Estimating the carbon footprint of milk in dual-purpose dairy production systems: Influence of animal productivity, functional unit, and emission allocation and quantification methods
デュアルパーパス乳製品生産システムにおける牛乳のカーボンフットプリント推定：動物生産性、機能単位、排出配分・定量化方法の影響 (AI 翻訳)
Md Bari, Mohammad Ashiqul Islam, Md. Harun‐ur‐Rashid, M.E. Uddin
🤖 gxceed AI 要約
日本語
この研究は、デュアルパーパス（乳肉兼用）乳製品生産システムにおける牛乳のカーボンフットプリント（CF）を推定し、Tier1とTier2の方法、異なる機能単位、配分方法の影響を比較した。Tier2はTier1より27%低いCFを示し、生産性向上はCF削減に効果的だった。不確実性分析では年間変動が大きく、乳量と群構成が主要因であることがわかった。腸内発酵が最大の排出源（48%）であった。
English
This study estimates the carbon footprint (CF) of milk in dual-purpose dairy production systems, comparing Tier 1 and Tier 2 methods, different functional units, and allocation approaches. Tier 2 resulted in 27% lower CF than Tier 1. Productivity improvement reduced CF similarly across functional units. Uncertainty analysis showed high interannual variability, with milk yield and herd structure as key drivers. Enteric fermentation was the dominant emission source (48%).
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
本論文は、低投入型のデュアルパーパス乳製品システムにおけるCF推定手法の選択が結果に大きく影響することを示しており、日本の畜産分野のGHG算定においても、Tier2の活用や配分方法の選択が重要であることを示唆する。ただし、日本の酪農は高投入型が主流であるため、直接的な適用には注意が必要。
In the global GX context
This paper highlights how methodological choices in carbon footprint estimation significantly affect results in low-input dual-purpose dairy systems. The findings are relevant for global dairy GHG accounting, especially for developing countries. The comparison of Tier 1 vs Tier 2 and allocation methods provides insights for improving accuracy in agricultural carbon footprinting, which is increasingly demanded under frameworks like TCFD and ISSB.
👥 読者別の含意
🔬研究者:Researchers can gain insights on methodological choices (functional unit, allocation, tier level) that affect carbon footprint estimates in dairy systems.
🏢実務担当者:Dairy farm managers can use the findings to understand that productivity improvements and choice of carbon accounting method significantly influence reported emissions.
🏛政策担当者:Policymakers can note that standardizing carbon footprint methodology for dairy is crucial for comparability and informed decision-making in agricultural GHG reduction.
📄 Abstract（原文）
). Tier 2 method, using system-specific factors, had 27% lower CF than Tier 1. Productivity gain had similar reduction effects on CF when using 100-kcal energy or 100 g protein as FU. Biophysical and economic allocation reduced CF by 47% and 36%, respectively, compared with no allocation. Uncertainty analysis showed wide CF variation across years, and sensitivity analysis identified milk yield and herd structure as the most influential drivers. Enteric fermentation was the dominant emission source (48%), followed by feed (24%), manure (20%), and farm energy (8%). These findings highlight the importance of productivity improvement and methodological choices for shaping CF estimates and evaluating sustainable dairy production in low-input dual-purpose systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3168/jds.2025-27448first seen 2026-06-23 05:58:55
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