Evidence map›Paper›PMID 40593977›Full record

ArticleScientific reports2025

Evaluating agricultural efficiency and sustainable development in China.

Muren, Xiaokun Sun, Junya Yao, Li Cao

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

4 authors.

MurenAcademy of Big Data and Interdisciplinary Sciences, Jilin University of Finance and Economics, Changchun, 130117, China.
Xiaokun SunDepartment of Statistics, Jilin University of Finance and Economics, Changchun, 130117, China.
Junya YaoDepartment of Statistics, Jilin University of Finance and Economics, Changchun, 130117, China. 1844080588@qq.com.
Li CaoDepartment of Computer Information, Inner Mongolia Medical University, Hohhot, 010110, China.

Funding

Key Scientific Research Project of Jilin Provincial Department of Education JJLH20250757KJNational Natural Science Foundation of China 72261030National Natural Science Foundation of China 72371115
6 · The paper itself

Abstract

China's Ministry of Agriculture and Rural Development has introduced the National Action Plan for Smart Agriculture (2024-2028) to modernize agriculture through technology and energy restructuring. This study analyzes spatial geographic big data (2014-2022) at the district and county levels to evaluate agricultural output value, influencing factors, and sustainable development potential. Using the Malmquist index, the efficiency of agricultural output across regions is assessed, identifying provinces with efficiency and inefficiency zones. A Spatiotemporal geographically weighted regression (GTWR) with interaction terms is applied to explore factors influencing agricultural output at national, efficiency, and inefficiency levels. In addition, the coupling coordination degree is used to measure the sustainable development capacity of different regions in five ways. The findings suggest the government should implement targeted policies based on agricultural efficiency disparities. Recommendations include improving mechanization, optimizing resource allocation, enhancing soil quality, and promoting sustainable practices in efficient zones. For inefficient zones, strategies should focus on technological advancement and resource management. Differentiated support is advised to balance economic, environmental, and population sustainability, fostering coordinated regional development.

Indexed as

EffectivenessFormulate precise policiesSustainability

Identifiers

PMID40593977
PMCPMC12216875

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.