Evidence map›Paper›PMID 36141496›Full record

ArticleInternational journal of environmental research and public health2022

From Fragmentation to Intensification: Land Reform in China's "New Era".

Qiang Wang, Liying Yu, Yueling Yang

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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.

Qiang WangSchool of Business, Shandong Management University, Jinan 250357, China.ORCID 0000-0003-0146-7668
Liying YuSchool of Business, Shandong Management University, Jinan 250357, China.
Yueling YangSchool of Business, Shandong Management University, Jinan 250357, China.ORCID 0000-0001-5282-9871

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Combining the current national conditions of China and the status quo of rural land, realizing the transformation of land from fragmentation to intensification is the only way for China to move towards agricultural modernization. We selected Feicheng City, Shandong Province, as the research area, conducted regression analysis on the data by means of questionnaires and key interviews, and identified the influencing factors that can affect and change farmers' willingness to transfer (WTT) their land and willingness to the duration (WTD) of land transfer. The study found that 82.54% of farmers are willing to transfer land, and the WTD is 9.34 years. Among them, five factors, including job stability, purchased houses in urban area, cultivated land roads, degree of policy understanding, and emotion for the land, can significantly affect the farmers' WTT. Six factors, namely, age, job stability, number of family members, purchased houses in urban area, non-agricultural income, emotion for the land, can significantly affect the farmers' WTD. Based on this, we propose the "MPEU theory" of farmers' land transfer. That is, by allowing farmers to change their minds, understand policies, increase the non-agricultural employment rate, and improve the level of urbanization, the farmers' WTT/WTD can be improved, and the level of land intensification can be improved. Finally, agricultural modernization, peasant citizenization, and rural urbanization will be realized.

Indexed as

AgricultureFarmersChinaCitiesHumansUrbanizationLand Reformwillingness to the duration of land transfer (WTD)willingness to transfer land (WTT)

Identifiers

PMID36141496
PMCPMC9517452

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.