Abstract
The escalating environmental risks in China posed by pesticides necessitate precise management and supervision strategies, yet such a national-scale framework is hindered by data gaps. Till now, only statistics of aggregated pesticide inputs are available in China as the sum of inputs of hundreds of active ingredients (AIs), highlighting the need for an AI-specific agricultural input datasets. The dataset was developed through field surveys covering 1181 respondents and 12 crop systems and order-dependent relationship quantitation of crop-specific pesticide usage patterns, combined with multi-objective optimization to minimize provincial-level prediction errors, with official statistics serving as constraint conditions. This approach integrated crop-specific application trends, registration timelines, and spatial disaggregation to produce AI-specific input estimates at 5-arcmin resolution (2001–2022). We performed technical validation by predicting riverine concentrations and further compared with measurements. The generative dataset was designed to follow a computational framework and be updated annually based on pesticide application data from field surveys, offering data support for policy makers for sustainable pesticide management strategies.
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Code availability
The code for developing pesticide input datasets was processed by using the R software version 3.6.2 and is available on Github(https://github.com/CauJane/CPI_model).
Data availability
Pesticide input datasets are available on Figshare (https://figshare.com/s/d37f04c47d0f11d90922).
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Acknowledgements
This work was financially supported by the National Key Research and Development Program of China (2021YFC3200804), the National Natural Science Foundation of China (21976205) and the China Scholarship Council (201913043).
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B.Z. contributed to conceptualization, methodology, visualization, curated the data, and wrote the original draft. H.M. contributed to conceptualization, methodology, curated the data, wrote the original draft, and revised the manuscript. H.L. contributed substantially to the methodology. S.G. contributed to the methodology. Y.Z. contributed to the methodology. W.A. supervised this project, contributed to the conceptualization, and reviewed the manuscript.
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Supplementary materials of Spatially explicit datasets of pesticide inputs integrating 146 active ingredients in China from 2001 to 2022
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Zhang, B., Mu, H., Li, H. et al. Spatially explicit datasets of pesticide inputs integrating 146 active ingredients in China from 2001 to 2022. Sci Data (2026). https://doi.org/10.1038/s41597-026-06704-7
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DOI: https://doi.org/10.1038/s41597-026-06704-7
