As of June 1, 2026, there are 75,647 Korean and beef cattle farms and 5,342 dairy cattle farms, and if you simply add up the sections with 50 or more cows, there are 23,815 operating units. Definitions of numbers and cautions when interpreting the market are explained.
Read the global livestock monitoring market's growth forecast from USD 1.65 billion in 2025 to USD 2.57 billion in 2031, and explain how methane monitoring businesses should distinguish between TAM, SAM, and SOM.
Trust in carbon credits begins with the connection between measurement boundaries, raw data, quality control, reporting and verification rather than calculation results. We outline how AI and digital records are assisting MRV work.
Carbon data used in ESG disclosure is not only emissions numbers. Boundaries, source data, calculation methods, estimates and uncertainties must be managed to create comparable information.
Explains what must be determined first when applying the organizational boundaries and greenhouse gas inventory principles of ISO 14064-1 to livestock business.
Using a hypothetical livestock business as an example, we look at the process of dividing enteric fermentation, manure, power, feed, and transportation into Scope 1, 2, and 3.
Carbon Score is not a single, internationally recognized formula, but is often a purpose-designed evaluation metric. You should look at the formula and data quality before the score.
Although measurement accuracy does not solely determine company value, it directly impacts product trust, business decisions, carbon claims, and verification costs.
It is more important to ensure that sensor data can be used for its intended purpose rather than collecting a lot of it. Covers time, completeness, scope, drift and review history.
Based on a hypothetical livestock methane project, we introduce the Digital MRV construction sequence connecting sensors, raw data, quality control, calculation version, and verification data room.
We introduce a research and development direction that extends industrial gas detection experience to livestock methane measurement, data quality, AI-assisted analysis, and field review.
Based on Patent 10-2990446 and approved field materials, this article explains how continuous collection, data quality, AI anomaly detection, and operator review connect in one monitoring system.
Drawing on both patents and approved field materials, this article explains why missing data, calibration, time alignment, and environmental context must be recorded for a reproducible reduction review.
Using the comparison logic of Patent 10-2990447, this article reviews baselines, application timing, equivalent conditions, and differences between observations and AI predictions.
Within the approved career scope and product catalog, this article examines how safety-instrumentation principles extend to barn methane collection and AI-assisted analysis.
Based on publicly usable agreements and the approved Sejong, Buan, and Paju scope, this article distinguishes each participant’s role in research design, device operations, field records, and commercialization decisions.