Cropland Use Transitions and Their Driving Factors in Poverty-Stricken Counties of Western Hubei Province, China

Jiangfeng Li,Xiaoqing Song,Jingwei Xiang

doi:10.3390/su11071997

Abstract

Agriculture is important for economic development in most poverty-stricken areas in China, but cropland use is facing challenges due to rapid industrialization and urbanization, causing serious issues for poverty alleviation and sustainable socioeconomic development. Cropland Use Transition (CUT) is one way to alleviate poverty and develop the economy in poverty-stricken areas. This paper chose 16 typical poverty-stricken counties in Western Hubei province as the case area. A morphology index system was established to evaluate CUT, and geographic information system software was used to analyze the temporal-spatial variations in CUT. Using the Radial Basis Function Neural Network (RBFNN) model, contributions of driving factors of population, economy, and industrial structure to CUT were analyzed. The results show that: (1) cropland use morphology can be divided into functional morphology and spatial morphology; (2) the spatial distribution of CUT was high in the north and low in the south, the temporal variation of CUT from 1995 to 2013 showed fluctuations, and the coefficient of CUT changed from 0.460 to 0.649 with a growth rate of 41%; (3) for the driving factors, population factors most significantly contributed to CUT, followed by industrial structure and economic factors. The results obtained in this study are in line with the findings of previous studies. The RBFNN model is suitable for evaluating the contributions of driving factors, which can solve the deficiency in previous studies caused by ignoring the internal relationship and target orientation of driving factors. This study suggests that poverty-stricken counties should narrow the urban–rural divide, encourage balanced labor and investment flow into cropland by formulating relevant economic policies, motivate farmers’ agricultural engagement, and use science and technology to promote CUT and the growth of the agricultural economy, poverty alleviation, and to coordinate urban–rural development.

Highlights

The social-economic level in poverty-stricken areas of China has significantly improved with the rapid development of industrialization, urbanization, and socioeconomic transition
Using the Radial Basis Function Neural Networks (RBFNN) model explained above, this paper found that the population factor contributed the most to Cropland Use Transition (CUT) in general, followed by industrial structure, while the economic factor was relatively weaker (Figure 6)
This paper considered 16 poverty-stricken counties in Western Hubei province in China as the study area and analyzed CUT from the perspectives of functional morphology and spatial morphology, along with driving factors, including population, economy, and industrial structure

Summary

Introduction

The social-economic level in poverty-stricken areas of China has significantly improved with the rapid development of industrialization, urbanization, and socioeconomic transition. Due to sterile soil and complex topographic and geographic conditions, the use of cropland is relevant for poverty alleviation and sustainable socioeconomic development [3], which promote the rapid transition of cropland use. Cropland use change is related to food security [23], farmland productivity [24], socioeconomic development [25,26], urbanization [27], and ecosystem services [28]. Due to the practical connection with poverty alleviation and sustainable socioeconomic development, the study of Cropland Use Transition (CUT) is receiving more attention, being widely practiced to meet the demands of the social economy

Objectives

Methods

Results

Discussion

Conclusion