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 • Previous Articles Next Articles
Zhan TIAN1,2,3, Yinghao JI1,2, Laixiang SUN4,5, Xinliang XU7,*(), Dongli FAN1,*(
), Honglin ZHONG4, Zhuoran LIANG6, Gunther FICSHER5
Received:
2017-03-06
Accepted:
2017-09-20
Online:
2018-11-20
Published:
2018-11-20
Contact:
Xinliang XU,Dongli FAN
E-mail:xuxl@lreis.ac.cn;fandl@sit.edu.cn
About author:
Author: TIan Zhan, E-mail: tianz@lreis.ac.cn
Supported by:
Zhan TIAN, Yinghao JI, Laixiang SUN, Xinliang XU, Dongli FAN, Honglin ZHONG, Zhuoran LIANG, Gunther FICSHER. 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.
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Table 1
The location information of selected 10 stations"
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 2
Comparison of cultivar parameters"
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 |
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."
Table 3
T-test analysis of rapeseed yield variation in the Yangtze River Basin under future climatic conditions"
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 4
Total rapeseed production changes for the upper, middle and lower reaches of the Yangtze River in the 2080s (million tons)"
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 |
[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.
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.
doi: 10.1029/2008GL033423 |
[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) |
[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) |
[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.
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.
doi: 10.1016/j.agrformet.2009.02.001 |
[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)
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) |
[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.
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: . |
[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. |
[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.
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.
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) |
[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.
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.
doi: 10.1016/j.envsoft.2014.07.009 |
[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.
doi: 10.1073/pnas.1312330110 |
[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)
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)
doi: 10.1016/j.rse.2005.08.012 |
[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)
doi: 10.11820/dlkxjz.2013.04.015 |
[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)
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)
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)
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.
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.
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)
doi: 10.5194/esd-6-45-2015 |
[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)
doi: 10.1117/12.2272988 |
[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.
doi: 10.1007/s10584-016-1866-z |
[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)
doi: 10.1016/j.agrformet.2008.11.004 |
[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) |
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