1932

Abstract

Agriculture stands on the cusp of a digital revolution, and the same technologies that created the Internet and are transforming medicine are now being applied in our farms and on our fields. Overall, this digital agricultural revolution is being driven by the low cost of collecting data on everything from soil conditions to animal health and crop development along with weather station data and data collected by drones and satellites. The promise of these technologies is more food, produced on less land, with fewer inputs and a smaller environmental footprint. At present, however, barriers to realizing this potential include a lack of ability to aggregate and interpret data in such a way that it results in useful decision support tools for farmers and the need to train farmers in how to use new tools. This article reviews the state of the literature on the promise and barriers to realizing the potential for Big Data to revolutionize agriculture.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-resource-100516-053654
2018-10-05
2024-07-04
Loading full text...

Full text loading...

/deliver/fulltext/resource/10/1/annurev-resource-100516-053654.html?itemId=/content/journals/10.1146/annurev-resource-100516-053654&mimeType=html&fmt=ahah

Literature Cited

  1. Adjemian MK, Brorsen BW, Hahn W, Saitone TL, Sexton RJ 2016. Thinning markets in US agriculture Econ. Inf. Bull. 148 Econ. Res. Serv., US Dep. Agric. Washington, DC:
    [Google Scholar]
  2. Ali A, Qadir J, ur Rasool R, Sathiaseelan A, Zwitter A, Crowcroft J 2016. Big data for development: applications and techniques. Big Data Anal 1:12
    [Google Scholar]
  3. Allen DW, Lueck D 2004. The Nature of the Farm: Contracts, Risk, and Organization in Agriculture. Cambridge, MA: MIT Press
    [Google Scholar]
  4. Antle JM, Jones JW, Rosenzweig C 2017. Next generation agricultural system models and knowledge products: synthesis and strategy. Agric. Syst. 155:179–85
    [Google Scholar]
  5. Aung MM, Chang YS 2014. Traceability in a food supply chain: safety and quality perspectives. Food Control 39:172–84
    [Google Scholar]
  6. Badia-Melis R, Mishra P, Ruiz-García L 2015. Food traceability: new trends and recent advances. A review. Food Control 57:393–401
    [Google Scholar]
  7. Balafoutis A, Beck B, Fountas S, Vangeyte J, Wal TVD et al. 2017. Precision agriculture technologies positively contributing to GHG emissions mitigation, farm productivity and economics. Sustainability 9:81339–67
    [Google Scholar]
  8. Ballenger N, Bastian C, Cammack K, Feuz B, Griffith G, Schaffer J 2016. 30 and Daisy: Where's the economics in beef cattle DNA testing. ? Choices 31:21–10
    [Google Scholar]
  9. Banham R 2014. Who owns farmers’ big data. ? Forbes July 8. http://www.forbes.com/sites/emc/2014/07/08/who-owns-farmers-big-data/#211977e53ce7
    [Google Scholar]
  10. Baumgart-Getz A, Stalker L, Floress K 2012. Why farmers adopt best management practice in the United States: a meta-analysis of the adoption literature. J. Environ. Manag. 96:117–25
    [Google Scholar]
  11. Beatty PH, Good AG 2011. Future prospects for cereals that fix nitrogen. Science 333:6041416–17
    [Google Scholar]
  12. Bennett JM 2015. Agricultural Big Data: utilisation to discover the unknown and instigate practice change. Farm Policy J 12:143–50
    [Google Scholar]
  13. Benton TG, Dougill AJ, Fraser EDG, Howlett DJB 2011. The scale for managing production versus the scale required for ecosystem service production. World Agric 2:114–21
    [Google Scholar]
  14. Bigelow D, Borchers A, Hubbs T 2016. U.S. farmland ownership, tenure, and transfer. Econ. Inf. Bull. 161 Econ. Res. Serv., US Dep. Agric. Washington, DC:
    [Google Scholar]
  15. Bradley K 2017. Ag Industry, Do we have a problem yet? Integr. Pest. Manag., Univ. Mo. https://ipm.missouri.edu/IPCM/2017/7/Ag_Industry_Do_we_have_a_problem_yet/
    [Google Scholar]
  16. Bronson K, Knezevic I 2016. Big Data in food and agriculture. Big Data Soc 3:1 https://doi.org/10.1177/2053951716648174
    [Crossref] [Google Scholar]
  17. Brown E 2017. Why big data hasn't yet made a dent on farms. Wall Street J May 15. https://www.wsj.com/articles/why-big-data-hasnt-yet-made-a-dent-on-farms-1494813720
    [Google Scholar]
  18. Bryan J, Deaton BJ, Weersink A 2015. Do landlord-tenant relationships influence rental contracts for farmland or the cash rental rate. ? Land Econ 91:4650–63
    [Google Scholar]
  19. Busse M, Schwerdtner W, Siebert R, Doernberg A, Kuntosch A et al. 2015. Analysis of animal monitoring technologies in Germany from an innovation system perspective. Agric. Syst. 138:55–65
    [Google Scholar]
  20. Campbell H 2009. Breaking new ground in food regime theory: Corporate environmentalism, ecological feedbacks and the ‘food from somewhere’ regime. ? Agric. Hum. Values 26:309–19
    [Google Scholar]
  21. Caracciolo F, Cicia G, Del Guidince T, Cembalo L, Krystallis A et al. 2016. Human values and preferences for cleaner livestock production. J. Clean. Prod. 112:121–30
    [Google Scholar]
  22. Carolan M 2017. Publicising food: big data, precision agriculture, and co‐experimental techniques of addition. Sociol. Rural. 57:2135–54
    [Google Scholar]
  23. Caron P, Bienabe E, Hainzelin E 2014. Making transition towards ecological intensification of agriculture a reality: the gaps in and the role of scientific knowledge. Curr. Opin. Environ. Sustain. 8:44–52
    [Google Scholar]
  24. Castillo MJ, Boucher S, Carter M 2016. Index insurance: using public data to benefit small-scale agriculture. Int. Food Agribus. Manag. Rev. 19:A93–114
    [Google Scholar]
  25. Chen M, Mao S, Liu Y 2014. Big data: a survey. Mobile Netw. Appl. 19:2171–209
    [Google Scholar]
  26. Chi H, Welch S, Vasserman E, Kalaimannan E 2017. A framework of cybersecurity approaches in precision agriculture. Proceedings of the ICMLG2017 5th International Conference on Management Leadership and Governance90–95 Reading, UK: Acad. Conf. Publ. Int.
    [Google Scholar]
  27. Clapp J, Fuchs D 2009. Corporate Power in Global Agrifood Governance Cambridge, MA: MIT Press
    [Google Scholar]
  28. Coble K, Griffin T, Ahearn A, Ferrell S, McFadden J et al. 2016. Advancing U.S. agricultural competitiveness with Big Data and agricultural economic market information, analysis, and research Rep. Counc. Food Agric. Resour. Econ. Washington, DC:
    [Google Scholar]
  29. Conf. Board Can. 2016. Sowing the seeds of growth: temporary foreign workers in agriculture Rep. Conf. Board Can. Ottawa: Dec. 1. http://www.conferenceboard.ca/e-library/abstract.aspx?did=8363
    [Google Scholar]
  30. Cukier K, Mayer-Schoenberger V 2013. The rise of Big Data: how it's changing the way we think about the world. Foreign Aff 92:228–40
    [Google Scholar]
  31. Currid-Halkett E 2017. The Sum of Small Things: Culture and Consumption in the 21st Century Princeton, NJ: Princeton Univ. Press
    [Google Scholar]
  32. Deichmann U, Goyal A, Mishra D 2016. Will digital technologies transform agriculture in developing countries. ? Agric. Econ. 47:21–33
    [Google Scholar]
  33. De Mauro A, Greco M, Grimaldi M 2016. A formal definition of Big Data based on its essential features. Libr. Rev. 65:3122–35
    [Google Scholar]
  34. Duffy M 2009. Economies of size in production agriculture. J. Hunger Environ. Nutr. 4:3–4375–92
    [Google Scholar]
  35. Edwards D 2016. The impact of genomics technology on adapting plants to climate change. Plant Genomics and Climate Change D Edwards, J Batley 173–78 New York: Springer
    [Google Scholar]
  36. Erickson B, Widmar DA 2015. Precision agricultural services dealership survey results Work. Pap. Purdue Univ. West Lafayette, IN: http://agribusiness.purdue.edu/files/resources/2015-crop-life-purdue-precision-dealer-survey.pdf
    [Google Scholar]
  37. Faulkner A, Cebul K 2014. Agriculture gets smart: the rise of data and robotics Rep. Cleantech Group San Francisco:
    [Google Scholar]
  38. Fleming L, Tempini N, Gordon-Brown H, Nichols GL, Sarran C et al. 2017. Big Data in environment and human health. Oxf. Res. Encycl. Environ. Sci. https://doi.org/10.1093/acrefore/9780199389414.013.541
    [Crossref] [Google Scholar]
  39. Gardner BL 1992. Changing economic perspectives on the farm problem. J. Econ. Lit. 30:162–101
    [Google Scholar]
  40. Garnett T, Appleby MC, Balmford A, Bateman IJ, Benton TG et al. 2013. Sustainable intensification in agriculture: premises and policies. Science 341:614133–34
    [Google Scholar]
  41. Gebbers R, Adamchuk VI 2010. Precision agriculture and food security. Science 327:5967828–31
    [Google Scholar]
  42. Goddard ME 2012. Uses of genomics in livestock agriculture. Anim. Prod. Sci. 52:373–77
    [Google Scholar]
  43. Gov. Can. 2016. CRTC establishes fund to attain new high-speed Internet targets News Release, Dec. 21. https://www.canada.ca/en/radio-television-telecommunications/news/2016/12/crtc-establishes-fund-attain-new-high-speed-internet-targets.html
    [Google Scholar]
  44. Green RE, Cornell SJ, Scharlemann JP, Balmford A 2005. Farming and the fate of wild nature. Science 307:5709550–55
    [Google Scholar]
  45. Griffin TW, Miller NJ, Bergtold J, Shanoyan A, Sharda A, Ciampitti IA 2017. Farm's sequence of adoption of information-intensive precision agricultural technology. Appl. Eng. Agric. 33:4521–27
    [Google Scholar]
  46. Howard PH 2016. Concentration and Power in the Food System: Who Controls What We Eat? New York: Bloomsbury
    [Google Scholar]
  47. IPCC (Int. Panel Clim. Change). 2014. Climate change 2014: synthesis report Contrib. Work. Groups I, II, III to 5th Assess. Rep IPCC Geneva:
    [Google Scholar]
  48. Janssen SJC, Porter CH, Moore AD, Athanasiadis IN, Foster I et al. 2017. Towards a new generation of agricultural system data, models and knowledge products: information and communication technology. Agric. Syst. 155:200–12
    [Google Scholar]
  49. Janzen T 2017. Dicamba will show us the promise—and limitations—of big data in 2017. Janzen Ag Law Blog Aug. 3. http://www.aglaw.us/janzenaglaw/2017/8/3/dicamba-big-data
    [Google Scholar]
  50. Ker AP, Barnett B, Jacques D, Tolhurst T 2017. Canadian business risk management: private firms, crown corporations, and public institutions. Can. J. Agric. Econ. 65:4591–612
    [Google Scholar]
  51. Koontz SB, Hoag DL, Thilmany DD, Green JW, Grannis JL 2006. The Economics of Livestock Disease Insurance: Concepts, Issues and International Case Studies Oxfordshire, UK: CABI
    [Google Scholar]
  52. Krintz C, Wolski R, Golubovic N, Lampel B, Kulkarni V et al. 2016. SmartFarm: improving agriculture sustainability using modern information technology Paper presented at the KDD 2016 Workshop on Data Science for Food, Energy, and Water San Francisco: Aug 13–17 http://www.cs.ucsb.edu/∼rich/publications/dsfew16.pdf
    [Google Scholar]
  53. Kshetri N 2016. Big Data's Big Potential in Developing Economies: Impact on Agriculture, Health and Environmental Security Boston, MA: CABI
    [Google Scholar]
  54. Leslie JE, Weersink A, Yang W, Fox G 2017. Actual versus environmentally recommended fertilizer application rates: implications for water quality and policy. Agric. Ecosyst. Environ. 240:1109–20
    [Google Scholar]
  55. Lesser A 2014. Analyst report: big data and big agriculture. Gigaom https://gigaom.com/report/big-data-and-big-agriculture/
    [Google Scholar]
  56. Lev-Ram M 2017. John Deere is paying $305 million for this Silicon Valley company. Fortune Sept. 6. http://fortune.com/2017/09/06/john-deere-blue-river-acquisition/
    [Google Scholar]
  57. Lindblom J, Lundstro C, Jonsson A 2017. Promoting sustainable intensification in precision agriculture: review of decision support systems development and strategies. Precis. Agric. 18:3309–31
    [Google Scholar]
  58. Lusk JL 2017. Consumer research with big data: applications from the food demand survey (FooDS). Am. J. Agric. Econ. 99:2303–20
    [Google Scholar]
  59. Luskin MS, Lee JSH, Edwards DP, Gibson L, Potts MD 2018. Study context shapes recommendations of land-sparing and sharing; a quantitative review. Glob. Food Secur. 16:29–35
    [Google Scholar]
  60. Lyson TA, Stevenson GW, Welsh R 2008. Food and the Mid-Level Farm: Renewing an Agriculture of the Middle Cambridge, MA: MIT Press
    [Google Scholar]
  61. MacDonald JM 2016. Concentration, contracting, and competition policy in U.S. agribusiness. Concurrences 1:3–9
    [Google Scholar]
  62. Marden E, Godfrey RN, Manion R 2016. The Intellectual Property–Regulatory Complex: Overcoming Barriers to Innovation in Agricultural Genomics Vancouver: UBC Press
    [Google Scholar]
  63. Marvin HJ, Janssen EM, Bouzembrak Y, Hendriksen PJ, Staats M 2017. Big data in food safety: an overview. Crit. Rev. Food Sci. Nutr. 57:112286–95
    [Google Scholar]
  64. Matson PA, Parton WJ, Power AG, Swift MJ 1997. Agricultural intensification and ecosystem properties. Science 277:5325504–9
    [Google Scholar]
  65. McAfee A, Brynjolfsson E, Davenport TH, Patil DJ, Barton D 2012. Big data: the management revolution. Harv. Bus. Rev. 90:1060–68
    [Google Scholar]
  66. McCluskey JJ, Kalaitzandonakes N, Swinnen J 2015. Media coverage, public perceptions, and consumer behavior: insights from new food technologies. Annu. Rev. Resour. Econ. 7:467–86
    [Google Scholar]
  67. Messer KD, Costanigro M, Kaiser HM 2017. Labeling food processes: the good, the bad and the ugly. Appl. Econ. Perspect. Policy 39:3407–27
    [Google Scholar]
  68. Mitchell S, Weersink A, Erickson B 2018. Adoption of precision agriculture technologies in Ontario crop production. Can. J. Plant Sci. In press
    [Google Scholar]
  69. Ng JMS, Han M, Beatty PH, Good A 2016. “Genes, meet gases”: the role of plant nutrition and genomics in addressing greenhouse gas emissions. Plant Genomics and Climate Change D Edwards, J Batley 149–72 New York: Springer
    [Google Scholar]
  70. Opara LU 2003. Traceability in agriculture and food supply chain: a review of basic concepts, technological implications, and future prospects. J. Food Agric. Environ. 1:101–6
    [Google Scholar]
  71. Palm-Forster LH, Swinton SM, Redder TM, DePinto JV, Boles CM 2016. Using conservation auctions informed by environmental performance models to reduce agricultural nutrient flows into Lake Erie. J. Great Lakes Res. 42:61357–71
    [Google Scholar]
  72. Pannell DJ 2006. Flat earth economics: the far-reaching consequences of flat payoff functions in economic decision making. Rev. Agric. Econ. 28:4553–66
    [Google Scholar]
  73. Pant LP, Odame HH 2017. Broadband for a sustainable digital future of rural communities: a reflexive interactive assessment. J. Rural Stud. 54:435–50
    [Google Scholar]
  74. Phalan B, Green RE, Dicks LV, Dotta G, Feniuk C et al. 2016. How can higher-yield farming help to spare nature. ? Science 351:6272450–51
    [Google Scholar]
  75. Pierpaoli E, Carli G, Pignatti E, Canavari M 2013. Drivers of precision agriculture technologies adoption: a literature review. Proc. Technol. 8:61–69
    [Google Scholar]
  76. Pitchbook. 2017. Agtech investment review Rep. Finistere Ventures Palo Alto, CA: https://files.pitchbook.com/website/files/pdf/Finistere_Ventures_PitchBook_2017_Agtech_Investment_Review.pdf
    [Google Scholar]
  77. Pizzuti T, Mirabelli G 2015. The Global Track&Trace System for food: general framework and functioning principles. J. Food Eng. 159:16–35
    [Google Scholar]
  78. Poon K, Weersink A 2014. Growing forward with agricultural policy: strengths and weaknesses of Canada's agricultural data sets. Can. J. Agric. Econ. 62:2191–218
    [Google Scholar]
  79. Popper N, Lohr S 2017. Blockchain: a better way to track pork chops, bonds, bad peanut butter. New York Times March 4. https://www.nytimes.com/2017/03/04/business/dealbook/blockchain-ibm-bitcoin.html
    [Google Scholar]
  80. Porter CH, Villalobos C, Holzworth D, Nelson R, White JW et al. 2014. Harmonization and translation of crop modeling data to ensure interoperability. Environ. Model. Softw. 62:495–508
    [Google Scholar]
  81. Rainie L, Anderson J 2017. The future of jobs and jobs training. Pew Res. Cent. Internet Technol. May 3. http://www.pewinternet.org/2017/05/03/the-future-of-jobs-and-jobs-training/
    [Google Scholar]
  82. Rajsic P, Weersink A 2008. Do farmers waste fertilizer? A comparison of ex post optimal nitrogen rates and ex ante recommendations by model, site and year. Agric. Syst. 97:156–67
    [Google Scholar]
  83. Ramundo L, Taisch M, Terzi S 2016. State of the art of technology in the food sector value chain towards the IoT. 2016 IEEE 2nd International Forum on Research and Technologies for Society and Industry Leveraging a Better Tomorrow (RTSI)1–6 New York: IEEE
    [Google Scholar]
  84. Rose DC, Sutherland WJ, Parker C, Lobley M, Winter M et al. 2016. Decision support tools for agriculture: towards effective design and delivery. Agric. Syst. 149:165–74
    [Google Scholar]
  85. Rotz S, Fraser EDG, Martin RC 2017. Situating tenure, capital and finance in farmland relations: implications for stewardship and agroecological health in Ontario, Canada. J. Peasant Stud. https://doi.org/10.1080/03066150.2017.1351953
    [Crossref] [Google Scholar]
  86. Schimmelpfennig D 2016. Farm profits and adoption of precision agriculture Econ. Res. Rep. 217 Econ. Res. Serv., US Dep. Agric. Washington, DC:
    [Google Scholar]
  87. Schmitz A 2010. Agricultural Policy, Agribusiness, and Rent-Seeking Behavior Toronto: Univ. Toronto Press
    [Google Scholar]
  88. Sexton RJ 2012. Market power, misconceptions, and modern agricultural markets. Am. J. Agric. Econ. 95:2209–19
    [Google Scholar]
  89. Shin DH, Choi MJ 2015. Ecological views of big data: perspectives and issues. Telemat. Inf. 32:2311–20
    [Google Scholar]
  90. Sonka S 2014. Big data and the ag sector: more than lots of numbers. Int. Food Agribus. Manag. Rev. 17:11–20
    [Google Scholar]
  91. Sonka S 2016. Big data: fueling the next evolution of agricultural innovation. J. Innov. Manag. 4:1114–36
    [Google Scholar]
  92. Stat. Can. 2017. A portrait of a 21st century agricultural operation Rep. Minist. Ind. Ottawa: http://www.statcan.gc.ca/pub/95-640-x/2016001/article/14811-eng.pdf
    [Google Scholar]
  93. Tey YS, Brindal M 2012. Factors influencing the adoption of precision agricultural technologies: a review for policy implications. Precis. Agric. 13:6713–30
    [Google Scholar]
  94. Tilman D 1998. The greening of the green revolution. Nature 396:6708211–12
    [Google Scholar]
  95. Tilman D, Cassman KG, Matson PA, Naylor R, Polasky S 2002. Agricultural sustainability and intensive production practices. Nature 418:6898671–77
    [Google Scholar]
  96. Van Rijswijk W, Frewer LJ 2012. Consumer needs and requirements for food and ingredient traceability information. Int. J. Consum. Stud. 36:3282–90
    [Google Scholar]
  97. Walker MJ, Burns M, Burns DT 2013. Horse meat in beef products—species substitution 2013. J. Assoc. Public Anal. 41:67–106
    [Google Scholar]
  98. Weersink A 2018. The growing heterogeneity in the farm sector and its implications. Can. J. Agric. Econ. 66:127–41
    [Google Scholar]
  99. Weersink A, Livernois J, Shogren JF, Shortle JS 1998. Economic instruments and environmental policy in agriculture. Can. Public Policy/Anal. Politiques 24:3309–27
    [Google Scholar]
  100. Weersink A, Pannell D 2017. Payments versus direct controls for environmental externalities in agriculture. Oxf. Res. Encycl. Environ. Sci. http://environmentalscience.oxfordre.com/view/10.1093/acrefore/9780199389414.001.0001/acrefore-9780199389414-e-520
    [Google Scholar]
  101. Weis T 2013. The Ecological Hoofprint: The Global Burden of Industrial Livestock London: Zed Books
    [Google Scholar]
  102. Wolfert S, Ge L, Verdouw C, Bogaardt M-J 2017. Big Data in Smart Farming—a review. Agric. Syst. 153:69–80
    [Google Scholar]
  103. Wong EHK, Hanner RH 2008. DNA barcoding detects market substitution in North American seafood. Food Res. Int. 41:8828–37
    [Google Scholar]
  104. Woodard JD 2016. Data science and management for large scale empirical applications in agricultural and applied economics research. Appl. Econ. Perspect. Policy 38:3373–88
    [Google Scholar]
/content/journals/10.1146/annurev-resource-100516-053654
Loading
  • Article Type: Review Article
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error