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Soil indigenous nutrients increase the resilience of
maize yield to climatic warming in China
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Environ. Res. Lett.
15
(2020) 094047
https://doi.org/10.1088/1748-9326/aba4c8
Environmental Research Letters
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PUBLISHED
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LETTER
Soil indigenous nutrients increase the resilience of maize yield to
climatic warming in China
Xi Deng
,
, Yao Huang
,
and Zhangcai Qin
1
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093,
People’s Republic of China
2
University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China
3
School of Atmospheric Sciences and Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies,
Sun Yat-sen University, Zhuhai 519000, People’s Republic of China
4
Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, People’s Republic of China
E-mail:
Keywords
maize yield, climate change, temperature sensitivity, soil indigenous nutrients, resilience
Abstract
Climate warming leads to crop yield loss. Although investigations have shown the region-specific
effect of climate warming on maize yield in China, the determinants of this region-specific effect
are poorly known. Using county-level data from 1980 to 2010 for China, we investigated the
dependence of yield change under climate warming on soil indigenous nutrients. Analysis of the
data indicated an average decrease of 2.6% in maize yield for 1
◦
C warming. Warming-related yield
loss occurred mostly in western China, the North China Plain, and the southwest region of
Northeast China. By contrast, climate warming did not decline maize yield in the northern region
of Northeast China, south, and southwest China. Summer maize is more sensitive to warming than
spring maize. A 1
◦
C warming resulted in an average loss of 3.3% for summer maize and 1.8% for
spring maize. The region-specific change in yield can be well quantified by a combination of soil
indigenous total nitrogen (STN), available phosphorus (SAP), and available potassium (SAK).
Under climate warming, maize yields in regions with high STN generally increased, while the risk
of yield reduction appeared in regions with high SAK. Areas that were vulnerable (defined as a
yield loss higher than 1% for a 1
◦
C increase) to climate warming accounted for 62%, while areas
that showed resilience (defined as a yield increase higher than 1% for a 1
◦
C increase) to climate
warming accounted for 27% of the planting area. An increase in nitrogen fertilizer application is
expected to reduce the risk of yield reduction in regions with low STN. Our findings highlight soil
resilience to climate warming and underline the practice of fertilizer management to mitigate yield
loss due to climate warming.
1. Introduction
China provides the world’s second largest maize pro-
duction with the largest planting area (FAOSTAT
). Since 1980, maize production and planting
area in China has been increasing by 5.43 million
tons and 0.69 million hectares per year, respect-
ively (National Bureau of Statistics of China,
). With climate change, the fre-
quency and intensity of extreme heat and drought
have been increasing in China (Chen and Sun
,
Sun
et al
), which have caused severe losses in
maize yield (Feng
et al
, Kim
et al
, Leng and
Hall
). Approximately 42% of the yield variability
was explained by climate variability over the period
1979–2008 (Ray
et al
). Future warming of 2
◦
C
lead to a 10% maize yield reduction in China
(Tigchelaar
et al
). Understanding the impacts of
climate variables on maize yield and seeking proper
adaptations is essential to cope with climate change
and maintain food security.
Numerous studies have assessed the impact of cli-
mate change on maize yield by building statistical
models (Tigchelaar
et al
, Leng
et al
). Based
on county-level data, the increased temperature in
growing-season from 1980 to 2008 caused an aver-
age loss of 5.8% in maize yield across China (Zhang
and Huang
). Changes in temperature showed
© 2020 The Author(s). Published by IOP Publishing Ltd
Environ. Res. Lett.
15
(2020) 094047
X Deng
et al
a stronger influence on the yield than changes in
precipitation (Lobell
et al
, Deng
et al
, Vogel
et al
). The results of the model ensemble showed
that maize yield would decrease by 8.0% for 1
◦
C
warming in China (Zhao
et al
Soil nutrients are crucial resources for maize
growth and development. Soils with higher indigen-
ous nutrients not only produced higher maize yield
without fertilization (Xu
et al
, Zhou
et al
but also improved the stability of maize yield under
fertilization (Xu
et al
). The response of maize
yield to nitrogen, phosphorus, and potassium applic-
ation increased with the decrease in soil indigenous
nutrients (Xu
et al
). The supply of nitrogen,
phosphorus, and potassium fertilizers can signific-
antly increase maize yield under different RCPs
(Representative Concentration Pathways; IPCC
(Zhang
et al
). Maize in China was observed
to be limited by nutrient availability (Mueller
et al
). Optimized soil nutrient management effect-
ively enhances resilience to climate change and closes
the yield gap (Song
et al
, Sz
´
eles
et al
Although the overall negative effect of climate
change is widely detected (Gaupp
et al
), local
differences in the response of maize yield to climate
change suggests extensive spatial heterogeneity (Ray
et al
). Climate trends during the maize growth
period reduced maize yield by 13.2–17.3% in south-
western China, but increased yield by 12.9–14.4% in
northwestern China (Tao
et al
). However, the
causes of the spatial pattern have not been fully invest-
igated. Field warming experiments have shown that
a higher nitrogen supply increases crop yield under
heat stress (Ord
´
oñeza
et al
, Elía
et al
Great spatial variation exists in soil indigenous nutri-
ents and in fertilizer application (Liu
et al
He
et al
, Ma
et al
), which likely explains
the spatial pattern of maize yield change. However,
the role of soil indigenous nutrients in regulating the
impacts of warming on maize yield remains poorly
understood.
We hypothesized that spatially uneven soil indi-
genous nutrients can help interpret the region-
specific response of maize yield to warming, and
optimized soil indigenous nutrients can increase the
resilience of maize yield to cope with the negative
impact of warming. We built the yield-climate rela-
tionship for each county using a multiple regression
model, and then obtained the temperature sensitiv-
ity of maize yield. We used the random forest model
to measure the impacts of soil indigenous nutrients
on temperature sensitivity. Our objectives were to
address the following: (1) what is the spatial pattern
of the impact of climatic warming on maize yield in
China, and (2) how do soil indigenous nutrients reg-
ulate the effects of climatic warming on maize yield.
By exploring these we provide insights into adaptive
strategies for climate change from nutrient manage-
ment perspectives.
2. Materials and methods
2.1. Data sources
County-level data of maize yield and planting area
were obtained from the Chinese Academy of Agricul-
tural Sciences (
). The data-
set includes data for the period 1980–2010 from
1767 counties with records for longer than 20 years
and from 477 counties with records for shorter than
20 years. Long-term recorded data were used to estab-
lish the yield-climate relationship.
We obtained the maize calendar dataset from the
China Meteorological Data Service Center (
). This dataset includes the sowing and
maturity dates from 272 agrometeorological stations.
We then interpolated the station level data to a ras-
ter surface by using ArcGIS to obtain the sowing and
maturity dates for each county (figure S1 (available
online at
stacks.iop.org/ERL/15/094047/mmedia
Maize was divided into spring maize and summer
maize according to the sowing dates (figure S2). We
used crop calendar data to compute cumulative or
average values in the maize growing season for each
climate variable.
Climate data were extracted from the AgMERRA
dataset (Ruane
et al
). AgMERRA comprises
gridded daily climate data at 0.25
◦
spatial resolution
over the period 1980–2010. Gridded daily climate
variables were averaged for each county by its bound-
ary. Mean temperature (
T
), total precipitation (
P
),
and average solar radiation (
R
) of the maize growing
season over the period 1980–2010 were then calcu-
lated for each county.
We used data on soil indigenous nutrients from
the Basic Nutrients Dataset of Soil Testing and Fertil-
ization Recommendation (NATESC
) to meas-
ure the relationship between yield change caused
by elevated temperatures and soil. The data cover
the field observations from 2563 counties. Soil was
sampled at 120–8500 sites in each county, depend-
ing on the area of cropland in a given county.
The larger the area of cropland, the more sampling
sites. The values of soil indigenous nutrients in each
county are the average of the measurements at dif-
ferent sites. The soil indigenous nutrients include
total nitrogen (STN), available phosphorus (SAP),
and available potassium (SAK). For counties without
soil indigenous nutrient data, we used the compre-
hensive gridded soil characteristics dataset of China
(Wei
et al
A summary of the variables and the abbreviation
used in this paper are presented in table
.
2.2. Identification of climate effects on maize yield
Linear regression models were used to assess the cli-
mate and yield trends from 1980 to 2010 for each
county. Only the significant trends were considered
(
p
< 0.05). We averaged county-level trends into
provincial level.
2
Environ. Res. Lett.
15
(2020) 094047
X Deng
et al
Table 1.
Target variables and features used in this study.
Abbrev.
Variable description
Y
Maize yield (kg ha
−
1
)
t
Time in years
T
Mean temperature during growing season of maize (
◦
C)
P
Total precipitation during growing season of maize (mm)
R
Mean solar radiation during growing season of maize (MJ m
−
2
d
−
1
)
STN
Soil total nitrogen (g kg
−
1
)
SAP
Soil available phosphorus (mg kg
−
1
)
SAK
Soil available potassium (mg kg
−
1
)
S
Y
,
T
Temperature sensitivity of maize yield (% per
◦
C)
The impact of climate variables on maize yield
was estimated using a multiple regression model
(equation (
)) for 1767 counties. This approach was
used in accordance with previous studies (Schlenker
and Lobell
, Zhu
et al
ln(
Y
i
,
t
)
=
β
0
+
β
1
t
+
β
2
t
2
+
β
3
T
i
,
t
+
β
4
T
i
,
t
2
+
β
5
P
i
,
t
+
β
6
P
i
,
t
2
+
β
7
R
i
,
t
+
ε
(1)
where ln(
Y
i,t
) is the natural logarithm of yield at
county
i
in year
t
. The quadratic time trend (
β
1
t
and
β
2
t
2
) was used to remove the effects of overall tech-
nological progress (e.g. artificial fertilization, and cul-
tivar shift) on maize yield (Schlenker and Lobell
,
Zhao
et al
). The quadratic terms of
T
and
P
sim-
ulate the nonlinear response of yield to temperature
and precipitation change, respectively.
R
represents
the average radiation during the maize growing sea-
son.
ε
is the model error.
The temperature sensitivity of maize yield
(equation (
)) was measured by the partial derivative
of equation (
) (Chen
et al
, Zhu
et al
).
S
Y
,
T
=
∂
Y
∂
T
100% = (
β
3
+ 2
β
4
T
i
)100%
(2)
where
T
i
represents the mean of
T
during the period
1980–2010 in county
i.
β
3
and
β
4
are the paramet-
ers of county
i
in equation (
S
Y,T
is the temperat-
ure sensitivity of maize yield, representing the yield
change (%) for 1
◦
C warming. We evaluated the
S
Y,T
of 1767 counties using equations (
) and (
No significant trends in precipitation were detec-
ted in most of the meteorological stations and regions
of China (Zhang
et al
, Sun
et al
). There-
fore, we did not consider the yield sensitivity to pre-
cipitation.
2.3. Quantification of the relationship between
temperature sensitivity and soil indigenous
nutrients
We employed a machine learning method, random
forest (Breiman
), to evaluate the relationship
between temperature sensitivity and soil indigenous
nutrients. The package ‘randomForest’ (Liaw and
Wiener
) that was built for R language was
used on the input data with the control parameters
ntree
=
500 (appropriate number of trees to ensure
the model has stable and low error rates) and mtry
=
1
(default number of variables considered for splitting)
in our study. In each tree, 36% of samples are left
out and these are called out-of-bag (OOB) data. The
OOB data are used to estimate the generalization
error. The importance of a variable is interpreted as
an increase in the mean square error (IncMSE; %)
due to random permutation of that variable. The
dependent variables were
S
Y,T
, and the independ-
ent variables were STN, SAP, and SAK. We conduc-
ted the Random Forest algorithm for both spring
maize and summer maize. We used 1767 samples to
build the random forest model. For the other 477
counties without
S
Y,T
, we simulated it by inputting the
data on STN, SAP, and SAK into the random forest
model.
3. Results
3.1.
Climate and yield trends from 1980 to 2010
Warming was more pronounced than the change in
precipitation and solar radiation. Significant trends
in the temperature of the maize growing season were
observed in 1386 counties, with an increase at the
rate of 0.36
◦
C per decade. Large areas of northern,
central, and eastern China showed positive trends in
temperature (figure
(a)). For spring maize, 15 of
the 22 Provinces showed significant warming trends
(table S1). However, no significant warming trend
was found in southwest China at the provincial scale.
For summer maize, the largest warming rate was
observed in Beijing (0.49
◦
C per decade). Only Shan-
dong (SD) and Yunnan Provinces had no significant
warming trend.
Only 218 counties showed significant trends in
precipitation at an average increase rate of 28.4 mm
per decade. Positive trends were found in parts
of northeastern China, while negative trends were
found in a narrow part of eastern and southwest-
ern China (figure
(b)). At the provincial level, Hei-
longjiang (HLJ) Province significantly became drier
(
−
24.0 mm per decade) during the spring maize
growing season, and Gansu (GS,
−
40.1 mm per dec-
ade) and Sichuan (
−
42.2 mm per decade) Provinces
3
Environ. Res. Lett.
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X Deng
et al
Figure 1.
Trend in temperature (a), precipitation (b), solar radiation (c), and maize yield (d) of each county for the period
1980–2010. Colored areas represent different gradients of trends. Gray areas indicate counties without significant trends. Blank
areas are counties without maize (a)–(c). For yield trends, we only showed the counties with more than 20 years of record (d).
Abbreviation of Provinces: HLJ, Heilongjiang; JL, Jilin; LN, Liaoning; IM, Inner Mongolia; BJ, Beijing; TJ, Tianjin; HE, Hebei; HA,
Henan; SD, Shandong; SX, Shanxi; SN, Shannxi; NX, Ningxia; GS, Gansu; QH, Qinghai; XJ, Xinjiang; HB, Hubei; HN, Hunan;
AH, Anhui; JX, Jiangxi; JS, Jiangsu; ZJ, Zhejiang; FJ, Fujian; TB, Tibet; SC, Sichuan; CQ, Chongqing; GZ, Guizhou; YN, Yunnan;
GX, Guangxi; GD, Guangdong; HI, Hainan.
became drier during the summer maize growing sea-
son (table S1). However, precipitation increased by
50.8 mm per decade in SD Province.
Downward trends in solar radiation occurred in
a part of northwestern, southwestern, and eastern
China, while upward trends occurred in a confined
area of central China (figure
(c)). The average
trend of 280 counties was
−
0.32 MJ m
−
2
d
−
1
per
decade. For spring maize, solar radiation signific-
antly decreased in Ningxia (NX) and GS Provinces
(table S1). For summer maize, Xinjiang (XJ) was the
only Province with a reduced rate in solar radiation.
Maize yield in China has been constantly
increasing over the past decades. Significant trends
were observed in 1570 counties (89% of maize
growing counties) at an average increase rate
of 120.4 kg ha
−
1
yr
−
1
. Positive trends (1559
counties) occurred extensively in maize planting
areas, especially in northeastern and northwest-
ern China (figure
(d)). Remarkable enhance-
ments were found in XJ (237.4 kg ha
−
1
yr
−
1
), HLJ
(167.8 kg ha
−
1
yr
−
1
), NX (155.9 kg ha
−
1
yr
−
1
), Jilin
(JL, 153.4 kg ha
−
1
yr
−
1
), and Inner Mongolia (IM,
153.0 kg ha
−
1
yr
−
1
) Provinces. Another 17 Provinces
recorded an increased yield of 100–140 kg ha
−
1
yr
−
1
.
Spring maize (124.3 kg ha
−
1
yr
−
1
) had stronger
elevated trends in yield than summer maize
(113.1 kg ha
−
1
yr
−
1
).
3.2. Temperature sensitivity of maize yield
Figure
shows the distributions of
S
Y,T
of spring
maize (figure
(a)) and summer maize (figure
simulated by equations (
) and (
). The change in
yield was concentrated in
±
20% for 1
◦
C warm-
ing for both spring maize and summer maize. The
area weighted average yield of spring maize in
1077 counties and summer maize in 690 counties
decreased by 1.1% and 2.9% for 1
◦
C warming,
respectively. Summer maize is more vulnerable than
spring maize.
S
Y,T
showed a clear spatial pattern. The
positive impacts of increased temperature were dis-
tributed in the northern areas of Northeast, Central,
and South China, and the negative impacts mainly
occurred in the southern areas of Northeast China,
Loess Plateau, and North China Plain (figure S3).
3.3. Relationship between S
Y,T
and soil indigenous
nutrients
The random forest model was built with data
from 1767 counties. It revealed that STN was
the most important predictor of
S
Y,T
for spring
maize (IncMSE
=
40.2%), followed by SAK
4
Environ. Res. Lett.
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X Deng
et al
Figure 2.
Frequency distributions of
S
Y,T
(% per
◦
C) based on equation (
). (a) Spring maize of 1077 counties and (b) summer
maize of 690 counties. The red line represents the frequency fitted by the amplitude version of the Gaussian peak function.
(IncMSE
=
17.7%) and SAP (IncMSE
=
16.8%).
The most important predictor for summer maize was
SAK, followed by STN and SAP, and their IncMSEs
were 12.8, 5.9, and 2.2%, respectively. The
R
2
of the
model was 0.78 for spring maize and 0.73 for summer
maize. The high
R
2
indicated that the soil indigenous
nutrients regulated the response of maize yield to
temperature increase and explained the spatial pat-
tern of
S
Y,T
well.
The partial dependence plots visualized the rela-
tionship between
S
Y,T
and soil indigenous nutrients
(figure
). Most of STN (95%) of spring maize and
summer maize were concentrated from 0.52 to 2.52
and from 0.61 to 2.14 g kg
−
1
, respectively. With the
increase of STN, the response of spring maize yield to
warming changed from negative (yield loss) to posit-
ive (yield increase) (figure
(a)), while summer maize
did not show a consistent dependency (figure
In agreement with the order of IncMSE, SAP also did
not show a consistent dependency (figures
(b) and
(e)). SAK of spring maize and summer maize were
mainly distributed from 88.9 to 93.7 and from 74.1
to 197.7 mg kg
−
1
, respectively. As SAK increase, the
risk of yield loss increased in a certain range of SAK
(figures
(c) and (f)).
We calculated the
S
Y,T
of an additional 477
counties that had less than 20 years of records through
the random forest model. Compared to
S
Y,T
sim-
ulated by climate variables (figure
),
S
Y,T
simu-
lated by soil indigenous nutrients was distributed in
a narrower interval (figure
). Counties with
S
Y,T
between
±
10% account for 89% and 90% of the
total for spring maize and summer maize, respectively
(figure
). The average yield losses of spring maize
and summer maize were 1.8% and 3.3% for 1
◦
C
warming, respectively. For all maize planting areas,
751 counties showed a positive response to warm-
ing (3.8%
◦
C
−
1
), but 1493 counties showed negative
responses (
−
5.8%
◦
C
−
1
). The area-weighted average
maize yield in 2244 counties decreased by 2.6% for
1
◦
C warming. Based on the average of the provincial
scale, maize yield in 25 of the 31 Provinces decreased
with increasing temperature (table S2).
3.4. Spatial distribution of soil indigenous
nutrients and S
Y,T
Great variation in soil indigenous nutrients exis-
ted across different regions (figure
). Northeast-
ern and southwestern China showed higher STN
content (figure
(a)). The STN of HLJ, JL, Tibet,
and Guizhou Provinces exceeded 2.00 g kg
−
1
(table
S2). However, the STN of northwestern China and
the North China Plain was lower, even less than
1.00 g kg
−
1
in Shanxi (SX), Shaanxi, NX, SD, Henan,
and Hebei Provinces. High SAP content was seen in
northeastern, southwestern China, and North China
Plain (figure
(b)). SD and SX Provinces showed the
highest (26.8 mg kg
−
1
) and lowest (12.5 mg kg
−
1
)
SAP content, respectively. SAK content was higher in
northern China than in southern China (figure
Most Provinces in the south showed less than
100 mg kg
−
1
of SAK, while in the north, values over
120 mg kg
−
1
of SAK were seen (table S2).
We built a framework to measure the capab-
ility of soil indigenous nutrients to mitigate the
impact of warming on maize yield. We classi-
fied different effects of soil indigenous nutrients
on
S
Y,T
into seven levels: extreme vulnerability
(
S
Y,T
≤ −
10% per
◦
C), moderate vulnerability
(
−
10% per
◦
C <
S
Y,T
≤ −
5% per
◦
C), low vulner-
ability (
−
5% per
◦
C <
S
Y,T
≤ −
1% per
◦
C), mar-
ginal (
−
1% per
◦
C <
S
Y,T
≤
1% per
◦
C), low resilience
(1% per
◦
C <
S
Y,T
≤
5% per
◦
C), moderate resilience
(5% per
◦
C <
S
Y,T
≤
10% per
◦
C), and high resilience
(
S
Y,T
> 10% per
◦
C). The areas of extreme vulner-
ability were mainly distributed in the Loess Plateau
(figure
(d)). The areas of moderate and low vulner-
ability were concentrated in the XJ and IM Provinces,
5
Environ. Res. Lett.
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X Deng
et al
Figure 3.
Partial dependence plots for (a), (d) STN, (b), (e) SAP, and (c), (f) SAK. The figures show the average deviation in
temperature sensitivity (
S
Y,T
; %
◦
C
−
1
) caused by a given variable. The dense short lines on
x
-axis indicate that most of the data
are distributed here. The red and blue lines represent the results of spring maize and summer maize, respectively.
Figure 4.
Frequency distributions of
S
Y,T
(% per
◦
C) simulated by soil indigenous nutrients. (a) Spring maize planted in 1457
counties and (b) summer maize planted in 787 counties. The red line represents the frequency fitted by the amplitude version of
the Gaussian peak function.
North China Plain, and in the southern regions of
Northeast China. Resilience mainly occurred in the
IM, Hunan, Guangxi Provinces, northern regions of
Northeast China, and the western part of Southwest
China. The spatial distribution of
S
Y,T
resembled that
of STN.
In the context of climate warming, maize yields in
regions with high STN generally increased, while the
risk of yield reduction appeared in the regions with
high SAK (table S2). In addition, the SAP concentra-
tion is likely not closely related to the warming-related
yield change (table S2).
3.5. Increased resilience of maize yield via soil
indigenous nutrients
Summer maize is more vulnerable to climate warm-
ing than spring maize. The vulnerable area of sum-
mer maize and spring maize were 8.47 Mha and 7.97
Mha, respectively, accounting for 72.1% and 53.6%
of the total planting area (figure S4). The areas of
summer and spring maize with resilience accounted
for 15.9% and 36.1%, respectively. Overall, the vul-
nerable area and the resilience area accounted for
62% and 27% of the total maize-planted area in
China.
6
Environ. Res. Lett.
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et al
Figure 5.
Spatial distribution of soil indigenous nutrients and
S
Y,T
. Colored areas represent different gradients of STN (a), SAP
(b), and SAK (c). Seven colors represent different effects of soil indigenous nutrients on
S
Y,T
(d), that is,
S
Y,T
≤ −
10% per
◦
C,
extreme vulnerability;
−
10% per
◦
C <
S
Y,T
≤ −
5% per
◦
C, moderate vulnerability;
−
5% per
◦
C <
S
Y,T
≤ −
1% per
◦
C, low
vulnerability;
−
1% per
◦
C <
S
Y,T
≤
1% per
◦
C, marginal; 1% per
◦
C <
S
Y,T
≤
5% per
◦
C, low resilience; 5% per
◦
C <
S
Y,T
≤
10% per
◦
C, moderate resilience;
S
Y,T
> 10% per
◦
C, high resilience.
We averaged the STN, SAP, and SAK for each
level of
S
Y,T
to obtain more insights into what kind of
soil indigenous nutrient conditions increase the resi-
lience of maize yield (table
S
Y,T
of spring maize
increased with increases in STN and SAP. The yields
decreased by more than 10% per
◦
C in the regions
where STN and SAP were less than 0.91 g kg
−
1
and
13.4 mg kg
−
1
, respectively. In regions where STN
exceeded 1.80 g kg
−
1
and SAP exceeded 19.6 mg kg
−
1
,
spring maize showed resilience to the impacts of
warming. STN and SAP in maize planting areas with
high resilience to warming were twice as high as in
areas that were extremely vulnerable to warming.
About 31% of the 1457 counties were resilient to the
negative impacts of warming because of favorable soil
indigenous nutrients. In barren spring maize planting
regions, the negative effects of temperature rise can be
reduced and yield can be improved by increasing the
application of nitrogen and phosphorus fertilizer.
S
Y,T
of summer maize increased with an increase
in STN (table
). Yield decreased by more than
10% for 1
◦
C warming in the regions with STN less
than 1.10 g kg
−
1
. Summer maize obtained resili-
ence to warming in regions with an STN of more
than 1.33 g kg
−
1
. However, only 18% of the 787
counties were resilient for temperature rise because
of appropriate soil indigenous nutrients. Maize yield
benefitted from a relatively low SAK. Increasing the
use of nitrogen fertilizer and appropriately decreas-
ing potassium fertilizer can increase the resilience of
summer maize against the negative impacts of elev-
ated temperature.
4. Discussion
Temperature is the
predominant factor affecting
maize yield (Lobell
et al
). Our study revealed
that for each degree Celsius increase in mean tem-
perature, the area-weighted average maize yield was
reduced by 2.6%. From a physiological perspect-
ive, the inhibitory effect of high temperature on leaf
photosynthesis was associated with the reduction of
Rubisco activase activity (Crafts-Brandner and Salv-
ucci
). High temperature also restrains the
duration of light capture, radiation use efficiency,
and harvest index (Cicchino
et al
, Edreira and
Otegui
). Heat stress, especially at anthesis and
grain filling phases, reduced pollen viability, kernel
number, and individual kernel mass (C
´
arcova and
Otegui
, Lizaso
et al
). The comprehens-
ive negative influences of warming ultimately lead to
yield losses.
However, the results of our study revealed
the positive impacts of warming on maize yield
7
Environ. Res. Lett.
15
(2020) 094047
X Deng
et al
Table 2.
The mean and standard error (SE) of STN, SAP, and SAK at seven levels of
S
Y,T
.
Spring maize
Summer maize
S
Y,T
n
STN
±
SE
SAP
±
SE SAK
±
SE
n
STN
±
SE
AP
±
SE
SAK
±
SE
(% per
◦
C)
(g kg
−
1
)
(mg kg
−
1
) (mg kg
−
1
)
(g kg
−
1
)
S (mg kg
−
1
) (mg kg
−
1
)
≤−
10
134
0.91
±
0.04 13.4
±
0.5 118.1
±
2.6
70
1.10
±
0.03 18.4
±
1.0
136.0
±
4.8
(
−
10,
−
5]
186
1.12
±
0.03 16.5
±
0.4 124.5
±
2.3
203
1.10
±
0.02 19.9
±
0.5
134.0
±
2.6
(
−
5,
−
1]
429
1.34
±
0.02 18.4
±
0.3 107.6
±
1.4
289
1.11
±
0.02 20.9
±
0.5
124.0
±
1.5
(
−
1, 1]
259
1.50
±
0.02 19.0
±
0.4 99.9
±
1.8
83
1.14
±
0.04 20.6
±
0.9
119.8
±
2.7
(1, 5]
312
1.80
±
0.03 19.6
±
0.3 110.4
±
2.1
105
1.33
±
0.06 19.8
±
0.7
125.3
±
2.9
(5, 10]
108
1.79
±
0.04 19.9
±
0.7 114.8
±
5.2
30
1.45
±
0.13 19.5
±
1.3
119.3
±
5.7
>10
29
2.08
±
0.17 21.5
±
1.4 128.5
±
11.2
7
1.61
±
0.25 15.7
±
2.3
82.5
±
9.8
in northeastern, central, and southwestern China
(figure
(d)), which implied effective adaptation of
those regions against warming. Suitable soil indigen-
ous nutrients, especially higher nitrogen contents,
were demonstrated to provide resilience for maize
to cope with warming (table
). Field experiments
revealed that high-nitrogen maize plants possess
higher values of maximum photosynthetic rate and
accumulate more plant biomass than low-nitrogen
maize plants (Zhao
et al
). Furthermore, high-
nitrogen maize plants are inclined to allocate a greater
proportion of biomass to aboveground parts (Chen
et al
). Radiation use efficiency and water use
efficiency also increase with the increase in nitro-
gen fertilizer applications (Teixeira
et al
). Thus,
regions with higher STN can increase resilience to
warming via these biophysical processes or response
mechanisms.
Our results indicated that extensive maize
planting areas were vulnerable to temperature rise
(figure
). Maize yield in 80% of the Provinces
decreased with increasing temperature (table S2).
SX is the most vulnerable Province with the largest
yield decrease rate (
−
11.5%
◦
C
−
1
) and the lowest
STN (0.82 g kg
−
1
) and SAP (12.5 mg kg
−
1
) con-
tent (table S2). The average application of nitrogen,
phosphorus, and potassium fertilizers from 1987 to
2010 in SX Province was 108.2 kg ha
−
1
, 52.6 kg ha
−
1
,
and 19.3 kg ha
−
1
, respectively (National Bureau of
Statistics of China,
), which is
much lower than the optimal fertilization for maize
to obtain high yield (Zhang
et al
). Under 1
◦
C
warming, maize supplied with 200 kg N ha
−
1
resulted
in about 14% more yield than maize that was supplied
with 100 kg N ha
−
1
(Ord
´
oñeza
et al
). Therefore,
the shortage of nitrogen supplies causes remarkable
losses in yield when the temperature increases.
We can enhance the resilience of maize yield to
the negative effects of warming by increasing the
application of nitrogen fertilizers in barren areas.
For instance, when the STN content in the regions
of spring maize was less than 0.91 g kg
−
1
, the
yield decreased by more than 10% for 1
◦
C warm-
ing. If the application of nitrogen fertilizer in these
regions increases the STN content to higher than
1.80 g kg
−
1
, the maize yield can benefit from
increased temperature (table
). A five-year warming
experiment conducted in Hailun County, Heilongji-
ang Province in Northeast China showed that maize
yield increased by 11.8
±
2.5% (mean
±
SD,
n
=
5)
for 1
◦
C warming (Qiao
et al
). STN, SAP, and
SAK in this experiment were 2.21 g kg
−
1
, 41.2 mg
kg
−
1
, and 228.9 mg kg
−
1
, respectively (Qiao
et al
). Our model calculation, using the soil indi-
genous nutrients as input, resulted in a maize yield
increase of 8.1% for 1
◦
C warming, which is in agree-
ment with Qiao
et al
. In the barren field with
0.08 g STN kg
−
1
, 10.5 mg SAP kg
−
1
, and 103.9 mg
SAK kg
−
1
, the maize yield decreased by as much as
13% for 1
◦
C warming (Abebe
et al
, Mina
et al
). These field-warming experiments support our
results that higher soil indigenous nitrogen increases
the resilience of maize yield to warming.
The impacts of increased temperature on veget-
ation are regulated by water supply (Wang
et al
, Reich
et al
). Maize grown in extreme
heat requires more water for carbon assimilation
and transpiration (Lobell
et al
). Although the
effects of precipitation (
P
) and temperature (
T
) were
measured as two separate model components in our
study (equation (
)),
S
Y,T
was inevitably affected by
P
. We fitted the influence of
P
on
S
Y,T
(figure
).
S
Y,T
increased with the increase in
P
, that is, yield
increased for 1
◦
C warming in the areas with higher
precipitation. The slopes in figures
(a) and (b) show
that spring maize and summer maize increased by
0.8% and 0.6% per
◦
C for a 100 mm increase in
P
,
respectively. The slopes show that although precipit-
ation significantly affects the response of maize yield
to elevated temperature, the degree of the impacts was
limited.
Irrigation considerably cooled the daytime land
surface temperature (Li
et al
, Yang
et al
).
Therefore, irrigation can lower the temperature sens-
itivity of maize and mitigate the effects of heat waves
and drought events induced by water stress on maize
yield (Schauberger
et al
, J
¨
agermeyr and Frieler
). In arid and semi-arid regions of China, includ-
ing IM Province, Loess Plateau, and western China,
maize growth depends on irrigation to meet the water
8
Environ. Res. Lett.
15
(2020) 094047
X Deng
et al
Figure 6.
Effects of precipitation (
P
) during the maize growing season on temperature sensitivity of yield (
S
Y,T
). (a) Spring maize;
(b) Summer maize. A significant relationship between
S
Y,T
, and
P
was fitted by linear regression.
requirements. Irrigation was not taken into account
in our model due to unavailable data on the area
and amount of irrigation, which leads to the uncer-
tainty of our simulations in irrigated systems. It is
expected that the effect of irrigation on maize yield in
the context of climate warming could be determined
when detailed irrigation data are available in the
future.
Another uncertainty in our study is that we
did not consider elevated CO
2
levels. CO
2
fertiliza-
tion increases photosynthesis and CO
2
enrichment
enhances the water-use efficiency under water stress
conditions (Ainsworth and Long
), which can fill
the yield gaps caused by climate change. However,
maize, being a C4 crop, benefits less from elevated
CO
2
(Long
et al
, Makowski
et al
). The res-
ults of the model simulation suggested that elevated
CO
2
only limitedly offsets maize yield loss (Jin
et al
, Schauberger
et al
5. Conclusion
Climate warming in China resulted in maize yield
loss generally. Warming-related yield loss occurred
mostly in western China, the North China Plain, and
the southwest region of Northeast China. Summer
maize was more sensitive to warming than spring
maize. The vulnerable area (defined as a yield loss
higher than 1% for a 1
◦
C increase) and the resi-
lience area (defined as a yield increase higher than
1% for a 1
◦
C increase) to climate warming accoun-
ted for 62% and 27% of the total maize-planted area,
respectively. Soil indigenous nutrients regulated the
effects of climate warming on maize yield, which
can be well determined by random forest model.
Higher soil indigenous nutrients increased the resi-
lience of maize yield to climate warming. An increase
in fertilization is expected to reduce the risk of
yield reduction in regions with low soil indigenous
nutrients.
Acknowledgments
This work was supported by the National Nat-
ural Science Foundation of China (Grant No.
41530533). We thank the Chinese Academy of Agri-
cultural Sciences for providing county-level crop
data, Chinese Meteorological Administration for
providing phenological data, the National Agro-
Tech Extension and Service Center, and the Cold
and Arid Regions Science Data Center at Lanzhou
(
) for providing soil nutri-
ent data. The climate dataset (AgMERRA) is access-
ible online at (
Data availability statement
The data that support the findings of this study are
available upon reasonable request from the authors.
ORCID iDs
Xi Deng
https://orcid.org/0000-0001-8694-9572
Yao Huang
https://orcid.org/0000-0002-0192-
Zhangcai Qin
https://orcid.org/0000-0001-9414-
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