International Journal of Innovative Approaches in Science Research
Abbreviation: IJIASR | ISSN (Print): 2602-4810 | ISSN (Online): 2602-4535 | DOI: 10.29329/ijiasr

Original article    |    Open Access
International Journal of Innovative Approaches in Science Research 2023, Vol. 7(1) 9-24

An Integrated Risk Sensitive Crop Allocation Model with Pollination Intelligence Algorithm

Catherine Ngozi Ejieji & Aki̇ntayo Aki̇nsunmade

pp. 9 - 24   |  DOI: https://doi.org/10.29329/ijiasr.2023.524.2

Published online: March 20, 2023  |   Number of Views: 82  |  Number of Download: 290


Abstract

A risk sensitive model for allocation of crops is considered in this work. The constructed model was designed to help farmers decision making process, thereby maximizing the use of agricultural land. Market price, cost of cultivation, yield of crops and climatic conditions were factors considered in the models. The theory of chance constraint programing was used to handle uncertainties that arise in crop planing. Data of known yield of crops were harvested and analyzed with the help of statistical tools. A class of Pollination Intelligence Algorithm was adopted to solve the model.

Keywords: Metaheuristic Algorithm, Agricultural Model, Risk Analysis


How to Cite this Article

APA 6th edition
Ejieji, C.N. & Aki̇nsunmade, A. (2023). An Integrated Risk Sensitive Crop Allocation Model with Pollination Intelligence Algorithm . International Journal of Innovative Approaches in Science Research, 7(1), 9-24. doi: 10.29329/ijiasr.2023.524.2

Harvard
Ejieji, C. and Aki̇nsunmade, A. (2023). An Integrated Risk Sensitive Crop Allocation Model with Pollination Intelligence Algorithm . International Journal of Innovative Approaches in Science Research, 7(1), pp. 9-24.

Chicago 16th edition
Ejieji, Catherine Ngozi and Aki̇ntayo Aki̇nsunmade (2023). "An Integrated Risk Sensitive Crop Allocation Model with Pollination Intelligence Algorithm ". International Journal of Innovative Approaches in Science Research 7 (1):9-24. doi:10.29329/ijiasr.2023.524.2.

References
  1. Ashourloo, D.,Matkan, A. A., Aghighi, H., Hosseni, A. & Gholampour, A. (2008). Irrigated and non-irrigated wheat classification based on cultivation calendar by HIS algorithm. Am. J. Agric. Biol. Sci. 3,602-609.  [Google Scholar]
  2. Bamiro O. M., Afolabi M. & Daramola (2012). Enterprise combination of cassava based food crop farming system in Nigeria; evidence from Ogun State. Greener Journal of Agricultural Sciences 2(1), 13-20.  [Google Scholar]
  3. Lowe, T. J., & Preckel, P. V. (2004). Decision technologies for agribusiness problems: A brief review of selected literature and a call for research. Manufacturing & Service Operations Management 6(3), 201-208.  [Google Scholar]
  4. Ahmed M. A., Ahmed I. A. & Fawzi S. A. (2012). Optimization of the cropping pattern in Saudi Arabia using a mathematical programing sector Model. Journal of Agic Econ-czech 58(2), 56-60.  [Google Scholar]
  5. Chowdhury M. A. & Chakrabarty D. (2015). Optimal crop yield under limited water availability: a linear programming approach. J. Basic and Applied Engineering Research 2(10), 892-895.  [Google Scholar]
  6. Mellaku, M. T., Reynolds, T. W., & Woldeamanuel, T. (2018). Linear programing-based cropland allocation to enhance performance of smallholder crop production: a pilot study in Abaro Kebele. Ethiopia. Journal of Resources 7, 1-15.  [Google Scholar]
  7. Angelo, A. F. (2013). Hybrid metaheuristics for crop rotation. Anais do Congresso de Matematica (CMAC)53-58  [Google Scholar]
  8. Ashutosh R. & Prakash S. (2018). Optimal allocation of agricultural land for crop planning in Hirakud canal command area using swarm intelligence techniques. ISH Journal of Hydraulic Engineering 1 -13.  [Google Scholar]
  9. Ejieji C. N. & Akinsunmade A. E. (2020), Agricultural Model for Allocation of Crops Using Pollination Intelligence Method. Hindawi Journal of Applied Computational Intelligence and Soft Computing 4830359, 6 pages.  [Google Scholar]
  10. Patel, N., Thaker, M., & Chaudhari, C. (2017). Agricultural land allocation to the major crops through linear programming model. International Journal of Science and Research (IJSR) 6(4), 519 - 522.  [Google Scholar]
  11. Akinsunmade A. E. & Ejieji C. N. (2021). Land suitability and crop pattern model using integrated pollination intelligence algorithm and remote sensing. Earthline Journal of Mathematical Sciences 5(1), 1 - 15.  [Google Scholar]
  12. Mirjalili S. (2015), Dragonfly Algorithm: A New Meta-heuristic Optimization Technique for Solving Single-Objective, Discrete, and Multi-objective Problems. Neural Comput. and Applic. 1-21.  [Google Scholar]
  13. Yang X. S. (2012), Flower Pollination Algorithm for Global Optimization. J. unconventional computation and Natural Computation 7445, 240-249.  [Google Scholar]
  14. McConnell, D. J. & Dillon, J. L. (1997). Farm management for Asia: a systems approach , FAO farms systems management series  [Google Scholar]
  15. Charnes, A. & Cooper, W. W.(1959) Chance constrained programming. Management Science 6, 73-79.  [Google Scholar]
  16. Sevruk, B. & Geiger, H. (1981). Selection of distribution types for extremes of precipitation. World Meteorological Organisation, Operational Hydrology Report, 15, WMO-No. 560, Geneva. [Google Scholar]