Evidence map›Paper›PMID 30972091›Full record

ArticleFrontiers in plant science2019

High Nitrogen Levels Alleviate Yield Loss of Super Hybrid Rice Caused by High Temperatures During the Flowering Stage.

Ke Liu, Jun Deng, Jian Lu, Xiaoyan Wang, Bilin Lu, Xiaohai Tian, Yunbo Zhang

Open access · goldAbstract read
In one paragraph

Article in Frontiers in plant science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
12.5field-weighted citation impact, top 2% 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

19 citing papers in PubMed, 81 citations in OpenAlex.

  1. Article
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  9. Genetic Research Progress: Heat Tolerance in Rice.International journal of molecular sciences · 2023
    Review
  10. Article
  11. Article
  12. Frontiers in plant science · 2022
    Article
  13. Article
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  15. Frontiers in plant science · 2021
    Article
  16. Article
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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

7 authors at 1 institution in 1 country.

Ke LiuHubei Collaborative Innovation Center for Grain Industry, Yangtze University, Jingzhou, China.
Jun DengHubei Collaborative Innovation Center for Grain Industry, Yangtze University, Jingzhou, China.
Jian LuHubei Collaborative Innovation Center for Grain Industry, Yangtze University, Jingzhou, China.
Xiaoyan WangHubei Collaborative Innovation Center for Grain Industry, Yangtze University, Jingzhou, China.
Bilin LuHubei Collaborative Innovation Center for Grain Industry, Yangtze University, Jingzhou, China.
Xiaohai TianHubei Collaborative Innovation Center for Grain Industry, Yangtze University, Jingzhou, China.
Yunbo ZhangHubei Collaborative Innovation Center for Grain Industry, Yangtze University, Jingzhou, China.
Yangtze University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The effect of high temperatures on rice production has attracted considerable research attention. It is not clear, however, whether nitrogen (N) management can be used to alleviate the damaging effects of high temperatures on flowering in rice. In this study, we compared the yields of five elite super hybrid rice varieties and examined their heat tolerance under four N treatments in two seasons with contrasting temperatures at flowering: 2015 (normal temperature) and 2016 (high temperature). The average daily temperature during the flowering stage in 2016 was 31.1°C, which was 4.5°C higher than that in 2015. There was a significant positive correlation between grain yield and N level (

Indexed as

climate changegrain yieldheat stresshigh temperaturenitrogensuper hybrid rice

Identifiers

PMID30972091
PMCPMC6443885
OpenAlexW2924698569

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.