Evidence map›Paper›PMID 37765427›Full record

ArticlePlants (Basel, Switzerland)2023

Study on the Key Autotoxic Substances of Alfalfa and Their Effects.

Bei Wu, Shangli Shi, Huihui Zhang, Yuanyuan Du, Fang Jing

Abstract read
In one paragraph

Article in Plants (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. 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

5 authors.

Bei WuKey Laboratory of Grassland Ecosystem of Ministry of Education, College of Pratacultural Science, Gansu Agricultural University, Lanzhou 730070, China.
Shangli ShiKey Laboratory of Grassland Ecosystem of Ministry of Education, College of Pratacultural Science, Gansu Agricultural University, Lanzhou 730070, China.
Huihui ZhangKey Laboratory of Grassland Ecosystem of Ministry of Education, College of Pratacultural Science, Gansu Agricultural University, Lanzhou 730070, China.
Yuanyuan DuKey Laboratory of Grassland Ecosystem of Ministry of Education, College of Pratacultural Science, Gansu Agricultural University, Lanzhou 730070, China.
Fang JingKey Laboratory of Grassland Ecosystem of Ministry of Education, College of Pratacultural Science, Gansu Agricultural University, Lanzhou 730070, China.

Funding

National Natural Science Foundation of China 32160330
6 · The paper itself

Abstract

Alfalfa is a leguminous plant with strong autotoxicity, which seriously affects regeneration and stability. In order to clarify the relationship between the key autotoxic substances and autotoxicity of alfalfa, this experiment determined the content of phenolic autotoxic substances in different varieties of alfalfa and the effect of different concentrations of alfalfa extracts on seed germination, seedling growth and physiology. The results showed that the content of single autotoxic substances in the eight alfalfa varieties was highest for total coumarin. The variety with the highest total coumarin content was "LZ", and the lowest content was "656". Principal component analysis of the autotoxicity of eight alfalfa varieties revealed that the variety with the strongest autotoxicity was "LZ" and the weakest was "656". After treatment with extracts, the germination potential, germination rate, germination index and vigor index of 656 were higher than those of LZ, and the seeds of LZ and 656 did not germinate when the concentration was higher than C

Indexed as

alfalfaautotoxicitycontentcoumarinphenolic autotoxic substances

Identifiers

PMID37765427
PMCPMC10538189

What OpenQuestion holds

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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.