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1 公里分辨率中国 2004-2016 年水稻、小麦和玉米磷素投入数据集。

1 km-resolution gridded dataset of phosphorus rate for rice wheat and maize in China over 2004-2016.

机构信息

College of Resources and Environmental Sciences, China Agricultural University, Beijing, China.

State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China.

出版信息

Sci Data. 2023 Jun 7;10(1):363. doi: 10.1038/s41597-023-02283-z.

Abstract

Crop-specific, high-resolution phosphorus rate information is essential for sustainable agricultural fertilizer management in China. However, substantial uncertainties exist in the current phosphorus fertilizer dataset because of only coarse national statistics used in dataset development and no crop-specific information provided. This study harmonized provincial and county-level phosphorus and component fertilizer statistics and crop distribution data to generate 1 km gridded maps of phosphorus rate for rice, wheat and maize in the years of 2004-2016 (CN-P). CN-P provides a comparable estimate on phosphorus rate for each crop over 2004-2016, and demonstrates an improved spatial heterogeneity. Existing dataset developed using national statistics tends to smooth out the variability within country and significantly underestimates actual phosphorus rate. CN-P shows that, during 2004-2016, wheat received the most phosphorus rate (8.7 g PO m), while maize showed the rapidest increasing trend (2.36% yr). The CN-P dataset has the potential to be widely applied in modeling studies on sustainable agricultural fertilizer management strategies and phosphorus pollution.

摘要

在中国,针对特定作物、高分辨率的磷素施肥率信息对可持续农业肥料管理至关重要。然而,由于当前数据集的开发仅使用了粗略的国家统计数据,且没有提供针对特定作物的信息,因此当前的磷素肥料数据集存在很大的不确定性。本研究通过协调省级和县级磷素及成分肥料统计数据和作物分布数据,生成了 2004-2016 年水稻、小麦和玉米的 1km 格网磷素施肥率图(CN-P)。CN-P 提供了 2004-2016 年期间每个作物的可比磷素施肥率估计值,并展示了更高的空间异质性。利用国家统计数据开发的现有数据集往往会使国内的变异性平滑化,并严重低估实际的磷素施肥率。CN-P 表明,在 2004-2016 年期间,小麦的磷素施肥率最高(8.7g PO m),而玉米则表现出最快的增长趋势(2.36% yr)。CN-P 数据集具有广泛应用于可持续农业肥料管理策略和磷素污染建模研究的潜力。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/832e/10247718/aedb324d4983/41597_2023_2283_Fig1_HTML.jpg

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