Supplementary Material: Variations in the potential climatic suitability
distribution patterns and grain yields for spring maize in Northeast China
under climate change
J. Zhao • X. Yang ( ) •Z. Liu • S. Lv
College of Resources and Environmental Sciences, China Agricultural University, 100193, Beijing,
China
E-mail: yangxg@cau.edu.cn
Phone: +86 010 62733939
Fax: +86 010 62733939
J. Wang
College of Resources and Environmental Sciences, China Agricultural University, 100193, Beijing,
China
Ningxia Institute of Meteorological Sciences, Yinchuan 750002, P.R.China.
S. Dai
School of Natural Resources, University of Nebraska Lincoln, Lincoln, NE 68583, USA.
1
SI 1. Geographical shift of the potential cropping areas for spring maize
Growing degree-days (GDD) during the growing season for spring maize was calculated in
each year for every study location in Northeast China (NEC). GDD during the growing season for
spring maize with the 20th percentile in each period was used to measure the climatic cropping
safety for spring maize. We used inverse distance weighting (IDW, SI 6) method in ArcGIS to
interpolate GDD across the entire region. Areas with GDD during the growing season for spring
maize greater than 2100 oC·d were defined as potential spring maize cropping areas (Gong, 1988;
Liu et al., 2010).
Fig. SI. 1 presents the geographical shift of northern limit for spring maize cropping and the
expansion of potential cropping area for spring maize in NEC during 1961–2010. In the context of
climate warming, climatic potential cropping area for spring maize expanded to the entire area of
Liaoning and Jilin, and northern Heilongjiang. Compared to period I (1961–1980), the total
regional potential maize cropping area increased by approximately 38679.4 km2 during period II
(1981–2010) in NEC.
SI 2. Description on the crop parameters, soil data and APSIM model set for
yield potential
Spring maize trial data for this study were collected from the 6 Agrometeorological Experimental
Stations operated by the China Meteorological Administration. The data set includes spring maize
phenological dates (emergence, end of juvenile, flowering and maturity dates), aboveground dry matter
and grain yields. Based on the observation, the cultivar parameters were calibrated with a
trial-and-error method and validated by Liu et al. (2012a, b). The comparisons included simulated and
observed flowering/maturity dates, total aboveground dry matter, and grain yield indices (i.e., thermal
time required from emergence to end of juvenile stage and from flowering to maturity, maximum grain
numbers per head, and grain-filling rate). The cultivar parameters were shown in Table SI. 1.
The soil data in NEC were collected from China Soil Scientific Database, including the soil bulk
density, saturated volumetric water content, drained upper limit, and 15Bar lower limit in different soil
layers. Through a linear interpolation weighted by soil type information extracted at each weather
station, the interpolated soil parameters were used for the simulations at weather stations which are not
collocated with experiment stations (Fig. 1). The range of soil profile properties across NEC is
summarized in Table SI. 2.
Based on the crop parameters and soil data, the APSIM-Maize model was run with historical
climate data (1961–2010) to quantify the yield potential for spring maize in NEC. At each site, we
selected the cultivar in Table SI. 1 for which the thermal time (sowing to maturity) was similar to the
thermal time of the local cultivar. To eliminate the impact of adaptation, the simulated crops were sown
on May 6, May 1, and April 20 each year in Heilongjiang, Jilin, and Liaoning, respectively. These dates
are the average actual sowing date from the experiment stations in each province, and a single maize
cultivar was used at each site for the entire study period. Water applications were set equal to the water
use of the maize crop and nutrient inputs were taken as no limiting to eliminate the effect of water and
nutrient stresses on simulated maize yield. Planting density (70000 plants·ha-1), sowing depth (50 mm),
and other management details are kept constant throughout the simulation period.
2
SI 3. Yield potential level and yield stability index
SI 3.1. Grading standard for yield potential level and yield stability
According to the grading standard for potential climatic suitability, the method of cumulative
frequency distribution (CFD) was used to grade the yield potential level and yield stability. For each
decade, the mean and coefficient of variation (CV) of yield potential were calculated for every station
in the potential maize cropping areas in NEC. According to the CFD in Table SI. 3, the level and CV of
yield potential grading can be calculated, which is listed in another column in Table SI. 3.
Inverse distance weighting (IDW, SI 6) method in ArcGIS was used to interpolate the mean and
CV of yield potential across the potential maize cropping area. Based on the grading values, the
potential maize cropping area was divided into 5 sub-regions with different yield potential level and
yield stability.
SI 3.2. Variations in yield potential level
Fig. SI. 2 shows the variations in yield potential level for spring maize in NEC from 1961 –2010.
Mean yield potential decreased from west to east in NEC, and the lowest yield potential for spring
maize occurred in northern Heilongjiang and western Jilin. This geophysical distribution pattern of
yield potential may be related to the decrease in solar radiation from west to east and decrease in GDD
from southwest to northeast (Liu et al., 2012; Liu et al., 2009). During period I, the very high (VH)
zone
was
located
in
the
west
of
Sanchahe-Changchun-Siping
in
Jilin,
the
west
of
Kaiyuan-Heishan-Yingkou-Xiongyue-Wafangdian, and Shenyang and Qingyuan in Liaoning, accouting
for 18% of the entire regional land area (Fig. SI. 2a). The high (H) zone and moderately high (MH)
zone were located throughout Liaoning, central Jilin and the west of Helen-Suihua-Harbin-Shangzhi in
Heilongjiang, covering 10% and 14% of the entire regional land area, respectively. Compared with
period I, the VH zone was scattered in western Jilin and Liaoning, accouting for only 1% of the entire
regional land area, yet was replaced by H zone and MH zone in period II (Fig. SI. 2b). Meanwhile, the
areas of marginally high (mH) zone and no high (NH) zone increased by 3% and 8% of the entire
regional land area during period II as compared to period I (Fig. SI. 2).
SI 3.3. Variations in yield potential stability
Fig. SI. 3 shows the variations in yield potential stability for spring maize in NEC during 1961–
2010. Yield potential stability for spring maize was relatively lower in northern Heilongjiang, eastern
Liaoning and Jilin. During period I, the very stable (Vs) zone was located in the west of
Sanchahe-Changchun-Siping in Jilin and the west of Keshan-Mingshui-Suihua-Shangzhi in
Heilongjiang, accounting for 23% of the entire regional land area (Fig. SI. 3a). The marginally stable
(ms) zone and no stable (Ns) zone were located in the north of Beian-Hegang in northern Heilongjaing,
the east of Jingyu-Dunhua in eastern Jilin, and southern Liaoning, accounting for 12% and 9% of the
entire regional land area, respectively. Compared with period I, the Vs zone was scattered in western
Liaoning, Heilongjiang, and central Heilongjiang, accounting for only 3% of the entire regional land
area (Fig. SI. 3b). The ms zone and Ns zone expanded to the east of Qingyuan-Benxi-Wafangdian in
eastern Liaoning, entire Jilin, and northern and southeastern Heilongjiang, accounting for 34% and 11%
of the entire regional land area, respectively.
3
SI 4. Discussions on the variations in yield potential level and yield stability
In the potential maize cropping area, maize yield potential decreased from west to east, and the
lowest maize yield potential occurred in northern Heilongjiang and western Jilin. With the
APSIM-Maize model, we obtained similar distribution patterns of photosynthesis and temperature
productivity for spring maize as with Agro-Ecological Zone (AEZ, SI 5) model together with level
correction method (Chen et al., 2011; Wei et al., 2010; Zhong et al., 2012a). In NEC, solar radiation
decreased from west to east, and the temperature decreased from southwest to northeast (Liu et al.,
2012; Liu et al., 2009). The geophysical distribution pattern of spring maize yield potential was
determined by temperature and solar radiation distribution patterns. Climate warming could expedite
crop growing and shorten the growing period for maize (Liu et al., 2013a; Liu et al., 2012; Wang et al.,
2010), which would negatively impact the grain yield. Yield stability is an important index to evaluate
the climatic suitability for maize production in the long run. In the past 46 years, the fluctuation in the
maize yield affected by temperature showed a decreasing trend in NEC (Zhang et al., 2009). In addition,
the increasing trend in CV of sunshine duration was detrimental to the yield potential stability for
spring maize in NEC.
SI 5. Description on the Agro-Ecological Zone (AEZ) model
The Agro-Ecological Zone (AEZ) model was developed by the Food and Agriculture
Organization of the United Nations (FAO) and the International Institute for Applied Systems
Analysis (IIASA) as a method of calculating potential productivity (Fischer and Sun, 2001). The
AEZ model is based on principles of land evaluation and can serve as an evaluative framework for
biophysical limitations and production potential of major food and fiber crops under various levels
of inputs and management scenarios at global and regional scales. In China, the AEZ model was
widely used to calculate the potential productivity, which is dependent upon the supply of water,
energy, nutrients, and physical support to plants (Chen et al., 2011; Liu et al., 2014; Zhong et al.,
2012a; Zhong et al., 2012b).
SI 6. Descriptions on the inverse distance weighted (IDW) interpolation
According to the ‘help’ module in ArcMap 10.0, inverse distance weighted (IDW)
interpolation explicitly implements the assumption that things that are close to one another are
more alike than those that are farther apart. To predict a value for any unmeasured location, IDW
uses the measured values surrounding the prediction location. The measured values closest to the
prediction location have more influence on the predicted value than those farther away. IDW
assumes that each measured point has a local influence that diminishes with distance. It gives
greater weights to points closest to the prediction location, and the weights diminish as a function
of distance, hence the name inverse distance weighted. IDW interpolation method was widely
used to interpolate the meteorological elements, yields across a region (Chen et al., 2012; Li et al.,
2014; Liu et al., 2013b).
4
References
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5
Figures
Fig. SI. 1 Geographical shift of the northern limit and expansion of potential climatic cropping
region for spring maize in Northeast China (NEC) during 1961–2010. The inset map shows the
position of expanded potential cropping region (shaded area) on the NEC map. Different styles of
bold line indicate different northern limits for spring maize during the two sub periods.
6
Fig. SI. 2 Variations in yield potential level for spring maize during period I (a) and period II (b)
in Northeast China. Different types of gray shade indicate different yield potential level zones for
spring maize. The pie charts show the percentages of each yield potential level zone area for
spring maize to the entire regional land area.
7
Fig. SI. 3 Variations in yield potential stability for spring maize during period I (a) and period II (b)
in Northeast China. Different types of gray shade indicate different yield potential level zones for
spring maize. The pie charts show the percentages of each yield potential level zone area for
spring maize to the entire regional land area.
8
Table SI. 1
Parameters for different varieties in APSIM-Maize at six study locations in Northeast China
Location
Hybrid
Thermal
time
Thermal
time
required
from
required
from
emergence to end
of juvenile
flowering to end of
Photoperiod
Maximum grain
Grain-filling
slope
numbers
rate
(°C·h-1)
maturity
(°C·d)
(°C·d)
per
(mg·grain-1·d-1)
head
Harbin
Sidan 19
90
790
22.0
600
9.0
Tailai
Baidan 9
140
700
23.0
650
10.0
Helen
Haiyu 6
50
720
23.0
550
9.5
Tonghua
Jidan 101
100
700
23.0
650
9.5
Huadian
Jidan 120
75
680
23.0
650
9.0
Benxi
Danyu 13
110
730
23.0
600
9.5
9
Table SI. 2
The range of soil profile properties across NEC.
Soil depth
Bulky density
Saturated volumetric water
Drained upper limit
15Bar lower limit
(cm)
(g·cm-3)
(mm·mm-1)
(mm·mm-1)
(mm·mm-1)
0–10
1.04–1.58
0.30–0.45
0.23–0.42
0.07–0.19
10–20
1.14–1.51
0.31–0.48
0.23–0.42
0.08–0.18
20–30
1.14–1.53
0.28–0.49
0.21–0.42
0.07–0.19
30–50
1.15–1.51
0.26–0.49
0.21–0.39
0.07–0.18
50–70
1.24–1.51
0.27–0.48
0.21–0.38
0.06–0.20
70–90
1.26–1.56
0.32–0.45
0.21–0.41
0.06–0.21
90–100
1.28–1.55
0.34–0.46
0.21–0.41
0.06–0.22
10
Table SI. 3
Grading standard for
Yield potential level
yield potential level and yield stability for spring maize in Northeast China
CFD (%) Mean yield
Yield potential stability CFD (%) CV of yield potential
potential
(kg/ha)
Very High (VH)
<20
>13914
Very Stable (Vs)
80-100
<0.14
High (H)
20-40
11952-13914
Stable (s)
60-80
0.14-0.17
Moderately High (MH) 40-60
10491-11952
Moderately Stable (Ms) 40-60
0.17-0.18
Marginally High (mH) 60-80
9560-10491
Marginally Stable (ms) 20-40
0.18-0.23
No High (NH)
<9560
No Stable (Ns)
>0.23
80-100
11
<20