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Informational Support for Agricultural Machinery Management in Field Crop Cultivation

Author

Listed:
  • Chavdar Z. Vezirov

    (Department of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria)

  • Atanas Z. Atanasov

    (Department of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria)

  • Plamena D. Nikolova

    (Department of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria)

  • Kalin H. Hristov

    (Department of Agricultural Machinery, Agrarian and Industrial Faculty, University of Ruse “Angel Kanchev”, 7017 Ruse, Bulgaria)

Abstract

This study explores the potential of freely available tools for collecting, processing, and applying information in the management of mechanized fieldwork. A hierarchical approach was developed, integrating operational, logistical, and strategic levels of decision-making based on crop type, land conditions, machinery, labor, and time constraints. Various technological and technical solutions were evaluated through simulations and manual data processing. The proposed methodology was applied to a real-world case in Kalipetrovo, Bulgaria. The results include a 3.5-fold reduction in required tractors and a 50% decrease in tractor driver needs, achieved through extended working hours and shift scheduling. Additional benefits were identified from replacing conventional tillage with deep tillage, resulting in higher fuel consumption but improved soil preparation. Detailed resource schedules were created for machinery, labor, and fuel, highlighting seasonal peaks and optimization opportunities. The approach relies on spreadsheets and free AI-assisted platforms, proving to be a low-cost, accessible solution for mid-sized farms lacking advanced digital infrastructure. The findings demonstrate that structured information integration can support the effective renewal and utilization of tractor and machinery fleets while offering a scalable basis for decision support systems in agricultural engineering.

Suggested Citation

  • Chavdar Z. Vezirov & Atanas Z. Atanasov & Plamena D. Nikolova & Kalin H. Hristov, 2025. "Informational Support for Agricultural Machinery Management in Field Crop Cultivation," Agriculture, MDPI, vol. 15(13), pages 1-26, June.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:13:p:1356-:d:1687150
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    References listed on IDEAS

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    1. Hamid Jalilnezhad & Yousef Abbaspour-Gilandeh & Vali Rasooli-Sharabiani & Aref Mardani & José Luis Hernández-Hernández & José Antonio Montero-Valverde & Mario Hernández-Hernández, 2023. "Use of a Convolutional Neural Network for Predicting Fuel Consumption of an Agricultural Tractor," Resources, MDPI, vol. 12(4), pages 1-14, March.
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