{"title":"评估印度农业氮过剩的时间动态:1966 年至 2017 年的地区规模数据。","authors":"Shekhar Sharan Goyal, Rohini Kumar, Udit Bhatia","doi":"10.1038/s41597-024-04023-3","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1191"},"PeriodicalIF":5.8000,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11531528/pdf/","citationCount":"0","resultStr":"{\"title\":\"Assessing temporal dynamics of nitrogen surplus in Indian agriculture: district scale data from 1966 to 2017.\",\"authors\":\"Shekhar Sharan Goyal, Rohini Kumar, Udit Bhatia\",\"doi\":\"10.1038/s41597-024-04023-3\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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.</p>\",\"PeriodicalId\":21597,\"journal\":{\"name\":\"Scientific Data\",\"volume\":\"11 1\",\"pages\":\"1191\"},\"PeriodicalIF\":5.8000,\"publicationDate\":\"2024-11-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11531528/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Data\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41597-024-04023-3\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Data","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41597-024-04023-3","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Assessing temporal dynamics of nitrogen surplus in Indian agriculture: district scale data from 1966 to 2017.
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.
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.