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    Geographical agglomeration of crops and its evolutionary mechanism in Henan Province during 1989-2009
    CHEN Taizheng, LI Erling, LI Wan
    PROGRESS IN GEOGRAPHY    2013, 32 (8): 1237-1245.   DOI: 10.11820/dlkxjz.2013.08.007
    Abstract1047)      PDF (2870KB)(836)      
    Taking 17 types of crops in Henan Province as an example, this paper analyses the geographical agglomeration and specialized level of crops from 1989 to 2009. Based on evolutionary economic geography, an analytic framework of "Stability-Variation-Selection-Adaption" was established to explain the crops' spatial agglomeration and its evolutionary mechanism. The results indicate that agglomeration of crops is low and in slow growth, especially for the general crops; rice has the highest level of clustering, which has a great impact on the whole agglomeration level of crops. Levels of agglomeration and specialization of the economic crops are higher than the food crops, and their distribution patterns are relatively stable, still concentrated in the initially favorable areas. Specialized level of vegetable crops is low, and their spatial distributions are dependent on the urbanization level of each city. Fruit crops constantly concentrate in the western mountainous area of Henan Province, especially Sanmenxia City, and some fruit crops, such as grapes and peaches, appear to be in a decentralized trend. The municipal differentiation degree of crops is low, and the pattern of division and specialization has just emerged.
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    Cited: CSCD(7)
    Simulation of the changes of farmer households’land-use behaviors in response to China’s Grain to Green policy: A case study in Gaoqu Commune of Mizhi County, Shaanxi Province
    CHEN Hai, XI Jing, LIANG Xiaoying, CHEN Shanshan
    PROGRESS IN GEOGRAPHY    2013, 32 (8): 1246-1256.   DOI: 10.11820/dlkxjz.2013.08.008
    Abstract839)      PDF (4182KB)(1076)      
    Analyzing the interactions between environmental policies and farmers' responses is an important dimension to understand the sustainability of agro-ecosystems. China's Grain to Green (GTG) policy has been the most influential one that has drawn the biggest participation from the farmers in the contemporary history of China. To what extent the implementation of the GTG policy has affected regional land-use changes and farmers' land-use behaviors at the household level in particular, has been a critical issue that concerned by both policy makers and researchers. This empirical study identified how the implementation of GTG policy during 1999-2006 has impacted farmers' land-use behaviors, and designed a "Farmer Group Decision-Making Model" to simulate the probability of land-use change in the case study area of Mizhi County of Shaanxi Province. Elevation, slope, and heterogeneous characteristics of the farmer households under study were identified to be the key factors influencing farmers' land-use decision-making process and land-use pattern. Classification and Regression Tree were used to classify farmer household groups, and Experience-Weighted Attraction learning model was used to analyze the land use behavior's change before and after the GTG policy. The results showed that farm households' land-use behaviors, namely land reclamation and land abandonment in the study area, has been significantly affected by the GTG program in China since 1999, and the results of simulation in the study showed that possible consequences of land reclamation and abandonment co-exist under all policy scenarios. The spatial distribution and magnitude of land to be affected by such land-use behaviors vary among the villages in the study area. Policy recommendations based on the findings of this study relate to how to protect the effectiveness of the GTG program and to improve the efficient use of the under-used farmland. How and to what extent distance and kinship affect farm households' land-use behavior need to be explored in future research.
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    Cited: CSCD(13)
    Spatial difference of grain yield changes during 1995-2010 and balanced potential output to increase in Shandong Province
    BAI Linchuan, WU Lanfang, SONG Xiaoqing
    PROGRESS IN GEOGRAPHY    2013, 32 (8): 1257-1265.   DOI: 10.11820/dlkxjz.2013.08.009
    Abstract1004)      PDF (11354KB)(821)      
    Based on spatial autocorrelation analysis of exploratory spatial data, spatial changes and disparities of grain yield per unit area at county level in Shandong Province during 1995-2010 are discussed by using ArcGIS and GeoDa software, and then the potential increase of grain yield per unit area and total yield at regional scale are accessed. The results show that: (1) During 1995-2010, the difference of grain yield per unit area among counties increased first, and then decreased. Among the counties, the northwestern plain and southwestern plain both had poor conditions initially, but the former increased the most quickly in grain yield per unit area and the latter increased slowly; the central and southern hills and Jiaodong Peninsula both had good conditions initially, but they increased slowly. (2) The global spatial autocorrelation of grain yield per unit area change is significantly positive and Global Moran's I is 0.5708. The changes showed a spatially clustering phenomenon on the whole and the characteristic of spatial clustering of regional high value and low value is significant. The regions with "high-high" and "low-low" correlation are the majority. The regions with "H-H" correlation are mostly located in the northwestern plain, however, the regions with "L-L" correlation are mainly distributed in the other three regions. (3) Shandong Province could be divided into 4 first-grade regions and 9 second-grade regions. The balanced potential output per unit area of the 4 first-grade regions could be sorted in descending order as the northwestern plain, central and southern hills, Jiaodong Peninsula, and southwestern plain. The total potential output of Shandong Province is 9.50×10 6 tons, and the 4 regions could be sorted as the central and southern hills, northwestern plain, southwestern plain, and Jiaodong Peninsula.
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    Cited: CSCD(12)