PARADIGMA EVOLUTIVO EN LA FORMULACIÓN DE RACIONES PARA GANADO BOVINO (EVOLUTIONARY PARADIGM IN THE FORMULATION OF RATIONS FOR BOVINE CATTLE)
Resumen
Resumen
El interés de la industria ganadera por la salud del animal ha impulsado el estudio de la preparación de raciones con los nutrientes necesarios para un mayor rendimiento en la producción de leche. El cuidado de una buena alimentación está directamente relacionado con la calidad y cantidad de leche producida por una vaca. Este trabajo propone una técnica evolutiva para la formulación de raciones con el objetivo de maximizar la producción de leche en el ganado bovino. El problema de la formulación de raciones es complejo debido a que no sólo se considera su peso, edad, especie y estado físico del animal, sino también, factores como la proteína cruda digerible, los nutrientes totales digeribles y la materia seca digerible son importantes en este proceso. En este trabajo, un Algoritmo Genético con representación binaria es propuesto para resolver el problema en la formulación de raciones. Los resultados obtenidos muestran que la aplicación del Algoritmo Genético en la preparación de raciones es una alternativa muy competitiva y eficiente, que alcanza un mayor rendimiento que los métodos tradicionales en la producción de leche para ganado bovino.
Palabra(s) Clave: Algoritmo genético, Nutrición animal, Nutrientes, Rendimiento.
Abstract
This work proposes an evolutionary technique for the formulation of food rations with the objective to maximize milk production in cattle. The interest of the livestock industry by the animal's health has promoted the study of the preparation of rations with the necessary nutrients for increased performance in the production of milk. The care of a good power supply guarantees the quality and quantity of milk produced by a cow. The problem of ration formulation is complex due to the fact that not only is considered your weight, age and physical condition of the animal, but in addition, factors such as the digestible crude protein, total digestible nutrients and the digestible dry matter are important in this process. A Genetic Algorithm with binary representation is proposed in the formulation of rations. The results are interesting and competitive reaching greater performance in the production of milk.
Keywords: Animal nutrition, Genetic algorithm, Nutrients, Performance.
Texto completo:
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Abbass, H. A. (2001). Marriage in honey-bee optimization (mbo): A haplometrosis polygynous swarming approach. Proc. The Congress on Evolutionary Computation (CEC2001), Seoul, Korea.
Afolayan, M. O. and Afolayan, M. (2008). Nigeria oriented poultry feed formulation software requirements. Journal of Applied Sciences Research. vol. 4, no. 11, pp. 1596– 1602.
Alexander, P. C. & Wood, G. R. (2006). Feeding strategies for maximizing gross margin in pig production, in Global optimization: Scientific and Engineering Case Studies, vol. 33, pp. 33–43.
B.M.A. & Cuzon, G. (1980). Improved nutrient specification for linear programming of penaeid rations. Aquaculture, vol. 19, pp. 313–323.
Chappell, A. E. (1974). Linear programming cuts costs in production of animal feeds. Operational Research Quarterly, vol. 25, no. 1, pp. 19–26.
Candler, W. (1960). Short-cut method for the complete solution of game theory and feed-mix problems. Econometrica. vol. 28, no. 3, pp. 618–634.
Cadenas, H. R. P. J. M. Pelta, D. A. and Verdegay, J. L. (2004). Application of fuzzy optimization to diet problems in argentinean farms. European Journal of Operational Research, vol. 158, pp. 218–228.
Council, N. R. (2000). Nutrient Requeriments of Beef Casttle. The National Academies Press.
Cortés, N. C. & Coello, C. A. C. (2003). Multiobjective optimization using ideas from the clonal selection principle. In GECCO, pp. 158–170.
Correa, H. (2001). El modelo NRC-2001. Nutrición Animal, Facultad de Ciencias Agropecuarias, vol. 412.
Eiben, A. E. & Smith, J. E. (2003). Introduction to Evolutionary Computing. SpringerVerlag.
Engelbrecht, E (2008). Optimising animal diets at the Johannesburg zoo. University of Pretoria: Unpublished Bachelor degree thesis.
Forsyth, D. M. (1985). IChapter 5: Computer programming of beef cattle diet. Academic Press: in Beef cattle feeding and nutrition. 2nd Ed.
Food and A. O. of the United Nations (2016). Fao’s role in animal production. Recuperado de urlhttp://www.fao.org/animal-production/en/
Furuya, T. S. T. & Minami, Y. (1997). Evolutionary programming for mix design. Computers and Electronics in Agriculture. vol. 18, pp. 129–135.
Goldberg, D. E. et al. (1989). Genetic algorithms in search optimization and machine learning. vol. 412. Addison-Wesley Reading Menlo Park.
Hillier, F. S. & Lieberman, G. J. (2008). Introduction to operations research. 8th ed. New York: Mc Graw-Hill International.
Karaboga, D. (2005). An idea based on honey bee swarm for numerical optimization. Computer Engineering Department: Technical Report-TR06. Erciyes University Engineering.
Guevara, V. R. (2004). Use the nonlinear programming to optimize performance response to energy density in broiler feed formulation. Poultry Science, vol. 83, pp. 147– 151.
Gupta R., M. C. & Kuntal, R. S. (2018). Heuristic approaches in solving nonlinear model of livestock ration formulation.
Jimenez, F. V. Leon-Borges, J. A. and Cruz-Cortes, N. (2014). An adaptive single-point algorithm for global numerical optimization. Expert Syst. Appl., vol. 41, no. 3, pp. 877–885.
López-Ramírez, B.C. & Mezura-Montes, E. (2007). Estudio del comportamiento en línea de algoritmos bio-inspirados usando medidas de desempeño en optimización con restricciones.
López-Ramírez, B.C. & Cruz-Cortés, N. (2014). Designing minimal sorting networks using a bio-inspired technique. Computación y Sistemas. vol. 18, no. 4.
Miller, W. J. & Cuhna, T. J. (1979). Dairy Cattle Feeding and Nutrition. Elsevier.
Notte, G., et al. (2012). Algoritmos evolutivos aplicados a sistemas pastoriles de producción de leche. Revista Argentina de Producción Animal, vol. 32, no. 1, pp. 21–79.
O’Connor, J. (1985). Least cost dairy cattle ration formulation model based on the degradable protein system. Journal of Dairy Science, vol. 72, pp. 2733–2745.
Rahman, R. Ramli, Abd, R. Jamari, Z. and Ku-Mahamud, K. R. (2015). Evolutionary algorithm approach for solving animal diet formulation. In 5th International Conference on Computing and Informatics (ICOCI). pp. 274–279.
Rehman, T. & Romero, C. (1987). Goal programming with penalty functions and livestock ration formulation. Agricultural Systems. vol. 23, no. 2, pp. 117–132.
Saxena, P. (2011). Comparison of linear and nonlinear programming techniques for animal diet. Applied Mathematics. vol. 1, no. 2, pp. 106–108.
Saxena, P. (2015). Animal diet formulation: optimization and simulation techniques. Journal of Veterinary Science & Technology. Gautam Buddha University, India.
SAGARPA. (20-11-2018). Programa de fomento ganadero. Recuperado de https://www.sagarpa.gob.mx/padron-de-beneficiarios/programa-de-fomento-ganadero-2
Sirisatien, M. D. D. Wood, G. R. and Morel, P. C. H. (2009). Two aspects of optimal diet determination for pig production: efficiency of solution. Journal of Global Optimum, vol. 43, pp. 249–261.
Sirisatien, D. Wood, G. R. Dong, M. & Morel, P. C. H. (2007). Two aspects of optimal diet determination for pig production: efficiency of solution and incorporation of cost variation. Journal of global optimization. vol. 43, no. 2, pp. 249– 261.
Waugh, F. V. (1951). The minimum-cost dairy feed. Journal of Farm Economics, vol. 33, pp. 299–310.
Zioganas, C. (1981). The determination of viable, parity and optimum sizes of family-type sheep farms in the Epirus Region of Greece (PhD thesis), Wye College-University of London.
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