Evidence map›Paper›PMID 40594063›Full record

ArticleScientific reports2025

Impacts of mechanized farmland transformation on ecological landscape patterns in hilly regions of Zhong County, China.

Xiaohui Xv, Jie Ren, Yongshi Ming

Abstract read
In one paragraph

Article in Scientific reports, 2025. 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.

Xiaohui XvSchool of Architecture and Environmental Art, Sichuan Fine Arts Institute, No. 56, University Town South Road, Shapingba District, Chongqing, 401331, China.
Jie RenSchool of Architecture and Environmental Art, Sichuan Fine Arts Institute, No. 56, University Town South Road, Shapingba District, Chongqing, 401331, China. renjie@scfai.edu.cn.
Yongshi MingSchool of Architecture and Environmental Art, Sichuan Fine Arts Institute, No. 56, University Town South Road, Shapingba District, Chongqing, 401331, China.

Funding

Chongqing Municipal Education Commission KJQN202201005
6 · The paper itself

Abstract

Ecological landscape patterns in agricultural areas are critical indicators of changes in ecological health and spatial structure in farmland utilization projects. In this study, the impacts of mechanized transformation on farmland ecosystems in Zhong County, Chongqing (2019-2024), were evaluated via remote sensing technology, landscape pattern indices, and Geographic Information System (GIS). A comprehensive assessment framework was developed across three dimensions: cropland quality enhancement, plot layout optimization, and topographic adaptation. The key findings included the following. (1) Mechanization upgraded 65.4% of inefficient farmland to high-standard agricultural land, significantly altering land use patterns. (2) Land use intensification increased markedly, with a 63.6% reduction in farmland patch number and a 64.3% decrease in patch density. Morphological improvements included an 18.8% decline in the landscape shape index (LSI) and a 37.3% reduction in edge density (ED). (3) Topographic simplification was achieved through reductions in slope complexity (6.1-18.9%) and elevation variation coefficients (2.96-6.28%). This research provides empirical evidence for optimizing ecological patterns through mechanization, and it offers actionable insights into sustainable agricultural development in hilly regions.

Indexed as

Agricultural efficiencyHilly regionLandscape patternMechanized farmland transformation

Identifiers

PMID40594063
PMCPMC12216460

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.