Associations Between Cultivated Land Fragmentation and Multifunctional Utilization in a Typical Economically Developed Region: A Case Study of the Yangtze River Delta, China

Under rapid urbanization and intensive land development, understanding the spatiotemporal patterns of cultivated land fragmentation (CLF) and its associations with cultivated land multifunctional utilization (CLMU) in economically developed regions is important for optimizing cultivated land use patterns, improving multifunctional cultivated land governance, and safeguarding regional food and ecological security. Based on multi-source data on land use, agricultural production, and socioeconomic conditions, this study uses ordinary least squares (OLS) and geographically weighted regression (GWR) models to examine year-specific associations between CLF and CLMU and their spatial variation in the Yangtze River Delta (YRD), a typical economically developed region in China. The results show that: (1) From 2000 to 2020, CLF in the YRD increased by 13.52%. Its dimensional attributes followed the order of spatial aggregation > shape regularity > resource scale, and all three dimensions declined by 9.59%, 4.30%, and 8.39%, respectively. Spatially, 94.87% of counties in the YRD showed an increase in CLF, and counties with large increases in CLF were mainly concentrated in northern Zhejiang, northern Anhui, and the northern and eastern coastal plains of Jiangsu. (2) During 2000–2020, the level of CLMU in the YRD declined slightly. Specifically, the ecological maintenance and economic support functions increased by 8.99% and 2.26%, respectively, whereas the agricultural production and social security functions decreased by 7.48% and 16.14%, respectively. Spatially, approximately 52.82% of counties showed a decline in CLMU, mainly in northern Anhui, Jiangsu, Shanghai, and Zhejiang. Counties with an increased CLMU were mainly distributed in central and southern Anhui, with increases mostly exceeding 7.4%. (3) CLF was consistently negatively correlated with CLMU during the study period, and the magnitude of this negative association varied across the five observation years. Spatially, the association between CLF and CLMU was generally negative in the north and positive in the south. The associations of CLF and different cultivated land landscape characteristics with agricultural production, economic support, social security, and ecological maintenance functions also showed significant spatio-temporal heterogeneity.

Authors

Institutions

Publication Details

Journal
Land
Published
2026-11-11
DOI
https://doi.org/10.3390/land15112158
Primary Topic
Land Use and Ecosystem Services
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

Associations Between Cultivated Land Fragmentation and Multifunctional Utilization in a Typical Economically Developed Region: A Case Study of the Yangtze River Delta, China

Jing Liu, Weiyi Xu, Tian Bing, Chao Chen
Land
Land Use and Ecosystem Services
article

Associations Between Cultivated Land Fragmentation and Multifunctional Utilization in a Typical Economically Developed Region: A Case Study of the Yangtze River Delta, China

Jing Liu, Weiyi Xu, Tian Bing, Chao Chen
article en

Abstract

Under rapid urbanization and intensive land development, understanding the spatiotemporal patterns of cultivated land fragmentation (CLF) and its associations with cultivated land multifunctional utilization (CLMU) in economically developed regions is important for optimizing cultivated land use patterns, improving multifunctional cultivated land governance, and safeguarding regional food and ecological security. Based on multi-source data on land use, agricultural production, and socioeconomic conditions, this study uses ordinary least squares (OLS) and geographically weighted regression (GWR) models to examine year-specific associations between CLF and CLMU and their spatial variation in the Yangtze River Delta (YRD), a typical economically developed region in China. The results show that: (1) From 2000 to 2020, CLF in the YRD increased by 13.52%. Its dimensional attributes followed the order of spatial aggregation > shape regularity > resource scale, and all three dimensions declined by 9.59%, 4.30%, and 8.39%, respectively. Spatially, 94.87% of counties in the YRD showed an increase in CLF, and counties with large increases in CLF were mainly concentrated in northern Zhejiang, northern Anhui, and the northern and eastern coastal plains of Jiangsu. (2) During 2000–2020, the level of CLMU in the YRD declined slightly. Specifically, the ecological maintenance and economic support functions increased by 8.99% and 2.26%, respectively, whereas the agricultural production and social security functions decreased by 7.48% and 16.14%, respectively. Spatially, approximately 52.82% of counties showed a decline in CLMU, mainly in northern Anhui, Jiangsu, Shanghai, and Zhejiang. Counties with an increased CLMU were mainly distributed in central and southern Anhui, with increases mostly exceeding 7.4%. (3) CLF was consistently negatively correlated with CLMU during the study period, and the magnitude of this negative association varied across the five observation years. Spatially, the association between CLF and CLMU was generally negative in the north and positive in the south. The associations of CLF and different cultivated land landscape characteristics with agricultural production, economic support, social security, and ecological maintenance functions also showed significant spatio-temporal heterogeneity.

LandVol. 15(11)
China University of Mining and Technology (CN), China Agricultural University (CN)
Land Use and Ecosystem Services
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon