Abstract
Abstract
Mathematical models have long supported nutrition management in livestock systems, progressing from early National Research Council empirical tables to complex mechanistic frameworks such as the Ruminant Nutrition System. What is less documented is how models are operationalized in commercial practice, particularly as data streams from precision livestock technologies, remote sensing, and other Internet of Things technologies are increasingly hybridized into existing modeling frameworks. A prevailing assumption is that greater sensing, automation, or algorithmic complexity will naturally translate into better decisions; however, adoption and impact in commercial systems remain uneven. To examine how models are actually used under real-world constraints, we conducted a literature review and semi-structured interviews (n = 11) with livestock producers, technology developers, consultants, and value-chain actors working at the interface of biology, data, models, and farm decision-making. Six cross-cutting themes emerged: 1) model augmentation of systems, 2) model predictive control (MPC), 3) human centered adoption, 4) technology fit, 5) data bottlenecks, and 6) data enlightened value chain (Figure 1). Collectively, these insights re-center nutrition as the integrating discipline for productivity, welfare, and sustainability, and emphasize MPC and open, interoperable data pipelines as prerequisites for moving beyond pilots toward durable adoption. Interviewees consistently emphasized that meaningful impact depends on aligning biological realism, software engineering, and value-chain incentives so that models support actionable decisions under commercial constraints, rather than pursuing automation for its own sake.