Climate change may interact with nitrogen fertilizer management leading to different ammonia loss in China’s croplands

Xiangrui Xu; Xiao Ouyang; Yining Gu; Kun Cheng; Pete Smith; Jianfei Sun; Yunpeng Li; Genxing Pan

doi:10.1111/gcb.15874

Climate change may interact with nitrogen fertilizer management leading to different ammonia loss in China’s croplands

Xiangrui Xu, Xiao Ouyang, Yining Gu, Kun Cheng^*, Pete Smith, Jianfei Sun, Yunpeng Li, Genxing Pan

^*Corresponding author for this work

Aberdeen Centre For Environmental Sustainability

Research output: Contribution to journal › Article › peer-review

29 Citations (Scopus)

Abstract

Despite research into the response of ammonia (NH₃) volatilization in farmland to various meteorological factors, the potential impact of future climate change on NH₃ volatilization is not fully understood. Based on a database consisting of 1063 observations across China, nonlinear NH₃ models considering crop type, meteorological, soil and management variables were established via four machine learning methods, including support vector machine, multi-layer perceptron, gradient boosting machine and random forest (RF). The RF model had the highest R² of 0.76 and the lowest RMSE of 0.82 kg NH₃-N ha⁻¹, showing the best simulation capability. Results of model importance indicated that NH₃ volatilization was mainly controlled by total input of N fertilizer, followed by meteorological factors, human managements and soil characteristics. The NH₃ emissions of China's cereal production (paddy rice, wheat and maize) in 2018 was estimated to be 3.3 Mt NH₃-N. By 2050, NH₃ volatilization will increase by 23.1−32.0% under different climate change scenarios (Representative Concentration Pathways, RCPs), and climate change will have the greatest impact on NH₃ volatilization in the Yangtze river agro-region of China due to high warming effects. However, the potential increase in NH₃ volatilization under future climate change can be mitigated by 26.1−47.5% through various N fertilizer management optimization options.

Original language	English
Pages (from-to)	6525-6535
Number of pages	11
Journal	Global Change Biology
Volume	27
Issue number	24
Early online date	3 Sept 2021
DOIs	https://doi.org/10.1111/gcb.15874
Publication status	Published - Dec 2021

Bibliographical note

Funding Information:
This work was financially supported by Natural Science Foundation of China under a grant number 41877546, and a BBSRC–Newton Fund project N‐Circle (BB/N013484/1). Global climate model data was extracted and converted by Dr. Jie Pan from Institute of Environment and Sustainable Development in Agricultural, Chinese Academy of Agricultural Sciences. The authors have no conflict of interest to declare.

Keywords

cereal
climate change scenario
machine learning
mineral nitrogen fertilizer
NH volatilization
nonlinear model

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1111/gcb.15874Licence: Unspecified

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title = "Climate change may interact with nitrogen fertilizer management leading to different ammonia loss in China{\textquoteright}s croplands",

abstract = "Despite research into the response of ammonia (NH3) volatilization in farmland to various meteorological factors, the potential impact of future climate change on NH3 volatilization is not fully understood. Based on a database consisting of 1063 observations across China, nonlinear NH3 models considering crop type, meteorological, soil and management variables were established via four machine learning methods, including support vector machine, multi-layer perceptron, gradient boosting machine and random forest (RF). The RF model had the highest R2 of 0.76 and the lowest RMSE of 0.82 kg NH3-N ha−1, showing the best simulation capability. Results of model importance indicated that NH3 volatilization was mainly controlled by total input of N fertilizer, followed by meteorological factors, human managements and soil characteristics. The NH3 emissions of China's cereal production (paddy rice, wheat and maize) in 2018 was estimated to be 3.3 Mt NH3-N. By 2050, NH3 volatilization will increase by 23.1−32.0% under different climate change scenarios (Representative Concentration Pathways, RCPs), and climate change will have the greatest impact on NH3 volatilization in the Yangtze river agro-region of China due to high warming effects. However, the potential increase in NH3 volatilization under future climate change can be mitigated by 26.1−47.5% through various N fertilizer management optimization options.",

keywords = "cereal, climate change scenario, machine learning, mineral nitrogen fertilizer, NH volatilization, nonlinear model",

author = "Xiangrui Xu and Xiao Ouyang and Yining Gu and Kun Cheng and Pete Smith and Jianfei Sun and Yunpeng Li and Genxing Pan",

note = "Funding Information: This work was financially supported by Natural Science Foundation of China under a grant number 41877546, and a BBSRC–Newton Fund project N‐Circle (BB/N013484/1). Global climate model data was extracted and converted by Dr. Jie Pan from Institute of Environment and Sustainable Development in Agricultural, Chinese Academy of Agricultural Sciences. The authors have no conflict of interest to declare. ",

year = "2021",

month = dec,

doi = "10.1111/gcb.15874",

language = "English",

volume = "27",

pages = "6525--6535",

journal = "Global Change Biology",

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T1 - Climate change may interact with nitrogen fertilizer management leading to different ammonia loss in China’s croplands

AU - Xu, Xiangrui

AU - Ouyang, Xiao

AU - Gu, Yining

AU - Cheng, Kun

AU - Smith, Pete

AU - Sun, Jianfei

AU - Li, Yunpeng

AU - Pan, Genxing

N1 - Funding Information: This work was financially supported by Natural Science Foundation of China under a grant number 41877546, and a BBSRC–Newton Fund project N‐Circle (BB/N013484/1). Global climate model data was extracted and converted by Dr. Jie Pan from Institute of Environment and Sustainable Development in Agricultural, Chinese Academy of Agricultural Sciences. The authors have no conflict of interest to declare.

PY - 2021/12

Y1 - 2021/12

N2 - Despite research into the response of ammonia (NH3) volatilization in farmland to various meteorological factors, the potential impact of future climate change on NH3 volatilization is not fully understood. Based on a database consisting of 1063 observations across China, nonlinear NH3 models considering crop type, meteorological, soil and management variables were established via four machine learning methods, including support vector machine, multi-layer perceptron, gradient boosting machine and random forest (RF). The RF model had the highest R2 of 0.76 and the lowest RMSE of 0.82 kg NH3-N ha−1, showing the best simulation capability. Results of model importance indicated that NH3 volatilization was mainly controlled by total input of N fertilizer, followed by meteorological factors, human managements and soil characteristics. The NH3 emissions of China's cereal production (paddy rice, wheat and maize) in 2018 was estimated to be 3.3 Mt NH3-N. By 2050, NH3 volatilization will increase by 23.1−32.0% under different climate change scenarios (Representative Concentration Pathways, RCPs), and climate change will have the greatest impact on NH3 volatilization in the Yangtze river agro-region of China due to high warming effects. However, the potential increase in NH3 volatilization under future climate change can be mitigated by 26.1−47.5% through various N fertilizer management optimization options.

AB - Despite research into the response of ammonia (NH3) volatilization in farmland to various meteorological factors, the potential impact of future climate change on NH3 volatilization is not fully understood. Based on a database consisting of 1063 observations across China, nonlinear NH3 models considering crop type, meteorological, soil and management variables were established via four machine learning methods, including support vector machine, multi-layer perceptron, gradient boosting machine and random forest (RF). The RF model had the highest R2 of 0.76 and the lowest RMSE of 0.82 kg NH3-N ha−1, showing the best simulation capability. Results of model importance indicated that NH3 volatilization was mainly controlled by total input of N fertilizer, followed by meteorological factors, human managements and soil characteristics. The NH3 emissions of China's cereal production (paddy rice, wheat and maize) in 2018 was estimated to be 3.3 Mt NH3-N. By 2050, NH3 volatilization will increase by 23.1−32.0% under different climate change scenarios (Representative Concentration Pathways, RCPs), and climate change will have the greatest impact on NH3 volatilization in the Yangtze river agro-region of China due to high warming effects. However, the potential increase in NH3 volatilization under future climate change can be mitigated by 26.1−47.5% through various N fertilizer management optimization options.

KW - cereal

KW - climate change scenario

KW - machine learning

KW - mineral nitrogen fertilizer

KW - NH volatilization

KW - nonlinear model

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VL - 27

SP - 6525

EP - 6535

JO - Global Change Biology

JF - Global Change Biology

IS - 24

ER -

Climate change may interact with nitrogen fertilizer management leading to different ammonia loss in China’s croplands

Abstract

Bibliographical note

Keywords

UN SDGs

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