Understanding the Relationship between Dominant Geo-Environmental Factors and Rural Poverty in Guizhou, China

Ge, Yong; Ren, Zhoupeng; Fu, Yangyang

doi:10.3390/ijgi10050270

Cited by 13 publications

(17 citation statements)

References 55 publications

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“…Spatial autocorrelation analysis is based on Tobler's first law of geography [36,37], in which the correlation of a specific variable is analyzed at different locations. It encompasses global and local autocorrelation analysis [38]. In this study, local autocorrelation was used to explore the spatial distribution patterns of the rice yield in Pucheng County.…”

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Rice Yield Simulation and Planting Suitability Environment Pattern Recognition at a Fine Scale

Liang

Wang

et al. 2021

IJGI

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Analyzing rice yields and multidimensional environmental factors at a fine scale facilitates the discovery of the planting environment patterns that guide the spatial layout of rice production. This study uses Pucheng County, Fujian Province, a demonstration county of China Good Grains and Oils, as the research area. Using actual rice yield sample data and environment data, a yield simulation model based on random forest regression is constructed to realize a fine-scale simulation of rice yield and its spatial distribution pattern in Pucheng County. On this basis, we construct a method system to identify spatial combination patterns between rice yields and fine-scale multidimensional environmental planting suitability using rice yield data and environmental planting suitability evaluation data. We categorize the areas into four combination model areas to analyze the spatial correlation model of planting suitability, multidimensional environment, and yield: higher-yield and higher-suitability cluster–comprehensive environmental-advantage areas, high-yield and high-suitability cluster–soil condition-limited areas, moderate-yield and moderate-suitability cluster–irrigation and drainage condition-limited areas, and low-yield and low-suitability cluster–site condition-limited areas. The following results are found. (1) The rice yield simulation model, which is based on random forest regression, considers the various complex relationships between yield and natural as well as human factors to realize the refined simulation of rice yields at a county scale. (2) The county rice yield has a strong positive spatial correlation, and the spatial clustering characteristics are obvious; these relationships can provide a basis for effectively implementing intensive rice planting in Pucheng County. (3) We construct a spatial combination pattern recognition method based on rice yield and environmental planting suitability. We can use this method to effectively identify the spatial relationship between yield and planting suitability as well as the shortcomings and advantages of different regions in terms of the climate, soil, irrigation, site, mechanical farming, and similar factors. On this basis, we can provide regional rice planting guidance for Pucheng County. In addition, this method system also provides a new perspective and method for research into spatial combination models and related spatial issues.

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Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Rice Yield Simulation and Planting Suitability Environment Pattern Recognition at a Fine Scale

Liang

Wang

et al. 2021

IJGI

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Section: Introductionmentioning

confidence: 99%

“…The geo-environment influences the development of human society and economy by influencing production activities and processes (Wang et al 2018 ; Ge et al 2021 ). The geo-environment can have a significant or even decisive impact on poverty (Xu et al 2019 ; Zhou et al 2020 ; Ge et al 2021 ; Zhou and Liu 2022 ). The poor are often identified in areas with unfavorable geo-environment, such as remote geographical location (Wang et al 2020 ), backward transportation (Benevenuto and Caulfield 2019 ), fragile ecology (Raleigh 2011 ; Chen et al 2021 ), and frequent disasters (Sawada and Takasaki 2017 ; Winsemius et al 2018 ).…”

Section: Introductionmentioning

confidence: 99%

“…Natural conditions, especially complex terrain are the main determinants of poverty in China (Liu and Xu, 2016 ; Zhou and Xiong 2018 ; Zhou et al 2020 ). Poverty and eco-environment degradation affect each other, especially in the southwest of the country (Chen et al 2021 ; Ge et al 2021 ; Liu et al 2021 ). Geographic capital affects farmers’ production activities and livelihood capital, thus restricting the increase of income (Jalan and Ravallio 2002 ).…”

Section: Introductionmentioning

confidence: 99%

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Geo-environmental and socioeconomic determinants of poverty in China: an empirical analysis based on stratified poverty theory

Zhou

Huang

2022

Environ Sci Pollut Res

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According to the stratified poverty theory, poverty includes individual (people) and regional (place) poverty. Understanding the interaction mechanism between individual poverty and regional poverty is crucial to achieving the UN goal of poverty eradication by 2030. However, at present, the relevant empirical research is still limited by the availability of data. To fill this important gap, based on the multi-source data of poverty census, geo-environmental and socio-economic data of China’s 1587 counties in 2013, we used exploratory spatial data analysis (ESDA) and spatial econometric models (spatial-lag and spatial-error model) to identify determinants of individual poverty and regional poverty in this county. Results show that the spatial distribution of the rural poor in China had strong spatial dependence (Global Moran’s I = 0.574). There was a high degree of spatial overlap between individual poverty and regional poverty. The poverty-causing factors were complex and varied across regions and individuals. Disease of family members was the leading factor driving rural areas in Northeast, Central, and Southwest China. Northeast China was mainly affected by the illness and lack of labor skills of family members. The complex terrain conditions were the determinants driving rural poverty in most areas of China. Improved transportation can greatly reduce rural poverty. Geographical isolation or lack of geographical capital caused by complex terrain conditions, backward transportation, and regional closure promoted regional poverty. In turn, regional poverty-causing factors further restricted the improvement of rural residents’ self-development ability and aggravated individual poverty. Our findings indicate that individual poverty and regional poverty have different poverty-causing mechanisms and poverty reduction priorities. Effective poverty reduction strategies require the coordinated promotion of individual and regional poverty reduction. The reduction of individual poverty should focus on enhancing the livelihood capital of the poor through differentiated policy intervention, while regional poverty alleviation should focus on creating a favorable development environment by increasing infrastructure investment and public service supply.

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“…The township boundary data (scale 1:250), digital elevation model (DEM), slope data, and terrain relief were obtained from the Resource and Environment Science and Data Center of Chinese Academy of Sciences [28]. Terrain relief was defined as the difference between the highest elevation and the lowest elevation within each township [29].…”

mentioning

confidence: 99%

Spatial Disparities and Correlated Variables of Community Care Facility Accessibility in Rural Areas of China

et al. 2021

Sustainability

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With the increasing aging of the world’s population, research on the equitable allocation of elderly care facilities has received increasing attention, but measuring the accessibility of community care facilities (CCFs) in rural areas has received little attention. In this study, which covered 7985 CCFs in 223,877 villages, we measured the accessibility of CCFs in rural areas of Hubei Province by using the nearest distance method. Based on the accessibility calculation, the spatial disparities and agglomeration characteristics of spatial accessibility were analyzed, and the correlated variables related to the accessibility were analyzed from both natural environment and socioeconomic aspects by employing a geographically weighted regression (GWR) model. Our results show that 87% of villages have a distance cost of less than 7121 m and 81% of townships have a distance cost of less than 5114 m; good spatial accessibility is present in the eastern and central regions, while poor spatial accessibility is shown in a small number of areas in the west. The results from the clustering analysis show that the hot spot areas are mainly clustered in the western mountainous areas and that the cold spot areas are mainly clustered around Wuhan city. We also observed that area, elevation, population aged 65 and above, and number of villages are significantly correlated with accessibility. The results of this study can be used to provide a reference for configuration optimization and layout planning of elderly care facilities in rural areas.

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Understanding the Relationship between Dominant Geo-Environmental Factors and Rural Poverty in Guizhou, China

Cited by 13 publications

References 55 publications

Rice Yield Simulation and Planting Suitability Environment Pattern Recognition at a Fine Scale

Rice Yield Simulation and Planting Suitability Environment Pattern Recognition at a Fine Scale

Geo-environmental and socioeconomic determinants of poverty in China: an empirical analysis based on stratified poverty theory

Spatial Disparities and Correlated Variables of Community Care Facility Accessibility in Rural Areas of China

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