{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T20:00:59Z","timestamp":1760731259615,"version":"build-2065373602"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T00:00:00Z","timestamp":1574035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Project of the Natural Science Foundation of Guangdong Province, China","award":["No. 2018A030313683"],"award-info":[{"award-number":["No. 2018A030313683"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>Changes in cropping intensity reflect not only changes in land use but also the transformation of land functions. Although both natural conditions and socioeconomic factors can influence the spatial distribution of the cropping intensity and its changes, socioeconomic developments related to human activities can exert great impacts on short term cropping intensity changes. The driving force of this change has a high level of uncertainty; and few researchers have implemented comprehensive studies on the underlying driving forces and mechanisms of these changes. This study produced cropping intensity maps in China from 2001 to 2016 using remote sensing data and analyzed the impacts of socioeconomic drivers on cropping intensity and its changes in nine major agricultural zones in China. We found that the average annual cropping intensity in all nine agricultural zones increased from 2001 to 2016 under rapid socioeconomic development, and the trends in the seven major agricultural zones were significantly increased (p &lt; 0.05), based on a Mann\u2013Kendall test, except for the Northeast China Plain (NE Plain) and Qinghai Tibet Plateau (QT Plateau). Based on the results from the Geo-Detector, a widely used geospatial analysis tool, the dominant factors that affected cropping intensity distribution were related to the arable land output in the plain regions and topography in the mountainous regions. The factors that affected cropping intensity changes were mainly related to the arable land area and crop yields in northern China, and regional economic developments, such as machinery power input and farmers\u2019 income in southern China. These findings provide useful cropping intensity data and profound insights for policymaking on how to use cultivated land resources efficiently and sustainably.<\/jats:p>","DOI":"10.3390\/ijgi8110519","type":"journal-article","created":{"date-parts":[[2019,11,18]],"date-time":"2019-11-18T11:18:48Z","timestamp":1574075928000},"page":"519","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Impacts of Rapid Socioeconomic Development on Cropping Intensity Dynamics in China during 2001\u20132016"],"prefix":"10.3390","volume":"8","author":[{"given":"Le","family":"Li","sequence":"first","affiliation":[{"name":"School of Management, Guangdong University of Technology, Guangzhou 510520, China"},{"name":"Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangzhou 510070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0444-8139","authenticated-orcid":false,"given":"Zurui","family":"Ao","sequence":"additional","affiliation":[{"name":"Guangdong Provincial Key Laboratory of Urbanization and Geo-Simulation, Sun Yat-sen University, Guangzhou 510275, China"}]},{"given":"Yaolong","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Geography, Guangdong Research Center of Smart Homeland Engineering, South China Normal University, Guangzhou 510631, China"}]},{"given":"Xulong","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Lab of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangzhou Institute of Geography, Guangzhou 510070, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1038\/nature13957","article-title":"Direct human influence on atmospheric co2 seasonality from increased cropland productivity","volume":"515","author":"Gray","year":"2014","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38-1","DOI":"10.1029\/2001GB001425","article-title":"Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in china","volume":"16","author":"Frolking","year":"2002","journal-title":"Global Biogeochem. 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