Evidence map›Paper›PMID 41817016›Full record

SynthesisAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

Optimal Grazing Exclusion Duration to Enhance Soil Carbon Sequestration in Degraded Grasslands.

Bin Zhang, Chongzhi Sun, Dongmei Xue, Paul Christiaan Struik, Tongrui Zhang, Jiayue Liu, Tingting Xing, Ziyang Liu, Shiming Tang, Ke Jin

Abstract readMeta-Analysis
In one paragraph

Synthesis in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Bin ZhangKey Laboratory for Model Innovation in Forage Production Efficiency, Ministry of Agriculture and Rural Affairs, Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot, China.
Chongzhi SunDepartment of Grassland Science, College of Grassland Science and Technology, China Agricultural University, Beijing, China.
Dongmei XueTianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin, China.
Paul Christiaan StruikCentre for Crop Systems Analysis, Department of Plant Sciences, Wageningen University & Research, Wageningen, Netherlands.
Tongrui ZhangKey Laboratory for Model Innovation in Forage Production Efficiency, Ministry of Agriculture and Rural Affairs, Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot, China.
Jiayue LiuKey Laboratory for Model Innovation in Forage Production Efficiency, Ministry of Agriculture and Rural Affairs, Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot, China.
Tingting XingKey Laboratory for Model Innovation in Forage Production Efficiency, Ministry of Agriculture and Rural Affairs, Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot, China.
Ziyang LiuKey Laboratory for Model Innovation in Forage Production Efficiency, Ministry of Agriculture and Rural Affairs, Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot, China.
Shiming TangKey Laboratory for Model Innovation in Forage Production Efficiency, Ministry of Agriculture and Rural Affairs, Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot, China.ORCID https://orcid.org/0000-0002-0234-6721
Ke JinKey Laboratory for Model Innovation in Forage Production Efficiency, Ministry of Agriculture and Rural Affairs, Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot, China.

Funding

China Postdoctoral Science Foundation 2024MD764025Chinese Academy of Agricultural Sciences Institute of Grassland Research talent program CAAS-ASTIP-27-GRI-02-CAASIGR2025JQ02National Key Research and Development Program of China 2024YFD1300300National Natural Science Foundation of China 32503298Natural Science Foundation of Inner Mongolia Autonomous Region of china 2025QN03130Northern Center for Agro-pastoral Industry Technology Innovation 2023RC-CYY-2Young Talents of Inner Mongolia 2023NMRC003
6 · The paper itself

Abstract

Soil organic carbon (SOC) plays a crucial role in mitigating climate change and ensuring food security. Grazing exclusion (GE) is widely recognized as an effective strategy for restoring degraded grasslands and enhancing SOC sequestration, yet the optimal duration for this practice remains uncertain. Here, we conducted a meta-analysis using 4257 paired observations from 125 studies in China to quantify the impact of GE on SOC content and stock, and to determine the optimal duration for maximizing carbon sequestration. We found that GE increased SOC content and stock by an average of 34.12% and 24.56%, respectively. This positive effect of GE on SOC becomes greater over time in areas with higher mean annual precipitation (MAP), but it is negative below 200 mm. These effects of GE remain consistent across alpine, meadow, desert, and typical grasslands, and are independent of other environmental attributes. Our results indicate that implementing GE on 70% of degraded grasslands nationwide could, on average, sequester 1.52 Pg C in soil over a period of 10 years (about 17% of global annual fossil fuel emissions), with larger gains expected in higher-MAP regions. Our synthesis provides an evidence-based framework for optimizing grassland restoration through enhanced soil carbon sequestration in degraded grasslands.

Indexed as

Carbon SequestrationConservation of Natural ResourcesGrasslandHerbivorySoilAnimalsCarbonChinaClimate ChangeCarbonSoilfencinggrassland restorationgrazing exclusion durationprecipitation dependencesoil carbon content

Identifiers

PMID41817016
PMCPMC13205864

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.