Advances in Crop Science and Technology
Open Access

Sorghum; Genetic variability; Heritability; Genetic advance as percent of mean

Introduction

Sunflower (Helianthus annuus L., 2n=34) is an important oilseed crop. It belongs to the family Asteracae (Compositae). Sunflower is widely produced for edible oil. Its oil is highly used in the human diet because of its high level of unsaturated fatty acids, lack of linolenic acid and bland flavor (Putman et al., 1990) [1]. Moreover, sunflower has many other uses such as forage, feed for ruminant animals plus swine and poultry, industrial applications and non-oilseed feed (use for bird feed or in human diets as a snack) (Putman et al., 1990). Its oil may also be used as a raw material to extract bio-diesel which can be used as fuel in diesel engines (Antolin et al., 2002 and Murugesan et al., 2009). Sunflower can contribute a big share in improving local edible oil production due to its short interval for maturity, high oil contents, better fitting in the cropping pattern, tolerance to drought and its high yield potential. The seed of sunflower contains 40% oil though some high yielding varieties produce up to 50% oil content (Skoric and Marinkovic, 1986; Kaya, 2008) with good adaptability under different agro-ecological conditions [2,3].

Sunflower is grown mostly as a source of vegetable oil and proteins in many countries (Leon et al., 1995 and Yue et al., 2010) and is the second most important oilseed crop after soybean worldwide (Paniego et al., 2002). Sunflower belongs to non-conventional oilseed crops and has great ability to increase oil yield per hectare (Raifq et al., 2016). Sunflower is grown in all continents and covers about 26.48 million hectares of the cultivated land of the world with production and productivity of 55.25 million metric tons and 2.09 metric tons per hectare, respectively (Westcott, 2010). The productivity, production and area coverage of sunflower in Ethiopia is about 1.266 tons/ha, 95,707.49 ton/ha and 7,560.56 ha, respectively which is lower by half than the world average (CSA, 2019). This low productivity problem is attached to certain yield constraints such as lack of improved varieties released in the country, biotic and a biotic stresses and sub optimal agronomic practices (Tesfaye et al., 2000) [4,5].

To overcome this low yield potential, presence of basic information on genetic variability is important to plan breeding strategy and make effective selection for desirable traits. Variations are very precious for any breeding programs (Hussain et al., 2015) as the success of breeding program mainly rely upon the extent of variation present in a germplasm for yield and its contributing traits (Qamar et al., 2015). Genotypic variations existing in the genotypes can be exploited efficiently as genetic resources in breeding programs (Sowmya et al., 2010) [6]. Yield being a complex character is collectively influenced by various component traits which are polygenically inherited. Heritability estimations support in determining the relative heritable portion in variation and thus help plant breeder in selecting the elite inbred from a diverse population. Heritability estimates along with adequate variability and higher genotypic variance are normally more helpful in forecasting genetic advance and the gain under selection than heritability estimates alone. A wide range of variation has been reported for seed yield and seed number (Zain, 2015) and other important components of yield (Virupakshappa and Sindagi, 1988). Therefore, improvement for yield cannot be achieved through simple phenotypic selection because of its polygenic nature and low heritability estimates [7,8].

High heritability is needed to execute effective selection scheme for the trait of interest. Heritability along with genetic advance is more reliable than heritability alone (Johnson et al., 1955). Though sunflower is an important crop to smallholder farmers, the production of this crop is distributed throughout the country with very limited area coverage (CSA, 2019) as compared to other oil crops [9]. This may be attributed to lack of availability of improved sunflower varieties and underestimation of the advantage of the crop, particularly in food insecure areas. In order to increase sunflower production and productivity in Ethiopia research efforts must plan at supplying farmers with improved varieties is crucial. The main challenge in sunflower breeding is getting uniform morphological characters in terms of height and head characters are the major once. This is mainly due to cross pollination and limited number of germplasms in the research system. So far, there are very limited reports on studies done to assess the level of genetic variability available in sunflower germplasm in Ethiopia except few studies made involving limited number of genotypes. Realizing the importance of the crop and the existing information gap the present study was conducted to quantify genetic variability, heritability, and genetic advance as percent of mean [10,11].

Materials and Methods

Study area description

Field experiment was conducted at Holeta Agricultural Research Center (HARC), Oromia, central Ethiopia in 2020/21 main cropping season. It is located at 9º 00’ N latitude and 38º30’ E longitude with an altitude of 2400 m.a.s.l. The mean annual rainfall was 1144 mm and temperature ranges from 6°C-22°C with rainy season from June to September. The dominant soil type is well drained Red Nitosols and characterized by soil pH 5.2-6.0 and 0.16% Nitrogen content with low organic carbon content 1.18% (Mekonen and Tilahun, 2019) [12] (Figure 1).

Figure 1: Map of study area.

Experimental materials description

A total of 220 Sunflower genotypes were used from EBI and national mid and highland oilseeds Research Program of Holeta Agricultural Research Center, Coordinating Center and three checks were used for this study. Those genotypes, 108 Accessions from EBI and 109 were advanced through head to row selection and three released varieties was used for performance evaluation of their genetic variability and related characters [13].

Experimental design and Field management

The experiment was arranged in 11x20 Alpha lattice designs with two replications. Each genotype was planted in single rows having 0.25m spacing between plants, 0.75m spacing between rows, and 1.5m row length. Fertilizer was applied at the rate of 23/23 kg NP2O5 kg per hectare. Other agronomic and management practices was applied uniformly as per the recommendation for the area. Yield and yield related data were collected from all plants. Grain yields were adjusted to moisture content 7% using grain moisture analysis [14].

Data collection

The following data were collected both on plant bases and/or plot bases for quantitative traits by using descriptors of International Board for Plant Genetic Resources (IBPGR, 1985) for sunflower. Number of Leaves per plant (LNPP), Plant height [Ph, (cm)], Head Diameter [HD (cm)], Petiole length [PL (cm)], Days to 50% flowering [DF (days)], Days to maturity [DM(days)], Stem Diameter [SD (cm)], Seed filling percentage [SFP (%)] Seed filling percent =(number of filled seeds/total number of filled + unfilled seeds)x100, Hundred Seed weight [HSWT (g)], Seed yield per plant [SYPP (g)], Oil content [OC (%)]-the seed oil content was determined through non-destructive method by utilizing nuclear magnetic resonance (NMR) technique in the laboratory of oil seeds research at HARC [15]. Sample of 25g of seeds were dried in an oven for 3hr at 78℃ and cooled for 3 hours. Then oil contents of seeds were measured using nuclear magnetic resonance machine from 22g working sample. Seed yield per hectare [SYPH (kg)], Oil yield [OYPH (kg/ha)], Oil yield in kg/ha was calculated by using the formula of Habib and Mehdi (2002). Oil yield kg/ha = [(seed yield (kg/ha) x oil content (%)]/100, Seed yield per plot [SYPP (g)], Stand percentage [stand (%), seed number per plant head (SNPP), Seed yield per plot [SYPt (g)] [16].

Data Analysis

Data were subjected to statistical analysis according to Gomez and Gomez (1984), using SAS version 9.3 (SAS Institute, 2014) computer software for quantitative data analysis. It considers the block term as nested in the replication (Block/Rep). Best linear unbiased predictor (BLUP) means were estimated using multivariate mixed model (REML) spatial analysis considering the Block/Rep + treatment as random effect for special correction of the nearest block errors to avoid the biased estimate of variance components at 5% level of significance. Tested genotypes were considered to be significant at (P≤0.05) level of significance. Least significant difference [LSD (±)] and at (P≤0.05) level of significance was used for mean comparison. Differences between genotypes for various characters were tested for significance using the technique of analysis of variance (Panse and Sukhatme, 1985 and Fisher, 1992) [17,18].

Phenotypic and genotypic variability: Phenotypic, genotypic and environmental variance components and their coefficients of variation were estimated based on the methods detailed in (Burton & Devane, 1953). According to Siva Subramanian and Menon (1973) genotypic coefficients of variance (GCV) and phenotypic coefficients of variance (PCV) Values greater than 20% are high, less than 10% are low and between 10% and 20% are medium.

Heritability and genetic advance: Broad sense heritability (H2) and genetic advance as percent of mean (GAM) were also estimated according to the formula in (Allard, 1960). The heritability percentage was categorized as low, moderate and high as suggested by Johnson et al. (1955). Genetic advance in absolute unit (GA) and as percent of the mean (GAM), assuming selection of superior 5% of the genotypes was estimated in accordance with the methods illustrated by Johnson et al. (1955). The GA as percent of mean was categorized as low, moderate and high as suggested by Johnson et al. (1955) Phenotypic and genotypic variance and coefficient of variation, heritability, and genetic advance were computed using the excel Microsoft program [19].

The ANOVA for individual location followed the following model:

�������� = �� + ���� + ������ + ���� + ��������

Where, Pijk= phenotypic value of ith genotype under jth replication and kth incomplete block within replication j; µ =grand mean; gi= the effect of ith genotype, bk(j) =the effect of incomplete block k within replication j; rj=the effect of replication j; and eijk= the residual or effect of random error.

Results and Discussion

Analysis of variance

Analysis of variance (ANOVA) for sixteen quantitative traits showing mean squares of 220 sunflower genotypes studied are presented in Table 1. Analysis of variances for quantitative traits showed highly significant or significant (P20].

For stem diameter (30.3%) followed by leaf number per plant (28.5%) and petiole length (28.4%). The genotypes impact on studied response variables were high and satisfactory with ranges of 0.62 to 0.99 as depicted by R2, coefficient of determination. This means that 62.4% to 99.9% of the variability in the studied characters was described the variability in sunflower genotypes. This finding is in confirmatory with the finding of scholars: Manjula et al. (2001), Sujatha et al. (2002), Salah and Abdellah (2009), Nabi Pur et al. (2010) and Abu (2019) which revealed significant difference among sunflower genotypes for plant height, head diameter, oil content and seed yield, oil yield and days to maturity but disagree in terms of stem diameter with those scholars [21,22] (Table 1).

Table 1: Mean square from analysis of variance for different sources of variation and the corresponding CV for quantitative traits of sunflower genotypes tested at Holeta.

Range and mean performance

Descriptive statistical analysis was done for sixteen quantitative characters of 220 sunflower genotypes and presented in Table 2. The genotypes showed yield performance which ranged from 210.66 kg/ha to 4795.4kg/ha with an average grand mean of genotypes 2165.78kg/ha. The maximum seed yield was recorded from ACC.228898SS (4795.4kg/ha) followed by ACC.17717WSS (4749.4kg/ha), ACC.28464GS (4657.8kg/ha) and ACC.35506WS (4601.3kg/ha); whereas the minimum seed yield was recorded from genotypes H15-HR-93-1 (248.4 kg/ha), H15-HR-11-1(256.2 kg/ha) and H15-HR-Adadi-1-7 (278.4kg/ha). Out of the 220 sunflower genotypes 48.63% genotypes showed better seed yield performance compared to the trial mean and 5.9% of the genotypes showed around the mean performance, 26.82% of the genotypes showed less than mean seed yield per hectare but 18.64% of the genotypes showed less than the national average seed yield per hectare (11.27kg/ha) reported by CSA (2019) [23].

Table 2: Descriptive statistics for studied Quantitative traits of sunflower genotypes.

The result identified considerable number of accessions which showed better performances of seed yield, oil yield and component traits relative to the national average and it is a golden opportunity for the breeding programs to utilize these materials. From breeding and genetics point of view variability expressed by seed yield is complex and might be the outcome of genetic variation among the genotypes and the environment [24].

Plant height is one of the important characters in the development of uniform OPV sunflower variety. Maximum plant height was recorded for ACC.35506WS (297.5cm) followed by ACC.29936LGS (291cm), H14-Adadi-3-4 (290.5cm) and ACC.17725BS (282.5cm) while the minimum was recorded for genotype H19-HR-13-4 (80.5 cm) followed by genotype H15-HR-15-15 (97.5cm), genotype H15-HR-93-1 and genotype H16-HR-13-6 (105cm) and genotype H14-Adadi-1-1 (106cm). About 49.5% of the genotypes was found less than an average value (186.7cm) of plant height and 42.7% of the genotypes was found greater than 2m and can be categorized as tall genotypes whereas 7.72% of the genotypes was having medium height genotypes (IBPGR, 1985). Generally, the results showed wide range would be expected to have variation among the genotypes that could be utilized in breeding program for improvement of plant height for sunflower [25].

Stem diameter: maximum stem diameter was recorded for ACC.208122WS (3.6cm) followed by ACC.28460WS (3.35cm) and ACC.228898SS (3.15cm) while the minimum stem diameter was recorded for genotypes H19-HR-13-4 and 144 (0.9cm) followed by genotype H15-HR-13-13 (1.1cm), genotype H15-HR-Adadi-1-7 (1.15cm) and ACC.28458WS (1.2cm). Nearly 20% of the genotypes was found an average value (2.06cm) of stem diameter and 42.73% of the genotypes was found greater than an average stem diameter and can be categorized as tough stem genotypes whereas 37.3% of the genotypes was having tin stem diameter and this may be sensitive to lodging and stem break problem. Generally, the results showed relatively wide range would be expected to have variation among the genotypes that could be utilized in breeding program for improvement of stem diameter and lodging for sunflower [26].

One of a major yield contributing character in sunflower is head diameter. The maximum head diameter was recorded for genotype H16-HR-15-1 (24.25cm) and genotype H14-Adadi-3-3 (24cm) followed by genotype H16-HR-6-1-6 (22.75cm) and Ayehu & genotype H14-Adadi-3-6 (22.5cm) and ACC.17717WSS (22.25cm) while the minimum head diameter was recorded for genotype H14-Oissa-4-4 (7.25cm) followed by genotype H15-HR-Adadi-1-7 & ACC.17723SGS (8.25cm), genotype H16-HR-9-3-12 & ACC.28472SWS (9cm) and ACC.17716WS (9.25cm). About 21.82% of the genotypes was found an average value (14-15cm) medium headed and 37.3% of the genotypes was found greater than an average head diameter and can be categorized as big-headed genotypes whereas 40.91% of the genotypes was having small headed genotypes and this may be related to low seed yield and oil yield. Generally, the results showed significantly wide differences would be expected to have variation among the genotypes that could be utilized in breeding program for improvement of head diameter with seed yield as well [27].

Number of leaves per plant ranged from 19.75 to 73.5. Maximum leaf number per plant was obtained from ACC.208122WS (73.5) followed by ACC.17726SGS (66.5), ACC.202490BS (64.25) and genotype H15-HR-1-13 (62.25) while the minimum leaf number per plant was recorded for genotype H19-HR-11-1 (19.75). Petiole length ranges from 6.5cm to 23.75cm with mean value of 13.15cm. The maximum petiole length was obtained from genotype H14-Adadi-1-8 & ACC.228898SS (23.75cm) while the minimum was recorded for genotype H19-HR-8-4 (6.5cm). Hundred seed weight ranges from 2.6g to 12.5g with mean value of 6g. The maximum hundred seed weight was obtained from genotype H16-HR-14-4-2 (12.5g) and that of minimum was obtained from genotype H16-HR-21-6 (2.6g) [28].

Sunflower is primarily grown for edible oil purpose in a current scenario. Oil content ranges from 12.2% - 42.1% with mean value of 27.2%. The highest oil content was obtained from Oissa (42.1%) followed by genotype H14-Adadi-3-1 (40.9), Ayehu (40.7) and ACC.208902LGS & ACC.17721WS (38.7%). The range for yield per plant head was from 6.84g to 298.3g with grand mean value of genotypes 76.57g per plant head. The highest seed yield per plant was recorded for ACC.17720BSS (298.31g) followed by ACC.208122WS (229.8g) and H19-HR-26-2 (212.8g) whereas low seed yield per plant was obtained from H16-HR-19-4 (6.84g) [29].

Oil yield per hectare ranges from 52.9 kg/ha to1387.9 kg/ha with mean value of 580.9 kg/ha. The highest oil yield was recorded for ACC.28474WS (1387.9kg/ha) while the minimum was observed for H15-HR-93-1 (52.9kg/ha). About 46.4% of the studied genotypes have high oil yield compared to mean oil yield value of genotypes and about 4.1% of the genotypes have nearly intermediate oil yield while about 49.5% of the genotypes performed less oil yield than the mean value of genotypes. Number of seed per plant ranged from 235.5 to 3844 with mean value of 1356.47. Maximum number of seed per plant was obtained from genotype ACC.17718BS (3844) followed by H16-HR-19-2 (3663.5) while the minimum number of seed per plant was recorded for H15-HR-93-1 (235.5) [30].

Days to flowering: intermediate number of days to flowering was recorded for 6.82% of studied genotypes (106.6 days) while the minimum was recorded for genotype H14-Oissa-4-1 (73days). The maximum days to flowering was recorded for genotype ACC.28465SGS (170days) followed by genotypes ACC.17717BS (160days), and ACC.17724BS (159days). Generally; in sunflower breeding early flowering is highly crucial related to early cessation of rain as well as terminal drought escape. The result depicts those characters having wider range would be predictable to have significant variation among sunflower genotypes and that could be a source for breeding stock to be utilized for the development of early flowering genotypes [31].

The highest seed filling percentage was recorded for genotype H15-HR-15-12 (99.3%) while the minimum was recorded for genotype H15-HR-93-1 (56.1%) with grand mean of genotypes 92.5%. Nearly 24.55% of the genotypes was found an intermediate seed filling percentage and 56.82% of the genotypes was found good seed filling percentage; greater than an average value whereas 18.64% of the genotypes was found low seed filling percentage and this affects the overall economic value of the genotype and also tremendous impact on seed physiology as well. Generally, the results obtained from this study indicated that characters which have wide range would be expected to have variation among the sunflower genotypes that could be blessing and utilized in breeding program for improvement of the specific desired character in sunflower [32].

Early maturity is one of highly important selection character for any plant breeding works in a dynamic agro ecological scenario. About 55.5% of the genotypes were found an early maturing (<158.4days) once and the minimum days to maturity was recorded for genotype H14-Oissa-4-1, nearly hundred days (102.5 days) followed by genotype H19-HR-13-10 (103.5days), genotype H15-HR-13-13 (120.5days) and ACC.28459LGS (122.5days). While about 38.2% of the genotypes showed late maturing once (>160days) and the maximum days to maturity was recorded for ACC.17717BS and ACC.29936LGS (231days) followed by ACC.28479WS and ACC.17719WSS (227days), ACC.28487SLGM (222days) and ACC.28482WS (221.5days). Generally; from the breeding point of view, this shows that the results obtained from the study having wide range would be predictable to have significant variation among sunflower genotypes and that could be a source for breeding work to utilized this important character in sunflower improvement program [33] (Table 2).

Variance components

Phenotypic (��2p) and genotypic (��2g) variance, Phenotypic and genotypic coefficient of variation (PCV% and GCV%, respectively), broad sense heritability (H2), genetic advance from selection (GA) and genetic advance as a percentage of the mean (GAM) for this study are showed below Table 3.

Table 3: Heritability, genetic advance and coefficients of variations in 220 sunflower genotypes.

Phenotypic and genotypic variations

Out of sixteen studied quantitative characters half of them have genotypic (��2g) and phenotypic (��2p) variance were larger than error variance (��2e) and vice versa. This shows that block number within replication used in evaluating 220 sunflower genotypes were nearly adequate to give a better estimation for the error variance of these characters but not for others which have less genotypic variance than error variance. High σ2g and σ2p were recorded for seed yield, plant height, number of seed per plant, days to maturity, days to flowering, oil yield and stand percentage whereas seed yield per hectare and per plant, seed number per plant, plant height, days to maturity and days to flowering showed the highest σ2g and σ2p. A wide range of phenotypic (��2p) variance was observed for seed yield per hectare followed by seed number per plant and oil yield per hectare. Estimated genotypic variance were higher than the corresponding environmental variance for most of studied characters but not for some characters. This justifies the existence of genetic variability among the studied sunflower genotypes for those characters. Lira et al. (2017) had reported similar results which indicate that the estimates of phenotypic variance were higher than genotypic variance for elite sunflower genotypes in Brazil [34].

Genotypic and phenotypic coefficients of variation

Estimated phenotypic coefficient of variation (PCV) and genotypic coefficient of variation (GCV) are shown in Table 3. Coefficient of variation also known as relative variability calculated as percentage is a measure to examine that how much variability exists for the selection. The genotypic coefficients of variance (GCV) were ranged from 3.52% for seed filling percentage to 47.99% for seed yield per plot followed by seed yield per hectare while the phenotypic coefficients of variance (PCV) were ranged from 8.11% for days to seed filling percentage to 48.34% for seed yield per plot followed by seed yield per hectare.

In the present study phenotypic coefficients of variation were higher than their corresponding genotypic coefficient of variation for all the studied characters. This depicts that the effect of environment on genotypes’ performance expression was high in terms of studied characters. According to Shivasubramanian and Menon (1973), almost all characters were categorized as high for phenotypic coefficient of variance (PCV) except days to flowering, maturity and seed filling percentage. Highest PCV was recorded for seed yield/ha (47.89%) and the least PCV were recorded for seed filling percentage (8.11%). Moderate PCV were recorded for days to flowering (16.65%) and days to maturity (19.57%).

High GCV were recorded for seed yield per plot (47.99%) followed by seed yield per hectare (47.54%), oil yield per hectare (46.68%), seed yield per plant (46.34%), seed number per plant head (45.96%), stand percentage (32.24%) and hundred seed weight (29.34%). Moderate PCV were recorded for plant height (17.68%) followed by head diameter (16.73%), oil content (12.12%), petiole length (10.83%) and leaf number per plant (10.23%). Low GCV were recorded for Seed filling percentage (3.52%) followed by days to maturity (8.8%), days to flowering (9.23%) and stem diameter (9.95%). High to moderate values of PCV and GCV observed in the present study indicated the existence of variability for such traits and selection may be effective based on those traits.

For low PCV and GCV recorded traits selection may not be effective. When the value of PCV and GCV proportional, indicating that high contribution of genotypic effect for phenotypic expression of those characters. From this point of view yield per plot, hundred seed weight, seed yield per hectare, oil yield per hectare seed number per plant head, seed yield per pant and stand percentage showed proportional value of GCV and PCV value. This shows that the performance expressions of these characters were less factored by environment. But for days to flowering, days to maturity, plant height, stem diameter, head diameter, leaf number per plant, petiole length and oil content PCV value were greater than GCV value which shows high influence of environment on genetic expression. This result is in accordance to the finding of Gangappa (1991) and Abu (2019) who reported low variability for these characters. But not in agreement with the findings of Reddy et al. (2012) who reported high PCV and GCV for these traits. This difference may be due to differences in genotypes source even genotype itself and studied area.

As we know genotypic coefficient of variance (GCV) provides information on the genetic variability present in quantitative traits in base population, but it is not possible to determine the amount of the variation that was heritable only from the GCV. Genetic coefficient of variance together with heritability estimates would give the best indication for the amount of advance to be expected from selection (Burton and Devane, 1953). Therefore, estimation of the heritable percentage of the variation could be more useful in quantitative genetics.

Heritability, genetic advance and genetic advance of mean

Heritability, genetic advance as the percentage of the mean (GAM) at 5% selection intensity and variance component are presented in Table 3. According to Johnson et al. (1955) heritability values more than 60% are regarded as high whereas, values less than 30% are considered to be low and values between 30% and 60% are to be moderate. Broad sense heritability ranged from 9.71% to 99.86% for stem diameter and seed number per plant, respectively. High heritability was recorded for hundred seed weight, seed yield per hectare, oil yield per hectare, seed number per plant, seed yield per plant head. It was due to high genotypic influence and low environmental influence. Therefore, Selection based on phenotypic performance of this character may help to improve those characters. Medium heritability was recorded for days to flowering, plant height and head diameter. Low heritability was observed for days to maturity, number of leaves per plant, stem diameter, petiole length, oil content and seed filling percentage. This shows that those traits are highly influenced by environment. Therefore, direct selection based on phenotypic performance may not be effective to improve those traits (Table 3).

Genetic advance (as percentage of mean) was estimated in order to determine the relative advantages of different traits that could be further utilized in the selection program of crop improvement. According to Johnson et al. (1955), genetic advance as percent of mean (GAM) was categorized as high (>20%), moderate (10-20%) and low (0-10). High GAM was obtained from plant height, head diameter, seed yield per plot, hundred seed weight, stand percentage, seed yield per hectare, oil yield per hectare and seed number per plant head. Moderate GAM for days to flowering and oil content. Low genetic advance was observed for days to maturity, leaf number per plant, stem diameter, petiole length and seed filling percentage. To estimate selection effects, heritability accompanied with genetic advance is useful than heritability alone Hanson (2003).

According to Panse and Sukhatme (1985) if a trait is governed by non-additive gene action it may give high heritability but low genetic advance, whereas, if it is governed by additive gene action heritability and genetic advance would be high. Comparison of heritability with genetic advance (% of mean) for the traits shows that seed yield per plot, seed number per plant, stand percentage, seed yield per hectare and oil yield per hectare had high heritability estimates along with high genetic advance (% of mean) for studied genotypes. Therefore; those characters are highly governed by additive gene action and can be improved through selection. High heritability along with moderate genetic advance as percent of mean (58.2%) observed for hundred seed weight showing the contribution of both additive and non-additive gene actions in the inheritance of this character. Therefore, hundred seed weight can be improved through any selection procedures targeting to exploit the additive gene effects. But it should be noted that this is broad sense heritability and hence it is not absolute indicator of efficiency (Singh, 1999). According to Singh (1999), if heritability of a trait is very high, say 97%, selection for that trait is Substantial.

Generally, highly heritable trait with high or moderate genetic advance could be further improved with individual plant selection. Traits with high heritability and low genetic advance indicated little scope for further improvement through individual plant selection. This finding is in agreement with the finding of Baraiya et al. (2018) which reported high heritability with moderate genetic advance as percent of mean for hundred seed weight but in contrary with the finding of Hassan et al. (2012) which showed high heritability with moderate genetic advance as percent of mean for oil content. And also, in contrast to Hassan et al. (2012) and Abu (2019) who reported high heritability for hundred seed weight but low genetic advance. The present result is in agreement with Baraiya et al. (2018). Recently, Lagiso et al. (2021) reported that high PCV, GCV and heritability in broad sense were obtained for seed yield, oil content, oil yield, and thousand seed weight. The authors also reported high heritability coupled with high genetic advance as percentages of mean for seed yield, oil content, oil yield per plot, head diameter and plant height and high heritability coupled with moderate genetic advance for days to maturity and days to 50% flowering, seed yield per plant, leaf number and reproductive phase; which is confirmatory with our study in some traits and also disagree on a few traits [35].

Conclusion and Recommendation

Knowledge on the extent of genetic variability and the degree of relationship among yield and other agronomic character are important in plant breeding program. Study of genetic variability and related parameters among sunflower genotypes is a pilar and starting point to identify and select high yielding, tolerant or resistance to biotic and abiotic factors plant materials to develop best sunflower varieties in general. In this study, 220 sunflower genotypes including three released varieties and 108 accessions from EBI collected from different locations of Ethiopia and the remaining 109 were from Holeta mid and highland oilseed research program selected as single plant selections (SPS) were evaluated in 2020 main growing season, at Holeta Agricultural Research Center using 11*20 Alpha Lattice design with two replications. The objectives of the study were to determine genetic variability genetic variability, heritability, and genetic advance as percent of mean among sunflower genotypes in central Highlands of Ethiopia.

The Analysis of variance showed highly significant (p<0.01) differences among the tested genotypes for days to 50% flowering, days to 80% maturity, plant height, head diameter, number of leaves per plant, petiole length, 100 seed weight, oil contents and seed yield per hectare and per plant, oil yield per hectare, seed number per plant head, seed filling percentage and stand percentage but not for stem diameter. Descriptive statistics also showed the presence of wide range of phenotypic variation among studied genotypes. High σ2g and σ2p were recorded for seed yield, plant height, number of seed per plant, days to maturity, days to flowering, oil yield and stand percentage whereas seed yield per hectare and per plant, seed number per plant, plant height, days to maturity and days to flowering showed the highest σ2g and σ2p. A wide range of phenotypic (��2p) variance was observed for seed yield per hectare followed by seed number per plant and oil yield per hectare.

Seed yield showed high PCV and GCV value followed by Oil yield of studied genotypes. The Highest heritability coupled with high genetic advance as percent of mean was observed by seed number per plant and seed yield per plant head. Low heritability and genetic advance as percent of mean was also recorded for days to maturity, stem diameter, leaf number per plant, petiole length and seed filling percentage depicts that those traits are highly influenced by environment factors an[d these traits are highly controlled by non-additive gene action. Some important characters showed high heritability coupled with high genetic advance as percent of mean such as seed number per plant, seed yield per plant head, oil yield and seed yield per hectare even hundred seed weight can be used for character improvement through direct and indirect selections. In addition, a comprehensive study supported by molecular data is recommended for identification of novel genes responsible for important traits including fatty acid profile analysis. Generally, further work should be made to improve sunflower genetic diversity in Ethiopia employing further germplasm collection from untouched potential areas of the country, germplasm introduction from potential countries and strong crossing program for specific traits.

1. Abu Edeo, Mohammed (2019) GENETIC VARIABILITY AND CHARACTER ASSOCIATION IN SUNFLOWER (Helianthus annus L.) GENOTYPES IN CENTRAL HIGHLANDS OF ETHIOPIA (Msc Thesis, JIMMA UNIVERSITY).

Indexed at, Google Scholar, Crossref

2. Allard RW (1960) Principal of Plant Breeding John Wiley and Sons. Inc., USA Wiley International Edition p85.

Google Scholar

3. Antolın G, Tinaut FV, Briceno Y, Castano V, Perez C, et al. (2002) Optimisation of biodiesel production by sunflower oil transesterification. Bioresource technology 83: 111-114.

Indexed at, Google Scholar, Crossref

4. Baraiya VK, Patel PJHR (2018) Genetic variability, heritability and genetic advance for seed yield in sunflower (Helianthus annus L.). Indian J Crop Sci 6: 2141-2143.

Google Scholar

5. Burton GW, Devane DE (1953) Estimating heritability in tall fescue (Festuca arundinacea) from replicated clonal material 1. Agronomy Journal 45: 478-481.

Indexed at, Google Scholar, Crossref

6. Central Statistical Agency (2019) Agricultural sample survey volume I, report on area and production of major crops (private peasant holdings, meher season). Statistical bulletin 589.

Google Scholar

7. Gangappa E (1991) Evaluation of sunflower germplasm. Crop Science 15: 77-78.

Google Scholar

8. Gomez KA, Gomez AA (1984) Statistical procedures for agricultural research. John Wiley & Sons.

Indexed at, Google Scholar, Crossref

9. Hassan SM, Iqbal MS, Rabbani G, Shabbir G (2012) Genetic variability, heritability and genetic advance for yield and yield components in sunflower (Helianthus annuus L.). Electronic Journal of Plant Breeding, 3: 707-710.

Indexed at, Google Scholar, Crossref

10. Hanson GH, Robinson HF, Comstock RE (2003) Biometrical studies of yield segregating population of Korean lespedeza. Agron J 48: 267-82.

Google Scholar

11. Hussain F, Iqbal J, Rafiq M, Mehmood Z, Ali M, et al. (2015) GENETIC IMPROVEMENT IN LINSEED (LINUM USITATISSIMUM L.) THROUGH VARIABILITY HERITABILITY AND GENETIC ADVANCE. J Agric Res 53(4).

Indexed at, Google Scholar, Crossref

12. IBPGR (1985) Descriptors for cultivated and wild sunflower, Rome.

Google Scholar

13. Johnson HW, Robinson HF, Comstock RE (1955) Estimates of genetic and environmental variability in soybean. Agron J 47: 314-18.

Indexed at, Google Scholar, Crossref

14. Kaya Y, Evci G, Pekcan V, Gucer T, Durak S, et al. (2008) The path analysis of yield traits in sunflower (Helianthus annuus L). Agronomijas Vestis 11: 72-77.

Indexed at, Google Scholar, Crossref

15. Lagiso TM, Singh BCS, Weyessa B (2021) Evaluation of sunflower (Helianthus annuus L.) genotypes for quantitative traits and character association of seed yield and yield components at Oromia region, Ethiopia. Euphytica 217: 1-18.

Google Scholar

16. Lira EG, Amabile RF, Fagioli M, Montalvão APL (2017) Genetic parameters, phenotypic, genotypic and environmental correlations and genetic variability on sunflower in the Brazilian Savannah. Ciência Rural 47(8).

Indexed at, Google Scholar, Crossref

17. Manjula K, Nadaf HL, Giriraj (2001) Genetic diversity in non-oilseed sunflower (Helianthus annuus L.) genotypes. Helia 24: 17-24.

Indexed at, Google Scholar, Crossref

18. Mekonen H, Tilahun M (2019) Seed coating; Genius Coat Cereal Disco AG L-439; effect on seed yield and yield related traits of malting barley (Hordeum vulgare L.) in central highlands of Ethiopia. International Journal of Agriculture and Biosciences 8:141-147.

Indexed at, Google Scholar, Crossref

19. Murugesan A, Umarani C, Subramanian R, Nedunchezhian N (2009) Bio-diesel as an alternative fuel for diesel engines-a review. Renewable and sustainable energy reviews 13: 653-662.

Indexed at, Google Scholar, Crossref

20. Nabi Pur AR, Andarkhor AA, Naser Qadimi F, Shahsavari MR, Soltani M, et al. (2010) Evaluation of yield and agronomic performance of introduced hybrid varieties of sunflower.

Indexed at, Google Scholar, Crossref

21. Paniego N, Echaide M, Muñoz M, Fernández L, Torales S, et al. (2002) Microsatellite isolation and characterization in sunflower (Helianthus annuus L.). Genome 45: 34-43.

Indexed at, Google Scholar, Crossref

22. Panse VG, Sukhatme PV (1985) Statistical methods for agricultural workers. Indian Council of Agricultural Research, New Delhi pp 359.

Google Scholar

23. Putman JD, David C, Molinillo JMG, David M, Varela RM, et al. (1990) Synthesis of heliannane skeletons. Facile preparation of heliannuol D. Tetrahedron 59: 1679-1683.

Indexed at, Google Scholar

24. Qamar R, Sadaqat HA, Bibi A, Tahir MHN (2015) Estimation of combining abilities for early maturity, yield and oil related traits in sunflower (Helianthus annuus L.). Int J Sci Nature 6: 110 -114.

Google Scholar

25. Rafiq M, Iqbal Z, Hussain F, Khan QAT, Din SS, et al. (2016) Evaluation of autumn planted sunflower (Helianthus annuus L.) Hybrids under Faisalabad agro-ecological zone. J Agric Res 54: 407-421.

Indexed at, Google Scholar, Crossref

26. Reddy SM, Reddy TD, Dudhe MY (2012) Analysis of genetic diversity in germplasm accessions of sunflower (Helianthus annuus L.). Madras Agricultural Journal 99: 457-460.

Indexed at, Google Scholar, Crossref

27. Salah BMA, Abdella WH (2009) Genetic yield stability in some sunflower (Helianthus annuus L.) hybrids under different environmental conditions of Sudan. J of Plant Breeding and Crop Sci 1: 016-021.

Google Scholar

28. Shivasubramanian S, Menon N (1973) Heterosis and inbreeding depression Genet 17: 381-387.

Google Scholar

29. Singh RK (1999) Principle of plant breeding and genetics. Kalayani Publishers, India.

Indexed at, Google Scholar, Crossref

30. Sowmya HC, Shadakshari YG, Pranesh KJ, Srivastava A, Nandini B, et al. (2010) Character association and path analysis in sunflower (Helianthus annuus L.). Ele J Plant Breed 1: 828-831.

Google Scholar

31. Sujatha HL, Chikkadevaiah, Nandini (2002) Genetic variability study in sunflower inbreds. Helia 37: 93-100.

Indexed at, Google Scholar

32. Tesfaye M, Adugna W, Terefe G, Weyessa B, Semahegn Y, et al. (2000) Achievements, Challenges and Future Prospects Edible Oilseeds Research and Development in Ethiopia. Agricultural Research for Ethiopian Renaissance.

Indexed at, Google Scholar, Crossref

33. Virupakshappa K, Sindagi SS (1988) A note on germplasm collections in sunflower. J. Oilseeds Res 5: 119-120.

Google Scholar

34. Yue B, Vick BA, Cai X, Hu J (2010) Genetic mapping for the Rf1 (fertility restoration) gene in sunflower (Helianthus annuus L.) by SSR and TRAP markers. Plant Breeding 129: 24-28.

Indexed at, Google Scholar, Crossref

35. Zain N (2015) Genetic variability among sunflower (Helianthus annuus L.) accessions for relative growth and seedling traits.

Google Scholar