26 Jul 2021

26 Jul 2021

Review status: this preprint is currently under review for the journal ESSD.

A 1 km global cropland dataset from 10000 BCE to 2100 CE

Bowen Cao1, Le Yu1,2, Xuecao Li3, Min Chen4,5, Xia Li6, and Peng Gong2,7 Bowen Cao et al.
  • 1Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing 100084, China
  • 2Ministry of Education Ecological Field Station for East Asian Migratory Birds, Beijing 100084, China
  • 3College of Land Science and Technology, China Agricultural University, Beijing 100083, China
  • 4Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 537061598, USA
  • 5Nelson Institute Center for Climatic Research, University of Wisconsin-Madison, 1225 W. Dayton St. Madison, WI 53706 USA
  • 6Ministry of Education Key Laboratory of Geographic Information Science, School of Geographic Sciences, East China Normal University, Shanghai 200241, China
  • 7Department of Geography and Department of Earth Sciences, University of Hong Kong, Hong Kong

Abstract. Cropland greatly impacts food security, energy supply, biodiversity, biogeochemical cycling, and climate change. Accurately and systematically understanding the effects of agricultural activities requires cropland spatial information with high resolution and a long time span. In this study, the first 1 km resolution global cropland proportion dataset for 10000 BCE–2100 CE was produced. With the cropland map initialized in 2010 CE, we first harmonized the cropland demands extracted from the History Database of the Global Environment 3.2 (HYDE 3.2) and the Land-Use Harmonization 2 (LUH2) datasets, and then spatially allocated the demands based on the combination of cropland suitability, kernel density, and other constraints. According to our maps, cropland originated from several independent centers and gradually spread to other regions, influenced by some important historical events. The spatial patterns of future cropland change differ in various scenarios due to the different socioeconomic pathways and mitigation levels. The global cropland area generally shows an increasing trend over the past years, from 0 million km2 in 10000 BCE grows to 2.8 million km2 in 1500 CE, 6.2 million km2 in 1850 CE, and 16.4 million km2 in 2010 CE. It then follows diverse trajectories under future scenarios, with the growth rate ranging from 18.6 % to 82.4 % between 2010 CE and 2100 CE. There are large area disparities among different geographical regions. The mapping result coincides well with widely-used datasets at present in both distribution pattern and total amount. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The spatiotemporally continuous and conceptually consistent global cropland dataset serves as a more comprehensive alternative for long-term earth system simulations and other precise analyses. The flexible and efficient harmonization and downscaling framework can be applied to specific regions or extended to other land use/cover types through the adjustable parameters and open model structure. The 1 km global cropland maps are available at (Cao et al., 2021a).

Bowen Cao et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-219', Anonymous Referee #1, 22 Aug 2021
  • RC2: 'Comment on essd-2021-219', Anonymous Referee #2, 01 Sep 2021

Bowen Cao et al.

Data sets

A 1 km global cropland dataset from 10000 BCE to 2100 CE Bowen Cao, Le Yu, Xuecao Li, Min Chen, Xia Li, Peng Gong

Bowen Cao et al.


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Short summary
In the study, the first 1 km global cropland proportion dataset for 10000 BCE–2100 CE was produced through the harmonization and downscaling framework. The mapping result coincides well with widely-used datasets at present. With improved spatial resolution, our maps can better capture the cropland distribution details and spatial heterogeneity. The dataset will be valuable for long-term simulations and precise analyses. The framework can be extended to specific regions or other land use types.