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Digital transformation for a sustainable agriculture in the United States: Opportunities and challenges

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  • Madhu Khanna
  • Shady S. Atallah
  • Saurajyoti Kar
  • Bijay Sharma
  • Linghui Wu
  • Chengzheng Yu
  • Girish Chowdhary
  • Chinmay Soman
  • Kaiyu Guan

Abstract

Agriculture faces key challenges of increasing productivity while reducing adverse impacts on the environment. Conventional practices that rely on tillage, inefficient and over‐application of chemicals, and monoculture row cropping are leading to growing resistance of weeds and pests to chemicals, nutrient and sediment run‐off, and declining soil carbon stocks in the United States. Digital technologies and artificial intelligence (AI) technologies are enabling the collection of vast amounts of geo‐referenced information about growing conditions within the field, automated implementation of spatially varying input applications, and reduced reliance on chemical inputs. We discuss the pathways by which digital agricultural technologies have the potential to address the challenge of herbicide‐resistant weeds, over‐application of nitrogen and irrigation water, and cover crop planting for restoring soil health and contribute to the environmental sustainability of agriculture. Then, we discuss the economic factors, behavioral preferences of farmers, peer pressure, and social networks that can be expected to play a role in adoption decisions. We conclude with a discussion of approaches for ex ante assessments of the determinants of farmer willingness to adopt digital technologies and their diffusion in a region.

Suggested Citation

  • Madhu Khanna & Shady S. Atallah & Saurajyoti Kar & Bijay Sharma & Linghui Wu & Chengzheng Yu & Girish Chowdhary & Chinmay Soman & Kaiyu Guan, 2022. "Digital transformation for a sustainable agriculture in the United States: Opportunities and challenges," Agricultural Economics, International Association of Agricultural Economists, vol. 53(6), pages 924-937, November.
  • Handle: RePEc:bla:agecon:v:53:y:2022:i:6:p:924-937
    DOI: 10.1111/agec.12733
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    as
    1. Daxini, Amar & Ryan, Mary & O’Donoghue, Cathal & Barnes, Andrew P., 2019. "Understanding farmers’ intentions to follow a nutrient management plan using the theory of planned behaviour," Land Use Policy, Elsevier, vol. 85(C), pages 428-437.
    2. Oriana Bandiera & Imran Rasul, 2006. "Social Networks and Technology Adoption in Northern Mozambique," Economic Journal, Royal Economic Society, vol. 116(514), pages 869-902, October.
    3. Shang, Linmei & Heckelei, Thomas & Gerullis, Maria K. & Börner, Jan & Rasch, Sebastian, 2021. "Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction," Agricultural Systems, Elsevier, vol. 190(C).
    4. Just, Richard E & Zilberman, David, 1983. "Stochastic Structure, Farm Size and Technology Adoption in Developing Agriculture," Oxford Economic Papers, Oxford University Press, vol. 35(2), pages 307-328, July.
    5. Barham, Bradford L. & Chavas, Jean-Paul & Fitz, Dylan & Salas, Vanessa Ríos & Schechter, Laura, 2014. "The roles of risk and ambiguity in technology adoption," Journal of Economic Behavior & Organization, Elsevier, vol. 97(C), pages 204-218.
    6. Marra, Michele & Pannell, David J. & Abadi Ghadim, Amir, 2003. "The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: where are we on the learning curve?," Agricultural Systems, Elsevier, vol. 75(2-3), pages 215-234.
    7. Swinton, Scott M. & King, Robert P., 1994. "A bioeconomic model for weed management in corn and soybean," Agricultural Systems, Elsevier, vol. 44(3), pages 313-335.
    8. Wolfert, Sjaak & Ge, Lan & Verdouw, Cor & Bogaardt, Marc-Jeroen, 2017. "Big Data in Smart Farming – A review," Agricultural Systems, Elsevier, vol. 153(C), pages 69-80.
    9. Elaine M. Liu, 2013. "Time to Change What to Sow: Risk Preferences and Technology Adoption Decisions of Cotton Farmers in China," The Review of Economics and Statistics, MIT Press, vol. 95(4), pages 1386-1403, October.
    10. Madhu Khanna, 2021. "Digital Transformation of the Agricultural Sector: Pathways, Drivers and Policy Implications," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 43(4), pages 1221-1242, December.
    11. Neil R. Miller, 2006. "Is Site-Specific Yield Response Consistent over Time? Does It Pay?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(2), pages 471-483.
    12. Isik, Murat & Khanna, Madhu, 2002. "Variable-Rate Nitrogen Application Under Uncertainty: Implications For Profitability And Nitrogen Use," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(1), pages 1-16, July.
    13. Le Pira, Michela & Marcucci, Edoardo & Gatta, Valerio & Inturri, Giuseppe & Ignaccolo, Matteo & Pluchino, Alessandro, 2017. "Integrating discrete choice models and agent-based models for ex-ante evaluation of stakeholder policy acceptability in urban freight transport," Research in Transportation Economics, Elsevier, vol. 64(C), pages 13-25.
    14. George W. Norton & Jeffrey Alwang, 2020. "Changes in Agricultural Extension and Implications for Farmer Adoption of New Practices," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 8-20, March.
    15. Babcock, Bruce A. & Pautsch, Gregory R., 1998. "Moving From Uniform To Variable Fertilizer Rates On Iowa Corn: Effects On Rates And Returns," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 23(2), pages 1-16, December.
    16. Khanna, Madhu & Isik, Murat & Winter-Nelson, Alex, 2000. "Investment in site-specific crop management under uncertainty: implications for nitrogen pollution control and environmental policy," Agricultural Economics, Blackwell, vol. 24(1), pages 9-21, December.
    17. Xin Zhang & Eric A. Davidson & Denise L. Mauzerall & Timothy D. Searchinger & Patrice Dumas & Ye Shen, 2015. "Managing nitrogen for sustainable development," Nature, Nature, vol. 528(7580), pages 51-59, December.
    18. Madhu Khanna & Jordan Louviere & Xi Yang, 2017. "Motivations to grow energy crops: the role of crop and contract attributes," Agricultural Economics, International Association of Agricultural Economists, vol. 48(3), pages 263-277, May.
    19. Jean‐Paul Chavas & Céline Nauges, 2020. "Uncertainty, Learning, and Technology Adoption in Agriculture," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 42-53, March.
    20. David J. Lewis & Bradford L. Barham & Brian Robinson, 2011. "Are There Spatial Spillovers in the Adoption of Clean Technology? The Case of Organic Dairy Farming," Land Economics, University of Wisconsin Press, vol. 87(2), pages 250-267.
    21. Schimmelpfennig, David, 2016. "Farm Profits and Adoption of Precision Agriculture," Economic Research Report 249773, United States Department of Agriculture, Economic Research Service.
    22. Zahniser, Steven & Taylor, J. Edward & Hertz, Thomas & Charlton, Diane, 2018. "Farm Labor Markets in the United States and Mexico Pose Challenges for U.S. Agriculture," Economic Information Bulletin 281161, United States Department of Agriculture, Economic Research Service.
    23. Margriet F. Caswell & David Zilberman, 1986. "The Effects of Well Depth and Land Quality on the Choice of Irrigation Technology," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 68(4), pages 798-811.
    24. Stefan Holm & Renato Lemm & Oliver Thees & Lorenz M. Hilty, 2016. "Enhancing Agent-Based Models with Discrete Choice Experiments," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 19(3), pages 1-3.
    25. D'Antoni, Jeremy M. & Mishra, Ashok K. & Powell, Rebekah R. & Martin, Steven W., 2012. "Farmers’ Perception of Precision Technology: The Case of Autosteer Adoption by Cotton Farmers," 2012 Annual Meeting, February 4-7, 2012, Birmingham, Alabama 119734, Southern Agricultural Economics Association.
    26. Schimmelpfennig, David & Ebel, Robert, 2016. "Sequential Adoption and Cost Savings from Precision Agriculture," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 41(1), pages 1-19, January.
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    Cited by:

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    2. Robert Finger, 2023. "Digital innovations for sustainable and resilient agricultural systems," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 50(4), pages 1277-1309.

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