モデル・データ融合による原料のクレードル・トゥ・ファームゲート炭素強度の不確実性低減

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🔬研究者:プロセスモデルとLCAを統合した圃場別CI算定の不確実性評価手法を、農業由来排出の精緻化研究に応用できる。

🏢実務担当者:農業サプライチェーンを持つ企業が、Scope 3排出の圃場別評価や低CI原料調達の根拠として活用可能。

🏛政策担当者:バイオ燃料CI規制や農業カーボンクレジット制度の設計において、圃場別・高精度なCI算定手法の採用を検討する際の参考になる。

Biofuel carbon intensity (CI) quantifies greenhouse gas (GHG) emissions across the biofuel supply chain, providing a transparent metric guiding renewable fuel policies and enabling market‐based mechanisms to reduce GHG emissions. In this study, we focus on the cradle‐to‐farmgate feedstock segment of biofuel CI, the portion attributable to feedstock production from cradle to farm gate and directly influenced by agricultural management decisions. We quantify field‐level cradle‐to‐farmgate CI of corn and soybean feedstocks by integrating a process‐based model ( ecosys ) with a life cycle assessment (LCA) tool (GREET R&D FD‐CIC). We first identified CI uncertainty sources at a “supersite,” a representative farm in Illinois with comprehensive GHG measurements, using a Monte Carlo sampling framework that varied soil properties, management practices, and plant trait parameters across plausible ranges to quantify each source's contribution to CI uncertainty. Unknown plant trait parameters were the largest CI uncertainty source for both crops, with fertilizer information also important for corn because these inputs affect yield, N 2 O emissions, and soil organic carbon (SOC) stocks. We revealed widespread field‐level variability in cradle‐to‐farmgate CI within the same county, demonstrating the need for field‐level assessment. This variability was most strongly associated with SOC change for soybean (total‐effect index, 38%) and N 2 O emissions for corn (43%). While inherent uncertainty is inevitable, we demonstrated that a Model‐Data Fusion (MDF) approach, which integrates observational data including in situ flux measurements and satellite‐based remote sensing to constrain model parameters with known processes, can substantially reduce cradle‐to‐farmgate CI uncertainty. Specifically, we used crop photosynthesis and yield data for plant trait parameter calibration, and incorporated tillage and cover crop information as model inputs, achieving a 41%–52% CI uncertainty reduction. This work establishes a CI quantification framework composed of process‐based models, an LCA tool, MDF, and satellite data, enabling scalable, cost‐effective, field‐level cradle‐to‐farmgate CI quantification across regions.

• semanticscholar https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/gcbb.70179first seen 2026-10-16 05:27:19 · last seen 2026-11-06 05:16:51
• openalex https://doi.org/10.1111/gcbb.70179first seen 2026-10-31 04:47:32 · last seen 2026-11-06 04:35:39