Arkansas Net Energy: A productive energy system sensitive to broiler performance and body composition

This study determined the sensitivity of productive energy (PE; Arkansas Net Energy) to performance and body composition and developed predictive models based on PE. Chicks (2,148 day-old ones) were placed in 96 pens and assigned to one of 12 dietary treatments, including varying concentrations of total digestible amino acids (TDAA), digestible CP (dCP), digestible fat (dFat), digestible starch, and non-starch polysaccharides (NSP). Eight replications per treatment, one per block, were used, being the block consecutive groups of birds weekly starting the seven-day experimental period. In each block, birds were control fed to meet similar ME intake, placed in calorimetry chambers to determine fed (HP) and fasting heat production (FHP), and scanned (Dual-Energy X-Ray Absorptiometry) to determine protein accretion (PAC) and energy gain. N-corrected apparent metabolizable energy (AMEn) and digestible nutrients were determined. Net energy for gain (NEg; kcal/kg), net energy for maintenance (NEm; kcal/kg; NEm = FHP), heat increment (HI; HI = HP - FHP), classic net energy (CNE; CNE = AMEn - HI; kcal/kg), PE (kcal/kg), and associated ratios were calculated. BW gain (BWG) and feed conversion ratio (FCR) were recorded. ANOVA and multiple comparisons were performed. Mixed models were used to build and validate models predicting BWG, FCR, and PAC based on digestible nutrients or PE. Treatments influenced (P < 0.05) NEg, NEm, PE, CNE, PE/AMEn, but not HI or CNE/AMEn. The treatments affected the efficiencies of AMEn or CNE for BWG or PAC (P < 0.05) but not PE ones. TDAA, dCP, and dFat favored BWG and FCR, but NSP affected FCR negatively (P < 0.05). TDAA and dCP favored PAC (P < 0.01). No influence of AMEn and CNE was observed on BWG, FCR, or PAC (P > 0.05), but CNE correlated with fat gain (P < 0.03). Models to predict BWG, FCR, and PAC based on PE were developed (R² > 0.86; P < 0.02). In conclusion, PE was shown to be an energy system able to predict broiler performance.