Document Type

Thesis - Open Access

Award Date

2026

Degree Name

Master of Science (MS)

Department / School

Animal Science

First Advisor

Hector Menendez

Second Advisor

Jameson Brennan

Abstract

Greenhouse gas (GHG) emissions from grazing ruminants represent a significant component of U.S. agricultural emissions. However, enteric methane (CH4) from cow– calf systems on extensive rangelands pose substantial measurement and management challenges. The Northern Great Plains (NGP) supports nearly one-quarter of the U.S. beef cow population, yet regionally representative, herd-level enteric emission data from commercial ranches remain limited. This thesis integrates empirical field measurements with modeling approaches to evaluate the feasibility of quantifying enteric emissions on extensive rangelands and to examine system-level drivers influencing data yield, costs, and operational performance. Enteric methane and carbon dioxide (CO2) emissions were measured from beef cattle and bison herds across four commercial ranches in the NGP using GreenFeed (GF) emission monitoring pasture systems (C-Lock, Inc., Rapid City, SD) during the summer grazing season. Herd-level emission estimates were generated using four published data aggregation methodologies differing in visit thresholds and temporal structure. Linear mixed-effects models and descriptive statistics were used to evaluate methodological effects on herd-level estimates, variance, data retention, and herd inclusion. Adoption and visitation varied widely across sites, with beef ranch adoption averaging 66% and bison adoption averaging 12.9%. No statistically significant differences were detected among aggregation methodologies for herd-level CH4 or CO2 estimates (P = 0.22), despite numerical variation. Mean CH4 estimates ranged from 259 to 281 g/day, and CO2 estimates ranged from 9,074 to 10,037 g/day, depending on methodology. More restrictive methods reduced variability but decreased herd representation and data retention by up to 95% of animals and visits. To further investigate drivers of data collection using precision livestock technologies (PLT), a system dynamics model was developed to evaluate interactions among animal visitation, equipment performance, staffing experience, and management logistics. Measures of accuracy indicated strong agreement between predicted and observed values, with the bias correction factor approaching unity (Cb = 1.0), minimal mean bias (0.283% of the mean observed value), and low RMSEP (631.4 units; 7.2% of the mean observed value). Precision measurements demonstrated very high predictive performance (R² = 0.996; MEF = 0.996; CCC = 0.99), reflecting highly consistent relationships between predicted and observed values. indicating that the simulated structure captured dominant drivers of data accumulation and machine utilization. The model also revealed unintended consequences of increased GF adoption and visitation, which were ultimately constrained by practical operational limits, particularly feed refilling capacity, technician labor availability, and equipment wear and failure. As use intensified, feed was depleted more rapidly, maintenance demands increased, and response times lengthened, resulting in diminishing returns and, in some cases, declines in system performance. Policy scenario analysis further demonstrated that these outcomes arise from fundamental system behaviors consistent with well-established system dynamics archetypes and principles, including Limits to Growth, Shifting the Burden, Limits to Success, Overshoot and Collapse, and Burning the Midnight Oil. In practical terms, these dynamics reflect straightforward operational realities: labor availability and refilling capacity cannot scale instantaneously, and sustained high levels of use accelerates equipment degradation, which ultimately constrains long-term data collection performance. Collectively, this thesis demonstrates that reliable herd-level enteric emission baselines can be established in commercial production systems on extensive rangelands despite logistical constraints and that aggregation methodology does not significantly alter herd-level estimates under variable visitation conditions. By combining empirical measurement with dynamic modeling, this work provides a foundation for more informed decision-making around emission monitoring strategies, resource allocation, and sustainability benchmarking in extensive grazing systems.

Publisher

South Dakota State University

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Rights Statement

In Copyright