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Data Science Applications in Indian Agriculture

Author

Listed:
  • Sripad K. Devalkar
  • Sridhar Seshadri
  • Chitrabhanu Ghosh
  • Allen Mathias

Abstract

Agricultural supply chains in the developing world face the daunting task of feeding a growing population in the coming decades. Along with the provision of food, sustaining livelihoods, enhancing nutrition and the ability to cope with rapid changes in the environment and marketplaces are equally important to millions of small farmers. Data science can help in many ways. In this article, we outline the beginnings of data science applications in Indian agriculture. We cover various initiatives such as data collection, visualization and information dissemination, and applications of algorithmic data analysis techniques for decision support. We describe one application under development that provides timely price information to farmers, traders, and policy makers.

Suggested Citation

  • Sripad K. Devalkar & Sridhar Seshadri & Chitrabhanu Ghosh & Allen Mathias, 2018. "Data Science Applications in Indian Agriculture," Production and Operations Management, Production and Operations Management Society, vol. 27(9), pages 1701-1708, September.
    • Handle: RePEc:bla:popmgt:v:27:y:2018:i:9:p:1701-1708
    DOI: 10.1111/poms.12834
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    Cited by:

    1. Jagjit Singh Srai & Nitin Joglekar & Naoum Tsolakis & Sandeep Kapur, 2022. "Interplay between Competing and Coexisting Policy Regimens within Supply Chain Configurations," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 457-477, February.
    2. Man Mohan Siddh & Sameer Kumar & Gunjan Soni & Vipul Jain & Charu Chandra & Rakesh Jain & Milind Kumar Sharma & Yigit Kazancoglu, 2022. "Impact of agri-fresh food supply chain quality practices on organizational sustainability," Operations Management Research, Springer, vol. 15(1), pages 146-165, June.
    3. Akshai Gaddam & Sravan Malla & Sandhya Dasari & Narayana Darapaneni & Mukesh Kumar Shukla, 2022. "Creating an Optimal Portfolio of Crops Using Price Forecasting to Increase ROI for Indian Farmers," Papers 2211.01951, arXiv.org.
    4. Sengupta, Tuhin & Narayanamurthy, Gopalakrishnan & Hota, Pradeep Kumar & Sarker, Tapan & Dey, Subhendu, 2021. "Conditional acceptance of digitized business model innovation at the BoP: A stakeholder analysis of eKutir in India," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    5. Nur Sunar & Jayashankar M. Swaminathan, 2022. "Socially relevant and inclusive operations management," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4379-4392, December.
    6. Jónas Oddur Jónasson & Kamalini Ramdas & Alp Sungu, 2022. "Social impact operations at the global base of the pyramid," Production and Operations Management, Production and Operations Management Society, vol. 31(12), pages 4364-4378, December.

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