Annotation:
Although Kazakhstan increases the area under soybean cultivation annually, its production
remains challenging in the northern and eastern regions due to the limited number of varieties adapted
to climatic conditions. Traditional methods of selecting parental forms for the creation of new ultraearly
and early soybean varieties require significant time and resource investments, so modern
breeding technologies are aimed at increasing the accuracy and efficiency of hybridization. In the
context of agricultural intensification and the need to adapt soybean varieties to specific agroecological
conditions, cluster analysis has proven to be an effective tool for evaluating and selecting
varietal material. Its application made it possible not only to structure the studied soybean accessions
by economically important traits, but also to identify the most promising forms for subsequent use in
breeding programs. The article presents the results of a study of 102 collection soybean accessions of
various ecological and geographical origins, evaluated by the main economically important traits in
order to define criteria for selecting sources and donors for breeding for high productivity, early
maturity, and high biochemical performance, using cluster analysis via Ward’s method with the
Statistica v.13 software.
To conduct a cluster analysis of 102 collection soybean accessions for systematization based
on economically important traits and to identify genetically promising parental forms to improve the
efficiency of the breeding process and accelerate the development of new varieties adapted to the
conditions of the northern and eastern regions of Kazakhstan.
The study was conducted using the hierarchical clustering method based on Ward’s method.
Statistical analysis was carried out using Statistica v.13.
As a result of studying 102 collection soybean accessions using cluster analysis, five clusters
were identified that differed in sets of economically important traits. From the first cluster, four
accessions were identified as sources and donors of high yield and high protein content in seeds. From
the second cluster, six accessions were identified as sources and donors of early maturity, and five as
sources of high protein content. From the third cluster, four early maturing accessions, three highyielding
accessions, five with high protein content, and three with high fat content were identified.
From the fourth cluster, three accessions were identified by yield level, two by 1000 seed weight, two
by protein content, and four by pod insertion height. In the fifth cluster, seven soybean accessions
stood out for yield and three for pod insertion height. Cluster analysis has proven its effectiveness as a digital breeding tool, facilitating the accelerated development of adapted soybean varieties and the
expansion of soybean cultivation in Kazakhstan.
Year of release:
2025
Number of the journal:
2(98)
Heading: Technical sciences and technologies