Reliable prediction of avian responses to dietary nutrients is essential for planning, management, and optimization activities in poultry nutrition. In this study, two bootstrapped neural network (BNN) models, each containing 100 separated neural networks (SNN), were developed for predicting average daily gain (ADG) and feed efficiency (FE) of broiler chicks in response to intake of protein and branched chain amino acids (BCAA) in the starter period. Using a re-sampling method, 100 different batches of data were generated for both the ADG and FE sets. Starting with 270 data lines extracted from eight studies in the literature, SNN models were trained, tested, and validated with 136, 67, and 67 data lines, respectively. All 200 SNN models developed, along with their respective BNN ones, were subjected to optimization (to find the optimum dietary protein and BCAA levels that maximize ADG and FE). Statistical analysis indicated that based on R2, the BNN models were more accurate in 76 and 56 cases (out of 100) compared with the SNN models developed for ADG and FE, respectively. Optimization of the BNN models showed protein, isoleucine, leucine, and valine requirements for maximum ADG were 231.80, 9.05, 14.03 and 10.90 g kg1of diet, respectively. Also, maximum FE was obtained when the diet contained 232.30, 9.07, 14.50, and 11.04 g kg1 of protein, isoleucine, leucine, and valine, respectively. The results of this study suggest that in meta-analytic modelling, bootstrap re-sampling algorithms should be used to better analyze available data and thereby take full advantage of them. This issue is of importance in the animal sciences as producing reliable data is both expensive and time-consuming.

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