Evaluation of genetic variability, heritability, genetic advance, and association of yield and yield contributing traits of indeterminate climbing common bean (Phaseolus vulgaris L.) genotypes

Evaluation of genetic variability, heritability, genetic advance, and association of yield and yield contributing traits of indeterminate climbing common bean (Phaseolus vulgaris L.) genotypes | 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 Article Evaluation of genetic variability, heritability, genetic advance, and association of yield and yield contributing traits of indeterminate climbing common bean (Phaseolus vulgaris L.) genotypes Temesgen Asmare Molla, Tiegist Dejene Abebe, Ephrem Worku Tesfa, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7490462/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Despite the importance of common bean as a cash crop, research and development have given limited attention to the indeterminate climbing bean. As a result, its productivity has remained low due to a lack of improved varieties. Hence this study was undertaken to estimate the extent of genetic variability, heritability, genetic advance, and associations of yield and yield contributing to 12 quantitative traits of indeterminate climbing common bean genotypes. The study used forty-nine genotypes of indeterminate- climbing type of common beans with a 7x7 triple lattice design. Variance analysis revealed a highly significant difference among 49 genotypes of beans for twelve morphological traits (< 0.01) under study. The coefficient of variation, both at the phenotypic and genotypic levels, indicated significant differences among the genotypes for measured traits. The result revealed that estimates of heritability (%) in a broad sense for 12 traits studied, ranged from (23.73%) to (85.85%) for seed per plant and hundred seed weight respectively. Genetic advance estimation in this study revealed that plant height, pod per plant, branches per plant, biological yield, harvest index, 100-seed weight, and grain yield had high heritability estimates coupled with high genetic advance as a percentage of the mean. The correlation analysis revealed that grain yield had a highly significant and positive phenotypic correlation with plant height, pod length, pod per plant, seed per pod, seed per plant, branch per plant, hundred seed weight, biological yield, and significant positive phenotypic correlation with harvest index. Path coefficient analysis demonstrated that higher positive direct effects were exerted by biological yield harvest index and hundred seed weight on grain yield at phenotypic and genotypic levels. The study reveals significant genetic variability in indeterminate climbing common bean genotypes, highlighting the potential for selective breeding to enhance yield-related traits. By identifying high-performing genotypes, the research supports targeted breeding programs aimed at increasing productivity and benefiting smallholder farmers in Ethiopia. Biological sciences/Genetics Biological sciences/Plant sciences correlation coefficient of variance genetic advance heritability indeterminate common bean path analysis variability Figures Figure 1 1. INTRODUCTION The common bean ( Phaseolus vulgaris L. ), also known as kidney bean, dry bean, or haricot bean, is a diploid and predominantly self-pollinated crop (Ibarra-Perez et al., 1997 ). It exhibits three growth habits: determinate bush, semi-indeterminate bush, and indeterminate climbing types. Globally, it is ranked as the second most important pulse after soybean (Gidago et al., 2011 ). The crop is cultivated across continents and serves as a direct food source for millions. It is estimated that about 300 million people in the tropics and roughly 100 million people in Africa depend on common beans as a staple (Sofi et al., 2011 ). Beyond being a dietary protein source, beans play an important role in household food security, livestock feed, and regional economies. While dry seeds are the major product, green pods are consumed as vegetables, leaves are eaten either fresh or cooked, and crop residues are used as animal fodder (Broughton et al., 2003 ). In Ethiopia, common bean is one of the most widely grown pulse crops and holds significant economic importance. It is cultivated by smallholder farmers as a sole crop or intercropped with cereals. National surveys have identified it as a leading pulse crop in terms of area coverage and production volume. According to the 2020/21 Agricultural Sample Survey by the Central Statistical Agency (CSA), the crop was planted on about 311,584 hectares, yielding 552,564 metric tons with an average productivity of 1.77 t/ha (CSA, 2020/21). Major producing regions include Oromia, Amhara, SNNPR, and Benishangul-Gumuz (Frehiwot Mulugeta, 2010). Indeterminate climbing beans are particularly valuable in Ethiopia, providing both food and income to smallholder farmers. Despite favorable agro-ecological conditions and farmers’ long-standing experience with these beans, their cultivation has largely remained confined to homesteads and is often intercropped with cereals. Research and breeding efforts on climbing types have been limited, leaving considerable room for productivity improvement. Genetic enhancement through structured breeding programs offers an important opportunity to increase yields and expand their production potential. The breeding process typically involves germplasm collection, hybridization, and subsequent selection to develop superior varieties. Among these steps, selection is especially critical, as it enables breeders to identify high-yielding genotypes based on variability in quantitative traits such as yield and its components. For selection to be effective, it is important to evaluate the magnitude of genetic variability and understand how different traits are interrelated. Genetic parameters such as heritability provide insights into the degree to which traits are transmitted from one generation to the next, while correlation analysis helps to reveal associations among traits that influence yield. Path coefficient analysis (Dewey and Lu, 1959 ) further refines this understanding by partitioning correlations into direct and indirect effects, thereby identifying traits that contribute most efficiently to yield improvement. Despite its significance, few studies have comprehensively addressed genetic variability, heritability, and trait associations in indeterminate climbing beans in Ethiopia. Such information is essential for designing effective breeding strategies and enhancing seed yield. Therefore, the present study was conducted to evaluate the extent of genetic variability, heritability, and genetic advance in indeterminate climbing common bean genotypes, and to examine the relationships among yield and yield-related traits using 49 genotypes. 2. MATERIALS AND METHODS 2.1. Description of the study area The experiment was conducted at Finoteselam Agricultural Research Subcenter during the 2019/2020 G.C main cropping seasons under rain feed conditions. Finoteselam is located 176 km from Bahir Dar, at 10.41’N Latitude, at 37.15’E Longitude, and an altitude of 1956 m.a.s.l. The average annual rainfall of the area is 884 mm and the maximum and minimum annual mean temperatures are 28.4°C and 12.6°C, respectively (Jabetena District Agricultural Office). The soil type of the site is Nitosoil. 2.2. Experimental materials and design Forty-eight genotypes of indeterminate climbing common bean ( Phaseolus vulgaris L.) obtained from Melkassa Agricultural Research Center which was introduced from CIAT ( International Center for Tropical Agriculture ) with one check, a total of forty-nine genotypes were used for this experiment. The experiment was laid out in a 7x7 triple lattice design and plot size of 4mx1.6m. Each plot consisted of four rows with 4m length and 40 cm inter-row and 10 cm intra-row spacing. The spacing between plots, blocks, and replications were 40cm, 1m, and 1.5m, respectively. The total experimental area was 34mx 43.8m. A seed rate of 90 to 100 kg ha-1 was used. The recommended rates of 100 kg ha-1 and 50 kg ha-1NPS and Urea respectively were applied. Table 1.1 Designation of indeterminate common bean ( Phaseolus vulgaris L.) genotypes Number Name of genotypes Source Number Name of genotypes Source 1 NUV187 CIAT 26 16371 CIAT 2 NUV100 CIAT 27 NUV140 CIAT 3 NUV1142 CIAT 28 NUV77 CIAT 4 NUV131 CIAT 29 NUV180 CIAT 5 16525 CIAT 30 NUV85b CIAT 6 Local seed Local market 31 NUV173 CIAT 7 228080 CIAT 32 16378 CIAT 8 16361 CIAT 33 211278 CIAT 9 NUV90 CIAT 34 211350 CIAT 10 NUV159 CIAT 35 NUV21 CIAT 11 NUV102 CIAT 36 NUV182 CIAT 12 214662 CIAT 37 NUV30 CIAT 13 208639 CIAT 38 NUV54 CIAT 14 16364 CIAT 39 215050 CIAT 15 21136 CIAT 40 211320 CIAT 16 NUV20 CIAT 41 16367 CIAT 17 NUV27 CIAT 42 NUV40 CIAT 18 NUV115 CIAT 43 207933 CIAT 19 9213 CIAT 44 212978 CIAT 20 213319 CIAT 45 NUV42 CIAT 21 211336 CIAT 46 NUV160 CIAT 22 NUV56 CIAT 47 NUV76 CIAT 23 NUV219-9 CIAT 48 15996 CIAT 24 215048 CIAT 49 201940 CIAT 25 211331 CIAT 2.3. Data Collected The following data were collected from the middle two rows of each plot on both a plot and plant basis. Data recorded on a plant basis Data were recorded following the standard procedures outlined by Debouck et al. ( 1986 ). Plant height (cm) Measured at the pod-filling stage from the ground level to the tip of the main stem on five randomly selected plants and the average were taken. Number of primary branches Counted as the number of branches arising from the main stem, recorded on randomly sampled plants, and expressed as the average per plant. Pod length (cm) Measured at harvest as the mean length of five pods sampled from each selected plant. Number of pods per plant Total pods per plant were counted on pre-marked, randomly chosen plants and expressed as the average value. Number of seeds per pod Determined as the mean number of seeds from five plants divided by the total number of pods harvested from the same plants. Number of seeds per plant Calculated as the average number of seeds per plant from randomly selected samples. Data recorded on a plot basis The following traits were measured using standard procedures described by Debouck et al. ( 1986 ): Days to 50% flowering The number of days from sowing to the point when approximately half of the plants in a plot had produced flowers. Days to maturity Recorded as the number of days from planting until about 90% of the plants in a plot reached physiological maturity, as indicated by yellowing of leaves and stems. Grain yield (GY) Measured after harvesting, threshing, and cleaning the seed. Grain weight per net plot was taken using an electronic balance and converted to kilograms per hectare after adjusting for seed moisture content. Hundred-seed weight (HSW) Determined by counting 100 seeds from each plot and weighing them using an electronic balance. Above-ground dry biomass (AGDB) Total dry matter (biological yield) per plot was measured and recorded after harvesting. Harvest index (HI) Calculated as the ratio of seed yield to biological yield for each plot, i.e., HI = seed yield (kg) / biological yield (kg). 2.4. Data analyses 2.4.1. Analysis of variance The collected data was subjected to statistical analysis according to the procedure of triple lattice design. SAS computer software version 9.2 (SAS, 2008) was used for data analysis. The relevant values generated on plot and plant basis were subjected to analysis of variance (ANOVA) using the linear model implemented in SAS software version 9.2. The Mathematical Model for Triple Lattice Design is: YIjk = µ + GI + Bk(j) + Rj + EIjk Where, YIjk = Phenotypic effect of ith genotype under jth replication and kth incomplete block within replication j, µ = grand mean, GIj = effects of ith genotype, Bk(j) = the effect of incomplete block within replication j Rj = the effect of replication j, EIjk, = the plot residual effect or effect of random error. 2.4.2 Estimation of phenotypic and genotypic variances The phenotypic and the genotypic variations were estimated according to the method suggested by Mirza et al. ( 2011 ) as follows. Environmental variance (δ 2 e) = MSE (square) Genotypic variance (δ 2 g) = Msg-Mse/r Phenotypic variance (δ 2 p) =δ 2 g + δ 2 e Where, Msg = mean square due to genotype; Mse = Environmental variance r = number of replications 2.4.2. Estimation of genotypic and phenotypic coefficient of variability Phenotypic and genotypic coefficients of variation are helpful to estimate the magnitude of variability present in a population. According to Singh ( 2001 ), the phenotypic and genotypic coefficients of variances are expressed by the following formula. Phenotypic coefficient of variation (PCV) = \(\:\sqrt{{{\sigma\:}}^{2}\text{P}}\) / X *100 Genotypic coefficient of variation (GCV) = \(\:\sqrt{{{\sigma\:}}^{2}\text{g}\:\:}\) / X *100 Where; X = Mean value of the trait. \(\:\sqrt{{{\sigma\:}}^{2}\text{g}\:\:}\) =Genotypic standard deviations \(\:\sqrt{{{\sigma\:}}^{2}\text{P}}\) =phenotypic standard deviations 2.4.3. Estimation of heritability and genetic advance Heritability provides the degree of transmissibility of a trait and indicates the effectiveness of selection. Further, estimates of heritability have to be considered in conjunction with genetic advances to find the expected genetic gain in the next generation (Shukla et al., 2006 ). Broad sense heritability (H 2 b) shall be expressed as the percentage of the ratio of the genotypic variance (δ 2 g) to the phenotypic variance (δ 2 p) and were estimated on genotype mean basis as described by Allard and Hansche ( 1964 ) as: H 2 b = δ 2 g /δ 2 p*100 Where; H 2 b = heritability in broad sense δ 2 p=phenotypic variance δ 2 g = genotypic variance The genetic advances for selection intensity (k) at 5% (2.06) were estimated by the formula of Johnson et al. ( 1955 ) Genetic advance (GA) = H 2 b *K*δp and Genetic advance as percent of the mean (GAM) = \(\:\:\frac{GA}{X}\) Where : δp = Phenotypic standard deviation; H 2 b = heritability in a broad sense and k = selection intensity X = Grand mean 2.4.4. Estimation of phenotypic and genotypic correlation coefficients Correlation studies help to find the degree of interrelationship among various traits and to evolve selection criteria for improvement. Phenotypic correlation is the relationship between two variables, which includes both genotypic and environmental effects while genotypic correlation is the inherited association between two variables. Phenotypic and genotypic correlation coefficients were estimated using the standard procedure suggested by Dewey and Lu ( 1959 ) from corresponding variance and co-variance components; $$\:phenotypic\:correlation=rp\left(xy\right)=\:\frac{{COV}_{p}\left(xy\right)}{\sqrt{{V}_{p}\left(x\right)\times\:\sqrt{{V}_{p}\left(y\right)}}}$$ $$\:Genotypic\:correlation=rg\left(xy\right)=\:\frac{{COV}_{g}\left(xy\right)}{\sqrt{{V}_{g}\left(x\right)\times\:\sqrt{{V}_{g}\left(y\right)}}}$$ Where ; \(\:{COV}_{p}\left(xy\right)\) and \(\:{COV}_{g}\left(xy\right)\) are phenotypic and genotypic covariance between x and y traits. Vp (x) and Vg(x) represent variances of x traits at phenotypic and genotypic levels Vp (y) and Vg(y) denote variance of y traits at phenotypic and genotypic levels, respectively. 2.4.5. Path coefficient analysis Path coefficient analysis was carried out to separate the observed correlation coefficients into direct and indirect effects of yield-related traits on grain yield. This approach helps to identify whether a trait contributes to yield primarily through its own direct influence or indirectly through its association with other traits. The analysis followed the method proposed by Dewey and Lu ( 1959 ), which applies standardized partial regression coefficients to estimate direct effects. Indirect effects were obtained by multiplying the correlation of an independent trait with an intermediate trait by the direct effect of that intermediate trait on yield. The residual effect was calculated to determine the proportion of variation in grain yield not explained by the traits included in the model. $$\:\varvec{r}\varvec{i}\varvec{j}=\varvec{P}\varvec{i}\varvec{j}+\sum\:\varvec{r}\varvec{i}\varvec{k}\varvec{P}\varvec{j}\varvec{k}$$ Where \(\:\:\varvec{r}\varvec{i}\varvec{j}\) = mutual association between independent variable (i) and dependent variable (j) as measured by phenotypic and genotypic correlation coefficient. \(\:\varvec{P}\varvec{i}\varvec{j}\) = component of a direct effect of independent variable (i) as measured by the phenotypic and genotypic path coefficient. \(\:\sum\:\varvec{r}\varvec{i}\varvec{k}\varvec{P}\varvec{j}\varvec{k}\) = summation of components of an indirect effect of a given independent variable (i) on a given dependent variable (j) via all other independent traits(K). 3. RESULTS AND DISCUSSION 3.1. Analysis of Variance (ANOVA) The analysis of variance revealed highly significant differences (p < 0.01) among the 49 bean genotypes for all twelve morphological traits studied (Table 3.1 ). This demonstrates the presence of substantial genetic variability, providing an opportunity to identify superior genotypes for future breeding and improvement programs. Similar results have been reported in previous studies. Ghimire and Mandal ( 2019 ) found significant variation among bean genotypes for traits such as flowering time, maturity, plant height, pod number, seed traits, seed weight, and grain yield. Bulyaba et al. ( 2020 ) likewise observed strong genotypic effects on flowering and maturity duration, plant height, pods per plant, seeds per pod, biomass, harvest index, branch number, and yield. In addition, Msolla and Mduruma ( 2007 ) reported marked differences in flowering time, maturity, and yield-related traits in common bean lines, further supporting the present findings. Table 3.1 Mean square for quantitative traits of bean genotypes Trait Treatment (Df = 48) Rep (Df = 2) Block (Df = 18) CV (%) R² Error Mean DF 68.94** 2.71 25.84 9.58 0.58 26.80 54.71 DM 162.00** 61.76 10.81 3.79 0.85 18.58 111.15 PH 1681.46** 366.24 148.18 10.69 0.87 144.85 111.99 PL 3.39** 0.36 0.22 8.88 0.75 0.72 9.09 PPP 62.47** 12.75 7.12 15.68 0.86 5.52 15.44 SPP 1.16** 0.06 0.22 10.17 0.65 0.36 5.72 SPPL 319.05** 309.14 105.55 27.24 0.53 165.03 46.83 BPP 0.87** 0.11 0.07 13.96 0.79 0.14 2.57 HSW 158.14** 12.72 7.79 12.34 0.92 8.24 22.75 BY 2933236.00** 96403.00 264222.00 10.46 0.89 192636.00 4181.38 GY 1182013.00** 44171.00 69038.00 15.07 0.90 75325.00 1821.81 HI 260.54** 23.72 26.66 13.84 0.80 36.41 42.91 Note: DF = days to flowering; DM = days to maturity; PH = plant height (cm); PL = pod length (cm); PPP = pod per plant; SPP = seed per pod; SPPL = seed per plant; BPP = branch per plant; HSW = hundred seed weight (g); BY = biological yield (kg ha-1); GY = grain yield (kg ha-1); HI = harvest index (%), CV% = coefficient of variation; and R2 = R-square. 3.2. Mean and range of measured traits The mean, minimum, and maximum values of the 12 traits are presented in Table 3.2 , showing wide variation among the 49 bean genotypes. This variability reflects the presence of substantial genetic differences across the genotypes studied. Yohannes and Berecha ( 2015 ) also reported considerable ranges in traits such as seed yield per plant, hundred-seed weight, seeds per pod, and flowering time, supporting the present findings. Days to 50% flowering ranged from 44 to 73.66 days, while days to maturity ranged from 98.66 to 125.33 days. The latest maturing genotype was 16525 (125.33 days), and the earliest was 211320 (98.66 days). Such variation provides opportunities to classify genotypes into early- and late-maturing groups, which is valuable for breeding programs targeting contrasting environments such as moisture-deficit areas and high-rainfall regions. Plant height ranged from 63.93 cm (NUV160) to 158.2 cm (208639), with a mean of 111.99 cm. Pod length varied between 5.8 and 11.07 cm, while pods per plant ranged from 5.13 to 25.66, with a mean of 15.43. Hundred-seed weight was highest in genotype 208639 (49.19 g) and lowest in 212978 (13.22 g), consistent with the variation reported by Yohannes and Berecha ( 2015 ). Grain yield ranged from 387.7 kg ha⁻¹ (NUV173) to 3246.9 kg ha⁻¹ (16525), which aligns with the results of Raffi and Nath ( 2004 ). The highest harvest index (60.86) was recorded in NUV140, while the lowest (13.24) was found in NUV173. Biological yield was also highest in genotype 16525 and lowest in 15996. Overall, the wide range of variation observed across genotypes provides a strong basis for selection. The presence of such diversity indicates good prospects for identifying and improving superior genotypes through breeding. Table 3.2 Range and mean values for 12 traits of 49 bean genotypes Trait Min Genotype Max Genotype Mean Range DF 44 214662 73.667 NUV30 54.71429 29.67 DM 98.667 211320 125.333 16525 111.1497 26.66 PH 63.933 NUV160 158.2 208639 111.9852 94.27 PL 5.8 NUV173 11.0667 208639 9.085714 5.27 PPP 5.133 201940 25.667 211350 15.43673 20.54 SPP 3.8 201940 7.1333 NUV219-9 5.722449 3.33 SPPL 17.27 201940 64.47 211350 46.82993 47.2 BPP 1.4667 15996 3.4667 NUV30 2.57415 2 HSW 13.22 212978 49.187 208639 22.74639 35.97 BY 2234 15996 7733.5 16525 4181.376 5499.5 GY 387.7 NUV173 3246.9 16525 1821.805 2859.2 HI 13.235 NUV173 60.862 NUV140 42.91218 47.62 Note: DF = days to flowering; DM = days to maturity; PH = plant height (cm); PL = pod length (cm); PPP = pod per plant; SPP = seed per pod; SPPL = seed per plant; BPP = branch per plant; HSW = hundred seed weight (g); BY = biological yield (kg ha-1); GY = grain yield (kg ha-1) and HI = harvest index (%). 3.3. Phenotypic and Genotypic Variations The phenotypic and genotypic variances, along with the coefficients of variation (PCV and GCV), for yield and related traits are presented in Table 3.3 . Following the classifications of Sivasubramaniam and Madhava Menon (1973) and Deshmukh et al. ( 1986 ), coefficients of variation were categorized as low ( 20%). Both PCV and GCV values revealed substantial variability among the genotypes. In line with the findings of Bashir et al. (2014), PCV values were generally higher than GCV values, indicating that environmental factors contributed to the observed variation. High GCV estimates were recorded for plant height, pods per plant, hundred-seed weight, biological yield, grain yield, and harvest index. These results are consistent with Singh ( 2001 ), who reported high genetic variance for seed yield and related traits in common bean. Traits such as pod length, seeds per plant, and branches per plant showed moderate GCV, suggesting that improvement through selection is feasible. In contrast, days to flowering and days to maturity exhibited low GCV values, implying that these traits are strongly influenced by the environment and thus less responsive to selection. High PCV values were observed for plant height, pods per plant, seeds per plant, branches per plant, hundred-seed weight, biological yield, grain yield, and harvest index. Moderate PCV values were recorded for days to flowering and pod length, while days to maturity exhibited the lowest PCV (< 10%). Similar trends were reported by Yohannes and Berecha ( 2015 ), who found higher PCV values for plant height, biological yield, pods per plant, harvest index, and seed weight, while traits like flowering time and seeds per pod showed moderate variation. The relatively large differences between PCV and GCV for traits such as seeds per plant, flowering time, and branch number suggest strong environmental influence, making direct selection less reliable for these traits. However, the smaller differences observed for most other traits indicate that genetic factors were the main source of variation, making them dependable targets for selection in breeding programs. 3.4. Heritability Broad-sense heritability estimates for the 12 traits are summarized in Table 3.3 , ranging from 23.73% for seeds per plant to 85.85% for hundred-seed weight. High heritability was recorded for plant height, hundred-seed weight, biological yield, grain yield, harvest index, branches per plant, days to maturity, and pods per plant. These results suggest that the expression of these traits is largely governed by genetic factors with minimal environmental influence, making phenotypic selection effective. Moderate heritability estimates were observed for seeds per pod and days to flowering, while seeds per plant exhibited low heritability, indicating greater environmental influence and reduced effectiveness of direct selection. Comparable findings have been reported in previous studies. Mallu et al. ( 2014 ) found high heritability for grain yield and hundred-seed weight, and Ghimire and Mandal ( 2019 ) reported heritability above 60% for seed weight, pod length, pod number, maturity period, yield, and plant height. Similarly, Wondwosen and Abebe ( 2017 ) observed strong heritability for yield, seed weight, and maturity-related traits, while Raffi and Nath ( 2004 ) documented values exceeding 60% for maturity, yield, seed weight, pod length, and plant height. More recently, Langat et al. ( 2019 ) reported high heritability (> 60%) for flowering, maturity, seeds per pod, hundred-seed weight, and grain yield under stress conditions. In French bean, Aklade et al. ( 2018 ) also reported high heritability for flowering, maturity, pod length, hundred-seed weight, and seeds per pod. Similarly, Ali et al. ( 2010 ) identified high heritability for biomass, grain yield, seed weight, and pod number, noting that these traits are reliable for selecting high-yielding genotypes. 3.5. Genetic Advance Genetic advance estimates are presented in Table 3.3 . Traits including plant height, pods per plant, branches per plant, biological yield, harvest index, hundred-seed weight, and grain yield showed high heritability combined with high genetic advance as a percentage of the mean. This indicates strong additive gene action, suggesting that these traits can be effectively improved through direct selection. In contrast, days to maturity, pod length, seeds per pod, and seeds per plant exhibited moderate genetic advance, while days to flowering showed low values, reflecting limited genetic variability and reduced potential for selection. These findings are consistent with Mohammed et al. ( 2019 ), who reported high genetic advance as a percentage of the mean (GAM) for grain yield, biomass yield, pods per plant, and hundred-seed weight, but lower GAM values for maturity, flowering, and seeds per pod. Table 3.3 Estimates of variances and coefficients of variability, heritability, and genetic advance of the 12 traits of 49 bean genotypes Traits σ 2 e σ 2 g σ 2 p PCV% GCV% H 2 GA GAM DF 26.80 14.05 40.84 11.68 6.85 34.39 4.53 8.28 DM 18.58 47.81 66.39 7.33 6.22 72.01 12.09 10.87 PH 144.85 512.20 657.05 22.89 20.21 77.95 41.16 36.76 PL 0.72 0.89 1.61 13.95 10.39 55.50 1.45 15.95 PPP 5.52 18.99 24.50 32.07 28.23 77.49 7.90 51.18 SPP 0.36 0.27 0.63 13.84 9.03 42.62 0.70 12.15 SPPL 165.03 51.34 216.37 31.41 15.30 23.73 7.19 15.35 BPP 0.14 0.24 0.38 23.97 19.13 63.68 0.81 31.44 HSW 8.24 49.97 58.21 33.54 31.08 85.85 13.49 59.32 BY 192636 913533.4 1106169 25.15 22.86 82.59 1789.29 42.79 GY 75325 368896 444222 36.58 33.34 83.04 1140.18 62.58 HI 36.41 74.71 111.12 24.56 20.14 67.24 14.60 34.02 Note: DF = days to flowering; DM = days to maturity; PH = plant height (cm); PL = pod length (cm); PPP = pod per plant; SPP = seed per pod; SPPL = seed per plant; BPP = branch per plant; HSW = hundred seed weight (g); BY = biological yield (kg ha-1); GY = grain yield (kg ha-1) and HI = harvest index (%); σ2e = environmental variance; σ2g = genotypic variance; σ2p = phenotypic variance; H2 = broad sense heritability; GA = genetic advance and GAM%= Genetic advances as percent of the mean. 3.6. Correlation of grain yield with other traits and correlation among traits 3.6.1. Phenotypic correlation Phenotypic correlation coefficients among the studied traits are shown in Table 3.4 . Grain yield displayed strong and positive associations with plant height, pod length, pods per plant, seeds per pod, seeds per plant, branch number, hundred-seed weight, and biological yield. A significant positive relationship was also observed with harvest index. These results suggest that selection based on these traits could indirectly enhance grain yield. Similar positive associations between yield and traits such as seeds per plant (Cokkizgin et al., 2013 ), seeds per pod (Roy et al., 2006 ), hundred-seed weight (Karasu and Oz, 2010 ), and branch number (Kulaz and Ciftci, 2012 ) have been reported in previous studies. Positive correlations of grain yield with days to flowering were also noted by González et al. (2016) and Bagheri et al. ( 2017 ). Conversely, yield exhibited a non-significant but positive correlation with days to maturity, indicating limited potential for indirect improvement through this trait. Pod length showed highly significant and positive correlations with seeds per pod, seeds per plant, and branches per plant, hundred-seed weight, biological yield, grain yield, and harvest index. This implies that improvement in pod length could lead to simultaneous gains in these traits, making it a useful selection criterion. However, pod length had a significant negative association with days to maturity, consistent with findings by Roy et al. ( 2006 ). Harvest index exhibited strong positive correlations with pod length, pods per plant, seeds per pod, seeds per plant, branch number, hundred-seed weight, biological yield, and grain yield, as well as a positive association with plant height. In contrast, it showed a significant negative correlation with days to maturity and a non-significant negative correlation with days to flowering. These patterns suggest that genotypes with higher values for positively correlated traits are likely to achieve greater harvest index. In general, the predominance of positive and significant phenotypic correlations indicates that many traits can be improved simultaneously through selection. Negative correlations, however, may constrain the possibility of achieving simultaneous improvement for certain trait combinations. 3.6.2. Genotypic correlations Genotypic correlation coefficients among yield and yield-related traits are presented in Table 3.4 . Grain yield showed highly significant and positive correlations with plant height, pod length, pods per plant, seeds per pod, seeds per plant, branches per plant, hundred-seed weight, biological yield, and harvest index. Seeds per plant were positively associated with several traits including pod length, branches per plant, biological yield, seeds per pod, harvest index, and pods per plant, indicating that these traits tend to improve together. Pod length also exhibited strong positive correlations with seeds per pod, seeds per plant, branches per plant, hundred-seed weight, biological yield, grain yield, and harvest index. Likewise, biological yield was strongly correlated with nearly all yield-related traits, underscoring its central role in influencing productivity. These findings are consistent with earlier reports. Agrawal et al. ( 2000 ) and Yohannes et al. ( 2020 ) documented strong genotypic correlations of grain yield with traits such as biological yield, pods per plant, seeds per pod, stem diameter, and leaf area index. In the current study, biological yield recorded the highest genotypic correlation with grain yield, which aligns with their findings. By contrast, grain yield showed non-significant but positive correlations with days to flowering (rg = 0.15) and days to maturity (rg = 0.05), suggesting limited potential for improving yield through these traits. Correlation analysis also revealed that days to flowering had significant positive associations with days to maturity and biological yield, while its relationships with pod length, pods per plant, seeds per pod, seeds per plant, branches per plant, and grain yield were positive but non-significant. Negative correlations were detected with plant height and harvest index. Similarly, days to maturity was significantly and positively correlated with days to flowering, biological yield, and pods per plant, while its associations with plant height, branches per plant, grain yield, and hundred-seed weight were positive but non-significant. Negative but non-significant relationships were observed with seeds per plant and pod length. In general, the predominance of positive and significant genotypic correlations suggests that many traits can be improved simultaneously through selection. However, negative correlations highlight potential trade-offs, which may limit the scope of simultaneous trait improvement. Table 3.4 Estimates of genotypic (below diagonal) and phenotypic (above diagonal) correlation coefficients among 12 traits in 49 bean genotypes Variable DF DM PH PL PPP SPP SPPl BPP HSW BY GY HI DF 1 0.25** -0.01 0.05 0.11 0.06 0.01 0.09 -0.06 0.24** 0.09 -0.11 DM 0.36* 1 0.19* -0.16* 0.26** -0.31** -0.01 0.06 0.12 0.32** 0.05 -0.32** PH -0.01 0.21 1 0.20* 0.06 0.1 0.16 0 0.50** 0.32** 0.34** 0.21* PL 0.13 -0.23 0.26 1 0.04 0.62** 0.35** 0.44** 0.53** 0.37** 0.53** 0.52** PPP 0.18 0.30* 0.04 0.06 1 0.18* 0.54** 0.23** -0.01 0.34** 0.37** 0.27** SPP 0.09 -0.34* 0.15 0.80** 0.23 1 0.45** 0.18* 0.23** 0.15 0.35** 0.49** SPPl 0.13 -0.07 0.19 0.48** 0.65** 0.59** 1 0.28** 0.12 0.35** 0.45** 0.39** BPP 0.18 0.07 -0.03 0.49** 0.30* 0.32* 0.44** 1 0.21* 0.54** 0.53** 0.31** HSW -0.07 0.1 0.55** 0.61** -0.02 0.30* 0.14 0.22 1 0.45** 0.57** 0.43** BY 0.34* 0.36* 0.35* 0.42** 0.36* 0.18 0.43** 0.62** 0.48** 1 0.82** 0.25** GY 0.15 0.05 0.39** 0.60** 0.40** 0.43** 0.59** 0.58** 0.61** 0.87** 1 0.74* HI -0.1 -0.37** 0.28 0.63** 0.33* 0.63** 0.59** 0.34* 0.48** 0.35* 0.76** 1 Note: DF = days to flowering; DM = days to maturity; PH = plant height (cm); PL = pod length (cm); PPP = pod per plant; SPP = seed per pod; SPPL = seed per plant; BPP = branch per plant; HSW = hundred seed weight (g); BY = biological yield (kg ha-1); GY = grain yield (kg ha-1) and HI = harvest index (%). 3.7. Path coefficient analysis Path coefficient analysis partitioned the correlations of different traits with grain yield into their direct and indirect effects. At the phenotypic level, biological yield, harvest index, and 100-seed weight exerted the strongest positive direct effects on yield, suggesting that direct selection for these traits would be effective. Conversely, traits such as plant height and pod number displayed negative direct effects but contributed indirectly through positive associations with yield components like biological yield and seed weight. The residual effect of 0.14 indicated that the variables included explained about 86% of the yield variation. At the genotypic level, biological yield and harvest index again showed the largest positive direct effects, followed by seed weight and seed number per plant. Negative direct effects were observed for plant height, pod length, and pods per plant, though these were compensated by positive indirect contributions. The residual value of 0.045 suggested that the considered traits explained more than 95% of yield variation at the genotypic level. 3.7.1. Phenotypic direct and indirect effects of various traits on grain yield At the phenotypic level, biological yield exerted the strongest direct effect on grain yield, followed by harvest index and hundred-seed weight. Traits showing strong direct positive effects can be used as effective selection criteria in breeding programs. Although pod number and plant height showed negative direct effects, they contributed positively to yield through strong indirect effects mediated by seed number, biological yield, and harvest index. Plant height and seeds per pod both had negative direct effects on yield but showed positive indirect effects through hundred-seed weight, biological yield, and harvest index. Similar results were reported by Espósito et al. ( 2009 ). Pods per plant also exhibited a negative direct effect on grain yield but remained positively correlated with it overall, as the negative effect was offset by strong positive indirect effects via seeds per plant, biological yield, and harvest index. This agrees with the findings of Huque et al., ( 2012 ). Other traits, such as pod length and branches per plant, also recorded negative direct effects on yield. In such cases, direct selection is unlikely to be effective; instead, improvement may be achieved indirectly through traits that compensate for their effects, such as biological yield and harvest index. Overall, the phenotypic path analysis highlighted biological yield, hundred-seed weight, and harvest index as the most influential contributors to grain yield. Indirect contributions from traits like pod number and plant height further emphasize the importance of considering both direct and indirect effects when designing selection strategies. Table 3.5 Estimation of direct (bold diagonal) and indirect effects (off-diagonal) of 9 traits on grain yield at phenotypic level in common bean genotypes Traits PH PL PPP SPP SPPl BPP HSW BY HI PH -0.01 0.00 0.00 0.00 0.00 0.00 0.02 0.22 0.12 PL 0.00 -0.02 0.00 -0.02 0.01 -0.01 0.02 0.25 0.31 PPP 0.00 0.00 -0.02 -0.01 0.01 0.00 0.00 0.23 0.16 SPP 0.00 -0.01 0.00 -0.04 0.01 0.00 0.01 0.10 0.29 SPPl 0.00 -0.01 -0.01 -0.02 0.02 0.00 0.00 0.24 0.23 BPP 0.00 -0.01 0.00 -0.01 0.00 -0.01 0.01 0.37 0.18 HSW -0.01 -0.01 0.00 -0.01 0.00 0.00 0.04 0.31 0.25 BY 0.00 -0.01 -0.01 -0.01 0.01 -0.01 0.02 0.68 0.15 HI 0.00 -0.01 0.00 -0.02 0.01 0.00 0.02 0.17 0.59 Residual effect = 0.14; Note: PH = plant height (cm); PL = pod length (cm); PPP = pod per plant; SPP = seed per pod; SPPL = seed per plant; BPP = branch per plant; HSW = hundred seed weight (g); BY = biological yield (kg ha-1) and HI = harvest index (%). 3.7.2. Genotypic direct and indirect effects of various traits on grain yield Genotypic path analysis (Table 3.6 ) showed that biological yield had the strongest direct positive effect on grain yield, followed by harvest index, hundred-seed weight, and number of seeds per plant. The high correlations of biological yield (r = 0.87) and harvest index (r = 0.76) with yield were largely explained by these direct contributions, underscoring their importance as selection criteria. These findings agree with Korat et al. ( 2010 ), who reported similar positive direct effects of harvest index, seed weight, and biological yield on seed yield. Seeds per pod had a small positive direct effect but contributed more strongly through indirect effects via seed number, hundred-seed weight, biological yield, and harvest index. Hundred-seed weight also exhibited modest direct influence but reinforced yield through its indirect associations with the same traits. In contrast, plant height and pod number per plant exerted negative direct effects on grain yield. However, their overall influence remained positive due to strong indirect pathways, especially through hundred-seed weight, biological yield, and harvest index. Comparable results were reported by Mammo et al. ( 2019 ) and Ambachew et al. ( 2015 ), who noted negative direct effects of pod number and seed weight on yield that were offset by indirect contributions. Conversely, Mesele ( 1997 ) found that seed number per plant was the most favorable contributor, while maturity and flowering time had negative direct effects. In the present study, pod length also showed a negative direct effect but was compensated by positive indirect influences through seed number, seed weight, biological yield, and harvest index. In general, the direct effects of traits did not always correspond with their correlation coefficients, highlighting the complexity of inter-trait relationships. The low residual effect (0.045) indicated that the traits included in the model accounted for about 95.5% of the variation in grain yield, leaving only 4.5% unexplained. Table 3.6 Estimation of direct (bold diagonal) and indirect effects (off-diagonal) of 9 traits on grain yield at genotypic level in common bean genotypes Traits PH PL PPP SPP SPPl BPP HSW BY HI PH -0.04 -0.02 0 0 0.01 0 0.05 0.25 0.15 PL -0.01 -0.09 0 0.01 0.02 -0.01 0.06 0.29 0.33 PPP 0 -0.01 -0.04 0 0.03 -0.01 0 0.25 0.17 SPP -0.01 -0.07 -0.01 0.01 0.03 -0.01 0.03 0.13 0.33 SPPl -0.01 -0.04 -0.03 0.01 0.05 -0.01 0.01 0.3 0.31 BPP 0 -0.04 -0.01 0 0.02 -0.02 0.02 0.43 0.18 HSW -0.02 -0.06 0 0 0.01 0 0.09 0.34 0.25 BY -0.01 -0.04 -0.02 0 0.02 -0.01 0.04 0.7 0.18 HI -0.01 -0.06 -0.01 0.01 0.03 -0.01 0.04 0.25 0.52 Residual effect = 0.045; Note: PH = plant height (cm); PL = pod length (cm); PPP = pod per plant; SPP = seed per pod; SPPL = seed per plant; BPP = branch per plant; HSW = hundred seed weight (g); BY = biological yield (kg ha-1) and HI = harvest index (%). 4. CONCLUSIONS This study demonstrated substantial genetic variation among the 49 climbing common bean genotypes across 12 quantitative traits, confirming the availability of a broad genetic base for improvement. Yield-related traits such as grain yield, pods per plant, and hundred-seed weight exhibited particularly wide variability, highlighting their potential as priority targets for breeding. High heritability coupled with notable genetic advance was observed for several traits, including plant height, pods per plant, branches per plant, biological yield, harvest index, hundred-seed weight, and grain yield. These results suggest that additive genetic effects play a major role in the expression of these traits, making direct selection an effective strategy. Grain yield, pod number, biomass, and seed weight were especially important because they combined strong heritability with high genetic advance values. Genotypic correlations were generally stronger than phenotypic ones, indicating that genetic factors were more influential than environmental effects in shaping trait associations. Grain yield showed significant positive correlations with multiple traits, including pod length, plant height, branch number, seeds per pod, seeds per plant, pods per plant, hundred-seed weight, biological yield, and harvest index. This implies that simultaneous improvement of yield and related traits is feasible through selection. Path coefficient analysis further identified biological yield and hundred-seed weight as the most important direct contributors to grain yield, reinforcing their relevance as key selection criteria for developing high-yielding indeterminate climbing bean genotypes. In general, the results highlight the presence of valuable genetic diversity in Ethiopian climbing common beans and demonstrate the potential for achieving substantial genetic gains through targeted selection. The identification of promising genotypes provides opportunities for their advancement and eventual release, thereby contributing to improved productivity and supporting smallholder farmers. However, the study was conducted at a single site and within one growing season, which may limit the broader applicability of the results. Multi-location and multi-season trials are needed to validate trait stability under diverse environments. Furthermore, molecular approaches such as marker-assisted selection were not applied but could provide additional insights into the genetic basis of yield and related traits, supporting more efficient breeding strategies. Declarations Data availability statement The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Funding statement This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. 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1","display":"","copyAsset":false,"role":"figure","size":12147,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 3.1 plots of the figure show genotypic and phenotypic variances, heritability, and genetic advance as the percent of the mean and genetic parameters discussed in common bean genotypes studied with 12 traits.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7490462/v1/c74140afc44e2d62ac6f20d9.png"},{"id":98444403,"identity":"72c3bccc-b833-4542-952c-6c082526b010","added_by":"auto","created_at":"2025-12-17 17:15:48","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1792907,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7490462/v1/13262c5f-d97c-41f7-81c9-a239f3b884c5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation of genetic variability, heritability, genetic advance, and association of yield and yield contributing traits of indeterminate climbing common bean (Phaseolus vulgaris L.) genotypes","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe common bean (\u003cem\u003ePhaseolus vulgaris L.\u003c/em\u003e), also known as kidney bean, dry bean, or haricot bean, is a diploid and predominantly self-pollinated crop (Ibarra-Perez et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). It exhibits three growth habits: determinate bush, semi-indeterminate bush, and indeterminate climbing types. Globally, it is ranked as the second most important pulse after soybean (Gidago et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The crop is cultivated across continents and serves as a direct food source for millions. It is estimated that about 300\u0026nbsp;million people in the tropics and roughly 100\u0026nbsp;million people in Africa depend on common beans as a staple (Sofi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Beyond being a dietary protein source, beans play an important role in household food security, livestock feed, and regional economies. While dry seeds are the major product, green pods are consumed as vegetables, leaves are eaten either fresh or cooked, and crop residues are used as animal fodder (Broughton et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn Ethiopia, common bean is one of the most widely grown pulse crops and holds significant economic importance. It is cultivated by smallholder farmers as a sole crop or intercropped with cereals. National surveys have identified it as a leading pulse crop in terms of area coverage and production volume. According to the 2020/21 Agricultural Sample Survey by the Central Statistical Agency (CSA), the crop was planted on about 311,584 hectares, yielding 552,564 metric tons with an average productivity of 1.77 t/ha (CSA, 2020/21). Major producing regions include Oromia, Amhara, SNNPR, and Benishangul-Gumuz (Frehiwot Mulugeta, 2010).\u003c/p\u003e\u003cp\u003eIndeterminate climbing beans are particularly valuable in Ethiopia, providing both food and income to smallholder farmers. Despite favorable agro-ecological conditions and farmers\u0026rsquo; long-standing experience with these beans, their cultivation has largely remained confined to homesteads and is often intercropped with cereals. Research and breeding efforts on climbing types have been limited, leaving considerable room for productivity improvement. Genetic enhancement through structured breeding programs offers an important opportunity to increase yields and expand their production potential.\u003c/p\u003e\u003cp\u003eThe breeding process typically involves germplasm collection, hybridization, and subsequent selection to develop superior varieties. Among these steps, selection is especially critical, as it enables breeders to identify high-yielding genotypes based on variability in quantitative traits such as yield and its components. For selection to be effective, it is important to evaluate the magnitude of genetic variability and understand how different traits are interrelated. Genetic parameters such as heritability provide insights into the degree to which traits are transmitted from one generation to the next, while correlation analysis helps to reveal associations among traits that influence yield. Path coefficient analysis (Dewey and Lu, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1959\u003c/span\u003e) further refines this understanding by partitioning correlations into direct and indirect effects, thereby identifying traits that contribute most efficiently to yield improvement.\u003c/p\u003e\u003cp\u003eDespite its significance, few studies have comprehensively addressed genetic variability, heritability, and trait associations in indeterminate climbing beans in Ethiopia. Such information is essential for designing effective breeding strategies and enhancing seed yield. Therefore, the present study was conducted to evaluate the extent of genetic variability, heritability, and genetic advance in indeterminate climbing common bean genotypes, and to examine the relationships among yield and yield-related traits using 49 genotypes.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Description of the study area\u003c/h2\u003e\u003cp\u003eThe experiment was conducted at Finoteselam Agricultural Research Subcenter during the 2019/2020 G.C main cropping seasons under rain feed conditions. Finoteselam is located 176 km from Bahir Dar, at 10.41\u0026rsquo;N Latitude, at 37.15\u0026rsquo;E Longitude, and an altitude of 1956 m.a.s.l. The average annual rainfall of the area is 884 mm and the maximum and minimum annual mean temperatures are 28.4\u0026deg;C and 12.6\u0026deg;C, respectively (Jabetena District Agricultural Office). The soil type of the site is Nitosoil.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Experimental materials and design\u003c/h2\u003e\u003cp\u003eForty-eight genotypes of indeterminate climbing common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.) obtained from Melkassa Agricultural Research Center which was introduced from \u003cem\u003eCIAT\u003c/em\u003e (\u003cem\u003eInternational Center for Tropical Agriculture\u003c/em\u003e) with one check, a total of forty-nine genotypes were used for this experiment. The experiment was laid out in a 7x7 triple lattice design and plot size of 4mx1.6m. Each plot consisted of four rows with 4m length and 40 cm inter-row and 10 cm intra-row spacing. The spacing between plots, blocks, and replications were 40cm, 1m, and 1.5m, respectively. The total experimental area was 34mx 43.8m. A seed rate of 90 to 100 kg ha-1 was used. The recommended rates of 100 kg ha-1 and 50 kg ha-1NPS and Urea respectively were applied.\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 1.1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDesignation of indeterminate common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.) 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=\"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\" 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colname=\"c2\"\u003e\u003cp\u003eNUV159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e35\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNUV21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNUV102\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e36\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c2\"\u003e\u003cp\u003e208639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e38\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNUV54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e39\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e215050\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e21136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e40\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e211320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNUV20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e41\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNUV27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e42\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNUV40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNUV115\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e43\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e207933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e19\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9213\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e44\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e212978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e213319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e45\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNUV42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e21\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e46\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNUV160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNUV56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e47\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNUV76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e23\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNUV219-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e48\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e24\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e215048\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e49\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e201940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003e25\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e211331\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCIAT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\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. Data Collected\u003c/h2\u003e\u003cp\u003eThe following data were collected from the middle two rows of each plot on both a plot and plant basis.\u003c/p\u003e\u003cp\u003e\u003cb\u003eData recorded on a plant basis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eData were recorded following the standard procedures outlined by Debouck et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1986\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePlant height (cm)\u003c/strong\u003e\u003cp\u003eMeasured at the pod-filling stage from the ground level to the tip of the main stem on five randomly selected plants and the average were taken.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNumber of primary branches\u003c/strong\u003e\u003cp\u003eCounted as the number of branches arising from the main stem, recorded on randomly sampled plants, and expressed as the average per plant.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003ePod length (cm)\u003c/strong\u003e\u003cp\u003eMeasured at harvest as the mean length of five pods sampled from each selected plant.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNumber of pods per plant\u003c/strong\u003e\u003cp\u003eTotal pods per plant were counted on pre-marked, randomly chosen plants and expressed as the average value.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNumber of seeds per pod\u003c/strong\u003e\u003cp\u003eDetermined as the mean number of seeds from five plants divided by the total number of pods harvested from the same plants.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eNumber of seeds per plant\u003c/strong\u003e\u003cp\u003eCalculated as the average number of seeds per plant from randomly selected samples.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eData recorded on a plot basis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe following traits were measured using standard procedures described by Debouck et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1986\u003c/span\u003e):\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDays to 50% flowering\u003c/strong\u003e\u003cp\u003eThe number of days from sowing to the point when approximately half of the plants in a plot had produced flowers.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eDays to maturity\u003c/strong\u003e\u003cp\u003eRecorded as the number of days from planting until about 90% of the plants in a plot reached physiological maturity, as indicated by yellowing of leaves and stems.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eGrain yield (GY)\u003c/strong\u003e\u003cp\u003eMeasured after harvesting, threshing, and cleaning the seed. Grain weight per net plot was taken using an electronic balance and converted to kilograms per hectare after adjusting for seed moisture content.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHundred-seed weight (HSW)\u003c/strong\u003e\u003cp\u003eDetermined by counting 100 seeds from each plot and weighing them using an electronic balance.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAbove-ground dry biomass (AGDB)\u003c/strong\u003e\u003cp\u003eTotal dry matter (biological yield) per plot was measured and recorded after harvesting.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eHarvest index (HI)\u003c/strong\u003e\u003cp\u003eCalculated as the ratio of seed yield to biological yield for each plot, i.e., HI\u0026thinsp;=\u0026thinsp;seed yield (kg) / biological yield (kg).\u003c/p\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Data analyses\u003c/h2\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.4.1. Analysis of variance\u003c/h2\u003e\u003cp\u003eThe collected data was subjected to statistical analysis according to the procedure of triple lattice design. SAS computer software version 9.2 (SAS, 2008) was used for data analysis. The relevant values generated on plot and plant basis were subjected to analysis of variance (ANOVA) using the linear model implemented in SAS software version 9.2.\u003c/p\u003e\u003cp\u003eThe Mathematical Model for Triple Lattice Design is: YIjk\u0026thinsp;=\u0026thinsp;\u0026micro;\u0026thinsp;+\u0026thinsp;GI\u0026thinsp;+\u0026thinsp;Bk(j)\u0026thinsp;+\u0026thinsp;Rj\u0026thinsp;+\u0026thinsp;EIjk\u003c/p\u003e\u003cp\u003eWhere,\u003c/p\u003e\u003cp\u003eYIjk\u0026thinsp;=\u0026thinsp;Phenotypic effect of ith genotype under jth replication and kth incomplete block within replication j,\u003c/p\u003e\u003cp\u003e\u0026micro;\u0026thinsp;=\u0026thinsp;grand mean,\u003c/p\u003e\u003cp\u003eGIj\u0026thinsp;=\u0026thinsp;effects of ith genotype,\u003c/p\u003e\u003cp\u003eBk(j)\u0026thinsp;=\u0026thinsp;the effect of incomplete block within replication j\u003c/p\u003e\u003cp\u003eRj\u0026thinsp;=\u0026thinsp;the effect of replication j,\u003c/p\u003e\u003cp\u003eEIjk, = the plot residual effect or effect of random error.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2 Estimation of phenotypic and genotypic variances\u003c/h2\u003e\u003cp\u003eThe phenotypic and the genotypic variations were estimated according to the method suggested by Mirza et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) as follows.\u003c/p\u003e\u003cp\u003eEnvironmental variance (δ\u003csup\u003e2\u003c/sup\u003ee)\u0026thinsp;=\u0026thinsp;MSE (square)\u003c/p\u003e\u003cp\u003eGenotypic variance (δ\u003csup\u003e2\u003c/sup\u003eg)\u0026thinsp;=\u0026thinsp;Msg-Mse/r\u003c/p\u003e\u003cp\u003ePhenotypic variance (δ\u003csup\u003e2\u003c/sup\u003ep) =δ\u003csup\u003e2\u003c/sup\u003eg\u0026thinsp;+\u0026thinsp;δ\u003csup\u003e2\u003c/sup\u003ee\u003c/p\u003e\u003cp\u003eWhere,\u003c/p\u003e\u003cp\u003eMsg\u0026thinsp;=\u0026thinsp;mean square due to genotype;\u003c/p\u003e\u003cp\u003eMse\u0026thinsp;=\u0026thinsp;Environmental variance\u003c/p\u003e\u003cp\u003er\u0026thinsp;=\u0026thinsp;number of replications\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003e2.4.2. Estimation of genotypic and phenotypic coefficient of variability\u003c/h2\u003e\u003cp\u003ePhenotypic and genotypic coefficients of variation are helpful to estimate the magnitude of variability present in a population. According to Singh (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), the phenotypic and genotypic coefficients of variances are expressed by the following formula.\u003c/p\u003e\u003cp\u003ePhenotypic coefficient of variation (PCV) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{{{\\sigma\\:}}^{2}\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e/\u003c/b\u003eX *100\u003c/p\u003e\u003cp\u003eGenotypic coefficient of variation (GCV) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{{{\\sigma\\:}}^{2}\\text{g}\\:\\:}\\)\u003c/span\u003e\u003c/span\u003e \u003cb\u003e/\u003c/b\u003eX *100\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhere;\u003c/b\u003e\u003c/p\u003e\u003cp\u003eX\u0026thinsp;=\u0026thinsp;Mean value of the trait.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{{{\\sigma\\:}}^{2}\\text{g}\\:\\:}\\)\u003c/span\u003e\u003c/span\u003e =Genotypic standard deviations\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sqrt{{{\\sigma\\:}}^{2}\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e=phenotypic standard deviations\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e2.4.3. Estimation of heritability and genetic advance\u003c/h2\u003e\u003cp\u003eHeritability provides the degree of transmissibility of a trait and indicates the effectiveness of selection. Further, estimates of heritability have to be considered in conjunction with genetic advances to find the expected genetic gain in the next generation (Shukla et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Broad sense heritability (H\u003csup\u003e2\u003c/sup\u003eb) shall be expressed as the percentage of the ratio of the genotypic variance (δ\u003csup\u003e2\u003c/sup\u003eg) to the phenotypic variance (δ\u003csup\u003e2\u003c/sup\u003ep) and were estimated on genotype mean basis as described by Allard and Hansche (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1964\u003c/span\u003e) as:\u003c/p\u003e\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003eb\u0026thinsp;=\u0026thinsp;δ\u003csup\u003e2\u003c/sup\u003eg /δ\u003csup\u003e2\u003c/sup\u003ep*100 Where;\u003c/p\u003e\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003eb\u0026thinsp;=\u0026thinsp;heritability in broad sense δ\u003csup\u003e2\u003c/sup\u003ep=phenotypic variance δ\u003csup\u003e2\u003c/sup\u003eg = genotypic variance\u003c/p\u003e\u003cp\u003eThe genetic advances for selection intensity (k) at 5% (2.06) were estimated by the formula of Johnson et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1955\u003c/span\u003e) Genetic advance (GA)\u0026thinsp;=\u0026thinsp;H\u003csup\u003e2\u003c/sup\u003eb *K*δp and Genetic advance as percent of the mean (GAM) =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\frac{GA}{X}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eWhere\u003c/b\u003e:\u003c/p\u003e\u003cp\u003eδp\u0026thinsp;=\u0026thinsp;Phenotypic standard deviation;\u003c/p\u003e\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003eb\u0026thinsp;=\u0026thinsp;heritability in a broad sense and k\u0026thinsp;=\u0026thinsp;selection intensity\u003c/p\u003e\u003cp\u003eX\u0026thinsp;=\u0026thinsp;Grand mean\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e2.4.4. Estimation of phenotypic and genotypic correlation coefficients\u003c/h2\u003e\u003cp\u003eCorrelation studies help to find the degree of interrelationship among various traits and to evolve selection criteria for improvement. Phenotypic correlation is the relationship between two variables, which includes both genotypic and environmental effects while genotypic correlation is the inherited association between two variables. Phenotypic and genotypic correlation coefficients were estimated using the standard procedure suggested by Dewey and Lu (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1959\u003c/span\u003e) from corresponding variance and co-variance components;\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:phenotypic\\:correlation=rp\\left(xy\\right)=\\:\\frac{{COV}_{p}\\left(xy\\right)}{\\sqrt{{V}_{p}\\left(x\\right)\\times\\:\\sqrt{{V}_{p}\\left(y\\right)}}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:Genotypic\\:correlation=rg\\left(xy\\right)=\\:\\frac{{COV}_{g}\\left(xy\\right)}{\\sqrt{{V}_{g}\\left(x\\right)\\times\\:\\sqrt{{V}_{g}\\left(y\\right)}}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eWhere\u003c/em\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{COV}_{p}\\left(xy\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{COV}_{g}\\left(xy\\right)\\)\u003c/span\u003e\u003c/span\u003e are phenotypic and genotypic covariance between x and y traits.\u003c/p\u003e\u003cp\u003eVp (x) and Vg(x) represent variances of x traits at phenotypic and genotypic levels Vp (y) and Vg(y) denote variance of y traits at phenotypic and genotypic levels, respectively.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e2.4.5. Path coefficient analysis\u003c/h2\u003e\u003cp\u003ePath coefficient analysis was carried out to separate the observed correlation coefficients into direct and indirect effects of yield-related traits on grain yield. This approach helps to identify whether a trait contributes to yield primarily through its own direct influence or indirectly through its association with other traits. The analysis followed the method proposed by Dewey and Lu (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1959\u003c/span\u003e), which applies standardized partial regression coefficients to estimate direct effects. Indirect effects were obtained by multiplying the correlation of an independent trait with an intermediate trait by the direct effect of that intermediate trait on yield. The residual effect was calculated to determine the proportion of variation in grain yield not explained by the traits included in the model.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:\\varvec{r}\\varvec{i}\\varvec{j}=\\varvec{P}\\varvec{i}\\varvec{j}+\\sum\\:\\varvec{r}\\varvec{i}\\varvec{k}\\varvec{P}\\varvec{j}\\varvec{k}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eWhere\u003c/strong\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\varvec{r}\\varvec{i}\\varvec{j}\\)\u003c/span\u003e\u003c/span\u003e= mutual association between independent variable (i) and dependent variable (j) as measured by phenotypic and genotypic correlation coefficient.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{P}\\varvec{i}\\varvec{j}\\)\u003c/span\u003e\u003c/span\u003e = component of a direct effect of independent variable (i) as measured by the phenotypic and genotypic path coefficient.\u003c/p\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\sum\\:\\varvec{r}\\varvec{i}\\varvec{k}\\varvec{P}\\varvec{j}\\varvec{k}\\)\u003c/span\u003e\u003c/span\u003e = summation of components of an indirect effect of a given independent variable (i) on a given dependent variable (j) via all other independent traits(K).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"3. RESULTS AND DISCUSSION","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Analysis of Variance (ANOVA)\u003c/h2\u003e\u003cp\u003eThe analysis of variance revealed highly significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) among the 49 bean genotypes for all twelve morphological traits studied (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3.1\u003c/span\u003e). This demonstrates the presence of substantial genetic variability, providing an opportunity to identify superior genotypes for future breeding and improvement programs.\u003c/p\u003e\u003cp\u003eSimilar results have been reported in previous studies. Ghimire and Mandal (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) found significant variation among bean genotypes for traits such as flowering time, maturity, plant height, pod number, seed traits, seed weight, and grain yield. Bulyaba et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) likewise observed strong genotypic effects on flowering and maturity duration, plant height, pods per plant, seeds per pod, biomass, harvest index, branch number, and yield. In addition, Msolla and Mduruma (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) reported marked differences in flowering time, maturity, and yield-related traits in common bean lines, further supporting the present findings.\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 3.1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean square for quantitative traits of bean genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTreatment (Df\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRep (Df\u0026thinsp;=\u0026thinsp;2)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eBlock (Df\u0026thinsp;=\u0026thinsp;18)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCV (%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR\u0026sup2;\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eError\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.94**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e54.71\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e162.00**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e61.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e10.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e18.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e111.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1681.46**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e366.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e148.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e144.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e111.99\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.39**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e8.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.47**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.68\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\u003e5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e15.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.16**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPPL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e319.05**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e309.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e105.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e165.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e46.83\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.87**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e158.14**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e22.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2933236.00**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e96403.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e264222.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e192636.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4181.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1182013.00**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e44171.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e69038.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e15.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e75325.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1821.81\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e260.54**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e23.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e26.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e36.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e42.91\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote: DF\u0026thinsp;=\u0026thinsp;days to flowering; DM\u0026thinsp;=\u0026thinsp;days to maturity; PH\u0026thinsp;=\u0026thinsp;plant height (cm); PL\u0026thinsp;=\u0026thinsp;pod length (cm); PPP\u0026thinsp;=\u0026thinsp;pod per plant; SPP\u0026thinsp;=\u0026thinsp;seed per pod; SPPL\u0026thinsp;=\u0026thinsp;seed per plant; BPP\u0026thinsp;=\u0026thinsp;branch per plant; HSW\u0026thinsp;=\u0026thinsp;hundred seed weight (g); BY\u0026thinsp;=\u0026thinsp;biological yield (kg ha-1); GY\u0026thinsp;=\u0026thinsp;grain yield (kg ha-1); HI\u0026thinsp;=\u0026thinsp;harvest index (%), CV% = coefficient of variation; and R2\u0026thinsp;=\u0026thinsp;R-square.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Mean and range of measured traits\u003c/h2\u003e\u003cp\u003eThe mean, minimum, and maximum values of the 12 traits are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3.2\u003c/span\u003e, showing wide variation among the 49 bean genotypes. This variability reflects the presence of substantial genetic differences across the genotypes studied. Yohannes and Berecha (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) also reported considerable ranges in traits such as seed yield per plant, hundred-seed weight, seeds per pod, and flowering time, supporting the present findings.\u003c/p\u003e\u003cp\u003eDays to 50% flowering ranged from 44 to 73.66 days, while days to maturity ranged from 98.66 to 125.33 days. The latest maturing genotype was 16525 (125.33 days), and the earliest was 211320 (98.66 days). Such variation provides opportunities to classify genotypes into early- and late-maturing groups, which is valuable for breeding programs targeting contrasting environments such as moisture-deficit areas and high-rainfall regions.\u003c/p\u003e\u003cp\u003ePlant height ranged from 63.93 cm (NUV160) to 158.2 cm (208639), with a mean of 111.99 cm. Pod length varied between 5.8 and 11.07 cm, while pods per plant ranged from 5.13 to 25.66, with a mean of 15.43. Hundred-seed weight was highest in genotype 208639 (49.19 g) and lowest in 212978 (13.22 g), consistent with the variation reported by Yohannes and Berecha (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Grain yield ranged from 387.7 kg ha⁻\u0026sup1; (NUV173) to 3246.9 kg ha⁻\u0026sup1; (16525), which aligns with the results of Raffi and Nath (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). The highest harvest index (60.86) was recorded in NUV140, while the lowest (13.24) was found in NUV173. Biological yield was also highest in genotype 16525 and lowest in 15996.\u003c/p\u003e\u003cp\u003eOverall, the wide range of variation observed across genotypes provides a strong basis for selection. The presence of such diversity indicates good prospects for identifying and improving superior genotypes through breeding.\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 3.2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRange and mean values for 12 traits of 49 bean 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=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTrait\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMin\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMax\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eGenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRange\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e214662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e73.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNUV30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e54.71429\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e98.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e211320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e125.333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e111.1497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.66\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.933\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNUV160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e158.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e208639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e111.9852\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e94.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNUV173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.0667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e208639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.085714\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.133\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e211350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.43673\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.1333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNUV219-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e5.722449\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPPL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e201940\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e64.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e211350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e46.82993\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.4667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.4667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNUV30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2.57415\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e212978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e49.187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e208639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.74639\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e35.97\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2234\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7733.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e4181.376\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5499.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e387.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNUV173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3246.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1821.805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2859.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13.235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNUV173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e60.862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNUV140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e42.91218\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e47.62\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: DF\u0026thinsp;=\u0026thinsp;days to flowering; DM\u0026thinsp;=\u0026thinsp;days to maturity; PH\u0026thinsp;=\u0026thinsp;plant height (cm); PL\u0026thinsp;=\u0026thinsp;pod length (cm); PPP\u0026thinsp;=\u0026thinsp;pod per plant; SPP\u0026thinsp;=\u0026thinsp;seed per pod; SPPL\u0026thinsp;=\u0026thinsp;seed per plant; BPP\u0026thinsp;=\u0026thinsp;branch per plant; HSW\u0026thinsp;=\u0026thinsp;hundred seed weight (g); BY\u0026thinsp;=\u0026thinsp;biological yield (kg ha-1); GY\u0026thinsp;=\u0026thinsp;grain yield (kg ha-1) and HI\u0026thinsp;=\u0026thinsp;harvest index (%).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Phenotypic and Genotypic Variations\u003c/h2\u003e\u003cp\u003eThe phenotypic and genotypic variances, along with the coefficients of variation (PCV and GCV), for yield and related traits are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e. Following the classifications of Sivasubramaniam and Madhava Menon (1973) and Deshmukh et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1986\u003c/span\u003e), coefficients of variation were categorized as low (\u0026lt;\u0026thinsp;10%), moderate (10\u0026ndash;20%), or high (\u0026gt;\u0026thinsp;20%).\u003c/p\u003e\u003cp\u003eBoth PCV and GCV values revealed substantial variability among the genotypes. In line with the findings of Bashir et al. (2014), PCV values were generally higher than GCV values, indicating that environmental factors contributed to the observed variation.\u003c/p\u003e\u003cp\u003eHigh GCV estimates were recorded for plant height, pods per plant, hundred-seed weight, biological yield, grain yield, and harvest index. These results are consistent with Singh (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), who reported high genetic variance for seed yield and related traits in common bean. Traits such as pod length, seeds per plant, and branches per plant showed moderate GCV, suggesting that improvement through selection is feasible. In contrast, days to flowering and days to maturity exhibited low GCV values, implying that these traits are strongly influenced by the environment and thus less responsive to selection.\u003c/p\u003e\u003cp\u003eHigh PCV values were observed for plant height, pods per plant, seeds per plant, branches per plant, hundred-seed weight, biological yield, grain yield, and harvest index. Moderate PCV values were recorded for days to flowering and pod length, while days to maturity exhibited the lowest PCV (\u0026lt;\u0026thinsp;10%). Similar trends were reported by Yohannes and Berecha (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), who found higher PCV values for plant height, biological yield, pods per plant, harvest index, and seed weight, while traits like flowering time and seeds per pod showed moderate variation.\u003c/p\u003e\u003cp\u003eThe relatively large differences between PCV and GCV for traits such as seeds per plant, flowering time, and branch number suggest strong environmental influence, making direct selection less reliable for these traits. However, the smaller differences observed for most other traits indicate that genetic factors were the main source of variation, making them dependable targets for selection in breeding programs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Heritability\u003c/h2\u003e\u003cp\u003eBroad-sense heritability estimates for the 12 traits are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e, ranging from 23.73% for seeds per plant to 85.85% for hundred-seed weight. High heritability was recorded for plant height, hundred-seed weight, biological yield, grain yield, harvest index, branches per plant, days to maturity, and pods per plant. These results suggest that the expression of these traits is largely governed by genetic factors with minimal environmental influence, making phenotypic selection effective. Moderate heritability estimates were observed for seeds per pod and days to flowering, while seeds per plant exhibited low heritability, indicating greater environmental influence and reduced effectiveness of direct selection.\u003c/p\u003e\u003cp\u003eComparable findings have been reported in previous studies. Mallu et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found high heritability for grain yield and hundred-seed weight, and Ghimire and Mandal (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported heritability above 60% for seed weight, pod length, pod number, maturity period, yield, and plant height. Similarly, Wondwosen and Abebe (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) observed strong heritability for yield, seed weight, and maturity-related traits, while Raffi and Nath (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) documented values exceeding 60% for maturity, yield, seed weight, pod length, and plant height. More recently, Langat et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported high heritability (\u0026gt;\u0026thinsp;60%) for flowering, maturity, seeds per pod, hundred-seed weight, and grain yield under stress conditions. In French bean, Aklade et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) also reported high heritability for flowering, maturity, pod length, hundred-seed weight, and seeds per pod. Similarly, Ali et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) identified high heritability for biomass, grain yield, seed weight, and pod number, noting that these traits are reliable for selecting high-yielding genotypes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Genetic Advance\u003c/h2\u003e\u003cp\u003eGenetic advance estimates are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3.3\u003c/span\u003e. Traits including plant height, pods per plant, branches per plant, biological yield, harvest index, hundred-seed weight, and grain yield showed high heritability combined with high genetic advance as a percentage of the mean. This indicates strong additive gene action, suggesting that these traits can be effectively improved through direct selection. In contrast, days to maturity, pod length, seeds per pod, and seeds per plant exhibited moderate genetic advance, while days to flowering showed low values, reflecting limited genetic variability and reduced potential for selection.\u003c/p\u003e\u003cp\u003eThese findings are consistent with Mohammed et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), who reported high genetic advance as a percentage of the mean (GAM) for grain yield, biomass yield, pods per plant, and hundred-seed weight, but lower GAM values for maturity, flowering, and seeds per pod.\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 3.3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimates of variances and coefficients of variability, heritability, and genetic advance of the 12 traits of 49 bean genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\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\u003eσ\u003csup\u003e2\u003c/sup\u003ee\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003eg\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eσ\u003csup\u003e2\u003c/sup\u003ep\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePCV%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGCV%\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eH\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eGA\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eGAM\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e26.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e40.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e4.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e8.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e47.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e66.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e7.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e6.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e72.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e10.87\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e144.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e512.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e657.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e22.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e77.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e41.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e36.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e55.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e15.95\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e32.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e28.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e77.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e51.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e13.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e9.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e42.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e12.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPPL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e165.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e216.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e15.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e23.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e15.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e23.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e19.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e63.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e31.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e58.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e31.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e85.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e13.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e59.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e192636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e913533.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1106169\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e22.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e82.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1789.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e42.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e75325\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e368896\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e444222\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e36.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e33.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e83.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1140.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e62.58\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e36.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e111.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e20.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e67.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e14.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e34.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: DF\u0026thinsp;=\u0026thinsp;days to flowering; DM\u0026thinsp;=\u0026thinsp;days to maturity; PH\u0026thinsp;=\u0026thinsp;plant height (cm); PL\u0026thinsp;=\u0026thinsp;pod length (cm); PPP\u0026thinsp;=\u0026thinsp;pod per plant; SPP\u0026thinsp;=\u0026thinsp;seed per pod; SPPL\u0026thinsp;=\u0026thinsp;seed per plant; BPP\u0026thinsp;=\u0026thinsp;branch per plant; HSW\u0026thinsp;=\u0026thinsp;hundred seed weight (g); BY\u0026thinsp;=\u0026thinsp;biological yield (kg ha-1); GY\u0026thinsp;=\u0026thinsp;grain yield (kg ha-1) and HI\u0026thinsp;=\u0026thinsp;harvest index (%); σ2e\u0026thinsp;=\u0026thinsp;environmental variance; σ2g\u0026thinsp;=\u0026thinsp;genotypic variance; σ2p\u0026thinsp;=\u0026thinsp;phenotypic variance; H2\u0026thinsp;=\u0026thinsp;broad sense heritability; GA\u0026thinsp;=\u0026thinsp;genetic advance and GAM%= Genetic advances as percent of the mean.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Correlation of grain yield with other traits and correlation among traits\u003c/h2\u003e\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\u003ch2\u003e3.6.1. Phenotypic correlation\u003c/h2\u003e\u003cp\u003ePhenotypic correlation coefficients among the studied traits are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e. Grain yield displayed strong and positive associations with plant height, pod length, pods per plant, seeds per pod, seeds per plant, branch number, hundred-seed weight, and biological yield. A significant positive relationship was also observed with harvest index. These results suggest that selection based on these traits could indirectly enhance grain yield. Similar positive associations between yield and traits such as seeds per plant (Cokkizgin et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), seeds per pod (Roy et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), hundred-seed weight (Karasu and Oz, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), and branch number (Kulaz and Ciftci, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2012\u003c/span\u003e) have been reported in previous studies. Positive correlations of grain yield with days to flowering were also noted by Gonz\u0026aacute;lez et al. (2016) and Bagheri et al. (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Conversely, yield exhibited a non-significant but positive correlation with days to maturity, indicating limited potential for indirect improvement through this trait.\u003c/p\u003e\u003cp\u003ePod length showed highly significant and positive correlations with seeds per pod, seeds per plant, and branches per plant, hundred-seed weight, biological yield, grain yield, and harvest index. This implies that improvement in pod length could lead to simultaneous gains in these traits, making it a useful selection criterion. However, pod length had a significant negative association with days to maturity, consistent with findings by Roy et al. (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHarvest index exhibited strong positive correlations with pod length, pods per plant, seeds per pod, seeds per plant, branch number, hundred-seed weight, biological yield, and grain yield, as well as a positive association with plant height. In contrast, it showed a significant negative correlation with days to maturity and a non-significant negative correlation with days to flowering. These patterns suggest that genotypes with higher values for positively correlated traits are likely to achieve greater harvest index.\u003c/p\u003e\u003cp\u003eIn general, the predominance of positive and significant phenotypic correlations indicates that many traits can be improved simultaneously through selection. Negative correlations, however, may constrain the possibility of achieving simultaneous improvement for certain trait combinations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\u003ch2\u003e3.6.2. Genotypic correlations\u003c/h2\u003e\u003cp\u003eGenotypic correlation coefficients among yield and yield-related traits are presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e. Grain yield showed highly significant and positive correlations with plant height, pod length, pods per plant, seeds per pod, seeds per plant, branches per plant, hundred-seed weight, biological yield, and harvest index. Seeds per plant were positively associated with several traits including pod length, branches per plant, biological yield, seeds per pod, harvest index, and pods per plant, indicating that these traits tend to improve together. Pod length also exhibited strong positive correlations with seeds per pod, seeds per plant, branches per plant, hundred-seed weight, biological yield, grain yield, and harvest index. Likewise, biological yield was strongly correlated with nearly all yield-related traits, underscoring its central role in influencing productivity.\u003c/p\u003e\u003cp\u003eThese findings are consistent with earlier reports. Agrawal et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2000\u003c/span\u003e) and Yohannes et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) documented strong genotypic correlations of grain yield with traits such as biological yield, pods per plant, seeds per pod, stem diameter, and leaf area index. In the current study, biological yield recorded the highest genotypic correlation with grain yield, which aligns with their findings. By contrast, grain yield showed non-significant but positive correlations with days to flowering (rg\u0026thinsp;=\u0026thinsp;0.15) and days to maturity (rg\u0026thinsp;=\u0026thinsp;0.05), suggesting limited potential for improving yield through these traits.\u003c/p\u003e\u003cp\u003eCorrelation analysis also revealed that days to flowering had significant positive associations with days to maturity and biological yield, while its relationships with pod length, pods per plant, seeds per pod, seeds per plant, branches per plant, and grain yield were positive but non-significant. Negative correlations were detected with plant height and harvest index. Similarly, days to maturity was significantly and positively correlated with days to flowering, biological yield, and pods per plant, while its associations with plant height, branches per plant, grain yield, and hundred-seed weight were positive but non-significant. Negative but non-significant relationships were observed with seeds per plant and pod length.\u003c/p\u003e\u003cp\u003eIn general, the predominance of positive and significant genotypic correlations suggests that many traits can be improved simultaneously through selection. However, negative correlations highlight potential trade-offs, which may limit the scope of simultaneous trait improvement.\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 3.4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimates of genotypic (below diagonal) and phenotypic (above diagonal) correlation coefficients among 12 traits in 49 bean genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"13\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDM\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003ePPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eSPPl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eBPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHSW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eBY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.25**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c12\"\u003e\u003cp\u003e0.53**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.52**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.30*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.23**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.34**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.37**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.27**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.09\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.34*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.80**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c10\"\u003e\u003cp\u003e0.21*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.54**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.53**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.31**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.55**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.61**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.30*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.45**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.57**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.43**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.34*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.36*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.35*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.42**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.36*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.43**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.62**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.48**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.82**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.25**\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.39**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.60**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.40**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.43**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.59**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.58**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.61**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.87**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e0.74*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-0.37**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.63**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.33*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.63**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.59**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.34*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.48**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.35*\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e0.76**\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNote: DF\u0026thinsp;=\u0026thinsp;days to flowering; DM\u0026thinsp;=\u0026thinsp;days to maturity; PH\u0026thinsp;=\u0026thinsp;plant height (cm); PL\u0026thinsp;=\u0026thinsp;pod length (cm); PPP\u0026thinsp;=\u0026thinsp;pod per plant; SPP\u0026thinsp;=\u0026thinsp;seed per pod; SPPL\u0026thinsp;=\u0026thinsp;seed per plant; BPP\u0026thinsp;=\u0026thinsp;branch per plant; HSW\u0026thinsp;=\u0026thinsp;hundred seed weight (g); BY\u0026thinsp;=\u0026thinsp;biological yield (kg ha-1); GY\u0026thinsp;=\u0026thinsp;grain yield (kg ha-1) and HI\u0026thinsp;=\u0026thinsp;harvest index (%).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.7. Path coefficient analysis\u003c/h2\u003e\u003cp\u003ePath coefficient analysis partitioned the correlations of different traits with grain yield into their direct and indirect effects. At the phenotypic level, biological yield, harvest index, and 100-seed weight exerted the strongest positive direct effects on yield, suggesting that direct selection for these traits would be effective. Conversely, traits such as plant height and pod number displayed negative direct effects but contributed indirectly through positive associations with yield components like biological yield and seed weight. The residual effect of 0.14 indicated that the variables included explained about 86% of the yield variation. At the genotypic level, biological yield and harvest index again showed the largest positive direct effects, followed by seed weight and seed number per plant. Negative direct effects were observed for plant height, pod length, and pods per plant, though these were compensated by positive indirect contributions. The residual value of 0.045 suggested that the considered traits explained more than 95% of yield variation at the genotypic level.\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003e3.7.1. Phenotypic direct and indirect effects of various traits on grain yield\u003c/h2\u003e\u003cp\u003eAt the phenotypic level, biological yield exerted the strongest direct effect on grain yield, followed by harvest index and hundred-seed weight. Traits showing strong direct positive effects can be used as effective selection criteria in breeding programs. Although pod number and plant height showed negative direct effects, they contributed positively to yield through strong indirect effects mediated by seed number, biological yield, and harvest index.\u003c/p\u003e\u003cp\u003ePlant height and seeds per pod both had negative direct effects on yield but showed positive indirect effects through hundred-seed weight, biological yield, and harvest index. Similar results were reported by Esp\u0026oacute;sito et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Pods per plant also exhibited a negative direct effect on grain yield but remained positively correlated with it overall, as the negative effect was offset by strong positive indirect effects via seeds per plant, biological yield, and harvest index. This agrees with the findings of Huque et al., (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOther traits, such as pod length and branches per plant, also recorded negative direct effects on yield. In such cases, direct selection is unlikely to be effective; instead, improvement may be achieved indirectly through traits that compensate for their effects, such as biological yield and harvest index.\u003c/p\u003e\u003cp\u003eOverall, the phenotypic path analysis highlighted biological yield, hundred-seed weight, and harvest index as the most influential contributors to grain yield. Indirect contributions from traits like pod number and plant height further emphasize the importance of considering both direct and indirect effects when designing selection strategies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3.5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimation of direct (bold diagonal) and indirect effects (off-diagonal) of 9 traits on grain yield at phenotypic level in common bean genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\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\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSPPl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHSW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eBY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.02\u003c/b\u003e\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\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.16\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e-0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPPl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00\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\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00\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\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.01\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\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.68\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.59\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\u003cp\u003eResidual effect\u0026thinsp;=\u0026thinsp;0.14; Note: PH\u0026thinsp;=\u0026thinsp;plant height (cm); PL\u0026thinsp;=\u0026thinsp;pod length (cm); PPP\u0026thinsp;=\u0026thinsp;pod per plant; SPP\u0026thinsp;=\u0026thinsp;seed per pod; SPPL\u0026thinsp;=\u0026thinsp;seed per plant; BPP\u0026thinsp;=\u0026thinsp;branch per plant; HSW\u0026thinsp;=\u0026thinsp;hundred seed weight (g); BY\u0026thinsp;=\u0026thinsp;biological yield (kg ha-1) and HI\u0026thinsp;=\u0026thinsp;harvest index (%).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section3\"\u003e\u003ch2\u003e3.7.2. Genotypic direct and indirect effects of various traits on grain yield\u003c/h2\u003e\u003cp\u003eGenotypic path analysis (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e3.6\u003c/span\u003e) showed that biological yield had the strongest direct positive effect on grain yield, followed by harvest index, hundred-seed weight, and number of seeds per plant. The high correlations of biological yield (r\u0026thinsp;=\u0026thinsp;0.87) and harvest index (r\u0026thinsp;=\u0026thinsp;0.76) with yield were largely explained by these direct contributions, underscoring their importance as selection criteria. These findings agree with Korat et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), who reported similar positive direct effects of harvest index, seed weight, and biological yield on seed yield.\u003c/p\u003e\u003cp\u003eSeeds per pod had a small positive direct effect but contributed more strongly through indirect effects via seed number, hundred-seed weight, biological yield, and harvest index. Hundred-seed weight also exhibited modest direct influence but reinforced yield through its indirect associations with the same traits.\u003c/p\u003e\u003cp\u003eIn contrast, plant height and pod number per plant exerted negative direct effects on grain yield. However, their overall influence remained positive due to strong indirect pathways, especially through hundred-seed weight, biological yield, and harvest index. Comparable results were reported by Mammo et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Ambachew et al. (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), who noted negative direct effects of pod number and seed weight on yield that were offset by indirect contributions. Conversely, Mesele (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) found that seed number per plant was the most favorable contributor, while maturity and flowering time had negative direct effects. In the present study, pod length also showed a negative direct effect but was compensated by positive indirect influences through seed number, seed weight, biological yield, and harvest index.\u003c/p\u003e\u003cp\u003eIn general, the direct effects of traits did not always correspond with their correlation coefficients, highlighting the complexity of inter-trait relationships. The low residual effect (0.045) indicated that the traits included in the model accounted for about 95.5% of the variation in grain yield, leaving only 4.5% unexplained.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3.6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eEstimation of direct (bold diagonal) and indirect effects (off-diagonal) of 9 traits on grain yield at genotypic level in common bean genotypes\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"10\"\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=\"char\" char=\".\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\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\u003ePH\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSPPl\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eBPP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHSW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eBY\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e-0.09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e-0.04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSPPl\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBPP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e-0.02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHSW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u003cb\u003e0.09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBY\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e0.7\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e0.52\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\u003cp\u003eResidual effect\u0026thinsp;=\u0026thinsp;0.045; Note: PH\u0026thinsp;=\u0026thinsp;plant height (cm); PL\u0026thinsp;=\u0026thinsp;pod length (cm); PPP\u0026thinsp;=\u0026thinsp;pod per plant; SPP\u0026thinsp;=\u0026thinsp;seed per pod; SPPL\u0026thinsp;=\u0026thinsp;seed per plant; BPP\u0026thinsp;=\u0026thinsp;branch per plant; HSW\u0026thinsp;=\u0026thinsp;hundred seed weight (g); BY\u0026thinsp;=\u0026thinsp;biological yield (kg ha-1) and HI\u0026thinsp;=\u0026thinsp;harvest index (%).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. CONCLUSIONS","content":"\u003cp\u003eThis study demonstrated substantial genetic variation among the 49 climbing common bean genotypes across 12 quantitative traits, confirming the availability of a broad genetic base for improvement. Yield-related traits such as grain yield, pods per plant, and hundred-seed weight exhibited particularly wide variability, highlighting their potential as priority targets for breeding.\u003c/p\u003e\u003cp\u003eHigh heritability coupled with notable genetic advance was observed for several traits, including plant height, pods per plant, branches per plant, biological yield, harvest index, hundred-seed weight, and grain yield. These results suggest that additive genetic effects play a major role in the expression of these traits, making direct selection an effective strategy. Grain yield, pod number, biomass, and seed weight were especially important because they combined strong heritability with high genetic advance values.\u003c/p\u003e\u003cp\u003eGenotypic correlations were generally stronger than phenotypic ones, indicating that genetic factors were more influential than environmental effects in shaping trait associations. Grain yield showed significant positive correlations with multiple traits, including pod length, plant height, branch number, seeds per pod, seeds per plant, pods per plant, hundred-seed weight, biological yield, and harvest index. This implies that simultaneous improvement of yield and related traits is feasible through selection.\u003c/p\u003e\u003cp\u003ePath coefficient analysis further identified biological yield and hundred-seed weight as the most important direct contributors to grain yield, reinforcing their relevance as key selection criteria for developing high-yielding indeterminate climbing bean genotypes.\u003c/p\u003e\u003cp\u003eIn general, the results highlight the presence of valuable genetic diversity in Ethiopian climbing common beans and demonstrate the potential for achieving substantial genetic gains through targeted selection. The identification of promising genotypes provides opportunities for their advancement and eventual release, thereby contributing to improved productivity and supporting smallholder farmers.\u003c/p\u003e\u003cp\u003eHowever, the study was conducted at a single site and within one growing season, which may limit the broader applicability of the results. Multi-location and multi-season trials are needed to validate trait stability under diverse environments. Furthermore, molecular approaches such as marker-assisted selection were not applied but could provide additional insights into the genetic basis of yield and related traits, supporting more efficient breeding strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Interest’s statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAgrawal, A. 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Genetic variability of common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.) genotypes under sole and maize-bean cropping systems in Bako, Western Oromia, Ethiopia. \u003cem\u003eAfr. J. Agric. Res.\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 419\u0026ndash;429 (2019). https://doi.org/10.5897/AJAR2018.13725\u003c/li\u003e\n \u003cli\u003eMesele, A. Genetic variation and interrelationships of agronomic traits in cowpea (\u003cem\u003eVigna unguiculata\u003c/em\u003e (L.) Walp). MSc thesis, Addis Ababa Univ. (1997).\u003c/li\u003e\n \u003cli\u003eMirza, M. Y. et al. Estimation of genetic parameters to formulate selection strategy for increased yield in linseed. \u003cem\u003ePak. J. Agric. Res.\u003c/em\u003e \u003cstrong\u003e24\u003c/strong\u003e, 1\u0026ndash;4 (2011).\u003c/li\u003e\n \u003cli\u003eMohammed, A., Tesso, B., Ojiewo, C. \u0026amp; Ahmed, S. 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(2015).\u003c/li\u003e\n \u003cli\u003eYohannes, S., Loha, G. \u0026amp; Gessese, M. K. Performance evaluation of common bean (\u003cem\u003ePhaseolus vulgaris\u003c/em\u003e L.) genotypes for yield and related traits at Areka, Southern Ethiopia. \u003cem\u003eAdv. Agric.\u003c/em\u003e \u003cstrong\u003e2020\u003c/strong\u003e, 1497530 (2020). https://doi.org/10.1155/2020/1497530\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"correlation, coefficient of variance, genetic advance, heritability, indeterminate common bean, path analysis, variability","lastPublishedDoi":"10.21203/rs.3.rs-7490462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7490462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDespite the importance of common bean as a cash crop, research and development have given limited attention to the indeterminate climbing bean. As a result, its productivity has remained low due to a lack of improved varieties. Hence this study was undertaken to estimate the extent of genetic variability, heritability, genetic advance, and associations of yield and yield contributing to 12 quantitative traits of indeterminate climbing common bean genotypes. The study used forty-nine genotypes of indeterminate- climbing type of common beans with a 7x7 triple lattice design. Variance analysis revealed a highly significant difference among 49 genotypes of beans for twelve morphological traits (\u0026lt;\u0026thinsp;0.01) under study. The coefficient of variation, both at the phenotypic and genotypic levels, indicated significant differences among the genotypes for measured traits. The result revealed that estimates of heritability (%) in a broad sense for 12 traits studied, ranged from (23.73%) to (85.85%) for seed per plant and hundred seed weight respectively. Genetic advance estimation in this study revealed that plant height, pod per plant, branches per plant, biological yield, harvest index, 100-seed weight, and grain yield had high heritability estimates coupled with high genetic advance as a percentage of the mean. The correlation analysis revealed that grain yield had a highly significant and positive phenotypic correlation with plant height, pod length, pod per plant, seed per pod, seed per plant, branch per plant, hundred seed weight, biological yield, and significant positive phenotypic correlation with harvest index. Path coefficient analysis demonstrated that higher positive direct effects were exerted by biological yield harvest index and hundred seed weight on grain yield at phenotypic and genotypic levels. The study reveals significant genetic variability in indeterminate climbing common bean genotypes, highlighting the potential for selective breeding to enhance yield-related traits. By identifying high-performing genotypes, the research supports targeted breeding programs aimed at increasing productivity and benefiting smallholder farmers in Ethiopia.\u003c/p\u003e","manuscriptTitle":"Evaluation of genetic variability, heritability, genetic advance, and association of yield and yield contributing traits of indeterminate climbing common bean (Phaseolus vulgaris L.) genotypes","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-12 11:03:25","doi":"10.21203/rs.3.rs-7490462/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"30079073081973127439986257539816277615","date":"2026-05-18T11:40:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-09T06:42:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-09T06:12:28+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-09-08T13:43:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T14:27:34+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-09-05T14:24:08+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5082f98f-2db8-4e48-b79f-5a107a99094d","owner":[],"postedDate":"December 12th, 2025","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"30079073081973127439986257539816277615","date":"2026-05-18T11:40:21+00:00","index":101,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59466929,"name":"Biological sciences/Genetics"},{"id":59466930,"name":"Biological sciences/Plant sciences"}],"tags":[],"updatedAt":"2025-12-12T11:03:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-12 11:03:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7490462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7490462","identity":"rs-7490462","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}