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Optimization of the Cropping Pattern Using Cuckoo Search Technique

  • Ashutosh RathEmail author
  • Sandeep Samantaray
  • Prakash Chandra Swain
Chapter
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 374)

Abstract

Agriculture is the major occupation of the people in Odisha state, India. More than seventy percentage of the population depend directly or indirectly on agriculture. In this work, cropping model is formulated for the study area to optimize the cropping pattern by using the Cuckoo Search technique to maximize the net annual benefit. The processes of crop planning and crop rotation have been given more emphasis, since optimal allocation of scarce water resources is highly necessary. To ensure correct assessment of the irrigation water availability, the sensor-based water measurement techniques such as ADV flow tracker and micro-ADV are used in the study. The crop water requirements of various crops are determined with CROPWAT software. The cropping models are developed by taking into account the opinion of local farmers and officials of agriculture department. The models are compared with the prevailing practice with respect to net annual benefits. The results indicate that that presently the farmers are getting benefits of 0.975 million USD. The cropping pattern suggested by LINDO yields a net benefit of 1.07 million USD per year. The optimal cropping pattern from Cuckoo Search technique yields a net benefit of 1.296 million USD.

Keywords

ADV flow tracker micro-ADV LINDO Cuckoo Search Optimization 

Notes

Acknowledgements

The authors thank the officials of Hirakud Dam authority and District Agriculture office, Sambalpur, for providing necessary assistance at the time of need to conduct this research.

References

  1. 1.
    Alabdulkader, A.M., Al-Amound, A.I., Awad, F.S.: Optimization of the Cropping Pattern in Saudi Arabia Using a mathematical programming sector model. Agric. Econ. Czech 58(2), 56–60 ( 2012)Google Scholar
  2. 2.
    Fister, I., Fister, D., Fister, I.: A comprehensive review of cuckoo search: variants and hybrids. Int. J. Math. Modell. Numer. Optim. 4(4), 387–409 (2013)zbMATHGoogle Scholar
  3. 3.
    FAO. Food and, : Agriculture Organization of the United Nations, Crop evapotranspiration guidelines for computing crop water requirements - FAO Irrigation and drainage paper, p. 56. Rome (1998)Google Scholar
  4. 4.
    Lalehzari, R., Nasab, S.B., Moazed, H., Haghighi, A.: Multi-objective Management of water allocation to sustainable irrigation planning and optimal cropping pattern. J. Irrig. Drain. Eng. 10.1061/ (ASCE) IR1943-4774.0000933, 05015008 (2015)Google Scholar
  5. 5.
    Fister, I., Fister, D., Fister, I.: Optimal reservoir management and crop planning using deterministic and stochastic inflows. Water Res. Bull. 16, 438–443 (1980)Google Scholar
  6. 6.
    Raju, K.S., Kumar, D.N.: Irrigation planning using genetic algorithms. Water Res. Manag. 18(163–176) (2004)Google Scholar
  7. 7.
    Reddy, M.J., Kumar, D.: Nagesh Evolving strategies for crop planning and operation of irrigation reservoir system using multi-objective differential evolution. Irrig. Sci. 26, 177–190 (2008)CrossRefGoogle Scholar
  8. 8.
    Sharma, J.L.: Inter-state disparities in growth of agriculture in India. Agric. Situat. India 45(7), 453–456 (1990)Google Scholar
  9. 9.
    Singh, A.: Optimizing the Use of Land and Water Resources for maximizing farm income by mitigating the Hydrological Imbalances. J. Hydrol. Eng. 19(7), ASCE (2014). ISSN 1084-0699/2014/7-1447-1451CrossRefGoogle Scholar
  10. 10.
    Feng Kuo, S., Jang Lin, B., Shieh, H.: CROPWAT model to evaluate crop water requirements in Taiwan. In: 1st Asian Regional Conference, Seoul (2001)Google Scholar
  11. 11.
    Sahoo, B., Lohani, A.K., Sahu, R.K.: Fuzzy multi-objective and linear programming based management models for optimal land-water-crop system planning. Water Resour. Manag. 20(6), 931–948 (2006)CrossRefGoogle Scholar
  12. 12.
    Xu, Y.P., Tung, Y.K.: rules for water resources management under uncertainty J. Water Res. Planning. Manag. 135: 3(149), 149–159 (2009)Google Scholar
  13. 13.
    Yang, X.S., Deb, S.: Engineering Optimization by Cuckoo Search. Int. J. Math. Modell. Numer. Optim. 1(4), 330–343 (2010)CrossRefGoogle Scholar
  14. 14.
    Yang, X.S., Deb, S.: Multiobjectives cuckoo search for design optimization. Comput. Oper. Res. 1616–1624 (2013)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Mohammadrezapour, O., Yoosefdoost, I., Ebrahimi, M.: Cuckoo optimization algorithm in optimal water allocation and crop planning under various weather conditions. Case study: Qazvin plain, Iran Neural Comput & Applic. (2017).  https://doi.org/10.1007/s00521-017-3160

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ashutosh Rath
    • 1
    Email author
  • Sandeep Samantaray
    • 1
  • Prakash Chandra Swain
    • 1
  1. 1.Department of Civil EngineeringVeerSurendraSai University of TechnologyOdishaIndia

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