Plant Soil Environ., 2024, 70(11):712-718 | DOI: 10.17221/361/2024-PSE

Information sources in agricultureOriginal Paper

Jan Jarolímek ORCID...1, Jakub Samek1, Pavel Šimek1, Michal Stočes1, Jiří Vaněk1, Jan Pavlík1
1 Department of Information Technologies, Faculty of Economics and Management, Czech University of Life Sciences Prague, Prague, Czech Republic

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.

Keywords: data map; data quality; usability of data; data analytics; farmers; data management plan

Received: July 7, 2024; Revised: September 7, 2024; Accepted: September 9, 2024; Prepublished online: October 4, 2024; Published: October 17, 2024  Show citation

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Jarolímek J, Samek J, Šimek P, Stočes M, Vaněk J, Pavlík J. Information sources in agriculture. Plant Soil Environ. 2024;70(11):712-718. doi: 10.17221/361/2024-PSE.
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    References

    1. Baldin M., Breunig T., Cue R., De Vries A., Doornink M., Drevenak J., Fourdraine R., George R., Goodling R., Greenfield R., Jorgensen M.W., Lenkaitis A., Reinemann D., Saha A., Sankaraiah C., Shahinfar S., Siberski C., Wade K.M., Zhang F., Cabrera V.E. (2021): Integrated decision support systems (IDSS) for dairy farming: a discussion on how to improve their sustained adoption. Animals, 11: 2017-2025. Go to original source... Go to PubMed...
    2. Beegle K., Carletto C., Himelein K. (2012): Reliability of recall in agricultural data. Journal of Development Economics, 98: 34-41. Go to original source...
    3. Braun A.T., Colangelo E., Steckel T. (2018): Farming in the Era of Industrie 4.0. Procedia CIRP, 72: 979-984. Go to original source...
    4. Chen D., Wu B., Chen T., Dong J. (2017): Development of distributed data sharing platform for multi-source IOT sensor data of agriculture and forestry. Transactions of the Chinese Society of Agricultural Engineering, 33: 300-307.
    5. Da Silveira F., da Silva S.L.C., Machado F.M., Barbedo J.G.A., Amaral F.G. (2023): Farmers' perception of the barriers that hinder the implementa-tion of agriculture 4.0. Agricultural Systems, 208: 103656. Go to original source...
    6. Estes-Zumpf W., Addis B., Marsicek B., Lee M., Nelson Z., Murphy M. (2022): Improving the sustainability of long-term amphibian monitoring: the value of collaboration and community science for indicator species management. Ecological Indicators, 134: 108451. Go to original source...
    7. Feng H., Abagandura G.O., Senturklu S., Landblom D.G., Lai L., Ringwall K., Kumar S. (2020): Soil quality indicators as influenced by 5-year diversified and monoculture cropping systems. The Journal of Agricultural Science, 158: 594-605. Go to original source...
    8. European Union (2018): Food safety - EU action. Available at: https://european-union.europa.eu/priorities-and-actions/actions-topic/food-safety_en
    9. Hwang C., Kim H., Jung H. (2018): Detection and correction method of erroneous data using quantile pattern and LSTM. Journal of Information and Communication Convergence Engineering, 16: 242-247.
    10. Jonák Z. (2003a): Data. Czech terminological database of librarianship and information science (TDLIS). National Library of the Czech Republic. Available at: https://aleph.nkp.cz/F/?func=direct&doc_number=000000442&local_base=KTD
    11. Jonák Z. (2003b): Information. Czech terminological database of librarianship and information science (TDLIS). National Library of the Czech Republic. Available at: https://aleph.nkp.cz/F/?func=direct&doc_number=000000456&local_base=KTD
    12. Jonák Z. (2003c): Piece of knowledge. Czech terminological database of librarianship and information science (TDLIS). National Library of the Czech Republic. Available at: https://aleph.nkp.cz/F/?func=direct&doc_number=000000484&local_base=KTD
    13. Jonák Z. (2003d): Knowledge. Czech terminological database of librarianship and information science (TDLIS). National Library of the Czech Republic. Available at: https://aleph.nkp.cz/F/?func=direct&doc_number=000000498&local_base=KTD
    14. Kar S., Garin V., Kholová J., Vadez V., Durbha S.S., Tanaka R., Iwata H., Urban M.O., Adinarayana J. (2020): SpaTemHTP: a data analysis pipeline for efficient processing and utilization of temporal high-throughput phenotyping data. Frontiers in Plant Science, 11: 552509. Go to original source... Go to PubMed...
    15. Oladele O.I., Fawole O.P. (2007): Farmers perception of the relevance of agriculture technologies in South-Western Nigeria. Journal of Human Ecology, 21: 191-194. Go to original source...
    16. Omar N., Zen H., Nicole N., Waluyo W. (2020): View of accuracy and reliability of data in IoT system for smart agriculture. International Journal of Integrated Engineering, 12: 105-116.., Rodný T., Okenka I. (2012): Precise irrigation process support by using a computer based algorithm of heuristics. Agris On-Line Papers in Economics and Informatics, 4: 49-54.
    17. Stočes M., Šilerová E., Vaněk J., Jarolímek J., Šimek P. (2018): Posibilities of using open data in sugar and sugar beet sector. Sugar and Sugar Beet Sector, 134: 117-121.
    18. Sylvere N., Jean D'amour R. (2020): Updates on modern agricultural technologies adoption and its impacts on the improvement of agricultural activities in Rwanda: a review. International Journal of Innovative Science and Research Technology, 5: 222-229.
    19. Tantalaki N., Souravlas S., Roumeliotis M. (2019): Data-driven decision making in precision agriculture: the rise of big data in agricultural systems. Journal of Agricultural and Food Information, 20: 344-380. Go to original source...
    20. Trapanese L., Hostens M., Salzano A., Pasquino N. (2024): Short review of current limits and challenges of application of machine learning algo-rithms in the dairy sector. Acta IMEKO, 13: 1-7. Go to original source...

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