低投入型および集約型レンズマメ生産体系の栽培段階カーボン会計：デジタルデータワークフローを用いた評価
Cultivation-Stage Carbon Accounting of Low-Input and Intensive Lentil Production Systems with Digital Data Workflow (原題)
Maria Lampridi, Vasso Marinoudi, Lefteris Th. Benos, Remigio Berruto, Gabriele Miserendino, Patrizia Busato, Vasileios Moysiadis, Dionysis Bochtis
🤖 gxceed AI 要約
日本語
イタリア中部の商業レンズマメ圃場25筆（低投入14・集約11）を2作期にわたり、圃場から貯蔵までの境界で栽培段階のGHGを定量した。面積当たり排出は低投入が555、集約が632 kg CO2eq/haだったが、収量が高い集約系は生産物当たりで約22%低い0.50対0.64 kg CO2eq/kgとなった。API連携のデータワークフローで運用記録・算定前提・メタデータ・排出量を整理し、機能単位の選択が結論を左右することを示した。
English
This study quantified cultivation-stage GHG emissions across 25 commercial lentil fields in Central Italy (14 low-input, 11 intensive) over two seasons, within a field-to-storage boundary. Area-normalized emissions were 555 vs. 632 kg CO2eq/ha, but the higher-yielding intensive system showed ~22% lower product-normalized intensity (0.50 vs. 0.64 kg CO2eq/kg). An API-supported data workflow organized operational records, assumptions, metadata, and outputs, showing that conclusions depend on the chosen functional unit.
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
食品・農業サプライチェーンのScope 3算定やSSBJ開示に向け、圃場レベルの一次データをAPIで構造化する手法は、日本の食品企業が一次データ収集・トレーサビリティ体制を構築する際の実務的参考になる。機能単位の選択が排出強度の評価を反転させ得る点は、開示指標設計の教訓として重要。
In the global GX context
As Scope 3 Category 1 (purchased goods) and agricultural emissions gain prominence under ISSB/CSRD and GHG Protocol land-sector guidance, this work demonstrates a transparent, API-driven field-to-storage accounting workflow. It underscores that functional-unit choice can reverse comparative emissions rankings, a key methodological caution for corporate disclosure and product-level footprinting.
👥 読者別の含意
🔬研究者:圃場単位のカーボン会計における機能単位選択とホットスポット分析の方法論的示唆を提供する。
🏢実務担当者:食品・農業企業がScope 3一次データをAPIで構造化し、生産物当たり排出強度を算定する際の実装例として活用できる。
🏛政策担当者:農業GHG算定の透明性・標準化と、機能単位に依存する指標設計の課題を規制設計に反映する材料となる。
📄 Abstract（原文）
Cultivation-stage carbon accounting is essential for supporting low-emission decision-making. In this context, transparent links between field operations, inputs, and reporting indicators are required. This study quantified gross cultivation-stage greenhouse gas (GHG) emissions from 25 commercial lentil fields in Central Italy. The assessment included the same 14 low-input and 11 intensive fields monitored over two cultivation seasons within a field-to-storage boundary. The observational design compared management bundles, and indicators were calculated at field level and summarized using arithmetic means across fields. A data workflow supported by an Application Programming Interface (API) was used to organize operational records, calculation assumptions, metadata, and emissions outputs. Mean field-level area-normalized emissions were 555 and 632 kg CO2eq ha−1 for the low-input and intensive systems, respectively. However, because of its higher yield, the intensive system had approximately 22% lower mean product-normalized GHG intensity than the low-input system, namely 0.50 vs. 0.64 kg CO2eq kg−1 of harvested lentils. Hotspot analysis at the operation level showed that mechanized operations, particularly tillage, harvesting, and seeding, dominated the low-input profile. In contrast, fertilization was the main hotspot in the intensive system. Using producer-provided crude-protein contents, estimated protein yield was higher in the intensive system, while protein-normalized emissions partly overlapped between systems. Overall, interpretation depended on the selected functional unit.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/agronomy16191927first seen 2026-10-07 05:02:06
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