Goyal Shekhar Sharan, Kumar Rohini, Bhatia Udit
Department of Earth Sciences, Indian Institute of Technology Gandhinagar, Palaj, Gandhinagar, Gujarat, 382055, India.
Computational Hydrosystems, Helmholtz Center for Environmental Research, UFZ, Leipzig, Germany.
Sci Data. 2024 Nov 2;11(1):1191. doi: 10.1038/s41597-024-04023-3.
Nitrogen (N) is essential for agricultural productivity, yet its surplus poses significant environmental risks. Currently, over half of applied nitrogen is lost, resulting in resource wastage, contributing to increased greenhouse gas emissions and biodiversity loss. Excess nitrogen persists in the environment, contaminating soil and water bodies for decades. Quantifying detailed historical N-surplus estimation in India remains limited, despite national and global-scaled assessments. Our study develops a district-level dataset of annual agricultural N-surplus from 1966-2017, integrating 12 different estimates to address uncertainties arising from multiple data sources and methodological choices across major elements of the N surplus. This dataset supports flexible spatial aggregation, aiding policymakers in implementing effective nitrogen management strategies in India. In addition, we verified our estimates by comparing them with previous studies. This work underscores the importance of setting realistic nitrogen management targets that account for inherent uncertainties, paving the way for sustainable agricultural practices in India, reducing environmental impacts, and boosting productivity.
氮（N）对农业生产力至关重要，但其过剩会带来重大的环境风险。目前，超过一半的施氮量流失，导致资源浪费，加剧温室气体排放和生物多样性丧失。过量的氮在环境中持续存在，数十年来污染土壤和水体。尽管有国家和全球层面的评估，但印度详细的历史氮过剩量化估计仍然有限。我们的研究编制了一个1966年至2017年年度农业氮过剩的县级数据集，整合了12种不同的估计方法，以解决因氮过剩主要要素的多个数据源和方法选择而产生的不确定性。该数据集支持灵活的空间汇总，有助于政策制定者在印度实施有效的氮管理策略。此外，我们通过与先前的研究进行比较来验证我们的估计。这项工作强调了设定考虑到内在不确定性的现实氮管理目标的重要性，为印度可持续农业实践、减少环境影响和提高生产力铺平了道路。
