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A process-based framework for national-scale estimation of agricultural soil N₂O emissions under variable climate and management

気候と管理の変動下における農業土壌N₂O排出の全国規模推定のためのプロセスベースフレームワーク (AI 翻訳)

Andrew Smerald, Hannes Imhof, Edwin Haas, David Kraus, Lioba Martin, Kathrin Fuchs, John Akubia, Ali Sakhaee, Cora Vos, Roland Fuß, Clemens Scheer, Ralf Kiese

プレプリント2026-07-16#政策Origin: EU対象セクター: agriculture
DOI: 10.5194/egusphere-2026-4010
原典: https://doi.org/10.5194/egusphere-2026-4010
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🤖 gxceed AI 要約

日本語

ドイツ全国の農業土壌からのN₂O排出を、プロセスベースモデルLandscapeDNDCを用いて高解像度で推定した。2017-2022年平均で35ktN/年と推定し、国家インベントリ報告より28%高いが不確実性範囲内。気候変動や管理の影響を明示的に考慮し、次世代GHGインベントリへの道筋を示す。

English

A process-based modeling framework (LandscapeDNDC) estimates national-scale agricultural N2O emissions for Germany, yielding 35 kt N/yr (2017-2022), 28% higher than the national inventory but within uncertainty. It captures climate variability and management effects, offering a pathway to next-generation GHG inventories.

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、農業分野のGHGインベントリはTier-1/2が中心であり、本フレームワークはTier-3への移行可能性を示す。国連報告や国内の排出削減目標の精緻化に寄与し、農業政策やJ-クレジット制度の評価にも応用可能。

In the global GX context

Globally, this framework demonstrates how process-based models can enhance national GHG inventories, aligning with IPCC Tier-3 ambitions. It provides a template for other countries to improve spatial and temporal resolution of agricultural emissions, supporting more effective mitigation policy.

👥 読者別の含意

🔬研究者:Process-based modeling can significantly improve national GHG inventory accuracy and capture climate variability.

🏢実務担当者:Provides a method for more detailed agricultural emissions reporting, potentially useful for corporate Scope 3 accounting.

🏛政策担当者:Supports development of next-generation national GHG inventories and evaluation of mitigation strategies.

📄 Abstract(原文)

Abstract. Agricultural soils are the dominant source of anthropogenic N2O emissions, yet their high spatial and temporal heterogeneity provides a major challenge for accurately quantifying emissions and evaluating mitigation options. Most national greenhouse gas inventories rely on empirical Tier-1 or Tier-2 emission-factor approaches and therefore do not fully capture the effects of climate variability, soil properties, or management practices. Here, we present a transferable, process-based modelling framework based on the biogeochemical model LandscapeDNDC for determining direct and indirect N2O emissions from major crops cultivated on mineral soils at the national scale. We apply the method to Germany making use of high-resolution input data provided by the national reporting agencies, estimating N2O emissions of 35 (29–44) kt N yr-1(2017–2022 average). This is 28 % higher than the national inventory report (submission 2025), but well within the uncertainty range. In contrast to conventional inventory methods, the framework explicitly accounts for interannual climate variability and can be spatially disaggregated at high resolution, taking into account local variations in soil type, weather and agricultural management practices. Because the model simulates coupled carbon and nitrogen cycling, it also quantifies multiple nitrogen loss pathways and potential changes in carbon stocks simultaneously, providing a consistent basis for evaluating mitigation strategies and their potential trade-offs. Our results demonstrate that process-based modelling can substantially improve the spatial and temporal resolution of agricultural N₂O emissions and provide a platform for developing next-generation national greenhouse gas inventories. While further work is required before the framework fully satisfies all IPCC Tier-3 requirements, it offers a pathway towards a more mechanistic and policy-relevant assessment of agricultural greenhouse gas emissions.

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