Recursive prediction of broiler growth response to feed intake by using a time-variant parameter estimation method

The objective of this study was to explore whether time-variant parameter estimation procedures allow modeling and predicting the dynamic growth response of broiler chickens to feed intake in real time. A recursive linear model was used that estimated the model parameters every 24 h based on a fixed number of actual and past measurements (i.e., time window). Based on 48 datasets, it was concluded that the mean relative prediction error (MRPE) of the recursive linear modeling approach had a minimum for a window size of 5 d. Weight of the birds could be predicted during the growth process 3 to 7 d ahead with a mean relative prediction error of 5% or less. In comparison with the prediction results of three static empirical growth models (one linear and two nonlinear models), the recursive modeling technique had a similar accuracy to the nonlinear empirical models (MRPE of 1.4% to 2.3% vs. 1.1% to 2.8%), but it was less accurate for larger prediction horizons (2 to 7 d). The compact recursive linear model was more accurate than the static linear growth model for prediction horizons of one up to 4 d, depending on the feeding strategy. Since such recursive modeling approach allows the prediction of broiler growth without any prior knowledge of the system and takes into account the time-variant (nonlinear) nature of the growth process based on only a small window of measured information, it is suitable for real-time integration in process management.