Quantifying greenhouse gas emissions in agricultural systems: a comparative analysis of process models

Yujie Tang, Yunfa Qiao, Yinzheng Ma, Weiliang Huang, Khan Komal and Shujie Miao

Ecological Modelling, 2024, vol. 490, issue C

Abstract: Agricultural ecosystems have long been recognized as significant greenhouse gas (GHG) sources. To accurately quantify GHG emissions, researchers have developed various process models. However, there are no summary studies of comparative model simulations of GHG emissions from different crop systems. This study compared four widely used process models: APSIM, DNDC, DayCent, and STICS, analyzing their mechanisms, input variables, and simulation results from different crops in simulating GHG emissions. In this study, a total of 94 relevant peer-review literature papers were considered. The research found that these models have strengths in simulating GHG emissions from different crops, but also have certain limitations. DNDC and DayCent performed better in simulating methane (CH4) emissions from rice, while APSIM was more effective in simulating nitrous oxide (N2O) emissions from maize and wheat. The main factors affecting the simulation results include model mechanisms, management practices, climate, and data availability. To improve model accuracy, it is recommended that future research expands model applicability, evaluates models using standardized measured data, and enhances adaptability by optimizing algorithms and incorporating simulations of key processes.

Keywords: Agriculture ecosystem; GHG; Process model (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecomod:v:490:y:2024:i:c:s0304380024000358

DOI: 10.1016/j.ecolmodel.2024.110646

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