Evidence map›Paper›PMID 32117352›Full record

ArticleFrontiers in plant science2019

Estimation of Vertical Leaf Nitrogen Distribution Within a Rice Canopy Based on Hyperspectral Data.

Jiaoyang He, Xiangbin Zhang, Wanting Guo, Yuanyuan Pan, Xia Yao, Tao Cheng, Yan Zhu, Weixing Cao, Yongchao Tian

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 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
4.0field-weighted citation impact, top 6% 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

13 citing papers in PubMed, 39 citations in OpenAlex.

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  10. Changes in Vertical Phenotypic Traits of Rice (Frontiers in plant science · 2022
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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

9 authors at 1 institution in 1 country.

Jiaoyang HeNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.
Xiangbin ZhangNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.
Wanting GuoNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.
Yuanyuan PanNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.
Xia YaoNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.
Tao ChengNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.
Yan ZhuNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.
Weixing CaoNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.
Yongchao TianNational Engineering and Technology Center for Information Agriculture, Nanjing Agricultural University, Nanjing, China.
Nanjing Agricultural University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate estimations of the vertical leaf nitrogen (N) distribution within a rice canopy is helpful for understanding the nutrient supply and demand of various functional leaf layers of rice and for improving the predictions of rice productivity. A two-year field experiment using different rice varieties, N rates, and planting densities was performed to investigate the vertical distribution of the leaf nitrogen concentration (LNC, %) within the rice canopy, the relationship between the LNC in different leaf layers (LNC

Indexed as

leaf nitrogen concentrationremote sensingricevegetation indexvertical distribution

Identifiers

PMID32117352
PMCPMC7031418
OpenAlexW3005630254

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.