Threshold-window framework reveals management-sensitive controls of CH4 and N2O emissions in flooded rice

Rice paddies are major sources of methane (CH 4 ) and nitrous oxide (N 2 O), yet co-managing water and nitrogen (N) to limit both gases remains challenging because emissions arise from non-linear interactions among multiple drivers. We combined a two-year field experiment in subtropical rice, with four realistic water–N treatments (CF/AWD × normal/reduced N), and a two-factor threshold-window framework to summarize these interactions into a few management-relevant states. For CH 4 , key windows were NH 4 + –N × soil water, DOC × soil water, and soil water × temperature; for N 2 O, windows involving mineral N with moisture, DOC, temperature, or pH captured distinct pathways. For most selected windows, especially for CH 4 , the low-emission state occurred when both drivers remained below their respective thresholds, whereas the NO 3 − –N × soil water window for N 2 O showed a crossover pattern. This threshold-window framework condenses complex controls into simple, management-sensitive rules to guide water and N strategies that reduce CH 4 while avoiding large N 2 O pulses in flooded rice.

Authors

Institutions

Publication Details

Journal
Geoderma
Published
2026-08-18
DOI
https://doi.org/10.1016/j.geoderma.2026.117960
Primary Topic
Soil Carbon and Nitrogen Dynamics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Threshold-window framework reveals management-sensitive controls of CH4 and N2O emissions in flooded rice

L.S. Tong, Guohan Si, Qi Peng, Jianing Wang et al.
Geoderma
Soil Carbon and Nitrogen Dynamics
article

Threshold-window framework reveals management-sensitive controls of CH4 and N2O emissions in flooded rice

L.S. Tong, Guohan Si, Qi Peng, Jianing Wang, Zhang Xian, Dabing Xu, Wanzhu Xi, Jie Song, Wei Liu, Xiangyu Xu, Chenglin Peng, Lulu Yue, Shujun Zhao
article en

Abstract

Rice paddies are major sources of methane (CH 4 ) and nitrous oxide (N 2 O), yet co-managing water and nitrogen (N) to limit both gases remains challenging because emissions arise from non-linear interactions among multiple drivers. We combined a two-year field experiment in subtropical rice, with four realistic water–N treatments (CF/AWD × normal/reduced N), and a two-factor threshold-window framework to summarize these interactions into a few management-relevant states. For CH 4 , key windows were NH 4 + –N × soil water, DOC × soil water, and soil water × temperature; for N 2 O, windows involving mineral N with moisture, DOC, temperature, or pH captured distinct pathways. For most selected windows, especially for CH 4 , the low-emission state occurred when both drivers remained below their respective thresholds, whereas the NO 3 − –N × soil water window for N 2 O showed a crossover pattern. This threshold-window framework condenses complex controls into simple, management-sensitive rules to guide water and N strategies that reduce CH 4 while avoiding large N 2 O pulses in flooded rice.

GeodermaVol. 473
Gansu Agricultural University (CN), Yangtze University (CN), Huazhong Agricultural University (CN), Institute of Plant Protection (CN), Experimental Station (US), Ministry of Agriculture (ID), Hubei Academy of Agricultural Sciences (CN)
National Natural Science Foundation of China, Hubei Key Laboratory of Regional Development and Environmental Response, Key Technologies Research and Development Program, National Key Research and Development Program of China
Climate action
Openalex Percentile: Top 13%
Soil Carbon and Nitrogen Dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.