Acta agriculturae Slovenica, 119/1, 1–11, Ljubljana 2023 doi:10.14720/aas.2023.119.1.2911 Original research article / izvirni znanstveni članek Correlation and path coefficient analysis of yield and yield components of some Ethiopian faba bean (Vicia faba L.) accessions Andualem Muche HIYWOTU 1, 2, Alemu ABATE 3 and Fisseha WOREDE 4 Received November 14, 2022; accepted February 16, 2023. Delo je prispelo 14. novembera 2022, sprejeto 16. februarja 2023 1 Bonga University, College of Agriculture and Natural Resources, Department of Plant Sciences, Bonga, Ethiopia 2 Corresponding author, e-mail: andumuche499@gmail.com 3 Bahir Dar University, College of Agriculture and Environmental Sciences, Department of Plant Sciences, Bahir Dar, Ethiopia 4 Ethiopian Institute of Agricultural Research, Fogera National Rice Research and Training Center, Bahir Dar, Ethiopia Correlation and path coefficient analysis of yield and yield components of some Ethiopian faba bean (Vicia faba L.) ac- cessions Abstract: The knowledge of correlation and path coeffi- cient analysis allow crop breeders to practice indirect selection to improve traits like grain yield which are complex in nature. The objectives of the present study were to measure association among yield and yield related traits and to identify important traits for indirect selection to improve faba bean grain yield. Eighty-one faba bean accessions were evaluated following 9 × 9 simple lattice design at one of the Bahir Dar University research sites at Mecha district in 2019 rainy cropping season. The re- sult of correlation analysis revealed that grain yield had highly significant (p < 0.01) and positive phenotypic and genotypic correlations with plant height, pod length, number of pods per plant, number of branches per plant, biomass yield, 100-seed mass and harvest index indicating the possibility of simultane- ous improvement of grain yield with these traits through selec- tion. Path coefficient analysis demonstrated that higher positive direct effects were exerted by biomass yield and harvest index on grain yield both at phenotypic and genotypic levels, as a re- sult, these traits could be used as indirect selection criteria to improve faba bean grain yield. Key words: correlation; faba bean; indirect selection; path analysis; selection criteria Korelacijska in usmerjene multipla regresijska analiza pridel- ka in njegovih komponent nekaterih etiopskih akcesij boba (Vicia faba L.) Izvleček: Poznavanje korelacije in usmerjene multiple regresijske analize omogoča žlahtniteljem posreden izbor za izboljšanje lastnosti kot je pridelek zrnja, na katerega vplivajo v naravi številni dejavniki. Namen te raziskave je bil izmeriti povezave med pridelkom in z njim povezanimi lastnostmi in določiti pomembne lastnosti za posreden izbor izboljšanja pri- delka zrnja pri bobu. Enainosemdeset akcesij boba je bilo ovre- dnotenih v 9 × 9 nepopolnem bločnem poskusu na raziskoval- nem polju Bahir Dar University, v območju Mecha, v deževni dobi pridelovalne sezone 2019. Rezultati korelacijske analize so odkrili, da je pridelek zrnja visoko značilno (p < 0.01) in pozi- tivno feno-in genotipsko povezan z višino rastlin, dolžino stro- kov, številom strokov na rastlino, številom poganjkov na rastli- no, biomaso, maso 100-semen. Usmerjena multipla regresijska analiza je pokazala, da so bili večji neposredni pozitivni učinki na pridelek zrnja izraženi s pridelkom biomase in žetvenim in- deksom na feno- in genotipski ravni. Na osnovi teh rezultatov bi se te lastnosti lahko uporabile kot posredni selekcijski krite- riji za za izboljšanje pridelka zrnja pri bobu. Ključne besede: korelacija; bob; posredna selekcija; usmerjena multipla regresijska analiza; selekcijski kriteriji Acta agriculturae Slovenica, 119/1 – 20232 A. M. HIYWOTU et al. 1 INTRODUCTION Faba bean (Vicia faba L.), is one of the earliest do- mesticated food legumes in the world (Singh et al., 2013). Faba bean is one of the most important legume crops and is believed to have originated in the Near East and cul- tivation started early in Neolithic times, 8000 B.C. (Cu- bero, 1974; Torres et al., 2006; Karkanis et al., 2018). Ethiopia is the second largest producer of faba bean in the world after China (Mussa Jarso and Gemechu Keneni, 2006). According to CSA (2016), a faba bean is important pulse covering 3.56 % (about 443,966.09 hec- tares) of the grain crop area. The report also revealed that the production obtained from faba bean, was 3.18 % (about 848654.57 ton) of the grain production. Between 2007 and 2017 the area under faba bean cultivation was decreased from 520,519 to 437,106 ha (16.02 %) whereas the production and productivity during the correspond- ing period was increased from 0.69 to 0.92 million ton (25  %) and 1.323 to 2.109 t ha-1(37.27  %), respectively (CSA, 2017). Faba bean has a versatile use and it is an important source of dietary protein to the majority of population in Ethiopia (Mussa Jarso and Fasil Assefa, 2014), while its dry seeds, green haulm and dry straw are used as animal fodder. Faba bean seeds contain proteins, carbohydrates, vitamin B, antioxidants and minerals. Protein content in different varieties varies from 26 % to 41 % (Picard, 1977). Carbohydrate contents varies from 51 % to 68 %, of which major proportion is contributed by starch (41–53 %) (Cerning et al., 1975). Studies on genotypic and phenotypic correlations among traits of crop plants are useful in planning, evalu- ating and setting selection criteria for the desired traits in breeding programs (Johanson et al., 1955). Correla- tions between different traits of crops may arise either from genotypic or environmental factors. Environmental correlations arise from the effect of environmental fac- tors that vary at different environments. Correlations due to genetic causes are mainly pleiotropic effects and linkage between genes affecting different traits (Falconer and Mackay, 1996). When more traits are considered in correlation studies, correlations of traits become more complex; therefore, correlation study followed by path analysis will help to identify yield attributing traits. Path analysis provides precise picture of character associations for formulating efficient selection strategy. It differs from simple correlation in that it points out the causes and their relative importance, whereas the later measures the mutual association ignoring the causation. The concept of path coefficient was developed by Wright (1921) and it was first used for plant selection by Dewey and Lu (1959). Path coefficient analysis is a standardized partial regression which measures the direct and indi- rect contribution of independent traits on a dependent trait. Hence, it measures the influence of a trait up on another; and permits the separation of the correlation coefficient into components of direct and indirect effects (Dewey and Lu, 1959). Therefore, the present study was conducted to assess association among yield and yield related traits and to identify important traits for indirect selection to improve faba bean grain yield. 2 MATERIALS AND METHODS 2.1 EXPERIMENTAL SITE, MATERIALS AND DESIGN The experiment was conducted at one of Bahir Dar University research sites in Mecha district of West Goj- jam zone of Amhara region, Ethiopia in 2019 main crop- ping season. Mecha is located about 525 km Northeast of Addis Ababa and 34 km Southwest of Bahir Dar. It is located at latitude of 10°30’ N and longitude of 37°29’ E. It receives annual rainfall of 1572 mm. The mean temper- ature ranges between 24 °C and 27 °C; and the altitude is 2009 m. a. s. l. The faba bean genotypes used in the present study include seventy-eight accessions obtained from Ethio- pian Biodiversity Institute, two standard checks obtained from Adet Agricultural Research Center, and a local check from local source; 81 in total (Table 1). The experimental design used was 9 × 9 simple lat- tice. Each accession was planted on two-row plot. Spac- ing between rows and between plants were 40 cm and 10 cm, respectively. The plot size was 0.8 m x 1 m (0.8 m2). Spacing between blocks was 1 meter. 2.2 DATA COLLECTION AND ANALYSES 2.2.1 Data collection Depending on the nature of traits data were col- lected on plot and plant basis. Plant height (cm), pod length (cm), number of pods per plant, number of branches were collected on plant basis for which mean of five plants from each plot was used for analysis. Days to 50 % flowering, grain yield (g/plot), 100-seed mass (g), biomass yield (g/plot), harvest index (%), diseases score were gathered on plot basis. Diseases score include the incidence of ascochyta blight, chocolate spot and rust diseases. Each disease was scored using 1-9 score meth- od (where 1 refers highly resistance and 9 indicate highly susceptible) according to Bernier et al. (1993). Acta agriculturae Slovenica, 119/1 – 2023 3 Correlation and path coefficient analysis of yield and yield components of some Ethiopian faba bean (Vicia faba L.) accessions Number Accession Collection Region District Number Accession Collection Region District 1 212565 Amhara Siyadebrina Wayu 42 235709 SNNP Dirashe Special 2 27279 Amhara Machakel 43 219089 Oromiya Amigna 3 25299 SNNP Angacha 44 25338 SNNP Meskanena Mareko 4 245140 SNNP Dila Zuria 45 215748 Amhara Dessie Zuria 5 212566 Oromiya Wuchalena Jido 46 213211 Oromiya Tiro Afeta 6 25006 Amhara Hulet Ej Enese 47 25336 SNNP Meskanena Mareko 7 212568 Amhara Siyadebrina Wayu 48 226125 Amhara Legambo 8 25018 Oromiya Wuchalena Jido 49 240497 SNNP Decha 9 27052 Oromiya Kofele 50 25346 SNNP Meskanena Mareko 10 25274 Oromiya Becho 51 25328 SNNP Selti 11 229303 Amhara Lay Betna Tach Bet 52 213214 Oromiya Limu Seka 12 212567 Amhara Siyadebrina Wayu 53 25335 SNNP Meskanena Mareko 13 25298 SNNP Angacha 54 25340 SNNP Meskanena Mareko 14 219355 Oromiya Adolana Wadera 55 25339 SNNP Meskanena Mareko 15 208085 Amhara Weremo Wajetuna Mida 56 215129 Amhara Mama Midrina Lalo 16 212811 Amhara Wegera 57 215128 Amhara Mama Midrina Lalo 17 203105 Oromiya Dedesa 58 228607 Amhara Goncha Siso Enese 18 235955 Amhara Debark 59 25337 SNNP Meskanena Mareko 19 25279 SNNP Cheha 60 25341 SNNP Meskanena Mareko 20 208114 Amhara Weremo Wajetuna Mida 61 25331 SNNP Selti 21 229310 Amhara Weremo Wajetuna Mida 62 235433 Tigray Kola Temben 22 212572 Amhara Weremo Wajetuna Mida 63 Tumsa     23 25003 Oromiya Kuyu 64 25325 SNNP Selti 24 25323 SNNP Selti 65 25329 SNNP Selti 25 25022 Oromiya Kuyu 66 25309 SNNP Angacha 26 25280 SNNP Cheha 67 25304 SNNP Angacha 27 235956 Amhara Debark 68 25330 SNNP Selti 28 220079 Tigray Adwa 69 25307 SNNP Angacha 29 212576 Amhara Lay Betna Tach Bet 70 25334 SNNP Selti 30 212575 Amhara Lay Betna Tach Bet 71 25306 SNNP Angacha 31 25277 SNNP Cheha 72 25311 SNNP Angacha 32 220076 Tigray Adwa 73 25332 SNNP Sodo 33 27290 Oromiya Jimma Arjo 74 25310 SNNP Angacha 34 25264 Oromiya Becho 75 25333 SNNP Selti 35 25292 SNNP Limo 76 25302 SNNP Angacha 36 25270 Oromiya Becho 77 25327 SNNP Selti 37 212580 Amhara Mama Midrina Lalo 78 Dosha     38 25017 Amhara Enarj Enawga 79 25303 SNNP Angacha 39 212578 Amhara Geramidirna Keya 80 25301 SNNP Angacha 40 25010 Oromiya Gerar Jarso 81 Local     41 25290 SNNP Limo Table 1: List of the 81 faba bean accessions used in the study Acta agriculturae Slovenica, 119/1 – 20234 A. M. HIYWOTU et al. 2.2.2 Correlation analysis Phenotypic correlation, the observable correlation between variables, which is the sum of genotypic and environmental effects were calculated from variance and covariance components using the formula of Miller et al. (1958) as follows: Genotypic correlation= σg xy / √ (σ 2 g x σ 2 g y) Phenotypic correlation= σp xy / √ (σ 2 p x σ 2 py) Where σp x y = phenotypic covariance between char- acter x and y, σ2p x = phenotypic variance for character x, σ2p y = phenotypic variance for character y, σg xy = genotypic covariance between characters x and y, σ2g x = genotypic variance for character x, and σ2g y = genotypic variance for character y The significance of phenotypic correlation coeffi- cients was tested by the formula of Singh and Chaudhary (1985): t’= rpxy √ (n-2 / 1-rp 2xy) The computed t’ value was tested against the tabu- lated t-value at n-2 degree of freedom where n was the number of genotypes studied; whereas the correlation coefficient at genotypic level was tested for significance using the formula proposed by Robertson (1959). The calculated ‘t’ value was compared with ‘t’ tabu- lated at (n-2) degree of freedom at 1 % and 5 % levels of significances. Where Where SErgxy is the standard error of genotypic cor- relation coefficient; Hx and Hy are heritability for traits x and y, respectively. 2.2.3 Path coefficient analysis Path coefficient analysis is a tool to partition the observed correlation coefficient into direct and indirect effects of yield components on grain yield. Based on cor- relation, path coefficient which refers to the direct and indirect effects of the yield attributing traits (independ- ent trait) on grain yield (dependent trait) were calculated as per Dewey and Lu (1959) as follows: rij = Pij + Σ rikpkj Where rij = mutual association between the in- dependent character (i) and dependent character (j) as measured by the genotypic correlation coefficients. Pij = direct effects of the independent character (i) on the de- pendent variable (j) as measured by the genotypic path coefficients, and Σrikpkj = Summation of components of indirect effects of a given independent character (i) on a given dependent character (j) via all other independent characters (k). Residual factor (R2), the contribution of the re- maining unknown factor was estimated using Singh and Chaudhury (1985) method: R2 = √ (1− ΣPij rij) 3 RESULTS AND DISCUSSION Mean squares of the 14 traits from analysis of vari- ance (ANOVA) as presented below in Table 2 highly significant (p < 0.01) differences among accessions were observed for all traits except days to maturity and num- ber of seeds per pod. This highly significant difference indicates the existence of variability among accessions for traits studied. The presence of variability in the ac- cessions offers an opportunity in improvement of yield and its contributing traits through selection in faba bean. Similar results were obtained by Gemechu Keneni et al. (2005) in 160 faba bean accessions. 3.1 CORRELATION AMONG TRAITS The interrelationship among the 12 traits was esti- mated through correlation coefficient at genotypic and phenotypic levels are presented in Table 2. 3.2 PHENOTYPIC CORRELATION OF GRAIN YIELD WITH OTHER TRAITS The result of correlation analysis revealed that grain yield had highly significant (p < 0.01) and positive pheno- typic correlations with plant height, pod length, number of pods per plant, number of branches per plant, biomass yield, 100-seed mass and harvest index. These results in- dicate that accessions with high plant height, pod length, number of pods per plant, number of branches per plant, biomass yield, 100-seed mass and harvest index produce high grain yield and vice versa. Therefore, these traits emerged as most important associates of grain yield in faba bean. The results suggested that selection of these traits may have good impact on yield improvement. Sim- ilarly, Lal (2019) reported that grain yield had highly sig- nificant and positive phenotypic correlations with plant height, number of pods per plant, number of branches per plant, biomass yield, 100-seed mass and harvest in- dex. Kumar et al. (2013) reported highly significant and positive correlation of seed yield with number of branch- es per plant, number of pods per plant, biomass yield and Acta agriculturae Slovenica, 119/1 – 2023 5 Correlation and path coefficient analysis of yield and yield components of some Ethiopian faba bean (Vicia faba L.) accessions Traits Mean square CV (%) R2 R.E. to RCBD Rep (DF = 1) Accessions (DF = 80) Rep x Block (16) Error (DF = 64) DF 0.10ns 18.57** 3.02ns 2.21 3.30 0.92 101.87 DM 43.56ns 25.29ns 28.53ns 18.28 3.64 0.68 103.76 PH 365.70ns 230.18** 212.36** 109.68 10.75 0.77 108.26 PL 2.57** 0.53** 0.24ns 0.19 11.74 0.80 101.12 NPP 1.03ns 45.44** 7.25ns 6.74 22.36 0.89 100.10 NSP 1.81** 0.17ns 0.09ns 0.12 12.32 0.68 95.91 NBP 0.00ns 0.61** 0.26* 0.17 24.16 0.81 103.24 BY 24966.00ns 9567533.60** 817978.00ns 691793.00 10.23 0.95 100.55 GY 31441.00ns 810072.84** 10553.00ns 14290.00 5.72 0.99 94.77 HSM 6.97ns 173.27** 47.64* 28.96 12.93 0.89 104.69 HI 3.21ns 106.81** 16.36ns 12.71 14.44 0.90 101.23 AB 12.20ns 532.02** 0.04ns 6.19 7.07 0.99 97.23 CS 3.05ns 547.10** 0.33ns 24.49 15.07 0.96 104.92 RT 76.22* 590.28** 0.12ns 14.48 14.30 0.98 100.00 Table 2: Analysis of variance for 14 traits of eighty-one faba bean accessions NOTE: ** = Highly significant (p < 0.01), * = Significant (p < 0.05), ns = Non-significant, CV = Coefficient of variation, DF = days to flowering, DM = days to maturity, PH = plant height(cm), PL = pod length, NPP = number of pods per plant, NSP = number of seeds per pod, NBP = number of branches per plant, BY = biomass yield, GY = grain yield, HSM = 100 seed mass, HI = harvest index, AB = ascochyta blight, CS = chocolate spot, RT = rust disease, R.E to RCBD = relative efficiency to RCBD harvest index. Verma et al. (2015) also found highly sig- nificant and positive correlation of seed yield with har- vest index and biomass yield. Grain yield had highly significant (p < 0.01) and negative phenotypic correlations with ascochyta blight, chocolate spot and rust disease scores. These results indi- cate that as disease severity increase grain yield becomes highly reduced. In addition, grain yield had significant (p < 0.05) and negative phenotypic correlations with days to flowering. This result showed that the early flowering ac- cessions have high yield potential than those of late flow- ering ones, indicating the scope for developing short- cycle varieties. The finding agrees with that of Nchimbi and Mduruma (2007) who reported negative association of days to flowering with grain yield in common bean. 3.3 PHENOTYPIC CORRELATION AMONG OTHER TRAITS Rust disease score showed highly significant (p < 0.01) and positive correlations with ascochyta blight and chocolate spot, indicating that diseases are comple- mentary to each other. Rust disease had highly signifi- cant negative correlations with plant height, pod length, number of pods per plant, number of branches per plant, biomass yield and100 seed mass. This indicates that rust is negatively associated with these traits. Chocolate spot had highly significant negative correlations with plant height, pod length, number of pods per plant, biomass yield and100 seed mass. Ascochyta blight had highly sig- nificant and negative correlations with plant height, pod length, biomass yield and100 seed mass and significant negative correlations with number of pods per plant. Generally, the results suggested that disease related traits were complementary to each other and negatively affect- ed growth and yield related traits. Harvest index showed highly significant (p < 0.01) positive phenotypic correlations with number of pods per plant. This showed that accessions with high num- ber of pods per plant producing high harvest index. However, harvest index had highly significant negative phenotypic correlations with biomass yield and then sig- nificant negative phenotypic correlations with days to flowering. Negative correlations arise due to competition among traits for common precursors which are having restricted supply (Madhur and Jinks, 1994). Hundred- seed mass had highly significant (p < 0.01) positive phenotypic correlations with plant height, pod length, biomass yield and significant (p < 0.05) positive pheno- Acta agriculturae Slovenica, 119/1 – 20236 A. M. HIYWOTU et al. typic correlations with number of pods per plant. Days to flowering had high significant negative phenotypic cor- relations with 100-seed mass. Similarly, Alghamdi (2007) and Mulualem et al. (2013) reported a positive and sig- nificant phenotypic correlation between pod length and thousand-seed mass. Similar results were also obtained by Lal (2019) that hundred-seed mass had highly sig- nificant positive phenotypic correlations with pod length and biomass yield. Biomass yield had highly significant (p < 0.01) positive phenotypic correlations with plant height, pod length, number of pods per plant and number of branch- es per plant. These traits had positive correlation with biomass yield which augurs well for providing correlated response during selection for improving biomass yield. These observations of positive associations between biomass yield and plant height, pod length, number of pods per plant and number of branches per plant are in agreement with the reports made previously (Kumar et al., 2013; Singh et al., 2015; Verma et al., 2015; Kumar et al., 2017) on faba bean. Similar results were obtained by Lal (2019) that biomass yield had highly significant posi- tive phenotypic correlations with plant height, number of pods per plant and number of branches per plant. Positive and highly significant (p < 0.01) phenotypic correlation was recorded between number of branches per plant with days to flowering, plant height, pod length, number of pods per plant. Similarly, Lal (2019) obtained number of branches per plant had highly significant and positive phenotypic correlations with plant height and number of pods per plant. Number of pods per plant showed highly significant and positive correlation with plant height and pod length. Azarpour et al. (2012), Sharifi (2014) and Abdalla et al. (2015) reported positive and significant phenotypic cor- relation of number of pods per plant with plant height. Pod length shows highly significant (p < 0.01) positive phenotypic correlations with plant height. Plant height negatively correlated with days to flowering. 3.4 GENOTYPIC CORRELATION OF GRAIN YIELD WITH OTHER TRAITS In the present study, grain yield had highly signifi- cant (p < 0.01) and positive genotypic correlations with plant height, pod length, number of pods per plant, num- ber of branches per plant, biomass yield, 100-seed mass and harvest index. These results indicated that accessions with high plant height, pod length, number of pods per plant, number of branches per plant, biomass yield, 100- seed mass and harvest index produce high grain yield. Singh et al. (2017) reported that seed yield had highly significant and positive genotypic correlations with num- ber of pods per plant, 100-seed mass and harvest index. Similar result was reported by Zakira et al. (2010) for harvest index in field pea. Grain yield had highly signifi- cant (p < 0.01) and negative genotypic correlations with Traits DF PH PL NPP NBP BY GY HSM HI AB CS RT DF 1 -0.198* -0.066ns -0.089ns 0.293** 0.016ns -0.179* -0.248** -0.192* 0.072ns 0.132ns 0.102ns PH -0.178ns 1 0.542** 0.588** 0.333** 0.456** 0.576** 0.491** 0.111ns -0.206** -0.412** -0.398** PL -0.071ns 0.560** 1 0.287** 0.280** 0.390** 0.360** 0.709** -0.026ns -0.239** -0.289** -0.263** NPP -0.078ns 0.628** 0.271* 1 0.446** 0.518** 0.755** 0.175* 0.228** -0.186* -0.348** -0.394** NBP 0.381** 0.369** 0.312** 0.454** 1 0.229** 0.336** 0.109ns 0.077ns -0.028ns -0.092ns -0.248** BY 0.026ns 0.524** 0.438** 0.538** 0.238* 1 0.562** 0.396** -0.458** -0.244** -0.505** -0.561** GY -0.190ns 0.680** 0.407** 0.789** 0.374** 0.579** 1 0.352** 0.418** -0.281** -0.483** -0.413** HSM -0.252* 0.510** 0.789** 0.136ns 0.061ns 0.418** 0.362** 1 -0.009ns -0.301** -0.411** -0.263** HI -0.227* 0.173ns -0.007ns 0.272* 0.108ns -0.427** 0.438** -0.009ns 1 0.003ns -0.032ns 0.189* AB 0.075ns -0.247* -0.289** -0.206ns -0.041ns -0.255* -0.286** -0.329** 0.004ns 1 0.339** 0.332** CS 0.142ns -0.516** -0.373** -0.373** -0.116ns -0.514** -0.499** -0.458** -0.060ns 0.348** 1 0.413** RT 0.105ns -0.495** -0.332** -0.429** -0.294** -0.578** -0.422** -0.283** 0.199ns 0.338** 0.428** 1 Table 3: Estimates of phenotypic (above diagonal) and genotypic (below diagonal) correlation coefficients of 12 traits of 81 faba bean accessions ** = Highly significant (p < 0.01), * = Significant (p < 0.05), ns = Non significant, DF = days to flowering, PH = plant height, PL = pod length, NPP = number of pods per plant, NBP = number of branches per plant, BY = biomass yield, GY = grain yield, HSM = 100-seed mass, HI = harvest index, AB = ascochyta blight, CS = chocolate spot, RT = rust disease score Acta agriculturae Slovenica, 119/1 – 2023 7 Correlation and path coefficient analysis of yield and yield components of some Ethiopian faba bean (Vicia faba L.) accessions ascochyta blight, chocolate spot and rust disease scores. This indicates disease plays a vital role in reduction of grain yield. 3.5 GENOTYPIC CORRELATION AMONG OTHER TRAITS Days to flowering were positively and highly sig- nificantly (p < 0.01) correlated with number of branches per plant. The result shows late flowering accessions are expected to produce high number of branches per plant. Days to flowering negatively correlated with 100-seed mass and harvest index. This result showed that the late flowering accessions have less yield potential than those of early flowering ones. These findings are in concur- rence with Alghamdi (2007), Tofiq et al. (2016). Kumar et al. (2020) also reported negative correlation of days to flowering with 100-seed mass. Plant height had positive and highly significant (p < 0.01) genotypic correlation with pod length, number pods per plant, number of branches per plant, biomass yield and 100-seed mass. These indicate accessions that have high plant height also had high pod length, number of pods per plant, number of branches per plant, biomass yield and 100-seed mass. Results by Lal (2019) revealed that number of branches per plant and biomass yield had positive and highly significant correlation with plant height in faba bean. Plant height had negative and highly significant (p < 0.01) correlation with chocolate spot and rust disease scores, and negatively correlated with as- cochyta blight. This indicates disease plays a vital role in reduction of plant height. Pod length had positive and highly significant (p < 0.01) correlation with number of branches per plant, biomass yield and 100-seed mass and positive significant correlation with number of pods per plant. In harmony with the present finding, Lal (2019) reported positive and highly significant correlation of pod length with 100- seed mass. Pod length also had negative and highly sig- nificant correlation with ascochyta blight, chocolate spot and rust disease scores. Number of pods per plant had positive and highly significant (p < 0.01) correlation with number of branches per plant, biomass yield and positive and significant correlation with harvest index. Improving these traits increases number of pods per plant that sup- port to increases grain yield. This result agrees with Singh et al. (2017) who reported number of pods per plant had positive and highly significant correlation with number of branches per plant and harvest index. Similar result also reported by Fikreselassie and Soboka (2012). Num- ber of pods per plant had negative and highly significant (p < 0.01) correlation with chocolate spot and rust dis- ease scores. Number of branches per plant had positive and significant correlation with biomass yield and nega- tive highly significant correlation with rust disease score. Biomass yield had positive and highly significant (p < 0.01) genotypic correlation with 100-seed mass. This indicates that breeding for increased biomass yield might result in bold seeds. This implies that the trait has posi- tive impact on faba bean improvement. Biomass yield had negatively significant (p < 0.01) genotypic correla- tion with harvest index, chocolate spot and rust disease scores and negative significant correlation with ascochy- ta blight. This implies that these traits inversely correlat- ed with biomass yield. Hundred-seed mass had negative and highly significant correlation with ascochyta blight and chocolate spot diseases but negative significant cor- relation with rust. These indicate that hundred-seed mass negatively affected by disease and leads to low yield. As- cochyta blight had positive and highly significant cor- relation with chocolate spot and rust disease scores and also chocolate spot had positive and highly significant correlation with rust disease. This result indicates the oc- currence of one disease simply favores another. 3.6 PATH COEFFICIENT ANALYSES Path coefficient analyses separated the phenotypic and the genotypic correlation coefficients into the corre- sponding direct and indirect effects. The phenotypic and genotypic direct and indirect effects of different traits on grain yield are presented in Table 3 and 4, respectively. 3.6.1 Phenotypic path coefficients In the present study, the path coefficient analysis was carried out by using phenotypic correlation coef- ficients among twelve traits. Plant height, pod length, number of pods per plant, number of branches per plant, biomass yield and harvest index had positive di- rect effects on grain yield. Higher positive direct effects on grain yield, however, were exerted by biomass yield (0.856) and harvest index (0.778) (Table 3). The results indicate biomass yield and harvest-index are most im- portant traits to which emphasis should be given during simultaneous selection aimed at improving grain yield in faba bean. These results are in close association with pre- vious workers (Bora et al., 1998; Kumar et al., 2013 Lal, 2019) for biomass yield and harvest-index in faba bean. Biomass yield exerted considerable positive indirect effects on grain yield via number of pods per plant, plant height, pod length and 100-seed mass. Harvest index ex- hibited high positive indirect effects on grain yield via Acta agriculturae Slovenica, 119/1 – 20238 A. M. HIYWOTU et al. number of pods per plant and plant height. These results suggested the importance of these traits in selection for the improvement of faba bean grain yield. However, bio- mass yield exerted considerable negative indirect effects on grain yield via harvest index chocolate spot and rust disease scores and harvest index exhibited high order of negative indirect effects on grain yield via biomass yield. Similarly, Lal (2019) observed biomass yield exerted con- siderable positive indirect effects on grain yield via num- ber of pods per plant, number of branches per plant and plant height. Harvest index exhibited high order of posi- tive indirect effects on grain yield via number of pods per plant and biomass yield on faba bean. In addition to the important direct contributions, biomass yield and har- vest index had considerable positive indirect effects via different traits, implying that biomass yield and harvest index are important traits to be considered during devis- ing selection strategy aimed at developing high yielding varieties in faba bean. Ulukan et al. (2003) and Tadesse et al. (2011) also observed the highest positive direct effect of number of pods per plant together with plant height. Kumar et al. (2017) also reported that the num- ber of branches per plant, number of pods per plant and pod length exhibited positive direct effects on yield. On the contrary, days to flowering, 100-seed mass, ascochyta blight and rust disease scores had negative direct effects on grain yield. Tofiq et al. (2016) also reported negative direct effect of 100-seeds mass on yield. The magnitude of residual effect (0.297) indicated that the traits included in the study accounted for most of the variability present in grain yield, indicating that the contribution of traits considered was 70.3 % and the rest 29.7 % was the contribution of other traits which were not considered in the present study. 3.6.2 Genotypic path coefficients The high positive direct effects on grain yield were exerted by biomass yield (0.844) and harvest index (0.756) (Table 4). Thus, biomass yield and harvest-index emerged as most important yield components on which emphasis should be given during simultaneous selection aimed at improving grain yield in faba bean. These results are in close agreement with those of Bora et al. (1998) and Kumar et al. (2013). Similar results also obtained by Lal (2019). The direct effects of the remaining traits were low to be considered important. Biomass yield exerted considerable positive indirect effects on grain yield via number of pods per plant, plant height, pod length and 100-seed mass but biomass yield exerted considerable negative indirect effects on grain yield via harvest index, chocolate spot and rust disease scores. Harvest index exhibited high order of positive indirect effects on grain yield via number of pods per plant and plant height but harvest index exhibited high order of negative indirect effects on grain yield via biomass yield. Similar results were obtained by Lal (2019) for biomass yield and har- vest index. In addition to their very high positive direct effects on grain yield, biomass yield and harvest index, having considerable positive indirect effects via differ- ent traits, also appeared as most important indirect yield components. In the present study, path analysis identified bio- Residual factor = 0.297. DF = days to flowering, PH = plant height, PL = pod length, NPP = number of pods per plant, NBP = number of branches per plant, BY = biomass yield, HSM = 100 seed mass, HI = harvest index, AB = ascochyta blight, CS = chocolate spot, and RT = rust disease score Traits DF PH PL NPP NBP BY HSM HI AB CS RT DF -0.052 -0.007 -0.001 -0.008 0.013 0.014 0.011 -0.149 -0.005 0.006 -0.002 PH 0.010 0.034 0.006 0.053 0.014 0.390 -0.022 0.087 0.013 -0.019 0.008 PL 0.003 0.019 0.011 0.026 0.012 0.333 -0.032 -0.020 0.016 -0.013 0.005 NPP 0.005 0.020 0.003 0.090 0.019 0.443 -0.008 0.178 0.012 -0.016 0.008 NBP -0.015 0.011 0.003 0.040 0.043 0.196 -0.005 0.060 0.002 -0.004 0.005 BY -0.001 0.016 0.004 0.047 0.010 0.856 -0.018 -0.356 0.016 -0.023 0.012 HSM 0.013 0.017 0.008 0.016 0.005 0.339 -0.045 -0.007 0.020 -0.019 0.005 HI 0.010 0.004 0.000 0.021 0.003 -0.392 0.000 0.778 0.000 -0.001 -0.004 AB -0.004 -0.007 -0.003 -0.017 -0.001 -0.209 0.014 0.003 -0.065 0.015 -0.007 CS -0.007 -0.014 -0.003 -0.031 -0.004 -0.432 0.018 -0.025 -0.022 0.045 -0.009 RT -0.005 -0.014 -0.003 -0.035 -0.011 -0.480 0.012 0.147 -0.022 0.019 -0.021 Table 4: Phenotypic direct (bold and diagonal) and indirect effects of 11 traits on grain yield of 81 faba bean accessions Acta agriculturae Slovenica, 119/1 – 2023 9 Correlation and path coefficient analysis of yield and yield components of some Ethiopian faba bean (Vicia faba L.) accessions mass yield followed by harvest-index as most important direct as well as indirect yield contributing traits. These indicate that both traits had true association with grain yield and their importance in determining this complex trait. Therefore, important consideration should be given while practicing selection aimed at the improvement of grain yield in faba bean. These results were in accord- ance with the result of Jivani et al. (2013) who reported the highest direct effects of harvest index and biomass yield on grain yield in chickpea. Plant height, number of pods per plant and number of branches per plant had positive direct effects on grain yield at genotypic level but days to flowering, pod length, 100-seed mass, ascochyta blight and rust disease scores had negative direct effects on grain yield. The residual effect shows how much the explanatory variables represent the variability of the dependent vari- able (Singh and Chaudhary, 1985). The residual effect at the genotypic path coefficient analysis was 0.272; as a re- sult, the studied traits explained 72.8 % of the variability in the seed yield and show that few traits were not con- sider which are related to grain yield. Residual factor = 0.272. DF = days to flowering, PH = plant height, PL = pod length, NPP = number of pods per plant, NBP = number of branches per plant, BY = biomass yield, HSM = 100 seed mass, HI = harvest index, AB = ascochyta blight, CS = chocolate spot, RT = rust disease. 4 CONCLUSION Grain yield had highly significant (p < 0.01) and positive phenotypic and genotypic correlation with plant height, pod length, number of pods per plant, number of branches per plant, biomass yield, 100-seed mass and harvest index. These results indicated the possibility of si- multaneous improvement of grain yield with these traits through selection. Path coefficient analysis showed that harvest index and biomass yield had the highest direct effects on yield both at phenotypic and genotypic levels, indicating the importance of these traits for indirect se- lection of faba bean accessions for improvement of grain yield. 5 ACKNOWLEDGEMENTS I would like to thank Bahir Dar University for providing me experimental plot for the field research. I would also like to thank Ethiopian Biodiversity Institute and Adet Agricultural Research Center for the provision of accessions and genotypes for the study. 6 REFERENCES Abdalla M.M., Shafik M.M., El-Mohsen M.I.A., Abo-Hegazy S.R. and Heba M.A.S. (2015). Investigation on faba beans, (Vicia faba L.) heterosis, inbreeding effects, GCA and SCA of diallel crosses of ssp. paucijuga and eu-faba. Journal of American Sciences, 11, 1-7. Alghamdi S.S. (2007). Genetic behavior of some selected faba bean genotypes. African Crop Science Conference Proceed- ings, 8, 709-714. 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DF = days to flowering, PH = plant height, PL = pod length, NPP = number of pods per plant, NBP = number of branches per plant, BY = biomass yield, HSM = 100 seed mass, HI = harvest index, AB = ascochyta blight, CS = chocolate spot, and RT = rust disease score Traits DF PH PL NPP NBP BY HSM HI AB CS RT DF -0.061 -0.011 0.001 -0.005 0.026 0.022 0.008 -0.172 -0.005 0.009 -0.002 PH 0.011 0.061 -0.006 0.042 0.025 0.442 -0.017 0.131 0.017 -0.034 0.008 PL 0.004 0.034 -0.010 0.018 0.021 0.370 -0.026 -0.005 0.020 -0.024 0.006 NPP 0.005 0.038 -0.003 0.066 0.031 0.454 -0.004 0.206 0.014 -0.024 0.007 NBP -0.023 0.022 -0.003 0.030 0.067 0.201 -0.002 0.082 0.003 -0.008 0.005 BY -0.002 0.032 -0.004 0.036 0.016 0.844 -0.014 -0.323 0.017 -0.034 0.010 HSM 0.015 0.031 -0.008 0.009 0.004 0.353 -0.032 -0.007 0.023 -0.030 0.005 HI 0.014 0.010 0.000 0.018 0.007 -0.361 0.000 0.756 0.000 -0.004 -0.003 AB -0.005 -0.015 0.003 -0.014 -0.003 -0.215 0.011 0.003 -0.068 0.023 -0.006 CS -0.009 -0.031 0.004 -0.025 -0.008 -0.434 0.015 -0.046 -0.024 0.066 -0.007 RT -0.006 -0.030 0.003 -0.029 -0.020 -0.488 0.009 0.150 -0.023 0.028 -0.017 Table 5: Genotypic direct (bold and diagonal) and indirect effects of 11 traits on grain yield for 81 faba bean accessions Acta agriculturae Slovenica, 119/1 – 202310 A. 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