Analysis of the nonlinear dynamics of daily broiler growth and feed intake.
Daily BW velocity (BWV) and acceleration (BWA) of individual birds have been demonstrated to be oscillatory. Daily feed intake velocity (FIV) and acceleration (FIA) were hypothesized to be oscillatory and to have a positive relationship with BWV and BWA, respectively. Forty-eight male broiler chicks were individually caged and provided a commercial starter feed ad libitum for 49 d. BW and feed intake (FI) were measured daily. Experiment 1 confirmed that, on a daily basis, BWV, BWA, FIV, and FIA were oscillatory. There was a positive correlation between BW and FI, BWV and FIV, and BWA and FIA. A Kohonen neural network (KNN) clustered BWV and FIV into two and three sequential phases. BWA and FIA analysis did not make definitive clusters. In experiment 2, it was hypothesized that correlation between BWV and FIV would increase with feeding of grower and finisher rations. It was hypothesized that KNN three phase clusters may provide more biologically ideal times of ration change (TORC) for starter, grower, and finisher rations. For 49 d, five treatments, nine birds per treatment, were fed starter, grower, and finisher rations singly or together with dietary changes according to an industry or KNN-determined TORC. Evaluation was made of BW, FI, and carcass characteristics. No significant mean differences were found. Compared to the industry group, the KNN group demonstrated significantly improved uniformity (i.e., smaller SD) of BW (bled out), FI, dressing percentage, and some of the carcass characteristics. Differences between KNN and industry TORC results might have been related to the length of time the birds were fed the starter, grower, and finisher diets.
Vandegrift
K J
KJ
Department of Poultry Science, Pennsylvania State University, University Park, Pennsylvania 16802-3501, USA.
Cravener
T L
TL
Hulet
R M
RM
Roush
W B
WB
D000375:Aging; D000818:Animals; D002645:Chickens / Q000254:growth & development*; D004435:Eating; D016014:Linear Models; D008297:Male; D016571:Neural Networks, Computer; D013997:Time Factors; D015430:Weight Gain