Have a personal or library account? Click to login
Evaluation of the growth, drought tolerance and biochemical compositions of introduced red currant cultivars and Russian breeding genotypes in temperate continental climate Cover

Evaluation of the growth, drought tolerance and biochemical compositions of introduced red currant cultivars and Russian breeding genotypes in temperate continental climate

Open Access
|Sep 2021

Figures & Tables

Figure 1

Disease scores of S. mors-uvae (A) and P. ribis (B) on the test genotypes in optimum and drought seasons. Averages of overall seasons that were compared for genotypes and different letters over the green columns were used to represent significant differences among the genotypes according to Tukey's test. Independent samples t-test was used to compare optimum and drought seasons separately for each genotype, *Represents significant difference and ns means non-significant.
Disease scores of S. mors-uvae (A) and P. ribis (B) on the test genotypes in optimum and drought seasons. Averages of overall seasons that were compared for genotypes and different letters over the green columns were used to represent significant differences among the genotypes according to Tukey's test. Independent samples t-test was used to compare optimum and drought seasons separately for each genotype, *Represents significant difference and ns means non-significant.

Figure 2

Berry weight (A), yield (B), raceme length (C) and the number of berries in a raceme (D) of test genotypes in optimum and drought seasons. Averages of overall seasons that were compared for genotypes and different letters over the green columns were used to represent significant differences among the genotypes according to Tukey's test. Independent samples t-test was used to compare optimum and drought seasons separately for each genotype, *Represents significant difference and ns means non-significant.
Berry weight (A), yield (B), raceme length (C) and the number of berries in a raceme (D) of test genotypes in optimum and drought seasons. Averages of overall seasons that were compared for genotypes and different letters over the green columns were used to represent significant differences among the genotypes according to Tukey's test. Independent samples t-test was used to compare optimum and drought seasons separately for each genotype, *Represents significant difference and ns means non-significant.

Figure 3

Soluble solid concentration (A), titratable acidity (B), ascorbic acid content (C) and total phenolic content (D) of test genotypes in optimum and drought seasons. Averages of overall seasons that were compared for genotypes and different letters over the green columns were used to represent significant differences among the genotypes according to Tukey's test. Independent samples t-test was used to compare optimum and drought seasons separately for each genotype, *Represents significant difference and ns means non-significant.
Soluble solid concentration (A), titratable acidity (B), ascorbic acid content (C) and total phenolic content (D) of test genotypes in optimum and drought seasons. Averages of overall seasons that were compared for genotypes and different letters over the green columns were used to represent significant differences among the genotypes according to Tukey's test. Independent samples t-test was used to compare optimum and drought seasons separately for each genotype, *Represents significant difference and ns means non-significant.

Figure 4

Correlation among selected traits (A), PCA – biplot with loadings (B), factor map (C) and hierarchical cluster (D) analysis of red currant genotypes.(1) ‘Jonkheer van Tets’; (2) ‘Hollandische Rote’; (3) ‘Viksne’; (4) ‘Shchedraya’; (5) ‘Natali’; (6) 129-21-61; (7) 111-19-81; (8) 261-65-19 and (9) 271-58-24. PCA, principal component analysis.
Correlation among selected traits (A), PCA – biplot with loadings (B), factor map (C) and hierarchical cluster (D) analysis of red currant genotypes.(1) ‘Jonkheer van Tets’; (2) ‘Hollandische Rote’; (3) ‘Viksne’; (4) ‘Shchedraya’; (5) ‘Natali’; (6) 129-21-61; (7) 111-19-81; (8) 261-65-19 and (9) 271-58-24. PCA, principal component analysis.

Figure 5

Correlation among selected water regime traits in soil and plant leaves of red currant plants in May (A), July (B), drought season (C) and optimum season (D). ASM.0–200, absolute soil moisture (%) at 0–200 mm; ASM.200–400, absolute soil moisture (%) at 200–400 mm; BW.in.L, bound water in leaves (%); Coef., coefficient of bound/free water (%); FW.in.L, free water in leaves (%); SM.0–200, soil moisture (%) in 0–200 mm; SM.200–400, soil moisture (%) in 200–400 mm; Transp., transpiration (mg · m−2 · h−1); WC.in.L, water content (%).
Correlation among selected water regime traits in soil and plant leaves of red currant plants in May (A), July (B), drought season (C) and optimum season (D). ASM.0–200, absolute soil moisture (%) at 0–200 mm; ASM.200–400, absolute soil moisture (%) at 200–400 mm; BW.in.L, bound water in leaves (%); Coef., coefficient of bound/free water (%); FW.in.L, free water in leaves (%); SM.0–200, soil moisture (%) in 0–200 mm; SM.200–400, soil moisture (%) in 200–400 mm; Transp., transpiration (mg · m−2 · h−1); WC.in.L, water content (%).

Figure 6

Comparison of the water content of leaves in May and July (A), optimum and drought seasons in May (B) and optimum and drought seasons in July (C); and comparison of transpiration of genotypes in May and July (D), optimum and drought seasons in May (E) and optimum and drought seasons in July (F). Averages of overall seasons that were compared for genotypes and different letters over the columns were used to represent significant differences among the genotypes according to Tukey's test. Independent samples t-test was used to compare May and July or optimum and drought seasons separately for each genotype, *Represents significant difference and ns means non-significant.
Comparison of the water content of leaves in May and July (A), optimum and drought seasons in May (B) and optimum and drought seasons in July (C); and comparison of transpiration of genotypes in May and July (D), optimum and drought seasons in May (E) and optimum and drought seasons in July (F). Averages of overall seasons that were compared for genotypes and different letters over the columns were used to represent significant differences among the genotypes according to Tukey's test. Independent samples t-test was used to compare May and July or optimum and drought seasons separately for each genotype, *Represents significant difference and ns means non-significant.

Some climatic characteristics of the experimental site_

YearAverage monthly air temperature (°C)Humidity (mm)Maximum soil temperature (°C)

MayJulyMayJulyMayJuly
20141619.137.12046.653
201519.520.844.279.15646
201617.721.247.266.534.531
201716.322.256.385.538.541
201817.721.231.41204229
Long-term average1318.536.388.8N/AN/A

Introduced and Russian red currant genotypes of the present study_

GenotypeOrigin

GeneticGeographic
Jonkheer van TetsFaya Plodorodnaya × London MarketThe Netherlands
NataliRibes vulgare × R. rubrum × Ribes petraeumRussia
Hollandische RoteR. rubrum × Ribes petraeumThe Netherlands
ViksneRibes warscewiczii Jancz.Latvia
ShchedrayaFaya Plodorodnaya × Houghton CastleRussia
129-21-61Jonkheer van Tets × Hollandische RoteRussia
261-65-19Jonkheer van Tets × Ribes atropurpureumRussia
111-19-81Jonkheer van Tets - free pollinationRussia
271-58-24Jonkheer van Tets × Ribes meyeriRussia

Disease scores followed for the observation of Sphaerotheca mors-uvae and Pseudopeziza ribis_

Disease scoreSphaerotheca mors-uvaePseudopeziza ribis
0No infection (healthy plant)No infection (healthy plant)
1.0Very weak disease damage (up to 10% of leaves and up to 1% of berries are affected)Very weak damage (up to 5% leaves are affected)
2.0Weak plant disease (up to ¼ of the shrub shoots, up to 25% of leaves and up to 3% of berries are affected)Weak plant damage (up to 10% of the leaves are damaged)
3.0Average plant disease (up to ⅓ of the shrub shoots, 26–50% of the leaves and up to 10% of the berries are affected)Average plant damage (up to 30% of leaves are damaged)
4.0Severe plant disease (⅓ to ¼ of the shrub shoots, 51–70% of the leaves and up to 20% of the berries are affected)Severe plant damage (up to 50% of the leaves are damaged)
5.0Very severe disease (more than ½ of the shrub shoots, more than 70% of the leaves and more than 20% of the berries are affected)Very severe damage (more than 50% of the leaves are damaged)

Biometric indicators of productivity components_

GenotypesRaceme lengthNumber of berries in a racemeBerry weight

Short (5.1–8.0)Medium (8.1–10.0)Long (10.1–12.0)Little (7–10)Average (11–14)Large (15–20)Small (0.3–0.45)Medium (0.46–0.65)Large (0.66–0.85)
Jonkheer van Tets + + +
Natali+ + +
Hollandische Rote+ + +
Viksne + + +
Shchedraya+ + +
129-21-61+ + +
261-65-19 + + +
111-19-81+ + +
271-58-24 + + +

Hydrothermal coefficient of the experimental site during the study period_

Period20142015201620172018
May0.270.811.211.110.62
July0.340.981.051.250.94
DOI: https://doi.org/10.2478/fhort-2021-0023 | Journal eISSN: 2083-5965 | Journal ISSN: 0867-1761
Language: English
Page range: 309 - 324
Submitted on: Jun 24, 2021
Accepted on: Jul 24, 2021
Published on: Sep 18, 2021
Published by: Polish Society for Horticultural Sciences (PSHS)
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year

© 2021 Olga Panfilova, Volkan Okatan, Mikhail Tsoy, Olga Golyaeva, Sergey Knyazev, İbrahim Kahramanoğlu, published by Polish Society for Horticultural Sciences (PSHS)
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.