Determination of the breeding value of sunflower varieties and hybrids with cluster and factor analyzes

Abstract. The creation of highly productive sunflower varieties and hybrids adapted to the conditions of cultivation regions depends on the use of source material with high genetic diversity. The purpose of the study is to study the variability of agronomic characters in sunflower varieties and hybrids created in various breeding centers, their correlation, and to group genotypes according to a set of economically valuable indicators, using multivariate statistics methods. Methods. The research was carried out on the Right bank of the Saratov region on the experimental field of the Federal Center of Agriculture Research of the South-East Region in 2021–2023. The objects of research were varieties and new experimental hybrids bred by epy Federal Center of Agriculture Research of the South-East Region (31 varieties and hybrids) and V. S. Pustovoyt All-Russian Research Institute of Oil Crops (12 hybrids). To evaluate the breeding material, generally accepted methodological recommendations were used. Results. 43 varieties and hybrids of sunflower were assessed according to 8 agronomic characters. It was established a low degree of variability in yield (V=9.4%); as well as of plant height (V = 7.8 %); anthode diameter (V = 5.2 %); in unit (V = 5.3 %); and of oil content (V = 5.7 %). Average variability was revealed in the weight of 1000 seeds (V = 16.0 %); anthode area (V = 11.1 %); oil crop (V = 15.8 %). Correlation analysis made it possible to identify 8 significant connections at the 5.0 % level. High correlation coefficients were established between oilseed yield and unit (r = 0.7), as well as between anthode diameter and anthode area (r = 0.83). As a result of factor analysis, 8 characteristics were reduced to 5 factors (loading above 5.0 %) with a total variance of 90.95 %. Clustering by minimum Euclidean distances allowed the studied genotypes to be grouped into 10 clusters, including from 1 to 16 genotypes at the 33rd step. The identified clusters can be considered as independent groups for inclusion in the selection process. Scientific novelty. An attempt was made to group varieties and new experimental sunflower hybrids created in various breeding centers according to the similarities and differences of the main agronomic characters in the arid conditions of the Saratov region.

Authors

Institutions

Publication Details

Journal
Agrarian Bulletin of the
Published
2024-08-05
DOI
https://doi.org/10.32417/1997-4868-2024-24-07-850-859
Citations
1
Primary Topic
Sunflower and Safflower Cultivation
Type
article
Field-Weighted Citation Impact
0.60
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Determination of the breeding value of sunflower varieties and hybrids with cluster and factor analyzes

Lyudmila Aleksandrovna Gudova, Andrey Vladimirovich Lekarev
1 citations
Agrarian Bulletin of the
Sunflower and Safflower Cultivation
0.60
article

Determination of the breeding value of sunflower varieties and hybrids with cluster and factor analyzes

Lyudmila Aleksandrovna Gudova, Andrey Vladimirovich Lekarev
article en
1 citations

Abstract

Abstract. The creation of highly productive sunflower varieties and hybrids adapted to the conditions of cultivation regions depends on the use of source material with high genetic diversity. The purpose of the study is to study the variability of agronomic characters in sunflower varieties and hybrids created in various breeding centers, their correlation, and to group genotypes according to a set of economically valuable indicators, using multivariate statistics methods. Methods. The research was carried out on the Right bank of the Saratov region on the experimental field of the Federal Center of Agriculture Research of the South-East Region in 2021–2023. The objects of research were varieties and new experimental hybrids bred by epy Federal Center of Agriculture Research of the South-East Region (31 varieties and hybrids) and V. S. Pustovoyt All-Russian Research Institute of Oil Crops (12 hybrids). To evaluate the breeding material, generally accepted methodological recommendations were used. Results. 43 varieties and hybrids of sunflower were assessed according to 8 agronomic characters. It was established a low degree of variability in yield (V=9.4%); as well as of plant height (V = 7.8 %); anthode diameter (V = 5.2 %); in unit (V = 5.3 %); and of oil content (V = 5.7 %). Average variability was revealed in the weight of 1000 seeds (V = 16.0 %); anthode area (V = 11.1 %); oil crop (V = 15.8 %). Correlation analysis made it possible to identify 8 significant connections at the 5.0 % level. High correlation coefficients were established between oilseed yield and unit (r = 0.7), as well as between anthode diameter and anthode area (r = 0.83). As a result of factor analysis, 8 characteristics were reduced to 5 factors (loading above 5.0 %) with a total variance of 90.95 %. Clustering by minimum Euclidean distances allowed the studied genotypes to be grouped into 10 clusters, including from 1 to 16 genotypes at the 33rd step. The identified clusters can be considered as independent groups for inclusion in the selection process. Scientific novelty. An attempt was made to group varieties and new experimental sunflower hybrids created in various breeding centers according to the similarities and differences of the main agronomic characters in the arid conditions of the Saratov region.

Agrarian Bulletin of theVol. 24(07)
Agricultural Research Institute for South-East Region (RU)
Zero hunger
Openalex Percentile: Top 22%
Sunflower and Safflower Cultivation
0.60
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.