Holocene spatiotemporal millet agricultural patterns in northern China: a dataset of archaeobotanical macroremains,Earth System Science Data

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Holocene spatiotemporal millet agricultural patterns in northern China: a dataset of archaeobotanical macroremains
Earth System Science Data ( IF 11.2 ) Pub Date : 2022-10-28 , DOI: 10.5194/essd-14-4777-2022
Keyang He , Houyuan Lu , Jianping Zhang , Can Wang

Millet agriculture, i.e., broomcorn millet (Panicum miliaceum) and foxtail millet (Setaria italica), initially originated in northern China and provided the basis for the emergence of the first state in the Central Plains. However, owing to the lack of a comprehensive archaeobotanical dataset, when, where, and how these two millet types evolved across different regions and periods remains unclear. Here, we presented a dataset of archaeobotanical macroremains (n=538) spanning the Neolithic and Bronze ages in northern China and suggested a significant spatiotemporal divergence of millet agriculture in the subhumid mid-lower Yellow River (MLY) and semiarid agro-pastoral ecotone (APE). The key timing of the diffusion and transition of millet agriculture occurred around 6000 cal. a BP, coinciding with the Holocene Optimum (8000–6000 cal. a BP) and Miaodigou Age (6200–5500 cal. a BP). It spread westward and northward from the MLY to APE and underwent a dramatic transition from low-yield broomcorn millet to high-yield foxtail millet. The combined influence of warm-wet climate, population pressure, and field management may have promoted the intensification, diffusion, and transition of millet agriculture around 6000 cal. a BP. Thereafter, the cropping patterns in the MLY were predominated by foxtail millet (∼ 80 %), while those in APE focused on both foxtail (∼ 60 %) and broomcorn millet under a persistent drying trend since the mid-Holocene. This study provided the first quantitative spatiotemporal cropping patterns during the Neolithic and Bronze ages in northern China, which can be used for evaluating prehistoric human subsistence, discussing past human–environment interaction, and providing a valuable perspective of agricultural sustainability for the future. The dataset is publicly available at https://doi.org/10.5281/zenodo.6669730 (He et al., 2022).

更新日期：2022-10-28

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