Journal of Integrative Agriculture ›› 2025, Vol. 24 ›› Issue (6): 2425-2437.DOI: 10.1016/j.jia.2024.08.023

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估算方法是中国农田硝酸盐淋洗估计不确定性的主要来源

  

  • 收稿日期:2024-04-25 修回日期:2024-08-27 接受日期:2024-07-17 出版日期:2025-06-20 发布日期:2025-05-13

The estimation method is the primary source of uncertainty in cropland nitrate leaching estimates in China

Xingshuai Tian*, Huitong Yu*, Jiahui Cong, Yulong Yin, Kai He, Zihan Wang, Zhenling Cui#   

  1. College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China

  • Received:2024-04-25 Revised:2024-08-27 Accepted:2024-07-17 Online:2025-06-20 Published:2025-05-13
  • About author:Xingshuai Tian, E-mail: tianxingshuai@126.com; Huitong Yu, E-mail: 18745141052@163.com; #Correspondence Zhenling Cui, Tel: +86-10-62733454, E-mail: cuizl@cau.edu.cn * These authors contributed equally to this study.
  • Supported by:
    This work was supported by the National Key Research and Development Program of China (2023YFD1902703) and the National Natural Science Foundation of China (Key Program) (U23A20158).

摘要:

农田硝酸盐淋失是氮(N)损失的主要途径,水体污染显著具有显著。然而,由于输入数据集和估算方法的差异,农田硝酸盐淋失的估计仍存在很大的不确定性。在本研究中,我们通过整合3种农田面积数据集、3种氮入数据集和3种估算方法中国农田硝酸盐淋洗进行了重新估计识别并量化了中国农田硝酸盐淋洗估计的不确定性来源。结果表明综合27种不同组合的估计结果,2010中国农田硝酸盐淋洗平均6.7±0.6 Tg N yr−1(平均值±标准误差),范围为2.9—15.8 Tg N yr−1。估算方法是硝酸盐淋洗估计不确定性的主要来源贡献了45.1%的不确定性;其次是入数据集与估算方法之间的交互作用,贡献了24.4%的不确定性。我们的研究强调了需要采用稳健的估算方法,并改善估算方法与氮入数据集之间的匹配性,以有效减少估计过程中的不确定性。本研究发现对准确估计农田硝酸盐淋失具有重要意义,进而为解决水体污染问题提供了科学支撑。

Abstract:

Cropland nitrate leaching is the major nitrogen (N) loss pathway, and it contributes significantly to water pollution.  However, cropland nitrate leaching estimates show great uncertainty due to variations in input datasets and estimation methods.  Here, we presented a re-evaluation of Chinese cropland nitrate leaching, and identified and quantified the sources of uncertainty by integrating three cropland area datasets, three N input datasets, and three estimation methods.  The results revealed that nitrate leaching from Chinese cropland averaged 6.7±0.6 Tg N yr−1 in 2010, ranging from 2.9 to 15.8 Tg N yr−1 across 27 different estimates.  The primary contributor to the uncertainty was the estimation method, accounting for 45.1%, followed by the interaction of N input dataset and estimation method at 24.4%.  The results of this study emphasize the need for adopting a robust estimation method and improving the compatibility between the estimation method and N input dataset to effectively reduce uncertainty.  This analysis provides valuable insights for accurately estimating cropland nitrate leaching and contributes to ongoing efforts that address water pollution concerns.


Key words: cropland nitrate leaching , uncertainty ,  cropland area ,  nitrogen input ,  estimation method