The preservation of farmland is a growing concern in China because the fact that China possesses limited land resources and the world's largest population presents a clear contradiction. Only when the value of far...The preservation of farmland is a growing concern in China because the fact that China possesses limited land resources and the world's largest population presents a clear contradiction. Only when the value of farmland is fully appreciated in commercial markets can farmland preservation be effectively achieved. The current study constructed a model to evaluate the economic, social, and ecological value of farmland in China according to the connotation of values. As a case study, the value of Chinese farmland was estimated in 1999, 2002, 2005, 2008, and 2011 using the established model. Although the amount of farmland was greatly reduced from 1999 to 2011 due to constructive occupation, agricultural restructuring, ecological restoration, and disaster destruction, the value of this farmland increased from 220.71×1012 to 736.26×1012 RMB Yuan as a result of the multifunctional nature and scarcity of farmland during the same period. The potential value of farmland in China was huge, but the value in the market was greatly underestimated, especially in regard to its social and ecological value. This study proposes a new method that integrates the discounted value of all future services provided by a natural resource(for the society and individuals) to evaluate the resource assets, provides a scientific foundation for the preservation and operation of farmland assets, and explores ways to increase farmers' property income.展开更多
This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 199...This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.展开更多
基金supported by the Fundamental Research Funds for the Central Universities(Grant Nos.XDJK2012C104)the National Science and Technology Major Project of the Ministry of Science and Technology of China(Grant Nos.2012ZX07104-004)the International S & T Cooperation Program of China(Grant Nos.2013DFG92520)
文摘The preservation of farmland is a growing concern in China because the fact that China possesses limited land resources and the world's largest population presents a clear contradiction. Only when the value of farmland is fully appreciated in commercial markets can farmland preservation be effectively achieved. The current study constructed a model to evaluate the economic, social, and ecological value of farmland in China according to the connotation of values. As a case study, the value of Chinese farmland was estimated in 1999, 2002, 2005, 2008, and 2011 using the established model. Although the amount of farmland was greatly reduced from 1999 to 2011 due to constructive occupation, agricultural restructuring, ecological restoration, and disaster destruction, the value of this farmland increased from 220.71×1012 to 736.26×1012 RMB Yuan as a result of the multifunctional nature and scarcity of farmland during the same period. The potential value of farmland in China was huge, but the value in the market was greatly underestimated, especially in regard to its social and ecological value. This study proposes a new method that integrates the discounted value of all future services provided by a natural resource(for the society and individuals) to evaluate the resource assets, provides a scientific foundation for the preservation and operation of farmland assets, and explores ways to increase farmers' property income.
基金Under the auspices of National Natural Science Foundation of China(No.40601073,41101192,41201571)Fundamental Research Funds for the Central Universities(No.2011PY112,2011QC041,2011QC091)Huazhong Agricultural University Scientific&Technological Self-innovation Foundation(No.2011SC21)
文摘This study used spatial autoregression(SAR)model and geographically weighted regression(GWR)model to model the spatial patterns of farmland density and its temporal change in Gucheng County,Hubei Province,China in 1999 and 2009,and discussed the difference between global and local spatial autocorrelations in terms of spatial heterogeneity and non-stationarity.Results showed that strong spatial positive correlations existed in the spatial distributions of farmland density,its temporal change and the driving factors,and the coefficients of spatial autocorrelations decreased as the spatial lag distance increased.SAR models revealed the global spatial relations between dependent and independent variables,while the GWR model showed the spatially varying fitting degree and local weighting coefficients of driving factors and farmland indices(i.e.,farmland density and temporal change).The GWR model has smooth process when constructing the farmland spatial model.The coefficients of GWR model can show the accurate influence degrees of different driving factors on the farmland at different geographical locations.The performance indices of GWR model showed that GWR model produced more accurate simulation results than other models at different times,and the improvement precision of GWR model was obvious.The global and local farmland models used in this study showed different characteristics in the spatial distributions of farmland indices at different scales,which may provide the theoretical basis for farmland protection from the influence of different driving factors.
