Biosystems Engineering

Biosystems Engineering publishes advances in engineering science that strengthen the understanding and management of biological systems. Biosystems of primary interest relate to land use and the environment, agriculture and amenities, bioproduction processes and the food chain.

The journal envisions engineering science as an interdisciplinary field that bridges the gap between theoretical science and practical engineering, focusing on applying fundamental principles of physics, mathematics, chemistry, and biology to solve complex problems.

Research should focus on the performance, optimisation and sustainable development of the studied biosystem through the application of quantitative, analytical, and design-based methods. Papers may report the results from experiments, modelling, theoretical analyses, data-driven findings, design of, or innovations related to, machines and mechanisation systems, processes or processing methods, equipment and buildings, experimental setups, laboratory and analytical techniques and instrumentation.

Submissions should:

• demonstrate new insights in engineering science (i.e. science-based novelty)

• provide a clear summary of relevant previous research on the topic and show how the presented work advances the state of the art

• and discuss how the knowledge gap has been narrowed and outline the key challenges that remain.

Science-based novelty is of crucial importance for our journal, and it is linked with our focus on Science4Impact. Thus, research in ‘biosystems engineering’ should aim to deliver ‘solutions for the future’ with respect to e.g. climate change, food security, biodiversity and animal welfare. Our journal wishes to demonstrate to its readership how its individual publications link to these societal challenges. Therefore, authors are encouraged to include a Science4Impact Statement, describing how their scientific findings could support advisory and/or decision-making processes with stakeholders(e.g. land managers, agricultural businesses, environmental pressure groups and charities, governments and regulators etc. ).

Biosystems Engineering does NOT wish to publish:

• findings obtained under conditions which are not sufficiently representative of practice, making the usefulness of the results and conclusions not well demonstrated;

• application of Artificial Intelligence (AI) and Machine Learning (ML) or Deep Learning (DL) techniques, without independent operational validation and/or analysis of their impact on the specific biological application under investigation;

• results from non-validated models, primarily based on assumptions;

• work where the novelty is centred on property testing of products being processed using standard techniques;

• calibration and verification results using well-known approaches for a specific application.