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Published on: July 19, 2018
Investigation of dynamic processes in liquid manure storage in an in vitro system: Implications for methane emissions
Aditya Rawat1, Zhenhua Song2, Barbara Amon3
1Leibniz Institute for Agricultural Engineering and Bioeconomy (ATB), Potsdam, Germany; Technische Universität Berlin, Faculty Ⅴ - Mechanical Engineering and Transport Systems, Berlin, Germany.
Abstract:
Manure storage is a major source of agricultural methane emissions, yet its underlying dynamics, which are strongly influenced by parameters such as substrate flux, microbial processes, are still insufficiently understood. This study aims to develop a controlled experimental platform that allows the investigation of dynamic processes, like continuous substrate supply and microbial adaptation, which are expected to significantly influence methane emissions during the storage of liquid manure. The laboratory setup, consists of four thermostatted tanks that enable daily substrate feeding, precise temperature control, and real-time gas measurement, simulating long-term storage conditions. Gas produced during storage was collected in bags and analyzed daily for volume and concentration, enabling high-resolution quantification of methane emissions. The continuous methane emission monitoring was coupled with high-resolution bacterial and archaeal community profiling (16S rRNA gene sequencing) to elucidate the temporal dynamics of microbial activity underlying methane production. In a three-month experiment under summer conditions, methane emissions showed a one-week lag due to microbial adaptation, followed by a rapid increase, peaking at 4.3 g kg-1 VS't d-1 on day 31. Emissions then stabilized at 1.7 g kg-1 VS't d-1, sustained by daily feeding of substrate. Cumulative CH4 emissions of 180.9 g kg-1 VS't were measured from the experiments, exceeding the 137.8 g kg-1 VS't predicted by the Arrhenius model, which underestimated emissions by not accounting for the continuous substrate addition. This continuous, high-resolution monitoring under realistic conditions provides a robust platform for investigating emission drivers and refining predictive models of greenhouse gas emissions from liquid manure storage.
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