[IEEE 2014 Third International Conference on Agro-Geoinformatics - Beijing, China (2014.8.11-2014.8.14)] 2014 The Third International Conference on Agro-Geoinformatics - Effects of drought on grain yield of spring maize in Northern China
Post on 22-Mar-2017
Effects of Drought on Grain Yield of Spring Maize in Northern China
Chaoyang Dong Tianjin Climate Center
Tianjin Meteorological Bureau Tianjin China
Kenan Li College of air traffic management Civil Aviation University of China
Chaoyang Dong, Xiaoguang Yang*, Zhijuan Liu, Kenan Li, Shuang Sun
College of Resources and Environmental Science China Agricultural University
Beijing China Email: email@example.com
AbstractNorthern China (NC) accounts for more than 80%
of the nations total maize planting areas and production, which is the major maize production areas in China, but drought has restrict the stable development of maize in this important area. This led us to investigate the impacts of drought on grain yield of spring maize in NC. We divided the study area into 6 precipitation zones (I~VI) based on the total precipitation during the growing season of spring maize with the interval of 100 mm. The APSIM-Maize model was calibrated and validated for maize phenology and yields in NC. The validated model was then used to estimate the effects of drought on grain yield of spring maize in each precipitation zone. The results for model calibration and evaluation show that simulated growth and development of maize are in good agreement with the corresponding observed values in NC. Maize yield reduction caused by drought decreased from northwest to southeast, which is consistent with the spatial distribution of precipitation during maize growing season. Moreover, maize yield reduction caused by drought reduced gradually from 99.3% in precipitation zone I to 14.8% in precipitation zone VI. The time trends of maize yield reduction caused by drought are not obvious in each precipitation zone during the study period of 1961~2010.
KeywordsNorthern China; spring maize; drought; yield reduction; APSIM-Maize model
I. INTRODUCTION Maize accounts for more than one-third of Chinese cereal production, which is the largest food crop in China . The statistical data from 2006 to 2008 showing that the annual average planting area of maize in Northern China (NC) account for 81.9% of Chinas total and grain production of maize in NC accounts for 84.9% of Chinas total . Therefore, Maize planting in NC plays a vital role in the crop production in China. On the other hand, the warming trend was obvious in NC in recent 50 years (1958~2007), the temperature in some areas has increased up to 4C . For the precipitation, it had a decreasing trend in Northeast China, most areas of North China,
and the southern part of Northwest China during the past 50 years . The rising temperature and the decreasing precipitation will lead to the aggravation of the climate arid in NC.
Drought is one of the most serious agro-meteorological disasters which affect agricultural production in China, and it is the main factor that restricting the stable development of agriculture and food security . Agricultural drought mainly occurred in Northeast China, Northwest China, and North China , among these regions, Northwest China is the most serious, approximately 80% of the whole area is arid or semi-arid regions, the severe drought occurred every 2 years, followed by North China and Northeast China, the frequency is 25.4% and 23%, respectively . Previous studies indicated that drought has caused serious damage on the growth and development of maize [8, 9]. Moreover, significant maize yield loss caused by drought are expected to increase in a changing climate with temperature rising and rainfall distribution changes in the key crop production areas [10-12].
In spite of the above mentioned studies, there has been little regional evidence relating the effects of drought on maize yield in NC. Consequently, analyzing the maize yield reduction caused by drought disaster is helpful to understand the constraints of drought on maize yield, furthermore, it can provide scientific references for drought resistance and securing food production in NC.
II. METHODS AND DATA
A. Study Site The study area is NC, including 9 provinces (Heilongjiang, Jilin, Liaoning, Hebei, Henan, Shandong, Shanxi, Shaanxi, and Gansu), 2 cities (Beijing and Tianjin), and 3 autonomous regions (Inner Mongolia, Ningxia Hui, and Xinjiang Uygur) (Fig. 1). We selected the areas with the 10C active accumulated temperature > 2000 C d in maize growing
Financial support by the Ministry of Science and Technology of China (National Science & Technology Pillar Program project: Grant No. 2012BAD20B04; 973 project: Grant No. 2010CB951502).
season (the beginning and ending date of the daily average temperature steadily pass 10C) during the period of 1961~2010 in NC as the major maize growing areas (Fig. 1). 217 stations in this region were selected from the weather stations operated by the National Meteorological Networks of China Meteorological Administration (CMA).
In order to up-scaling location-specific estimates of yield reduction to regional levels, the maize-growing region has been divided into 6 precipitation zones (I~VI) based on the total precipitation during maize growing season with the interval of 100 mm (Fig. 2). The 10C active accumulated temperature during maize growing season ranged from 2190 to 5467 C d. The sunshine hours ranged from 891 to 2050 h and total precipitation during maize growing season ranged from 11 to 916 mm (Fig. 3).
Fig. 1 The study area in NC. The blue part is the study area, while the green part is the no-study area. The name of provinces, cities, and autonomous regions are mark in map.
Fig. 2 The locations selected for simulation of maize yield in NC. The solid circles indicate locations used for simulating yield. The bold lines indicate the precipitation contour lines with the interval of
100 mm (values are shown in bold font). The name of the precipitation zones are mark in each region (I~VI).
Fig. 3 The 10C active accumulated temperature (a), sunshine hours (b), and precipitation (c) during maize growing season from 1961 to 2010.
B. Climate, soil, maize phenology, and on-farm yield data The climatic data including daily maximum and minimum temperature, sunshine hours, and precipitation are available from 1961 to 2010 at each weather station. Sunshine duration was converted into the daily solar radiation by using the Angstrom formula [13, 14].
The soil data including the soil bulk density (BD), the drained upper limit (DUL), and the 15Bar lower limit (LL15) in different soil layers of 10 km10 km lattice data from Nanjing soil research institute, Chinese academy of sciences.
The maize phenology (sowing, emergence, flowering, and maturity dates), hybrid type, grain yields, and field management practices were obtained from Agro-meteorological Experimental Stations (AESs) in NC.
C. Crop modeling and simulation The Agricultural Production Systems Simulator (APSIM) has proven to be an effective tool to investigate the potential impacts of climate variability on maize productivity [15-17]. In this study, we used the APSIM model to identify the effects of drought on maize yields in NC. The AESs data sets were divided into two groups (Table 1). APSIM was calibrated based on field-measured phenology, and grain yield of maize in the first group (Table 1) by finding the crop parameters that optimize the model performance with a trial-and-error method . The comparisons included simulated and observed emergence/flowering/maturity dates, and yield. The calibrated model was then validated using data from the second group described in Table 1.
To determine the yield reduction caused by drought, we designed yields under two scenarios: potential (non-water limited) and rain-fed (no irrigation). For potential yields, water applications were set equal to the water use of the maize crop
(Unit: C d)(Unit: h)
and nutrient inputs were taken as non-limiting in order to eliminate the effect of water and nutrient stresses on simulated maize yield. For rain-fed yields there were no irrigations throughout the simulation period (1961-2010). To eliminate the impacts of changes of hybrids, planting density, sowing depth, and other management, these factors were kept constant throughout the simulation period.
D. Data analysis In our analysis, we define the potential yield (Yp) as the yield achieved for a given maize hybrid in a given location with the optimal water and no nutrient limitation, rain-fed yield (Yr) as yield achieved for a given maize hybrid under the conditions of local precipitation with no nutrient limitation. From this, we define the yield reduction due to drought (Y):
The Y, a value from 0 to 100%, indicates the percentage of yield reduction caused by drought.
E. Statistical evaluation of model performance To evaluate model performance, statistical indicators of decision coefficient (R2), root mean square error (RMSE), normalized root mean square error (NRMSE), D value, and comments on the absolute error (MAE) were computed from simulated and observed variables, which included emergence days after sowing, flowering days after sowing, maturity days after sowing and grain yield.
A. Model calibration and validation Evaluation of the APSIM-Maize model with the experiment data collected at 4 locations (Table 1) indicated that the model
predicted growth stages and grain yield reasonably well (Fig. 4). The average simulated emergence, flowering, and maturity days after sowing in these experiments were 12.0, 80.3, and 135.5 days as against observed values of 13.1, 80.9, and 135.6 days, respectively, whereas the simulated grain yield was 5640 kg ha-1 and the observed grain yield was 5453 kg ha-1.
The R2 values of simulated and observed days from sowing to for emergence, flowering, and maturity were 0.77, 0.95 and 0.95 respectively, whereas the R2 values of simulated and observed grain yield was 0.76. The NRMSE values of simulated and observed days from sowing to for emergence, flowering, and maturity were 27%, 4% and 3%, whereas the NRMSE values of simulated and observed grain yield was 16%. In addition, the D values for simulated emergence, flowering, and maturity days after sowing were higher than 0.90. These indicated that the APSIM-Maize model was able to simulate the duration to emergence, flowering, and maturity reasonably well a good agreement between the simulated and observed values. Compared with simulated and observed grain yield, the NRMSE, R2, and D values were 16%, 0.76, and 0.93, respectively, indicated that the model estimated the growth of maize reasonably well (Table 2). Table 1 Maize hybrids and years of crop data used for model calibration and validation at 4 selected stations in study area
Locations Hybrids Years of crop data used for Calibration Validation
Jiamusi Dongnong 248 1994-1999 2000-2002, 2004-2006 Changling Zhongdan 2 1988-1990 1991-1993 Tangshan Zhengdan 958 1984-1986 1981, 1983, 1992
Xifengzhen Zhongdan 2 1994-1995, 1998-1999 1996, 2000-2001, 2003
y = 1.05 x - 6.61R2 = 0.95
100 120 140 160 180
y = 0.91 x + 0.68R2 = 0.76
0 5 10
y = 0.95 x - 0.37R2 = 0.77
0 10 20 30
y = 0.97 x + 1.90R2 = 0.95
60 80 100 120
Emergence Flowering Maturity Yield
Observed days after sowing Observed yield (t ha-1)
Simulated yield (t ha -1)hh
Fig. 4 Comparison of simulated and observed emergence (a), flowering (b), maturity (c) days after sowing, and grain yield (d). The dashed line and solid line were 1:1 line and regression trend line, respectively.
Table 2 Validation results of APSIM-Maize model Item R2 RMSE NRMSE D MAE
Emergence days after sowing 0.77 3.5 27% 0.93 2.81 Flowering days after sowing 0.95 3.1 4% 0.99 2.56 Maturity days after sowing 0.95 4.0 3% 0.99 3.60 Grain yield 0.76 879.2 16% 0.93 717.17
B. Effects of drought on grain yield of spring maize in NC Fig. 5 shows the spatial distribution of maize yield reduction caused by drought in NC. The yield reduction showed a zonal distribution, decreasing from northwest to southeast. The highest yield reduction caused by drought (>80%) during the study period (1961-2010) mainly located in Xinjiang Uygur
(a) (b) (c) (d)
autonomous region, the north part of Gansu province, and the west part of Inner Mongolia autonomous region. The yield reduction ranged from 60% to 80% is mainly located in the middle part of Inner Mongolia autonomous region, the west part of Hebei province, most of Shanxi province, and the north part of Shaanxi province. The yield reduction approximately 40~60% is mainly located in the west of Jilin province, most of Hebei province, the north part of Henan province, the west part of Shandong province, and the middle part of Shaanxi province. The yield reduction in 20~40% is mainly located in the west part of Heilongjiang province, the middle part of Jilin province, most of Shandong province, the middle part of Henan province, and the south part of Shaanxi province. The lowest values of yield reduction ( 500 mm). The smallest difference between the two consecutive zones is precipitation zone I and II, it was 3.7%; while the biggest difference between the two consecutive zones is precipitation zone II and III, it was 35.6%.
Fig. 6 The yield reduction caused by drought during 1961 to 2010 in the 6 precipitation zones (I~VI) in NC
Fig. 7 shows the decadal averages of maize yield reduction caused by drought during the study period (1961-2010) in the 6 precipitation zones in NC. Over the past 50 years, only in precipitation zone I maize yield reduction has a significant decreasing trend, with the rate of 0.15% per decade. In precipitation zone II, maize yield reduction maintain at a relative higher level and has a fluctuation rising trend, it is highest in 2000s (97.0%). Moreover, maize yield reduction showed a rising trend in recent 30 years in precipitation zone III and IV, and a decreasing trend in recent 20 years in precipitation zone V and VI.
Fig. 7 The decadal averages of maize yield reduction caused by drought during 1961 to 2010 in the 6 precipitation zones (I~VI) in NC.
IV. CONCLUSIONS In this study, we explored the applicability of APSIM-Maize model which was used in the simulation of growing stages and grain yield of spring maize in NC and evaluated the effects of drought on grain yield of spring maize in each precipitation zone. The results showed that the APSIM-Maize model can be successfully used to simulate growth and grain yield of spring maize in NC. Maize yield reduction caused by drought showed a zonal distribution, with the highest values in northwest and lowest in southeast, which is consistent with the spatial distribution of precipitation. The time trends of maize yield reduction caused by drought are not obvious in each
precipitation zone during the past 50 years, but it fluctuated largely in each decade.
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