Transition Toward Regenerative Resource Models in Agro-Food Supply Networks

Authors

  • Dr. P. Wang Technical College, Bhutan

Keywords:

Regenerative systems, Agro-food supply chain, Circular economy, Supply chain resilience

Abstract

The agro-food sector is increasingly challenged by resource depletion, environmental degradation, and systemic inefficiencies embedded within conventional linear supply chain models. These models, characterized by extract-produce-consume-dispose patterns, have intensified ecological pressures and undermined long-term sustainability. In response, regenerative resource models have emerged as a transformative paradigm aimed at restoring ecological balance, optimizing resource cycles, and enhancing system resilience. This study critically examines the transition toward regenerative resource models in agro-food supply networks, integrating perspectives from circular economy theory, supply chain risk management, and energy system optimization.

The research employs a conceptual and analytical synthesis of existing literature to develop a comprehensive framework for regenerative agro-food systems. It explores the interplay between resource circularity, supply chain resilience, and technological innovation, with particular emphasis on energy integration through microgrid systems and advanced optimization techniques. The study further investigates the role of supply chain design under uncertainty, highlighting the importance of robust network configurations in mitigating disruptions and ensuring sustainability.

Findings suggest that regenerative models significantly improve resource efficiency by enabling closed-loop material flows, reducing waste, and enhancing energy self-sufficiency. The integration of microgrid-based energy systems and decentralized resource management mechanisms is identified as a key enabler of regenerative supply networks. However, the transition is constrained by economic, technological, and institutional barriers, including high implementation costs, complexity of system integration, and lack of coordinated policy frameworks.

The study concludes that regenerative resource models represent a viable pathway for transforming agro-food supply networks into sustainable and resilient systems. It emphasizes the need for interdisciplinary approaches, combining economic, technological, and environmental strategies to facilitate large-scale adoption. The research contributes to the academic discourse by providing a structured framework for understanding the dynamics of regenerative systems and their implications for future agro-food sustainability.

References

Agarwal, R., Sri Varshni, J., Harini, P. (2025). Adoption of Circular Economy in Food and Agriculture. In: Kandpal, V., Gunasekaran, A., Jaswal, A., Mukherjee, D. (eds) Rethinking Resources. Approaches to Global Sustainability, Markets, and Governance. Springer, Singapore. https://doi.org/10.1007/978-981-96-9055-8_16

Azad, N. and H. Davoudpour. And G. Saharidis. And M. Shiripour. ( 2014 ). A new model to mitigating random disruption risks of facility and transportation in supply chain network design. The International Journal of Advanced Manufacturing Technology, 70 ( 9-12 ), 1757–1774.

Baghalian, A., S. Rezapour, and R.Z. Farahani. ( 2013 ). Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case. European Journal of Operational Research, 227 ( 1 ), 199–215.

Bai Linquan, Wang Jianhui, Wang Chengshan, et al. Distribution locational marginal pricing (DLMP) for congestion management and voltage support[J]. IEEE Transactions on Power Systems, 2018, 33 ( 4 ): 4061–4073.

Christopher, M. and H. Lee. ( 2004 ). Mitigating supply chain risk through improved confidence. International journal of physical distribution & logistics management, 34 ( 5 ), 388–396.

Heckmann, I., T. Comes, and S. Nickel. ( 2015 ). A critical review on supply chain risk-Definition, measure and modeling. Omega, 52, 119–132.

Hendricks, K.B. and V.R. Singhal. ( 2003 ). The effect of supply chain glitches on shareholder wealth. Journal of operations Management, 21 ( 5 ), 501–522.

Jing Tianjun, Tan Yuangang, Yang Minghao. Optimal operation model for microgrid in rural areas[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28 ( 14 ): 127–132.

Kleindorfer, P.R. and G.H. Saad. ( 2005 ). Managing disruption risks in supply chains. Production and operations management, 14 ( 1 ), 53–68.

Liu Yixin, Guo Li, Wang Chengshan. Optimal bidding strategy for microgrids in electricity distribution market[J]. Power System Technology, 2017, 41 ( 8 ): 2469–2476.

Oke, A. and M. Gopalakrishnan. ( 2009 ). Managing disruptions in supply chains: A case study of a retail supply chain. International journal of production economics, 118 ( 1 ), 168–174.

Sadghiani, N.S., S. Torabi, and N. Sahebjamnia. ( 2015 ). Retail supply chain network design under operational and disruption risks. Transportation Research Part E: Logistics and Transportation Review, 75, 95–114.

Simchi-Levi, D., P. Kaminsky, and E. Simchi-Levi. ( 2004 ). Managing the Supply Chain: Definitive Guide : Tata McGraw-Hill Education.

Tang, C.S. ( 2006 ). Perspectives in supply chain risk management. International journal of production economics, 103 ( 2 ), 451–488.

Xie rm, Ji Xiang, Ke Shaojia, et al. Autonomous optimized economic dispatch of active distribution power system with multi-microgrids based on analytical target cascading theory[J]. Proceedings of the CSEE, 2017, 37 ( 17 ): 4911–4921.

Xu Qingshan, Li Lin, Sheng Yehong, et al. Day-ahead optimized economic dispatch of active distribution power system with combined cooling, heating and power-based microgrids[J]. Power System Technology, 2018, 42 ( 6 ): 1726–1734.

Xu Qiongguo, Zhang Weitao, Liu Guangwei, et al. Research on Optimal Scheduling of DC Microgrid Considering Energy Storage Regulation Characteristics[J]. Electric Drive, 2022, 52 ( 15 ): 53–60.

Downloads

Published

2026-02-28

How to Cite

Dr. P. Wang. (2026). Transition Toward Regenerative Resource Models in Agro-Food Supply Networks. Emerging Frontiers Library for The American Journal of Applied Sciences, 8(2), 104–112. Retrieved from http://emergingsociety.org/index.php/efltajas/article/view/1298

Issue

Section

Articles