Effects of Different Target Trajectories on the Broiler Performance in Growth Control

Abstract

Applying altered trajectories in broiler growth control with early feed restriction and a consequent accelerated catch-up growth has been approved to result in a better feed conversion ratio and a reduction in mortality. The properties of the growth trajectory and the resulting time and duration of the feed restriction can be crucial for animal welfare and production performance. The objective of this work was to test broiler growth control strategy online in field conditions using different target trajectories. Several experiments were conducted, and the best target trajectory has been proven to result in an end weight of 2,616 g and feed conversion ratio of 1.54 for Ross-type birds and an end weight of 2,472 g and a feed conversion ratio of 1.67 for Cobb-type birds.

Neural Predictive Control of Broiler Chicken Growth

“…The absence of the coccidiostats should not affect the growth or feed conversion, but it is not evident from the feed analysis if other minor changes were made to the feed composition between the two deliveries that could have affected the growth. In contrast to findings by Cangar et al [2008] in these trials the Ross 308 bird appeared to be capable of recovery growth (see Figure 5), i.e. the broilers were capable of regaining weight in excess of equivalent growth by the standard growth curve beyond 31 days.…”

“…Excluding this period reduced the mean relative error to 1.9%. Overall the mean relative error in this work is much lower than the 7-9% reported by Cangar et al [2008]. The authors suggested that this high error might be largely due to different conditions and systems for the weighing and feed delivery used for generating data for creating and validating the model (small scale, "ideal" conditions) and for the validation of the control algorithm.…”

“…For the reasons discussed above, a data-based approach was followed on laboratory scale by Aerts et al [2003a] and at a larger scale by Cangar et al [2008], in which the quantity of feed presented was controlled using model predictive control. They used a recursive linear model with time varying parameters to predict weight 3-7 days ahead [Aerts et al, 2003b].…”

Neural predictive control of broiler chicken and pig growth

“…For the reasons discussed above, a data-based approach was followed on laboratory scale by Aerts et al (2003a) and at a larger scale by Cangar et al (2008), in which the quantity of feed presented was controlled using model predictive control. They used a recursive linear models with time varying parameters to predict weight 3-7 days ahead (Aerts et al, 2003b;Cangar et al, 2008). Using online prediction of the feed quantity, control of broiler growth along a target trajectory proved possible within certain boundary conditions.…”

“…reported by Cangar et al (2008). The authors suggested that this high error might be largely due to different conditions and systems for the weighing and feed delivery used for generating data for creating and validating their model (small scale, "ideal" conditions) and for the validation of the control algorithm (commercial conditions).…”