Evidence map›Paper›PMID 40048483›Full record

ArticlePloS one2025

Unveiling the digital revolution: Catalyzing total factor productivity in agriculture.

Jing He, Zhuangyu Wei, Xiaokai Lei

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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.

Jing HeCollege of Agriculture, Guangxi University, Nanning, Guangxi, China.
Zhuangyu WeiSchool of Economics, Guangxi University, Nanning, Guangxi, China.ORCID https://orcid.org/0009-0006-5530-904X
Xiaokai LeiBrandeis International Business School, Brandeis University, Boston, Massachusetts, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Drawing upon panel data spanning the years 2011 to 2022 and encompassing 30 provinces across China, this research employs empirical methodologies, specifically the difference GMM and system GMM methods, to scrutinize the impact of the digital economy on the total factor productivity (TFP) within the agricultural sector. The study reveals a significant augmentation of China's agricultural TFP attributable to the digital economy, a finding robust to various methodological examinations. Notably, the influential role of the digital economy on agricultural TFP is more conspicuous in the central and western regions, as well as in locales characterized by lower productivity levels. Mechanistic analysis underscores that the digital economy exerts a positive influence on agricultural TFP through the stimulation of innovation and marketization effects. Furthermore, strategic recommendations emerge from this study, advocating for the reinforcement of institutional and mechanistic reforms to cultivate an enabling external milieu for the digital economy to propel agricultural TFP. It is posited that regional development strategies should be tailored based on individual resource endowments and the extent of digital economic development. Additionally, there is a call to refine mechanisms promoting high-quality development in agriculture, with an overarching goal of comprehensively elevating agricultural TFP. The implications of this research extend to the imperative need for a nuanced and context-specific approach to advancing agricultural productivity across diverse regions in China.

Indexed as

AgricultureEconomic DevelopmentChinaEfficiencyHumans

Identifiers

PMID40048483
PMCPMC11884720

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.