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Journal of Geographical Sciences    2018, Vol. 28 Issue (11) : 1700-1714     DOI: 10.1007/s11442-018-1538-1
Special Issue: Land system dynamics: Pattern and process |
Changes in production potentials of rapeseed in the Yangtze River Basin of China under climate change:A multi-model ensemble approach
TIAN Zhan1,2,3,JI Yinghao1,2,SUN Laixiang4,5,XU Xinliang7,*(),FAN Dongli1,*(),ZHONG Honglin4,LIANG Zhuoran6,FICSHER Gunther5
1. Shanghai Institute of Technology, Shanghai 200030, China
2. Shanghai Climate Center, Shanghai Meteorological Bureau, Shanghai 200030, China
3. School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, Guangdong, China
4. Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
5. International Institute for Applied Systems Analysis (IIASA), A-2361 Laxenburg, Austria
6. Hangzhou Meteorological Bureau, Hangzhou 310051, China
7. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100039, China
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Abstract  

Rapeseed is one of the major oil crops in China and it is very sensitive to climate change. The Yangtze River Basin is the main rapeseed production area in China. Therefore, a better understanding of the impact of climate change on rapeseed production in the basin is of both scientific and practical importance to Chinese oil industry and food security. In this study, based on climate data from 5 General Circulation Models (GCMs) with 4 representative concentration pathways (RCPs) in 2011-2040 (2020s), 2041-2070 (2050s) and 2071-2100 (2080s), we assessed the changes in rapeseed production potential between the baseline climatology of 1981-2010 and the future climatology of the 2020s, 2050s, and 2080s, respectively. The key modelling tool - the AEZ model - was updated and validated based on the observation records of 10 representative sites in the basin. Our simulations revealed that: (1) the uncertainty of the impact of climate change on rapeseed production increases with time; (2) in the middle of this century (2050s), total rapeseed production would increase significantly; (3) the average production potential increase in the 2050s for the upper, middle and lower reaches of the Yangtze River Basin is 0.939, 1.639 and 0.339 million tons respectively; (4) areas showing most significant increases in production include southern Shaanxi, central and eastern Hubei, northern Hunan, central Anhui and eastern Jiangsu.
Keywords climate change      rapeseed production      AEZ      Yangtze River Basin     
Fund:National Natural Science Foundation of China, No.41671113, No.51761135024, No.41601049, No.41475040;China’s National Science & Technology Pillar Program, No.2016YFC0502702
Corresponding Authors: XU Xinliang,FAN Dongli     E-mail: xuxl@lreis.ac.cn;fandl@sit.edu.cn
Issue Date: 21 December 2018
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TIAN Zhan
JI Yinghao
SUN Laixiang
XU Xinliang
FAN Dongli
ZHONG Honglin
LIANG Zhuoran
FICSHER Gunther
Cite this article:   
TIAN Zhan,JI Yinghao,SUN Laixiang, et al. Changes in production potentials of rapeseed in the Yangtze River Basin of China under climate change:A multi-model ensemble approach[J]. Journal of Geographical Sciences, 2018, 28(11): 1700-1714.
URL:  
http://www.geogsci.com/EN/10.1007/s11442-018-1538-1     OR     http://www.geogsci.com/EN/Y2018/V28/I11/1700
Figure 1  Distribution of rapeseed observation stations in China
Station Province Latitude Longitude
Neijiang Sichuan 29°35′N 105°5′E
Nanbu Sichuan 31°21′N 106°3′E
Ankang Shaanxi 32°43′N 109°2′E
Changde Hunan 29°3′N 111°41′E
Wuchang Hubei 30°21′N 114°19′E
Nanchang Jiangxi 28°33′N 115°57′E
Hefei Anhui 31°52′N 117°14′E
Gaochun Jiangsu 31°19′N 118°53′E
Longyou Zhejiang 29°2′N 119°11′E
Jiaxing Zhejiang 30°47′N 120°44′E
Table 1  The location information of selected 10 stations
Cultivar Original parameters New parameters
Cya+Cyb HI TS1n TS1x Cya+Cyb HI TS1n TS1x
Wrs 1 35+105 0.25 1500 2100 55+85 0.25 1100 1600
Wrs 2 40+120 0.25 1600 2400 65+90 0.25 1200 1800
Wrs 3 45+135 0.25 1700 2700 75+95 0.25 1300 1950
Wrs 4 45+150 0.25 1800 3000 85+100 0.25 1400 2100
Srs 1 0+150 0.20 1400 1850 0+105 0.23 1200 2150
Srs 2 0+120 0.21 1500 2100 0+120 0.23 1300 2300
Srs 3 0+135 0.22 1600 2350 0+135 0.23 1400 2400
Srs 4 0+150 0.23 1700 2600 0+150 0.23 1500 2500
New 1 0+150 0.25 1500 2500
New 2 0+165 0.25 1650 2600
New 3 0+180 0.24 1800 2700
New 4 0+195 0.24 2000 2800
New 5 0+210 0.23 2150 2900
New 6 0+225 0.23 2300 3000
Table 2  Comparison of cultivar parameters
Figure 2  Technological roadmap
Figure 3  Comparison of simulated attainable yields with observed yields
Figure 4  The ensemble yields of rain-fed winter rapeseed in the 2020s (green), 2050s (purple), and 2080s (red). The plus represents the 30-year average rain-fed rapeseed yield from 1981 to 2010 simulated with the observation data as the baseline. The horizontal line denotes the ensemble median.
Sites Baseline (kg/ha) Scenarios Sig Significance
2020s 5.37E-04 ***
Neijiang 3252 2050s 1.41E-06 ***
2080s 8.52E-01
2020s 9.59E-14 ***
Nanbu 3338 2050s 7.27E-03 ***
2080s 1.28E-01
2020s 9.75E-16 ***
Ankang 3207 2050s 1.53E-07 ***
2080s 1.04E-01
2020s 3.19E-04 ***
Changde 3317 2050s 9.57E-03 ***
2080s 7.26E-03 ***
2020s 1.05E-01
Wuchang 3125 2050s 7.30E-01
2080s 5.35E-02 *
2020s 1.65E-04 ***
Nanchang 3309 2050s 2.13E-04 ***
2080s 1.44E-04 ***
2020s 6.54E-02 *
Hefei 2992 2050s 5.69E-06 ***
2080s 1.34E-03 ***
2020s 5.92E-01
Gaochun 2975 2050s 1.71E-02 **
2080s 1.60E-03 ***
2020s 9.98E-09 ***
Longyou 3342 2050s 4.33E-08 ***
2080s 3.06E-03 ***
2020s 1.47E-03 ***
Jiaxing 3280 2050s 1.95E-02 **
2080s 5.04E-01
Table 3  T-test analysis of rapeseed yield variation in the Yangtze River Basin under future climatic conditions
Scenarios climate models Upper reaches (mt) Middle reaches (mt) Lower reaches (mt)
RCP2.6 GFDL-ESM2M 0.826 2.528 0.507
NorESM1-M 1.911 2.583 0.385
RCP4.5 GFDL-ESM2M 0.802 3.316 0.927
NorESM1-M 1.441 2.263 0.223
RCP6.0 GFDL-ESM2M -0.122 -2.333 -0.217
NorESM1-M 0.748 1.705 0.241
RCP8.5 GFDL-ESM2M 0.779 0.240 -0.005
NorESM1-M 1.130 2.810 0.648
Average 0.939 1.639 0.339
Table 4  Total rapeseed production changes for the upper, middle and lower reaches of the Yangtze River in the 2080s (million tons)
Figure 5  Rapeseed yield change from the baseline to the mid-21st century (2050s) under RCP2.6, RCP4.5, RCP6.0 and RCP8.5 scenarios produced with GFDL-ESM2M (a-d) and NorESM-M (e-h) climate models
[1] Cai C Z, 2007. Rape yield potential analysis of cropping system regions in China based on AEZ model.Chinese Journal of Agricultural Resources and Regional Planning, 28(1): 37-37. (in Chinese)http://en.cnki.com.cn/Article_en/CJFDTotal-ZGNZ200701009.htm
[2] Cai C Z, Liang Y, 2009. An analysis on the yield per uint of Chinese Cole based on yield potential prediction.Guizhou Agricultural Sciences, 37(6): 57-59. (in Chinese)http://www.cabdirect.org/abstracts/20103121941.html
[3] Challinor A J, Watson J, 2014. A meta-analysis of crop yield under climate change and adaptation.Nature Climate Change, 4(4): 287-291.http://www.nature.com/articles/nclimate2153
doi: 10.1038/nclimate2153
[4] Daniel R, César I, Allison M, 2009. Long-term climate change impacts on agricultural productivity in eastern China.Agricultural and Forest Meteorology, 149(6/7): 1118-1128.https://linkinghub.elsevier.com/retrieve/pii/S0168192309000380
doi: 10.1016/j.agrformet.2009.02.001
[5] Deligios P A, Farci R, Sulas L, 2013. Predicting growth and yield of winter rapeseed in a Mediterranean environment: Model adaptation at a field scale.Field Crops Research, 144(6): 100-112.https://linkinghub.elsevier.com/retrieve/pii/S0378429013000336
doi: 10.1016/j.fcr.2013.01.017
[6] FAO, 2003. World Agriculture: Towards 2015/2030: An FAO perspective. Available at: www.fao.org/docrep/005/y4252e/y4252e00.htm.
[7] FAO/IIASA/ISRIC/ISSCAS/JRC, 2009. Harmonized World Soil Database (version 1.1).
[8] FAOSTAT, 2015. Available at: .www.faostat.fao.org
[9] Fischer G, Van V H T, Shah M M, 2002. Global agro-ecological assessment for agriculture in the 21st century: Methodology and results. IIASA Research Report.
[10] Hansen J W, Challinor A J, Ines A, 2006. Translating climate forecasts into agricultural terms: Advances and challenges.Climate Researh, 33(1): 27-41.http://www.int-res.com/abstracts/cr/v33/n1/p27-41/
doi: 10.3354/cr033027
[11] Holzworth D P, Huth N I, 2014. APSIM-evolution towards a new generation of agricultural systems simulation.Environmental Modelling & Software, 62: 327-350.http://www.sciencedirect.com/science/article/pii/S1364815214002102
doi: 10.1016/j.envsoft.2014.07.009
[12] IIASA/FAO, 2012. Global Agro-Ecological Zones (GAEZ v3.0).
[13] Liu J Y, Buheaosier, 2000. Study on spatial-temporal feature of modern land-use change in China: Using remote sensing techniques.Quaternary Sciences, 20(3): 229-239. (in Chinese)
[14] Liu J Y, Kuang W H, Zhang Z X, 2014. Spatiaotemporal characteristics, patterns and causes of land-use changes in China since the late 1980s.Geographical Sciences, 24(2): 195-210. (in Chinese)http://link.springer.com/10.1007/s11442-014-1082-6
doi: 10.1007/s11442-014-1082-6
[15] Liu J Y, Liu M L, Tian H Q, 2005. Spatial and temporal patterns of China's cropland during 1990-2000: An analysis based on Landsat TM data.Remote Sensing of Environment, 98(4): 442-456. (in Chinese)http://linkinghub.elsevier.com/retrieve/pii/S003442570500266X
doi: 10.1016/j.rse.2005.08.012
[16] Liu J Y, Zhang Z X, Xu X L, 2010. Spatial patterns and driving forces of land use change in China during the early 21st century. Journal of Geographical Sciences, 20(4): 483-494. (in Chinese)http://link.springer.com/10.1007/s11442-010-0483-4
doi: 10.1007/s11442-010-0483-4
[17] Liu J Y, Zhang Z X, Zhang D F, 2003. A study on the spatial-temporal dynamic changes of land-use and driving forces analyses of China in the 1990s.Geographical Research, 22(1): 1-12. (in Chinese)http://en.cnki.com.cn/Article_en/CJFDTOTAL-DLYJ200301000.htm
doi: 10.1007/BF02873097
[18] Luo Y, Guo W, 2008. Development and problems of crop models.Transactions of the Chinese Society of Agricultural Engineering, 24(5): 307-312. (in Chinese)http://dl.sciencesocieties.org/publications/tcsae/abstracts/2008/5/2008.5.053
doi: 10.3901/JME.2008.09.177
[19] Masutomi Y, Takahashi K, Harasawa H, 2009. Impact assessment of climate change on rice production in Asia in comprehensive consideration of process/parameter uncertainty in general cirulation models.Agriculture, Ecosystems & Environment, 131(3): 281-291.http://www.sciencedirect.com/science/article/pii/S0167880909000498
doi: 10.1016/j.agee.2009.02.004
[20] Moss R H, Edmonds J A, Hibbard K A, 2010. The next generation of scenarios for climate change research and assessment.Nature, 463(7282): 747-756.http://www.nature.com/doifinder/10.1038/nature08823
doi: 10.1038/nature08823 pmid: 20148028
[21] Tang Q H, Yin Y, Liu X, 2015. A multi-model analysis of change in potential yield of major crops in China under climate change.Earth System Dynamics, 6(1): 45-59. (in Chinese)https://www.earth-syst-dynam.net/6/45/2015/
doi: 10.5194/esd-6-45-2015
[22] Tao F, Zhang Z, Liu J, 2009. Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection.Agricultural and Forest Meteorology, 149(8): 1266-1278. (in Chinese)https://linkinghub.elsevier.com/retrieve/pii/S0168192309000501
doi: 10.1016/j.agrformet.2008.11.004
[23] Taylor K E, Stouffer R J, Meehl G A, 2012. An overview of CMIP5 and the experiment design,Bulletin of the American Meteorological Society, 93(4): 485-498.http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1
doi: 10.1175/BAMS-D-11-00094.1
[24] Tebaldi C, Lobell D B, 2008. Towards probabilistic projections of climate change impacts on global crop yields,Geographical Research Letters, 35(8): 307-315.http://onlinelibrary.wiley.com/doi/10.1029/2008GL033423/full
doi: 10.1029/2008GL033423
[25] Tian Z, Ding Q Y, Liang Z R, 2014a. Advances of researches in the impact on oil crops under climate change.Chinese Agricultural Science Bulletin, 30(15): 1-6. (in Chinese)http://en.cnki.com.cn/Article_en/CJFDTotal-ZNTB201415001.htm
doi: 10.1117/12.675868
[26] Tian Z, Liang Z R, Fischer G, 2013. Analysis of impact on china wheat potential productivity of climate change during 1961-2010.Chinese Agricultural Science Bulletin, 29(9): 61-69. (in Chinese)
[27] Tian Z, Zhong H L, Shi R H, 2012. Estimating potential yield of wheat production in China based on cross-scale data-model fusion,Frontiers of Earth Science, 6(4): 364-372.http://link.springer.com/10.1007/s11707-012-0332-0
doi: 10.1007/s11707-012-0332-0
[28] Tian Z, Zhong H L, Sun L X, 2014b. Improving performance of agro-ecological zone (AEZ) modeling by cross-scale model coupling: An application to japonica rice production in Northeast China.Ecological Modelling, 290: 155-164.https://linkinghub.elsevier.com/retrieve/pii/S0304380013005681
doi: 10.1016/j.ecolmodel.2013.11.020
[29] Wang S, 2014. Effect of climate change and management practices on rapeseed production in Australia and China [D]. Yangling: Northwest A&F University. (in Chinese)
[30] Wang X C, 2011. Simulation of climate change and the response of cropping systems on the Loess Plateau of China [D]. Yangling: Northwest A&F University. (in Chinese)
[31] Warszawski L, Frieler K, Huber V, 2014. The Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP): project framework.Proceedings of the National Academy of Sciences, 111(9): 3228-3232.http://www.pnas.org/lookup/doi/10.1073/pnas.1312330110
doi: 10.1073/pnas.1312330110
[32] Xiao J, 2009. The rapeseed that conquered China.Chinese National Geography, (6): 60-73. (in Chinese)
[33] Yang X, Chen B D, Tian Z, 2013. Uncertainty of ensemble winter wheat yield simulation in North China based on CMIP5. Progess in Geography, 32(4): 627-636. (in Chinese)http://en.cnki.com.cn/Article_en/CJFDTOTAL-DLKJ201304017.htm
doi: 10.11820/dlkxjz.2013.04.015
[35] Yang X, Tian Z, Sun L X, 2017. The impacts of increased heat stress events on wheat yield under climate change in China.Climatic Change, 140(3): 605-620.http://link.springer.com/10.1007/s10584-016-1866-z
doi: 10.1007/s10584-016-1866-z
[36] Zhang H, Tian Z, Yang J, 2011. Study on Canola yield simulation in Yangtze River Region under the impact of climate change.Chinese Agricultural Science Bulletin, 27(21): 105-111. (in Chinese)http://en.cnki.com.cn/Article_en/CJFDTOTAL-ZNTB201121022.htm
[34] Yang X, Chen B D, Tian Z, 2014. Impacts of climate change on wheat yield in China simulated by CMIP5 multi-model ensemble projections.Scientia Agricultura Sinica, 47(15): 3009-3024. (in Chinese)http://adsabs.harvard.edu/abs/2017SPIE10405E..0WX
doi: 10.1117/12.2272988
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