Use of artificial neural networks to estimate production variables of broilers breeders in the production phase.
1. Although the poultry industry uses state-of-the-art equipment and up-to-date services, in Brazil it generally makes decisions involving all its production variables based on purely subjective criteria. This paper reports the use of artificial neural networks to estimate performance in production birds belonging to a South Brazilian poultry farm. 2. Recorded data from 22 broiler production breeder flocks were obtained, from April, 1998 to December, 1999, which corresponded to 689 data lines of weekly recordings. 3. These data were processed by artificial neural networks using the software NeuroShell 2 version 4.0 (Ward Systems Group). The artificial neural network models generated were compared and selected based on their largest determination coefficient (R2), lowest Mean Squared Error (MSE), as well as on a uniform scatter in the residual plots. The authors conclude that it is possible to explain the performance variables of production birds, with the use of artificial neural networks. 4. The method allows the decisions made by the technical staff to be based on objective, scientific criteria, allows simulations of the consequences related to these decisions, and reports the contribution of each variable to the variables under study.
Salle
C T P
CT
Centre for Diagnostics and Research in Avian Pathology, Faculty of Veterinary Medicine, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil. ctps@ufrgs.br
Guahyba
A S
AS
Wald
V B
VB
Silva
A B
AB
Salle
F O
FO
Nascimento
V P
VP
D000821:Animal Feed; D000822:Animal Husbandry / Q000379:methods* / Q000592:standards; D000818:Animals; D001938:Brazil; D002645:Chickens / Q000502:physiology*; D005260:Female; D008137:Longitudinal Studies; D008297:Male; D016571:Neural Networks, Computer; D010058:Oviposition; D011237:Predictive Value of Tests; D012189:Retrospective Studies