IDEAS home Printed from https://ideas.repec.org/a/bla/agecon/v53y2022i6p924-937.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/agec.12733
    Download Restriction: no

    File URL: https://libkey.io/10.1111/agec.12733?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. Schimmelpfennig, David, 2016. "Farm Profits and Adoption of Precision Agriculture," Economic Research Report 249773, United States Department of Agriculture, Economic Research Service.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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.
    25. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wolfert, Sjaak & Verdouw, Cor & van Wassenaer, Lan & Dolfsma, Wilfred & Klerkx, Laurens, 2023. "Digital innovation ecosystems in agri-food: design principles and organizational framework," Agricultural Systems, Elsevier, vol. 204(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Khanna, Madhu, 2021. "Digital Transformation for a Sustainable Agriculture: Opportunities and Challenges," 2021 Conference, August 17-31, 2021, Virtual 315052, International Association of Agricultural Economists.
    2. Wang, Tong & Jin, Hailong & Sieverding, Heidi & Kumar, Sandeep & Miao, Yuxin & Rao, Xudong & Obembe, Oladipo & Mirzakhani Nafchi, Ali & Redfearn, Daren & Cheye, Stephen, 2023. "Understanding farmer views of precision agriculture profitability in the U.S. Midwest," Ecological Economics, Elsevier, vol. 213(C).
    3. Khanna, Madhu & Atallah, Shadi & Kar, Saurajyoti & Sharma, Bijay & Wu, Linghui & Yu, Chengzheng, 2021. "Digital Transformation for a Sustainable Agriculture in the US: Opportunities and Challenges," 2021 Conference, August 17-31, 2021, Virtual 313799, International Association of Agricultural Economists.
    4. Wang, Tong & Jin, Hailong & Sieverding, Heidi L. & Rao, Xudong & Miao, Yuxin & Kumar, Sandeep & Redfearn, Daren & Nafchi, Ali, 2022. "Understanding farmer perceptions of precision agriculture profitability in the U.S. Midwest," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322502, Agricultural and Applied Economics Association.
    5. 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.
    6. Margherita Masi & Marcello Rosa & Yari Vecchio & Luca Bartoli & Felice Adinolfi, 2022. "The long way to innovation adoption: insights from precision agriculture," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-17, December.
    7. Nadia A. Streletskaya & Samuel D. Bell & Maik Kecinski & Tongzhe Li & Simanti Banerjee & Leah H. Palm‐Forster & David Pannell, 2020. "Agricultural Adoption and Behavioral Economics: Bridging the Gap," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 54-66, March.
    8. McFadden, Jonathan & Njuki, Eric & Griffin, Terry, 2023. "Precision Agriculture in the Digital Era: Recent Adoption on U.S. Farms," USDA Miscellaneous 333550, United States Department of Agriculture.
    9. 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.
    10. Madhu Khanna & Ruiqing Miao, 2022. "Inducing the adoption of emerging technologies for sustainable intensification of food and renewable energy production: insights from applied economics," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(1), pages 1-23, January.
    11. Omotuyole Isiaka Ambali & Francisco Jose Areal & Nikolaos Georgantzis, 2021. "Improved Rice Technology Adoption: The Role of Spatially-Dependent Risk Preference," Agriculture, MDPI, vol. 11(8), pages 1-13, July.
    12. Murat Isik & Madhu Khanna, 2003. "Stochastic Technology, Risk Preferences, and Adoption of Site-Specific Technologies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 305-317.
    13. Wu, Haixia & Ge, Yan & Li, Jianping, 2023. "Uncertainty, time preference and households’ adoption of rooftop photovoltaic technology," Energy, Elsevier, vol. 276(C).
    14. Oksana Hrynevych & Miguel Blanco Canto & Mercedes Jiménez García, 2022. "Tendencies of Precision Agriculture in Ukraine: Disruptive Smart Farming Tools as Cooperation Drivers," Agriculture, MDPI, vol. 12(5), pages 1-15, May.
    15. Lijing Gao & J. Arbuckle, 2022. "Examining farmers’ adoption of nutrient management best management practices: a social cognitive framework," Agriculture and Human Values, Springer;The Agriculture, Food, & Human Values Society (AFHVS), vol. 39(2), pages 535-553, June.
    16. Ahsanuzzaman, & Priyo, Asad Karim Khan & Nuzhat, Kanti Ananta, 2022. "Effects of communication, group selection, and social learning on risk and ambiguity attitudes: Experimental evidence from Bangladesh," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 96(C).
    17. Crentsil, Christian & Gschwandtner, Adelina & Wahhaj, Zaki, 2020. "The effects of risk and ambiguity aversion on technology adoption: Evidence from aquaculture in Ghana," Journal of Economic Behavior & Organization, Elsevier, vol. 179(C), pages 46-68.
    18. Isik, Murat & Coble, Keith H. & Hudson, Darren & House, Lisa O., 2003. "A model of entry-exit decisions and capacity choice under demand uncertainty," Agricultural Economics, Blackwell, vol. 28(3), pages 215-224, May.
    19. Freudenreich, H., 2018. "Explaining Mexican Farmers Adoption of Hybrid Maize Seed - The Role of Social Psychology, Risk and Ambiguity Aversion," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277410, International Association of Agricultural Economists.
    20. Bert Lenaerts & Yann de Mey & Matty Demont, 2022. "Revisiting multi‐stage models for upstream technology adoption: Evidence from rapid generation advance in rice breeding," Journal of Agricultural Economics, Wiley Blackwell, vol. 73(1), pages 277-300, February.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:agecon:v:53:y:2022:i:6:p:924-937. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/iaaeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.