The Structure of Phenotypic Variation in Seven Segregating F2 Soybean Populations in an Extremely Hot and Dry Season in Southern Russia

Variation in segregating soybean populations has rarely been decomposed into between- and within-population components, and not yet for the southern Russian ecotype. We phenotyped 1062 individually harvested plants from seven F2 populations for thirteen quantitative traits in Krasnodar Krai, 2024. The season was extreme on the following four measures: 4415 °C as the sum of active temperatures, 93 days above 30 °C, 30 days above 35 °C (of which 26 were during heat waves lasting 19 and 7 days each), and 215 mm of rainfall. All four lie outside the six preceding years’ ranges. On average, 76.4% of variation resided within populations, ranging from 52.3% for plant height to 93.6% for productive nodes. The negative protein–oil correlation held in all seven populations (ρ −0.36 to −0.73) but differed in strength. Protein was unrelated to seed mass per plant, and the F2 median exceeded the parental values measured in another nursery in one of six crosses. The growing regime was completely confounded with the parental pairs, so the two groups of crosses are not comparable as treatments, making pseudoreplication measurable: twelve traits separate the groups when the plant is the unit, and only one when the cross is the unit. Pooled-threshold selection repeated the error, assigning 42 out of 78 candidates to one population. Within-population selection retained 20 plants from all seven crosses.

Authors

Institutions

Publication Details

Journal
Agriculture
Published
2026-11-10
DOI
https://doi.org/10.3390/agriculture16222456
Primary Topic
Soybean genetics and cultivation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
article

The Structure of Phenotypic Variation in Seven Segregating F2 Soybean Populations in an Extremely Hot and Dry Season in Southern Russia

М. В. Климушина, Щегольков Альберт Валерьевич, М.Г. Дивашук, Bogdan Paliev et al.
Agriculture
Soybean genetics and cultivation
article

The Structure of Phenotypic Variation in Seven Segregating F2 Soybean Populations in an Extremely Hot and Dry Season in Southern Russia

М. В. Климушина, Щегольков Альберт Валерьевич, М.Г. Дивашук, Bogdan Paliev, Pavel Yu. Kroupin, Gennady I. Karlov, Dmitriy E. Zima
article en

Abstract

Variation in segregating soybean populations has rarely been decomposed into between- and within-population components, and not yet for the southern Russian ecotype. We phenotyped 1062 individually harvested plants from seven F2 populations for thirteen quantitative traits in Krasnodar Krai, 2024. The season was extreme on the following four measures: 4415 °C as the sum of active temperatures, 93 days above 30 °C, 30 days above 35 °C (of which 26 were during heat waves lasting 19 and 7 days each), and 215 mm of rainfall. All four lie outside the six preceding years’ ranges. On average, 76.4% of variation resided within populations, ranging from 52.3% for plant height to 93.6% for productive nodes. The negative protein–oil correlation held in all seven populations (ρ −0.36 to −0.73) but differed in strength. Protein was unrelated to seed mass per plant, and the F2 median exceeded the parental values measured in another nursery in one of six crosses. The growing regime was completely confounded with the parental pairs, so the two groups of crosses are not comparable as treatments, making pseudoreplication measurable: twelve traits separate the groups when the plant is the unit, and only one when the cross is the unit. Pooled-threshold selection repeated the error, assigning 42 out of 78 candidates to one population. Within-population selection retained 20 plants from all seven crosses.

AgricultureVol. 16(22)
All-Russian Research Institute of Agricultural Biotechnology Russian Academy of Agricultural Sciences (RU)
Soybean genetics and cultivation
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.

Laika access coming soon