gxceed
← 論文一覧に戻る

Data for Helfenstein et al. 2026, Continental-scale evidence of farm management impacts on soil carbon, Global Change Biology

ヘルフェンシュタインら2026年、農地管理が土壌炭素に与える影響に関する大陸規模の証拠、Global Change Biology (AI 翻訳)

Julian Helfenstein

Zenodo (CERN European Organization for Nuclear Research)データセット2026-05-01#炭素会計Origin: Global
DOI: 10.5281/zenodo.19948606
原典: https://doi.org/10.5281/zenodo.19948606

🤖 gxceed AI 要約

日本語

本データセットは、欧州全域の農地管理と土壌有機炭素（SOC）変化の関係を分析した研究に付随する。LUCAS調査（2009年・2018年）とFADNデータを結合し、耕起強度、肥料投入、輪作多様性などの管理変数がSOC動態に与える影響を評価する。プライバシー保護のため個別農場の管理値は含まれないが、確率的予測値を提供する。

English

This dataset accompanies a study on continental-scale evidence of farm management impacts on soil carbon across Europe. It integrates LUCAS soil survey data (2009, 2018) with farm management variables from the FADN dataset, including tillage, fertilization, and crop rotation, to assess effects on soil organic carbon change. Point values are withheld for privacy; predicted management probabilities are provided.

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

日本では農地土壌炭素の算定・報告が今後拡大する可能性があり、本データが示す管理変数と炭素変化の関係は、日本の農業分野におけるGX実践（例：カーボンファーミング、J-クレジット）のエビデンス基盤として参考になる。

In the global GX context

This paper provides large-scale empirical evidence linking farm management practices to soil carbon changes, directly relevant to global carbon accounting frameworks (e.g., IPCC guidelines, carbon farming initiatives). It supports corporate scope 3 accounting for agricultural supply chains and informs policy on agricultural decarbonization.

👥 読者別の含意

🔬研究者:Provides a validated dataset and methodology for analyzing management impacts on soil carbon across large scales.

🏢実務担当者:Offers evidence-based insights on which management practices (e.g., tillage, fertilization) influence soil carbon sequestration.

🏛政策担当者:Supports design of agricultural carbon credit programs by quantifying the link between management and SOC change.

📄 Abstract（原文）

Dataset accompanying the publication - Title: Continental-scale evidence of farm management impacts on soil carbon - Authors: Julian Helfenstein, Nick van Dijk, Anna Edlinger, Gabriel Y.K. Moinet, Sophie Q. van Rijssel, Alexandre M.J.-C. Wadoux, Rachel Creamer, Carmen Vazquez, Vera L. Mulder - Journal: Global Change Biology Notes: For data sources see Supplementary Table S4 of Helfenstein et al. 2026, Continental-scale evidence of farm management impacts on soil carbon, Global Change Biology. Point values of management variables are not included to protect privacy of land owners. Variable descriptions LC1_orig = original LUCAS LC1 land cover LC1_JH = simplified LC1 for this study LC0_JH = simplified LC0 land cover Depth = soil depth OC_2018 = soil organic carbon concentration in g kg-1 from LUCAS survey year 2018 OC_2009 = soil organic carbon concentration in g kg-1 from LUCAS survey year 2009 ParMat1 = parent material1 ParMat2 = parent material2 Sand/silt/clay = soil texture component in % WRB_LV1 = world reference base soil group Elevation.y = elevation in m Coarse = coarse fragment content in % Aridity = aridity ET0 = potential evapotranspiration in mm Koppen = Köppen-Geiger climate classification MAT = mean annual temperature MAP = mean annual precipitation PCZ = pedoclimatic zone from Feeney et al. 2024 PCZ_imputed = predicted pedoclimatic zone dSOC =change in SOC concentration between 2009 and 2018 r.dSOC = relative change in SOC stock_2018 = soil organic C stocks in Mg C ha-1 median_SOC_stock = median SOC stocks per PCZ stock_benchmarked = observed SOC stock divided by median stock bdref = bulk density n = number of nearby individual farms in FADN dataset having the same LC1 used for predicting most likely farm management crich = crop richness organic = probability of organic cultivation rot = crop rotational diversity LU = livestock units ha-1 N_min/P_min/K_min = mineral fertilizer application in kg ha-1 N_input/P_input/K_input = mineral plus animal-based fertilizer application in kg ha-1 N_org/P_org/K_org = share of manure in total nutrient input till = tillage intensity cer = % cereals in crop rotation roo = % roots and tubers in crop rotation pul = % pulses in crop rotation rap = % rapeseed in crop rotation sun = % sunflower in crop rotation ind= % other industrial crops in crop rotation maize = % maize in crop rotation veg = % vegetables in crop rotation frleg = % fodder legumes in crop rotation ley = % ley in crop rotation ley_forage = sum of ley and frleg intensity = aggregate management intensity index

🔗 Provenance — このレコードを発見したソース

🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。

gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。