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eISSN: 2083-5906
ISSN: 1230-1485
ISSN: 1230-1485
ORIGINAL RESEARCH
Exploring Multivariate Relationships Among
Seed Morphometric and Yield-Related Traits
in Bread Wheat (Triticum aestivum L.)
1
Institute of Molecular Biology and Biotechnology, The University of Lahore, Lahore
2
Department of Botany, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
3
Department of Botany, University of Sargodha, Sargodha
4
Institute of Molecular Biology and Biotechnology, University of Lahore Sargodha Campus
5
Department of Agronomy, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur,
Bahawalpur, 63100, Pakistan
6
College of Agronomy, Northwest A & F University, Yangling, 712100 Shaanxi, China
7
Department of Food Science and Nutrition, College of Agriculture Food Science, King Saud University,
Riyadh, Saudi Arabia
8
Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
Submission date: 2023-12-24
Final revision date: 2024-03-11
Acceptance date: 2024-04-20
Online publication date: 2024-09-03
Publication date: 2025-01-28
Corresponding author
Muhammad Jamil
Department of Botany, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Department of Botany, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Usman Zulfiqar
Department of Agronomy, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Department of Agronomy, Faculty of Agriculture and Environment, The Islamia University of Bahawalpur, Bahawalpur, 63100, Pakistan
Pol. J. Environ. Stud. 2025;34(3):2003-2010
KEYWORDS
digital image analysis (DIA)digital ground cover (DGC)PCAstructural equation modeling (SEM)grain yield (GY)
TOPICS
ABSTRACT
Correlation and path analysis for seed morphometric as well as yield-related traits were performed
on five bread wheat varieties, each sown on an area of one acre; this investigation was carried out to
check the influence of these traits on the grain yield of bread wheat (Triticum aestivum L.). Digital
image analysis (DIA) is a quantitative technique to phenotype seed morphometric characteristics with
high accuracy. Multivariate analysis was performed to determine the relationship bdeetween ground
cover (GC) and seed size and grain yield (GY). Significantly important traits were identified by principal
component analysis (PCA). Applying the multiple linear regression model, grain yield was predicted
with the help of ground cover and seed shape traits. The association of the studied traits was quantified
with a structural equation model. The impact of spikes per meter square (SPMS) and thousand kernel
weights (TKW) on grain yield was significant (p-value <.01). Plant height has a detrimental impact
on grain yield, and the grain yield was influenced by two features: spikes per meter square (SPMS)
and grain weight per spike (GWS), which have a direct impact on thousand kernel weight (TKW).
The present research aims to feature the relationship between yield-contributing characteristics and the digital ground cover of the wheat crop. The present work uncovered the fact that digital ground
cover (DGC) influences the seed length, area, and perimeter.
CONFLICT OF INTEREST
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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| eISSN: | 2083-5906 |
| ISSN: | 1230-1485 |
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