ORIGINAL ARTICLE

Impacts of observed growing-season warming trends since 1980on crop yields in China

Wei Xiong • Ian P. Holman • Liangzhi You •

Jie Yang • Wenbin Wu

Received: 14 September 2012 / Accepted: 27 January 2013 / Published online: 13 February 2013

� Springer-Verlag Berlin Heidelberg 2013

Abstract This study explores the effects of observed

warming trends since 1980 on crop yields at both national

and regional scales for the four main staple crops (rice,

wheat, maize, and soybean) in China, using gridded climate

and observed crop yield data, and identifies the areas in

China where food production is susceptible to warming.

National scale yield–temperature relationships show that

there are clear negative yield responses of maize, wheat,

and soybean to increased growing-season temperature.

Regional scale yield–temperature relationships show that

over 50 % of the arable land exhibited yield susceptibility

to past warming trends, with maize showing the highest

vulnerability and rice the lowest vulnerability. However, in

most of the main food-producing areas, crops experienced

increases or insignificant changes in yields due to better

agronomic management. The Loess plain is revealed as the

most vulnerable region to the past warming, as at least two

food crops have exhibited the signs of warming suscepti-

bility in the majority of the area. We also find considerable

yield reductions in spring wheat in the central northeast,

winter wheat in the Yellow River basin, and maize in

Southwest China. These findings of hotspot areas are

valuable in prioritizing future climate adaptation strategies

for Chinese agriculture.

Keywords Climate warming � Impact � Crop yields

Introduction

Crop yields in China have increased substantially over the

last three decades. The national-average yields of rice,

wheat, maize, and soybean have increased from 4,144,

1,891, 3,079, and 1,101 kg ha-1 in 1980 to 6,548, 4,749,

5,460, and 1,771 kg ha-1 in 2010 (FAO 2011), which are

relative increases of 58, 151, 77, and 61 %, respectively.

Improvements in yield are mainly due to new and better

seeds, expansion of the irrigated area, greater use of fer-

tilizers and mechanization, and the gradual adoption of

new agricultural products and technologies (e.g., pesticides

and plastic films) (Zhang et al. 2010b; Rozelle and Huang

2000). Despite the contribution of those factors, crop pro-

duction remains highly dependent on climate as growing-

season temperature and precipitation are important drivers

of crop growth, plant diseases, and pest infestations

(Murugan et al. 2012). Many aspects of China’s agricul-

tural production remain vulnerable to climate variability

W. Xiong (&)

Institute of Environment and Sustainable Development

in Agriculture, Chinese Academy of Agricultural Sciences,

Beijing 100081, China

e-mail: xiongwei8848@hotmail.com

W. Xiong

Ecosystems Services and Management Program, International

Institute for Applied Systems Analysis, 2361 Laxenburg, Austria

I. P. Holman

Cranfield Water Science Institute, Cranfield University,

Cranfield, Bedfordshire MK43 0AL, UK

L. You

International Food Policy Research Institute,

Washington, DC, USA

J. Yang

College of Resources and Environmental Sciences,

China Agricultural University, Beijing 100193, China

W. Wu

Institute of Agricultural Resources and Regional Planning,

Chinese Academy of Agricultural Sciences,

Beijing 100081, China

123

Reg Environ Change (2014) 14:7–16

DOI 10.1007/s10113-013-0418-6

and future climate change due to high exposure to

climate-related stresses and low adaptive capacity. Agri-

cultural losses due to climate-related hazards (such as

droughts and floods or climate-sensitive pests and diseases)

have increased since the 1970s (Piao et al. 2010; Li et al.

2011).

Significant spatial variations in yield trends have been

recognized in China (Li et al. 2010), which are likely to

have been influenced by recent climate trends (Kucharik

and Serbin 2008). A number of studies have investigated

the changing trends in crop growing-season climate and

yield responses in China, with the purposes of identifying

the climate risks and hot spots. The scales of these studies

include the whole county, main food-producing regions,

and provinces, with climate data coming from the regional

averages or representative sites and crop data from official

reporting. For example, Tao et al. (2008) reported that

warming trends contributed to yield increases for rice in

northeast China and for soybean in north and northeast

China, but decreased yield for wheat in east China. You

et al. (2009) suggested a small negative impact of past

warming on wheat for the whole country, but provincial

yields were reported by Li et al. (2010) to be significantly

correlated with climate in a few provinces. These studies

highlight the complexity of crop–climate relationships in

China. Understanding the growing-season climate risks and

crop yield responses were prerequisites for effective

adaptation (Lobell et al. 2011a, b), whereas this informa-

tion is still unclear in China, particularly at the regional

level.

Increasing surface air temperature is perhaps the most

tangible aspect of recent climate change. The observed

warming trend throughout China (?1.2 �C from 1960 to

2010) is well established (e.g., Ding et al. 2007). However,

precipitation and other climatic trends are more spatially

and temporally variable. Although yield responses to the

warming are different between crops and locations, due to

the differences in base temperature, warming extent, and

response mechanisms, etc., observed or projected warming

has acted as one of the main drivers for current develop-

ment of adaptation in China, such as the expansion of

winter wheat planting in north China (Yang et al. 2007)

and adoption of heat-resistant crop cultivars in the North

China Plain (Tao and Zhang 2010). A better understanding

of the yield responses to past warming and their spatial

variation will therefore facilitate the development of

locally specific adaptation strategies, through the identifi-

cation of the vulnerable production systems and locations

and possible mechanisms.

We address these by analyzing the relationships between crop

yields and growing-season temperatures at a fine spatial reso-

lution, with the aims to (1) identify the spatial patterns of

growing-season temperature–yield relationships; (2) investigate

the causes of spatial variation in yield response; and (3)

highlight the warming-risk areas for food production in

China.

Methodology

Study area and spatial scale

Mainland China lies between latitudes 18� and 53�N and

the longitudes 74� and 134�E. Although China’s agricul-

tural output is the largest in the world, only approximately

15 % of its total land can be cultivated. We considered the

four main food crops, namely rice, wheat, maize, and

soybean, which account for 75 % of the cultivated area.

The yield responses to past warming were investigated at a

0.5� 9 0.5� grid scale. A total of 2,429 grids were classi-

fied as arable grids and used as the main spatial units of

analysis, according to the land use map of China (Liu et al.

2002). Based on agro-ecological zones, administrative

boundaries, differences in crop types, management prac-

tices, and soil characteristics, we divided China into seven

food-producing regions: northeast (NE), north China plain

(NCP), Loess plateau (LP), Yangtze River basin (YRB),

south and southeast (SSE), southwest (SW), and northwest

(NW) (See Fig. 1). The majority of rice is grown in the

YRB and SSE, although the area for rice cultivation has

increased in the NE since 1980 (Yang et al. 2007). Wheat is

the most-prevalent grain crop, grown in most parts of the

country and especially in the NCP, LP, and parts of the

YRB as winter-sown wheat, and in NE and NW as spring-

sown wheat. Maize and soybean are grown in the NCP and

NE.

Climate and crop datasets

Gridded (0.5� 9 0.5�) monthly temperature (T) and pre-

cipitation (P) for the period 1981–2006 were obtained from

the Chinese Meteorological Bureau, interpolated from

roughly 2,400 climate stations using the climatological

optimal interpolation method (Zhang et al. 2008; Shen

et al. 2010). Growing seasons T and P were computed for

each crop and grid cell from monthly values according to

crop calendars which vary by locations and crops. Gener-

ally, in the NE and NW, a single crop of rice, spring wheat,

maize, or soybean is mainly grown from May to Septem-

ber. In the southern parts of China, winter wheat is grown

from October to June, and two crops of rice are grown from

April to October.

Other environmental and management factors were also

used as follows. Time series of sown area and yields for the

four crops in each county were obtained from China’s

annual grain production database. The original metadata

8 W. Xiong et al.

123

for this database comes from the China Agricultural

Yearbook (Chinese Agriculture Press, 1981–2006) and has

been carefully checked and corrected by county-scale

sample survey data by the Ministry of Agriculture. To

match the resolution of the climate data grid, the county-

level data were aggregated to the 0.5� 9 0.5� grid using the

following area-weighting approach:

y ¼Xn

i¼1

yi � ðai=a0 Þ ð1Þ

where y is the aggregated output of any given grid, n is the

number of counties that intersect or are contained within

that grid, yi is the census yield data of the ith county, ai is

the intersecting area of the ith county and the specific grid,

and a0 is the area of the grid.

Evaluation of yield–climate relationships at national

and pixel scales

To evaluate the relationship between the time series of

changes in yield (DY) and climate (DC), we first minimized

the influence of slowly changing factors such as crop

management by applying a widely used approach by first-

differencing time series for yield and climate (i.e., the

difference in values from 1 year to the next) (Tao et al.

2008; Lobell and Field 2007). We then performed linear

regression as Eq. (2):

DY ¼ aþ bDC þ e ð2Þ

where DY is the first difference in yield; a is the yield

change due to management and other nonclimatic factors;

DC is the first differences of growing-season temperature

or precipitation; b is the yield response to this change; and eis the residual error.

At the national scale, we used the national-average yield

and climate to establish the regression. National-average

crop yield and growing-season temperature and precipita-

tion were computed from the grid values using the yearly

spatial distribution of crop area as a weight. The time series

of both observed yield and the temperature and precipita-

tion would be used to estimate the above equation. The

model’s significance was evaluated at the 95 % confidence

level using Student’s t test.

At the grid scale, we repeated the regression analysis

(Eq. 2) for each grid, but only selected DT as the predictor

variable because increase in temperature was the most

pronounced and widespread change in the current and

future climates. For grids where the regressions were sig-

nificant at the 95 % confidence level, we considered them

as warming-sensitive areas, and the net effects caused by

the change in growing-season temperature were estimated

by applying the observed temperature trends to the

regression relationships. Different crops and regions may

have different yield response to the warming, with climate

warming favoring some crops in some areas, but causing

determent in others. In order to investigate the variations

between regions, for each food-producing region, we split

the warming-sensitive grids into sub-datasets with positive

Fig. 1 Division of the seven

food-producing regions and

sowing areas of the four staple

crops in (1981–2006)

Impacts of warming trends on crop yields in China 9

123

or negative yield responses, and plotted the estimated net

yield effects (DY) and observed DT to estimate the warm-

ing contributions.

Results

National scale climate–yield relationships

Multiple regression models of first differences between

national area-weighted yields and growing-season tem-

peratures and precipitations were significant (p \ 0.05) for

wheat, maize, and soybean, accounting for at least 19 % of

the variance in interannual yield (Table 1). The multiple

regression models, when applied to the observed climate

trends, suggest that the past T and P trends significantly

decreased yields for wheat (-103 kg ha-1), maize (-261

kg ha-1), and soybean (-24 kg ha-1) over the period of

1981–2006, suggesting the past T and P changes had

explicitly offset the yield promotion for the three crops,

with effects ranging from -4 to -15 %. For rice yield,

factors other than growing-season T and P played principal

roles, indicated by the weak, not significant, regression

model, but it suggests a possible positive contribution of

DT and DP on past yield increase.

In all cases, warming was the main player for estimated

climate-driven yield changes, as precipitation had only

minor effects on yields due to relatively small changes and

contributing rates in the period 1981–2006. It moderately

suppressed yield increases for maize and wheat, slightly

offset the soybean yield increase, while stimulated the

yield advance for rice.

Yield–temperature relationships at pixel level

The majority of the arable grids experienced pronounced

increases for both crop yields and growing-season tem-

peratures over the period (data not shown), but only a small

portion of grids exhibited significant yield correlation with

growing-season temperature. Linear regression between

the first difference of yields (DY) and growing-season

T (DT) in each grid demonstrated that the DY–DT relation-

ships were significant (p \ 0.05) in approximately

15–40 % of arable grids, dependent on crop. This suggests

that past warming is likely to have had detectable impacts

on the crop yields in these areas.

Figure 2 shows the estimated DY–DT relationships for

each crop, namely the change in yield for 1 �C growing-

season warming, estimated by taking the sum of the slope

of the linear regression from 1981 to 2006 and expressing

this as a per cent change in yield from the 1981 value. The

grids with insignificant DY–DT relationships are not shown

in the figure. Converting the amount of grid to crop

planting area based on the average crop area in each grid,

the proportion of the crop areas with significant DY–

DT relationships were 21, 34, 35, and 25 % for rice, wheat,

maize, and soybean, respectively, with regression model R2

averaged across the areas being 0.24, 0.23, 0.27, and 0.31,

respectively. These suggest more than 20 % of the vari-

ances in year-to-year yield changes can be explained by

changes in the growing season T in those inferred warm-

ing-affected areas.

Variations of yield response to the warming

among regions

In order to investigate the regional variations of yield

response, Fig. 3 shows the yield responses (expressed as

per cent change in yield from the 1981 value) to the

warming in different regions, subdivided according to

whether grids exhibited significant positive/negative cor-

relations between yield changes and observed warming

trends.

Within the inferred warming-affected areas, more than

half of the rice and wheat area exhibited yield gains under

past warming, with estimated contribution rates of 15.3 %

(Fig. 3a rice) and 20.3 % (Fig. 3a wheat) per �C growing-

season warming, respectively. For rice, widespread areas

Table 1 Changes in national yield and growing-season climate from 1981 to 2006, and statistics of regression models between yield and climate

first-differences

Rice Wheat Maize Soybean

Actual yield change 1981–2007 (kg ha-1) 1,510** 1,920** 1,850** 537**

Growing-season DT (base T) (�C) 0.98** (21.7) 1.52** (7.1) 0.95** (19.6) 1.05** (18.9)

Growing-season DP (base P) (mm) -19 (1,062) 7 (295) -27 (627) -10 (629)

Regression equations DY = 67.6DT - 0.24DP ? 52.8

DY = -133.8DT - 0.41DP ? 102.8

DY = -304.3DT

? 2.2DP ? 87.2

DY = -70.9DT

- 0.3DP ? 47.3

Model R2 0.12 0.21* 0.42** 0.19*

DT, DP-driven yield change (kg ha-1) 124 -103 -261 -24

* Significant at p \ 0.05; ** significant at p \ 0.01. National yield and climate were averaged from all grid values with area-weight method

10 W. Xiong et al.

123

showed yield increase under the warming, particularly in

north parts of China (Fig. 2a). The warming contribution

rate was greater in the north than in the south; for example,

NE experienced the highest warming contribution rate of

15.7 %/�C among the 7 regions. A few areas also exhibited

decreased yield under the warming, with contribution rates

being significant in all regions except SSE and NW, and

ranging from -31.8 to -7.2 %/�C (Fig. 3b rice). In con-

trast, for wheat, only NCP and a few northernmost areas

showed yield increases under the warming (Fig. 2b), with

contribution rates of 9.0 and 27.2 %/�C, respectively, but

yield depression was obvious under the warming in other

regions, with significant decreasing rates in all regions

except NW, ranging from -65.3 to -9 %/�C (Fig. 3b

wheat).

Maize and soybean suffered more from the past warming in

terms of the area of yield decrease. Within the inferred

warming-affected areas, roughly 88 % (maize) and 67 %

(soybean) of the planting area exhibited declined yield

(Fig. 2c, d), with decreasing rates of -20.3 %/�C (Fig. 3b

Maize) and -20 %/�C (Fig. 3b Soybean), respectively.

Maize experienced yield decreases in all regions except east

of NE and parts of NW, with decreasing rates between

-45.6 %/�C in NCP and -14.2 %/�C in SSE. The LP and

SW were the regions showing the greatest warming suscep-

tibility for maize, indicated by the larger proportion of the

maize area with decreased yield, with decreasing rates of

-19.5 and -20.3 %/�C, respectively. Soybean has the same

spatial pattern of yield response as maize, but only the LP

appeared as the main region for yield decline under the warming.

Fig. 2 Estimated yield responses (%) (compared to the regression-estimated yield from the 1981 value) for a 1 �C increase in T during the

growing season for a rice, b wheat, c maize, d soybean

Impacts of warming trends on crop yields in China 11

123

y = 15.3**x - 0.3

0

15

30

45

60

0 0.4 0.8 1.2 1.6 2 2.4Δ Y

ield

(%)

ΔT( )

NE:

NCP:

LP:

YRB:

SW:

SSE:

NW:

(a) Ricey=15.7**x+5.7

y=13.8**x+6.2

y=5.8x+16.9

y=11.4**x-2.8

y=3.3x+2.7

y=8.4x+10.9

y=11.7**x-0.8

y = -15.9**x + 3.4

-60

-45

-30

-15

0

15

-0.4 0 0.4 0.8 1.2 1.6 2 2.4

Δ Yie

ld(%

)

ΔT( )

NE:

NCP:

LP:

YRB:

SW:

SSE:

NW:

(b) Rice

y=-15.0**x+4.0

y=-12.1**x-0.1

y=-24.8**x+3.6

y=-7.2**x+0.6

y=-31.8**x-4.6

y=-3.6x-1.2

y=-83.5x+76.5

y = 20.3**x + 3.20

15

30

45

60

75

0 0.4 0.8 1.2 1.6 2 2.4

Δ Yie

ld(%

)

ΔT( )

NE:

NCP:

LP:

YRB:

SW:

SSE:

NW:

(a) Wheaty=27.2**x+7

y=9.0*x+4.5

y=-5.9x+41.7

y=26.3**x-7.7

y=-6.6x+31.0

y=6.2**x+8.3

y = -19.0**x + 1.5

-60

-45

-30

-15

0

15

0 0.4 0.8 1.2 1.6 2 2.4

Δ Yie

ld(%

)

ΔT( )

NE:

NCP:

LP:

YRB:

SW:

SSE:

NW:

(b) Wheat

y=-27.2**x+7.0

y=-9.0*x-4.5

y=-26.0**x+17.7

y=-31.0**x+14.4

y=-8.3x-5.5

y=-65.3**x+72.7

y=-19.0**x-1.5

y = 17.5**x + 2.7

0

15

30

45

60

0 0.4 0.8 1.2 1.6 2

Δ Yie

ld(%

)

ΔT( )

NE:

NCP:

YRB:

SW:

SSE:

NW:

(a) Maizey=13.2**x+9.8

y=17.7**x-5.7

y=25.5*x+1.9

y=0.3**x+3.1

y=53.8**x-10.5

y=2.9x+4.8

y = -20.3**x + 4.7

-60

-45

-30

-15

0

15

-0.4 0 0.4 0.8 1.2 1.6 2 2.4

Δ Yie

ld(%

)

ΔT( )

NE:

NCP:

LP:

YRB:

SW:

SSE:

NW:

(b) Maize

y=-26.3**x+4.5

y=-45.6**x+22.0

y=-19.5**x-8.0

y=-17.3**x-0.7

y=-20.3**x+1.9

y=-14.2**x-3.6

y=-25.3**x+5.4

y = 19.9**x + 2.5

-5

10

25

40

55

70

0 0.4 0.8 1.2 1.6 2 2.4

Δ Yie

ld(%

)

ΔT( )

NE:

NCP:

LP:

YRB:

SW:

SSE:

NW:

(a) Soybean y=13.4**x+9.2

y=9.7x+8.3

y=16.7**x+4.2

y=11.4x+5.8

y=23.7**x-1.7

y=33.3*x+2.5

y = -20.0**x - 3.2

-60

-45

-30

-15

0

0 0.4 0.8 1.2 1.6 2 2.4

Δ Yie

ld(%

)

ΔT( )

NE:

NCP:

LP:

YRB:

SW:

SSE:

NW:

(b) Soybean

y=-35.8**x+24.2

y=-28.0**x+5.6

y=-19.6**x-5.2

y=-33.9**x-5.5

y=-7.0**x-3.2

y=-16.2**x-2.0

y=-8.6x-40.4

Fig. 3 Scatter plots of

estimated yield changes and

corresponding observed trends

in growing season T, a in areas

with significant (p \ 0.05)

positive yield response to T, and

b in areas with significant

negative yield response to

T. The best-fit linear regressions

are provided for all datasets and

regional subsets. (Note:

*p \ 0.05; **p \ 0.01)

12 W. Xiong et al.

123

The warming-risk areas for food production

and the loss in production

Figure 4 shows the locations which experienced decreased

yields for multiple crops under the warming and the esti-

mated loss in production due to the warming trend across

the whole China. The combined loss in production of the

four crops, represented as a per cent of production in 1981,

is based on the average sown area from 1981 to 2006. The

area which exhibited decreased yield for at least one crop

was considered as the warming vulnerable area, and its

proportion of the total arable land (average for 1981–2006)

and the mean production loss are presented in Table 2.

A total of 52.6 % of the arable land exhibited yield

decreases for at least one crop in China, indicating the

possible warming susceptibility in these areas (Table 2).

These areas are located in all the main food-producing

areas, except eastern part of NE, and the NCP (Fig. 4a),

with production loss averaged across these areas at 7.0 %.

Much of the LP, central NE, southern SW, and parts of

RYB experienced yield decreases for at least two crops,

taking up 18.7 % of the arable land. Only a small portion

(4.0 %) of land showed the depressed yields due to

warming for at least three crops, largely concentrated in the

LP.

The LP showed the largest warming susceptibility of all

the seven food-producing regions, as the majority of its

arable land (90.6 %) experienced yield reductions under

the warming for at least one crop, more than half (54.8 %)

of the land exhibited yield decreases for two crops, and

roughly 15 % of the land had decreased yields for three

crops. The loss in food production across these areas of the

LP was -10.9 %, with most losses coming from yield

decreases in maize, soybean, and wheat. (Table 2; Fig. 4a).

The NE, YRB, and SW also appear as large warming-risk

areas due to the past warming (Table 2), indicated by more

than half of their arable land experiencing decreased yields

due to warming. Wheat in the central belt and maize in the

south contributed to the susceptibility in the NE, leading to

an average loss of 8.3 % in production. Negative warming

responses of maize in the southern corner of SW and wheat

in the YRB were the main reason for the warming risks in

the SW and YRB, respectively, but they only caused small

losses in production under the past warming (-3.0 % in

SW and -4.9 % in YRB), due to the relatively small

increases in growing-season temperatures and the low

importance of the two crops in these regions.

Discussion

Comparison with previous studies

The impacts of past warming trends have been examined in

China by a limited number of previous studies, in which

most have used similar empirical models to gauge the

effects. For example, Lobell et al. (2011a, b), using

national-averaged yield and climate, inferred that China’s

past climate change (T and P) since 1980 had increased

yields for rice by roughly 2.5 %, but reduced yields for

wheat, maize, and soybean, by approximately -5, -1.5,

and -2 %, respectively. Using provincial data, Tao et al.

(2008) reached similar conclusions except for a slight yield

increase for soybean. You et al. (2009) and Zhang and

Huang (2012) gave more detailed estimations for specific

crops, suggesting a 3–10 % yield loss in wheat with a 1 �C

Fig. 4 a The number of food crops (rice, wheat, maize, and soybean) which exhibited depressed yields to the past warming, and b estimated

decreases in food production due to the past warming trends (compared to the reported production in 1981)

Impacts of warming trends on crop yields in China 13

123

growing-season warming and insignificant positive effects

on rice.

The estimated effects vary across these studies,

depending on the scales of evaluation, data used, periods of

assessment, etc. but a clear message emerges that past

warming trends increased rice yield, but decreased yields

for wheat, maize, and soybean in China. Although the yield

and climatic data used in this study are aggregated from

county and grid values, which are different from previous

studies, our analysis agrees well with some of the previous

studies. Compared to the yields in 1981, our estimations on

climate-driven change (DT and DP) in yields are 2.8, -4.9,

and -2.1 % for rice, wheat, and soybean (Table 1),

respectively, which are consistent with the estimations of

Lobell et al. (2011a). In the case of maize, we project a

larger yield reduction (-8.6 %) compared to value of

Lobell et al. (2011a), but it roughly matches the estimation

of Tao et al. (2008) (-10 %).

Identification of vulnerable crops and areas

Regarding the areas affected by warming identified in the

analysis, our study provides more detailed spatial infor-

mation of the yield response, which previous studies usu-

ally failed to provide. The inferred areas with significant

responses to the temperature indicate possible detectable

impacts of past warming, suggesting the target crops or

hotspots for adaptation. In addition, the significant contrast

in yield response between adjacent regions for the same

crop, or between crops in a region, implies specific

mechanisms for their responses, which is a prerequisite for

effective adaptation.

Maize is identified as the most vulnerable crop in terms

of having the largest area with pronounced decreases in

yield (30.9 % of the maize area exhibited yield decrease),

and greatest yield loss due to past warming trends

(Table 1). Possible reasons for its yield decrease include

shorter growth period, increased spikelet sterility and

chloroplast damages, and increased energy consumption.

Although detrimental impacts of warming on rice have

been observed in many Asian counties (Peng et al. 2004;

Welch et al. 2010), our study demonstrates that past

warming may still remain within the optimal growth tem-

perature range for rice in much of the rice areas. There is

only 8.2 % of the rice area showing yield declines with

past warming, and these areas are randomly located in a

few rice planting areas, namely southern NE and northern

NCP, and parts of YRB (Fig. 2a). For wheat and soybean,

the increase in temperature tends to decrease their yields,

indicated by roughly 10 % of their areas showing depres-

sed yields, but in their principal planting regions, that is,

soybean in the NE, wheat in the NCP, yields present

positive responses to the warming. This partly can be

explained by the management differences between the

principal and marginal producing regions, for example,

soybean cultivated in the NE and wheat in the NCP are

usually have better irrigation systems and more use of new

cultivars.

The LP is identified as the most vulnerable region to

warming for food production in China due to its largest

proportion of vulnerable planting area and substantial

decrease in yields for many crops (Fig. 4; Table 2). Using

a composite of crop simulation results and proxy indica-

tors, Lin (1996) also identified the LP as the most vulner-

able region to climate change. The possible reasons for its

vulnerability include great land degradation, and low soil

fertility and water-holding capacity. The SW and YRB in

the south of China are also identified as warming vulner-

able regions. Negative yield responses to the warming for

the less importance crops, such as wheat in the YRB, and

maize in the southern corner of SW, also contribute to their

vulnerabilities. Increased heat and water stresses under the

warmer climate can partly explain the vulnerability in these

two areas, in part because wheat and maize in these areas

are often planted as rotation crops under rain-fed condi-

tions, and in part because of the correlation between

increased extreme climate events (e.g., extreme high tem-

peratures) and warming trends in much of the areas (Zhang

and Huang 2012). Although the current warming trend is

small in south China, particularly in the southern corner of

SW, which only led to a small loss in the food production,

the significant negative yield responses in these areas imply

potential risks for future food production with the antici-

pated warming trends.

The NE has been widely acknowledged as a region

which may benefit under a warmer climate, due to its low

base temperature, decreased cold stresses, and good

Table 2 Proportion of cultivation area with decreasing crop yields

due to the past warming, and aggregated loss in food production, for

different food-producing regions

Regions Proportion of vulnerable area

(%)

Mean production loss in

the vulnerable areas (%)

(compared to production

in 1981)For

one

crop

For

two

crops

For

three

crops

For

four

crops

NE 65.6 22.8 2.4 0.6 -8.3

NCP 31.0 8.3 1.7 0.5 -8.2

LP 90.6 54.8 14.9 1.5 -10.9

YRB 58.1 20.6 4.6 0.5 -4.9

SW 88.4 39.7 8.1 0.0 -3.0

SSE 48.8 11.2 2.7 0.0 -2.1

NW 49.1 22.6 3.6 0.0 -11.5

China 52.6 18.7 4.0 0.5 -7.0

14 W. Xiong et al.

123

irrigation infrastructure (Yang et al. 2007; Wang et al.

2005). Our analysis reveals that only the east of NE,

namely the NE plain, exhibits pronounced gains from past

warming on yields, with all crops except wheat showing

pronounced yield increases in the plain. However, central

NE still shows the signs of vulnerability to warming

(Fig. 4b). Yields of wheat decreased with the past warm-

ing, which caused a loss of 8.3 % in production in these

areas. Cultivation of spring-sown wheat whose reproduc-

tive stage collides with the hottest season, low soil fertility,

and lack of irrigation due to the mountainous landscape are

likely the causes.

Other factors affecting the vulnerability and limitations

of the study

This study tries to identify the spatial vulnerability to future

warming through the relationships between past warming

and crop yields, with the inferred areas with decreased crop

yields suggesting possible risks under warming. However,

factors other than the growing-season warming may also

play a role.

Firstly, changes in other climatic variables, such as

precipitation and radiation, may contribute to vulnerability

in some areas. For example, correlations between increas-

ing temperature and decreasing precipitation have been

reported in NE, particularly in the south and central NE

(Zhang and Huang 2012). This usually results in increased

yield losses in areas without insufficient irrigation, which

partly explains the negative yield responses observed for

maize and wheat in parts of the NE (Fig. 2). In YRB, the

decrease in radiation is often associated with increasing

temperature, contributing to the negative warming

responses for rice and wheat in much of the area. Xiong

et al. (2012) and Zhang et al. (2010a) have both identified

reduced radiation as important contributors to past climate-

driven yield decreases for rice in China. In addition,

extreme weather events such as drought, extreme high

temperature are likely to have more impacts on crop yields

than changes in the mean climate (Piao et al. 2010). They

tend to cause the warming vulnerability if there is covari-

ance between a specific extreme event and temperature

increase, which is the case for crops in parts of the YRB

where increased yield losses can partly be explained by the

increasing heat stresses under the warmer climate.

Secondly, vulnerability to warming is clearly affected by

differences in management. We find that crops with better

management, such as irrigation, tend to exhibit positive or

insignificant warming responses. For example, compared to

other crops, rice has benefited more from past warming, as

over 95 % of the rice area is fully irrigated in China (Xiong

et al. 2009). Furthermore, crops in most of the main food-

producing areas, which would be expected to have better

agronomic management than in more marginal production

areas, also show positive or insignificant warming responses,

such as wheat and maize in the NCP and rice in the YRB.

Possible mechanisms include better irrigation, higher soil

fertility, and more investment on development and use of

new cultivars. Although we did not carry out the yield

response analyses for different management practices due to

the unavailability of such data, this study indicates that

agronomic measures to increase the crop resilience are

important strategies to cope with future warming.

Finally, there is some evidence that farmers in the north-

ern latitudes of China have already adapted to the warmer

temperature, which has contributed to the positive yield

responses in some areas. For example, the sowing dates of

wheat and maize in the NCP have advanced by about

5–10 days since 1980s (Wang et al. 2012), which combined

with the adoption of new crop cultivars with longer growth

periods, have significantly promoted yields of winter wheat

and summer maize (Liu et al. 2010; Chen et al. 2010; Fu et al.

2009). Increased cultivation of rice in cooler regions, toge-

ther with the development of cold-tolerant cultivars, irriga-

tion, etc., has substantially increased rice yields in the NE

(Yang et al. 2007; Wang et al. 2005). Although many of the

adaptation cannot be extrapolated to other regions and crops,

we believe that effective adaptation can contribute to main-

taining food production under the future warmer climate,

particularly in northern parts of China.

Conclusion

The present study found that the warming rates since 1980

are statistically significant across much of China and have

had discernible impacts on China’s crop productivity. The

impacts of warming are variable across regions and

between crops. National scale yield–climate relationships

demonstrate that the effects of the widespread warming are

considerable for maize and wheat, and moderate for soy-

bean, but less pronounced for rice. Grid scale analysis

indicates that maize is the most sensitive crop to the past

observed warming, while the impact on rice has been

small. Over 50 % of the arable land exhibits yield vul-

nerability to past warming, with different causal mecha-

nisms between crops and regions. The loess plateau is most

vulnerable to warming, where at least two crops have been

shown to be susceptible to the warming. In addition, yield

reductions in spring wheat in the central northeast, wheat in

the Yellow River basin, and maize in Southwest China are

also observed.

As with other empirical studies, this study contains

uncertainties, including those associated with the selection

of the statistical approach (e.g., a simple linear model and

the first-difference method), choice of predictor variables

Impacts of warming trends on crop yields in China 15

123

(only T at the regional scale), and the quality of the raw

data (county data). Despite these uncertainties, our study

produces consistent results to previous studies in terms of

the yield impacts caused by past warming. However, our

fine resolution analysis of the temperature–yield relation-

ships has importantly highlighted the potential warming-

risk areas for food production and indicated the hotspots

for adaptation prioritization for future climate change in

China.

Acknowledgments This research was jointly supported by National

Basic Research Program of China (project no. 2010CB951504,

2012CB95904) and National Natural Science Foundation of China

(Grant No. 41171093), and National Scientific Program (No.

2012BAC19B01). We thank the reviewers for their constructive

suggestions that improved our initial manuscript. We acknowledge

the weather data groups in China Meteorological Information Center

for providing their data for analysis.

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