Skip to content
#diffusion models Open access

Lower methane emissions from full-scale stockpiles of the solid fraction of separated digested slurry with biochar amendment

Sep 2026 · Biosystems Engineering · 39 references

Abstract

Field application of biochar can be challenging due to loss through dust release and uneven spreading, and incorporation of biochar into organic fertilisers has been proposed as a practical application strategy. If biochar is added to the solid fraction prior to field application, it must be incorporated before or during storage, making its effect on storage emissions important to investigate. This study investigated how biochar addition to the solid fraction from separated digestate affected emissions during storage. Emissions from two full-scale stockpiles were determined over 85 days using the backward Lagrangian stochastic dispersion model combined with up- and downwind concentration measurements. One stockpile was amended with 10% (w/w) biochar, while the other was unamended. The CH 4 emissions were consistently lower from the biochar-amended stockpile during both covered and uncovered periods. Higher oxygen concentrations across depths and elevated core temperatures in the biochar-amended stockpile indicate improved aeration and enhanced aerobic degradation. Emissions of N 2 O and NH 3 were below measurable levels in both treatments. To the best of current knowledge, this is the first field-scale study to quantify the effect of biochar amendment on gas emissions from stockpiled solid fractions of anaerobically digested slurry. The results provide field-scale evidence that biochar enhances gas diffusion and shifts decomposition towards aerobic pathways, suppressing methanogenesis without increasing NH 3 or N 2 O emissions. These findings advance the state of the art beyond small-scale and composting studies by demonstrating that the effect of biochar on CH 4 emissions is detectable and consistent at full scale under practical field conditions.

View source

Similar papers

#computer vision Open access Jun 2016

Software Development in Startup Companies: The Greenfield Startup Model

The results are packaged in the Greenfield Startup Model (GSM), which explains the priority of startups to release the product as quickly as possible, and the need to shorten time-to-market, by speeding up the development through low-precision engineering activities.

Carmine Giardino, Nicolò Paternoster, M. Unterkalmsteiner et al. · 178 citations · ⚡14
#computer vision Open access Oct 2016

Software Startups - A Research Agenda

Software startup companies develop innovative, software-intensive products within limited timeframes and with few resources, searching for sustainable and scalable business models.

M. Unterkalmsteiner, P. Abrahamsson, Xiaofeng Wang et al. · 157 citations · ⚡17
#machine learning Review Open access Oct 2016

“Failures” to be celebrated: an analysis of major pivots of software startups

This study conducts a case survey study based on the secondary data of the major pivots happened in 49 software startups, and demonstrates that customer need pivot is the most common among all pivot types.

Sohaib Shahid Bajwa, Xiaofeng Wang, Anh Nguyen-Duc et al. · 127 citations · ⚡15
#computer vision Review Open access May 2015

A survey study on major technical barriers affecting the decision to adopt cloud services

The comparison of adopter and non-adopter sample reveals three potential adoption inhibitor, security, data privacy, and portability, which underlines the importance of the technical and security perspectives for research investigating the adoption of technology.

Nattakarn Phaphoom, Xiaofeng Wang, S. Samuel et al. · 111 citations · ⚡8
#computer vision Open access Feb 2018

Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors

This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.

Henry Edison, Nina M. Smørsgård, Xiaofeng Wang et al. · 78 citations · ⚡6
#computer vision Conference Sep 2010

Exploring the Sources of Waste in Kanban Software Development Projects

The application of agile software methods and more recently the integration of Lean practices contribute to the trend of continuous improvement in the software industry. One such area warranting proper empirical evidence is a project’s operational efficiency when using the Kanban method. This short paper takes a new angle and explores waste in the Kanban-driven software development project context. A preliminary research model is presented for helping the consequent replication of the study. The results from the empirical analysis suggest Kanban can be an effective method in visualizing and organizing the current work, but does not prevent waste from creeping in, although the overall project outcome may be successful.

Marko Ikonen, Petri Kettunen, Nilay V. Oza et al. · 67 citations · ⚡9

Related blog posts

MIT News · Artificial Intelligence Sep 14, 2026

New method enables AI for safety-critical situations

The “HardFlow” algorithm could help generative AI models produce high-quality outputs that obey strict requirements when “pretty close” doesn’t cut it.

GPT-Lab Sep 10, 2026

Responsible AI Must Consider Its Afterlife

AI may appear weightless, but every model depends on physical infrastructure. To understand responsible AI, we need to look beyond algorithms and consider the entire lifecycle of the hardware behind them. The post Responsible AI Must Consider Its Afterlife appeared first on GPT-Lab.

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.