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

乳牛におけるメタン焦点の遺伝的選抜シナリオのモンテカルロシミュレーションデータ、2025–2050 (AI 翻訳)

RAMANATHAN, KASIMANICKAM

Zenodoデータセット2026-06-04#その他Origin: Global
DOI: 10.5281/zenodo.20548424
原典: https://zenodo.org/records/20548424

🤖 gxceed AI 要約

日本語

本データセットは、乳牛の遺伝的選抜によるメタン排出削減の可能性を評価するモンテカルロシミュレーションの入力パラメータ、出力、再現可能なコードを含む。1万回のシミュレーションで、生物学的・遺伝的・経済的不確実性を考慮し、年間および累積の温室効果ガス削減量、生産者導入率、経済効果を推定している。

English

This dataset provides Monte Carlo simulation inputs, outputs, summary statistics, and reproducible code to evaluate methane-focused genetic selection in global dairy cattle (2025-2050). It incorporates uncertainty in biological, genetic, demographic, adoption, and economic parameters, estimating annual and cumulative GHG mitigation, adoption rates, per-cow reductions, and economic benefits from avoided methane emissions.

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

📝 gxceed 編集解説 — Why this matters

日本のGX文脈において

日本では畜産業からのメタン排出削減が課題であり、遺伝的選抜は将来の対策の一つとなり得る。本データは日本の乳牛改良プログラムや農業GHG削減目標の検討に参照可能。

In the global GX context

This dataset offers a transparent, reproducible framework for evaluating genetic selection as a climate-smart agriculture strategy to reduce enteric methane, a potent GHG. It is relevant for global livestock emissions mitigation, carbon pricing scenarios, and climate policy evaluation.

👥 読者別の含意

🔬研究者:Provides a simulation framework and data for modeling methane mitigation via genetic selection, useful for livestock GHG research.

🏢実務担当者:Dairy breeding companies can use the adoption and economic parameters to assess potential benefits of methane-inclusive breeding programs.

🏛政策担当者:Offers scenario-based evidence for integrating methane reduction targets into agricultural climate policies.

📄 Abstract（原文）

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.

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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。