Measuring what matters: soil health as the missing metric in climate-smart agriculture monitoring
重要なものを測定する：気候スマート農業モニタリングにおける欠落指標としての土壌健康 (AI 翻訳)
Leigh Winowiecki, Aida Bargués‐Tobella, Romy Chevallier, Hanna Linden, Sabrina Trautman, Djalal Arinloye Ademonla, Jules Bayala, Robin Chacha, Jacqueline Hannam, Anthony Kimaro, Lukelysia Mwangi, Isaac Betserai Nyoka, Luke Ouko, Zampela Pittaki, Sieglinde Snapp, Zachary Stewart, Bertin Takousing, Tor‐Gunnar Vågen, Rattan Lal
🤖 gxceed AI 要約
日本語
気候スマート農業（CSA）は食料安全保障と気候変動の課題に対処する枠組みだが、これまでは実践の採用率に焦点が当たり、生態学的成果の測定が不足していた。本稿は土壌健康がCSAの3目的（適応・緩和・生産性）を支える中核指標であり、体系的なモニタリングが重要だと主張。LDSFなどの枠組みを通じた標準化された景観モニタリングの実現可能性を示し、多層的政策枠組みへの土壌健康指標の組み込みが進んでいることを報告する。
English
Climate-smart agriculture (CSA) has focused on measuring practice adoption rather than ecological outcomes. This article argues that systematic soil health monitoring is a critical missing metric, as soil health underpins adaptation, mitigation, and productivity. It shows that standardized landscape monitoring frameworks like the Land Degradation Surveillance Framework (LDSF) are feasible and can support performance-based climate finance. Recent policy embedding of soil health indicators signals political support for consistent monitoring.
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
日本では農林水産省が「みどりの食料システム戦略」で土壌炭素貯留を推進しており、本稿の提唱する成果ベースのモニタリングは国内の農業GHG削減手法の実効性検証に示唆を与える。
In the global GX context
Globally, this paper fills a gap in climate-smart agriculture verification by proposing soil health as an outcome-based metric, aligning with ISSB/TCFD nature-related disclosure and performance-based climate finance mechanisms like results-based payments for carbon sequestration.
👥 読者別の含意
🔬研究者:Highlights the need for outcome-based soil health monitoring in CSA research and provides a framework for longitudinal data collection.
🏢実務担当者:Offers guidance for agricultural sustainability teams on integrating soil health metrics into monitoring systems for climate finance eligibility.
🏛政策担当者:Supports the case for embedding soil health indicators in national climate and agricultural policies to improve accountability and enable performance-based funding.
📄 Abstract（原文）
Climate-smart agriculture (CSA) has become a central framework for addressing the intersecting challenges of food security and climate change. Assessments of CSA have largely relied on measuring the adoption of practices and technologies rather than the ecological outcomes they are intended to deliver. This article argues that the absence of systematic soil health monitoring represents a critical measurement gap in CSA, limiting the ability to verify whether interventions genuinely enhance resilience, contribute to adaptation and mitigation, and sustain productivity. Drawing on two decades of research, we show that soil health underpins all three CSA objectives - adaptation, mitigation and productivity - through its role in regulating water availability, nutrient cycling, carbon storage and biodiversity conservation. We propose scaling outcome-based monitoring to complement practice-based monitoring, positioning soil health as an integrating metric that aligns climate, land degradation and biodiversity agendas. Standardized landscape monitoring approaches, including repeated georeferenced measurements of soil health indicators through frameworks such as the Land Degradation Surveillance Framework (LDSF), demonstrate that operationalisation is feasible and can generate longitudinal datasets. The recent embedding of soil health indicators within multi-level policy frameworks and reporting systems signals political support for implementing consistent soil health monitoring frameworks. Strengthening soil monitoring infrastructure is therefore essential for improving accountability, informing implementation and enabling performance-based climate finance.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3389/fsufs.2026.1814366first seen 2026-07-06 04:43:29
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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。