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Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm

Author

Listed:
  • Bhavana G. Thummar

    (Marwadi University)

  • Vijendra Kumar

    (Dr. Vishwanath Karad MIT World Peace University)

  • Sanjaykumar M. Yadav

    (SVNIT)

  • Prabhakar Gundlapalli

    (Nuclear Power Corporation of India Limited)

Abstract

A pioneering teaching learning-based optimization (TLBO) model is introduced to optimize cropping patterns by efficiently allocating available resources, such as land and water. The objective of the TLBO model is to maximize the net benefit derived from the command area of the Nyari-2 reservoir, considering various constraints like land allocation, water allocation, storage continuity, evaporation, and overflow. Specifically, TLBO models are formulated for a 75% dependability level of inflow, determined using the Weibull formulation. These models are developed for different combinations of population sizes (25, 50, 75, and 100) and iteration numbers (10, 22, and 100). The results obtained from various linear programming models (LPM) are meticulously analyzed for maximum net benefits and optimal crop areas. Subsequently, the outcomes of the TLBO model are compared with those of the LPM75 model. The analysis reveals that the TLBO model outperforms the LPM75 model, providing valuable insights for cultivators to make informed decisions on the types of crops to cultivate in greater quantities in the command area of the Nyari-2 reservoir.

Suggested Citation

  • Bhavana G. Thummar & Vijendra Kumar & Sanjaykumar M. Yadav & Prabhakar Gundlapalli, 2024. "Optimum Cropping Pattern in the Command Area of Nyari-2 Reservoir Using Teaching Learning-Based Optimization Algorithm," SN Operations Research Forum, Springer, vol. 5(2), pages 1-18, June.
    • Handle: RePEc:spr:snopef:v:5:y:2024:i:2:d:10.1007_s43069-024-00324-w
    DOI: 10.1007/s43069-024-00324-w
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    References listed on IDEAS

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    1. Misgana Muleta & John Nicklow, 2004. "Joint Application of Artificial Neural Networks and Evolutionary Algorithms to Watershed Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(5), pages 459-482, October.
    2. Knox, J.W. & Kay, M.G. & Weatherhead, E.K., 2012. "Water regulation, crop production, and agricultural water management—Understanding farmer perspectives on irrigation efficiency," Agricultural Water Management, Elsevier, vol. 108(C), pages 3-8.
    3. Playan, Enrique & Mateos, Luciano, 2006. "Modernization and optimization of irrigation systems to increase water productivity," Agricultural Water Management, Elsevier, vol. 80(1-3), pages 100-116, February.
    4. Vijendra Kumar & S. M. Yadav, 2019. "Optimization of Cropping Patterns Using Elitist-Jaya and Elitist-TLBO Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(5), pages 1817-1833, March.
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