Abstract Agent-based modeling (ABM) is a powerful tool that offers the flexibility to represent the stochasticity exhibited by swine systems due to individual animal nutritional requirements. ABM can also accurately mimic the biological dynamic growth performance of pigs. Addressing and simulating the biological growth dynamics enables precision nutrition and sustainable production management. To realistically replicate the nutrient requirement and growth performance of growing-finishing pigs, we developed an ABM based on the principles and equations from the eleventh Swine Nutrient Requirement Council Model. Each individual pig (agent) in the model had a starting weight of 20 kg and a fixed finishing weight of 140 kg, with a variable number of days on feed. Our model considers three sexes: gilt, barrow, and intact male. Pigs move through the farm, feed, and interact with the environment. Our model dynamically simulates the complex interactions between feed intake, metabolism, and growth processes of growing pigs, thereby being a virtual visualizable growing-finishing swine farm (Figure 1). The basic parameters of the biological growth of pigs, including the body weight, feed intake, metabolizable energy (ME) intake, maintenance ME requirements, protein deposition (Pd), lipid deposition (Ld), and probe backfat thickness (Figure 2), were modeled based on NRC equations. In addition to the above parameters, the daily amino acid, calcium (Ca), and phosphorus (P) requirements for individual pigs were calculated daily. To demonstrate ABM responsiveness to feeding strategies, ractopamine supplementation is included to evaluate the impact on protein deposition. The ABM offers more personalized nutrition management by considering unique pig growth potential and nutritional needs based on characteristics such as age and sex, which can lead to improved growth performance and feed efficiency. Besides, the ABM enables the modeling of population-level dynamics and the evaluation of different feeding scenarios to optimize growth outcomes by allocating optimized feed based on individual nutritional needs. Our ABM will be used to test different feeding management strategies and scenarios, providing valuable insights into the impact of various factors on pig nutrition and growth outcomes while minimizing both time and financial expenditures. Our ABM aims to provide a robust framework for simulating and optimizing feeding strategies and enhancing overall pig production efficiency by leveraging the comprehensive and scientifically validated guidelines provided in the NRC. To verify the accuracy and reliability of the proposed ABM, the results generated by the model were rigorously evaluated and compared with the NRC model results. The results demonstrated a highly satisfactory prediction performance by our ABM, paving the way for more real-time decisions to be made for daily feed management on growing-finishing swine operations.
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