Evidence map›Paper›PMID 41719272›Full record

ArticlePloS one2026

Projecting spatiotemporal changes in Soil Organic Carbon stocks in Mexico under coupled socioeconomic and climate scenarios.

Yuan Wang, Xiaoxiong Li, Sha Liu

Abstract read
In one paragraph

Article in PloS one, 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

3 authors.

Yuan WangFaculty of Geography and Resource Sciences, Sichuan Normal University, Chengdu, Sichuan, China.
Xiaoxiong LiNanjing Gago Group, Nanjing, Jiangsu, China.ORCID https://orcid.org/0009-0000-6530-1232
Sha LiuYunnan Provincial Key Laboratory of Soil Carbon Sequestration and Pollution Control, Faculty of Environmental Science and Engineering, Kunming University of Science and Technology, Kunming, Yunnan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soil organic carbon (SOC) sequestration is a pivotal strategy for achieving global climate goals, yet current projections often rely on static landscape assumptions that fail to capture the complex interplay between climatic forcing and socioeconomic development. This limitation is particularly acute in Mexico, where rapid agricultural transformation threatens to destabilize historic carbon sinks. To bridge this gap, we projected spatiotemporal SOC dynamics (2021-2100) across Mexico's diverse ecoregions by coupling a calibrated Random Forest model (R2 = 0.45) with downscaled climate and dynamic land-use data under four Shared Socioeconomic Pathways (SSPs). We identified a current baseline mean SOC stock of 2.96 kg C/m² and revealed stark, pathway-dependent future trajectories. While sustainable and intermediate pathways (SSP126, SSP245, SSP370) stabilize or slightly increase national stocks (~2% gain), the fossil-fueled development scenario (SSP585) drives a continuous decline below baseline levels. Novel analysis of agricultural systems reveals a critical tipping point: under SSP585, the proportion of degrading cropland escalates from 36.7% to 63.2%, transitioning the sector from a net carbon sink to a net source after 2070. These findings provide quantitative evidence that future SOC stability is not merely a climatic outcome but is critically determined by policy-driven land-use choices, offering a data-driven basis for immediate intervention in threatened agricultural zones.

Indexed as

CarbonClimateClimate ChangeSoilAgricultureCarbon SequestrationMexicoSocioeconomic FactorsCarbonSoil

Identifiers

PMID41719272
PMCPMC12922974

What OpenQuestion holds

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LicenceCC BY
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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.