+443308186230
Genotypic and phenotypic Correlation and Path Coefficient Analysis for Yield and Yield-related Traits in Bread Wheat (Triticum aestivum L.) Genotypes
Received: 01-Feb-2024 / Manuscript No. acst-24-127426 / Editor assigned: 04-Feb-2024 / PreQC No. acst-24-127426 / Reviewed: 18-Feb-2024 / QC No. acst-24-127426 / Revised: 22-Feb-2024 / Manuscript No. acst-24-127426 / Published Date: 29-Feb-2024
Abstract
Bread wheat (Triticum aestivum L.) is a self-pollinated annual crop belonging to the family (Poaceae), tribe (Triticeae) and genus Triticum. This study aimed to assess the magnitude of genotypic and phenotypic association among traits, and identify promising bread wheat genotypes for further breeding. A total of 100 bread wheat genotypes were evaluated in alpha-lattice design with two replications in the 2022 main cropping season at Liban Jawi District, West Shewa Ethiopia. The results of correlation analysis revealed that grain yield had a positive and significant correlation with spike length, kernels per spike, biomass yield, harvest index, thousand seed weight, productive tillers, and awn length; while negative significant correlation with days to 50% heading, days to 90% maturity and non-significant correlation with grain filling period, plant height, spikelet per spike, flag leaf length, flag leaf width and peduncle length at genotypic level whereas positive and significant correlation with kernels per spike, biomass yield, harvest index, thousand seed weight, productive tillers, awn length, negative significant correlation for days to heading and non-significant for the left traits at phenotypic level.
Keywords
Wheat; Correlation; Genotype; Path coefficient; Phenotypic; Traits
Introduction
Bread wheat (Triticum aestivum L.), is a self-pollinated annual crop that belongs to the family Gramineae (Poaceae), tribe Triticeae, genus Triticum and species aestivum (Acquaah, 2007). It is an allohexaploid species with 3 different genome configurations (A, B, D) of 42 chromosomes (2n=6x= 42) and perfect flowers which enforced to reproduction as autogamous crop with 1-4% natural cross-pollination (Hei et al., 2017). Bread wheat is one of the oldest cereal crops regarded as the ‘king of cereals’ since it shares a large area under production, has high productivity and holds a prominent position in the international food grain trade (Hazra et al., 2019b) [1].
In Ethiopia wheat is produced under rain-fed conditions and nowadays broadly cultivated under irrigation. Wheat is widely grown in the high-lands and mid-altitudes of Ethiopia. In spite of the existence of wide agro-ecological suitability for wheat production; demand for wheat is increased because of population growth, urbanization, and expansion of agro-industries in which wheat production is left behind by 25 to 30% to its demand in Ethiopia (Hodson et al., 2020).
Wheat is the major essential cereal crop occupying 1.79 million hectares of land with total production of 5.8 million tons and productivity was 3.046 t/ha in Ethiopia (CSA, 2021). Wheat is one of the main food crops and has been the basic staple food (Haas et al., 2018). Wheat has great importance for economic, commercial, and industrial, diets for human beings and also using for livestock feed (Shewry and Sandra, 2015). It is one of the staple foods and the main source of calories in the major producing areas (Shiferaw et al., 2013) [2].
For effective utilization of the genetic stock in crop improvement, direct selection for yield is frequently misleading in wheat as wheat yield is a polygenic trait. Therefore, it is necessary to know the mutual association between yield and yield-related traits (Alemu et al., 2019). Generating information about the genetic variability, correlations and mechanisms of inheritance of the genetic traits involved are the key tasks in the genetic improvement of any crop (Naik et al., 2015). To develop improved varieties it is essential to have detailed information on correlation among the traits (Mary and Gopalan, 2006).
Path coefficient analysis also provides a more effective means of separating the components of the correlation coefficient into direct and indirect effects through other characters and shows the relationship in a more meaningful way (Alemu et al., 2019) [3]. However, limited information is available on the association of yield and yield-related traits among bread wheat genotypes especially for a large number of new accessions for further improvement. Therefore, this research was conducted using one hundred different bread wheat genotypes with the objectives of:
• To evaluate the association of traits in bread wheat accessions.
• To determine the direct and indirect effects of traits on grain yield
Materials and Methods
Description of the Study Area
The experiment was conducted at Liban Jawi District, West Shewa Zone, Oromia Regional State of Ethiopia. Liban Jawi is located 173 km away from Addis Ababa and 47 km from Ambo town. The altitude of the district ranges from 1800 to 3098 m.a.s.l (meters above sea level) and receives an annual rainfall of 1000 mm to 1800 mm with an average temperature of 10.4-290c. It has three different agro-climatic conditions namely high land, moderate altitude and low land constituting 27%, 65% and 8%, respectively. The district is bordered in the north by Chaliya and Mida Kegni district, in the south by Dire Inchini and Jibat district, in the east by Toke Kutaye district and in the west by Dano, Chaliya and Jibat district. The dominant soil type of the test site is loamy soil with a PH of 6-7 (LJAO, 2021 unpublished data) (Figure 1) [4].
Figure 1: Map of the study area showing Ethiopia, Liban Jawi District and Erensa Kebele (study site) (Source: Adopted from Arc. GIS Computation and Ethiopian map, 2019).
Planting Materials
A total of hundred bread wheat accessions were grown at Liban Jawi in 2021 cropping season. These bread wheat accessions were collected from different sources such as: Ethiopian Biodiversity Institute (EBI), Kulumsa Agricultural Research Center (KARC) and Holeta Agricultural Research Center (HARC). Nine released varieties (Alidoro, Dandaa, Digelu, Enkoy, Hidase, Kingbird, Kubsa, Ogolcho and Wane) were used as check varieties (Table 1).
| S.N | Accession | Source | S.N | Accession | Source | S.N | Accession | Source |
|---|---|---|---|---|---|---|---|---|
| 1 | 31169 | Amara | 35 | 33907 | Amara | 69 | 34737 | Tigray |
| 2 | 31224 | Oromia | 36 | 33909 | Amara | 70 | 34804 | Amara |
| 3 | 31257 | Oromia | 37 | 33911 | Amara | 71 | 34821 | Oromia |
| 4 | 31258 | Oromia | 38 | 33915 | Tigray | 72 | 34856 | Amara |
| 5 | 31296 | Amara | 39 | 33917 | Amara | 73 | 36255 | Oromia |
| 6 | 31394 | Oromia | 40 | 33919 | Amara | 74 | 36503 | Amara |
| 7 | 31395 | Oromia | 41 | 33921 | Amara | 75 | EBW192364 | KARC |
| 8 | 31430 | Amara | 42 | 33924 | Amara | 76 | EBW192398 | KARC |
| 9 | 31542 | Oromia | 43 | 33972 | Oromia | 77 | EBW192299 | KARC |
| 10 | 31543 | Oromia | 44 | 34029 | Amara | 78 | EBW192344 | KARC |
| 11 | 31600 | Oromia | 45 | 34037 | Amara | 79 | EBW192345 | KARC |
| 12 | 31551 | Oromia | 46 | 34039 | Amara | 80 | EBW192362 | KARC |
| 13 | 31554 | Oromia | 47 | 34043 | Oromia | 81 | EBW192610 | KARC |
| 14 | 31593 | Amara | 48 | 34045 | Amara | 82 | BW184033 | KARC |
| 15 | 31627 | Oromia | 49 | 34053 | Oromia | 83 | EBW192875 | KARC |
| 16 | 31630 | Oromia | 50 | 34073 | Amara | 84 | EBW192865 | KARC |
| 17 | 31632 | Oromia | 51 | 34086 | Amara | 85 | EBW192348 | KARC |
| 18 | 31643 | Oromia | 52 | 34097 | Oromia | 86 | EBW192489 | KARC |
| 19 | 31644 | Oromia | 53 | 34098 | Oromia | 87 | EBW192872 | KARC |
| 20 | 31786 | Amara | 54 | 34137 | Oromia | 88 | BWKU13383 | KARC |
| 21 | 31790 | Amara | 55 | 34145 | Oromia | 89 | EBW192398 | KARC |
| 22 | 31813 | Amara | 56 | 34152 | Amara | 90 | EBW194030 | KARC |
| 23 | 31818 | Amara | 57 | 34157 | Amara | 91 | EBW192870 | KARC |
| 24 | 33206 | Amara | 58 | 34159 | Amara | 92 | Alidoro | HARC |
| 25 | 33387 | Oromia | 59 | 34161 | Oromia | 93 | Danda,a | HARC |
| 26 | 33389 | Oromia | 60 | 34169 | Oromia | 94 | Digelu | KARC |
| 27 | 33516 | Amara | 61 | 34190 | Oromia | 95 | Enkoy | KARC |
| 28 | 33556 | Oromia | 62 | 34239 | SNNP | 96 | Hidase | KARC |
| 29 | 33597 | Amara | 63 | 34280 | Tigray | 97 | Kingbird | KARC |
| 30 | 33682 | Amara | 64 | 34667 | Oromia | 98 | Kubsa | KARC |
| 31 | 33794 | Amara | 65 | 34706 | Oromia | 99 | Ogolcho | KARC |
| 32 | 33828 | Amara | 66 | 34720 | Tigray | 100 | Wane | KARC |
| 33 | 33893 | Amara | 67 | 34728 | Tigray | |||
| 34 | 33901 | Tigray | 68 | 34735 | Tigray | |||
| Key: S.N =Serial Number, KARC=Kulumsa Agricultural Research, HARC = Holeta Agricultural Research Center | ||||||||
Table 1: List and source of 100 bread wheat accessions grown in 2021.
Experimental design and trial management
The field experiment was carried out in 4x25 alpha lattice design with two replications during the main rainy season of 2022 at one location. The total area including the border was 18.8 m x 17 m (319.6 m2) out of these effective trial area was 12.8 x 11 (140.8 m2). The replication had four blocks, in each block contains twenty-five plots with two plots at the border being used to minimize the bordering effect. The plot per block dimension was 27 rows of 2 m length with 0.20 m row spacing. It means twenty-five (25) entries and two borders which were 27 rows applied per block. The width of the block was 0.2 m x 27 which was 5.4 m per replication. The dimension of an individual block area was 5.4 m width x 2 m length (10.8m2). The spacing between blocks was 1 m and a space between plots was 0.2 m. Terraces were formed over the block to protect the plot from soil erosion [5]. The experimental field was well plowed four times before sowing and then planting rows were ready by exploitation of hand force row marker.
Planting was done by hand drilling method at a depth of 5 cm with a seed rate of 150 kg/ha. Planting was carried out in the first week of July. NPS and Urea fertilizers were applied at the rate of 100 kg/ha. Urea was applied in split application: first split (1/3) and the second split (2/3) of the total dose at planting and mid-tillering stages, respectively. Weeds were manually eradicated from the experimental field.
Data collection
Data were collected both on a plant and plot basis by random sampling technique with the use of descriptors for wheat (IBPGR, 1985).
Plant basis
Ten plants were randomly taken from the central plants for recording the following observations and the mean values for the treatments were computed.
Plant height (cm): The distance in cm between the ground level to the tip of the spike of ten plants (excluding the awns) at maturity was measured.
Number of productive tillers per plant (PT): The actual count of the fertile numbers of tillers of ten plants (spike bearing) per plant.
Spike length (cm): Length measured in cm from the base of the spike to the tip of the highest spikelet of ten plants (excluding the awns) in cm at maturity.
Spikelet per spike (SPS): The total number of spikelets on the main spike of all ten plants was counted at the time of maturity and the average was recorded.
Number of kernels per spike (KPS): The average number of seeds were recorded from the ten sampled plants.
Flag Leaf length (cm): Average length of the uppermost leaf on ten randomly selected plants at physiological maturity.
Flag Leaf width (cm): Average width of the uppermost leaf at the widest point on ten randomly selected plants at physiological maturity.
Peduncle length (cm): The length of peduncle from the last node to the tip of the peduncle during maturity on ten randomly tagged plants.
Awn length (cm): Awn length from the end of the spike to the tip of the awn was measured and the average for ten randomly tagged plants was recorded [6].
Plot basis
The data on the following attributing traits were collected based on the plots.
Days to 50% heading (DH): It was recorded by counting the number of days from sowing to the date when at least 50% of the heads in the plot fully exerted from the boom or flowered.
Grain filling period (GFP): It was the result obtained from the number of days to maturity minus the number of days to heading.
Days to 90% maturity: Recorded by counting the number of days from sowing to the days when 90% of the heads in the plot were physiologically matured.
Grain yield per plot (g): Moisture was adjusted to the standard moisture content at 12.5% moisture basis after threshing using a moisture tester and the adjusted yield per plot was converted to quintal per hectare.
Thousand seed weight (g): The weight (g) of 1000 seeds from randomly sampled seeds per plot is measured by using sensitive balance.
Biological yield or biomass (g): Was determined by weighing the total air dried above-ground biomass of the plot and converted to quintal per hectare.
Harvest index (%): It was calculated by dividing grain yield per plot to total above ground dry biomass yield per plot and then multiplied by hundred [7].
Data analysis
Analysis of variance (ANOVA)
All data were subjected to analyses of variance (ANOVA) using a general linear model (GLM) a procedure of SAS Statistical version 9.4 software (SAS, 2013). Least significance difference (LSD) was used to separate differences in parameters means of genotypes where significant variation was observed at a 5% probability level.
Analysis of variance was done using the following model:-
Yijl = μ + ��i + ��j + ρl (j) + ��ijl
Where; μ is the overall (grand) mean, ��i is the effect due to the ith treatment, (i=1, 2, 3…, t), γj is the effect due to the jth replication, and (j=1, 2…, r), ρl (j) is block inside replicate effect, εijl is that the error term wherever the error terms, are independent observations from an approximately normal distribution with mean = 0 and constant variance σ² ε (Table 2) [8].
| SV | DF | Mean square | F values | Expected mean square |
|---|---|---|---|---|
| Replication(r) | r-1 | Msr | Msr/Mse | σ2r + σ2b + σ2g + σ2e |
| Block(rep) | r(b-1) | Msb | Msb/Mse | rσ2b + σ2g + σ2e |
| Genotypes(g) | g-1 | Msg | Msg/Mse | rbσ2g + σ2e |
| Error | rg-rb-g+1 | Mse | σ2e | |
| Total | rg-1 | Mst | ||
| Key: SV= source of variation, DF= degree of freedom, r= number of replication, b= block, g= genotypes, Msr= mean square of replication, Msg= mean square of genotypes, Msb= mean square of block within replication, Mse= mean square of error, Mst= mean square of total. | ||||
Table 2: Skeleton of analysis of variance table for alpha lattice design.
Phenotypic and genotypic correlation analysis
The phenotypic and genotypic correlation coefficients were worked out to determine the degree of association of a character with yield and also among the yield components by using the formula given by Johnson et al. (1955) and Sharma (1998). rp= Pcovxy/√(Vpx.Vpy): Where: rp= Phenotypic correlation coefficient, Pcovxy= Phenotypic covariance between variables x and y, Vpx= Phenotypic variance of variable x and Vpy= Phenotypic variance of variable.
rg= Gcovxy/(√Vgx. Vgy): Where: rg= Genotypic correlation coefficient, Gcovxy= Genotypic covariance between variables x and y, Vgx= Genotypic variance of variable x and Vgy= Genotypic variance of variable y.
rg= Gcovxy/(√Vgx.Vgy): Where: rg= Genotypic correlation coefficient, Gcovxy= Genotypic covariance between variables x and y, Vgx= Genotypic variance of variable x and Vgy= Genotypic variance of variable y.
The calculated phenotypic correlation coefficient values were tested for significance using t test: t= rph/SE (rp) Where, rp= Phenotypic correlation; SE (rp)= Standard error of phenotypic correlation obtained using the following formula (Sharma, 1998). SE(rp)= (√1-r2ph/h-2): Where, n is the number of genotypes tested, r2p is phenotypic correlation coefficient. The coefficients of correlations at genotypic levels were tested for their significance by the formula described by Robertson (1959) as indicated below: t= rgxy/SErgxy; SErgxy= (√1-r2gxy.h2y)/h2x.The calculated ''t'' was compared with the tabulated ''t'' value at (n-2) degree of freedom at 5% level of significance.Where, n is number of genotype, h2x= Heritability of trait x, h2y= Heritability of trait y [9].
Path coefficient analysis
By considering the phenotypic and genotypic correlation coefficients between traits, path coefficient analysis was carried out to know the direct and indirect effects of yield-related traits on grain yield following (Dewey and Lu, 1959). rij= pij+∑rik.pkj, where; rij= mutual association between the independent character (i) and dependent character (j) as measured by the correlation coefficient, Pij= component of direct effects of the independent character (i) on the dependent character (j) as measured by the path coefficient and, ∑rik pkj= summation of components of indirect effect of a given independent character (i) on the given dependent trait (j) via all other independent traits (k) [10].
The residual effect, which determines how best the causal factors account for the variability of the dependent factor yield, was computed using the formula; 1= p2R + Σpijrij, where, p2R is the residual effect. Pijrij= the result of direct effect of any variable and its correlation association with yield.
Results and Discussion
Association of characters
Genotypic correlation coefficient (GCC)
The genotypic correlation coefficients of traits are given. Days to heading had a strong and positive association with days to maturity, plant height, spike length, spikelet per spike, biomass yield, productive tillers, flag leaf length, flag leafwidth, awn length, peduncle length and significant positive associated with thousand seed weight. However, the trait revealed negative, but significant association with grain filling period, number of kernel per spike, harvest index and grain yield.
Days to maturity had highly significant and positive correlation with plant height, spike length, spikelet per spike, biomass yield, flag leaf length, flag leaf width and peduncle length whereas significant and positive correlation with productive tillers at genotypic level. This trait showed highly significant and negative genotypic correlation with grain filling period, kernels per spike and harvest index while non-significant for the rest traits. Plant height had a highly significant and positive association with spike length, biomass yield, productive tillers, flag leaf length, flag- leaf width, awn length and peduncle length at genotypic level and showed positive and significant genotypic correlation with spikelet per spike and thousand seed weight. The trait showed highly significant and negative association with kernel per spike and harvest index [11].
Spike length was a positive and highly significant correlation with the number of spikelet per spike, biomass yield, productive tillers, flag leaf length, awn length and a positive significant association with flag leaf width and peduncle length. The character revealed non-significant and negative associations with the rest characters. Positive highly significant correlation was observed for a number of kernels per spike with the harvest index. The trait had a negative highly significant association with productive tillers, flag leaf length, flag leaf width and peduncle length and was non-significant with the rest characters in positive and negative directions at the genotypic level.
Biomass yield showed a positive highly significant correlation with productive tillers, flag leaf length, flag leaf width, awn length and peduncle length. This trait showed a positive significant genotypic correlation with thousand seed weight and a negative significant genotypic correlation with harvest index. The result of correlation analysis revealed that grain yield had a positive highly significant genotypic correlation with kernel per spike, biomass yield, harvest index, thousand seed weight and productive tillers while it showed positive significance with spike length and awn length. This trait showed negative significance with days to heading, days to maturity and non-significance correlation with grain filling period, plant height, spikelet per spike, flag leaf length, flag leaf width and peduncle length irrespective of direction [12].
Similarly, Adhiena et al. (2016) reported a significant negative correlation between grain yield and number of days to maturity. Alemu et al. (2016) and Din et al. (2020) also reported number of productive tillers, spike length, thousand seed weight, biomass yield and harvest index had a positive correlation with grain yield.
Phenotypic correlation coefficient (PCC)
From result of the analysis for a number of days to heading showed significant and positive phenotypic correlation with days to maturity, plant height, spike length, number of productive tillers, number of spikelets per spike, biomass yield, flag leaf length, flag leaf width, awn length and peduncle length. However, the same trait showed a negative and significant correlation with grain filling period, kernel per spike and harvest index at the phenotypic level. It showed a non-significant association with the remainder of the characters. Significant and positive phenotypic correlation was observed for days to maturity with plant height, spike length, number of spikelets per spike, biomass yield, thousand seed weight, flag leaf length, flag leaf width and peduncle length whereas positive non-significant phenotypic correlation with productive tillers and awn length. Plant height had a positive and highly significant association with spike length, biomass yield, productive tillers, flag leaf length, flag leaf width, awn length, peduncle length and a positive significant correlation with the number of spikelets per spike and thousand seed weight [13].
It showed insignificant phenotypic positive for grain yield and negative correlations with the rest characters. Spike length revealed a highly significant positive association with days to heading, days to maturity, plant height, number of spikelets per spike, biomass yield, flag leaf length and awn length whereas significant association with productive tillers at phenotypic level. It had negative and significant association with grain filling period and harvest index while showed positive non-significant for the rest characters at phenotypic level.
The number of kernels per spike had a highly significant and positive association with the grain filling period, spikelet per spike and harvest index. However it had a negative highly significant correlation with days to heading, plant height, productive tillers, flag leaf length, flag leaf width and peduncle length whereas non-significant positive and negative correlation with the rest characters. Thousand seed weight had highly significant and positive associations with spikelet per spike and it showed a positive significant association with days to maturity, plant height, biomass yield, harvest index, flag leaf width and awn length. The character exhibited positive and negative non-significant phenotypic association with all the left traits [14].
Grain yield had significant positive phenotypic associations with kernels per spike, biomass yield, harvest index, thousand seed weight, productive tillers, awn length and negative significantly associated with days to heading whereas non-significant for days to maturity, grain filling period, plant height, spike length, spikelet per spike, flag leaf length, flag leaf width and peduncle length. Birhanu et al. (2017) also reported number of productive tillers; kernels per spike, spike length, thousand seed weight, biomass yield and harvest index had a positive correlation with grain yield at the phenotypic level [15].
Path coefficient analysis (PCA)
Phenotypic path coefficient analysis
Characters that had a significant relationship with grain yield were included in the path analysis (Dewy and Lu, 1959). The phenotypic direct and indirect effects of traits on grain yield are presented [16].
Phenotypic path coefficients showed that kernels per spike, biomass yield and harvest index exerted positive direct effects on grain yield with the range of 0.05 for kernels per spike to 0.78 for both biomass yield and harvest index. High values of direct effects exhibited that the actuality relationship and direct selection for these traits may additionally increase and provide a higher response for improvement of grain yield and may be major selection criteria in bread wheat breeding programs.
On the other hand, the negative direct effect on grain yield was exhibited through days to heading (-0.66), thousand seed weight (-0.002), productive tillers (-0.004) and awn length (-0.02). The negative direct effects on grain yield would show that the selection for these characters would not be rewarding for yield improvement. The indirect effects via most different characters were negligible [17,18].
The phenotypic residual effect (0.033) indicated that about 96.7% of the variability in total grain yield was contributed by the seven characters studied in path analysis. About 3.3% of the variability towards bread wheat genotypes grain yield in the present study might be due to many reasons that were not studied (Table 3) [19].
Traits |
DH | KPS | BY | HI | TSW | PT | AL | Rp |
|---|---|---|---|---|---|---|---|---|
| DH | -0.7 | -0.01 | 0.2 | -0.33 | -0 | -8E-04 | -0.005 | -0.16* |
| KPS | 0.15 | 0.05 | 0.09 | 0.31 | -0 | 0.001 | -5E-04 | 0.4** |
| BY | -0.2 | 0.005 | 0.78 | -0.15 | -0 | -0.002 | -0.004 | 0.61** |
| HI | 0.28 | 0.02 | -0.15 | 0.78 | -0 | 4E-04 | 0.0005 | 0.63** |
| TSW | -0.1 | 0.006 | 0.12 | 0.13 | -0.002 | 4E-04 | -4E-04 | 0.22** |
| PT | -0.2 | -0.01 | 0.36 | -0.08 | 2E-04 | -0.004 | -0.004 | 0.28** |
| AL | -0.2 | 0.001 | 0.18 | -0.02 | -0 | -7E-04 | -0.02 | 0.15* |
| Residual effect = 0.033 Key: DH= days to heading, KPS= kernels per spike, BY= biomass yield, HI= harvest index, TSW= thousand seed weight, PT= productive tillers and AL= awn length. |
||||||||
Table 3: Direct (bold diagonal) and indirect effects (off diagonal) at phenotypic level of traits on yield of bread wheat genotypes at West Shewa.
Genotypic path coefficient analysis
The result of a genotypic path analysis revealed that kernels per spike (3.32), thousand seed weight (3.06), awn length (1.37), biomass yield (1.22), spike length (1.01) and harvest index (0.36) exerted positive genotypic direct effects on grain yield. The genotypic direct effects of these traits ranged between 0.36 for harvest index and 3.32 for kernels per spike. The direct effects of those traits on total grain yield indicate that, improvement in these traits will increase the grain yield of bread wheat yield. Genotypic negative direct effects of traits on grain yield were in the range between -0.46 for productive tillers and -3.84 for days to maturity. The Maximum negative direct effect was exerted by days to maturity (-3.84) followed by days to heading (-0.79) [20].
The negative direct effect of the productive tillers was compensated by its positive indirect effects via harvest index, thousand seed weight and awn length and rendered the genotypic correlation coefficient positive and significant. The genotypic residual effect (0.11) indicated that about 89% of the variability in grain yield was contributed by the nine characters studied in path analysis. About 11% of the variability toward yield in the present study might be due to many reasons that were not studied (Table 4) [21].
| Traits | DH | DM | SL | KPS | BY | HI | TSW | PT | AL | Rg |
|---|---|---|---|---|---|---|---|---|---|---|
| DH | -0.79 | -3.38 | 0.58 | -1.1 | 0.34 | -0.16 | 0.51 | -0.12 | 0.38 | -0.16* |
| DM | 6.96 | -3.84 | 0.62 | -0.82 | 0.39 | -0.16 | -0.39 | -0.08 | -0.003 | -0.15* |
| SL | 4.54 | -2.34 | 1.01 | -0.1 | 0.65 | -0.09 | 0.4 | -0.11 | 0.36 | 0.16* |
| KPS | -2.63 | 0.95 | -0.03 | 3.32 | 0.14 | 0.18 | 0.3 | 0.12 | 0.04 | 0.48** |
| BY | 2.19 | -1.23 | 0.54 | 0.38 | 1.22 | -0.07 | 0.5 | -0.23 | 0.31 | 0.62** |
| HI | -3.5 | 1.69 | -0.25 | 1.67 | -0.24 | 0.36 | 0.6 | 0.07 | -0.03 | 0.62** |
| TSW | 1.33 | -0.49 | 0.13 | 0.33 | 0.2 | 0.07 | 3.06 | 0.06 | 0.28 | 0.25** |
| PT | 2.09 | -0.69 | 0.25 | -0.85 | 0.61 | -0.05 | -0.39 | -0.46 | 0.28 | 0.3** |
| AL | 2.2 | 0.01 | 0.26 | 0.09 | 0.28 | -0.01 | 0.63 | -0.09 | 1.37 | 0.15* |
| Residual effect= 0.11 Key: DH= days to heading, DM= days to maturity, SL= spike length, KPS= kernels per spike, BY= biomass yield, HI= harvest index, TSW= thousand seed weight, PT= productive tillers and AL= awn length |
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Table 4: Direct (bold diagonal) and indirect effects (off-diagonal) at genotypic level of traits on yield of bread wheat genotypes at West Shewa.
Conclusions and Recommendation
Knowledge of the degree of relationship between yield and other agronomic traits is important as they provide the basis for successful breeding strategies. In the present research work, a total of 100 bread wheat genotypes were evaluated for their correlation during the main cropping season of 2022 in West Shewa, central Ethiopia. The correlation and path analysis indicated that kernels per spike, biomass yield and harvest index showed positive association and direct effect on grain yield. Therefore, to bring an effective improvement of grain yield, more attention should be given to these traits which showed high positive phenotypic and genotypic correlation coefficients with considerable direct and indirect effect on grain yield. According to present findings' improvement in grain yield could be achieved by direct or indirect selection for yield components positively associated to yield. Accordingly, traits like kernels per spike, bio mass yield and harvest index had positive direct effect on grain yield and hence could be used as selection criteria to improve grain yield.
Acknowledgements
I want to express my deepest gratitude and appreciation to Raya University for the opportunity and financial support during my study. Thanks to Ethiopia Biodiversity Institute (EBI), Kulumsa Agricultural Research Center (KARC) and Holeta Agricultural Research Center (HARC) who has provided me with bread wheat germplasm.
Conflict of Interests
The authors have declared none conflict of interests.
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Citation: Gelgu F, Chandra Sekhar Singh B, Nepir G (2024) Genotypic andphenotypic Correlation and Path Coefficient Analysis for Yield and Yield-relatedTraits in Bread Wheat (Triticum aestivum L.) Genotypes. Adv Crop Sci Tech 12: 670.
Copyright: © 2024 Gelgu F, et al. This is an open-access article distributed underthe terms of the Creative Commons Attribution License, which permits unrestricteduse, distribution, and reproduction in any medium, provided the original author andsource are credited.
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