Transdisciplinary model-based systems engineering in the development of the Ruminant Farm Systems model

This study adopts a transdisciplinary model-based systems engineering (MBSE) approach to support the development of the Ruminant Farm Systems (RuFaS) model, an advanced on-farm decision support tool. Using the cloud-based MBSE platform Innoslate (SPEC Innovations, Manassas, VA), we identified key stakeholders, constructed use cases, defined system boundaries, refined stakeholder requirements, and outlined the system architecture and subsystem interfaces for RuFaS. To demonstrate RuFaS’s ability to meet stakeholder requirements, we selected a specific use case focused on comparing whole farm impacts across different manure management scenarios. For the current case, we defined 12 scenarios from 4 manure management strategies and 3 diet-climate conditions based on U.S. regions. The scenarios included two bedding types (sawdust vs. sand), two storage methods [anaerobic digestion with lagoon (ADL) vs. slurry storage (SS)], and three regions (R1, R2, R3). RuFaS predictions were responsive to changes in scenario conditions, with whole farm greenhouse gas (GHG) emissions ranging from 1.23 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mo>±</mml:mo></mml:math> 4.64 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mo>×</mml:mo></mml:math> 10 −3 to 1.61 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mo>±</mml:mo></mml:math> 9.45 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mo>×</mml:mo></mml:math> 10 −3 kg CO 2 -eq/kg fat-and protein-corrected milk (FPCM). Regional variations influenced whole herd enteric CH 4 intensity, with R2 scenarios showing the highest emissions (0.472 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mo>±</mml:mo></mml:math> 3.65 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mo>×</mml:mo></mml:math> 10 −3 kg CO 2 -eq/kg FPCM), followed by R1 (0.458 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M7"><mml:mo>±</mml:mo></mml:math> 4.19 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M8"><mml:mo>×</mml:mo></mml:math> 10 −3 kg CO 2 -eq/kg FPCM) and R3 (0.449 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M9"><mml:mo>±</mml:mo></mml:math> 3.45 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M10"><mml:mo>×</mml:mo></mml:math> 10 −3 kg CO 2 -eq/kg FPCM), driven by differences in dry matter intake, and milk production and composition. Manure storage methods also impacted emissions, with ADL scenarios producing 0.146 kg CO 2 -eq/kg FPCM lower whole farm GHG emissions than SS scenarios, due to the combined effects of reduced manure storage CH 4 emissions associated with anaerobic digestion and associated increased NH 3 emissions and subsequent indirect N 2 O emissions. These findings highlight the complex interactions among RuFaS model components and confirm its ability to support effective comparisons of manure management practices to meet specific stakeholder needs. Our transdisciplinary MBSE approach provides a robust framework for ongoing RuFaS evaluation, ensuring alignment with stakeholder requirements. This study represents a pioneering milestone in the application of MBSE to agricultural system model development, highlighting its potential to advance decision-making in sustainable dairy farm management.