Based on classification data of remote sensing images, using kernel density, the minimal cumulative resistance model of road traffic accessibility, and a logistic regression model, the spatial pattern and the main factors influencing it were quantitatively examined in Guangdong Province from 1990 to 2013. The framework of the research concerning rural settlement evolution and its effect mechanisms were also discussed and generalized for the future. The results are as follows: (1) The spatial distribution of rural settlements showed some spatial orientation to low altitude and to low slope, and were significantly close to rivers and towns; thus formed a special pattern of "dense on the plains, and sparse in the mountainous region", including two high density core regions of rural settlements in the Chaoshan plain and the Zhanjiang tableland. The rural settlements were distributed along the rivers, valleys, and roads in the mountainous region surrounding the Pearl River Delta. (2) In addition to the spatial orientation of the open road, the accessibility of road traffic to the township has the greatest influence on the spatial distribution of the rural settlements. The connected transport network between towns and villages is significant for rural transformation as a comprehensive increase in township production and service capacity will be the key to optimizing the town-village system in rural areas. (3) Elevation and slope were two basic but influential factors that have affected the distribution, scale, and form of rural settlements. The attributes of the natural geography are the first elements in optimizing village layout and planning spatial reconstruction. (4) In the current Internet and social media era, the reconstruction of market network system orders is connected with the global market network system in the rural areas. The rural life service circle will be constructed with the township at its core to explore the theory and practice of spatial reconstruction, including its production, life and ecology, and social-cultural heritage and protection. It will also allow for exploration of the rural settlements' evolution, rural spatial production, rural social networks, group behavior, social autonomy, and social and cultural fields, which will be the core focus of China's rural spatial reconstruction research under the background of globalization.

In order to explore the spatial structure of rural residential land transition and its relevance to economic development, the spatial layout and agglomeration characteristics of rural residential land transition in Shandong Province were studied by applying theoretical hypothesis on rural housing land transition and spatial correlation analysis methods, such as global spatial autocorrelation analysis and hot spot analysis. Economic growth stages at county scale were divided based on per capita GDP, and the coupling relationship between rural residential land transition and economic development were revealed from the integrated angle of global scale at the provincial level and "point-line-face" feature unit. The results showed that, the rural residential land transition index from 2005 to 2014 displayed a significantly increasing trend in the east-west direction and a gradually descending trend from south to north. Based on the global spatial autocorrelation analysis, Moran's index, which reached 0.6317, indicated that the rural residential land transition showed a significant pattern of high-high and low-low spatial clustering. Furthermore, by applying hot spot analysis, it was found that the hot spots and hot sub-spots were intensively distributed in the western inadated plain of the Yellow River, southwest Huaihe plain and central Yimeng Mountains of Shandong Province; the cold sub-spots were distributed in the periphery of the hot sub-spots, such as cities of Zaozhuang, Jinan, Zibo, Binzhou and Weifang; and the cold spots were mostly distributed in Jiaodong hilly region and the northern Yellow River Delta. Moreover, obvious correlation of mathematical statistics and spatial coupling between rural residential transition indices and economic development level were indicated. The decreasing tendency from low to high economic gradient at both global scale and provincial level and feature units of "Five counties - Three belts - Four regions" were revealed by all rural residential land transition characteristics, with the significant relationship of power exponent trend. This paper explored the spatial characteristics of rural residential land transition, and made up for the deficiency of the single non-spatial analysis of land use transition; and the results verified the previous theoretical hypothesis successfully.

It is a China's strategic demand to systematically and accurately measure the poverty characteristics of poverty-stricken areas from the perspective of village-level poverty. In this context, this paper constructs a multidimensional poverty evaluation model from the perspective of spatial poverty, and combines the decomposition of indicator contribution degree and linear regression method to explore poverty contributing factors. Then it adopts LSE (Least Square Error) model and geostatistics analysis model to classify the villages' poverty types and spatial distribution difference, so as to grasp the whole poverty characteristics, poverty contributing factors and poverty types of poor villages all over the country and to provide auxiliary decision-making support services for eradicate poverty in 2020. Taking 51461 villages in China as a case, we can draw some conclusions as follows: (1) The distribution of poverty-stricken villages has an obvious regional characteristic from the perspectives of poverty level and poverty size, and most poor villages are concentrated in contiguous destitute areas, showing a pattern of high in the west and low in the east. In view of poverty level, there exists an obvious olive-shaped structure of big in the middle and small on two sides, and nearly half of the poverty-stricken villages are moderately poor. (2) China's poor villages have four main poverty contributing factor dimensions, i.e. , harsh transportation conditions, frequent natural disasters, low income and poor labor force qualities, of which, the significant poverty contributing factors are road access ratio, terrain type, suffered frequency of natural disaster, per capita net income, labor force ratio, ratio of illiterate labor forces, etc. (3) China's poor villages are driven by multiple poverty types, among which, those villages with three-factor dominated, four-factor collaborative and five-factor combined types have a relatively high proportion. (4) "Entire-village Advancement" poverty reduction strategy has achieved an initial performance, improved the village's sustainable development environment and raised the income of poor population. However, each village has its own poverty characteristics, therefore, anti-poverty measures should be adjusted according to its respective local conditions.

This paper establishes a model of coupling relationship between grain yield and agricultural labor changes, and preliminarily discusses the coupling process of the changes of grain yield and agricultural labor proportion at county level. Using county-level statistical data of farmland and agricultural labor in 1991, 2000 and 2010, two coefficients were developed to analyze the coupling relationship between grain yield and agricultural labor changes, one is grain-labor elasticity coefficient (GLEC), and the other is agricultural labor-transfer effect coefficient (ALTEC). Results showed that: (1) During the process of agricultural development transformation in China, different types of coupling relationship modes between grain yield and agricultural labor changes coexist at the same time. During 1991-2010, the number of counties with three different coupling modes, i.e., grain yield increasing and agricultural labor decreasing, both increasing of grain yield and agricultural labor and both decreasing of grain yield and agricultural labor, account for 48.85%, 29.11% and 19.74% of the total counties in the study area, respectively. Interestingly, the coupling mode of grain yield increasing and agricultural labor decreasing is mainly found in the traditional farming area of China. The coupling mode of both increasing of grain yield and agricultural labor is located in the pastoral areas and agro-pastoral ecotone in the underdeveloped western China, while the coupling mode of both decreasing of grain yield and agricultural labor is distributed in the areas experiencing rapid agricultural development transition, especially in the developed southeastern coastal China. (2) From 1991 to 2010, the number of counties which experienced a decline of agricultural labor proportion was 1961, among which 1452 counties with an increasing of grain yield, accounting for 72.38% of the total number of counties. The coupling curve between grain yield and agricultural labor proportion changes showed a stepped fluctuation, and the discrete trend of the coupling relationship was continually strengthened. (3) The mean of GLEC and ALTEC increased from -0.25 and -2.93 during 1991-2000 to -0.16 and -1.78 during 2000-2010, respectively. The changes of GLEC and ALTEC illustrated that the influence of agricultural labor changes on increasing grain yield is diminishing gradually. (4) "Hu Huanyong Line" is a significant boundary presenting the coupling relationship between grain yield and agricultural labor changes.

Agricultural production will be confronted with more serious challenges under climate change. Understanding households' adaptive behaviors and the influence of government policies on them is the fundamental step to improve relevant policies for enhancing farmers' ability of adaptation against drought. The econometrics method is used in this study to evaluate the farmers’ adaptive behaviors and their influencing factors, especially the regulation of government policies, like early-warning and policy supports, based on a household and village survey in three provinces of the North China Plain (NCP). The results show that: (1) Most of households are apt to take adaptive measures when facing drought; and the more serious droughts are, the more the adaption measures will be taken. (2) The adaptation behaviors of farmers are affected by the regulation of government policies. The enthusiasm to take adaptations of households can be enhanced obviously by the governments' early-warning information and relevant policy supports, especially when the warning information is released by multiple channels and the subsides or multiple supports are provided by the township government and the village. However, most of the households have no support from governments at all levels. Besides, TV as the present main channel has very little effect on the farmers' hehaviors. (3) Adaptive behaviors vary along with households' characteristics. Farmers who live in irrigated areas, and who have more agricultural members in the family with larger cultivated land block acreage, are more inclined to take adaptive measures. These findings can provide references for the government to take measures to combat drought.