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A model to optimize broiler productivity.

Robert Mervyn Gous

Open publisher page 6 citations

Abstract

Optimizing the feeding of commercial broilers is made difficult because of the many interacting factors influencing their growth and food intake. Not all broilers are the same, nor are they housed in the same environments, and the costs of feeding and the revenue derived from the sale of the product differs markedly from one locality to another. When making decisions about how to maximize an economic index, such as margin/m2/year or breast meat yield in a commercial broiler operation, all of these interacting factors should be considered simultaneously. This is now possible, using optimization techniques, but only where food intake can be accurately predicted. The basis of such an optimization process is that specifications for feeds and feeding schedules are passed to a feed formulation program, which produces the least-cost feeds and passes these on to a broiler growth model that, in turn, evaluates the suggested feeding programme. By following certain rules the optimizer continues to alter the specifications of the feeds and/or feeding programme until no improvement can be made in the objective function. Without an accurate prediction of the amount of food that a given broiler will consume in the given environment, such an optimization process is bound to fail. The broiler growth model described here predicts food intake accurately under most circumstances that would be experienced in a commercial broiler operation, making it possible now to optimize the feeding programme of commercial broilers under a wide range of biological, environmental and economic circumstances, using many different objective functions. In this chapter a method of predicting food intake, and hence the rate of growth of the body and its components in broilers is described, and the basis of the optimization process that determines the most profitable feeding programme for broilers is outlined. Some examples are given of the effect of changing ingredient prices, revenue and the objective function on the amino acid composition of the resultant optimum feeds and on the optimum feeding programme.

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Optimizing the feeding of commercial broilers is made difficult because of the many interacting factors influencing their growth and food intake. Not all broilers are the same, nor are they housed in the same environments, and the costs of feeding and the revenue derived from the sale of the product differs markedly from one locality to another. When making decisions about how to maximize an economic index, such as margin/m2/year or breast meat yield in a commercial broiler operation, all of these interacting factors should be considered simultaneously. This is now possible, using optimization techniques, but only where food intake can be accurately predicted. The basis of such an optimization process is that specifications for feeds and feeding schedules are passed to a feed formulation program, which produces the least-cost feeds and passes these on to a broiler growth model that, in turn, evaluates the suggested feeding programme. By following certain rules the optimizer continues to alter the specifications of the feeds and/or feeding programme until no improvement can be made in the objective function. Without an accurate prediction of the amount of food that a given broiler will consume in the given environment, such an optimization process is bound to fail. The broiler growth model described here predicts food intake accurately under most circumstances that would be experienced in a commercial broiler operation, making it possible now to optimize the feeding programme of commercial broilers under a wide range of biological, environmental and economic circumstances, using many different objective functions. In this chapter a method of predicting food intake, and hence the rate of growth of the body and its components in broilers is described, and the basis of the optimization process that determines the most profitable feeding programme for broilers is outlined. Some examples are given of the effect of changing ingredient prices, revenue and the objective function on the amino acid composition of the resultant optimum feeds and on the optimum feeding programme.

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Available abstract

Optimizing the feeding of commercial broilers is made difficult because of the many interacting factors influencing their growth and food intake. Not all broilers are the same, nor are they housed in the same environments, and the costs of feeding and the revenue derived from the sale of the product differs markedly from one locality to another. When making decisions about how to maximize an economic index, such as margin/m2/year or breast meat yield in a commercial broiler operation, all of these interacting factors should be considered simultaneously. This is now possible, using optimization techniques, but only where food intake can be accurately predicted. The basis of such an optimization process is that specifications for feeds and feeding schedules are passed to a feed formulation program, which produces the least-cost feeds and passes these on to a broiler growth model that, in turn, evaluates the suggested feeding programme. By following certain rules the optimizer continues to alter the specifications of the feeds and/or feeding programme until no improvement can be made in the objective function. Without an accurate prediction of the amount of food that a given broiler will consume in the given environment, such an optimization process is bound to fail. The broiler growth model described here predicts food intake accurately under most circumstances that would be experienced in a commercial broiler operation, making it possible now to optimize the feeding programme of commercial broilers under a wide range of biological, environmental and economic circumstances, using many different objective functions. In this chapter a method of predicting food intake, and hence the rate of growth of the body and its components in broilers is described, and the basis of the optimization process that determines the most profitable feeding programme for broilers is outlined. Some examples are given of the effect of changing ingredient prices, revenue and the objective function on the amino acid composition of the resultant optimum feeds and on the optimum feeding programme.

Key concepts: Broiler, Productivity, Revenue, Product (mathematics), Yield (engineering), Process (computing), Computer science, Agricultural engineering

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