FORMATION OF INFORMATION SUPPORT SYSTEM FOR THE MANAGEMENT OF AGRICULTURAL ENTERPRISES

##plugins.themes.bootstrap3.article.main##

##plugins.themes.bootstrap3.article.sidebar##

Published: Nov 30, 2022

  Vitalii Vakulenko

  Liu Xiaowei

Abstract

The purpose of the article is to generalize and present the peculiarities of the process of forming a system of information support for the management of agricultural enterprises in Ukraine. Methodology. General scientific (generalization, comparison, induction and deduction) and empirical and theoretical methods (analysis, synthesis) were used in the research. The use of system-structural analysis made it possible to identify the main features of the process of forming a system of information support for the management of agricultural enterprises in Ukraine. The results of the study showed that with the help of big data analysis in agriculture it is possible to remotely detect problems that can be used to identify nutrient deficiencies, diseases, lack or excess of water, pest and weed infestation, insect damage, etc. It is determined that the use of analytical tools based on the analysis of geographic information systems data is useful in modeling and mapping, which can be used to predict crop yields. Practical implications. The results of the study can be used in the management of agricultural enterprises in Ukraine. The obtained results can be directed to further research on the analysis of big data in agriculture in the management of agricultural enterprises. Value / originality. The scientific novelty of the results obtained is determined by the solution of an important scientific task, which is to develop theoretical provisions and practical recommendations for the formation of a system of information support for the management of agricultural enterprises in Ukraine. The work has further developed research on the use and analysis of big data in agriculture in the management of agricultural enterprises in Ukraine.

How to Cite

Vakulenko, V., & Xiaowei, L. (2022). FORMATION OF INFORMATION SUPPORT SYSTEM FOR THE MANAGEMENT OF AGRICULTURAL ENTERPRISES. Economics & Education, 7(3), 6-11. https://doi.org/10.30525/2500-946X/2022-3-1
Article views: 165 | PDF Downloads: 114

##plugins.themes.bootstrap3.article.details##

Keywords

agricultural enterprise, information support, management

References

Liu, X., Zhai, H., Shen, Y., Lou, B., Jiang, C., Li, T., Hussain, S. B., & Shen, G. (2020). Large-scale crop mapping from multisource remote sensing images in Google Earth engine, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 13, no. 13, pp. 414–427.

Ang, K. L.-M., & Sen, J. K. P. (2021). Big Data and Machine Learning With Hyperspectral Information in Agriculture, IEEE Access, vol. 9, pp. 36699–36718. DOI: https://doi.org/10.1109/ACCESS.2021.3051196

Kamilaris, A., Kartakoullis, A., & Prenafeta-Boldú, F. X. (2017). A review on the practice of big data analysis in agriculture, Comput. Electron. Agricult., vol. 143, no. 1, pp. 23–37.

Bhat, S., & Huang, N.-F. (2021). Big Data and AI Revolution in Precision Agriculture: Survey and Challenges. IEEE Access. 9. 110209–110222. DOI: https://doi.org/10.1109/ACCESS.2021.3102227

Nandyala, C. S. (2016). Big and Meta Data Management for U-Agriculture Mobile Services, International Journal of Software Engineering and Its Applications, vol. 10, no. 2.

Kshetri, N. (2014). The emerging role of Big Data in key developmental issues; opputunities, challenges and concerns, Big Data and Society.

Gartner (2013). E-source: http://www.gartner.com/it-glossary/big-data

Abawajy, J. (2015). Comprehensive analysis of big data variety landscape. International journal of parallel, emergent and distributed systems, 30(1), 5–14.

Yan-e, D. (2011). Design of Intelligent Agriculture Management Information System Based on IoT, 2011 Fourth International Conference on Intelligent Computation Technology and Automation, Shenzhen, Guangdong, 2011, pp. 1045–1049.

Waga, D., & Rabah, K. (2014). Environmental conditions’ big data management and cloud computing analytics for sustainable agriculture. World Journal of Computer Application and Technology, vol. 2(3), pp. 73–81.

Lokers, R., Knapen, R., Janssen, S., van Randen, Y., & Jansen, J. (2016). Analysis of Big Data technologies for use in agro-environmental science. Environmental Modelling & Software, vol. 84, pp. 494–504.

Garg, R., & Himanshu, A. (2016). Big Data Analytics Recommendation Solutions for Crop Disease using Hive and Hadoop Platform. Indian Journal of Science and Technology, vol. 9, p. 32.

Weersink, A., Fraser, E., Pannell, D., Duncan, E., & Rotz, S. (2018). Opportunities and challenges for Big Data in agricultural and environmental analysis. Annual Review of Resource Economics, vol. 10, pp. 19–37.

Javaregowda, M., & Indiramma, M. (2019). Role of Big Data in Agriculture. International Journal of Innovative Technology and Exploring Engineering, vol. 9, pp. 3811–3821.

The Organization for Economic Co-operation and Development (OECD). (2019). Digital Opportunities for Better Agricultural Policies. 234 p. E-source: https://www.oecd-ilibrary.org/agriculture-and-food/digital-opportunities-for-better-agriculturalpolicies_571a0812-en

United States Agency for International Development (USAID). (2013). Crowdsourcing Applications For Agricultural Development In Africa Introduction. USAID. 6 p. E-source: https://www.agrilinks.org/sites/default/files/resource/files/Crowdsourcing_Application s_for_Agricultural_Development_in_Africa.pdf

Tanha, T., Dhara, S., Nivedita, P., Hiteshri, Y., & Manan, S. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, vol. 4, рр. 58–73. E-source: https://www.sciencedirect.com/science/article/pii/S258972172030012X

Alan, S. (2021). Data Storage Methods for Businesses. Vxchnge. E-source: https://www.vxchnge.com/blog/business-data-storage-methods

Food and Agriculture Organization (FAO) and International Telecommunication Union (ITU). (2019). EAgriculture in Action: Blockchain for Agriculture. 72 p. E-source: http://www.fao.org/3/CA2906EN/ca2906en.pdf

Michael, H. C., Bradley, D. L., & Joe, D. L. (2016). Factors Influencing Producer Propensity for Data Sharing & Opinions Regarding Precision Agriculture and Big Farm Data. University of Nebraska, Lincoln. Vol. 3. 26 p. E-source: https://digitalcommons.unl.edu/ageconworkpap/48/

State Statistics Service of Ukraine (2022). E-source: http://www.ukrstat.gov.ua/

Eurostat. (2022). E-source: https://ec.europa.eu/eurostat

National Investment Council of Ukraine. (2018). Agricultural sector of Ukraine: Securing the global food supply.

Ligonenko, L. O., & Lanova, O. L. (2021). Digitalization of the agricultural sphere: state, problems and prospects. Economics: time realities. Scientific journal, vol. 1 (53), рр. 84–92. DOI: https://doi.org/10.15276/ETR.01.2021.9