Early Warning of Poverty Returning against the Background of Rural Revitalization: A Case Study of Two Counties in Guangxi Province, China

• Published in: Agriculture (Basel) Vol. 13; no. 5; p. 1087
• Main Authors: Du, Yaqi, Zhao, Rong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2023
• Subjects: Algorithms; Analytic hierarchy process; Back propagation networks; Case studies; China; Early warning systems; Farmers; Hierarchies; Households; Human capital; Income; Intellectual capital; Labor; Living conditions; Low income groups; Minority & ethnic groups; Neural networks; Poverty; Regeneration; Rural areas; rural development; Guangxi Province; poverty returning; AHP analytic hierarchy process; BP neural network algorithm; Surveys; Warning systems; China
• ISSN: 2077-0472; 2077-0472
• DOI: 10.3390/agriculture13051087

China has achieved the goal of building a moderately prosperous society in a well-rounded way by 2020. At this stage, effectively dealing with poverty and not returning to it has become the bottom-line task of rural revitalization. The purpose of this study is to construct a poverty-return early warning and evaluation system for X and Y counties in Guangxi. Based on the field survey data of 150 households from the questionnaire survey in X County and Y County of Guangxi Province, an early warning evaluation system for returning to poverty in the two counties of Guangxi Province is constructed. The AHP analytic hierarchy process is used to evaluate the early warning of returning to poverty for farmers. The BP neural network algorithm is used to verify the rationality of the method; the overall poverty relief situation in the two counties is stable and the living conditions are good. The early warning results are as follows: One household in X County has a severe early warning, six households have a slight early warning, and sixty-four households have no early warning; in Y County, six households had severe early warning, six households had mild early warning, and sixty-seven households had no early warning. For farmers, serious early warnings are mainly caused by the lack of labor force and low annual per capita net income, as well as the lack of the main means of livelihood and capacity. The characteristics of mild early warnings for farmers are mainly that the proportion of non-labor income is relatively high, and the farmers lack the ability and way of long-term development. Different suggestions are put forward for farmers with different early-warning levels, focusing on improving their viability and development ability.