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A mixed integer nonlinear programming model for biomass production

Author

Listed:
  • J. Contreras

    (Universidad CentroOccidental Lisandro Alvarado (UCLA))

  • H. Lara

    (Universidad CentroOccidental Lisandro Alvarado (UCLA))

  • G. Nouel-Borges

    (Biominbloq CA.)

Abstract

In this paper, a nonlinear model to maximize biomass production with specific nutritional quality is proposed. The model decides about kind of grasses and legumes to cultivate, quantities of each grasses and legumes chosen, the use of resources, and the proper time of harvest at which the biomass with specific nutritional quality is maximized. Model works with sufficient information about biomass yield, nutrient content, water requirements and fertilizer requirements of several crops, and it can explore all possible harvest times and choose the right time in which biomass production is maximized with desired nutritional quality. Furthermore, the solution gives to the producers additional information on weekly irrigation plan and weekly fertilizers plan for m2 of cultivated grass. The model was tested on six scenarios using GAMS and obtained solutions are the global solution in each scenario.

Suggested Citation

  • J. Contreras & H. Lara & G. Nouel-Borges, 2019. "A mixed integer nonlinear programming model for biomass production," Operational Research, Springer, vol. 19(1), pages 39-57, March.
  • Handle: RePEc:spr:operea:v:19:y:2019:i:1:d:10.1007_s12351-016-0283-4
    DOI: 10.1007/s12351-016-0283-4
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    References listed on IDEAS

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    1. Munford, Alan G., 1996. "The use of iterative linear programming in practical applications of animal diet formulation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 42(2), pages 255-261.
    2. Fred Glover, 1975. "Improved Linear Integer Programming Formulations of Nonlinear Integer Problems," Management Science, INFORMS, vol. 22(4), pages 455-460, December.
    3. Frederick V. Waugh, 1951. "The Minimum-Cost Dairy FeedAn Application of "Linear Programming"," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 33(3), pages 299-310.
    4. Rehman, Tahir & Romero, Carlos, 1987. "Goal programming with penalty functions and livestock ration formulation," Agricultural Systems, Elsevier, vol. 23(2), pages 117-132.
    5. Agrell, Per J. & Stam, Antonie & Fischer, Gunther W., 2004. "Interactive multiobjective agro-ecological land use planning: The Bungoma region in Kenya," European Journal of Operational Research, Elsevier, vol. 158(1), pages 194-217, October.
    6. Vassalos, Michael & Dillon, Carl R. & Freshwater, David & Karanikolas, Pavlos, 2010. "Modeling Multifunctionality Of Agriculture At A Farm-Level: The Case Of Kerkini District, Northern Greece," APSTRACT: Applied Studies in Agribusiness and Commerce, AGRIMBA, vol. 4(3-4), pages 1-6.
    7. Jena, Sanjay Dominik & Poggi, Marcus, 2013. "Harvest planning in the Brazilian sugar cane industry via mixed integer programming," European Journal of Operational Research, Elsevier, vol. 230(2), pages 374-384.
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