Agricultural big data and methods and models for food security analysis—a mini-review
Background Big data and data analysis methods and models are important tools in food security (FS) studies for gap analysis and preparation of appropriate analytical frameworks. These innovations necessitate the development of novel methods for collecting, storing, processing, and extracting data. M...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-06-01
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| Online Access: | https://peerj.com/articles/13674.pdf |
| _version_ | 1797419011881828352 |
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| author | Khalil A. Ammar Ahmed M.S. Kheir Ioannis Manikas |
| author_facet | Khalil A. Ammar Ahmed M.S. Kheir Ioannis Manikas |
| author_sort | Khalil A. Ammar |
| collection | DOAJ |
| description | Background Big data and data analysis methods and models are important tools in food security (FS) studies for gap analysis and preparation of appropriate analytical frameworks. These innovations necessitate the development of novel methods for collecting, storing, processing, and extracting data. Methodology The primary goal of this study was to conduct a critical review of agricultural big data and methods and models used for FS studies published in peer-reviewed journals since 2010. Approximately 130 articles were selected for full content review after the pre-screening process. Results There are different sources of data collection, including but not limited to online databases, the internet, omics, Internet of Things, social media, survey rounds, remote sensing, and the Food and Agriculture Organization Corporate Statistical Database. The collected data require analysis (i.e., mining, neural networks, Bayesian networks, and other ML algorithms) before data visualization using Python, R, Circos, Gephi, Tableau, or Cytoscape. Approximately 122 models, all of which were used in FS studies worldwide, were selected from 130 articles. However, most of these models addressed only one or two dimensions of FS (i.e., availability and access) and ignored the other dimensions (i.e., stability and utilization), creating a gap in the global context. Conclusions There are certain FS gaps both worldwide and in the United Arab Emirates that need to be addressed by scientists and policymakers. Following the identification of the drivers, policies, and indicators, the findings of this review could be used to develop an appropriate analytical framework for FS and nutrition. |
| first_indexed | 2024-03-09T06:42:26Z |
| format | Article |
| id | doaj.art-78b15e880c5c4151b6e480639e385ef7 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T06:42:26Z |
| publishDate | 2022-06-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-78b15e880c5c4151b6e480639e385ef72023-12-03T10:48:11ZengPeerJ Inc.PeerJ2167-83592022-06-0110e1367410.7717/peerj.13674Agricultural big data and methods and models for food security analysis—a mini-reviewKhalil A. Ammar0Ahmed M.S. Kheir1Ioannis Manikas2International Center for Biosaline Agriculture, ICBA, Dubai, United Arab EmiratesInternational Center for Biosaline Agriculture, ICBA, Dubai, United Arab EmiratesFaculty of Business, University of Wollongong in Dubai, Dubai, UAE, United Arab EmiratesBackground Big data and data analysis methods and models are important tools in food security (FS) studies for gap analysis and preparation of appropriate analytical frameworks. These innovations necessitate the development of novel methods for collecting, storing, processing, and extracting data. Methodology The primary goal of this study was to conduct a critical review of agricultural big data and methods and models used for FS studies published in peer-reviewed journals since 2010. Approximately 130 articles were selected for full content review after the pre-screening process. Results There are different sources of data collection, including but not limited to online databases, the internet, omics, Internet of Things, social media, survey rounds, remote sensing, and the Food and Agriculture Organization Corporate Statistical Database. The collected data require analysis (i.e., mining, neural networks, Bayesian networks, and other ML algorithms) before data visualization using Python, R, Circos, Gephi, Tableau, or Cytoscape. Approximately 122 models, all of which were used in FS studies worldwide, were selected from 130 articles. However, most of these models addressed only one or two dimensions of FS (i.e., availability and access) and ignored the other dimensions (i.e., stability and utilization), creating a gap in the global context. Conclusions There are certain FS gaps both worldwide and in the United Arab Emirates that need to be addressed by scientists and policymakers. Following the identification of the drivers, policies, and indicators, the findings of this review could be used to develop an appropriate analytical framework for FS and nutrition.https://peerj.com/articles/13674.pdfUnited Arab EmiratesData extractionData infrastructureGapsChallengesMulti-model approach |
| spellingShingle | Khalil A. Ammar Ahmed M.S. Kheir Ioannis Manikas Agricultural big data and methods and models for food security analysis—a mini-review PeerJ United Arab Emirates Data extraction Data infrastructure Gaps Challenges Multi-model approach |
| title | Agricultural big data and methods and models for food security analysis—a mini-review |
| title_full | Agricultural big data and methods and models for food security analysis—a mini-review |
| title_fullStr | Agricultural big data and methods and models for food security analysis—a mini-review |
| title_full_unstemmed | Agricultural big data and methods and models for food security analysis—a mini-review |
| title_short | Agricultural big data and methods and models for food security analysis—a mini-review |
| title_sort | agricultural big data and methods and models for food security analysis a mini review |
| topic | United Arab Emirates Data extraction Data infrastructure Gaps Challenges Multi-model approach |
| url | https://peerj.com/articles/13674.pdf |
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