Opportunities in Farming Research from an Operations Management Perspective

Gupta, Sushil; Berenji, Hossein Rikhtehgar; Shukla, Manish; Murthy, Nagesh N.

Abstract

We review and analyze the farming (upstream agribusiness supply chain) research literature since 1965 to identify farming research opportunities for operations management (OM) researchers. A majority of reviewed papers in our corpus, until the turn of the 21st century, primarily focus on improving operational efficiency and effectiveness of farming using optimization techniques. However, during the last two decades, farmers’ welfare and the interests of other stakeholders have drawn OM researchers’ attention. This expanded focus on farming research has become possible due to the proliferation of mobile communication devices and the Internet, as well as advancements in information technology platforms and social media. Our review also shows that there is a paucity of OM literature that leverages increased data availability from the emergence of precision agriculture and blockchain to address major challenges for the farming sector emanating from climate change, natural disasters, food security, and sustainable and equitable agriculture, among others. Big data, in conjunction with opportunities for field-based experimentation, artificial intelligence and machine learning, and integration of predictive and prescriptive analytics, can be leveraged by OM scholars engaged in farming research. We zero in on specific questions, issues, and opportunities for research in farming.

Citation

Gupta, S., Berenji, H. R., Shukla, M., & Murthy, N. N. (2023). Opportunities in Farming Research from an Operations Management Perspective. Production and Operations Management, 32(6), 1577-1596. https://doi.org/10.1111/poms.13967

• Journal Article Type: Article
• Acceptance Date: Feb 24, 2023
• Online Publication Date: Mar 1, 2023
• Publication Date: 2023
• Deposit Date: Mar 24, 2023
• Publicly Available Date: Mar 2, 2025
• Journal: Production and Operations Management
• Print ISSN: 1059-1478
• Electronic ISSN: 1937-5956
• Publisher: Wiley
• Peer Reviewed: Peer Reviewed
• Volume: 32
• Issue: 6
• Pages: 1577-1596
• DOI: https://doi.org/10.1111/poms.13967
• Public URL: https://durham-repository.worktribe.com/output/1178452