Evidence map›Paper›PMID 40118854›Full record

ArticleScientific reports2025

Comprehensive evaluation of sugar beet varieties based on genotype × yield × trait (GYT) in different environments.

Dang Xinwang, Lin Yuhang, Hu Xiaohang, Li Yanli

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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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.

Dang XinwangAcademy of Modern Agriculture and Ecology Environment, Heilongjiang University, Harbin, , 150008, Heilongjiang, China.
Lin YuhangAcademy of Modern Agriculture and Ecology Environment, Heilongjiang University, Harbin, , 150008, Heilongjiang, China.
Hu XiaohangAcademy of Modern Agriculture and Ecology Environment, Heilongjiang University, Harbin, , 150008, Heilongjiang, China. 2005142@hlju.edu.cn.
Li YanliAcademy of Modern Agriculture and Ecology Environment, Heilongjiang University, Harbin, , 150008, Heilongjiang, China.

Funding

Research on soil improvement and carbon emission in black soil area by returning filtered mud from agricultural waste sugar plant to field, Heilongjiang Department of Ecology and Environment No. HST2022TR003Research on Sugar Beet Ferti-lization Decision-making Model Based on UAV Remote Sensing Technology No. CARS-170202
6 · The paper itself

Abstract

The aim of this study was to assess the agronomic traits and genotype by yield × trait (GYT) interactions of sugar beet across seven ecological pilot areas in Northeast and North China using GYT biplot analysis. Utilizing correlation analysis and implementing the AMMI model, GGE biplot, and GYT biplot, the study analyzed the agronomic characteristics including plant height, leaf length, leaf width, leaf stalk diameter, SPAD value, root rot, brown spot, sugar content, and sugar yield of 16 sugar beet varieties in the 2023 regional experiment across seven planting ecological pilot areas. The pairwise correlation between yield and trait combinations was analyzed using the GYT biplot technique. Principal components PC1 and PC2 accounted for 80.19 and 12.31% of genotype-trait interactions. KWS7748 (G1) exhibited broader adaptability and superior yield compared to other varieties across different planting areas. Among the varieties tested in the regional sugar beet experiment, KWS7748 (G1) demonstrated the best comprehensive performance. Simultaneously, in the multi-character evaluation methods of sugar beet varieties, the results of GYT biplot analysis and evaluation proved to be more reliable than those of GGE and GT biplot, indicating its effectiveness as a scientific evaluation method for sugar beet varieties.

Indexed as

Beta vulgarisGene-Environment InteractionGenotypeQuantitative Trait, HeritableChinaPhenotypePlant LeavesGYT biplotIdeal indexRegional testSugar beetsYield-trait combination

Identifiers

PMID40118854
PMCPMC11928685

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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.