Phenotyping and combining ability Analysis of sorghum [Sorghum bicolor (l) Moench] Genotypes in dryland Environments

Phenotyping and combining ability Analysis of sorghum [Sorghum bicolor (l) Moench] Genotypes in dryland Environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Phenotyping and combining ability Analysis of sorghum [Sorghum bicolor (l) Moench] Genotypes in dryland Environments Temesgen Begna, Techale Birhan, Taye Tadesse This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5838770/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 Jul, 2025 Read the published version in Discover Sustainability → Version 1 posted 8 You are reading this latest preprint version Abstract Sorghum has profound role in ensuring food security across the globe, especially in dry lowland regions. However, substantial sorghum productivity has been curtailed by severe and prolonged drought stress due to the limitation of climate smart and superior sorghum varieties for moisture stress areas of Ethiopia. Therefore, this study was conducted to identify and develop superior sorghum genotypes through investigating gene action and combining abilities for yield and agronomic traits. In total, 42 sorghum genotypes were assessed in alpha lattice design with two replication. There was considerable differences amongst genotypes for yield and agronomic characteristics. Best performing hybrids such as P-9534 × Melkam (6.32 tha -1 ), B6 × ICRS-14 (5.92 tha -1 ) TX-623 × ICRS-14 (5.88 tha -1 ), P9511 × Melkam (5.78 tha -1 ) and P-850341 × ICRS-14 (5.57tha -1 ) were identified with yield advantage of 32.49%, 24%, 23%, 21% and 16.68% over the standard check (ESH-4) (4.77 tha -1 ) respectively. Inbred lines P-9534 and P-9505 were identified as the best general combiners for the plant height and days to flowering, while P-9501 and B5 were found to be the best general combiner parents for stay green. In terms of thousand-seed weight, the best general combiners were P-850341, MARC2, and MARC6 inbred lines. This signified the traits were principally governed by additive gene action and early generation selection was the most preferred strategies for further improvement. The hybrids P-9534 × Melkam, B6 × ICRS-14, and MARC3 × Melkam were identified as best specific combiners for grain yield, while TX-623 × ICRS-14 was the best specific combiner for panicle width, 1000-seed weight, days to flowering and days to maturity. This demonstrated non-additive gene action mainly controlled the traits and heterotic breeding strategies was advised to develop superior sorghum hybrids. Since the ratio of specific to general combining ability was more than unity for all traits except plant height, the investigation demonstrated the preponderance of non-additive gene action. In conclusion, after further investigation for stability and adaptability over years across locations, the drought-resistant and high-yielding hybrids (TX-623 × ICRS-14, MARC3 × Melkam, MARC3 × ICRS-14, P-9511 × Melkam, P-850341 × ICRS-14, P-9534 × Melkam, and B6 × ICRS-14) would be utilized commercially. Gene action Combining ability Climate smart Sorghum Drought resilience Figures Figure 1 Figure 2 1. INTRODUCTION Sorghum [ Sorghum bicolor (L.) Moench] is an annual cereal crop belonging to poaceae family [ 1 ]. Sorghum is a short-day, principally a self-pollinating, diploid (2n = 20) crop with a genome size of 730 Mb [ 2 ]. Sorghum is a C 4 carbon cycle crop with a high photosynthetic efficiency that can adapt to diverse agro-ecologies particularly dryland regions [ 3 ]. Sorghum plays a critical role in ensuring global food security and is the most preferred crop for climate-smart agriculture [ 4 ]. Sorghum has historically been grown in arid and semi-arid regions and is the fifth most significant cereal crop in the world, behind maize, wheat, rice, and barley [ 5 ]. Sorghum is widely recognized for its remarkable resilience and broad adaptation to multiple biotic and abiotic stressors [ 6 ]. Due to these qualities, sorghum has gained attention as a possible star crop to address the issue of global food security [ 7 ]. Sorghum is produced in 40.25 million ha in the world with a production of 58.70 million tons with the productivity of 1.46 tha − 1 [ 5 ]. In terms of production, sorghum stands third in Africa behind rice (37.2 million metric tons) and maize (96.6 million metric tons) [ 5 ]. It is the second most important cereal crop in terms of area coverage (28.1 million hectares) preceded by maize (42.5 million hectares) and followed by rice (15.8 million hectares) [ 5 ]. In countries where food insecurity is a serious problem, sorghum provides food for almost half a billion people [ 8 ]. It is an excellent source of carbohydrates, including amylopectin (80.22%), starch (69.15%), and amylose (19.78%) [ 9 ]; ash (1.8%), oil (3.3%), protein (13.8%), and fiber (17.3%) [ 10 ]; minerals: calcium (336 mg/kg), zinc (12.0–23.0 mg/kg), iron (17.5 mg/kg), potassium (1548.5 mg/kg) and sodium (36.6 mg/kg) [ 11 ]. Sorghum provides an alternative source of nutrition for those with celiac disease or gluten sensitivity because it is gluten-free [ 12 ]. Sorghum is the most vital strategic cereal crop for improving the food security and economic well-being of millions of people globally, particularly in developing countries like Ethiopia [ 13 ]. Despite its crucial significance, sorghum productivity has declined because of several production constraints [ 3 ]. Among the production constraints, drought stress is the leading and catastrophic production constraints that drastically reduces sorghum productivity on a regional and worldwide level [ 14 ]. Devastating drought stress has a detrimental effect on sorghum productivity and production at different stages, but it drastically decreases sorghum productivity during the flowering and grain filling stages [ 15 ]. The growth and development of sorghum affected by drought stress, which ultimately results in considerable yield reduction [ 16 ]. Drought stress is becoming more frequent and causing more loss, which is a critical problem for crop production globally [ 17 ]. Drought stress causes alterations in morphology, physiology, biochemistry in sorghum [ 18 ]. These alterations have an impact on the growth and development of sorghum, which drastically decreases its yields [ 18 ]. Drought stress reduced sorghum productivity to 36% at the vegetative stage and 55% at the reproductive stage respectively [ 19 ]. Drought stress significantly reduces the amount and quality of grain sorghum, affecting its physio-chemical properties and development from germination to the reproductive and grain filling stages [ 10 ]. Drought in the tropic region surpass 17% yield loss, with up to 60% in the drastically affected environments [ 20 ]. Drought during grain filling is the most detrimental abiotic stress on sorghum production, causing yield losses of between 45% and 50% [ 21 ]. De Souza et al . [ 22 ] found that pre-flowering drought stress reduced sorghum grain production by approximately 40%, whereas post-flowering drought stress caused 50–90% reductions in grain yield [ 23 ]. Sorghum's phenological stage, frequency, and intensity are the main factors that determine the severity of drought stress [ 24 ]. Therefore, to enhance productivity and ensure local and global food security, it is vital to develop sorghum varieties with high productive, nutrient-dense, market-preferred and resilient to drought stress. Thus, the investigation of combining ability and gene action are essential to identify and select superior and potential parental lines and hybrids varieties from best general and specific combiners of genetic materials [ 25 ]. Combining ability and gene action are playing vital roles in the identification of the superior and promising sorghum genotype for yield and agronomic traits. It also assists in determining and designing the most effective breeding strategies based on the traits' genetic inheritance [ 3 ]. In order to identify and develop superior sorghum genotypes for yield and agronomic characteristics, numerous investigations have been conducted on the combining ability and gene action of sorghum in different times. However, there is a dearth of information regarding combining ability and gene action of grain yield and agronomic traits to develop superior and well-adapted sorghum varieties. Previously, several open-pollinated and few hybrid sorghum varieties have been released for production for diverse agro-ecologies of Ethiopia [ 26 ]. These varieties adopted in very low rates due to different constraints; lack of drought adapted traits and lack farmers’ multiple desired traits, especially biomass, grain size, and earliness [ 26 , 27 ]. To address the high demand for improved sorghum varieties and farmers’ preferred traits issues, the development of superior open pollinated and hybrids varieties are very critical [ 26 ]. Therefore, gene action and combining ability play an important role for tackling adaptation issues and farmers' preferences for various traits, particularly in regions where drought is causing detrimental productivity reduction [ 26 ]. According to Indhubala et al . [ 28 ], specific combining ability assists in developing of superior hybrids in terms of yield and agronomic characteristics, whereas general combining ability aids in the development of superior parental lines. For future sorghum improvement program, the development of hybrids by heterotic breeding and population enhancement through effective selection are essential and appropriate methods of breeding. There are very few commercially available sorghum hybrids for Ethiopia's moisture-stressed regions, despite the reality that hybrids are superior to open-pollinated sorghum varieties in terms of earliness, adaptability, and stability [ 13 ]. Hence, the study was conducted with objectives to identify the best general and specific combining ability of sorghum genotypes for yield and agronomic traits, to determine the type of gene action involved in the inheritance of yield and agronomic traits, and design the best breeding strategies and genetic standards for parents and hybrids to improve sorghum in the future. 2. MATERIALS AND METHODS 2.1 Location of the Experiment The trial was grown in two representative lowland agro-ecologies of Ethiopia under moisture stress environments, where investigations geared toward drought were undertaken. These were Mieso in Eastern Ethiopia and Kobo in Northern Ethiopia. Drought is a major productivity constraint at the particular research areas, where sorghum crop is the primary grown. These locations have been designated as vital national drought adaptation testing locations because they have long been adversely affected by recurrent drought. These areas are well known for growing sorghum and were recognized as models for drought adaptation [ 29 ]. A detailed description of Kobo and Mieso's geographical features found in table-1. Table 1 Description of the experimental locations Location Altitude (m.a.s.l) Rainfall (mm) Global position T o Soil type Region Latitude Longitude Mieso 1470 763 8°30΄N 39°21΄E 23.0 Vertisols Oromia Kobo 1479 650 12°09΄N 39°38΄E 22.0 Vertisols Amara Source : National metrology data of 2019 growing season for meterological information; m.a.s.l is altitude meters above sea level; mm is rainfall in millimeter (July-November, 2019) and T o is Mean monthly temperature in degree centigrade [ 29 ]. 2.2 Materials A total of 42 sorghum genotypes, encompassing 26F1 (hybrids), 13 seed parents (A-lines), 2 pollen parents (R-lines), and one standard check (hybrid) were investigated in Ethiopia's sorghum-producing regions under moisture stress environments. The hybrids were developed using irrigation at the Werer Agricultural Research Center in 2017. 13 A-lines and 2 R-lines were crossed in a line × tester mating design to develop 26 F1 (hybrids). In the process of creating hybrid sorghum seed, the female parent line (A-line) is male sterile, while the male parent line (R-line) is fertile and provides pollen to the A-line. The A-line is a male sterile line in A1 cytoplasm, developed by backcrossing with maintainer line (B-line) in normal cytoplasm. In order to restore hybrid fertility with the A1 cytoplasm, restorer lines (R-lines) carry a dominant nuclear gene. The four A-lines (MARC1B, MARC2B, MARC3B, and MARC6B) were developed by the Ethiopian national sorghum research program, whereas the other nine A-lines (TX-623B, P-9501B, P-9505B, P-9534B, P-851015B, P-850341B, P-9511B, B5, and B6) were introduced from Purdue University. The A-lines were tested for broad-adaptation, drought-resilience, high yielding and early maturing. The R-lines (Melkam and ICSR-14) were developed for dryland agro-ecologies by the Indian International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and the Ethiopian National Sorghum Improvement Program respectively. These pollen parents are well known for their stability, high yielding performance and ability to adapt to moisture stress environments. The hybrid (ESH-4) variety was developed by Ethiopian national sorghum improvement program and used as a standard check for drought adaptation. The check hybrid (ESH-4) variety was released with the merit of drought adaptation and high yielding performance in drought prone environments. Table 2 Details of materials harnessed in the experiment. S.N Lines Type Source S.N Crosses Type Source 1 TX-623B B-line 2018 MW CAS #8B 21 P-851015A × ICSR − 14 Hybrid 2018 MW CAS #15A × 34R 2 P-9501B B-line 2018 MW CAS #9B 22 P-850341A × ICSR-14 Hybrid 2018 MW CAS #17A × 34R 3 P-9505B B-line 2018 MW CAS #10B 23 A5 × ICSR-14 Hybrid 2018 MW CAS #21A × 34R 4 P-9534B B-line 2018 MW CAS #14B 24 A6 × ICSR-14 Hybrid 2018 MW CAS #22A × 34R 5 P-851015B B-line 2018 MW CAS #15B 25 MARC1A × ICSR-14 Hybrid 2018 MW CAS #23A × 34R 6 P-850341B B-line 2018 MW CAS #17B 26 MARC2A × ICSR-14 Hybrid 2018 MW CAS #24A × 34R 7 B5 B-line 2018 MW CAS #21B 27 MARC3A × ICSR-14 Hybrid 2018 MW CAS #25A × 34R 8 B6 B-line 2018 MW CAS #22B 28 MARC6A × ICSR-14 Hybrid 2018 MW CAS #28A × 34R 9 MARC1B B-line 2018 MW CAS #23B 29 P-9511A × ICSR-14 Hybrid 2018 MW CAS #32A × 34R 10 MARC2B B-line 2018 MW CAS #24B 30 TX-623A × Melkam Hybrid 2018 MW CAS #8A × 33R 11 MARC3B B-line 2018 MW CAS #25B 31 P-9501A × Melkam Hybrid 2018 MW CAS #9A × 33R 12 MARC6B B-line 2018 MW CAS #28B 32 P-9505A × Melkam Hybrid 2018 MW CAS #10A × 33R 13 P-9511B B-line 2018 MW CAS #32B 33 P-9534A × Melkam Hybrid 2018 MW CAS #14A × 33R Testers 34 P-851015A × Melkam Hybrid 2018 MW CAS #15A × 33R 14 Melkam R-tester 2018 BS Inc. 35 P-850341A × Melkam Hybrid 2018 MW CAS #17A × 33R 15 ICSR-14 R-tester 2018 BS Inc. 36 A5 × Melkam Hybrid 2018 MW CAS #21A × 33R Check 37 A6 × Melkam Hybrid 2018 MW CAS #22A × 33R 16 ESH-4 Hybrid 2018 BS Inc. 38 MARC1A × Melkam Hybrid 2018 MW CAS #23A × 33R Crosses 39 MARC2A × Melkam Hybrid 2018 MW CAS #24A × 33R 17 TX-623A × ICSR-14 Hybrid 2018 MW CAS #8A × 34R 40 MARC3A × Melkam Hybrid 2018 MW CAS #25A × 33R 18 P-9501A × ICSR-14 Hybrid 2018 MW CAS #9A × 34R 41 MARC6A × Melkam Hybrid 2018 MW CAS #28A × 33R 19 P-9505A × ICSR-14 Hybrid 2018 MW CAS #10A × 34R 42 P-9511A × Melkam Hybrid 2018 MW CAS 32A × 33R 20 P-9534A × ICSR-14 Hybrid 2018 MW CAS #14A × 34R Source : Melkassa miscellaneous sorghum working collection documents [ 30 ] 2.2 Experimental Design and Trial Management The experiment was conducted using alpha lattice design with two replications at both locations in the cropping season of 2019. Each replicate embraced 42 genotypes, planted over six blocks, with seven experimental units per block. Each genotype was planted in two rows of 5 m length with spacing between rows and plants 0.75 m and 0.20 m respectively. The distances between each block and replication were 1 m and 1.5 m respectively. The alpha lattice design was used to overcome restrictions on the number of genotypes that could be taken into consideration and to account for irregular block sizes. The large number of genotypes restrict the use of RCBD and the non-square number of genotypes inhibits the use of lattice design. Therefore, alpha-lattice designs resolved restrictions on the number of genotypes to be taken into consideration and their relationship to the block size needed for lattice designs [ 31 ]. The seeds were manually drilled at a rate of 12 kgha − 1 and thinned to 0.20 m between plants at knee-stages to optimize population. The recommended fertilizer rate of phosphorus fertilizer (46 kgha − 1 P2O5) in the form of diammonium phosphate (DAP) and nitrogen fertilizer (23 kgha − 1 nitrogen in the form of urea) were applied at planting and 35 days after planting respectively. The experimental plots were kept free of pests during the growing seasons. 2.3 Data Collection The data were collected using descriptors of sorghum from plot and plant randomly [ 32 ]. Traits Description Days to 50% flowering (DTF in days) The number of days from the start of emergence till half of the plants in a plot start flowering halfway through the panicle Days to 95% maturity (DTM in days) The duration in days from the time of emergence until 95% of the plants reached physiological maturity Plant height (PTH in cm) The height of five randomly tagged plants during flowering, measured from the base to the tip of the panicle Stay green score (SG in scale) A scale of 1 to 5 was used to measure the level of greenness at maturity; 1 = fully green, normal-sized leaves (no leaf death), 2 = 25% of the leaves died, 3 = 26 to 50% of the leaves died, 4 = 51 to 75% are dead and 5 = 76 to 100% of the leaves and stem are dead (total plant death) [ 33 ]. Panicle length (PL in cm) The distance between the panicle tip and the lowest panicle branch from five randomly tagged plants Panicle width (PW in cm) The average width at the center of the panicle head of the five randomly selected plants Total leaf area (LA in cm 2 ) The length × width of the fourth leaf from the flag leaf multiplied by 0.747 is the total leaf area of five randomly selected plants [ 34 ]. Panicle exertion (PE in cm) The distance between the panicle bases and the flag leaf was utilized to measure the panicle exertion of five randomly selected plants 1000 seed weight (TSW in g) The gram weight of 1,000 grains collected from a plot that had a 12.5% moisture content Grain yield (GY in tonha − 1 ) Grain yield is calculated from the total harvest of the plot and converted to tha − 1 once the ideal seed moisture content has been adjusted 2.4 Data Analysis The mixed linear model in SAS [ 35 ] was used to perform the combined analysis of variance (ANOVA). After error variance homogeneity was confirmed by the Bartlett's test, combined analyses were performed for traits that demonstrated significant genotypic variations in individual location. In the combined analysis, genotypes were considered as fixed effects, while environments, replications within environments, and blocks within replications were considered as random effects [ 36 ]. The Fisher's least significant difference \(\:\left(\dot{\text{L}\text{S}\text{D}=\text{S}\text{D}\left(d\right)\:\times\:{t}_{\left(0.05\right)}\:\text{a}\text{t}\:\text{e}\text{r}\text{r}\text{o}\text{r}\:\text{o}\text{f}\:\text{d}\text{f}}\:or\:LSD=\left(\sqrt{\frac{2MSe}{r}}\right)\times\:{t}_{\left(0.05\right)}\:\text{a}\text{t}\:\text{e}\text{r}\text{r}\text{o}\text{r}\:\text{o}\text{f}\:\text{d}\text{f}\right)\) test was used to separate the means at a 5% probability level [ 37 ]. Where, SD is the standard error of the difference ̇ \(\:{SD(}_{d)}=\sqrt{\frac{2MSe}{r}}\) ). The coefficient of variation (CV) is a measure of the experiment's reliability that shows how accurately the experimental genotype compare one another [37]. It was computed as \(\:\:CV=\frac{\sqrt{MSe}}{GM}\:\times\:100\) , where, MSe is error mean square; GM is grand mean. The coefficient of determination (R²) is a statistical metric in a regression model that determines the percentage of variance in the dependent variable that can be explained by the independent variable. A model's efficiency of fit can be measured by its R 2 value, which is expressed as a percentage and ranges from 0–100%. It was calculated as; $$\:\dot{{\text{R}}^{2}}=\frac{{\text{S}\text{S}}_{\text{r}\text{e}\text{g}\text{r}\text{e}\text{s}\text{s}\text{i}\text{o}\text{n}}}{{\text{S}\text{S}}_{\text{t}\text{o}\text{t}\text{a}\text{l}}}$$ Where, SS regression is the sum of squares due to regression; SS total is the total sum of squares. The following model was used in the pooled analysis of variance over location to measure the total variation among the genotypes: $$\:\:{\text{Y}}_{\text{i}\text{j}\text{k}\text{s}}={\mu\:}+{l}_{\left(s\right)}+\:{g}_{\left(i\right)}+{r}_{\left(j\right)}\:+{b}_{\left(jk\right)}+{(g\:\times\:\text{l}\:}_{\left(is\right)}+\:{e}_{\left(ijks\right)}\:$$ Where Y ijks is the observation; µ is grand mean; l s is the effect of sth locations; g i is the fixed effect of the ith genotype; r j is the effect of the jth replicate; b jk is the effect of the kth incomplete block within the jth replicate; (g×l) (is) is the interaction effects of isth between genotype and location; e ijks is the residual or effect of random error (Table − 3). Table 3 ANOVA skeleton for combined analysis across locations Source of variation DF MS F-Values Location (L) L-1 MSL MSL/MSe Replication (Loc) L(r-1) MSr MSr/MSe Block (rep) rL(b-1) MSb MSb/MSe Genotypes g-1 MSg MSg/MSe Hybrids h-1 MSh MSh/MSe Parents p-1 MSp MSp/MSe Check c-1 MSc MSc/MSe Lines l-1 MSl MSl/MSe Testers t-1 MSt MSt/MSe Line × Tester (l-1) (t-1) MS (l×t) MS (l×t)/MSe Hybrid vs Parent 1 MS (h vs p) MS (hvsp)/MSe Hybrid vs Check 1 MS (h vs c) MS (hvsc)/MSe Parent vs Check 1 MS (p vs c) MS (pvsc)/MSe Genotype × L (g-1) (L-1) MSg×L MS (g×L)/MSe Hybrid × L (h-1) (L-1) MSh×L MS (h×L)/MSe Parent × L (p-1) (L-1) MSp×L MS (p×L)/MSe Check × L (c-1) (L-1) MSc×L MS (c×L)/MSe Lines× L (l-1) (L-1) MSl×L MS (l×L)/MSe Testers × L (t-1) (L-1) MSt×L MS (t×L)/MSe Line × Tester×L (l-1) (t-1) (L-1) MSl×t×L MS (l×t×L)/MSe Hybrid vs Parent×L 1(L-1) MS (h vs p) ×L MS (h vs p) ×L/MSe Hybrid vs check × L 1(L-1) MS (h vs c) ×L MS (h vs c) ×L/MSe Parent vs Check×L 1(L-1) MS (p vs c) ×L MS (p vs c) ×L /MSe Error L(r − 1)(g − 1) MSe Total lrg − 1 MST Source: Sharma et al ., [ 38 ]. Key: df is degree of freedom, L is locations, r is replications, g is number of genotypes, b is block, p is number of parents, l is number of lines, t is number of testers, c is number of checks, l × t is line by tester, h vs c is hybrid vs check, h vs p is hybrid vs parent, p vs c is parent vs check, MS is mean square, MSe is mean square of error, MST is mean square of the total. 2.4.1 Combining Ability Analysis The general and specific combining ability analyses were performed for both parents and hybrids using the methodologies developed by Kempthorne [ 39 ]. 2.4.1.1 General combining ability (GCA ) - The term "general combining ability effect of lines and testers" refers to the deviation of the line and tester means from the hybrid mean. GCA effects were calculated using Kempthorne's [ 39 ] pattern for each trait. The GCA effects of lines (g i ) and testers (g j ) were calculated as: $$\:\:{GCA}_{i}=\frac{{X}_{i}}{\text{t}\text{r}}-\frac{\text{X}}{\text{l}\text{t}\text{r}}$$ $$\:\:{GCA}_{j}=\frac{{X}_{j}}{\text{l}\text{r}}-\frac{\text{X}}{\text{l}\text{t}\text{r}}$$ where g i is the GCA effect for the i th lines; g j is the GCA effect for the j th testers; X i is the total of the ith line over all testers (t) and replications (r); X j is the total of the jth tester over all lines (l) and replications (r); X is the grand total, where l is the number of lines, t is the number of testers, and r is the number of replications. 2.4.1.2 Specific combining ability (SCA) - SCA effects were measured as the deviation of each cross mean from all hybrid means, adjusted for the corresponding GCA effects of parents [ 39 ]. The following is how the computation was done: $$\:{SCA}_{ij}=\frac{{X}_{iJ}}{\text{r}}-\frac{{X}_{i}}{\text{t}\text{r}}-\frac{{X}_{j}}{\text{l}\text{r}}+\frac{\text{X}}{\text{l}\text{t}\text{r}}$$ Where S ij is the SCA effect of the ij th crosses; X ij is the value of the total number of ith lines with jth testers over all replications (r); X i is the total number of ith over all testers; X j is the total number of jth testers over all lines; X is the grand total crosses; l is the number of lines; t is the number of testers; and r is the number of replication. The significance of GCA or SCA effects were tested by dividing the GCA effects of a particular line or males and SCA effects of a particular hybrid by its respective standard error. The following formulas were used for calculating the significance of the GCA and SCA effects: $$\:\text{S}\text{i}\text{g}\text{n}\text{i}\text{f}\text{i}\text{c}\text{a}\text{n}\text{c}\text{e}\:\text{o}\text{f}\:\text{G}\text{C}\text{A}\:\text{e}\text{f}\text{f}\text{e}\text{c}\text{t}\text{s}\:\text{o}\text{f}\:\text{l}\text{i}\text{n}\text{e}\text{s}\:\text{t}\text{e}\text{s}\text{t}\text{e}\text{d}\left(\text{l}\right)={g}_{i}/\text{S}\text{E}\left({g}_{i}\right)\:$$ $$\:\text{S}\text{i}\text{g}\text{n}\text{i}\text{f}\text{i}\text{c}\text{a}\text{n}\text{c}\text{e}\:\text{o}\text{f}\:\text{G}\text{C}\text{A}\:\text{o}\text{f}\:\text{t}\text{e}\text{s}\text{t}\text{e}\text{r}\text{s}\:\text{t}\text{e}\text{s}\text{t}\text{e}\text{d}\left(\text{t}\right)={g}_{i}/\text{S}\text{E}\left({g}_{j}\right)\:$$ $$\:\text{S}\text{i}\text{g}\text{n}\text{i}\text{f}\text{i}\text{c}\text{a}\text{n}\text{c}\text{e}\:\text{o}\text{f}\:\text{S}\text{C}\text{A}\:\text{e}\text{f}\text{f}\text{e}\text{c}\text{t}\text{s}\:\text{h}\text{y}\text{b}\text{r}\text{i}\text{d}\text{s}\:\text{t}\text{e}\text{s}\text{t}\text{e}\text{d}\left(\text{h}\right)={g}_{ij}/\text{S}\text{E}\left({s}_{ij}\right)\:$$ The standard error for general and specific combining ability effects were calculated as: $$\:\text{S}\text{t}\text{a}\text{n}\text{d}\text{a}\text{r}\text{d}\:\text{e}\text{r}\text{r}\text{o}\text{r}\:\text{o}\text{f}\:\text{G}\text{C}\text{A}\:\text{f}\text{o}\text{r}\:\text{l}\text{i}\text{n}\text{e}\text{s}\left({g}_{i}\right)=\sqrt{\text{m}\text{s}\text{e}/\text{t}\text{r}}$$ $$\:\text{S}\text{t}\text{a}\text{n}\text{d}\text{a}\text{r}\text{d}\:\text{e}\text{r}\text{r}\text{o}\text{r}\:\text{o}\text{f}\:\text{G}\text{C}\text{A}\:\text{f}\text{o}\text{r}\:\text{t}\text{e}\text{s}\text{t}\text{e}\text{r}\text{s}\left({g}_{j}\right)=\sqrt{\text{m}\text{s}\text{e}/\text{r}\text{l}}$$ $$\:\text{S}\text{t}\text{a}\text{n}\text{d}\text{a}\text{r}\text{d}\:\text{e}\text{r}\text{r}\text{o}\text{r}\:\text{o}\text{f}\:\text{S}\text{C}\text{A}\:\text{f}\text{o}\text{r}\:\text{h}\text{y}\text{b}\text{r}\text{i}\text{d}\text{s}\left({s}_{ij}\right)=\sqrt{\text{m}\text{s}\text{e}/\text{r}}$$ 2.4.2 Computation of combining abilities variance components The GCA variance of the lines, testers and the SCA variance of hybrids were computed using the mean sum of squares expectations using the formula proposed by Singh and Chaudhary [ 40 ]. $$\:{{\sigma\:}}^{2}{gca}_{i}=\frac{\text{M}\text{l}\:-\:\text{M}\text{l}\text{t}}{l*r}=\frac{1}{4}{{\sigma\:}}^{2}\text{A}=\:{{\sigma\:}}^{2}\text{G}\text{C}\text{A}$$ $$\:{{\sigma\:}}^{2}{gca}_{j}=\frac{\text{M}\text{t}-\text{M}\text{l}\text{t}}{t*r}=\:\frac{1}{4}{{\sigma\:}}^{2}\text{A}={{\sigma\:}}^{2}\text{G}\text{C}\text{A}$$ $$\:{{\sigma\:}}^{2}{sca}_{ij}=\frac{\text{M}\text{l}\text{t}-\text{M}\text{e}}{r}=\:{{\sigma\:}}^{2}\text{S}\text{C}\text{A}=(\text{C}\text{o}\text{v}\:\text{F}\text{S}-2\text{C}\text{o}\text{v}\:\text{H}\text{S})$$ $$\:\text{A}\text{v}\text{e}\text{r}\text{a}\text{g}\text{e}\:\text{v}\text{a}\text{r}\text{i}\text{a}\text{n}\text{c}\text{e}=\frac{\text{X}+\text{Y}}{\text{l}+\text{t}+\text{r}}$$ $$\:{{\sigma\:}}^{2}\text{A}=4\:\text{x}\:\frac{\text{X}+\text{Y}}{\text{l}+\text{t}+\text{r}}$$ $$\:{{\sigma\:}}^{2}\text{D}=4\:\text{x}\:{{\sigma\:}}^{2}\text{s}\text{c}\text{a}$$ Where σ 2 gca i is the variance due to general combining ability for lines, σ 2 gca j is the variance due to general combining ability for testers, and σ 2 sca ij is the variance due to specific combining ability, r is the number of replications, l is the number of lines, t is the number of testers, Ml is the mean square due to lines, Mt is the mean square due to testers, Mlt is the mean square due to hybrids, and Me is the mean square due to error, HS is the covariance of half-sib and FS is the covariance of full-sib. Because both lines and testers were deemed inbred, the inbreeding coefficient F is 1 was used to determine the additive and dominant genetic variance. To determine the relative importance of additive versus non-additive gene actions, two ratios were used: 𝜎 2 gca/𝜎 2 sca and 𝜎 2 𝐷/𝜎 2 𝐴 [ 40 ]. 2.4.3 Estimation of the proportional contribution different variance components The following algorithm was used to determine the proportionate contribution of lines, testers, and line × tester interaction to total variance [ 40 ]. $$\:\text{C}\text{o}\text{n}\text{t}\text{r}\text{i}\text{b}\text{u}\text{t}\text{i}\text{o}\text{n}\:\text{o}\text{f}\:\text{l}\text{i}\text{n}\text{e}\text{s}\left(\text{l}\right)=\frac{\text{S}\text{S}\text{l}}{\text{S}\text{S}\text{h}}\text{x}100$$ $$\:\text{C}\text{o}\text{n}\text{t}\text{r}\text{i}\text{b}\text{u}\text{t}\text{i}\text{o}\text{n}\:\text{o}\text{f}\:\text{t}\text{e}\text{s}\text{t}\text{e}\text{r}\text{s}\left(\text{t}\right)=\frac{\text{S}\text{S}\text{t}}{\text{S}\text{S}\text{h}}\text{x}100$$ $$\:\text{C}\text{o}\text{n}\text{t}\text{r}\text{i}\text{b}\text{u}\text{t}\text{i}\text{o}\text{n}\:\text{o}\text{f}\:\text{l}\text{i}\text{n}\text{e}\text{s}\:\text{x}\:\text{t}\text{e}\text{s}\text{t}\text{e}\text{r}\text{s}\left(\text{l}\text{x}\text{t}\right)=\frac{\text{S}\text{S}\text{l}\text{x}\text{t}}{\text{S}\text{S}\text{h}}\text{x}100$$ 2.5 Estimation of genetic components Broad and narrow sense heritability were computed for each characteristic based on the formula developed by Allard [ 41 ]. $$\:{\text{H}}^{2}=\frac{{{\sigma\:}}^{2}\text{g}}{{{\sigma\:}}^{2}\text{p}}$$ $$\:{\text{h}}^{2}=\frac{{{\sigma\:}}^{2}\text{a}}{{{\sigma\:}}^{2}\text{p}}$$ $$\:{{\delta\:}}^{2}\text{g}\:=\left(\frac{\text{M}\text{S}\text{g}-\text{M}\text{S}\text{g}\text{l}}{\text{r}\text{l}}\right)$$ $$\:{{\delta\:}}^{2}\text{p}={{\delta\:}}^{2}\text{g}+\left(\frac{{{\delta\:}}^{2}\text{g}\text{l}}{\text{l}}\right)+\left(\frac{{{\delta\:}}^{2}\text{e}}{\text{r}\text{l}}\right)$$ Where H² is the broad-sense heritability, h 2 is the narrow-sense heritability, σ²g is the genotypic variance and σ²p is the phenotypic variance, δ²gl is genotypic by environmental variance, δ²e is environmental variance, MSg is mean square of genotype, MSgl is mean square due to genotype by environment interaction, l is number of locations, and r is number of replications. 3. RESULTS AND DISCUSSION 3.1 Analysis of Variance (ANOVA) The combined analysis of variance revealed highly significant variation among genotypes for days to flowering, days to maturity, plant height, stay green, panicle length, panicle width, leaf area, panicle exertion, 1000 seed weight and grain yield traits. This result signified the presence of superior and well-adapted genotypes to select for targeted locations. Numerous similar studies found significant variation for grain yield and agronomic traits of sorghum in Ethiopia [ 42 – 45 ]. The investigation identified highly significant difference in genotype by environment interactions for days to flowering, days to maturity, plant height, 1000 weight and grain yield whereas significant difference observed for panicle width trait. This result indicated the differential response of genotypes across environments with best adaptation to moisture stressed environments. The environment had significant effect on the traits expressions. Therefore, this confirmed that why the plant breeders conduct trial across locations and over years, particularly during the final stages of variety development. Several researchers have previously reported similar findings in sorghum for yield and yield-related parameters [ 46 , 3 ]. Table 4 Combined analysis of variance of sorghum genotypes for yield and agronomic traits Traits Fixed effect Random effect MS E (DF = 72) CV R 2 MS G (DF = 41) MS L (DF = 1) MS GL (= 41) Days to flowering 13.23** 1080.21** 5.51** 2.62 2.29 0.91 Days to maturity 15.10** 1494.05** 13.74** 4.67 1.95 0.89 Plant height 7615.51** 14359.70** 332.80** 70.47 4.43 0.98 Stay green 0.78** 63.14** 0.51 ns 0.35 22.15 0.83 Panicle length 27.08** 117.66** 3.80 ns 2.99 6.13 0.87 Panicle width 3.86** 308.34** 1.10* 0.65 9.85 0.92 Leaf area 5662.39** 439598.44** 3919.83 ns 2812.38 16.84 0.81 Panicle exersion 31.36** 388.87** 7.47 ns 5.31 28.33 0.85 1000 seed weight 60.41** 7100.60** 13.25** 6.02 9.37 0.96 Grain yield 5106.56** 858491.96** 1708.55** 869.54 21.75 0.94 3.2. Mean Performance of Genotypes for Grain Yield The highest grain yield was obtained from hybrid P-9534 × Melkam (6.32 tha − 1 ) followed by the hybrids B6 × ICRS-14 (5.92 tha − 1 ), TX-623 × ICRS-14 (5.88 tha − 1 ), P-9511 × Melkam (5.78 tha − 1 ) and P-850341 × ICRS-14 (5.57tha − 1 ) compared to check (ESH-4) (4.77 tha − 1 ) as indicated in Figure-1. This signified the availability of drought resilient hybrids and excelled the parents (lines and testers) and standard check (ESH-4) in terms of yield. Besides, to yield performance, growth and morphological traits were considered as selection criteria in the development of drought-tolerant genotypes. It was identified that early maturing and drought-tolerant hybrid varieties were among the drought strategies for adaptation that demanded further investigation. Therefore, the greatest attention should be provided to hybrid sorghum development to boost genetic gain for moisture stress environments. Future breeding efforts should focus to develop drought-tolerant hybrids with optimum plant height and maturity profiles that qualify to the particular conditions of areas where sorghum is grown. Thus, the most outstanding and potential sorghum genotypes were identified to be harnessed in drought-prone environments. Many authors have previously reported similar superior sorghum hybrids [ 46 – 48 ]. It is becoming increasingly important to adopt high-performing hybrids like P-9534 × Melkam (6.32 tha − 1 ) with farmer-preferred traits like biomass and earliness, as this improves smallholder farmers' livelihoods in the drought prone environments. 3.3 Combining Ability Analyses for Yield and Agronomic traits Combining ability analysis is a useful strategy for determining the type of gene action governing characteristics and for selecting parents and potential hybrids according to the effects of both general and specific combining abilities. It is essential to consider that the magnitude of gene action can be determined by GCA and SCA variations, which assists in design appropriate breeding strategies for future breeding programs. Combining ability analysis is the most crucial methods for identifying the best combiners to determine the effective breeding strategies in sorghum improvements [ 25 ]. Significant GCA and SCA variances with desirable direction were demonstrated due to females and males and interaction of females × males for yield and other agronomic traits. This suggested the females and males significantly contribute to GCA variance, whereas females and males interact to SCA variance. This also signified the preponderance of both additive and dominance gene action in development of high yielding and adaptable sorghum varieties in the moisture stress areas. GCA variances of lines were significantly high for days to flowering, plant height, stay green, panicle length, panicle exertion and 1000 seed weight traits, whereas highly significant GCA variance due to testers were demonstrated for plant height and leaf areas (Table-5). This signified the additive gene action played a leading role in controlling these traits and the possibility scenarios for improving superior sorghum parental lines in the dryland regions. These parents can be utilized for the development of high yielding and drought resistant hybrids. The SCA variances of line x tester interactions were highly significant for panicle exertion, whereas significant variations revealed in plant height and panicle width, demonstrated the dominant gene action played a vital role in governing these traits (Table-5). Rao et al . [ 49 ] and Maftuchah et al . [ 50 ] reported similar results in GCA and SCA variations among lines, testers and line x tester interactions for yield and agronomic traits. Generally, the combining abilities results identified best general combiner parents (lines and testers) and best specific combiner hybrids for the investigated traits. This signified the characteristics were controlled by both additive and dominance gene actions. Therefore, population improvement and heterosis breeding methods are advised to develop high yielding and drought adaptation varieties [ 48 , 51 ]. Table 5 Pooled analysis of variance for combining ability for yield and agronomic traits in sorghum SV DF DTF DTM PHT SG PL PW LA PE TSW GY Location 1 1080.21 ** 1494.05 ** 14359.70 ** 63.14 ** 117.66 ** 308.34 ** 439598.44 ** 388.87 ** 7100.60 ** 858491.93 ** Rep (L) 1 0.001 ns 7.29 ns 1955.70 ** 0.06 ns 5.42 ns 13.03 ** 16.35 ns 104.97 ** 0.86 ns 5700.15 ** Parents 14 28.15 ** 8.88* 10121.19 ** 1.15 ** 19.44 ** 2.44 ** 4837.30 * 55.76 ** 81.06 ** 1678.97 ** Hybrids 25 5.10 ** 7.41 ns 4288.77 ** 0.64 * 11.79 ** 1.62 ns 2958.4 ns 19.85 ** 41.95 ** 1264.76 ns Lines 12 8.12 ** 9.63 ns 8724.51 ** 0.88 ** 19.27 ** 1.25 ns 2606.25 ns 30.35 ** 76.62 ** 1390.63 ns Testers 1 3.47 ns 0.47 ns 572.46 ** 0.24 ns 2.28 ns 0.34 ns 18171.24 ** 0.16 ns 13.37 ns 318.85 ns Lines × Testers 12 2.22 ns 5.76 ns 162.71 * 0.42 ns 5.10 ns 2.10 * 2042.97 ns 11.17 ** 9.65 ns 1217.71 ns Parent Vs Hybrids 1 45.55 ** 198.48 ** 95086.83 ** 2.34 * 468.66 ** 109.99 ** 96749.62 ** 4.42 ns 792.48 ** 167591.62 ** Parent × L 14 13.41 ** 28.74 ** 350.14 ** 1.08 ** 4.25 ns 1.11 ns 2549.44 ns 11.42 ns 10.96 ns 617.10* Hybrid × L 25 1.86 ns 4.90 ns 349.75 ** 0.31 ns 3.59 ns 0.83 ns 3586.11 ns 5.48 ns 11.74 ** 1020.53 ns Lines × L 12 2.45 ns 6.71 ns 618.29 ** 0.32 ns 5.57 ns 1.38 ns 3651.38 ns 3.73 ns 18.23 ** 1474.88 ns Testers × L 1 7.00 ns 7.00 ns 48.74 ns 0.24 ns 1.16 ns 0.01 ns 3681.66 ns 0.06 ns 10.15 ns 608.61 ns Lines ×Testers × L 12 0.84 ns 2.92 ns 106.29 ns 0.30 ns 1.82 ns 0.34 ns 3512.88 ns 7.86 ns 5.39 ns 600.51 ns Hybrid v parent×L 1 11.97 ns 198.48 ** 448.11 ns 1.51 ns 0.68 ns 9.65 ** 26771.48 ** 2.94 ns 124.94 ** 41045.72 ** Error 72 2.62 4.67 70.47 0.35 2.99 0.65 2812.38 5.31 6.02 869.54 CV (%) 2.29 1.95 4.43 22.15 6.13 9.85 16.84 28.33 9.37 21.75 LSD (5%) 2.28 3.04 11.83 0.83 2.44 1.14 74.75 3.24 3.45 1314.4 R 2 0.91 0.89 0.98 0.83 0.87 0.92 0.81 0.85 0.96 0.94 3.3.1 Estimates of general combining ability effects of sorghum for Yield and agronomic traits The GCA represents an additive gene action and indicates the average performance of parental lines. The GCA effect of the A-line and R-line parents presented in Table-6 revealed significant in different direction for the investigated traits. GCA variance for days to flowering, days to maturity, plant height and 1000 seed weight appeared to be highly and positively significant in MARC2 line, whereas positive significant GCA variance revealed in P-851015 line for days to flowering (Table-6). Significant and negative GCA difference demonstrated in lines (P-9505, P-9534 and P-9511) for days to flowering trait (Table-6). This indicated the days to flowering trait is controlled by additive genetic action. Negative GCA effects for days to flowering have a positive effect in mitigating the adverse impacts of terminal drought stress in environments where drought stress is a severe problem. As a result, the P-9505, P-9534, and P-9511 lines were identified and recommended for utilization in the development of early maturing varieties. This result is in agreement with those obtained by [ 46 , 50 , 52 ]. In order to counteract significant drought-induced yield reductions, the crop can reach the stages of yield formation and grain filling, before episodes of limited soil water and excessive atmospheric temperatures. This is made possible by the most notable phenological drought escape mechanisms, which include early flowering in the season and increased early vigor [ 53 ]. The sorghum's developmental timeline is measured in days to flowering, which is essential for ensuring grain-filling duration under ideal environmental circumstances. This characteristic is directly related to the crop's ability to adapt to various growing seasons and climates. The experiment was executed in dryland environments, where terminal drought stress is critical problematic for long maturity lines. Hence, the lines with negative GCA values (P-9505, P-9534 and P-9511) were selected as best general combiner and recommended for their earliness in such drought prone environments. Significant and positive GCA effects (2.44) for days to maturity was observed in MARC2 line whereas no significant discrepancies was revealed in the rest lines and testers (Table-6). However, negative GCA effects are desirable for days to maturity to escape the drastic effect of drought stress in dry lowland environments. Negative and significant GCA effects are preferable for days to maturity in order to escape the drastic drought stress in the dry lowlands environment. To mitigate the serious effects of drought in semiarid tropical regions where seasonal rainfall is either irregular or infrequent, early-maturing sorghum cultivars are recommended. These cultivars might not always produce more than long-maturing cultivars, but by escaping terminal drought, they might produce more consistent yields in water-stressed environments. Drought stress during the pre-flowering stage results damages, abortion of the florets, reduced panicle size, and significant reduction in seed size, finally result in decreased yield potential [ 54 ]. The GCA effect signified highly significant in all lines except lines (P-851015, P-850341 and B5) and testers (Melkam and ICSR-14) for plant height (Table-6). Highly significant and positive GCA variances were observed in MARC1, MARC2, MARC3 and MARC6 lines, whereas highly significant and negative GCA variances were revealed in TX-623, P-9501, P-9505, P-9534, B6 and P-9511 lines for plant height (Table-6). This demonstrated that the existence of useful genes to improve the physiological development for both tallness and shortness traits. To mitigate the catastrophic effects of drought stress, significant and negative GCA values are recommended in dryland environments. In order to refrain from severe terminal drought stresses, the short-stature sorghum lines (TX-623, P-9501, P-9505, P-9534, B6, and P-9511) were selected. As a result, lines with significant and negative GCA values are considered the best general combiners for environments under moisture stress, while lines with significant and positive GCA effects can be used for biomass research regardless of the current moisture stressed experiment. Plant height is a vital trait that influences yield as well as the plant's ability to withstand lodging and maximize light capture. Breeding for optimal height achieves a balance between need to reduce lodging, where plants fall before harvest, lowering yield and quality, and the advantages of taller plants, which frequently yield more. This result indicated the additive gene action predominates and population enhancement is advised as a breeding strategy to improve plant height. This result agreed with the findings reported by Rini et al . [ 52 ] and Trikoesoemaningtyas et al . [ 55 ]. GCA variance for stay green trait was significantly high and positive in P-9501 and B5 lines (Table-6). According to Ndlovu et al . [ 56 ], leaf senescence is a stage of maturity in a plant's life cycle during which its leaves undergo chlorosis due to a multitude of reasons, including aging, biotic and abiotic stressors. Post-flowering drought stress causes premature leaf senescence, abortion and a decrease in grain yield in senescent sorghum genotypes [ 56 ]. Post-flowering drought stress can only be tolerated by genotypes that have an integrated drought adaptation traits called stay green, which delays leaf senescence [ 57 ]. At first, it was believed that this characteristic of sorghum was cosmetic, implying that it delayed the onset of leaf senescence and decreased its rate, which might happen simultaneously or separately [ 58 ]. Flowering period, sink strength, and environmental factors all influence stay green expression [ 59 ]. Van Oosterom et al . [ 58 ] state that in sorghum genotypes, the duration of green leaf area, the rate of senescence, and the timing of senescence onset are all independently inherited. Stay green expression has been determined by additive gene action. Therefore, P-9501 and B5 lines were best general combiner and preferred to develop drought resistance varieties in moisture-stressed environments. GCA effects in P-9534 line was highly significant and positive for panicle length, whereas significantly high and negative GCA effects observed in MARC2, MARC3 and MARC6 lines (Table-6). However, significant and positive GCA effect is desired for panicle length as it is directly associated with in increasing productivity. Therefore, P-9534 line was best general combiner for panicle length and selected to develop superior hybrid in moisture stress areas. The GCA results for panicle exertion was positive and significantly high in P-9505, P-850341 and MARC1 lines whereas positive and significant difference revealed in P-9511 line and ICRS-14 tester. In addition, negative and highly significant difference GCA effect revealed for panicle exertion in MARC2 line while negative and significant difference GCA results observed in P-9501, B5 line and Melkam tester. This meant the additive gene actions were more likely important to enhance the panicle exertion trait. The most significant characteristic linked to drought tolerance in sorghum is excellent exertion, which suggests that lines with higher exertion are more resilient to moisture stress. Therefore, P-9505, P-850341, MARC1, P-9511 and ICRS-14 lines were identified as best general combiner under moisture stress environments. The line with higher panicle exertion decreases disease risk, enhances grain filling, and is essential for the convenience of mechanical harvesting. The finding is in agreement with the results reported by Rini et al . [ 52 ] and Maftuchah et al . [ 50 ]. The GCA results for 1000-grain weight was positive and highly significantly different in MARC2 and MARC6 lines and positive and significant different GCA results observed in MARC3 line whereas negative and highly significant variation was revealed in P-850341 line and negative and significant difference observed in P-851015 and B5 lines. Although a positive and significant GCA, result required 1000-seed weight trait to increase genetic gain in sorghum. As a result, lines (MARC2, MARC6 and MARC3) were best general combiners and indicating additive gene action involved in controlling the trait. The findings were in agreement with earlier research by El-Kady et al . [ 48 ] and Ibrahim et al . [ 51 ]. Generally, these lines appeared worthy of being exploited in recombination breeding programs because they generally have high general combining ability effects that correspond with additive and additive x additive interaction [ 60 ] and represent fixable genetic components of variation. Table 6 Estimates of general combining ability effects of sorghum for Yield and agronomic traits Lines Traits DTF DM PTH SG PL PW LA PE TSW GY TX-623 0.51 ns 0.07 ns -22.08 ** -0.17 ns 0.89 ns -0.07 ns 10.64 ns -1.33 ns 0.44 ns 562.5 ns P-9501 -0.60 ns -1.18 ns -27.48 ** 0.57 ** 0.54 ns -0.52 ns 2.07 ns -1.53 * -1.47 ns -165.0 ns P-9505 -1.23 * -0.80 ns -30.93 ** 0.32 ns -0.01 ns -0.75 ns 26.35 ns 1.81 ** -2.27 ns -216.5 ns P-9534 -1.23 * -1.55 ns -28.83 ** -0.42 * 2.29 ** 0.05 ns -22.62 ns -0.98 ns 1.60 ns 492.5 ns P-851015 1.26 * 0.31 ns -5.63 ns -0.17 ns 0.91 ns 0.09 ns 16.54 ns -0.80 ns -3.09 * -473.0 ns P-850341 0.02 ns 0.81 ns -12.38 ns -0.04 ns 0.69 ns 0.37 ns 5.85 ns 4.24 ** -4.43 ** 277.5 ns B5 -0.48 ns -0.68 ns -16.33 ns 0.57 ** 1.41 ns -0.27 ns -17.99 ns -1.68 * -3.45 * -14.5 ns B6 0.02 ns 0.31 ns -24.13 ** 0.07 ns 0.49 ns -0.20 ns -5.62 ns -0.38 ns -1.29 ns 258.0 ns MARC1 -0.35 ns -0.68 ns 48.07 ** 0.07 ns -0.16 ns 0.47 ns -3.99 ns 2.46 ** 3.34 * 400.5 ns MARC2 2.02 ** 2.44 ** 56.22 ** -0.29 ns -2.51 ** 0.64 ns -23.41 ns -2.40 ** 4.01 ** -681.0 ns MARC3 0.39 ns 0.81 ns 38.56 ** -0.17 ns -2.63 ** 0.29 ns -25.43 ns -0.06 ns 3.34 * -480.5 ns MARC6 0.89 ns 0.94 ns 42.06 ** -0.42 ns -2.35 ** 0.04 ns 19.94 ns -0.88 ns 4.65 ** -318.5 ns P-9511 -1.23 * -0.80 ns -17.13 ** 0.07 ns 0.44 ns -0.15 ns 17.68 ns 1.56 * -1.35 ns 358.0 ns SE (Lines) 0.53 0.87 8.04 0.19 0.80 0.39 20.52 0.65 1.45 412.53 Testers Melkam 0.18 ns 0.07 ns 2.34 ns 0.05 ns 0.15 ns -0.06 ns -13.21 ns -0.10 * -0.35 ns 55.34 ns ICRS-14 -0.18 ns 0.07 ns 2.34 ns -0.05 ns 0.15 ns 0.06 ns 13.21 ns 0.10 * 0.35 ns -55.34 ns SE(Testers) 0.25 0.25 0.77 0.04 0.11 0.01 5.94 0.02 0.31 76.49 3.3.2 Estimates of specific combining ability effects of sorghum for yield and agronomic traits Specific combining ability is playing vital roles in the identification of best performing lines, which can be used as parents in hybrid variety development [ 61 ]. The development of superior hybrids is primarily due to the non-additive gene action caused by dominance or over-dominance gene effects, which are linked to specific combining ability. In this study, equal SCA values obtained in magnitude and opposite in direction. This might be because of both testers having similar backgrounds in specific combining abilities and having the same gene-regulating influence on the traits. Similar findings was reported in sorghum (35 testers and 2 lines) [ 46 ]. Highly significant and positive SCA effects demonstrated in hybrid B6 × ICRS-14 for days to flowering, whereas significant and positive differences observed in TX-623 × Melkam and MARC2 × Melkam hybrids (Table-7). Highly significant and negative SCA effects displayed in B6 × Melkam hybrid while negative and significant difference obtained in TX-623 × ICRS-14 and MARC2 × ICRS-14 hybrids. This indicated that non-additive gene action was vital in controlling this trait. The hybrid with negative SCA values is preferred because this investigation was conducted in dryland environments. Therefore, in dry lowland environments, the hybrids with negative SCA values (B6 × Melkam, TX-623 × ICRS-14 and MARC2 × ICRS-14) flowered earlier and ensured earliness (Table-7). Thus, B6 × Melkam, TX-623 × ICRS-14 and MARC2 × ICRS-14 hybrids were identified as best specific combiner and preferred for moisture stress areas to escape the drastic effect of drought. Therefore, heterosis breeding is the most effective and recommended breeding strategy to enhance the genetic material for the days to flowering trait. Similarly, Mengistu et al . [ 27 ]; Wagaw et al . [ 46 ]; El-Kady et al . [ 48 ] and Ibrahim et al . [ 51 ] reported positive and negative significant estimates of SCA effects for days to flowering. The SCA analysis for days to maturity displayed both positive and negative significant difference in eight hybrids. In order to mitigate the severe effects of terminal drought, moisture-stressed agro-ecologies prefer negative SCA effects. Therefore, hybrids (TX-623 × ICRS-14, P-850341 × ICRS-14, MARC2 × ICRS-14 and P-9511 × ICRS-14) with negative and significant effects of SCA were identified as best specific combiner to develop extra-early maturing varieties (Table-7). The use of early-maturing sorghum varieties is encouraged to overcome the drastic effects of drought in semiarid tropical regions where either seasonal rainfall is short or its distribution is erratic. These varieties might not always superior than long-maturing cultivars, but by escaping terminal drought, they might produce more consistent yields in water-stressed environments. Rachman et al . [ 62 ] similarly reported highly significant negatives for days to maturity in the direction to be utilized in breeding programs to introduce genes for early maturation. The SCA variance for plant height demonstrated significant and positive difference in hybrids MARC3 × Melkam whereas negative and significant variation revealed in hybrid MARC3 × ICRS-14. However, significant and negative SCA value is preferred for plant height to develop early and lodging free varieties. Therefore, only MARC3 × ICRS-14 hybrid showed significant and negative SCA effects for plant height (Table-7). The plant height trait is governed by additive gene action in general and the effect of GCA was more vital to SCA effects. Mengistu et al . [ 27 ] and Wagaw et al. [ 46 ] reported both negative and positive significant SCA effects in crosses of sorghum lines. Ensuring yield and quality requires lodging resistance, especially in areas that are vulnerable to prolong drought and high winds. In order to maintain the plant's weight and withstand environmental challenges, breeding efforts focus on strengthening the roots and stalks. The SCA results for panicle length were highly significant in (TX-623 × Melkam, TX-623 × ICRS-14, B6 × Melkam and B6 × ICRS-14) hybrids, whereas significant difference was observed for panicle length in other hybrids (P-9501 × Melkam, P-9501 × ICRS-14, MARC2 × Melkam and MARC2 × ICRS-14) (Table-7). Positive SCA effects are required for panicle length to enhance grain yield per unit area. Therefore, those hybrids (TX-623 × ICRS-14, B6 × ICRS-14, P-9501 × Melkam and MARC2 × Melkam) with significant and positive SCA effects were identified as best specific combiner to further enhance of the panicle length trait. In line with the present investigation, Mengistu et al . [ 27 ] and Wagaw et al . [ 46 ] showed both negative and positive estimates of SCA impacts in crosses of sorghum panicle length. The SCA variance for panicle width displayed significantly high differences and significant differences in eighteen hybrids, indicating that the panicle width trait was governed by dominance gene actions and the heterosis-breeding scheme could be harnessed for enhancing this trait. The SCA effects for panicle exertion exhibited significantly high in hybrid (P-9505 × Melkam and P-9505 × ICRA-14). However, positive SCA values are desirable for panicle exertion. Hence, the hybrid (P-9505 × ICRS-14) is identified as best specific combiner and well-exerted hybrids. A well-exerted hybrid is preferred for improving the quality of grains and avoid fungal and insects to devastation. The current finding is supported by extremely significant and positive SCA for panicle exertion reported by Wagaw et al . [ 46 ] and Mindaye et al . [ 26 ]. The SCA effects for 1000 seed weight trait was highly significant in TX-623 × Melkam and TX-623 × ICRS-14 hybrids whereas significant difference was displayed in (P-9505 × Melkam and P-9505 × ICRS-14) hybrids (Table-7). The study showed that additive gene action predominated over non-additive gene action in the 1000 seed weight trait, suggesting that the GCA was more crucial than the SCA to increase this trait. The hybrids with the highest and positive SCA values are preferred to increase the productivity. The SCA results found to be highly significant in hybrids (P-9534 × Melkam, P-9534 × ICRS-14, B6 × Melkam and B6 × ICRS-14) for grain yield, whereas significant variances revealed in (MARC3 × Melkam and MARC3 × ICRS-14) hybrids (Table-7). These hybrids are vital for increasing grain yield per unit area because they have the best and positive SCA effects. Therefore, the hybrids (P-9534 × Melkam, B6 × ICRS-14 and MARC3 × Melkam) with positive SCA effects are identified as best specific combiner and because dominance gene action predominated, the SCA effects on grain yield significantly higher than the GCA effects. To develop superior sorghum hybrids that can satisfy the expanding demands of global agriculture, it is crucial to comprehend the complex interactions between this trait and how they react to various environmental conditions. Eventually, grain yield trait can be improved through heterosis breeding method since non-additive genes involved for the expression of traits. Several authors have reported both positive and negative SCA for sorghum yield [ 46 , 51 , 63 ]. Table 7 Estimates of specific combining ability effects of sorghum for yield and agronomic traits Line Tester DTF DMT PHT SG PL PW LA PE TSW GY TX-623 Melkam 0.69 * 1.18 * -1.09 ns 0.32 ns -1.24 ** -0.79 ** 2.52 ns -1.20 ns -2.03 ** -331.34 ns TX-623 ICRS-14 -0.69 * -1.18 * 1.09 ns -0.32 ns 1.24 ** 0.79 ** -2.52 ns 1.20 ns 2.03 ** 331.34 ns P-9501 Melkam 0.31 ns 0.43 ns -4.29 ns -0.17 ns 1.01 * 0.20 ns 21.86 ns 0.19 ns -0.49 ns 40.15 ns P-9501 ICRS-14 -0.31 ns -0.43 ns 4.29 ns 0.17 ns -1.01 * -0.20 ns -21.86 ns -0.19 ns 0.49 ns -40.15 ns P-9505 Melkam -0.30 ns -0.19 ns -5.84 ns 0.33 ns -0.05 ns 0.58 ** 6.09 ns -2.70 ** 1.83 * 5.65 ns P-9505 ICRS-14 0.30 ns 0.19 ns 5.84 ns -0.33 ns 0.05 ns -0.58 ** -6.09 ns 2.70 ** -1.83 * -5.65 ns P-9534 Melkam -0.31 ns -0.44 ns 1.55 ns -0.17 ns 0.65 ns 0.78 ** 6.83 ns 0.54 ns 1.21 ns 727.65 ** P-9534 ICRS-14 0.31 ns 0.44 ns -1.55 ns 0.17 ns -0.65 ns -0.78 ** -6.83 ns -0.54 ns -1.21 ns -727.65 ** P-851015 Melkam 0.44 ns -0.06 ns -5.44 ns 0.08 ns 0.23 ns -0.06 ns 16.29 ns 0.92 ns -1.14 ns -428.84 ns P-851015 ICRS-14 -0.44 ns 0.06 ns 5.44 ns -0.08 ns -0.23 ns 0.06 ns -16.29 ns -0.92 ns 1.141 ns 428.84 ns P-850341 Melkam 0.44 ns 1.18 * -3.49 ns -0.29 ns -0.14 ns -0.59 ** 10.60 ns -1.02 ns 0.17 ns -310.34 ns P-850341 ICRS-14 -0.44 ns -1.18 * 3.49 ns 0.29 ns 0.14 ns 0.59 ** -10.60 ns 1.02 ns -0.17 ns 310.34 ns B5 Melkam 0.19 ns -0.06 ns 5.65 ns 0.07 ns -0.37 ns -0.34 ns -2.74 ns 0.74 ns 0.19 ns 163.65 ns B5 ICRS-14 -0.19 ns 0.06 ns -5.65 ns -0.07 ns 0.37 ns 0.34 ns 2.74 ns -0.74 ns -0.19 ns -163.65 ns B6 Melkam -1.06 ** -1.06 ns -2.04 ns 0.08 ns -1.59 ** -0.41 * -6.77 ns 0.64 ns 0.58 ns -675.84 ** B6 ICRS-14 1.06 ** 1.06 ns 2.04 ns -0.08 ns 1.59 ** 0.41 * 6.77 ns -0.64 ns -0.58 ns 675.84 ** MARC1 Melkam -0.18 ns -0.06 ns -3.04 ns -0.17 ns 0.80 ns 0.55 ** 18.84 ns -0.40 ns 0.72 ns 15.65 ns MARC1 ICRS-14 0.18 ns 0.06 ns 3.04 ns 0.17 ns -0.80 ns -0.55 ** -18.84 ns 0.40 ns -0.72 ns -15.65 ns MARC2 Melkam 0.69 * 1.30 * 3.20 ns -0.29 ns 0.90 * 0.63 ** -4.37 ns -0.32 ns 0.92 ns -163.84 ns MARC2 ICRS-14 -0.69 * -1.30 * -3.20 ns 0.29 ns -0.90 * -0.63 ** 4.37 ns 0.32 ns -0.92 ns 163.84 ns MARC3 Melkam -0.43 ns -1.06 ns 7.55 * 0.07 ns 0.22 ns -0.06 ns -4.73 ns 1.32 ns -0.20 ns 527.65 * MARC3 ICRS-14 0.43 ns 1.06 ns -7.55 * -0.07 ns -0.22 ns 0.06 ns 4.73 ns -1.32 ns 0.20 ns -527.65 * MARC6 Melkam 0.06 ns 0.05 ns 5.45 ns 0.32 ns -0.59 ns -0.41 * -40.01 ns 0.24 ns -1.44 ns 104.65 ns MARC6 ICRS-14 -0.06 ns -0.05 ns -5.45 ns -0.32 ns 0.59 ns 0.41 * 40.01 ns -0.24 ns 1.44 ns -104.65 ns P-9511 Melkam -0.55 ns -1.19 * 1.85 ns -0.17 ns 0.20 ns -0.06 ns 8.17 ns 1.04 ns -0.32 ns 325.15 ns P-9511 ICRS-14 0.55 ns 1.19 * -1.85 ns -0.17 ns -0.20 ns 0.06 ns -8.17 ns -1.04 ns 0.32 ns -325.15 ns SE( ij ) 0.31 0.58 3.59 0.18 0.45 0.19 20.13 0.95 0.78 263.22 Table 8 Summary of best general and specific combiners of sorghum lines, testers and hybrids for yield and agronomic traits Traits Best general combiners Best specific combiners DTF P-9505, P-9534 & P-9511 TX-623 × ICRS 14, B6 × Melkam& MARC2 × ICRS-14 DTM - TX-623 × ICRS 14, P-850341 × ICRS-14, MARC2 × ICRS-14 & P-9511 × Melkam PTH TX-623, P-9501, P-9505, P-9534, B6 & P-9511 MARC3 × ICRS-14 SG P-9501 & B5 - PL P-9534 TX-623 × ICRS-14, P-9501 × Melkam, B6 × ICRS-14& MARC2 × Melkam PW - TX-623 × ICRS-14, P-9505 × Melkam, P-9534 × Melkam, P-850341 × ICRS-14, B6 × ICRS-14, MARC1 × Melkam, MARC2 × Melkam & MARC6 × ICRS-14 LA - - PE P-9505, P-850341, MARC1 & MARC6 P-9505 × ICRA-14 TSW MARC1, MARC2, MARC3 & MARC6 TX-623 × ICRS 14& P-9505 × Melkam GY - P-9534 × Melkam, B6 × ICRS-14 & MARC3 × Melkam 3.4 Estimation of variances of combining ability effect, gene action and heritability Understanding genetic variability, the role of genes action, and combining ability is essential for increasing sorghum yield. The relative importance of each variance was determined using GCA/SCA ratio of mean squares. Non-additive genetic variance (dominance or epitasis) is more significant than additive gene action in controlling these traits, demonstrated by the GCA/SCA ratio being less than unity for all investigated characteristics with the exception of the plant height (Table-8). All characteristics under investigation, with the exception of plant height, showed the genetic variation connected to specific combining ability (σ 2 sca), suggesting that dominant gene action was more important in determining the traits than additive type. Therefore, the selection and development of superior genotypes take time until segregation generation is fixed. Making decisions about the next stage of a breeding program requires an extensive understanding of the GCA and SCA consequences. Similar findings regarding the role of gene action and combining ability effect of sorghum crops were reported by Amelework et al . [ 64 ] and Wagaw et al . [ 46 ]. All of the traits under study had a degree of dominance (σ 2 D/σ 2 A) greater than unity, with the exception of days to flowering, plant height, and leaf area. This suggests that non-additive gene action controls the inheritance of traits that contribute to yield, as well as the most effective method to develop superior hybrids is through heterosis-breeding. The findings showed that the degree of dominance (σ 2 D/σ 2 A) for days to flowering, plant height and leaf area was less than unity, indicating that additive genes regulate these traits and that parent selection should be given more weight in breeding strategies. According to Wagaw et al . [ 46 ], the significance mean squares of lines and testers reveal the significance additive variance (σ 2 A), however the significance mean squares of line x testers provide the significance of dominance variance (σ 2 D). The broad sense heritability (H 2 ) values ranged from 8.99% for days to maturity to 95.63% for plant height. The highest heritability was obtained for plant height (95.63%), panicle length (85.97%), 1000-seed weight (77.98%), panicle exertion (74.87%), panicle width (71.88%) and grain yield (66.54%), whereas days to flowering (58.31%), stay green (35.00%) and leaf area (30.77%) revealed moderate heritability. These results indicating the presence of positive response of sorghum improvement through selection of these traits because of their higher heritability. Thus, phenotypic and high heritability in the hybrid population are crucial for the selection of genetically superior generations. Gaddameedi et al . [ 65 ], Mengistu et al . [ 27 ], Wagaw et al . [ 46 ], and Veldandi et al . [ 66 ] have also reported similar results in sorghum. Table 9 Variance components of combining ability analysis and estimates of gene effects Traits Combining ability variances Ratio (gca/sca) Gene action Ratio (𝜎²D/𝜎²A) Phenotypic variance Genotypic variance Heritability σ 2 gca σ 2 sca 𝜎²𝐴 𝜎²𝐷 H 2 (%) h 2 (%) DTF 0.09 -0.01 -7.06 0.36 -0.05 -0.14 3.31 1.93 58.31 11 DTM 0.03 0.85 0.04 0.13 3.39 26.33 3.78 0.34 8.99 3.4 PTH 118.32 42.48 2.78 473.29 169.90 0.35 1903.88 1820.67 95.63 25 SG 0.01 0.04 -0.16 0.02 0.14 6.13 0.20 0.07 35.00 10 PL 0.19 0.95 0.20 0.75 3.80 5.01 6.77 5.82 85.97 11 PW 0.001 0.20 0.001 0.01 0.80 745.37 0.96 0.69 71.88 0 LA 44.46 -396.55 -0.11 177.85 -1586.19 -8.91 1415.60 435.64 30.77 13 PE 0.26 2.93 0.09 1.03 5.86 5.70 7.48 5.60 74.87 14 TSW 0.91 1.60 0.56 3.62 5.86 1.75 15.12 11.79 77.98 24 GY 0.21 183.08 0.01 0.86 732.32 851.53 1276.64 849.50 66.54 0.7 3.6 Estimation of the Proportional Contribution different variance components (lines, testers and line × tester) The proportional contribution of lines, testers and their interaction to the total variance showed that lines showed greater contribution than tester for all traits except leaf width in which line × tester interaction revealed greater contribution than the lines and testers (Table-9). Generally, the contribution of lines are higher than testers and line x tester interactions in all traits except leaf width, indicating the lines had excellent contribution in hybrid variety development. For every traits except plant height, the line x tester interactions contributed more than the testers. Plant height was the most significant contribution from lines, followed by 1000 seed weight, days to flowering, panicle length, panicle exertion, stay green, and days to maturity. The maximum contribution of lines was 95.43% in plant height and the minimum was 42.63 in leaf area whereas the testers contribution ranged from 0.02% in days to maturity to 24.35% in leaf area and the line × tester contributed the highest 46.31% in grain yield and the lowest 1.82% in plant height. This finding demonstrated that lines contributed adequate variation in expressing the characteristics under evaluation. It suggested the hybrid-breeding program should be focused the selection of parent lines. Rachman et al . [ 62 ], Wagaw et al . [ 46 ], and Rini et al . [ 52 ] were similarly in agreement with this finding as well. Table 10 Proportional contribution of different component (lines, testers and line × tester) towards total variances for yield and various agronomic traits. Traits Contribution (%) Lines Testers Line × Tester DTF 77.03 1.53 20.45 DTM 58.75 0.02 39.25 PTH 95.43 2.52 1.82 SG 68.47 1.08 33.57 PL 73.12 0.97 19.76 PW 51.97 0.25 44.76 LA 42.63 24.35 33.04 PE 73.37 0.03 27.00 TSW 89.39 1.20 11.66 GY 52.80 0.64 46.31 4. SUMMARY AND CONCLUSIONS Enhancing sorghum crop through identifying and selecting of best general and specific combiners of superior sorghum lines and hybrids for grain yield and agronomic traits is vital to tackle the problems of acute food insecurity and malnutrition in arid and semi-arid regions of Ethiopia. The current study identified promising and drought-adapted sorghum parental lines and hybrids for all traits based on the substantial effect of both additive and non-additive gene actions. In order to develop new sorghum varieties with outstanding grain yield and agronomic traits, it is vital to select lines and testers that have significant GCA effects and crosses that have significant SCA effects. The best general combining ability of the potential parents were determined by their consistent performance and their desired direction for each traits. The best general combiner lines, P-9505, P-9534, and P-9511 were identified for the days to flowering, whereas all lines were identified to be the best general combiners for plant height except some lines and both testers. The investigation demonstrated that additive genes control the expression of plant height, and selection-breeding strategy could help the population associated with this trait. The best general combiners were identified to be lines (P-9501, P-9534, B5) for stay green, lines (P 9534) for panicle length, lines (P-9501, P-9505, P-850341, B5, MARC1, MARC2, P-9511, Melkam and ICRS-14) for panicle exertion and lines (MARC1, MARC2, MARC3 and MARC6) for 1000 seed weight. Additive gene actions governed each of the aforementioned lines for their respective traits, leading to effective selection for improving the population. The best specific combiners hybrids (TX-623 × ICRS-14, B6 × Melkam and MARC2 × ICRS-14) for days to flowering, (TX-623 × ICRS-14, P-850341 × ICRS-14, MARC2 × ICRS-14 and P-9511 × ICRS-14) for days to maturity, (MARC3 × ICRS-14) for plant height, (TX-623 × ICRS-14, P-9501 × Melkam, B6 × ICRS-14 and MARC2 × Melkam) for panicle length, (TX-623xICRS 14, P-9505 x Melkam, P-9534 x Melkam, 6x15, P-850341 x ICRS-14, MARC1 × Melkam, MARC2 × Melkam and MARC6 × ICRS-14) for panicle width, (P-9505 × ICRA-14) for panicle exertion, (TX-623 × ICRS-14) for 1000 seed weight and (P-9534 × Melkam, B6 × ICRS-14 and MARC3 × Melkam) for grain yield were identified. Due to the expression of these traits were governed by dominant gene actions, heterosis-breeding techniques are recommended to enhance these traits. The ratio SCA variation was greater than GCA variation for the traits under investigation except plant height. Hence, non-additive gene action played a crucial role in determining the investigated traits. In conclusion, the parental lines P-9534, MARC2, MARC3, MARC6, and P-9511 and hybrids P-9534 × Melkam, P-9534 × ICRS-14, B6 × Melkam, B6 × ICRS-14, MARC3 × Melkam, and MARC3 × ICRS-14 were identified as promising sorghum genotypes that could be used after in-depth scrutiny for superiority and yield stability across locations over years. The development of hybrids by heterotic breeding and population improvement through efficient selection were both recommended as appropriate breeding strategies for future breeding programs. As a prospect for the future, modern breeding techniques such as multi-trait gene editing, genomic prediction, and machine learning can precisely speed up breeding efforts to increase sorghum productivity. Therefore, revitalizing sorghum hybrid development is the primary prerequisite for enhancing sorghum productivity in Ethiopia. Declarations Author contributions Temesgen Begna : Writing - review & editing, Writing– original draft, Visualization, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. Techale Birhan : Writing– review & editing, Writing– original draft, Validation, Supervision, Project administration, Investigation, Conceptualization. Taye Tadesse : Writing– original draft, Visualization, Resources, Project administration, Investigation, Funding acquisition, Conceptualization. Funding Statement The study was funded by the Ethiopian National Sorghum Improvement Program. Data availability statement All the data are included in the article. Ethics approval and clinical trial declarations : Not applicable Consent to publish declaration : Not applicable Conflict of interest: The authors declare no competing interests. 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Agronomy. 2023;13(11):2788. Assefa Y, Staggenborg SA, Prasad VP. Grain sorghum water requirement and responses to drought stress: A review. Crop Manage. 2010;9(1):1–1. Borrell A, Jordan D, Mullet J, Henzell B, Hammer G. Drought adaptation in sorghum. InDrought adaptation in cereals. 2024; (335–399). CRC Press. Wortmann CS, Mamo M, Mburu C, Letayo E, Abebe G, Kayuki KC, Chisi M, Mativavarira M, Xerinda S, Ndacyayisenga T. Atlas of Sorghum (Sorghum bicolor (L.) Moench): production in eastern and Southern Africa. De Souza AA, de Carvalho AJ, Bastos EA, Portugal AF, Torres LG, Batista PS, Julio MP, Julio BH, de Menezes CB. Grain sorghum grown under drought stress at pre-and post-flowering in Semiarid environment. Harris K, Subudhi PK, Borrell A, Jordan D, Rosenow D, Nguyen H, Klein P, Klein R, Mullet J. Sorghum stay-green QTL individually reduce post-flowering drought-induced leaf senescence. J Exp Bot. 2007;58(2):327–38. Wang Z, Nie T, Lu D, Zhang P, Li J, Li F, Zhang Z, Chen P, Jiang L, Dai C, Waller PM. Effects of Different Irrigation Management and Nitrogen Rate on Sorghum (Sorghum bicolor L.) Growth, Yield and Soil Nitrogen Accumulation with Drip Irrigation. Agronomy. 2024;14(1):215. Begna T. Combining ability and heterosis in plant improvement. Open J Plant Sci. 2021;6(1):108–17. Mindaye TT, Mace ES, Godwin ID, Jordan DR. Heterosis in locally adapted sorghum genotypes and potential of hybrids for increased productivity in contrasting environments in Ethiopia. Crop J. 2016;4(6):479–89. Mengistu G, Shimelis H, Laing M, Lule D, Mashilo J. Combining ability and heterosis among sorghum (Sorghum bicolor [L.] Moench) lines for yield, yield-related traits, and anthracnose resistance in western Ethiopia. Euphytica. 2020;216(2):33. Indhubala M, Ganesamurthy K, Punitha D. Heterosis for quality attributes in sweet sorghum hybrids using cytoplasmic genic male sterile lines. 2010; 309–11. Melkasa Agricultural Research Centre (MARC). Meteorological weather data report. Ethiopia: East wollo and West Hararghe Oromia; 2020. Melkassa miscellaneous sorghum working collection documents. 2018. John JA, Williams ER. The construction of efficient two-replicate row–column designs for use in field trials. J Royal Stat Soc Ser C: Appl Stat. 1997;46(2):207–14. IBPGR ICRISAT. Descriptors for sorghum [Sorghum bicolor (L.) Moench]. International Board for plant genetic Resources. Rome, Italy: International Crops Research Institute for the Semi-Arid Tropics, Patancheru, India.; 1993. Haussmann BI, Obilana AB, Ayiecho PO, Blum A, Schipprack W, Geiger HH. Quantitative-genetic parameters of sorghum [Sorghum bicolor (L.) Moench] grown in semi-arid areas of Kenya. Euphytica. 1999;105:109–18. Stickler FC, Pauli AW. Leaf Removal in Grain Sorghum. I. Effects of Certain Defoliation Treatments on Yield and Components of Yield 1. Agron J. 1961;53(2):99–102. SAS Institute Inc. (2018). SAS/STAT users guide. Version 9.4, 4th Ed. Cat, NC. Moore KJ, Dixon PM. Analysis of combined experiments revisited. Agron J. 2015;107(2):763–71. Gomez KA, Gomez KA, Gomez AA. Statistical procedures for agricultural research. Wiley; 1984. Sharma KR, Leung P, Zaleski HM. Technical, allocative and economic efficiencies in swine production in Hawaii: a comparison of parametric and nonparametric approaches. Agric Econ. 1999;20(1):23–35. Kempthorne O. An introduction to genetic statistics. Link: Wiley; 1957. https://bit.ly/3qofss9 . Singh RK, Chaudhary BD. Biometrical Methods in Quantitative Genetics Analysis. New Delhi, India. 1985. Allard RW. Principles of plant breeding. Wiley; 1999. Gebeyehu C, Bulti T, Dagnachew L, Kebede D. Additive main effect and multiplicative interactions (AMMI) and regression analysis in sorghum [Sorghum bicolor (L). Moench] varieties. J Appl Biosci. 2019;136:13877–86. Worede F, Mamo M, Assefa S, Gebremariam T, Beze Y. Yield stability and adaptability of lowland sorghum (Sorghum bicolor (L.) Moench) in moisture-deficit areas of Northeast Ethiopia. Cogent Food Agric. 2020;6(1):1736865. Birhanu C, Bedada G, Dessalegn K, Lule D, Chemeda G, Debela M, Gerema G. Genotype by environment interaction and grain yield stability analysis for Ethiopian sorghum [Sorghum bicolor (L.) Moench] genotypes. Int J Plant Breed Crop Sci. 2021;8:975–86. Enyew M, Feyissa T, Geleta M, Tesfaye K, Hammenhag C, Carlsson AS. Genotype by environment interaction, correlation, AMMI, GGE biplot and cluster analysis for grain yield and other agronomic traits in sorghum (Sorghum bicolor L. Moench). PLoS ONE. 2021;16(10):e0258211. Wagaw K, Tadesse T. Combining ability and heterosis of sorghum (Sorghum bicolor L. Moench) hybrids for grain and biomass yield. Am J Plant Sci. 2020;11(12):2155–71. Rosenow DT, Quisenberry JE, Wendt CW, Clark LE. Drought tolerant sorghum and cotton germplasm. Agric Water Manage. 1983;7(1–3):207–22. El Kady YM, Abd Elraheem OA, Hafez HM. Combining ability and heterosis for agronomic and yield traits in some grain sorghum genotypes. Egypt J Plant Breed. 2022;26(1):59–74. Rao BD, Anis M, Kalpana K, Sunooj KV, Patil JV, Ganesh T. Influence of milling methods and particle size on hydration properties of sorghum flour and quality of sorghum biscuits. LWT-Food Sci Technol. 2016;67:8–13. Maftuchah H, Widyaningrum A, Zainudin, Sulistyawati HA, Reswari. and H. Sulistiyanto. Combining ability and heterosis in sorghum (Sorghum bicolor L.). 2022: 30–43. Ibrahim MU, Khaliq A, Hussain S, Murtaza G. Sorghum water extract application mediates antioxidant defense and confers drought stress tolerance in wheat. J Plant Growth Regul. 2022;41(2):863–74. Rini EP, Wirnas D, Sopandie D. Genetic analysis on agronomic and quality traits of sorghum hybrids in Indonesia. SABRAO J Breed Genet. 2017;49(2). Hadebe ST, Modi AT, Mabhaudhi T. Drought tolerance and water use of cereal crops: A focus on sorghum as a food security crop in sub-Saharan Africa. J Agron Crop Sci. 2017;203(3):177–91. Jabereldar AA, El Naim AM, Abdalla AA, Dagash YM. Effect of water stress on yield and water use efficiency of sorghum (Sorghum bicolor L. Moench) in semi-arid environment. Int J Agric Forestry. 2017;7(1):1–6. Trikoesoemaningtyas FA, Burnama PW, Rahayu F, Rachman F, Hariadi RE, Marwiyah S, Sopandie D, Wirnas D. Genetic variations in sorghum segregating populations based on yield and amylose content. SABRAO J Breed Genet. 2024;56(4):1357–66. Ndlovu E, Maphosa M, van Staden J. Pre-anthesis morpho-physiological response of tropical sorghum to combined drought and heat stress. South Afr J Bot. 2024;172:448–61. Borrell AK, van Oosterom EJ, Mullet JE, George-Jaeggli B, Jordan DR, Klein PE, Hammer GL. Stay‐green alleles individually enhance grain yield in sorghum under drought by modifying canopy development and water uptake patterns. New Phytol. 2014;203(3):817–30. Van Oosterom EJ, Jayachandran R, Bidinger FR. Diallel analysis of the stay-green trait and its components in sorghum. Crop Sci. 1996;36(3):549–55. Harris K, Subudhi PK, Borrell A, Jordan D, Rosenow D, Nguyen H, Klein P, Klein R, Mullet J. Sorghum stay-green QTL individually reduce post-flowering drought-induced leaf senescence. J Exp Bot. 2007;58(2):327–38. Griffing BR. Concept of general and specific combining ability in relation to diallel crossing systems. Australian J Biol Sci. 1956;9(4):463–93. Fasahat P, Rajabi A, Rad JM, Derera JJ. Principles and utilization of combining ability in plant breeding. Biometrics Biostatistics Int J. 2016;4(1):1–24. Rachman F, TRIKOESOEMANINGTYAS T, Wirnas D. REFLINUR R. Estimation of genetic parameters and heterosis through line× tester crosses of national sorghum varieties and local Indonesian cultivars. Biodiversitas J Biol Divers. 2022;23(3). Maiga AM, Diallo AG, Touré A. Combining ability for grain yield and grain components of sorghum hybrid containing high lysine, threonine, iron and zinc content in mali. 2021. Amelework B, Shimelis H, Laing M. Genetic variation in sorghum as revealed by phenotypic and SSR markers: implications for combining ability and heterosis for grain yield. Plant Genetic Resour. 2017;15(4):335–47. Gaddameedi A, Sheraz S, Kumar A, Li K, Pellny T, Gupta R, Wan Y, Moore KL, Shewry PR. The location of iron and zinc in grain of conventional and biofortified lines of sorghum. J Cereal Sci. 2022;107:103531. Veldandi S, Maheswaramma S, Sravanthi K, Sujatha K, Ramesh S, Yamini KN, Shivani D, Kumar CS. Heterosis and combining ability studies to identify the superior hybrids and parents for grain yield and yield contributing traits in sorghum (Sorghum bicolor L. Moench). 2022. Additional Declarations No competing interests reported. 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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-5838770","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":444231835,"identity":"02d76d38-2971-4970-a45f-405f027b633e","order_by":0,"name":"Temesgen Begna","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYHACNjDJDyISCohQzwPTItkA0mJAihaDA2CSCC327N1pDz78uiNvfH514ocHBgzy/GIHCNjCc3a74cy+Z4bbbrzdLAF0mOHM2QkEtEjkbpPm7TnMuO3G2Q0gLQkGtwlpkX+7Tfpvz2H7zTPObv5BnBYJ3m3SDD8OJ27g791GpC1ncrcb9jYcTp5xg3ebRYKBBGG/sLef3fbgx5/Dtv39Zzff/FFhI88vTUALGDC2AQkJsEoJIpSDwR8g5j9ArOpRMApGwSgYaQAAxwZJNOWtq20AAAAASUVORK5CYII=","orcid":"","institution":"Ethiopian Institute of Agricultural Research, Addis Ababa","correspondingAuthor":true,"prefix":"","firstName":"Temesgen","middleName":"","lastName":"Begna","suffix":""},{"id":444231836,"identity":"5c346e20-4925-4866-8d77-c9b5d62fd476","order_by":1,"name":"Techale Birhan","email":"","orcid":"","institution":"Jimma University","correspondingAuthor":false,"prefix":"","firstName":"Techale","middleName":"","lastName":"Birhan","suffix":""},{"id":444231837,"identity":"53315b70-64c1-44ac-a858-87cbff70a454","order_by":2,"name":"Taye Tadesse","email":"","orcid":"","institution":"Ethiopian Institute of Agricultural Research, Addis Ababa","correspondingAuthor":false,"prefix":"","firstName":"Taye","middleName":"","lastName":"Tadesse","suffix":""}],"badges":[],"createdAt":"2025-01-16 05:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5838770/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5838770/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s43621-025-01411-6","type":"published","date":"2025-07-01T15:58:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80876614,"identity":"86121560-25c8-4541-aca8-594170f8b47b","added_by":"auto","created_at":"2025-04-18 06:40:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92591,"visible":true,"origin":"","legend":"\u003cp\u003eMean performance of genotypes for combined locations\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5838770/v1/d518a367e4c874c1c3e8368a.png"},{"id":80876615,"identity":"2c99cb1c-8ce0-44c2-a920-57c5821a5bce","added_by":"auto","created_at":"2025-04-18 06:40:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":89204,"visible":true,"origin":"","legend":"\u003cp\u003eProportional contribution of lines, testers, line x tester, GCA and SCA to the total variances.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5838770/v1/f703a4c842a1bd5d1c1c59c6.png"},{"id":86179647,"identity":"e83398f2-b943-4909-9b71-65fa7c8c3560","added_by":"auto","created_at":"2025-07-07 16:18:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2494736,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5838770/v1/d64aa707-b22a-4223-9d47-5180e2ccf2ca.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Phenotyping and combining ability Analysis of sorghum [Sorghum bicolor (l) Moench] Genotypes in dryland Environments","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eSorghum [\u003cem\u003eSorghum bicolor (L.)\u003c/em\u003e Moench] is an annual cereal crop belonging to poaceae family [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Sorghum is a short-day, principally a self-pollinating, diploid (2n\u0026thinsp;=\u0026thinsp;20) crop with a genome size of 730 Mb [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Sorghum is a C\u003csub\u003e4\u003c/sub\u003e carbon cycle crop with a high photosynthetic efficiency that can adapt to diverse agro-ecologies particularly dryland regions [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Sorghum plays a critical role in ensuring global food security and is the most preferred crop for climate-smart agriculture [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Sorghum has historically been grown in arid and semi-arid regions and is the fifth most significant cereal crop in the world, behind maize, wheat, rice, and barley [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Sorghum is widely recognized for its remarkable resilience and broad adaptation to multiple biotic and abiotic stressors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Due to these qualities, sorghum has gained attention as a possible star crop to address the issue of global food security [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSorghum is produced in 40.25\u0026nbsp;million ha in the world with a production of 58.70\u0026nbsp;million tons with the productivity of 1.46 tha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In terms of production, sorghum stands third in Africa behind rice (37.2\u0026nbsp;million metric tons) and maize (96.6\u0026nbsp;million metric tons) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. It is the second most important cereal crop in terms of area coverage (28.1\u0026nbsp;million hectares) preceded by maize (42.5\u0026nbsp;million hectares) and followed by rice (15.8\u0026nbsp;million hectares) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In countries where food insecurity is a serious problem, sorghum provides food for almost half a billion people [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. It is an excellent source of carbohydrates, including amylopectin (80.22%), starch (69.15%), and amylose (19.78%) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]; ash (1.8%), oil (3.3%), protein (13.8%), and fiber (17.3%) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]; minerals: calcium (336 mg/kg), zinc (12.0\u0026ndash;23.0 mg/kg), iron (17.5 mg/kg), potassium (1548.5 mg/kg) and sodium (36.6 mg/kg) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Sorghum provides an alternative source of nutrition for those with celiac disease or gluten sensitivity because it is gluten-free [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSorghum is the most vital strategic cereal crop for improving the food security and economic well-being of millions of people globally, particularly in developing countries like Ethiopia [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Despite its crucial significance, sorghum productivity has declined because of several production constraints [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Among the production constraints, drought stress is the leading and catastrophic production constraints that drastically reduces sorghum productivity on a regional and worldwide level [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Devastating drought stress has a detrimental effect on sorghum productivity and production at different stages, but it drastically decreases sorghum productivity during the flowering and grain filling stages [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The growth and development of sorghum affected by drought stress, which ultimately results in considerable yield reduction [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Drought stress is becoming more frequent and causing more loss, which is a critical problem for crop production globally [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDrought stress causes alterations in morphology, physiology, biochemistry in sorghum [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These alterations have an impact on the growth and development of sorghum, which drastically decreases its yields [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Drought stress reduced sorghum productivity to 36% at the vegetative stage and 55% at the reproductive stage respectively [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Drought stress significantly reduces the amount and quality of grain sorghum, affecting its physio-chemical properties and development from germination to the reproductive and grain filling stages [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Drought in the tropic region surpass 17% yield loss, with up to 60% in the drastically affected environments [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Drought during grain filling is the most detrimental abiotic stress on sorghum production, causing yield losses of between 45% and 50% [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. De Souza \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] found that pre-flowering drought stress reduced sorghum grain production by approximately 40%, whereas post-flowering drought stress caused 50\u0026ndash;90% reductions in grain yield [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Sorghum's phenological stage, frequency, and intensity are the main factors that determine the severity of drought stress [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, to enhance productivity and ensure local and global food security, it is vital to develop sorghum varieties with high productive, nutrient-dense, market-preferred and resilient to drought stress. Thus, the investigation of combining ability and gene action are essential to identify and select superior and potential parental lines and hybrids varieties from best general and specific combiners of genetic materials [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Combining ability and gene action are playing vital roles in the identification of the superior and promising sorghum genotype for yield and agronomic traits. It also assists in determining and designing the most effective breeding strategies based on the traits' genetic inheritance [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn order to identify and develop superior sorghum genotypes for yield and agronomic characteristics, numerous investigations have been conducted on the combining ability and gene action of sorghum in different times. However, there is a dearth of information regarding combining ability and gene action of grain yield and agronomic traits to develop superior and well-adapted sorghum varieties. Previously, several open-pollinated and few hybrid sorghum varieties have been released for production for diverse agro-ecologies of Ethiopia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These varieties adopted in very low rates due to different constraints; lack of drought adapted traits and lack farmers\u0026rsquo; multiple desired traits, especially biomass, grain size, and earliness [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. To address the high demand for improved sorghum varieties and farmers\u0026rsquo; preferred traits issues, the development of superior open pollinated and hybrids varieties are very critical [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, gene action and combining ability play an important role for tackling adaptation issues and farmers' preferences for various traits, particularly in regions where drought is causing detrimental productivity reduction [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. According to Indhubala \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], specific combining ability assists in developing of superior hybrids in terms of yield and agronomic characteristics, whereas general combining ability aids in the development of superior parental lines. For future sorghum improvement program, the development of hybrids by heterotic breeding and population enhancement through effective selection are essential and appropriate methods of breeding. There are very few commercially available sorghum hybrids for Ethiopia's moisture-stressed regions, despite the reality that hybrids are superior to open-pollinated sorghum varieties in terms of earliness, adaptability, and stability [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Hence, the study was conducted with objectives to identify the best general and specific combining ability of sorghum genotypes for yield and agronomic traits, to determine the type of gene action involved in the inheritance of yield and agronomic traits, and design the best breeding strategies and genetic standards for parents and hybrids to improve sorghum in the future.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Location of the Experiment\u003c/h2\u003e\n \u003cdiv class=\"BlockQuote\"\u003e\n \u003cp\u003eThe trial was grown in two representative lowland agro-ecologies of Ethiopia under moisture stress environments, where investigations geared toward drought were undertaken. These were Mieso in Eastern Ethiopia and Kobo in Northern Ethiopia. Drought is a major productivity constraint at the particular research areas, where sorghum crop is the primary grown. These locations have been designated as vital national drought adaptation testing locations because they have long been adversely affected by recurrent drought. These areas are well known for growing sorghum and were recognized as models for drought adaptation [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. A detailed description of Kobo and Mieso\u0026apos;s geographical features found in table-1.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDescription of the experimental locations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eLocation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eAltitude (m.a.s.l)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eRainfall (mm)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth colspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eGlobal position\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eT\u003csup\u003eo\u003c/sup\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth rowspan=\"2\" align=\"left\"\u003e\n \u003cp\u003eSoil type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLatitude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLongitude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMieso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1470\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e763\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u0026deg;30΄N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u0026deg;21΄E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVertisols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOromia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKobo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u0026deg;09΄N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u0026deg;38΄E\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVertisols\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAmara\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eSource\u003c/strong\u003e: National metrology data of 2019 growing season for meterological information; m.a.s.l is altitude meters above sea level; mm is rainfall in millimeter (July-November, 2019) and T\u003csup\u003eo\u003c/sup\u003e is Mean monthly temperature in degree centigrade [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003ch2\u003e2.2 Materials\u003c/h2\u003e\n\u003cp\u003eA total of 42 sorghum genotypes, encompassing 26F1 (hybrids), 13 seed parents (A-lines), 2 pollen parents (R-lines), and one standard check (hybrid) were investigated in Ethiopia\u0026apos;s sorghum-producing regions under moisture stress environments. The hybrids were developed using irrigation at the Werer Agricultural Research Center in 2017. 13 A-lines and 2 R-lines were crossed in a line \u0026times; tester mating design to develop 26 F1 (hybrids). In the process of creating hybrid sorghum seed, the female parent line (A-line) is male sterile, while the male parent line (R-line) is fertile and provides pollen to the A-line. The A-line is a male sterile line in A1 cytoplasm, developed by backcrossing with maintainer line (B-line) in normal cytoplasm. In order to restore hybrid fertility with the A1 cytoplasm, restorer lines (R-lines) carry a dominant nuclear gene. The four A-lines (MARC1B, MARC2B, MARC3B, and MARC6B) were developed by the Ethiopian national sorghum research program, whereas the other nine A-lines (TX-623B, P-9501B, P-9505B, P-9534B, P-851015B, P-850341B, P-9511B, B5, and B6) were introduced from Purdue University. The A-lines were tested for broad-adaptation, drought-resilience, high yielding and early maturing. The R-lines (Melkam and ICSR-14) were developed for dryland agro-ecologies by the Indian International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) and the Ethiopian National Sorghum Improvement Program respectively. These pollen parents are well known for their stability, high yielding performance and ability to adapt to moisture stress environments. The hybrid (ESH-4) variety was developed by Ethiopian national sorghum improvement program and used as a standard check for drought adaptation. The check hybrid (ESH-4) variety was released with the merit of drought adaptation and high yielding performance in drought prone environments.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDetails of materials harnessed in the experiment.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLines\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.N\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrosses\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTX-623B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #8B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-851015A \u0026times; ICSR \u0026minus;\u0026thinsp;14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #15A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9501B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #9B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-850341A \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #17A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9505B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #10B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA5 \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #21A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9534B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #14B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA6 \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #22A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-851015B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #15B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC1A \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #23A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-850341B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #17B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC2A \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #24A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #21B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC3A \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #25A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #22B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC6A \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #28A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC1B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #23B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9511A \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #32A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC2B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #24B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTX-623A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #8A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC3B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #25B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9501A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #9A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC6B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #28B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9505A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #10A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9511B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eB-line\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #32B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9534A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #14A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTesters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-851015A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #15A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMelkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-tester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 BS Inc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-850341A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #17A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eR-tester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 BS Inc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA5 \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #21A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCheck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA6 \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #22A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eESH-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 BS Inc.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC1A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #23A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCrosses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC2A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #24A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTX-623A \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #8A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC3A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #25A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9501A \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #9A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMARC6A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #28A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9505A \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #10A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9511A \u0026times; Melkam\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS 32A \u0026times; 33R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP-9534A \u0026times; ICSR-14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2018 MW CAS #14A \u0026times; 34R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp id=\"Sec4\" class=\"Section2\"\u003e\u003cstrong\u003eSource\u003c/strong\u003e: Melkassa miscellaneous sorghum working collection documents [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Experimental Design and Trial Management\u003c/h2\u003e\n \u003cp\u003eThe experiment was conducted using alpha lattice design with two replications at both locations in the cropping season of 2019. Each replicate embraced 42 genotypes, planted over six blocks, with seven experimental units per block. Each genotype was planted in two rows of 5 m length with spacing between rows and plants 0.75 m and 0.20 m respectively. The distances between each block and replication were 1 m and 1.5 m respectively. The alpha lattice design was used to overcome restrictions on the number of genotypes that could be taken into consideration and to account for irregular block sizes. The large number of genotypes restrict the use of RCBD and the non-square number of genotypes inhibits the use of lattice design. Therefore, alpha-lattice designs resolved restrictions on the number of genotypes to be taken into consideration and their relationship to the block size needed for lattice designs [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. The seeds were manually drilled at a rate of 12 kgha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and thinned to 0.20 m between plants at knee-stages to optimize population. The recommended fertilizer rate of phosphorus fertilizer (46 kgha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e P2O5) in the form of diammonium phosphate (DAP) and nitrogen fertilizer (23 kgha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e nitrogen in the form of urea) were applied at planting and 35 days after planting respectively. The experimental plots were kept free of pests during the growing seasons.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Data Collection\u003c/h2\u003e\n \u003cp\u003eThe data were collected using descriptors of sorghum from plot and plant randomly [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to 50% flowering (DTF in days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe number of days from the start of emergence till half of the plants in a plot start flowering halfway through the panicle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDays to 95% maturity (DTM in days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe duration in days from the time of emergence until 95% of the plants reached physiological maturity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePlant height (PTH in cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe height of five randomly tagged plants during flowering, measured from the base to the tip of the panicle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStay green score (SG in scale)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eA scale of 1 to 5 was used to measure the level of greenness at maturity; 1 =\u0026nbsp;fully green, normal-sized leaves (no leaf death), 2\u0026thinsp;=\u0026thinsp;25% of the leaves died, 3\u0026thinsp;=\u0026thinsp;26 to 50% of the leaves died, 4\u0026thinsp;=\u0026thinsp;51 to 75% are dead and\u0026nbsp;5\u0026thinsp;=\u0026thinsp;76 to 100% of the leaves and stem are dead (total plant death)\u0026nbsp;[\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanicle length (PL in cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe distance\u0026nbsp;between the panicle tip and the lowest panicle branch from five randomly tagged plants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanicle width (PW in cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe average width at the center of the panicle head of the five randomly selected plants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal leaf area (LA in cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe length \u0026times; width of the fourth leaf from the flag leaf multiplied by 0.747 is the total leaf area of five randomly selected\u0026nbsp;plants [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePanicle exertion (PE in cm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe distance between the panicle bases and the flag leaf was utilized to measure the panicle exertion of five randomly selected plants\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1000 seed weight (TSW in g)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe gram weight of 1,000 grains collected from a plot that had a 12.5% moisture content\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrain yield (GY in tonha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGrain yield is calculated from the total harvest of the plot and converted to tha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e once the ideal seed moisture content has been adjusted\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e\n \u003cp\u003eThe mixed linear model in SAS [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e] was used to perform the combined analysis of variance (ANOVA). After error variance homogeneity was confirmed by the Bartlett\u0026apos;s test, combined analyses were performed for traits that demonstrated significant genotypic variations in individual location. In the combined analysis, genotypes were considered as fixed effects, while environments, replications within environments, and blocks within replications were considered as random effects [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. The Fisher\u0026apos;s least significant difference \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\left(\\dot{\\text{L}\\text{S}\\text{D}=\\text{S}\\text{D}\\left(d\\right)\\:\\times\\:{t}_{\\left(0.05\\right)}\\:\\text{a}\\text{t}\\:\\text{e}\\text{r}\\text{r}\\text{o}\\text{r}\\:\\text{o}\\text{f}\\:\\text{d}\\text{f}}\\:or\\:LSD=\\left(\\sqrt{\\frac{2MSe}{r}}\\right)\\times\\:{t}_{\\left(0.05\\right)}\\:\\text{a}\\text{t}\\:\\text{e}\\text{r}\\text{r}\\text{o}\\text{r}\\:\\text{o}\\text{f}\\:\\text{d}\\text{f}\\right)\\)\u003c/span\u003e\u003c/span\u003e test was used to separate the means at a 5% probability level [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. Where, SD is the standard error of the difference ̇\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{SD(}_{d)}=\\sqrt{\\frac{2MSe}{r}}\\)\u003c/span\u003e\u003c/span\u003e). The coefficient of variation (CV) is a measure of the experiment\u0026apos;s reliability that shows how accurately the experimental genotype compare one another [37]. It was computed as\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:CV=\\frac{\\sqrt{MSe}}{GM}\\:\\times\\:100\\)\u003c/span\u003e\u003c/span\u003e, where, MSe is error mean square; GM is grand mean. The coefficient of determination (R\u0026sup2;) is a statistical metric in a regression model that determines the percentage of variance in the dependent variable that can be explained by the independent variable. A model\u0026apos;s efficiency of fit can be measured by its R\u003csup\u003e2\u003c/sup\u003e value, which is expressed as a percentage and ranges from 0\u0026ndash;100%. It was calculated as;\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equa\" class=\"mathdisplay\"\u003e$$\\:\\dot{{\\text{R}}^{2}}=\\frac{{\\text{S}\\text{S}}_{\\text{r}\\text{e}\\text{g}\\text{r}\\text{e}\\text{s}\\text{s}\\text{i}\\text{o}\\text{n}}}{{\\text{S}\\text{S}}_{\\text{t}\\text{o}\\text{t}\\text{a}\\text{l}}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere, SS regression is the sum of squares due to regression; SS total is the total sum of squares. The following model was used in the pooled analysis of variance over location to measure the total variation among the genotypes:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equb\" class=\"mathdisplay\"\u003e$$\\:\\:{\\text{Y}}_{\\text{i}\\text{j}\\text{k}\\text{s}}={\\mu\\:}+{l}_{\\left(s\\right)}+\\:{g}_{\\left(i\\right)}+{r}_{\\left(j\\right)}\\:+{b}_{\\left(jk\\right)}+{(g\\:\\times\\:\\text{l}\\:}_{\\left(is\\right)}+\\:{e}_{\\left(ijks\\right)}\\:$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere Y\u003csub\u003eijks\u003c/sub\u003e is the observation; \u0026micro; is grand mean; l\u003csub\u003es\u003c/sub\u003e is the effect of sth locations; g\u003csub\u003ei\u003c/sub\u003e is the fixed effect of the ith genotype; r\u003csub\u003ej\u003c/sub\u003e is the effect of the jth replicate; b\u003csub\u003ejk\u003c/sub\u003e is the effect of the kth incomplete block within the jth replicate; (g\u0026times;l)\u003csub\u003e(is)\u003c/sub\u003e is the interaction effects of isth between genotype and location; e\u003csub\u003eijks\u003c/sub\u003e is the residual or effect of random error (Table \u0026minus;\u0026thinsp;3).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eANOVA skeleton for combined analysis across locations\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource of variation\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMS\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eF-Values\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLocation (L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSL/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReplication (Loc)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL(r-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSr/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBlock (rep)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003erL(b-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSb/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotypes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eg-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSg/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrids\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eh-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSh/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ep-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSp/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCheck\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ec-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSc\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSc/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003el-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSl/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTesters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003et-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSt/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLine \u0026times; Tester\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(l-1) (t-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (l\u0026times;t)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (l\u0026times;t)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid vs Parent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (h vs p)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (hvsp)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid vs Check\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (h vs c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (hvsc)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParent vs Check\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (p vs c)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (pvsc)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGenotype \u0026times; L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(g-1) (L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSg\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (g\u0026times;L)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid \u0026times; L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(h-1) (L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSh\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (h\u0026times;L)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParent \u0026times; L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(p-1) (L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSp\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (p\u0026times;L)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCheck \u0026times; L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(c-1) (L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSc\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (c\u0026times;L)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLines\u0026times; L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(l-1) (L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSl\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (l\u0026times;L)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTesters \u0026times; L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(t-1) (L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSt\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (t\u0026times;L)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLine \u0026times; Tester\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(l-1) (t-1) (L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSl\u0026times;t\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (l\u0026times;t\u0026times;L)/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid vs Parent\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (h vs p) \u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (h vs p) \u0026times;L/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHybrid vs check \u0026times; L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (h vs c) \u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (h vs c) \u0026times;L/MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParent vs Check\u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(L-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (p vs c) \u0026times;L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMS (p vs c) \u0026times;L /MSe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eError\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eL(r \u0026minus;\u0026thinsp;1)(g \u0026minus;\u0026thinsp;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMSe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003elrg\u0026thinsp;\u0026minus;\u0026thinsp;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eSharma \u003cem\u003eet al\u003c/em\u003e., [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eKey: df is degree of freedom, L is locations, r is replications, g is number of genotypes, b is block, p is number of parents, l is number of lines, t is number of testers, c is number of checks, l \u0026times; t is line by tester, h vs c is hybrid vs check, h vs p is hybrid vs parent, p vs c is parent vs check, MS is mean square, MSe is mean square of error, MST is mean square of the total.\u003c/p\u003e\n \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\n \u003ch2\u003e2.4.1 Combining Ability Analysis\u003c/h2\u003e\n \u003cp\u003eThe general and specific combining ability analyses were performed for both parents and hybrids using the methodologies developed by Kempthorne [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.4.1.1 General combining ability (GCA\u003c/strong\u003e) - The term \u0026quot;general combining ability effect of lines and testers\u0026quot; refers to the deviation of the line and tester means from the hybrid mean. GCA effects were calculated using Kempthorne\u0026apos;s [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] pattern for each trait. The GCA effects of lines (g\u003csub\u003ei\u003c/sub\u003e) and testers (g\u003csub\u003ej\u003c/sub\u003e) were calculated as:\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equc\" class=\"mathdisplay\"\u003e$$\\:\\:{GCA}_{i}=\\frac{{X}_{i}}{\\text{t}\\text{r}}-\\frac{\\text{X}}{\\text{l}\\text{t}\\text{r}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equd\" class=\"mathdisplay\"\u003e$$\\:\\:{GCA}_{j}=\\frac{{X}_{j}}{\\text{l}\\text{r}}-\\frac{\\text{X}}{\\text{l}\\text{t}\\text{r}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere g\u003csub\u003ei\u003c/sub\u003e is the GCA effect for the i\u003csup\u003eth\u003c/sup\u003e lines; g\u003csub\u003ej\u003c/sub\u003e is the GCA effect for the j\u003csup\u003eth\u003c/sup\u003e testers; X\u003csub\u003ei\u003c/sub\u003e is the total of the ith line over all testers (t) and replications (r); X\u003csub\u003ej\u003c/sub\u003e is the total of the jth tester over all lines (l) and replications (r); X is the grand total, where l is the number of lines, t is the number of testers, and r is the number of replications.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e2.4.1.2 Specific combining ability (SCA)\u003c/strong\u003e - SCA effects were measured as the deviation of each cross mean from all hybrid means, adjusted for the corresponding GCA effects of parents [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. The following is how the computation was done:\u003c/p\u003e\n \u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Eque\" class=\"mathdisplay\"\u003e$$\\:{SCA}_{ij}=\\frac{{X}_{iJ}}{\\text{r}}-\\frac{{X}_{i}}{\\text{t}\\text{r}}-\\frac{{X}_{j}}{\\text{l}\\text{r}}+\\frac{\\text{X}}{\\text{l}\\text{t}\\text{r}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere S\u003csub\u003eij\u003c/sub\u003e is the SCA effect of the ij\u003csup\u003eth\u003c/sup\u003e crosses; X\u003csub\u003eij\u003c/sub\u003e is the value of the total number of ith lines with jth testers over all replications (r); X\u003csub\u003ei\u003c/sub\u003e is the total number of ith over all testers; X\u003csub\u003ej\u003c/sub\u003e is the total number of jth testers over all lines; X is the grand total crosses; l is the number of lines; t is the number of testers; and r is the number of replication.\u003c/p\u003e\n \u003cp\u003eThe significance of GCA or SCA effects were tested by dividing the GCA effects of a particular line or males and SCA effects of a particular hybrid by its respective standard error. The following formulas were used for calculating the significance of the GCA and SCA effects:\u003c/p\u003e\n \u003cdiv id=\"Equf\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equf\" class=\"mathdisplay\"\u003e$$\\:\\text{S}\\text{i}\\text{g}\\text{n}\\text{i}\\text{f}\\text{i}\\text{c}\\text{a}\\text{n}\\text{c}\\text{e}\\:\\text{o}\\text{f}\\:\\text{G}\\text{C}\\text{A}\\:\\text{e}\\text{f}\\text{f}\\text{e}\\text{c}\\text{t}\\text{s}\\:\\text{o}\\text{f}\\:\\text{l}\\text{i}\\text{n}\\text{e}\\text{s}\\:\\text{t}\\text{e}\\text{s}\\text{t}\\text{e}\\text{d}\\left(\\text{l}\\right)={g}_{i}/\\text{S}\\text{E}\\left({g}_{i}\\right)\\:$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equg\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equg\" class=\"mathdisplay\"\u003e$$\\:\\text{S}\\text{i}\\text{g}\\text{n}\\text{i}\\text{f}\\text{i}\\text{c}\\text{a}\\text{n}\\text{c}\\text{e}\\:\\text{o}\\text{f}\\:\\text{G}\\text{C}\\text{A}\\:\\text{o}\\text{f}\\:\\text{t}\\text{e}\\text{s}\\text{t}\\text{e}\\text{r}\\text{s}\\:\\text{t}\\text{e}\\text{s}\\text{t}\\text{e}\\text{d}\\left(\\text{t}\\right)={g}_{i}/\\text{S}\\text{E}\\left({g}_{j}\\right)\\:$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equh\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equh\" class=\"mathdisplay\"\u003e$$\\:\\text{S}\\text{i}\\text{g}\\text{n}\\text{i}\\text{f}\\text{i}\\text{c}\\text{a}\\text{n}\\text{c}\\text{e}\\:\\text{o}\\text{f}\\:\\text{S}\\text{C}\\text{A}\\:\\text{e}\\text{f}\\text{f}\\text{e}\\text{c}\\text{t}\\text{s}\\:\\text{h}\\text{y}\\text{b}\\text{r}\\text{i}\\text{d}\\text{s}\\:\\text{t}\\text{e}\\text{s}\\text{t}\\text{e}\\text{d}\\left(\\text{h}\\right)={g}_{ij}/\\text{S}\\text{E}\\left({s}_{ij}\\right)\\:$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe standard error for general and specific combining ability effects were calculated as:\u003c/p\u003e\n \u003cdiv id=\"Equi\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equi\" class=\"mathdisplay\"\u003e$$\\:\\text{S}\\text{t}\\text{a}\\text{n}\\text{d}\\text{a}\\text{r}\\text{d}\\:\\text{e}\\text{r}\\text{r}\\text{o}\\text{r}\\:\\text{o}\\text{f}\\:\\text{G}\\text{C}\\text{A}\\:\\text{f}\\text{o}\\text{r}\\:\\text{l}\\text{i}\\text{n}\\text{e}\\text{s}\\left({g}_{i}\\right)=\\sqrt{\\text{m}\\text{s}\\text{e}/\\text{t}\\text{r}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equj\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equj\" class=\"mathdisplay\"\u003e$$\\:\\text{S}\\text{t}\\text{a}\\text{n}\\text{d}\\text{a}\\text{r}\\text{d}\\:\\text{e}\\text{r}\\text{r}\\text{o}\\text{r}\\:\\text{o}\\text{f}\\:\\text{G}\\text{C}\\text{A}\\:\\text{f}\\text{o}\\text{r}\\:\\text{t}\\text{e}\\text{s}\\text{t}\\text{e}\\text{r}\\text{s}\\left({g}_{j}\\right)=\\sqrt{\\text{m}\\text{s}\\text{e}/\\text{r}\\text{l}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equk\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equk\" class=\"mathdisplay\"\u003e$$\\:\\text{S}\\text{t}\\text{a}\\text{n}\\text{d}\\text{a}\\text{r}\\text{d}\\:\\text{e}\\text{r}\\text{r}\\text{o}\\text{r}\\:\\text{o}\\text{f}\\:\\text{S}\\text{C}\\text{A}\\:\\text{f}\\text{o}\\text{r}\\:\\text{h}\\text{y}\\text{b}\\text{r}\\text{i}\\text{d}\\text{s}\\left({s}_{ij}\\right)=\\sqrt{\\text{m}\\text{s}\\text{e}/\\text{r}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.4.2 Computation of combining abilities variance components\u003c/h2\u003e\n \u003cp\u003eThe GCA variance of the lines, testers and the SCA variance of hybrids were computed using the mean sum of squares expectations using the formula proposed by Singh and Chaudhary [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv id=\"Equl\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equl\" class=\"mathdisplay\"\u003e$$\\:{{\\sigma\\:}}^{2}{gca}_{i}=\\frac{\\text{M}\\text{l}\\:-\\:\\text{M}\\text{l}\\text{t}}{l*r}=\\frac{1}{4}{{\\sigma\\:}}^{2}\\text{A}=\\:{{\\sigma\\:}}^{2}\\text{G}\\text{C}\\text{A}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equm\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equm\" class=\"mathdisplay\"\u003e$$\\:{{\\sigma\\:}}^{2}{gca}_{j}=\\frac{\\text{M}\\text{t}-\\text{M}\\text{l}\\text{t}}{t*r}=\\:\\frac{1}{4}{{\\sigma\\:}}^{2}\\text{A}={{\\sigma\\:}}^{2}\\text{G}\\text{C}\\text{A}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equn\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equn\" class=\"mathdisplay\"\u003e$$\\:{{\\sigma\\:}}^{2}{sca}_{ij}=\\frac{\\text{M}\\text{l}\\text{t}-\\text{M}\\text{e}}{r}=\\:{{\\sigma\\:}}^{2}\\text{S}\\text{C}\\text{A}=(\\text{C}\\text{o}\\text{v}\\:\\text{F}\\text{S}-2\\text{C}\\text{o}\\text{v}\\:\\text{H}\\text{S})$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equo\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equo\" class=\"mathdisplay\"\u003e$$\\:\\text{A}\\text{v}\\text{e}\\text{r}\\text{a}\\text{g}\\text{e}\\:\\text{v}\\text{a}\\text{r}\\text{i}\\text{a}\\text{n}\\text{c}\\text{e}=\\frac{\\text{X}+\\text{Y}}{\\text{l}+\\text{t}+\\text{r}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equp\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equp\" class=\"mathdisplay\"\u003e$$\\:{{\\sigma\\:}}^{2}\\text{A}=4\\:\\text{x}\\:\\frac{\\text{X}+\\text{Y}}{\\text{l}+\\text{t}+\\text{r}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equq\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equq\" class=\"mathdisplay\"\u003e$$\\:{{\\sigma\\:}}^{2}\\text{D}=4\\:\\text{x}\\:{{\\sigma\\:}}^{2}\\text{s}\\text{c}\\text{a}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u0026sigma;\u003csup\u003e2\u003c/sup\u003egca\u003csub\u003ei\u003c/sub\u003e is the variance due to general combining ability for lines, \u0026sigma;\u003csup\u003e2\u003c/sup\u003egca\u003csub\u003ej\u003c/sub\u003e is the variance due to general combining ability for testers, and \u0026sigma;\u003csup\u003e2\u003c/sup\u003esca\u003csub\u003eij\u003c/sub\u003e is the variance due to specific combining ability, r is the number of replications, l is the number of lines, t is the number of testers, Ml is the mean square due to lines, Mt is the mean square due to testers, Mlt is the mean square due to hybrids, and Me is the mean square due to error, HS is the covariance of half-sib and FS is the covariance of full-sib.\u003c/p\u003e\n \u003cp\u003eBecause both lines and testers were deemed inbred, the inbreeding coefficient F is 1 was used to determine the additive and dominant genetic variance. To determine the relative importance of additive versus non-additive gene actions, two ratios were used: 𝜎\u003csup\u003e2\u003c/sup\u003egca/𝜎\u003csup\u003e2\u003c/sup\u003esca and 𝜎\u003csup\u003e2\u003c/sup\u003e𝐷/𝜎\u003csup\u003e2\u003c/sup\u003e𝐴 [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.4.3 Estimation of the proportional contribution different variance components\u003c/h2\u003e\n \u003cp\u003eThe following algorithm was used to determine the proportionate contribution of lines, testers, and line \u0026times; tester interaction to total variance [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv id=\"Equr\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equr\" class=\"mathdisplay\"\u003e$$\\:\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{i}\\text{b}\\text{u}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{o}\\text{f}\\:\\text{l}\\text{i}\\text{n}\\text{e}\\text{s}\\left(\\text{l}\\right)=\\frac{\\text{S}\\text{S}\\text{l}}{\\text{S}\\text{S}\\text{h}}\\text{x}100$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equs\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equs\" class=\"mathdisplay\"\u003e$$\\:\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{i}\\text{b}\\text{u}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{o}\\text{f}\\:\\text{t}\\text{e}\\text{s}\\text{t}\\text{e}\\text{r}\\text{s}\\left(\\text{t}\\right)=\\frac{\\text{S}\\text{S}\\text{t}}{\\text{S}\\text{S}\\text{h}}\\text{x}100$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equt\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equt\" class=\"mathdisplay\"\u003e$$\\:\\text{C}\\text{o}\\text{n}\\text{t}\\text{r}\\text{i}\\text{b}\\text{u}\\text{t}\\text{i}\\text{o}\\text{n}\\:\\text{o}\\text{f}\\:\\text{l}\\text{i}\\text{n}\\text{e}\\text{s}\\:\\text{x}\\:\\text{t}\\text{e}\\text{s}\\text{t}\\text{e}\\text{r}\\text{s}\\left(\\text{l}\\text{x}\\text{t}\\right)=\\frac{\\text{S}\\text{S}\\text{l}\\text{x}\\text{t}}{\\text{S}\\text{S}\\text{h}}\\text{x}100$$\u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.5 Estimation of genetic components\u003c/h2\u003e\n \u003cp\u003eBroad and narrow sense heritability were computed for each characteristic based on the formula developed by Allard [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e].\u003c/p\u003e\n \u003cdiv id=\"Equu\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equu\" class=\"mathdisplay\"\u003e$$\\:{\\text{H}}^{2}=\\frac{{{\\sigma\\:}}^{2}\\text{g}}{{{\\sigma\\:}}^{2}\\text{p}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equv\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equv\" class=\"mathdisplay\"\u003e$$\\:{\\text{h}}^{2}=\\frac{{{\\sigma\\:}}^{2}\\text{a}}{{{\\sigma\\:}}^{2}\\text{p}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equw\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equw\" class=\"mathdisplay\"\u003e$$\\:{{\\delta\\:}}^{2}\\text{g}\\:=\\left(\\frac{\\text{M}\\text{S}\\text{g}-\\text{M}\\text{S}\\text{g}\\text{l}}{\\text{r}\\text{l}}\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equx\" class=\"Equation\"\u003e\n \u003cdiv id=\"FileID_Equx\" class=\"mathdisplay\"\u003e$$\\:{{\\delta\\:}}^{2}\\text{p}={{\\delta\\:}}^{2}\\text{g}+\\left(\\frac{{{\\delta\\:}}^{2}\\text{g}\\text{l}}{\\text{l}}\\right)+\\left(\\frac{{{\\delta\\:}}^{2}\\text{e}}{\\text{r}\\text{l}}\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere H\u0026sup2; is the broad-sense heritability, h\u003csup\u003e2\u003c/sup\u003e is the narrow-sense heritability, \u0026sigma;\u0026sup2;g is the genotypic variance and \u0026sigma;\u0026sup2;p is the phenotypic variance, \u0026delta;\u0026sup2;gl is genotypic by environmental variance, \u0026delta;\u0026sup2;e is environmental variance, MSg is mean square of genotype, MSgl is mean square due to genotype by environment interaction, l is number of locations, and r is number of replications.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Analysis of Variance (ANOVA)\u003c/h2\u003e \u003cp\u003eThe combined analysis of variance revealed highly significant variation among genotypes for days to flowering, days to maturity, plant height, stay green, panicle length, panicle width, leaf area, panicle exertion, 1000 seed weight and grain yield traits. This result signified the presence of superior and well-adapted genotypes to select for targeted locations. Numerous similar studies found significant variation for grain yield and agronomic traits of sorghum in Ethiopia [\u003cspan additionalcitationids=\"CR43 CR44\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. The investigation identified highly significant difference in genotype by environment interactions for days to flowering, days to maturity, plant height, 1000 weight and grain yield whereas significant difference observed for panicle width trait. This result indicated the differential response of genotypes across environments with best adaptation to moisture stressed environments. The environment had significant effect on the traits expressions. Therefore, this confirmed that why the plant breeders conduct trial across locations and over years, particularly during the final stages of variety development. Several researchers have previously reported similar findings in sorghum for yield and yield-related parameters [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\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\u003eCombined analysis of variance of sorghum genotypes for yield and agronomic traits\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=\"left\" 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=\"left\" 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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eRandom effect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMS\u003csub\u003eE\u003c/sub\u003e(DF\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMS\u003csub\u003eG\u003c/sub\u003e(DF\u0026thinsp;=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003csub\u003eL\u003c/sub\u003e(DF\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMS\u003csub\u003eGL\u003c/sub\u003e(=\u0026thinsp;41)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays to flowering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.23**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1080.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.51**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDays to maturity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15.10**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1494.05**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.74**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7615.51**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14359.70**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e332.80**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e70.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStay green\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.78**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63.14**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.51\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanicle length\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27.08**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e117.66**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.80\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanicle width\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.86**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e308.34**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.10*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLeaf area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5662.39**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e439598.44**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3919.83\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2812.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePanicle exersion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.36**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e388.87**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.47\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1000 seed weight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e60.41**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7100.60**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.25**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGrain yield\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5106.56**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e858491.96**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1708.55**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e869.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.94\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=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Mean Performance of Genotypes for Grain Yield\u003c/h2\u003e \u003cp\u003eThe highest grain yield was obtained from hybrid P-9534 \u0026times; Melkam (6.32 tha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) followed by the hybrids B6 \u0026times; ICRS-14 (5.92 tha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), TX-623 \u0026times; ICRS-14 (5.88 tha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e), P-9511 \u0026times; Melkam (5.78 tha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and P-850341 \u0026times; ICRS-14 (5.57tha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) compared to check (ESH-4) (4.77 tha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) as indicated in Figure-1. This signified the availability of drought resilient hybrids and excelled the parents (lines and testers) and standard check (ESH-4) in terms of yield. Besides, to yield performance, growth and morphological traits were considered as selection criteria in the development of drought-tolerant genotypes. It was identified that early maturing and drought-tolerant hybrid varieties were among the drought strategies for adaptation that demanded further investigation. Therefore, the greatest attention should be provided to hybrid sorghum development to boost genetic gain for moisture stress environments. Future breeding efforts should focus to develop drought-tolerant hybrids with optimum plant height and maturity profiles that qualify to the particular conditions of areas where sorghum is grown. Thus, the most outstanding and potential sorghum genotypes were identified to be harnessed in drought-prone environments. Many authors have previously reported similar superior sorghum hybrids [\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. It is becoming increasingly important to adopt high-performing hybrids like P-9534 \u0026times; Melkam (6.32 tha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) with farmer-preferred traits like biomass and earliness, as this improves smallholder farmers' livelihoods in the drought prone environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Combining Ability Analyses for Yield and Agronomic traits\u003c/h2\u003e \u003cp\u003eCombining ability analysis is a useful strategy for determining the type of gene action governing characteristics and for selecting parents and potential hybrids according to the effects of both general and specific combining abilities. It is essential to consider that the magnitude of gene action can be determined by GCA and SCA variations, which assists in design appropriate breeding strategies for future breeding programs. Combining ability analysis is the most crucial methods for identifying the best combiners to determine the effective breeding strategies in sorghum improvements [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Significant GCA and SCA variances with desirable direction were demonstrated due to females and males and interaction of females \u0026times; males for yield and other agronomic traits. This suggested the females and males significantly contribute to GCA variance, whereas females and males interact to SCA variance. This also signified the preponderance of both additive and dominance gene action in development of high yielding and adaptable sorghum varieties in the moisture stress areas.\u003c/p\u003e \u003cp\u003eGCA variances of lines were significantly high for days to flowering, plant height, stay green, panicle length, panicle exertion and 1000 seed weight traits, whereas highly significant GCA variance due to testers were demonstrated for plant height and leaf areas (Table-5). This signified the additive gene action played a leading role in controlling these traits and the possibility scenarios for improving superior sorghum parental lines in the dryland regions. These parents can be utilized for the development of high yielding and drought resistant hybrids. The SCA variances of line x tester interactions were highly significant for panicle exertion, whereas significant variations revealed in plant height and panicle width, demonstrated the dominant gene action played a vital role in governing these traits (Table-5). Rao \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] and Maftuchah \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] reported similar results in GCA and SCA variations among lines, testers and line x tester interactions for yield and agronomic traits.\u003c/p\u003e \u003cp\u003eGenerally, the combining abilities results identified best general combiner parents (lines and testers) and best specific combiner hybrids for the investigated traits. This signified the characteristics were controlled by both additive and dominance gene actions. Therefore, population improvement and heterosis breeding methods are advised to develop high yielding and drought adaptation varieties [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\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\u003ePooled analysis of variance for combining ability for yield and agronomic traits in sorghum\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDF\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\u003eDTM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePHT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eTSW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e 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\u003cp\u003e308.34\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e439598.44\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e388.87\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e7100.60\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e858491.93\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRep (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.29\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1955.70\u003csup\u003e**\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\u003e5.42\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.03\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.35\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e104.97\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.86\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5700.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.88*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10121.19\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.44\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.44\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4837.30\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e55.76\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e81.06\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1678.97\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHybrids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.10\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.41\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4288.77\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.79\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.62\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2958.4 \u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19.85\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e41.95\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1264.76\u003csup\u003ens\u003c/sup\u003e\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\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.12\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.63\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8724.51\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.88\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.27\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.25\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2606.25\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e30.35\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e76.62\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1390.63\u003csup\u003ens\u003c/sup\u003e\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\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.47\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e572.46\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.28\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18171.24\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.16\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.37\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e318.85\u003csup\u003ens\u003c/sup\u003e\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\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.22\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.76\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162.71\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.10\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.10\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2042.97\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.17\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.65\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1217.71\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent Vs Hybrids\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.55\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198.48\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95086.83\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.34\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e468.66\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e109.99\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e96749.62\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e4.42\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e792.48\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e167591.62\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParent \u0026times; L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.41\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.74\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e350.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.25\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.11\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2549.44\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e11.42\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.96\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e617.10*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHybrid \u0026times; L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.86\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.90\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e349.75\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.59\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.83\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3586.11\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.48\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.74\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1020.53\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLines \u0026times; L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.45\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.71\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e618.29\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.57\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.38\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3651.38\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.73\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e18.23\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1474.88\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTesters \u0026times; L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.00\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.00\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.74\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.16\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.01\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3681.66\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.06\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.15\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e608.61\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLines \u0026times;Testers \u0026times; L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.92\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e106.29\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.82\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.34\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3512.88\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.86\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.39\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e600.51\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHybrid v parent\u0026times;L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.97\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e198.48\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e448.11\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.68\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.65\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26771.48\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.94\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e124.94\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e41045.72\u003csup\u003e**\u003c/sup\u003e\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\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2812.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e5.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e869.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e9.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e16.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e28.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e21.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD (5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1314.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Estimates of general combining ability effects of sorghum for Yield and agronomic traits\u003c/h2\u003e \u003cp\u003eThe GCA represents an additive gene action and indicates the average performance of parental lines. The GCA effect of the A-line and R-line parents presented in Table-6 revealed significant in different direction for the investigated traits. GCA variance for days to flowering, days to maturity, plant height and 1000 seed weight appeared to be highly and positively significant in MARC2 line, whereas positive significant GCA variance revealed in P-851015 line for days to flowering (Table-6). Significant and negative GCA difference demonstrated in lines (P-9505, P-9534 and P-9511) for days to flowering trait (Table-6). This indicated the days to flowering trait is controlled by additive genetic action. Negative GCA effects for days to flowering have a positive effect in mitigating the adverse impacts of terminal drought stress in environments where drought stress is a severe problem. As a result, the P-9505, P-9534, and P-9511 lines were identified and recommended for utilization in the development of early maturing varieties. This result is in agreement with those obtained by [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn order to counteract significant drought-induced yield reductions, the crop can reach the stages of yield formation and grain filling, before episodes of limited soil water and excessive atmospheric temperatures. This is made possible by the most notable phenological drought escape mechanisms, which include early flowering in the season and increased early vigor [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The sorghum's developmental timeline is measured in days to flowering, which is essential for ensuring grain-filling duration under ideal environmental circumstances. This characteristic is directly related to the crop's ability to adapt to various growing seasons and climates. The experiment was executed in dryland environments, where terminal drought stress is critical problematic for long maturity lines. Hence, the lines with negative GCA values (P-9505, P-9534 and P-9511) were selected as best general combiner and recommended for their earliness in such drought prone environments.\u003c/p\u003e \u003cp\u003eSignificant and positive GCA effects (2.44) for days to maturity was observed in MARC2 line whereas no significant discrepancies was revealed in the rest lines and testers (Table-6). However, negative GCA effects are desirable for days to maturity to escape the drastic effect of drought stress in dry lowland environments. Negative and significant GCA effects are preferable for days to maturity in order to escape the drastic drought stress in the dry lowlands environment. To mitigate the serious effects of drought in semiarid tropical regions where seasonal rainfall is either irregular or infrequent, early-maturing sorghum cultivars are recommended. These cultivars might not always produce more than long-maturing cultivars, but by escaping terminal drought, they might produce more consistent yields in water-stressed environments. Drought stress during the pre-flowering stage results damages, abortion of the florets, reduced panicle size, and significant reduction in seed size, finally result in decreased yield potential [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GCA effect signified highly significant in all lines except lines (P-851015, P-850341 and B5) and testers (Melkam and ICSR-14) for plant height (Table-6). Highly significant and positive GCA variances were observed in MARC1, MARC2, MARC3 and MARC6 lines, whereas highly significant and negative GCA variances were revealed in TX-623, P-9501, P-9505, P-9534, B6 and P-9511 lines for plant height (Table-6). This demonstrated that the existence of useful genes to improve the physiological development for both tallness and shortness traits. To mitigate the catastrophic effects of drought stress, significant and negative GCA values are recommended in dryland environments. In order to refrain from severe terminal drought stresses, the short-stature sorghum lines (TX-623, P-9501, P-9505, P-9534, B6, and P-9511) were selected. As a result, lines with significant and negative GCA values are considered the best general combiners for environments under moisture stress, while lines with significant and positive GCA effects can be used for biomass research regardless of the current moisture stressed experiment. Plant height is a vital trait that influences yield as well as the plant's ability to withstand lodging and maximize light capture. Breeding for optimal height achieves a balance between need to reduce lodging, where plants fall before harvest, lowering yield and quality, and the advantages of taller plants, which frequently yield more. This result indicated the additive gene action predominates and population enhancement is advised as a breeding strategy to improve plant height. This result agreed with the findings reported by Rini \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and Trikoesoemaningtyas \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGCA variance for stay green trait was significantly high and positive in P-9501 and B5 lines (Table-6). According to Ndlovu \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], leaf senescence is a stage of maturity in a plant's life cycle during which its leaves undergo chlorosis due to a multitude of reasons, including aging, biotic and abiotic stressors. Post-flowering drought stress causes premature leaf senescence, abortion and a decrease in grain yield in senescent sorghum genotypes [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Post-flowering drought stress can only be tolerated by genotypes that have an integrated drought adaptation traits called stay green, which delays leaf senescence [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. At first, it was believed that this characteristic of sorghum was cosmetic, implying that it delayed the onset of leaf senescence and decreased its rate, which might happen simultaneously or separately [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Flowering period, sink strength, and environmental factors all influence stay green expression [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Van Oosterom \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e] state that in sorghum genotypes, the duration of green leaf area, the rate of senescence, and the timing of senescence onset are all independently inherited. Stay green expression has been determined by additive gene action. Therefore, P-9501 and B5 lines were best general combiner and preferred to develop drought resistance varieties in moisture-stressed environments.\u003c/p\u003e \u003cp\u003eGCA effects in P-9534 line was highly significant and positive for panicle length, whereas significantly high and negative GCA effects observed in MARC2, MARC3 and MARC6 lines (Table-6). However, significant and positive GCA effect is desired for panicle length as it is directly associated with in increasing productivity. Therefore, P-9534 line was best general combiner for panicle length and selected to develop superior hybrid in moisture stress areas.\u003c/p\u003e \u003cp\u003eThe GCA results for panicle exertion was positive and significantly high in P-9505, P-850341 and MARC1 lines whereas positive and significant difference revealed in P-9511 line and ICRS-14 tester. In addition, negative and highly significant difference GCA effect revealed for panicle exertion in MARC2 line while negative and significant difference GCA results observed in P-9501, B5 line and Melkam tester. This meant the additive gene actions were more likely important to enhance the panicle exertion trait. The most significant characteristic linked to drought tolerance in sorghum is excellent exertion, which suggests that lines with higher exertion are more resilient to moisture stress. Therefore, P-9505, P-850341, MARC1, P-9511 and ICRS-14 lines were identified as best general combiner under moisture stress environments. The line with higher panicle exertion decreases disease risk, enhances grain filling, and is essential for the convenience of mechanical harvesting. The finding is in agreement with the results reported by Rini \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] and Maftuchah \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GCA results for 1000-grain weight was positive and highly significantly different in MARC2 and MARC6 lines and positive and significant different GCA results observed in MARC3 line whereas negative and highly significant variation was revealed in P-850341 line and negative and significant difference observed in P-851015 and B5 lines. Although a positive and significant GCA, result required 1000-seed weight trait to increase genetic gain in sorghum. As a result, lines (MARC2, MARC6 and MARC3) were best general combiners and indicating additive gene action involved in controlling the trait. The findings were in agreement with earlier research by El-Kady \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and Ibrahim \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Generally, these lines appeared worthy of being exploited in recombination breeding programs because they generally have high general combining ability effects that correspond with additive and additive x additive interaction [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] and represent fixable genetic components of variation.\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\u003eEstimates of general combining ability effects of sorghum for Yield and agronomic traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLines Traits\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\u003eDM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePTH\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eTSW\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTX-623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.51\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-22.08\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.17\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.89\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.64\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.33\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.44\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e562.5\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-9501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.60\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.18\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-27.48\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.54\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.52\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.07\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-1.53\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.47\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-165.0\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-9505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.23\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e 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\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.56\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.35\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e358.0\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE (Lines)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e412.53\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=\"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 \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 \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMelkam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.18\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-13.21\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-0.10\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.35\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e55.34\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICRS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.18\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.34\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.05\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.15\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.06\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e13.21\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.10\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.35\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-55.34\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE(Testers)\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\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e76.49\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=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Estimates of specific combining ability effects of sorghum for yield and agronomic traits\u003c/h2\u003e \u003cp\u003eSpecific combining ability is playing vital roles in the identification of best performing lines, which can be used as parents in hybrid variety development [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. The development of superior hybrids is primarily due to the non-additive gene action caused by dominance or over-dominance gene effects, which are linked to specific combining ability. In this study, equal SCA values obtained in magnitude and opposite in direction. This might be because of both testers having similar backgrounds in specific combining abilities and having the same gene-regulating influence on the traits. Similar findings was reported in sorghum (35 testers and 2 lines) [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Highly significant and positive SCA effects demonstrated in hybrid B6 \u0026times; ICRS-14 for days to flowering, whereas significant and positive differences observed in TX-623 \u0026times; Melkam and MARC2 \u0026times; Melkam hybrids (Table-7).\u003c/p\u003e \u003cp\u003eHighly significant and negative SCA effects displayed in B6 \u0026times; Melkam hybrid while negative and significant difference obtained in TX-623 \u0026times; ICRS-14 and MARC2 \u0026times; ICRS-14 hybrids. This indicated that non-additive gene action was vital in controlling this trait. The hybrid with negative SCA values is preferred because this investigation was conducted in dryland environments. Therefore, in dry lowland environments, the hybrids with negative SCA values (B6 \u0026times; Melkam, TX-623 \u0026times; ICRS-14 and MARC2 \u0026times; ICRS-14) flowered earlier and ensured earliness (Table-7). Thus, B6 \u0026times; Melkam, TX-623 \u0026times; ICRS-14 and MARC2 \u0026times; ICRS-14 hybrids were identified as best specific combiner and preferred for moisture stress areas to escape the drastic effect of drought. Therefore, heterosis breeding is the most effective and recommended breeding strategy to enhance the genetic material for the days to flowering trait. Similarly, Mengistu \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]; Wagaw \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]; El-Kady \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] and Ibrahim \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] reported positive and negative significant estimates of SCA effects for days to flowering.\u003c/p\u003e \u003cp\u003eThe SCA analysis for days to maturity displayed both positive and negative significant difference in eight hybrids. In order to mitigate the severe effects of terminal drought, moisture-stressed agro-ecologies prefer negative SCA effects. Therefore, hybrids (TX-623 \u0026times; ICRS-14, P-850341 \u0026times; ICRS-14, MARC2 \u0026times; ICRS-14 and P-9511 \u0026times; ICRS-14) with negative and significant effects of SCA were identified as best specific combiner to develop extra-early maturing varieties (Table-7). The use of early-maturing sorghum varieties is encouraged to overcome the drastic effects of drought in semiarid tropical regions where either seasonal rainfall is short or its distribution is erratic. These varieties might not always superior than long-maturing cultivars, but by escaping terminal drought, they might produce more consistent yields in water-stressed environments. Rachman \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] similarly reported highly significant negatives for days to maturity in the direction to be utilized in breeding programs to introduce genes for early maturation.\u003c/p\u003e \u003cp\u003eThe SCA variance for plant height demonstrated significant and positive difference in hybrids MARC3 \u0026times; Melkam whereas negative and significant variation revealed in hybrid MARC3 \u0026times; ICRS-14. However, significant and negative SCA value is preferred for plant height to develop early and lodging free varieties. Therefore, only MARC3 \u0026times; ICRS-14 hybrid showed significant and negative SCA effects for plant height (Table-7). The plant height trait is governed by additive gene action in general and the effect of GCA was more vital to SCA effects. Mengistu \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Wagaw \u003cem\u003eet al.\u003c/em\u003e [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] reported both negative and positive significant SCA effects in crosses of sorghum lines. Ensuring yield and quality requires lodging resistance, especially in areas that are vulnerable to prolong drought and high winds. In order to maintain the plant's weight and withstand environmental challenges, breeding efforts focus on strengthening the roots and stalks.\u003c/p\u003e \u003cp\u003eThe SCA results for panicle length were highly significant in (TX-623 \u0026times; Melkam, TX-623 \u0026times; ICRS-14, B6 \u0026times; Melkam and B6 \u0026times; ICRS-14) hybrids, whereas significant difference was observed for panicle length in other hybrids (P-9501 \u0026times; Melkam, P-9501 \u0026times; ICRS-14, MARC2 \u0026times; Melkam and MARC2 \u0026times; ICRS-14) (Table-7). Positive SCA effects are required for panicle length to enhance grain yield per unit area. Therefore, those hybrids (TX-623 \u0026times; ICRS-14, B6 \u0026times; ICRS-14, P-9501 \u0026times; Melkam and MARC2 \u0026times; Melkam) with significant and positive SCA effects were identified as best specific combiner to further enhance of the panicle length trait. In line with the present investigation, Mengistu \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and Wagaw \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] showed both negative and positive estimates of SCA impacts in crosses of sorghum panicle length. The SCA variance for panicle width displayed significantly high differences and significant differences in eighteen hybrids, indicating that the panicle width trait was governed by dominance gene actions and the heterosis-breeding scheme could be harnessed for enhancing this trait.\u003c/p\u003e \u003cp\u003eThe SCA effects for panicle exertion exhibited significantly high in hybrid (P-9505 \u0026times; Melkam and P-9505 \u0026times; ICRA-14). However, positive SCA values are desirable for panicle exertion. Hence, the hybrid (P-9505 \u0026times; ICRS-14) is identified as best specific combiner and well-exerted hybrids. A well-exerted hybrid is preferred for improving the quality of grains and avoid fungal and insects to devastation. The current finding is supported by extremely significant and positive SCA for panicle exertion reported by Wagaw \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e] and Mindaye \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The SCA effects for 1000 seed weight trait was highly significant in TX-623 \u0026times; Melkam and TX-623 \u0026times; ICRS-14 hybrids whereas significant difference was displayed in (P-9505 \u0026times; Melkam and P-9505 \u0026times; ICRS-14) hybrids (Table-7). The study showed that additive gene action predominated over non-additive gene action in the 1000 seed weight trait, suggesting that the GCA was more crucial than the SCA to increase this trait. The hybrids with the highest and positive SCA values are preferred to increase the productivity.\u003c/p\u003e \u003cp\u003eThe SCA results found to be highly significant in hybrids (P-9534 \u0026times; Melkam, P-9534 \u0026times; ICRS-14, B6 \u0026times; Melkam and B6 \u0026times; ICRS-14) for grain yield, whereas significant variances revealed in (MARC3 \u0026times; Melkam and MARC3 \u0026times; ICRS-14) hybrids (Table-7). These hybrids are vital for increasing grain yield per unit area because they have the best and positive SCA effects. Therefore, the hybrids (P-9534 \u0026times; Melkam, B6 \u0026times; ICRS-14 and MARC3 \u0026times; Melkam) with positive SCA effects are identified as best specific combiner and because dominance gene action predominated, the SCA effects on grain yield significantly higher than the GCA effects. To develop superior sorghum hybrids that can satisfy the expanding demands of global agriculture, it is crucial to comprehend the complex interactions between this trait and how they react to various environmental conditions. Eventually, grain yield trait can be improved through heterosis breeding method since non-additive genes involved for the expression of traits. Several authors have reported both positive and negative SCA for sorghum yield [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\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\u003eEstimates of specific combining ability effects of sorghum for yield and agronomic traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e 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colname=\"c12\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTX-623\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelkam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.69\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.09\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.32\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.24\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e 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colname=\"c12\"\u003e \u003cp\u003e331.34\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-9501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelkam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-4.29\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e21.86\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.19\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.49\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e40.15\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-9501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.31\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.43\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.29\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-1.01\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.20\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-21.86\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-0.19\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.49\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-40.15\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-9505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelkam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.30\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.19\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.84\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.33\u003csup\u003ens\u003c/sup\u003e\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 \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.58\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.09\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-2.70\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.83\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.65\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-9505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.30\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.84\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.33\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.05\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.58\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-6.09\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2.70\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-1.83\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-5.65\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-9534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelkam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.31\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.44\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.65\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" 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align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-104.65\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-9511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMelkam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.55\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-1.19\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.85\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.20\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.06\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.17\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1.04\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-0.32\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e325.15\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP-9511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICRS-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.19\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.85\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.17\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.20\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-8.17\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e-1.04\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.32\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-325.15\u003csup\u003ens\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSE(\u003csub\u003eij\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e20.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e263.22\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=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of best general and specific combiners of sorghum lines, testers and hybrids for yield and agronomic traits\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \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\u003eBest general combiners\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBest specific combiners\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=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-9505, P-9534 \u0026amp; P-9511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTX-623 \u0026times; ICRS 14, B6 \u0026times; Melkam\u0026amp; MARC2 \u0026times; ICRS-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTX-623 \u0026times; ICRS 14, P-850341 \u0026times; ICRS-14, MARC2 \u0026times; ICRS-14 \u0026amp; P-9511 \u0026times; Melkam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTX-623, P-9501, P-9505, P-9534, B6 \u0026amp; P-9511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMARC3 \u0026times; ICRS-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-9501 \u0026amp; B5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-9534\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTX-623 \u0026times; ICRS-14, P-9501 \u0026times; Melkam, B6 \u0026times; ICRS-14\u0026amp; MARC2 \u0026times; Melkam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTX-623 \u0026times; ICRS-14, P-9505 \u0026times; Melkam, P-9534 \u0026times; Melkam, P-850341 \u0026times; ICRS-14, B6 \u0026times; ICRS-14, MARC1 \u0026times; Melkam, MARC2 \u0026times; Melkam \u0026amp; MARC6 \u0026times; ICRS-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP-9505, P-850341, MARC1 \u0026amp; MARC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-9505 \u0026times; ICRA-14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMARC1, MARC2, MARC3 \u0026amp; MARC6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTX-623 \u0026times; ICRS 14\u0026amp; P-9505 \u0026times; Melkam\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP-9534 \u0026times; Melkam, B6 \u0026times; ICRS-14 \u0026amp; MARC3 \u0026times; Melkam\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 \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Estimation of variances of combining ability effect, gene action and heritability\u003c/h2\u003e \u003cp\u003eUnderstanding genetic variability, the role of genes action, and combining ability is essential for increasing sorghum yield. The relative importance of each variance was determined using GCA/SCA ratio of mean squares. Non-additive genetic variance (dominance or epitasis) is more significant than additive gene action in controlling these traits, demonstrated by the GCA/SCA ratio being less than unity for all investigated characteristics with the exception of the plant height (Table-8). All characteristics under investigation, with the exception of plant height, showed the genetic variation connected to specific combining ability (σ\u003csup\u003e2\u003c/sup\u003esca), suggesting that dominant gene action was more important in determining the traits than additive type. Therefore, the selection and development of superior genotypes take time until segregation generation is fixed. Making decisions about the next stage of a breeding program requires an extensive understanding of the GCA and SCA consequences. Similar findings regarding the role of gene action and combining ability effect of sorghum crops were reported by Amelework \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] and Wagaw \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll of the traits under study had a degree of dominance (σ\u003csup\u003e2\u003c/sup\u003eD/σ\u003csup\u003e2\u003c/sup\u003eA) greater than unity, with the exception of days to flowering, plant height, and leaf area. This suggests that non-additive gene action controls the inheritance of traits that contribute to yield, as well as the most effective method to develop superior hybrids is through heterosis-breeding. The findings showed that the degree of dominance (σ\u003csup\u003e2\u003c/sup\u003eD/σ\u003csup\u003e2\u003c/sup\u003eA) for days to flowering, plant height and leaf area was less than unity, indicating that additive genes regulate these traits and that parent selection should be given more weight in breeding strategies. According to Wagaw \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], the significance mean squares of lines and testers reveal the significance additive variance (σ\u003csup\u003e2\u003c/sup\u003eA), however the significance mean squares of line x testers provide the significance of dominance variance (σ\u003csup\u003e2\u003c/sup\u003eD).\u003c/p\u003e \u003cp\u003eThe broad sense heritability (H\u003csup\u003e2\u003c/sup\u003e) values ranged from 8.99% for days to maturity to 95.63% for plant height. The highest heritability was obtained for plant height (95.63%), panicle length (85.97%), 1000-seed weight (77.98%), panicle exertion (74.87%), panicle width (71.88%) and grain yield (66.54%), whereas days to flowering (58.31%), stay green (35.00%) and leaf area (30.77%) revealed moderate heritability. These results indicating the presence of positive response of sorghum improvement through selection of these traits because of their higher heritability. Thus, phenotypic and high heritability in the hybrid population are crucial for the selection of genetically superior generations. Gaddameedi \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e], Mengistu \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], Wagaw \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and Veldandi \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] have also reported similar results in sorghum.\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\u003eVariance components of combining ability analysis and estimates of gene effects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\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\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCombining ability variances\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003cp\u003e(gca/sca)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eGene action\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRatio\u003c/p\u003e \u003cp\u003e(\u0026#120590;\u0026sup2;D/\u0026#120590;\u0026sup2;A)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePhenotypic variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGenotypic variance\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eHeritability\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003egca\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003esca\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026#120590;\u0026sup2;\u0026#119860;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026#120590;\u0026sup2;\u0026#119863;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eh\u003csup\u003e2\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\u003eDTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.09\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-7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e58.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTM\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\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e118.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e473.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e169.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1903.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1820.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e95.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG\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\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.16\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\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e35.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e85.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\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\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e745.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e71.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e44.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-396.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e177.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1586.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-8.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1415.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e435.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e30.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e74.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e77.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e183.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e732.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e851.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1276.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e849.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e66.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.7\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 \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Estimation of the Proportional Contribution different variance components (lines, testers and line \u0026times; tester)\u003c/h2\u003e \u003cp\u003eThe proportional contribution of lines, testers and their interaction to the total variance showed that lines showed greater contribution than tester for all traits except leaf width in which line \u0026times; tester interaction revealed greater contribution than the lines and testers (Table-9). Generally, the contribution of lines are higher than testers and line x tester interactions in all traits except leaf width, indicating the lines had excellent contribution in hybrid variety development. For every traits except plant height, the line x tester interactions contributed more than the testers. Plant height was the most significant contribution from lines, followed by 1000 seed weight, days to flowering, panicle length, panicle exertion, stay green, and days to maturity.\u003c/p\u003e \u003cp\u003eThe maximum contribution of lines was 95.43% in plant height and the minimum was 42.63 in leaf area whereas the testers contribution ranged from 0.02% in days to maturity to 24.35% in leaf area and the line \u0026times; tester contributed the highest 46.31% in grain yield and the lowest 1.82% in plant height. This finding demonstrated that lines contributed adequate variation in expressing the characteristics under evaluation. It suggested the hybrid-breeding program should be focused the selection of parent lines. Rachman \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], Wagaw \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], and Rini \u003cem\u003eet al\u003c/em\u003e. [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] were similarly in agreement with this finding as well.\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\u003eProportional contribution of different component (lines, testers and line \u0026times; tester) towards total variances for yield and various agronomic traits.\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\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eContribution (%)\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 \u003cp\u003e\u003cb\u003eLines\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTesters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eLine\u003c/b\u003e \u0026times; \u003cb\u003eTester\u003c/b\u003e\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\u003e77.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDTM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e58.75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePTH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e95.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.82\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44.76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTSW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGY\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.31\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":"4. SUMMARY AND CONCLUSIONS","content":"\u003cp\u003eEnhancing sorghum crop through identifying and selecting of best general and specific combiners of superior sorghum lines and hybrids for grain yield and agronomic traits is vital to tackle the problems of acute food insecurity and malnutrition in arid and semi-arid regions of Ethiopia. The current study identified promising and drought-adapted sorghum parental lines and hybrids for all traits based on the substantial effect of both additive and non-additive gene actions. In order to develop new sorghum varieties with outstanding grain yield and agronomic traits, it is vital to select lines and testers that have significant GCA effects and crosses that have significant SCA effects. The best general combining ability of the potential parents were determined by their consistent performance and their desired direction for each traits. The best general combiner lines, P-9505, P-9534, and P-9511 were identified for the days to flowering, whereas all lines were identified to be the best general combiners for plant height except some lines and both testers. The investigation demonstrated that additive genes control the expression of plant height, and selection-breeding strategy could help the population associated with this trait.\u003c/p\u003e \u003cp\u003eThe best general combiners were identified to be lines (P-9501, P-9534, B5) for stay green, lines (P 9534) for panicle length, lines (P-9501, P-9505, P-850341, B5, MARC1, MARC2, P-9511, Melkam and ICRS-14) for panicle exertion and lines (MARC1, MARC2, MARC3 and MARC6) for 1000 seed weight. Additive gene actions governed each of the aforementioned lines for their respective traits, leading to effective selection for improving the population. The best specific combiners hybrids (TX-623 \u0026times; ICRS-14, B6 \u0026times; Melkam and MARC2 \u0026times; ICRS-14) for days to flowering, (TX-623 \u0026times; ICRS-14, P-850341 \u0026times; ICRS-14, MARC2 \u0026times; ICRS-14 and P-9511 \u0026times; ICRS-14) for days to maturity, (MARC3 \u0026times; ICRS-14) for plant height, (TX-623 \u0026times; ICRS-14, P-9501 \u0026times; Melkam, B6 \u0026times; ICRS-14 and MARC2 \u0026times; Melkam) for panicle length, (TX-623xICRS 14, P-9505 x Melkam, P-9534 x Melkam, 6x15, P-850341 x ICRS-14, MARC1 \u0026times; Melkam, MARC2 \u0026times; Melkam and MARC6 \u0026times; ICRS-14) for panicle width, (P-9505 \u0026times; ICRA-14) for panicle exertion, (TX-623 \u0026times; ICRS-14) for 1000 seed weight and (P-9534 \u0026times; Melkam, B6 \u0026times; ICRS-14 and MARC3 \u0026times; Melkam) for grain yield were identified. Due to the expression of these traits were governed by dominant gene actions, heterosis-breeding techniques are recommended to enhance these traits.\u003c/p\u003e \u003cp\u003eThe ratio SCA variation was greater than GCA variation for the traits under investigation except plant height. Hence, non-additive gene action played a crucial role in determining the investigated traits. In conclusion, the parental lines P-9534, MARC2, MARC3, MARC6, and P-9511 and hybrids P-9534 \u0026times; Melkam, P-9534 \u0026times; ICRS-14, B6 \u0026times; Melkam, B6 \u0026times; ICRS-14, MARC3 \u0026times; Melkam, and MARC3 \u0026times; ICRS-14 were identified as promising sorghum genotypes that could be used after in-depth scrutiny for superiority and yield stability across locations over years. The development of hybrids by heterotic breeding and population improvement through efficient selection were both recommended as appropriate breeding strategies for future breeding programs. As a prospect for the future, modern breeding techniques such as multi-trait gene editing, genomic prediction, and machine learning can precisely speed up breeding efforts to increase sorghum productivity. Therefore, revitalizing sorghum hybrid development is the primary prerequisite for enhancing sorghum productivity in Ethiopia.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTemesgen Begna\u003c/strong\u003e: Writing - review \u0026amp; editing, Writing– original draft, Visualization, Validation, Supervision, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. \u003cstrong\u003eTechale Birhan\u003c/strong\u003e: Writing– review \u0026amp; editing, Writing– original draft, Validation, Supervision, Project administration, Investigation, Conceptualization. \u003cstrong\u003eTaye Tadesse\u003c/strong\u003e: Writing– original draft, Visualization, Resources, Project administration, Investigation, Funding acquisition, Conceptualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was funded by the Ethiopian National Sorghum Improvement Program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data are included in the article.\u0026nbsp;\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eEthics approval and clinical trial declarations\u003c/strong\u003e:\u0026nbsp;Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish declaration\u003c/strong\u003e: Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor hosting the trials, the Ethiopian National Sorghum Improvement Program has been acknowledged by the authors.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eXiao MZ, Sun Q, Hong S, Chen WJ, Pang B, Du ZY, Yang WB, Sun Z, Yuan TQ. 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Genetic variation in sorghum as revealed by phenotypic and SSR markers: implications for combining ability and heterosis for grain yield. Plant Genetic Resour. 2017;15(4):335\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaddameedi A, Sheraz S, Kumar A, Li K, Pellny T, Gupta R, Wan Y, Moore KL, Shewry PR. The location of iron and zinc in grain of conventional and biofortified lines of sorghum. J Cereal Sci. 2022;107:103531.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVeldandi S, Maheswaramma S, Sravanthi K, Sujatha K, Ramesh S, Yamini KN, Shivani D, Kumar CS. Heterosis and combining ability studies to identify the superior hybrids and parents for grain yield and yield contributing traits in sorghum (Sorghum bicolor L. Moench). 2022.\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":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gene action, Combining ability, Climate smart, Sorghum, Drought resilience","lastPublishedDoi":"10.21203/rs.3.rs-5838770/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5838770/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e \u003cem\u003eSorghum has profound role in ensuring food security across the globe, especially in dry lowland regions. However, substantial sorghum productivity has been curtailed by severe and prolonged drought stress due to the limitation of climate smart and superior sorghum varieties for moisture stress areas of Ethiopia. Therefore, this study was conducted to identify and develop superior sorghum genotypes through investigating gene action and combining abilities for yield and agronomic traits. In total, 42 sorghum genotypes were assessed in alpha lattice design with two replication. There was considerable differences amongst genotypes for yield and agronomic characteristics. Best performing hybrids such as P-9534\u003c/em\u003e \u0026times; \u003cem\u003eMelkam (6.32 tha\u003c/em\u003e\u003csup\u003e\u003cem\u003e-1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e), B6\u003c/em\u003e \u0026times; \u003cem\u003eICRS-14 (5.92 tha\u003c/em\u003e\u003csup\u003e\u003cem\u003e-1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) TX-623\u003c/em\u003e \u0026times; \u003cem\u003eICRS-14 (5.88 tha\u003c/em\u003e\u003csup\u003e\u003cem\u003e-1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e), P9511\u003c/em\u003e \u0026times; \u003cem\u003eMelkam (5.78 tha\u003c/em\u003e\u003csup\u003e\u003cem\u003e-1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) and P-850341\u003c/em\u003e \u0026times; \u003cem\u003eICRS-14 (5.57tha\u003c/em\u003e\u003csup\u003e\u003cem\u003e-1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) were identified with yield advantage of 32.49%, 24%, 23%, 21% and 16.68% over the standard check (ESH-4) (4.77 tha\u003c/em\u003e\u003csup\u003e\u003cem\u003e-1\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e) respectively. Inbred lines P-9534 and P-9505 were identified as the best general combiners for the plant height and days to flowering, while P-9501 and B5 were found to be the best general combiner parents for stay green. In terms of thousand-seed weight, the best general combiners were P-850341, MARC2, and MARC6 inbred lines. This signified the traits were principally governed by additive gene action and early generation selection was the most preferred strategies for further improvement. The hybrids P-9534\u003c/em\u003e \u0026times; \u003cem\u003eMelkam, B6\u003c/em\u003e \u0026times; \u003cem\u003eICRS-14, and MARC3\u003c/em\u003e \u0026times; \u003cem\u003eMelkam were identified as best specific combiners for grain yield, while TX-623\u003c/em\u003e \u0026times; \u003cem\u003eICRS-14 was the best specific combiner for panicle width, 1000-seed weight, days to flowering and days to maturity. This demonstrated non-additive gene action mainly controlled the traits and heterotic breeding strategies was advised to develop superior sorghum hybrids. Since the ratio of specific to general combining ability was more than unity for all traits except plant height, the investigation demonstrated the preponderance of non-additive gene action. In conclusion, after further investigation for stability and adaptability over years across locations, the drought-resistant and high-yielding hybrids (TX-623\u003c/em\u003e \u0026times; \u003cem\u003eICRS-14, MARC3\u003c/em\u003e \u0026times; \u003cem\u003eMelkam, MARC3\u003c/em\u003e \u0026times; \u003cem\u003eICRS-14, P-9511\u003c/em\u003e \u0026times; \u003cem\u003eMelkam, P-850341\u003c/em\u003e \u0026times; \u003cem\u003eICRS-14, P-9534\u003c/em\u003e \u0026times; \u003cem\u003eMelkam, and B6\u003c/em\u003e \u0026times; \u003cem\u003eICRS-14) would be utilized commercially.\u003c/em\u003e\u003c/p\u003e","manuscriptTitle":"Phenotyping and combining ability Analysis of sorghum [Sorghum bicolor (l) Moench] Genotypes in dryland Environments","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-18 06:40:38","doi":"10.21203/rs.3.rs-5838770/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-05-27T11:28:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-07T10:43:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-21T04:12:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27099409956678950654483836951713619135","date":"2025-04-20T06:12:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77275327777966545290445869582764877215","date":"2025-04-17T07:24:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-17T07:07:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-15T04:06:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Sustainability","date":"2025-03-27T12:48:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-sustainability","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"disu","sideBox":"Learn more about [Discover Sustainability](https://www.springer.com/43621)","snPcode":"","submissionUrl":"","title":"Discover Sustainability","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04a57568-839c-4a8e-9ac4-fa4ead3b574d","owner":[],"postedDate":"April 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T16:09:12+00:00","versionOfRecord":{"articleIdentity":"rs-5838770","link":"https://doi.org/10.1007/s43621-025-01411-6","journal":{"identity":"discover-sustainability","isVorOnly":false,"title":"Discover Sustainability"},"publishedOn":"2025-07-01 15:58:43","publishedOnDateReadable":"July 1st, 2025"},"versionCreatedAt":"2025-04-18 06:40:38","video":"","vorDoi":"10.1007/s43621-025-01411-6","vorDoiUrl":"https://doi.org/10.1007/s43621-025-01411-6","workflowStages":[]},"version":"v1","identity":"rs-5838770","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5838770","identity":"rs-5838770","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}