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Information sources in agriculture

Author

Listed:
  • Jan Jarolímek

    (Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic)

  • Jakub Samek

    (Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic)

  • Pavel Šimek

    (Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic)

  • Michal Stočes

    (Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic)

  • Jiří Vaněk

    (Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic)

  • Jan Pavlík

    (Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic)

Abstract

The aim of this study is to define data sources and propose methods for effective and secure data management in an agricultural enterprise in the context of using data for decision support. Current developments in information and communication technology (ICT) have contributed towards the increase in the amount of generated data in various fields. The main data sources for agricultural enterprises are the farm itself, suppliers, government, market, and research. The use of smart solutions, artificial intelligence, and other innovative practices in agriculture is discussed at many conferences, in various journals, strategies and project plans. Data is the essential raw material for all these solutions. Large amounts of data cannot be analysed efficiently with spreadsheet programs. Currently, there are trends in the use of data, for example, in business intelligence (decision-making systems), e.g. tools using online transaction processing (OLAP) or process automation or the possibility of e.g. tracing the origin of food. The availability and possibility of creating large data sets bring many challenges related to managing that data. To effectively manage farm data, it is essential to have a well-developed data management plan (DMP) used to formalise the processes related to handling. A DMP mainly addresses archiving, backup, licensing and other important aspects of data management. The challenges and developments in farm data management include incorporating artificial intelligence into data analysis and security. Food is classified as an "Entity of Critical Importance" in the NIS2 EU Directive, which also deals with cybersecurity issues.

Suggested Citation

  • Jan Jarolímek & Jakub Samek & Pavel Šimek & Michal Stočes & Jiří Vaněk & Jan Pavlík, 2024. "Information sources in agriculture," Plant, Soil and Environment, Czech Academy of Agricultural Sciences, vol. 70(11), pages 712-718.
    • Handle: RePEc:caa:jnlpse:v:70:y:2024:i:11:id:361-2024-pse
    DOI: 10.17221/361/2024-PSE
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    References listed on IDEAS

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    1. Beegle, Kathleen & Carletto, Calogero & Himelein, Kristen, 2012. "Reliability of recall in agricultural data," Journal of Development Economics, Elsevier, vol. 98(1), pages 34-41.
    2. da Silveira, Franco & da Silva, Sabrina Letícia Couto & Machado, Filipe Molinar & Barbedo, Jayme Garcia Arnal & Amaral, Fernando Gonçalves, 2023. "Farmers' perception of the barriers that hinder the implementation of agriculture 4.0," Agricultural Systems, Elsevier, vol. 208(C).
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