数字农科院2.0

Greenhouse gas emissions from the open-air storage of corn stover: Dynamics and environmental drivers over a two-year period

文献类型： 外文期刊

作者： Li, Qichen;Yao, Zonglu;Zhao, Lixin;Fu, Guohao;Huo, Lili

作者机构：

关键词： Straw storage;Decomposition;Greenhouse gas;Emission dynamics;Multivariate analysis

期刊名称： FUEL

ISSN： 0016-2361

年卷期： 2025 年 412 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Greenhouse gas (GHG) emissions during biomass storage vary considerably and are strongly influenced by site-specific conditions. However, investigations of year-round dynamics and their environmental drivers are scarce, with most studies relying on short-term or qualitative assessments. In this study, CO2, CH4, and N2O emissions and key climatic variables were continuously monitored for two years from corn straw stockpiles established on a hard paved ground (PG) and on agricultural open-field (OF) surfaces. Multivariate models were developed to quantify the influence of climatic factors on emissions. The results showed that CO2 emissions dominated under both storage conditions, accounting for 84 % and 99 % of the total GHG emissions for PG and OF, respectively. CH4 emissions on PG were over two orders of magnitude higher than those on OF, contributing up to 15 % of total GHG emissions during warm and humid months. In contrast, CH4 on OF was < 0.2 %. N2O emissions accounted for less than 2 % under both storage conditions. Climatic variables were the primary drivers of GHG dynamics during open-air storage, which explained up to 72 % of emission variability on PG. CO2 and CH4 emissions on PG were more predictable than those on OF. The accuracy of multivariate models was higher for PG, with coefficient of determination (R-2) values of 0.72 and 0.54, compared with 0.63 and 0.04 for OF. This suggests that microenvironment conditions on OF were more complex and heterogeneous, making emissions more difficult to capture by the models. Overall, this study provides a process-level understanding of biomass storage emissions, which can help reduce uncertainties in carbon accounting and inform climate-smart agricultural residue management strategies.

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