Volume 02, Issue 06 (June 2013)

Effects of Climate Change on Maize Yield (Zea Mays L.) in Hebei Province of China

DOI : 10.17577/IJERTV2IS60896

Download Full-Text PDF Cite this Publication
Eric-Desire Tomoro, Yeping Zhu, Hailong Liu, Shijuan Li, Kaimeng Sun, E Yue, 2013, Effects of Climate Change on Maize Yield (Zea Mays L.) in Hebei Province of China, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 02, Issue 06 (June 2013),

Text Only Version

Effects of Climate Change on Maize Yield (Zea Mays L.) in Hebei Province of China

Effects Of Climate Change On Maize Yield (Zea Mays L.) In Hebei Province Of China

Eric-Désiré Tomoro, Yeping Zhu, HaiLong Liu, Shijuan Li, KaiMeng Sun, E Yue. Agricultural Information Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Agri-information Service Technology, Ministry of Agriculture, China 100081.

Abstract

Maize is one of major crop in Hebei province in northeast China which is under threat for yield instability due to climate change effects. Previous studies argued on the significance of the impact of climate change on maize yield variability. We used weather data from 2000 to 2010 then we simulated predictive maize yields under regional PRECIS A2 and B2 scenario using Maize Production Emulation System (MPES). Our results projected that, the LAI , for the same field there is different LAI and ET Significant upward trend in the growth of maize during the average temperature rise high amplitude were 0.07°C/year and 0.067 °C

reached to the significant level with P 5%

r=0.64**, r=0.58** . The maize yield change up to 20% change is expected due to rain increase amount, but this change will fail down by 2050s and even serious around 2070s in most simulated sites in Hebei province.

Keywords: Climate Change, Maize Yield, Regional Scale, PRECIS scenario, Hebei Province China.

  1. Introduction

    Maize (Zea mays L.) is one of the most important feed crops in China and it is also one of the main crops in Huang-Hai-Plain (HHP) china including Hebei province. For example HHP, known as the bread basket of China (World Bank, 2002), has only 7.7% of national water resources, yet it produces 39.2% of national grain supply and 32.4% of gross

    domestic product (Tian, 2006). Thus, the ability to meet future demand may hinge on proper assessment of medium- and long-term agricultural vulnerability to climate change and the measures taken to adapt accordingly (Lin, 1996).

    The decline of the productivity in this area continues to be a major concern to policy makers due to its impact on food security (Liu, et al. 2012). At national and regional scale, differences in climate projections often have larger influence on changes in crop yields. How significant these changes in climate affect maize yield and food security?

    Previous studies showed different views on the significance of the impact of climate change on maize yield. Fulu Tao et al. (2009) demonstrated that for all maize cultivation grids across Henan province China, the maize yield changes based on super ensemble simulations with 60% probability maize yield s could decrease by 8.4% by 2020 and 13.3% by 2050 under A1F1 scenario and 18.1% under B1 scenario.

    Allison et al. 2006 showed in the study of climate change impacts on agriculture in Huang-Hai Plain (HHP) that, the specific development under scenario A2 or B2 did not significantly alter the sequestration crop potential of the region but future precipitation increases simulated would prove beneficial to both crop yields and soil organic matter.

    Chi-Chung Chen et al. (1999) investigated on the yield variability influenced by climate change has an interesting positive impact, so this might have been achieved at the expense of

    increased yields risk confirming work by Anderson and Hazel. More rainfall causes maize yield level to rise, while decreasing yield variance. Temperature has the reverse effect on maize yield levels and variance.

    Some other results have shown by Meng Wang et al., (2011) that, using uncertainties of the future climate change projection, the maize yield in Jilin province China is highly likely to decline in western and central regions of the province but increases in the east part. The average maize yield in west and central regions is projected to decrease 15% or more by 2050 and around 30% in 2070 under A2 and B2 scenarios respectively. David B. Lobell and Marshall B. Burke (2010) used CERES to show that with +2°C increase on temperature, the maize yield loss is between 11.4% and 12.7%.

    However, Liming Ye et al, (2012) carried out some research on the same area. Their results predict that food crop yield will increase from+3% to 11% under A2 and +4% under B2 scenarios by 2030 and 2050 respectively. The maize yield in northeast China is projected to respond negatively to climate change the 2015 under SRES A2 scenario but overall, the maize yield is projected to increase at 0.2-0.3% per year at national scale during period 2011-2040 under A2 scenario and the yield change of 10% in 2040 under B2. Their demonstrated that their results showed largely positive effects of climate change on crop yield in China. This could be the hope for the maize yield increase.

    Zhijuan Liu et al. (2012) have presented that within Northeast China (NEC) maize yields were, on average, only 51% of the potential yields including a large exploitable gap, which provides an opportunity to significantly increase production by effective irrigation, fertilization and planting density. However in the past 30 years, the time from sowing to maturity has increased, including that farmers adapted new hybrids varieties. The adoption of longer season hybrids would contribute to the yield increases.

    This show that the new varieties role in the adaption to the climate change in term to expect maize yield increase.

    Some other studies on climate change impact on maize yield at national or regional scale showed very interesting results. Chi-Chung Chen et al. (1999) ; Bruce A. McCarl and David

    E. (1999); Allison et al.(2006); Fulu Tao et al., (2009); David B. Lobell and Marshall B. Burke; Meng Wang et al. (2011) affirm in their result that there is significant worries about maize yields. All these, using different methods and different IPCC scenarios describe the maize yield loss due to the climate change effects.

    At the other side, some other research argues on this idea. For example Liming Ye et al. (2012) used Food Security Index (FSI) to demonstrate that their results show largely positive effect of climate change on crop yield in China. ZhiJuan Liu et al. (2012) show that climate change is no longer a worry by using the good farm management (new varieties, planting density etc.).

    The argument between two sides is a challenge because of uncertainty of the climate change issues. We propose to examine this problem using MPES simulation model to simulate the impact of the change in rain and temperature under regional PRECIS A2 and B2 scenarios. It is an additional view to the existing results.

    This research examines the impact of climate change on maize yield in Hebei province China. We use Maize Production Emulation System (MPES) model, Li Shijuan and Yeping Zhu (2008) to simulate maize yield variability under regional climate change scale then compare the result to the observed maize yield.

    The objectives of this work are focusing on the evaluation of the performance of the MPES for simulating maize growth, development and yield under climate change conditions with the climate drivers (Temperature, Rain); the identification of the impact of the local climate

    change on the maize yield variability; the prediction of the future maize yield under PRECIS A2 and B2 climate scenarios and the display of different thematic map using GIS.

  2. Material and Methods

    1. Study area

      In this study, 9 simulation sets in different 9 counties (Quzhou; shenzhou; xinji; ningjin; zhengding; qingxian; dahe; luanxian and huailai) from 2000 to 2010 locate in 7 different cities(Handan; Hengshui; Shijiazhuang; Xingtai; Cangzhou; Tangshan and Zhangjiakou

      ) of Hebei province China( 36° 01'to 42° 37' North and113°31 to 119°53East ), Figure.1. Hebei province has a continental monsoon climate, with cold, dry winters, and hot, humid summers. Temperatures average 16 to 3 °C in January and 20 to 27 °C in July; the annual precipitation ranges from 400 to 800 mm, concentrated heavily in summer. The main annual temperature is between11-13°C.

    2. MPES model description

      The MPES model is a computer software system serving field experiment and simulation of the intelligent agriculture laboratory of the Chinese academy of agricultural sciences. MPES is issued from the CERES-maize model adapted on the cropping situation in Northern China, Li Shijuan and Zhu Yeping (2007). MPES integrated many technologies such as system theory, simulation; artificial intelligent; visualization and network technology, Yan Ding-Chun et al., (2007).

      This MPES model can provide plant management mean and decision making information after processing computer simulation. Basic parameter database consists of the information about location, fertilizer type and management, irrigation management, cultivation variety, phenology data, soil texture and soil parameter, among which phenology data; also the water content before sowing, and

      organic matter percent, volume weight, NH4-N, NO3-N and PH of every soil layer, Li Shijuan and Zhu Yeping (2008). This model can simulate the effects of cultivars, sowing date and density, solar radiation, air temperature, rainfall, soil moisture, and nitrogen on crop growth, development and yield. Simulating the crop yield according to the weather drivers such as Temperature, Sun radiation and Precipitation, and then forecasting the maximum yield is one of the important functions of MPES, Yeping Zhu and Zhang Jian Bin (2001).

    3. Data collection

      In this study, 9 meteorological stations in Hebei province china were selected from the weather stations operated by the National Meteorological Networks of China Meteorological Administration. 9 simulation sites across Hebei province were selected based on 10 years (2000-2010) length data. In each simulation site, we used the nearest meteorological data where maize growth and yield observed information are available. Hence, we used 4 of the 10 stations were used in all the 9 sites for the model calibration and validation by utilizing available phenological and climate data Table 1.

      The soil parameters and cultivars were obtained from database of the institute of soil fertilizer of Hebei provinces academy of agricultural sciences, Zhao Tong-ke et al., (2001). Soil nutrient in 2007 with reference to the location in samples depth (0-100cm); bulk density 1.26-1.4g/cm3); PH (5.8-8.83); NH4+_N

      and N03-_N, organic matter (OM). The cultivar using in this work are the zhenda958 and nongda108 the maize varieties adapted to the Hebei Provinces environment, Table 2. The monthly mean sun radiation was from NASA website

      http://power.larc.nasa.gov/cgi-bin/cgiwrap/solar/ agro.cgi?email=agroclim@larc.nasa.gov . The mean temperature range during the growing period is between 26.8°C and 23.7°C, the maximum temperature range is between 41.1°C and 39.2°C and the minimum temperature 15.9°C and 11.9° . The future climate scenario using in this work is PRECIS A2 and B2 scenario from Inter-governmental Panel of Climate Change (IPCC).This future climate is generated using WGEN based on 2000-2010 baseline Hebei Province weather data Figure2.

      2.2 Research Method

      A three-step approach has been developed in this research. First, we collected weather, soil, crop, and management data from 4 different meteorological stations of the China Meteorological Administration and the solar radiation from NASA as well as the historical observed maize yield from the Chinese statistical book year. Secondly we inputted weather data into the MPES model after checking its sensibility to weather drivers (Temperature (0%,

      ±20%), Rain (0%; ±20%)) and simulated the maize yield relative to each under the same soil parameters. After the simulation, we compared the maize yield simulated using correlation r and RMSE with the observed yield. Thirdly, we simulated the future yield according to the PRECIS A2 and B2 scenarios and the predictive

      auto-regressive process for region i and time period t, demonstrated by Im, Pesaran, and Shin(1997, 2003 and 2005) propose as a series of unit root test statistics in dynamic heterogeneous panels based on individual Dickey-Fuller, David E. Schimmelpfennig et al., (2007), regressions as equation (3) using the Maximum Likelihood Error

      (3) yi,t=i+iXi,t-1 +i,t ; i=1,,N ; t=1,.,T

      Where yi,t =yi,t yi,t-1 is the variation of the yield simulated at i simulation site at year t, Xi,t represents the weather factors (Temperature, Rain) is the MPES model parameter and represents error. And the RSME calculations were obtained using the following relation. The root mean square error is defined by the relation (4).

      maize yield relative to the predict climate

      display using GIS at provincial scale.

      2.4.2 Data Analysis

      4RMSE

      n i1

      (Yobs,i

      Ymo del,i )

      2

      2

      n ,

      This research used the Time-series to define to relation between the yield and climate factors relation (1) Nathaniel Beck (2006) and Lence (2009), which can be seen as (2) Saha, Schumway and Talpaz (1994). The difference between two yields at the same site at different period as shown in (3) as maximum likelihood error, Saha, Havenner and Talpaz (1997); David

      E. Schimmelpfennig et al., (1999).The Pearson correlation coefficient r; RMSE was calculated as shown in relation (4).

      We analyzed the spatial and temporal maize yield change, evapotranspiration (ET) and leaf area index (LAI) using ANOVA for SAS version

        1. and SigmaPlot version 11.0 as well as different results graphics under PRECIS A2 and B2 scenarios. And then the ArcGis software version 9.3 is used to show the different maps display of the results.

          1. Log yt =0+1Tt +2Pt+t Where yt , Tt , Pt

            represent respectively in year t yield, growing average temperature, and growing period total precipitation, 0-2 represent model parameters to be fit and t is an error, t=1T .

            The (1) can be written as below:

          2. yt = f(Tt, Pt)expt Where y is a maize yield, f

            1. represents an production function, and expt represents an error.

      Suppose that the variable of interest, yi,t has a representation as a stochastic first-order

      Where Yobs,i and Ymodel,i represent respectively the yield observed and simulated at site i.

  3. Results and Discussion

    1. MPES model calibration

      Maize Production Emulation System (MPES) was used by Li Shijuan and Zhu Yeping in 2008, to show that using actual experiment data under deferent water conditions in 1996 and 1997 in Hebei province, we calibrated and validated Maize Production Emulation System based on cooperative models. Our results indicated the system has good prediction performance and strong applicability. The RMSE of yield and biomass predicted by system were 426.3kg/ha, 477.5kg/hm2 and 1029.8 kg/ha, 1356.0 kg/ha respectively. The relative RMSE were 6.78%, 5.55% and 7.60%, 7.50%,

      all less than 10%.

      The maize varieties using in this simulation are nongda 108 and zhengda958. The initial parameters in the first simulation with 10% change the results show different with relative RMSE gap of 546.5Kg/ha in Luanxian and the smallest 42.72Kg/ha in Shenzhou . For different cultivars with new parameters including the

      grain per plant per hectare but the harvest time difference is no significant.

      3.2. MPES sensibility to weather

      The maize yield changes according to the high temperature and low temperatre change and with an additional rain during the 2000-2010 growth periods. The LAI indicated that, for the same field there is different LAI and ET Significant upward trend in the growth of maize during the average temperature rise high amplitude were 0.07°C/year and 0.067 °C

      reached to the significant level with P 5%

      (r=0.6421**, r=0.576** ) the graphics show in different site across the study area in Figure3a.

        1. Climate Change effects on maize yield

          1. Rain impact under PRECIS A2 and B2 scenario

            The annual rain in this study is between 627 and 461 mm per year, this amount is mostly during the maize growing season from late may to September. There is significant change in the maize growth due to the change in the rain as its shown by We noticed with the change of 20% in rain, the yield change as well in all simulation sites. Figure3b.

          2. Temperature impact under PRECIS A2 and B2 scenario

            The temperature is around 40°C as a maximum and 39°C as a minimum in the different selected meteorological stations but during the growing period, the maximum is about 30°C to 35°C. With an additional degree on the usual temperature, the ET changed as shown by the figure. It shown that the change in the yields is no significant, about -5% to 2% change in yield in the grain per plant and the late harvest time in north part (Huailai, Luanxian) and early in the south (Qingxian, Quzhou, Shenzhou and Ningjin) and center (Xinji, Luquan and Zhengding) Figure3c.

          3. Temperature and Rain impact under PRECIS A2 and B2 scenario

      In all simulation sites, the combination of

      the change under A2 and B2, showed yield trends in 2020s, 2050s and 2070s.There is serious decrease in yield in the 2070s scenario due to high additional rain (17%) and Temperature (4.5°C) for A2 scenario and the B2 scenario Table3. This trend will affect seriously the crop production thus the food security by the end of the century.

      However there is no significant difference between the yield trends under A2 and B2 in most of the sites during 2020s.

  4. Conclusion

The maize yield is in threat under PRECIS A2 and B2 scenarios. We noticed that the elevated in temperature contributed to the variation in the harvest period. There is not significant change in maize yield due to temperature. The rain is a key factor in the growing system. In fact there is serious change in the yield due rain variation in all simulation sites as shown in the results.

The argument on the impact of climate change on crop production whether the yield will increase or decrease depending on the position and the soil fertilizer uptake is no longer in debate. All information we have now notify clearly the crop will be in threat due to environmental change. To insure the under increasing maize production in Hebei Province Chine farmer should improve the sustainable management techniques focusing on cultivars improvement and the efficient fertilizer application otherwise water and soil degradation will speed up then the total agricultures environment in danger.

Acknowledgements

This work was supported by National High Technology Research and Development Program of China (2013AA102305).

We would like to thank the dynamic team of the Key Laboratory of Agri-Information of

the Agricultural Information Institute of Chinese Academy of Agricultural Sciences and for funding this research. Thanks to the Department of Soil and Fertilizer of Hebei Academy of Agricultural Sciences for their crucial help by allowing access to their data bank.

References

  1. Allison M. Thomson, R. Cesar Izaurralde, Norman

    J. Rosenberg, Xiaoxia He, (2005). Climate Change impacts on agriculture and soil carbon sequestration potential in the Huang-Hai Plain of China. Agriculture, Ecosystems and Environment 114 (2006) 195-209.

  2. Asseng S, Fillery IRP, Anderson GC, Dolling PJ, Dunin FX, Keating BA (1998) Use of the APSIM wheat model to predict yield, drainage, and NO3-leaching for deep sand. Australian Journal of Agricultural Research 49 (1998) 363378.

  3. Boogaard, (1998). Climate change and India: vulnerability assessment and adaptation. Universities press (2003) 45.

  4. Boote, K.J., J.W. Jones, and G. Hoogenboom (1997). Simulation of crop growth: CROPGRO

    model. Agricultural Systems Modeling (1997) 651-692.

  5. Budong Qian, Reinder De Jong, Jingyi Yang, Hong Wang, Sam Gameda, (2011). Comparing simulated crop yields with observed and synthetic weather data. Agriculture and Forest Meteorology 151(2011) 1781-1791.

  6. Castrignano A., N. Katerji, F. Karam, M. Mastrorilli and A. Hamdy (1998). A modified version of CERES-maize model for predicting crop response to salinity stress.Ecological Modeling 111 (1998) 107-120.

  7. Chao Chen, Enli Wang, and Qiang Yu, (2010). Modeling Wheat and Maize Productivity as Affected by Climate Variation and Irrigation Supply in North China Plain. Agronomy Jounal-Article 102(2010) 1037-1049.

  8. Christopher J. Kucharik and Shawn P. Serbin, (2008). Iimpacts of recent climate change on Wisconsin corn and soybean yields trends.

    Environment Research Letters 3(2008) 034003(10pp)

  9. Climate change (2007): The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, UK.

  10. David B. Lobell and Marshall B. Burke, (2010). On the use of the statistical models to predict crop yield response to climate change. Journal of Agriculture and Forest Meteorology (2010) in press.

  11. Evans LT, Fischer RA (1999) Yield potential: its definition, measurement, and significance. Crop Science 39 (1999) 15441551.

  12. David E. Schimmelpfennig, Bruce A. McCarl and Chi-Chun Chen (2007) Yield Variability as Influenced by Climate: A Statistical Investigation available at

    http://www.usgcrp.gov/usgcrp/nacc/agriculture/che n4.pdf

  13. Fulu Tao, Zhao Zhang, Jiyuan Liu, Masayuki, Yokozawa (2009). Modelling the impacts of weather and climate variability on crop productivity over large area: A new super-ensemble-based probabilistic projection. Agriculture and Forest Meteorology 149(2009) 1266-1278.

  14. Fulu Tao, Masayuki Yokozawa, Yinlong Xu Yousay Hayashi and Zhao zhang (2006). Climate change and trends in phenology and yields of field crops in China, 1981-2000. Agriculture and Forest Meteorology 138(2006)82-92.

  15. Im, K.S., M.H.Pesaran, Y. Shin, (1997). Testing for Unit Roots in Heterogenous Panels. Working Paper 9526, Department of Applied Economics, University of Cambridge.

  16. International panel on Climate Change (IPCC 2001), Climate change (2001): Impacts, Adaptation & Vulnerability. Contribution of Working Group 2 to the Third Assessment Report: 1000p.

  17. IPCC (2007) Regional Climate Projections. Climate Change (2007): The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental

    Panel on Climate Change (eds Solomon S, Qin D, Manning M, Chen Z, Marquis M, Averyt KB, Tignor M, Miller HL). Cambridge University Press (2007) 881885.

  18. IPCC, 2007. Climate Change 2007: The Physical Science Basis. Paris: 18, IPCC. 2007. Summary for Policymakers.

  19. Jensen Olesen Olesen J.E., P.K. Bøcher T. 2000. Comparison of scales of climate and soil data for aggregating simulated yields of winter wheat in Denmark.

  20. Jones J.W., G. Hoogenboom, C.H. Porter, K.J. Boote, W.D. Batchelor, L.A. Hunt, P.W. Wilkens,

    U. Singh, A.J. Gijsman, J.T. Ritchie 2003. The DSSAT cropping system model. European Journal of. Agronomy 18 (2003) 235-265.

  21. Jia Yingsuo and Xie Junliang Maize in Hebei 2006 Supported by international project of science and Technology number 2006DFB02480; pp39-59 and pp 135-44. (In Chinese)

  22. Keating BA, Carberry PS, Hammer GL 2003. An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy 18(2003) 267288.

  23. Lence S.H. AJAE Appendix 2009: Joint Estimation of Risk Preferences and Technology: Flexible Utility or Futility. Unpublished manuscript available at

    http://ageconsearch.umn.edu/.

  24. Li shijuan, Zhu Yeping, sun Kai meng and Yue E. 2003 Wheat-corn field production simulation model for intelligent decision making.

  25. Li Shijuan and Zhu Yeping, 2007. Simulating field production by wheat-maize continuous cropping intelligent decision system. China Academic Journal Electronic Publishing House (2007) 162-165.

  26. Li Shijuan and Yeping Zhu, 2008. Maize Production Emulation System Based on Cooperative Models. Computer and Computing Technologies in Agriculture Volume2. The international Federation for Information Processing Volume 259(2008) 1213-1221.

  27. Liming Ye, Wei Xiong, Zhengguo Li, Peng Yang, Wenbin, Guixia Yang, Yijiang Fu, Jinqiu Zou, zhongxin Chen, Eric Van Ranst and Huajun Tang 2012. Climate Change impact on China food security in 2050. Agronomy Sustainable Development (2012) DOI 10.1007/s13593-012-0102-0.

  28. Liu Hai Long ,YANG Jing-yi, HE Ping, BAI You-lu, JIN Ji-yun, Craig F Drury, ZHU Ye-ping, YANG,Xue-ming, LI Wen-juan, XIE Jia-gui,YANG Jing-min and Gerrit Hoogenboom 2012. Optimizing Parameters of CSM-CERES-Maize Model to Improve Simulation Performance of Maize Growth and Nitrogen Uptake in Northeast China. Journal of Integrative Agriculture (11)2012, 1898-1913.

  29. Long Stephen P. Andrew D.B. Leakey, Martin Uribelarrea, Elizabeth A. Ainsworth, Shawna L. Naidu, Alistair Rogers, Donald R. Ort. (2006) Photosynthesis, Productivity, and Yield of Maize Are Not Affected by Open-Air Elevation of CO2 Concentration in the Absence of Drought. Journal Plant Physiology 140 (2)779-790.

  30. Long Stephen P., Elizabeth A. Ainsworth, Andrew D. B. Leakey, Donald R. Ort,

    Josef Nösberger, and David Schimel, 2007. Food for Thought: Lower-Than-Expected Crop Yield Stimulation with Rising CO2 Concentrations. Science Journals26: 459-460.

  31. Nathaniel Beck, (2006). Time-SeriesCross-Section Methods. [32]Nakicenovic, N., Alcamo, J., Davis, G., de Vries,

B., Fenhann, J., Gaffin, S., Gregory, K., Grübler, A., Jung, T.Y., Kram, T., La Rovere, E.L., Michaelis, L., Mori, S., Morita, T., Pepper, W., Pitcher, H., Price, L., Riahi, K., Roehrl, A., Rogner, H.-H., Sankovski, A., Schlesinger, M., Shukla, P., Smith, S., Swart, R., van Rooijen, S., Victor, N., Dadi Z. (2000): IPCC Special Report on Emissions Scenarios. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA. 599pp.

[33]NBSC (National Bureau of Statistics of China, P.R.China), 2010. China Statistical Yearbook 2010. China Statistical Press, Beijing, China.

  1. Panmao Zhai, Xuebin Zhang, Hui Wan, and Xiaohua Pan, (2004). Trends in Total Precipitation and Frequency of Daily Precipitation Extremes over China. Journal of Climate 18(2004)1096-1108.

  2. Parry M., Rosenzweig C., Iglesias A., Fischer, G. and Livermore M., (1999). Climate change and world food security: a new assessment, Global Environmental Change 9(1999)5167.

  3. Peter C. Le Roux, Melodie A. McGeoch, Mawethu J. Nyakatya, Steven L. Chown, 2005. Effects of a short-term climate change experiment on a sub-Antarctic keystone plant species. Journal of Global Change Biology11 (2005) 1628-1639.

  4. Saha, A., C.R. Shumway, and H. Talpaz (1994). Joint Estimation of Risk Preference Structure and Technology Using Expo-Power Utility. American Journal of Agricultural Economics76:173-84, D.E. (1996) Uncertainty in Economic Models of Climate Change Impacts. Climatic Change 33:213-234.

  5. Schimmelpfennig, D.E. and G. Yohe. (1999) Vulnerability of Agricultural Crops to Climate Change: A Practical Method of Indexing, Global Environmental Change and Agriculture:

    Assessing the Impacts, G. Frisvold and B. Kuhn, eds. Northampton, MA: Edward Elgar

    Publishing Limited, pp.193-217.

  6. Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and

    H.L. Miller. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.

  7. Timothy D. Mitchell, Timothy R. Carter,Philip D. Jones, Mike Hulme and Mark New 2003 . A comprehensive set of high-resolution grids of

    List of Figures and Tables

    monthly climate for Europe and the globe: the observed record (19012000) and 16 scenario

    (20012100)

  8. Van Ittersum 2003 and Amthor, 2001. Crop Physiology: Applications for Genetic Improvement and Agronomy (chapter 20: Crop physiology, Modeling and Climate Change). Academic Press (2009)524-543.

  9. Villeneuve, C. et Richard, F., 2007. Vivre les changements climatiques : « Quoi de neuf? » Éditions MultiMondes (in French) [44]William E. Easterling1, Netra Chhetri and

Xian zheng Niu 2003. Adaptation to Climate Change: Simulation Technological Substitution. Kluwer Academic Publisher 149-221.

[45]Wood, R. 2008. Natural ups and downs. Nature, Vol. 453:43-44.

[46]X. Yang , Ch. Chen, Q. Luo, L. Li, Q. Yu 2010

Climate change effects on wheat yield and water use in oasis cropland.

  1. Zhang Jian Bin and YePing Zhu, 2001 Intelligent Simulation of Wheat-corn Continuous Cropping System and Its Implementation on Internet. Master Thesis 2006 (In Chinese).

  2. Zhao Tong-ke, Zhang Guo-yin, Ma Li-min and Sun Zu-yu, 2001. The contents, Form and Distribution of Sulfur in soil of Hebei. Plant Nutrition and Fertilizer Science 7(2), (2001)

    178-182.

  3. Zhijuan Liu, Xiaoguang Yang, Kenneth G. Hubbard, Xiao maolin 2012.

    Maize potential yields and yield gaps in the changing climate of Northeast China. Primary Research Article.

  4. Zhu Yeping 2001. Wheat Continuous Cropping Environment Simulation and Intelligent Decision System WMCCIDS. Journal of computer and Agriculture 11(2001) 41-44(in Chinese)

Experiment Site

Soil Depth (cm)

Bulk Density (g/cm3)

Water content(w/w)%

pH

NH4+_N

(mg/kg)

NO3-_N

(mg/kg)

OM

(g/kg)

Soil Type

Maize varieties

Figure1: Study Area ( Hebei province with simulation sites )

Meteorological station ID and location

Total Solar Radiation MJ/m^2/year

Mean Temper ature (°C)

Highest Temperat ure

(°C)

Lowest Tempe rature (°C)

Annual rainfall (mm)

cultivar

Climate zone

54602 Baoding satation 38°27N115°30E

(Xinji, Dahe, zhengding)

9403

26.8

41.1

-26.9

461.6

Zhengda 958

Continen tal Monsoon

54405 Huailai station

40°30N115°40E

(huailai)

9048

23.7

39.2

-15.7

507.23

Nongda1 08

Continen tal Monsoon

54534 Tangshan station38°55N117°31E (Luanxian)

9203

25.7

39.6

-25.2

549

Zhengda 958

Continen tal Monsoon

54616 Cangzhou station 37°4N115°6E

(Qingxian,Quzhou,Shenz hou,ningjin)

9960

26.5

40.5

-11.9

627

Zhengda 958

Continen tal Monsoon

Table1: Different nearest meteorolgical stations to the simulation sites

Quzhou County

(in Handan region)

0-20

1.26

21.12

7.3

2.33

19.3

12.1

light loam

Zhengda 958

20-40

1.31

19.52

7.3

3.35

16.2

10.6

light loam

40-60

1.31

18.51

7.6

2.67

15.6

7.83

heavy loam

60-80

1.4

22.32

7.6

2.32

12.3

5.6

middle loam

80-100

1.4

19.15

7.8

2.19

14.5

4.3

light loam

Shenzhou County

(in Hengshui region)

0-20

1.34

13.65

8.1

1.44

15.1

11.5

light loam

Zhengda 958

20-40

1.34

12.05

8.1

2.36

12.4

11.5

light loam

40-60

1.41

16.36

8.1

2.62

10.5

10.3

middle loam

60-80

1.41

15.63

7.6

2.98

19.6

6.33

heavy loam

80-100

1.45

18.15

7.6

1.41

19.1

5.22

light loam

Xinji City (in Shijiazhuang east region)

0-20

1.26

8.63

7.8

0.891

32.58

17

light loam

Zhengda 958

20-40

1.26

9.16

7.8

0.884

23.82

11.4

light loam

40-60

1.31

11.25

7.6

1.025

15.31

9

middle loam

60-80

1.31

12.26

7.8

1.166

2.15

7.22

middle loam

80-100

1.4

14.61

8.1

0.899

6.43

5.22

middle loam

Ningjin County

(in Xingtai region)

0-20

1.31

12.11

7.1

1.122

15.34

13.1

light loam

Zhengda 958

20-40

1.32

13.26

7.6

1.462

8.31

11.3

light loam

40-60

1.41

12.84

7.6

1.583

9.20

9.84

middle loam

60-80

1.33

18.21

7.5

2.056

3.97

8.11

middle loam

80-100

1.46

19.37

7.5

1.044

4.72

5.6

middle loam

Zhengding

0-20

1.35

18.55

7.2

1.056

20.39

16.55

light

Zhengda

County

3

loam

958

(in

20-40

1.35

18.69

6

7.2

2.356

15.53

12.58

light loam

Shijiazhuang

region)

40-60

1.35

19.25

5

7.5

2.547

11.36

9.23

middle loam

60-80

1.33

21.33

5

7.2

2.261

11.53

5.12

middle loam

80-100

1.4

24.68

3

7.4

1.011

15.59

5.73

middle loam

Qingxian

0-20

1.452255

14.48

8.8

1.418

23.64

20.1

light

Zhengda

County

3

loam

958

in

20-40

1.510926

14.67

8

8.4

1.548122

19.06

11.8

middle loam

Cangzhou

region)

40-60

1.592388

16.60

3

8.3

1.519129

9.93

10.9

middle loam

60-80

1.582327

18.89

8

8.3

1.176138

11.26

8.97

light loam

80-100

1.521559

17.13

2

8.4

0.519283

5.88

8.4

light loam

Dahe

0-20

1.45

14.925

8.6

0.911101

38.29

19.1

middle

Zhengda

(in

loam

958

Shijiazhuang

20-40

1.5

13.249

8.4

0.416318

30.93

8.94

heavy loam

west region)

40-60

1.51

13.400

8.3

0.331968

31.78

7.16

middle loam

60-80

1.46

14.605

8.4

0.382434

32.95

7.06

middle loam

80-100

1.49

14.86

8.2

0.293288

22.27

7.1

middle loam

Luanxian

0-20

1.459006

4.32

5.8

3.157904

6.472

8.8

light

Zhengda

County

loam

958

(in

20-40

1.594037

8.58

8

6.0

2.193278

13.98

5.58

sandy loam

Tangshan

region)

40-60

1.56977

9.87

6

6.2

2.041851

13.21

3.63

middle loam

60-80

1.603483

11.40

7

6.3

1.727244

12.24

1.93

sandy loam

80-100

1.571273

11.88

6.2

1.833677

12.59

1.07

sandy

9

loam

Huailai County (in

Zhangjiakou region)

0-20

1.45

8.9

8.1

2.331

14.4

15.31

middle loam

Nongda 108

20-40

1.46

7.8

8.2

3.233

14.6

12.14

middle loam

40-60

1.41

12.6

8.1

3.512

15.6

8.49

middle loam

60-80

1.51

14.3

7.6

2.561

15.6

9.24

heavy loam

80-100

1.33

11.5

7.7

1.843

18.1

6.29

middle loam

Table2: Simulation sites and soil information

Figure2: (a) the year sun radiation in Hebei Province (b) Yearly distribution of the Precipitation in Hebei Province (c) year temperature (Maximum and Minimum) in Hebei province (baseline 1980-2010)

7000

6500

6000

5500

5000

7000

6500

6000

5500

5000

7500

Shenzhou

Yield(Kg/ha)

Yield(Kg/ha)

4500

1998 2000 2002 2004 2006 2008 2010 20

Year

Year vs Observed Yield

Year vs Observed Yield Year vs Simulated Yield

Year vs Observed Yield

Year vs Observed Yield Year vs Simulated Yield

R² = 0.075

RMSE=193.35kg/ha

7000

6500

6000

5500

5000

4500

7000

6500

6000

5500

5000

4500

7500

Dahe

Yield(Kg/ha)

Yield(Kg/ha)

Figure3a LAI of different simulation sites

ET in Luanxian(Lx) baseline 2070s

4000

1998 2000 2002 2004 2006 2008 2010 20

Year

Year vs Observed Yield

Year vs Observed Yield Year vs Simulated Yield

RMSE=1026.441kg/ha

Luquan(Dahe) under A2andB2(2020s)

6000

Xinji

19000 5500

Yield(Kg/ha)

Yield(Kg/ha)

18500 5000

18000

4500

Yield(Kg/ha)

Yield(Kg/ha)

17500

17000

4000

3500

16500

3000

16000

15500

2500

19290800

2000 2002 2004 2006 2008 2010 20

Year

15000

2018 2020 2022 2024 2026 2028 2030 20 Year vs Observed Yield

Year vs Observed Yield

Year

Year vs Simulated Yield

Simulated Yield under A2and B2(2020s)

Maize Yield under A2 scenario Maize Yield under B2 Scenario

RMSE=193.35 kg/ha

Xinji under A2andB2(2020s)

22000

Luquan(Dahe) under A2andB2(2050s)

21000

18000

20000

17000

19000

18000

Yield(Kg/ha)

Yield(Kg/ha)

16000

17000

15000

Yield(Kg/ha)

Yield(Kg/ha)

16000

15000

14000

13000

2048 2050 2052 2054 2056 2058 2060 20

Year

Simulated Yield under A2and B2(2050s)

Maize Yield under A2 scenario

14000

2018 2020 2022 2024 2026 2028 2030 20

Year

Simulated Yield under A2and B2(2020s)

Maize Yield under A2 scenario Maize Yield under B2 Scenario

Maize Yield under B2 Scenario

Xinji under A2andB2(2050s)

Luquan(Dahe) under A2andB2(2070s)

22000

16500

21000

16000

20000

15500

Yield(Kg/ha)

Yield(Kg/ha)

15000

14500

14000

13500

19000

Yield(Kg/ha)

Yield(Kg/ha)

18000

17000

16000

13000

12500

2068 2070 2072 2074 2076 2078 2080 20

Year

Simulated Yield under A2and B2(2070s)

Maize Yield under A2 scenario

15000

2048 2050 2052 2054 2056 2058 2060 20

Year

Simulated Yield under A2and B2(2050s)

Maize Yield under A2 scenario Maize Yield under B2 Scenario

Maize Yield under B2 Scenario

19000

Xinji under A2andB2(2070s)

20000

Zhengding under A2andB2(2050s)

18000

17000

19000

Yield(Kg/ha)

Yield(Kg/ha)

16000

Yield(Kg/ha)

Yield(Kg/ha)

18000

15000

17000

14000

13000

2068 2070 2072 2074 2076 2078 2080 20

Year

Simulated Yield under A2and B2(2070s)

Maize Yield under A2 scenario Maize Yield under B2 Scenario

16000

15000

2048 2050 2052 2054 2056 2058 2060 20

Year

Zhending

Simulated Yield under A2and B2(2050s)

Maize Yield under A2 scenario Maize Yield under B2 Scenario

9000

8800

8600

20000

18000

16000

Zhengding under A2andB2(2070s)

Yield(Kg/ha)

Yield(Kg/ha)

8400

14000

Yield(Kg/ha)

Yield(Kg/ha)

12000

8200

10000

8000

8000

6000

19798800

2000 2002 2004 2006 2008 2010 20

Year

Year vs Observed Yield

Year vs Observed Yield Year vs Simulated Yield

4000

2000

2068 2070 2072 2074 2076 2078 2080 20

Year

Simulated Yield under A2and B2(2070s)

Maize Yield under A2 scenario Maize Yield under B2 Scenario

RMSE=147.98kg/ha

Zhengding under A2andB2(2020s)

7500

Quzhou

12000

7000

10000

8000

6500

Yield(Kg/ha)

Yield(Kg/ha)

6000

Yield(Kg/ha)

Yield(Kg/ha)

6000

5500

4000

5000

2000

0

2018 2020 2022 2024 2026 2028 2030 20

19495800

2000 2002 2004 2006 2008 2010 20

Year

Year

Simulated Yield under A2and B2(2020s)

Maize Yield under A2 scenario

Year vs Observed Yield

Year vs Observed Yield Year vs Simulated Yield

Maize Yield under B2 Scenario

RMSE= 334.41kg/ha

Quzhou under A2andB2(2020s)

Quzhou under A2andB2(2050s)

3000

2800

2900

2800

2700

Yield(Kg/ha)

Yield(Kg/ha)

2700

Yield(Kg/ha)

Yield(Kg/ha)

2600

2600

2500

2500

2400

2400

2300

2018 2020 2022 2024 2026 2028 2030 20

Year

2300

2200

Simulated Yield under A2and B2(2020s)

Maize Yield under A2 scenario Maize Yield under B2 Scenario

2100

2048 2050 2052 2054 2056 2058 2060 20

Year

Quzhou under A2andB2(2020s)

Simulated Yield under A2and B2(2050s)

Maize Yield under A2 scenario

3400

Maize Yield under B2 Scenario

3200

3000

Quzhou under A2andB2(2050s)

Yield(Kg/ha)

Yield(Kg/ha)

2800

3400

2600

3200

2400

3000

2200

2018 2020 2022 2024 2026 2028 2030 20

Year

2800

Yield(Kg/ha)

Yield(Kg/ha)

2600

Year vs Simulated YieldA2(20s) Maize Yield under A2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(20s) with Rain

2400

2200

2000

2048 2050 2052 2054 2056 2058 2060 20

Year

Year vs Simulated YieldA2(20s) Maize Yield under A2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(50s) with Rain

Quzhou under A2andB2(2070s)

7000

6000

Yield(Kg/ha)

Yield(Kg/ha)

5000

4000

3000

Qingxian

Yield(Kg/ha)

Yield(Kg/ha)

2600

2500

2400

2300

2200

2100

2000

2600

2500

2400

2300

2200

2100

2000

1900

2068 2070 2072 2074 2076 2078 2080 20

Year

Simulated Yield under A2and B2(2070s)

Maize Yield under A2 scenario

Maize Yield under B2 Scenario

Simulated Yield under A2and B2(2070s)

Maize Yield under A2 scenario

Maize Yield under B2 Scenario

Ningji

2000

1998 2000 2002 2004 2006 2008 2010 20

Year

Year vs Observed Yield

Year vs Observed Yield Year vs Simulated Yield

Year vs Observed Yield

Year vs Observed Yield Year vs Simulated Yield

RMSE=68.59kg/ha

7500

7000

6500

2700

2600

Qingxian under A2andB2(2020s)

Yield(Kg/ha)

Yield(Kg/ha)

6000

2500

Yield(Kg/ha)

Yield(Kg/ha)

2400

5500

2300

5000

2200

2100

19495800

2000 2002 2004 2006 2008 2010 20

Year

Year vs Observed Yield

Year vs Observed Yield Year vs Simulated Yield

2000

1900

2018 2020 2022 2024 2026 2028 2030 20

Year

Simulated Yield under A2and B2(2020s)

Maize Yield under A2 scenario

Maize Yield under B2 Scenario

RMSE=527.57kg/ha

Luanxian

Qingxian under A2andB2(2050s)

9000 2800

Yield(Kg/ha)

Yield(Kg/ha)

8000 2600

Yield(Kg/ha)

Yield(Kg/ha)

7000

2400

6000

2200

5000

2000

4000

1998 2000 2002 2004 2006 2008 2010 20

Year

Year vs Observed Yield

Year vs Observed Yield Year vs Simulated Yield

1800

2048 2050 2052 2054 2056 2058 2060 20

Year

Simulated Yield under A2and B2(2050s)

Maize Yield under A2 scenario Maize Yield under B2 Scenario

RMSE=546.54kg/ha

2500

2400

2300

2200

2100

2000

1900

2500

2400

2300

2200

2100

2000

1900

2600

Qingxian under A2andB2(2070s)

4500

Huailai under A2andB2(2020s)

4000

Yield(Kg/ha)

Yield(Kg/ha)

1800

2068 2070 2072 2074 2076 2078 2080 20

Year

Simulated Yield under A2and B2(2070s)

Maize Yield under A2 scenario Maize Yield under B2 Scenario

The RMSE in Qingxian for 20s; 50s and 70s respectively. RMSE= 181.2385kg/ha;

RMSE= 128.4438kg/ha RMSE=488.4484kg/ha

3500

Yield(Kg/ha)

Yield(Kg/ha)

3000

2500

2000

1500

2018 2020 2022 2024 2026 2028 2030 20

Year

Simulated Yield under A2and B2(2020s)

Maize Yield under A2 scenario Maize Yield under B2 Scenario

Huailai under A2andB2(2050s)

4000

3500

6000

Huailai

3000

Yield(Kg/ha)

Yield(Kg/ha)

2500

5000

2000

Yield(Kg/ha)

Yield(Kg/ha)

4000

3000

1500

2048 2050 2052 2054 2056 2058 2060 20

Year

Simulated Yield under A2and B2(2050s)

Maize Yield under A2 scenario

Maize Yield under B2 Scenario

2000

1000

1998 2000 2002 2004 2006 2008 2010 20

Year

3600

Huailai under A2andB2(2070s)

Year vs Observed Yield

Year vs Observed Yield Year vs Simulated Yield

3400

3200

RMSE=152.4Kg/ha

3000

Yield(Kg/ha)

Yield(Kg/ha)

2800

2600

2400

2200

2000

2068 2070 2072 2074 2076 2078 2080 20

Year

Simulated Yield under A2and B2(2070s) Maize Yield under A2 scenario

Maize Yield under B2 Scenario

2700

2600

Shenzhou under A2andB2(2020s)

The RMSE in Shenzhou for 20s, 50s and 70s respectively are: RMSE=42.72416; RMSE=284.9282 and RMSE= 418.347

2500

Yield(Kg/ha)

Yield(Kg/ha)

2400

Ningji under A2andB2(2020s)

2300

3000

2200

2100

2900

Yield(Kg/ha)

Yield(Kg/ha)

2800

2000

2700

1900

2018 2020 2022 2024 2026 2028 2030 20

Year

2600

2500

Year vs Simulated YieldA2(20s) Maize Yield under A2 and B2 scenario

2400

Maize Yield under B2 Scenario Simlted Year under A2(20s)

2300

2018 2020 2022 2024 2026 2028 2030 20

Year

2800

Shenzhou under A2andB2(2050s)

Year vs Simulated YieldA2(20s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(20s)

Ningji under A2andB2(2050s)

2600

2800

2700

Yield(Kg/ha)

Yield(Kg/ha)

2400

Yield(Kg/ha)

Yield(Kg/ha)

2600

2200

2500

2400

2000

2300

1800

2048 2050 2052 2054 2056 2058 2060 20

Year

Year vs Simulated YieldA2(50s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(50s)

2200

2100

2048 2050 2052 2054 2056 2058 2060 20

Year

Year vs Simulated YieldA2(50s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(50s)

2600

2500

Shenzhou under A2andB2(2070s)

2700

2600

2500

Ningji under A2andB2(2070s)

2400

2400

Yield(Kg/ha)

Yield(Kg/ha)

2300

2300

Yield(Kg/ha)

Yield(Kg/ha)

2200

2100

2000

1900

2200

2100

2000

1900

2068 2070 2072 2074 2076 2078 2080 20

Year

1800

2068 2070 2072 2074 2076 2078 2080 20

Year

Year vs Simulated YieldA2(70s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(70s)

The RMSE in Ningji for 20s; 50s and 70s

Year vs Simulated YieldA2(70s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(70s)

respectively.

RMSE= 181.2385; RMSE= 128.4438

RMSE= 488.4484

Luanxian under A2andB2(2020s)

3000

2800

2600

2400

2200

2800

2600

2400

2200

Luanxian under A2andB2(2070s)

Yield(Kg/ha)

Yield(Kg/ha)

3200

3000

2800

2600

3200

3000

2800

2600

3400

2400

2018 2020 2022 2024 2026 2028 2030 20

Year

Year vs Simulated YieldA2(20s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(20s)

Year vs Simulated YieldA2(20s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(20s)

Yield(Kg/ha)

Yield(Kg/ha)

2000

2068 2070 2072 2074 2076 2078 2080 20

Year

Year vs Simulated YieldA2(70s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(70s)

Year vs Simulated YieldA2(70s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(70s)

R² = 0.0032

The RMSE in Luanxian for 20s; 50s and 70s respectively: RMSE=46.88501; RMSE=284.0237; RMSE=468.5486

3000

2900

2800

2700

2600

2500

2400

3000

2900

2800

2700

2600

2500

2400

3100

Luanxian under A2andB2(2050s)

Yield(Kg/ha)

Yield(Kg/ha)

Figure3 3b 3c: simulation yield and observed yield under PRECIS A2 and B2 scenario in different sites.

2300

2048 2050 2052 2054 2056 2058 2060 20

Year

Year vs Simulated YieldA2(50s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(50s)

Year vs Simulated YieldA2(50s) Maize Yield under A2 and B2 scenario

Maize Yield under B2 Scenario Simlted Year under A2(50s)

Leave a Reply