Journal of Geographical Sciences ›› 2012, Vol. 22 ›› Issue (3): 563-573 .doi: 10.1007/s11442-012-0947-9

• Land Use Change • Previous Articles     Next Articles

Farmland changes and the driving forces in Yucheng, North China Plain

CHEN Zhao1,2, LU Changhe1, FAN Lan1,2   

  1. 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2011-06-20 Revised:2011-11-01 Online:2012-06-15 Published:2012-05-04
  • Contact: Lu Changhe, Professor, E-mail: luch@igsnrr.ac.cn E-mail:luch@igsnrr.ac.cn
  • About author:Chen Zhao (1978-), Ph.D Candidate, specialized in land use and food security. E-mail: chenc.09b@igsnrr.ac.cn
  • Supported by:

    National Natural Science Foundation of China, No.41071063; National Basic Research Program of China (973 Program), No.2012CB955304

Abstract:

Taking Yucheng, a typical agricultural county in Shandong Province as a case, this study applied Logistic regression models to spatially identify factors affecting farmland changes. Using two phases of high resolution imageries in 2001 and 2009, the study obtained the land use and farmland change data in 2001-2009. It was found that the farmland was reduced by 5.14% in the period, mainly due to the farmland conversion to forest land and built-up land, although part of forest land and unused land was converted to farmland. The results of Logistic regressions indicated that location, population growth and farmer income were main factors affecting the farmland conversion, while soil types and pro-curvature were main natural factors controlling the distribution of farmland changes. Regional differences and temporal-spatial variables of farmland changes affected fitting capability of the Logistic regression models. The ROC fitting test indicated that the Logistic regression models gave a good explanation of the regional land-use changes. Logistic regression analysis is a good tool to identify major factors affecting land use change by quantifying the contribution of each factor.

Key words: farmland changes, driving factors, Logistic regression model