The global divide in data-driven farming

被引:0
|
作者
Zia Mehrabi
Mollie J. McDowell
Vincent Ricciardi
Christian Levers
Juan Diego Martinez
Natascha Mehrabi
Hannah Wittman
Navin Ramankutty
Andy Jarvis
机构
[1] University of British Columbia,The UBC School of Public Policy and Global Affairs
[2] University of British Columbia,Institute for Resources, Environment and Sustainability
[3] University of British Columbia,Center for Sustainable Food Systems
[4] Helmholtz Centre for Environmental Research - UFZ,Department of Computational Landscape Ecology
[5] International Center for Tropical Agriculture,CGIAR Research Program on Big Data in Agriculture
来源
Nature Sustainability | 2021年 / 4卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
Big data and mobile technology are widely claimed to be global disruptive forces in agriculture that benefit small-scale farmers. Yet the access of small-scale farmers to this technology is poorly understood. We show that only 24–37% of farms of <1 ha in size are served by third generation (3G) or 4G services, compared to 74–80% of farms of >200 ha in size. Furthermore, croplands with severe yield gaps, climate-stressed locations and food-insecure populations have poor service coverage. Across many countries in Africa, less than ~40% of farming households have Internet access, and the cost of data remains prohibitive. We recommend a digital inclusion agenda whereby governments, the development community and the private sector focus their efforts to improve access so that data-driven agriculture is available to all farmers globally.
引用
收藏
页码:154 / 160
页数:6
相关论文
共 50 条
  • [1] The global divide in data-driven farming
    Mehrabi, Zia
    McDowell, Mollie J.
    Ricciardi, Vincent
    Levers, Christian
    Martinez, Juan Diego
    Mehrabi, Natascha
    Wittman, Hannah
    Ramankutty, Navin
    Jarvis, Andy
    NATURE SUSTAINABILITY, 2021, 4 (02) : 154 - 160
  • [2] A data-driven future for Scottish farming?
    Loeb, Josh
    VETERINARY RECORD, 2022, 190 (01) : 7 - 7
  • [3] Feeding the World with Data: Visions of Data-Driven Farming
    Steup, Rosemary
    Dombrowski, Lynn
    Su, Norman Makoto
    PROCEEDINGS OF THE 2019 ACM DESIGNING INTERACTIVE SYSTEMS CONFERENCE (DIS 2019), 2019, : 1503 - 1515
  • [4] Data-driven techniques for divide and conquer adaptive control
    Bertolissi, E
    Birattari, M
    Bontempi, G
    Duchâteau, A
    Bersini, H
    CONTROL APPLICATIONS OF OPTIMIZATION 2000, VOLS 1 AND 2, 2000, : 59 - 64
  • [5] Data-driven agriculture and sustainable farming: friends or foes?
    Offer Rozenstein
    Yafit Cohen
    Victor Alchanatis
    Karl Behrendt
    David J. Bonfil
    Gil Eshel
    Ally Harari
    W. Edwin Harris
    Iftach Klapp
    Yael Laor
    Raphael Linker
    Tarin Paz-Kagan
    Sven Peets
    S. Mark Rutter
    Yael Salzer
    James Lowenberg-DeBoer
    Precision Agriculture, 2024, 25 : 520 - 531
  • [6] Data-driven agriculture and sustainable farming: friends or foes?
    Rozenstein, Offer
    Cohen, Yafit
    Alchanatis, Victor
    Behrendt, Karl
    Bonfil, David J.
    Eshel, Gil
    Harari, Ally
    Harris, W. Edwin
    Klapp, Iftach
    Laor, Yael
    Linker, Raphael
    Paz-Kagan, Tarin
    Peets, Sven
    Rutter, S. Mark
    Salzer, Yael
    Lowenberg-DeBoer, James
    PRECISION AGRICULTURE, 2024, 25 (01) : 520 - 531
  • [7] Harnessing Data-Driven Technologies for Sustainable Farming Practices
    Velez, Sergio
    Alvarez, Sara
    AGRONOMY-BASEL, 2024, 14 (12):
  • [8] Data Lake Architecture for Smart Fish Farming Data-Driven Strategy
    Benjelloun, Sarah
    El Aissi, Mohamed El Mehdi
    Lakhrissi, Younes
    El Haj Ben Ali, Safae
    APPLIED SYSTEM INNOVATION, 2023, 6 (01)
  • [9] Data-driven decision making in pig farming: A review of the literature
    van Klompenburg, Thomas
    Kassahun, Ayalew
    LIVESTOCK SCIENCE, 2022, 261
  • [10] DATA-DRIVEN GLOBAL DYNAMICS OF THE INDIAN OCEAN
    Li Z.
    Yan W.
    Kang J.
    Jiang J.
    Hong L.
    Lixue Xuebao/Chinese Journal of Theoretical and Applied Mechanics, 2021, 53 (09): : 2595 - 2602