Rural population non- agriculturalization is a geographical phenomenon conforming to the development trend and social progress of rural areas, and its study can provide important insights on the modernization of Chinese agriculture, acceleration of urban development, and resolution of the "Three Rural Issues". China's rural population non-agriculturalization reflects the evolution of the economic and social structures of the countryside. The prominent feature of this change is the transfer of rural laborers to non-agricultural sectors on a large scale. According to the "Report on the Development of China's Floating Population in 2010", China's proportion of non-agricultural employment will reach 65% by 2015 and 70% by 2020; the population of urban residents will rise to 77% by 2050, which means that hundred millions of rural population will transform into urban residents in the next 30 or 40 years. From a cross-disciplinary perspective, this study uses statistical data since the reform and opening up of the late 1970s and the exploratory spatial data analysis (ESDA) and partial least squares (PLS) regression analysis methods to explore the spatial-temporal patterns of the rural population non-agriculturalization process and its impacting factors, for making appropriate policies and taking proper measures to facilitate the future development of non-agricultural employment of the rural population. The results indicate that: (1) In the temporal dimension, the non-agriculturalization of rural population has experienced three distinctive stages: nationwide and in the four regions, its growth rate has gone through an "inverted U- shaped" process of growth→fluctuating growth→steady growth since 1978. (2) Spatially, China's rural population non-agriculturalization shows clear regional differences. (3) Rural population non-agriculturalization has an apparent spatial polarization character—the growth hotspots moved from the north to the south and then to the central region, that is, the growth hotspots were first in two core agglomerations, then concentrated in one core area, and later diffused from a center of growth to the periphery in declining intensity. (4) The spatio-temporal differences of China's rural population non-agriculturalization is mainly controlled by the level of industrialization, urbanization, development of service industries, education, and agricultural modernization and the interaction of these factors, and the role each factor played during different time periods varied significantly. (5) Confronted with the new situations and challenges of non-agriculturalization of rural population in the transitional period at present, the research community of geography should come up with necessary theoretical frameworks for the study of this process and countermeasures for problems that occur.

Rapid urbanization and industrialization are accompanied with rural-urban migration and the recombination and interaction of socioeconomic development factors, which result in the restructuring of rural socioeconomic system and spatial configuration, including the migration of rural people, non- agricultural transition of employment, industrial development, and land use change. These changes break the traditional rural-urban dual structure and result in the overall rural transformation and development. In reality, there is often a lack of coordination between the transformation of rural development factors, which means the transition speed of one rural development factor is faster or slower than that of others. To some extent, this uncoordinated transformation hinders the sustainable development of the rural or the whole rural-urban system. Therefore, it is particularly important to analyze the coordination of transition of rural development factors. Shandong Province, located in the coastal area of eastern China, has experienced drastic changes in land use since the initiation of economic reforms in 1978. Rapid urbanization and industrialization boosted the development in the rural areas. Shandong has similar primary industry structure and similar urbanization rate to China's average. In addition, there is a clear gradient of regional economic growth from the eastern to the central and western parts of the province, which is extremely similar to the general spatial pattern of economic development in China. As such, Shandong Province can be considered a miniature of rural economic development in China. This paper selects Shandong Province as the case study area to analyze the development of rural area from the viewpoints of transformation and coordination. This paper integrates the methods of traditional geographical research and analysis, simulation of quantitative model and decision support system based on the "3S" technology to establish a coordinated transformation degree model and a three- dimensional space of rural transformation degree, integrated coordination degree and coordinated transformation degree, based on the three major rural development elements of population, land and industry. The relationship between rural transformation speed and the coordination characteristics of transformations of rural development elements in Shandong Province was analyzed from the viewpoint of "transformation" and "coordination". The main conclusions are as follows. (1) The spatial pattern of rural transformation degree gradually changed from local clustering to overall dispersion; the distribution of integrated coordination degree took on a dispersion pattern with some small clusters; the spatial pattern of coordinated transformation degree was coupled with that of rural transformation degree during 1990-1995 and 1995-2000, coupled with that of rural transformation degree and integrated coordination degree during 2000-2005, and coupled with the spatial pattern of integrated coordination degree during 2005-2009. (2) The correlation coefficient of rural transformation degree and coordinated transformation degree, and that of integrated coordination degree and coordinated transformation degree are 0.92 and 0.61, respectively. These results indicate that the overall flow of the development elements of population, land and industry between rural and urban systems will promote the coordinated transformation of rural areas. (3) "Transformation" and "coordination" represent the "quantity" and "quality" of development, which should be paid equal attention during the process of rural transformation and development.

Farmers are the demanders and users of agricultural technology. Only when farmers accept, assimilate and use new technologies, can agricultural science and technology achievements be transformed into practical productive forces. So, it is worth paying attention to what farmers need, which factors influence farmers' behavior significantly and the relationship between these factors. To address these questions and understand the mechanisms of influence, we take potato planting technology adoption in Dingxi City, Gansu Province as an example. Questionnaire survey and interview were conducted to acquire data. Based on 575 survey samples, a Logistic regression model and the interpretative structure model (ISM), we analyze the significant factors influencing farmers' adoption behavior in the process of potato planting technology diffusion, and investigate the relationship among the various factors. The results are as follows. (1) Farmers' behaviors of adopting new potato planting technology is affected by five major characteristic variables, including farmers' background, awareness, production management, resource endowment, and environmental characteristics. These variables influence farmers' behavior to various degrees. (2) The result of Logistic regression shows that farmers' level of education, family structure, degree of specialization, degree of organization, awareness of science and technology park, awareness of new technology, agricultural income, climatic productivity, topography of the planting areas, grade of nearby roads and location are significant factors influencing farmers' adoption behavior. (3) The ISM model shows that there are various factors influencing farmers' adoption behavior, and these factors are at different levels of a hierarchy, which are interrelated and independent. Among these factors, degree of specialization, agricultural income, climatic productivity, location, terrain, and grade of nearby roads are the direct influencing factors. Awareness of science and technology park and awareness of new technology are the indirect factors. Education of farmers, family structure and the degree of organization are the root influencing factors. Based on these results, we recommend strengthening the development of production by orders from business and consumers, reconstructing diffusion model and adopting the annular diffusion model of "leading by enterprises, supporting by the government and participating by farmers". These measures will improve the enthusiasm of farmers to adopt new technology effectively. In addition, it is necessary to raise the farmers' awareness of science park and new technology and expand the range of influence of the Dingxi Agricultural Science and Technology Park, especially for the information-deprived remote villages, and enable them to be exposed to new technologies promptly and adequately. Likewise, strengthening infrastructure development and increasing agricultural income also should not be overlooked. Improving incentive structures can encourage farmers to adopt new technology. The government should invest in transportation infrastructure and improve accessibility to attract business and optimize the sales channels of agricultural products. Future research of rural agricultural technology diffusion should be directed toward these directions.

Given a market for land circulation, cropland abandonment due to lack of labor forces will be alleviated. Therefore, the correlation between agricultural laborer and cropland abandonment at the farm household level will be weakened to some degree. In this paper, we aimed to identify a reasonable level for analysis of agricultural laborer and size of cropland abandonment. Because land use property transfer primarily occurs within the village unit, we supposed this relationship may be more pronounced at the village level. To test this hypothesis, this study examines to what extent agricultural laborer impacts the size of cropland abandonment at the village level and at the farm household level, and compares these results. The data from a survey of 308 farm households in 40 villages from Wulong County of Chongqing Municipality were used in the analysis. Data for the two variables (agricultural laborer per unit of farm land area and abandoned cropland area in the household) were collected through the survey for each household, and they were aggregated to form the village level data for the corresponding village units. We first conducted two Logistic regressions for the two levels respectively. The results show that the size of agricultural laborer can effectively influence the cropland abandonment decision at the village level, but it had no significant influence at the farm household level. This finding confirms our hypothesis that the size of agricultural laborer affects cropland abandonment much stronger at the village level than the household level. We further explored the reason of this relationship by using a partial correlation analysis between agricultural laborer per unit of land area and the size of abandoned land at both the household level and the village level. Furthermore, the results are compared between with and without controlling for the rate of land transfer. The Spearman correlation between agricultural laborer for each unit of farm land (mu) and abandoned cropland area are not significant at the household level (the correlation coefficient is -0.027), but this relationship is remarkably strong when controlling the land circulation rate, with a coefficient of -0.273. The result at the village level shows that the two variables are significantly correlated with or without controlling for land circulation, and the two correlation coefficients are very close: -0.273 for the controlled analysis and -0.279 without land transfer control. That is, the correlation between the two variables is slightly enhanced at the village level with a market for land circulation. The Pearson partial correlation analysis shows that the correlation between agricultural laborer and cropland abandonment was weakened by land circulation at the farm household level, but enhanced at the village level. Through this analysis, we conclude that village level is a reasonable level for analysis of agricultural laborer affecting cropland abandonment. Due to the land circulation effects, at the household level agricultural laborer cannot be an effective explanatory variable for cropland abandonment. We recommend that the relationship between agricultural laborer and cropland abandonment should be analyzed at the village level.