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The increase in greenhouse gas (GHG) emissions has resulted in climate change and global warming. Human activities in many sectors, including agriculture, contribute to approximately 9.2% of total GHG emissions from Annex I countries. An argument on issues of livestock being the highest contributor to GHG emissions has grown since FAO’s 2006 report Livestock’s Long Shadow. The issue has continued growing, conflicting the importance of the industry in terms of food security and livelihoods, thus, monitoring GHG emission from this sector is vital. The most commonly used methods for calculating GHG emissions from the livestock sector are life cycle assessment (LCA) and the GHG inventory. Although the LCA presents information on the impacts of the livestock industry on the environment, the GHG inventory is the main tool used internationally for GHG reporting. This review comprehensively discusses the source of GHG emissions from the livestock industry and its estimation methodology, as well as the current strategies for mitigating these emissions.

DEVS (Discrete Event System Specification) is widely used in modeling and simulation fields to design, validate, and implement complex response systems. DEVS provides a robust formalism for system design using event-driven, state-based models with explicitly defined temporal information. We extend the RTL-DEVS model based on DEVS formalism to enable part of Verilog simulation in DEVS-based simulation tools. The simulation based on RTL-DEVS methodology, which imitates Verilog’s testbench and behavioral module, confirmed through experiments that RTL simulation can be performed sufficiently through the code elaboration process. In multiple simulation results, Verilog simulation and RTL-DEVS-based simulation were able to output equivalent results under limited conditions. DEVS formalism-based modeling can be extended to other DEVS-based simulators when using model-type exchange tools, and this means that the advanced functions or classes of RTL simulation tools can be applied using higher-level language tools.

The DEVS model, designed for general discrete event simulation, explores the event status and time advance of all DEVS atomic models deployed at the time of the simulation, and then performs the scheduled simulation step. Each simulation step is accompanied by a re-exploration the event status and time advance, which is needed for maintaining the casual order of the entire model. It is time consuming to simulate a large-scale DEVS model. In a similar vein, attempts to perform an HDL simulation in a DEVS space increase simulation costs by incurring repeated search costs for model transitions. In this study, we performed a statistical analysis of engine behavior to improve simulation speed and we proposed a DP-based memoization technique for the coupled model. Through our method, we can expect significant performance improvements that range statistically from 7.4 to 11.7 times.