Research Article

Evaluation of Genetic Variability and Yellow Rust in Selected Wheat Lines

Muhammad Sadiq1, Nadia1, Abdur Rauf1*, Khilwat Afridi2, Muhammad Qayash1, Saman Yaqub1, Kashmala Jabbar1, Guleena Khan1, Ikramullah Khan1, Adeel Khan1, Tahseen Ullah1, Tanweer Kumar3, Muhammad Arif3, Muhammad Ismail3 and Muntaha Munir4

1Garden Campus, Abdul Wali Khan University, Mardan, Khyber Pakhtunkhwa, Pakistan; 2Cereal Crop Research Institute Pirsabak, Nowshera, Khyber Pakhtunkhwa, Pakistan; 3Sugar Crops Research Institute (SCRI), Mardan, Khyber Pakhtunkhwa, Pakistan; 4Institute of Botany, University of Punjab Lahore, Pakistan.

Received | January 13, 2025; Accepted | March 07, 2025; Published | March 26, 2025

*Correspondence | Abdur Rauf, Garden Campus, Abdul Wali Khan University, Mardan, Khyber Pakhtunkhwa, Pakistan; Email: rauf77@awkum.edu.pk

Citation | Sadiq, M., Nadia, A. Rauf, K. Afridi, M. Qayash, S. Yaqub, K. Jabbar, G. Khan, I. Khan, A. Khan, T. Ullah, T. Kumar, M. Arif, M. Ismail and M. Munir. 2025. Evaluation of genetic variability and yellow rust in selected wheat lines. Pakistan Journal of Weed Science Research, 31(1): 16-36.

DOI | https://dx.doi.org/10.17582/journal.PJWSR/2025/31.1.16.36

Keywords | GCV, PCV, Heritability, Variability, Correlation, F3 populations, Lines

Copyright: 2025 by the authors. Licensee ResearchersLinks Ltd, England, UK.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Introduction

Wheat is among the most widely cultivated crops globally, serving as one of the earliest domesticated food sources and a primary provider of calories and proteins, especially in developing nations (Arzani and Ashraf, 2017). However, global wheat production, estimated at approximately 766 million tons, predominantly sustains 35% of the world’s population (Singh et al., 2023). It has grown across more than 125 countries, covering approximately 216 million hectares of land (Pequeno et al., 2021). Asia leads in wheat production, followed by Europe, the Americas, Oceania, and Africa worldwide (Erenstein et al., 2022). Notably, China, India, the Russian Federation, the USA, and France collectively contribute over 50% of the global wheat output. Europe and Asia exhibit high wheat productivity due to favorable environmental conditions (Sharma et al., 2021).

One of the main concerns is food security. The agricultural growth rate was calculated at 4.0% in 2017–2018, 0.6% in 2018–2019, and roughly 2.7% in 2019–2020, following the 2019–2020 National Economic Survey of Pakistan (Islam et al., 2023). Moreover, Pakistan’s wheat production is concentrated in the Punjab province, which contributes about 70% of the country’s total wheat production, followed by Sindh province, which contributes about 20% (Shahzad et al., 2022). Conversely, in the context of Pakistan, which is a developing country with a low per capita income, agriculture remains the most promising assurer of food security and increasing ability to feed a growing population. There is pressure on the cultivated land due to population increment, particularly in the urban areas, and relatively slow progression of the cultivated land area if the population growth rate continues to be sluggish, the population will double by 2050 (Ezeh et al., 2012). The provisional level of wheat production in Pakistan included spring wheat raised as a Rabi crop grown in Sindh, Baluchistan, NWFP, and Punjab. Winter wheat is grown in the northern region of Baluchistan (Bussay and Expert, 2009). Punjab and Sindh are the largest crop-producing provinces in Pakistan. In Punjab, cultivation occurred on irrigated land, only 10% was rainfed. Punjab accounts for the total wheat production in Pakistan (Akhtar and Athar, 2020). Punjab produced 19178.50 thousand tonnes of wheat in 2021-2022 (Rizwan et al., 2024). About 13.38% of Pakistan’s wheat is produced by Sindh (second largest producer), accounting for 26.4 million tonnes in 2021-2022 and Khyber Pakhtunkhwa contributed to 8.22% of the total wheat production (Rizwan et al., 2023). Wheat production in Pakistan is influenced by various factors such as water availability, soil fertility, temperature, pest and disease management, and inputs such as seeds and fertilizers. Despite the increase in wheat production, Pakistan still faces many challenges in ensuring food security and self-sufficiency. One of the major challenges is the low productivity of wheat crops, which is significantly lower than other wheat-producing countries. Compared to other South Asian nations, Pakistan has a comparatively high fertility rate, which could lead to social disputes over food (Islam et al., 2023). Agricultural sectors noted that the increase in population rate steadily affected nutritional availability. Pakistan is an agricultural country, with the highest population in Asia, raising several food security issues. Critical food difficulties have also been exacerbated by water scarcity and unfavorable climate changes. According to the 2022 Global Hunger Index, Pakistan has a severe level of hunger and is ranked 99th out of 121 nations (Rehmat et al., 2023). It is assessed that due to the rapid population growth and climatic risks, the wheat crop per capita in Pakistan was 198 Kg in 2014, and it is expected to be 105 Kg in 2031 and 84 Kg in 2050 (Tariq et al., 2014). An important staple food, wheat occupies about 45% of the global area under wheat and Asia has a significant share in the global wheat area. The second biggest challenge to high productivity in Pakistan is disease. In Pakistan, yellow rust is a threat to public health because it affects about 70 percent of wheat crops and results in outbreaks that lead to heavy losses (Ali et al., 2022). The best and cheapest long-term control measure that has received considerable attention is genetic resistance. In Pakistan, India, Bangladesh, China, and Russia, Triticum aestivum L. is the most important crop, while American consumers use it differently (Badar et al., 2023).

Bread wheat (soft wheat) primarily contributes to global wheat productivity, accounting for approximately 90 to 95% of production (Singh et al., 2023). Among over 300,000 plant species that have the potential to be edible, only around 100 are regularly grown, including wheat (Reynolds and Braun, 2022). The three major cereal grains are wheat, rice, and maize, all of which contribute to approximately 60% of people’s calories, while wheat supplies about 20% of the total calories and protein of food intake (Reynolds and Braun, 2022). Wheat belongs to the tribe Triticeae of the genus Triticum L. of the family Poaceae and the subfamily Pooideae. Different morphological, cytogenetic, and genomic characteristics have been used to classify wheat into different groups (Krishnappa et al., 2022).

Given the set lineage of descent from the wild to cultivated wheat, Caius and Pfisterer limn that it was the diploid (AA) einkorn, and tetraploid (AABB) emmer species, initially cultivated. Evidence suggests that the origin of these varieties may point to southeast Egypt (Shewry, 2009). It only appeared about 9,000 years ago when cultivation had reached the Near East, and then the current hexaploid bread wheat evolved. The landraces were chosen from the wild inhabitants by the agriculturalists due to their superior productivity and other excellent attributes (Shewry, 2009). Through breeding, highly adaptable varieties were evaluated, including tetraploid, and hexaploid, which are considered modern wheat to have strong compatibility with the environment (Yang et al., 2022).

Out of the 16 species, two are commercially available including bread (Triticum aestivum), macaroni (Triticum durum), and three growing patterns (winter, spring, and facultative wheat) (Iqbal et al., 2022). The T. aestivum (2n= 42, hexaploid, AABBDD) contributed 95% of global wheat production. It is again divided into soft wheat and hard wheat depending upon the grain’s hardness. It is mostly used as a flour for making different food items (Yang et al., 2022). T. durum (2n= 28, tetraploid, AABB) roughly shares 35 to 40% of total wheat production globally. This variety is adaptable to hot and dry weather conditions. Other varieties are Spelt (hexaploid), Emmer (tetraploid), and Einkorn (diploid). Their grains are not as well harvested through threshing as the other grains. From a marketing perspective, grain harvesting is identified as a wheat property among the harvesting properties (Iqbal et al., 2022).

Wheat is a valuable source of food, globally humans use it as a source of protein and carbohydrates, and animals use it as a dietary fiber. It also contains minerals, fats, and vitamins which are the sources of micronutrients and dietary fibers (Iqbal et al., 2022). The developed countries used it as only bread, noodles, cakes, pastries, and lactogen (Sharma et al., 2019). Along with nutrients, wheat is also used as a therapeutic. Other minerals in the form of phosphates and other mineral salts are present in the inner bran coatings according to (Kumar et al., 2011). The outer bran gives some supplements needed while the non-digestible parts help in digestion (Awulachew, 2020). Some of the fatalities include wheat germ, which is gotten during the purification process and contains vital vitamin E that if not taken, can lead to heart disease (Kumar et al., 2011). Moreover, the increased constipation of other gastrointestinal problems and nutritional diseases result from the absence of vitamins and minerals found in refined wheat flour. Whole wheat eliminates different diseases including constipation, heart illnesses, diverticulum, obesity, appendicitis, ischemic diseases, and diabetes among others (Iqbal et al., 2022).

Global climate fluctuations, high temperature and cold, and dryness in turn with disease risks are tremendous factors (Girardin, 2024). Nonetheless, by 2018, the wheat crop was grown on about 217 M ha of land, thus it is the most produced crop globally (Erenstein et al., 2022). Wheat from an agronomic point of view prefers the temperate climate. As for production, the world’s total production of wheat reached more than 765 million metric tons, while the growth was more than 30 million tonnes compared to the year 2019 (Sendhil et al., 2023). The three largest producers of wheat are Asian and European countries, and there has been a progressive trend in the concern production rates. Currently, Asian countries use 45% (2020 TE) of the global production; European countries use 35% (2020 TE); while Americans use 15% (2020) with a smaller portion by African and Oceanian countries (3-4%) (Ramadas et al., 2023). Due to the relatively stable global assets in stock of wheat in the past half century, the yield level of wheat has been on the rise, which according to our values can be attributed to a moderate production level (Erenstein et al., 2022). Total wheat trade is also likely to rise to 12.4% from 122.1 million MT in 2014 to 137.2 million in 2024. Due to the role that wheat plays in the fight against hunger in the world, the production of wheat in the world must reach 840 million tonnes by 2050 (Sendhil et al., 2023). But the need for land, which is reducing day by day, overpopulation, diseases, and many other requirements for production, are some of the problems that hinder the production of the required amount of wheat. Globally, per capita food consumption of wheat stands at 65.6 Kg per year, of which 37% is the average annual cereal consumption of 175 Kg globally excluding beverages (FAOSTAT, 2020; Erenstein et al., 2022). Currently, the developing Asian countries in South and East Asia, consume the most wheat, followed by the European Union and the Soviet Union (TM et al., 2023). Currently, more than 800 million global citizens are affected by hunger, and among them, the maximum percentage is from Sub-Saharan Africa and South Asia (Gupta et al., 2023).

The record shows that food production has been a target of pests and diseases (PandD). It has been reported that wheat suffers from almost 200 pests and diseases, of which 50 affect the yield significantly (Singh et al., 2016). The cost of wheat rust pathogens is approximately 15 million tonnes worldwide annually (Bhavani et al., 2022). The yellow rust resulted in yield losses of 5.5 million tonnes per year globally (Figueroa et al., 2022). Both biotic (rust pathogen) and abiotic (climate change, heat, cold, and drought stresses) are serious threats, which steadily decrease grain yield and affect morphological traits. Wheat stripe rust (yellow rust) is caused by Puccinia triticina, and Puccinia striiformis f.sp. tritici (Chen et al., 2014). The first external symptoms of clear and bright yellow, spotted, or flecked patches on wheat indicate the attack of stripe rust, which could be easily noticed morphologically (Badar et al., 2023). The likelihood is independent of the development stage, with an equal risk of new infection at any one of the plant leaf stages (Chen, 2017). Signs of illnesses develop about 1 week after infection, while sporulation occurs approximately two weeks later, with all required conditions of an environment. These fungal pathogens, after attacking the crops, form the tiny yellow to orange-colored rust pustules referred to as uredia, containing thousands of urediniospores, which are not even visible to the naked eye (Morin et al., 1992). Around 200 rust-resistance genes have been reported (Mapuranga et al., 2022) and disease management can be performed using resistant varieties instead of old and traditional varieties (Gupta et al., 2017). The available fungicides such as Tilt, Evito, Quadri, Prosaro, Stratego, and Quilt, are also very helpful in controlling pathogenesis (Carmona et al., 2020). There are certain cultural methods, including a combination of crop choices, selection of the right time for cultivation, removal of tillers of cereals, and volunteer plants that support the stripe rust.

To improve yield potential and morphological adaptation, wheat breeders are introducing new varieties having high grain yield and resistance to changing climates. Thus, the selection on the foundation of phenotypic variation in the wheat yield improvement program is not effective unless the genetic variation of the breeding material is adequately assessed and explored (Tessemav et al., 2020). Hence it becomes important for them to get information on the variation, which is phonotypical and genotypic, hereditary, and genetic. Success in selecting high-yielding genotypes requires information on the genetic variation and the relation between morphological agronomic traits and grain yield. Genetic variability, heritability, correlation coefficients, and other related parameters can aid in improving grain yield through targeted selection of specific traits and their relationship with overall productivity. Generally, heritability is a measure of the ways by which a character is passed from one generation to the next, ecological conditions. The mature selection ability of the wheat population relies mostly on the degree of heritable difference. There exists an interaction between PVE and IVE, which showed that the primary responsibility of wheat breeders was to assess wheat in terms of variability. The yield components of bread wheat genotypes were analyzed by the researchers, and the researchers suggested potential differences (Din et al., 2018). Productivity and yield improvement remain the objective of the prehistoric phase and contemporary breeding initiatives. The present study was performed to estimate genetic variability, heritability, (PCV), and (GCV) in the F3 wheat population.

Materials and Methods

The experiment was carried out to examine genetic variability, heritability, and correlation among the different wheat genotypes, along with yellow rust disease and its correlation with different morpho-grain related traits at the Cereal Crops Research Institute, Pirsabak, Nowshera, Khyber Pakhtunkhwa, Pakistan. The CCRI is situated in the North of Khyber Pakhtunkhwa province of Pakistan. Its location is at 34ᵒdegree North latitude, 71degree East longitude, and 288m altitude from sea level, the climatic condition of the area is considerably semi-arid.

Experimental details

This experiment was carried out in a proper irrigated condition with uniform nutrient availability and cultural practices. The experimental materials consisted of twenty-seven (27) wheat genotypes, which were sown in a Complete Randomized Block Design (RCBD) on 30 November 2022. The layout was appropriately managed for 27 wheat genotypes, each trail consisted of three replications. Each replication was sown in two (2) rows, the length of the individual row was six meters, and the distance was kept 0.25 cm between the rows, to make it easier for data collection. The agronomic practices and inputs were used on all the entries from sown until the harvest time to reduce the environmental variation in the trial.

Data collection

Data were recorded in F3 wheat genotypes on different morphological and grain-related traits including leaf yellow rust disease using the standard procedures. Data from fifteen parameters were collected, by randomly selecting six healthy plants in each replication. These parameters included the days to heading, days to maturity, flag leaf area, plant height, tillers per plant, chlorophyll content, spike length, spikelets per spike, spike weight, grain per spike, grain weight per spike, grain yield, biological yield, thousand-grain weight, and yellow rust.

Leaf yellow rust scoring

Cobb scale method was used to record rust data (Peterson et al., 1948; Grafton et al., 1985; Ali et al., 2014) on flag leaves of individual plants. It is based on the severity of the leaf surface covered with uredinia. Multiplying of disease severity (DS) and constant values of infection types (IT) were shown based on: Immune= I, Resistant= R, Tolerant= T, Moderately Susceptible= MS, Susceptible= S, Moderate resistant to Moderate Susceptible= M, Resistant to Moderate Resistant= RMR, Moderate Resistant=MR and Susceptible to Moderate Susceptible=SMS. These host reaction types are converted to values through 0, 0.2, 0.1, 0.8, 1, 0.6, 0.3, 0.4, and 0.9 respectively. To calculate the coefficient of infection (C.I.) for data analysis, the severity and host response ratings were multiplied together for severity percentage: 0–100.

Coefficient of Infection (C.I.) = Host reaction value × Severity

Statistical analysis of data

Mean values of different parameters were subjected to ANOVA to determine the variance in a complete randomized block design among genotypes for different traits. The grand mean, standard of error, and coefficient of variation were calculated as indicators of trait variability according to the statistical methods (Gomez and Gomez, 1984), at 5% probability (Rahman et al., 2003). For the comparison of means, the least significant test was used. In this study, we calculated the phenotypic and genotypic correlations using the methodology outlined by Singh and Chaundry (1985). Our analysis was focused on determining the positive and negative correlations among various morpho-yield characters. Various traits were estimated according to Kwon and Torrie (1964) and the Genotypic (rg) and phenotypic (rp) correlation coefficients. The following mathematical model was used to calculate the randomized complete block design’s analysis of variance:

µ + rj + gi + Ɛij = Yij

Where: Yij is the trait Y’s observed value for the ith genotype in the jth replication. µ = trait Y’s overall mean rj = the impact of jth replication. εij is the experimental error related to the trait y for the ith genotype in the jth replication, while gi is the effect of ith genotype.

 

Table 1: Names of 27 wheat genotypes of the F3 population.

S. No	F3 population	S. No	F3 population	S. No	F3 population
1	Auqaab-2000	10	Auqaab-2000 × YR-5	19	Khattakwal × YR-5
2	Fateh Jung-16	11	Auqaab-2000 × YR-10	20	Khattakwal × YR-15
3	Anmol-91	12	Auqaab-2000 × YR-15	21	Khattakwal × YR-5
4	Khattakwal	13	Fateh jung-16 × YR-5	22	KT-06 ×YR-5
5	KT-06	14	Fateh jung-16 ×YR-10	23	KT-06 × YR-10
6	PR-128	15	Fateh jung-16 × YR-15	24	KT-06 ×YR-5
7	YR-5	16	Anmol-91 ×YR-5	25	PR-128 × YR-5
8	YR-10	17	Anmol-9l ×YR-10	26	PR-128 × YR-10
9	YR-15	18	Anmol-19 ×YR-15	27	PR-128 × YR-15

 

The CV percentage for each character and LSD between genotypes were further computed from the given formula.

α = t- value at 5% and 1% probability level.

Estimation of genetic parameters

The computation of Genotypic Coefficients of Variance (GCV) and Phenotypic Coefficient of Variance (PCV) was conducted following the methodology provided by Singh and Chaudhary (1985), supplemented by the approach described by Johnson et al. (1955), Genotypic variance.

Where: MSg= mean square due to genotypes, MSe= error mean square, r = the number of replications, Environmental variance (σ2e)= error mean square= MSe.

Where; x = grand mean of character.

To determine the heritability (h2) of traits, we employed Falconer (1996) formula, which calculates broad-sense heritability. Following Allard and Alder (1960) approach, the estimation of broad-sense heritability (h2) was based on the genotype mean base. This calculation is expressed as a percentage and is derived from the ratio of genotypic variance (σ2 g) to phenotypic variance (σ2 p).

Where; H2 = heritability. Phenotypic variation is symbolized by σ2p and genotypic variance is symbolized by σ2g.

According to Robinson et al. (1951), the heritability percentage was divided into three categories: It can be concluded as low (0–30%), moderate (30–60%), and high (≥ 60%).

Result and Discussion

Days to heading

The analysis of variance for days to heading estimated the highly significant genetic variation at 0.05 probability among the diverse wheat parental lines/cultivars and F3 populations. Moreover, among the parental and F3 wheat populations, the maximum days to heading ranged from 109.66 to 116.66 with 1.10 LSD at 5% probability. Furthermore, the maximum means of days to heading were observed in the Khattakwal (116.66) and Khattakwal×YR10 (115.33) respectively, however, desirable minimum days to heading were reported by PR-128 (109.66). All the genotypes for days to heading predicted significant genotypic differences with a high broad sense of heritability (H2) i.e., 59%. It is therefore suggested that an improvement for days to heading could be possible in the early generation selection i.e., F2 generation. The Coefficient of Variation (CV) was 1.03 for days to heading which predicted the authenticity of the trail for the said trait. The PCV and GCV were exhibited 1.61, and 1.24, respectively (Table 3). The value of GCV was slightly less than PCV, revealing that environmental factors were mostly involved rather than internal factors in variation. The past report days to heading revealed the same result for heritability estimation in the F3 wheat population (Jan et al., 2015; Rauf et al., 2023). However, genetic variance reported significant results for all genotypes (Jan et al., 2015; Rauf et al., 2023; Ali et al., 2024).

Days to maturity

ANOVA findings showed significant differences among parental and F3 segregating populations of spring wheat for days to maturity. In the days to maturity, the performance of genotypes varied from 149 to 156. F3 wheat population i.e., Khattakwal × YR-15 and Aquaab-2000 ×YR-15 exhibited maximum mean performance of 156 and 155 respectively (Table 2). While the F3 population i.e., PR-128 × YR-15 and susceptible variety Aquaab-2000 demonstrated a minimum mean (149, 150.7) for days to maturity with 2.19 LSD at 5% probability. Similarly, within genotypes, days to maturity exhibited non-significant genetic variation at 5% probability. Furthermore, the CV value i.e., 1.51 demonstrated the authenticity of the experiment for days to maturity. However, a broad sense of heritability for days to maturity was low, i.e., 15%. It is therefore concluded that selection for improvement in days to maturity should be carried out in the later generations, i.e., in F6 segregants. Moreover, PCV and GCV exhibited 1.64, and 0.63 values, respectively. The low value of GCV was a sign of less involvement of genetic factors in variations. The present work did not support Kumar et al. (2016) past experiment, which exhibited different results for days to maturity, which was significant in genetic variance. Similarly, for heritability, the present report showed the same as the past report (Ali et al., 2024; Asghar et al., 2016).

 

Table 2: Mean performance of 27 genotypes of F3 wheat population for days to heading, days to maturity, spike length, spikelet per spike, plant height, grain yield, and tillers per plant.

F3 Genotypes	Days to heading	Days to maturity	Spike length	Spikelet per spike	Plant height	Tillers per plant	Grain yield
Auqaab-2000	112	151	11	18	76	4	19
Fateh jung-16	113	151	10	17	83	6	24
Anmol-91	113	154	10	18	76	6	18
Khattakwal	117	154	14	20	111	8	38
KT-06	114	151	10	18	79	5	35
PR-128	110	153	11	19	79	5	39
YR-5	114	156	10	18	70	9	63
YR-10	114	156	9	18	73	8	37
YR-15	114	154	9	18	75	10	33
Auqaab-2000 × YR-5	113	154	11	19	85	8	32
Auqaab-2000 × YR-10	111	154	11	19	84	6	32
Auqaab-2000 × YR-15	112	155	11	19	80	8	33
Fateh jung-16 × YR-5	114	154	10	18	72	5	38
Fateh jung-16 ×YR-10	114	153	10	18	80	6	29
Fateh jung-16 × YR-15	112	153	10	18	81	7	45
Anmol-91 ×YR-5	113	153	12	18	81	6	28
Anmol-9l ×YR-10	112	152	10	18	80	7	33
Anmol-91×YR-15	111	153	10	18	80	6	30
Khattakwal × YR-5	110	155	10	16	73	5	22
Khattakwal × YR-15	115	156	12	19	95	6	27
Khattakwal × YR-5	114	155	10	18	82	5	24
KT-06 ×YR-5	114	152	10	17	74	5	24
KT-06 × YR-10	114	152	10	18	78	5	23
KT-06 ×YR-5	114	154	10	17	78	6	25
PR-128 × YR-5	113	153	12	18	76	6	38
PR-128 × YR-10	111	152	10	17	76	5	26
PR-128 × YR-15	112	149	10	17	78	6	25
CV	1.03	1.51	7.89	4.79	5.47	18.70	17.71
PCV	1.61	1.64	12.10	6.04	10.96	26.23	17.71
GCV	1.24	0.63	9.19	3.69	9.50	18.40	00
LSD	1.10	2.19	0.78	0.82	4.12	1.11	5.17
H2 %	59	15	57	37	57	49	00

 

LSD= Least Significant Differences, CV= Coefficient of Variation, PCV= Phenotypic Coefficient of Variation, GCV= Genotypic Coefficient of Variation, H2=Heritability.

 

Table 3: Mean performance of 27 genotypes of F3 wheat population for chlorophyll content, leaf yellow rust, 1000 grain weight, biological yield, flag leaf area, spike weight, grain per spike, grain weight per spike.

F3 Genotypes	Chlorophyll content	Leaf yellow rust	Biological yield	Spike weight	Grain per spike	1000 grain weight	Grain weight per spike	Flag leaf area
Auqaab-2000	42	15	19	2	55	39	2	14
Fateh jung-16	54	0	26	3	56	42	2	14
Anmol-91	37	28	24	3	51	40	2	15
Khattakwal	48	31	49	3	49	45	2	15
KT-06	52	0	30	3	57	48	3	14
PR-128	51	5	35	4	76	45	3	14
YR-5	52	0	33	4	71	48	3	15
YR-10	51	0	41	3	60	42	3	14
YR-15	50	4	34	3	62	38	2	14
Auqaab-2000 × YR-5	44	30	34	4	68	42	3	14
Auqaab-2000 × YR-10	48	12	46	4	68	46	3	14
Auqaab-2000 × YR-15	42	21	35	3	63	43	3	16
Fateh jung-16 × YR-5	51	4	29	3	58	49	3	14
Fateh jung-16 ×YR-10	51	12	41	3	54	49	3	13
Fateh jung-16 × YR-15	53	11	31	3	63	50	3	14
Anmol-91 ×YR-5	50	18	34	4	64	43	3	13
Anmol-9l ×YR-10	52	32	32	3	58	43	3	14
Anmol-91×YR-15	45	23	26	3	51	39	2	16
Khattakwal × YR-5	39	67	24	2	38	48	2	14
Khattakwal × YR-15	43	13	38	3	57	41	2	14
Khattakwal × YR-5	44	27	27	3	57	45	2	15
KT-06 ×YR-5	49	21	27	3	47	51	2	13
KT-06 × YR-10	49	11	24	3	39	52	2	14
KT-06 ×YR-5	49	28	29	3	50	51	3	16
PR-128 × YR-5	48	5	33	4	53	56	3	14
PR-128 × YR-10	47	3	34	4	60	49	3	13
PR-128 × YR-15	42	20	26	3	61	43	3	16
CV	9.19	18.55	19.21	19.10	18.86	13.50	19.97	7.95
PCV	12.10	98.11	26.79	22.17	21.66	14.99	22.61	9.05
GCV	7.87	96.34	16.67	11.26	10.64	6.51	10.60	4.32
LSD	4.14	2.67	5.79	0.26	10.22	5.81	0.49	1.08
H2 %	42	96	49	26	24	19	22	23

 

LSD= Least Significant Differences, CV= Coefficient of Variation, PCV= Phenotypic Coefficient of Variation, GCV= Genotypic Coefficient of Variation, H2=Heritability.

 

To analyze flag leaf area, ANOVA was estimated as highly significant genetic variation at 0.05 probability among diverse wheat parental lines/cultivars and F3 populations. Moreover, among parental and F3 wheat populations the maximum flag leaf area ranged from 13 to 16 cm with 1.08 LSD at 5% probability. Furthermore, desirable maximum means flag leaf area was observed F3 wheat segregant Auqaab-2000× YR-15 (16 cm), however, minimum flag leaf area was reported by Anmol-19×YR-5 i.e. (13 cm) (Table 2). All the genotypes for flag leaf area predicted significant genotypic differences with low broad sense heritability (H2) i.e., 23%. It is therefore suggested that improvement in the flag leaf area could be possible in the later generation selection, i.e., F6 generation. The Coefficient of Variation (CV) was 7.95 for flag leaf area, which predicted the authenticity of the trait for the said trait. The (PCV) and (GCV) were exhibited at 9.05 and 4.32, respectively (Table 4). The value of GCV was slightly less than PCV, which revealed that environmental factors were mostly involved rather than internal factors in variation. The past investigation of Prabha et al. (2022) showed that all the genotypes for the flag leaf area were significantly varied, truly matching the present report, including high heritability and variability values following the previous experiment of Emmadishetty and Gurjar (2022).

ANOVA finding for plant height estimated highly significant genetic variation at 0.05 probability among diverse wheat parental lines/cultivars and F3 populations. Moreover, among parental and F3 wheat populations the maximum plant height ranged from 69.8 to 111.4 cm with 4.12 LSD at 5% probability (Table 2). Furthermore, the maximum means plant height was presented by landrace variety Khattakwal (111.4), however, the desirable minimum plant height was reported by PR-128 (69.8 cm). All the genotypes for plant height predicted significant genotypic variances with a high broad sense heritability (H2) of 75%. It is therefore suggested that improvement of plant height could be possible in the early generation selection, i.e., F2 generation. The Coefficient of Variation (CV) was 5.47 for plant height, which predicted the authenticity of the trail for the said trait. The PCV and GCV were exhibited at 10.96, and 9.50, respectively. The value of GCV was slightly less than that of PCV, revealing that environmental factors were mostly involved rather than internal factors in variation (Table 2). The same result for genetic differences in F3 genotypes was demonstrated by Nizamani et al. (2020) for plant height. According to previous findings, all genotypes were significant in their performance, moreover, for heritability, F3 genotypes were more valuable than parental lines. Jan et al. (2015) also showed the same result in genetic variability and heritability.

ANOVA’s findings showed significant differences among parental and F3 segregating populations of spring wheat for the yield component of the tillers per plant. In tillers per plant, the mean performance of genotypes varied from 8 to 13. Resistive lines, i.e., YR-15 and YR-5, exhibited a maximum mean performance of 13 and 12 respectively. While the F3 hybrid (Khattakwal × YR-15) and susceptible variety Auqaab-2000 demonstrated both a minimum means (8) for tillers per plant with 1.30 LSD at 5% probability (Table 2). Similarly, within genotypes, tillers per plant exhibited significant genetic variation at 0.05 probability. Furthermore, the CV value of 14.17 demonstrated the authenticity of the experiment for tillers per plant. However, the broad sense of heritability for the tillers per plant was low, i.e., 36%. It is therefore concluded that the selection for improvement in tillers per plant should be carried out in a later generation, i.e., in F6 segregants. Moreover, PCV and GCV exhibited 18.49, and 11.13 values, respectively. The low value of GCV was a sign of less involvement of genetic factors in variations. According to past work, all the genotypes were significant in genetic differences, while for heritability and variability analysis the present findings truly match the past investigation (Ali et al., 2024; Asghar et al., 2016; Nizamani et al., 2020).

ANOVA estimation is highly significant for spike length and shows genetic variation at 0.05 probability among diverse wheat parental lines/cultivars and F3 populations. Moreover, among parental and F3 wheat populations the maximum spike length ranged from 9.1 to 14.5 cm with 0.78 LSD at 5% probability (Table 2). Furthermore, desirable maximum means spike length was presented by landrace variety Khattakwal (14 cm) and F3 wheat segregant i.e., Khattakwal× YR-10 (11.7 cm), however, minimum spike length was reported by Resistive lines i.e., YR-10 (9.1cm) and YR-15 (9.4 cm). All the genotypes for spike length predicted significant genotypic differences with high broad sense heritability (H2) i.e., 57%. It is therefore suggested that improvement of spike length could be possible in the early generation selection, i.e., F2 generation. The Coefficient of Variation (CV) was 7.89 for spike length, which predicted the authenticity of the trail for the said trait. The PCV and GCV were exhibited at 12.10, and 9.18, respectively (Table 4). The value of GCV was slightly less than PCV, which revealed that environmental factors were mostly involved rather than internal factors in variation. In a past paper, Jan et al. (2015) estimated the same result as the present work in genetic significance and variability, but different in heritability, which is moderate to low. In the present report, spike length revealed the same as a past result for heritability estimation in the F3 wheat population (Jan et al., 2015). However, the genetic variance (Ali et al., 2024; Jan et al., 2015) reported significant results for all genotypes.

Spikelets per spike

The ANOVA finding revealed significant differences among parental and F3 segregating populations of spring wheat for yield component spikelets per spike (Table 2). In spikelets per spike, the mean performance of genotypes varied from 17 to 20.4. Khattakwal landrace variety exhibited a maximum mean performance of 20.4, while the F3 wheat population i.e., Khattakwal × YR-05 (16) demonstrated the minimum mean spikelets per spike with a 0.82 LSD at 5% probability. Similarly, within genotypes, spikelets per spike exhibited significant genetic variation at (0.05) probability. Furthermore, the CV value i.e., 4.79 demonstrated the authenticity of the experiment for spikelets per spike (Table 4). However, heritability in a broad sense for spikelets spike-1 was low, i.e., 37%. It is therefore concluded that selection for improvement in spikelets per spike should be carried out in a later generation, i.e., in F6 segregants. Moreover, PCV and GCV exhibited 6.04, and 3.69 values respectively. The low value of GCV was a sign of less involvement of genetic factors in variations. According to past work, all genotypes were significant in genetic differences for heritability and variability analysis, while the present findings truly match the past investigation (Ali et al., 2024; Asghar et al., 2016; Nizamani et al., 2020).

Spike weight

The ANOVA findings showed significant differences among parental and F3 segregating populations of spring wheat for spike weight. In spike weight, the performance of genotypes varied from 1.6 to 4.4 gm. Wheat lines, i.e., PR-128 and YR-5 exhibited maximum mean performance (4.4 gm) while the F3 population i.e., Khattakwal × YR-5 demonstrated minimum mean (1.6) for spike weight with 0.60 LSD at 5% probability (Table 2). Similarly, within genotypes spike weight exhibited significant genetic variation at (0.05) probability. Furthermore, the CV value, i.e., 19.10 demonstrated the authenticity of the experiment for spike weight. However, a broad sense of heritability for spike weight was low, i.e., 26%. It is therefore concluded that selection for improvement in spike weight should be carried out in ‘later generations i.e., in F6 segregants. Moreover, PCV and GCV exhibited 22.17 and 11.26 values respectively. The value of GCV was slightly less than PCV, which revealed that environmental factors were mostly involved rather than internal factors in variation (Table 4). The same result for genetic differences in F3 genotypes was demonstrated by Nizamani et al. (2020) for spike weight. According to previous findings, all genotypes were significant in their performance, moreover, for heritability, F3 genotypes were more valuable than parental lines. In the past, Nizamani et al. (2020) and Aziz et al. (2018) also exhibited the same result in genetic variability and heritability for spike weight.

 

The ANOVA analysis for chlorophyll content estimated highly significant genetic variation at 0.05 probability among diverse wheat parental lines/cultivars and F3 populations. Moreover, among parental and F3 wheat populations the maximum chlorophyll content ranged from 37.1 to 53.8 with 4.14 LSD at 5% probability (Table 3). Furthermore, desirable maximum means chlorophyll content was observed by cultivar Fateh jung-16 (53.8) and followed by F3 wheat segregant i.e., Fateh jung-16× YR-15 (53.8), however, minimum chlorophyll content was reported by susceptible variety Anmol- 91 i.e. (37.1). All the genotypes for chlorophyll content predicted significant genotypic differences with moderate broad sense heritability (H2) i.e., 42%. It is therefore suggested that improvement of chlorophyll content could be possible in the later generation selection, i.e., F4 generation. The Coefficient of Variation (CV) was 9.19 for chlorophyll content, which predicted the authenticity of the trail for the said trait. The PCV and GCV were exhibited at 12.10 and 7.87, respectively. The value of GCV was slightly less than PCV, which revealed that environmental factors were mostly involved rather than internal factors in variation. The past investigation of Prabha et al. (2022) showed that all the genotypes for chlorophyll content were significantly varied, truly matching the present report, including high heritability and variability values following the previous experiment of Emmadishetty and Gurjar (2022).

Grain per spike

The ANOVA findings presented significant differences among parental and F3 segregating populations of spring wheat for grain per spike. In grain per spike means performance of genotypes varied from 37.1 to 76.1. Wheat resistant lines, i.e., PR-128 and YR-5 exhibited maximum mean performance (76, 71) while the F3 population i.e., Khattakwal× YR-5 demonstrated minimum mean (38) for grain per spike with 10.22 LSD at 5% probability (Table 3). Similarly, within genotypes, grain per spike exhibited significant genetic variation at (0.05) probability. Furthermore, the CV value i.e., 18.86 demonstrated the authenticity of the experiment for grain spike-1. However, the heritability for grain per spike was low, i.e., 24%. It is therefore concluded that the selection for improvement in grain per spike should be carried out in a later generation, i.e., in F6 segregants. Moreover, PCV and GCV exhibited 21.66 and 10.64 values respectively (Table 4). The value of GCV was slightly less than PCV, which revealed that environmental factors were mostly involved rather than internal factors in variation. The same result for genetic differences in F3 genotypes was demonstrated by Nizamani et al. (2020) for grain per spike. According to previous findings, all genotypes were significant in their performance, moreover, for heritability, F3 genotypes were more valuable than parental lines. In the past, Nizamani et al. (2020) and Aziz et al. (2018) also revealed the same result in genetic variability and heritability for grain per spike (Jan et al., 2015; Shah et al., 2019).

ANOVA findings observed that grain weight spike-1 was highly significant in genetic variation at 0.05 probability among diverse wheat parental lines/cultivars and F3 populations (Table 3). Moreover, among parental and F3 wheat populations, maximum grain weight per spike ranged from 2 to 3 gm with 0.49 LSD at 5% probability. Furthermore, desirable maximum means grain weight per spike was observed by lines KT-06, YR-5, PR-128 (3gm) and followed by F3 wheat segregant i.e., Fateh Jung-16× YR-5, Anmol × YR-5 (3gm), however, minimum grain weight per spike was reported by susceptible variety Anmol- 91, Auqaab-2000 i.e. (2 gm). All the genotypes for grain weight per spike predicted significant genotypic differences with low broad sense heritability (H2) i.e., 22%. It is therefore suggested that improvement of grain weight per spike could be possible in the later generation selection, i.e., F6 generation. The Coefficient of Variation (CV) was 19.97 for weight per spike, which predicted the authenticity of the trail for the said trait. The PCV and GCV were exhibited at 22.61, and 10.60, respectively (Table 4). The value of GCV was slightly less than PCV, which revealed that environmental factors were mostly involved rather than internal factors in variation. Grain weight per spike ultimately enhanced the yield quantity of wheat, Ali et al. (2024) in past results supported by present work.

To estimate genetic variation in grain yield, ANOVA was performed and estimated significant data at 0.05 probability among diverse wheat parental lines/cultivars and F3 populations. Moreover, among parental and F3 wheat populations, the maximum grain yield per plant ranged from 22 to 63gm with 5.17 LSD at 5% probability (Table 2). Furthermore, the desirable maximum means grain yield per plant was presented by line YR-5 (63gm) and F3 wheat segregant i.e., Fateh Jang-16× YR-15 (45gm), however, the minimum grain yield per plant was reported by F3 wheat segregant Khattakwal×YR- 5 (22gm). All the genotypes for grain yield per plant predicted non-significant genotypic differences with low broad sense heritability (H2) i.e., 00%. An improvement in grain yield per plant could be suggested, possibly in late generation selection, i.e., F6 generation. The Coefficient of Variation (CV) was 17.71 for grain yield per plant, which predicted the authenticity of the trail for the said trait (Table 4). The PCV and GCV were exhibited at 17.71, 00, respectively. The value of GCV was slightly less than PCV, which revealed that environmental factors were mostly involved rather than internal factors in variation. Jan et al. (2015) estimated the same result as the present work in genetic significance and variability but different in heritability, which is moderate to low. In the present report, grain yield per plant revealed the same as past results for heritability estimation in the F3 wheat population (Jan et al., 2015). However, the genetic variance (Ali et al., 2024; Jan et al., 2015) reported significant results for all genotypes, which are not followed by the present work.

The ANOVA finding estimated highly significant genetic variation for 1000-grain weight at 0.05 probability among diverse wheat parental lines/cultivars and F3 populations. Moreover, among parental and F3 wheat populations, a maximum 1000 grain weight ranged from 38.4 to 56.4 (g) with 5.81 LSD at 5% probability (Table 3). Furthermore, the desirable maximum means 1000 grain weight was observed by F3 wheat segregant i.e., PR-128× YR-5 (56.4 g) however, the minimum 1000 grain weight was reported by resistive line YR-15 i.e., 38.4 g. All the genotypes for 1000 grain weight predicted non-significant genotypic differences with low broad sense heritability (H2) i.e., 19%. It is therefore suggested that improvement of 1000-grain weight could be possible in the later generation selection, i.e., F6 generation. The Coefficient of Variation (CV) was 13.50 for 1000 grain weight, which predicted the authenticity of the trail for the said trait. The PCV and GCV were exhibited on 14.99 and 6.51 respectively. The value of GCV was slightly less than PCV, revealing that environmental factors were mostly involved rather than internal factors in variation. In a past paper, Jan et al. (2015) estimated contrast results which are not supported by present work in genetic differences for all genotypes were significant and heritability was moderate to low. In the yield component, 1000 grain weight plays a significant role in increasing the yield quantity. However, the present estimation supported the past experiment in heritability and variability analysis (Aziz et al., 2018).

The ANOVA findings revealed significant differences among parental and F3 segregating populations of spring wheat for biological yield. In biological yield, the mean performance of genotypes varied from 18.9 to 48.3 (g). Landrace variety, i.e., Khattakwal exhibited maximum mean performance (48.3 g) while yellow rust susceptible variety i.e., Auqaab-2000 demonstrated minimum mean (18.9 g) for biological yield with 5.79 LSD at 5% probability (Table 2). Similarly, within genotypes biological yield exhibited significant genetic variation at 0.05 probability. Furthermore, the CV value i.e., 19.21 demonstrated the authenticity of the experiment for biological yield. However, the broad sense of heritability for biological yield was moderate, i.e., 49%. It is therefore concluded that selection for improvement in biological yield should be carried out in later generations, i.e., in F4 segregants. Moreover, PCV and GCV exhibited 26.67 and 16.67 values respectively (Table 4). The value of GCV was slightly less than PCV, which revealed that environmental factors were mostly involved rather than internal factors in variation. The same result for genetic differences in F3 genotypes was demonstrated by Nizamani et al. (2020) for biological yield. According to previous findings, all genotypes were significant in their performance, moreover, for heritability, F3 genotypes were more valuable than parental lines. In the past, Nizamani et al. (2020) and Aziz et al. (2018) also presented the same result in genetic variability and heritability for biological yield (Jan et al., 2015; Shah et al., 2019).

For the yellow rust, the coefficient of infection analysis of variance in parental lines/varieties and F3 wheat segregants revealed highly significant variation. Similarly, all genotypes were significantly varied at 5% probability. The coefficient of Variation (CV) was 18.55, showing the normalization of the experiment field. In yellow, the scoring coefficient of infection (C.I.) ranged from 0 to 67 with 2.67 LSD at 5% probability. In the mean performance, a maximum coefficient of infection was observed by Kattakwal x YR-5 i.e. C.I=67 and followed by F3 segregant Anmol x YR-10 (C.I=32). However, the minimum mean for the coefficient of infection was shown by Fateh Jung-16, KT-06, YR-5, and YR-10 i.e. C.I= 0, followed by PR-128 x YR-10 (C.I=3) Fateh Jung-16 x YR-5 (C.I=4), respectively (Table 4). However, the wheat yellow rust broad sense heritability was high i.e. 96%. It is therefore concluded that the improvement in yellow rust should be carried out in the early generation, i.e. in F2 segregants. Moreover, PCV and GCV exhibited 98.11 and 96.34 values, respectively. The value of GCV was slightly less than PCV, which revealed that environmental factors were mostly involved rather than internal factors in variation. Yellow rust is an undesired component, according to Afridi (2016), all the F1 and F2 wheat were significant in their genetic difference, which was truly supported by the present work for yellow rust scoring. Similarly, Aglan et al. (2020) estimated a high value for broad-sense heritability, which was supported by the present experiment.

Correlation analysis

Days to heading were significantly correlated with spikelets per spike (0.39**). Similarly, considerable 0.21, 0.27, 0.08, and 0.09 positive association of days to heading with days to maturity, spike length, biological yield, chlorophyll content, and spike weight but negative phenotypic correlation with the 1000 grain weight (-0.02), tillers per plant (-0.01), grain per spike (-0.03), grain weight per spike (-0.01), flag leaf area (-0.12) (Table 4). Furthermore, days to heading negatively correlated with the incidence of yellow rust (-0.30) while positively correlated with grain yield (0.25). The correlation analysis for days to heading showed the same result revealed in their previous findings (Ali et al., 2024; Maqsood et al., 2014).

Days to maturity are significantly positively associated with biological yield (0.52*). While negatively correlated with days to heading, tillers per plant (-0.03, -0.13), and chlorophyll contents (-0.04). Similarly, days to maturity also showed significantly negative phenotypic correlation with the yellow rust (-0.03), while positively correlated plant height (0.24), spike length (0.11), spikelets per spike (0.12), grain per spike (0.17), flag leaf area (0.35), grain weight per spike (0.11), grain yield (0.16), and 1000 grain weight (0.06). The present work follows the past works of (Ghallab et al., 2017; Jan et al., 2015).

The flag leaf area revealed significantly positive phenotypic correlation with plant height (0.37*), grain yield-1 (0.48*) and spike length (0.49*), while days to maturity (0.34), days to interval (0.22), spike length (0.35), chlorophyll content (0.08), grain per spike (0.30), grain weight per spike (0.15) and yellow rust (0.29) also exhibited a positive association with the flag leaf area. Moreover, thousand-grain weight, biological yield, and tillers plant-1 and spikelets per spike i.e. (-0.24, -0.67, -0.06, -0.33) were negatively correlated with flag leaf area, following the previous experiments of (Ali et al., 2024; Asghar et al., 2016).

Phenotypic correlation analysis of plant height revealed a positive association with spike length (0.76**), spikelets spike-1 (0.60**), and (0.52**) for biological yield, while plant height significantly negatively correlated with days to heading (-0.85**). Furthermore, all the parameters were positively correlated with plant height, respectively, except for 1000-grain weight (-0.07), and yellow rust (-0.11) which showed a negative correlation. The same results were demonstrated by (Ali et al., 2024; Maqsood et al., 2014; Nizamani et al., 2020).

Tillers per plant exhibited significant and negative genotypic association with days to heading (-0.63**), plant height (-0.75**), spike length (-0.75**), and spikelets per spike (-0.39*) respectively. Moreover, 1000-grain weight (-0.07), chlorophyll contents (-0.09), flag leaf area (-0.05), and yellow rust (-0.09) also negatively correlated with tillers per plant. Similarly, the tillers per plant showed a positive phenotypic correlation with days to maturity (0.10), spike weight (0.09), grain per spike (0.17), grain weight per spike (0.11), grain yield (0.19), and biological yield (0.15). Ali et al. (2024) and Jan et al. (2015) reported significant results for all traits in correlation analysis.

Spike length displayed a significant and positive correlation with plant height (0.91**), (0.41*) for biological yield, spikelets spike-1 (0.78**), and grain yield (0.59**), while spike length significantly negatively correlated with days to heading (-0.80**). Furthermore, days to maturity (-0.36), chlorophyll content (-0.01), 1000-grain weight (-0.02), and yellow rust (-0.32) were also negatively correlated with spike length. While flag leaf area (0.09), tillers per plant (0.03), spike weight (0.15), and grain per spike (0.15), showed a negative correlation with spike length, as shown in the following the previous experiments of (Ali et al., 2024; Asghar et al., 2016).

Spikelets per spike exhibited significant and positive phenotypic association with biological yield (0.47), day to maturity (0.52**), spike length (0.91**), and plant height (0.96**), respectively. While days to interval (-0.60**), (-0.30) for flag leaf area and thousand-grain weight (-0.02) revealed negative association with spikelets spike-1. Moreover, tillers per plant, spike weight, chlorophyll content, grain spike-1, grain weight spike-1, and grain yield (i.e., 0.03, 0.42, 0.15, 0.27, 0.23, and 0.32) were positively correlated with spikelets per spike, respectively. Furthermore, yellow rust (-0.32) also estimated a negative correlation with the spikelets per spike. The present estimation supported the past experiment in correlation analysis (Aziz et al., 2018).

Days to maturity (0.46**), spikelets per spike (0.57**), grain yield (0.83**), and tillers per plant (0.99**) were significant and positive phenotypic correlation spike weight Similarly all the parameters were positively associated with spike weight, days to heading, plant height, spike length, biological yield, chlorophyll content, grain per spike and grain weight per spike, 1000 grain weight, and biological yield (i.e., 0.24, 0.18, 0.18, 0.04, 0.69, 0.86, 0.24 and 0.42), respectively. Furthermore, spike weight was negatively correlated with yellow rust (-0.52**) and flag leaf area (-0.17) (Table 2). The present findings truly match the past investigation (Ali et al., 2024; Asghar et al., 2016; Nizamani et al., 2020).

Chlorophyll content displayed significant and positive phenotypic correlation with days to heading (0.75**), flag leaf area (0.48*), and grain yield (0.39*). While chlorophyll content significantly negatively correlated with spike length (-065**), biological yield (-0.43*) and yellow rust (-0.43*). Furthermore, tillers per plant (-0.09) and plant height (-0.35) were also negatively correlated with chlorophyll content, respectively. Moreover, spikelets per spike, days to maturity, grain per spike, grain weight per spike, and thousand-grain weight (i.e. 0.02, 0.03, 0.18, 0.34, 0.15) showed positive correlation with chlorophyll content (Table 4). In the past, Nizamani et al. (2020) and (Aziz et al., 2018; Ali et al., 2024) also showed the same result in correlation analysis for chlorophyll content.

The phenotypic correlation analysis of grain per spike estimated that grain per spike was significantly positively correlated with days to maturity (0.71**), spike length (0.99**), spikelets per spike (0.61**), spike weight (0.73**) and tillers per plant (0.95**) respectively. Furthermore, grain weight per spike (0.88**) and grain yield (0.68**) also positively correlated with grain per spike, respectively. Moreover, flag leaf area, chlorophyll content, 1000-grain weight, and yellow rust showed a negative correlation with grain per spike. The correlation analysis for grain per spike showed the same result revealed by (Ali et al., 2024; Maqsood et al., 2014) in their previous findings.

The analysis of correlation revealed that the grain weight per spike was significantly positively correlated with spike length (0.41*), spikelets per spike (0.56**), tillers per plant (0.49**), spike weight (0.85**) and grain per spike (0.83**), respectively. Further, days to heading had a phenotypic correlation of 0.05 with plant height and 0.26 with days to maturity, 1000-grain weight was 0.32, and biological yield was 0.31. The characters also showed negative phenotypic correlation with spike length and yellow rust being -0.21 and –0.53**, respectively. In addition, flag leaf area: chlorophyll content (-0.21) was significantly negative with grain weight per spike. The present estimation supplemented the previous study by conducting correlation analysis in the past experiment (Aziz et al., 2018). The phenotypic correlation analysis of grain yield plant-1 estimated that grain yield plant-1 was considerably positively associated with spike length (0.96**), spikelets per spike (0.71**), flag leaf area (0.39*), chlorophyll content (0.43*), spike weight (0.93**), grain weight spike-1 (0.95**), thousand-grain weight (0.49*) and biological yield (0.74**), respectively. Furthermore, days to heading (0.17), and plant height (0.35) also positively correlated with grain yield per plant. However, days to maturity (-0.09), tillers per plant (-0.08), and yellow rust (-0.58**) correspondingly negatively correlated with yield per plant (Okuyama et al., 2004; Ali et al., 2024).

The results of the correlation study showed a strong positive relationship between the number of tillers per plant (0.79**) and the thousand-grain weight and plant height (0.53**). Additionally, there was a significant association found between 1000-grain weight and days to heading (0.10), chlorophyll content (0.07), spike weight (0.17), grain weight per spike (0.33), biological yield (0.12), and grain yield (0.11). Additionally, there was a significantly negative phenotypic connection between 1000 grain weight and spike length (-0.17), spikelets per spike (-0.64**), flag leaf area (-0.09), and grain per spike (-0.28), respectively. Additionally, there was a negative correlation (-0.13) between yellow rust and 1000 grain weight. Jan et al. (2015) and Masood et al. (2014) similarly showed that there was an association between 1000-grain weight and other traits.

Days to maturity (0.71**), flag leaf area (0.69*), spikelets per spike (0.97**), tillers per plant (0.99**), chlorophyll content (0.44*), spike weight (0.70*), grain per spike (0.68*), and grain yield (0.60**) all showed a significant and positive phenotypic correlation with biological yield. In contrast, there was a substantial negative correlation between biological yield and yellow rust (-0.15) and days to heading (-0.90**). Additionally, there was a negative connection between biological yield and plant height (0.27), spike length (0.16), grain weight per spike (0.15), and 1000-grain weight (0.16). In the previous findings, Masood et al. (2014), Nizamani et al. (2020) also showed the same result.

According to the correlation study, there was a positive correlation between the yellow rust and spike length (0.83**), spikelets per spike (0.98**), days to maturity (0.45*), and plant height (0.99**). Additionally, the same study found a significant and negative phenotypic correlation between yellow rust and 1000 grain weight (-0.12), biological yield (-0.08), chlorophyll content (-0.42*), grain weight per spike (-0.23), spike weight (-0.64**), tillers per plant (-0.90**), and grain weight per spike (-0.56**) same work was reported by (Afridi, 2016).

Conclusions and Recommendations

Based on the results, the following deductions were made. All the parental lines were significant as well as the F3 hybrids except days to maturity, grain yield, and thousand-grain weight. Parental resistant variety PR-128 and F3-hybrid (PR-128 x YR-15) showed desirable phonological value with a desirable short plant height appearing in early maturing genotypes, which can be focused for further utilization in the varietal improvement program. For desirable traits, YR-5, YR-10, and YR-15 revealed maximum tiller per plant and grain yield per plant and estimated high resistivity to yellow rust.

High heritability estimates along with expected genetic gain were recorded for all the studied traits under both environments. For early selection days to heading, plant height, spike length, and yellow rust could be suggested for future breeding programs. Wheat genotypes PR-128, PR-128 x YR-15, and YR-5, YR-10 were found to be high-yielding, and these promising genotypes are recommended for further evaluation and improvement.

Acknowledgement

We are pleased to the Cereal Crop Research Institute (CCRI), Pirsabak Nowshera, Pakistan, for providing the research materials and facilitating this project.

Novelty Statement

The genotypes YR-5, Fateh Jung-16×YR-5, and PR-128× YR-5 showed maximum grain yield and high resistivity to yellow rust, which are recommended for further evaluation and future breeding programs.

Author’s Contribution

Muhammad Sadiq: Research Experiment Executed/MS drafting.

Nadia: Data collection.

Abdur Rauf: Research Supervision/MS drafting and proofreading.

Khilwat Afridi: Field Trial Supervision.

Muhammad: Qayash, Saman Yaqoob, Tanweer Kumar, Muhammad Ismail, Muhammad Arif and Muntaha Munir: Proofreading.

Kashmala Jabbar, Guleena Khan, Adeel Khan and Tahseen Ullah: Data analysis.

Ikramullah Khan Proofreading/Data analysis.

Conflict of interest

The authors have declared no conflict of interest.

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