NON-LINEAR MODELING OF BROILER GROWTH USING A TIME-VARIANT PARAMETER ESTIMATION METHOD

Abstract

Controlling the growth trajectory of broiler chickens is a possibility to reduce the negative growth responsesrelated to fast growth, such as increased body fat deposition, a decrease of reproduction capacity, metabolic diseases, a highincidence of skeletal diseases, and sudden death syndrome. In previous research, a growth control algorithm for broilers wasdeveloped based on a compact adaptive growth prediction model, assuming a linear relationship between cumulative foodintake and animal weight. Because the growth response of broilers to cumulative food intake is a non-linear process, thisarticle investigates the validity of the assumption of linearity in an adaptive modeling approach in terms of predictionaccuracy. The dynamic growth response of the broiler chickens was modeled and predicted using a time-variant parameterestimation procedure. A recursive non-linear model was used to estimate the model parameters and to predict the growthresponse every 24 h based on a fixed number of actual and past measurements. Tests were performed on 43 data sets. Acomparison was made with the prediction accuracy of the recursive linear modeling approach. The non-linear modelingapproach made it possible to predict the growth of the broiler chickens up to 7 days ahead with a mean relative predictionerror of 5%, or less. This non-linear model reduces the prediction error significantly with a maximum of 1.5% in comparisonto the linear modeling approach. It can be concluded that for growth control purposes the dynamic growth response of broilerchickens is slightly better modeled assuming non-linear dynamics in a short time window.