Evaluation of confectionary sunflower germplasm accessions and their derived hybrids

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However, seed quality of confectionary sunflower differs from non-oil seed types. In the present study, 71 sunflower accessions were selected for the evaluation of seed quality traits. Significant genetic variability was observed for traits such as 100-seed mass and seed length, as indicated by high genotypic coefficients of variability. Correlation analysis revealed a positive correlation between 100-seed mass and seed size, protein content, and oleic acid content. Path coefficient analysis suggested that accessions with high sugar content may be selected to develop superior germplasm for confectionary products. Biplot analysis was conducted to identify suitable accessions with favorable confectionary traits. Accessions ‘Hybrid 100’, ‘GOR101’, and ‘Odesskijj112’ exhibited high sugar content, while ‘Yawn’ demonstrated comparatively higher oleic acid content. Accessions ‘Vinimik 6931’ and ‘HA 305’ displayed high 100-seed mass, while seed length was greater in accessions ‘Comet’ and H. × multiflorous. Combining ability analysis were performed to assess the breeding value of accessions. ‘Comet’ exhibited the highest general combining ability (GCA) effects for seed yield per plant, head diameter and 100-seed mass; while ‘G.OR.104’ had positive GCA for seed yield per plant but negative combining ability for other traits. ‘Tenissiei’ displayed positive GCA for all traits. Among the testers, accession ‘Universal’ showed positive GCA for seed yield per plant, while ‘HA-292’ exhibited positive GCA for head diameter. Accessions Achene Correlation Fatty acid profile Sugar contents Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Sunflower ( Helianthus annuus L.) is a versatile crop cultivated for its oilseed, confectionary products, snacks, and as food for birds and small animals. It ranks as the fourth largest oilseed crop globally, with cultivation spanning over 70 countries (Rauf 2019 ). According to FAO data from 2022, the total harvestable area dedicated to sunflower cultivation reached 29.25 million hectares, yielding a total production of 54.3 million metric tons. Notably, countries with the largest cultivated areas of sunflower include the Russian Federation, Ukraine, Argentina, China, India, and the USA (FAO, 2022 ). Sunflower cultivars are categorized into distinct types, broadly classified into two groups: oilseed types and non-oilseed types. Non-oilseed types encompass confectionary cultivars characterized by large seed and low oil content. Confectionary sunflower is prominently cultivated in Eastern European countries such as Turkey, Russia, Romania, Bulgaria, and Ukraine (Aldemir et al., 2016 ). Sunflower seeds are commonly crushed to extract oil, leaving behind seed meal that serves as a byproduct. This meal can be repurposed for various applications such as animal feed or incorporated directly into human consumption in confectionary and baking products. However, sunflower meal is frequently considered of low value due to its relatively low energy and protein content, alongside the presence of anti-nutritional components (González-Pérez, 2015 ). Comparative analysis across various species indicates that sunflower meal typically contains a lower percentage of protein (approximately 30% crude protein) compared to cotton (approximately 42%) and soybean (approximately 50%). Consequently, there arises a necessity to enhance the protein content in sunflower seeds, possibly at the expense of polysaccharides, in order to increase the value of the hull contents. Improving hulling efficiency by reducing fiber content not only augments protein levels but also positively impacts oil extraction. Research indicates significant variability in protein and oil contents among different sunflower elite germplasm, with maximum protein levels ranging from 35–50% (Warburton et al., 2017 ). This variability is primarily attributed to differences in hull contents. Thus, enhancing protein content entails reducing fiber content and improving hull composition. Additionally, within sunflower germplasm, variation is observed concerning anti-nutritional components such as chlorogenic acid. High protein content is correlated with a higher kernel-to-hull ratio and reduced fiber content, which in turn enhances hull digestibility (Demir, 2021 ). Improving the nutritional value of sunflower meal by increasing its protein content is a pivotal breeding objective. While sunflower meal provides all essential amino acids except lysine, efforts can be directed towards enhancing lysine content by leveraging initial variations within elite or breeding lines (Nenova & Drumeva, 2012 ). Confectionary sunflower, characterized by large seed size, low oil, and high polysaccharide and protein contents, serves various purposes. Its seeds are suitable for roasting or snacks when inshelled, and for baking, bird feed, and flour used in various bakery products when shelled. Roasted sunflower seeds offer a cost-effective alternative to nuts and are often served during social gatherings. Confectionary sunflower seeds can be identified based on their seed coat color, which may be albino, striped, or colored, and they are typically larger in size. Confectionary sunflower seeds exhibit a higher 100-seed mass, approximately 8–13 g per 100 seeds, compared to oilseed types, which typically contain only 4–6 g per 100 seeds. Dehulling of confectionary sunflower seeds is easier compared to oilseed types, and the kernels are loosely packed. Sunflower breeders generally select confectionary lines based on criteria such as seed yield potential, protein content, ease of dehulling, hull/kernel ratio, among others. Key characteristics of confectionary sunflower cultivars are seed yield potential (approximately 6 metric tons per hectare), plant height (approximately 175 cm), protein content (> 25%), hull ratio ( 60%), oil content (30–35%), ease of dehulling, and sweet and nutty taste (Rauf et al., 2019). The primary objectives of confectionary sunflower breeding align with those of oilseed types; however, there are distinct differences in seed morphology and biochemical traits. Confectionary types exhibit characteristics such as larger seed size, higher protein contents, ease of dehulling, and colorful testa (Feng et al., 2022 ). The testa of confectionary sunflower is easily dehulled, and the kernel is removed intact during shelling (Feng et al., 2022 ). Experimental confectionary hybrids have demonstrated significant commercial heterosis, particularly for traits like seed yield, head diameter, and seed weight (Pekcan et al., 2015 ). Notably, two confectionary hybrids (09 TRÇ 003 and 09 TRÇ 004) were submitted for registration based on their superior performance in terms of seed yield and uniformity (Pekcan et al., 2015 ). Considering this understanding, a research study was initiated to assess genetic variation in sunflower accessions concerning traits relevant to confectionary sunflower and their potential utilization in a hybrid breeding program. This initiative aims to capitalize on the distinct characteristics of confectionary sunflower to develop improved hybrids with desirable traits. Materials and Methods In 2023, experiments were conducted at the Department of Plant Breeding & Genetics, College of Agriculture, University of Sargodha (Pakistan). The germplasm used in the study was obtained by ordering through the Germplasm Resources Information Network (GRIN) of the United States Department of Agriculture in 2020. The germplasm was multiplied and maintained for two years before being subjected to evaluation trials. The list of germplasm utilized in the study is provided in Table 1 . Table 1 List of the germplasm accessions used in the study Accession Origin Accession Origin Comet Transvaal, South Africa HA 288 North Dakota, United States USSR Franslever Former, Soviet Union HA 292 North Dakota, United States Laan Transvaal, South Africa HA 305 North Dakota, United States Short Russian MN 34% oil Transvaal, South Africa RHA 273 United States Universal Transvaal, South Africa RHA 294 North Dakota, United States A 9345 France RHA 298 North Dakota, United States C 1957 France RHA 299 North Dakota, United States Tenissei France RHA 801 North Dakota, United States V 8883 France HA 61 Texas, United States Saratovski MN 49% oil Former, Soviet Union HA 89 Texas, United States No. 513 South Africa HA 304 North Dakota, United States Turkey RHA 271 United States I-7999-V. 56 Uruguay RHA 293 North Dakota, United States Beacon South Africa 'Peredovik' Bulgaria I-29444 Uruguay 'Novi Sad 61' Former S&M Yugovostok Former Soviet Union Vniimk 6540 Former, Soviet Union Peredovick Former Soviet Union 'Vniimk 8931' Former, Soviet Union Vniimk 6540 Former Soviet Union 'Zelenk 61' Uncertain Vniimk 8883 Former Soviet Union 'VR Bulgarian' Bulgaria Chernianka 35 Former Soviet Union 'DDR 1' Uncertain Girassol São Paulo, Brazil 'MN17' North Dakota, United States Peredovic Former Soviet Union Jupiter Zimbabwe Black Sayar Pakistan 'Yawne Zimbabwe H. × multiflorus 33 Portugal Russian Giant Zimbabwe Manfredi INTA (3-WAY X)11 Argentina Comet Zimbabwe USSR Vniimk 6540 '66 Former Soviet Union G.O.R. 104 Zimbabwe USSR Armavirskij3497'66 Former Soviet Union G.O.R. 101 Zimbabwe USSR Vniimk 8931 '66 Former Soviet Union 803495 Zimbabwe B-12 Former Serbia and Montenegro (S&M) 803496 Zimbabwe D-75-10 Former S&M Hybrid 100 Zimbabwe N 3/2 − 1 Former S&M 803504 Zimbabwe PO 6/4 − 2 Former S&M 'Sunrise' Zimbabwe R-201/4 Former S&M 'Dukn' Zimbabwe V 8931 2/2 − 1 Former S&M Odesskij 19 Former Soviet Union Peredovik ul Former Soviet Union Voronezskij 151 Former Soviet Union Odesskij 113 Former Soviet Union Plant material and sowing of experiments Experimental trials were conducted by initiating a field trial on 20th February 2022 at the College of Agriculture, University of Sargodha. On 20th February 2022, a total of 71 germplasm accessions from various countries were sown to screen potential breeding lines with useful genetic variations related to confectionery sunflower at the College of Agriculture, University of Sargodha. These selected plants may be further utilized in the development of inbred lines and hybrid breeding. Additionally, they may be exploited in the breeding program to estimate the general combining ability (GCA) in sunflower. Crop husbandry The experiments were sown on raised beds with a plant-to-plant distance of 24 cm and a row distance of 75 cm. Weed control was implemented using pre-emergence pesticide (dual gold®). Each breeding line was sown in a single row spanning approximately 6 meters. Initially, three seeds were manually dibbled into each hole, with subsequent thinning to a single plant per hole upon germination (approximately 14 days after sowing). The breeding material received fertilization with 50 kg per acre of diammonium phosphate and 25 kg per acre of sulfate of potash. To prevent insect damage, a recommended pesticide, lufenuron with dichlorobenzine at a rate of 30 ml per 20 liters, was sprayed. The experiments were irrigated with canal water to prevent water stress, and insect control was implemented with the recommended insecticide following pest scouting conducted by an entomologist. Maintenance of germplasm Each plant was individually tagged and covered with a net bag to prevent pollen contamination from pollinators. To maintain a breeding line, pollen from two selected neighboring plants was collected and used for pollination, a process known as sib mating. Selected plants were pollinated until all the anthers were withered, ensuring controlled pollination and genetic purity within the breeding lines. Crossing for combining ability test Parental lines were carefully selected based on all traits related to 100-seed mass, sugar contents, protein contents, and seed length (Table 2 ). Six breeding lines ('HA-305', 'VNIIMIK 8931', 'TENISSIE', 'Odeskij 113', and 'Vniimk89') were mated with three male lines ('HA-292', 'I-29444', and 'Universal'). This mating strategy aimed to combine desirable traits from the female and male parental lines, thereby creating potential hybrids with improved characteristics for further evaluation and selection in the breeding program. Table 2 Mean values of the selected accessions used in the line × tester analysis Accessions 100-seed weight Days to flowering Oleic acid (%) Oil percentage Protein content Seed length Sugar content ‘Comet’ 9.37 65.33 41.67 27.00 15.33 2.27 114.99 ‘Universal’ 8.73 59.33 40.33 25.33 18.00 2.40 102.23 ‘Tenissei’ 9.37 54.67 46.33 27.00 18.33 1.40 113.00 ‘Odesskij 113’ 6.17 59.00 52.33 42.67 15.33 0.97 135.00 ‘HA 292’ 14.50 80.67 34.33 26.00 18.67 1.40 124.00 ‘HA 305’ 12.87 61.00 43.33 25.67 18.33 1.03 116.00 ‘Vniimk 8931' 11.23 61.33 52.67 20.00 19.33 1.30 106.00 ‘Comet’ 7.30 62.33 41.33 30.33 15.33 1.23 123.67 ‘I-29444’ 8.57 74.67 47.67 25.33 18.67 1.63 109.67 Development of half sib offspring They were developed through the process of crossing the female lines with the male lines. Prior to sunrise, the female lines were manually emasculated to remove their anthers, ensuring that only the desired pollen from the male lines would be used for pollination. Both the female and male lines' capitula were covered with net bags to prevent pollen contamination by insect pollinators. The female lines were pollinated until all the stigmas were withered. After pollination, seeds from each crossed head were manually threshed once the plants reached physiological maturity. This was indicated by the heads turning brown. The seeds were then dried and cleaned to remove all chaff and stored at room temperature until reaching physiological maturity. In total, there were 18 crosses made, resulting from combinations of 6 female lines and 3 male lines. These crosses aimed to create a diverse set of hybrids for further evaluation and selection in the breeding program. Evaluation of half sib offspring The offspring were sown during the autumn season on 18th August 2023. The trial was conducted in loam soil, which was well-prepared beforehand. Raised beds were utilized for sowing the half-sib offspring. The soil was fertilized with 12 kg of diammonium phosphate and 5 kg of sulfate of potash. All materials were sown in single rows spanning 6 meters, with a plant-to-plant distance of 22 cm and a row-to-row distance of 60 cm. Each seed was manually dibbled into the soil. To control weed growth, the field was sprayed with the pre-emergence herbicide "Dual Gold" (metolachlor). Irrigation was administered when the soil moisture content fell below the field capacity, which was approximately 18% on a weight basis. Additionally, the crop was sprayed with chlorpyrifos at a rate of 250 g per acre to prevent insect infestation in the field. Trait evaluation The following traits were evaluated in all accessions according to the details provided: 100-seed mass . A total of 100 seeds were counted using a digital seed counter and subsequently weighed on the seed counter. Days to 50% flowering . Each row was tagged, and the number of days to anthesis for 50% of the plants in each row was observed and recorded. Achene oil content . Achenes weighing 10 g were utilized to determine the oil contents using a Soxhlet apparatus. Kernels were crushed gently and placed in a thimble for oil extraction through hexane until all oil was extracted from the seed lot. The oil contents of the kernels were calculated using the formula: Achene oil contents = [(Achene mass before extraction – Achene mass after oil extraction)/Achene mass] Fatty acid profile . Oil was extracted manually using a small expeller from a 40 g seed sample, and the expelled oil was cleaned to remove any seed material. Oil samples were thereafter refrigerated at 4°C for further analysis. α-tocopherol was determined using high-performance liquid chromatography (HPLC). Oleic acid contents were determined through gas chromatography. Oil samples were methylated using KOH, and methylated fatty acids were extracted with hexane. Analysis was performed on a fused capillary column with a flame ionizing detector. Sugar content . It was determined using the phenol-sulfuric acid method. Samples were treated with phenol-sulfuric acid solution and incubated for 30 minutes before being cooled to room temperature. Standard curves of glucose and sucrose were prepared, and absorbance was measured using a spectrophotometer at 490 nm. Each of the above traits was evaluated meticulously to gather comprehensive data for further analysis and selection of superior accessions in the breeding program. At physiological maturity, the following traits were evaluated in the half-sib offspring for further combing ability analysis: Seed yield head -1 . The heads were manually harvested and dried under shade to ensure proper drying without exposure to direct sunlight. After drying, the heads were manually threshed to separate the seeds from the heads. The threshed seeds were then cleaned to remove any debris or chaff. Subsequently, the cleaned seeds were placed in kraft paper bags and dried to achieve a moisture content of 12%. This drying process helps preserve the quality of the seeds during storage. To calculate the seed yield per head, all the harvested seeds from a single head were measured using a digital balance. This measurement provided accurate data on the quantity of seeds obtained from each individual head, allowing for the assessment of seed yield potential. 100-seed mass . A total of 100 seeds were counted using a digital counter to ensure consistency in the sample size. Subsequently, the mass of these 100 seeds was measured using a digital balance to obtain accurate data on seed mass. This process allows for precise assessment of seed size and weight, which are important factors in determining seed quality and yield potential. Head diameter. The diameter of the capitula was measured using a measuring tape from the center of the capitula to the front side, and all data were recorded in centimeters. This measurement provides valuable information about the size and morphology of the capitula, which can influence seed production and overall plant performance. Results Table 3 presents mean values of various traits, ranges, genotypic and phenotypic coefficient of variation (GCV and PCV) showing variability among the accessions for all traits. The highest variation was observed in seed length, with GCV and PCV estimates of 29% and 31% respectively. The range for seed length was 1.39–2.40 cm among the accessions. Considerable variation was also observed for 100-seed mass, with a range of 3.4–14.5 g and GCV and PCV estimates of 27%. Sugar content exhibited the lowest variation, with GCV and PCV estimates of 9%. Seed sugar contents ranged between 120–139 mg g -1 . Seed oil contents range was 34–49%, with GCV and PCV estimates at 19%. Protein contents range was 16–19%, while oleic acid percentage range was 39–53%, thus indicating that the accessions were generally suitable for confectionary purposes. Moreover, the range of oleic acid was low to medium, and no high oleic acid accessions were present within the exotic germplasm. Table 3 Germplasm statistics of seed quality traits based on 71 accessions Statistics 100-Seed mass (g) Days to flowering Oleic acid (%) Oil content (%) Protein content (%) Seed length (cm) Sugar contents (mg g -1 ) Mean 7.78 62.95 39.30 33.85 16.10 1.39 120.37 Maximum 14.50 98.00 52.67 49.00 19.33 2.40 138.67 Min 3.40 46.33 23.33 20.00 11.33 0.67 102.00 GCV 26.73 12.20 18.47 19.20 13.61 28.84 8.88 PCV 27.40 12.20 20.63 19.61 14.72 31.42 9.30 GCV: genotypic coefficient variation; PCV: phenotypic coefficient of variation Trait associations Table 4 presents the estimated genotypic correlations among various traits. The 100-seed mass exhibited a positive relationship with protein contents and seed length, indicating that larger seeds tend to have higher protein contents and longer lengths. Conversely, it showed a negative relationship with oil percentage, suggesting that accessions with higher seed masses tend to have lower oil percentages. Days to flowering displayed negative correlations with oil percentage and positive correlations with protein contents and seed length. These correlations suggest that accessions suitable for confectionary purposes tend to mature later. Seed length showed positive relationships with 100-seed mass and protein contents, indicating that longer seeds tend to have higher masses and protein contents The 100-seed mass exhibited a positive relationship with oleic acid and protein contents (Table 5 ), thereby suggesting that larger seeds tend to have higher oleic acid and protein contents. It also displayed a negative relationship with oil percentage, reinforcing the earlier observation that accessions with higher seed masses tend to have lower oil percentages. Days to flowering had a positive relationship with both protein contents and seed length, while displaying negative relationships with oil percentage. This indicates that accessions with later flowering times tend to have higher protein contents and seed lengths, but lower oil percentages. Table 4 Genotypic correlation coefficient among traits related seed quality Traits 100-SM DTF OA Oil% PC% SL 100-SM 1.00 0.06 NS 0.16* -0.57** 0.40** 0.28** DTF 0.06 NS 1.00 -0.18 NS -0.23** 0.31** 0.16** OA 0.16* -0.18 NS 1.00 -0.13 NS 0.09 NS -0.07 NS Oil% -0.57** -0.23** -0.13 NS 1.00 -0.59** -0.28** PC 0.40** 0.31** 0.09 NS -0.59** 1.00 0.17* SL 0.28** 0.16* -0.07 NS -0.28** 0.17* 1.00 SC 0.01 NS -0.07 NS 0.06 NS 0.34** -0.48** -0.05 NS NS * and ‘’ indicate non-significant ( P ≥ 0.05), * significant ( P ≤ 0.05) and highly significant (P ≤ 0.01) genotypic correlations, respectively. They were tested using a two-tail t test with degree of freedom as number of genotypes – 2. 100-SM: seed mass; DTF: days to flowering; OA: oleic acid percentage; Oil%: oil percentage; PC: protein content percentage; SL: seed length Table 5 Genotypic correlation coefficient among traits related seed quality Traits 100-SM DTF OA Oil% PC% SL 100-SM 1.00 0.04 NS 0.14* -0.55** 0.37** 0.23** DTF 0.04 NS 1.00 -0.13 NS -0.22** 0.27** 0.14** OA 0.14* -0.13 NS 1.00 -0.13 NS 0.06 NS -0.06 NS Oil% -0.55** -0.22** -0.13 NS 1.00 -0.54** -0.25** PC% 0.37** 0.27** 0.06 NS -0.54** 1.00 0.14* SL 0.25** 0.14* -0.06 NS -0.24** 0.14* 1.00 SC 0.01 NS -0.07 NS 0.05 NS 0.31** -0.41** -0.03 NS * and ‘’ indicate non-significant ( P ≥ 0.05), * significant ( P ≤ 0.05) and highly significant (P ≤ 0.01) genotypic correlations, respectively. They were tested using a two-tail t test with degree of freedom as number of genotypes – 2. 100-SM: seed mass; DTF: days to flowering; OA: oleic acid percentage; Oil%: oil percentage; PC: protein content percentage; SL: seed length Path analysis In the partitioning of genotypic correlations, it was found that sucrose contents, followed by protein contents, had the strongest direct effect on 100-seed mass. However, oil percentage exerted the highest negative direct effect on 100-seed mass. Additionally, protein contents exhibited positive indirect effects on seed mass via oil percentage, indicating that accessions with higher protein contents may indirectly be selected through lower oil percentages. Similarly, path analysis based on phenotypic correlations revealed that sucrose contents, followed by protein contents, had positive direct effects on 100-seed mass (Table 6 ). Conversely, oil percentage exerted a direct negative effect on 100-seed mass, suggesting that breeding lines with high oil content may be selected through genotypes with lower 100-seed mass. (Table 7 ). Table 6 Partitioning of phenotypic coefficients in to direct and indirect effects through phenotypic path coefficient analysis affected by various phenological, quality and seed characteristics on dependent variable 100-seed mass (Residual 0.18) Traits DTF OA Oil% PC% SL SC DTF -0.13 -0.01 0.11 0.08 0.03 -0.02 OA 0.02 0.04 0.07 0.02 -0.0 0.02 Oil% 0.03 -0.01 -0.50 -0.16 -0.04 0.100 PC% -0.04 0.00 0.29 0.26 0.02 -0.14 SL -0.02 -0.00 0.13 0.04 0.13 -0.01 SC 0.01 0.00 -0.17 -0.13 -0.01 0.30 DTF: days to flowering; OA: oleic acid percentage; Oil%: oil percentage; PC: protein content percentage; SL: seed length; SC: sugar content percentage Table 7 Partitioning of phenotypic coefficients in to direct and indirect effects through phenotypic path coefficient analysis affected by various phenological various phenological, quality and seed characteristics on dependent variable 100-seed mass (Residual 0.21) Traits DTF OA Oil% PC% SL SC DTF -0.12 -0.01 0.11 0.06 0.02 -0.02 OA 0.02 0.04 0.07 0.01 -0.01 0.01 Oil% 0.03 -0.01 -0.49 -0.11 -0.03 0.07 PC% -0.03 0.00 0.26 0.22 0.02 -0.10 SL -0.02 -0.00 0.12 0.03 0.13 -0.01 SC 0.01 0.00 -0.14 -0.09 -0.00 0.25 DTF: days to flowering; OA: oleic acid percentage; Oil%: oil percentage; PC: protein content percentage; SL: seed length; SC: sugar content percentage Biplot analysis The genetic variation within the 71 germplasm accessions was characterized into several components based on the traits under study. The first component accounted for 34.5% of the total genetic variation, followed by the second component carrying about 17.8% of the variation (Fig. 1 ). Therefore, it was concluded that the first two factors cumulatively carried 52% of the total genetic variation, thus highlighting the importance of selecting key traits within each component to capture genetic variability within the germplasm. Figure 2 illustrates the relative contribution of various traits within each component. Percent oil and protein contents were relatively important traits in the first component, followed by 100-seed weight. In contrast, oleic acid, days to flowering, and 100-seed mass were traits with high relative contribution in the second component. Furthermore, traits were partitioned into two major groups, as indicated by Fig. 3 . The first group included traits such as 100-seed mass, seed length, percent oil, and days to flowering, which may have positive relationships. On the other hand, the second group included sugar contents and percent oil, suggesting that accessions with higher sugar content may also contain accessions with higher oil percentage. Biplot analysis conducted using R software further characterized the accessions based on their traits (Fig. 4 ). For instance, accessions such as ‘HA-61’, ‘Peredovik’, and ‘HA-89’ exhibited high oil contents, while accessions like ‘Hybrid 100’, ‘GOR101’, and ‘Odesskijj112’ had high sugar contents. Accession ‘Yawn’ showed comparatively higher oleic acid contents than others. Additionally, accessions ‘Vinimik 6931’ and ’HA 305’ displayed high 100-seed mass, while ‘Comet’ and H. × multiflorous exhibited higher seed length. Accession ‘Garissol’ showed higher days to flowering, indicating a later maturity. Combining ability analysis This analysis aimed to determine the type of gene action involved in the studied traits and formulate breeding strategies for enhancing target traits. Moreover, GCA effects were estimated to assess the breeding value of the selected plant material. A good general combiner carries lesser genetic load and may produce more uniform offspring when mated with a tester population. Combining ability analysis of the selected accessions revealed significant variation for all traits due to crosses, lines, and line × tester interactions (Table 8 ). Significant values indicated that crosses and selected lines significantly differed for the traits under study (Table 8 ). Mean values of seed yield per head and head diameter are presented in Table 9 . ‘Comet’ exhibited the highest seed yield and head diameter across all crosses, while ‘Vniimk 8931’ showed the lowest trait values in all crosses (Table 9 ). ‘Odeskij 113’ and ‘Tenisseiä displayed higher values of seed yield when crossed with the tester ‘Universal’ (Table 9 ). For 100-seed mass, ‘Comet’ displayed the highest mean value across all crosses, while ‘HA-305’ exhibited the highest 100-seed mass following the ‘HA-292’ tester (Table 10 ). ‘Tenissiei’ also demonstrated high 100-seed mass following its cross with tester ‘1-29444’. The cross of ‘Comet’ with ‘Universal’ also resulted in high seed mass (Table 10 ). GCA effects were estimated following a line × tester design (Table 11 ). ‘Comet’ showed the highest GCA effects for seed yield per plant, head diameter, and 100-seed mass (Table 11 ). ‘G.OR.104’ was a positive general combiner for seed yield per plant but a negative combiner for other traits. ‘Tenissiei’ exhibited positive GCA for all traits under study. Among the testers, ‘Universal’ displayed positive GCA for seed yield per plant, while ‘HA-292’ exhibited positive GCA for head diameter (Table 11 ). Table 8 Analysis of variance for the yield related traits following line × tester mating design for the accessions selected on the basis of traits related to the confectionary sunflower Source of variation Degrees of freedom Mean sum of square 100-seed mass Head diameter Seed yield per plant Replications 2 2.07* 2.57 NS 12.07 NS Crosses 17 5.64* 35.05** 780.71** Lines 5 12.37* 103.46** 2454.63** Testers 2 0.57 NS 2.46 NS 132.91* Lines × Testers 10 3.28* 7.37* 71.82* Error 34 1.47 1.86 34.58 Total 53 Genotypic variance 1.19 10.83 256.21 Phenotypic variance 2.65 12.69 290.80 Environment variance 1.47 1.87 34.58 Heritability 0.48 0.85 0.88 NS insignificant (P ≥ 0.05), * significant when probability (P ≤ 0.05) and ** highly significant when probability (P ≤ 0.01). Table 9 Mean values for the seed yield head -1 and head diameter following the line × tester mating design for the accessions selected on the basis of traits related to the confectionary sunflower Seed yield head -1 Head diameter (cm) Testers Lines ‘HA-292’ ‘I-29444’ ‘Universal’ ‘HA-292’ ‘I-29444’ ‘Universal’ ‘Comet’ 138.67 135.33 143.00 26.33 27.67 27.33 ‘G.O.R. 104’ 116.37 126.67 122.67 18.67 20.67 18.33 ‘HA-305’ 115.00 111.00 121.33 24.33 20.67 22.67 ‘Odeskij 113’ 110.67 116.00 132.67 21.33 19.00 20.33 ‘Tenissei’ 124.00 119.00 132.67 21.67 22.33 25.33 ‘Vniimk 8931’ 93.67 88.33 84.67 19.33 17.00 16.33 Table 10 Mean values for 100-seed mass following the line × tester mating design for the accessions selected on the basis of traits related to the confectionary sunflower Testers Lines ‘HA-292’ ‘I-29444’ ‘Universal’ ‘Comet’ 12.33 12.67 13.00 ‘G.O.R. 104’ 8.33 11.33 9.67 ‘HA-305’ 13.00 11.00 12.00 ‘Odeskij 113’ 10.33 10.00 10.33 ‘Tenissei’ 11.67 13.67 11.00 ‘Vniimk 8931’ 11.00 10.00 11.00 Table 11 General combining ability effects for yield related traits estimated following the line × tester mating design for the accessions selected on the basis of traits related to the confectionary sunflower Seed yield per plamt Head diameter 100-seed mass Lines ‘Comet’ 21.19 5.48 1.43 ‘G.O.R. 104’ 4.185 -2.41 -1.46 ‘HA-305’ -2.04 0.93 0.76 ¨Odeskij 113’ -1.82 -1.41 -1.01 ‘Tenissei’ 7.41 1.48 0.87 ¨Vniimk 8931’ -28.93 -4.07 -0.57 Testers ‘HA-292’ -1.37 0.32 -0.13 ‘I-29444’ -1.76 -0.41 0.20 ‘Universal’ 3.13 0.09 -0.07 Discussion The screening of sunflower germplasm for confectionary traits revealed significant variability among the traits, with GCV ranging between 9–29%. Previous research also reported broad genetic variability among confectionary sunflower cultivars (Velasco et al. 2014 ; Aldemir et al. 2016 ; Chen et al. 2020 ), thereby highlighting the potential for further improvement in this crop. A study on the landraces sampled in Spain for confectionary sunflower revealed wide variability within sunflower functional molecules within this germplasm (Velasco et al. 2014 ). The seven accessions that were obtained from Cordoba had high phytosterol contents and obtained accessions were valuable source for improvement of this crop. Another study on newly developed confectionary sunflower types indicated an achene length of 15.2 mm and width of 8.50 mm, with dehulled seed oil contents of 49% and protein contents of 28% (Sandrinelli et al. 2022 ). The fatty acid profile showed high levels of linoleic acid (56%) followed by oleic acid (34%). Correlation and path coefficient analyses revealed associations between various confectionary traits. For instance, 100-seed mass was positively correlated with protein contents, seed length, and oleic acid, suggesting that accessions with high 100-seed mass tend to have higher protein contents, seed length, and oleic acid contents. Additionally, genotypes with high sugar contents may be selected to develop confectionary sunflower varieties with high seed mass. Previous research emphasized protein contents as a crucial criterion for the selection of confectionary sunflower (Hladni et al. 2015 ; Hladni et al. 2016 ; Vedmedeva et al. 2023 ). Path coefficient analysis has been used to determine the direct and indirect effects of seed-related traits on protein yield (Hladni et al. 2015 ). Notably, oil contents showed a significant negative direct effect on protein yield (Hladni et al. 2015 ). Reinert et al. ( 2020 ) showed a negative association between seed length and floret traits such as pollen tube length. Combining ability analysis of selected confectionary sunflower types revealed high heritability estimates and a preponderance of dominance variance for traits such as 100-seed mass and head diameter, suggesting the potential for recurrent selection to develop breeding lines with high seed mass. Accessions showing positive GCA effects may be preferred for breeding high-yielding confectionary sunflower hybrids (Li et al. 1995 ; Ćiric et al. 2013 ; Rauf, 2019 ). For example, ‘Comet’ exhibited the highest GCA effects for seed yield per plant, head diameter, and 100-seed mass. ‘Tenissiei’ also showed positive GCA for all traits under study. Accessions like ‘Universal’ and ‘HA-292’ displayed positive combining ability for specific traits, thus offering valuable insights for breeding programs aimed at enhancing confectionary sunflower cultivars. Declarations Author Contribution SR did the planning and mobilised resources for this research. AL, SR and MN did the field research and data analysis. Results interpretation was done together by AL, SR, MN and RO. SR provided the first draft, whose main editing was done by RO. Alll authors contributing to this manuscript accept this last version. References Aldemir M, Tan AS, Altunok A (2016) Performance of some confectionary sunflower ( Helianthus annuus L.) varieties in Aegean region of Turkey. In Proceedings, 19th International Sunflower Conference, 29 May–3 June 2016, Edirne, Turkey. International Sunflower Association, Paris, France. pp 548–555. Chen S, Zhang H, Huang Y, Cai R, Mei G, Cao D, Ruan G (2020) Difference and genetic analysis of main agronomic characters between oil sunflower and edible sunflower in Zhejiang. Field Crop 3:1–12. DOI: 10.5376/fc.2020.03.0007 Ćiric M, Jocic S, Cvejic S, Jockovic M, Čanak P, Marinkov R, Ivanovic M (2013) Combining abilities of new inbred lines of sunflower. Genetika 45: 289–296. Demir I (2021) The evaluation of confectionery sunflower ( Helianthus annuus L.) cultivars and populations for yield and yield components. Intl J Agric Environ Biores 6: 179–186. Feng J, Jan CC, Seiler G (2022) Breeding, production, and supply chain of confection sunflower in China. OCL 29: 11. DOI: 10.1051/ocl/2022004 González-Pérez S (2015) Sunflower proteins. In Martínez-Force, E, Dunford NT, Salas JJ (eds) Sunflower. AOCS Press, Champaign, Illinois. pp 331–393. FAO (2022) Food and Agriculture Organization, Rome Italy. Data retrieved from https://www.fao.org/faostat/en/#data/QCL (Accessed on 15th March 2024) Hladni N, Jocić S, Miklič V, Miladinović D, Zorić M, Radić V (2016) Correlations and path coefficient analysis of confectionery sunflower ( Helianthus annuus L.). In Proceedings, 19th International Sunflower Conference, 29 May–3 June 2016, Edirne, Turkey. International Sunflower Association, Paris, France. pp 478–478. Hladni N, Miklič V, Mijić A, Jocić S, Miladinović D (2015) Correlation and path coefficient analysis for protein yield in confectionary sunflower ( Helianthus annuus L.). Genetika 47: 811–818. Hladni N, Terzić S, Mutavdžić B, Zorić M (2017) Classification of confectionary sunflower genotypes based on morphological characters. J Agric Sci 155(10): 1594–1609. Li YM, Chaney RL, Schneiter AA, Miller JF (1995) Combining ability and heterosis estimates for kernel cadmium level in sunflower. Crop Sci 35: 1015–1019. Nenova N, Drumeva M (2012) Investigation on protein content and amino acid composition in the kernels of some sunflower lines. Helia 35: 41–46. Pekcan V, Goksel E, Yilmaz İM, Yalcin K (2015) Developing confectionery sunflower hybrids and determination of their yield performances in different environmental conditions. Ekin J Crop Breed Genet 1: 47–55. Rauf S (2019) Breeding strategies for sunflower ( Helianthus annuus L.) genetic improvement. In: Al-Khayri JM (ed) Advances in Plant Breeding Strategies: Industrial and Food Crops, 6th ed. Springer, New York. pp 637–673. Reinert S, Gao Q, Ferguson B, Portlas Z M, Prasifka JR, Hulke BS (2020) Seed and floret size parameters of sunflower are determined by partially overlapping sets of quantitative trait loci with epistatic interactions. Mol Genet Genom 295: 143–154. Sandrinelli TR, Alvarez D, Silva MP, Aguilar R, Pazos A, Balzarini M, Martínez MJ (2022) Morpho-chemical characterization of new confectionery sunflower ( Helianthus annuus L.) genotypes from Argentina. AgriScientia 39: 45–56. Vedmedeva K, Nosal O, Poliakova I, Machova T (2023) Correlations of confectionary seed traits in different head zones sunflower. Helia 46: 215–231. Velasco L, Fernández-Cuesta Á, Fernández-Martínez JM (2014) Variability of seed quality traits in a collection of Spanish landraces of confectionery sunflower. Crop Past Sci 65: 242–249. Warburton ML, Rauf S, Marek L, Hussain MM, Ogunola O, de Jesus J, Gonzalez S (2017) The use of crop wild relatives for crop improvement. Crop Sci 57: 1227–1240. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 02 Sep, 2024 Read the published version in Genetic Resources and Crop Evolution → Version 1 posted Editorial decision: Revision requested 12 Aug, 2024 Reviews received at journal 12 Aug, 2024 Reviews received at journal 01 Aug, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers agreed at journal 15 Jul, 2024 Reviewers agreed at journal 01 Jul, 2024 Reviewers agreed at journal 26 Jun, 2024 Reviewers invited by journal 22 Jun, 2024 Editor assigned by journal 29 May, 2024 Submission checks completed at journal 17 Mar, 2024 First submitted to journal 17 Mar, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4118769","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280643028,"identity":"e2af121b-d064-4683-9f82-0c52cfe06d23","order_by":0,"name":"Abdul Latif","email":"","orcid":"","institution":"University of Sargodha","correspondingAuthor":false,"prefix":"","firstName":"Abdul","middleName":"","lastName":"Latif","suffix":""},{"id":280643029,"identity":"f7ce21cc-b195-413b-8516-c7dba31662a8","order_by":1,"name":"Saeed Rauf","email":"","orcid":"","institution":"University of Sargodha","correspondingAuthor":false,"prefix":"","firstName":"Saeed","middleName":"","lastName":"Rauf","suffix":""},{"id":280643030,"identity":"c62e268a-d316-4df0-ad6f-d43c6a1dc1c4","order_by":2,"name":"Maham Nazish","email":"","orcid":"","institution":"University of Sargodha","correspondingAuthor":false,"prefix":"","firstName":"Maham","middleName":"","lastName":"Nazish","suffix":""},{"id":280643031,"identity":"ca68c7b3-a683-4ca2-8348-50baeb4bce01","order_by":3,"name":"Rodomiro Ortiz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYHACAzACgwQGGwY+UrQwNiQwpDGwEaeFAaqFgeEwYS3m7c0bH90ouCfHwN77/MGDP+ft2SQSmD98wKNF5syxYuMcg2JjBp7jhg0JPLcT2yQS2CRn4NEiIZFjJp1jkJDYIJEG9IvE7QSgLWzMPMRrMTgHdtjnP8RrSTjACHQYgzQ+70vwgP2SYMzGc4xxRsKB5MQ2nodtkj34tLA3b3yc8ydBjp+9jeHjjz929vzsyYc//MBnDQwgRQcofkbBKBgFo2AUUAQAaZhCJgAaMNQAAAAASUVORK5CYII=","orcid":"","institution":"Swedish University of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Rodomiro","middleName":"","lastName":"Ortiz","suffix":""}],"badges":[],"createdAt":"2024-03-17 21:29:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4118769/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4118769/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10722-024-02136-7","type":"published","date":"2024-09-02T15:57:40+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":53063399,"identity":"c2c7e020-8939-4f4c-967b-4535dbbd187f","added_by":"auto","created_at":"2024-03-20 07:53:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":14860,"visible":true,"origin":"","legend":"\u003cp\u003eCharacterization of relative contribution of various components to the total genetic variability within germplasm.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4118769/v1/5a3cdb56e56c93def6021c4b.png"},{"id":53063509,"identity":"c08ef77a-b2f8-4387-9b92-2d7f5d216758","added_by":"auto","created_at":"2024-03-20 07:53:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":17697,"visible":true,"origin":"","legend":"\u003cp\u003eRelative contribution of each trait within each component\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4118769/v1/9409bbc459795b164d546b6b.png"},{"id":53063395,"identity":"8b65c0b1-df28-4314-a750-e1d1170d17d5","added_by":"auto","created_at":"2024-03-20 07:53:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":24406,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship among traits under the study\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4118769/v1/c1509c2b6ef69f0314b1b029.png"},{"id":53063544,"identity":"93ecfcb4-974b-4a35-8ec3-9ca6920248ba","added_by":"auto","created_at":"2024-03-20 07:53:49","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":489328,"visible":true,"origin":"","legend":"\u003cp\u003eBiplot analysis partitioned accessions on the basis of their relative importance with respect to the traits\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4118769/v1/33b1413d41bc0b3f70342ad5.jpeg"},{"id":64186030,"identity":"f9a9bfd8-ee56-4b20-8b28-9dc599f660db","added_by":"auto","created_at":"2024-09-09 16:23:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1544653,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4118769/v1/3ff0497e-a31f-4de5-aadc-86e679660dc9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of confectionary sunflower germplasm accessions and their derived hybrids","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e L.) is a versatile crop cultivated for its oilseed, confectionary products, snacks, and as food for birds and small animals. It ranks as the fourth largest oilseed crop globally, with cultivation spanning over 70 countries (Rauf \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). According to FAO data from 2022, the total harvestable area dedicated to sunflower cultivation reached 29.25\u0026nbsp;million hectares, yielding a total production of 54.3\u0026nbsp;million metric tons. Notably, countries with the largest cultivated areas of sunflower include the Russian Federation, Ukraine, Argentina, China, India, and the USA (FAO, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSunflower cultivars are categorized into distinct types, broadly classified into two groups: oilseed types and non-oilseed types. Non-oilseed types encompass confectionary cultivars characterized by large seed and low oil content. Confectionary sunflower is prominently cultivated in Eastern European countries such as Turkey, Russia, Romania, Bulgaria, and Ukraine (Aldemir et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSunflower seeds are commonly crushed to extract oil, leaving behind seed meal that serves as a byproduct. This meal can be repurposed for various applications such as animal feed or incorporated directly into human consumption in confectionary and baking products. However, sunflower meal is frequently considered of low value due to its relatively low energy and protein content, alongside the presence of anti-nutritional components (Gonz\u0026aacute;lez-P\u0026eacute;rez, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eComparative analysis across various species indicates that sunflower meal typically contains a lower percentage of protein (approximately 30% crude protein) compared to cotton (approximately 42%) and soybean (approximately 50%). Consequently, there arises a necessity to enhance the protein content in sunflower seeds, possibly at the expense of polysaccharides, in order to increase the value of the hull contents. Improving hulling efficiency by reducing fiber content not only augments protein levels but also positively impacts oil extraction.\u003c/p\u003e \u003cp\u003eResearch indicates significant variability in protein and oil contents among different sunflower elite germplasm, with maximum protein levels ranging from 35\u0026ndash;50% (Warburton et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This variability is primarily attributed to differences in hull contents. Thus, enhancing protein content entails reducing fiber content and improving hull composition. Additionally, within sunflower germplasm, variation is observed concerning anti-nutritional components such as chlorogenic acid. High protein content is correlated with a higher kernel-to-hull ratio and reduced fiber content, which in turn enhances hull digestibility (Demir, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Improving the nutritional value of sunflower meal by increasing its protein content is a pivotal breeding objective. While sunflower meal provides all essential amino acids except lysine, efforts can be directed towards enhancing lysine content by leveraging initial variations within elite or breeding lines (Nenova \u0026amp; Drumeva, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConfectionary sunflower, characterized by large seed size, low oil, and high polysaccharide and protein contents, serves various purposes. Its seeds are suitable for roasting or snacks when inshelled, and for baking, bird feed, and flour used in various bakery products when shelled. Roasted sunflower seeds offer a cost-effective alternative to nuts and are often served during social gatherings. Confectionary sunflower seeds can be identified based on their seed coat color, which may be albino, striped, or colored, and they are typically larger in size. Confectionary sunflower seeds exhibit a higher 100-seed mass, approximately 8\u0026ndash;13 g per 100 seeds, compared to oilseed types, which typically contain only 4\u0026ndash;6 g per 100 seeds. Dehulling of confectionary sunflower seeds is easier compared to oilseed types, and the kernels are loosely packed. Sunflower breeders generally select confectionary lines based on criteria such as seed yield potential, protein content, ease of dehulling, hull/kernel ratio, among others. Key characteristics of confectionary sunflower cultivars are seed yield potential (approximately 6 metric tons per hectare), plant height (approximately 175 cm), protein content (\u0026gt;\u0026thinsp;25%), hull ratio (\u0026lt;\u0026thinsp;25%), kernel ratio (\u0026gt;\u0026thinsp;60%), oil content (30\u0026ndash;35%), ease of dehulling, and sweet and nutty taste (Rauf et al., 2019).\u003c/p\u003e \u003cp\u003eThe primary objectives of confectionary sunflower breeding align with those of oilseed types; however, there are distinct differences in seed morphology and biochemical traits. Confectionary types exhibit characteristics such as larger seed size, higher protein contents, ease of dehulling, and colorful testa (Feng et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The testa of confectionary sunflower is easily dehulled, and the kernel is removed intact during shelling (Feng et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Experimental confectionary hybrids have demonstrated significant commercial heterosis, particularly for traits like seed yield, head diameter, and seed weight (Pekcan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notably, two confectionary hybrids (09 TR\u0026Ccedil; 003 and 09 TR\u0026Ccedil; 004) were submitted for registration based on their superior performance in terms of seed yield and uniformity (Pekcan et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Considering this understanding, a research study was initiated to assess genetic variation in sunflower accessions concerning traits relevant to confectionary sunflower and their potential utilization in a hybrid breeding program. This initiative aims to capitalize on the distinct characteristics of confectionary sunflower to develop improved hybrids with desirable traits.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eIn 2023, experiments were conducted at the Department of Plant Breeding \u0026amp; Genetics, College of Agriculture, University of Sargodha (Pakistan). The germplasm used in the study was obtained by ordering through the Germplasm Resources Information Network (GRIN) of the United States Department of Agriculture in 2020. The germplasm was multiplied and maintained for two years before being subjected to evaluation trials. The list of germplasm utilized in the study is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of the germplasm accessions used in the study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOrigin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccession\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOrigin\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransvaal, South Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHA 288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Dakota, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSSR Franslever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer, Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHA 292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Dakota, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransvaal, South Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHA 305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Dakota, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShort Russian MN 34% oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransvaal, South Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHA 273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUniversal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTransvaal, South Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHA 294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Dakota, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eA 9345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHA 298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Dakota, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC 1957\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHA 299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Dakota, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTenissei\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHA 801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Dakota, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV 8883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHA 61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTexas, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSaratovski MN 49% oil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer, Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHA 89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTexas, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. 513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHA 304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Dakota, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTurkey\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHA 271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnited States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI-7999-V. 56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUruguay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRHA 293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Dakota, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBeacon\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSouth Africa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e'Peredovik'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBulgaria\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI-29444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUruguay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e'Novi Sad 61'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormer S\u0026amp;M\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYugovostok\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVniimk 6540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormer, Soviet Union\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeredovick\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e'Vniimk 8931'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFormer, Soviet Union\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVniimk 6540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e'Zelenk 61'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVniimk 8883\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e'VR Bulgarian'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBulgaria\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChernianka 35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e'DDR 1'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUncertain\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGirassol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS\u0026atilde;o Paulo, Brazil\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e'MN17'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNorth Dakota, United States\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeredovic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJupiter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack Sayar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e'Yawne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eH.\u003c/em\u003e \u0026times; \u003cem\u003emultiflorus\u003c/em\u003e 33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePortugal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRussian Giant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eManfredi INTA (3-WAY X)11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArgentina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSSR Vniimk 6540 '66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG.O.R. 104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSSR Armavirskij3497'66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG.O.R. 101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUSSR Vniimk 8931 '66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e803495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eB-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Serbia and Montenegro (S\u0026amp;M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e803496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD-75-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer S\u0026amp;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHybrid 100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN 3/2\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer S\u0026amp;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e803504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePO 6/4\u0026thinsp;\u0026minus;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer S\u0026amp;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e'Sunrise'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR-201/4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer S\u0026amp;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e'Dukn'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZimbabwe\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV 8931 2/2\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer S\u0026amp;M\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOdesskij 19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeredovik ul\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVoronezskij 151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOdesskij 113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Soviet Union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePlant material and sowing of experiments\u003c/h2\u003e \u003cp\u003eExperimental trials were conducted by initiating a field trial on 20th February 2022 at the College of Agriculture, University of Sargodha. On 20th February 2022, a total of 71 germplasm accessions from various countries were sown to screen potential breeding lines with useful genetic variations related to confectionery sunflower at the College of Agriculture, University of Sargodha. These selected plants may be further utilized in the development of inbred lines and hybrid breeding. Additionally, they may be exploited in the breeding program to estimate the general combining ability (GCA) in sunflower.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCrop husbandry\u003c/h2\u003e \u003cp\u003eThe experiments were sown on raised beds with a plant-to-plant distance of 24 cm and a row distance of 75 cm. Weed control was implemented using pre-emergence pesticide (dual gold\u0026reg;). Each breeding line was sown in a single row spanning approximately 6 meters. Initially, three seeds were manually dibbled into each hole, with subsequent thinning to a single plant per hole upon germination (approximately 14 days after sowing). The breeding material received fertilization with 50 kg per acre of diammonium phosphate and 25 kg per acre of sulfate of potash. To prevent insect damage, a recommended pesticide, lufenuron with dichlorobenzine at a rate of 30 ml per 20 liters, was sprayed. The experiments were irrigated with canal water to prevent water stress, and insect control was implemented with the recommended insecticide following pest scouting conducted by an entomologist.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMaintenance of germplasm\u003c/h2\u003e \u003cp\u003eEach plant was individually tagged and covered with a net bag to prevent pollen contamination from pollinators. To maintain a breeding line, pollen from two selected neighboring plants was collected and used for pollination, a process known as sib mating. Selected plants were pollinated until all the anthers were withered, ensuring controlled pollination and genetic purity within the breeding lines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eCrossing for combining ability test\u003c/h2\u003e \u003cp\u003eParental lines were carefully selected based on all traits related to 100-seed mass, sugar contents, protein contents, and seed length (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Six breeding lines ('HA-305', 'VNIIMIK 8931', 'TENISSIE', 'Odeskij 113', and 'Vniimk89') were mated with three male lines ('HA-292', 'I-29444', and 'Universal'). This mating strategy aimed to combine desirable traits from the female and male parental lines, thereby creating potential hybrids with improved characteristics for further evaluation and selection in the breeding program.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean values of the selected accessions used in the line \u0026times; tester analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100-seed weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDays to flowering\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOleic acid (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil percentage\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProtein content\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSeed length\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSugar content\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Comet\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e65.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e114.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Universal\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e102.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Tenissei\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e113.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Odesskij 113\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e42.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e135.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;HA 292\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e34.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e26.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e124.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;HA 305\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e116.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Vniimk 8931'\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e61.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e106.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Comet\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e123.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;I-29444\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e74.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e25.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e109.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDevelopment of half sib offspring\u003c/h2\u003e \u003cp\u003eThey were developed through the process of crossing the female lines with the male lines. Prior to sunrise, the female lines were manually emasculated to remove their anthers, ensuring that only the desired pollen from the male lines would be used for pollination. Both the female and male lines' capitula were covered with net bags to prevent pollen contamination by insect pollinators. The female lines were pollinated until all the stigmas were withered. After pollination, seeds from each crossed head were manually threshed once the plants reached physiological maturity. This was indicated by the heads turning brown. The seeds were then dried and cleaned to remove all chaff and stored at room temperature until reaching physiological maturity. In total, there were 18 crosses made, resulting from combinations of 6 female lines and 3 male lines. These crosses aimed to create a diverse set of hybrids for further evaluation and selection in the breeding program.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of half sib offspring\u003c/h2\u003e \u003cp\u003eThe offspring were sown during the autumn season on 18th August 2023. The trial was conducted in loam soil, which was well-prepared beforehand. Raised beds were utilized for sowing the half-sib offspring. The soil was fertilized with 12 kg of diammonium phosphate and 5 kg of sulfate of potash. All materials were sown in single rows spanning 6 meters, with a plant-to-plant distance of 22 cm and a row-to-row distance of 60 cm. Each seed was manually dibbled into the soil. To control weed growth, the field was sprayed with the pre-emergence herbicide \"Dual Gold\" (metolachlor). Irrigation was administered when the soil moisture content fell below the field capacity, which was approximately 18% on a weight basis. Additionally, the crop was sprayed with chlorpyrifos at a rate of 250 g per acre to prevent insect infestation in the field.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTrait evaluation\u003c/h2\u003e \u003cp\u003eThe following traits were evaluated in all accessions according to the details provided:\u003c/p\u003e \u003cp\u003e \u003cb\u003e100-seed mass\u003c/b\u003e. A total of 100 seeds were counted using a digital seed counter and subsequently weighed on the seed counter.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDays to 50% flowering\u003c/b\u003e. Each row was tagged, and the number of days to anthesis for 50% of the plants in each row was observed and recorded.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAchene oil content\u003c/b\u003e. Achenes weighing 10 g were utilized to determine the oil contents using a Soxhlet apparatus. Kernels were crushed gently and placed in a thimble for oil extraction through hexane until all oil was extracted from the seed lot. The oil contents of the kernels were calculated using the formula:\u003c/p\u003e \u003cp\u003eAchene oil contents = [(Achene mass before extraction \u0026ndash; Achene mass after oil extraction)/Achene mass]\u003c/p\u003e \u003cp\u003e \u003cb\u003eFatty acid profile\u003c/b\u003e. Oil was extracted manually using a small expeller from a 40 g seed sample, and the expelled oil was cleaned to remove any seed material. Oil samples were thereafter refrigerated at 4\u0026deg;C for further analysis. α-tocopherol was determined using high-performance liquid chromatography (HPLC). Oleic acid contents were determined through gas chromatography. Oil samples were methylated using KOH, and methylated fatty acids were extracted with hexane. Analysis was performed on a fused capillary column with a flame ionizing detector.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSugar content\u003c/b\u003e. It was determined using the phenol-sulfuric acid method. Samples were treated with phenol-sulfuric acid solution and incubated for 30 minutes before being cooled to room temperature. Standard curves of glucose and sucrose were prepared, and absorbance was measured using a spectrophotometer at 490 nm.\u003c/p\u003e \u003cp\u003eEach of the above traits was evaluated meticulously to gather comprehensive data for further analysis and selection of superior accessions in the breeding program. At physiological maturity, the following traits were evaluated in the half-sib offspring for further combing ability analysis:\u003c/p\u003e \u003cp\u003e \u003cb\u003eSeed yield head\u003c/b\u003e \u003csup\u003e \u003cb\u003e-1\u003c/b\u003e \u003c/sup\u003e. The heads were manually harvested and dried under shade to ensure proper drying without exposure to direct sunlight. After drying, the heads were manually threshed to separate the seeds from the heads. The threshed seeds were then cleaned to remove any debris or chaff. Subsequently, the cleaned seeds were placed in kraft paper bags and dried to achieve a moisture content of 12%. This drying process helps preserve the quality of the seeds during storage. To calculate the seed yield per head, all the harvested seeds from a single head were measured using a digital balance. This measurement provided accurate data on the quantity of seeds obtained from each individual head, allowing for the assessment of seed yield potential.\u003c/p\u003e \u003cp\u003e \u003cb\u003e100-seed mass\u003c/b\u003e. A total of 100 seeds were counted using a digital counter to ensure consistency in the sample size. Subsequently, the mass of these 100 seeds was measured using a digital balance to obtain accurate data on seed mass. This process allows for precise assessment of seed size and weight, which are important factors in determining seed quality and yield potential.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHead diameter.\u003c/b\u003e The diameter of the capitula was measured using a measuring tape from the center of the capitula to the front side, and all data were recorded in centimeters. This measurement provides valuable information about the size and morphology of the capitula, which can influence seed production and overall plant performance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents mean values of various traits, ranges, genotypic and phenotypic coefficient of variation (GCV and PCV) showing variability among the accessions for all traits. The highest variation was observed in seed length, with GCV and PCV estimates of 29% and 31% respectively. The range for seed length was 1.39\u0026ndash;2.40 cm among the accessions. Considerable variation was also observed for 100-seed mass, with a range of 3.4\u0026ndash;14.5 g and GCV and PCV estimates of 27%. Sugar content exhibited the lowest variation, with GCV and PCV estimates of 9%. Seed sugar contents ranged between 120\u0026ndash;139 mg g\u003csup\u003e-1\u003c/sup\u003e. Seed oil contents range was 34\u0026ndash;49%, with GCV and PCV estimates at 19%. Protein contents range was 16\u0026ndash;19%, while oleic acid percentage range was 39\u0026ndash;53%, thus indicating that the accessions were generally suitable for confectionary purposes. Moreover, the range of oleic acid was low to medium, and no high oleic acid accessions were present within the exotic germplasm.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGermplasm statistics of seed quality traits based on 71 accessions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100-Seed mass (g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDays to flowering\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOleic acid (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil content (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProtein content (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSeed length (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSugar contents (mg g\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e62.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e120.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e98.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e52.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e49.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e138.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e102.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e20.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e31.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eGCV: genotypic coefficient variation; PCV: phenotypic coefficient of variation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eTrait associations\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the estimated genotypic correlations among various traits. The 100-seed mass exhibited a positive relationship with protein contents and seed length, indicating that larger seeds tend to have higher protein contents and longer lengths. Conversely, it showed a negative relationship with oil percentage, suggesting that accessions with higher seed masses tend to have lower oil percentages. Days to flowering displayed negative correlations with oil percentage and positive correlations with protein contents and seed length. These correlations suggest that accessions suitable for confectionary purposes tend to mature later. Seed length showed positive relationships with 100-seed mass and protein contents, indicating that longer seeds tend to have higher masses and protein contents The 100-seed mass exhibited a positive relationship with oleic acid and protein contents (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), thereby suggesting that larger seeds tend to have higher oleic acid and protein contents. It also displayed a negative relationship with oil percentage, reinforcing the earlier observation that accessions with higher seed masses tend to have lower oil percentages. Days to flowering had a positive relationship with both protein contents and seed length, while displaying negative relationships with oil percentage. This indicates that accessions with later flowering times tend to have higher protein contents and seed lengths, but lower oil percentages.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenotypic correlation coefficient among traits related seed quality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100-SM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDTF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePC%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100-SM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.16*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.57**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.40**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.28**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.06\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.18\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.23**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.16**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.16*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.18\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.07\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.57**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.23**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.59**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.28**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.40**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.59**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.17*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.16*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.28**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.34**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.48**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.05\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eNS\u003c/sup\u003e * and \u0026lsquo;\u0026rsquo; indicate non-significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05), * significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) and highly significant (P\u0026thinsp;\u0026le;\u0026thinsp;0.01) genotypic correlations, respectively. They were tested using a two-tail t test with degree of freedom as number of genotypes \u0026ndash; 2. 100-SM: seed mass; DTF: days to flowering; OA: oleic acid percentage; Oil%: oil percentage; PC: protein content percentage; SL: seed length\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenotypic correlation coefficient among traits related seed quality\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e100-SM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDTF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOil%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePC%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e100-SM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.55**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.37**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.23**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.04\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.13\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.06\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.06\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.55**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.22**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.25**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.37**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.27**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.54**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.14*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.06\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.24**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.07\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.31**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csup\u003eNS\u003c/sup\u003e * and \u0026lsquo;\u0026rsquo; indicate non-significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.05), * significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05) and highly significant (P\u0026thinsp;\u0026le;\u0026thinsp;0.01) genotypic correlations, respectively. They were tested using a two-tail t test with degree of freedom as number of genotypes \u0026ndash; 2. 100-SM: seed mass; DTF: days to flowering; OA: oleic acid percentage; Oil%: oil percentage; PC: protein content percentage; SL: seed length\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003ePath analysis\u003c/h2\u003e \u003cp\u003eIn the partitioning of genotypic correlations, it was found that sucrose contents, followed by protein contents, had the strongest direct effect on 100-seed mass. However, oil percentage exerted the highest negative direct effect on 100-seed mass. Additionally, protein contents exhibited positive indirect effects on seed mass via oil percentage, indicating that accessions with higher protein contents may indirectly be selected through lower oil percentages. Similarly, path analysis based on phenotypic correlations revealed that sucrose contents, followed by protein contents, had positive direct effects on 100-seed mass (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Conversely, oil percentage exerted a direct negative effect on 100-seed mass, suggesting that breeding lines with high oil content may be selected through genotypes with lower 100-seed mass. (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePartitioning of phenotypic coefficients in to direct and indirect effects through phenotypic path coefficient analysis affected by various phenological, quality and seed characteristics on dependent variable 100-seed mass (Residual 0.18)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eDTF: days to flowering; OA: oleic acid percentage; Oil%: oil percentage; PC: protein content percentage; SL: seed length; SC: sugar content percentage\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePartitioning of phenotypic coefficients in to direct and indirect effects through phenotypic path coefficient analysis affected by various phenological various phenological, quality and seed characteristics on dependent variable 100-seed mass (Residual 0.21)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDTF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOil%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePC%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOil%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePC%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eDTF: days to flowering; OA: oleic acid percentage; Oil%: oil percentage; PC: protein content percentage; SL: seed length; SC: sugar content percentage\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eBiplot analysis\u003c/h2\u003e \u003cp\u003eThe genetic variation within the 71 germplasm accessions was characterized into several components based on the traits under study. The first component accounted for 34.5% of the total genetic variation, followed by the second component carrying about 17.8% of the variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Therefore, it was concluded that the first two factors cumulatively carried 52% of the total genetic variation, thus highlighting the importance of selecting key traits within each component to capture genetic variability within the germplasm. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the relative contribution of various traits within each component. Percent oil and protein contents were relatively important traits in the first component, followed by 100-seed weight. In contrast, oleic acid, days to flowering, and 100-seed mass were traits with high relative contribution in the second component. Furthermore, traits were partitioned into two major groups, as indicated by Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The first group included traits such as 100-seed mass, seed length, percent oil, and days to flowering, which may have positive relationships. On the other hand, the second group included sugar contents and percent oil, suggesting that accessions with higher sugar content may also contain accessions with higher oil percentage. Biplot analysis conducted using R software further characterized the accessions based on their traits (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). For instance, accessions such as \u0026lsquo;HA-61\u0026rsquo;, \u0026lsquo;Peredovik\u0026rsquo;, and \u0026lsquo;HA-89\u0026rsquo; exhibited high oil contents, while accessions like \u0026lsquo;Hybrid 100\u0026rsquo;, \u0026lsquo;GOR101\u0026rsquo;, and \u0026lsquo;Odesskijj112\u0026rsquo; had high sugar contents. Accession \u0026lsquo;Yawn\u0026rsquo; showed comparatively higher oleic acid contents than others. Additionally, accessions \u0026lsquo;Vinimik 6931\u0026rsquo; and \u0026rsquo;HA 305\u0026rsquo; displayed high 100-seed mass, while \u0026lsquo;Comet\u0026rsquo; and \u003cem\u003eH.\u003c/em\u003e \u0026times; \u003cem\u003emultiflorous\u003c/em\u003e exhibited higher seed length. Accession \u0026lsquo;Garissol\u0026rsquo; showed higher days to flowering, indicating a later maturity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCombining ability analysis\u003c/h2\u003e \u003cp\u003eThis analysis aimed to determine the type of gene action involved in the studied traits and formulate breeding strategies for enhancing target traits. Moreover, GCA effects were estimated to assess the breeding value of the selected plant material. A good general combiner carries lesser genetic load and may produce more uniform offspring when mated with a tester population. Combining ability analysis of the selected accessions revealed significant variation for all traits due to crosses, lines, and line \u0026times; tester interactions (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Significant values indicated that crosses and selected lines significantly differed for the traits under study (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). Mean values of seed yield per head and head diameter are presented in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. \u0026lsquo;Comet\u0026rsquo; exhibited the highest seed yield and head diameter across all crosses, while \u0026lsquo;Vniimk 8931\u0026rsquo; showed the lowest trait values in all crosses (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). \u0026lsquo;Odeskij 113\u0026rsquo; and \u0026lsquo;Tenissei\u0026auml; displayed higher values of seed yield when crossed with the tester \u0026lsquo;Universal\u0026rsquo; (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e). For 100-seed mass, \u0026lsquo;Comet\u0026rsquo; displayed the highest mean value across all crosses, while \u0026lsquo;HA-305\u0026rsquo; exhibited the highest 100-seed mass following the \u0026lsquo;HA-292\u0026rsquo; tester (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). \u0026lsquo;Tenissiei\u0026rsquo; also demonstrated high 100-seed mass following its cross with tester \u0026lsquo;1-29444\u0026rsquo;. The cross of \u0026lsquo;Comet\u0026rsquo; with \u0026lsquo;Universal\u0026rsquo; also resulted in high seed mass (Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e). GCA effects were estimated following a line \u0026times; tester design (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). \u0026lsquo;Comet\u0026rsquo; showed the highest GCA effects for seed yield per plant, head diameter, and 100-seed mass (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e). \u0026lsquo;G.OR.104\u0026rsquo; was a positive general combiner for seed yield per plant but a negative combiner for other traits. \u0026lsquo;Tenissiei\u0026rsquo; exhibited positive GCA for all traits under study. Among the testers, \u0026lsquo;Universal\u0026rsquo; displayed positive GCA for seed yield per plant, while \u0026lsquo;HA-292\u0026rsquo; exhibited positive GCA for head diameter (Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of variance for the yield related traits following line \u0026times; tester mating design for the accessions selected on the basis of traits related to the confectionary sunflower\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSource of variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDegrees of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eMean sum of square\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100-seed mass\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHead diameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSeed yield per plant\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReplications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.07*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12.07\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCrosses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.64*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.05**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e780.71**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLines\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.37*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e103.46**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2454.63**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTesters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.57\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.46\u003csup\u003eNS\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132.91*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLines \u0026times; Testers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.28*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.37*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71.82*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eError\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenotypic variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e256.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenotypic variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e290.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironment variance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeritability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eNS\u003c/sup\u003e insignificant (P\u0026thinsp;\u0026ge;\u0026thinsp;0.05), * significant when probability (P\u0026thinsp;\u0026le;\u0026thinsp;0.05) and ** highly significant when probability (P\u0026thinsp;\u0026le;\u0026thinsp;0.01).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean values for the seed yield head\u003csup\u003e-1\u003c/sup\u003e and head diameter following the line \u0026times; tester mating design for the accessions selected on the basis of traits related to the confectionary sunflower\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eSeed yield head\u003csup\u003e-1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c7\" namest=\"c5\"\u003e \u003cp\u003eHead diameter (cm)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTesters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLines\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lsquo;HA-292\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lsquo;I-29444\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lsquo;Universal\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lsquo;HA-292\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lsquo;I-29444\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lsquo;Universal\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Comet\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e138.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e135.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e143.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e27.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e27.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;G.O.R. 104\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e126.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e122.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;HA-305\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e115.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e111.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e121.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e22.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Odeskij 113\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e110.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Tenissei\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e132.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Vniimk 8931\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e88.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e84.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean values for 100-seed mass following the line \u0026times; tester mating design for the accessions selected on the basis of traits related to the confectionary sunflower\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eTesters\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLines\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lsquo;HA-292\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lsquo;I-29444\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lsquo;Universal\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Comet\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;G.O.R. 104\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.67\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;HA-305\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Odeskij 113\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Tenissei\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Vniimk 8931\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral combining ability effects for yield related traits estimated following the line \u0026times; tester mating design for the accessions selected on the basis of traits related to the confectionary sunflower\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeed yield per plamt\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHead diameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100-seed mass\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLines\u003c/span\u003e\u003c/p\u003e \u003cp\u003e\u0026lsquo;Comet\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;G.O.R. 104\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-2.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;HA-305\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uml;Odeskij 113\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Tenissei\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026uml;Vniimk 8931\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-28.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTesters\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;HA-292\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;I-29444\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lsquo;Universal\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe screening of sunflower germplasm for confectionary traits revealed significant variability among the traits, with GCV ranging between 9\u0026ndash;29%. Previous research also reported broad genetic variability among confectionary sunflower cultivars (Velasco et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Aldemir et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Chen et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), thereby highlighting the potential for further improvement in this crop. A study on the landraces sampled in Spain for confectionary sunflower revealed wide variability within sunflower functional molecules within this germplasm (Velasco et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The seven accessions that were obtained from Cordoba had high phytosterol contents and obtained accessions were valuable source for improvement of this crop. Another study on newly developed confectionary sunflower types indicated an achene length of 15.2 mm and width of 8.50 mm, with dehulled seed oil contents of 49% and protein contents of 28% (Sandrinelli et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The fatty acid profile showed high levels of linoleic acid (56%) followed by oleic acid (34%).\u003c/p\u003e \u003cp\u003eCorrelation and path coefficient analyses revealed associations between various confectionary traits. For instance, 100-seed mass was positively correlated with protein contents, seed length, and oleic acid, suggesting that accessions with high 100-seed mass tend to have higher protein contents, seed length, and oleic acid contents. Additionally, genotypes with high sugar contents may be selected to develop confectionary sunflower varieties with high seed mass.\u003c/p\u003e \u003cp\u003ePrevious research emphasized protein contents as a crucial criterion for the selection of confectionary sunflower (Hladni et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Hladni et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Vedmedeva et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Path coefficient analysis has been used to determine the direct and indirect effects of seed-related traits on protein yield (Hladni et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Notably, oil contents showed a significant negative direct effect on protein yield (Hladni et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Reinert et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) showed a negative association between seed length and floret traits such as pollen tube length.\u003c/p\u003e \u003cp\u003eCombining ability analysis of selected confectionary sunflower types revealed high heritability estimates and a preponderance of dominance variance for traits such as 100-seed mass and head diameter, suggesting the potential for recurrent selection to develop breeding lines with high seed mass. Accessions showing positive GCA effects may be preferred for breeding high-yielding confectionary sunflower hybrids (Li et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1995\u003c/span\u003e; Ćiric et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rauf, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For example, \u0026lsquo;Comet\u0026rsquo; exhibited the highest GCA effects for seed yield per plant, head diameter, and 100-seed mass. \u0026lsquo;Tenissiei\u0026rsquo; also showed positive GCA for all traits under study. Accessions like \u0026lsquo;Universal\u0026rsquo; and \u0026lsquo;HA-292\u0026rsquo; displayed positive combining ability for specific traits, thus offering valuable insights for breeding programs aimed at enhancing confectionary sunflower cultivars.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSR did the planning and mobilised resources for this research. AL, SR and MN did the field research and data analysis. Results interpretation was done together by AL, SR, MN and RO. SR provided the first draft, whose main editing was done by RO. Alll authors contributing to this manuscript accept this last version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAldemir M, Tan AS, Altunok A (2016) Performance of some confectionary sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e L.) varieties in Aegean region of Turkey. In Proceedings, 19th International Sunflower Conference, 29 May\u0026ndash;3 June 2016, Edirne, Turkey. International Sunflower Association, Paris, France. pp 548\u0026ndash;555.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Zhang H, Huang Y, Cai R, Mei G, Cao D, Ruan G (2020) Difference and genetic analysis of main agronomic characters between oil sunflower and edible sunflower in Zhejiang. Field Crop 3:1\u0026ndash;12. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5376/fc.2020.03.0007\u003c/span\u003e\u003cspan address=\"10.5376/fc.2020.03.0007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eĆiric M, Jocic S, Cvejic S, Jockovic M, Čanak P, Marinkov R, Ivanovic M (2013) Combining abilities of new inbred lines of sunflower. Genetika 45: 289\u0026ndash;296.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemir I (2021) The evaluation of confectionery sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e L.) cultivars and populations for yield and yield components. Intl J Agric Environ Biores 6: 179\u0026ndash;186.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng J, Jan CC, Seiler G (2022) Breeding, production, and supply chain of confection sunflower in China. OCL 29: 11. DOI: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1051/ocl/2022004\u003c/span\u003e\u003cspan address=\"10.1051/ocl/2022004\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonz\u0026aacute;lez-P\u0026eacute;rez S (2015) Sunflower proteins. In Mart\u0026iacute;nez-Force, E, Dunford NT, Salas JJ (eds) Sunflower. AOCS Press, Champaign, Illinois. pp 331\u0026ndash;393.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFAO (2022) Food and Agriculture Organization, Rome Italy. Data retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.fao.org/faostat/en/#data/QCL\u003c/span\u003e\u003cspan address=\"https://www.fao.org/faostat/en/#data/QCL\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (Accessed on 15th March 2024)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHladni N, Jocić S, Miklič V, Miladinović D, Zorić M, Radić V (2016) Correlations and path coefficient analysis of confectionery sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e L.). In Proceedings, 19th International Sunflower Conference, 29 May\u0026ndash;3 June 2016, Edirne, Turkey. International Sunflower Association, Paris, France. pp 478\u0026ndash;478.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHladni N, Miklič V, Mijić A, Jocić S, Miladinović D (2015) Correlation and path coefficient analysis for protein yield in confectionary sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e L.). Genetika 47: 811\u0026ndash;818.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHladni N, Terzić S, Mutavdžić B, Zorić M (2017) Classification of confectionary sunflower genotypes based on morphological characters. J Agric Sci 155(10): 1594\u0026ndash;1609.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi YM, Chaney RL, Schneiter AA, Miller JF (1995) Combining ability and heterosis estimates for kernel cadmium level in sunflower. Crop Sci 35: 1015\u0026ndash;1019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNenova N, Drumeva M (2012) Investigation on protein content and amino acid composition in the kernels of some sunflower lines. Helia 35: 41\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePekcan V, Goksel E, Yilmaz İM, Yalcin K (2015) Developing confectionery sunflower hybrids and determination of their yield performances in different environmental conditions. Ekin J Crop Breed Genet 1: 47\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRauf S (2019) Breeding strategies for sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e L.) genetic improvement. In: Al-Khayri JM (ed) Advances in Plant Breeding Strategies: Industrial and Food Crops, 6th ed. Springer, New York. pp 637\u0026ndash;673.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eReinert S, Gao Q, Ferguson B, Portlas Z M, Prasifka JR, Hulke BS (2020) Seed and floret size parameters of sunflower are determined by partially overlapping sets of quantitative trait loci with epistatic interactions. Mol Genet Genom 295: 143\u0026ndash;154.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSandrinelli TR, Alvarez D, Silva MP, Aguilar R, Pazos A, Balzarini M, Mart\u0026iacute;nez MJ (2022) Morpho-chemical characterization of new confectionery sunflower (\u003cem\u003eHelianthus annuus\u003c/em\u003e L.) genotypes from Argentina. AgriScientia 39: 45\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVedmedeva K, Nosal O, Poliakova I, Machova T (2023) Correlations of confectionary seed traits in different head zones sunflower. Helia 46: 215\u0026ndash;231.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVelasco L, Fern\u0026aacute;ndez-Cuesta \u0026Aacute;, Fern\u0026aacute;ndez-Mart\u0026iacute;nez JM (2014) Variability of seed quality traits in a collection of Spanish landraces of confectionery sunflower. Crop Past Sci 65: 242\u0026ndash;249.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWarburton ML, Rauf S, Marek L, Hussain MM, Ogunola O, de Jesus J, Gonzalez S (2017) The use of crop wild relatives for crop improvement. Crop Sci 57: 1227\u0026ndash;1240.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Accessions, Achene, Correlation, Fatty acid profile, Sugar contents","lastPublishedDoi":"10.21203/rs.3.rs-4118769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4118769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eConfectionary sunflower has gained popularity due to its utilization in roasting as snacks, poultry, bird food, and bakery products. However, seed quality of confectionary sunflower differs from non-oil seed types. In the present study, 71 sunflower accessions were selected for the evaluation of seed quality traits. Significant genetic variability was observed for traits such as 100-seed mass and seed length, as indicated by high genotypic coefficients of variability. Correlation analysis revealed a positive correlation between 100-seed mass and seed size, protein content, and oleic acid content. Path coefficient analysis suggested that accessions with high sugar content may be selected to develop superior germplasm for confectionary products. Biplot analysis was conducted to identify suitable accessions with favorable confectionary traits. Accessions \u0026lsquo;Hybrid 100\u0026rsquo;, \u0026lsquo;GOR101\u0026rsquo;, and \u0026lsquo;Odesskijj112\u0026rsquo; exhibited high sugar content, while \u0026lsquo;Yawn\u0026rsquo; demonstrated comparatively higher oleic acid content. Accessions \u0026lsquo;Vinimik 6931\u0026rsquo; and \u0026lsquo;HA 305\u0026rsquo; displayed high 100-seed mass, while seed length was greater in accessions \u0026lsquo;Comet\u0026rsquo; and \u003cem\u003eH.\u003c/em\u003e \u0026times; multiflorous. Combining ability analysis were performed to assess the breeding value of accessions. \u0026lsquo;Comet\u0026rsquo; exhibited the highest general combining ability (GCA) effects for seed yield per plant, head diameter and 100-seed mass; while \u0026lsquo;G.OR.104\u0026rsquo; had positive GCA for seed yield per plant but negative combining ability for other traits. \u0026lsquo;Tenissiei\u0026rsquo; displayed positive GCA for all traits. Among the testers, accession \u0026lsquo;Universal\u0026rsquo; showed positive GCA for seed yield per plant, while \u0026lsquo;HA-292\u0026rsquo; exhibited positive GCA for head diameter.\u003c/p\u003e","manuscriptTitle":"Evaluation of confectionary sunflower germplasm accessions and their derived hybrids","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-20 07:49:20","doi":"10.21203/rs.3.rs-4118769/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-08-12T16:16:39+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-12T15:54:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-01T07:40:53+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"279001248694758866682223881746175757487","date":"2024-07-15T14:21:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"185328993520404057482869195649900308307","date":"2024-07-15T12:30:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224828138940573648518212559833567705442","date":"2024-07-01T12:02:29+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109470228370384762827979310295962098590","date":"2024-06-26T10:23:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-22T17:10:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-29T04:51:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-18T01:31:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Genetic Resources and Crop Evolution","date":"2024-03-17T21:27:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"genetic-resources-and-crop-evolution","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gres","sideBox":"Learn more about [Genetic Resources and Crop Evolution](https://www.springer.com/journal/10722)","snPcode":"10722","submissionUrl":"https://submission.nature.com/new-submission/10722/3","title":"Genetic Resources and Crop Evolution","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"c3e6013f-31cd-493f-b85c-5fb02fd19e44","owner":[],"postedDate":"March 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-09-09T16:14:57+00:00","versionOfRecord":{"articleIdentity":"rs-4118769","link":"https://doi.org/10.1007/s10722-024-02136-7","journal":{"identity":"genetic-resources-and-crop-evolution","isVorOnly":false,"title":"Genetic Resources and Crop Evolution"},"publishedOn":"2024-09-02 15:57:40","publishedOnDateReadable":"September 2nd, 2024"},"versionCreatedAt":"2024-03-20 07:49:20","video":"","vorDoi":"10.1007/s10722-024-02136-7","vorDoiUrl":"https://doi.org/10.1007/s10722-024-02136-7","workflowStages":[]},"version":"v1","identity":"rs-4118769","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4118769","identity":"rs-4118769","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}