Variability, heritability and association among Grain yield and yield contributing traits of Sorghum genotypes at Western Ethiopia

Variability, heritability and association among Grain yield and yield contributing traits of Sorghum genotypes at Western Ethiopia | 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 Variability, heritability and association among Grain yield and yield contributing traits of Sorghum genotypes at Western Ethiopia Habtamu Alemu, Tokuma Legesse This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7352596/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Sorghum is a globally significant multipurpose crop primarily utilized for food, animal feed, and industrial uses. However, its productivity remains constrained, necessitating focused efforts for enhancement. To boost sorghum yields, it is crucial to have access to genetically diverse sorghum genotypes that exhibit various agronomic traits, as well as to understand the relationships among these traits. This study aimed to assess the genetic diversity and the interrelationships among different agronomic traits in selected sorghum genotypes with the objective of pinpointing the most promising genotype that could improve sorghum production or act as a valuable asset for breeding programs. Sixty three sorghum genotypes were evaluated at Assosa, Bako and Mange research stations during the 2023/24 growing season. Data on important agronomic traits were collected: days to 50% flowering (DTF), days to physiological maturity (DTM), plant height (PHT), over all agronomic score (PAS), thousand kernels weight (TKW), Anthracnose disease resistant score and grain yield (GY). Higher genotypic coefficient of variations (GCV) was recorded for TKW (27.14%), GY (24.53%), PAS (23.99), while higher phenotypic coefficient of variations (PCV) were recorded for GY (53.59%), followed by PAS (35.64%), and TKW (33.02%). High broad-sense heritability was recorded for PHT (79.79), DTM (78.48), TKW (68.25), and DTF (65.64). Similarly highest genetic advance was recorded for PHT (44.54). Grain yield was positively and significantly correlated with TKW (r = 0.59), PHT (r = 0.40) while negatively associated with DTM (-0.63) and DTF (-0.55).The path analysis revealed that TKW (0.33) and PHT (0.14) exerted significant positive direct effects of on GY. Generally, the observed variability and the information obtained from this study can be used for the genetic improvement of sorghum which can result in the development of high-yielding varieties. sorghum variability association heritability direct effect genetic advance Figures Figure 1 INTRODUCTION Sorghum (Sorghum bicolor L.) is a highly adaptable and resilient cereal crop that thrives in a variety of agro-ecological zones, including semi-arid, sub-tropical, tropical, and temperate climates (Ngidi et al., 2024 ). It is recognized globally as the fifth most significant cereal, following wheat, rice, maize, and barley, due to its nutritional value and ability to withstand challenging environmental conditions where other crops may fail (Ritter et al., 2007 ). The origins of sorghum can be traced back between 5,000 and 7,000 years in northeastern Africa, particularly in Ethiopia (T. Mesfin and F. Tileye, 2013). Sorghum is a crucial crop for food security in many South Asian and sub-Saharan African nations, especially in Ethiopia. It is well-suited to a variety of agro-ecological zones throughout the country, although it is primarily grown in the lowland and moisture-stressed areas (Tesso et al., 2007 ). Despite its considerable nutritional benefits, sorghum often goes unrecognized. It is a source of essential carbohydrates, including amylose (19.78%), starch (69.15%), and amylopectin (80.22%), and boasts a protein content of 13.8%, along with 1.8% ash, 3.3% oil, and 17.3% fiber. Furthermore, sorghum is abundant in vital minerals, providing 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) (A. S. Gerrano et al., 2016). Genetic heterogeneity among crop genetic resources is crucial to create new cultivars with desired agronomic features. The test population's genetic composition and the growing environment determine phenotypic variance. To choose superior individuals with desired qualities from a genetically varied population, it is crucial for plant breeding operations that there is genetic variety in agro-morphological features (R. H. Gichile, 2022 ). Selecting parental genotypes with a broad genetic basis for additional genetic improvement requires an understanding of the degree of genetic diversity. Numerous academics have noted that Ethiopian sorghum landraces have a very high level of genetic diversity, suggesting that breeding programs might provide substantial improvements (B. Fantaye, 2024 ). The extent of genetic variability present in the source material and the effectiveness of selection highly dictate the success of any crop improvement (Ranjith et al., 2017 ). Genotypic Coefficient of Variation (GCV) is a measure of the amount of variation within a population for a particular trait due to genetic differences while phenotypic Coefficient of Variation PCV measures the total variation of a trait, encompassing both genetic and environmental influences. A comparison of PCV and GCV is very important to determine the extent to which a trait is influenced by genetic makeup as against environmental factors. The higher PCV compared to GCV suggests that the environment has greater influence on the expression of a given trait. Heritability is a statistical concept that describes how much of the variation in a given trait can be attributed to genetic variation (Falconer DS and Mackay F, 1996). It does not indicate what proportion of a trait is determined by genes and what proportion is by environment but indicates the extent of the variability in the trait in a population is due to genetic differences among the population. Genetic advance (GA) is a measure of the expected improvement or gain in a specific trait's mean genotypic value when selection is applied to a population. The combination of Heritability estimates with genetic advance is more accurate than heritability alone in predicting the genetic gain under selection (Demeke, et al., 2023). Since grain yield is controlled by several genes and impacted by complex gene interactions, it is essential to fully understand the kind and degree of gene activity at involved (Tirkey Sheetal et al., 2021). Analysis of correlations shows how closely basic traits are related to one another. A thorough grasp of the extent to which yield and yield-contributing qualities are related is essential to improving selection efficiency for high yielding (Shimelis H. 2006). The primary genetic factors used to choose superior genotypes and assess breeding tactics are phenotypic and genotypic correlations. Correlations between agronomic parameters with high heritability facilitate the selection process for complex traits such as grain yield (Gurmu F, et al., 2018 ). It is crucial to perform a path coefficient analysis, which separates the correlated variables into direct and indirect effects and thus envisions the fundamental relationship in a more meaningful way, since correlation analysis demonstrated the relationship between two or more series of characters (C. Endalamaw and Z. Semahegn, 2020). In sorghum genotypes, especially in lowland regions of western Ethiopia, there is a dearth of information on genetic diversity, correlation, and path-efficient analysis of grain production and yield-related factors. Using quantitative characteristics for breeding, genetic variation among most of the sorghum genotypes analyzed in the region still needs to be investigated. Therefore, this study's objectives were to assess genetic variability, determine the kind and extent of phenotypic and genotypic relationships among the quantitative traits being studied, and identify the most important features for indirect selection in future sorghum breeding projects. MATERIAL AND METHODS 2.1. Description of the Study Area The field experiment was conducted from June to December 2023 at Assosa, Mange and Bako during the main cropping season. Assosa found at 667 km to the west of Addis Ababa, the country’s capital city while Bako located at the 285 km on the way from Addis Ababa to Assosa. Mange found at 50 km west of Assosa, the capital city of Benishangul gumuz region. Table 1 Description of the testing environment Environment Longitude Latitude Altitude m.a.s.l Annual rainfall (mm) Min-Max T o Assosa 34° 57’ E 10 0 04’ 1553 1275 17 0c -32 0c Mange 34˚43'30.809''E 10˚20'8.17''N 1123 771.5 14.3 0C -34.2 0c Bako 37° 09’E 09 0 06’ 1650 1425 13.1–31.4 0 C 2.2. Experimental materials description The study was conducted by using a total of 61 sorghum inbreed lines along with two standard checks, Melkam and Bonsa varieties. Table 2 List of Sorghum genotypes used in the experiment Genotype Genotype SSD18-114 SSD18-6 SSD18-142 ARG-16 SSD18-103 SSD18-2 SSD18-97 SSD18-48 SSD18-26 SSD18-159 Melkam (Check) SSD18-166 SSD18-148 SSD18-129 SSD18-132 SSD9-170 SSD18-150 SSD18-147 SSD18-42 P9830 SSD18-133 SSD9-118 Bonsa (Check) TAM428 SSD18-91 SSD18-117 SSD18-122 SSD18-210 SSD18-56 SSD18-44 SSD18-38 SSD18-170 SSD18-98 SSD18-36 SSD18-71 SSD18-10 SSD18-179 SSD18-128 SSD18-70 SSD18-50 SSD18-99 SSD18-8 SSD18-186 SSD18-64 SSD18-116 SSD18-28 SSD18-7 SSD18-167 SSD9-185 SSD18-66 SSD18-164 SSD18-80 SSD18-193 SSD18-109 SSD18-127 SSD18-100 SSD18-121 SSD18-40 SSD18-52 SSD18-41 SSD18-131 ARG-25 SSD9-30 2.3. Source of the planting material used Sixty three sorghum genotypes were obtained from Sorghum Research Program of Assosa Agricultural Research Center, Ethiopian Institute of Agricultural Research (EIAR) for this study. The materials were selected and collected purposively for their better agronomic performance as well as good disease resistance level in the open field conditions of western Ethiopia. The list of sorghum genotypes used in this study is presented in Table 2 . 2.4. Experimental design and Field management Randomized complete block design (RCBD) with two replications were used. The genotypes were sown on a plot of two rows with five (5m) meters length having the spacing between rows, plants, and replications, 75cm, 15cm 1.5m respectively. The seeds were thoroughly drilled in the prepared ridge followed by appropriate thinning at around knee height. Experimental units received the same amount and rate of 100 kg/ha DAP and 100 kg/ha Urea fertilizers. 2.5. Data collection Data for main phonological and growth parameters including yield and important yield components were recorded using the appropriate procedure. Plant and plot based data was collected. Plant-based data were collected from randomly selected and representative five plants in the plot, while the plot-based data were collected from the whole harvestable plots. Days to flowering (DF), Days to maturity (DM), Plant height (PH) (cm), Agronomic score (PAS), Thousand seed weight (TSW) (g), and Grain Yield (kg/ha) were the recorded quantitative data collected in the study. 2.6. Data Analysis Analysis of variance was computed following the methods proposed by (Gomez & Gomez, 1984). META-R and R soft wares were used for data analysis. Best linear unbiased predictor (BLUP) means were estimated using multivariate mixed model (REML) spatial analysis considering the Block/Rep + treatment as random effect for special correction of the nearest block errors to avoid the biased estimate of variance components at 5% level of significance (Vargas et al., 2013 ). Phenotypic, genotypic and environmental variance components and their coefficients of variation were estimated based on the methods detailed in (Burton & Devane, 1953 ). The simple correlation coefficient was subjected to path analysis (Dewey and Lu, 1959 ). Broad sense heritability (H2) and genetic advance as percent of mean (GAM) were also estimated according to the formula in (Allard, 1960 ). Results and Discussion 3.1. Analysis of Variance Analysis of variance for Grain yield (GY (kg/ha), days to flowering (DTF), DTM (days to maturity), Plant height (PHT cm), Thousand seed weight (TSW in g), Over all agronomic score (PAS) and Anthracnose disease resistance showed highly significant difference among the tested genotypes and the test locations at (P < 0.01) level of significance (Table 3 ). This result implies that the tested genotypes were different in their performance of the evaluated traits and the tested locations were also varied significantly for the performance of the evaluated genotypes. Similar research findings reported by many authors like Gebregergs & Mekbib, ( 2020 ), (Amare et al., 2015 ), Tariq et al., 2012 . The genotype by location interaction was also highly significant (P < 0.01) for Grain yield (GY), Days to flowering (DTF), Days to maturity (DTM) and Agronomic Score (PAS). This showed that the performance of the genotypes evaluated in this trial varied significantly across the tested environments. Table 3 ANOVA and Mean performance sorghum genotypes evaluated at the humid lowland agro-ecology of western Ethiopia Genotype GY Kg/Ha DTF DTM TKW(g) PAS PHT(cm) AnthSc ARG-16 2423 73.5 127 21.13 3.333 126.5 3.17 ARG-25 1278 76 125.7 20.1 4.333 77.9 2.83 Bonsa 2966 75.67 129.5 22.99 3.083 145.6 3.00 Melkam 3061 74.33 124.3 33.64 2.167 146.1 2.08 P9830 2259 70.17 123.7 23.3 2.583 136 3.25 SSD18-10 2109 70 126 27.66 2.667 143 3.00 SSD18-100 1866 74.5 125.8 18.6 3.25 139.7 3.17 SSD18-103 3232 76.33 127.2 28.36 2 146.4 3.17 SSD18-109 1881 74 124.2 20.66 2.917 126 2.67 SSD18-114 3526 73.83 126 22.54 2.083 115.2 2.17 SSD18-116 2687 79.17 128.2 20.66 2.167 133 2.08 SSD18-117 2243 78.67 127.7 21.44 2.083 160.2 1.83 SSD18-121 2545 73.67 127.5 24.35 2.5 156.5 2.75 SSD18-122 2845 71 122.7 25.23 2.667 147.9 3.17 SSD18-127 2571 72.5 123.8 24.23 2.417 147.4 2.67 SSD18-128 2107 73.67 121.7 23.32 2.25 136.7 3.00 SSD18-129 2312 72.67 124 26.04 2.833 141.5 2.83 SSD18-131 2453 77.5 125 22.07 2.083 141.8 2.83 SSD18-132 3016 75.33 128.3 25.89 2.5 125.1 3.17 SSD18-133 2991 71.5 125.5 28.84 2.167 143.7 2.83 SSD18-142 3352 74.5 129.8 27.59 1.667 154.9 2.42 SSD18-147 2274 72.83 124.8 27.73 2.667 146 2.83 SSD18-148 3025 75 125.5 25.93 2.25 146.8 2.50 SSD18-150 2996 71.67 126.7 22.55 2.167 155.9 2.83 SSD18-159 2342 70.5 126.2 27.14 2.583 146 2.33 SSD18-164 2628 72.33 124.3 26.21 2.5 118.4 2.50 SSD18-166 2320 75 125.7 24.42 2.667 131.8 2.58 SSD18-167 1999 73.17 121.8 20.98 2.833 125.2 2.42 SSD18-170 2144 72 121 19.11 2.667 152.7 2.33 SSD18-179 2755 74.17 125.3 25.12 2.5 129.9 2.08 SSD18-186 2702 76.33 126.2 19.35 2.35 150.5 1.92 SSD18-193 2583 71.5 123.8 27.02 2.333 154.4 3.17 SSD18-2 2400 72.83 127.5 28.63 2.333 137 2.92 SSD18-210 2242 72.17 121.2 23.81 3.5 137 3.17 SSD18-26 3117 75 128.5 26.17 2 162.9 3.33 SSD18-28 2006 76 126.2 26.38 2.5 145.7 3.17 SSD18-36 2130 75.5 123.8 23.4 2.871 140.3 3.33 SSD18-38 2816 75.83 127 24.01 2.083 145.9 2.50 SSD18-40 1692 76.83 127.7 18.32 3 122 3.00 SSD18-41 1664 70.83 121.2 18.95 2.667 143.5 2.67 SSD18-42 2991 76.83 129.2 21.44 2.167 168.3 2.25 SSD18-44 2190 74 121 21.59 2.75 129.7 3.33 SSD18-48 2345 77 126.2 21.58 2.333 140.3 3.17 SSD18-50 2090 72.5 124.2 23.46 2.667 152.9 2.67 SSD18-52 2529 72.5 121.5 23.36 2.667 149.8 2.83 SSD18-56 2821 79.33 130 25.88 2.083 141.1 3.17 SSD18-6 2424 76 128 22.53 2.25 139.2 2.83 SSD18-64 2034 68.33 122.2 29.53 3.083 134.8 2.75 SSD18-66 1916 73.17 122.3 21.47 3 133.8 2.83 SSD18-7 2661 72.17 122.5 28.44 2.333 134.2 2.42 SSD18-70 2748 74 126.7 22.85 2.25 134.2 2.67 SSD18-71 2793 73 125.7 25.74 2.25 164.9 3.00 SSD18-8 2081 73.33 123.3 25.59 2.667 125.1 3.17 SSD18-80 1902 72.17 124.8 23.56 2.75 140.2 3.33 SSD18-91 2912 68 124.7 25.11 2.667 160.4 3.17 SSD18-97 3128 73.17 127 21.48 2.083 150.3 3.17 SSD18-98 2810 72.17 126 22.94 2.333 154.3 2.75 SSD18-99 2743 69.33 124.5 23.27 2.583 169.3 3.33 SSD9-118 2251 73.17 129 26.39 3.083 119.8 2.67 SSD9-170 2275 75.33 125.2 22.58 3.167 118.6 3.33 SSD9-185 2656 74.33 126.8 26.2 2.75 145.5 2.25 SSD9-30 1155 70.5 123 26.56 3.583 151.1 3.17 TAM428 2248 71.33 121.8 24.64 2.583 140.6 2.83 Grand Mean 2464 73.65 125.29 24.16 2.576 140.97 2.806 CV 18.1 3.3 3.2 19.7 19.1 7.2 23.6 LSD 875.1 4.74 8.03 9.384 0.9704 19.989 1.30 Genotype ** ** ** ** ** ** ** Location ** ** ** ** ** ** ** Genotype * Location ** ** ** NS ** NS NS Where GY = grain yield, DTF = Days to flowering, DTM = Days to maturity, TKW = Thousand kernel weight, PAS = Agronomic score, PHT = Plant height, AnthSc = Sorghum anthracnose score, CV = coefficient of variation, LSD = List significant difference, ** = significant at p < 0.001, NS = Non-significantly different The highest grain yield (3526 kg ha-1) was recorded for genotypes SSD18-114 while the lowest one (1155 kg ha-1) for genotypes SSD9-30. Genotype SSD18-91 flowered earlier than others (68) while genotype SSD18-56 flowered lately (79.33). Genotype SSD18-170 was early maturing than others (121) while genotype SSD18-56 late maturing (130). The largest thousand seed weight (33.64g) was recorded for Melkam which was the standard check while the lowest thousand seed weight (18.32g) recorded for SSD18-40. Similarly, the tallest plant height (169.3cm) was recorded for genotype SSD18-99 while the shortest (77.9cm) was genotype ARG-25. This wider genetic variation of the genotypes for the evaluated traits showed that utilizing the broader genetic variability in the evaluated genotypes as a source of breeding material for the enhancement of traits of interest across various objectives may enhance sorghum production and productivity. Different scholars report similar findings to this research results so far ((Tariq et al., 2012 , Abraha et al., 2015 , and Gebregergs & Mekbib, ( 2020 ). Like all the above agronomic traits, the evaluated genotypes varied for their reaction to Anthracnose disease under open environment conditions. Even though Genotype SSD18-117 was considered as resistant genotype having average disease score of 1.30 across the test environments on 1–5 scale, most of the genotypes showed resistant to moderately resistant reaction. 3.2. Variance components Genotypic variance (δ2 g), phenotypic variance (δ2p), environmental variance (δ2e), broad sense heritability (H2), genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV) and GAM for yield and yield contributing traits of the sorghum genotypes are presented in Table 4 . The calculated variance components for the assessed characteristics show that the PCV was larger than the GCV, suggesting that the trait's expression was impacted by the environment. The GCV and PCV for the measured traits in the current study ranged in between 3.88 and 7.14 for DTF to 24.55 and 53.59 for GY respectively which imply the presence of significant amount of genetic variability among the tested genotypes for different traits. Similarly, (Gebregergs & Mekbib, 2020 ), (Amare et al., 2015 ), (Abraha et al., 2015 ) reported higher GCV and PCV for Plant height and Grain yield; Alemu and Demelash, 2022 reported for higher GCV and PCV for TKW. In comparison, DTF and DTM showed low PCV and GCV. In other words, DTF and DTM provide little heritable genetic (additive) component to the following generation, meaning that there is no need for investment to enhance these characteristics in order to improve sorghum. 3.3. Heritability and Genetic advance Heritability is a statistical metric used to measure the degree to which genetic variants among members of a community account for variances in a characteristic. It basically calculates the percentage of observable variation in a characteristic, or phenotypic variation that may be attributed to genetic variation. The result of broad sense heritability estimation (Table 4 ) showed that the highest broad sense heritability (79.79, 78.48, 68.25 and 65.64) was recorded for Plant height, Days to maturity, Thousand Kernel weight and days to flowering respectively. On the other hand, the lowest (45.32 and 45.77) recorded for agronomic score and grain yield respectively. The higher heritability estimation for some characters revealed that selecting sorghum genotypes for these traits will have a positive response on sorghum improvement. Alemu and Demelash, ( 2022 ) reported similar findings of higher heritability for Plant height, Days to maturity, Thousand Kernel weight and days to flowering. It is more convenient to have the combination of higher heritability with higher estimates of GCV and GAM in order to assure successful selection for improvement, because high broad sense heritability alone does not necessarily guarantee high prediction of genetic gain. For traits to be effective to select for improvement of sorghum grain yield, their respective estimate of GCV and GAM have to be higher. Table 4 Estimated variance components for agronomic traits of sorghum genotypes Traits GY DTF DTM TKW PAS PHT Genetic variance 365411.02 18.16 27.43 43.00 0.38 585.80 Phenotypic variance 1744288.03 27.67 34.95 63.64 0.84 734.15 Environmental Variance 2109699.04 19.51 7.52 55.63 0.46 148.35 PCV 53.59 7.14 6.25 33.02 35.64 19.22 GCV 24.53 5.79 4.18 27.14 23.99 17.17 H2 45.77 65.64 78.48 68.25 45.32 79.79 GA 5.70 3.20 7.21 2.07 0.86 44.54 GAM 23.13 4.34 5.76 8.55 33.27 31.59 Where GY = grain yield, DTF = Days to flowering, DTM = Days to maturity, TKW = Thousand kernel weight, PAS = Agronomic score, PHT = Plant height, PCV = Phenotypic coefficient of variation, GCV = Genotypic coefficient of variation, H 2 = broad sense heritability, GA = Genetic advance, GAM = Genetic advance as percent of mean The highest genetic advance was recorded for plant height (44.54) followed by days to physiological maturity (7.21) and grain yield (5.70) while the lowest score was for agronomic score (PAS) which was 0.86 (Table 3 ). The estimated Genetic advance as percent of mean was ranged from 4.34% for days to flowering to 33.27% for agronomic score (Table 3 ). As stated by Johnson et al. (1955), high genetic advance as percent of mean was recorded for grain yield (23.13%), agronomic score (33.27%) and Plant height (31.59) while low GAM was recorded for other characters like days to flowering (4.34%), days to physiological maturity (5.76) and thousand kernel weight (8.55). Similarly higher estimate of GAM were reported by (Ranjith et al., 2017 ) and (Gebregergs & Mekbib, 2020 ) for grain yield and plant height. High GAM is associated with effective selection of sorghum genotypes with plant height. High genetic advance values and heritability show the presence of additive gene activity, which is highly heritable and suggests that choosing such characteristics, might enhance crops. To create precise selection indices for creating sorghum genotypes with high grain production capability, priority should be given to traits that demonstrated significant heritability and genetic advancements. 3.4. Correlations among agronomic traits Estimated genotypic and phenotypic correlation coefficients among each pair of agronomic traits are presented in (Fig. 1 ). The evaluation of the pairwise relationships between some agronomic traits showed that some of them were positively correlated while others were negatively correlated. This result suggested that improving one trait had either positive or negative impact on the other traits. Since grain yield is a complex traits governed by many (polygenic) genes and attributed by many traits, having the information of how these traits interact to each other is very crucial. To establish effective selection methods, considering the association between these traits and their correlation with grain yield is very important (Saleh et al 2020 ). Grain yield showed positive genotypic and phenotypic correlations with PHT, and TKW, while negative correlations with DTF and DTM. This positive correlation infers that selection for these traits can assist to exploit higher grain yielding potential in sorghum. Similar results of strong positive correlation of grain yield with plant height have been reported by Alemayehu (2003), Keba & Tamir ( 2023 ) and Ngidi et al. ( 2024 ). PHT showed positive association with DTM and TKW while negatively correlated to DTF and PAS. DTF and DTM had strongly positive correlated to each other. This implies that selecting for better plant height directly will indirectly resulted in better DTM and TKW and selecting for better DTM will also improve DTF. This finding is in agreement with the finding of Keba & Tamir ( 2023 ). 3.5. Path coefficient analysis for agronomic traits The present study showed that positive direct effects were exerted on grain yield by Thousand kernel weight (0.33), followed by Plant height (0.14) while negative direct effects on grain yield were observed by days to maturity (-0.51), agronomic score (-0.15), and days to flowering (-0.08) respectively(Table 5 ). The positive and high correlation of some of the agronomic traits revealed the actual impacts these traits have to improve sorghum grain yield. Dissecting the complex relationships between grain yield and its component traits into direct and indirect effects allowed researchers to determine whether the influences directly affect yield or take a different route that ultimately affects yield. The most yield-contributing traits were then used for both direct and indirect selection. Similar result of direct positive correlation of thousand kernel weights and plant height on grain yield was reported by Keba & Tamir ( 2023 ), Mengesha et al., ( 2019 ), and Chittapur and Biradar ( 2015 ). Plant height and thousand kernel weight had positive direct correlation with grain yield and had positive and significant genotypic correlations with grain yield (Table 5 ). Some of the traits like days to flowering, days to maturity and agronomic score has negative direct correlation with grain yield. This implies that selection for early maturing genotypes resulted in significant yield reduction in the testing environments as the locations suited for long maturing varieties. Similarly, selecting for higher agronomic score values ended in poor yielding genotypes as the smallest score for the trait is desirable. Hence, those traits showed negative direct effect has to be considered carefully as they play a role in grain yield reduction in the study area. Thousand kernel weights have also positive indirect effect on grain yield through plant height (0.08), agronomic score (0.015). These implied that the positive indirect effects contributed to increase grain yield via increasing those traits. The result of positive indirect impact of thousand kernel weight through plant height is in line with the findings of Keba & Tamir ( 2023 ), and Khan et.al., ( 2013 ). Table 5 Path analysis estimates for important agronomic traits in sorghum genotypes DTF DTM PHT PAS TKW DTF -0.08 -0.26 -0.05 0.03 -0.19 DTM -0.04 -0.51 -0.03 0.07 -0.12 PHT 0.03 0.12 0.14 0.03 0.08 PAS 0.02 0.24 -0.03 -0.15 0.015 TKW 0.05 0.18 0.03 -0.01 0.33 Conclusion The result of the current study revealed that the evaluated genotypes were statistically significantly different at (P < 0.01) for all the traits examined. Results also showed that larger genetic variance over environmental variance for all characteristics except grain yield. High genetic and phenotypic coefficient of variation was also recorded for grain yield, agronomic score (PAS) and thousand kernel weight while lower for days to flowering and days to maturity. High broad sense heritability recorded for all characteristics. It confirms a positive response for the effectiveness of selection based on the traits with high and medium PCV and GCV values for trait of interest improvement. The study further found positive associations among the examined traits. The significant positive association between traits suggests that the traits can be enriched simultaneously through direct selection. Whereas, the strong negative association between traits implies the need for growing and selection among large segregating populations to break the unfavorable association. Hence, the selection process for these genotypes will be effective if focused on the highly heritable traits with higher estimate of GCV and GAM. Declarations Ethics declaration : not applicable. Consent to Participate declaration: not applicable Consent to Publish declaration: not applicable. Funding declaration: This activity receives no special funds. Data Availability Statement: The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Permissions to collect the plants/plant parts: Not applicable Source of the plant used in your study: All the sorghum seed/planting materials used in this experiment were collected from Assosa Agricultural research center of Ethiopian Institute of Agricultural Research References Demeke, B., Dejene, T., & Abebe, D. (2022). Genetic variability, heritability, and genetic advance of morphological, yield related and quality traits in upland rice ( Oryza Sativa L.) genotypes at pawe, northwestern Ethiopia. Cogent Food & Agriculture , 9 (1). https://doi.org/10.1080/23311932.2022.2157099 Tirkey Sheetal, Jawale LN and More AW. (2021). 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Moench] at Humera, Western Tigray, Ethiopia,” Cogent Food & Agriculture 6, no. 1 (2020): 1764181, https://doi.org/10.1080/23311932.2020.1764181. Tesso, T., Gutema, Z., Deressa,A., & Ejeta,G. (2007). An inte grated Striga management option offers effective control of Striga in Ethiopia. Integrating New Technologies for Striga Control: Towards Ending the Witch Hunt, 199-212. https://doi.org/10.1142/9789812771506_ 0015 A. Gerrano, M. Labuschagne, A. Van Biljon, N. Shargie, Quantification of mineral composition and total protein content in sorghum [Sorghum bicolor (L.) Moench] genotypes, Cereal Res. https://doi.org/10.1556/0806.43.2015.046 Al-Younis, Abdel Hamid Ahmed. 1993. Production and improvement of field crops. Dar Al-Kutub Publishing and Printing Foundation. Ministry of Higher Education and Scientific Research. Mosul University Press. College of Agriculture and Forestry. p. 469. R. H. Gichile, Genetic Variability and Trait Associations among Sorghum [Sorghum Bicolor (L.) Moench] Genotypes for Yield and Yield Related Traits in West Hararghe (Dire Dawa, Ethiopia: Haramaya University, 2022). Fantaye B (2024) Genetic Variation in Dry Lowland Sorghum Land races of Abergelle, Northern Ethiopia. Adv Crop Sci Tech 12: 726. P. Ranjith, R. B. Ghorade, V. V. Kalpande, and A. M. Dange (2017). “Genetic Variability, Heritability and Genetic Advance for Grain Yield and Yield Components in Sorghum,” In ternational Journal of Farm Sciences 7(1): 90–93. Gebremedhn Gebregergs & Firew Mekbib | (2020) Estimation of genetic variability, heritability, and genetic advance in advanced lines for grain yield and yield components of sorghum [Sorghum bicolor (L.) Moench] at Humera, Western Tigray, Ethiopia, Cogent Food & Agriculture, 6:1, 1764181, https://doi.org/10.1080/23311932.2020.1764181 Falconer DS, Mackay FC (1996). Introduction to quantitative genetics. Volume 464. New York: Longman. Tirkey Sheetal, Jawale LN and More AW (2021). Genetic variability, correlation and path analysis studies in B parental lines of kharif sorghum (Sorghum bicolor (L.) Moench). The Pharma Innovation Journal; 10(8): 624-628 Shimelis, H. A. (2006). Associations of yield and yield components among selected durum wheats (Triticum turgidum L.). South African Journal of Plant and Soil, 23(4), 305–309. https://doi.org/10.1080/02571862.2006.10634770 Gurmu F, Shimelis HA, Laing MD. Correlation and path-coefficient analyses of root yield and related traits among selected sweet potato genotypes. S Afr J Plant Soil. 2018; 35:179–86. Endalamaw Chalachew, and Zigale Semahegn (2020): "Genetic Variability and Yield Performance of Sorghum (sorghum bicolor L.) Genotypes Grown in Semi-Arid Ethiopia." International Journal of Advanced Biological and Biomedical Research 8, no. 2 193-213. Vargas, M., Combs, E., Alvarado, G., Atlin, G., Mathews, K., & Crossa, J. (2013). META: A suite of SAS programs to analyze multienvironment breeding trials. Agronomy Journal, 105(1), 11–19. https://doi.org/10.2134/ agronj2012.0016 Burton, G. W., & Devane, E. (1953). Estimating heritability in tall fescue (Festuca Arundinacea) from replicated clonal material 1. Agronomy Journal, 45(10), 478–481. https://doi.org/10.2134/agronj1953. 00021962004500100005 Dewey DR and Lu KH 1959. A correlation and path coefficient analysis of components of crested wheatgrass seed production. Agron. J. 51: 515- 518 Allard, R. (1960). Principies of plant breeding John Wiley. John Wiley and Sons. Inc. Amare, K., Zeleke, H., & Bultosa, G. (2015). Variability for yield, yield related traits and association among traits of sorghum (Sorghum Bicolor (L.) Moench) varieties in Wollo, Ethiopia. Journal of Plant Breeding and Crop Science, 7(5), 125–133. https://doi.org/10.5897/ JPBCS2014.0469 Tariq, A. S., Akram, Z., Shabbir, G., Gulfraz, M., Khan, K. S., Iqbal, M. S., & Mahmood, T. (2012). Character associa tion and inheritance studies of different sorghum gen otypes for fodder yield and quality under irrigated and rainfed conditions. African Journal of Biotechnology, 11 (38), 9189–9195. https://doi: 10.5897/AJB11.2561. Abraha, T., Githiri, S. M., Kasili, R., Araia, W., & Nyende, A. B. (2015). Genetic variation among sorghum (Sorghum bicolor L. Moench) landraces from eritrea under post-flowering drought stress conditions. American Journal of Plant Sciences, 6(9), 1410. https://doi.org/10.4236/ajps.2015.69141 H. A. Keba and H. D. Tamir (2023). Genotypic and phenotypic correlation and path coefficient analysis for yield and other traits of sorghum (Sorghum bicolour L. Moench) land races at humid lowland and intermediate agro ecology of Ethiopia. Journal of Genetics, Genomics & Plant Breeding 7(3)62-67. H. Alemu and H. Demelash (2022). Genetic Variability, Heritability and Genetic Advance for Agronomic Traits of Ethiopian Sorghum [Sorghum bicolor (L.) Moench] Landraces. Asian J. Adv. Agric. Res., vol. 20, no. 3, pp. 1-9. Saleh, M. M., Salem, K. F., & Elabd, A. B. (2020). Definition of selection criterion using correlation and path coefficient analysis in rice (Oryza sativa L.) genotypes. Bulletin of the National Research Centre , 44 , 1-6. Mengesha, G.H., Hailemariam, F.M., Mindaye T.T., Lakew, B. and Verma, R.P.S. (2019). Correlation and path analysis of yield, yield contributing and malt quality traits of Ethiopian sorghum (Sorghum bicolor (L.) Moench) genotypes. African J. Plant Sci., 13(8): 209-220, Alemayehu, A. (2003). Genetic variability and breeding potential of barley (Hordeum vulgare L.) landraces from North Shewa in Ethiopia,” PhD Thesis, Faculity of Natural and Agricultural Sciences University of Free State, Bloemfontein, South Africa. 226. Ngidi, A., Shimelis, H., Abady, S., Chaplot, V., & Figlan, S. (2024). Genetic variation and association of yield, yield components, and carbon storage in sorghum (Sorghum bicolor [L.] Moench) genotypes. BMC Genomic Data , 25 (1), 74. https://doi.org/10.1186/s12863-024-01256-4 Chittapur, R. and Biradar, B.D. (2015). Association studies between quantitative and qualitative traits in rabi sorghum. Indian J. Agric. Sci., 49(5): 468–471. Khan, H.A., Shad, S.A. and Akram, W. (2013). Resistance to new chemical insecticides in the house fly, Musca domestica L., from dairies in Punjab, Pakistan. Parasitology Res., 112 (5):2049 54. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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1","display":"","copyAsset":false,"role":"figure","size":118562,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations among grain yield and agronomic traits of sorghum genotypes\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7352596/v1/e32c8b5c731001d9836367ca.png"},{"id":96251582,"identity":"864de596-b41a-430d-8430-a130f097bc05","added_by":"auto","created_at":"2025-11-19 07:39:49","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1551985,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7352596/v1/248ba823-48d9-470b-99f0-c2fa0054a491.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Variability, heritability and association among Grain yield and yield contributing traits of Sorghum genotypes at Western Ethiopia","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSorghum (Sorghum bicolor L.) is a highly adaptable and resilient cereal crop that thrives in a variety of agro-ecological zones, including semi-arid, sub-tropical, tropical, and temperate climates (Ngidi et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). It is recognized globally as the fifth most significant cereal, following wheat, rice, maize, and barley, due to its nutritional value and ability to withstand challenging environmental conditions where other crops may fail (Ritter et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The origins of sorghum can be traced back between 5,000 and 7,000 years in northeastern Africa, particularly in Ethiopia (T. Mesfin and F. Tileye, 2013). Sorghum is a crucial crop for food security in many South Asian and sub-Saharan African nations, especially in Ethiopia. It is well-suited to a variety of agro-ecological zones throughout the country, although it is primarily grown in the lowland and moisture-stressed areas (Tesso et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Despite its considerable nutritional benefits, sorghum often goes unrecognized. It is a source of essential carbohydrates, including amylose (19.78%), starch (69.15%), and amylopectin (80.22%), and boasts a protein content of 13.8%, along with 1.8% ash, 3.3% oil, and 17.3% fiber. Furthermore, sorghum is abundant in vital minerals, providing 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) (A. S. Gerrano et al., 2016).\u003c/p\u003e\u003cp\u003eGenetic heterogeneity among crop genetic resources is crucial to create new cultivars with desired agronomic features. The test population's genetic composition and the growing environment determine phenotypic variance. To choose superior individuals with desired qualities from a genetically varied population, it is crucial for plant breeding operations that there is genetic variety in agro-morphological features (R. H. Gichile, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Selecting parental genotypes with a broad genetic basis for additional genetic improvement requires an understanding of the degree of genetic diversity. Numerous academics have noted that Ethiopian sorghum landraces have a very high level of genetic diversity, suggesting that breeding programs might provide substantial improvements (B. Fantaye, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe extent of genetic variability present in the source material and the effectiveness of selection highly dictate the success of any crop improvement (Ranjith et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Genotypic Coefficient of Variation (GCV) is a measure of the amount of variation within a population for a particular trait due to genetic differences while phenotypic Coefficient of Variation PCV measures the total variation of a trait, encompassing both genetic and environmental influences. A comparison of PCV and GCV is very important to determine the extent to which a trait is influenced by genetic makeup as against environmental factors. The higher PCV compared to GCV suggests that the environment has greater influence on the expression of a given trait. Heritability is a statistical concept that describes how much of the variation in a given trait can be attributed to genetic variation (Falconer DS and Mackay F, 1996). It does not indicate what proportion of a trait is determined by genes and what proportion is by environment but indicates the extent of the variability in the trait in a population is due to genetic differences among the population. Genetic advance (GA) is a measure of the expected improvement or gain in a specific trait's mean genotypic value when selection is applied to a population. The combination of Heritability estimates with genetic advance is more accurate than heritability alone in predicting the genetic gain under selection (Demeke, et al., 2023).\u003c/p\u003e\u003cp\u003eSince grain yield is controlled by several genes and impacted by complex gene interactions, it is essential to fully understand the kind and degree of gene activity at involved (Tirkey Sheetal et al., 2021). Analysis of correlations shows how closely basic traits are related to one another. A thorough grasp of the extent to which yield and yield-contributing qualities are related is essential to improving selection efficiency for high yielding (Shimelis H. 2006). The primary genetic factors used to choose superior genotypes and assess breeding tactics are phenotypic and genotypic correlations. Correlations between agronomic parameters with high heritability facilitate the selection process for complex traits such as grain yield (Gurmu F, et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is crucial to perform a path coefficient analysis, which separates the correlated variables into direct and indirect effects and thus envisions the fundamental relationship in a more meaningful way, since correlation analysis demonstrated the relationship between two or more series of characters (C. Endalamaw and Z. Semahegn, 2020).\u003c/p\u003e\u003cp\u003eIn sorghum genotypes, especially in lowland regions of western Ethiopia, there is a dearth of information on genetic diversity, correlation, and path-efficient analysis of grain production and yield-related factors. Using quantitative characteristics for breeding, genetic variation among most of the sorghum genotypes analyzed in the region still needs to be investigated. Therefore, this study's objectives were to assess genetic variability, determine the kind and extent of phenotypic and genotypic relationships among the quantitative traits being studied, and identify the most important features for indirect selection in future sorghum breeding projects.\u003c/p\u003e"},{"header":"MATERIAL AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Description of the Study Area\u003c/h2\u003e\u003cp\u003eThe field experiment was conducted from June to December 2023 at Assosa, Mange and Bako during the main cropping season. Assosa found at 667 km to the west of Addis Ababa, the country\u0026rsquo;s capital city while Bako located at the 285 km on the way from Addis Ababa to Assosa. Mange found at 50 km west of Assosa, the capital city of Benishangul gumuz region.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDescription of the testing environment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnvironment\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLongitude\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLatitude\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAltitude m.a.s.l\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAnnual rainfall (mm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMin-Max T\u003csup\u003eo\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\u003eAssosa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u0026deg; 57\u0026rsquo; E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10\u003csup\u003e0\u003c/sup\u003e04\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1553\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17\u003csup\u003e0c\u003c/sup\u003e -32 \u003csup\u003e0c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMange\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34˚43'30.809''E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10˚20'8.17''N\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e771.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.3\u003csup\u003e0C\u003c/sup\u003e -34.2\u003csup\u003e0c\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBako\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37\u0026deg; 09\u0026rsquo;E\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e09\u003csup\u003e0\u003c/sup\u003e06\u0026rsquo;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.1\u0026ndash;31.4 \u003csup\u003e0\u003c/sup\u003eC\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=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Experimental materials description\u003c/h2\u003e\u003cp\u003eThe study was conducted by using a total of 61 sorghum inbreed lines along with two standard checks, Melkam and Bonsa varieties.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of Sorghum genotypes used in the experiment\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-114\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eARG-16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-103\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-159\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMelkam (Check)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-129\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD9-170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-147\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eP9830\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD9-118\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBonsa (Check)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTAM428\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-210\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-170\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-10\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-179\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-50\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-186\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-64\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-116\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-167\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD9-185\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-109\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-100\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSSD18-41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD18-131\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eARG-25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSSD9-30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Source of the planting material used\u003c/h2\u003e\u003cp\u003eSixty three sorghum genotypes were obtained from Sorghum Research Program of Assosa Agricultural Research Center, Ethiopian Institute of Agricultural Research (EIAR) for this study. The materials were selected and collected purposively for their better agronomic performance as well as good disease resistance level in the open field conditions of western Ethiopia. The list of sorghum genotypes used in this study is presented in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Experimental design and Field management\u003c/h2\u003e\u003cp\u003eRandomized complete block design (RCBD) with two replications were used. The genotypes were sown on a plot of two rows with five (5m) meters length having the spacing between rows, plants, and replications, 75cm, 15cm 1.5m respectively. The seeds were thoroughly drilled in the prepared ridge followed by appropriate thinning at around knee height. Experimental units received the same amount and rate of 100 kg/ha DAP and 100 kg/ha Urea fertilizers.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Data collection\u003c/h2\u003e\u003cp\u003eData for main phonological and growth parameters including yield and important yield components were recorded using the appropriate procedure. Plant and plot based data was collected. Plant-based data were collected from randomly selected and representative five plants in the plot, while the plot-based data were collected from the whole harvestable plots. Days to flowering (DF), Days to maturity (DM), Plant height (PH) (cm), Agronomic score (PAS), Thousand seed weight (TSW) (g), and Grain Yield (kg/ha) were the recorded quantitative data collected in the study.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Data Analysis\u003c/h2\u003e\u003cp\u003eAnalysis of variance was computed following the methods proposed by (Gomez \u0026amp; Gomez, 1984). META-R and R soft wares were used for data analysis. Best linear unbiased predictor (BLUP) means were estimated using multivariate mixed model (REML) spatial analysis considering the Block/Rep\u0026thinsp;+\u0026thinsp;treatment as random effect for special correction of the nearest block errors to avoid the biased estimate of variance components at 5% level of significance (Vargas et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Phenotypic, genotypic and environmental variance components and their coefficients of variation were estimated based on the methods detailed in (Burton \u0026amp; Devane, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1953\u003c/span\u003e). The simple correlation coefficient was subjected to path analysis (Dewey and Lu, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1959\u003c/span\u003e). Broad sense heritability (H2) and genetic advance as percent of mean (GAM) were also estimated according to the formula in (Allard, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1960\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Analysis of Variance\u003c/h2\u003e\u003cp\u003eAnalysis of variance for Grain yield (GY (kg/ha), days to flowering (DTF), DTM (days to maturity), Plant height (PHT cm), Thousand seed weight (TSW in g), Over all agronomic score (PAS) and Anthracnose disease resistance showed highly significant difference among the tested genotypes and the test locations at (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) level of significance (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This result implies that the tested genotypes were different in their performance of the evaluated traits and the tested locations were also varied significantly for the performance of the evaluated genotypes. Similar research findings reported by many authors like Gebregergs \u0026amp; Mekbib, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Amare et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), Tariq et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e. The genotype by location interaction was also highly significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01) for Grain yield (GY), Days to flowering (DTF), Days to maturity (DTM) and Agronomic Score (PAS). This showed that the performance of the genotypes evaluated in this trial varied significantly across the tested environments.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eANOVA and Mean performance sorghum genotypes evaluated at the humid lowland agro-ecology of western Ethiopia\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=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGY Kg/Ha\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\u003eTKW(g)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePHT(cm)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eAnthSc\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eARG-16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e126.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eARG-25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e77.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBonsa\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2966\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e145.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMelkam\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3061\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e33.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e146.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eP9830\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2259\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD18-10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e27.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e143\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD18-100\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1866\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e139.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD18-103\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3232\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e76.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e28.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e146.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD18-109\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1881\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD18-114\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e115.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD18-116\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e79.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD18-117\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e78.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e127.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e160.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" 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colname=\"c3\"\u003e\u003cp\u003e71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e122.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e147.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD18-127\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2571\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e72.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD18-99\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e124.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e169.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD9-118\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2251\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e73.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e129\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.083\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e119.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD9-170\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e125.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.167\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e118.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD9-185\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2656\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e126.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e145.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSSD9-30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e70.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e26.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e151.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTAM428\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2248\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e71.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e121.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e140.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e2.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGrand Mean\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e2464\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e73.65\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e125.29\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e24.16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e2.576\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e140.97\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e2.806\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e18.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e3.3\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e3.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e19.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e19.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e7.2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e23.6\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLSD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e875.1\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e4.74\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e8.03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e9.384\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.9704\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e19.989\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e1.30\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGenotype\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eLocation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGenotype * Location\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003eNS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003eNS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003eNS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eWhere GY\u0026thinsp;=\u0026thinsp;grain yield, DTF\u0026thinsp;=\u0026thinsp;Days to flowering, DTM\u0026thinsp;=\u0026thinsp;Days to maturity, TKW\u0026thinsp;=\u0026thinsp;Thousand kernel weight, PAS\u0026thinsp;=\u0026thinsp;Agronomic score, PHT\u0026thinsp;=\u0026thinsp;Plant height, \u003cb\u003eAnthSc\u003c/b\u003e\u0026thinsp;=\u0026thinsp;Sorghum anthracnose score, CV\u0026thinsp;=\u0026thinsp;coefficient of variation, LSD\u0026thinsp;=\u0026thinsp;List significant difference, ** = significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, NS\u0026thinsp;=\u0026thinsp;Non-significantly different\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe highest grain yield (3526 kg ha-1) was recorded for genotypes SSD18-114 while the lowest one (1155 kg ha-1) for genotypes SSD9-30. Genotype SSD18-91 flowered earlier than others (68) while genotype SSD18-56 flowered lately (79.33). Genotype SSD18-170 was early maturing than others (121) while genotype SSD18-56 late maturing (130). The largest thousand seed weight (33.64g) was recorded for Melkam which was the standard check while the lowest thousand seed weight (18.32g) recorded for SSD18-40. Similarly, the tallest plant height (169.3cm) was recorded for genotype SSD18-99 while the shortest (77.9cm) was genotype ARG-25. This wider genetic variation of the genotypes for the evaluated traits showed that utilizing the broader genetic variability in the evaluated genotypes as a source of breeding material for the enhancement of traits of interest across various objectives may enhance sorghum production and productivity. Different scholars report similar findings to this research results so far ((Tariq et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Abraha et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, and Gebregergs \u0026amp; Mekbib, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eLike all the above agronomic traits, the evaluated genotypes varied for their reaction to Anthracnose disease under open environment conditions. Even though Genotype SSD18-117 was considered as resistant genotype having average disease score of 1.30 across the test environments on 1\u0026ndash;5 scale, most of the genotypes showed resistant to moderately resistant reaction.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Variance components\u003c/h2\u003e\u003cp\u003eGenotypic variance (δ2 g), phenotypic variance (δ2p), environmental variance (δ2e), broad sense heritability (H2), genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV) and GAM for yield and yield contributing traits of the sorghum genotypes are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The calculated variance components for the assessed characteristics show that the PCV was larger than the GCV, suggesting that the trait's expression was impacted by the environment. The GCV and PCV for the measured traits in the current study ranged in between 3.88 and 7.14 for DTF to 24.55 and 53.59 for GY respectively which imply the presence of significant amount of genetic variability among the tested genotypes for different traits. Similarly, (Gebregergs \u0026amp; Mekbib, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Amare et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), (Abraha et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) reported higher GCV and PCV for Plant height and Grain yield; Alemu and Demelash, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e reported for higher GCV and PCV for TKW.\u003c/p\u003e\u003cp\u003eIn comparison, DTF and DTM showed low PCV and GCV. In other words, DTF and DTM provide little heritable genetic (additive) component to the following generation, meaning that there is no need for investment to enhance these characteristics in order to improve sorghum.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Heritability and Genetic advance\u003c/h2\u003e\u003cp\u003eHeritability is a statistical metric used to measure the degree to which genetic variants among members of a community account for variances in a characteristic. It basically calculates the percentage of observable variation in a characteristic, or phenotypic variation that may be attributed to genetic variation. The result of broad sense heritability estimation (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) showed that the highest broad sense heritability (79.79, 78.48, 68.25 and 65.64) was recorded for Plant height, Days to maturity, Thousand Kernel weight and days to flowering respectively. On the other hand, the lowest (45.32 and 45.77) recorded for agronomic score and grain yield respectively. The higher heritability estimation for some characters revealed that selecting sorghum genotypes for these traits will have a positive response on sorghum improvement. Alemu and Demelash, (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) reported similar findings of higher heritability for Plant height, Days to maturity, Thousand Kernel weight and days to flowering.\u003c/p\u003e\u003cp\u003eIt is more convenient to have the combination of higher heritability with higher estimates of GCV and GAM in order to assure successful selection for improvement, because high broad sense heritability alone does not necessarily guarantee high prediction of genetic gain. For traits to be effective to select for improvement of sorghum grain yield, their respective estimate of GCV and GAM have to be higher.\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\u003eEstimated variance components for agronomic traits of sorghum genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTraits\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGY\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\u003eTKW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePHT\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGenetic variance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e365411.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e43.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e585.80\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhenotypic variance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1744288.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e27.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e34.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e734.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eEnvironmental Variance\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2109699.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e55.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e148.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePCV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e53.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e35.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGCV\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e23.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eH2\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45.77\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e78.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e45.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e79.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGA\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e44.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eGAM\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e23.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e31.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eWhere GY\u0026thinsp;=\u0026thinsp;grain yield, DTF\u0026thinsp;=\u0026thinsp;Days to flowering, DTM\u0026thinsp;=\u0026thinsp;Days to maturity, TKW\u0026thinsp;=\u0026thinsp;Thousand kernel weight, PAS\u0026thinsp;=\u0026thinsp;Agronomic score, PHT\u0026thinsp;=\u0026thinsp;Plant height, PCV\u0026thinsp;=\u0026thinsp;Phenotypic coefficient of variation, GCV\u0026thinsp;=\u0026thinsp;Genotypic coefficient of variation, H\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;broad sense heritability, GA\u0026thinsp;=\u0026thinsp;Genetic advance, GAM\u0026thinsp;=\u0026thinsp;Genetic advance as percent of mean\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe highest genetic advance was recorded for plant height (44.54) followed by days to physiological maturity (7.21) and grain yield (5.70) while the lowest score was for agronomic score (PAS) which was 0.86 (Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The estimated Genetic advance as percent of mean was ranged from 4.34% for days to flowering to 33.27% for agronomic score (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As stated by Johnson et al. (1955), high genetic advance as percent of mean was recorded for grain yield (23.13%), agronomic score (33.27%) and Plant height (31.59) while low GAM was recorded for other characters like days to flowering (4.34%), days to physiological maturity (5.76) and thousand kernel weight (8.55). Similarly higher estimate of GAM were reported by (Ranjith et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and (Gebregergs \u0026amp; Mekbib, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for grain yield and plant height. High GAM is associated with effective selection of sorghum genotypes with plant height. High genetic advance values and heritability show the presence of additive gene activity, which is highly heritable and suggests that choosing such characteristics, might enhance crops. To create precise selection indices for creating sorghum genotypes with high grain production capability, priority should be given to traits that demonstrated significant heritability and genetic advancements.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Correlations among agronomic traits\u003c/h2\u003e\u003cp\u003eEstimated genotypic and phenotypic correlation coefficients among each pair of agronomic traits are presented in (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The evaluation of the pairwise relationships between some agronomic traits showed that some of them were positively correlated while others were negatively correlated. This result suggested that improving one trait had either positive or negative impact on the other traits. Since grain yield is a complex traits governed by many (polygenic) genes and attributed by many traits, having the information of how these traits interact to each other is very crucial. To establish effective selection methods, considering the association between these traits and their correlation with grain yield is very important (Saleh et al \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Grain yield showed positive genotypic and phenotypic correlations with PHT, and TKW, while negative correlations with DTF and DTM. This positive correlation infers that selection for these traits can assist to exploit higher grain yielding potential in sorghum. Similar results of strong positive correlation of grain yield with plant height have been reported by Alemayehu (2003), Keba \u0026amp; Tamir (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) and Ngidi et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). PHT showed positive association with DTM and TKW while negatively correlated to DTF and PAS. DTF and DTM had strongly positive correlated to each other. This implies that selecting for better plant height directly will indirectly resulted in better DTM and TKW and selecting for better DTM will also improve DTF. This finding is in agreement with the finding of Keba \u0026amp; Tamir (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Path coefficient analysis for agronomic traits\u003c/h2\u003e\u003cp\u003eThe present study showed that positive direct effects were exerted on grain yield by Thousand kernel weight (0.33), followed by Plant height (0.14) while negative direct effects on grain yield were observed by days to maturity (-0.51), agronomic score (-0.15), and days to flowering (-0.08) respectively(Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The positive and high correlation of some of the agronomic traits revealed the actual impacts these traits have to improve sorghum grain yield. Dissecting the complex relationships between grain yield and its component traits into direct and indirect effects allowed researchers to determine whether the influences directly affect yield or take a different route that ultimately affects yield. The most yield-contributing traits were then used for both direct and indirect selection. Similar result of direct positive correlation of thousand kernel weights and plant height on grain yield was reported by Keba \u0026amp; Tamir (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), Mengesha et al., (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), and Chittapur and Biradar (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Plant height and thousand kernel weight had positive direct correlation with grain yield and had positive and significant genotypic correlations with grain yield (Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Some of the traits like days to flowering, days to maturity and agronomic score has negative direct correlation with grain yield. This implies that selection for early maturing genotypes resulted in significant yield reduction in the testing environments as the locations suited for long maturing varieties. Similarly, selecting for higher agronomic score values ended in poor yielding genotypes as the smallest score for the trait is desirable. Hence, those traits showed negative direct effect has to be considered carefully as they play a role in grain yield reduction in the study area. Thousand kernel weights have also positive indirect effect on grain yield through plant height (0.08), agronomic score (0.015). These implied that the positive indirect effects contributed to increase grain yield via increasing those traits. The result of positive indirect impact of thousand kernel weight through plant height is in line with the findings of Keba \u0026amp; Tamir (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and Khan et.al., (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\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\u003ePath analysis estimates for important agronomic traits in sorghum genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDTF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDTM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePHT\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePAS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTKW\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDTF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.19\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\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.51\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePHT\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\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTKW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.33\u003c/b\u003e\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":"Conclusion","content":"\u003cp\u003eThe result of the current study revealed that the evaluated genotypes were statistically significantly different at (P \u0026lt; 0.01) for all the traits examined. Results also showed that larger genetic variance over environmental variance for all characteristics except grain yield. High genetic and phenotypic coefficient of variation was also recorded for grain yield, agronomic score (PAS) and thousand kernel weight while lower for days to flowering and days to maturity. High broad sense heritability recorded for all characteristics. It confirms a positive response for the effectiveness of selection based on the traits with high and medium PCV and GCV values for trait of interest improvement. The study further found positive associations among the examined traits. The significant positive association between traits suggests that the traits can be enriched simultaneously through direct selection. Whereas, the strong negative association between traits implies the need for growing and selection among large segregating populations to break the unfavorable association. Hence, the selection process for these genotypes will be effective if focused on the highly heritable traits with higher estimate of GCV and GAM.\u003c/p\u003e\n"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003cstrong\u003e:\u003c/strong\u003e not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration:\u003c/strong\u003e 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\u003eFunding declaration:\u0026nbsp;\u003c/strong\u003eThis activity receives no special funds.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003ePermissions to collect the plants/plant parts:\u003c/strong\u003e Not applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSource of the plant used in your study:\u0026nbsp;\u003c/strong\u003eAll the sorghum seed/planting materials used in this experiment were collected from Assosa Agricultural research center of Ethiopian Institute of Agricultural Research\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDemeke, B., Dejene, T., \u0026amp; Abebe, D. (2022). Genetic variability, heritability, and genetic advance of morphological, yield related and quality traits in upland rice (\u003cem\u003eOryza Sativa\u003c/em\u003e L.) genotypes at pawe, northwestern Ethiopia. \u003cem\u003eCogent Food \u0026amp; Agriculture\u003c/em\u003e, \u003cem\u003e9\u003c/em\u003e(1). https://doi.org/10.1080/23311932.2022.2157099\u003c/li\u003e\n\u003cli\u003eTirkey Sheetal, Jawale LN and More AW. (2021). Genetic variability, correlation and path analysis studies in B parental lines of kharif sorghum (Sorghum bicolor (L.) Moench) .The Pharma Innovation Journal 10(8): 624-628.\u003c/li\u003e\n\u003cli\u003eRitter, K. B., Mcintyre, C. L., Godwin, I. D., Jordan, D. R., \u0026amp; Chapman, S. C. (2007). An assessment of the genetic relationship between sweet and grain sorghums, within Sorghum bicolor ssp. bicolor (L.) Moench, using AFLP markers. Euphytica, 157(1\u0026ndash;2), 161\u0026ndash;176. \u003cu\u003ehttps://doi.org/10.1007/s10681-007-9408-4\u003c/u\u003e\u003c/li\u003e\n\u003cli\u003eT. Mesfn and F. Tileye, \u0026ldquo;Genetic Diversity of Wild Sorghum (Sorghum bicolor Ssp. Verticilliforum (L.) Moench) Germplasm from Ethiopia as Revealed by ISSR Markers,\u0026rdquo; Asian Journal of Plant Science 12, no. 3 (2013): 137144.\u003c/li\u003e\n\u003cli\u003eG. Gebregergs and F. Mekbib, \u0026ldquo;Estimation of Genetic Variability, Heritability, and Genetic Advance in Advanced Lines for Grain Yield and Yield Components of Sorghum [Sorghum Bicolor (L.) Moench] at Humera, Western Tigray, Ethiopia,\u0026rdquo; Cogent Food \u0026amp; Agriculture 6, no. 1 (2020): 1764181, https://doi.org/10.1080/23311932.2020.1764181.\u003c/li\u003e\n\u003cli\u003eTesso, T., Gutema, Z., Deressa,A., \u0026amp; Ejeta,G. (2007). An inte grated Striga management option offers effective control of Striga in Ethiopia. Integrating New Technologies for Striga Control: Towards Ending the Witch Hunt, 199-212. https://doi.org/10.1142/9789812771506_ 0015\u003c/li\u003e\n\u003cli\u003eA. Gerrano, M. Labuschagne, A. Van Biljon, N. Shargie, Quantification of mineral composition and total protein content in sorghum [Sorghum bicolor (L.) Moench] genotypes, Cereal Res. https://doi.org/10.1556/0806.43.2015.046\u003c/li\u003e\n\u003cli\u003eAl-Younis, Abdel Hamid Ahmed. 1993. Production and improvement of field crops. Dar Al-Kutub Publishing and Printing Foundation. Ministry of Higher Education and Scientific Research. Mosul University Press. College of Agriculture and Forestry. p. 469.\u003c/li\u003e\n\u003cli\u003eR. H. Gichile, Genetic Variability and Trait Associations among Sorghum [Sorghum Bicolor (L.) Moench] Genotypes for Yield and Yield Related Traits in West Hararghe (Dire Dawa, Ethiopia: Haramaya University, 2022). \u003c/li\u003e\n\u003cli\u003eFantaye B (2024) Genetic Variation in Dry Lowland Sorghum Land races of Abergelle, Northern Ethiopia. Adv Crop Sci Tech 12: 726.\u003c/li\u003e\n\u003cli\u003eP. Ranjith, R. B. Ghorade, V. V. Kalpande, and A. M. Dange (2017). \u0026ldquo;Genetic Variability, Heritability and Genetic Advance for Grain Yield and Yield Components in Sorghum,\u0026rdquo; In ternational Journal of Farm Sciences 7(1): 90\u0026ndash;93.\u003c/li\u003e\n\u003cli\u003eGebremedhn Gebregergs \u0026amp; Firew Mekbib | (2020) Estimation of genetic variability, heritability, and genetic advance in advanced lines for grain yield and yield components of sorghum [Sorghum bicolor (L.) Moench] at Humera, Western Tigray, Ethiopia, Cogent Food \u0026amp; Agriculture, 6:1, 1764181, https://doi.org/10.1080/23311932.2020.1764181\u003c/li\u003e\n\u003cli\u003eFalconer DS, Mackay FC (1996). Introduction to quantitative genetics. Volume 464. New York: Longman.\u003c/li\u003e\n\u003cli\u003eTirkey Sheetal, Jawale LN and More AW (2021). Genetic variability, correlation and path analysis studies in B parental lines of kharif sorghum (Sorghum bicolor (L.) Moench). The Pharma Innovation Journal; 10(8): 624-628\u003c/li\u003e\n\u003cli\u003eShimelis, H. A. (2006). Associations of yield and yield components among selected durum wheats (Triticum turgidum L.). South African Journal of Plant and Soil, 23(4), 305\u0026ndash;309. https://doi.org/10.1080/02571862.2006.10634770\u003c/li\u003e\n\u003cli\u003eGurmu F, Shimelis HA, Laing MD. Correlation and path-coefficient analyses of root yield and related traits among selected sweet potato genotypes. S Afr J Plant Soil. 2018; 35:179\u0026ndash;86.\u003c/li\u003e\n\u003cli\u003eEndalamaw Chalachew, and Zigale Semahegn (2020): \u0026quot;Genetic Variability and Yield Performance of Sorghum (sorghum bicolor L.) Genotypes Grown in Semi-Arid Ethiopia.\u0026quot; International Journal of Advanced Biological and Biomedical Research 8, no. 2 193-213.\u003c/li\u003e\n\u003cli\u003eVargas, M., Combs, E., Alvarado, G., Atlin, G., Mathews, K., \u0026amp; Crossa, J. (2013). META: A suite of SAS programs to analyze multienvironment breeding trials. Agronomy Journal, 105(1), 11\u0026ndash;19. https://doi.org/10.2134/ agronj2012.0016\u003c/li\u003e\n\u003cli\u003eBurton, G. W., \u0026amp; Devane, E. (1953). Estimating heritability in tall fescue (Festuca Arundinacea) from replicated clonal material 1. Agronomy Journal, 45(10), 478\u0026ndash;481. https://doi.org/10.2134/agronj1953. 00021962004500100005\u003c/li\u003e\n\u003cli\u003eDewey DR and Lu KH 1959. A correlation and path coefficient analysis of components of crested wheatgrass seed production. Agron. J. 51: 515- 518\u003c/li\u003e\n\u003cli\u003eAllard, R. (1960). Principies of plant breeding John Wiley. John Wiley and Sons. Inc.\u003c/li\u003e\n\u003cli\u003eAmare, K., Zeleke, H., \u0026amp; Bultosa, G. (2015). Variability for yield, yield related traits and association among traits of sorghum (Sorghum Bicolor (L.) Moench) varieties in Wollo, Ethiopia. Journal of Plant Breeding and Crop Science, 7(5), 125\u0026ndash;133. https://doi.org/10.5897/ JPBCS2014.0469\u003c/li\u003e\n\u003cli\u003eTariq, A. S., Akram, Z., Shabbir, G., Gulfraz, M., Khan, K. S., Iqbal, M. S., \u0026amp; Mahmood, T. (2012). Character associa tion and inheritance studies of different sorghum gen otypes for fodder yield and quality under irrigated and rainfed conditions. African Journal of Biotechnology, 11 (38), 9189\u0026ndash;9195. https://doi: 10.5897/AJB11.2561.\u003c/li\u003e\n\u003cli\u003eAbraha, T., Githiri, S. M., Kasili, R., Araia, W., \u0026amp; Nyende, A. B. (2015). Genetic variation among sorghum (Sorghum bicolor L. Moench) landraces from eritrea under post-flowering drought stress conditions. American Journal of Plant Sciences, 6(9), 1410.\u003c/li\u003e\n\u003cli\u003ehttps://doi.org/10.4236/ajps.2015.69141\u003c/li\u003e\n\u003cli\u003eH. A. Keba and H. D. Tamir (2023). Genotypic and phenotypic correlation and path coefficient analysis for yield and other traits of sorghum (Sorghum bicolour L. Moench) land races at humid lowland and intermediate agro ecology of Ethiopia. Journal of Genetics, Genomics \u0026amp; Plant Breeding 7(3)62-67.\u003c/li\u003e\n\u003cli\u003eH. Alemu and H. Demelash (2022). Genetic Variability, Heritability and Genetic Advance for Agronomic Traits of Ethiopian Sorghum [Sorghum bicolor (L.) Moench] Landraces. Asian J. Adv. Agric. Res., vol. 20, no. 3, pp. 1-9.\u003c/li\u003e\n\u003cli\u003eSaleh, M. M., Salem, K. F., \u0026amp; Elabd, A. B. (2020). Definition of selection criterion using correlation and path coefficient analysis in rice (Oryza sativa L.) genotypes. \u003cem\u003eBulletin of the National Research Centre\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e, 1-6.\u003c/li\u003e\n\u003cli\u003eMengesha, G.H., Hailemariam, F.M., Mindaye T.T., Lakew, B. and Verma, R.P.S. (2019). Correlation and path analysis of yield, yield contributing and malt quality traits of Ethiopian sorghum (Sorghum bicolor (L.) Moench) genotypes. African J. Plant Sci., 13(8): 209-220,\u003c/li\u003e\n\u003cli\u003eAlemayehu, A. (2003). Genetic variability and breeding potential of barley (Hordeum vulgare L.) landraces from North Shewa in Ethiopia,\u0026rdquo; PhD Thesis, Faculity of Natural and Agricultural Sciences University of Free State, Bloemfontein, South Africa. 226.\u003c/li\u003e\n\u003cli\u003eNgidi, A., Shimelis, H., Abady, S., Chaplot, V., \u0026amp; Figlan, S. (2024). Genetic variation and association of yield, yield components, and carbon storage in sorghum (Sorghum bicolor [L.] Moench) genotypes. \u003cem\u003eBMC Genomic Data\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(1), 74. https://doi.org/10.1186/s12863-024-01256-4\u003c/li\u003e\n\u003cli\u003eChittapur, R. and Biradar, B.D. (2015). Association studies between quantitative and qualitative traits in rabi sorghum. Indian J. Agric. Sci., 49(5): 468\u0026ndash;471.\u003c/li\u003e\n\u003cli\u003eKhan, H.A., Shad, S.A. and Akram, W. (2013). Resistance to new chemical insecticides in the house fly, Musca domestica L., from dairies in Punjab, Pakistan. Parasitology Res., 112 (5):2049 54. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sorghum, variability, association, heritability, direct effect, genetic advance","lastPublishedDoi":"10.21203/rs.3.rs-7352596/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7352596/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSorghum is a globally significant multipurpose crop primarily utilized for food, animal feed, and industrial uses. However, its productivity remains constrained, necessitating focused efforts for enhancement. To boost sorghum yields, it is crucial to have access to genetically diverse sorghum genotypes that exhibit various agronomic traits, as well as to understand the relationships among these traits. This study aimed to assess the genetic diversity and the interrelationships among different agronomic traits in selected sorghum genotypes with the objective of pinpointing the most promising genotype that could improve sorghum production or act as a valuable asset for breeding programs. Sixty three sorghum genotypes were evaluated at Assosa, Bako and Mange research stations during the 2023/24 growing season. Data on important agronomic traits were collected: days to 50% flowering (DTF), days to physiological maturity (DTM), plant height (PHT), over all agronomic score (PAS), thousand kernels weight (TKW), Anthracnose disease resistant score and grain yield (GY). Higher genotypic coefficient of variations (GCV) was recorded for TKW (27.14%), GY (24.53%), PAS (23.99), while higher phenotypic coefficient of variations (PCV) were recorded for GY (53.59%), followed by PAS (35.64%), and TKW (33.02%). High broad-sense heritability was recorded for PHT (79.79), DTM (78.48), TKW (68.25), and DTF (65.64). Similarly highest genetic advance was recorded for PHT (44.54). Grain yield was positively and significantly correlated with TKW (r\u0026thinsp;=\u0026thinsp;0.59), PHT (r\u0026thinsp;=\u0026thinsp;0.40) while negatively associated with DTM (-0.63) and DTF (-0.55).The path analysis revealed that TKW (0.33) and PHT (0.14) exerted significant positive direct effects of on GY. Generally, the observed variability and the information obtained from this study can be used for the genetic improvement of sorghum which can result in the development of high-yielding varieties.\u003c/p\u003e","manuscriptTitle":"Variability, heritability and association among Grain yield and yield contributing traits of Sorghum genotypes at Western Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-01 17:03:57","doi":"10.21203/rs.3.rs-7352596/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ca6ef11f-bbac-40c3-9813-bdf1f3bc9068","owner":[],"postedDate":"October 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T05:23:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-01 17:03:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7352596","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7352596","identity":"rs-7352596","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}