Evidence map›Paper›PMID 33854171›Full record

ArticleScientific reports2021

Shortened key growth periods of soybean observed in China under climate change.

Qinghua Tan, Yujie Liu, Liang Dai, Tao Pan

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
5.8field-weighted citation impact, top 4% of its field
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

9 citing papers in PubMed, 39 citations in OpenAlex.

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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 at 3 institutions in 1 country.

Qinghua TanKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Yujie LiuKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. liuyujie@igsnrr.ac.cn.
Liang DaiKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Tao PanKey Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Institute of Geographic Sciences and Natural Resources Research · CNChinese Academy of Sciences · CNUniversity of Chinese Academy of Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Phenology is an important indicator of global climate change. Revealing the spatiotemporal characteristics of crop phenology is vital for ameliorating the adverse effects of climate change and guiding regional agricultural production. This study evaluated the spatiotemporal variability of soybean's phenological stages and key growth periods, and assessed their sensitivity to key climatic factors, utilizing a long-term dataset (1992-2018) of soybean phenology and associated meteorological data collected at 51 stations across China. The results showed that (1) during the soybean growing seasons from 1992 to 2018, the average temperature (0.34 ± 0.09 ℃ decade

Indexed as

ChinaClimate ChangeCrops, AgriculturalGlycine maxSpatio-Temporal AnalysisTemperature

Identifiers

PMID33854171
PMCPMC8047036
OpenAlexW3154969888

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.