Supplementary Material: Variations in the Potential Climatic Suitability Distribution Patterns

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

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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 NebraskaLincoln, Lincoln, NE 68583, USA.

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, climaticpotential 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 AgrometeorologicalExperimental Stations operated by the ChinaMeteorological Administration. The data set includes spring maize phenologicaldates (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.

Thesoil data in NEC were collected from China Soil Scientific Database,includingthe soil bulk density,saturated volumetric water content, drained upper limit, and 15Bar lower limit in different soil layers.Through alinear interpolation weighted by soil type informationextracted at each weather station, the interpolatedsoil parameters were used for the simulations at weather stationswhich are not collocated with experiment stations(Fig. 1).The range of soil profile properties across NEC issummarized in Table SI.2.

Based on the crop parameters and soil data, the APSIM-Maize model was run withhistorical climate data (1961–2010) to quantify the yield potential for spring maize in NEC. At each site, we selected thecultivar 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 wateruse of the maize crop and nutrient inputs were taken as nolimitingto eliminate the effect of water and nutrient stresseson simulated maize yield. Planting density (70000 plants·ha-1), sowing depth (50 mm),and other management details are kept constant throughoutthe simulation period.

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 foronly 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 during1961–2010. 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 for23% 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 for12% 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.

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 withlevel 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).

References

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Figures

Fig. SI. 1Geographical shift of the northern limit and expansionof 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 limitsfor spring maize during the two subperiods.

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.

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.

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Table SI. 1

Parameters for different varieties in APSIM-Maize at six study locations in Northeast China

Location / Hybrid / Thermal time required from emergence to end of juvenile
(°C·d) / Thermal time required from flowering to end of maturity
(°C·d) / Photoperiod slope
(°C·h-1) / Maximum grain numbers per head / Grain-filling rate
(mg·grain-1·d-1)
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

Table SI. 2

The range of soil profile properties across NEC.

Soil depth
(cm) / Bulky density
(g·cm-3) / Saturated volumetric water
(mm·mm-1) / Drained upper limit
(mm·mm-1) / 15Bar lower limit
(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

Table SI. 3

Grading standard for yield potential level and yield stability for spring maize in Northeast China

Yield potential level / CFD (%) / Mean yield potential
(kg/ha) / Yield potential stability / CFD (%) / CV of yield potential
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) / 80-100 / <9560 / No Stable (Ns) / <20 / >0.23

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