Negative effects of climate warming on maize yield are reversed by the changing of sowing date and cultivar selection in Northeast China

Negative effects of climate warming on maize yield arereversed by the changing of sowing date and cultivarselection in Northeast ChinaZH I JUAN L IU * † , K ENNETH G . HUBBARD † , X IAOMAO L IN ‡ § and XIAOGUANG YANG*

*College of Resources and Environmental Sciences, China Agricultural University, No. 2 Yuanmingyuan West Rd., Haidian

District, Beijing 100193, China, †School of Natural Resources, University of Nebraska-Lincoln, 3310 Holdrege Street, Lincoln, NE

68583, USA, ‡Department of Agronomy, Kansas State University, 2108 Throckmorton Hall, Plant Sciences Center, Manhattan,

KS 66506, USA, §LI-COR Biosciences, 4647 Superior Street, Lincoln, NE 68504, USA

Abstract

Northeast China (NEC) accounts for about 30% of the nation’s maize production in China. In the past three decades,

maize yields in NEC have increased under changes in climate, cultivar selection and crop management. It is impor-

tant to investigate the contribution of these changing factors to the historical yield increases to improve our under-

standing of how we can ensure increased yields in the future. In this study, we use phenology observations at six

sites from 1981 to 2007 to detect trends in sowing dates and length of maize growing period, and then combine these

observations with in situ temperature data to determine the trends of thermal time in the maize growing period, as a

measure of changes in maize cultivars. The area in the vicinity of these six sites accounts for 30% of NEC’s total maize

production. The agricultural production systems simulator, APSIM-Maize model, was used to separate the impacts

of changes in climate, sowing dates and thermal time requirements on maize phenology and yields. In NEC, sowing

dates trended earlier in four of six sites and maturity dates trended later by 4–21 days. Therefore, the period from

sowing to maturity ranged from 2 to 38 days longer in 2007 than it was in 1981. Our results indicate that climate

trends alone would have led to a negative impact on maize. However, results from the adaptation assessments indi-

cate that earlier sowing dates increased yields by up to 4%, and adoption of longer season cultivars caused a substan-

tial increase in yield ranging from 13% to 38% over the past 27 years. Therefore, earlier sowing dates and

introduction of cultivars with higher thermal time requirements in NEC have overcome the negative effects of climate

change and turned what would have otherwise been a loss into a significant increase in maize yield.

Keywords: climate change, crop simulation model APSIM, cultivar, maize, Northeast China, sowing date, yield

Received 18 December 2012; revised version received 11 June 2013 and accepted 14 June 2013

Introduction

Northeast China (NEC), located at ca. 38.9–53.0°N,

118.8–135.1°E in China, comprised Heilongjiang, Jilin

and Liaoning Provinces and is one of the major agricul-

tural production areas in China. Moreover, the total

maize area accounts for more than 36% of the total crop

area in NEC and the grain production of maize in NEC

accounts for 30% of the nation’s total [(National Bureau

of Statistics of China (NBSC) (2001–2008)]. Therefore,NEC undoubtedly plays a vital role in securing food

production in China. On the other hand, during the

past 50 years, NEC has experienced a warming trend in

surface average air temperature equal to 0.38 °C per

decade (Liu et al., 2012). If other factors were constant

(fixed genotypes and management practices) this

would result in a negative impact on maize production;

the warmer climate would speed up crop development

and lead to a reduction in the length of the growing

season (Tubiello et al., 2000; Olesen, 2005; Porter, 2005;

Liu et al., 2010). However, in NEC, yields of maize have

steadily increased since the 1980s (Chen et al., 2011; Liu

et al., 2012) due to the improved cultivars and manage-

ment (Yun et al., 2005; Ma et al., 2007; Gao et al., 2010).

As demand for food and energy increase with increases

in population, society will be pressed to increase agri-

cultural production, especially in China. Therefore, it is

important to understand and quantify how climate

change, cultivar selection, and management changes

contributed to the historical yield increases in NEC; the

knowledge will help to develop future strategies to

sustain or increase yield.

Farmers always have a number of possible counter-

measures or options to adapt to climate change. These

adaptation strategies may potentially lessen the

yield losses from climate change or may improve

yields in regions where beneficial climate changesCorrespondence: Xiaoguang Yang, tel. +86 010 6273 3939,

fax +86 010 6273 3939, e-mail: [email protected]

© 2013 John Wiley & Sons Ltd 3481

Global Change Biology (2013) 19, 3481–3492, doi: 10.1111/gcb.12324

occur (Southworth et al., 2000). The adjustments for

dealing with the effect of climate change include

changes in sowing dates and cultivars, changes in

material inputs such as irrigation, nitrogen fertilizers

and techniques to conserve soil water (Alexandrov

et al., 2002). NEC is located at relatively high latitude

(from about 39 to 53°N), where growers are usually

challenged by a later arrival of spring and a shorter

average growing season than the rest of China (May to

September) which limits the region to one-crop a year

in contrast to other parts of China. Climate warming in

NEC mainly occurs in winter and spring (Ju et al.,

2007), which provides the warmer conditions in later

April that favour early sowing, especially in Heilongji-

ang province. Moreover, temperature increases extend

over the potential growing season in NEC (Yang et al.,

2007). Therefore, the simple and effective measures of

adaptation to climate change in NEC are shifting the

sowing dates earlier and have led to the use of cultivars

with longer growing season (greater thermal require-

ments), which are being autonomously developed

and applied by the local farmers (Tao et al., 2006;

Liu et al., 2012; Xiong et al., 2012; Chen et al., 2013).

Early planting allows for longer growing-season culti-

vars to be used with confidence as long as these culti-

vars are tolerant of relatively low (nonfreezing)

temperatures that can occur after sowing. Early plant-

ing also increases the likelihood that plant physiologi-

cal maturity will occur before killing frosts occur in the

fall (Gupta, 1985; Kucharik, 2006; Sacks & Kucharik,

2011).

It is difficult to separate the effects of climate change,

cultivar selection and management improvements since

these factors are changing simultaneously (Sacks &

Kucharik, 2011). Therefore, simulation models have

been used extensively to estimate the impacts of these

factors on crop productivity (e.g. Ludwig & Asseng,

2006; Sadras & Monzon, 2006; Luo et al., 2009; Chen

et al., 2010a). In spite of the above mentioned studies,

there is little evidence relating the effects of climate

warming, sowing dates, and changing cultivars on the

maize production in NEC. The Agricultural Production

Systems Simulator (APSIM) has proven to be an effec-

tive tool to investigate the potential impacts of climate

variability on crop productivity (Asseng et al., 2004,

2011; Liu et al., 2012; Zheng et al., 2012). Therefore, we

used the APSIM model to separate these factors and

determined their contributions to the historical yield

increases, which would help to increase maize yields

sustainably in NEC. The objectives of this study are (i)

to identify observed trends in sowing dates, length of

the growing period and changes in thermal require-

ments of maize cultivars; and (ii) to separate the effects

of observed climate change, sowing dates and cultivar

selection on the duration of maize growing periods and

grain yields, and estimate how much these observed

trends contributed to the historical yield increase over

the past 27 years in NEC.

Materials and methods

Study sites

In this study, six sites in NEC were selected from the climate

stations of the National Meteorological Networks of China

Meteorological Administration (CMA), including Qinggang,

Boli and Tailai in Heilongjiang province, Siping in Jilin

province, Xinmin and Benxi in Liaoning province (Fig. 1).

Fig. 1 The locations selected for simulation of maize yields in Northeast China (NEC). Solid circles indicate locations used for simulat-

ing yield (names are shown in regular font). The grey shading indicates mean maize production (104 t) from 2001 to 2008 for each city

in NEC. Lines show province boundaries. Provinces are named in italic. The left figure is a map of China and location of NEC within

China.

© 2013 John Wiley & Sons Ltd, Global Change Biology, 19, 3481–3492

3482 Z. LIU et al.

These sites are also Agrometeorological Experimental Stations

(AESs) for maize with detailed management and yield data

from 1981 to 2007. These sites represent six different climate

regions classified by average temperature and total precipita-

tion during the maize growing season (Table 1). More detailed

information on the climate of the sites is provided in Table 1.

The areas surrounding these sites represent 30% of total maize

production in NEC.

Climate, biological and soil data

Climate data include daily mean, maximum and minimum

temperatures, daily sunshine hours and daily precipitation

from 1981 to 2007 at each station. Sunshine duration was con-

verted into daily solar radiation using the �Angstr€om formula

(Black et al., 1954; Jones, 1992).

The experiment data on maize phenology (sowing, flower-

ing and maturity dates), cultivar type, yields and management

practices were obtained from local AESs in NEC. These obser-

vations have uniform CMA observing standards and guide-

lines for collecting surface climate, phenology and maize yield

data across the country (Liu et al., 2012). In this study, we

analysed maize sowing, flowering and maturity date and the

length of development periods. We referred to the period

from sowing to start of flowering (R1) as the vegetative period,

from flowering (R1) to physiological maturity (R6) as the

reproductive period, and from sowing to physiological

maturity (R6) as the whole growing period.

The soil data used in our study include the soil bulk den-

sity, saturated volumetric water content (SAT, pF = 0),

drained upper limit and 15Bar lower limit (LL15, pF = 4.2) in

different soil layers. These data were also taken from the

AESs. The details of these sites and data are given by Liu et al.,

2012.

Calculation of thermal time during maize growing period

Daily thermal time is calculated in most maize simulation

models. A response function allows thermal time to increase

linearly between a base temperature of 8 °C and an optimum

of 34 °C, then declines linearly to 0 at 44 °C (Fig. 2a) (Wilson

et al., 1995). However, this procedure ignores the observation

Table 1 The trends of average temperature (Tave), maximum temperature (Tmax), minimum temperature (Tmin), total precipitation

(P), and solar radiation (SR) during the maize growing season from 1981 to 2007 at the six study sites, and the climate regions char-

acteristics classified by average temperature and total precipitation during the maize growing season in Northeast China

Qinggang Boli Tailai Siping Xinmin Benxi

Climate factors Tave (°C per decade) 0.56** 0.35* 0.59** 0.64** 0.29 0.47**

Tmax (°C per decade) 0.50** 0.28 0.71** 0.52** 0.39* 0.52**

Tmin (°C per decade) 0.64** 0.40** 0.53** 0.71** 0.19 0.48**

P (mm per decade) �65.4* �65.6** �36.2 �37.3 �19.7 �53.3

SR (MJ m�2 day�1 per decade) 0.32 0.26 �0.27 �0.36 �0.52* 0.82*

Represent the

region with

Tave (°C) 17.6–19.0 17.6–19.0 19.1–20.5 19.1–20.5 20.6–22.1 20.6–22.1

P (mm) 328–450 451–600 328–450 451–600 451–600 601–856

SR was calculated using the �Angstr€om formula (Black et al., 1954; Jones, 1992). Coefficients a and b of the �Angstr€om formula are

0.25 and 0.5 respectively.

*Significant at P < 0.05.

**Significant at P < 0.01.

0

5

10

15

20

25

30

0 10 20 30 40 50 0 10 20 30 40 500

5

10

15

20

25

30

Ther

mal

tim

e (o

C d

)

Temperature (oC)

(a) (b)Topt

Tmax

Topt

TmaxTbase

Fig. 2 Responses of daily thermal time to temperature (a) with an 8 °C base (Tbase), an optimum of 34 °C (Topt) and a maximum of

44 °C (Tmax) and (b) the same as (a) but modified to include a different linear response between 0 and 18 °C.


IMPACTS OF SOWING DATE AND CULTIVARS TO MAIZE 3483

that leaf appearance is not a linear function of thermal time

at low temperatures (Wilson et al., 1995; Zhang et al., 2008).

Therefore, the relationship used in APSIM-Maize model was

modified so that the leaf appearance rate at low tempera-

tures approximates a nonlinear response between 0 and

34 °C (Fig. 2b). To minimize error associated with diurnal

temperature variation, daily thermal time is calculated using

the eight averaged 3-hourly air temperatures, which are

interpolated from the daily maximum and minimum tem-

peratures (Jones & Kiniry, 1986). These daily thermal time

values are accumulated into a thermal time summation,

which is used to determine the duration of each phase in

APSIM-Maize model.

We used observed maize phenology data to define the days

in each stage and the daily minimum and maximum tempera-

ture data to calculate the thermal time during maize growing

period for each station-year combination, using the same rela-

tionship with APSIM-Maize model which is shown in Fig. 2b.

For each station and each year, we then calculated the accu-

mulated thermal time during the vegetative period (TTveg),

reproductive period (TTreprod) and the whole growing period

(TTwhole).

Crop modelling

In the APSIM-Maize model, there are eleven crop stages and

commencement of each stage (except for sowing to germina-

tion which is driven by soil moisture) is determined by accu-

mulation of thermal time (Equation in Fig. 2b). Therefore, the

APSIM-Maize model assumed that accumulation of thermal

time for a given cultivar was constant (McCown et al., 1996;

Wang et al., 2002), similar to other crop models such as

CERES (Jones & Kiniry, 1986; Gungula et al., 2003), ORYZA2000

(Bouman et al., 2001; Bouman & van Laar, 2006). The phase

between germination and emergence was determined by the

thermal time affected by the depth of sowing; therefore, this

thermal time is assumed to be a constant for a given location

over the period (1981–2007) in this study. The duration

between emergence and flag leaf appearance is determined by

the total number of leaves destined to appear on the plant,

and the rate at which they appear, which is determined by

temperatures (see below). The total number of leaves is equal

to the number in the seed at germination plus the number sub-

sequently initiated, until floral initiation is reached. Hence, the

timing of floral initiation will determine the total leaf number

and the timing of the appearance of the flag leaf. Between the

end of the juvenile phase and floral initiation, the thermal

development rate is sensitive to photoperiod if the cultivar is

photoperiod sensitive. In APSIM-Maize model, the default

assumption is that maize, as a short day plant, will have a

longer phase between the end of the juvenile phase and initia-

tion if photoperiods exceed 12.5 h. Detailed descriptions of

the APSIM-Maize module can be found in Keating et al. (2003)

or at the APSIM website: http://www.apsim.info/apsim/. In

our study, we further assumed photoperiod sensitivity

remained constant within the cultivars grown over this period

(1981–2007). On the basis of these assumptions, we selected

the total thermal time (TT) from emergence to end of juvenile

stage (we called TT_emerg_to_endjuv in APSIM-Maize crop

parameters) to define the observed cultivar changes in terms

of TTveg, and thermal time from flowering to maturity (we

called TT_flower_to_maturity in APSIM-Maize crop parame-

ters) to define the observed cultivar changes in terms of

TTreprod (Table 2).In our previous study (Liu et al., 2012), we demonstrated

that APSIM was able to reproduce the observed maize

growth and yield in NEC. The results indicated that the

APSIM-Maize model was able to simulate the observed

duration from emergence to flowering, and emergence to

maturity reasonably well for most treatments and at all the

locations selected from NEC. In addition, simulations of

total above ground dry matter agreed very well with the

observations. The simulated yields compared favourably

with observed yields, indicating that the model estimated

the growth of maize reasonably well. More details are given

by Liu et al., 2012. APSIM-Maize was run with historical cli-

mate data (1981–2007) to quantify the impacts of changes in

climate, sowing dates and cultivars on the duration of

growing period and yields of maize in NEC. We designed

four simulations to examine these effects. These simulations

differed in two sets: sowing dates, and two ‘cultivar’ vari-

ables including TT_emerg_to_endjuv and

TT_flower_to_maturity. The four simulations then encom-

passed the different possible combinations of fixed vs. tran-

sient management for these two sets of variables (Table 2).

The CONTROL run represents what would have happened

if sowing dates and cultivar remained constant at the 1981

values and reflects the historical effects of climate change

on maize yield throughout the period 1981–2007. The SOW-

ING + CULTIVAR run represents actual, historical manage-

ment changes in sowing dates and cultivars. The other two

runs represent some combination of these two, to separate

Table 2 Model runs performed in this study

Name Description Sowing date TT_emerg_to_endjuv TT_flower_to_maturity

CONTROL No trends in sowing dates

or cultivars

Fixed Fixed Fixed

SOWING Trends in sowing dates only Observed time series Fixed Fixed

CULTIVAR Trends in cultivars only Fixed Linearized

observations

Linearized observations

SOWING + CULTIVAR Trends in both sowing dates

and cultivars

Observed time series Linearized

observations

Linearized observations


3484 Z. LIU et al.

the effects of changing sowing dates and changing culti-

vars.

In the runs with transient sowing dates (SOWING and

SOWING + CULTIVAR), we used the actual, observed time

series for sowing date at each site. In the runs with ‘fixed’

sowing dates (CONTROL and CULTIVAR), we used the sow-

ing dates from the year 1981. In the runs with transient culti-

vars (CULTIVAR and SOWING + CULTIVAR), we used the

linearized values of the time series after removing any interan-

nual variability around this trend line, because we try to cap-

ture the true management changes. We assumed that changes

in cultivars would be gradual, which would not be large varia-

tions from year to year. In the runs with fixed cultivars (CON-

TROL and SOWING), we used fixed value during the first

year 1981 (Sacks & Kucharik, 2011).

Simulations assumed that water applications were set equal

to the water use of the maize crop and nutrient inputs were

taken as nonlimiting to eliminate the effect of water and nutri-

ent stresses on simulated maize yield. Planting density, sow-

ing depth and other management details are kept constant

throughout the simulated years 1981–2007, for which we had

maize phenological observations.

Data analysis

Linear regression analysis was used to detect trends in

observed sowing dates, flowering dates, maturity dates as

well as the duration of growing periods (vegetative period,

reproductive period and whole period). The slope of the linear

regression line against time was evaluated using Student’s

t-test at 95% or 99% confidence levels. To determine the

changes in maize cultivars from 1981 to 2007 during the past

27 years, the linear regression analysis was also used to detect

trends in thermal time of vegetative period and reproductive

period of maize.

To separate the impacts of climate change, sowing date

shifting and cultivar selection on the duration of growing peri-

ods and yield of maize, we first detected the linear trends of

yield in the CONTROL run as the effects of climate change.

We then examined linear trends of the differences between

each experimental run (SOWING, CULTIVAR and SOW-

ING + CULTIVAR) and the CONTROL run as the effects of

sowing date shifting, cultivars changing, and both of them on

maize. Finally, total changes from 1981 to 2007 were calculated

by multiplying the trends by 27 years.

Results

In the following sections, we first identified observed

trends in sowing, flowering and maturity dates and the

length of growing period, and changes in thermal

requirements of maize cultivars, which rely on observa-

tions. Then in sections three to five, we used the

APSIM-Maize model to separate the contributions of

the observed phenology and yield trends that were

attributed by sowing dates, and the contributions that

were attributed to changes in cultivars.

Trends in observed maize sowing, flowering and maturitydate and the length of growing period

Maize sowing dates have advanced 1.4, 0.6, 6.6 and

1.9 days per decade in Qinggang, Boli, Tailai and Benxi

stations from 1981 to 2007 respectively. The trend is sig-

nificantly different from zero in Tailai station, therefore,

sowing date was 18 days earlier around 2007 than it

was around 1981 (Table 3). April and May warming

may have contributed to this shift to earlier sowing

dates (Fig. 3). The trends of maize flowering date were

inconclusive throughout the six stations in NEC.

Among our study locations, the trends of maize matu-

rity dates were consistent and indicated significant

Table 3 Averages and trends of observed sowing, flowering and maturity dates, and the duration of vegetative period (sowing to

flowering), reproductive period (flowering to maturity) and the whole period (sowing to maturity) of maize at the six study sites

over the period 1981–2007. Trends are given in days per decade

Qinggang Boli Tailai Siping Xinmin Benxi

Sowing date Average (Day of year) 125 128 125 114 117 120

Trend (days per decade) �1.4 �0.6 �6.6** 4.8** 3.0 �1.9

Flowering date Average (DOY) 211 210 209 205 203 205

Trend (days per decade) �0.5 �1.5 0.7 2.6** 0.1 �0.9

Maturity date Average (DOY) 268 262 265 256 260 259

Trend (days per decade) 5.3** 5.2* 7.6** 5.3** 1.4** 3.5**

Vegetative period Average (days) 87 83 84 92 87 86

Trend (days per decade) 0.9 �0.9 7.3** �2.2 �2.9 1.0

Reproductive period Average (days) 57 53 57 51 56 53

Trend (days per decade) 5.8** 6.7** 6.9** 2.7** 4.3** 4.4**

Whole period Average (days) 144 136 141 143 143 139

Trend (days per decade) 6.7** 5.8** 14.2** 0.6 1.4 5.4**

Asterisk in the table indicates significant at **P < 0.01, and *P < 0.05, and the values are shown in bold font.



delays (P < 0.05) from 1.4 to 7.6 days per decade from

1981 to 2007 (Table 3).

The length of vegetative period of maize changed

slightly and not significant in NEC; however, the length

of reproductive period has increased significantly

(P < 0.01) at all study stations, ranging from 2.7 to

6.9 days per decade.

Changes in maize sowing, flowering and maturity

dates were not uniform across NEC, while there was a

general shift to earlier sowing dates and delayed matu-

rity dates. Therefore, the total maize growing season

has increased during the past three decades in NEC.

Trends of thermal time during maize growing period

Although the trend of vegetative period for maize

changed slightly, there was a trend to greater TTveg

(Fig. 4). TTreprod has generally followed a trend similar

to TTveg, but with a larger magnitude, with increases

ranging from 24.6 to 67.8 °C days per decade for sta-

tions during the 1981 to 2007 period. Therefore, TTreprod

were 66–183 °C days higher by 2007 than it was in

1981; this is equivalent to the 9–29% increase in

TTreprod. Five of six stations experienced a significant

increase in TTreprod; the exception was Siping, for which

the trend did not differ significantly from zero (Fig. 4).

Simulated impacts of climate change on growing periodand yield of maize

The warming trend significantly shortened the vegeta-

tive period, ranging from 1.8 to 3.1 days per decade

from 1981 to 2007 at different stations in NEC in the

absence of sowing date and cultivar changes; the mag-

nitude of the reproductive period trend was slightly

reduced (0.5–2.4 days per decade). Furthermore, the

trends of reproductive period were statistically signifi-

cant at the 95% confidence level only for two out of six

stations. In all, the length of the whole period (period

between sowing and maturity) reduced by 2.3 to

4.8 days per decade depending on the locations

(Table 4).

The bottom row shown in Table 4 is the trend of sim-

ulated yield of maize in the run of CONTROL, which is

the impact of climate change on yields of maize if there

are no changes in sowing dates and fixed cultivars

throughout the simulation period (1981–2007). The

CONTROL runs show a declining trend in yields of

maize except for Benxi station, which matches our

expectation that the yields of maize will generally

decrease in a climate warming situation unless mitigat-

ing actions are taken. During the past 27 years, solar

radiation in Benxi station increased 0.17 MJ m�2 day�1

per decade (Table 1); this is a possible reason for the

increase in maize yield even though the growing period

was considered reduced due to the climate warming.

At five stations, climate change led to yield decreases

that vary from 5% at Qinggang station to 18% at

Xinmin station in the past 27 years (1981–2007). More-

over, maize yield decreased by 4–22% for each 1 °Cincrease in growing-season mean temperature.

Simulated impacts of sowing dates and cultivar selectionon growing period of maize

In this and the next section, we use APSIM to deter-

mine to what portion of the observed phenology and

yield trends can be attributed to the trend towards ear-

lier sowing, and what portion can be attributed to

changes in cultivars. The results above indicate that

recent climate trends were a negative impact on maize

production if there were no changes in sowing date

and cultivars in NEC. However, in the past 27 years,

farmers have practiced earlier sowing dates (Table 3)

and the thermal time from sowing to maturity has

increased (Fig. 4), indicating that farmers adopted new

‘longer season’ cultivars, which was also reported by

Chen et al. (2011).

(a) (b)

Fig. 3 Relationships between sowing date with monthly average temperature in April (a) and May (b) among selected locations across

Northeast China (NEC). Straight lines are the linear regression lines. All relationships were highly significant (P < 0.001).


3486 Z. LIU et al.

Table 5 shows the effects of changing sowing dates

and cultivars on the length of vegetative period, repro-

ductive period and the whole period of maize in NEC.

Earlier sowing with fixed cultivars, in the four stations

mentioned above (Table 3), led to a slight extension of

vegetative period except for the Boli station, but this

trend is not significant. However, earlier sowing led to

the opposite trend in the reproductive period compared

with the vegetative period, resulting in a slight shorten-

ing of the length of reproductive period. Because earlier

sowing pushes the vegetative period into cooler times

of year (early May), the crop requires more days to

accumulate a given number of TTveg. However, earlier

sowing pushes the reproductive period into warmer

days of the summer, so fewer days are required to

accumulate a given number of TTreprod (Table 5).

For runs with changes in cultivars but with fixed

sowing dates, the length of vegetative period was

extended. Similarly, changes in cultivars led to an

extension of the reproductive period but with a larger

Table 4 Simulated trends of length of vegetative period (sowing to flowering), reproductive period (flowering to maturity), whole

period (sowing to maturity) and yields of maize under CONTROL run at the six study sites over the period 1981–2007

Items Qinggang Boli Tailai Siping Xinmin Benxi

Vegetative period (days per decade) �2.8** �2.1* �2.8** �3.1** �1.8* �2.4**

Reproductive period (days per decade) �2.0 �0.6 �1.4 �1.5* �0.5 �2.4*

Whole period (days per decade) �4.7** �2.7 �4.3** �4.7** �2.3 �4.8**

Yield (kg ha�1per decade) �167 �370 �728* �630 �438 432


y = 0.99 x – 1028R 2 = 0.01

y = 6.47 x – 12 271R 2 = 0.42**

Year

Ther

mal

tim

e (o

C d

)

Qinggang Boli

Tailai Siping

Xinmin Benxi

y = 6.12 x – 11 068R 2 = 0.26**

y = 2.46 x – 4172R 2 = 0.12

y = 3.96 x – 6804R 2 = 0.17*

y = 6.15 x – 11 492R 2 = 0.38**

y = 4.81 x – 8563R 2 = 0.24**

y = 6.02 x – 11 374R 2 = 0.31**

0

400

800

1200

1600

TTvegTTreprod

y = 11.14 x – 21 120R 2 = 0.55**

y = 6.78 x – 12 812R 2 = 0.35**

0

400

800

1200

1600

y = 0.89 x – 624R 2 = 0.01

y = 3.86 x – 6841R 2 = 0.17*

0

400

800

1200

1600

1980 1990 2000 2010 1980 1990 2000 2010

Fig. 4 Time series of thermal time during the vegetative period (open circles) and reproductive period (open boxes) for maize at the

six study sites in Northeast China (NEC) from 1981 to 2007. Straight lines are the linear regression lines against years. **P < 0.01, and

*P < 0.05.



magnitude (up to 10.0 days per decade), which is in

agreement with the changing of TTveg and TTreprod

(Fig. 4). Thus, the length of whole period extended sig-

nificantly (P < 0.01) due to changes in cultivars with

higher thermal time requirements (Table 5).

The observed increase in the length of vegetative per-

iod (from sowing to flowering) was driven by earlier

sowing and changing to longer growing-season culti-

vars, whereas the increase in the length of reproductive

period (from flowering to maturity) was mostly driven

by a shift to longer growing-season cultivars.

Simulated impacts of sowing dates and selection ofcultivars on maize yields

There are slight increases in yields due to the earlier

sowing, and the Tailai station had the highest yield

increases because it had the greatest trend towards ear-

lier sowing (6.6 days per decade). However, these

trends were not significant (Fig. 5).

Selection of longer season cultivars alone resulted in

a significant increasing trend in yields, ranging from

470 to 1030 kg ha�1 per decade, except for Benxi sta-

tion, for which the trend was not significant (Fig. 5).

The yield trends are equivalent to 5% to 14% per dec-

ade across the six locations, indicating that the chang-

ing of cultivars in the past 27 years has led to 13% to

38% yield increases in different locations. The yield

increase results from the increasing the value of ther-

mal time in the growing season, especially in the repro-

ductive period, which allows for more grain

production. The relation between maize yield trends

and thermal time trends are shown in Fig. 6. The

highest yield trends were found in the locations where

the TTreprod trends are larger (Yield trend (% per

decade) = 0.97 9 TTreprod trend (% per decade) + 0.02).

The combined effect of trends in sowing dates and

changes in cultivars is similar to the effect of trends in

cultivars alone, since changes in sowing date had a

slight effect on maize yield in NEC. This combined

effect on yields is positive in all stations, increasing

6–12% per decade in different locations, although the

increase is not statistically significant in Siping, Xinmin

or Benxi stations. The yield trends indicate that the

change of sowing dates and cultivars in the past

27 years has led to 17–33% yield increases among loca-

tions in NEC. Moreover, the southern sites have the

least significant benefits of the sowing date shifts and

cultivars selections (Fig. 6), and these sites are also

located in the areas with lower maize production

(Fig. 1).

Overall, for simulations with earlier sowing dates,

the yield changes were positive (up to 4% in Tailai sta-

tion over the past 27 years). Selection of longer season

cultivars resulted in yield increases with larger magni-

tude, varying from 13% to 38% over the past 27 years

in different locations. Therefore, the negative impact of

climate warming was offset to a large extent by the

changing of sowing dates and cultivars in NEC.

Discussion

Maize yields have significantly increased in many

countries during the past century. Globally, maize

yields doubled during 1961–2002, from 1.9 to

4.3 Mg ha�1, a linear increase of 61 kg ha�1 yr�1

Table 5 The effects of sowing dates and change in cultivars on the length of periods: vegetative period (sowing to flowering),

reproductive period (flowering to maturity) and the whole period (sowing to maturity) of maize at the six study sites over the per-

iod 1981–2007. The data show the trends of differences between the experiment runs (SOWING, CULTIVAR, and SOWING + CUL-

TIVAR) and CONTROL run. SOWING shows the difference when sowing dates vary but cultivars remain fixed, CULTIVAR shows

the difference when cultivars vary but sowing dates remain fixed, and SOWING + CULTIVAR shows the difference when both

sowing dates and cultivars are changing (see Table 2). Trends are given in days per decade

Growing period Experiment runs Qinggang Boli Tailai Siping Xinmin Benxi

Vegetative period SOWING 0.4 �0.1 2.6** �2.1** �1.2 0.7

CULTIVAR 3.9** 0.0 7.3** 3.6** 0.0 3.4**

SOWING + CULTIVAR 4.5** �0.1 10.0** 1.5* �1.2 4.2**

Reproductive period SOWING �0.3 �0.2 �0.7 0.5 0.6 �0.1

CULTIVAR 8.4** 5.8** 10.0** 3.2** 2.9** 7.4**

SOWING + CULTIVAR 6.8** 5.5** 7.0** 4.4** 4.2** 6.9**

Whole period SOWING 0.1 �0.3 1.9** �1.6** �0.6 0.6

CULTIVAR 12.3** 5.8** 17.4** 6.9** 2.9** 10.8**

SOWING + CULTIVAR 11.3** 5.5** 17.0** 5.9** 3.0** 11.1**



3488 Z. LIU et al.

(Duvick, 2005). Therefore, a better understanding of

these yield increases under a changing climate in NEC

will help ensure sustainable yields in the future.

Numerous studies have quantified factors responsible

for the historical yield increases using the statistical

and/or empirical methods (Edm�e et al., 2005; Kucharik,

2008; Egli & Cornelius, 2009). However, crop models

better capture the true mechanisms by which these fac-

tors’ impact yield, rather than empirically calculating

the contributions to yield increases (Anapalli et al.,

2005) even though the APSIM-Maize model assumed

that the start of each growing stage was determined by

accumulation of thermal time with no water and

nutrient stresses (Boote et al., 2013).

Over the last 27 years, if there was no change in the

cultivars grown by farmers and no improvement in

management practices, the simulations indicate that cli-

mate change would have caused a negative impact on

the duration of growing period and yields of maize in

NEC. Because the warmer climate would speed up

Fig. 5 The effects of sowing dates and cultivar changes on simulated maize yield. Bars indicate the trends in kg ha�1 per decade

(1981–2007) calculated from differences between the experiment runs (SOWING, CULTIVAR, and SOWING + CULTIVAR) and

CONTROL run at the six study sites in Northeast China (NEC). In each subfigure, Run2 (SOWING run) shows the difference when

sowing dates vary but cultivars remain fixed, Run3 (CULTIVAR run) shows the difference when cultivars vary but sowing dates

remain fixed, and Run4 (SOWING + CULTIVAR run) shows the difference when both sowing dates and cultivars change (see Table 2).

**P < 0.01, and *P < 0.05. The solid circles show locations of stations used in this study.



development and lead to a reduction in the number of

days in the growing period for maize and therefore a

shorter time for processes like photosynthesis and the

filling of grain there would be a reduction in yield. Sim-

ilar simulation results were found in maize (Lobell &

Field, 2007; Chen et al., 2010b; Liu et al., 2010). Further-

more, our results indicate that maize yield decreased

by 4–22% for each 1 °C increase in growing-season

mean temperature in NEC. For maize in the United

Staes, previous modelling studies predict that yield of

maize declines by 11–22% for a 3 °C temperature

increase (Brown & Rosenberg, 1997). However, the

observed sensitivity is higher than previously expected.

Lobell & Asner (2003) showed a roughly 17% relative

decrease in observed maize yield occurred for each

degree increase in the growing-season temperature

over a 17-year period. At the global scale, maize exhib-

ited negative impacts for global net loss of 3.8%, rela-

tive to what would have occurred without the climate

trends in 1980–2008 (Lobell et al., 2011), which indi-

cated that maize yield declined by 8.3% for each 1 °Cincrease in average monthly minimum and maximum

temperatures during the growing season (Lobell &

Field, 2007).

The above discussion pertains to the climate change

impacts under the situation of no change in crop man-

agement in response to new climatic conditions. How-

ever, in NEC, yields of maize have steadily increased

since the 1980s (Chen et al., 2011; Liu et al., 2012) since

the options for autonomous adaptation were realized

by farmers; this is similar to the results reported by

Olesen & Bindi (2002), in high latitudes of Europe

increases in air temperature can have beneficial impacts

on crop productivity if adaptations are taken. Our sim-

ulation design attempted to isolate the effects of sowing

date and the selection of longer season cultivars on

maize production. Therefore, we allow irrigation appli-

cation to respond to water demand in all four runs. We

also assumed no N limitation, although fertilizer appli-

cation may need to increase to meet the possibly

increased demand from longer season cultivars.

The simple possible adaptation to the new climatic

conditions that we explored in our study was a shift in

the planting dates to allow crop development during

more favourable conditions. The simulations showed

that advancing the sowing date of maize in NEC is an

effective measure to compensate the negative impact of

reduced crop growth period on crop yield due to the

increase in temperature, probably because earlier sow-

ing date led to an extension of vegetative period. Stud-

ies of maize in central USA suggest a similar result,

which indicate that a significant relationship (P < 0.05)

existed between first differences of planting dates and

yields of maize in six of the 12 states (Kucharik, 2008).

This is consistent with various field studies (Swanson &

Wilhelm, 1996; Kwabiah, 2004; Williams, 2008). How-

ever, it is likely that this result is also cropping system

dependent; for instance, the results for double cropping

systems in North China Plain indicates that delaying

the planting date of winter wheat led to overall 4–6%increase in total grain yield of the wheat–maize system

(Wang et al., 2012).

A secondary management change that we explored

in our study was a shift towards planting higher yield-

ing cultivars in these areas so they can take advantage

of the resulting growing-season extension. The newer

cultivars have a longer growing period, especially for

the period of grain fill (calculated as time from flower-

ing to maturity) in NEC, which increases the length of

time devoted to yield accumulation. Thus, the newer

cultivars increased the efficiency in use of the growing

season in NEC. Our modelling results also indicated

that an increase in thermal time requirements is highly

effective in overcoming the negative effects of climate

change and has contributed significantly to crop yield

increase in recent decades. Several studies showed that

cultivar improvements have contributed to the yield

increase of maize in many regions of the world during

the latter half of the 20th century, especially in those

places where maize is grown as a commercial crop

(Duvick, 2005). For example, in the United States, maize

yield increases were attributed (about 50–60%) to culti-

var improvements (Duvick & Cassman, 1999). Niu et al.

(2013) provide an estimate of 31.3% for the contribution

y = 0.97 x + 0.02

(R 2 = 0.75, P < 0.05)

0%

5%

10%

15%

0% 5% 10% 15%

Qinggang

Boli

Tailai

Siping

Xinmin

Benxi

Thermal time trend (decade–1)

Yie

ld tr

end

(dec

ade–

1 )

Fig. 6 The relationship between yield trends expressed in

percent per decade and increase in thermal time expressed in

percent per decade for reproductive period. The linear

regression equations are shown and location names are shown

in regular font.


3490 Z. LIU et al.

of genetic improvement to maize yield increases in

NEC at high-yielding cultivation levels, during the per-

iod 1950s to 2000s. However, we emphasize that other

cultivar genetic advances (e.g. leaf angle, drought toler-

ance, more tolerance of stresses due to higher plant

density and so on) have simultaneously improved over

the past decades, which was not taken into consider-

ation in this study and should therefore be considered

in future studies. In addition, a subsequent study

should quantify the yield gains of higher yielding

modern cultivars in more detail.

Acknowledgements

This study was supported by the Ministry of Science and Tech-nology of China (‘973’ project: Grant No. 2010CB951502 andNational Key Technology R & D Programme: Grant No.2012BAD20B04), the Special Fund for Agro-scientific Researchin the Public Interest (No. 200903003), and also supported bythe China Scholarship Council (CSC). Support was alsoprovided by the School of Natural Resources at the Universityof Nebraska-Lincoln.

References

Alexandrov V, Eitzinger J, Cajic V, Oberforster M (2002) Potential impact of climate

change on selected agricultural crops in north-eastern Austria. Global Change

Biology, 8, 372–389.

Anapalli SS, Ma L, Nielsen DC, Vigil MF, Ahuja LR (2005) Simulating planting date

effects on corn production using RZWQM and CERES-Maize models. Agronomy

Journal, 97, 58–71.

Asseng S, Jamieson PD, Kimball B, Pinter P, Sayre K, Bowden JW, Howden SM (2004)

Simulated wheat growth affected by rising temperature, increased water deficit

and elevated atmospheric CO2. Field Crops Research, 85, 85–102.

Asseng S, Foster I, Turner NC (2011) The impact of temperature variability on wheat

yields. Global Change Biology, 17, 997–1012.

Black JN, Bonython CW, Prescott JA (1954) Solar radiation and the duration of

sunshine. Quarterly Journal of the Royal Meteorological Society, 80, 231–235.

Boote KJ, Jones JW, White JW, Asseng S, Lizaso JI (2013) Putting mechanisms into

crop production models. Plant, Cell and Environment, doi: 10.1111/pce.12119.

Bouman BAM, van Laar HH (2006) Description and evaluation of the rice growth

model ORYZA2000 under nitrogen-limited conditions. Agricultural Systems, 87,

249–273.

Bouman BAM, Kropff MJ, Tuong TP, Wopereis MCS, ten Berge HFM, van Laar HH

(2001) ORYZA2000: Modeling Lowland Rice. International Rice Research Institute/

Wageningen University and Research Centre, Los Banos, Philippines/Wagenin-

gen, The Netherlands.

Brown RA, Rosenberg NJ (1997) Sensitivity of crop yield and water use to change in a

range of climatic factors and CO2 concentrations: a simulation study applying

EPIC to the central USA. Agricultural and Forest Meteorology, 83, 17l–203.

Chen C, Wang E, Yu Q (2010a) Modelling the effects of climate variability and water

management on crop water productivity and water balance in the North China

Plain. Agricultural Water Management, 97, 1175–1184.

Chen C, Wang E, Yu Q, Zhang Y (2010b) Quantifying the effects of climate trends in

the past 43 years (1961–2003) on crop growth and water demand in the North

China Plain. Climatic Change, 100, 559–578.

Chen C, Lei C, Deng A, Qian C, Hoogmoed W, Zhang W (2011) Will higher minimum

temperatures increase corn production in Northeast China? An analysis of histori-

cal data over 1965–2008. Agricultural and Forest Meteorology, 151, 1580–1588.

Chen X, Chen F, Chen Y et al. (2013) Modern maize hybrids in Northeast China exhi-

bit increased yield potential and resource use efficiency despite adverse climate

change. Global Change Biology, 19, 923–936.

Duvick DN (2005) The contribution of breeding to yield advances in maize (Zea Mays

L.). Advances in Agronomy, 86, 83–145.

Duvick DN, Cassman KG (1999) Post-green revolution trends in yield potential of

temperate maize in the north-central United States. Crop Science, 39, 1622–1630.

Edm�e SJ, Miller JD, Glaz B, Tai PYP, Comstock JC (2005) Genetic contribution to

yield gains in the Florida sugarcane industry across 33 years. Crop Science, 45, 92–97.

Egli DB, Cornelius PL (2009) A regional analysis of the response of soybean yield to

planting date. Agronomy Journal, 101 (2), 330–335.

Gao Q, Feng G, Wang Z (2010) Present situation of fertilizer application on spring

maize in Northeast China. Chinese Agricultural Science Bulletin, 26, 229–231.

Gungula DT, Kling JG, Togun AO (2003) CERES-Maize predictions of maize phenol-

ogy under nitrogen-stressed conditions in Nigeria. Agronomy Journal, 95, 892–899.

Gupta SC (1985) Predicting corn planting dates for moldboard and no-till tillage

systems in the Corn Belt. Agronomy Journal, 77, 446–455.

Jones HG (1992) Plant and Microclimate: A Quantitative Approach to Environmental Plant

Physiology (2nd edn). Cambridge University Press, Cambridge.

Jones CA, Kiniry JR (1986) CERES Maize: A Simulation Model of Maize Growth and

Development (1st edn). Texas A & M University Press, College Station.

Ju H, Xiong W, Xu Y, Lin E (2007) Climate change and its impacts in Northeast China.

Chinese Agricultural Science Bulletin, 23, 345–349.

Keating BA, Carberry PS, Hammer GL et al. (2003) An overview of APSIM, a model

designed for farming systems simulation. European Journal of Agronomy, 18, 267–288.

Kucharik CJ (2006) A multidecadal trend of earlier corn planting in the Central USA.

Agronomy Journal, 98, 1544–1550.

Kucharik CJ (2008) Contribution of planting date trends to increased maize yields in

the Central United States. Agronomy Journal, 100, 328–336.

Kwabiah AB (2004) Growth and yield of sweet corn (Zea mays L.) cultivars in

response to planting date and plastic mulch in a short-season environment. Scien-

tia Horticulturae, 102, 147–166.

Liu Y, Wang E, Yang X, Wang J (2010) Contributions of climatic and crop varietal

changes to crop production in the North China Plain, since 1980s. Global Change

Biology, 16, 2287–2299.

Liu Z, Yang X, Hubbard KG, Lin X (2012) Maize potential yields and yield gaps in

the changing climate of Northeast China. Global Change Biology, 18, 3441–3454.

Lobell DB, Asner GP (2003) Climate and management contributions to recent trends

in U.S. agricultural yields. Science, 299, 1032.

Lobell DB, Field CB (2007) Global scale climate–crop yield relationships and the

impacts of recent warming. Environmental Research Letter, 2, 1–7.

Lobell DB, Schlenker W, Costa-Robert J (2011) Climate trends and global crop pro-

duction since 1980. Science, 333, 616–620.

Ludwig F, Asseng S (2006) Climate change impacts on wheat production in a Medi-

terranean environment in Western Australia. Agricultural Systems, 90, 159–179.

Luo Q, Bellotti W, Williams M, Wang E (2009) Adaptation to climate change of wheat

growing in South Australia: analysis of management and breeding strategies. Agri-

culture, Ecosystems and Environment, 129, 261–267.

Ma S, Wang Q, Guo J, Shen Z (2007) Geographical change law of effects of corn plas-

tic mulching on increasing temperature and production in Northeast China. Trans-

actions of the Chinese Society of Agricultural Engineering, 23, 66–71.

McCown RL, Hammer GL, Hargreaves JNG, Holzworth DP, Freebairn DM (1996)

APSIM: a novel software system for model development, model testing and simu-

lation in agricultural systems research. Agricultural Systems, 50, 255–271.

National Bureau of Statistics of China (NBSC) (2001–2008) China Statistics Yearbook.

China Statistics Press, Beijing.

Niu X, Xie R, Liu X, Zhang F, Li S, Gao S (2013) Maize yield gains in Northeast China

in the last six decades. Journal of Integrative Agriculture, 12 (4), 630–637.

Olesen JE (2005) Climate change and CO2 effects on productivity of Danish agricul-

tural systems. Journal of Crop Improvement, 13, 257–274.

Olesen JE, Bindi M (2002) Consequences of climate change for European agricultural

productivity, land use and policy. European Journal of Agronomy, 16, 239–262.

Porter JR (2005) Rising temperatures are likely to reduce crop yields. Nature, 436, 174.

Sacks WJ, Kucharik CJ (2011) Crop management and phenology trends in the U.S.

Corn Belt: impacts on yields, evapotranspiration and energy balance. Agricultural

and Forest Meteorology, 151, 882–894.

Sadras VO, Monzon JP (2006) Modelled wheat phenology captures rising tempera-

ture trends: shortened time to flowering and maturity in Australia and Argentina.

Field Crops Research, 99, 136–146.

Southworth J, Randolph JC, Habeck M, Doering OC, Pfeifer RA, Rao DG, Johnston JJ

(2000) Consequences of future climate change and changing climate variability on

maize yields in the midwestern United States. Agriculture, Ecosystems and Environ-

ment, 82, 139–158.

Swanson SP, Wilhelm WW (1996) Planting date and residue rate effects on growth,

partitioning, and yield of corn. Agronomy Journal, 88, 205–210.



Tao F, Yokozawa M, Xu Y, Hayashi Y, Zhang Z (2006) Climate changes and trends in

phenology and yields of field crops in China, 1981–2000. Agricultural and Forest

Meteorology, 138, 82–92.

Tubiello FN, Donatelli M, Rosenzweig C, Stockle CO (2000) Effects of climate change

and elevated CO2 on cropping systems: model predictions at two Italian locations.

European Journal of Agronomy, 12, 179–189.

Wang E, Robertson MJ, Hammer GL et al. (2002) Development of a generic crop

model template in the cropping system model APSIM. European Journal of Agron-

omy, 18, 121–140.

Wang J, Wang E, Yang X, Zhang F, Yin H (2012) Increased yield potential of wheat-

maize cropping system in the North China Plain by climate change adaptation.

Climatic Change, 113, 825–840.

Williams MM II (2008) Sweet corn growth and yield responses to planting dates of

the North Central United States. HortScience, 43, 1775–1779.

Wilson DR, Muchow RC, Murgatroyd CJ (1995) Model analysis of temperature and

solar radiation limitations to maize potential productivity in a cool climate. Field

Crops Research, 43, 1–18.

Xiong W, Holman I, Lin E, Conway D, Li Y, Wu W (2012) Untangling relative con-

tributions of recent climate and CO2 trends to national cereal production in

China. Environmental Research Letters, 7, 1–9.

Yang X, Lin E, Ma S et al. (2007) Adaptation of agriculture to warming in Northeast

China. Climatic Change, 84, 45–58.

Yun Y, Fang X, Wang Y, Tao J, Qiao D (2005) Main grain crops structural change and

its climate background in Heilongjiang province during the past two decades.

Journal of Natural Resources, 20, 697–705.

Zhang T, Zhu J, Yang X (2008) Non-stationary thermal time accumulation reduces the

predictability of climate change effects on agriculture. Agricultural and Forest Mete-

orology, 148, 1412–1418.

Zheng B, Chenu K, Dreccer MF, Chapman SC (2012) Breeding for the future: what

are the potential impacts of future frost and heat events on sowing and flowering

time requirements for Australian bread wheat (Triticum aestivium) varieties? Global

Change Biology, 18, 2899–2914.


3492 Z. LIU et al.

Documents

Negative effects of climate warming on maize yield are reversed by the changing of sowing date and cultivar selection in Northeast China