Productive energy, as determined by Arkansas net energy, predicts broiler processing weights, carcass market value,...

This study assessed the sensitivity of Productive Energy (PE) to breast meat production, feed efficiency, and broiler economics, developed models for processing traits and economics, and examined economics dynamics. The experimental design involved 96 pens, 12 per block and eight blocks, with each pen within block receiving one of 12 dietary treatments for one week. Treatment diets, varying in amino acid concentrations, starch, and energy densities, were control-fed. The weights of processing body parts and net energy for gain (NEg) were determined with Dual-Energy X-Ray Absorptiometry, and fed heat production, fasting heat production (FHP), and net energy for maintenance (NEm) with calorimetry chambers. N-corrected apparent metabolizable energy (AMEn) was determined. PE was calculated as NEg + NEm, and classic net energy (CNE) as AMEn - heat increment (heat increment = fed heat production - FHP). Feed, protein, and energy efficiencies, and economic parameters (market value gain, MVG; gross profit gain, GPG; cents/bird per day) were calculated. Mixed models predicted the growth rate of processing parts and economic traits, and non-linear models characterized economic dynamics as the birds aged. AMEn (P < 0.04) and CNE (P < 0.02) efficiencies varied across treatments, but PE efficiency remained stable (P ≥ 0.07). PE was 2.0 and 2.7 times more sensitive (P < 0.05) than CNE to changes in breast meat gain and feed efficiency, respectively. PE, but not AMEn or CNE influenced processing and economic traits (P < 0.05), and models predicting these characteristics based on PE showed high precision (R² > 0.97). Amino acid calories supported MVG and GPG better than those from other nutrients (P < 0.01), and PE influenced the age at maximum GPG (P = 0.01). In conclusion, PE outperformed AMEn and CNE in predicting meat production and economic traits, offering a more sensitive and precise tool for broiler diet optimization.