Physiological and biochemical pathway: understanding their influence on maize grain yield through genotype–trait interactions

An experiment using a randomized complete block design (RCBD) with three replications was conducted on 10 corn genotypes to evaluate the effect of genotype × trait interaction on grain yield. Analysis of variance revealed that all genotypes differed significantly (p < 0.01) across all traits. Duncan's multiple range test identified KSC704 and KSC706 as favorable genotypes, while SC540 and KSC260 were considered unfavorable. Correlation analysis showed a positive association between grain yield and chlorophyll a, chlorophyll b, phosphorus, carotenoids, sodium, catalase, and potassium. Principal component analysis indicated that the first five components accounted for over 79% of the total data variance. Based on the first two principal components, the genotypes were grouped into four distinct clusters, and the traits into three. A graphical assessment of genotype performance was also conducted. According to the polygon view, genotypes KSC705, KSC400, KSC706, DC370, SC540, and KSC260 were identified as favorable. Among them, KSC705 and KSC400 were selected as the most desirable genotypes based on trait-based and ideal-genotype ranking diagrams. The biplot analysis further confirmed the grouping of genotypes into four distinct clusters.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-05-26
DOI
https://doi.org/10.1038/s41598-025-02628-1
Citations
1
Primary Topic
Genetics and Plant Breeding
Type
article
Field-Weighted Citation Impact
1.75

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Physiological and biochemical pathway: understanding their influence on maize grain yield through genotype–trait interactions

Seyed Mohammad Nasir Mousavi, Pooria Mazloom, Seyed Habib Shojaie
1 citations
Scientific Reports
Genetics and Plant Breeding
1.75
article

Physiological and biochemical pathway: understanding their influence on maize grain yield through genotype–trait interactions

Seyed Mohammad Nasir Mousavi, Pooria Mazloom, Seyed Habib Shojaie
article en
1 citations

Abstract

An experiment using a randomized complete block design (RCBD) with three replications was conducted on 10 corn genotypes to evaluate the effect of genotype × trait interaction on grain yield. Analysis of variance revealed that all genotypes differed significantly (p < 0.01) across all traits. Duncan's multiple range test identified KSC704 and KSC706 as favorable genotypes, while SC540 and KSC260 were considered unfavorable. Correlation analysis showed a positive association between grain yield and chlorophyll a, chlorophyll b, phosphorus, carotenoids, sodium, catalase, and potassium. Principal component analysis indicated that the first five components accounted for over 79% of the total data variance. Based on the first two principal components, the genotypes were grouped into four distinct clusters, and the traits into three. A graphical assessment of genotype performance was also conducted. According to the polygon view, genotypes KSC705, KSC400, KSC706, DC370, SC540, and KSC260 were identified as favorable. Among them, KSC705 and KSC400 were selected as the most desirable genotypes based on trait-based and ideal-genotype ranking diagrams. The biplot analysis further confirmed the grouping of genotypes into four distinct clusters.

Scientific ReportsVol. 15(1)
Dalhousie University (CA), University of Debrecen (HU), Islamic Azad University, Science and Research Branch (IR), Islamic Azad University of Chalous (IR)
Debreceni Egyetem
Zero hunger
Openalex Percentile: Top 15%
Genetics and Plant Breeding
1.75
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.