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Data Analytics in Agriculture: Enhancing Decision-Making for Crop Yield Optimization and Sustainable Practices

Author

Listed:
  • Dua Weraikat

    (Department of Mechanical and Industrial Engineering, Rochester Institute of Technology-Dubai, Dubai P.O. Box 341055, United Arab Emirates)

  • Kristina Šorič

    (Rochester Institute of Technology-Croatia, 10000 Zagreb, Croatia)

  • Martin Žagar

    (Rochester Institute of Technology-Croatia, 10000 Zagreb, Croatia)

  • Mateo Sokač

    (Department of Software Engineering, Algebra University, 10000 Zagreb, Croatia)

Abstract

Collaboration across the agriculture supply chain is essential to address the high-yield demand and sustainable practices amid global overpopulation. Limited resources, such as soil and water, are compromised by excessive chemical agents and nutrient use. The Internet of Things (IoT) and smart farming offer solutions by optimizing agent applications, data analysis, and farm monitoring. Evidence from numerous studies indicates that collaboration in the supply chain, including farmers, can improve efficiency and productivity, reduce costs, and enhance crop quality. This paper investigates the transformation of traditional agriculture into smart farming through the integration of IoT technology and community partnerships. It presents a case study focused on educating farm owners about advanced technologies to enhance decision-making, improve crop yields, and promote sustainability. Additionally, the paper highlights the role of data analytics in agriculture. Farmers in the southern region of Zagreb, Croatia, were trained on the use of sensors and yield monitoring. Small farms in that region face challenges in improving yields due to limited capacity and lack of entrepreneurial experience. The DMAIC methodology was employed to address these issues and measure relevant parameters. The paper also discusses consistent patterns between electrical conductivity (EC) measurements and potassium levels in soil. It explains the potential of estimating potassium concentrations based on EC readings, or vice versa. Leveraging EC as a proxy for potassium levels could offer a cost-effective means of assessing soil fertility and nutrient dynamics. Additionally, Principal Component Analysis (PCA) biplot analysis is presented, showing that pH values behaved independently. Understanding these dynamics enhances knowledge of soil variability and informs sustainable soil management practices.

Suggested Citation

  • Dua Weraikat & Kristina Šorič & Martin Žagar & Mateo Sokač, 2024. "Data Analytics in Agriculture: Enhancing Decision-Making for Crop Yield Optimization and Sustainable Practices," Sustainability, MDPI, vol. 16(17), pages 1-12, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:17:p:7331-:d:1464216
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    References listed on IDEAS

    as
    1. Qiongjie Kou & Quanyou Zhang & Laiqun Xu & Yaohui Li & Yong Feng & Huiting Wei, 2022. "Mobile Learning Strategy Based on Principal Component Analysis," International Journal of Information Systems in the Service Sector (IJISSS), IGI Global Scientific Publishing, vol. 14(3), pages 1-12, July.
    2. Xinping Chen & Zhenling Cui & Mingsheng Fan & Peter Vitousek & Ming Zhao & Wenqi Ma & Zhenlin Wang & Weijian Zhang & Xiaoyuan Yan & Jianchang Yang & Xiping Deng & Qiang Gao & Qiang Zhang & Shiwei Guo , 2014. "Producing more grain with lower environmental costs," Nature, Nature, vol. 514(7523), pages 486-489, October.
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