Research Article

Performance Evaluation, Estimation of Genetic and Phenotypic Correlation Coefficients, and Path Coefficient Analysis of Quantitative Traits in Triticale Genotypes Grown under Different Seeding Rates

Hussein Ali Hindi1, Liwaa Nahedh Faisal٢, Yaseen Obaid Noori Ahmed Sharif*3 and Tariq Raad Thaer Al-Mafarji4

1Department of Field Crops, College of Agriculture, Tikrit University, Tikrit, Iraq; 2Department Education. Samarra Industrial Secondary School. Salah al-Din Education Directorate . Vocationa. Ministry of Education. Iraq; 3Department of Field Crops, College of Agriculture, Kirkuk University, Kirkuk, Iraq; 4Department of Medicinal and Industrial Plants, College of Medicinal and Industrial Plants, University Kirkuk, Kirkuk, Iraq.

Received | July 17, 2025; Accepted | October 17, 2025; Published | February 23, 2026

*Correspondence | Yaseen Obaid Noori Ahmed Sharif, Department of Field Crops, College of Agriculture, Kirkuk University, Kirkuk, Iraq; Email: [email protected]

Citation | Hindi, H.A., L.N. Faisal. Y.O.N.A. Sharif and T.R.T. Al-Mafarji. 2026. Performance evaluation, estimation of genetic and phenotypic correlation coefficients, and path coefficient analysis of quantitative traits in triticale genotypes grown under different seeding rates. Pakistan Journal of Agricultural Research, 39(1): 77-88.

DOI | https://dx.doi.org/10.17582/j.pjar/2026/39.1.77.88

Keywords | Triticale, Genotypes, Seeding rates, Path coefficient analysis

Copyright: 2026 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

Triticale, also known as hexaploid wheat, represents a successful attempt by scientists to develop a man-made crop combining the traits of wheat (Triticum) and rye (Secale). This is achieved by chromosome doubling of the sterile hybrid to produce a polyploid plant. Triticale is characterized by a higher protein content and lysine level compared to wheat, with an increase of about 4% (Mohammed et al., 2025).

This crop has numerous favorable features. It can be grown in areas that experience spotty rainfall, alongside barley or as a substitute for it, as it is better adapted to drought conditions. This is significant in areas plagued by limited rainfall during the winter growing months of the year, or with scarce water supplies stemming from the construction of dams and subsequent water releases stemming from international agreements. Triticale also performs well on soil that is not suitable for other crops such as sandy, highly acidic, and nutritionally deficient soil varieties (Ismail and Al-Fahady, 2012; Fayhaa et al.,2025; Mohammed et al., 2025). Despite the fact that triticale contributes to economic returns and plays a dual role in crop production, Iraqi farmers have not given enough attention to its cultivation, which is rather being given to soft wheat, durum wheat, and barley. Even though the purchase of triticale is approved by the Iraqi Ministry of Trade at all silos in the country, the number of areas of cultivation has remained very limited, if not nonexistent. This reflects the need to encourage its cultivation and adopt scientific practices that ensure high productivity. Seeding rates are one of the most important of these practices; it establishes plant density and provides, under conditions of vegetative growth, an appropriate environment for the growth of the crop. The optimal rate reduces intraspecific competition, while during the grain-filling stage, it increases (Alnori and Ahmad, 2012; Younis and Al-Hasans, 2019).

The use of promising genotypes is a very important factor for increasing triticale grain yield. Introduction of new genotypes by insertion, along with exploiting genetic variation, is required to identify the most productive varieties in specified environmental conditions. A genotype that gives good performance in a specific environment might not yield under a similar intensity in another environment. Grain yield is a complex character in all breeding and improvement programs for triticale because it is governed by a large number of genes and highly influenced by environmental conditions (Al-Mafarji and Al-Jubouri, 2023a). The genetic and phenotypic correlation studies between grain yield and its components aim to determine the most influential characters and establish them as indicators for selection for improving the yield. There are two types of yield indicators used in this regard: direct indicators, represented by the estimates of heritability, and indirect indicators, such as correlation coefficients and path coefficient analysis (Mahmood et al., 2025).

The objectives of the study are the evaluation of the productivity of tested genotypes, determination of the most promising ones, and specifying the best seeding rate that provides the highest grain yield. Determination of genetic and phenotypic correlations of certain growth traits and yield components with grain yield was also estimated in this paper, in addition to carrying out path coefficient analyses to identify the direct and indirect effects of the most influential traits on grain yield, thus enabling their adoption as effective selection indicators for improving triticale productivity.

Materials and Methods

A field experiment was conducted during the 2024–2025 winter cropping season at the Samarra Directorate of Agriculture Research Station, Salah Al-Din Governorate, located at 34.09° N latitude and 43.51° E longitude. The study aimed to evaluate the response of growth and yield traits of six Triticale (Triticosecale × Wittmack) genotypes to three seeding rates, estimate genetic parameters, and perform path coefficient analysis. The experiment included two factors: the first factor was three seeding rates (140, 160, and 180 Kg ha-¹), and the second factor was six Triticale genotypes, as detailed in Table 1.

 

Table 1: Names, pedigree, and sources of Triticale genotypes.

No.	Genotype	Pedigree	Source
1	Farah	LOCAL CHECK*	Ministry of Science
2	Amal 7	LOCAL CHECK*	Ministry of Science
3	Al-Muhand	LOCAL CHECK*	Ministry of Science
4	LIRON-3	LIRON_2/5/DIS B5/...	MXI07-08
5	LIRON-4	LIRON_2/5/DIS B5/...	MXI07-08
6	LIRON-6	LIRON_2/5/DIS B5/...	MXI07-08

 

Soil samples were randomly collected from different field locations at 30 cm depth before sowing to estimate physical and chemical properties, as shown in Table 2.

The experimental field was plowed using a moldboard plow, followed by disc harrowing. Each replication was divided into three main plots randomly assigned to the three seeding rates. Each main plot included

 

Table 2: physical and chemical properties of field soil at 30 cm depth.

Trait	pH	E.C	Available N	Available P	Available K	Organic matter	Sand	Clay	Silt	Texture
Unit	–	dS m-¹	mg kg-¹ soil	mg kg-¹ soil	mg kg-¹ soil	%	%	%	%	–
Value	7.7	3.2	22	7	45	0.9	79.7	17.0	3.3	Sandy loam

 

six subplots randomly assigned to the six genotypes. Nitrogen fertilizer in the form of urea (N 46%) was applied at 200 Kg ha-¹ in two splits: the first at sowing and the second 25 days after sowing. Phosphorus was applied as triple superphosphate (P2O5 46%) at 200 Kg ha-¹ at soil preparation. Potassium was applied as potassium sulfate at 200 Kg ha-¹ at soil preparation (Sebahi, 2011).

The weeds were controlled using a pesticide 2.4, D before planting (Sharif et al; 2024a).

Each main plot area was 12 m². Five rows were planted per experimental unit with 20 cm spacing between rows. Seeds were sown manually in each row. Each replication included 18 experimental units, totaling 54 units. Sowing was conducted on 25 November 2024. Irrigation was applied as needed, and weeds were removed manually. At full maturity (25 May 2025), experimental units were harvested, and the following traits were recorded:

Vegetative growth traits:

• Number of tillers per m² (X1)
• Flag leaf area (cm2) (X2)
• Days from sowing to maturity (X3)
• Spike length (cm) (X4)

Yield and yield components:

• Number of grains per spike (grain spike-¹) (X5)
• 500-kernel weight (g) (X6)
• Total biological yield (Kg ha-¹) (X7)
• Grain yield (Kg ha-¹) (X8)

Data were statistically analyzed using a randomized complete block design with split-plot arrangement. Means were compared using Duncan’s multiple range test (Al-Rawi and Khalaf Allah, 2000).

Genetic analysis

Phenotypic and genetic correlation coefficients between traits and grain yield were estimated following (Walter, 1975) and tested for significance against tabulated r-values at the appropriate degrees of freedom. Path coefficient analysis was performed to determine direct and indirect effects of studied traits on grain yield according to (Al-Zubaidi and Al-Jubouri, 2016). The scale used for path effects is shown in Table 3.

 

Table 3: Scale of path coefficient effects.

Effect	Negligible	Low	Moderate	High	Very high
Value range	0–0.09	0.10–0.19	0.20–0.29	0.30–0.99	>1.00

 

Results and Discussion

Analysis of variance

The results of the analysis of variance (Table 4) showed that the effect of seeding rates was highly significant for all studied traits, except for days from sowing to maturity, which was significant at the 5% probability level. This is because seeding rate is a major factor influencing the optimal environment for crop growth by balancing plant competition, which affects vegetative growth and yield components (Al-Dahi, 2021; Sharif et al., 2024b).

The effect of genotypes was significant at 1% probability for most traits, except grain yield per unit area, which was significant at a lower level. This variation was ascribed to differences in the genetic makeup of the genotypes, which manifested in growth and yield attributes. This is in view of (Al-Dulaimi 2020; Alshamary et al., 2025). The interaction between the two factors was highly significant for most traits and statistically significant for days to maturity and spike length, while the number of grains per spike was not statistically significant. It follows, therefore, that the tested genotypes reacted to the three seeding rates differently, which consequently brought about variation in growth traits and grain yield components.

Total number of tillers (m²)

It can be seen in Table 5 that as determined by Duncan’s multiscale test, seeding rates, genotypes, and their interaction had a significant impact on the total number of tillers m². The highest average yield of 302.55 tillers m² was observed when the seeding rate was 140 kg ha-¹, whereas with a seeding rate of

 

Table 4: Mean squares of the studied traits from ANOVA.

M.S.								d.f	S.O.V
X8	X7	X6	X5	X4	X3	X2	X1
7434.47	2022.22	0.143	0.629	1.46	5.01	1.02	10.720	2	R
*17706674.11	2099605.11 **	34.79 **	252.29 **	74.12 **	45.90*	276.99 **	3148.38 **	2	A
8263.30	97686.11	0.17	0.48	0.268	4.57	0.93	11.520	4	R(A)
683363.86*	5097373.33 **	30.81 **	164.11 **	21.940 **	11.84**	248.38 **	6993.760 **	5	B
218945.79**	398212.22 **	0.25 **	3.051	0.99*	1.32*	3.83**	352.58**	10	AB
33832.79	63184.81	0.08	1.642	0.400	0.566	0.539	27.548	30	Error

 

180 kg ha-¹, it followed with the least average yield of 276.11 tillers m². It is likely that because of the lower seeding rate, there was decreased competition for nutrients and environmental resources, which allowed for better vegetative growth, whereas a higher seeding rate reduced the number of lateral branches (Batool et al., 2022).

 

Table 5: Effect of seeding rates, genotypes, and their interaction on total tiller number (m²).

Genotype	140 Kg ha-¹	160 Kg ha-¹	180 Kg ha-¹	Mean
Farah	345.00 a	311.33 ef	294.33 hi	350.22 a
Amal 7	331.00 b	330.33 b	300.00 ef	326.33 b
Al-muhand	310.67 cd	317.67 c	291.67 f	300.78 b
Liron-3	259.00 hij	253.00 jk	245.33 k	252.44 e
Liron-4	267.67 h	258.00 j	254.67 j	260.11 d
Liron-6	302.67 de	296.67 ef	281.67 g	293.67 c
Mean	302.55 a	288.83 b	276.11 c	x-=289.17

 

Observed significant differences between the means, with the highest average for Farah (350.22 tillers m²). The most inferior, Liron-3, came up with the lowest average of 252.44 tillers m². In addition, this variation shows the genetic reason, whether the genotypes are responding to environmental and climatic conditions, referring to previous studies of Nwry et al., 2021.

In the case of interaction between seeding rates and genotypes, the mean for Farah × 140 kg ha-¹ was highest (345.00 tillers m²), while Liron-3 × 180 kg ha-¹ recorded the minimum average under these conditions (245.33 tillers m²).

Flag leaf area (cm²)

Significant differences between the means of seeding rates, genotypes, and their interactions are indicated in Table 6 for flag leaf area. The highest mean (31.326 cm²) was recorded for the 140 kg ha-¹ rate, while the lowest mean (23.55 cm²) was given by the 180 kg ha-rate. This has been attributed to increased leaf area at low seeding rates due to less intra-plant competition that enabled the plants to utilize the growth resources optimally. The above explanation is consistent with the findings by Shah et al. (2016).

The genetic differences between the genotypes were also highly significant, and the highest mean flag leaf area was seen in Farah with 34.828 cm², while the lowest, 21.293 cm², was seen in Liron-3. Indeed, these differences reflected genetic responses under favorable environmental conditions, as stated by Al-Mafarji and Al-Jubouri (2024).

The interaction of seeding rates and genotypes was also significant, and the highest flag leaf area was 39.350 cm² from Farah × 140 kg ha-¹ while the lowest was 18.093 cm² belonging to Liron-3 × 180 kg ha-¹.

 

Table 6: Effect of seeding rates, genotypes, and their interaction on flag leaf area (cm²).

Genotype	140 Kg ha-¹	160 Kg ha-¹	180 Kg ha-¹	Mean
Farah	39.350 a	35.236 b	32.943 c	34.828 a
Amal 7	26.033 b	30.780 d	25.373 g	30.464 b
Al-Muhand	29.103 e	31.326 d	27.390 f	30.553 b
Liron-3	24.133 g	21.653 h	18.093 f	21.293 e
Liron-4	30.993 d	28.300 ef	22.233 h	27.175 c
Liron-6	25.300 g	22.200 h	19.143 i	22.214 d
Mean	31.326 a	28.382 b	23.55 c	x-=27.754

 

Days from sowing to full maturity (days)

There are substantial differences in the number of days from planting to full maturity due to seeding rates, genotypes, and their interactions, according to the results shown on Table 7. The 140 kg ha-¹ seeding rate had the shortest period of 164.055 days, while the longest was at the 180 kg ha-¹ seeding rate, at 167 days. It has been explained that the shorter duration in cases of low seeding rates is because competition for growth resources is minimal at an early stage, hence accelerating the plant’s transition from vegetative to productive growth (Al-Zaidi and Al-Abody, 2024).

Among genotypes, Al-Muhandis was the earliest at an average of 164.111 days, followed by Amal 7 and Liron-6, while Liron-3 was the slowest and reached up to 167.111 days. This variation is because of genetic cause and the response of different genotype to temperature and photoperiod, as Ahmad (2017) .

There was a significant interaction between seeding rates and genotype. The shortest maturation period was noted for the cross of Amal 7 × 140 kg ha-¹ with 162.666 days, while the longest was observed for the cross of Liron-3 × 180 kg ha-¹ with 169.333 days.

 

Table 7: Effect of seeding rates, genotypes, and their interaction on days from sowing to full maturity (days).

Genotype	140 Kg ha-¹	160 Kg ha-¹	180 Kg ha-¹	Mean
Farah	164.333 gh	166.00 c-f	164.000 i-j	166.11 b
Amal 7	162.666 k	164.666 f-i	163.00 j-k	164.777 cd
Al-Muhand	168.00 b	163.00 b-k	165.333 e-h	164.111 d
Liron-3	165.666 d-g	166.333 c-e	169.333 a	167.111 a
Liron-4	164.000 j-i	165.000 e-i	167.333 b-c	165.444 b-c
Liron-6	163.666 k-i	163.666 j-k	165.666 d-g	164.333 d
Mean	167.00 a	164.777 b	164.055 b	x-=163.314

 

Spike length (cm)

Significantly, as shown by the results in Table 8, there is a difference between seeding rates, genotypes, and their interactions with respect to spike length. The rate of 140 kg ha-¹ scored the highest average (12.111 cm), while 180 kg ha-¹ had the lowest average (8.055 cm). A high rate of seeding causes heightened competition for light and nutrients during ear formation that negatively affects vegetative growth, hence reducing the length of ears, as evidenced in the work by Basietva, (2023).

The average length of spikes in the genotypes was highest in Amal 7, which was 12.111 cm, while Liron-3 and Liron-6 showed the minimum, 8.666 and 8.111 cm, respectively. This variation reflects differences in the duration of the period from spike formation to flowering completion, which affected spike growth, consistent with Kaleri et al. (2016).

The interaction between seeding rates and genotypes was significant. The combination Amal 7 × 140 Kg ha-¹ gave the highest spike length (15.00 cm), whereas Liron-6 × 180 Kg ha-¹ recorded the lowest (6.666 cm). This result was supported by the significant positive phenotypic correlation between spike length and days from sowing to physiological maturity (Table 13).

 

Table 8: Effect of seeding rates, genotypes, and their interaction on spike length (cm).

Genotype	140 Kg ha-¹	160 Kg ha-¹	180 Kg ha-¹	Mean
Farah	13.333 b	11.333 cd	9.333 g-h	11.333 b
Amal 7	15.00 a	12.333 bc	9.000 g-h	12.111 a
Al-Muhand	12.666 b	10.666 de	8.333 hi	10.555 c
Liron-3	10.333 d-f	8.333 hi	7.333 i-j	8.666 e
Liron-4	11.333 cd	9.333 g-h	7.666 i-j	9.444 d
Liron-6	10.000 e-f	7.666 i-j	6.666 j	8.111 e
Mean	12.111 a	9.944 b	8.055 c	x-=10.037

 

Number of grains per spike (grain.spike-¹)

Results in Table 9 showed significant differences among seeding rates, genotypes, and their interaction for grain number per spike. The lowest seeding rate (140 Kg ha-¹) achieved the highest mean (43.751 grain.spike-¹), significantly higher than the medium rate (39.661 grain.spike-¹), while the highest rate (180 Kg ha-¹) gave the lowest mean (36.274 grain.spike-¹). The decrease at higher seeding rates is due to higher plant density, which limits dry matter accumulation and spike development, especially during early grain formation, consistent with (Zangana and Aljburi, 2023; Dumlu, 2025).

Genotypic differences were evident. Farah recorded the highest mean (45.983 grain.spike-¹), whereas Amal 7 gave the lowest (32.973 grain.spike-¹). The superior performance of Farah is associated with its later maturity, allowing prolonged leaf activity and greater flag leaf area, enhancing photosynthesis and dry matter accumulation. This result was supported by the genetic correlation between flag leaf area and the number of grains per spike (Table 13), and similar findings were reported by Abd El-Aty et al. (2025).

The interaction between seeding rates and genotypes was significant. Farah × 140 Kg ha-¹ gave the highest grain number per spike (49.480), whereas Amal 7 × 180 Kg ha-¹ recorded the lowest (29.523 grain.spike-¹). This can be explained by the significant positive phenotypic correlation between flag leaf area and the number of grains per spike (Table 13).

 

Table 9: Effect of seeding rates, genotypes, and their interaction on number of grains per spike (grain.spike-¹).

Genotype	140 Kg ha-¹	160 Kg ha-¹	180 Kg ha-¹	Mean
Farah	49.480 a	45.960 b	42.190 cd	45.983 a
Amal 7	36.403 g-i	32.993 j	29.523 k	32.973 e
Al-Muhand	42.510 cd	38.493 fg	34.247 i-j	38.310 d
Liron-3	43.913 bc	41.550 cd	38.870 ef	41.444 b
Liron-4	45.527 b	38.213 fg	35.233 i-j	39.657 c
Liron-6	44.993 b	40.757 de	37.263 f-g	41.004 b
Mean	43.751 a	39.661 b	36.274 c	x-=39.895

 

Weight of 500 grains (g)

Results in Table 10 indicated significant differences among seeding rates, genotypes, and their interactions for 500-grain weight. The 140 Kg ha-¹ seeding rate recorded the highest mean (21.694 g), significantly higher than the 180 Kg ha-¹ rate, which gave the lowest mean (18.916 g). The reduction at higher seeding rates is due to increased plant density, which reduces dry matter accumulation in source organs (leaves) and its translocation to sinks (grains), thus lowering grain weight, consistent with (Al-Hasan and Anwar, 2016).

Genotypic variation was evident. Amal 7 and Liron-4 recorded the highest means (22.277 and 22.444 g, respectively), while Farah showed the lowest mean (18.111 g). This result can be attributed to the negative correlation between grain number per spike and 500-grain weight, as Farah had the highest grain number per spike (Table 9), it is worth noting that there is an inverse relationship between the number of grains per spike and the 500-grain weight, and this result is consistent with (Al-Mafarji and Al-Jubouri, 2024).

The interaction between seeding rates and genotypes was significant. Amal 7 × 140 Kg ha-¹ and Liron-4 × 140 Kg ha-¹ recorded the highest 500-grain weights (24.00 and 24.333 g, respectively), whereas Farah × 180 Kg ha-¹ gave the lowest (16.833 g). This was supported by a significant positive phenotypic correlation between spike length and 500-grain weight (Table 13).

 

Table 10: Effect of seeding rates, genotypes, and their interaction on weight of 500 grains (g).

Genotype	140 Kg ha-¹	160 Kg ha-¹	180 Kg ha-¹	Mean
Farah	19.333 g-h	18.166 h	16.833 i	18.111 d
Amal 7	24.000 a	22.166 b	18.833 g	22.277 a
Al-muhand	20.000 de	20.666 c	17.833 h	18.888 c
Liron-3	22.166 b	20.666 c	19.666 e-f	20.833 b
Liron-4	24.333 a	22.166 b	20.833 c	22.444 a
Liron-6	20.333 cd	19.166 g-h	17.666 h	19.055 c
Mean	21.694 a	20.194 b	18.916 c	x-=20.268

 

Table 11: Effect of seeding rates, genotypes, and their interaction on total biomass yield (kg ha-¹).

Genotype	140 Kg ha-¹	160 Kg ha-¹	180 Kg ha-¹	Mean
Farah	12,210 e-g	12,440 c-f	12,766.7 a	12,472.233 a
Amal 7	11,853 c	11,936.7 g	12,410 b	12,066.8 c
Al-Muhand	11,850 gh	11,943.3 g	12,206.7 e-g	12,000 c
Liron-3	11,946.7 g	12,066.7 fg	12,326.7 d-g	12,113.366 c
Liron-4	11,233.3 i	11,266.7 i	11,450 i	11,316.666 d
Liron-6	12,110 d	12,490 b	12,623.3 b	12,407.766 b
Mean	11,867. 216 b	12,023.9 a	12,297.233 a	x-=12,062.797

 

Total biomass yield (kg.ha-¹)

Data in Table 11 revealed that seeding rates 160 and 180 Kg ha-¹ achieved the highest total biomass yields, recording means of 12,297.233 and 12,023.916 Kg ha-¹, respectively, compared to 140 Kg ha-¹, which had the lowest mean (11,867.216 Kg ha-¹). The increase in total biomass at higher seeding rates is attributed to higher plant density, leading to greater overall vegetative production, in agreement with (Zawar et al., 2025).

Genotype Farah recorded the highest mean for this trait (12,472.233 kg ha-¹), whereas genotype Liron-4 had the lowest mean (11,316.666 kg ha-¹). This increase in the Farah genotype can be attributed to its higher mean biological yield per plant, which consequently enhanced the total biological yield. Moreover, the genetic correlation was positive and significant between 500-grain weight and total biological yield (Table 13), and similar results were reported by (Ahmed and Alhabbar, 2025).

The interaction between genotype Farah and the seeding rate of 180 kg ha-¹ achieved the highest mean value for this trait (12,766.7 kg ha-¹), whereas the interaction between genotype Liron-4 and the seeding rate of 140 kg ha-¹ recorded the lowest mean (11,233.3 kg ha-¹). This can be explained by the significant positive phenotypic correlation between 500-grain weight and biological yield, as well as the significant negative phenotypic correlation of the same trait with the number of tillers per m².

Total grain yield (kg.ha-¹)

Results in Table 12 showed significant differences among seeding rates, genotypes, and their interactions for total grain yield. The 140 Kg ha-¹ seeding rate recorded the highest mean (5,312.61 kg.ha-¹), while 180 Kg ha-¹ gave the lowest mean (3,336.47 kg.ha-¹). The superior performance of the 140 Kg ha-¹ rate is attributed to its favorable impact on the main yield components, namely total spike number per m², grains per spike, and 500-grain weight, which collectively enhanced total grain yield per unit area, in agreement with (Abdalkarim et al., 2022; Al-Samarrai and Hindi., 2024).

The genotype Farah recorded the highest mean grain yield of 4690.94 kg ha-¹, whereas the genotypes Liron-3 and Al-Muhandis exhibited the lowest means of 3905.19 and 4020.59 kg ha-¹, respectively. This superiority of genotype Farah can be attributed to its advantage in one of the main yield components, namely the number of grains per spike, which resulted from the increased flag leaf area and consequently enhanced the total grain yield per unit area. This finding was supported by the significant positive genetic correlations between the number of tillers per m² and spike length with grain yield (Table 13) and is consistent with the results reported by (Ahmed and Alhabbar., 2025; Al-Mafarji and Al-Jubouri 2023b).

The interaction between genotype Farah and the seeding rate of 140 kg ha-¹ produced the highest mean grain yield (6199.2 kg ha-¹), whereas the interaction between genotype Al-Muhandis and the seeding rate of 180 kg ha-¹ gave the lowest mean (3199.1 kg ha-¹). This can be explained by the phenotypic correlations between the number of tillers per m², spike length, and grain yield of Triticale (Table 13). Moreover, this result was further confirmed by the high direct effects revealed by the path coefficient analysis between the number of grains per spike and 500-grain weight with grain yield of Triticale (Figure 1). These findings suggest that the two mentioned traits can be effectively used as selection criteria for improving grain yield.

 

Table 12: Effect of seeding rates, genotypes, and their interaction on total grain yield (kg ha-¹).

Genotype	140 Kg ha-¹	160 Kg ha-¹	180 Kg ha-¹	Mean
Farah	6,199.2 a	4,496.1 de	3,377.5 g	4,690.94 a
Amal 7	5,401.7 b	4,474.2 de	3,518.1 g	4,464.68 b
Al-Muhand	4,888.9 cd	3,973.7 f	3,199.1 g	4,020.59 d
Liron-3	4,634.9 cd	3,916.1 f	3,302.4 g	3,951.90 d
Liron-4	5,461.2 b	3,946.6 f	3,302.4 g	4,236.77 c
Liron-6	5,289.8 b	4,245.3 ef	3,316.9 g	4,283.99 c
Mean	5,312.61 a	4,175.36 b	3,336.47 c	x-=4,274.811

 

Genetic and phenotypic correlation with total grain yield

Table 13 presents genetic and phenotypic correlation coefficients between the studied traits and total grain yield. The genetic correlation of total grain yield was significantly positive with total spike number per m² (0.964**), spike length (0.598**), and positively significant with total biomass yield (0.546*). Total spike number per m² was highly positively correlated with spike length (0.641**) and total biomass yield (0.708**). Leaf area was highly positively correlated with grains per spike (0.654**). Days from sowing to physiological maturity showed a significant positive genetic correlation with spike length (0.538**). Spike length was positively correlated with total biomass yield (0.538*), and 500-grain weight had a highly significant positive genetic correlation with total biomass yield (0.596**).

Phenotypic correlation analysis showed that total grain yield was highly positively correlated with total spike number per m² (0.744**) and total biomass yield (0.619**), and significantly positive with spike length (0.554*). Spike length had a significant negative phenotypic correlation with total spike number per m² (-0.576*) but positively correlated with days to maturity (0.572*). Grains per spike had

 

Table 13: Genetic and phenotypic correlations of studied traits with total grain yield.

Traits	X8	X1	X2	X3	X4	X5	X6	X7
X8	1.000	-0.964**	0.256	-0.059	0.598**	-0.181	0.345	0.546*
X1	-0.744**	1.000	-0.252	-0.133	-0.641**	-0.032	-0.079	-0.708**
X2	0.269	-0.215	1.000	-0.383	0.397	0.654**	0.358	-0.068
X3	-0.001	-0.015	-0.363	1.000	-0.638**	0.134	-0.342	0.233
X4	0.554*	-0.576*	0.392	-0.572*	1.000	-0.222	0.538*	-0.008
X5	-0.161	-0.033	0.642**	0.115	-0.227	1.000	-0.412	0.241
X6	0.297	-0.064	0.342	-0.306	0.522*	-0.402	1.000	-0.596**
X7	0.619**	-0.571*	-0.007	0.219	0.031	0.211	-0.558*	1.000

 

 

a highly significant positive phenotypic correlation with leaf area (0.642**). Five-hundred-grain weight was positively correlated phenotypically with spike length (0.522*) and total biomass yield (0.558*) and negatively correlated with total spike number per m² (-0.571*). These results are consistent with (Aziz and Al-Zubaidy, 2024; Mishra et al., 2025; Madab., 2025; Assad and Hindi., 2025). who reported both positive and negative significant genetic and phenotypic correlations among yield-related traits.

Path coefficient analysis

Path coefficient analysis (Figure 1) revealed that the direct effects of traits on total grain yield varied considerably. The direct effect was high for total spike number per m² (0.537), grains per spike (0.728), and 500-grain weight (1.353), while it was low for the remaining traits. Indirect effects were also notable: total spike number per m² exhibited a strong negative indirect effect via total biomass yield (-0.885). Leaf area showed a moderate positive indirect effect through spike length, and a high positive indirect effect via grains per spike (0.316) and 500-grain weight (0.484). Days from sowing to physiological maturity had a high negative indirect effect on 500-grain weight (-0.462) but a high positive indirect effect on total biomass yield (0.702). Spike length exerted a high positive indirect effect on 500-grain weight, while grains per spike had a high positive indirect effect on both 500-grain weight and total biomass yield. Total biomass yield exhibited a strong positive indirect effect on total grain yield (0.745).

These results align with (Sabri and Ahmed, 2018; Neykov, 2024; Omer and Al-Layla, 2024; Sharif et al., 2024) who reported that specific yield components exerted significant direct effects on the total grain yield of Triticale. The analysis highlights the critical role of grains per spike, 500-grain weight, and total spike number in determining yield, and emphasizes the importance of considering both direct and indirect pathways in selection and breeding programs.

Conclusions

Based on the obtained results of this study, it can be concluded that all six tested triticale genotypes could be successfully grown under conditions of Samarra city using a seeding rate of 140 kg/ha-¹. This rate gave the highest averages for vegetative growth traits, as well as for total grain yield and its components, thus making it economically feasible for triticale growers. Among the genotypes, Farah showed the highest total grain yield per unit area, performing best under these conditions. The number of grains per spike and 500-grain weight traits may prove effective selective indicators in a triticale yield improvement program, since path coefficient analysis showed a strong direct effect of these two traits on total grain yield.

Acknowledgments

There are no individuals or organizations to acknowledge for this work.

Novelty Statement

This presents a new avenue in the realm of the genetic performance evaluation of triticale plant species through the assessment of performance, estimation of genetic and phenotypic correlation coefficients, and path coefficient analysis for quantitative traits under different cultivation rates. The results, thus obtained, provide precise scientific data that could enable the researcher and farmer to identify the best genotypes and to come up with improved agricultural management for better yields.

Authors’ Contributions

Hussein Ali Hindi and Liwa Nahed Faisal: Contributed to the practical part of the research.

Yassin Obaid Nouri Ahmed Sharif and Tariq Raad Thaer Al-Mufarji: Contributed to the theoretical part by writing the article and conducting a statistical data analysis.

All authors have read and approved the final manuscript.

Conflict of interest

Conflict of Interest The authors declare no financial, personal, or professional conflicts of interest that might influence the outcome or interpretation of the content of the study.

Generative AI and AI-assisted technology statement

The artificial intelligence analysis of all sections of this manuscript showed that none of the text plagiarized and was with a score of zero.

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