Process-based modelling in farm greenhouse gas assessment reveals site-specific dynamics and limitations of emission factor methods
農場温室効果ガス評価におけるプロセスベースモデリングは、サイト固有のダイナミクスと排出係数法の限界を明らかにする (AI 翻訳)
Di He, Enli Wang, Maartje Sevenster, Stuart Brown, John Kirkegaard, Julianne M. Lilley
🤖 gxceed AI 要約
日本語
本研究は、プロセスベースの農業システムモデル（APSIM）と排出係数法を統合し、オーストラリアの農場におけるGHG排出強度を評価した。その結果、正味排出量は圃場や季節によって大きく変動し、排出係数法は直接N2O排出を過大評価し、圃場レベルの変動を捉えられないことが明らかになった。また、長期的には土壌有機炭素が平衡に達し、システムが炭素吸収源から排出源に転じることを示し、管理戦略の重要性を強調している。
English
This study integrates a process-based agricultural systems model (APSIM) with emission-factor calculations to assess GHG emissions from Australian cropping fields. It finds that net emissions vary widely across fields and seasons, and that emission factor methods overestimate direct N2O emissions and fail to capture field-scale variability. Long-term simulations show that soil organic carbon reaches equilibrium and the system transitions from carbon sink to source, highlighting the need for advanced management strategies.
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
日本の農業分野では、食品企業のサプライチェーン排出量（Scope 3）算定において、排出係数法が広く用いられているが、本論文はその限界を明確に示し、プロセスベースモデルの有用性を提起している。SSBJやTCFDに基づく気候関連情報開示において、農業由来排出の精度向上に寄与する知見である。
In the global GX context
This paper addresses critical limitations of emission factor methods in agricultural LCA, which is highly relevant for global Scope 3 carbon accounting under TCFD/ISSB frameworks. It demonstrates the need for process-based models to capture field-scale variability and long-term carbon dynamics, offering an improved methodology for corporate sustainability reporting.
👥 読者別の含意
🔬研究者:Provides a framework combining LCA with process-based modelling that improves accuracy of agricultural GHG assessments and reveals limitations of static emission factors.
🏢実務担当者:Corporate sustainability teams in food and agriculture can adopt this approach to better estimate Scope 3 emissions from agricultural supply chains, accounting for management and climate variability.
🏛政策担当者:Highlights the necessity of moving beyond simple emission factors in agricultural carbon accounting standards to include dynamic, process-based methods.
📄 Abstract（原文）
Abstract Life cycle assessment (LCA) is a well-recognized tool to assess the environmental impact of food production. To assess partial life-cycle greenhouse gas emissions in agricultural systems with variable weather and management, this study integrated process-based agricultural systems modelling (APSIM) with emission-factor-based calculations to develop a system modelling framework. We assessed the greenhouse gas emission intensity of 11 fields from 2016 to 2021 at Boorowa Agricultural Research Station, representing an Australian cropping farm with comprehensive management records. Net greenhouse gas emissions varied widely across fields and seasons, ranging from −3.87 to 6.10 t CO 2 −e ha⁻ 1 . Emissions were not only determined by seasonal climate but also prior-year management decisions, highlighting the need for a system-level perspective. Compared to the LCA-APSIM approach, averaged emission factors tend to overestimate direct N 2 O emission and fail to capture field-scale variability driven by climate and management. This highlights the limitations of the emission factor-based approach. Long-term scenario simulations for a continuous cropping system (canola-wheat-wheat) and a phased pasture-crop system (lucerne (×3)-canola-wheat-wheat, with lucerne ungrazed and 50% cut for hay) clearly demonstrated the trade-off between greenhouse gas emissions and nitrogen input. From a long-term perspective, regardless of management practices or cropping systems, soil organic carbon in agricultural systems will eventually reach equilibrium, after which the system will transition from a carbon sink to a carbon source. To optimize environmental sustainability and food security, advanced farm management strategies must delay the attainment of equilibrium and maximize soil carbon storage potential while maintaining productivity. This study provides new insights into field-scale variability in greenhouse gas emissions, soil organic carbon equilibrium timing, and biases in static N₂O emission methods that have not been quantified in earlier LCA–APSIM applications.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s13593-026-01104-yfirst seen 2026-05-05 08:08:34 · last seen 2026-05-05 19:14:34
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