TY - JOUR
T1 - Enhanced nitrous oxide emission factors due to climate change increase the mitigation challenge in the agricultural sector
AU - Li, Linchao
AU - Lu, Chaoqun
AU - Winiwarter, Wilfried
AU - Tian, Hanqin
AU - Canadell, Josep G.
AU - Ito, Akihiko
AU - Jain, Atul K.
AU - Kou-Giesbrecht, Sian
AU - Pan, Shufen
AU - Pan, Naiqing
AU - Shi, Hao
AU - Sun, Qing
AU - Vuichard, Nicolas
AU - Ye, Shuchao
AU - Zaehle, Sönke
AU - Zhu, Qing
N1 - This work is supported by the OECD Co\u2010operative Research Program fellowship, USDA AFRI (2023\u201067019\u201039252), NSF Grant (1903722 and 1945036). We acknowledge the modeling groups for making their simulations available for this analysis. We thank two anonymous reviewers for their valuable feedback in improving this manuscript. This study contributes to the Global Nitrous Oxide Budget Synthesis, a joint international initiative of the Global Carbon Project in collaboration with the International Nitrogen Initiative. Q.Z. is supported by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Scientific Focus Area Project, sponsored by the Earth and Environmental Systems Modeling (EESM) Program under the Office of Biological and Environmental Research of the US Department of Energy Office of Science.
This work is supported by the OECD Co-operative Research Program fellowship, USDA AFRI (2023-67019-39252), NSF Grant\u00A0(1903722 and 1945036). We acknowledge the modeling groups for making their simulations available for this analysis. We thank two anonymous reviewers for their valuable feedback in improving this manuscript. This study contributes to the Global Nitrous Oxide Budget Synthesis, a joint international initiative of the Global Carbon Project in collaboration with the International Nitrogen Initiative. Q.Z. is supported by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Scientific Focus Area Project, sponsored by the Earth and Environmental Systems Modeling (EESM) Program under the Office of Biological and Environmental Research of the US Department of Energy Office of Science.
PY - 2024/8
Y1 - 2024/8
N2 - Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N2O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N2O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process-based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low-ambition nitrogen regulation policies, EF would increase from 1.18%–1.22% in 2010 to 1.27%–1.34% by 2050, representing a relative increase of 4.4%–11.4% and exceeding the IPCC tier-1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%–0.35% (relative increase of 11.9%–17%). In contrast, high-ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying–wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N2O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N2O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio-economic assessments and policy-making efforts.
AB - Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N2O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N2O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process-based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low-ambition nitrogen regulation policies, EF would increase from 1.18%–1.22% in 2010 to 1.27%–1.34% by 2050, representing a relative increase of 4.4%–11.4% and exceeding the IPCC tier-1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%–0.35% (relative increase of 11.9%–17%). In contrast, high-ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying–wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N2O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N2O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio-economic assessments and policy-making efforts.
KW - climate change
KW - emission factors (EFs)
KW - global warming
KW - nitrogen regulation pathways
KW - nitrous oxide (NO)
KW - sustainable development goals
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U2 - 10.1111/gcb.17472
DO - 10.1111/gcb.17472
M3 - Article
C2 - 39158113
AN - SCOPUS:85201541164
SN - 1354-1013
VL - 30
JO - Global change biology
JF - Global change biology
IS - 8
M1 - e17472
ER -