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Applying Data Analytics in Food Security

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Handbook of Sustainability Science in the Future

Abstract

Technology advancement enables food security system to interact with various sources of digital data in a faster, cheaper, and better way to support analytics-based decision-making in the global community. This chapter starts with the definition of food security and data analytics. Next, the sciences of data-driven decision-making framework using food security data to develop models, enhance decisions, and convert into value are discussed. Four pillars which can support end-to-end analytics processes, namely acquisition, organization, analysis, and delivery complement the framework. Both the principles of data analytics framework and four types of analytics (descriptive, diagnostic, predictive, and prescriptive) in precision agriculture would solve the challenges of food security. The discussion on predictive analytics in food safety reveals that researchers have successfully adopted new technologies to improve food security by raising crop yields. Machine learning is a promising solution to facilitate and sustain crop yields globally. The nine steps of data analytics addressing food insecurity for actionable insights are elaborated. Data analytics are integrated into food security to achieve Sustainable Development Goal (SDG) Target 2.1 to stop hunger and safeguard food accessibility by the global community and SDG Target 2.2 to stop entire forms of malnutrition. This chapter paves the way for future research in the development of data analytics in food security.

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Acknowledgment

The work that led to the publication of this chapter was funded by the Universiti Sains Malaysia Research University (RUI) Grant, No. 1001/PMGT/8011118. This research is conducted while the corresponding author is on sabbatical leave.

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Authors and Affiliations

  1. School of Management, Universiti Sains Malaysia, Penang, Malaysia

    Sin Yin Teh

  2. Centre for Global Sustainability Studies, Universiti Sains Malaysia, Penang, Malaysia

    Theam Foo Ng

  3. Faculty of Art, Computing and Creative Industry, Universiti Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia

    Shir Li Wang

Authors
  1. Sin Yin Teh
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  2. Theam Foo Ng
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  3. Shir Li Wang
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Corresponding author

Correspondence to Sin Yin Teh .

Editor information

Editors and Affiliations

  1. FTZ-ALS, HAW Hamburg, Hamburg, Hamburg, Germany

    Walter Leal Filho

  2. University of Coimbra, Coimbra, Portugal

    Anabela Marisa Azul

  3. Department of Business and Law, University of Milano-Bicocca, MILANO, Milano, Italy

    Federica Doni

  4. University of Passo Fundo, Sao Jose, Rio Grande do Sul, Brazil

    Amanda Lange Salvia

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Teh, S.Y., Ng, T.F., Wang, S.L. (2022). Applying Data Analytics in Food Security. In: Leal Filho, W., Azul, A.M., Doni, F., Salvia, A.L. (eds) Handbook of Sustainability Science in the Future. Springer, Cham. https://doi.org/10.1007/978-3-030-68074-9_52-1

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  • DOI: https://doi.org/10.1007/978-3-030-68074-9_52-1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68074-9

  • Online ISBN: 978-3-030-68074-9

  • eBook Packages: Springer Reference Earth and Environm. ScienceReference Module Physical and Materials ScienceReference Module Earth and Environmental Sciences

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