ASAS-NANP symposium: mathematical modeling in animal nutrition: contributions of mathematical modeling to life cycle assessment to support environmental sustainability of animal production

Abstract

Life cycle assessment (LCA) provides a standardized framework for evaluating the environmental impacts of animal production systems through its four key steps: 1) goal and scope definition, 2) inventory analysis, 3) impact assessment, and 4) interpretation. However, traditional approaches using surveys and experimental data face limitations in capturing complex interactions among biological processes and management practices. This paper reviews how mathematical modeling can enhance LCA methodology for animal production systems, overcoming these constraints and supporting more robust environmental impact assessments. Mathematical models contribute significantly to LCA methodology at multiple scales and stages. At the inventory analysis stage, models predict feed intake, growth, production, and excretion of nutrients in response to animal characteristics and management practices. These range from nutritional metabolic models of average animals to sophisticated individual-based models that account for variability among animals in a herd. A systematic workflow could be followed for developing stochastic, individual-based models that generate comprehensive life cycle inventories through a bottom-up approach. Process-based models also improve emission estimates from animals and manure, progressing from simple Tier 1 default emission factors to complex Tier 3 mechanistic approaches that capture interactions between management practices and environmental factors. However, significant challenges remain in modeling manure emissions due to complex data requirements and microbial dynamics. Beyond inventory development, mathematical modeling enhances LCA’s utility for decision support through optimization models that identify mitigation strategies balancing environmental and economic objectives. Individual-based models enable environmental phenotyping for genetic selection by quantifying how individual animal traits affect system-level impacts. These approaches represent promising developments for sustainable livestock production. Mathematical modeling transforms LCA from a descriptive tool to a predictive framework capable of evaluating numerous scenarios across different production contexts. Further development should focus on integrating performance and emission models, implementing optimization approaches for mitigation strategy identification, and expanding applications to regional and national scales to support evidence-based policies.