Variability and Driving Mechanisms of Cropland N₂O Emission Factors: A Review
農地N₂O排出係数の変動性と駆動メカニズム:レビュー (AI 翻訳)
Peiyi Zhang
🤖 gxceed AI 要約
日本語
本レビューは、農地からの亜酸化窒素(N₂O)排出係数(EF)の変動性とその駆動要因を包括的に整理した。従来の固定EF法は大規模算定に適するが、気候・土壌・管理の多様性を反映できず、近年は機械学習や解釈可能モデルを用いた動的・地域別EFの開発が進んでいる。将来はメカニズム制約付きのパラメータ枠組みへの移行が提唱され、農地N₂Oインベントリと緩和策の科学的基盤強化に寄与する。
English
This review synthesizes knowledge on the variability and driving mechanisms of cropland nitrous oxide (N₂O) emission factors (EF). It highlights that fixed EFs fail to capture spatial and temporal heterogeneity, and recent advances in machine learning enable dynamic, regionalized EF estimation. The authors advocate for mechanism-constrained parameter frameworks to improve N₂O inventories and mitigation 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
日本の農業由来温室効果ガスインベントリではN₂O排出係数の精緻化が課題であり、本レビューの知見は国内の農地管理や施肥最適化に示唆を与える。また、J-クレジット制度における農業分野の排出削減算定にも関連する。
In the global GX context
Globally, this review informs the refinement of agricultural GHG inventories under IPCC guidelines and supports the development of dynamic emission factors for climate mitigation policies. It also provides a scientific basis for integrating N₂O dynamics into carbon accounting frameworks.
👥 読者別の含意
🔬研究者:Provides a comprehensive overview of N₂O EF variability and methodological advances, useful for those modeling agricultural emissions.
🏢実務担当者:Offers insights into optimizing fertilizer management to reduce N₂O emissions, relevant for agricultural sustainability reporting.
🏛政策担当者:Highlights the need for updating emission factors in national inventories to reflect regional variability, supporting more accurate mitigation targets.
📄 Abstract(原文)
Nitrous oxide (N₂O) is an important component of agricultural greenhouse gas emissions, and nitrogen inputs to croplands represent one of the major anthropogenic sources of N₂O. The emission factor (EF), which links nitrogen input to direct N₂O emissions, has long been used in national greenhouse gas inventories and agricultural mitigation assessments. The conventional fixed-EF approach is simple and suitable for large-scale accounting, but extensive field observations have shown that cropland EFs vary substantially across climate zones, soil backgrounds, cropping systems, and management practices. A single fixed coefficient cannot adequately capture the spatial heterogeneity, interannual variability, and event-driven pulse characteristics of cropland N₂O emissions. In recent years, with the development of global field observation databases, meta-analyses, machine learning, and interpretable modelling approaches, EF research has gradually shifted from estimating average coefficients to explaining the mechanisms underlying EF variability. Existing studies indicate that cropland N₂O emissions are jointly regulated by nitrification, denitrification, and N₂O reduction processes. Their variability is mainly controlled by nitrogen substrate availability, soil moisture and oxygen diffusion, soil pH, soil organic carbon, temperature, precipitation, and agricultural management practices. Acidic soils, high SOC, high nitrogen input, humid conditions, and heavy rainfall after fertilization are more likely to create high-emission risks. Meanwhile, drying–rewetting, freeze–thaw cycles, and extreme rainfall events can trigger short-term emission peaks, further challenging the fixed-EF approach. This review synthesizes current knowledge on the variability, driving mechanisms, multifactorial interactions, and methodological advances in cropland N₂O EF research. It further suggests that future EF systems should move from fixed empirical coefficients toward regionalized, dynamic, and mechanism-constrained parameter frameworks, thereby improving the scientific basis of cropland N₂O inventories and mitigation strategies.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://wepub.org/index.php/IJNRES/article/download/6217/6718first seen 2026-08-05 05:36:28
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