A Systematic Review and Bibliometric Analysis on BlockChain Technology in Sustainable Food Agro-Processing and Dairy Sector
Main Article Content
Abstract
The unprecedented situation of COVID-19 disrupts significant sectors of the supply chain, like the Milk Supply Chain (MSC). Eradication of hunger and providing nutrition to all sections of the demographic population is a global agenda of the G-20 summit. Sustainable Development Goals (SDG) 12 is adopting a green economy and technological practices in production and consumption patterns in agricultural and dairy farming to save the planet, people and their overall health. This article critically reviews the systematic literature on the emergence of technological adoption and digitalisation in the Indian Agro-Processing and Dairy sector through statistical models and mathematical tools. This paper is based on data extraction from , Web of Science , SCIVAL and Scopus sources of information. Quantitative methods represent a diverse range of epistemological, theoretical and disciplinary perspectives. About 325 articles from blockchain and 250 articles from dairy supply chain (DSC) retrieved from peer-reviewed journals were accessed from the database. This data was analysed from a review of literature and bibliometric analysis. Sustainability is a core sub-theme of reviewed literature in Blockchain technology in the Indian milk and dairy supply chain (DSC).Beneficiation of research are researchers and academicians tend to explore extensive and systematic literature in a related field of study by using statistical tools and methods to develop substantive research. It provides a better lens to identify present, past and future trends in literature to research and publish.Vosviewer software was used to develop analytical models from the data retrieved from the present published literature during 2022-23. This research is based on adopting blockchain technology in sustainable agriculture and dairy farming. Research is limited to the theoretical aspect of the study. Citation analysis may be both positive and negative.