Abstract
The aim of the study was to assess the genetic structure and quantitative heritability of cotton yield, yield components and fiber quality parameters under well-watered (100%) and deficit (50%) irrigation conditions using a complete full diallel cross method with six cotton genotypes. The F1 and F2 generations were obtained to study the effect of irrigation conditions on genotypic variation and maternal effects on the traits studied. Parental, F1 and F2 generations were growed under full and deficit irrigation conditions and selected agronomic and fiber quality traits were measured. The data were analysed using various methods, including Griffing Method I, Model I analysis of variance, full diallel table analysis of variance, Jinks-Hayman diallel hybrid analysis. The Jinks-Hayman analysis of variance showed that dominant effects were more important in the inheritance of all traits, as indicated by the negative value of the difference between the additive variance and the dominance variance (D-H1). In addition, the average degree of dominance (H1/D)1/2 was greater than 1, indicating the predominance of dominant gene action in inheritance. The study concluded that starting selection in the F5–F6 generation is appropriate for drought resistant cotton breeding studies. It also emphasised the importance of conducting drought resistant cotton breeding under drought conditions due to the differences in gene movement between well-watered irrigation and drought stress conditions. The study highlighted the importance of population selection, environmental factors, data collection and analysis interpretation in breeding studies. It suggested that the Griffing diallel analysis method is suitable for hybrid breeding studies, while the Jinks-Hayman type analysis method is suitable for studying the genetic structures of populations.
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Introduction
The objective of breeding programmes in cotton farming countries is to cultivate cotton genotypes with high yield and fiber quality that are well-matched for new technologies. Since most of these characteristics are quantitatively inherited, many breeders who have relied on simple genetic models comprising only a few genetic parameters have faced challenges in attaining their intended goals (Verhalen and Murray 1967). The diallel analysis technique offers breeders a means to overcome these difficulties. A systematic approach has been developed for selecting parents in crossbreeding and determining their combining ability during the early stages of breeding. This approach facilitates the production parents crosses with superior desired traits (Yates 1947). Additionally, it assists breeders in choosing the most effective breeding method by enabling estimation of various genetic parameters (Jinks and Hayman 1953; Hayman 1954b, 1960; Jinks 1956; Griffing 1956).
When seeking to enhance any trait, an essential piece of information for breeders is identifying the genetic variation present in genotypes that are appropriate for breeding purposes, as well as the genetic variance that may arise in the early generations of the population hybrids (Çiçek 2007). To create variations, plant breeders produce crosses using the genetic material. The breeder requires the material in order to develop varieties appropriate for their objectives. In early generations of newly developed hybrid populations, the breeder wishes to distinguish between parents and hybrid offspring in terms of agronomic traits, and to subsequently select those with superior traits. Predicting hybrid performance and selecting the best is contingent upon the mean values of the parents with regards to traits. Expert parents (Poehlman and Sleeper 1995) can be identified through the phenomenon of heterosis (Knott 1965), whereby their hybrid performance can be enhanced. Parents (Poehlman and Sleeper 1995) can be identified through the phenomenon of heterosis (Knott 1965), whereby their hybrid performance can be enhanced. The success rate of breeding programmes can be increased by determining the genetic structure of the parents and inheritance information of traits. Obtaining such basic information will enable a high level of success in breeding programmes. Understanding the heritability of traits is also crucial. This understanding will aid in making informed decisions for successful breeding outcomes (Yıldırım, 2005). In addition, knowledge of the type of gene effect involved in the formation of the character to be transferred is crucial when considering breeding methods. It is also essential to understand the general and specific combination abilities of the parents with regards to desired traits. Selecting the appropriate generation to begin breeding can be determined by the purpose, thereby avoiding unnecessary combinations. The research was conducted to facilitate the development of varieties and enhance the aforementioned trait.
The diallel analysis method is commonly utilised in plant breeding to assess the genetic structure of hybrid progeny populations, identify promising hybrid combinations, and determine the general combining ability of parents and specific combining ability of hybrids (Yıldırım et al 1979). This method, which has a broad range of applications in both self and cross-fertilised plants, was developed by researchers including Schmidt (1919), Yates (1947), Jinks and Hayman (1953), Hayman (1954a, b), Jinks (1954, 1956), and Griffing. In Türkiye, the application of the principles on cotton plants has been studied by various researchers such as (1956), Hayman (1958, 1960), Gençer (1978), Turan (1979), Kaynak (1990), Efe and Gençer (1995), Başal (2001), Efe (1994), Akışcan (2011), and Akgöl (2012).
The majority of traits inheritance is controlled by nuclear genes. Nevertheless, in some instances, inheritance is regulated by cytoplasmic factors or genes. If the transmission of traits from parents to offspring is regulated by cytoplasmic genes situated on mitochondria and chloroplasts, it is known as the maternal effect (non-nuclear inheritance, cytoplasmic inheritance, non-Mendelian). Inheritance has been noted to play a role in adaptability (fitness) among individuals in several populations, with the phenotype of an individual's parents being as important as its own phenotype (Kirkpatrick and Lande 1989; Karakaya 2002; Kumar et al. 2004). The chloroplast genome encodes only 10% of the total cell protein, with the remaining 90% encoded by the cell nucleus (Gatehouse 2008). The presence of non-nuclear substances, known as cytoplasmic inheritance, also contributes to this process. As elements are entirely inherited maternally, it can be concluded that the cytoplasmic inheritance effect on offspring characteristics originates from the mother. In this type of inheritance, the offspring’s phenotype is determined by the DNA found in the chloroplast or mitochondria. Research demonstrates that these organelles are uniparentally transmitted to the offspring (Maheshwari & Barbash 2011). Understanding the role of the maternal effect in breeding studies is important for the purpose and duration of breeding. However, it is inaccurate to solely attribute the reciprocal effect in hybrids to the maternal effect. Numerous researchers have reported that the interaction between stoplasm cytoplasm and environment also plays a role in transferring adaptations, including adaptation. The study investigates whether maternal inheritance influences certain traits that have been developed over generations through the mother’s adaptation to the environment and survival. Previous research suggests that some traits may be subject to this type of inheritance (Bernardo 1996; Mousseau and Fox 1998; Maestripieri and Mateo 2009). However, a comprehensive study is still lacking regarding the extent of reduction in agronomic, physiological and biochemical traits in cotton under water-deficit stress (Chattha et al. 2021b). The purpose of this study is to provide insights for future research on improving these traits. The study investigates whether maternal inheritance influences certain traits that have been developed over generations through the mother's adaptation to the environment and survival. Technical terms are introduced with their abbreviations explained upon first use.
In breeding drought-tolerant varieties, two contrasting methods exist with respect to the environmental conditions for selection. One view is that selection should occur under optimal (full irrigation, 100%) conditions, where high-yielding lines will also exhibit high yields under unfavorable conditions (Piepho 2000; Rizza et al. 2004; Ober et al. 2005). Based on yield (empirical or pragmatic breeding) (Simmonds, 1984; Ludlow and Muchow 1990; Muchow 1990; Ceccarelli 1996; Ceccarelli and Grando 1997; Shakoor et al 2010). Advocates of the latter view support the development of varieties through the inclusion of physiological and morphological mechanisms for drought or salt stress in breeding programmes (Morgan 1998; Winter et al. 1988; Foulkes et al. 2002; Muhammad et al. 2023), rather than solely focusing on yield.
The aim of this study was to investigate the genetic structure of three drought stress tolerant and three drought stress sensitive cotton genotypes in F1 and F2 generations obtained as a result of crossing in accordance with the full diallel crossing method; (1) to determine the genetic structure of yield, yield components and fiber quality parameters in cotton and quantitative heritability of the studied traits in F1and F2 generations under optimum (100%) and deficit (50%) irrigation conditions, (2) to determine the effect of full and deficit irrigation conditions on genotypic variation in the studied traits, and (3) The aim of this study is to help future variety breeding studies by investigating the maternal effect on the traits examined.
Materıals and methods
Material
Six cotton genotypes, three of which are tolerant to drought stress (DAK 66/3, NIAB 999, Nazilli M503 (93/7) and the other three are sensitive to drought stress (NP-Ege, Nazilli 84S and Gloria), which constitute the initial material of the study, were determined by using the results of the project titled “Development of Cotton Lines with Improved Fiber and Quality Properties Tolerant to Drought Stress by Using Molecular Technologies” supported by TUBITAK (The Scientific and Technological Research Council of Türkiye). The geometric mean yield values and drought sensitivity index values of cotton varieties were used in the selection of genotypes used in the study (Sezener et al. 2015).
Experimental design and management practices
The thesis study was carried out in Aydın Adnan Menderes University Faculty of Agriculture Research and Training Farm.
Year one (2019)
In the first phase of the study, in order to establish hybrid populations, six cotton genotypes (three drought stress tolerant and three drought stress sensitivewere planted at the experimental area of the Faculty of Agriculture of Aydın Adnan Menderes University (37°45′ N, 27°45′ E) located in the Aegean Climatic Zone, in Türkiye during the 2019 summer main crop planting season (April–May). Six cotton genotypes (DAK 66/3, NIAB 999, Nazilli M503 (93/7), NP-Ege, Nazilli 84S and Gloriawere planted in the crossing garden on 30.04.2019 as 70 cm inter-row and 20 cm intra-row, with 2 rows of parents from each variety and one row of paternal parents from each variety. The six cotton genotypes mentioned above were crossed in accordance with the full diallel crossing method, and the techniques specified by Poehlman (1959) were applied in the crosses. At the same time, seeds belonging to the F1 generation obtained as a result of the full diallel crossing carried out in the previous year (2018) using the above-mentioned cotton genotypes were sown and seeds belonging to the F2 generation were obtained by selfing one flower on each plant.
Year two (2020)
The F1, F2 generations of hybrid combinations and parents, which will be used as material in the study, were sown on May 12, 2020 at the experimental area of the Faculty of Agriculture of Aydın Adnan Menderes University in one-row plots with a row spacing of 70 cm between rows, 12 cm above rows and 6 m in row length, according to the Randomized Complete Block Design with 4 replications (see Fig. 1).
Images taken by drone at different times from the experiment area
Determination of ırrigation time and ırrigation operations
In the experiment, drip irrigation method was preferred for easier adjustment of irrigation levels. The irrigation water required for irrigation of the trial plots was obtained from the underground water source (artesian). Monitoring of soil moisture in the experimental plots; In accordance with the gravimetric method, moisture content was calculated as percent (%) in samples taken from three soil layers up to 90 cm. Degraded soil samples were taken from the determined layers before each irrigation according to Petersen and Calvin (1965). Irrigation time was determined as the moment when the amount of available water in the plots with full irrigation decreased to 50%. Full irrigation (100%) plots were irrigated with all of the moisture deficit and limited irrigation (50%) plots were irrigated with half of the moisture deficit.
In order to determine the moisture change in the soil since planting, soil samples were taken from the above-mentioned soil layers according to the gravimetric method and moisture contents were determined; the relationship given by James (1988) was used to determine plant water consumption.
In the equation; ET: Plant water consumption (mm), I: Irrigation water (mm), R: Effective rainfall (mm), Cr: Capillary rise (mm), Dp: Deep infiltration (mm), Rf: Surface runoff losses (mm), ΔS: Moisture change in soil profile (mm).
Since the land where the trials were planned to be carried out has a deep structure without drainage and salinity problems, capillary water rise (Cr) from the ground water was neglected. Since it is assumed that there is no surface runoff (Rf) due to irrigation with drip irrigation system, this parameter was also ignored in the calculation. No restricted irrigation was applied in the first year of the experiment. The plant was given all the water it needed. In the second year of the experiment, a total of 7 irrigations were applied under full and restricted irrigation conditions. Irrigation dates and amounts are as follows (Table 2). Irrigation dates and rates in the experiment showed Table 1.
Parameters investigated
Seed cotton yield (kg/da)
F1, F2 and their parents, obtained by full diallel hybridization grown under full and limited irrigation conditions, were collected by hand from separate parcels, their seed cotton was weighed and calculated as a ratio per decare (Table 2).
Number of bolls per plant (piece/plant)
During the harvest period, the number of bolls that had opened or could be collected on 10 plants taken randomly from each plot was counted.
Single boll seed cotton weight (g)
The average weight of a bolls was found by weighing the bolls of 25 randomly collected bolls of each genotype from the plots under both limited and full irrigation conditions, on a scale sensitive to 0.01 g.
Water use efficiency (WUE)
In the experiment, water use efficiency values were determined by ratioing the cotton seed yield obtained from the subjects to the seasonal plant water consumption (Howell et al. 1990).
The equation used in the calculation;
In equality; WUE = Total water use efficiency (kgm−3), Y = Mass yield (kg/da−1), ET = Seasonal plant water consumption (mm).
Irrigation water use efficiency (IWUE)
Water use efficiency was determined using the following equations according to the principles given in Howell and Hiler (1975).
Y = Mass yield (kg da−1), I = Amount of irrigation water (mm).
Plant height (cm)
The length between the cotyledon nodes and the growth terminal point of 10 randomly selected plants from the plot was determined by measuring and taking the average.
Ginning percantage (%)
Ginning percentage was calculated by using formula suggested by Ghule et al. (2013).
\({\text{Ginning}}\,{\text{percentage }}\left( \% \right) \, = \, \left( {{\text{Weight}}\,{\text{of}}\,{\text{lint }}/{\text{ Weight}}\,{\text{of}}\,{\text{seed}}\,{\text{cotton}}} \right) \, \times \, 100\).
Number of monopodial branches (pcs/plant)
The number of monopodial branches of 10 consecutive plants from each plot was determined.
Number of sympodal branches (pcs/plant)
The number of Sympodal branches of 10 consecutive plants from each plot was determined.
Fiber quality characteristics
In addition, fiber samples taken from each parcel were analyzed with the HVI (High Volume Instrument);
-
a.
Fiber length (mm),
-
b.
Fiber fineness,
-
c.
Fiber strength (gr/tex),
-
d.
Elongation coefficient (elongation)
has been determined.
Genetic analysis methods
Among the analyses in this study, preliminary analysis of variance according to Randomized Complete Block Design was performed using the JMP statistical package (Version < 2020 > . SAS Institute Inc, Cary, NC, 1989–2021), analysis of variance of diallel tables was performed according to the diallel analysis of variance method proposed by Jinks and Hayman (1953), Mather and Jinks (1971) and in SAS/Sashadiall package program (Makumbi et al. 2018) SAS software, version 9.4 of the SAS System, Copyright 2016, Sas Institute, Inc.”, Jinks-Hayman type diallel analysis and Griffing type diallel analysis methods were performed in Tarpopgen package program (Açıkgöz and Özcan 1999) (Tables 3 and 4).
Results and discussion
Number of bolls per plant
In the conducted study, it was observed that the number of bolls per plant for all genotypes grown under full irrigation conditions exhibited a higher count compared to those grown under deficit irrigation conditions. This reduction in boll number under deficit irrigation conditions was found to have a negative impact on boll yield. Numerous studies have previously reported a negative correlation between water stress and boll number (Marani and Amirav 1971; Shimshi and Marani 1971; Marani 1973; Krieg 2000; Ertek and Kanber 2003; Pettigrew 2004; Mert 2005; Basal et al. 2009; Price 2009; Ünlü et al 2011; Gören and Başal 2020) However, some researchers have reported that water deficit did not affect boll number (Önder et al. 2009; Hussein et al. 2011).
The Jinks-Hayman type analysis method was employed to evaluate the boll number trait for both irrigation conditions and generations. The results indicated (H1/D)1/2 for both irrigation conditions and generations, where a value greater than 1 signifies superior dominance. Additionally, for both generations, KD/KR was greater than 1 under deficit irrigation conditions, emphasizing the prevalence of dominant alleles. In contrast, under full irrigation conditions, KD/KR was less than 1 in the F1 generation, indicating the dominance of recessive alleles (Tables 5 and 6). Notably, irregularity was observed in the irrigation conditions for this trait. Furthermore, utilizing the Griffing type analysis method, it was revealed that the GCA/SCA ratio was higher than 1 for both irrigation conditions and generations, signifying that the inheritance of this trait is influenced by additive genes. The reciprocal effect was found to be significant, except for the DS F2 population (Table 3).The influence of genes on the inheritance of boll number has been a subject of varied conclusions among researchers. Kapoor (2000) and Ilyas et al. (2007) identified non-additive gene effects, while Başal (2001), Çiçek, and Kaynak (2008) found both additive and dominant gene effects. Additionally, Bertini et al. (2001) discovered dominant gene effects, and Ahmad et al. (2003), Karademir (2005), Soomro et al. (2008), and Iqbal et al. (2011) reported additive gene effects. Moreover Rauf et al. (2006), and Kiani et al. (2007) reported both additive and non-additive genes, while Murtaza (2005) and Ali et al. (2011) highlighted the significance of superior dominant gene effects. Also, bolls per plant have been suggested in previous studies to show high heritability and genetic improvement through additive gene effect and therefore, this trait can be used as a means of selection in future breeding programmes of drought tolerance (Chattha et al 2021a).
In the analysis of GCA and SCA for boll number across all populations, it was observed that the parents Nazilli 84-S (2) and NP Ege (3) exhibited positive overall ability to adapt. Additionally, when considering all populations, the hybrid Nazilli M-503 × DAK 66:3 stood out with high SCA. However, in both full irrigation and deficit irrigation conditions, the hybrids Nazilli 84-S x DAK 66:3, NIAB 99 × Glora, NIAB 99 × Nazilli 84-S, and NIAB 99 × NP Ege displayed high SCA in the F1 generation. In the F2 generation, apart from the Nazilli M-503 × DAK 66:3 hybrid, no other hybrid was found to be distinctive for this characteristic (Tables 7 and 8).
Ginning percentage (%)
In the study conducted, it was observed that genotypes grown under deficit irrigation conditions had higher ginning percentage results compared to those grown under full irrigation conditions. This phenomenon has been attributed by several researchers to the increase in seed weight due to the prolongation of the his could be correlated with seed cotton yield. This finding is consistent with previous studies by Ertek and Kanber (2003), Pettigrew (2004), Balkcom et al. (2006) and Basal et al. In addition, Pettigrew (2004), Mert (2005), Rai (2011), and Cave (2013) reported higher ginning percentage under deficit irrigation.
In the Jinks-Hayman type analysis method, the evaluation of the ginning percentage trait for both irrigation conditions and generations revealed that (H1 /D)1/2 being greater than 1 emphasizes superior dominance, and KD/KR being greater than 1 indicates that dominant alleles are in the majority (Tables 5 and 6). Similarly, the Griffing-type analysis method indicated that the ratio of GCA/SCA was greater than 1 for both irrigation conditions and generations, suggesting that the inheritance of this trait was under the influence of additive genes. Furthermore, the reciprocal effect was found to be significant in DS F1, DS F2, and WW F1 populations (Table 3). Previous studies have reported varied findings regarding the genetic effects influencing ginning yield. Kapoor (2000) reported additive gene effects, Mukhtar et al. (2000) reported superior dominance, Başal (2001) reported both additive and dominant gene effects, Bertini et al. (2001) reported additive gene effects, Karademir (2005) reported additive gene effects, Rauf et al. (2006) reported both additive and non-additive gene effects, and Ilyas et al. (2007) reported non-additive gene effects. Additionally, Çiçek and Kaynak (2008) reported additive gene effects, while Mohamed et al. (2009) documented both additive and non-additive gene effects. Karademir and Gençer (2010) stated that non-additive gene effects were present.
In the analysis of GCA and SCA for ginning percentage, Gloria (1) and NIAB 999 (6) parents exhibited positive general adaptability in all populations. In the F1 generation, Nazilli 84-SxNP Ege and Gloria x NIAB 999 hybrids displayed high special adaptability and stood out in both full irrigation and deficit irrigation. In the F2 generation, Nazilli M-503 × NIAB 999 showed positive SCA in both irrigation conditions (Tables 9 and 10).
Seed cotton yield(kg/da)
The study found that the mean yield values of populations grown under full irrigation conditions exceeded those under deficit irrigation conditions. Other studies have previously shown that deficit irrigation decreases yield (Tekinel and Kanber 1978; Krieg 1997, 2000; Ertek and Kanber 2003; McWilliams 2004; Pettigrew 2004; Mert 2005; Balkcom et al. 2006; Mills 2010; Rai 2011; Cave 2013).
When the seed cotton yield trait was evaluated for Jinks-Hayman type analysis method for both irrigation conditions and generations; the negative value of the difference between the additive variance and the variance of dominance (D-H1) revealed that dominant effects were more important in the inheritance of this trait and the degree of dominance (H1 /D)1/2 being greater than 1 revealed that there was superior dominance.
Evaluating the data for the yield trait using the Griffing type analysis method, it was found that the ratio GCA/SCA was greater than 1 for both F1 and F2 generations under both deficit and full irrigation, and it was concluded that the inheritance of the trait was under the influence of additive genes(Table 3). However, no reciprocal effect was observed in the populations for the yield trait. In the previous diallel analysis studies conducted for yield value; Kapoor (2000), Ahmad et al. (2003), De Aguiar et al. (2007) reported additive gene effects; Khorgade et al. (2000) reported superior dominance; Başal (2001), Bertini et al. (2001) and Çiçek and Kaynak (2008) reported dominant gene effects; Cheatham et al. (2003) both additive and dominant gene effects; Rauf et al. (2006), Kiani et al. (2007), Mohamed et al. (2009) and Méndez-Natera et al. (2012) both additive and non-additive genes; Karademir (2005), Ilyas et al. (2007) and Chattha et al. (2019) reported that non-additive gene effects were important.
In the analysis of GCA and SCA for yield value, it was observed that Nazilli 84-S (2) was the better parent in all populations, and NP Ege x Gloria hybrids exhibited high special adaptability and stood out in both full irrigation and deficit irrigation. In the F2 generation, Nazilli 84-S x NP Ege, and Nazilli 84-S x NIAB 999 showed positive SCA in both irrigation conditions (Tables 11 and 12).
IWUE (İrrigation water use efficiency)
For irrigation water use efficiency in F1 and F2 generations, it was found that irrigation water use efficiency was low in the populations where full irrigation was applied, while it was high in the populations where water restriction was applied. Previous studies have shown that irrigation water use efficiency has low values under full irrigation conditions and higher values under drought conditions. This result supporWW our data (Basal et al. 2009; Dağdelen et al. 2009; Peynircioğlu 2014).
When IWUE trait were evaluated for Jinks-Hayman type analysis method for both irrigation conditions and generations; (H1/D)1/2 being greater than 1 indicates superior dominance, while KD/KR being greater than 1 for DS F1, WW F1, DS F2 and WW F2 revealed that dominant alleles were in majority in the population.
Furthermore, the Griffing type analysis method, when IWUE was evaluated, it was found that the ratio of GCA/SCA was greater than 1 for both generations and irrigation conditions and it was concluded that the inheritance of these traits was under the influence of additive genes. However, no reciprocal effect was detected for IWUE in the populations. Farshadfar et al. (2011) stated in their study that this trait is effected by superior dominance. For irrigation water use efficiency (IWUE), Nazilli 84-S was the parent with the highest overall adaptability in all populations. For both irrigation conditions and generations, Gloria x Nazilli 84-S combination was the hybrids with the highest IWUE (Tables 13 and 14).
Fiber length (mm)
In this study, fiber length was found to be high in F1 and F2 generations in the populations where full irrigation was applied, while fiber length decreased in the populations where water deficit was applied. Previous research has indicated that water stress is the most important factor affecting fiber quality traits, and fiber length shortens with increasing water stress (Marani 1973; McWilliams 2004; Pettigrew 2004; Mert 2005; Başal et al. 2009; Price 2009; Rai 2011) Hussein et al. This is supported by the findings of Marani (1973), McWilliams (2004), Pettigrew (2004), Mert (2005), Başal et al. (2009), Price (2009), Rai (2011), Hussein et al. (2011), Karademir et al. (2011), Reeves (2012), and Cave (2013). However, the study by Ünlü et al. (2011) reported that different levels of irrigation did not have a significant impact on fiber quality parameters.
The Jinks-Hayman type analysis method was utilized to evaluate the fiber length trait for both irrigation conditions and generations. A result of (H1/D)1/2 greater than 1 and this indicates superior dominance, while a result of KD/KR greater than 1, indicates dominant alleles are in majority for both generations and irrigation conditions (Tables 5 and 6).
The ratio of GCA/SCA was found to be greater than 1 for both generations and irrigation conditions, leading to the conclusion that dominant alleles are present in majority. The inheritance of this trait was determined by additive genes, with a significant reciprocal effect observed in F1 and F2 populations under full irrigation conditions (Table 4).While previous studies of the fiber length trait have mostly suggested an additive gene inheritance pattern (Başal 2001; Karademir 2005; Rauf et al. 2006; De Aguiar et al 2007; Çiçek and Kaynak 2008), Cheatham et al. (2003) have reported both additive and dominant gene effects, while Murtaza et al. (2004) have reported epistatic gene effects. Additionally, Partial dominance was reported in 2008, while Hussaini et al. reported non-additive genetic effects in 2010, with superior dominance dominating the inheritance of this trait according to Mukhtar et al.
Analysis of GCA and SCA for fiber length across all populations found strong overall adaptability in Nazilli 84-S (2). However, in the F1 generation, NIAB 999 and Nazilli 84-S were particularly noteworthy. The combination of Nazilli M-503 with Gloria showed promise for the DS F1 population, while the combination of Nazilli M-503 with NIAB 99 was found to be promising for the WW F1 population. Moreover, the hybrids of Nazilli 84-S with Nazilli M-503 exhibited promising results for the F2 generation (Tables 15 and 16). İn addition to, In recent years, in addition to classical breeding methods, there have been studies whose main focus is to investigate the transcript levels of long fiber length-related genes at different stages of cotton fiber development (Muhammad et al. 2023).
Fiber fineness (microner)
For the feature fiber fineness, it was found that fiber fineness was higher in the F1 and F2 generations in the populations where full irrigation was applied, while the fibers were thinner in the populations where water deficit was applied. Mert (2005) found a decrease in fiber fineness in their study. Similarly, in their studies, Basal et al. (2009) and Price (2009) reported that fiber coarsening occurred due to water restriction. Additionally, Pettigrew (2004) did not observe any definitive effects on fiber fineness under different irrigation conditions.
When evaluating the fiber fineness trait using the Jinks-Hayman analysis method under both irrigation conditions and generations, a value of (H1/D)1/2 greater than 1 indicates superior dominance. In addition, the value of KD/KR greater than 1 for both generations and irrigation conditions indicates a dominance of the alleles (Tables 5 and 6).
Using the Griffing type analysis method, it was determined that the GCA/SCA ratio was greater than 1 for both generations and irrigation conditions, leading to the conclusion that the trait is under the influence of additive genes.
Significant reciprocal effects were observed in all populations except for the DS F1 population. Previous studies on fiber fineness, including those by Başal (2001) and Çiçek and Kaynak (2008), reported similar results. Karademir (2005) found that additive effects; Rauf et al. (2006), Ilyas et al. found that only non-additive gene effects were effective. Minhas et al. (2008) reported that partial dominance and additive gene effects were effective, while Karademir and Gençer (2010) found that non-additive gene effects were effective and Méndez-Natera et al. (2012) reported that both additive and non-additive genes were effective.When considering fiber fineness, an analysis of GCA and SCA across all populations revealed that NIAB 99(6) and Nazilli 84-S (2) in the F1 generation; NP Ege and Nazilli 84-S parents in the F2 generation exhibited the suitable according to the criteria for fiber fineness quality. The Nazilli 84-S x Nazilli 503-M hybrid in the F1 generation and the Nazilli 84-S x Gloria and Nazilli 84-S x Nazilli 503-M hybrids in the F2 generation demonstrated the highest SCA under both irrigation conditions (Tables 17 and 18).
Fiber strength (gr/tex)
For fiber strength, it was found that the endurance of fiber strength was high in F1 and F2 generations in the populations where full irrigation was applied, while it was lower in the populations where water deficit was applied. Several previous studies; McWilliams (2004), Başal et al. (2009), Rai (2011), Karademir et al. (2011) and Price (2009) reported that water stress negatively affected fiber strength; Ünlü et al. However, as reported by Ünlü et al. (2011), the effect on fiber quality parameters was not was not significant.
When assessing the fiber strength trait by the Jinks-Hayman type analysis method for both irrigation conditions and generations, (H1 /D)1/2 greater than 1 indicates superior dominance, while for both generations and irrigation conditions, KD/KR greater than 1 indicates that dominant alleles are in the majority (Tables 5 and 6).
For the Griffing type analysis method; when evaluated, it was found that for both generations and irrigation conditions, the ratio GCA/SCA was greater than 1 and it was concluded that the inheritance of this trait was under the influence of additive genes. No reciprocal effect was observed regarding the fiber strength in the populations (Table 4). In the research on the inheritance of fiber strength, Başal (2001), Rauf et al, (2006), De Aguiar et al. (2007), Çiçek and Kaynak (2008), and Ilyas et al. (2007). Cheatham et al. (2003) and Murtaza et al. (2004) have concluded that both additive and dominant gene effects exist. Karademir (2005), and Karademir and Gençer (2010) found that non-additive gene effects were at work; Minhas et al. (2008) found that partial dominance and additive gene effects were important.
When GCA and SCA were examined for fiber strength: NIAB 999 was the parent with the the most extensive adaptability among all populations. Although SCA was not significant in any of the populations, Nazilli 84-S x Nazilli M-503 differed from the other combinations and became the outstanding hybrid combination for the fiber strength (Tables 19 and 20).
Fiber elongation (%)
Fiber elongation was observed to be lower in full irrigation populations in F1 and F2 generations, whereas it was higher in water deficit populations (Table 21). Previous studies have reported contrasting effects on fiber elengation; while Karademir et al. (2011) and Avsar (2021) found a negative impact, Hussein et al. (2011) observed no effect from irrigation conditions.
When evaluating the fiber elongation trait using the Jinks-Hayman analysis method for both irrigation conditions and generations, (H1/D)1/2 greater than 1 indicates superior dominance, KD/KR greater than 1 for DS F1, DS F2 and WW F2 indicates dominant alleles, while KD/KR less than 1 in the WW F1 population indicates that recessive alleles are in the majority (Tables 5 and 6).
Hence evaluating the Griffing type analysis method, it was found that for both generations and irrigation conditions, the ratio GCA/SCA was greater than 1 and it was concluded that the inheritance of this trait was under the effect of additive genes. Furthermore, it was discovered that the reciprocal effect was significant in all populations (Table 4).Previous research by Cheatham et al. (2003) found significant additive and dominant gene effects, which is consistent with the results of this study, whereas Karademir (2009) and De Aguiar et al. (2007) found significant additive gene effects only.
When examining GCA and SCA for fiber elengation, Nazilli 84-S and NP Ege demonstrated the highesT GCA across populations. For both irrigation conditions, the hybrids that demonstrated the highest SCA were Gloria x Nazilli 84-S, Nazilli 84-S x NP Ege, and Nazilli M-503 × NP Ege in the F1 generation, and Gloria x Nazilli M-503 combinations in the F2 generation (Tables 21 and 22).
Conclusion
Preliminary analysis of variance revealed that there were significant genetic differences in agronomic and fiber quality parameters, for both generations under both full and limited irrigation conditions.
In the Jinks-Hayman type variance analysis for all populations and traits superior dominance is important whileIn the Griffing-type analysis, it was discovered that the inheritance of the traits was under the influence of additive genes. Despite the fact that additive and non-additive genes work together for the studied traits in this analysis method, the fact that the ratio GCA/SCA is greater than 1 indicates that additive genes are in the majority.
The scope of information obtainable from the Griffing type diallel analysis is comparatively restricted in comparison to the Jinks-Hayman type analysis. Nevertheless, both methods have different areas of application. In the Griffing type analysis method, the emphasis is on producing hybrid varieties from populations comprising of varieties or lines, and the variances of GCA and SCA prove adequate for the intended purpose. The Griffing type diallel analysis method has found wide application in breeding hybrid varieties, while the Jinks-Hayman type analysis method has been widely used to investigate the genetic structure of populations. In other words, the Jinks-Hayman type analysis method belongs to the branch of genetics, which is a basic science (Yıldırım et al. 1979). Gilbert (1958) provided a thorough explanation of the reasons for discrepancies in diallel analysis methods. Based on all of the mentioned, it is concluded that the selection of the population to which the method will be applied, the environmental factor, the way of obtaining the values and the interpretation of the analysis are essential to the breeder. Based on all of the above, it can be inferred that the breeder has complete control over the selection of the population to which the method is applied, environmental factors, obtaining values and analyzing the results in diallel studies. Therefore, it is the breeder and their decisions that hold significance in this context.
As a result of this 6 × 6 full diallel study, in F1 generation, for fiber length and fiber fineness under full irrigation, for ginning percantege, number of bolls under full and deficit irrigation conditions AND in F2 generation, for ginning percantage, fiber strenght under deficit irrigation conditions, for number of bolls, fiber length parameters under full irrigation conditions; for fiber fineness and fiber elengation under both full and deficit irrigation conditions the variance of maternal effect was calculated as significant (in both full diallel analysis of variance results (c and d components) and Griffing type diallel analysis method).
Therefore, heritability can serve as a criterion for selecting desirable traits (Yıldırım et al. 1979). Hence, the use of heritability as a selection criterion must be approached with caution. Furthermore, in this study, all traits exhibited low narrow-sense heritability, and the Jinks-Hayman diallel analysis method revealed that dominant genes had a greater effect on trait inheritance. The decrease in narrow-sense heritability observed in the F2 generation compared to the F1 generation leads us to the conclusion that the additive gene effect has been replaced by non-additive or dominant gene effects. The breeding of dominant genes is typically carried out in advanced generations due to the need for a thorough understanding of their genetic behavior and interactions. Dominant genes exert a significant influence on the phenotype of an organism, and their expression can mask the effects of recessive alleles. Therefore, it is crucial to conduct breeding programs in advanced generations to accurately assess the performance and stability of dominant genes in various genetic backgrounds (Zeng et al. 2013; Wu et al. 2019). Based on the findings of this study, it was conculed that selection in the F5–F6 generation is appropriate in breeding programmes based on drought stress.
When the analysis of variance of diallel tables under both irrigation conditions was evaluated, fiber length, fiber fineness, strength, fiber elongation, IWUE and seed cotton yield values were found to be statistically significant in the F1 generation. Similarly, for the F2 generation, there are traits whose gene action change under different irrigation conditions, as well as traits that show the same effect pattern in both conditions. The difference in gene movement for hybrids under full irrigation and drought stress conditions suggests that drought resistant plant breeding should be carried out under drought conditions.
Data availability
The data used in this study are available upon reasonable request from the corresponding author.
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Gören, H.K., Canavar, Ö. Genetic analysis of yield and fiber quality traits in cotton (Gossypium hirsutum L.) under full and deficit irrigation conditions using full diallel method. Euphytica 220, 115 (2024). https://doi.org/10.1007/s10681-024-03348-9
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DOI: https://doi.org/10.1007/s10681-024-03348-9