Transforming Beef Farming Through Digital Twin Technology and Triple Bottom Line Balance

Rationale:

In the contemporary agricultural landscape, small-hold beef farming is at a crossroads, facing multifaceted challenges that span environmental, social, and economic dimensions. This sector significantly contributes to environmental emissions, undermining efforts to combat climate change and reduce the ecological footprint of agriculture. Economically, the productivity gaps between small-hold and large-scale farming operations underscore the urgent need for innovative interventions to enhance competitiveness and sustainability. Socially, issues such as varying beef quality, high calf mortality rates, and growing concerns about animal welfare demand a comprehensive approach that aligns with the Triple Bottom Line (TBL) principles of balancing environmental integrity, social equity, and economic viability.

Precision farming, heralded as a transformative force in agriculture, offers a promising solution to these challenges. However, its potential remains largely untapped in the context of beef farming. The underutilization of precision farming techniques, including real-time data management and analysis, hinders the sector's ability to make informed decisions that could mitigate environmental impacts, improve animal welfare, and enhance economic outcomes.

Objectives:

This PhD project aims to address these challenges by pursuing the following objectives:

*Develop an Interdisciplinary Framework: To create a comprehensive framework that incorporates Digital Twin (DT) technology for real-time management in beef farming, enabling stakeholders to monitor, simulate, and optimize farming operations dynamically.

*Analyse and balance TBL Trade-offs: To assess the environmental, social, and economic trade-offs inherent in beef farming, employing DT technology to identify and implement best practices that balance these considerations effectively.

*Explore New Business and Economic Models: To investigate the economic implications of DT implementation in beef farming, identifying innovative business models that enhance sustainability and profitability within the sector.

Significance:

This research holds critical significance for the transformation of UK beef farming into a practice that is not only sustainable but also resilient in the face of climate change, evolving social expectations, and economic pressures. By pioneering the use of DT in managing extensive grazing systems, this project has the potential to revolutionize traditional farming practices, setting a new standard for the industry.

The adoption of DT and the optimization of TBL objectives offer a path forward for small-hold beef farmers, empowering them with the tools and strategies needed to thrive in a rapidly changing world. This approach not only addresses immediate operational challenges but also positions the sector to contribute positively to global sustainability goals.

Possible Methodology:

The methodology for this study will involve a blend of case studies and action research, with a focus on implementing DT models on partner farms. This hands-on approach will allow for the direct measurement of DT's impact on farm productivity, environmental footprint, and animal welfare. The insights gained from these interventions will be crucial in developing new business and economic models tailored to the needs and realities of the beef farming industry.

Potential Impact:

The anticipated outcomes of this research include:

*A scalable DT framework applicable across various beef farming practices, offering a blueprint for integrating advanced technologies into traditional agricultural operations.
*Strategic guidelines and best practices for achieving TBL optimization in beef farming, providing a roadmap for sustainable development within the sector.
*Insights into innovative business models that leverage DT for enhanced economic efficiency and sustainability, offering new avenues for growth and profitability.
*Policy recommendations aimed at supporting SMEs in the transition towards precision farming, contributing to the broader agenda of agricultural innovation and sustainability.
Conclusion:

The integration of Digital Twin technology within the framework of Triple Bottom Line optimization presents a novel and promising approach to addressing the complex challenges faced by the UK beef farming sector. This PhD project aims not only to advance academic understanding and practical application in this area but also to contribute to a broader transformation in global agricultural practices. Through innovative research and collaboration with industry partners, this project seeks to empower farmers with the knowledge and tools necessary to ensure that their practices are economically viable, environmentally responsible, and socially beneficial.

• Have some working experience in the relevant industry.
• Have a backup business or have access to business data for the PhD study.
• Ideal candidates should possess analytical prowess, a strong foundation in economic and business theory, and a keen interest in emerging business trends.
• Background in either of the following relevant subject areas: Information Science / Tech Management; Entrepreneurship / Innovation / Strategy

Experience in qualitative and quantitative research methodologies is highly desirable.

This is a self-funded PhD project for UK applicants.