30-60 cm, 60-100 cm, and 100-
200 cm. Spatial maps of several soil properties for each layer
are given in the supplementary material (Figures
S1
-
S6
). In
addition, these data are provided for each country at 5 arc-
minute resolution. To make them congruent with the climatic
grids, we aggregated 5 arc-minute grids into 15 arc-minutes.
2.4 APSIM simulations
We implemented the APSIM (Agricultural Production System
sIMulator,
https://www.apsim.info/
) crop model version 7.10 to
simulate the dynamics of maize growth and development.
APSIM is structured around soil, plant, atmosphere, and
management modules, making it a comprehensive model
capable of simulating manifold biophysical processes in
response to environmental variations (Holzworth et al.,
2014
).
Many studies have successfully used the APSIM maize module
to quantify the impacts of climate change on maize yield in
China (Wang et al.,
2018
; Xiao et al.,
2020
; Zhu et al.,
2022
).
The APSIM model was originally developed in Australia
but since inception has been used with success in numerous
countries across the world (Harrison et al.,
2014b
). When used
in other regions, cultivar traits should be re-parameterized
only if local and robust datasets exist (Harrison et al.,
2012
;
Harrison et al.,
2019
). In this study, we obtained the genetic
parameters of six maize genotypes (Table
S1
) for the six cul-
tivating regions from the study of Huang et al. (
2020
), who
reported that the calibrated maize genotypes can well repre-
sent observed yield of maize cultivated in the belt with R
2
being 0.74 and NRMSE being 17.7%. Then, we set up a
long-term simulation (1961-2016) for each grid across
CMB. Climate and soil inputs for each grid have been de-
scribed above. Sowing date was determined as multi-year av-
erage sowing date of the nearest agrometeorological observa-
tional site for each grid (Huang et al.,
2020
). Details can be
found in Figure
S7
. Maize planting density, depth, and row
space were same for all regions, i.e., 67,500 plants ha
−
1
, 5 cm,
and 60 cm, respectively. The fertilizer at sowing was 180 kg
ha
−
1
urea-based N. These are common farming management
practices across China (Huang et al.,
2022
; Ren et al.,
2016
;
Zheng et al.,
2021
). With same management practices across
regions, we were able to focus on the effects of climate and
soil on maize yield in subsequent analysis.
It should be noted that climate, soil, cultivar, and manage-
ment practices have been changing during past decades.
Nonetheless, we did not focus on the effects of cultivar change
but on the responses of current maize planting to climate
change. Thus, we used one calibrated cultivar and kept soil
inherent properties and other management practices constant
over the study period. Moreover, to exclude any
“
carry-over
”
effects from previous seasons, initial soil water and nitrogen
were reset to 20% of maximum soil available water and 80 kg
ha
−
1
NO
3
-N and 12.5 kg ha
−
1
NH
4
-N on the 1st of January
each year. Fallow was performed prior to sowing.
Table 1
Climatic and growth
information of the six maize
regions across China
’
s Maize
Belt.
CS
cropping system;
TGS
typical growing season of maize;
AE
average elevation; AMT,
AMP, and AMR are annual mean
temperature, precipitation, and
solar radiation, respectively.
Region
No. of grids
CS
TGS
AE (m)
AMT
(°C)
AMP
(mm)
AMR
(MJ·m
−
2
)
I
713
Single
1 May
–
30 Sep
267
2.1
550
15.8
II
338
Single
1 May
–
30 Sep
387
4.6
620
16.6
III
375
Single
1 May
–
30 Sep
529
6.9
589
17.6
IV
680
Single
1 May
–
30 Sep
1471
8.5
458
19.0
V
950
Double
1 Jun
–
30 Sep
388
13.2
677
17.9
VI
1227
Mixed
1 Apr
–
30 Sep
1380
15.6
1120
18.2
85
Page 4 of 13
P. Feng et al.
Page 5
2.5 Identification of temperature sensitivity
We implemented a widely used panel data model, ordinary
least squares regression with quadratic terms (Deng et al.,
2020
; Schlenker and Lobell,
2010
; Zhu et al.,
2019
), to esti-
mate temperature sensitivity of maize yield (S
Y,T
). Growing
season mean temperature (T), total precipitation (P), and mean
solar radiation (R) were used as the explanatory variables.
ln
Yield
i
;
t
¼
a
1
T
i
;
t
þ
a
2
T
2
i
;
t
þ
b
1
P
i
;
t
þ
b
2
P
2
i
;
t
þ
c
1
R
i
;
t
þ
c
2
R
2
i
;
t
þ
ε
ð
1
Þ
where ln(
Yield
i
,
t
) is the natural logarithm of yield at grid
i
in
year
t
. As the APSIM model simulated maize yield can be
viewed as climate-driven yield, we did not normalize for lon-
gitudinal yield gains associated with technological progress
(e.g., breeding, fertilizer, and pesticide application).
Quadratic terms are included for three climatic variables to
simulate their nonlinear impacts on maize yield.
a
,
b
, and
c
are regression coefficients.
ε
represents the model error term.
Then, the S
Y,T
can be defined as
S
Y
;
T
¼
∂
Y
∂
T
∙
100%
¼
a
1
þ
2
a
2
T
i
∙
100%
ð
2
Þ
where
T
i
denotes the mean temperature during the study pe-
riod 1961-2016 in grid
i
.
a
1
and
a
2
stand for the regression
coefficients derived from Eq.
1
. As the response variable
(maize yield) has been log-transformed, the estimated temper-
ature sensitivity indicates the percentage change of yield for
1°C warming. We calculated the S
Y,T
for all of the grids across
CMB using Eq.
1
and Eq.
2
. The performance of the model
was evaluated using two metrics, namely coefficient of deter-
mination (R
2
) and normalized root mean square error
(NRMSE), given by the following equations:
R
2
¼
∑
n
i
¼
1
O
i
−
O
P
i
−
P
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
∑
n
i
¼
1
O
i
−
O
2
r
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
∑
n
i
¼
1
P
i
−
P
2
r
0
B
B
@
1
C
C
A
2
ð
3
Þ
NRMSE
¼
1
O
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
1
n
∑
n
i
¼
1
O
i
−
P
i
ð
Þ
2
r
ð
4
Þ
where
n
is the number of samples,
O
i
and
P
i
denote observa-
tional and predicted values, and
O
represents the mean of
observational values. Generally, the model with higher R
2
and lower NRMSE is considered to be a better-performance
model.
2.6 Contributions of soil properties to temperature
sensitivity
We implemented the random forest (RF) to study the contri-
butions of various soil properties to temperature sensitivity.
RF, also known as random decision forest, is an advanced
tree-based ensemble machine learning algorithm (Breiman,
2001
). Except for developing predictive regression or classi-
fication models, RF is also commonly used for investigating
the complicated relationships among variables, as it can ac-
count for nonlinear and hierarchical relationships between the
response and predictor variables (Dibari et al.,
2020
). For this
purpose, two built-in functions, namely variable importance
measures and partial dependence plots, can be employed after
an RF model has been built. In this study, we first built an RF
regression model with temperature sensitivity as the depen-
dent variable and multiple soil properties as independent var-
iables. The accuracy-based importance metric was used to
evaluate variable importance. This was generated using an
out-of-bag (OOB) validation procedure. In brief, during the
model building phase, about one-third of all input values were
randomly selected and set aside for subsequent OOB model
validation. Then, the prediction accuracy on the OOB sample
was determined. The mean decrease in prediction accuracy
when the values of a certain variable in the OOB sample were
randomly shuffled was defined as the importance value of the
variable (Heung et al.,
2014
), expressed as the mean square
error (MSE):
MSE
OOB
¼
1
n
∑
n
k
¼
1
K
i
−
P
kOOB
2
ð
5
Þ
where
n
denotes the number of observations,
K
i
indicates
known value, and
P
kOOB
represents the average of all OOB
predictions across all trees.
We also used partial dependence plot (PDP) to evaluate the
marginal effects of a selected explanatory predictor on the
response variable. A PDP can show whether the relationship
between the response and a predictor is linear, monotonic, or
more complex, marginalizing over the values of all other input
predictor variables (the
“
complement
”
features) (Friedman,
2001
). In this study, we used the
“
randomForest
”
package
sourced in the R software to build the RF model and derived
variable importance values and PDPs.
3 Results
3.1 Climatic and yield trends from 1961 to 2016
Temporal trends of climate and simulated maize yield in all
the grids over CMB for the period of 1961
–
2016 are illustrat-
ed in Figure
3
. A significant increasing trend was detected for
Soil properties resulting in superior maize yields upon climate warming
Page 5 of 13
85
Page 6
growing season mean temperature in nearly all central and
northern grids. Specifically, temperature increased faster in
inland grids (>0.2°C/10a). The trends varied in the southern
region (region VI), with a part in its east showing decreased
temperature. Solar radiation significantly increased in central
regions and parts of north-eastern regions, with a trend over
0.2 MJ m
−
2
/10a. The linear trends of solar radiation in remain-
ing regions were not significant. For growing season total
precipitation, no significant trends were detected in most of
the regions. Maize yields generally increased in many grids,
e.g., northern regions, part of mid-eastern regions, and the
northeast part of the southern region (region VI). In some
grids of central and southern regions (regions IV and VI),
maize yield decreased. As model simulated maize yields were
climate-driven, it can be derived that yield trends were mainly
attributed to climate variations during 1961
–
2016. In addition,
as the trends of precipitation and solar radiation were not
significant in most grids, we did not consider the yield sensi-
tivity to them in subsequent analysis.
3.2 Temperature sensitivity of maize yield
The performance of the panel data model in each region is pre-
sented in Figure
4
. Though with some fluctuations, the R
2
values
for the six regions were mainly around 0.5, and the NRMSE
values were mainly lower than 1%, meaning that maize yield
variations could be largely explained by climate variables.
As rising temperatures are a uniform and consistent feature
associated with climate change, we separately analyzed the
contributions of temperature to maize yield (hereafter, temper-
ature sensitivity or S
Y,T
). The results for all the grids across the
belt are illustrated in Figure
5
. Temperature sensitivity varied
Trend of T (°C/10a)
<0
0~0.1
0.1~0.2
0.2~0.3
0.3~0.4
>0.4
NS
(a)
Trend of P (mm/10a)
<−20
−20~−10
−10~0
0~10
10~20
>20
NS
(b)
Trend of R (MJ m
−
2
/10a)
0.2~0.4
0.4~0.6
>0.6
NS
(c)
Trend of yield (kg ha
−
1
/10a)
<−20
−20~−10
−10~0
0~10
10~20
>20
NS
(d)
Fig. 3
Linear trend of growing season mean temperature (
a
), precipitation (
b
), solar radiation (
c
), and maize yield (
d
) in each grid of China
’
s Maize Belt
for the period 1961
–
2016. NS: not significant (
P
> 0.05). In addition, mean values of the four variables for the study period were illustrated in Figure
S8
.
85
Page 6 of 13
P. Feng et al.
Page 7
(a)
0
1
2
3
0.0
0.2
0.4
0.6
0.8
1.0
R
2
Probability Density
I
II
III
IV
V
VI
(b)
0
2
4
6
8
1
2
3
NRMSE (%)
Probability Density
Fig. 4
Distributions of R
2
(coefficient of determination) and
NRMSE (normalized root mean
square error) derived from the
panel data model for all grids in
each region. The probability
density curves are derived from
Gaussian kernel density estimate.
The area under a density curve
should sum up to a total of 1.
They are smoothed versions of
histograms, showing the
distributions of continuous data.
The peaks of a density curve help
display where values are
concentrated.
<−15
−10
−5
0
5
10
>15
%/°C
VI
0
100
200
−20−15−10 −5 0
5 10 15 20
S
Y,T
(%/°C)
Count
IV
0
25
50
75
−20 −15 −10 −5
0
5
10 15 20
S
Y,T
(%/°C)
Count
V
0
20
40
60
−20 −15 −10 −5
0
5
10 15 20
S
Y,T
(%/°C)
Count
III
0
20
40
60
−20 −15 −10 −5
0
5
10 15 20
S
Y,T
(%/°C)
Count
II
0
20
40
60
−20 −15 −10 −5
0
5
10 15 20
S
Y,T
(%/°C)
Count
I
0
25
50
75
100
−20−15−10 −5 0
5 10 15 20
S
Y,T
(%/°C)
Count
Fig. 5
Spatial distributions of S
Y,T
(%/°C). S
Y,T
is the temperature sensitivity of maize yield, representing the yield change (%) for 1°C warming.
Histograms show the distributions of S
Y,T
values for all grids in each sub-region. The vertical dotted line indicates the average of each region.
Soil properties resulting in superior maize yields upon climate warming
Page 7 of 13
85
Page 8
greatly in different grids. The most noticeable feature was that
S
Y,T
values in region V were rather smaller than other regions.
Specifically, the values in region V were generally smaller
than 0 and the mean value was about
−
11%/°C. This indicated
that maize yield in region V negatively responded to temper-
ature increase and yield normally decreased by 11% for every
1°C increase. On the other hand, we also noticed that grids
with positive temperature sensitivity were mainly located in
the northeast, especially region I. Region I was the only one
with mean S
Y,T
exceeding 0% per °C, reaching 1.5% per °C.
The values in other regions generally showed a normal distri-
bution with the mean values in the range of
−
5 to 0 %/°C.
Thus, in general, temperature increase could contribute to
yield losses in most regions of CMB, except for region I.
3.3 Impacts of soil properties on temperature
sensitivity
Figure
6
shows the performance of the RF model in
explaining the spatial variance of temperature sensitivity
based on soil properties. We selected input soil properties
according to three standards. First, they were APSIM param-
eters so that their impacts could be captured by modeling
methods. Second, their characteristics did not change greatly
under conventional farming management practices. Third,
they were commonly available in gridded soil datasets.
Thus, we selected six soil properties, namely soil organic car-
bon content (SOC), bulk density (BD), sand content (SC),
wilting point (WP), field capacity (FC), and saturated water
content (SWC). Weighted averages of these soil properties by
layers were used as explanatory predictors to develop the RF
model. The model explained 72% variation of S
Y,T
with low
error (2.74% °C
−
1
), indicating that temperature sensitivity was
largely under the modulation of soil inherent properties.
Next, we analyzed the relative importance of input predic-
tors through their marginal effects on temperature sensitivity
(Figure
7
). SOC ranked highest with an importance value of
28.9%, showing a positive effect on temperature sensitivity.
While SOC content was highest in region I, it was lowest in
region V, partly explaining the differences of temperature sen-
sitivity in the two regions (Figure
7a
). Next highest was
wilting point (20.2%), showing a negative effect on tempera-
ture sensitivity (Figure
7b
). Moreover, wilting point in region
I was lowest, suggesting that this variable also contributed to
the high temperature sensitivity of region I. Sand content and
bulk density showed a similar negative impact and their im-
portance values were also similar (Figure
7c
and
d
, noting that
the bulk density in regions I and V was larger than other
regions). The latter two predictors were field capacity and
saturated water content (Figure
7e
and
f
), two hydraulic prop-
erties, showing positive and negative impacts on temperature
sensitivity respectively. The two soil properties were similarly
distributed across CMB, with region VI highest and region IV
lowest.
4 Discussion
Ensuring food security is the second most important
Sustainable Development Goals of the United Nations during
the period of 2015-2030. The agriculture sector is struggling
to fulfill this goal under the background of the climate crisis.
Temperature increase is the typical feature of climate change.
It is observed that global average land surface temperature has
increased by ~1°C in comparison with 1850-1900 and is go-
ing to increase by another 0.5°C over the next 20 years (IPCC
AR6). In our study, we also found increasing trends of grow-
ing season temperature in most grids of the CMB (Fig.
3
),
with some grids increasing even faster than global averages.
Temperature has been previously reported as the predominant
factor affecting maize yield (Lobell et al.,
2011
); thus, any
changes on temperature are likely to cause substantial impacts
on maize yields. Our study revealed that for each unit increase
in growing season mean temperature, the maize yield across
the belt was generally reduced by 3.6% (Fig.
4
). This is con-
sistent with Deng et al. (
2020
)
’
s study which also reported a
negative response of maize yield in China to climate warming.
A main reason is that increased temperature hastens phenolo-
gy and reduces the growth cycle, resulting in fewer days for
yield formation (Casali et al.,
2021
; Ibrahim et al.,
2019
).
Meanwhile, the adverse effects of high temperature are also
associated with increased maintenance respiration rates (Innes
−20
−15
−10
−5
0
5
10
15
20
−20 −15 −10 −5
0
5
10
15
20
S
Y,T
(% °C
−
1
)
RF−predicted S
Y,T
(% °C
−
1
)
Fig. 6
Comparison of RF-predicted S
Y,T
and actual S
Y,T
for all of 4283
grids across China
’
s Maize Belt. The RF model was run based on a 10-fold
cross validation procedure. The dashed line is the 1:1 ratio line. The orange
line is the linear regression fit. Fitted equation
y
= 0.68
x
−
1.06, R
2
= 0.72,
RMSE = 2.74% °C
−
1
. Comparisons of RF-predicted S
Y,T
and actual S
Y,T
for each sub-region are shown in Figure
S9
.
85
Page 8 of 13
P. Feng et al.
Page 9
et al.,
2015
) and decreased net photosynthesis (Rezaei et al.,
2015
). Nevertheless, we also noticed the positive impacts of
climate warming on maize yield, specifically in north-eastern
of the belt (Fig.
4
). This might be related to antecedent low
temperature conditions, under which increased temperature
still lied within the optimum temperature range of 18-25°C
(Muchow et al.,
1990
) for maize growth and yield.
The soil-plant-atmosphere continuum is a connected holis-
tic system (Harrison et al.,
2012
) such that changes in one part
of the system influence feedbacks in other parts. Given this,
we would expect soil properties to influence crop-climate re-
sponses, thereby contributing to the spatial variation in S
Y,T
.
This was confirmed by our results that in grids at a same
latitude, the response of maize yield to rising temperature
could also vary greatly (Fig.
4
). Our results also illustrated
that among multiple soil properties, SOC contributed most
to the sensitivity of maize productivity to climate warming
(Fig.
5
), in particular the resilience to warming. Many previ-
ous studies have demonstrated that crop yields are under the
modulation of soil carbon stocks and higher SOC content can
normally lead to higher pasture and/or crop yields (Harrison
et al.,
2021
; Osanai et al.,
2020
; Stockmann et al.,
2013
). Here
we further demonstrated that SOC could help buffer the ad-
verse effects of climate warming. This might be related to the
improvement of soil quality by SOC. Soil organic carbon
content is a fundamental representation of soil quality (Lal,
2016
), supporting multiple soil functions determining soil
physical, chemical, and biological features (Reeves,
1997
)
Imp = 28.9%
−4
−2
0
S
Y,T
(% °C
−
1
)
(a)
VI
V
IV
III
II
I
0
5
10
15
20
25
30
SOC (g kg
−
1
)
Imp = 20.2%
−4
−3
−2
−1
S
Y,T
(% °C
−
1
)
(b)
VI
V
IV
III
II
I
0.06 0.08 0.10 0.12 0.14 0.16 0.18
WP (mm mm
−
1
)
Imp = 16.0%
−4
−3
−2
−1
S
Y,T
(% °C
−
1
)
(c)
VI
V
IV
III
II
I
20
30
40
50
60
70
80
SC (%)
−
1
Imp = 15.7%
−5
−4
−3
−2
−1
S
Y,T
(% °C
−
1
)
(d)
VI
V
IV
III
II
I
1.3
1.4
1.5
1.6
1.7
BD (g cm
−
3
)
Imp = 9.7%
−3.3
−3.1
−2.9
−2.7
S
Y,T
(% °C
−
1
)
(e)
VI
V
IV
III
II
I
0.10 0.15 0.20 0.25 0.30 0.35
FC (mm mm
−
1
)
Imp = 9.6%
−3.4
−3.0
−2.6
−2.2
S
Y,T
(% °C
−
1
)
(f)
VI
V
IV
III
II
I
0.38
0.40
0.42
0.44
0.46
SWC (mm mm
−
1
)
Fig. 7
Partial dependence of S
Y,T
on input soil properties and
distributions of each property in different regions. The orange lines are
smoothed representations of the response. The trend of the line, rather
than the actual values, describes the nature of the dependence between the
response and predictors. The percentages denote the relative importance
of each predictor generated from the random forest model. Box plots
indicate the distributions of each soil property in different regions. Box
boundaries indicate the 25th and 75th percentiles across grids, and
whiskers on the left and right of the box indicate the 10th and 90th
percentiles. The black lines within each box indicate the median value.
SOC, WP, SC, BD, FC, and SWC represent soil organic carbon content,
wilting point, sand content, bulk density, field capacity, and saturated
water content, respectively. Regions I-VI correspond to those shown in
Fig.
2
.
Soil properties resulting in superior maize yields upon climate warming
Page 9 of 13
85
Page 10
which can significantly affect the productive ability of soils
for food production. SOC correlates with multiple soil biodi-
versity dimensions, e.g., community structure, microbial bio-
mass, and its activities (Mau et al.,
2015
). Decomposition of
SOC mainly releases absorbable nitrogen and higher nitrogen
content has been previously demonstrated to provide resil-
ience for maize to cope with warming (Deng et al.,
2020
). In
addition, SOC can also increase soil structure (e.g., aggregate
stability and porosity) and water retention (Bronick and Lal,
2005
; Karhu et al.,
2011
). In this case, crops can normally
obtain more available water to maintain high productivity
via evapotranspiration during high temperature conditions
(Huang et al.,
2021a
; Williams et al.,
2016
).
Our results also show that wilting point largely accounted
for the spatial variations of temperature sensitivity, more im-
portant than other two hydraulic features, field capacity and
saturated water content. This might be also due to the differ-
ences of plant available water capacity (PAWC) in different
regions. The PAWC is determined as the difference between
field capacity and wilting point. In CMB, the variations of
wilting point in different regions were relatively larger than
field capacity (Fig.
5
); thus, it was the wilting point that ac-
counted more for the variations of PAWC as well as S
Y,T
.
Sand content ranked third and it showed negative impacts of
S
Y,T
. This was consistent with results obtained by Rezaei et al.
(
2018
), reporting that wheat yield reduced significantly by
24% grown on sandy soil substrate with increasing air tem-
perature in a chamber-based experiment and with Van
Ittersum et al. (
2003
)
’
s study monitoring a declined of wheat
yield in a sandy soil under warmer (increase of temperature up
to 3°C) scenarios in western Australia. This was mainly due to
that high wilting point usually represented low water-holding
capacity (Huang et al.,
2021a
). Bulk density also showed neg-
atively influenced S
Y,T
, as higher bulk density normally re-
sulted in lower soil porosity (Song et al.,
2015
).
Our results also reveal feasible, reversible pathways for
farmers to take action against global climate change. Over
the past few decades, intensive farming practices, e.g., exces-
sive inorganic fertilization and tillage, have been widely
adopted to enhance crop productivity to meet the increasing
domestic food demand in China. These practices degrade soil
quality, meaning more unfavorable soil conditions for crops to
grow (Droste et al.,
2020
; Waqas et al.,
2020
). Degraded soil
quality will also make a cropping system more vulnerable to
warming according to our results. These problems can be
alleviated through improving soil quality. With appropriate
farming management practices at long-term context, farmers
can control soil quality (unlike the weather) to produce high
crop yields under current climate conditions, as well as main-
tain yields despite climate change (Macholdt et al.,
2020
;
Manns and Martin,
2018
). For example, Song et al. (
2015
)
conducted a 22-year field experiment in northeast China and
claimed that compared to inorganic fertilizer treatments,
organic matter amendments (crop straw or farmyard manure)
can not only increase maize yield but also maintain an increas-
ing trend. As demonstrated by Song et al. (
2015
), organic
amendments can mitigate the negative and promote the posi-
tive effects of climate warming on maize production through
increasing SOC. Farming practices that increase SOC can
usually enable soils to keep higher levels of biodiversity, sup-
ply more plant nutrients, have better water-holding capacity,
and be less vulnerable to erosion (Manns and Martin,
2018
;
Minasny et al.,
2017
). Moreover, increasing SOC is identified
as a main approach for greenhouse gas emission mitigation
(Farina et al.,
2021
; Lal et al.,
2007
); thus, it can also contrib-
ute to the mitigation of climate change. In addition, some
conservation agriculture practices, such as no tillage
(Figure
1
), are also proved to improve soil quality (Sithole
et al.,
2019
; Valkama et al.,
2020
). Nevertheless, different
regions might be varied in most suitable practices. Thus, fur-
ther studies are needed to explore what farming practices can
maximize the benefit to soil quality in certain regions to create
resilient and sustainable agro-ecosystems in face of climate
change.
5 Conclusions
This study is the first one to quantify the potential of soil inherent
properties to mitigate the effects of increased growing season
temperature on maize yield across the CMB. Climate warming
caused yield losses (up to 20% decline for 1°C warming) in most
areas but gains in north-eastern regions (up to 10% increase for
1°C warming). Around 72% of the spatial variation of yield
sensitivity could be attributed to the variation in soil properties.
Soil organic carbon contributed most to the temperature sensi-
tivity of yield, with positive correlations. As previous intensive
farming practices have been widely carried out across the belt,
soil degradation potentially reduced agriculture
’
s resilience to
climate warming and thus food security. Here we provided evi-
dence that preservation of soil carbon and improved soil quality
reduced yield losses due to climate warming.
Supplementary Information
The online version contains supplementary
material available at
https://doi.org/10.1007/s13593-022-00818-z
.
Code availability
Not applicable.
Authors
’
contributions
Funding acquisition: Jing Wang and Kelin Hu.
Data collection and formatting: Puyu Feng, Mingxia Huang, and De Li
Liu. Data analysis: Puyu Feng and Bin Wang. Writing original draft:
Puyu Feng, Bin Wang, Matthew Tom Harrison, Jing Wang, Ke Liu,
and Qiang Yu. Writing, review, and editing: all co-authors.
Funding
Open Access funding enabled and organized by CAUL and its
Member Institutions. This study was supported by the Strategic Priority
Research Program of the Chinese Academy of Sciences (XDA28060200)
and the National Key R&D Program of China (2016YFD0201202).
85
Page 10 of 13
P. Feng et al.
Page 11
Data availability
The datasets generated during and/or analyzed during
the present study are available from the corresponding author on reason-
able request.
Declarations
Ethical approval
Not appropriate.
Consent to participate
Not appropriate.
Consent for publication
Not appropriate.
Conflict of interest
The authors declare no competing interests.
Open Access
This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing, adap-
tation, distribution and reproduction in any medium or format, as long as
you give appropriate credit to the original author(s) and the source, pro-
vide a link to the Creative Commons licence, and indicate if changes were
made. The images or other third party material in this article are included
in the article's Creative Commons licence, unless indicated otherwise in a
credit line to the material. If material is not included in the article's
Creative Commons licence and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this
licence, visit
http://creativecommons.org/licenses/by/4.0/
.
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