Submitted Successfully!
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Ver. Summary Created by Modification Content Size Created at Operation
1 -- 1190 2023-07-21 20:41:11 |
2 layout -3 word(s) 1187 2023-07-24 05:00:24 |

Video Upload Options

Do you have a full video?


Are you sure to Delete?
If you have any further questions, please contact Encyclopedia Editorial Office.
Sakas, D.P.; Giannakopoulos, N.T.; Margaritis, M.; Kanellos, N. Supply Chain Firms in the Fertilizer Market. Encyclopedia. Available online: (accessed on 30 November 2023).
Sakas DP, Giannakopoulos NT, Margaritis M, Kanellos N. Supply Chain Firms in the Fertilizer Market. Encyclopedia. Available at: Accessed November 30, 2023.
Sakas, Damianos P., Nikolaos T. Giannakopoulos, Markos Margaritis, Nikos Kanellos. "Supply Chain Firms in the Fertilizer Market" Encyclopedia, (accessed November 30, 2023).
Sakas, D.P., Giannakopoulos, N.T., Margaritis, M., & Kanellos, N.(2023, July 21). Supply Chain Firms in the Fertilizer Market. In Encyclopedia.
Sakas, Damianos P., et al. "Supply Chain Firms in the Fertilizer Market." Encyclopedia. Web. 21 July, 2023.
Supply Chain Firms in the Fertilizer Market

The improvement of supply chain firms in the fertilizer sector through the increase of their stock market price can be impacted by various factors in the global economic landscape. The potential utilization of big data extracted from the cryptocurrency market is focused, decentralized finance applications, and blockchain technology to model and predict the trajectory of the stock market price of supply chain firms in the fertilizer sector.

cryptocurrency blockchain supply chain fertilizer market big data analysis big data decentralized finance innovation

1. Introduction

The fertilizer sector aims to provide sufficient supplies across the world to provide the necessary fertilizer products for the smooth operation of the global market. Such a task requires the utilization of supply chain firms’ participation. Their role is critical to the development of modern economies. Supply chain firms seek to exploit any available technologies that would enhance their profitability, such as the increase of their stock price. Towards the satisfaction of the objective of supply chain firms, in the fertilizer industry, the capitalization of various digital innovations would be fruitful. Digital innovations like blockchain technology, applications, and the cryptocurrency market provide sufficient data to assist the aim of supply chain firms in achieving profitability. Thus, big data derived from decentralized finance applications like the cryptocurrency markets could be harvested in favor of enhancing the stock market price of supply chain firms in the fertilizer industry, hence their profitability.
Many issues arose from the improper use of terms (Peráček 2021), thus the authors opted to provide a clear definition of Bitcoin. Bitcoin (BTC) is a cryptocurrency, or virtual currency, meant to function as money and a means of exchange independent of a single individual, organization, or organization, hence eliminating the need for third-party participation in transactions involving money. It is given to blockchain producers as payment for their efforts in verifying transactions and may be acquired on multiple markets (Frankenfield 2023). The operation of the stock market exchange is a sensitive matter and would require more concise supervision regarding the security of trading (Sidak et al. 2023).

2. Supply Chain Firms in the Fertilizer Market and Blockchain Applications

Various initiatives, like innovation advancement, studies, blog monitoring, and advertising, are critical for the expansion of fertilizer and agribusiness supply chain enterprises (Singh et al. 2022). Agribusinesses make contributions to the economy by increasing agricultural productivity, creating jobs, and supplying materials to multiple food supply chain enterprises (Qingxue and Wu 2016). The area set aside for agribusiness is small, but the need for agricultural output is significant. As a result, fulfilling demand with fewer resources is somewhat difficult, as sustainability solutions must be employed to achieve a sustainable future (Cappelli et al. 2022).
Multiple digital innovations across the fertilizer and supply chain context seek to transform the sector and facilitate additional flexibility in manufacturing processes, effective utilization of resources, and procedure optimization from smartphone tracking to the last-end delivery using innovations, such as the assimilation of cyber-physical systems (CPS), IoT, real-time customer engagement, digital applications, etc. (Kaburuan and Jayadi 2019). Therefore, for the fertilizer industry to increase production and sustainability, incorporating data and knowledge has grown increasingly important (Ghorbel et al. 2022). Internet of Things (IoT) innovations (Ketu and Mishra 2022; Frikha et al. 2021) greatly expand the availability and utility of data collecting, storing, interpretation, and usage in the sector.
The usage of blockchain is not just associated with cryptocurrencies, but additionally with other industries that are beginning to engage in specific application scenarios, such as Industry 4.0 and 5.0 (Xu et al. 2021). Leng et al. (2018) proposed a decentralized blockchain-based agricultural supply chain infrastructure. Li et al. (2006) developed an innovative modeling technique for agricultural and fertilizer supply chain firms. As a consequence, they were capable of maximizing output, raising efficiencies, and reducing waste. Surasak et al. (2019) demonstrated a blockchain-based IoT monitoring platform tailored specifically for agricultural goods.

3. Cryptocurrency Markets and Decentralized Finance Applications

Decentralized finance, or DeFi, is an economy of financial services developed on a blockchain network (Binance 2023a). The purpose of DeFi should be to develop an alternative financial infrastructure that does not rely on financial institutions or trusted third parties. Cryptocurrencies were founded to decentralize finance, enabling simpler transactions, drastically cutting the duration required to move cash, and lowering processing costs. However, authority can be accumulated in the control of a handful of businesses, even with Bitcoin, if customers opt to employ centralized fiduciary facilities (Kumar et al. 2020).
Much experimental research has been conducted to investigate the possible factors of cryptocurrency values. Such variables are classified in terms of macroeconomic context and societal awareness (Clark et al. 2023). In terms of the macroeconomic landscape, stock market values, currency exchange, asset prices, and fiscal policy volatility have been highlighted as determinants of cryptocurrency pricing and returns. Numerous publications have examined the fiscal capacities of cryptocurrencies, particularly Bitcoin, by investigating their involvement in the market and where they stand in comparison to other commodities (Corbet et al. 2018, 2019).
Cryptocurrency markets are the principal and, in many cases, the only place to buy, sell, and swap cryptocurrencies and coins. Several trades run 24 h a day, seven days a week, with no regional restrictions. Investors from virtually everywhere are allowed to participate in trading with no constraints or limitations. Many markets allow every investor to register and begin exchanging in seconds, unless an authentication procedure is necessary, which can involve anything from a couple of seconds to days (Saleh 2018).

4. Innovative Utilization of Big Data Analytics in Modeling Initiatives

Big data has provided fresh options for doing data processing work to enhance decision-making assistance mechanisms (Power 2015). Big databases are becoming more widely accessible as technology progresses in commercial operations. Big Data Analytics can alter enterprises and offer them the knowledge management to adjust to existing prospects and problems (Seles et al. 2018). Physically acquiring, retrieving, and evaluating data would not be necessary anymore (Falahat et al. 2023). This opens the way for utilizing Big Data Analytics in modeling and predicting share prices course.
Big Data Analytics is applying superior analysis techniques, both quantitative and qualitative, to massive amounts of organized and unorganized information. Forecast analytics (Schoenherr and Speier-Pero 2015), digital marketing analytics, big data analytics, and supply chain analytics (Wang et al. 2016) are examples of such research. Forecast analytics, for instance, is a significant element in Supply Chain Management (SCM), in projecting market trends and projected consumption, limiting inventory levels sometimes throughout situations of unexpected demand, such as in the latest years. It may be utilized to uncover SCM’s latent capability in terms of necessary competencies (Schoenherr and Speier-Pero 2015).
Apart from the referred application of Big Data Analytics, such tools could be used in producing important financial insights, such as the prediction of stock price variations. These data are capable of providing sufficient information for the development of innovative models to achieve digital marketing efficiency (Sakas et al. 2022b, 2022c). Capitalization of Big Data Analytics, combined with blockchain applications’ innovativeness, would be capable of producing the required value of data for potential investors in specific markets. Hence, while building a more universal product that may deliver analytical benefits to even more sectors may be difficult, it is not out of the realm of possibility through the utilization of Big Data Analytics (Mousavian et al. 2023).


  1. Peráček, T. 2021. A few remarks on the (im)perfection of the term securities: A theoretical study. Juridical Tribune-Tribuna Juridica 11: 135–49.
  2. Frankenfield, Jake. 2023. What Is Bitcoin? How to Mine, Buy and Use It. Available online: (accessed on 2 June 2023).
  3. Sidak, Mikuláš, Andrea Slezáková, Edita Hajnišová, and Stanislav Filip. 2023. Determination of Public Supervision Aspects and Legal Pillars of Activities of Financial Agents in Central European Countries. Administrative Sciences 13: 78.
  4. Singh, Rajat, Rajesh Singh, Anita Gehlot, Shaik Vaseem Akram, Neeraj Priyadarshi, and Bhekisipho Twala. 2022. Horticulture 4.0: Adoption of Industry 4.0 Technologies in Horticulture for Meeting Sustainable Farming. Applied Sciences 12: 12557.
  5. Li, Qingxue, and Huariu Wu. 2016. Research on vegetable growth monitoring platform based on facility agricultural IoT. In International Conference on Geo-Informatics in Resource Management and Sustainable Ecosystem. Singapore: Springer.
  6. Cappelli, Irene, Ada Fort, Alessandro Pozzebon, Marco Tani, Nicola Trivellin, Valerio Vignoli, and Mara Bruzzi. 2022. Autonomous IoT Monitoring Matching Spectral Artificial Light Manipulation for Horticulture. Sensors 22: 4046.
  7. Kaburuan, Emil Robert, and Riyanto Jayadi. 2019. A design of IoT-based monitoring system for intelligence indoor micro-climate horticulture farming in Indonesia. Procedia Computer Science 157: 459–64.
  8. Ghorbel, Oussama, Tarek Frikha, Abir Hajji, Raed Alabdali, Rami Ayadi, and Mohammed Abbas Elmasry. 2022. Blockchain-Based Supply Chain System for Olive Fields Using WSNs. Computational Intelligence and Neuroscience 2022: 9776776.
  9. Ketu, Shwet, and Pramod Kumar Mishra. 2022. A contemporary survey on IoT based smart cities: Architecture, applications, and open issues. Wireless Personal Communications 125: 2319–67.
  10. Frikha, Tarek, Ahmed Chaari, Faten Chaabane, Omar Cheikhrouhou, and Atef Zaguia. 2021. Healthcare and fitness data management using the IoT-based blockchain platform. Journal of Healthcare Engineering 2021: 9978863.
  11. Xu, Xun, Yuqian Lu, Birgit Vogel-Heuser, and Lihui Wang. 2021. Industry 4.0 and industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems 61: 530–35.
  12. Leng, Kaijun, Ya Bi, Linbo Jing, Han-Chi Fu, and Inneke Van Nieuwenhuyse. 2018. Research on agricultural supply chain system with double chain architecture based on blockchain technology. Future Generation Computer Systems 86: 641–49.
  13. Li, Dong, Dennis Kehoe, and Paul Drake. 2006. Dynamic planning with a wireless product identification technology in food supply chains. International Journal of Advanced Manufacturing Technology 30: 938–44.
  14. Surasak, Thattapon, Nungnit Wattanavichean, Chakkrit Preuksakarn, and Scott C. H. Huang. 2019. SCH &ai agriculture products traceability system using blockchain and internet of things. System 14: 15.
  15. Binance. 2023a. Buy, Trade, and Hold 350+ Cryptocurrencies on Binance. Available online: (accessed on 10 January 2023).
  16. Kumar, Manoj, Nikhil Nikhil, and Riya Singh. 2020. Decentralising Finance using Decentralised Blockchain Oracles. Paper presented at 2020 International Conference for Emerging Technology (INCET), Belgaum, India, June 5–7; pp. 1–4.
  17. Clark, Ephraim, Amine Lahiani, and Salma Mefteh-Wali. 2023. Cryptocurrency return predictability: What is the role of the environment? Technological Forecasting & Social Change 189: 122350.
  18. Corbet, Shaen, Andrew Meegan, Charles Larkin, Brian Lucey, and Larisa Yarovaya. 2018. Exploring the dynamic relationships between cryptocurrencies and other financial markets. Economics Letters 165: 28–34.
  19. Corbet, Shaen, Brian Lucey, Andrew Urquhart, and Larisa Yarovaya. 2019. Cryptocurrencies as a financial asset: A systematic Analysis. International Review of Financial Analysis 62: 182–99.
  20. Saleh, Mohamad. 2018. Cryptonomics: Investment Behaviour in the Cryptocurrency Market. Available online: (accessed on 12 March 2023).
  21. Power, Daniel J. 2015. Big Data’ Decision Making Use Cases. In ICDSST 2015. Lecture Notes in Business Information Processing. Decision Support Systems V–Big Data Analytics for Decision Making. Cham: Springer, p. 216.
  22. Seles, Bruno Michel Roman Pais, Ana Beatriz Lopes de Sousa Jabbour, Charbel Jose Chiappetta Jabbour, Paula de Camargo Fiorini, Yusliza Mohd-Yusoff, and Antonio Marcio Tavares Thomé. 2018. Business opportunities and challenges as the two sides of the climate change: Corporate responses and potential implications for big data management towards a low carbon society. Journal of Cleaner Production 189: 763–74.
  23. Falahat, Mohammad, Phaik Kin Cheah, Jayamalathi Jayabalan, Corrinne Mei Jyin Lee, and Sia Bik Kai. 2023. Big Data Analytics Capability Ecosystem Model for SMEs. Sustainability 15: 360.
  24. Schoenherr, Tobias, and Cheri Speier-Pero. 2015. Data Science, Predictive Analytics, and Big Data in Supply Chain Management: Current State and Future Potential. Journal of Business Logistics 36: 120–32.
  25. Wang, Gang, Angappa Gunasekaran, Eric W. T. Ngai, and Thanos Papadopoulos. 2016. Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications. International Journal of Production Economics 176: 98–110. Available online:;h=repec:eee:proeco:v:176:y:2016:i:c:p:98-110 (accessed on 10 January 2023).
  26. Sakas, Damianos P., Nikolaos T. Giannakopoulos, Nikos Kanellos, and Christos Tryfonopoulos. 2022b. Digital Marketing Enhancement of Cryptocurrency Websites through Customer Innovative Data Process. Processes 10: 960.
  27. Sakas, Damianos P., Nikolaos T. Giannakopoulos, Nikos Kanellos, and Stavros P. Migkos. 2022c. Innovative Cryptocurrency Trade Websites’ Marketing Strategy Refinement, via Digital Behavior. IEEE Access 10: 63163–76.
  28. Mousavian, Seyedmohammad, Shah J. Miah, and Yifan Zhong. 2023. A design concept of big data analytics model for managers in hospitality industries. Personal and Ubiquitous Computing.
Subjects: Business, Finance
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to : , , ,
View Times: 94
Revisions: 2 times (View History)
Update Date: 24 Jul 2023