Evaluation of genetic diversity among some superior Cucumber (Cucumis sativus) by using Morphological and Pomological characteristics in the greenhouse under warm condition

doi:10.21203/rs.3.rs-2958926/v1

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Evaluation of genetic diversity among some superior Cucumber (Cucumis sativus) by using Morphological and Pomological characteristics in the greenhouse under warm condition

https://doi.org/10.21203/rs.3.rs-2958926/v1

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Genetic variability in a crop population is essential for successful plant breeding. Fifteen cucumber (Cucumis sativus L.) genotypes were evaluated in the greenhouse under the warm condition to estimate the magnitude of their genetic variability and heritability as also morphological and pomological characteristics for three years. Genotypes were also classified into five groups based on the performance and determination of the highest discriminating trait that accounted for more significant variability using cluster analysis and principal component analysis (PCA). The measured characteristics were cucurbit chlorotic yellows virus and fungal severity, off-type plant, vine, internode, leaf, petiole, fruit and fruit trail length, leaf width, male flower percentage, fruit color and groove, fruit diameter, fruit fresh weight, fruit dry matter and ash percentage, fruit pH, EC and TSS, fruit yield, and total fruit number. The IR4 and IR5 genotypes showed the highest fruit yield (460.85 and 425.86 kg/plot) and number (108.72 and 84.22 fruit/plant). IR11 had the highest fruit length of 16.60 cm. High broad-sense heritability was associated with all the traits except for cucurbit chlorotic yellows virus and fungal severity, fruit pH, and total fruit yield. Cluster analysis and its comparison of means showed that IR4 and IR5 from the fifth cluster expressed the best agronomic traits and yield potentials in the warm condition. Hence, selection for any trait would favor genotypes in these clusters. The PCA involved fruit dry matter and TSS as the most discriminating trait that accounted for more significant cucumber variability, which should be considered in cucumber improvement programs.

Cucumber

Superior genotype

Heritability

Correlation

Principal component

yield

The family Cucurbitaceae consists of 98 genera and about 950–980 species of food and ornamental plants that are annual or perennial herbs native to temperate and tropical areas (Chomicki et al. 2020). The genus of Cucumis includes ~ 66 species that Cucumis sativus (2n = 24) is one of the most important species originated in the southern Himalayan foothills region of Asia (Weng 2021) and have been domesticated from wild Indian C. sativus var. hardwickii, which has small bitter-tasting or sour fruits that are ellipsoid or subglobose and 5–8 cm long (Qi et al. 2013). Cucumber is an annual, monoecious vine with trailing stems up to 10 m long; the root system is extensive and largely superficial. Leaves alternate, simple; petiole 5–20 cm long. Male flowers in three to seven-flowered fascicles, with pedicel 0.5–2 cm long, stamens three; female flowers solitary, with pedicel short and thick up to 0.5 cm long, ovary inferior. Fruit a globose to cylindrical berry up to over 30 cm long. Seedling with epigeal germination (Nicodemo et al. 2012).

Because of its low calories and high soluble dietary fiber, cucumber is an ideal source for hydration and obesity management (Sharma et al. 2020). Despite having 96.4% moisture, it contains 2.8% carbohydrates, 0.4% protein, 0.3% minerals, and 0.1% fat. Also, its seeds have high amounts of protein, fat, and minerals, as mentioned in Table 1 (Abiodun & Adeleke 2010). Fruits have carminative and antacid confidants and help remove constipation and aid digestion (Dhiman et al. 2012; Saboo et al. 2013). Mature cucumber is used to relieve celiac disease and strengthen the skin. Furthermore, unripe cucumber can be used to treat dysentery. Seed juice is also helpful for expelling parasitic worms (Uthpala et al. 2020).

Table 1

The nutritional profile of Cucumis sativus L. whole fruit and fruit seeds
Nutritional profile	Fruit	Seeds
Protein	0.4%	33.85%
Carbohydrates	2.8%	10.3%
Fats	0.1%	45.2%
Calcium	0.01%	2.0%
Phosphorus	0.03%	-
Iron	1.5 mg/100g	21.4 ppm
Vit C	7 mg/100g	-
Vit B	30 IU/100g	-
Source: Abiodun and Adeleke (2010)

Cucumber is among the most cultivated and consumed vegetable crops in the world. In 2020, cucumber was grown on ~ 2,260,000 hectares of land with a total production of 91.2 million tons, which shows an increase of 2.95 and 9.74%, respectively, compared to 2016. The top five producers are China, Iran, Russia, Turkey, and the United States, with China being the largest producer accounting for 79.81% and 56.61% total production and acreage of the world, respectively. Also, Iran is one of the largest cucumber producers accounting for 1.32% and 1.79% total production and acreage of the world, respectively. (FAO 2020).

Extensive research has been conducted worldwide on selecting high-quality cucumber genotypes from seed masses, and well-known cultivars have been introduced to select and evaluate genotypes. In a study on the evaluation of 21 genotypes of cucumber, it was shown that the cucumber length ranged from 5.50 to 20.95 cm with an average of 12.19 cm, and the cucumber width was between 3.00 to 8.30 cm with an average of 5.97 cm, and also, their heritability was 84.36 and 93.23%, respectively (Shet et al. 2018). In another research on the genetic evaluation of 38 genotypes of cucumber, the fruit length ranged from 6.57 to 35.71 cm with a mean of 20.73 cm; the fruit width was between 2.25 to 10.71 cm with a mean of 6.44 cm. The fruit fresh weight was between 65.85 to 540 gr with a mean of 278.32 gr, and their Coefficient of variation (CV) was 7.71, 9.50, and 17.78%, respectively (Veena et al. 2012). Pal et al. (2017) indicated that the fruit length ranged from 8.83 to 20.82 cm with a mean of 15.83 cm, the fruit diameter was between 2.89 to 5.56 cm with a mean of 4.53 cm, the fruit fresh weight was between 75.25 to 310.42 gr with a mean of 206.40 gr and the fruit TSS were between 3.80 to 5.38% with a mean of 4.31% and their CV was 6.64, 6.05, 6.79 and 7.02%, respectively by evaluating 30 genotypes of cucumber. Research on the evaluation of 30 genotypes of cucumber indicated that the Severity of powdery mildew ranges from 8.50 to 29.40% with a mean of 19.62%, the Severity of anthracnose was between 7.70 to 26.20% with a mean of 15.20%, the fruit fresh weight was between 95.00 to 430.00 gr with a mean of 249.63 gr, and the fruit TSS were between 2.03 to 4.07% with a mean of 2.75% (Kumar et al. 2013).

This study was conducted to investigate and determine Morphological, Phenological, and Pomological characteristics, select the best cucumber genotypes, and record their characteristics in greenhouse conditions under warm conditions.

2.1 Experimental design

The study was carried out in the research greenhouse of the Agricultural Research Center of Tehran Province, with a latitude of 35 degrees and, a longitude of 51 degrees, an altitude of about 1300 meters above sea level. The research was arranged based on a completely randomized design with three replications. In this research, in three replications, 11 superior cucumber genotypes from selected populations and four control cultivars were examined in terms of quantitative and qualitative traits for three years. Each replication consisted of 70 plants. Genotypes were coded, and morphological and pomological studies were conducted for each genotype based on Table 2.

2.2 Plant materials and growth condition

The seeds of the cucumber genotypes were disinfected using sodium hypochlorite and washed twice with distilled water (Hakimi et al. 2021); then, they were sowed in plots of 15m×1.2m in two rows and irrigated using a drip irrigation system. Seedlings were grown under average temperature (28°C/22°C day/night) and relative humidity of ∼60% for 45 days. The temperature was gradually increased by two °C each day to avoid any osmotic shock to seedlings up to 40°C/32°C day/night. This temperature was maintained for 60 days.

2.3 Phenotypic, Genotypic variances, and Heritability

Genotypic variance (Al-Jibouri et al. 1958), phenotypic variance (Al‐Jibouri et al. 1958), and broad sense heritability (Lush 1949) were computed as per standard formulas.

Genetic variance\((Vg)=\frac{{MSV - VE}}{r}\)

V_g=Genotypic variance, MSV = Mean square for varieties, V_E=Error mean square and r = Number of replications

Phenotypic variance\((Vp)=Vg+VE\) V_P=Phenotypic variance, V_g=Genotypic variance and V_E=Error variance

Heritability\(({h^2})=\frac{{Vg}}{{VP}} \times 100\) h² = Heritability (broad sense), V_g= Genotypic variance and V_P=Phenotypic variance

2.4 Statistical analysis

Analysis of variance (ANOVA), Duncan multiple ranges mean comparison test (DMRT), trait correlation (Pearson method), cluster analysis (Ward method), and principal component analysis were conducted using SPSS software version 26 (SPSS, Chicago, USA), and data were recorded, and graphs were drawn using Excel software. Values are means ± SD of three replicates.

Table 2

Traits evaluated in the experiment related to cucumber genotype
Character	Abbreviation	Unit	Measurement method
Cucurbit Chlorotic Yellows Virus	CCYV	%	(sensitive plant/total plant)×100
fungal severity	FS	%	(sensitive plant/total plant)×100
Offtype plant	OP	%	(offtype plant/total plant)×100
Vine length	VL	m	Tape measure
Internode length	IL	cm	Ruler
Leaf length	LL	cm	Ruler
Leaf width	LW	cm	Ruler
Petiole length	PL	cm	Ruler
Male flower percentage	MFP	%	(male flower/total flower)×100
Fruit color	FC	Code	1 = very light green, 2 = light green 3 = normal green, 4 = dark green
Fruit groove	FG	Code	1 = without groove, 2 = normal groove 3 = deep groove
Fruit length	FL	cm	Ruler
Fruit diameter	FD	mm	Caliper
Fruit trail length	FTL	cm	Ruler
Fruit fresh weight	FFW	gr	Scale
Fruit dry matter	FDM	%	(sample dry weight/sample weight)×100
Fruit ash	FA	%	(sample ash weight/sample dry weight)×100
Fruit pH	pH	0–14	pH meter
Fruit EC	EC	µS.m^− 1	EC meter
Fruit TSS	TSS	^◦Brix	Refractometer
Fruit yield per plot	FYP	kg	Scale
Fruit number	FN	N	Counting

The variance analysis showed that the studied genotypes had significant differences among all the traits due to the diversity in the measured characteristics. Therefore, it was possible to select the genotypes for different trait values. Characteries with a high coefficient of variation have a more comprehensive range of character quantities and provide a broader range of choices for selection (Mousavi et al. 2015).

3.1 Cucurbit Chlorotic Yellows Virus severity

The CV observed in the CCYV severity trait among genotypes is 34.47 %, and the range of CCYV severity in the studied genotypes was from 15.96 to 54.47 %, with a mean of 30.31%. The most resistant genotype to CCYV was the 'Nagene' genotype control, with a mean of 15.96%. The genotype IR2 was the most sensitive, respectively 38.51, 29.14, 30.85, and 24.56 % higher than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The heritability of this trait among genotypes was 69% (Fig 1-A). A significant negative correlation was observed between the CCYV with the FG (r = -0.377), FL (r = -0.364), FDM (r = -0.363), FA (r = -0.369), and TSS (r = -0.364) and vice versa. Also, a significant positive correlation was observed between the CCYV with the FS (r = 0.834) and OP (r = 0.640) and vice versa (Table 6).

3.2 Fungal severity

The mean fungal severity varied from 5.72 to 49.52% and had a total mean of 23.78%. Also, the CV of this trait was 65.41%. The most resistant genotype to fungal severity also belongs to the 'Nagene' cultivar, which is respectively 14.28, 17.90, and 24.19 % less than 'Veolla', 'Datis', and 'Ballistic' cultivars. The genotype IR2 was the most sensitive, which was respectively 43.8, 29.52, 37.14, and 31.42 % higher than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The heritability of this trait among genotypes was 38.94% (Fig 1-B). There was a significant positive correlation between mean FS with OP (r = 0.688) and vice versa. Also, a significant negative correlation was observed between the FS with the FG (r = -0.328), FL (r = -0.295), FDM (r = -0.303), FA (r = -0.306), TSS (r = -0.304) and FYP (r = -0.313) and vice versa (Table 6).

3.3 Off-type plant

Based on the results of the present study, 20% of the genotypes showed the off-type plant between 30-45%, 33.33% of the genotypes showed the off-type plant between 15-30%, and 46.67% of the genotypes showed the off-type plant between 0-15%. Also, the mean of off-type plants among all genotypes was 18.25%, and the CV observed in the off-type plant trait among genotypes was 67.42%. The heritability of this trait among genotypes was 93.37% (Fig 1-C). A significant negative correlation was observed between the OP with the VL (r = -0.415), PL (r = -0.383), FC (r = -0.456), FG (r = -0.506), FL (r = -0.474), FFW (r = -0.486), FDM (r = -0.435), FA (r = -0.441), and TSS (r = -0.432) and vice versa. Also, a significant positive correlation was observed between the OP with the MFP (r = 0.309) and vice versa (Table 6).

3.4 Vine length

The CV observed in the vine length among genotypes is 12.62%, and the range of vine length in the studied genotypes was from 5.62 to 8.66 cm with a mean of 7.01cm. The highest vine length belonged to the IR9 genotype, with a mean of 8.66cm. The second highest vine length, with an amount of 8.13cm, was IR4. The genotype IR8 has the lowest vine length, which was 15.99, 13.80, 24.46, and 13.27% lower than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The heritability of this trait among genotypes was 96.83% (Table 3). There was a significant positive correlation between mean VL with FL (r = 0.381), FYP (r = 0.299), and FN (r = 0.362) and vice versa. Also, a significant negative correlation was observed between the VL with the IL (r = -0.446), PL (r = -0.319), and FD (r = -0.300), and vice versa (Table 6).

3.5 Internode length

The mean internode length varied from 5.27 to 11.21cm and had a total mean of 7.94cm. Also, the CV of this trait was 18.77%. The highest internode length also belongs to the IR8 cultivar, respectively 21.98, 11.88, 24.42, and 43.90 % more than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The genotype IR11 has the lowest internode length. The heritability of this trait among genotypes was 97.33% (Table 3). A significant negative correlation was observed between the IL and FN (r = -0.316) and vice versa. Also, a significant positive correlation was honored with the LL (r = 0.340), PL (r = 0.644), FC (r = 0.365), FD (r = 0.346), FDM (r = 0.472), FA (r = 0.473), EC (r = 0.340) and TSS (r = 0.469) and vice versa (Table 6).

3.6 Leaf length

The trait of leaf length has an average of 25.44 cm, and the obtained CV was 13.85%. The results show a low difference between genotypes in this trait. The highest leaf length with, 33.57 and 29.83 cm, belongs to the 'Ballistic' and 'Nagene' genotypes, respectively. The lowest leaf length was obtained with the amount of 20.20 cm in the IR5 genotype, which was respectively 32.28, 10.50, 16.97, and 39.83% lower than 'Nagene', 'Veolla', 'Datis' and Ballistic cultivars. The heritability of this trait among genotypes was 92.98% (Table 3). There was a significant positive correlation between mean LL with LW (r = 0.895), PL (r = 0.705), FC (r = 0.362), FD (r = 0.357), FFW (r = 0.583), FDM (r = 0.723), pH (r = 0.402), EC (r = 0.887), and TSS (r = 0.716) and vice versa. Also, a significant negative correlation was observed with the FA (r = -0.725) and FN (r = -0.365) and vice versa (Table 6).

3.7 Leaf width

The CV observed in the leaf width among genotypes is 12.32%, and the range of leaf width in the studied genotypes was from 21.53 to 32.87 cm with a mean of 25.20 cm. The highest leaf width belonged to 'Ballistic', with a mean of 32.87 cm. The second highest leaf width, with 27.50cm, was 'Nagene'. The genotype IR7 has the lowest leaf width, which was respectively 21.71, 5.28, 8.50, and 34.50% lower than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The heritability of this trait among genotypes was 84.35% (Table 3). No significant negative correlation was observed between the LW with other characteristics. However, a significant positive correlation was observed with the PL (r = 0.594), FC (r = 0.318), FD (r = 0.322), FFW (r = 0.513), FDM (r = 0.564), FA (r = 0.566), pH (r = 0.502), EC (r = 0.895) and TSS (r = 0.558) and vice versa (Table 6).

3.8 Petiole length

The mean petiole length varied from 17.20 to 25.63cm and had a total mean of 20.45cm. Also, the CV of this trait was 13.92%. The highest petiole length also belongs to 'Nagene', respectively 9.67, 25.21, and 0.79 % more than 'Veolla', 'Datis', and 'Ballistic' cultivars. The genotype IR11 has the lowest internode length, which was respectively 32.89, 26.40, 15.97, and 32.36% less than 'Nagene', 'Veolla', 'Datis', and Ballistic cultivars. The heritability of this trait among genotypes was 86.86% (Table 3). There was a significant positive correlation between mean PL with FC (r = 0.344), FG (r = 0.464), FD (r = 0.353), FFW (r = 0.658), FDM (r = 0.771), FA (r = 0.768). pH (r = 0.297), EC (r = 0.705), and TSS (r = 0.764) and vice versa, and also a significant negative correlation was observed with the MFP (r = -0.318) and FN (r = -0.455) and vice versa (Table 6).

Table 3. Comparison of the mean morphological characters in the studied genotypes of Cucumber
Genotypes	Vine length (m)	Internode length (cm)	Leaf length (cm)	Leaf width (cm)	Petiole length (cm)
IR1	8.06±0.16bc	8.58±0.35d	27.63±0.71c	26.65±0.55bc	20.74±0.33c
IR2	7.27±0.13f	7.63±0.24ef	28.20±1.25bc	28.70±0.62b	20.67±1.68c
IR3	6.16±0.12i	7.59±0.20ef	23.57±1.50ef	23.50±1.50ef	18.20±1.11ef
IR4	8.13±0.20b	6.81±0.32g	24.50±0.64ef	24.07±0.55ef	18.63±1.15f
IR5	7.81±0.15cd	6.51±0.27g	20.20±1.06h	22.13±0.45ef	17.70±0.66ef
IR6	6.31±0.14hi	8.26±0.13d	25.50±0.50d	26.10±0.66cd	18.87±1.00cdef
IR7	6.11±0.25i	7.28±0.28f	21.30±1.04gh	21.53±0.50f	19.47±0.60cde
IR8	5.62±0.18j	11.21±0.27a	28.30±0.89bc	27.27±1.12bc	22.97±0.95b
IR9	8.66±0.08a	7.55±0.28ef	25.37±1.39d	24.45±2.50de	20.33±0.68cd
IR10	6.40±0.11ghi	6.81±0.11g	24.50±0.96de	24.07±1.89de	18.63±1.48def
IR11	7.55±0.22de	5.27±0.34h	23.50±0.50ef	23.27±0.80ef	17.20±0.82f
Nagene	6.69±0.13g	9.19±0.25c	29.83±1.04b	27.50±1.08bc	25.63±1.55a
Veolla	6.52±0.12gh	10.02±0.15b	22.57±0.72fg	22.73±1.00ef	23.37±0.75b
Datis	7.44±0.17ef	9.01±0.22c	24.33±0.78de	23.53±1.27ef	20.47±0.93cd
ballistic	6.48±0.18gh	7.79±0.20e	33.57±0.57a	32.87±1.99a	25.43±1.10a
Mean	7.01	7.94	25.44	25.20	20.45
CV (%)	12.62	18.77	13.85	12.32	13.92
Heritability (%)	96.83	97.33	92.98	84.35	86.86
*The means with at least one common letter using Duncan's test at the 5% probability level are not significantly different in each column

3.9 Male flower percentage

The trait of male flower percentage has an average of 33.52%, and the obtained CV was 66.10%. The highest male flower percentage of 73.00 and 68.25% belongs to IR11 and IR7 genotypes, respectively. The lowest male flower percentage was obtained, with the amount of 4.66% in the IR10 genotype. The heritability of this trait among genotypes was 96.35% (Fig 2-A). A significant negative correlation was observed between the MFP with the FC (r = -0.453), FG (r = -0.317), pH (r = -0.309), and vice versa (Table 6).

3.10 Fruit color

The CV observed in the fruit color among genotypes is 23%, and the range of fruit color in the studied genotypes was from 1.3 (very light green) to 4.00 (dark green) with a mean of 2.61 (standard green). Based on the results, 6.67, 20, 66.67, and 6.66% of genotypes have very light green, light green, standard green, and dark green fruits, respectively. The highest fruit color belonged to IR9, with a mean of 4. The second highest fruit color with an amount of 3 was IR6. The heritability of this trait among genotypes was 90.96% (Fig 2-B). There was a significant positive correlation between mean FC with FD (r = 0.511), FFW (r = 0.393), and EC (r = 0.362) and vice versa, and also a significant negative correlation was observed with the FN (r = -0.313) and vice versa (Table 6).

3.11 Fruit groove

The mean fruit groove varied from 1 (without groove) to 2.87 (deep groove) and had a total mean of 1.95 (normal groove). Also, the CV of this trait was 28.05%. Based on the results, 13.33, 66.67 and 20.00% of genotypes have without groove, normal groove, and deep groove fruits, respectively. The highest fruit groove also belongs to 'Veolla', 'Nagene', and 'Ballistic'. The genotypes IR6 and IR8 have the lowest groove. The heritability of this trait among genotypes was 90.72% (Fig 2-C). A significant positive correlation was observed between the FG with the FL (r = 0.560), FFW (r = 0.364), FDM (r = 0.296), FA (r = 0.301), pH (r = 0.573) and TSS (r = 0.300) and vice versa (Table 6).

3.12 Fruit length

The trait of fruit length has an average of 13.86 cm, and the obtained CV was 9.00%. The highest fruit length of 16.60 and 15.00 cm belongs to IR11 and Ballistic genotypes, respectively. The lowest fruit length was obtained with the amount of 11.12 cm in the IR6 genotype, which was respectively 25.57, 20.80, 21.63, and 25.87% lower than the 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The heritability of this trait among genotypes was 98.47% (Table 4). There was a significant positive correlation between mean FL with FDM (r = 0.360), FA (r = 0.363), pH (r = 0.374), and TSS (r = 0.359) and vice versa, and also a significant negative correlation was observed with the FD (r = -0.331) and vice versa (Table 6).

3.13 Fruit diameter

The CV observed in the fruit diameter among genotypes is 8.89%, and the range of fruit diameter in the studied genotypes was from 26.11 to 35.35 mm with a mean of 29.41 mm. The highest fruit diameter belonged to IR6. The second highest fruit diameter, with an amount of 33.28 mm, was IR9. The genotype IR4 has the lowest fruit diameter. The heritability of this trait among genotypes was 99.23% (Table 4). A significant negative correlation was observed between the FD with the FYP (r = -0.471) and FN (r = -0.673) and vice versa, and also a significant positive correlation was observed between the FD with the FTL (r = 0.561), FFW (r = 0.718) and EC (r = 0.357) and vice versa (Table 6).

3.14 Fruit trail length

The mean fruit trail length varied from 4.41 to 7.02 cm and had a total mean of 5.60cm. Also, the CV of this trait was 15.04%. The highest fruit trail length also belongs to 'Nagene', respectively 17.59, 17.79, and 41.25 % more than 'Veolla', 'Datis', and 'Ballistic' cultivars. The genotype IR4 has the lowest fruit trail length, which was respectively 37.18, 26.13, 26.01, and 11.27% less than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The heritability of this trait among genotypes was 97.98% (Table 4). There was a significant positive correlation between mean FTL with FFW (r = 0.527) and vice versa. Also, a significant negative correlation was observed with the FYP (r = -0.441) and FN (r = -0.602) and vice versa (Table 6).

3.15 Fruit fresh weight

The trait of fruit fresh weight has an average of 78.73 gr, and the obtained CV was 13.93%. The highest fruit fresh weight, 104.17 and 94.37 gr, belong to the 'Ballistic' and 'Nagene' genotypes, respectively. The lowest fruit fresh weight was obtained with the amount of 60.47 gr in the IR4 genotype, respectively, 35.92, 23.33, 21.16, and 41.95% lower than 'Nagene', 'Veolla', 'Datis' and Ballistic cultivars. The heritability of this trait among genotypes was 97.94% (Table 4). A significant negative correlation was observed between the FFW with the FYP (r = -0.383) and FN (r = -0.750) and vice versa, and also a significant positive correlation was observed with the FDM (r = 0.587), FA (r = 0.592), EC (r = 0.583) and TSS (r = 0.592) and vice versa (Table 6).

3.16 Fruit dry matter

The CV observed in the fruit dry matter among genotypes is 7.28%, and the range of fruit dry matter in the studied genotypes was from 3.77 to 4.90% with a mean of 4.17%. The highest fruit dry matter has belonged to 'Nagene'. The second highest fruit dry matter, with an amount of 4.67%, was IR8. The genotype IR3 has the lowest fruit dry matter, which was respectively 1.13, 0.37, 0.36, and 0.77% lower than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The heritability of this trait among genotypes was 98.96% (Table 4). There was a significant positive correlation between mean FDM with FA (r = 0.931), pH (r = 0.363), EC (r = 0.716), and TSS (r = 0.942) and vice versa, and also a significant negative correlation was observed with the FN (r = -0.440) and vice versa (Table 6).

3.17 Fruit ash

The mean fruit ash percentage varied from 10.85 to 14.10% and had a total mean of 12.02%. Also, the CV of this trait was 7.21%. The highest fruit ash percentage also belongs to 'Nagene', respectively 2.29, 2.33, and 1.03 % more than 'Veolla', 'Datis', and 'Ballistic' cultivars. The genotype IR3 has the lowest fruit ash percentage, which was respectively 3.25, 0.96, 0.92, and 2.22% lower than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The heritability of this trait among genotypes was 97.56% (Table 4). A significant negative correlation was observed between the FA and FN (r = -0.437) and vice versa. Also, a significant positive correlation was observed between the FA with the pH (r = 0.364), EC (r = 0.718), and TSS (r = 0.937) and vice versa (Table 6).

Table 4. Comparison of the mean pomological characters in the studied genotypes of Cucumber
Genotypes	Fruit length (cm)	Fruit diameter (mm)	Fruit trail length (cm)	Fruit fresh weight (gr)	Fruit dry matter (%)	Fruit ash (%)
IR1	13.68±0.12e	26.54±0.23k	4.96±0.09f	68.87±0.70i	4.12±0.01ef	11.86±0.03ef
IR2	13.57±0.06ef	27.85±0.10i	4.95±0.07f	70.67±1.40hi	4.06±0.03f	11.70±0.09f
IR3	13.56±0.10ef	27.97±0.16i	4.77±0.12fg	69.27±1.11i	3.77±0.08j	10.85±0.23j
IR4	13.06±0.16g	26.11±0.09l	4.41±0.10h	60.47±1.30j	4.08±0.02ef	11.74±0.07ef
IR5	14.47±0.08c	27.14±0.26j	4.72±0.16g	72.20±1.11g	3.86±0.04i	11.11±0.13i
IR6	11.12±0.14i	35.35±0.26a	6.64±0.18b	86.17±1.56d	3.92±0.03h	11.28±0.10h
IR7	12.87±0.12gh	29.35±0.23g	6.68±0.09b	79.57±1.76e	3.97±0.04g	11.44±0.12g
IR8	13.33±0.13f	30.76±0.23d	4.83±0.13fg	76.30±1.45f	4.67±0.02b	13.46±0.07b
IR9	14.85±0.09b	33.28±0.28b	6.27±0.10c	90.63±1.36c	4.31±0.02d	12.42±0.06d
IR10	12.66±0.23h	29.88±0.33f	6.34±0.12c	76.47±1.32f	4.09±0.01ef	11.78±0.03ef
IR11	16.60±0.14a	26.15±0.25l	5.52±0.19e	76.30±1.67f	4.13±0.03e	11.89±0.10e
Nagene	14.94±0.22b	30.33±0.35e	7.02±0.11a	94.37±1.78b	4.90±0.02a	14.10±0.07a
Veolla	14.04±0.23d	28.56±0.24h	5.97±0.15d	78.87±2.20ef	4.10±0.02ef	11.81±0.07ef
Datis	14.19±0.20d	30.01±0.19ef	5.96±0.18d	76.70±2.45f	4.09±0.01ef	11.77±0.04ef
ballistic	15.00±0.22b	31.87±0.18c	4.97±0.16f	104.17±2.06a	4.54±0.03c	13.07±0.09c
Mean	13.86	29.41	5.60	78.73	4.17	12.02
CV (%)	9.00	8.89	15.04	13.93	7.28	7.21
Heritability (%)	98.47	99.23	97.98	97.94	98.96	97.56
*The means with at least one common letter using Duncan's test at the 5% probability level are not significantly different in each column

3.18 Fruit pH

The trait of fruit acidity has an average of 5.62, and the obtained CV was 5.04%. The highest fruit acidity, with 6.11 and 5.92, belongs to the 'Ballistic' and 'Nagene' genotypes, respectively. The lowest fruit acidity was obtained at 5.22 in the IR6 genotype. The heritability of this trait among genotypes was 57.66% (Fig 3-A). A significant positive correlation was observed between the fruit pH with the EC (r = 0.402) and TSS (r = 0.362) and vice versa (Table 6).

3.19 Fruit EC

The CV observed in the fruit EC among genotypes is 13.87%, and the range of fruit EC in the studied genotypes was from 224.42 to 372.92 µS.m^-1 with a mean of 288.66 µS.m^-1. The highest fruit EC belonged to Ballistic. The second highest fruit EC with an amount of 331.45 µS.m^-1 was 'Nagene'. The genotype IR5 has the lowest fruit EC. The heritability of this trait among genotypes was 92.98% (Fig 3-B). There was a significant positive correlation between mean fruit EC with TSS (r = 0.716) and vice versa. Also, a significant negative correlation was observed with the FN (r = -0.365) and vice versa (Table 6).

3.20 Fruit TSS

The mean fruit TSS varied from 3.88 to 5.04 °Brix and had a total mean of 4.30 °Brix. Also, the CV of this trait was 7.33%. The highest fruit TSS also belongs to 'Nagene', respectively 19.43, 19.71, and 7.92% more than 'Veolla', 'Datis', and Ballistic cultivars. The genotype IR3 has the lowest fruit TSS, respectively 23.02, 8.06, 7.84, and 16.92% lower than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The heritability of this trait among genotypes was 99.02% (Fig 3-C). A significant negative correlation was observed between the fruit TSS with the FN (r = -0.441) and vice versa (Table 6).

3.21 Total yield

The trait of total yield has an average of 347.14 kg/plot, and the obtained CV was 17.49%. The highest total yield, 460.85 and 425.86 kg/plot, belongs to IR4 and IR5 genotypes, respectively. The lowest total yield was obtained with the amount of 263.53 in the IR9 genotype, which was respectively 24.26, 26.46, 29.98, and 24.33% lower than 'Nagene', 'Veolla', 'Datis' and 'Ballistic' cultivars. The heritability of this trait among genotypes was 55.55% (Fig 4-A). A significant positive correlation was observed between the FYP with the FN (r = 0.882) and vice versa (Table 6).

3.22 Fruit number

The CV observed in the fruit number among genotypes is 27.10%, and the range of fruit number in the studied genotypes was from 41.44 to 108.72 N.plant^-1 with a mean of 64.78 N.plant^-1. The highest fruit number belonged to IR4. The second highest fruit number, with an amount of 84.22, was IR5. The genotype IR9 has the lowest fruit number, respectively 21.20, 36.12, 40.99, and 13.43% lower than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. The heritability of this trait among genotypes was 85.89% (Fig 4-B).

3.23 Yield component

The highest yield was observed in genotype IR4 with a value of 113.57 kg/plot in 2^nd 10harvests, respectively 25.19, 28.94, 38.42, and 31.40% higher than 1^st, 3^rd, 4^th, and 5^th ten harvests. The highest mean yield was also observed with the value of 91.50 kg/plot in the 2^nd ten harvests, where 20.38, 19.83, 36.30, and 33.86 kg/plot were more than the 1^st, 3^rd, 4^th, and 5^th ten harvests.

Table 5. Comparison of the mean Yield component (kg/plot) in the studied genotypes of Cucumber
Genotypes	1^st 10harvests (kg/plot)	2^nd 10harvests (kg/plot)	3^rd 10harvests (kg/plot)	4^th 10harvests (kg/plot)	5^th 10harvests (kg/plot)
IR1	64.73±13.69ab	81.94±16.49bc	69.33±12.73c	59.00±5.77bcd	58.68±14.13bcd
IR2	73.07±14.82ab	89.67±11.18abc	76.32±11.37abc	62.97±2.32bc	67.66±9.96abc
IR3	70.01±7.76ab	88.13±9.46bc	77.91±11.17abc	67.84±10.63ab	74.72±9.18ab
IR4	90.72±17.08a	113.57±11.17a	88.08±8.22ab	82.05±9.41a	86.43±4.42a
IR5	72.00±9.62ab	97.62±14.51ab	90.79±6.82a	78.57±10.78a	86.89±12.47a
IR6	65.82±13.43ab	89.31±10.55abc	64.05±3.38cd	49.09±8.84cdef	45.69±23.15cd
IR7	64.15±5.49ab	86.10±12.66bc	66.58±4.92c	42.98±1.42def	37.02±7.89d
IR8	52.49±14.03b	80.90±9.15bc	63.05±6.43cd	42.48±0.56ef	50.71±7.21bcd
IR9	68.26±27.76ab	72.15±16.88c	51.28±11.77d	35.86±12.80f	35.97±24.91d
IR10	75.97±16.77ab	92.22±15.21abc	63.72±7.25cd	44.92±4.33def	34.12±4.68d
IR11	69.77±9.95ab	93.02±11.78abc	69.24±3.19c	49.91±5.38cdef	50.65±7.82bcd
Nagene	78.19±20.88ab	89.19±17.28abc	67.07±9.25c	54.48±3.83bcde	59.00±10.91bcd
Veolla	71.97±15.23ab	99.34±13.75ab	77.60±3.90abc	55.31±9.26bcde	54.11±11.18bcd
Datis	82.13±9.85a	100.04±7.07ab	76.02±0.81abc	51.26±6.24cdef	66.91±9.20abc
ballistic	67.45±2.02b	99.33±10.73ab	74.05±9.56bc	51.34±16.23cdef	56.08±21.80bcd
Mean	71.12	91.50	71.67	55.20	57.64
CV (%)	20.75	15.66	16.66	26.29	34.13
*The means with at least one common letter using Duncan's test at the 5% probability level are not significantly different in each column

Table 6- Simple correlation coefficients of some quantitative and qualitative traits measured in cucumber genotypes.
	CCYV	FS	OP	VL	IL	LL	LW	PL	MFP	FC	FG
CCYV	1
FS	0.834**	1
OP	0.640**	0.688**	1
VL	-0.155	-0.206	-0.415**	1
IL	-0.036	-0.018	-0.138	-0.446**	1
LL	0.92	-0.001	-0.263	-0.159	0.340*	1
LW	0.207	0.116	-0.183	-0.152	0.252	0.895**	1
PL	-0.104	-0.141	-0.383**	-0.319*	0.644**	0.705**	0.594**	1
MFP	0.089	0.199	0.309*	-0.224	-0.231	-0.260	-0.204	-0.318*	1
FC	0.084	-0.129	-0.456**	0.254	0.365*	0.362*	0.318*	0.344*	-0.453**	1
FG	-0.377*	-0.328*	-0.506**	0.55	0.034	0.175	0.162	0.464**	-0.317*	0.037	1
FL	-0.364*	-0.295*	-0.474**	0.381**	-0.231	0.154	0.079	0.211	0.171	0.128	0.560**
FD	0.124	-0.092	-0.185	-0.300*	0.346*	0.357*	0.322*	0.353*	-0.246	0.511**	-0.179
FTL	-0.123	-0.138	-0.143	-0.209	0.117	-0.031	-0.156	0.224	-0.260	0.047	0.072
FFW	-0.118	-0.244	-0.486**	-0.222	0.191	0.583**	0.513**	0.658**	-0.151	0.393**	0.364*
FDM	-0.363*	-0.303*	-0.435**	-0.149	0.472**	0.723**	0.564**	0.771**	-0.232	0.282	0.296*
FA	-0.369*	-0.306*	-0.441**	-0.152	0.473**	-0.725**	0.566**	0.768**	-0.236	0.285	0.301*
pH	-0.155	-0.122	-0.195	-0.002	-0.114	0.402**	0.502**	0.297*	-0.309*	0.014	0.573**
EC	0.092	-0.001	-0.263	-0.159	0.340*	0.887**	0.895**	0.705**	-0.260	0.362*	0.175
TSS	-0.364*	-0.304*	-0.432**	-0.151	0.469**	0.716**	0.558**	0.764**	-0.223	0.281	0.300*
FYP	-0.211	-0.313*	-0.074	0.299*	-0.271	-0.149	-0.081	-0.168	-0.046	-0.190	0.228
FN	-0.097	-0.088	0.181	0.362*	-0.316*	-0.365*	-0.277	-0.455**	0.008	-0.313*	-0.023
, *: Significant difference at the 5% and 1% of probability levels, respectively.

Continued table 6- Simple correlation coefficients of some quantitative and qualitative traits measured in cucumber genotypes.
	FL	FD	FTL	FFW	FDM	FA	pH	EC	TSS	FYP	FN
FL	1
FD	-0.331*	1
FTL	-0.116	0.561**	1
FFW	0.265	0.718**	0.527**	1
FDM	0.360*	0.288	0.208	0.587**	1
FA	0.363*	0.291	0.211	0.592**	0.931**	1
pH	0.374*	-0.067	-0.103	0.284	0.363*	0.364*	1
EC	0.154	0.357*	-0.031	0.583**	0.716*	0.718*	0.402*	1
TSS	0.359*	0.291	0.209	0.592**	0.942**	0.937**	0.362*	0.716**	1
FYP	0.064	-0.471**	-0.441**	-0.383**	-0.247	-0.251	0.135	-0.149	-0.248	1
FN	-0.091	-0.673**	-0.602**	-0.750**	-0.440*	-0.437*	0.011	-0.365*	-0.441**	0.882**	1
, *: Significant difference at the 5% and 1% of probability levels, respectively.

3.24 Cluster analysis

At a distance of 5 out of 25, the genotypes were divided into five main groups, and each group was divided into smaller clusters with more common characteristics. Genotype cluster mean values are shown in Table 7. Comparing cluster means with studied traits revealed considerable variation among different groups. The first group includes two subgroups that include characteristics such as high CCYV (35.68%), FS (29.40%), IL (8.56 cm), and FC (2.73: normal green) and consisted of 'Veolla', 'Datis', IR2 and IR3. The second group consisted of 'Nagene' and 'ballistic' and included traits such as high IL (8.49 cm), LL (31.70 cm), LW (30.19 cm), PL (25.53 mm), FC (2.80; normal green), FG (2.74: deep groove), FL (14.97 cm), FD (31.10 mm), FFW (99.28 gr), FDM (4.72 %), FA (13.59 %) and TSS (4.86 ^◦Brix). The third group consists of one subgroup that includes traits such as high FS (30.00%), OP (28.34%), MFP (70.63%), FL (14.74 cm), and FTL (6.10cm), which consist of IR7 and IR11. The fourth group consists of two subgroups with IR9, IR10, IR1, IR6, and IR8 genotypes that have traits such as high FS (25.43%), IL (8.48cm), FC (2.94; normal green) and FD (31.16 mm). The fifth group has traits such as VL (7.97 m), FYP (443.36 kg/plot), and FN (96.47 fruits/plant) and consists of IR4 and IR5 genotypes.

Table 7. Mean comparison of cucumber traits among cluster group
Group	CCYV (%)	FS (%)	OP (%)	VL (m)	IL (cm)	LL (cm)	LW (cm)	PL (cm)	MFP (%)	FC (code)
1^st	35.68a	29.40a	23.57b	6.85b	8.56a	24.67c	24.62b	20.68b	25.92cd	2.73a
2^nd	22.94c	11.91b	3.10d	6.59c	8.49a	31.70a	30.19a	25.53a	23.55d	2.80a
3^rd	29.67b	30.00a	28.34a	6.83b	6.28b	22.40d	22.40c	18.34c	70.63a	1.65c
4^th	31.97ab	25.43a	17.24c	7.01b	8.48a	26.26b	25.71b	20.31b	27.09c	2.94a
5^th	23.45c	14.05b	15.24c	7.97a	6.45b	21.74d	22.95c	17.40d	37.68b	2.33b
*The means with at least one common letter using Duncan's test at the 5% probability level are not significantly different in each column

Continued table 7. Mean comparison of cucumber traits among cluster group
Group	FG (code)	FL (cm)	FD (mm)	FTL (cm)	FFW (gr)	FDM (%)	FA (%)	TSS (^◦Brix)	FYP (kg)	FN (number)
1^st	2.19b	13.84b	28.60b	5.41c	73.88c	4.01cd	11.53cd	4.12cd	370.74b	71.96b
2^nd	2.74a	14.97a	31.10a	6.00ab	99.27a	4.72a	13.59a	4.86a	348.09bc	50.23c
3^rd	1.80c	14.74a	27.75c	6.10a	77.94b	4.05c	11.67c	4.17c	314.72c	57.77c
4^th	1.51d	13.13c	31.16a	5.81b	79.69b	4.22b	12.16b	4.35b	302.35c	54.99c
5^th	1.96c	13.77b	26.63d	4.57d	66.34d	3.97d	11.43d	4.09d	443.36a	96.47a
*The means with at least one common letter using Duncan's test at the 5% probability level are not significantly different in each column

3.25 Principal component analysis (PCA)

The PCA is a powerful multivariate statistical method that can put the number of evaluated attributes into influential groups. Using PCA, various traits can be placed in the factors or components, each containing several characteristics. This analysis can clarify the main differences between the genotypes studied and also reduce the amount of data. The relative variance of each component indicates the importance of the component in the variance of the studied traits and is expressed as percentages. In this study, Principal component analysis expressed 22 evaluated traits as six main components, among which the first, second, and third components had the largest share in justifying variance. A total of six main and independent components with more than one variance could explain 85.14% of the total variance.

The first component (PC1) was correlated with nine traits, including leaf length and width, petiole length, fruit fresh weight, fruit dry weight, fruit ash, fruit EC, fruit TSS and fruit number, which explained 36.46% of the contribution of variance. In PC2, the traits including CCYV, fungal severity, off-type plant, vine length, fruit groove, fruit length, fruit diameter, and fruit yield were found, which accounted for 16.91% of the variance. Trait, including fruit trail length, was in the PC3 and included 11.39% of the variance. The PC4 had the MFP and fruit color, which explained 7.87% of the variance. In PC5, internode length was found and explained 6.84% of the variance. The PC6 explained 5.67% of the variance and correlated with fruit pH.

Table 8. Eigenvectors and total percentage variation for the PCA of cucumber genotypes evaluated
Character	Component
	1	2	3	4	5	6
CCYV	-0.227	0.690	0.467	0.244	0.282	0.057
Fungal severity	-0.274	0.648	0.479	-0.035	0.262	0.033
Offtype plant	-0.535	0.576	0.380	-0.260	-0.059	0.145
Vine length	-0.191	-0.569	-0.052	0.515	0.323	-0.336
Internode length	0.504	0.308	-0.041	0.020	-0.630	-0.074
Leaf length	0.825	0.099	0.460	0.102	0.030	-0.074
Leaf width	0.702	0.120	0.580	0.150	0.108	0.020
Petiole length	0.864	0.035	0.100	-0.071	-0.189	0.125
MFP	-0.357	0.139	0.071	-0.634	0.199	-0.445
Fruit color	0.477	0.060	-0.125	0.698	0.067	-0.268
Fruit groove	0.395	-0.584	-0.008	-0.120	0.169	0.498
Fruit length	0.301	-0.622	0.004	-0.266	0.508	-0.250
Fruit diameter	0.525	0.542	-0.386	0.318	0.022	0.078
Fruit trail length	0.304	0.302	-0.697	-0.065	0.103	0.347
Fruit fresh weight	0.810	0.135	-0.269	-0.009	0.329	0.139
Fruit dry matter	0.902	-0.119	0.040	-0.225	-0.146	-0.170
Fruit ash	0.901	-0.118	0.039	-0.224	-0.145	-0.169
Fruit pH	0.419	-0.381	0.380	-0.071	0.272	0.481
Fruit EC	0.825	0.099	0.460	0.102	0.030	-0.074
Fruit TSS	0.902	-0.118	0.039	-0.226	-0.147	-0.170
Fruit yield	-0.324	-0.644	0.332	0.171	-0.303	0.171
Fruit number	-0.609	-0.533	0.395	0.154	-0.352	0.046
% of variance	36.46	16.91	11.39	7.87	6.84	5.67
Cumulative %	36.46	53.37	64.76	72.63	79.47	85.14

Around the world, extensive germplasm collections are maintained by governments, universities, botanical gardens, and even privately by individuals or industries. By maintaining diverse perennial plants in the same location across multiple years, germplasm collections offer an essential opportunity to describe phenotypic and interannual variation in large numbers of accessions living under typical conditions (Migicovsky et al. 2019). Evaluation of the genetic diversity of cucumber germplasm to select new genotypes with desirable traits is one of the advantages of germplasm preservation (Hakimi et al. 2022).

The lowest CCYV among genotypes except for 'Nagene' was observed in the IR4 genotype with 21.14%, which is 4.19, 2.48 and 8.77% less than 'Veolla', 'Datis', and 'Ballistic' cultivars, respectively (Fig. 1-A). Cucurbit chlorotic yellows virus (CCYV) belongs to the genus Crinivirus and is part of a complex of whitefly-transmitted viruses that cause yellowing disease in cucurbits (Kavalappara et al. 2022). This virus has been widely reported in most countries, resulting in lower yields and poorer quality fruit, but one of the best ways to deal with it is to identify genotypes resistant to this disease (Sun et al. 2017). According to the results, as the amount of groove, length, dry matter, ash percentage, and TSS of fruits increases, the amount of CCYV decreases and vice versa. Selecting genotypes with these traits can lead to CCYV control (Table 6). The lowest fungal severity among genotypes except for 'Nagene' was observed in the IR5 genotype with 11.90%, which is 8.10, 0.48, and 6.2% less than 'Veolla', 'Datis', and 'Ballistic' cultivars, respectively (Fig. 1-B). The most important fungal diseases in cucumber production are downy mildew, powdery mildew, anthracnose, Cercospora leaf spot, and Alternaria leaf blight, which lead to rotting of the root and aerial part, and as a result, yield decreases (Meena et al. 2019). Identifying genotypes resistant to fungal diseases and management methods can help better control these pathogens. Based on the results, as the amount of groove, length, dry matter, ash percentage, TSS, and yield of fruits increases, the amount of fungal severity decreases (Table 6). IR4 and IR5 genotypes can be used as promising genotypes in cucumber breeding programs to produce resistant cucumber varieties due to their relative resistance to viral and fungal diseases. Among the studied genotypes, except for the control cultivars, IR5 and IR9 showed the lowest off-type plants (Fig. 1-C). In order to produce hybrid cultivars with high degree of purity, it is necessary to remove off-type plants from inbred lines (Brandes et al. 2009). According to the results, as the amount of length of vine and petiole and color, groove, length, fresh weight, dry matter, ash percentage and TSS of fruits increases, the amounts of off-type plants decrease (Table 6). The comparison of the cluster groups also shows that except for the second cluster which includes the control cultivars, these three traits in the fifth cluster show the least amount compared to other clusters (Table 7). Also, these three traits belong to the second main component, which expresses a state of resistance to diseases and physiological disorders (Table 8).

The vine length of IR9 and IR4 genotypes is respectively 16.40 and 9.27% higher than the 'Datis' (Table 3). based on the results, as the amount of fruit length, fruit yield, and fruit number increases, the amount of vine length increase (Table 6). The heritability for this trait is above 95%. Based on the correlation results, selecting genotypes with higher vine lengths can be crucial in producing cultivars with higher yield and fruit length. These results are consistent with the results of Kumari (2017). The internode length of the IR11 genotype is respectively 42.66, 47.41, 41.51 and 32.35% less than the 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars (Table 3). The selection of genotypes with long vine lengths, short internode, and a high number of fruits per node is vital for breeding and production programs (Pushpalatha et al. 2016). The heritability for this trait is above 95%, and according to the results, as the amount of leaf length, petiole length, color, dry matter, ash content, EC, and TSS of fruits decreases, the amounts of internode decrease (Table 6), which is similar to Sharma et al. (Sharma et al. 2018). Also, the internode belongs to the fifth main component, which expresses its highest variance (Table 8). It seems that among the leaf-related indices, control genotypes have superiority over others. 'Ballistic' has 31.96 and 30.44% more leaf length and width than the mean index. Also, 'Nagene' has 25.33% more petiole than the mean index (Table 3). It has been shown that a higher leaf area index and a larger canopy lead to an increase in temperature among plants, and warm conditions, it can lead to the closing of the leaf stomas and an increase in evaporation and transpiration, resulting in a decrease in plant growth and yield (Jeon et al. 2022).

For this reason, in high-temperature greenhouses such as this study, choosing genotypes with medium leaf dimensions is the best option to deal with this issue because it improves the airflow among the plants and reduces evaporation and transpiration. Based on this, IR4 and IR6 genotypes seem to be the best option. Also, the correlation results showed that the number of fruits decreases with the increase in the dimensions of the leaves (Table 6). Also, these three traits belong to the first main component, which expresses a state of vegetation and yield components of vines (Table 8).

MFP of IR10 was respectively 7.82, 4.16, 18.76, and 29.95% lower than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. It has been shown that the number of male flowers increases with increasing temperature (Miao et al. 2011). It seems that under warm greenhouse conditions, genotypes IR11, IR7, IR5, IR8, and IR3 showed more sensitivity to high temperatures and produced more male flowers (Fig. 2-A). It seems that except for IR9 (dark green), IR10 (light green), IR11 (light green), and IR7 (very light green) genotypes, other genotypes showed normal green fruit color, which is the most desirable color in greenhouse cucumber production (Fig. 2-B). According to the results, the control cultivars showed the deepest fruit groove (Fig. 2-C). Also, the correlation results showed that the fruit groove increases with the increase in the fruit length, fresh weight, dry matter, and ash (Table 6). In other words, genotypes with deeper grooves have better-quality fruit.

The internode length of the IR11 genotype is 11.11, 18.23, 16.98, and 10.67% more than the 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars (Table 4). According to the results, as the length of the fruit increases, the amount of Fruit dry matter, fruit ash percentage, acidity, and TSS increases. It can be concluded that fruit length is directly related to quality traits, and probably genotypes with longer fruit length and deep grooves have longer post-harvest and shelf life (Table 6). These results are consistent with the results of Kumari (2017). The maximum length of the fruit in Kumar et al.'s study (2013) was obtained at 22.76 cm in the LC-25 genotype, which is 37.11% more than IR11 genotype in this study. According to the results, the heritability for this trait is above 95% and belongs to the second main component (Table 8). Fruits with a smaller diameter are more desirable among people, so choosing genotypes with a smaller width is more appropriate. The fruit diameter of IR4 has the lowest amount, respectively, 13.91, 8.58, 13.00, and 18.07 are less than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars (Table 4). According to the correlation table, the lower the fruit diameter, the higher the yield and number of fruit (Table 6). This correlation was also observed in Bozorgzad and Golabadi (2019). Among the cluster group, the fifth cluster's fruit diameter is 6.89, 14.37, 4.04, and 14.54% less than the 1st, 2nd, 3^rd, and 4th clusters (Table 7). The lowest fruit diameter was observed in Sadiq et al.'s study (2019), with a value of 2.5 cm in the 'Liza' variety, which is 6.12% less than the IR4 genotype in this study.

Based on the results, 33.33, 46.67, and 20% of genotypes had fruit fresh weight between 60–75, 75–90, and 90–105 gr, respectively (Table 4). Most fruit fresh weight (99.27 gr) belonged to the 2nd cluster, which was 34.37, 27.37, 24.57, and 49.64% more than the 1st, 3rd, 4th, and 5th clusters, respectively (Table 7). Ullah et al. (2012) showed that fruit fresh weight ranged between 82.33–303.30 gr by evaluating 12 cucumber genotypes. Based on the correlation results, increasing fruit fresh weight decreased Fruit yield and number (Table 6). For this reason, selecting genotypes with medium fruit weight and higher quality leads to higher yield than genotypes with higher fruit weight is more critical. However, these results do not agree with the results that Kumar et al. (2022) obtained. The dry matter percentage of 'Nagene' is 0.80, 0.81, and 0.36% higher than 'Veolla', 'Datis', and 'Ballistic', respectively. 26.67% of the genotypes are above the average fruit dry matter, and the rest are below the average (Table 4). Beilari et al. (2022), by evaluation of 9 cucumber genotypes, showed that fruit dry matter percentage ranged between 1.269–6.599%. Most fruit dry matter percentages (4.72%) belonged to the 2nd cluster, 0.71, 0.67, 0.50, and 0.75% more than the 1st, 3rd, 4th, and 5th clusters, respectively (Table 7). Based on the correlation results, increasing fruit dry matter percentage increased Fruits' pH, EC, and TSS (Table 6). Valverde-Miranda et al. (2021) showed a positive correlation between dry matter and TSS of fruits with shelf-life. As a result, selecting genotypes with a higher percentage of dry matter leads to higher shelf life in post-harvest conditions. The heritability of fresh weight, dry matter percentage, and ash of fruits are above 95% according to the results and belong to the first main component, which expresses a state of vegetation and yield components of vines (Table 8).

The range of fruit acidity of genotypes was between 5.22–6.11. Fruit acidity of IR6 was respectively 11.82, 5.95, 8.58, and 14.57% lower than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars (Fig. 3-A). Based on the correlation results, increasing the dimensions of the leaves and fruits increases the acidity of the fruits (Table 6). It belongs to the fifth principal component and constitutes the largest share of the variance of this factor (Table 8). Fruit EC of IR5 was respectively 32.29, 10.49, 16.99, and 39.82% lower than 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars (Fig. 3-B). Like fruit acidity, increasing the dimensions of the leaves and fruits increases the electrical conductivity of the fruits (Table 6). However, this trait belongs to the first principal component (Table 8). TSS is one of the most essential marketability traits among horticultural products, including cucumber, which is influenced by carbohydrates, organic acids, proteins, fats, and minerals (Valverde-Miranda et al. 2021). Except for the 'Nagene', the highest amount of fruit TSS was observed in IR8, which is 13.98, 14.25 and 3.00% higher than 'Veolla', 'Datis', and 'Ballistic' cultivars, respectively (Fig. 3-C). Kumar et al. (2013) showed that TSS ranged between 2.07–4.07 ^◦Brix by evaluating 30 cucumber genotypes. Based on the results, acidity, electrical conductivity, and TSS positively correlate with each other, which means that increasing each trait increases the other (Table 6).

The total yield per plot of the IR4 genotype is respectively 32.45, 28.61, 22.45, and 32.33% more than the 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. Also, the total yield per plot of the IR5 genotype is 22.40, 18.85, 13.16 and 22.29% more than the 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars (Fig. 3-A). These two genotypes showed the highest yield per plot and also belonged to the fifth cluster in the cluster grouping, whose yield per plot is 19.59, 27.37, 40.87, and 46.64% higher than 1st, 2nd, 3^rd, and 4th clusters, respectively (Table 7). Furthermore, this character belonged to the second main component (Table 8). The correlation results also showed that the product yield decreased significantly with the increase in the fruit's size and weight. These results are in line with the findings of Bozorgzad and Golabadi (2019). The total fruit number per plant of the IR4 genotype is 106.73, 67.60, 54.81, and 127.12% more than the 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars. Also, the total fruit number per plant of the IR5 genotype is 60.14, 29.83, 19.92 and 75.93% more than the 'Nagene', 'Veolla', 'Datis', and 'Ballistic' cultivars (Fig. 3-B). These two genotypes showed the highest total fruit number per plant and also belonged to the fifth cluster in the cluster grouping, whose total fruit number per plant is 34.06, 92.06, 66.99, and 75.43% higher than 1st, 2nd, 3^rd, and 4th cluster, respectively (Table 7). Furthermore, this character belonged to the first main component (Table 8). Increasing the greenhouse temperature in the first days has increased the mean total yield up to 2nd ten harvests while keeping the temperature at 40 degrees Celsius has gradually decreased yield. It was observed that the mean yield of the 2nd ten harvests is 28.66, 27.67, 65.76, and 58.74% higher than the 1st, 3rd, 4th, and 5th of 10 harvests, respectively (Table 5).

Breeding of horticultural plants is significantly increasing due to the motivation to preserve genetic diversity. While many cucumber cultivars have been registered worldwide, more breeding programs are still needed to produce better quality cucumber genotypes to solve production problems such as sensitivity to fungal and viral diseases.

In conclusion, we observed a high morphology and phenology variation between superior cucumber genotypes. Evaluating biochemical characteristics as selection markers can complete phenotypic data to study genetic variation in cucumber populations. In addition, knowledge of biochemical traits such as protein, phenolic compounds, and secondary metabolite is critical to introduce a superior genotype with high fruit quality. Based on the results, the selected genotypes were superior in various aspects of phenotypic characteristics as well as fruit quality. Therefore, these selected superior genotypes not only have the potential to be introduced as commercial cultivars soon but also can be used as a source of desirable genes for the future cucumber breeding program.

Here, a thorough comparison with established cucumber cultivars (Nagene, Veolla, Datis, and Ballistic) showed that 'IR4' and 'IR5' are relatively superior because of their low severity of CCYV and fungal disease, highest vine length, high yield, and high fruit number per plant, and can serve as a contribution to cucumber cultivation in the world while offering a genetic resource for cucumber crossbreeding in the future.

Author Contributions Statement

Conceptualization, KAS; methodology, YH; software, YH; validation, KAS; formal analysis, YH; investigation, YH; resources, YH; data curation, YH; writing—original draft preparation, YH; writing—review and editing, KAS; supervision, KAS; project administration, YH. All authors have read and agreed to the published version of the manuscript.

Conflict of Interest Declaration

The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analysis, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.

Declare statement

The authors have no relevant financial or non-financial interests to disclose.

Abiodun OA, Adeleke RO (2010) Comparative studies on nutritional composition of four melon seeds varieties. Pakistan Journal of Nutrition 9(9):905–908.
Al‐Jibouri H, Miller PA, Robinson HF (1958) Genotypic and environmental variances and covariances in an upland Cotton cross of interspecific origin 1. Agronomy Journal 50(10):633–636.
Beilari A, Olfati JA, Esfahani M, Pirmoradian N (2022) Evaluation of Yield Stability of a Number of Cucumber Lines (Cucumis sativus L.) during Spring and Autumn Cropping Seasons. Journal of Crops Improvement 24(3):961–975. https://doi.org/10.22059/jci.2022.322875.2545
Bozorgzad G, Golabadi M (2019) Appointment of Importance and Portion of Morphological Traits on Fruit Yield in Breeding Hybrid Cultivars of Greenhouse Cucumber. Journal of Agronomy and Plant Breeding 15(2):1–34.
Brandes C, Joachimsthaler A, Stöger A, Ruppitsch W, Zimmermann H (2009) Genetic characterization of the origin of offtype plants in maize inbred lines. Seed Science and Technology 37(2):423–435. https://doi.org/10.15258/sst.2009.37.2.15
Chomicki G, Schaefer H, Renner SS (2020) Origin and domestication of Cucurbitaceae crops: insights from phylogenies, genomics and archaeology. New Phytologist 226(5):1240–1255.
Dhiman K, Gupta A, Sharma DK, Gill NS, Goyal A (2012) A review on the medicinally important plants of the family Cucurbitaceae. Asian Journal of Clinical Nutrition 4(1):16–26.
FAO (2020) Food and Agriculture Organization of the United Nations-Statistic Division.
Hakimi Y, Fatahi R, Naghavi MR, Zamani Z (2021) Seed germination indices of Papaver bracteatum populations under different temperature treatments. 1th International & 5th National Seed Science and Technology Conference of Iran.
Hakimi Y, Fatahi R, Zamani Z (2022) Evaluation of genetic diversity among some selected walnut by using morphological ‎and pomological characteristics. Iranian Journal of Horticultural Science 53(1):209–224. https://doi.org/10.22059/ijhs.2021.318674.1896
Jeon Y, Cho L, Park S, Kim S, Lee C, Kim D (2022) Canopy Temperature and Heat Flux Prediction by Leaf Area Index of Bell Pepper in a Greenhouse Environment: Experimental Verification and Application. Agronomy 12(8):1807. https://doi.org/10.3390/agronomy12081807
Kavalappara SR, Riley DG, Cremonez PSG, Perie JD, Bag S (2022) Wild Radish (Raphanus raphanistrum L.) Is a Potential Reservoir Host of Cucurbit Chlorotic Yellows Virus. Viruses 14(3):593. https://doi.org/10.3390/v14030593
Kumar R, Das Munshi A, Behera TK, Jat GS, Choudhary H, Talukdar A, Dash P, Singh D (2022) Association mapping, trait variation, interaction and population structure analysis in cucumber (Cucumis sativus L.). Genetic Resources and Crop Evolution 69(5):1901–1917. https://doi.org/10.1007/s10722-022-01352-3
Kumar S, Kumar D, Kumar R, Thakur KS, Dogra BS (2013) Estimation of genetic variability and divergence for fruit yield and quality traits in cucumber (Cucumis sativus L.) in North-Western Himalayas. Universal Journal of Plant Science 1(2):27–36.
Kumari B (2017) Evaluation of phenotypic trait analysis of cucumber germplasm. International Journal of Engineering and Applied Sciences 4(9):257375.
Lush JL (1949) Heritability of quantitative characters in farm animals. Proceedings of 8th Congress of Genetics and Hereditas 356–375.
Meena AK, Godara SL, Meena PN, Meena AK (2019) Foilar Fungal Pathogens of Cucurbits. In The Vegetable Pathosystem (pp. 203–228). Apple Academic Press.
Miao M, Yang X, Han X, Wang K (2011) Sugar signalling is involved in the sex expression response of monoecious cucumber to low temperature. Journal of Experimental Botany 62(2):797–804. https://doi.org/10.1093/jxb/erq315
Migicovsky Z, Warschefsky E, Klein LL, Miller AJ (2019) Using living germplasm collections to characterize, improve, and conserve woody perennials. Crop Science 59(6):2365–2380.
Mousavi SA, Tatari M, Moradi H, Hasani D (2015) Evaluation of Genetic Diversity Among the Superior Walnut Genotypes Based on Pomological and Phenological Traits in Chahar Mahal va Bakhtiari Province‎. Seed and Plant Improvement Journal 31(2):365–389.
Nicodemo D, Malheiros EB, Jong DD, Couto RHN (2012) Floral biology of greenhouse-grown of Japanese cucumber plants (Cucumis sativus L.). Científica (Jaboticabal) 40(1):35–40.
Pal S, Sharma HR, Yadav NE (2017) Evaluation of cucumber genotypes for yield and quality traits. Journal of Hill Agriculture 8(2):144–150.
Pushpalatha N, Anjanappa M, Devappa V, Pitchaimuthu M (2016) Genetic variability and heritability for growth and yield in cucumber (Cucumis sativus L.). Journal of Horticultural Sciences 11(1):33–36.
Qi J, Liu X, Shen DI, Miao H, Xie B, Li X, Huang S (2013) A genomic variation map provides insights into the genetic basis of cucumber domestication and diversity. Nature Genetics 45(12):1510–1515.
Saboo SS, Thorat PK, Tapadiya GG, Khadabadi SS (2013) Ancient and recent medicinal uses of cucurbitaceae family. International Journal of Therapeutic Applications 9:11–19.
Sadiq GA, Omerkhil N, Zada KA, Safdary AJ (2019) Evaluation of growth and yield performance of five cucumbers (Cucumis sativus L.) genotypes; Case study Kunduz province, Afghanistan. Evaluation 4(6):22–28.
Sharma S, Kumar R, Chatterjee S, Sharma HR (2018) Correlation and path analysis studies for yield and its attributes in cucumber (Cucumis sativus L.). International Journal of Chemical Studies 6(2):2045–2048.
Sharma V, Sharma L, Sandhu KS (2020) Cucumber (Cucumis sativus L.). In Antioxidants in Vegetables and Nuts-Properties and Health Benefits (pp. 333–340). Springer.
Shet RM, Shantappa T, Gurumurthy SB (2018) Genetic variability and correlation studies for productivity traits in cucumber (Cucumis sativus L.). International Journal of Chemical Studies 6(5):236–238.
Sun X, Wang Z, Gu Q, Li H, Han W, Shi Y (2017) Transcriptome analysis of Cucumis sativus infected by Cucurbit chlorotic yellows virus. Virology Journal 14(1):18. https://doi.org/10.1186/s12985-017-0690-z
Ullah M, Hasan MJ, Chowdhury A, Saki A, Rahman A (2012) Genetic variability and correlation in exotic cucumber (Cucumis sativus L.) varieties. Bangladesh Journal of Plant Breeding and Genetics 25(1):17–23.
Uthpala TGG, Marapana RAUJ, Lakmini K, Wettimuny DC (2020) Nutritional bioactive compounds and health benefits of fresh and processed cucumber (Cucumis sativus L.). Sumerianz Journal of Biotechnology 3(9):75–82.
Valverde-Miranda D, Díaz-Pérez M, Gómez-Galán M, Callejón-Ferre ÁJ (2021) Total soluble solids and dry matter of cucumber as indicators of shelf life. Post-harvest Biology and Technology 180:111603. https://doi.org/10.1016/j.postharvbio.2021.111603
Veena R, Sidhu AS, Pitchaimuthu M, Souravi K (2012) Genetic evaluation of Cucumber [Cucumis sativus L.] genotypes for some yield and related traits. Electronic Journal of Plant Breeding 3(3):945–948.
Weng Y (2021) Cucumis sativus chromosome evolution, domestication, and genetic diversity: Implications for cucumber breeding. Plant Breeding Reviews 44:79–111.

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Evaluation of genetic diversity among some superior Cucumber (Cucumis sativus) by using Morphological and Pomological characteristics in the greenhouse under warm condition

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Abstract

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1 Introduction

2 Materials and Methods

2.1 Experimental design

2.2 Plant materials and growth condition

2.3 Phenotypic, Genotypic variances, and Heritability

2.4 Statistical analysis

3 Results

4 Discussion

5 Conclusions

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