Pulse Brain · Growing Health Evidence Index
Tier 4 — Narrative / commentaryPeer-reviewed

Modelling spatio-temporal patterns of soil carbon and greenhouse gas emissions in grazing lands: Current status and prospects

Junye Wang, Yumei Li, Edward W. Bork, G. M. Richter, Hyung‐Il Eum, Changchun Chen, Syed Hamid Hussain Shah, Symon Mezbahuddin

The Science of The Total Environment · 2020

Read source ↗ All evidence

Summary

This review synthesises current modelling approaches for simulating soil carbon storage and greenhouse gas emissions across grazing landscapes. As suggested by the title and journal scope, the authors evaluate existing model capabilities and limitations, likely proposing improvements to better predict and manage climate impacts of pastoral systems. The work appears positioned to support evidence-based policy and management strategies for emissions mitigation in grazing lands.

UK applicability

The findings are directly applicable to UK pastoral farming, where grazing systems dominate livestock production and contribute substantially to agricultural emissions. The review's insights into modelling approaches could inform UK climate reporting (e.g., IPCC inventory methods) and evidence for sustainable intensification of grassland systems.

Key measures

Soil carbon dynamics; greenhouse gas emissions (CO₂, CH₄, N₂O); spatio-temporal modelling frameworks; grazing system variables

Outcomes reported

The study reviews current modelling approaches for predicting spatio-temporal patterns of soil carbon stocks and greenhouse gas emissions in grazing lands. It assesses the status of existing models and identifies prospects for improved prediction and management.

Theme
Climate & resilience
Subject
Climate & greenhouse gas mitigation
Study type
Narrative Review
Study design
Narrative review
Source type
Peer-reviewed study
Status
Published
Geography
International
System type
Pasture-based livestock
DOI
10.1016/j.scitotenv.2020.139092
Catalogue ID
SNmohxvn79-p390br

Topic tags

Pulse AI · ask about this record

Dig deeper with Pulse AI.

Pulse AI has read the whole catalogue. Ask about this record, its theme, or how the findings apply to UK farming and policy — every answer cites the underlying studies.