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Controlling the Growth of Broilers by Using Food Supply as Control Input

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Paper number  024069,  2002 ASAE Annual Meeting . (doi: 10.13031/2013.10577) @2002
Keywords:   Broilers, growth control, model-based control algorithm

Selection programs for broilers, combined with improvements in nutrition, have led to a reduction of the age at which the animals reach the commercially desired slaughter weight. This evolution has also resulted in several negative growth responses (increased body fat deposition, a decrease of reproduction capacity, metabolic diseases and a high incidence of skeletal diseases). A possible way to correct for these negative growth responses is to alter the growth trajectory in such a way that the initial growth is lowered followed by an accelerated growth, i.e. compensatory growth.

The objective of the reported research was to control the growth trajectory of broiler chickens during the production process based on an adaptive compact dynamic process model. More specifically, the daily food supply was calculated, based on a model-based control algorithm, with the aim to follow a predefined (compensatory) growth trajectory as close as possible.

For the modelling of the dynamic growth response of broiler chickens to the control input food supply, an on-line parameter estimation procedure was used. More specifically, the algorithm estimated the parameter values on-line (every time new process information becomes available) based on a small window of actual and past measured data of process input(s) and output(s). In this research, parameters were estimated every day based on measured information of food supply and animal weights of past five days.

Six experiments with model-based growth control were conducted on a small scale (50 animals) and on a large scale (2900 animals). To test the usefulness of the model-based algorithm, different predefined reference trajectories were followed. More specifically, 2 strong restricted, 2 weak restricted and 1 strong compensatory growth trajectories. In 85 % of the cases, the control algorithm managed to follow the predefined reference trajectory with a Mean Relative Error (MRE) that varied between 3.7 % and 5.3 %.

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