NPKGRIDS：173 种作物的氮、五氧化二磷和氧化钾施肥量的全球地理参照数据集。

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2024-10-30 DOI:10.1038/s41597-024-04030-4

Thu Ha Nguyen, Fiona H M Tang, Giulia Conchedda, Leon Casse, Griffiths Obli-Laryea, Francesco N Tubiello, Federico Maggi

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引用次数: 0

摘要

我们介绍的 NPKGRIDS 是一个新的地理空间数据集，它首次提供了截至 2020 年 173 种作物的所有三种主要植物养分，即氮（N）、磷（P，以五氧化二磷表示）和钾（K，以氧化钾表示）的施肥量数据，地理空间分辨率为 0.05°（赤道约 5.6 千米）。NPKGRIDS 的开发采用了数据融合方法，将作物掩膜信息与八个已发布的化肥施用量数据集整合在一起，这些数据集由地理参照数据或国家和国家以下各级统计数据编制而成。此外，N、P2O5 和 K2O 的总施用量以粮农组织和国际肥料协会 (IFA) 提供的国家级信息为基准，并与国家统计局 (NSO) 提供的数据进行了验证。NPKGRIDS 可用于全球建模、决策和政策制定，以帮助最大限度地提高作物产量，同时减少对环境的影响。

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NPKGRIDS: a global georeferenced dataset of N, P₂O₅, and K₂O fertilizer application rates for 173 crops.

We introduce NPKGRIDS, a new geospatial dataset, providing for the first time data on application rates for all three main plant nutrients, nitrogen (N), phosphorus (P, in terms of phosphorus pentoxide, P₂O₅) and potassium (K, in terms of potassium oxide, K₂O) across 173 crops as of 2020, with a geospatial resolution of 0.05° (approximately 5.6 km at the equator). Development of NPKGRIDS adopted a data fusion approach to integrate crop mask information with eight published datasets of fertilizer application rates, compiled from either georeferenced data or national and subnational statistics. Furthermore, the total applied mass of N, P₂O₅, and K₂O were benchmarked against the country level information from FAO and the International Fertilizers Association (IFA) and validated against data available from National Statistical Offices (NSOs). NPKGRIDS can be used in global modelling, and decision and policy making to help maximize crop yields while reducing environmental impacts.

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.