Mitigating nitrogen losses with almost no crop yield penalty during extremely wet years

Author:

Liu Wenfeng123ORCID,Li Mengxue123ORCID,Huang Yuanyuan4ORCID,Makowski David5ORCID,Su Yang678ORCID,Bai Yawei123ORCID,Schauberger Bernhard910ORCID,Du Taisheng123ORCID,Abbaspour Karim C.11ORCID,Yang Kun1213ORCID,Yang Hong11,Ciais Philippe8

Affiliation:

1. State Key Laboratory of Efficient Utilization of Agricultural Water Resources, Beijing 100083, China.

2. National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture in Wuwei of Gansu Province, Wuwei 733000, China.

3. Center for Agricultural Water Research in China, College of Water Resources and Civil Engineering, China Agricultural University, Beijing 100083, China.

4. Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China.

5. UMR Applied Mathematics and Computer Science (MIA518), INRAE AgroParisTech, Université Paris-Saclay, Palaiseau, France.

6. UMR ECOSYS, INRAE UVSQ, Université Paris-Saclay, 91190 Gif-sur-Yvette, France.

7. Département d'Informatique, École Normale Supérieure - PSL, 75005 Paris, France.

8. Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France.

9. University of Applied Sciences Weihenstephan-Triesdorf, Department of Sustainable Agriculture and Energy Systems, Am Staudengarten 1, 85354 Freising, Germany.

10. Potsdam Institute for Climate Impact Research (PIK), Member of the Leibniz Association, 14473 Potsdam, Germany.

11. 2w2e Environmental Consulting GmbH, Mettlenweg 3, Dübendorf, 8600 Zürich, Switzerland.

12. Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Institute for Global Change Studies, Tsinghua University, Beijing100084, China.

13. National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System and Resource Environment, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China.

Abstract

Climate change–induced precipitation anomalies during extremely wet years (EWYs) result in substantial nitrogen losses to aquatic ecosystems (N w ). Still, the extent and drivers of these losses, and effective mitigation strategies have remained unclear. By integrating global datasets with well-established crop modeling and machine learning techniques, we reveal notable increases in N w , ranging from 22 to 56%, during historical EWYs. These pulses are projected to amplify under the SSP126 (SSP370) scenario to 29 to 80% (61 to 120%) due to the projected increases in EWYs and higher nitrogen input. We identify the relative precipitation difference between two consecutive years (diffPr) as the primary driver of extreme N w . This finding forms the basis of the CLimate Extreme Adaptive Nitrogen Strategy (CLEANS), which scales down nitrogen input adaptively to diffPr, leading to a substantial reduction in extreme N w with nearly zero yield penalty. Our results have important implications for global environmental sustainability and while safeguarding food security.

Publisher

American Association for the Advancement of Science (AAAS)

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