Monte Carlo simulation data for global methane-focused genetic selection scenarios in dairy cattle, 2025–2050

Title: Monte Carlo Simulation Data for Global Methane-Focused Genetic Selection Scenarios in Dairy Cattle (2025–2050)

Summary:

This dataset contains the complete input parameters, simulation outputs, summary statistics, and reproducible code used to evaluate the potential contribution of methane-inclusive genetic selection to greenhouse gas mitigation in the global dairy sector from 2025 to 2050. 

The simulation framework uses Monte Carlo methods to incorporate uncertainty in biological, genetic, demographic, adoption, and economic parameters associated with methane-focused livestock breeding programs. A total of 10,000 simulation iterations were conducted to estimate annual and cumulative greenhouse gas mitigation, producer adoption rates, realized per-cow methane reductions, and economic benefits resulting from avoided methane emissions.

Scientific Background:

Enteric methane emissions from ruminant livestock represent a major source of agricultural greenhouse gas emissions worldwide. Recent advances in methane phenotyping, genomic selection, and climate-smart breeding have created opportunities to incorporate methane emissions directly into livestock breeding objectives. The purpose of this dataset is to provide a transparent, reproducible framework for evaluating the long-term climate and economic impacts of methane-inclusive genetic selection under a range of biological and policy scenarios.

Methodological Overview

The simulation framework combines:

Biological Parameters

·         Methane-trait heritability

·         Genetic response

·         Generation interval

·         Replacement rate

Genomic Parameters

·         Genomic prediction accuracy

·         Selection intensity

Population Parameters

·         Global dairy population size

·         Population growth rate

Adoption Parameters

·         Logistic producer adoption function

·         Dissemination of improved genetics

Economic Parameters

·         Dynamic carbon pricing

·         Carbon valuation scenarios

Uncertainty was propagated through all model components using Monte Carlo sampling.

 Intended Uses:

This dataset may be used for:

• methane genetics research
• livestock breeding studies
• climate-smart agriculture research
• greenhouse gas forecasting
• carbon valuation analyses
• sustainability assessments
• policy evaluation
• educational purposes 

Limitations: The dataset represents scenario-based simulations rather than deterministic forecasts.

Results depend upon assumptions regarding:

• methane-trait heritability
• genomic prediction accuracy
• producer adoption
• carbon pricing
• population growth
• replacement rates

Actual outcomes may differ depending on future biological, technological, economic, and policy developments.