Next Article in Journal
Katagnymene terrestris sp. nov. (Gomontiellaceae, Cyanobacteria) Isolated from the Soil between Rocks in the Republic of Korea
Next Article in Special Issue
Comprehensive Assessment of Morphological Diversity in Bottle Gourd (Lagenaria siceraria) Accessions: A Focus on Roots and Morpho-Agronomic Traits
Previous Article in Journal
Phenotypic Polymorphism in Two Endosymbiotic Bacteria of the Ciliate Paramecium: Pseudolyticum multiflagellatum and “Ca. Megaira venefica”
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Combining Ability and Heterosis among Bottle Gourd [Lagenaria siceraria (Molina) Standl.] Selections for Yield and Related Traits under Drought-Stressed and Non-Stressed Conditions

1
African Centre for Crop Improvement (ACCI), University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3209, South Africa
2
Department of Biochemistry, Microbiology and Biotechnology, School of Molecular and Life Sciences, University of Limpopo, Private Bag X1106, Sovenga, Polokwane 0727, South Africa
3
Limpopo Department of Agriculture and Rural Development, Towoomba Research Centre, Agriculture Regulatory and Technology Development, Crop Science Directorate, Private Bag X1615, Bela-Bela 0480, South Africa
*
Author to whom correspondence should be addressed.
Diversity 2023, 15(8), 925; https://doi.org/10.3390/d15080925
Submission received: 6 July 2023 / Revised: 3 August 2023 / Accepted: 11 August 2023 / Published: 14 August 2023
(This article belongs to the Special Issue Genetic Diversity of Cucurbit Crops)

Abstract

:
Bottle gourd [Lagenaria siceraria (Molina) Standl.] is cultivated for multiple utilities, including as a leafy vegetable, for fresh and dried fruits and seeds. It is an under-researched and -utilized crop, and modern varieties are yet to be developed and deployed in sub-Saharan Africa (SSA). There is a dire need for pre-breeding and breeding of bottle gourds for commercialization in SSA. Therefore, this study aimed to determine the combining ability and heterosis among selected genotypes of bottle gourd for fruit yield and related traits under drought-stressed and non-stressed conditions to select the best parents and hybrids. Eight preliminarily selected and contrasting parents with drought tolerance were crossed using a half-diallel mating design. The 8 parents and 28 crosses were evaluated under non-stressed (NS) and drought-stressed (DS) conditions across two growing seasons (2020/21 and 2021/22) using a 6 × 6 alpha lattice design with three replicates. Data were collected on fruit yield and related traits and subjected to analysis of variance, combining ability and heterosis analyses. Significant (p < 0.05) specific combining ability (SCA) and general combining ability (GCA) effects were computed for fruit yield per plant (FYPP). The SCA × environment and GCA × environment interaction effects were highly significant (p < 0.001) for FYPP and SYPP. The results suggest that genetic effects were affected by the test environment. Parental genotypes BG-58 and GC recorded positive and significant GCA effects for FYPP under the DS condition, whereas GC recorded positive and significant GCA effects for FYPP under the NS condition. The two genotypes are ideal breeding parents for population development to select genotypes with high fruit and seed yields. Crosses BG-27 × BG-79, BG-79 × BG-52, BG-79 × BG-70, BG-80 × BG-70, BG-80 × GC, and BG-70 × GC recorded high and positive SCA effects for FYPP and SYPP under DS condition. Crosses BG-81 × BG-52, BG-81 × GC, BG-27 × BG-79, BG-27 × GC, BG-79 × GC, BG-80 × BG-70, BG-81 × BG-58, BG-27 × BG-80, BG-27 × BG-58, BG-79 × BG-52, BG-52 × BG-58, BG-80 × BG-58, and BG-58 × BG-70 recorded high and positive SCA effects for FYPP and SYPP under NS condition. Crosses BG-80 × BG-58, BG-27 × BG-79, BG-79 × BG-52, BG-27 × BG-52, and BG-52 × BG-80 showed high and positive mid- and better-parent heterosis under DS condition for FYPP and SYPP. Crosses BG-27 × GC, BG-79 × GC, BG-27 × BG-58, and BG-27 × BG-79 showed high and positive mid- and better parent heterosis under NS condition for FYPP and SYPP. The newly selected families are recommended for multi-environment evaluation forrelease and commercialization in South Africa or similar agroecologies.

1. Introduction

Bottle gourd [Lagenaria siceraria (Molina) Standl., 2n = 2x = 22] is a multi-purpose crop belonging to the Cucurbitaceae family. It is a highly valued food security crop in Africa and Asia [1,2,3,4,5]. In Africa, the large and tender leaves are cooked and consumed as a leafy vegetable. The young, tender fruits are harvested and boiled until soft and consumed by adding sugar, peanuts, salt, or milk, depending on consumer preferences. The matured seeds extracted from dried fruits are processed to prepare livestock feed. The fruits of bottle gourd are an excellent source of essential macro- and micro-nutrients, including minerals (e.g., iron, phosphorus, potassium, calcium, and magnesium), vitamins B, C, and E, carbohydrates, and dietary fiber [1,3,4,6,7,8,9,10,11]. The fruits are also a good source of essential amino acids, including aspartic acid, threonine, serine, glutamic acid, alanine, valine, phenylalanine, lysine and arginine, and phytochemical compounds, including phenolics, flavonoids and terpenoids [9,10]. Wild fruits of the crop are bitter due to the presence of cucurbitacins, which possess pharmaceutical values including anti-cancer and anti-diabetes [10,11,12]. The leaves are a good source of essential nutrients such as zinc, nitrogen, manganese, and copper [8]. The seeds are a source of crude protein, crude lipids, crude fiber, and carbohydrates [13,14,15]. The seeds are valued for their antioxidants, sterols, and vitamins, including ascorbic acid, thiamine, riboflavin, niacin, pantothenic acid, and vitamin B-6 [13]. In addition, the seeds contain essential amino acids, including glutamic acid, leucine acid, arginine, lysine, and aspartic acid [16].
Bottle gourd is one of the widely used rootstocks for grafted watermelon. It confers desirable qualities such as better fruit yield and quality and resistance to biotic (e.g., fungal and viral diseases) and abiotic (heat and drought) stress factors in grafted watermelons [17,18,19,20,21,22]. In Africa, bottle gourd is cultivated using genetically unimproved landrace varieties that are phenotypically and genetically diverse with low yield potential [2,5,23]. In farmers’ fields, different accessions are often planted in companion with the major crops, mainly maize, sorghum, soybeans, and rice. The landrace accessions display genetic variation and are selected by growers for their long handles/necks to make containers, and corrugated fruit with or without fruit necks are mainly for consumption. The round fruit-shaped gourds are used to make containers called “Kgapa” in the indigenous and local Sepedi language of South Africa. Planting diverse accessions in close proximity by farmers allows for cross-pollination, leading to high genetic recombination and genetic variation. The reported genetic diversity in different regions includes bottle gourd fruit having long curved-neck shapes, fruits with long straight-neck lengths, circular, oblate, pyriform, cavate, cylindrical fruit shapes, dark green fruit color and and other traits such as variation in the number of leaves and plant height [2,5,24,25,26,27]. In India, Turkey, India, China, and the USA, higher genetic variability of the crop has been reported [2,25,28,29,30,31]. The high genetic variation in bottle gourd allows for new variety designs with desirable consumer attributes, including high fruit and seed yields and other valuable traits such as fruit shape, size, color, and neck to enhance the crop’s market value in Africa.
The phenotypic variability of bottle-gourd genetic resources is documented via agronomic and horticultural traits. These include aboveground plant traits (e.g., plant height, number of primary and secondary branches), flower traits (e.g., number of male and female flowers), fruit traits (e.g., fruit shape, fruit weight, fruit yield, fruit texture, fruit color, fruit with or without neck, fruit neck length, fruit neck bending) and seed traits (e.g., number of seeds per fruit, seed length, seed width, seed size, hundred seed weight, seed yield, seed coat color, seed texture) [2,25,27,28,32]. These traits are useful for targeted selection to develop new varieties targeting the various end-use values of the crop. For example, leaf traits such as leaf number, size, and plant height are essential to developing varieties suited for leaf vegetable and livestock fodder. Increasing the proportion of female than male flowers can aid the development of high-yielding hybrids. Increased fruit number and weight can improve fruit yield, whereas a high count of seeds per plant and increased seed weight are essential attributes to breeding high seed-yielding varieties. Some economic traits in bottle gourd are positively correlated and useful for selecting genotypes with better fruit and seed yields. For instance, fruit weight and fruit number have a direct positive effect on seed yield per fruit and fruit yield, suggesting their simultaneous selection and improvement [33,34]. These associations will allow for the breeding of bottle gourd varieties incorporating multiple traits.
Genotype selection with a desirable and complementary product profile requires progeny evaluation based on combining ability and heterosis analyses. Combining ability analysis has aided the selection of parental genotypes and progenies with high fruit yield for genetic advancement [33,35,36,37,38]. Fruit yield and related traits in bottle gourd were conditioned by non-additive gene action [31]. Complex gene action, including duplicate gene interaction, complimentary gene action, or non-allelic interaction, was reported for fruit yield in bottle gourd [38,39]. Refs. [40,41] reported high GCA compared to SCA effects for fruit yield, indicating the involvement of additive gene action conditioning their inheritance. Analysis of heterosis in bottle gourd identified the dominant form of heterosis for plant height, fruit length, and the number of branches, aiding the identification of hybrids for use in strategic breeding and variety release [40].
Presently, in Africa, bottle gourd is an under-researched and -utilized crop, and modern varieties are yet to be developed and deployed. There is a dire need for pre-breeding and breeding bottle gourds with increased fruit and seed yield to enhance the market value of the crop. In previous studies, Ref. [42] identified accessions of bottle gourd with desirable agronomic attributes, including high fruit and seed yields useful for hybrid breeding. Ref. [43] recently developed F1 hybrids of bottle gourd derived from unimproved accessions for cultivation in the cooler environments of KwaZulu-Natal Province of South Africa. These newly developed hybrids performed better regarding fruit yield than the parental landrace accessions, indicating the possibility of developing cultivars with high yield potential and other desirable farmer-preferred traits. The next generation of improved bottle gourd varieties should comprise traits and attributes with multiple uses, including fodder, seed, and fruit, to serve varied value chains in the food, feed, and processing industries. Therefore, the objective of this study was to determine the combining ability and heterosis among selected genotypes of bottle gourd for fruit yield and related traits under drought-stressed and non-stressed conditions to select the best parents and hybrids for breeding.

2. Plant Material and Generation of Hybrids

The study used eight selected bottle gourd landrace accessions as parental genotypes for hybrid development. The selected bottle gourd accessions are widely grown in the Limpopo Province of South Africa by small-holder farmers for food (Table 1). The accessions are phenotypically and genetically divergent based on previous studies [27,32]. Additionally, the accessions exhibit varied responses to drought stress [42,44,45]. The Limpopo Department of Agriculture and Rural Development maintains the landrace accessions at Toowoomba Agricultural Development Centre (TADC), Bela-Bela, South Africa.
The eight parental accessions were grown in a 5 L capacity polyethylene plastic pots under glasshouse conditions at the University of Limpopo (−25°36′54″ S, 28°0′59.76″ E, 1312 m above sea level), South Africa. Five seeds per accession were sown in well-drained polyethylene plastic containing a loamy soil collected from the University of Limpopo, Syferskuil Experimental farm (−23°53′9.60″ S, 29°44′16.80″ E, 1312 m above sea level). Three plants were retained per accession in each pot two weeks after emergence and were watered daily to maintain soil moisture content approximately at field capacity (i.e., 40% v/v). Plants were allowed to grow until the development of male and female flowers, which occurred approximately 38 and 46 days after planting, respectively. The male flowers were brushed gently onto the female flower to ensure sufficient pollen for cross-pollination. The crosses were developed using a half-diallel mating design aiming for 28 crosses. The fully developed fruits from each of the crosses were labeled and sun-dried for up to four months. The seeds were extracted from the fruits, sun-dried, placed in labeled paper bags, and then stored in a dry, cool place for later use.

2.1. Study Site and Experimental Design

Field experiments were conducted at the University of Limpopo’s Syferskuil research farm, Mankweng, South Africa, during the 2020/21 and 2021/22 growing seasons. The area is characterized by sandy and loamy soils. The average rainfall received during the 2020/21 and 2021/22 growing seasons were 243 and 198 mm, respectively. The maximum temperature and relative humidity ranged from 26 to 34.8 °C and 60% to 88% for both growing seasons. The 8 parental genotypes and 28 successful crosses were evaluated under non-stressed (NS) and drought-stressed (DS) conditions using a 6 × 6 α-lattice design with three replications. In each block, three plants were established for parental accessions and crosses. The two water conditions and growing seasons provided four testing environments. Parents and crosses were planted at an intra-and-inter row spacing of 5 × 5 m apart. Sprinkler irrigation was used to water the plants. In the first two weeks after planting, the second true leaf stage, plants under both DS and NS conditions were watered weekly with approximately 27 mm of water. Thereafter, supplemental irrigation of approximately 27 mm of water was applied per week for plants grown under NS condition, whereas plants under DS condition were rain-fed. The total amount of water received by the plants grown under the NS condition was approximately 670 mm, whereas those under the DS condition received approximately 256 mm during the 2020/21 growing season. During the 2021/22 growing season, plants under the NS condition received approximately 680 mm of water, whereas those under the DS condition received approximately 269 mm of water. The plants under DS condition experienced a drought stress intensity of about 0.7 during the first and second seasons, which was calculated using the following formula:
DSI = X ¯ ns X ¯ ds X ¯ ns
where DSI is drought stress intensity; X ¯ ns , mean fruit yield averaged across all the genotypes tested under NS condition; X ¯ ds mean fruit yield averaged across all the genotypes tested under DS condition [46,47].

2.2. Data Collection

Data were collected on a single randomly selected and tagged plant out of the three plants in each block for parental genotypes and crosses. The following agronomic traits were measured: total number of male and female flowers per plant, sex ratio calculated as the total number of male flowers per plant to the total number of female flowers per plant, number of leaves per plant, plant height measured from the base of the plant to the tip of the main vine in meters, number of fruits per plant, single fruit weight of dried fruit (kg), fruit circumference (cm) measured as the horizontal distance around the boundary of the fruit, fruit yield per plant (kg), number of seeds per fruit, hundred seed weight (g) and seed yield per plant (kg). The fruit-related traits were measured on a single fully developed fruit per plant.

2.3. Data Analysis

2.3.1. Analysis of Variance

Analysis of variance was performed using GenStat version 18 [48]. The Least Significant Difference (LSD) test was computed to compare treatment means at the 5% level of significance.

2.3.2. Estimates of Best Linear Unbiased Predictors

Best Linear Unbiased Predictors (BLUPs) were calculated using META-R (Multi Environment Trail Analysis with R for Windows) Version 6.0 [49]. The BLUPs estimates were computed based on the lattice design procedure using the following linear model:
Yijkl = µ + Loci + Repj(Loci) + Blockk(LociRepj) + Genl + Loci × Genl + εijkl
where,
Yijkl = the trait of interest,
µ = overall mean effect,
Loci = effects of the ith environment,
Repj = effects of the jth replicate,
Blockk (Repi) = effects of the kth incomplete block within the jth replicate,
Loci × Genl = environment × genotype interaction,
Genj = effects of the lth genotype,
εijkl = error associated with the ith replication, jth incomplete block and the kth genotype, which is assumed to be normally and independently distributed, with mean zero and homocedastic variance σ2. Genotypes, environment, and interactions were treated as random factors effects to calculate BLUPs.

2.4. Estimates of the GCA and SCA Effects

The significant tests for GCA and SCA effects were estimated using PBTools version 1.4 [47]. The GCA and SCA effects and genetic variance components were estimated using AGD-R (Analysis of Genetic Designs in R) Version 5.0 [50] using a half-diallel mating design, method II, and model I. The analysis was performed using the following fixed-effect model:
Yijk = μ + gi + gj + sij + eijk
where,
Yijk = value for the ijth cross in the kth replication
μ = the population mean,
gi and gj = GCA effects for the ith and jth parents
sij = the SCA effect of the cross of the ith and jth parents
eijk = error term associated with the cross of the ith and jth parents in the kth replication

2.5. Gene Action and Heritability Estimates

Broad sense heritability (h2B), narrow sense heritability (h2n), additive variance (σ2A), and dominance variance (σ2D) were calculated according to [49,51] using the following formula:
h 2 B = σ 2 g σ 2 g + σ 2 g e n L o c + σ 2 ε / ( n L o c × n R e p )
where,
σ2 g = genotypic variance,
σ2ε = error variance,
nRep = number of replicates,
σ2ge = G × E interaction variance,
nLoc = number of environments in the analysis
h 2 n =   σ   2 A   σ   2 A + σ   2 D +   σ   2 E  
where,
σ2A = additive variance component,
σ2D = dominance variance component,
σ2E = environmental variance
σ 2 A =     4 σ   2 g c a
where gca = general combining ability effect
σ 2 D =     4 σ   2 s c a
where sca = specific combining ability effect

2.6. Heterosis Estimates

Mid-parent heterosis (MPH) and better-parent heterosis (BPH) were computed according to the following equations [52]:
M P H = 100 × ( F 1 M P M P   )                 and
B P H = 100 × ( F 1 B P B P )
where,
F1 = mean performance of F1,
MP = mean of the two parents making the cross and
BP = mean of the better parent for that particular cross.

2.7. Correlation Analysis

The BLUPs estimates were used to compute Pearson correlation coefficients to determine the associations between assessed agronomic traits using SPSS version 25 (SPSS Inc., Chicago, IL, USA, 2018).

3. Results

3.1. Genotype, Water Condition, and Their Interaction Effects

Analysis of variance showing the main effects of genotype, water conditions, and their interaction for the studied agronomic traits are shown in Table 2. Significant genotypic effects (p < 0.001) were recorded for all traits except for SR. The effects of water conditions were highly significant (p < 0.001) for all the traits. Genotype × environment interaction effects were significant (p < 0.001) for all assessed traits.

3.2. Performance of Bottle Gourd Parents and Hybrids for Assessed Traits

BLUPs estimates for the assessed traits for parents and their hybrids under DS and NS conditions across the two growing seasons are presented in Table 3 and Table 4, respectively. An approximately 50% increase in the performance of the different parental genotypes and hybrids was recorded for NMF, NFF, PH, NFPP, FC, NSPF, and SYPP under the NS condition compared to the DS condition. Under DS condition, a high FYPP of >0.4 kg was recorded for crosses BG-52 × BG-58, BG-79 × BG-52, BG-80 × BG-58, and BG-80 × GC, while a low FYPP of <0.2 kg was recorded for approximately 18% of the crosses. Higher FYPP of >0.4 kg was recorded for parental genotypes GC, and low FYPP of <0.2 kg was recorded for five parental genotypes, including BG-27, BG-52, BG-79, BG-80, and BG-81. Under NS condition crosses, BG-27 × BG-52 recorded a higher FYPP of >9.5 kg, whereas BG-80 × BG-70 recorded the least FYPP of 0.4 kg. Parents BG-70 and GC recorded high FYPP of 1.6 and 2.8 kg, respectively, whereas all the other parents recorded a low FYPP of <1 kg.

3.3. The GCA and SCA Effects

The ANOVA summary showing mean squares and significant tests for GCA and SCA effects for the assessed traits across the two growing seasons are presented in Table 5. The environmental effect was significant for all traits except for HSW. The genotypic effect was significant for all traits except for SR, PH, FC, and SYPP. The genotypic × environmental effect was significant for all traits except for SR, FW, FC, and NSPF. The GCA effects were significant for NFF, NFPP, FW, FC, and FYPP, whereas SCA effects were significant for NMF, NFF, NL, NFPP, FYPP, HSW, and SYPP. The GCA × environment interaction effects were significant for NMF, NFF, NL, NFPP, FYPP, HSW, and SYPP, whereas SCA × environment effects were significant for NMF, NFF, NL, PH, NFPP, FYPP, HSW, and SYPP.

3.3.1. General Combining Effects of Parental Genotypes

General combining effects of the parental genotypes for yield and related traits under DS and NS conditions across the two growing seasons are presented in Table 6. The parental lines exhibited varied GCA effects for the different assessed traits. Significant positive and high positive GCA effects were considered desirable for the different traits. Under the DS condition, BG-58 and GC were the best-performing parental genotypes that recorded high positive and significant GCA effects for NL, PH, NFPP, FW, and FYPP. In addition, BG-58 recorded a high positive and significant GCA effect of 0.24 for SYPP. Under the NS condition, parental genotype GC recorded significant and high positive and GCA effects of 18.3, 3.7, 1.4, 0.02, and 0.5 for NMF, NFF, NFPP, FW, and FYPP, in that order. BG-52 and BG-79 recorded a high and negative GCA effect of −0.1 for SYPP.

3.3.2. Specific Combining Ability Effects of the Crosses

Specific combining ability effects of the crosses for the assessed traits under DS and NS conditions across the two growing seasons are presented in Table 7 and Table 8, respectively. The SCA effect varied widely among the 28 hybrids. Under DS condition, positive and significant SCA effects of 0.4, 0.5, and 0.7 for FYPP were recorded for BG-79 × BG-52, BG-27 × BG-79, and BG-79 × BG-70, in that order. In addition, positive and significant SCA effects of 0.6 for SYPP were recorded for BG-80 × BG-70. Under NS condition, BG-81 × BG-52, BG-27 × BG-79, and BG-79 × GC recorded positive and significant SCA effects of 0.5, 0.6, and 1 for FYPP, in that order. Positive SCA effects of 0.2, 0.3, and 0.4 for SYPP were recorded for BG-81 × BG-58, BG-79 × BG-52, and BG-27 × GC, in that order.

3.4. Gene Action and Heritability Estimates

There were differences in the gene action and heritability among the assessed traits under the DS and NS conditions (Table 9). Under the DS condition, the broad-sense heritability (h2B) was higher than the narrow-sense heritability (h2n) for all traits. Under the DS condition, h2B varied from 0.76 to 0.94 for all traits except for SR and HSW, which recorded h2B of 0.13 and 0.23, respectively. A h2n of zero was recorded for all traits except for NL. Similarly, under the NS condition, the h2B was higher than the h2n for all traits. Overall, the dominance variance (σ2D) was higher compared to the additive variance (σ2A) for all traits.

3.5. Heterosis under Drought-Stressed and Non-Stress Conditions

Heterosis estimates for the studied traits amongst the F1 bottle gourd evaluated under DS and NS conditions across the two growing seasons are presented in Supplemental Table S1. High positive heterosis was considered desirable for the assessed traits. Under DS condition, high and positive mid-parent heterosis (MPH) of 298%, 101%, 91%, 58% and 86% for FYPP was recorded for crosses BG-80 × BG-58, BG-27 × BG-79, BG-79 × BG-52, BG-27 × BG-52 and BG-52 × BG-80, whereas better-parent heterosis (BPH) of 184%, 70%, 59%, 51% and 32% for FYPP was recorded for the same crosses, respectively. High and positive MPH of 244% and 265% and BPH of 236% and 174% for SYPP were recorded for crosses BG-70 × GC and BG-79 × BG-52, in that order. Under the NS condition, high and positive MPH of 58% and 52%, and BPH of 50% and 31% for FYPP were recorded for crosses BG-27 × GC and BG-79 × GC, in that order. At the same time, BPH of 52% and 59% for SYPP were recorded for crosses BG-27 × BG-58 and BG-27 × BG-79. In addition, MPH of 29% and 34% for SYPP were recorded for crosses BG-27 × BG-58 and BG-27 × BG-79, respectively.

3.6. Associations of the Agronomic Traits under Drought and Non-Stressed Conditions

Pearson’s correlation coefficients showing the associations between the assessed traits under DS and NS conditions across the two growing seasons are presented in Table 10. A highly significant and moderate positive correlation was recorded between several traits. Under DS condition, significant and positive correlations were recorded between FW with FYPP (r = 0.8) and SYPP (r = 0.7). Additionally, a significant and positive correlation was recorded between FYPP and SYPP (r = 0.8). Whereas under NS condition, high and positive correlations were recorded between NFPP with FW (r = 0.8) and SYPP (r = 0.8). NFPP exhibited a significantly low correlation with FYPP (r = 0.3). FW positively correlated with FYPP (r = 0.6) and SYPP (r = 0.9). A moderate and positive correlation was recorded between FYPP and SYPP (r = 0.5).

4. Discussion

Bottle gourd has niche market opportunities in SSA, requiring the breeding of new, well-adapted, and high-yielding varieties that possess good agronomic and horticultural traits and acceptable market standards. The present study determined the combining ability and heterosis of fruit yield and related traits among South African bottle gourd accessions under drought-stressed and non-stressed conditions for breeding and variety release.
Analysis of variance revealed significant genotypic effects (Table 2), suggesting substantial differences among the parental genotypes and their progenies for economic traits, including fruit and seed yields (Table 4 and Table 5). Bottle gourd is a morphologically diverse crop with variations reported for agronomic traits, including male and flowering capacity, fruit yield and related traits, and seed yield and related traits [2,25,27,28,32,42,53]. Drought stress reduced flowering and fruit capacity (Table 4 and Table 5). Fruit and seed yield were reduced by 71 and 62% across the tested genotypes, respectively. These indicated that bottle gourd can grow under drought-stressed environments and produce reasonable yields. The increasing drought episodes in SSA require concerted efforts to develop drought-resilient bottle gourd varieties. The variation observed in the present study will allow for selecting of desirable genotypes for new variety design and commercialization. For example, crosses BG-52 × BG-58, BG-79 × BG-52, BG-80 × BG-58, BG-80 × GC, BG-81 × BG-80, and BG-81 × GC were high fruit yielders and are recommendable for further selection. Additionally, crosses BG-52 × BG-58, BG-79 × BG-52, BG-27 × BG-52, BG-27 × BG-58, BG-27 × BG-70 and BG-52 × BG-80 were the best performers for seed yield. These are ideal families for gene introgression, genetic advancement, and variety release. The significant genotype-by-environment interaction effects for the studied traits suggested environmental influence on the performance of genotypes requiring multi-environment testing to identify and recommend genotypes with specific and wide adaptation in targeted production environments.
The significant SCA effect indicated non-additive gene action for fruit and seed yield and some related agronomic traits (Table 7 and Table 8). The non-additive gene action is non-fixable and challenging to transform, suggesting that such crosses should be used in direct production to increase the fruit and seed yield of bottle gourd in South Africa. There were significant GCA × environment effects for fruit and seed yields (Table 6). This indicated that the effects are dependent on the environment for their expression. A significant SCA × environment interaction effect existed for fruit and seed yields, suggesting that the environment played a significant role in expressing the effects. Therefore, multi-environment testing of the parents and hybrids is crucial to categorize each genotype’s performance and identify genotypes with adaptation to certain environments to optimize the fruit and seed yield.
In the present study, crosses such as BG-27 × BG-79 and BG-79 × BG-70 with high and significant SCA for fruit yield per plant were derivatives of parental genotypes BG-27, BG-70, and BG-79. Interestingly, these parents had low and non-significant GCA effects on fruit yield per plant. Previous studies in bottle gourd revealed crosses with significant SCA effects for fruit yield and yield-related traits that are derived from both or at least a parent that is a good combiner for the trait [38,39,41,54]. The recorded high SCA effects in the current study may be due to dominant × dominant non-allelic gene interaction producing over-dominance, thus challenging to modify using breeding programs [55,56]. In addition to dominance and epistasis, the SCA variation includes aberrations due to genotype × environment interactions [57]. In the current study, crosses such as BG-81 × BG-80 had a high positive and significant SCA effect for the number of female flowers. This cross manifested from good × poor general combiner parents for the trait may be attributed to favorable additive gene effects of the good general combiner parent (BG-81) and non-additive effects of the poor general combiner (BG-80) [56]. Parental genotypes such as BG-58 and GC (Table 6) with high GCA effects for fruit and seed yield serve as valuable germplasm for future breeding. Crosses such as BG-81 × BG-52, BG-81 × GC, BG-27 × BG-79, BG-27 × GC, BG-79 × GC, BG-80 × BG-70, BG-81 × BG-58, BG-27 × BG-80, BG-27 × BG-58, BG-79 × BG-52, BG-52 × BG-58, BG-80 × BG-58, and BG-58 × BG-70 (Table 7 and Table 8) with the high positive SCA effect for fruit and seed yield are potential genetic resources for further selection and multi-environment testing for release and commercialization in South Africa.
Heritability analysis is useful to provide information about the potential transmissibility of traits from parents to offspring [58,59]. The larger broad-sense heritability compared to narrow-sense heritability for all traits (Table 9) indicates that additive gene action is conditioning these traits in bottle gourd, showing that genetic gains are achievable using selection.
The exploitation of heterosis via intensive evaluation of hybrids identifies diverse genetic donors and allows for the identification of heterotic crosses [37,38]. High and positive mid- and better-parent heterosis observed in the cross BG-80 × BG-58, BG-27 × BG-79, BG-79 × BG-52, BG-27 × BG-52, and BG-52 × BG-80 for fruit yield per plant and cross, BG-70 × GC and BG-79 × BG-52 for seed yield per fruit. Hence, these crosses are essential for strategic breeding and variety release in South Africa.
Trait correlation analyses aid in the simultaneous selection of multiple traits. The positive correlations between the number of female flowers per plant with the number of fruits per plant and the number of fruits per plant with the number of seeds per fruit, fruit yield per plant, and seed yield per fruit suggested simultaneous improvement in these traits is possible. Additionally, these suggested the linkages of desirable genes controlling the expression of the studied traits [60,61]. Therefore, these traits are recommended for further selection in the newly developed bottle gourd hybrids to deliver varieties that meet market needs and standards and multiple crop characteristics required by sub-Saharan African growers.

5. Conclusions

The current study assessed the combining ability and heterosis for fruit yield and related traits. Crosses were made using genetically distant parents of South African bottle gourd accessions under non-stressed and drought-stressed conditions to select drought-tolerant parents and new hybrids for production under water-stressed environments in South Africa or similar agroecologies globally. Drought stress reduced flowering and fruit capacity, fruit and seed yields in the presently assessed bottle gourd populations. Nevertheless, the studied genotypes produced reasonable yield levels under drought-stressed conditions, indicating the possibility of breeding for enhanced drought tolerance in bottle gourds. Parental genotypes BG-58 and GC were identified as valuable germplasm for future breeding targeting high fruit and seed yields in water-limited environments. The newly bred F1 hybrids BG-81 × BG-52, BG-81 × GC, BG-27 × BG-79, BG-27 × GC, BG-79 × GC, BG-80 × BG-70, BG-81 × BG-58, BG-27 × BG-80, BG-27 × BG-58, BG-79 × BG-52, BG-52 × BG-58, BG-80 × BG-58, and BG-58 × BG-70 with high fruit and seed yields were drought-tolerance and are recommended for release and commercialization in South Africa following multi-environment testing.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/d15080925/s1, Table S1: Estimates of mid parent and better parent heterosis for the studied agronomic traits under drought stress and non-stress conditions across two growing seasons in South Africa.

Author Contributions

Conceptualization, P.M., H.S., and J.M.; methodology, P.M., H.S., and J.M.; validation, P.M., H.S., and J.M.; formal analysis, P.M., and J.M.; investigation, P.M.; resources, P.M., and J.M.; data curation, P.M.; writing—original draft preparation, P.M.; writing—review and editing, P.M., H.S., and J.M.; supervision, H.S., and J.M.; project administration, P.M., H.S., and J.M.; funding acquisition, P.M., H.S., and J.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The University of KwaZulu-Natal (UKZN), the University of Limpopo (UL), and the Limpopo Department of Agriculture and Rural Development (LDARD) are acknowledged for support provided during this study. P. Ndwambi is acknowledged for assisting with trial implementation and management. M. Malatji and the rest of the University of Limpopo farm employees are acknowledged for their assistance and guidance during the trial.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Morimoto, Y.; Mvere, B. Lagenaria siceraria. In Vegetables Plant Resources of Tropical Africa 2; Grubben, G.J.H., Denton, O.A., Eds.; Backhuys Publishers/CTA: Wageningen, The Netherlands; Leiden, The Netherlands, 2004; pp. 353–358. [Google Scholar]
  2. Morimoto, Y.; Maundu, P.; Fujimaki, H.; Morishima, H. Diversity of landraces of the white-flowered gourd (Lagenaria siceraria) and its wild relatives in Kenya: Fruit and seed morphology. Gen. Res. Crop Evol. 2005, 52, 737–747. [Google Scholar] [CrossRef]
  3. Upaganlawar, A.; Ramchandran, R. Bottle gourd (Lagenaria Siceraria) “A vegetable food for human health”—A comprehensive review. Pharmacologyonline 2009, 1, 209–226. [Google Scholar]
  4. Hart, T.G.B. The significance of African vegetables in ensuring food security for South Africa’s rural poor. Agric. Hum. Values 2011, 28, 321–333. [Google Scholar] [CrossRef]
  5. Mashilo, J.; Shimelis, H.; Odindo, A. Phenotypic and genotypic characterization of bottle gourd [Lagenaria siceraria (Molina) Standl.] and implications for breeding: A Review. Sci. Hortic. 2017, 222, 136–144. [Google Scholar] [CrossRef]
  6. Ojiako, O.A.; Igwe, C.U. Nutritional and anti-nutritional compositions of Cleome rutidosperma, Lagenaria siceraria, and Cucurbita maxima seeds from Nigeria. J. Med. Food. 2007, 10, 735–738. [Google Scholar] [CrossRef]
  7. Mohale, D.S.; Dewani, A.P.; Saoji, A.N.; Khadse, C.D. Antihyperlipidemic activity of isolated constituents from the fruits of Lagenaria siceraria in albino rats. Int. J.Green Pharm. 2008, 2, 104–107. [Google Scholar] [CrossRef]
  8. Sithole, J.N.; Modi, A.T.; Pillay, K. An assessment of minerals and protein contents in selected South African bottle gourd landraces [Lagenaria siceraria (Mol.) Standl.]. Hum. Ecol. 2015, 51, 279–286. [Google Scholar] [CrossRef]
  9. Mahapatra, S.; Sureja, A.M.; Behera, T.K.; Bhardwaj, R.; Verma, M. Variability in antioxidant capacity and some mineral nutrients among ninety-one Indian accessions of bottle gourd [Lagenaria siceraria (Molina) Standl.]. S. Afr. J. Bot. 2023, 152, 50–62. [Google Scholar] [CrossRef]
  10. Attar, U.A.; Ghane, S.G. Optimized extraction of anti-cancer compound—Cucurbitacin I and LC–MS identification of major metabolites from wild Bottle gourd (Lagenaria siceraria (Molina) Standl.). S. Afr. J. Bot. 2018, 119, 181–187. [Google Scholar] [CrossRef]
  11. Attar, U.A.; Ghane, S.G. In vitro antioxidant, antidiabetic, antiacetylcholine esterase, anticancer activities and RP-HPLC analysis of phenolics from the wild bottle gourd (Lagenaria siceraria (Molina) Standl.). S. Afr. J. Bot. 2019, 125, 360–370. [Google Scholar] [CrossRef]
  12. Saurabh, C.K.; Ghosh, S.K.; Sanyal, B. Novel detection method to rapidly quantify toxic cucurbitacin in Lagenaria siceraria (bottle gourd). J. Food Sci. Technol. 2023, 60, 160–170. [Google Scholar] [CrossRef]
  13. Chung, H.; Choi, Y.; Shin, Y.; Youn, S. Chemical composition, quality evaluation and characteristics of immature fruits of Korean native bottle gourd (Lagenaria siceraria). J. Hortic. Sci. 2000, 41, 319–328. [Google Scholar]
  14. Deshpande, J.R.; Mishra, M.R.; Meghre, V.S.; Wadodkar, S.G.; Dorle, A.K. Free radical scavenging activity of Lagenaria siceraria (Mol.) Standl. fruit. Nat. Prod. Rad. 2007, 6, 127–130. [Google Scholar]
  15. Ogunbusola, E.M. Nutritional and anti-nutritional composition of calabash and bottle gourd seed flours (var Lagenaria siceraria). J. Culin. Sci. Technol. 2018, 16, 326–335. [Google Scholar] [CrossRef]
  16. Ogunbusola, M.E.; Fagbemi, T.N.; Osundahunsim, O.F. Amino acid composition of Lagenaria siceraria seed flour and protein fractions. J. Food Sci. Technol. 2010, 47, 656–661. [Google Scholar] [CrossRef]
  17. Yetişir, H.; Sari, N. Effects of different rootstock on plant growth, yield and quality of watermelon. Aust. J. Exp. Agric. 2003, 43, 1269–1274. [Google Scholar] [CrossRef]
  18. Cohen, R.; Burger, Y.; Horev, C. Introducing grafted cucurbits to modern agriculture: The Israeli experience. Plant Sci. 2007, 91, 916–923. [Google Scholar] [CrossRef]
  19. Kousik, C.S.; Levi, A.; Ling, K.; Wechter, W.P. Potential sources of resistance to Cucurbit powdery mildew in U.S. plant introductions of bottle gourd. HortScience 2008, 43, 1359–1364. [Google Scholar] [CrossRef]
  20. Keinath, A.P.; Hassell, R.L. Control of Fusarium wilt of watermelon by drafting onto bottle gourd or interspecific hybrid squash despite colonization of rootstocks by Fusarium. Plant Dis. 2014, 98, 255–266. [Google Scholar] [CrossRef]
  21. Kousik, C.S.; Ikerd, J.L.; Mandal, M.K.; Adkins, S.; Webster, C.G.; Turechek, W.W. Powdery Mildew–Resistant Bottle Gourd Germplasm Lines: USVL351-PMR and USVL482-PMR. HortScience 2018, 53, 1224–1227. [Google Scholar] [CrossRef]
  22. Morales, C.; Riveros-Burgos, C.; Espinoza Seguel, F.; Maldonado, C.; Mashilo, J.; Pinto, C.; Contreras-Soto, R.I. Rootstocks Comparison in Grafted Watermelon under Water Deficit: Effects on the Fruit Quality and Yield. Plants 2023, 12, 509. [Google Scholar] [CrossRef] [PubMed]
  23. Koffi, K.K.; Anzera, G.K.; Malice, M.; Dey, Y.; Bertin, P.; Baudoin, J.P.; Zoro Bi, I.A. Morphological and allozyme variation in a collection of Lagenaria siceraria (Molina) Standl. from Cote D’Ivoire. Biotechnol. Agron. 2009, 13, 257–270. [Google Scholar]
  24. Decker-Walter, D.S.; Wilkins-Ellert, M.; Chung, S.; Staub, J.E. Discovery and genetic assessment of wild bottle gourd [Lagenaria siceraria (Mol.) Standley, Cucurbitaceae] from Zimbabwe. Econ. Bot. 2004, 58, 501–508. [Google Scholar] [CrossRef]
  25. Morimoto, Y.; Maundu, P.; Kawase, M. RAPD polymorphism of the white-flowered gourd (Lagenaria siceraria (Molina) Standl. landraces and its wild relatives in Kenya. Genet. Resour. Crop Eval. 2006, 53, 963–974. [Google Scholar] [CrossRef]
  26. Ibrahim, E.A.; Abed, M.Y.; Moghazy, M.A. Improving the seeds productivity of bottle gourd (Lagenaria siceraria) under drought conditions through selection. HortSci. J. 2013, 1, 127–133. [Google Scholar]
  27. Mashilo, J.; Shimelis, H.; Odindo, A. Genetic diversity of bottle gourd (Lagenaria siceraria (Molina) Standl.) landraces of South Africa assessed by morphological traits and simple sequence repeat markers. S. Afr. J. Plant Soil 2015, 33, 113–124. [Google Scholar] [CrossRef]
  28. Decker-Walters, D.; Staub, J.; Lόpez-Sesè, A.; Nakata, E. Diversity in landraces and cultivars of bottle gourd (Lagenaria siceraria, Cucurbitacaea) as assessed by random amplified polymorphic DNA. Gen. Resour. Crop Evol. 2001, 48, 369–380. [Google Scholar] [CrossRef]
  29. Wang, Y.; Wu, X.; Li, Y.; Feng, Z.; Mu, Z.; Wang, J.; Wu, X.; Wang, B.; Lu, Z.; Li, G. Identification and Validation of a Core Single-Nucleotide Polymorphism Marker Set for Genetic Diversity Assessment, Fingerprinting Identification, and Core Collection Development in Bottle Gourd. Front. Plant Sci. 2021, 12, 747940. [Google Scholar] [CrossRef]
  30. Bonthala, B.; Abdin, M.Z.; Arya, L. Genome-wide SSR markers in bottle gourd: Development, characterization, utilization in assessment of genetic diversity of National Genebank of India and synteny with other related cucurbits. J. Appl. Genet. 2022, 6, 237–263. [Google Scholar] [CrossRef]
  31. Mahapatra, S.; Sureja, A.K.; Behera, T.K.; Verma, M. Assessment of genetic diversity of ninety-one bottle gourd [Lagenaria siceraria (Mol.) Standl.] genotypes from fourteen different agro-climatic zones of India using agro-morphological traits and SSR markers. Mol. Biol. Rep. 2022, 49, 6367–6383. [Google Scholar] [CrossRef]
  32. Mashilo, J.; Shimelis, H.; Odindo, A.; Amelework, B. Genetic diversity of South African bottle gourd [Lagenaria siceraria (Molina) Standl.] landraces revealed by simple sequence repeat markers. HortScience 2016, 51, 120–126. [Google Scholar] [CrossRef]
  33. Pandit, M.K.; Mahato, B.; Sarkar, A. Genetic variability and genetic advance for some fruit characters and yield in bottle gourd (Lagenaria siceraria Molina. Standl.) genotypes. Acta Hortic. 2009, 809, 221–226. [Google Scholar] [CrossRef]
  34. Yao, K.A.G.; Koffi, K.K.; Ondo-Azi, S.A.; Baudoin, J.P.; Zoro, B.I.A. Seed yield component identification and analysis for exploitation recombinative heterosis in bottle gourd. Int. J. Veg. Sci. 2015, 21, 441–453. [Google Scholar] [CrossRef]
  35. Yadav, Y.C.; Kumar, S. Estimation of heterosis for yield and yield contributing traits in bottle gourd [Lagenaria siceraria (Molina) Standl]. Asian J. Hortic. 2012, 7, 310–314. [Google Scholar]
  36. Kumar, A.; Yadav, G.C.; Pandey, V.; Patel, M.S. Studies on combining ability for yield and its related traits in bottle gourd [Lagenaria siceraria (Mol.) Stndl.]. Ann. Agric. Biol. Res. 2014, 19, 140–143. [Google Scholar]
  37. Behera, T.; Sirohi, P.; Pal, A.; Singh, I. “Pusa Santushti” a new high yielding bottle gourd variety. Int. J. Veg. Sci. 2015, 21, 323–328. [Google Scholar] [CrossRef]
  38. Janaranjani, K.G.; Kanthaswamy, V.; Kumar, R. Heterosis, combining ability, and character association in bottle gourd for yield attributes. Int. J. Veg. Sci. 2016, 22, 490–515. [Google Scholar] [CrossRef]
  39. Doust, A.N.; Lukens, L.; Olsen, K.M.; Mauro-Herrera, M.; Meyer, A.; Rogers, K. Beyond the single gene: How epistasis and gene-by-environment effects influence crop domestication. Proc. Natl. Acad. Sci. USA 2014, 17, 6178–6183. [Google Scholar] [CrossRef]
  40. Yadav, Y.C.; Kumar, S. Specific combining ability analysis for yield improvement in bottle gourd [Lagenaria siceraria (Molina) Standl.]. Environ. Ecol. 2012, 30, 18–23. [Google Scholar]
  41. Mishra, S.; Pandey, S.; Kumar, N.; Satrughan, G.; Pandey, C.; Pandey, V.; Singh, T. Studies on combining ability and gene action in kharif season bottle gourd [Lagenaria siceraria (Molina) standl.]. J. Pharmacogn. Photochem. 2019, 8, 11–18. [Google Scholar]
  42. Mashilo, J.; Shimelis, H.; Odindo, A.; Amelework, B. Genetic differentiation of bottle gourd [Lagenaria siceraria (Molina) Standl.] landraces assessed by fruit qualitative traits and simple sequence repeat markers. Sci. Hortic. 2017, 216, 1–11. [Google Scholar] [CrossRef]
  43. Nkosi, L.S.; Ntuli, N.R.; Mavengahama, S. Morpho-Agronomic evaluation of Lagenaria siceraria landraces and their F1 populations. Plants 2002, 11, 1558. [Google Scholar] [CrossRef] [PubMed]
  44. Mashilo, J.; Shimelis, H.; Odindo, A. Drought tolerance of selected bottle gourd [Lagenaria siceraria (Molina) Standl.] landraces assessed by leaf gas exchange and photosynthetic efficiency. Plant Physiol. Biochem. 2017, 120, 75–87. [Google Scholar] [CrossRef] [PubMed]
  45. Mashilo, J.; Odindo, A.; Shimelis, H.; Musenge, P.; Tesfay, S.Z.; Magwaza, L.S. Photosynthetic response of bottle gourd [Lagenaria siceraria (Molina) Standl.] to drought stress, Relationship between cucurbitacins accumulation and drought tolerance. Hortic. Sci. 2018, 231, 133–143. [Google Scholar] [CrossRef]
  46. Sio-Se Mardeh, A.; Ahmadi, A.; Poustini, K.; Mohammadi, V. Evaluation of drought resistance indices under various environmental conditions. Field Crops Res. 2006, 98, 222–229. [Google Scholar] [CrossRef]
  47. Belko, N.; Cisse, N.; Diop, N.N.; Zombre, G. Selection for post flowering drought resistance in short-and medium-duration cowpeas using stress tolerance indices. Crop Sci. 2014, 54, 25–33. [Google Scholar] [CrossRef]
  48. Payne, R.W.; Murray, D.A.; Harding, S.A.; Baired, D.; Soultar, D. GenStat Windows, 17th ed.; VSN International: Hemel Hempstead, UK, 2017; pp. 15–16. [Google Scholar]
  49. Alvarado, G.; Rodrígueza, F.M.; Pachecoa, A.; Juan Burgueñoa, J.; Crossa, J.; Vargasa, M.; Pérez-Rodríguezd, P.; Lopez-Cruz, M.A. META-R: A software to analyse data from multi-environment plant breeding trials. Crop J. 2020, 8, 745–756. [Google Scholar] [CrossRef]
  50. Griffing, B. Concept of general and specific combining ability in relation to diallel crossing systems. Aust. J. Sci. Res. 1956, 9, 463–493. [Google Scholar] [CrossRef]
  51. Rodríguez, F.; Alvarado, G.; Pacheco, Ă.; Crossa, J.; Burgueňo, J. AGD-R (Analysis of Genetic Designs with R for Windows) Version 5.0; CIMMYT: Mexico City, Mexico, 2018; p. 13. [Google Scholar]
  52. Allard, R.W. Principles of Plant Breeding, 2nd ed.; John Wiley and Sons: New York, NY, USA, 1999; pp. 172–191. [Google Scholar]
  53. Falconer, D.S.; Mackay, T.F.C. Introduction to Quantitative Genetics; Longman: London, UK, 1996; pp. 32–46. [Google Scholar]
  54. Kumar, R.; Kumar, R.; Prasad, B.D.; Solankey, S.S.; Kumar, J.; Bamaniya, B.S. Genetic variation study using morphological and DNA marker-based genotyping in bottle gourd (Lagenaria siceraria (Mol.) Standl.). Curr. J. Appl. Sci. Technol. 2018, 31, 1–10. [Google Scholar] [CrossRef]
  55. Comstock, R.E.; Robinson, H.F. Estimation of the Average Dominance of Genes; Heterosis: Raleigh, NC, USA, 1952; pp. 494–516. [Google Scholar]
  56. Ojas, B.A.; Sprague, F. A Comparison of Variance Components in Corn Yield Trials: III. General and Specific Combining Ability and Their Interaction with Locations and Years 1. J. Agron. 1952, 44, 462–466. [Google Scholar]
  57. Begna, T. Combining ability and heterosis in plant improvement. J. Plant Sci. 2021, 6, 108–117. [Google Scholar]
  58. Gouda, G.; Sabarinathan, S.; Gupta, M.K.; Donde, R.; Dash, G.K. Quantitative Trait Locus Mapping in Rice. Appl. Bioinform. Rice Res. 2021, 10, 55–82. [Google Scholar]
  59. Abdin, B.M.; Arya, L.; Sureja, D.S.A.K.; Verma, M. Population structure and genetic diversity in bottle gourd [Lagenaria siceraria (Mol.) Standl.] germplasm from India assessed by ISSR markers. Plant Syst. Evol. 2014, 300, 767–773. [Google Scholar]
  60. Adhikari, B.N.; Joshi, B.P.; Shrestha, J.; Bhatta, N.R. Genetic variability, heritability, genetic advance and correlation among yield and yield components of rice (Oryza sativa L.). J. Agric. Natl. Resour. 2018, 1, 149–160. [Google Scholar] [CrossRef]
  61. Dey, S.S.; Singh, A.K.; Chandel, D.; Behera, T.K. Genetic diversity of bitter gourd (Momordica charanita L.) genotypes revealed by RAPD markers and agronomic traits. HortScience 2006, 109, 21–28. [Google Scholar]
Table 1. List and attributes of selected bottle gourd genotypes used in the cross.
Table 1. List and attributes of selected bottle gourd genotypes used in the cross.
Accession DesignationDrought ResponseFruit ShapeFruit Neck LengthPrimary Fruit Color
BG-27TolerantCavateLongDark green
BG-52TolerantCavateLongMedium green
BG-58SusceptibleElongatedLongDark green
BG-70SusceptibleElongatedNo neckDark green
BG-79TolerantPyriformShortLight green
BG-80SusceptibleElongatedShortDark green
BG-81SusceptiblePyriformNo neckLight green
GCTolerantPyriformNo neckLight green
Table 2. Analysis of variance showing mean squares and significant tests for the parental genotypes and their crosses evaluated for agronomic/horticultural traits under non-stressed and drought-stressed conditions across two growing seasons in South Africa.
Table 2. Analysis of variance showing mean squares and significant tests for the parental genotypes and their crosses evaluated for agronomic/horticultural traits under non-stressed and drought-stressed conditions across two growing seasons in South Africa.
Source of Variation Traits
dfNMFNFFSRNLPHNFPPFWFCFYPPNSPFHSWSYPP
Incomplete Block (IB)58922.00 **432.2 **12.94 *11,562.00 ns15.68 ns16.9 ns0.01 ns3292.20 **0.39 ns157,280.00 **24.83 ns0.15 ns
IB × Replication (Reps)104309.00 ns129.90 ns7.45 ns4050.00 ns68.71 ns5.39 ns0.00 ns114.3 ns0.09 ns9818.00 ns16.31 ns0.01 ns
Reps262.00 ns55.10 ns2.92 ns24,012.00 ns57.91 ns30.68 ns0.00 ns196.2 ns0.69 ns82,067.00 ns21.45 ns0.01 ns
Genotype (Gen)352691.80 **157.34 **7.45 ns27,966 **76.65 **23.68 **0.03 **1409.70 **0.69 **128,815.00 **24.24 **5.24 **
Environment (Env)325,075.00 **2958.70 **31.41 **774,455 **749.79 **683.71 **0.04 **7604.5 **12.46 **1,149,414.00 **115.08 **9.54 **
Gen × Env1052219.2 **104.68 **5.43 ns16,872 **61.67 *11.08 **0.02 **1061.6 **0.31 **75,629.00 **15.22 **12.12 **
Residual287160760.495.5519,68145.464.110.04674.60.0762,49110.774.47
Note: df, degrees of freedom; * and ** denote significant differences at 5 and 1% probability levels, respectively; ns, non significant; NMF, number of male flowers per plant, NFF; the number of female flowers per plant; SR, sex ratio, NL = the number of leaves per plant; PH, plant height (m); NFPP, the number of fruits per plant, FW = fruit weight (kg/fruit); FC, fruit circumference (cm); FYPP, fruit yield per plant (kg); NSPF, the number of seeds per fruit; HSW, hundred seed weight (g/100 seed); SYPP, seed yield per plant (kg).
Table 3. BLUPs estimates of bottle gourd parents and their hybrids for assessed traits evaluated under drought-stressed conditions across two growing seasons in South Africa.
Table 3. BLUPs estimates of bottle gourd parents and their hybrids for assessed traits evaluated under drought-stressed conditions across two growing seasons in South Africa.
GenotypeTraits
NMFNFFSRNLPHNFPPFWFCFYPPNSPFHSWSYPP
Crosses
BG-27 × BG-5247.648.926.8888.684.262.880.0759.890.21144.7815.530.1
BG-27 × BG-5864.2412.286.496.114.013.360.0758.160.29320.7416.630.29
BG-27 × BG-7067.7314.786.14119.844.543.520.0848.120.35334.7516.380.24
BG-27 × BG-7964.2411.426.6387.885.474.160.0747.940.31364.6916.070.24
BG-27 × BG-8088.5311.77.2759.952.332.080.0441.250.14359.8616.310.14
BG-27 × GC44.579.116.4771.252.022.080.0559.290.14194.0416.880.11
BG-52 × BG-5872.910.947.05144.697.275.920.0858.630.52459.5216.950.52
BG-52 × BG-7076.9411.76.40136.36.254.480.0851.980.41444.3816.340.33
BG-52 × BG-8067.1711.86.5185.942.462.720.0781.990.20268.116.490.16
BG-52 × GC66.211.226.38113.863.632.560.0757.80.20406.3915.430.15
BG-58 × BG-7080.9912.866.54148.576.414.960.0785.920.39419.2716.950.4
BG-58 × GC88.9415.546.38115.164.794.160.0847.830.38289.8316.260.23
BG-70 × GC59.649.976.72106.62.852.080.0635.410.14227.0416.530.12
BG-79 × BG-5283.6412.386.75122.745.834.960.0853.40.49482.3816.350.48
BG-79 × BG-5863.5410.656.5986.592.992.40.0651.670.18208.0516.860.12
BG-79 × BG-7079.1815.456.48123.712.653.040.0740.670.24201.6116.740.14
BG-79 × BG-8046.669.46.43118.554.343.840.0653.430.27144.9416.20.11
BG-79 × GC43.738.537.04143.565.014.000.0866.590.37186.3116.890.14
BG-80 × BG-5861.5911.86.09143.47.265.760.0971.710.59224.4717.020.23
BG-80 × BG-7059.6410.076.8296.593.063.520.0750.880.27317.8416.530.21
BG-80 × GC81.9714.016.34124.196.645.280.0965.610.49273.5716.80.29
BG-81 × BG-2789.6415.266.34105.964.93.680.0755.110.28311.7315.390.17
BG-81 × BG-5286.7117.476.21143.893.244.160.0747.710.29483.1815.350.30
BG-81 × BG-5862.1511.426.3676.093.052.720.0649.710.28242.3415.880.13
BG-81 × BG-7041.088.636.42124.364.793.520.0851.740.28150.816.410.12
BG-81 × BG-7971.3613.246.4855.431.992.080.0533.410.13351.1716.550.13
BG-81 × BG-8092.4315.936.72109.354.623.360.0752.130.25441.8116.680.25
BG-81 × GC63.4110.176.78150.185.463.840.0951.670.42360.9915.960.24
Parents
BG-2752.3810.366.5874.322.732.40.0543.260.14208.0515.850.12
BG-5259.3611.96.1998.212.771.920.0434.570.13264.2316.590.11
BG-5866.211.616.4122.264.793.840.0751.420.31269.5516.180.18
BG-7050.859.596.698.213.3340.0664.240.21197.4216.040.14
BG-7950.159.496.57107.732.832.880.0549.660.17199.1916.650.14
BG-8073.7312.286.42132.754.073.840.0673.450.19269.8716.970.21
BG-8173.5912.866.5287.882.662.40.0766.920.18228.3317.290.14
GC94.8116.796.17187.476.975.60.0777.870.49432.1516.810.32
Mean67.7111.996.53111.344.233.560.0755.310.29296.7616.410.21
LSD (%)388.491.4759.42.892.340.0317.990.28210.962.360.27
CV (%)52.7180.8735.9547.3556.6358.6234.9124.6589.7563.1820.26122.95
p-value>0.05<0.05>0.05<0.05>0.05<0.05<0.001<0.001<0.001<0.001<0.001<0.05
NMF, number of male flowers per plant; NFF, the number of female flowers per plant; SR, sex ratio; NL, the number of leaves per plant; PH, plant height (m); NFPP, the number of fruits per plant; FW, fruit weight (kg/fruit); FC, fruit circumference (cm); FYPP, fruit yield per plant (kg); NSPF, the number of seeds per fruit; HSW, hundred seed weight (g/100 seed); SYPP, seed yield per plant (kg). CV, coefficient of variation; LSD, least significant difference.
Table 4. BLUPs estimates of bottle gourd parents and their hybrids for assessed traits evaluated under non-stressed condition across two growing seasons in South Africa.
Table 4. BLUPs estimates of bottle gourd parents and their hybrids for assessed traits evaluated under non-stressed condition across two growing seasons in South Africa.
GenotypeTraits
NMFNFFSRNLPHNFPPFWFCFYPPNSPFHSWSYPP
Crosses
BG-27 × BG-52119.0715.607.84201.558.708.961.0861.549.63574.1614.380.64
BG-27 × BG-58145.9925.346.77419.9210.658.800.1451.241.26554.9815.790.67
BG-27 × BG-70144.8518.797.75209.558.827.800.0950.440.74773.2013.730.70
BG-27 × BG-79170.4927.907.19322.539.748.960.1190.071.01469.9914.400.55
BG-27 × BG-80150.4118.798.19282.549.596.970.0948.550.77330.2415.090.30
BG-27 × GC150.9823.107.20213.959.079.130.1861.531.64402.9812.800.41
BG-52 × BG-58181.8824.707.60221.558.987.970.1385.211.06436.9815.980.45
BG-52 × BG-70164.9423.267.42291.1410.098.960.1067.910.94391.9115.910.56
BG-52 × BG-80133.8917.197.88328.138.807.130.0853.180.60546.4816.600.62
BG-52 × GC178.8924.867.74240.559.188.800.1073.150.86692.1613.730.72
BG-58 × BG-70145.8519.277.80201.559.388.460.0855.730.79354.9515.470.39
BG-58 × GC150.1220.237.83208.158.855.640.07109.780.44486.2015.740.34
BG-70 × GC183.1730.936.98168.168.677.970.1480.081.09387.5614.070.42
BG-79 × BG-52155.1123.587.16206.159.208.300.1077.290.86372.1517.220.50
BG-79 × BG-58156.8221.357.56301.9418.429.300.1078.740.98574.9513.750.67
BG-79 × BG-70160.0918.638.34145.968.157.970.1067.490.77824.9912.520.67
BG-79 × BG-80140.7217.358.80328.939.4910.790.1159.931.26370.9614.190.51
BG-79 × GC162.3725.987.05186.818.6910.130.1375.891.36334.0014.890.44
BG-80 × BG-58166.7924.547.28323.539.7110.960.1187.891.22533.2412.130.61
BG-80 × BG-70160.5221.997.62213.358.818.130.1072.900.79524.3415.340.56
BG-80 × GC161.9423.587.33332.5310.028.460.1284.150.99573.9615.060.67
BG-81 × BG-27176.0429.506.92368.5310.229.130.1277.011.06668.6414.150.71
BG-81 × BG-52165.2225.507.58387.5211.097.970.1281.550.95668.4412.650.54
BG-81 × BG-58146.7023.587.06184.569.659.130.1170.611.00595.3113.250.62
BG-81 × BG-70147.8423.267.12374.3310.4011.290.1379.491.53272.1314.090.41
BG-81 × BG-79143.5722.157.53382.7210.139.790.0852.420.94347.6415.660.46
BG-81 × BG-80162.0926.147.17302.3410.7710.460.1172.841.17598.4713.440.74
BG-81 × GC179.3227.907.14363.1310.4810.290.1580.061.60687.6112.130.71
Parents
BG-27144.0018.478.02351.938.596.970.0956.600.67355.9416.140.36
BG-52133.1717.197.71290.948.887.470.1168.410.84400.8115.420.48
BG-58164.9423.587.42267.949.049.300.1076.980.96438.7614.020.51
BG-70179.8926.147.38422.9210.2612.290.1280.781.59306.1314.760.52
BG-79132.8921.836.93148.368.386.470.0960.210.61326.6814.810.32
BG-80157.2420.397.95176.568.237.800.1285.570.91407.9215.410.52
BG-81158.6723.107.35185.759.048.300.0855.380.69580.4816.510.81
GC184.0226.787.37441.3211.2213.780.1680.202.48364.4414.020.65
Mean157.2422.857.50277.709.708.890.1171.411.28486.9414.590.55
LSD (%)39.127.811.79125.737.333.280.0328.160.57213.283.600.25
CV (%)23.3830.5030.0234.4698.7531.9025.6634.4845.0333.7324.4138.79
p-value>0.05<0.05>0.05<0.05>0.05<0.05<0.001<0.001<0.001<0.001<0.001<0.05
NMF, number of male flowers per plant; NFF, the number of female flowers per plant; SR, sex ratio; NL, the number of leaves per plant; PH, plant height (m); NFPP, the number of fruits per plant; FW, fruit weight (kg/fruit); FC, fruit circumference (cm); FYPP, fruit yield per plant (kg); NSPF, the number of seeds per fruit; HSW, hundred seed weight (g/100 seed); SYPP, seed yield per plant (kg). CV, coefficient of variation; LSD, least significant difference.
Table 5. Analysis of variance showing mean squares and significant tests for parental genotypes and their hybrids for the assessed traits across two growing seasons in South Africa.
Table 5. Analysis of variance showing mean squares and significant tests for parental genotypes and their hybrids for the assessed traits across two growing seasons in South Africa.
Source of VariationTraits
dfNMFNFFSRNLPHNFPPFWFCFYPPNSPFHSWSYPP
Environment (Env)3.0025,074.90 **4125.34 **31.79 *1,009,293.0 **785.16 **684.26 **0.051 **10,863.87 **13.17 **1,269,586.00 **125.94 ns3.23 **
Env(Rep)6.00269.09 ns42.93 ns2.60 ns10,572.48 **31.45 ns12.10 **0.00 ns412.96 ns0.21 **25,322.75 ns38.04 **0.07 **
Env (RepBlock)40.002112.83 **145.49 **6.93 ns8314.86 **48.66 ns7.05 **0.00 ns876.45 ns0.19 **53,420.55 ns14.54 *0.09 **
Genotype (Gen)35.003966.34 *157.33 *7.45 ns27,965.56 *76.65 ns23.68 **0.03 **1409.69 ns0.69 **128,815.50 *24.53 *0.15 ns
Gen × Env105.002219.24 **104.68 **5.43 ns16,872.5 **61.67 *23.78 **0.00 ns1061.55 ns0.31 **75,628.94 ns15.21 **0.12 **
GCA7.004839.62 ns229.01 *7.99 ns13,288.07 ns52.32 ns11.08 **0.03 *2902.99 *1.02 *100,802.40 ns11.54 ns0.11 ns
SCA28.003748.02 *139.42 ns7.32 ns31,634.94 *82.73 ns25.67 *0.02 *1036.37 ns0.61 **135,818.70 *27.78 **0.16 ns
GCA × Env21.002513.755 **96.00 **4.66 ns16,083.49 **39.91 ns23.18 **0.00 ns936.45 ns0.31 **53,004.61 ns20.88 **0.14 **
SCA × Env84.002145.60 **106.84 **5.62 ns17,081.00 **67.10 *11.65 **0.00 ns1092.80 ns0.31 **81,285.03 ns13.79 *0.11 **
Residual100.0030,125.0626.925.05464.4643.972.880.00−174.760.02−950.838.750.01
Note: GCA, general combining ability; SCA, specific combining ability; df, degrees of freedom; * and ** denote significant difference at 5 and 1% probability levels; ns, non-significant; NMF, number of male flowers per plant; NFF, the number of female flowers per plant; SR, sex ratio; NL, the number of leaves per plant; PH, plant height (m); NFPP, the number of fruits per plant; FW, fruit weight (kg/fruit); FC, fruit circumference (cm); FYPP, fruit yield per plant (kg); NSPF, the number of seeds per fruit; HSW, hundred seed weight (g/100 seed); SYPP, seed yield per plant (kg).
Table 6. General combining effects for the assessed traits among the parental genotypes evaluated under drought-stressed and non-stressed conditions across two growing seasons in South Africa.
Table 6. General combining effects for the assessed traits among the parental genotypes evaluated under drought-stressed and non-stressed conditions across two growing seasons in South Africa.
ParentsTraits
NMFNFFSRNLPHNFPPFWFCFYPPNSPFHSWSYPP
Drought-stressed
BG-8115.44 ns6.39 **−0.72 ns−10.09 ns−1.04 **−1.01 **0.00 ns−1.41 ns−0.11 ns35.55 ns−1.80 **−0.11 ns
BG-27−14.51 ns−0.11 ns−0.49 ns−15.54 **−0.83 ns−0.76 ns−0.01 **−2.36 ns−0.10 ns−60.72 ns−1.01 ns−0.06 ns
BG-79−13.96 ns−3.16 ns0.18 ns−17.14 **−0.70 ns−0.46 ns−0.01 **−6.13 **−0.07 ns−29.17 ns0.67 ns−0.04 ns
BG-528.14 ns1.09 ns0.09 ns−5.49 ns−0.27 ns−0.36 ns0.00 ns0.31 ns−0.02 ns71.03 ns0.11 ns0.11 ns
BG-80−1.71 ns−1.16 ns0.37 ns−6.39 ns−0.22 ns−0.12 ns0.00 ns4.32 ns−0.04 ns−62.27 ns0.97 ns−0.08 ns
BG-5816.39 ns1.54 ns−0.12 ns23.76 **1.93 **1.19 **0.01 **4.16 ns0.16 **17.43 ns1.44 ns0.24 **
BG-70−13.76 ns−3.13 ns0.42 ns−1.69 ns−0.34 ns0.44 ns0.00 ns−2.75 ns−0.02 ns−52.05 ns−0.27 ns−0.01 ns
GC3.99 ns−1.26 ns0.27 ns32.56 **1.46 **1.19 **0.02 **3.85 ns0.19 **80.18 ns−0.10 ns0.04 ns
Non-stressed
BG-817.24 ns3.49 **−0.65 **56.38 **2.26 **1.03 ns0.00 ns−3.53 ns0.14 ns171.58 **−0.76 ns0.27 ns
BG-27−5.91 ns−0.31 ns−0.15 ns5.63 ns−0.07 ns−0.98 ns0.00 ns−0.31 ns−0.14 ns30.73 ns1.28 ns0.02 ns
BG-79−7.26 ns−0.96 ns0.20 ns−13.78 ns−0.84 ns0.08 ns−0.01 **−1.55 ns−0.07 ns−91.13 **0.06 ns−0.11 **
BG-52−24.46 **−3.41 **−0.02 ns−35.58 **−1.15 **−1.78 **−0.02 **1.20 ns−0.37 **−26.23 ns1.97 **−0.07 **
BG-806.09 ns−1.46 ns0.62 **−26.83 ns−0.94 ns−0.53 ns0.01 **1.43 ns−0.02 ns−44.48 ns0.33 ns−0.03 ns
BG-583.29 ns0.09 ns−0.04 ns−14.23 ns−0.22 ns−0.18 ns0.00 ns2.07 ns−0.06 ns19.53 ns−0.94 ns−0.02 ns
BG-702.69 ns−1.11 ns0.44 ns3.28 ns0.13 ns0.98 ns0.00 ns−0.29 ns0.06 ns−26.08 ns−0.56 ns−0.05 ns
GC18.34 **3.69 **−0.39 ns25.13 ns0.83 ns1.38 **0.02 **0.97 ns0.47 **−33.93 ns−1.39 ns0.01 ns
Notes: ** denote significant at 1% probability level of t-values based on a two-tailed test, respectively; ns, non-significant; NMF, number of male flowers per plant; NFF, the number of female flowers per plant; SR, sex ratio; NL, the number of leaves per plant; PH, plant height (m); NFPP, the number of fruits per plant; FW, fruit weight (kg/fruit); FC, fruit circumference (cm); FYPP, fruit yield per plant (kg); NSPF, the number of seeds per fruit; HSW, hundred seed weight (g/100 seed); SYPP, seed yield per plant (kg).
Table 7. Specific combining effects for the studied agronomic traits among the crosses evaluated under drought-stressed conditions across two growing seasons in South Africa.
Table 7. Specific combining effects for the studied agronomic traits among the crosses evaluated under drought-stressed conditions across two growing seasons in South Africa.
Cross Traits
NMFNFFSRNLPHNFPPFWFCFYPPNSPFHSWSYPP
BG-81 × BG-27−6.50 ns−6.40 ns0.80 ns−38.09 **−1.45 ns−0.71 ns−0.01 ns5.44 ns−0.08 ns−242.12 **4.12 **0.00 ns
BG-81 × BG-7949.95 **7.60 ns−0.71 ns35.86 ns3.63 **2.54 **0.01 ns4.82 ns0.22 ns−21.85 ns−5.28 **0.02 ns
BG-81 × BG-5237.90 ns8.65 ns−0.17 ns−45.54 **−2.57 ns−1.26 ns−0.02 **−8.69 ns−0.15 ns386.60 **3.02 ns0.03 ns
BG-81 × BG-8021.30 ns15.90 **−1.12 ns−19.69 ns−2.54 ns−1.36 ns−0.02 **−10.42 ns−0.18 ns77.40 ns−3.33 ns−0.19 ns
BG-81 × BG-5838.65 ns15.15 **−1.16 ns21.71 ns1.31 ns0.49 ns0.00 ns2.85 ns0.01 ns317.20 **−0.79 ns0.19 ns
BG-81 × BG-70−28.45 ns−6.05 ns0.12 ns−14.44 ns−2.07 ns−1.41 ns−0.03 **−8.77 ns−0.27 ns−207.50 ns−0.81 ns−0.22 ns
BG-81 × GC−74.30 **−15.70 **0.04 ns27.01 ns2.28 ns0.84 ns0.01 ns7.57 ns0.09 ns−253.32 **0.26 ns−0.10 ns
BG-27 × BG-79−32.05 ns−12.75 ns1.41 ns71.26 **2.87 ns1.59 ns0.06 ns1.75 ns0.45 **185.75 ns−1.32 ns0.27 ns
BG-27 × BG-52−20.10 ns−5.40 ns0.74 ns−22.19 ns−1.72 ns−1.21 ns−0.01 ns0.26 ns−0.11 ns−128.58 ns0.09 ns−0.12 ns
BG-27 × BG-8042.35 ns6.15 ns−0.63 ns60.91 **6.12 ns3.49 **0.02 **1.68 ns0.25 ns185.37 ns−2.23 ns0.18 ns
BG-27 × BG-58−66.25 **−14.10 **0.92 ns−16.24 ns−0.04 ns−0.61 ns0.00 ns0.73 ns−0.11 ns−306.83 **1.41 ns−0.29 ns
BG-27 × BG-7038.60 ns−1.85 ns2.79 **−50.84 **−3.14 ns−1.76 ns−0.04 ns−25.26 **−0.20 ns173.47 ns0.52 ns−0.04 ns
BG-27 × GC−12.00 ns0.95 ns0.02 ns16.51 ns−0.43 ns0.34 ns0.01 ns9.45 ns0.05 ns304.77 **−0.19 ns0.36 ns
BG-79 × BG-5244.65 ns21.80 **−3.41 **66.96 **2.41 ns1.59 ns0.04 ns−4.85 ns0.42 **194.25 ns1.65 ns0.24 ns
BG-79 × BG-80−57.10 **−9.75 ns−0.47 ns−68.79 **−5.10 **−3.16 **−0.03 ns12.91 ns−0.40 **−271.98 **3.94 **−0.22 ns
BG-79 × BG-58−25.20 ns−3.30 ns−0.44 ns−29.99 ns−2.62 **−1.81 ns−0.02 **−13.40 **−0.20 ns−160.18 ns−1.93 ns−0.16 ns
BG-79 × BG-7065.70 **6.45 ns1.09 ns108.36 ns6.57 ns4.59 **0.06 ns22.57 **0.69 **627.62 ns0.48 ns0.96 ns
BG-79 × GC−60.95 **−9.30 ns−0.32 ns27.26 ns1.84 ns1.44 ns0.03 **2.85 ns0.15 ns−191.58 ns−1.59 ns−0.11 ns
BG-52 × BG-80−7.05 ns−2.00 ns0.26 ns−66.39 **−4.35 **−3.46 **−0.02 **7.72 ns−0.40 **−189.28 ns3.97 **−0.33 ns
BG-52 × BG-5838.10 ns5.85 ns−0.12 ns−39.44 ns−3.47 **−1.71 ns−0.04 ns−12.07 ns−0.25 ns−167.80 ns0.76 ns−0.19 ns
BG-52 × BG-70−65.65 **−9.20 ns0.77 ns14.81 ns1.11 ns0.54 ns0.02 **12.75 ns0.11 ns−330.53 **−0.55 ns−0.22 ns
BG-52 × GC−33.90 ns−0.30 ns−2.50 **−58.29 **−3.53 **−2.01 ns−0.05 ns−28.64 ns−0.30 **−350.58 **1.32 ns−0.44 **
BG-80 × BG-58−30.05 ns−7.05 ns0.83 ns−24.89 ns−3.37 **−2.16 **0.00 ns23.12 **−0.24 ns−220.78 **−0.07 ns−0.25 ns
BG-80 × BG-7020.85 ns−1.75 ns1.51 ns61.96 **5.32 **3.44 **0.02 **6.00 ns0.37 **272.52 **3.08 ns0.63 **
BG-80 × GC62.00 **3.60 ns1.40 ns60.41 **5.51 **3.69 **0.04 ns8.19 ns0.53 **266.50 **−1.44 ns0.34 ns
BG-58 × BG-70−5.75 ns−2.45 ns0.37 ns−53.34 **−4.38 **−3.56 **−0.01 ns7.09 ns−0.45 ns−15.23 ns−2.78 ns−0.33 ns
BG-58 × GC10.80 ns−0.30 ns0.07 ns5.01 ns−0.77 ns−0.81 ns−0.01 ns1.84 ns−0.13 ns−50.48 ns1.00 ns−0.02 ns
BG-70 × GC−26.30 ns−4.50 ns−2.73 ns39.86 ns4.29 **3.29 **0.02 **10.63 ns0.44 **−89.18 ns−0.05 ns−0.02 ns
Note: ** denote significant differences at 1% probability level of t-values based on a two-tailed test, respectively; ns, non-significant; NMF, number of male flowers per plant; NFF, the number of female flowers per plant; SR, sex ratio; NL, the number of leaves per plant; PH, plant height (m); NFPP, the number of fruits per plant; FW, fruit weight (kg/fruit); FC, fruit circumference (cm); FYPP, fruit yield per plant (kg); NSPF, the number of seeds per fruit; HSW, hundred seed weight (g/100 seed); SYPP, seed yield per plant (kg).
Table 8. Specific combining effects for the studied agronomic traits among the crosses evaluated under non-stressed condition across two growing seasons in South Africa.
Table 8. Specific combining effects for the studied agronomic traits among the crosses evaluated under non-stressed condition across two growing seasons in South Africa.
CrossTraits
NMFNFFSRNLPHNFPPFWFCFYPPNSPFHSWSYPP
BG-81 × BG-27−8.32 ns−3.66 ns0.45 ns−149.11 **−5.04 **−0.91 ns−0.04 **−3.56 ns−0.47 **−64.07 ns5.27 **0.36 ns
BG-81 × BG-7951.33 **6.14 ns0.22 ns30.14 ns1.16 ns−3.41 **0.01 ns0.86 ns−0.37 ns201.28 **0.82 ns−0.01 ns
BG-81 × BG-521.18 ns5.79 ns−1.47 **96.04 **2.98 **4.04 **0.01 ns−0.83 ns0.52 **−375.87 ns1.09 ns−0.38 **
BG-81 × BG-8029.38 ns10.24 **−1.21 ns102.84 **3.34 **−2.11 ns0.01 ns−0.43 ns−0.21 ns332.73 **−4.86 **−0.07 ns
BG-81 × BG-58−15.17 ns−5.21 ns1.01 ns76.09 ns2.31 ns2.64 ns0.00 ns−3.02 ns0.35 ns10.98 ns−0.58 ns0.22 **
BG-81 × BG-70−43.37 **−8.26 **0.65 ns−115.51 **−3.02 **−2.71 ns−0.02 ns1.05 ns−0.42 ns107.98 ns−3.62 ns−0.22 **
BG-81 × GC−0.27 ns1.44 ns−0.52 ns61.49 ns1.84 ns1.64 ns0.03 **10.48 ns0.51 **−402.42 ns1.32 ns−0.41 ns
BG-27 × BG-79−6.42 ns−2.86 ns0.43 ns47.14 ns1.46 ns1.74 ns0.03 **−0.98 ns0.57 **253.43 **−4.72 **0.14 ns
BG-27 × BG-5228.98 ns−0.06 ns0.96 ns−49.11 ns−1.87 ns2.09 ns−0.02 ns−0.58 ns0.14 ns−263.37 **1.27 ns−0.16 **
BG-27 × BG-8033.33 ns10.09 **−1.26 ns57.29 ns2.31 ns2.04 ns0.01 ns23.44 **0.27 ns−116.52 **−1.69 ns0.01 **
BG-27 × BG-58−92.97 ns−11.46 **−0.18 ns10.09 ns0.90 ns−0.61 ns−0.03 **2.63 ns−0.18 ns197.08 **−0.45 ns0.19 **
BG-27 × BG-7017.48 ns0.09 ns0.79 ns4.84 ns−0.82 ns0.64 ns0.04 ns−8.60 ns0.42 ns−300.17 **−0.06 ns−0.33 **
BG-27 × GC−24.72 ns−0.96 ns−0.98 ns116.24 **4.13 **0.29 ns0.06 ns−4.52 ns0.55 **254.83 **2.99 ns0.38 **
BG-79 × BG-52−31.62 ns−5.76 ns0.27 ns−96.26 **−3.29 **−2.36 ns−0.03 **−11.59 ns−0.45 ns442.93 ns−1.87 ns0.33 **
BG-79 × BG-80−10.77 ns1.94 ns−0.77 ns−24.11 ns−0.65 ns−0.76 ns−0.03 **−1.07 ns−0.51 **−152.72 ns−2.28 ns−0.25 ns
BG-79 × BG-58−29.32 ns−4.76 ns−0.12 ns−96.81 **−4.23 **−5.01 **−0.04 ns−13.04 **−0.75 **−0.67 ns0.57 ns−0.09 ns
BG-79 × BG-7033.88 ns5.19 ns−0.56 ns−54.01 ns1.04 ns1.84 ns0.01 ns8.58 ns0.24 ns−148.07 ns3.47 ns−0.03 ns
BG-79 × GC23.33 ns0.24 ns0.57 ns138.74 **4.10 **4.09 **0.05 ns4.43 ns1.00 **−90.82 ns−0.87 ns0.00 ns
BG-52 × BG-8013.63 ns−0.31 ns0.50 ns53.14 ns2.64 ns2.74 ns0.01 ns−9.57 ns0.31 ns272.18 **−3.74 ns0.43 ns
BG-52 × BG-58−54.77 **−11.11 **2.14 **−87.36 ns−3.63 **−2.91 ns−0.01 ns−4.07 ns−0.45 ns595.78 ns−4.64 **0.33 **
BG-52 × BG-708.08 ns−0.41 ns0.31 ns−10.21 ns−0.98 ns−1.81 ns0.01 ns4.10 ns−0.39 ns−135.37 ns5.24 **−0.17 ns
BG-52 × GC−38.92 **−7.36 ns0.57 ns−47.21 ns−3.52 **−0.81 ns0.01 ns6.62 ns0.04 ns−218.97 **−1.60 ns−0.22 **
BG-80 × BG-585.53 ns−0.31 ns0.29 ns−33.46 ns−1.50 ns−0.56 ns−0.03 **−4.70 ns−0.22 ns218.78 **3.00 ns0.32 **
BG-80 × BG-7058.83 **5.64 ns0.65 ns67.44 ns1.37 ns1.59 ns0.05 ns10.47 ns0.61 **−239.72 **5.93 **−0.10 ns
BG-80 × GC20.43 ns2.34 ns−0.08 ns15.94 ns1.53 ns1.44 ns0.00 ns−21.73 **0.17 ns−220.62 **−0.41 ns−0.14 ns
BG-58 × BG-7022.78 ns3.04 ns−0.04 ns−14.41 ns0.38 ns0.04 ns−0.03 **−8.07 ns−0.49 ns297.73 **−3.49 ns0.28 **
BG-58 × GC−10.52 ns0.24 ns−0.47 ns−49.71 ns−2.64 **−2.31 ns0.00 ns5.37 ns−0.33 ns−197.97 **1.15 ns−0.28 **
BG-70 × GC24.28 ns5.69 ns−0.68 ns−9.31 ns2.86 **2.34 ns−0.01 ns10.23 ns0.20 ns154.03 ns−6.76 **0.04 ns
Note: ** denote significant differences at 1% probability level of t-values based on a two-tailed test; respectively, ns non-significant; NMF, number of male flowers per plant; NFF, the number of female flowers per plant; SR, sex ratio; NL, the number of leaves per plant; PH, plant height (m); NFPP, the number of fruits per plant; FW, fruit weight (kg/fruit); FC, fruit circumference (cm); FYPP, fruit yield per plant (kg); NSPF, the number of seeds per fruit; HSW, hundred seed weight (g/100 seed); SYPP, seed yield per plant (kg).
Table 9. Gene action and heritability estimates for the assessed traits under drought-stressed and non-stressed conditions across two growing seasons in South Africa.
Table 9. Gene action and heritability estimates for the assessed traits under drought-stressed and non-stressed conditions across two growing seasons in South Africa.
Traitsσ2Aσ2Dh2Bh2n
Drought-stressed
NMF0.001825.330.850.00
NFF0.0075.250.760.00
SR0.080.840.130.01
NL758.153548.410.860.15
PH0.0014.060.910.00
NFPP0.047.650.880.00
FW0.000.000.870.01
FC12.35755.560.940.02
FYPP0.000.090.890.00
NSPF0.0069,636.260.890.00
HSW0.001.420.230.00
SYPP0.000.090.850.00
Non-stressed
NMF248.281292.970.820.0.13
NFF10.8464.270.860.12
SR0.501.530.230.06
NL0.0042,735.770.950.00
PH0.007.810.080.00
NFPP0.3515.350.890.02
FW0.000.030.930.00
FC83.30988.200.880.06
FYPP0.060.600.930.08
NSPF0.00118,919.200.950.00
HSW0.0015.450.820.00
SYPP0.000.100.900.00
Note, σ2A, additive variance; σ2D, dominance variance; h2B, broad-sense heritability; h2n, narrow-sense heritability; NMF, number of male flowers per plant; NFF, the number of female flowers per plant; SR, sex ratio; NL, the number of leaves per plant; PH, plant height (m); NFPP, the number of fruits per plant; FW, fruit weight (kg/fruit); FC, fruit circumference (cm); FYPP, fruit yield per plant (kg); NSPF, the number of seeds per fruit; HSW, hundred seed weight (g/100 seed); SYPP, seed yield per plant (kg).
Table 10. Pearson correlation coefficients showing associations between assessed traits among the parental genotypes and their crosses under drought-stressed (upper diagonal) and non-stressed (lower diagonal) conditions across two growing seasons in South Africa.
Table 10. Pearson correlation coefficients showing associations between assessed traits among the parental genotypes and their crosses under drought-stressed (upper diagonal) and non-stressed (lower diagonal) conditions across two growing seasons in South Africa.
TraitsNMFNFFSRNLPHNFPPFWFCFYPPNSPFHSWSYPP
NMF 0.866 **0.00 ns0.277 **0.365 **0.378 **0.00 ns0.20 ns−0.06 ns0.611 **−0.03 ns−0.11 ns
NFF0.525 ** 0.12 ns0.333 **0.350 **0.401 **−0.06 ns0.11 ns−0.17 ns0.577 **0.05 ns−0.20 ns
SR0.04 ns0.21 ns 0.04 ns0.11 ns0.06 ns−0.14 ns−0.03 ns−0.248 *0.00 ns0.19 ns−0.17 ns
NL0.431 **0.08 ns−0.15 ns 0.772 **0.712 **−0.18 ns0.453 **−0.285 *0.405 **0.12 ns−0.251 **
PH−0.500 **0.333 **0.286 *−0.398 ** 0.861 **−0.24 ns0.462 **−0.339 **0.533 **0.03 ns−0.22 ns
NFPP−0.24 ns0.511 **0.307 *−0.12 ns0.866 ** −0.23 ns0.401 **−0.361 **0.453 **0.05 ns−0.266 *
FW−0.474 **0.334 **0.382 **−0.438 **0.946 **0.783 ** −0.17 ns0.839 **−0.04 ns−0.02 ns0.683 **
FC0.690 **0.403 **−0.08 ns0.598 **−0.335 **−0.02 ns−0.356 ** −0.282 **0.20 ns−0.06 ns−0.23 ns
FYPP−0.13 ns0.22 ns0.22 ns−0.22 ns0.443 **0.293 *0.631 **−0.12 ns −0.12 ns0.08 ns0.830 **
NSPF0.17 ns−0.04 ns0.07 ns0.02 ns−0.21−0.254 *−0.17 ns0.06 ns−0.13 ns −0.18 ns−0.12 ns
HSW−0.02 ns0.320 **0.07 ns−0.14 ns0.375 **0.357 **0.327 **−0.23 ns0.02 ns−0.14 ns 0.19 ns
SYPP−0.545 **0.295 *0.313 **−0.476 **0.981 **0.827 **0.969 **−0.400 **0.463 **−0.19 ns0.19 ns
Notes: * and ** denote significant at 5 and 1% probability level of t-values based on a two-tailed test, respectively; ns, non-significant; NMF, number of male flowers per plant; NFF, the number of female flowers per plant; SR, sex ratio; NL, the number of leaves per plant; PH, plant height (m); NFPP, the number of fruits per plant; FW, fruit weight (kg/fruit); FC, fruit circumference(cm); FYPP, fruit yield per plant (kg); NSPF, the number of seeds per fruit; HSW, hundred seed weight (g/100 seed); SYPP, seed yield per plant (kg).
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Mkhize, P.; Shimelis, H.; Mashilo, J. Combining Ability and Heterosis among Bottle Gourd [Lagenaria siceraria (Molina) Standl.] Selections for Yield and Related Traits under Drought-Stressed and Non-Stressed Conditions. Diversity 2023, 15, 925. https://doi.org/10.3390/d15080925

AMA Style

Mkhize P, Shimelis H, Mashilo J. Combining Ability and Heterosis among Bottle Gourd [Lagenaria siceraria (Molina) Standl.] Selections for Yield and Related Traits under Drought-Stressed and Non-Stressed Conditions. Diversity. 2023; 15(8):925. https://doi.org/10.3390/d15080925

Chicago/Turabian Style

Mkhize, Phumzile, Hussein Shimelis, and Jacob Mashilo. 2023. "Combining Ability and Heterosis among Bottle Gourd [Lagenaria siceraria (Molina) Standl.] Selections for Yield and Related Traits under Drought-Stressed and Non-Stressed Conditions" Diversity 15, no. 8: 925. https://doi.org/10.3390/d15080925

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop