Genetic Variability in Bread Wheat (Triticum aestivum L.) Genotypes under Irrigated Condition

Introduction

Bread wheat is one of the most important cereal crops in the world, farmed both irrigated and rain-fed, and serves as a reliable food source for many populations ⁹. The study of bread wheat is crucial due to its value as a cash crop, high level of production per unit area, and its role in meeting the dietary needs of society ¹⁷. The major producers of wheat worldwide are China, India, and Russia, whereas South Africa and Ethiopia are Africa’s major producers ¹⁴. Even though wheat has been grown in Ethiopia since time immemorial, bread wheat, the third-most important cereal crop of the country, is a newly introduced crop ⁴. Wheat roughly accounts for 17% of the nation’s annual grain production². Any breeding population’s performance improvement depends on available and created genetic variability ¹³.

Previous research has been conducted on the production and major producers of wheat globally, as well as its importance in Ethiopia. There is a lack of research on the yield potential, genetic variability, and heritability of bread wheat genotypes grown under irrigated conditions in the central highlands of Ethiopia. The development of high-yielding adaptive genotypes for off-season cropping in the central highlands of Ethiopia is in high demand, and this study aims to evaluate the yield potential, genetic variability, and heritability of bread wheat genotypes grown under irrigated conditions in Ginchi, Ethiopia. What are the yield potential, genetic variability, and heritability of bread wheat genotypes grown under irrigated conditions in the central highlands of Ethiopia? This study aims to evaluate the yield potential, genetic variability, and heritability of bread wheat genotypes grown under irrigated conditions in the central highlands of Ethiopia. It is hypothesized that the bread wheat genotypes grown under irrigated conditions in the central highlands of Ethiopia will have high yield potential, genetic variability, and heritability.

Materials and Methods

Description of Study Area

The study was carried out in the Ginchi sub-station Ambo Agricultural Research Center experimental field during the off-season (November twenty-one to April twenty, 2021). The site is located at an altitude of 2304 meters above sea level at 9^o 01′ 2.2″ N latitude and 38^o 06′ 11.8″ E longitude. The region has average minimum and maximum temperatures of 11°C and 21°C, and a relative humidity of 64.4% as well as the region has 1050 mm of regular annual rainfall. The major soil type of the area is heavy vertisol with a pH of 7.8 for the uppermost topsoil. (0–30 cm) ⁷.

Figure 1: Study Area Map. Click here to view Figure

Experimental Materials and Design

Experimental materials were constituted of sixty-two bread wheat advanced pure lines in Table 1, obtained from Ambo Agricultural Research Center (AARC) and two released varieties from Sinana and Bako Agricultural Research Center used as standard checks. The advanced lines were originally sourced from the International Maize and Wheat Improvement Center and advanced by the Kulumsa National Wheat Program located at Kulumsa Agricultural Research Center (Table 2). The experiment was laid out in an 8 × 8 simple lattice design in two replications with a plot size length of 2.5 m x 1.2 m =3m². Total plot 128; Spacing between rows, adjacent plots, blocks, and replication were 20 cm, 1 m, 1 m, and 2 m respectively. The seed rate for irrigated conditions was 125 kg ha^-1, and NPS was applied by drilling at sowing time. While urea fertilizers were employed at a rate of 100 kg ha^-1, urea was applied in equal parts during the tillering and heading stages. Weeds were manually managed as necessary. The data were collected from a net plot area of 2m².

Table 1: A list of bread wheat genotypes that were evaluated in the study

Code	Genotype	Source	Code	Genotype	Source	Code	Genotype	Source
G1	BW17 4382	KU18BW OE-5-26	G28	BW17 2582	KU18BW OE-3-6	G55	BW17 4421	KU18BW OE-7-35
G2	BW17 2655	KU18BW OE-3-27	G29	BW17 2580	KU18BW OE-7-10	G56	BW17 4424	KU18BW OE-1-20
G3	BW17 2683	KU18BW OE-4-36	G30	BW17 2596	KU18BW OE-4-35	G57	BW17 4416	KU18BW OE-10-25
G4	BW17 2653	KU18BW OE-6-20	G31	BW17 2598	KU18BW OE-4-9	G58	BW174397	KU18BW OE-6-33
G5	BW17 2682	KU18BW OE-1-23	G32	BW17 4101	KU18BW OE-10-4	G59	BW17 4399	KU18BW OE-3-25
G6	BW17 2661	KU18BW OE-3-35	G33	BW17 2581	KU18BW OE-3-16	G60	BW174420	KU18BWOE-9-20
G7	BW17 2726	KU18BW OE-10-14	G34	BW17 2575	KU18BW OE-7-34	G61	BW17 4393	KU18BWOE-9-17
G8	BW17 2750	KU18BW OE-5-34	G35	BW17 2592	KU18BW OE-5-10	G62	BW17 4390	KU18BW OE-5-22
G9	BW17 2712	KU18BW OE-9-29	G36	BW17 2576	KU18BW OE-7-2	G63	Obora	Sinana
G10	BW17 2719	KU18W OE-9-31	G37	BW12 0131	KU18BW OE-3-30	G64	Liben	Bako
G11	BW17 2722	KU18BW OE-3-29	G38	BW12 0097	KU18BW OE-6-4
G12	BW17 2711	KU18BW OE-3-28	G39	BW12 0094	KU18BW OE-3-2
G13	BW17 2710	KU18BW OE-6-26	G40	BW12 0103	KU18BW OE-10-34
G14	BW17 2695	KU18BW OE-5-31	G41	BW12 0136	KU18BW OE-3-17
G15	BW17 4395	KU18BW OE-2-2	G42	BW12 0124	KU18BW OE-10-18
G16	BW17 2646	KU18BWOE-2-25	G43	BW12 0113	KU18BW OE-4-33
G17	BW17 2631	KU18BW OE-2-1	G44	BW12 0091	KU18BW OE-6-12
G18	BW17 2650	KU18BW OE-4-27	G45	BW12 0117	KU18BW OE-3-22
G19	BW17 2642	KU18BW OE-4-1	G46	BW12 0151	KU18BW OE-1-18
G20	BW17 2640	KU18BW OE-1-33	G47	BW12 0074	KU18BW OE-2-21
G21	BW17 2638	KU18BW OE-1-24	G48	BW17 4386	KU18BW OE-2-10
G22	BW17 2623	KU18BW OE-6-21	G49	BW12 0078	KU18BW OE-10-27
G23	BW17 2618	KU18BW OE-4-7	G50	BW12 0075	KU18BW OE-7-3
G24	BW17 2632	KU18BW OE-1-36	G51	BW12 0081	KU18BW OE-9-30
G25	BW17 2624	KU18BW OE-5-18	G52	BW12 0076	KU18BW OE-1-29
G26	BW17 2626	KU18BW OE-8-5	G53	BW17 4455	KU18BW OE-5-36
G27	BW17 2583	KU18BW OE-10-15	G54	BW17 4342	KU18BW OE-4-30

 Data Collected

Data was collected using the International Plant Genetic Resources Institute procedure ³. The following plant-based data were recorded: the number of tillers per plant (NTPT), plant height (PH), spike length (SL), number of spikelets  spike^-1 (SPS), and number of kernels  spike-¹ (KPS). Total net plot:- Data on days to 50% emergence (DTE), days to 50% heading (DTH), grain filling period (GFP), days to 90% physiological maturity (DM), thousand kernel weight (TKW), Hectoliter weight(HLW), Grain protein content (GPC), biomass yield (BY), grain yield (GY), and harvest index (HI) traits were collected.

Data Analysis

PBIB design analysis of variance, Tukey’s honest significance difference means performance test, and variability analysis were done using agricolae, and variability packages of R software (version 4.1.0).

Results and Discussion

Analysis of Variance

Analysis of variance revealed significant differences (P<0.01) across genotypes for every trait except the number of tillers per plant (Table 2). Similarly, ^{1, 6} discovered extremely significant variations between genotypes in those mentioned traits. These include the number of days to heading, days to maturity, grain filling period, thousand kernel weight, plant height, spike length, number of spikelets spike^-1, and number of grains plant^-1, as well as the amount of grain produced  plot^-1, harvest index, and hectoliter weight. Analysis of variance reveals that the tested genotypes differed in the majority of traits. Thus, it might be exploited in future breeding efforts.

Table 2: Mean squared analysis of variance for fourteen parameters of 64 bread wheat genotypes examined in Dandi District.

Traits	Means square				CV%
	Rep	Block within Rep (Adj.)	Genotypes  (Adj.)	Residual (inter-block)
	(DF=1)	(DF=14)	(DF=63)	(DF=49)
Days to 50% emergency	4.5**	0.29**	0.25**	0.087	2.70
Days to 50% heading	2.53	1.82	37.78**	1.82	1.30
Days to 90% maturity	5.28	4.39	49.54**	4.309	1.20
Grain filling period	0.5	4.5	18.41**	3.6036	2.50
Plant height(cm)	47.37**	24.04**	49.79**	5.16	1.80
Number of tillers plant-1	0.0001	0.22	0.198ns	0.152	9.50
Spike length (cm)	0.206	0.39**	0.63**	0.14	2.80
Number of spikelets spike-1	0.67	0.89	3.66**	0.66	2.90
Number of kernels spike-1	3.85	30.83**	48.47**	12.38	4.70
Thousand kernels weight	2.51	6.23	56.72**	10.02	4.20
Hectoliter weight (Kg)	7.5	6.48	10.027**	1.55	1.63
Biomass yield (Kg/ha)	9098114	2612827	3009801**	1321392	10.90
Grain yield (kg/ha))	28375**	5180*	9664**	4139	6.20
Harvesting index (%)	0.0007	13.691	22.58**	7.365	5.20
Grain protein content (%)	0.18	0.699**	2.43**	0.178	1.08

Key ***, **, * Significant at P<0.001, 0.01 and 0.05 levels, respectively, SV=source of variation, DF=degree of freedom CV=coefficient variation

Mean Performance of Genotypes

The mean grain yield numbers varied from 3763 kg ha^-1 for G45 to 6811 kg ha^-1 for G31.  The total population mean was 5561 kg ha^-1 (Table 3). G31 (6811 kg ha^-1) and G11 (6806 kg ha^-1) were the highest grain yields. For the left genotypes yield next to above their order, which was chosen for the materials as good yielders as follows:   G51 (6797 kg ha^-1), G6 (6575 kg ha^-1), G3 (6556 kg ha^-1), G34 (6548 kg ha^-1), G23 (6430 kg ha^-1), G5 (6325 kg ha^-1), G41 (6304 kg ha^-1), G59 (6213 kg ha^-1), G36 (6177 kg ha^-1), G46 (6157 kg ha^-1), and G25 (6153 kg ha^-1). The lowest yield values were reported for genotype G45 (3763 kg ha^-1), followed by G19 (3913 kg ha^-1), G29 (4126 kg ha^-1), and G14 (4231 kg ha^-1). When compared to check varieties, thirteen genotypes showed higher mean grain yield values than the standard check Liben, whereas forty genotypes outperformed the standard check Obora.

The grain protein content values for the examined genotypes ranged from 10.4% to 14.9%, with a population mean of 12.34% (Appendix Table 1). The grain protein content was highest for G17, followed by G14, G20, G4, G19, G55, G27, G18, and G29. Low grain protein content was found for the Obora variety, followed by G42, G5, and G34. All genotypes showed higher mean grain protein content than the standard check, Obora.  Aside from this, sixteen genotypes showed a higher mean grain protein content than the normal check Liben. This conclusion was consistent with that of ¹¹, who found an average wheat grain protein content of 12%.  Hectoliter weight readings varied from 66.95 kg ha^-1 to 76.8 kg ha^-1, with an average value of 71.83 kg ha^-1. According to the analyzed data, the maximum hectoliter weight was observed on G55 (76.8 kg ha^-1).  The left genotypes were the following: G12 (76.4), G54 (76), G57 (75.6), G62 (75.6), G9 (75.1), and G1 (75.0). The lowest hectoliter weights were observed for G41 (74.8), G48 (74.8), G59 (74.4), G40 (74.3), and G23 (74.2). In general, sixty genotypes had a greater hectoliter weight than the standard check, Liben.

 According to earlier research, nine genotypes were classified as early maturing based on their mean performance(Appendix Table 1 ). These include G17, G35, G29, G8, G19, G3, G4, G25, and G11. The G63 has been shown to mature at approximately 136 days, making it the latest mature genotype. Next to this, G45, G43, G41, and G40 have a similar value of days to maturity at 129.5 days. The top ten most promising genotypes were chosen for comparison with the check varieties based on their performance outcomes across all variables (Table 3 Appendix Table 1). These genotypes performed above average on attributes of interest. These include days to 50% heading, number of kernels spike-1, thousand kernel weight, biomass yield, the yield of grain hectare^-1, and harvesting index. In addition, they had intermediate values for days to maturity, grain filling duration, plant height, and hectoliter weight.

Furthermore, they found lower values for days to 50% emergence, spike length, number of spikelets, and grain protein content. This finding was consistent with ^{5, 12}, who reported similar results on traits such as days to maturity, plant height, grain filling period, thousand kernel weight, hectoliter weight, spike length, number of spikes per spike, grain spike spike-1, biomass yield, harvest index, and grain yield. As a result, the selected genotypes can go to the next stage of the bread wheat advancement program.

Table 3: Mean performance of top ten genotypes along with standard checks evaluated at Dandi District 2021 off-season Click here to view Table

Estimation of Variance Components

Genotypic and phenotypic variances were more than 20%. For multiple traits that were investigated. These include days to maturity, plant height, thousand kernel weight, number of kernels spike^-1, biomass yield, and grain yield. For these traits, genotypic differences were higher than environmental variances. The lowest genetic variation was seen for days to 50% emergence, grain protein content, and spike length.

Genotypic and Phenotypic Coefficients Variation

Grain yield, biomass yield, thousand kernel weight, and number of kernel spike^-1 characteristics all showed moderate phenotypic coefficients of variation (PCV). The features with the lowest PCV were days to 50% heading, 50% grain filling period, days to maturity, plant height, and spike length (Table 4). The genotypic coefficient variation (GCV) was less than 20% for all characteristics except grain yield. The previous results were in line with those of Haydar ¹⁰. Because grain yield exhibited low GCV, it must be considered when using grain yield as a selection measure for improving these genotypes. In general, the PCV estimation was somewhat higher than the matching GCV, showing the importance of external factors in character expression. This was consistent with the findings of ^{5, 10}.

Heritability and genetic advance as percent of mean  

The majority of the traits: days to 50% heading, grain filling period, days to maturity, plant height, number of spikelets spike^-1, thousand kernels weight, grain protein content, and hector liter weight; lay on high heritability class. The other four traits: number of kernels spike^-1, spike length, grain yield hector^-1, and harvesting index; were moderately heritable and the remaining two (days to emergence and biomass yield) traits showed low heritability results (Table 4). Similar findings were reported by ¹⁵ also observed high heritability for spike length, kernel spike^-1, and grain yield. These research findings of heritability showed traits of consideration are least influenced by the test environment.

Expected genetic advance as a percentage of the mean (GAM) values ranged from 3.73% for days to 50% emergency to 16.90% for thousand kernel weight (Table 4). Moderate GAM was observed in thousand kernels weights followed by grain protein content, grain yield hector^-1, number of kernels spike^-1, days to 50% heading, and number of spikelets spike^-1. This result is similar to ^8,16 for grain yield hector^-1 and number of kernels spike^-1. Whereas, genetic advance as a percent of mean values was low for days to 50% emergency, hectoliter weight, harvest index, days to maturity, spike length, grain filling period, biomass yield, and plant height. Comparable results were reported on days to 50% of emergency, plant height, and days to 90% maturity ¹⁸.  Those characteristics with moderate genetic progress as a percentage of mean values also had high to moderate heritability (i.e. thousand kernel weight, grain protein content, and grain yield ha^-1). It demonstrated that the phenotypes accurately described their genotypes, and that selection based on phenotypic performance would be reliable for improving bread wheat.

Table 4: Variability, heritability, and genetic advance estimation for traits of the genotype in the study Click here to view Table

Conclusion

In conclusion, the results of this study demonstrate the significant variations among the tested bread wheat genotypes for all considered traits. The study successfully identified high-yielding genotypes and determined the variation in genes, heritability, and advancement of yield-determining variables in bread wheat genotypes under irrigation conditions in Ethiopia. The identified high-yielding genotypes, G31, G11, G51, G6, G3, G34, G23, G5, G41, and G59, have shown promising potential for advancing the breeding objectives in the field of irrigated wheat. The findings of this study can be used to guide future breeding programs aimed at developing high-yielding and adaptive bread wheat genotypes for low-altitude areas and off-season irrigated farming in Ethiopia.

Declaration of competing interest

The authors declare that they have no known financial or interpersonal conflicts that might have looked to have influenced the research presented in this study.

Acknowledgements

First of all, we would like to thank Almighty GOD who gave us health, strength, and patience to complete this job. We would like to acknowledge the Ethiopian Institute of Agricultural Research (EIAR), Ambo University, and Ambo Agricultural Research Center for their financial and technical support.

Funding Sources

During this experiment, there was no support for publication.

Authors Contributions

Proposed proposal, study conception and design,

Data collection and data management,

Data analysis and interpretations of result and manuscript wrote

2nd. Author. Ermias Estifanos Desalegn

Interpretation of results

Reviewed and comment

Written paper comment

Field supervisor and advise way of data collection

3rd. Author. Negash Galata Ayena

Interpretation of results

Reviewed and comment

Written paper comment

Field supervisor and advise way of data collection

References

1. Alemu Dabi Firew Mekbib and Tadesse Desalegn, (2019). Genetic variability studies on bread wheat (Triticum aestivum L.) genotypes. Journal of Plant Breeding and Crop Science. 11(2): p. 41-54.
   CrossRef
2. Anteneh Adugnaw, Asrat Dagninet %J Cogent Food, and Agriculture, (2020). Wheat production and marketing in Ethiopia: Review study. https://doi.org/10.1080/23311932.2020.1778893. 6(1): p. 1778893.
   CrossRef
3. Bonjean Alain P and Angus William J, The world wheat book: a history of wheat breeding. 2001: Lavoisier Publishing.
   CrossRef
4. Desalegn Negasa and Chauhan Dinesh Kumar, (2016). Variability, Heritability, and Genetic Advances in Wheat (Triticum aestivum L.) Breeding lines grown at Horro Guduru Wollega Zone, Western Ethiopia. International Journal of Advanced Scientific Research and Management. 1(1): p. 24-28.
5. Endashaw Girma  and  Alemnesh Sisay, (2018). Genetic variability, heritability, and genetic advance for yield and quality traits in M2-4 generations of bread wheat (Triticum aestivum L.) genotypes. Turkish Journal Of Field Crops. 23(2): p. 173-179.
   CrossRef
6. Endashaw Girma , Wosene Gebreselassie, and Berhane Lakew, (2021). Correlation and Path Coefficient Analysis of Ethiopian Bread Wheat (Triticum Aestivum L.) Varieties.
   CrossRef
7. Getachew Agegnehu Abraham Feyissa, Gemechu Keneni and Mussa Jarso, (2007). Chickpea Varietal Response to Drainage on Vertisol of Ginchi in the Central Highlands of Ethiopia. Ethiopian Journal of Natural Resources. 9(2): p. 191-207.
   CrossRef
8. Gezahegn Fikre, Sentayehu Alamerew, and Zerihun Tadesse, (2015). Genetic variability studies in bread wheat (Triticum aestivum L.) genotypes at Kulumsa agricultural research center, South East Ethiopia. Journal of Biology, Agriculture and Healthcare. 5(7): p. 89-98.
   CrossRef
9. Godfray H Charles J, Beddington John R, Crute Ian R, Haddad Lawrence, Lawrence David, Muir James F, Pretty Jules, Robinson Sherman, Thomas Sandy M, and Toulmin Camilla, (2010). Food security: the challenge of feeding 9 billion people. science. 327(5967): p. 812-818.
   CrossRef
10. Haydar FMA, Ahamed MS, Siddique AB, Uddin GM, Biswas KL, and Alam MF, (2020). Estimation of genetic variability, heritability, and correlation for some quantitative traits in wheat (Triticum aestivum L.). Journal of Bio-Science. 28: pp. 81-86.
    CrossRef
11. Hussain A, Larsson Hans, Kuktaite Ramune, Prieto-Linde Maria Luisa, and Johansson Eva, (2009). Protein content and composition in organically grown wheat: influence of genotype. Agronomy Research. 7(Special Issue 2): p. 599-605.
12. M. F. Amin M. Hasan, N. C. D. Barma, M. A. Rahman and Rahman And M. M., (2017). Character association and path co-efficient analysis in wheat (Triticum aestivum L.). Bangladesh Journal of Agricultural Research. 42(3): p. 571-588.
    CrossRef
13. Majumder DAN, Shamsuddin AKM, Kabir MA, and Hassan L, (2008). Genetic variability, correlated response, and path analysis of yield and yield contributing traits of spring wheat. Journal of the Bangladesh Agricultural University. 6(2): p. 227-234.
    CrossRef
14. Nigus Mulu, Shimelis Hussein, Mathew IsacK, and Abady Seltene, (2022). Wheat production in the highlands of Eastern Ethiopia: opportunities, challenges and coping strategies of rust diseases. Acta Agriculturae Scandinavica, Section B—Soil & Plant Science: p. 1-13.
    CrossRef
15. Nukasani Vamshikrishna, Potdukhe Nilkanth Ramchandra, Bharad Swati, Deshmukh Shradha, and Shinde Sachin Murlidhar, (2013). Genetic variability, correlation and path analysis in wheat. Journal of Cereal Research. 5(2).
16. Sandeep Kumar, Naresh Pratap Singh, Vaishali Aastha, Vishakha Burman, and Khyati Lehri Neelesh Kapoor3, (2021). Analysis of Genetic Diversity in Wheat (Triticum Aestivum L.) Using Simple Sequence Repeats Marker. DOI:10.37273/chesci.cs2051101006v Chem Sci Rev Lett 2021, 10 (37), 94-102.
17. Sandeep Kumar Naresh Pratap Singh, Vaishali, Aastha, Vishakha Burman, Khyati Lehri and Neelesh Kapoor, (2021). Analysis of Genetic Diversity in Wheat (Triticum Aestivum L.) Using Simple Sequence Repeats Marker.
18. Tiwari Dev Nidhi, Tripathi Santosh Raj, Tripathi Mahendra Prasad, Khatri Narayan, and Bastola Bishwas Raj, (2019). Genetic variability and correlation coefficients of major traits in early maturing rice under rainfed lowland environments of Nepal. Advances in Agriculture. 2019.
    CrossRef

Appendix Table 1 Click here to view Table