Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Spatially explicit datasets of pesticide inputs integrating 146 active ingredients in China from 2001 to 2022

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月7日

查看全文
  • Bin Zhang,
  • Hongyu Mu,
  • Hua Li,
  • Siqi Gao,
  • Yalan Zhou,
  • Wei An

摘要

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.