Research on targeted land poverty alleviation patterns based on the precise identification of dominant factors of rural poverty: a case study of Siyang County, Jiangsu Province, China

Poverty is a common problem faced by various social and economic forms. Eliminating poverty is a major issue of common concern and research all over the world. Land policies for poverty alleviation are an effective means of affecting land agriculture development, increasing the productivity of cultivated land, and promoting growth in farmers’ income and production. In recent years, it has played an important role in promoting poverty alleviation in poverty-stricken areas. However, the poverty alleviation needs of township units have not been precisely identified, and one-size-fits-all land policies are unable to effectively allocate poverty alleviation resources. Consequently, the actual efficiency of policies has been severely restricted, leading to regional differences in poverty alleviation. In response to this bottleneck, this study takes Siyang County in Jiangsu Province as a case study and adopts the geographical detector model to diagnose and identify the spatial differentiation characteristics and dominant factors of poverty and in various townships. The results can be summarised as ‘four dominant factors of poverty—four regional types of poverty townships—four targeted land poverty alleviation patterns’. Detailed results are as follows: the dominant factors affecting differentiation of rural poverty in Siyang County are labour, technology, land resources, and financial support. The various townships of the county were thus classified into four types according to the main factor affecting poverty: labour-constrained, land-constrained, capital-constrained, and technique-constrained, and proposed four targeted land poverty alleviation patterns for the different constraint types and then to improve the effectiveness of land development policies to successfully achieve precise poverty alleviation and reinforce strategies revitalising rural communities.