Impacts of Rapid Socioeconomic Development on Cropping Intensity Dynamics in China during 2001-2016

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 < 0.05), based on a Mann–Kendall 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’ 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.

[1]  P. Verburg,et al.  Downscaling of land use change scenarios to assess the dynamics of European landscapes , 2006 .

[2]  Patrick Hostert,et al.  Mapping cropland-use intensity across Europe using MODIS NDVI time series , 2016 .

[3]  Le Yu,et al.  High Resolution Mapping of Cropping Cycles by Fusion of Landsat and MODIS Data , 2017, Remote. Sens..

[4]  H. Long,et al.  Analysis of arable land loss and its impact on rural sustainability in Southern Jiangsu Province of China. , 2010, Journal of environmental management.

[5]  Jiewei Chen.  Rapid urbanization in China: A real challenge to soil protection and food security , 2007 .

[6]  Hui-Ju Kuo,et al.  The socioeconomic geography of organic agriculture in the United States , 2017 .

[7]  Xiao Wang,et al.  Driving forces and their interactions of built-up land expansion based on the geographical detector – a case study of Beijing, China , 2016, Int. J. Geogr. Inf. Sci..

[8]  Christopher O. Justice,et al.  Estimating Global Cropland Extent with Multi-year MODIS Data , 2010, Remote. Sens..

[9]  Yang Ren.  The Remote Sensing Inversion for Spatial and Temporal Changes of Multiple Cropping Index and Detection for Influencing Factors Around Bohai Rim in China , 2013 .

[10]  A. Challinor,et al.  Crop yield response to climate change varies with cropping intensity , 2015, Global change biology.

[11]  Zengxiang Zhang,et al.  Spatial patterns and driving forces of land use change in China during the early 21st century , 2010 .

[12]  Changsheng Li,et al.  Combining remote sensing and ground census data to develop new maps of the distribution of rice agriculture in China , 2002 .

[13]  Raja Sengupta,et al.  Mapping Crop Types, Irrigated Areas, and Cropping Intensities in Heterogeneous Landscapes of Southern India Using Multi-Temporal Medium-Resolution Imagery: Implications for Assessing Water Use in Agriculture , 2012 .

[14]  Steve Frolking,et al.  New district-level maps of rice cropping in India: a foundation for scientific input into policy assessment , 2006 .

[15]  Chongcheng Chen,et al.  Mapping cropping intensity trends in China during 1982–2013 , 2017 .

[16]  Yan-sui Liu,et al.  Accelerated restructuring in rural China fueled by 'increasing vs. decreasing balance' land-use policy for dealing with hollowed villages , 2012 .

[17]  Mark A. Friedl,et al.  Mapping Asian Cropping Intensity With MODIS , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Wenquan Zhu,et al.  A Shape-matching Cropping Index (CI) Mapping Method to Determine Agricultural Cropland Intensities in China using MODIS Time-series Data , 2012 .

[19]  Rui Xiao,et al.  Exploring the driving forces of farmland loss under rapidurbanization using binary logistic regression and spatial regression: A case study of Shanghai and Hangzhou Bay , 2018, Ecological Indicators.

[20]  Xiubing Li,et al.  Urban land expansion and arable land loss in China - a case study of Beijing-Tianjin-Hebei region , 2005 .

[21]  L. Hein,et al.  Spatial characterization of landscape functions , 2008 .

[22]  Yuan Zhang,et al.  Ecological effects associated with land-use change in China's southwest agricultural landscape , 2006 .

[23]  Xiaoping Liu,et al.  Capturing the varying effects of driving forces over time for the simulation of urban growth by using survival analysis and cellular automata , 2016 .

[24]  Petra Döll,et al.  Global Patterns of Cropland Use Intensity , 2010, Remote. Sens..

[25]  Xin Fu,et al.  Deriving suitability factors for CA-Markov land use simulation model based on local historical data. , 2018, Journal of environmental management.

[26]  W. Dong,et al.  Opportunistic Market‐Driven Regional Shifts of Cropping Practices Reduce Food Production Capacity of China , 2018 .

[27]  W. Parton,et al.  Agricultural intensification and ecosystem properties. , 1997, Science.

[28]  Xi Chen,et al.  Assessing the impact of urban expansion on potential crop yield in China during 1990–2010 , 2014, Food Security.

[29]  Mark A. Friedl,et al.  Mapping Crop Cycles in China Using MODIS-EVI Time Series , 2014, Remote. Sens..

[30]  M. McKinney,et al.  Urbanization, Biodiversity, and Conservation , 2002 .

[31]  Qihao Weng.  A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China , 2001 .

[32]  Geli Zhang,et al.  Variation in Cropping Intensity in Northern China from 1982 to 2012 Based on GIMMS-NDVI Data , 2016 .

[33]  Huajun Tang,et al.  Mapping Single‐, Double‐, and Triple‐crop Agriculture in China at 0.5° × 0.5° by Combining County‐scale Census Data with a Remote Sensing‐derived Land Cover Map , 2003 .

[34]  Chi-Farn Chen,et al.  A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam , 2013, Remote. Sens..

[35]  Shuwen Zhang,et al.  Spatial patterns and driving forces of land use change in China during the early 21st century , 2010 .

[36]  W. Zeng,et al.  Simulating Spatial-Temporal Changes of Land-Use Based on Ecological Redline Restrictions and Landscape Driving Factors: A Case Study in Beijing , 2018 .

[37]  Tonglin Zhang,et al.  A measure of spatial stratified heterogeneity , 2016 .

[38]  Yi Hu,et al.  Environmental health risk detection with GeogDetector , 2012, Environ. Model. Softw..

[39]  P. Gong,et al.  A Global Geospatial Ecosystem Services Estimate of Urban Agriculture , 2018 .

[40]  C. Yuill,et al.  Cultivated Land Changes and Their Driving Forces——A Satellite Remote Sensing Analysis in the Yellow River Delta, China , 2004 .

[41]  Swedish agriculture during the twentieth century in relation to sustainability , 2008 .

[42]  K. Hakala,et al.  Crop responses to climate and socioeconomic change in northern regions , 2013, Regional Environmental Change.

[43]  E. Kalnay,et al.  Impact of urbanization and land-use change on climate , 2003, Nature.

[44]  S. Carpenter,et al.  Global Consequences of Land Use , 2005, Science.

[45]  L. You,et al.  Global cropping intensity gaps: Increasing food production without cropland expansion , 2018, Land Use Policy.

[46]  Mark A. Friedl,et al.  Direct human influence on atmospheric CO2 seasonality from increased cropland productivity , 2014, Nature.

[47]  Hong-yu Liu,et al.  Socioeconomic Drivers of PM2.5 in the Accumulation Phase of Air Pollution Episodes in the Yangtze River Delta of China , 2016, International journal of environmental research and public health.

[48]  Xiaohua Wei,et al.  Geographical modeling of spatial interaction between human activity and forest connectivity in an urban landscape of southeast China , 2014, Landscape Ecology.

[49]  Xiaoying Zheng,et al.  Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China , 2010, Int. J. Geogr. Inf. Sci..

[50]  Zhiliang Zhu,et al.  Long-Term Land Cover Dynamics (1986-2016) of Northeast China Derived from a Multi-Temporal Landsat Archive , 2019, Remote. Sens..