Literature DB >> 30569453

How should we turn data into decisions in AgriFood?

Liliya Serazetdinova1, James Garratt2, Alan Baylis3, Sokratis Stergiadis4, Martin Collison5, Simon Davis6,7.   

Abstract

BACKGROUND: The AgriFood supply chain is under significant pressures related to food security, climate change, and consumer demands for affordable and higher quality food. Various technologies are already deployed producing a large amount of data, which can be utilised to guide decision-making to improve productivity, reduce wastage, and increase traceability across the AgriFood supply chain.
RESULTS: Several examples of the use of data are given, including improving efficiency in livestock production, supporting automation and use of robotics in crop production, increasing food safety and evidencing its provenance. The opportunities and ways forward were discussed at a workshop in November 2017, run by the Society of Chemical Industry and the Knowledge Transfer Network in the United Kingdom.
CONCLUSION: This article presents a summary of the key messages from the presentations and focus-group discussions during this event, as interpreted by the authors. A number of challenges in digitalisation of the AgriFood supply chain are discussed, such as low inter-operability of different data sets, silo mentality, low willingness to share data and a significant skills gap. Various approaches are presented that could help to unlock the benefits of using data, from practical support to producers and addressing skills gaps, to industrial leadership and the role of government departments and regulatory bodies in leading by example. Looking forward, data are already revolutionising the AgriFood supply chain, however, the benefits will remain piecemeal until the leaders of today are able to bring together the disparate groups into a cohesive whole.
© 2018 Society of Chemical Industry. © 2018 Society of Chemical Industry.

Keywords:  AgriFood; artificial intelligence (AI); big data; decision support; digitalisation; supply chain

Mesh:

Year:  2019        PMID: 30569453     DOI: 10.1002/jsfa.9545

Source DB:  PubMed          Journal:  J Sci Food Agric        ISSN: 0022-5142            Impact factor:   3.638


  2 in total

1.  Integrative analysis of the microbiome and metabolome in understanding the causes of sugarcane bitterness.

Authors:  Weijuan Huang; Donglei Sun; Lijun Chen; Yuxing An
Journal:  Sci Rep       Date:  2021-03-16       Impact factor: 4.379

2.  A Search Engine Concept to Improve Food Traceability and Transparency: Preliminary Results.

Authors:  Caterina Palocci; Karl Presser; Agnieszka Kabza; Emilia Pucci; Claudia Zoani
Journal:  Foods       Date:  2022-03-29
  2 in total

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