Studies on correlation and path analysis of yield attributes in cucumber (Cucumis sativus L.)

The present investigation was carried out to assess the correlation and path coefficient analysis for fruit yield and its component traits in 26 cucumber (Cucumis sativus L.) genotypes evaluated during Summer and Kharif seasons (2024). The experiment was laid out in a Randomized Block Design (RBD) with three replications. Observations were recorded on 18 quantitative and qualitative traits. Significant genotypic and phenotypic correlations were observed among the traits in both seasons and in the pooled data. Fruit yield per plant and fruit yield per hectare exhibited strong positive genotypic correlations with average fruit weight, number of seed per fruit, number of fruits per plant, fruit length, fruit diameter, and number of primary branches per plant across seasons. In the pooled data, average fruit weight had a highly significant and positive correlation with average fruit weight (r = 0.915**), and fruit length (r = 0.913**). Genotypic and phenotypic path coefficient analysis revealed that fruit yield per plant had a strong and significant direct positive effect on Fruit yield (q/ha) across all seasons. Among individual traits, fruit length, average fruit weight, and number of seeds per fruit consistently showed high direct positive effects on yield in all three analyses. In the Kharif season, genotypic path analysis showed the highest direct effects from average fruit weight (0.8491) and number of fruits per plant (0.0485), whereas phenotypically, average fruit weight (0.5249) and number of fruits per plant (0.0226) were most influential. During the Summer season, average fruit weight, number of fruits per plant, number of seed per fruit showed the highest direct phenotypic effects on FYH, with supporting genotypic effects from internodal length and fruit length. The pooled analysis further validated these findings, with FL (0.920**), average fruit weight (0.915**), and number of seed per fruit (0.853**) contributing the most substantial direct effects genotypically. Traits such as vine length, number of fruits per plant, and fruit diameter also contributed positively but to a lesser degree. Conversely, traits like days to staminate flower anthesis, node number to first staminate flower appearance, and internodal length showed consistently negative direct effects on Fruit yield (q/ha) in all seaso.

Authors

Publication Details

Journal
International Journal of Advanced Biochemistry Research
Published
2025-01-01
DOI
https://doi.org/10.33545/26174693.2025.v9.i7i.4818
Primary Topic
Advances in Cucurbitaceae Research
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Studies on correlation and path analysis of yield attributes in cucumber (Cucumis sativus L.)

Nimit Singh, Ashish Kumar Singh, Shivakumar Km, T. Prameela Devi
International Journal of Advanced Biochemistry Research
Advances in Cucurbitaceae Research
article

Studies on correlation and path analysis of yield attributes in cucumber (Cucumis sativus L.)

Nimit Singh, Ashish Kumar Singh, Shivakumar Km, T. Prameela Devi
article en

Abstract

The present investigation was carried out to assess the correlation and path coefficient analysis for fruit yield and its component traits in 26 cucumber (Cucumis sativus L.) genotypes evaluated during Summer and Kharif seasons (2024). The experiment was laid out in a Randomized Block Design (RBD) with three replications. Observations were recorded on 18 quantitative and qualitative traits. Significant genotypic and phenotypic correlations were observed among the traits in both seasons and in the pooled data. Fruit yield per plant and fruit yield per hectare exhibited strong positive genotypic correlations with average fruit weight, number of seed per fruit, number of fruits per plant, fruit length, fruit diameter, and number of primary branches per plant across seasons. In the pooled data, average fruit weight had a highly significant and positive correlation with average fruit weight (r = 0.915**), and fruit length (r = 0.913**). Genotypic and phenotypic path coefficient analysis revealed that fruit yield per plant had a strong and significant direct positive effect on Fruit yield (q/ha) across all seasons. Among individual traits, fruit length, average fruit weight, and number of seeds per fruit consistently showed high direct positive effects on yield in all three analyses. In the Kharif season, genotypic path analysis showed the highest direct effects from average fruit weight (0.8491) and number of fruits per plant (0.0485), whereas phenotypically, average fruit weight (0.5249) and number of fruits per plant (0.0226) were most influential. During the Summer season, average fruit weight, number of fruits per plant, number of seed per fruit showed the highest direct phenotypic effects on FYH, with supporting genotypic effects from internodal length and fruit length. The pooled analysis further validated these findings, with FL (0.920**), average fruit weight (0.915**), and number of seed per fruit (0.853**) contributing the most substantial direct effects genotypically. Traits such as vine length, number of fruits per plant, and fruit diameter also contributed positively but to a lesser degree. Conversely, traits like days to staminate flower anthesis, node number to first staminate flower appearance, and internodal length showed consistently negative direct effects on Fruit yield (q/ha) in all seaso.

International Journal of Advanced Biochemistry ResearchVol. 9(7)
Zero hunger
Openalex Percentile: Top 80%
Advances in Cucurbitaceae Research
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.