Evaluation of Suitability Areas for Maize in China Based on GIS and Its Variation Trend on the Future Climate Condition

  • Chao-jie Jia
  • Le-le Wang
  • Xiao-li Luo
  • Wei-hong Zhou
  • Ya-xiong Chen
  • Guo-jun Sun
Chapter
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 254)

Abstract

Climate change is one of the most significant factors for the migration of the suitable planted areas of maize. The hyper-resolution data of daily mean precipitation, temperature, soil, and topography that supported by Institute of Soil Science and National Climate Center were collected to assess the effect of climate change on migration of the suitable planted maize areas, and it provides a foundation for macro-management decisions of agriculture. Climate data mentioned above was simulated by regional climate model, which showed the experiments of the twentieth century and the forecast tests of twenty-first century. Based on the factors above, relevant criteria, suitability levels, and their weights for each factor were defined. And the database of climate and soil was established. In this article, we use the multi-criteria evaluation (MCE) approach based on GIS to identify the suitable planting areas for maize in China. The results demonstrated that the most suitable degrees of active accumulated temperature, maximum and minimum temperature in growth period moving north dramatically, and the precipitation moving slightly in the future, which will lead to the planting areas of maize moving northward, and the areas were increased by 4.61 × 105 km2.

Keywords

Maize Geographic information system (GIS) Climate change Multi-criteria evaluation Kriging interpolation Analytical hierarchy process 

Notes

Acknowledgments

This work is financial supported by the ISTCP 2010DFA92800 and 2012DFG31450.

References

  1. 1.
    Southworth, J., Randolph, J.C., Habeck, M., Doering, O.C., Pfeifer, R.A., Rao, D.G., Johnston, J.J.: Consequences of future climate change and changing climate variability on maize yields in the midwestern United States. Agric. Ecosyst. Environ. 82(1–3), 139–158 (2000)CrossRefGoogle Scholar
  2. 2.
    Corbett, J.H.: GIS and environmental modeling: progress research issues. In: Goodchild, M.F., Steyaert, T.L., Parks, O.B. (eds.) pp. 117–122. USA (1996)Google Scholar
  3. 3.
    Belton, V., Stewart, T.J.: Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer Academic Publishers, Boston (2002)CrossRefGoogle Scholar
  4. 4.
    Barredo, C.J.I.: Systems Information Geographical Evaluation Multi-Criteria laordenacio n deltetritorio. Editorial RA-MA Editorial, Madrid, EsPana (1996)Google Scholar
  5. 5.
    Mendoza, G.A., Martins, H.: Multi-criteria decision analysis in natural resource management: a critical review of methods and new modelling paradigms. For. Ecol. Manage. 230(1–3), 1–22 (2006)CrossRefGoogle Scholar
  6. 6.
    Jones, P.G., Beebe, S.E., Tohme, J., Galwey, N.W.: The use of geographic information systems in biodiversity exploration and conservation. Biodivers. Conserv. 6, 947–958 (1997)CrossRefGoogle Scholar
  7. 7.
    Mariuki, J.N., De Klerk, H.M., Williams, P.H., Bennun, L.A., Crowe, T.M., Berge, E.V.: Using patterns of distribution and diversity of Kenyan birds to select and prioritize areas for conservation. Biodivers. Conserv. 6, 191–210 (1997)CrossRefGoogle Scholar
  8. 8.
    Janssen, R., Rietved, P.: Geographical Information Systems for Urban and Regional Planning, pp. 129–138. Kluwer press, Dordrecht (1990)Google Scholar
  9. 9.
    Carver, S.J.: Integrating multi-criteria evaluation with geographical information systems. Int. J. Geogr. Inf. Syst. 5(3), 321–339 (1991)CrossRefGoogle Scholar
  10. 10.
    Malczewski, J.A.: GIS-based approach to multiple criteria group decision-making. Int. J. Geog. Inf. Sci. 10(8), 321–339 (1996)Google Scholar
  11. 11.
    Eastman, J.R., Jin, w., Kyem, A.K., Toledano, J.: Raster procedures for multi-criteria/multi-objective decisions. Photogram. Eng. Sens. 61(5), 539–547 (1995)Google Scholar
  12. 12.
    Yeh, W.H., Li, L., Mansuripur, M.: Vector diffraction and polarization effects in an optical disk system. Appl. Opt. 37(29), 6983–6988 (1998)Google Scholar
  13. 13.
    Pereira, J.M.C., Duckstein, L.: A multiple criteria decision-making approach to GIS-based land suitability evaluation. Int. J. Geog. Inf. Sci. 7(5), 407–424 (1993)CrossRefGoogle Scholar
  14. 14.
    Aguilera, H.N.: Panorama Agriculture Mexicana, pp. 58–69. De geografia, Mexico (1986)Google Scholar
  15. 15.
    Heywood, I., Oliver, J., Tomlinson, S.: Innovations of GIS, pp. 127–136. Taylor and Francis, UK (1995)Google Scholar
  16. 16.
    Huang, H.Q.: The Influence of global food shortage on Chinese economy. Contemp. Econ. 7, 6–7 (2008)Google Scholar
  17. 17.
    Ceballos-silva, A., Lo’pez-blanco, J.: Delineation of suitable areas for crops using a multicriteria evaluation approach and land use/cover mapping: a case study in central Mexico. Agric. Syst. 77(2), 117–136 (2003)Google Scholar
  18. 18.
    Shi, B.S., Wen, Z.P.: The weighted method for the score that experts give. China Acad. J. Electron. Publishing House (5), 57–58 (1996)Google Scholar
  19. 19.
    Guo, R., Chen, Y., Wan, F., Li, F., Liu, J., Sun, G.: Delineation of suitable areas for potato in China using a multi-criteria evaluation approach and geographic information system. Sens. Lett. 8(1), 167–172 (2010)CrossRefGoogle Scholar
  20. 20.
    Ceballos-Silva, A., Lopez-Blanco, J.: Delineation of suitable areas for crops using a multi-criteria evaluation approach and land use/cover mapping: a case study in Central Mexico. Agric. Syst. 95, 117–136 (2003)Google Scholar
  21. 21.
    Li, Y.Z., Dong, X.W., Liu, G.L., Tao, F.: Effects of light and temperature factors on yield and its components in maize. Chin. J. Eco-Agri. 10(2), 86–89 (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chao-jie Jia
    • 1
  • Le-le Wang
    • 1
  • Xiao-li Luo
    • 1
  • Wei-hong Zhou
    • 1
  • Ya-xiong Chen
    • 1
  • Guo-jun Sun
    • 1
  1. 1.Institute of Arid Agroecology, School of Life SciencesLanzhou UniversityLanzhouChina

Personalised recommendations