Morphological characterization, variability, and diversity among amaranth genotypes from Ethiopia

Amaranths are versatile, dicotyledonous plants with the potential for high yields. It has been extensively investigated as a model C4 plant. They have great photosynthetic performance as a result of eliminating the rival photorespiration mechanism. This increases the advantages of expanding the adaptation and cultivation of amaranth in Ethiopia. The objectives of the current study were to estimate genetic diversity, heritability, and genetic advance for yield and yield-contributing traits of amaranth genotypes based on agro-morphological traits. One hundred twenty amaranth genotypes were evaluated over two years using an alpha lattice design with two replications. The analysis of variance indicated that the mean square due to year and genotype-by-year interaction varied significantly for most measured traits. The estimates of variability, heritability, and genetic advance found in this study indicate the incredible genetic diversity in amaranth genotypes and the strength of selection response for these traits in the population. The findings showed that very high to moderately high heritability, high to moderate genetic advance, and genetic variability was observed for the traits basal lateral branch length, axillary inflorescence length, leaf area, branch number, plant height at flowering, plant height at maturity, stem diameter, days to flowering, grain filling periods, leaf width, and leaf length. Furthermore, the potential for amaranth improvement through appropriate selection is revealed by the existence of significant differences between the number of superior and inferior genotypes for the majority of examined traits. This suggests that these traits are governed more by additive gene action and that selection based on these traits might be successful in achieving the desired genetic gains for improvement.


Introduction
The genus Amaranthus (L.) belongs to the order Caryophyllales, which includes quinoa, spinach, and beetroot (D'Amico & Schoenlechner 2017).Amaranth is a dicotyledonous mesophyte that uses a special C4 carbon-fixation pathway (Assad et al. 2017;Stetter & Schmid 2017).Amaranth is a herbaceous plant or shrub that is found all over the world and is either annual or perennial (Stevens 2012).According to reports, there are 87 species in the genus Amaranthus (Jacobsen et al. 2000).It is now referred to be a third-millennium crop plant (Rastogi & Shukla 2013).The genus encompasses both cultivated and wild species, according to historical records.The Mayan civilization of South and Vol:.( 1234567890) Central America was the first to domesticate and cultivate amaranths some 8000 years ago (Thapa & Blair 2018;Ruth et al. 2021).They are one of the few non-kinds of grass that produce considerable amounts of small-seeded grain (Santra & Schoenlechner 2017).
A successful breeding program depends heavily on the type and degree of genetic diversity present in the genotypes since increased genetic variability would make it possible to pick genotypes with a better chance of success (Bhatia et al. 2017).The success of the selection is often determined by heritability and genetic advance (Kumar et al. 2013), although genetic diversity, is crucial for continuous genetic improvement (Sandhu et al. 2015).Yield and its component is a multi-genic trait, impacted by the contribution of many loci across the genome for various physiological, abiotic, and biotic stress tolerance factors that all interact over time during the growing season to determine final yield (Das 2016).Direct selection of yieldrelated traits, which are easier to precisely quantify than the yield itself, has also been an effective yield improvement technique (Samonte et al. 1998;Kumar et al. 2014).Thus, selection based on yield coupled with yield components can be efficient since yieldcontributing traits are less complex in heredity and less impacted by the environment (Gatti et al. 2005;Kumar et al. 2014).
Amaranth has long been a component of traditional African agriculture and is semi-domesticated, mostly as a vegetable, in Ethiopia and other East African countries (Alemayehu et al. 2015).Amaranth is now grown as a grain crop in far-flung areas such as Ethiopia's mountains, South India's hills, Nepal's Himalayas, and Mongolia's plains, with exceptional seed quality and the highest potential for use as a food ingredient (Brink et al. 2006;Sokolova et al. 2021).The widespread usage of local names and the large genetic variation present in Ethiopia have also been suggested as a hub of amaranth diversity.In the Flora region of Ethiopia and Eritrea, eleven species have been identified (Demissew 2010).Ethiopia is endowed with a wide range of food crops, the majority of which have received little to no attention in terms of research and the creation of regulatory frameworks that can encourage efficient commercial and industrial exploitation.One such underappreciated and neglected but double-duty species is amaranth.
The morphological diversity of Amaranth is remarkable, as is their adaptation to a wide range of eco-geographical conditions (Lee et al. 2008), elevation is not a severe limitation (Council 2006;D'Amico & Schoenlechner 2017), has a high tolerance to arid conditions, poor soils, withstand heat, drought, and pests, and they easily acclimate to new habitats, including ones that are unfavorable to conventional cereal crops (Yadav et al. 2014).Because of these benefits amaranth is cultivated in highly diverse areas, such as tropical lowlands and mountainous regions up to 3500 m altitude (D'Amico & Schoenlechner 2017).Despite the enormous morphological variability between and within Amaranthus species, there are only a few taxonomic traits that are unique to the genus (Juan et al. 2007;Wolosik & Markowska 2019;Aderibigbe et al. 2022).Amaranths have a considerable genetic variation, which indicates that they have a lot of room for improvement.The determination of genetic diversity relies heavily on agro morphological genotype characterization.As a result, it is required for the successful conservation of amaranth biodiversity and crop enhancement programs (Shah et al. 2018).
Amaranth is a high-yielding, climate-smart, nutrient-rich plant that is essential for meeting rising world demand while also reducing dependency on a few cereal crops.Ethiopians may not be aware of the crop's production or use (Yosef Alemu et al. 2018).This could be due to insufficient use, a dearth of better varieties, inadequate agronomic practices, or a lack of understanding of the crop's significance.Nonetheless, it is exclusively popular and consumed in a few parts of Ethiopia.Amaranth, an easy-to-grow nutrient-rich vegetable, has been recommended for enhancing food security and lowering malnutrition in populations in Ethiopia that depend on subsistence farming (Alemayehu et al. 2015).This genus contains a large number of edible plants, some of which are grown primarily for their leaves.In Ethiopia, two evaluated and released varieties are most frequently utilized (AC-NL and Madiira II).The amaranth plant not only thrives in a variety of climates (Lakshmi & Vimala 2000), but it is also one of the few exceptionally nutritionally multi-purpose crops (Ruth et al. 2021).It is used as a vegetable, cereal, medicinal plant, dye plant, forage plant, ornamental plant, as well as a fuel source (Mlakar et al. 2009;Sheikh & Singh 2013).As a result, it's critical to carefully consider the available germplasm and select improved genotypes with great potential for leaf and grain yield.
The introduction of genetic variability, selection, and usage of variance found in selected genotypes are all components of the contemporary breeding program used to produce new breeding materials.Heritability and variance components are important parameters of interest to disclose the genetic expression controlling the target trait.A genetic advance is also a potent instrument for looking for the first advance predicted by selection.To take advantage of the existing variations in amaranth genotypes, determining the extent of genetic diversity using markers of agro-morphological traits is crucial.Historic and less expensive morphological markers, which are the most straightforward measures of phenotypes, are frequently used to evaluate the extent of genetic diversity in populations (Jifar Daba 2018;Samarina et al. 2022).Therefore, this study was done to 1) quantify the extent of genetic variability, heritability, and expected genetic gain in Amaranths genotypes using markers of agro-morphological traits 2) to suggest an effective selection scheme and specify promising genotypes for future Amaranths breeding programs.

Experimental site
In the years 2020 and 2021, the experiment was carried out at Hawassa University, which is located at the agricultural experimental site.Geographically, the location is in Ethiopia's Sidama region, around 275 km from the capital, Addis Ababa.The experimental area is located at latitude 7° 2′ 54.7503′′ N and longitude 38° 30′ 17.1608′′ E, at an elevation of 1709 m above sea level.The soil texture class in the experimental area was clay loam, and the pH ranged from 6.0 to 6.5.The district's mean monthly minimum and maximum temperatures are 14.1.0°C and 27.9 °C, respectively.For two growing seasons, the experimental farm received an average of 1379.16 mm of rain throughout the growing seasons.
Table 1 List and location of plant materials included in the study

Easting
Northing Altitude Tigray Regions of Ethiopia in 2019 and characterized for various agro-morphological traits (Fig. 1).One hundred and eighteen members have passport information, while the other two do not and are considered released varieties.A two-season experiment was conducted at Hawassa University in Ethiopia to achieve the aforementioned objective.

Experimental layout and crop management
The experimental field was cleared, properly plowed, and harrowed by a tractor, with ridge preparation done by hand hoe.The experimental design was alpha lattice.A layout with 2 replications, 16 blocks, and 15 plots per block for amaranths was conducted.Each unit plot was separated by a 0.60-m way between plots, a one-meter between blocks, and a three-meter between replications, with a plot size of 1.80 m in length and 1.50 m width of 2.7 m 2 area.Each season required a total space of 1345.2sq.m (38 m × 35.4 m).On April 15th, 2020, and 2021, the seed was sown at one location in each season, which is under ideal growing circumstances and during the agricultural season.One growing season on the experimental site in one year is considered the environment.Seeds of various genotypes (Table 1) were consistently sowed in two rows with a gap of 0.75 m between them.Seeds are quite tiny, with sizes ranging from 0.37 to 1.21 g per 1000 seed weight (Paredes-Lopez 2018), and they were planted in seedbeds and covered with powdered, finely diced cow farmyard manure after being combined with sand in a 1:4 ratio.At 14 and 22 days after sowing (DAS), thinning was done twice at a distance of 75 cm between rows and 30 cm between plants.
According to Grubben and Van Sloten (1981a) and Shukla et al. (2006a, b), the experiment followed standard cultural practices.Hand-hoeing was used to control weeds at 2-week intervals following germination and whenever necessary.A total of 12 plants were maintained in each plot.During the first season of the trial cutworms, were identified as important amaranth pests.Diseases such as root rot, stem rot, and white rot were also observed on some plants.But no pests were identified in the second season.
Five percent karate was sprayed twice a day for 5 days at a rate of 25 ml per 15 L of water to control cutworm and leaf folder.250 EC (0.05%) was sprayed around the collar region to reduce root rot, and Redomil Gold (0.2%) was sprayed twice to control white rust.The number of branches was counted along the main stem at the maturity growth stage Terminal inflorescence stalk length (cm) TISL The mean length of 10 randomly selected terminal inflorescence stalks was measured with a meter rule starting from the base up to the tip of the inflorescence at the maturity stage Terminal inflorescence laterals length (cm) TILL The mean length of 10 randomly selected terminal inflorescence laterals length (cm) was measured at the maturity stage Number of nodes on the main stem (number) NN Many nodes were counted along the main stem at the flowering stage Grain yield ( t ha −1 ) GY Grain yield was estimated at an adjusted moisture content of 12% and weighed to determine the dry matter of the net plot and then extrapolated per hectare Leaf area(cm 2 ) LA Three different representative leaf sizes (small, middle, and large) per plant were measured by using a digital leaf area meter 1000 Seed weight (g) TSW A Sample of thousand seeds from each genotype was counted and weighed using an analytical balance Number of leaf (number) LN The main countable leaves were counted along the main stem at the flowering stage Days to maturity (days) DM The number of days between 90% emergence and the physiological maturity of 80% of plants.(The time of plant maturity is when the seed taken from the central part of the inflorescence does not change shape when pressed between fingers and when the inflorescence changes its color from green to brown) Days to flowering (days) DF From the 90% emergence date to the stage where ears had appeared in the terminal inflorescence from 50% of the plants in a row, days to 50% bloom Basal lateral branch length (cm) at maturity BLBL Measured from the base of the stem branches to the top of a branch at the maturity stage by using a meter tap

Data collection and measurements
To characterize the material under study, observations were made on various morphological traits at distinct phenological stages (Table 2).In each plot, the phenotypic characteristics of 10 randomly tagged plants were assessed.Plants were grown to maturity and defined using amaranth descriptors, as recommended by the International Board for Plant Genetic Resources based on taxonomic keys (Grubben & Van Sloten 1981b).Characters not on the list that were considered necessary for the characterization were included.In this investigation, a total of 24 quantitative characters were recorded.In each season, data was collected from the field at four phases of growth: germination, vegetative stage, and 50% of the plants' developing inflorescences, and plant maturity shortly before and after harvesting.Plant height at flowering and maturity (cm), petiole length (cm), leaf width (cm), leaf thickness (mm), terminal inflorescence stalk length (cm), terminal inflorescence lateral length (cm), number of branches per plant (number), number of leaves (number), number of nodes (number), stem diameter (mm), number of days to emergence (days), number of days to flowering (days), number of days to maturity (days), leaf area (cm 2 ), and 1000-seed weight (g), leaf yield tone ha -1 , grain yield tone ha −1 , grain filling period (days), grain filling rate (kg ha −1 day −1 ) are among them.Seedling data, vegetative data, inflorescence data, and seed data were among the 24 agro-morphological variables evaluated, which were assessed four times per plot for each replication.Except for days to emergence, days to flowering, and days to maturity, which were recorded at the plot level, all ten planted genotypes for each population (two replications) were used to collect quantitative trait data.For the traits, the mean value of 10 plants per plot was measured and recorded.Three different leaf length, leaf thickness, leaf width, petiole length, and leaf area (small, medium, and large) were randomly picked per plant for measurement.Leaf length, breadth, thickness, petiole length, and area were computed using the averages of 30 measurements (10 plants per plot for each of the 3 different leaf and petiole sizes).The harvest was done manually; panicles were carefully removed to minimize grain spilling, then the panicles were threshed by utilizing a flail, a sieve, and a basket.After the removal of fine debris, the seeds were cleaned, and they were held in an electric oven at 100 °C for 48 h to regulate the moisture content to 12%, as advised by (Biru 1978;de Jesus Souza et al. 2016).A digital caliper was used to measure the diameter of the stem and the thickness of the leaves (Digimatic Solar DC-S15 m, Mitutoyo, Japan).The LI-3100 AREA METER, an electronic leaf area meter, was used to measure the leaf area (LI, Cor, Inc, Lincoln, Nebraska, USA).

Analysis of variance
The SAS computer program first confirmed Bartlett's test for homogeneity of variance before executing ANOVA for each year's analysis.The F-max technique of Hartley (1950), which is based on the ratio of the larger mean square error (MSE) from the separate analysis of variance to the smaller MSE, was also used to test the homogeneity of error variance for the combined ANOVA.The error variance is considered homogenous if the bigger MSE is not three times greater than the smaller MSE (Gomez & Gomez 1984).After determining that the error variance was homogeneous, the combined ANOVA was carried out using the SAS PROC GLM procedure.Descriptive statistics were also done for the mean, range, standard deviation, and standard error of the mean.The genotype and year effects were both accounted for using a linear random model.The degrees of freedom for these combined mean squares were estimated following Satterthwaite's approximation approach, and the mean square of the random effect was compared to the sum of replication and interaction mean squares minus residual mean squares.The interaction effect, however, was assessed against the residual mean square whereas the genotype random effect was tested against the interaction (genotype by year) mean square.So, using the SAS statistical tool, analysis of variance (ANOVA) was performed on the pooled data from a two-year alpha lattice design.The quantitative data was evaluated using a two-way analysis of variance (ANOVA) in SAS 9.4 (Lehman et al. 2013), taking into account all sources of variation as random effect blocks, replications, genotypes, and year of planting as variables.All sources of variation were considered random effects, and the interactions between genotypes and years were evaluated.Following Gomez and Gomez (1984), a mean comparison between years was performed using Duncan's Multiple Range Test (DMRT) at a 5% probability level.The numerator degree of freedom for the year F-test was Y − 1 while the denominator (combined mean squares) degree of freedom was estimated using Satterthwaite's (Satterthwaite 1946) formula as indicated below.
where Fl is F-test for the year; MSy is the mean square of the year; MSr is the mean square of replication; MSgy is the mean square of genotype by year interaction; MSe is the mean square of error; DFcms is the degrees of freedom for combined mean square; r is replication; y is the year, and g is genotype.
The statistical models for each year and combined across the two years are presented below.The statistical model of the alpha lattice design for individual years is given by: The statistical model of the alpha lattice design across years is given by: where yijk is the measured response of genotypes i at year j and block k; μ is the overall mean; G (k) is the k-th genotype random effect; Y (i) is the i-th year random effect; GY (ki) is the genotype k by year i interaction is random effect; Bj is the j th block random effect, and ε (ijkl) is the random error.

Comparison of selected genotypes with the original population
To compare with the original population, the means of the top 5% genotypes for each trait were independently computed.The student t-test table was used to determine the significance of the difference between the sample mean and population parameter.When the calculated t-value is higher than the tabulated t-value, the difference is deemed significant (Singh et al. 2001).To compare the performance of the 5% best-selected genotypes with the size of a population, the absolute t-value was obtained using the student's t-test formula as follows: (1) (3) where n is the number of genotypes selected from the size of a population for better performance, x̄ is the mean of the genotypes that were selected, S is the sample standard deviation, and μ is the mean of the size of the population.

Estimation of variance components
The total variance components are partitioned into different components using ANOVA by assuming the mean square of each source of variation is equal to their expected mean squares (Table 3), as suggested by Singh and Chaudhary (1977).The mean, phenotypic, genotypic, and coefficient of variation were all measured to evaluate the population's level of diversity.According to the approach recommended by Johnson et al. (1955), the combined over-year mean squares computed using SAS statistical techniques and the causes of variations in alpha lattice design were utilized to estimate variance components as follows: (6) σ 2 y = (MSy + MSe) − (MSr + MSgy) gr� where δ 2 y is the variation over the year; The term "MSy" refers to the mean square of a year.g represents the number of genotypes, r represents the number of replications, δ 2 r represents the replication variance, MSr represents the mean square of replication, MSe represents the mean square of error, and δ 2 g represents the genotypic variance.MSgy stands for the mean square of genotype by year interaction."y" specifies the number of years, while MSg refers to the genotypes' mean square.The phenotypic variance is represented by δ 2 p, the environmental variance by δ 2 e, and the genotype by year interaction variance is represented by δ 2 gy.On the other hand, the technique suggested by Singh and Chaudhary (1977) was also applied to estimate the genotypic (GCV), phenotypic (PCV), and environmental coefficients of variation (ECV).These estimates of variance were categorized as low when values were less than 10%, medium when values were between 10 and 20%, and high when values were over 20% (Johnson et al. 1955;Sivasubramanian & Menon 1973;Deshmukh et al. 1986).( 10) where GCV denotes the genotypic coefficient of variation, PCV denotes the phenotypic coefficient of variation, and ECV denotes the environmental coefficient of variation.Whereas the genotypic variation is represented by δ 2 g, the phenotypic variation is represented by δ 2 p, the environmental variation by δ 2 e, μ is the mean of a trait, and the genotype by year interaction variation is represented by δ 2 gy.

Estimation of heritability
Heritability was defined by (Allard (1960); Falconer 1989), as a proportion of the (δ 2 g) the genotypic variance, and phenotypic variance (δ 2 p).Singh et al. (2001) and Pandey and Singh (2011) determined that the heritability estimates were classed as low when they were less than 40%, medium when they were between 40 and 59%, fairly high when they were between 60 and 79, and very high when they were higher than 80% estimated these values as follows: whereas h 2 bs= heritability in broad sense; δ 2 g stands for genotypic variance, and δ 2 p for phenotypic variance

Estimation of genetic advance
Using the approach described by Allard (Allard 1960), expected genetic advancement as a component of the mean (GA) for each characteristic at 5% selection intensity (k = 2.06) was calculated.Additionally, using the Comstock and Robinson (Comstock & Robinson 1952) method, the expected genetic advance as a percentage of the mean (GAM) was calculated to examine the magnitude of the predicted advance of various characteristics under selection.Johnson (Johnson et al. 1955) defined the estimated GAM values as low when values were less than 10%, medium when values were between 10 and 20%, and high when values were greater than 20%.
where GA is the genetic advance; K is the standardized selection differential at 5% selection intensity (k = 2.06); √ σ 2 p is the square root of pheno- typic variance; h 2 bs is heritability in broad sense; GAM is the genetic advance as percent mean; and X is the mean of the population in which selection employed.

Analysis of variance (ANOVA)
Table 4 illustrates the variance results from a pooled analysis of all traits for 120 genotypes.For all characteristics other than leaf thickness (mm), petiole length (cm), leaf yield (t/ha), top lateral branch length (cm), and thousand seed weight (g), the mean squares resulting from genotypes differed significantly among genotypes (P ≤ 0.001).The majority of the replicates showed no discernible differences.The effect of cropping season was significant for all traits, including plant height at flowering (days), leaf area (cm 2 ), leaf yield (t/ha), branch number (number), top lateral branch length (cm), auxiliary inflorescence length (cm), terminal inflorescence stalk length (cm), grain filling periods (days), and thousand seed weight (g).Similarly, genotype by cropping season's interaction effects was significant for all morphological and agronomic traits, except leaf yield (t/ha), and thousand seed weight (g).Significant genotype by cropping season's interaction effects was mostly a 'cross-over' type; i.e., interactions were associated with rank order changes among the genotypes (Fig. 2).

Descriptive statistics
The mean, range, standard deviation, and standard error of the mean for all 24 quantitative showed remarkable variations among amaranths genotypes in the studied phenotypic traits (Table 5).( 16) Vol.: (0123456789)

Effect of test cropping seasons
Table 6 indicates how experimental cropping seasons impacted the mean performance of agromorphological characters in amaranth genotypes.Several agro-morphological parameters of the amaranth genotypes were significantly different between the two cropping seasons.Days to emergence (days), days to flowering (days), leaf length (cm), leaf width (cm), leaf thickness (mm), petiole length (cm), node number (number), days to maturity (days), plant height at maturity (cm), stem diameter (cm), terminal lateral inflorescence length (cm), grain sink filling rate (kg ha −1 day −1 ), thousand seed weight and grain yield (t ha −1 ) were all significantly higher in 2020 than in 2021.Whereas, plant height at flowering (cm), leaf area (cm 2 ), leaf yield (t/ha), branch number (number), top lateral branch length (cm), auxiliary inflorescence length (cm), terminal inflorescence stalk length (cm), and grain filling periods (days) did not differ significantly between cropping seasons.The mean performance of all traits was greater in the cropping seasons in 2020 than in 2021.

Mean performance of genotypes
Over several traits, genotype differences were substantial (Fig. 3).It was clear that most genotypes of amaranth outperformed or had superior agromorphological performances when compared to the overall mean of the population and newly released variety (AC-NL and Madiira II), including the thousand seed weight (g), leaf number (number), leaf area (cm 2 ), leaf length (cm), leaf width (cm), leaf thickness (mm), days to emergence (days), petiole length (cm), and node number (number), top lateral branch length (cm), and days to maturity (days) (Fig. 3A).In contrast, several newly introduced varieties outperformed the population means when evaluated across all agro-morphological parameters, particularly grain, and leaf yield traits.So, comparisons at both levels (with population and average performances of released varieties) showed that there were genotypes comparable to the released genotypes for all 24 traits (Fig. 3B).The differences between the numbers of superior and inferior genotypes for most studied traits were higher when the tested genotypes were compared with the mean of released varieties than the mean of the population.
As shown in Table 7, the performance of the top 5% of leaf yielder amaranth genotypes in comparison with the bottom 5% of leaf yielder genotypes, the population means, and the mean of two released varieties.The findings showed that in the majority of evaluated phenotypic traits, the top leaf yielder genotypes were superior to the least leaf yielder genotypes, the population means, and the mean of released varieties.The top leaf yielder genotypes, however, were inferior to the least leaf yielder genotypes, the population means, and released variety mean in terms of days to emergence (days), basal lateral branch length (cm), top lateral branch length (cm), auxiliary inflorescence length (cm), terminal inflorescence lateral length (cm), terminal inflorescence stalk length  (cm), grain filling periods (days), and thousand seed weight.Comparably, Table 7 compares the performance of the top 5% grain yielder amaranth genotypes with the lowest 5% grain yielder genotypes, the population means, and the mean of two released varieties.The most grain genotypes outperformed the least grain-yielding genotypes in terms of leaf area (cm 2 ), leaf length (cm), leaf width (cm), leaf yield (t/ ha), top lateral branch length (cm), auxiliary inflorescence length (cm), terminal inflorescence lateral length (cm), terminal inflorescence stalk length (cm), grain sink filling rate (kg ha −1 day −1 ), thousand seed weight (g).On the other hand, the top grain yielder genotypes were inferior to the least grain yielder genotypes in terms of the number of leaf days to emergence (days), days to flowering (days), plant height at flowering (cm), leaf number (number), branch number (number), basal lateral branch length (cm), days to maturity (days), plant height at maturity (cm), and grain filling periods (days).Table 8 indicates the results of the top 5% of grain yielder genotypes for the assessed phenotypic variables.The top 5% of grain yielder genotypes were KEN-016, KEN-020, KAZ-060, and 225,715.The first top grain yielder genotype (KEN-016) had a higher top lateral branch length (cm), terminal lateral inflorescence length (cm), and grain filling rate (kg ha −1 day −1 ).The second top seed yielder genotype (KEN-020) gave a higher basal lateral branch length (cm) and grain filling rate (kg ha −1 day −1 ).The traits of the third-best grain yielder genotype were longer terminal inflorescence stalk length (cm) and auxiliary inflorescence length (KAZ-060).Higher top lateral branch length (cm), auxiliary inflorescence length (cm), and terminal inflorescence stalk length (cm) were obtained by the fourth-best grain-yielding genotype (KEN-010).The fifth-best grain-yielding genotype, KEN 018, exhibited the most days to emergence (days), days to flowering (days), plant height at flowering (cm), leaf number (number), leaf area (cm 2 ), leaf length (cm), and leaf breadth (cm).The top 5% leaf yielder genotypes were 225,713,242,530,and 212,890.Higher days to flowering (days), leaf area (cm 2 ), leaf thickness (mm), and petiole length (cm) were found in the first top leaf yielder genotype (KAZ-059).The genotype with the secondhighest leaf yield (225,713) had higher plant height at blooming (cm), leaf length (cm), branch number (number), basal lateral branch length (cm), days to  Vol:. ( 1234567890) maturity (days), plant height at maturity (cm), stem diameter (cm), and grain filling durations (days).The third top grain yielder genotype (KAZ-058).haslarger leaves (cm) and longer terminal inflorescence stalks (cm).The fourth-best leaf-yielding genotype (KEN-019) developed more leaves and nodes (number).Using certain agro-morphological parameters, the performance of the top 5% of genotypes is compared with the population means and the mean of the released variety (Table 9).When compared to the released varieties, no single genotype consistently outperformed the top 5% of best performers for various parameters such as grain yield, grain sink filling rate (kg ha −1 day −1 ), days to maturity (days), and grain filling time (days).Many other superior genotypes for multiple traits of agronomic significance, including grain yield, were also identified.The best genotypes for grain yield (t ha −1 ) were found to be superior by 71.22-138.85% to the population mean and 53.55-114.9% to the mean of grain yield performance of the released varieties; leaf yield (t/ha) 43.06-110.38% to the population mean and 37.59-102.33% to the mean leaf yield performance of the released varieties; plant height at maturity (cm) 36.86-64.09% to the population mean and 45.85-74.86% to the mean plant height at maturity performance of the released varieties; and grain sink filling rate (kg ha −1 day −1 ) 67.92-212.19% to the population mean and 15.42-28.97% to the released varieties' mean grain sink filling rate performance; A student t-test was used to compare the mean of the top 5% genotypes to the mean of the population was varied (P ≤ 0.0001) for all measured phenotypic traits (Table 10).The t-test showed highly significant differences between means of the selected subsets of the top 5% best genotypes (x) and the population parameters (µ) for days to emergence (days), days to flowering (days), plant height at flowering (cm), leaf area (cm 2 ), leaf length (cm), leaf yield (t/ha), leaf width (cm), petiole length (cm), leaf thickness (mm), top lateral branch length (cm), node number (number), days to maturity (days), plant height at maturity (cm), leaf number (number), stem diameter (cm), auxiliary inflorescence length (cm), terminal lateral inflorescence length (cm), branch number (number), basal lateral branch length (cm), terminal inflorescence stalk length (cm), grain filling periods (days), grain sink filling rate (kg ha −1 day −1 ), grain yield (t ha −1 ), and thousand seed weight (g).The findings showed that the top 5% of genotypes had greater relative advantages in all agro-morphological variables, with differences between them and the population means performances ranging from 15.90% for leaf thickness to 184.11% for basal lateral branch length.
Each day, the top 5% of genotypes produced 119.05% greater grain, per hectare than the population as a whole.Additionally, the top 5% of genotypes exhibited advantages in leaf area (cm 2 ), leaf yield (t ha −1 ), terminal inflorescence stalk length (cm), thousand seed weight (g), and grain yield (t ha −1 ) of 61.77, 56.66, 54.53, and 114.03% above the mean performances of the population, respectively, showing the occurrence of various degrees of amaranths enhancements through selection.

Variance component and coefficient of variation
Genetic parameters such as genotypic variance (δ 2 g), phenotypic variance (δ 2 p), environmental variances (δ 2 e), genotypic variance with year interactions (δ 2 gy), genotypic co-efficient of variation (GCV), phenotypic coefficient of variation (PCV), and environmental co-efficient of variation (ECV) were calculated for each of the evaluated quantitative traits indicated in the (Table 11).For all of the traits, the assessment of the variance's components revealed a wide range of variation and substantial disparities.The result reveals that for all examined variables, the phenotypic variance was greater than the comparable genotypic variance.Concerning all examined phenotypic variables; the phenotypic variance was likewise greater than the corresponding genotype-byyear interaction variance and environmental variance.The genotypic variance overshadowed genotype-byyear interactions, except for days to emergence, leaf thickness, petiole length, leaf yield, top lateral branch length, node number, terminal lateral inflorescence length, and terminal inflorescence stalk length, grain sink filling rate, thousand seed weight, and grain yield.Similarly, the genotypic variance was higher than the corresponding environmental variance in the days to flowering, plant height at flowering, leaf area, leaf length, leaf width, branch number, basal lateral branch length, days to maturity, plant height  (PCV and GCV, respectively).Plant height at flowering, leaf area, branch number, auxiliary inflorescence length, basal lateral branch length, plant height at maturity, grain sink filling rate, and grain yield all had high genotypic and phenotypic coefficients of variation.The traits with medium GCV and PCV comprised days to flowering, leaf length, leaf width, days to maturity, terminal lateral inflorescence length, stem diameter, days to grain filling periods, and thousand seed weight.Leaf yield and top lateral branch length showed high PCV and low GCV values, but days to emergence, the number of leaves, petiole length, and the number of nodes showed medium PCV and low GCV values.Leaf thickness is revealed by low PCV and GCV.Likewise, estimates of the environmental coefficient of variation (ECV) ranged from 6.88 (for days to maturity) to 58.76 for grain sink filling rate.For each trait, the estimates of the genotypic coefficient (GCV) were lower than the corresponding phenotypic coefficient of variation (PCV).The petiole length, leaf yield, basal lateral branch length, node number, top lateral branch length, terminal lateral inflorescence length, grain sink filling rate, thousand seed weight, and grain yield showed a wide difference between the phenotypic and genotypic coefficient of variations, while the remaining traits all exhibited a slight difference.

Discussion
Superior genotypes must be investigated utilizing several traits and multi-environment experiments to make sure that the chosen genotypes perform well in a variety of environments within the targeted area.Due to the extremely significant changes between the seasons and interactions between genotypes and seasons, the best genotypes for specific traits during the planting season were not always the best genotypes for the subsequent planting season.For the majority of the studied variables, the extent of significant differences was seen among years, genotypes, and the genotype-by-year interaction.In most of the studied traits in amaranth genotypes, the test year had a significant impact.Because weather and farming practices, such as soil characteristics, field management, or weather, affect how genes are expressed, this may help to explain the scenario (Yao et al. 2008;Persaud et al. 2022).Debelo et al. (2001), andMbwambo et al. (2013) also observed comparable findings the irregular variations in rainfall from year to year due to the genotypic difference often have an impact on Ethiopia's agriculture and could influence most of the plant variables in a complex way resulting in their plastic responses.
The mean squares of genotypes from the analysis of variance demonstrated that there was significant variation among the genotypes for the studied variables, except for the leaf thickness, node number, petiole length, leaf yield, and top lateral branch length.The considerable observed variances among the genotypes under study suggest that there was a substantial amount of inherent variability among amaranth genotypes for the variables under analysis.Various researchers have also reported that amaranth genotypes exhibit significant variability (Andini et al. 2013;Thapa & Blair 2018;Trivedi et al. 2022).The evaluation of both genetic and environmental factors may be made more precisely and efficiently by studying the interaction between genotype and environment.Stable genotypes are required for sustainable and reliable agriculture production (Kanfany et al. 2021).In the current studies, the genotypes by year interactions were found significant for DE, DF, LN, LL, LW, DM, PHM, SD, TILL, GSFR, and GY.This is due to both the discrepancy response of genotypes to the test year and the influence of the test year on the genotypes differently.Additionally, the inconsistent performance of the genotypes across years suggested the potential for exploring and cultivating superior genotypes in a variety of environmental conditions (Olaniyi 2007) and attributed genotype x year to variations in ecological distribution and genetic variations among the genotypes.Contrarily, the observed significant interactions for the PHF, BN, BLBL, AIL, TISL, and GFP traits were only due to the differential response of genotypes to the test years.However, the observed significant interactions for the remaining three (about 12.5%) traits were only due to differential responses of years to the test genotypes.Because growing conditions can vary, it is typical to expect that a genotype's performance will fluctuate in a variety of environments (Keneni 2012;Mohammadi 2017;Fekadu et al. 2022).
The estimated 24 quantitative traits showed a massive genetic variation among the genotypes, and a similar variation was confirmed by (Andini et al. 2013;Kumar 2015;Nyasulu et al. 2021).There are three plausible explanations for the increased variety in morphological appearance that may be in Ethiopian amaranth genotypes: they may have not passed through genetic bottlenecks during the process of speciation; they may not have experienced strong negative natural selection under control or cultivation (Chan & Sun 1997); and self-pollination is more likely to occur, with mean outcrossing rates ranging from 3 to 32% (Jain et al. 1982).This variation suggests that it would be possible to use it for amaranth varietal enhancement.
The observed crossover interactions in GY were a result of genotypic performance changes brought on by variable environments, which made it more difficult to create genotypes with stable performance.Significant genotype by environment interaction effects was mostly of the 'cross-over' type; i.e., interactions were associated with rank order changes among the genotypes.This indicated that the two environments were distinctly different for some of the characters and that better genotypes in one environment may not be better performers in another (Temesgen et al. 2015;Sossou et al. 2021).Moreover, the significant presence of extensive crossover GY interactions in the two cropping seasons suggests that a systematic effort is needed to screen different genotypes across various environments to identify those that perform well there or within a particular target region of environments (environment trials).The inconsistent genotype rankings for the investigated traits would make it difficult to generate genotypes with stability for these traits (Moghaddam & Pourdad 2009).The results, however, point to significant variations in genotype ranks between the environments; therefore, effort must be used while breeding these characteristics, particularly for grain production.
Plant height, days to flowering, leaf number, leaf area, leaf length, and individual leaf width are all key contributors to amaranth's leaf yield, as are a variety of other yield elements.Das (2016), supported similar findings.The observed high plant height in the top 5% leaf yielder genotypes might be due to the inherent genetic variation, strong light competition, and partition of more assimilate for stem elongation.Similar findings were observed by Yarnia et al. (2010) in amaranths and taller plants outcompete weeds more successfully than shorter ones (Fageria et al. 2004).Similarly, the leaf area is crucial in determining the yield (Sarker & Oba 2021).The top 5% of leaf yielder genotypes exhibited the biggest leaf area.This is because each leaf's area is estimated as the sum of its leaf length and leaf width, and when water availability grew, plants were able to photosynthesize more effectively (Shongwe et al. 2010).The leaf area intercepts sunlight, takes up CO 2 and inorganic nitrogen, and performs photosynthesis and biomass accumulation, among other factors, determining the yield reported in several studies (Puntel 2012).Moreover, photosynthesis has been the precondition for a successful breeding program to increase photo-assimilate production in high-yielding genotypes (Haritha et al. 2017).Although, light absorption and the rate of dry matter production increase as leaf number and size increase during crop growth (Remison & Akinleye 1979).Besides intercepting most of the solar radiation falling on the crop canopy, high leaf area indices ensure the optimum use of other available environmental factors like moisture, carbon dioxide, and nutrients, to achieve high productivity.Overall, our findings indicate that the genotypes that can be selected to increase biomass have superior photosynthetic efficiency (Vikram et al. 2016).Therefore, a successful yield improvement strategy has been the direct selection of yield-related traits, which are simpler to quantify precisely than the yield itself (Kumar et al. 2014).The genotypes of amaranth examined for potential grain and leaf yields generally showed genotypic heterogeneity.These variations in agro-morphological trait performance indicate the amaranth's potential for success in future development efforts for various uses.
Grain yield is an incredibly complex feature that cannot be determined by itself., according to Voss-Fels et al. (2019).It is a resultant effect of actions and interactions of its component traits.Therefore, the identification of plant traits that contribute to high grain yield is essential for breeding efforts.The observed grain yield was markedly higher in the top 5% of grain yielder genotypes, which may be explained by a stronger relationship between GY and GY-related traits LN, LA, LL, LW, LY, BN, AIL, TILL, TISL, GSFR, and TSW.The relative effect of PHM and DM, however, was less significant for grain yield performance in the top 5% of yielder genotypes.This may be tied to the top grain genotypes' considerably shorter plant heights and earlier maturation.Modern high-yielding genotypes have improved grain production mostly through the reduction of plant height, which boosts the harvest index due to lower yields of straw and increased lodging resistance.The photosynthesis and respiration of the shorter plants are better balanced and thus require less maintenance respiration (Peng et al. 1994).The study's findings also revealed that genotypes with high grain production typically had shorter plant heights (Shah et al. 2018).According to Sogbohossou and Achigan-Dako (2014), and Brenner et al. (2000), grain amaranths were chosen for reduced plant heights.Higher photosynthetic efficiency, lower respiration, and increased grain carbohydrate storage are significant physiological processes that can be used to increase yield potential (Sharma-Natu & Ghildiyal 2005).Additionally, similar to the photosynthetic rate, leaf area greatly increase grain output (Raza et al. 2019).On the other hand, it seems reasonable that any component that influences a plant's ability to fix carbon and/or transfer available or stored assimilates to the grain will likely also affect physiological maturity.For instance, terminal drought or nitrogen stress, which is known to accelerate leaf senescence, could shorten the filling period and advance physiological maturity (Smith & Hamel 2012).The top 5% of genotypes for grainyielding plants began to bloom earlier but took longer for GFP, which resulted in higher GY due to better use of growth resources.The length of the vegetative growth stage may also have an impact on grain yield (Feng et al. 2021).A shorter vegetative growth period makes it possible to reserve more growth resources for the reproductive phase, which raises GY due to the effective use of growth resources for yield production (Feng et al. 2021).The final yield is greatly influenced by the grain filling rate (GFR) and is a positive impact on the final grain weight (Khan et al. 2014).High temperature also increases the rate of grain filling to compensate for the shortened grain growth period.Selecting genotypes with high GFR is therefore probably a wise way to proceed for enhancing grain yield under stress.
Any breeding material must have a high level of genetic diversity since it not only serves as a foundation for selection but also offers important insights into the choice of varied parents for use in hybridization programs (Singh et al. 2016;Upadhyay et al. 2019).To determine the additive or heritable portion of variability, agronomic traits must be divided into genotypic, phenotypic, and environmental influences because they are quantitative and interact with the environment being studied.The extent of phenotypic variances was comparatively higher for all agro-morphological traits in amaranth genotypes in the current study compared to the corresponding genotypic and the interaction of genotypic by year variances, indicating a relatively high level of environmental influence on the expression of these traits.For 50% of the examined variables, the magnitude of genotypic (heritable) variance was higher than the corresponding environmental (non-heritable) variances.This suggests that the examined traits were mostly influenced by the genotypic component of variance.
In the current study's variability analysis, all of the characteristics exhibited greater phenotypic than genotypic coefficients of variation, which is generally consistent with the findings of (Sravanthi et al. 2012;Parveen et al. 2013;Yadav et al. 2014;Malaghan et al. 2018;Showemimo et al. 2021).They consequently suggested that the environment had an impact on how they expressed themselves.High genotypic and phenotypic coefficients of variation were revealed in the plant height at flowering, leaf area, branch number, auxiliary inflorescence length, basal lateral branch length, plant height at maturity, grain sink filling rate, and grain yield.Higher PCV values and correspondingly higher GCV values for these traits suggest that they are of economic importance and are under the control of genetics.As a result, these traits can be relied upon, and simple selection can be used to enhance these traits.Results of a comparable ilk have been reported by Malaghan et al. (2018) for a number of a branch, and plant height at flowering.A significant difference between PCV and GCV estimates for the traits viz., petiole length, leaf yield, leaf thickness, node number, top lateral branch length, terminal lateral inflorescence length, and thousand seed weight points to a higher level of environmental control or the contribution of non-additive gene effects.The same findings were reported by Rana et al. (2005) and Showemimo et al.(2021).But it was found that the differences between PCV and GCV were comparatively very small for the traits of days to flowering, leaf length, leaf width, days to maturity, and stem diameter.This suggests that these traits had a lot of genetic diversity that could be exploited and it is revealed that this estimated phenotypic variability is a reasonable signal of genotypic variability.As a result, environmental influences had a smaller impact on phenotypic performance (Sawadogo et al. 2014).Six of the examined traits had low ECV estimates (about 25%), suggesting that these traits are less responsive to environmental variables.For the traits of leaf length, leaf breadth, days to maturity, and plant height at maturity, the observed moderate-to-high PCV and moderate GCV along with the accompanying low ECV estimations revealed that improvement in those traits would be possible by direct selection.
The value of selection for a specific characteristic depends mainly on its heritability since selection Vol.: (0123456789) operates on genetic differences (Allard 1960).Heritability, a degree of the genetic link between parent and offspring, has been frequently used to determine how much a character may be passed down from one generation to the next.Estimates of heritability reveal the degree to which a trait is under genetic control, as well as the accuracy of phenotypic prediction of its breeding value (Ndukauba et al. 2015).Understanding heritability is crucial because it aids breeders in determining how much improvement is feasible through selection (Robinson et al. 1949;Ene et al. 2016).The current research revealed that, for assessed phenotypic traits, the estimated heritability generally showed significant variability.This great genetic improvement potential of the traits under study was suggested by the vast range of variability in the studied traits since the degree of wide variability essentially provides better scope for selection.
The number of branches, plant height at maturity, stem diameter, and leaf width, plant height at flowering, grain filling periods, basal lateral branch length, days to maturity, auxiliary inflorescence length, leaf area, days to flowering, and leaf length all demonstrated substantial heritability in the current investigations.According to Manal (2009) and Yanti (2016), high heritability signifies that environmental influences on the expression of these traits were relatively low and that the dominant genetic influence, due to the presence of high additive gene action on the expression of these traits, was largely responsible for the manifested phenotypic performance.For this reason, special consideration should be given to these particular traits that are passable and may be regulated by additive gene action.Therefore, to make selection effective for improving amaranth, these traits can be enhanced using mass selection or the pedigree technique.Similarly, Showemimo et al. (2021) also obtained high heritability estimates for, the number of branches, and leaf width.Similar findings in amaranth for traits including plant height, stem diameter, and the number of branches were made by Sravanthi et al. (2012), Yadav et al. (2014), Mobina and Jagatpati (2015), and Selvan et al. (2013).Similar results have also been reported by Trivedi et al. (2022) for the traits of days to flowering, plant height, and stem diameter.The estimations of heritability, however, were moderate for the length of the terminal inflorescence stalk, the number of leaves, and the number of days to emergence.Selection based on the phenotypic performance of these traits may not be beneficial for improvement due to the medium heritability of these traits, which suggested that the environmental effect was relatively strong on the expression of these traits.Moreover, moderate heritability, indicating a weak correlation of phenotype with genotypic value and reflecting the high influence of season by genotypes interaction effects.According to Singh et al. (1993), the selection is made significantly more challenging by the medium to low heritability estimates since the environment has a considerable obscuring impact on the genotypic effects.So, the genetic potential of such traits would be harnessed by heterosis breeding followed by recurrent selection.The significant influence of non-additive gene action on their expression.The existence of considerable genotype by environment interactions in 22 (or almost 92%) of the examined traits may be the key cause of the low heritability estimates that were observed in the current study.
There is a sign of genetic variation in the genotypes of amaranth that can be selected, according to the estimates of GA and GAM in the present study, which varied significantly across the observed traits.Whenever we select the top 5% of high-yielding genotypes as parents, the mean performance of the offspring is anticipated to improve based on the assessment of genetic gain in the current study.In light of this, it is expected that the genotypic performance of the new population (progeny) will increase, from 1.39 to 1.83 t ha −1 for grain yield to 10.31-11.76t ha −1 for leaf yield.Similarly, leaf area (69.91-99.17cm 2 ), leaf length (14.39-17.58cm), leaf width (7.09-8.70 cm), grain sink filling rate (30.11-40.96kg ha −1 day −1 ), plant height at maturity (209.25-272.25 cm), plant height at flowering (73.7-95.29 cm), thousand seed weight (0.86-1.03 g), auxiliary inflorescence length (12.04-18.71cm), terminal lateral inflorescence length (17.91-20.94cm), and terminal inflorescence stalk length (28.96-35.93cm) are expected to be improved.The genetic gain (GAM) that could be estimated from selecting the top 5% of the genotypes as a percent of the mean ranged from 0.0% for leaf thickness to 86.1% for basal lateral branch length.
Only heritability-based trait selection, however, may occasionally be successful since the broad definition of heredity takes into account total genetic variance, which includes additive, dominant, and epistatic variances (i.e.interaction between variations that is not additive).Measuring the heritability of a Vol:.( 1234567890) group of genotypes in conjunction with rapid genetic advance is, therefore, more precise and efficient for the selection of desirable traits for a subset of the population (Ali et al. 2002;Bhargava et al. 2004;Amegan et al. 2020).High heritability combined with high genetic advance as a percentage of the mean was a more valuable and powerful tool for predicting the effect of selection and producing the resultant effect for selecting the best individuals (Shukla et al. 2006a, b;Kuralarasan et al. 2018;Chauhan & Singh 2019) because heritability is a separate numerical expression of the ratio of the two variances, which may not result in success if the selection is based on heritability estimates alone.High heritability and GA for a particular characteristic show that it is controlled by additive gene action, which makes it the best candidate for selection (Mohsin et al. 2009).Furthermore, it is thought that traits with high heritability's but little genetic advance, which restricts the possibility of improvement through selection, are controlled by non-additive gene action.However, if the trait is governed by additive gene action, it will be highly heritable and have a significant genetic advance, allowing for a significant improvement through selection.High broad-sense heritability (h 2 bs) traits, therefore, may not always result in high genetic gain (Lipi et al. 2020).Estimates of very high to moderately high heritability accompanied with high GAM were identified for basal lateral branch length, auxiliary inflorescence length, leaf area, branch number, plant height at maturity, plant height at flowering, stem diameter, days to flowering, grain filling periods, leaf width, and leaf length.Similar results were recorded by Popa et al. (2010), Venkatesh et al. (2014), and Mobina and Jagatpati (2015).This implies that additive genetic variables had a significant impact on the development of these traits.The above suggests the existence of gene effects that are additive and, as a result, a significant genetic gain under phenotypic selection.The remaining traits' moderate to low heritability estimates and moderate to low genetic advance as a percentage mean suggested that non-additive genetic variance played a role in how they manifested themselves.The traits for petiole length, top lateral branch length, and nod number showed low heritability and genetic gain.Through hybridization, these traits with low genetic advance and heritability can be improved (Samadia 2005;Rajan 2012).The superior genotypes of the segregating population obtained from repeated crosses can be accumulated in the lines, whilst the traits that exhibit high heritability with moderate or poor genetic advance can be improved via inter-mating.

Conclusions
The results of this study demonstrated the existence of considerable variation among 120 Amaranth genotypes evaluated based on markers of agro-morphological traits collected over two-year periods in Ethiopia.Furthermore, most of the traits were significantly affected by the test year, suggesting that the test year affects the traits differently.The majority of the assessed traits also showed significant variance in the genotype-by-year interaction.There were estimates of very high to moderately high heritability, high to moderate genetic advance, and genetic variability for the traits BLBL, AIL, LA, BN, PHF, PHM, SD, DF, GFP, and LW.This suggests that selection based on these features may be successful in generating the needed genetic gains for improvement.These traits are thought to be more strongly influenced by additive gene action.By selecting genotypes with high leaf yield together with traits like increased LA, BN, PHM, and PHF, it might be possible to boost leaf yield in amaranth genotypes.Grain yield improvement in amaranth genotypes, on the other hand, might be successful if the selection was carried out with high GY along with greater LA, AIL, TILL, and TISL.Because of this, the selected amaranth genotypes, 225,713,242,530,and 212,890, had high leaf yields coupled with superior LA, LW, LL, LN, and PH.Conversely, genotypes KEN-016; KEN-020; KAZ-060; and 225,715 generated high GY coupled with improved LN, LA, AIL, TILL, TISL, GSFR, and TSW.These genotypes were chosen to improve the genetic gain in the amaranth breeding program by boosting the genotypes' existing leaf and grain yield productivity.Before making the preferred genotypes available to the farming community, it will be crucial to evaluate the genotypes under various environmental conditions.

Fig. 1
Fig. 1 Map of Ethiopia showing the collection site for the different genotypes of Amaranths from different agro-ecological Zone.Open Source Geospatial Foundation; 2020.http:// qgis.osgeo.org

Fig. 3
Fig. 3 The number of superior and inferior amaranth genotypes over (A) the population means and (B) the mean of released varieties grown in 2020 and 2021.Abbreviated names (codes) of different traits; DE =days to emergence; AIL = axillary inflorescence length; LY = leaf yield; DF = days to 50% flowering; GY = Grain yield; DM = days to maturity;LA = leaf area; LL = leaf length; LW = leaf width; BN = branch number;

Fig. 4
Fig. 4 Heritability, genetic advance, and genetic advance as percent of the mean for 24 traits of 120 amaranth genotypes grown in the 2020 and 2021 cropping seasons.Abbreviated names (codes) of different traits; DE = days to emergence; AIL = axillary inflorescence length; LY = leaf yield; DF = days to 50% flowering; GY = grain yield; DM = days to maturity; LA = Leaf area; LL = leaf length; LW = leaf width; BN = branch number; LN = leaf number; NN = node num-

Table 2
List of quantitative agro-morphological traits used along with their phenotype code, description, and phenotype scores PL Three different representative petiole sizes (small, middle, and large) per plant were measured in centimeters from the base of the stem to the petiole of the leaf with the help of a meter scale at the flowering stage.measured from the point of insertion of the petiole to the beginning of the leaf blade Number of the branch (number) BN

Table 3
Combined analysis of variance (ANOVA) and expected mean square for the random model of alpha lattice design σ 2 g = genotypic variance; σ 2 = variance; σ 2 gy = genotype by year interaction variance; y is the number of year; r = number of replication; b = block; g = genotype and σ 2 e = environmental variance

Table 5
Mean, range, standard deviation, and standard error of the mean for 24 traits of 120 amaranth genotypes grown in the 2020 and 2021 cropping seasons StDev, Standard deviation; SEM ( ±), Standard error of the mean Vol.: (0123456789)

Table 7
Compares the mean of 5% high and low leaf and grain yielder amaranth genotypes to the mean of the population and released varieties for 24 amaranth traits grown in the 2020 and 2021 cropping seasons Abbreviated names (codes) of different traits; DE,

Table 8
Mean traits performance of top 5% leaf and grain yielder genotypes for 24 traits of amaranth grown in the 2020/2021 cropping season

Table 10
Comparison of mean performances of selected top 5% genotypes with the population mean performances for 24 traits of amaranth grown in the 2020 and 2021 cropping seasons Vol:. (1234567890)