Pulse Brain · Growing Health Evidence Index
Tier 3 — Observational / field trialPeer-reviewed

Predicting soil carbon with biodiversity

Crowther, T.W. et al.

2019

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Summary

This Nature paper by Crowther et al. (2019) presents a large-scale empirical analysis exploring whether measures of soil biodiversity can be used to predict soil organic carbon across global ecosystems. The study likely draws on extensive observational datasets to demonstrate that biotic variables — alongside or beyond conventional abiotic predictors such as climate and texture — contribute meaningfully to explaining variation in soil carbon stocks. The findings carry significant implications for Earth system modelling and land management strategies aimed at enhancing carbon sequestration.

UK applicability

Although conducted at a global scale, the findings are broadly applicable to UK land management, where soil biodiversity is increasingly recognised within policy frameworks such as the Environmental Land Management scheme (ELMs) as a lever for improving soil health and carbon outcomes. UK practitioners and policymakers seeking to use biodiversity indicators as proxies for soil carbon potential may find the modelling approach informative.

Key measures

Soil organic carbon stocks (Mg C ha⁻¹); soil biodiversity indices (species richness or community composition); predictive model fit (e.g. R², variance explained)

Outcomes reported

The study examined the relationship between soil biodiversity (including microbial and faunal communities) and soil organic carbon stocks across global datasets, assessing whether biodiversity metrics can improve predictions of carbon storage. It likely reported that higher soil biodiversity is positively associated with greater soil carbon retention, with biodiversity explaining variance in carbon stocks beyond abiotic factors alone.

Theme
Farming systems, soils & land use
Subject
Soil biology & carbon cycling
Study type
Research
Study design
Observational / global synthesis (likely combining large-scale field survey data and modelling)
Source type
Peer-reviewed study
Status
Published
Geography
Global
System type
Mixed / natural and managed terrestrial ecosystems
Catalogue ID
XL0450

Topic tags

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