IDEAS home Printed from https://ideas.repec.org/a/caa/jnlpse/v70y2024i11id361-2024-pse.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://pse.agriculturejournals.cz/doi/10.17221/361/2024-PSE.html
    Download Restriction: free of charge
    File URL: http://pse.agriculturejournals.cz/doi/10.17221/361/2024-PSE.pdf
    Download Restriction: free of charge
    File URL: https://libkey.io/10.17221/361/2024-PSE?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Klasen, Stephan & Reimers, Malte, 2017. "Looking at Pro-Poor Growth from an Agricultural Perspective," World Development, Elsevier, vol. 90(C), pages 147-168.
    2. de Nicola, Francesca & Giné, Xavier, 2014. "How accurate are recall data? Evidence from coastal India," Journal of Development Economics, Elsevier, vol. 106(C), pages 52-65.
    3. Peterson-Wilhelm, Bailey & Schwab, Benjamin, 2024. "How does recall bias in farm labor impact separability tests?," Food Policy, Elsevier, vol. 128(C).
    4. Amadu, Festus O. & McNamara, Paul E. & Davis, Kristin E., 2021. "Soil health and grain yield impacts of climate resilient agriculture projects: Evidence from southern Malawi," Agricultural Systems, Elsevier, vol. 193(C).
    5. Micheels, Eric T. & Nolan, James F., 2016. "Examining the effects of absorptive capacity and social capital on the adoption of agricultural innovations: A Canadian Prairie case study," Agricultural Systems, Elsevier, vol. 145(C), pages 127-138.
    6. Dang, Hai-Anh H & Carletto, Calogero, 2022. "Recall Bias Revisited: Measure Farm Labor Using Mixed-Mode Surveys and Multiple Imputation," IZA Discussion Papers 14997, Institute of Labor Economics (IZA).
    7. Chaoran Chen & Diego Restuccia & Raül Santaeulàlia-Llopis, 2023. "Land Misallocation and Productivity," American Economic Journal: Macroeconomics, American Economic Association, vol. 15(2), pages 441-465, April.
    8. Alistair Munro, 2020. "Using experimental manipulation of questionnaire design and a Kenyan panel to test for the reliability of reported perceptions of climate change and adaptation," Climatic Change, Springer, vol. 162(3), pages 1081-1105, October.
    9. James R. Stevenson & Karen Macours & Douglas Gollin, 2023. "The Rigor Revolution: New Standards of Evidence for Impact Assessment of International Agricultural Research," Annual Review of Resource Economics, Annual Reviews, vol. 15(1), pages 495-515, October.
    10. Douglas Gollin & Christopher Udry, 2021. "Heterogeneity, Measurement Error, and Misallocation: Evidence from African Agriculture," Journal of Political Economy, University of Chicago Press, vol. 129(1), pages 1-80.
    11. Florence Kondylis & Valerie Mueller & S. Zhu, 2015. "Measuring agricultural knowledge and adoption," Agricultural Economics, International Association of Agricultural Economists, vol. 46(3), pages 449-462, May.
    12. Chegere, Martin Julius, 2018. "Post-harvest losses reduction by small-scale maize farmers: The role of handling practices," Food Policy, Elsevier, vol. 77(C), pages 103-115.
    13. Vaiknoras, Kate & Larochelle, Catherine & Alwang, Jeffrey, 2020. "IFAD RESERACH SERIES 64 - How the adoption of drought-tolerant rice varieties impacts households in a non-drought year: Evidence from Nepal," IFAD Research Series 308809, International Fund for Agricultural Development (IFAD).
    14. Zezza, Alberto & Federighi, Giovanni & Kalilou, Amadou Adamou & Hiernaux, Pierre, 2016. "Milking the data: Measuring milk off-take in extensive livestock systems. Experimental evidence from Niger," Food Policy, Elsevier, vol. 59(C), pages 174-186.
    15. Wollburg, Philip & Tiberti, Marco & Zezza, Alberto, 2021. "Recall length and measurement error in agricultural surveys," Food Policy, Elsevier, vol. 100(C).
    16. Abay,Kibrom A. & Barrett,Christopher B. & Kilic,Talip & Moylan,Heather G. & Ilukor,John & Vundru,Wilbert Drazi, 2022. "Nonclassical Measurement Error and Farmers’ Response to Information Reveal Behavioral Anomalies," Policy Research Working Paper Series 9908, The World Bank.
    17. De Magalhães, Leandro & Santaeulàlia-Llopis, Raül, 2018. "The consumption, income, and wealth of the poorest: An empirical analysis of economic inequality in rural and urban Sub-Saharan Africa for macroeconomists," Journal of Development Economics, Elsevier, vol. 134(C), pages 350-371.
    18. McCullough, Ellen B., 2017. "Labor productivity and employment gaps in Sub-Saharan Africa," Food Policy, Elsevier, vol. 67(C), pages 133-152.
    19. Cavanagh,Jack & Fliegner,Jasmin Claire & Kopper,Sarah & Sautmann,Anja, 2023. "A Metadata Schema for Data from Experiments in the Social Sciences," Policy Research Working Paper Series 10296, The World Bank.
    20. Susan Godlonton & Manuel A Hernandez & Mike Murphy, 2018. "Anchoring Bias in Recall Data: Evidence from Central America," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 100(2), pages 479-501.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:caa:jnlpse:v:70:y:2024:i:11:id:361-2024-pse. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ivo Andrle (email available below). General contact details of provider: https://www.cazv.cz/en/home/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.