Per Capita Cropland Estimations for Traditional Agricultural Areas of China over Past Millennium

Author:

Li Meijiao12,He Fanneng2,Yang Fan3ORCID,Hao Ruifei4

Affiliation:

1. College of Resources and Environment, Shanxi University of Finance and Economics, Taiyuan 030006, China

2. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

3. Key Research Institute of Yellow River Civilization and Sustainable Development & Collaborative Innovation Center on Yellow River Civilization Jointly Built by Henan Province and Ministry of Education, Henan University, Kaifeng 475001, China

4. School of Materials Engineering, Taiyuan Institute of Technology, Taiyuan 030008, China

Abstract

Studying changes in land use per capita is critical for understanding the interactions between humans and ecosystems, and for modeling the impacts of land use changes on climate systems. However, many uncertainties in historical estimates significantly hinder climate modeling. This study estimated the per capita cropland area in traditional agricultural regions of China over the past millennium using historical-document-based and modern statistical cropland and population data. The findings showed that changes in the per capita cropland area in the provinces of the middle and lower reaches of the Yellow River could be characterized into three stages: slow decrease, rapid increase, and fluctuating decrease, whereas, in the provinces of the middle and lower reaches of the Yangtze River, there was a continuous decrease. Spatially, the per capita cropland area was higher in the middle and lower reaches of the Yellow River and lower in the middle and lower reaches of the Yangtze River during the study period. The per capita cropland areas showed clear differences in the HYDE dataset and our study; the corresponding values of our study were 2.1–8.0, 1.7–8.2, and 1.6–8.8 times higher than those from the HYDE dataset for the early Song, Yuan, and Ming dynasties, respectively.

Funder

National Natural Science Foundation of China

National Key R&D Program of China

“Strategic Priority Research Program” of the Chinese Academy of Sciences

Publisher

MDPI AG

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