Low Nitrogen Narrows down Phenotypic Diversity in Durum Wheat

Abstract

Breeding for nitrogen use efficiency has become the major global concern and priority to improve agricultural sustainability. In an attempt to quantify genetic variation and identify traits for optimum and low N environments, 200 durum wheat genotypes were evaluated at three locations in the central highlands of Ethiopia during the 2020 growing season. The experiments were arranged in alpha lattice design with two replications. The results revealed significant differences among genotypes for all studied traits under both N conditions, indicating ample opportunities for genetic improvement. All traits except days to heading and maturity, grain filling period and grain protein content were higher under optimum than under low N. High values of genotypic and phenotypic coefficients of variations, broad sense heritability and genetic advance as percent of the mean were observed for number of fertile tillers and number of seed per spike (NSPS) under optimum, and spike length and NSPS under low N conditions. Cluster analysis classified the durum wheat genotypes into thirteen and eight clusters under optimum and low N, respectively. Principal component analysis detected five and four components which explained 81.29% and 73.63% of the total variations under optimum and N stress conditions, respectively. The present study confirmed the existence of wide genetic variability among the durum wheat genotypes under optimum and low N conditions; and low N lowers the level of diversity. Thus, our study paves the possibility for improvement of durum wheat genotypes through selection and hybridization for increased grain yield and adaptation to N stressed conditions.

Keywords:

• cluster
• durum wheat
• genetic divergence
• NDVI
• nitrogen efficiency
• principal components

Public Interest Statements

Durum wheat is one of the indigenous cereal species in Ethiopia. It has significant economic value and provides the raw materials for food industries in the nutrition of the global population and suitable for preparing traditional recipes in Ethiopia. However, the national productivity of the crop is far below the world average due to biotic and abiotic stresses resulted from global climate changes. Therefore, identification of genotypes performed well under the changing environmental conditions is mandatory to develop climate smart durum wheat varieties. Yield is a polygenic trait that is the result of numerous interrelated factors. Thus, understanding the relationships between traits and extent of genetic diversity in durum wheat is crucial to the success of durum wheat breeding programs in maximizing yield to feed the rapidly growing population.

1. Introduction

Durum wheat (Triticum turgidum var. durum Desf) is the oldest tetraploid (2n = 4× = 28, AABB) wheat species in the world (Royo et al., 2009). The origin of durum wheat was the result of two successful domestication events by ancient farmers; first from wild emmer to domesticated emmer, and second from cultivated naked forms of emmer to durum (Gioia et al., 2015). It is the second most important among the cultivated species of wheat grown in the world next to common wheat (Elias, 1995). Durum wheat is suitable for the preparation of traditional dishes in Ethiopia (Belay et al., 2013) and as raw material for food industries worldwide (Sissons et al., 2005).

Ethiopia is a center of diversity for tetraploid wheat and more than 7000 local durum wheat accessions are available in the national gene bank of Ethiopia (Tsegaye & Berg, 2006; Mengistu & Pè, 2016), and these could be used for conservation as well as sustainable exploitation for higher grain yields to meet the nutritional demands of the growing population. Wheat grain yield is determined by genotype, environment, and their interactions, with fertilizers being one of the environmental factors influencing productivity (Asthir et al., 2017). Nitrogen (N) fertilizer is an essential nutrient used in high concentration for increasing grain yield as well as grain quality in wheat production (Koutroubas et al., 2014). However, reports indicated that crop uptake of available nitrogen was about 50% due to restrictions imposed by N losses and inefficiencies in N uptake and utilization by crop plants (Hawkesford, 2014). Furthermore, N loss in the field leads to alteration of the quality of surface and groundwater resources and air pollution, which have a negative impact on human health and agricultural sustainability (Hawkesford, 2014). Moreover, N fertilizer is the main expense of farmers, and the high costs and low returns of N fertilizer use particularly burden smallholder farmers in developing countries, forcing them to grow their crops under sub-optimal N application. Consequently, this calls for the use of nitrogen-efficient crop varieties (Dethier et al., 2012).

To achieve this goal, the identification of durum wheat genotypes that have exploitable variation for the traits of interest is the first step in durum wheat breeding programs. Genetic diversity contributes to the understanding of genetic relationships among populations and consequently leads to specific heterogeneous groups of parents for hybridization (Khodadadi et al., 2011) and provides an option to address the above limitation. Variability among durum wheat genotypes can be assessed based on different qualitative and quantitative traits. Estimates of genetic parameters such as variances and heritability allow understanding the nature and magnitude of genetic variability in a population (Bartaula et al., 2019), whereas cluster and principal component analysis are convenient methods for identifying homogeneous groups of genotypes and reducing variables (Azad et al., 2012). Although the presence of genetic diversity has been reported among different wheat species under varying N levels (Barraclough et al., 2014; Belay et al., 2017; Hawkesford, 2017), several studies in Ethiopia (Mengistu et al., 2015; Haile et al., 2013; Lemma et al., 2021; Letta et al., 2013; Negisho et al., 2021) described the existence of genetic variations in grain yield, adaptive traits, resistance to biotic and abiotic stresses among durum wheat germplasm under optimum N conditions. However, there are no evidences on the genetic variability of durum wheat under low N conditions. Therefore, the objectives of this study were: (i) to estimate genetic variability under optimum and low N availability; and (ii) to quantify the level of variation among genotypes under both N environments; and (iii) to identify suitable traits for future breeding efforts.

2. Materials and methods

2.1. Description of Study Areas

The experiments were carried out at Debre Zeit, Chefe Donsa and Minjar in the central highlands of Ethiopia during the main season of 2020. Descriptions of the study areas are given in Table 1.

Table 1. Geographical locations, climatic conditions and soil types of the experimental sites

Parameter	Locations
	Debre Zeit	Chefe Donsa	Minjar
Altitude (m.a.s.l)	1900	2435	2257
Latitude and longitude	8° 44’ N and 38° 58’E	8 ° 57’ and 39° 16’E	8°46´ N and 39°16´ E
Mean annual rainfall (mm)	984	1020	865
Maximum Temperature (^oC)	26.84	20	28.77
Minimum temperature (^oC)	11.39	8	12.3
Soil type	Black vertisol	Black vertisol	Vertisol
Agro-ecology	Mid-altitude	Highland	Mid-altitude

Source: DZARC (2020)

2.2. Experimental Plant Materials, Design and Layout

A total of 200 durum wheat genotypes were used in the study (Table S 1), and of these comprised 67 genotypes from the Ethiopian Biodiversity Institute (EBI), 83 from the International Maize and Wheat Improvement Center (CIMMYT), 13 from the International Center for Agricultural Research in the Dry Areas (ICARDA), and 37 from durum wheat breeding program of Debre Zeit Agricultural Research Center (DZARC). The experiments were planted on a field that was previously sown to tef [Eragrostis tef (Zucc.) Trotter]. A composite soil sample was taken from each site before planting, and soil total nitrogen analysis was performed following standard procedure (Table 2). The fields that are low/very low in total nitrogen (TN) content were selected to establish the experiments following (Tadesse et al., 1991) soil rating based on total nitrogen (TN) content. Two sets of experiments (Set-I with recommended N fertilizer and Set-II without any N fertilizer application) were conducted at each location, and the same genotypes were used for both sets of experiments. The experiments were arranged in an alpha lattice design with two replications. The plot size was 1 m × 1 m (1 m²) and the distances between rows, plots, blocks and replications were 0.2, 0.4, 0.5, and 1 meter, respectively. The Set-I experiments (Optimum N condition) received 92 kg per hectare nitrogen fertilizer in split applications at the time of sowing and as top-dressing during the tillering stage. Conversely, no N was applied to the Set-II experiments (Low N condition). A recommended (10 kg P per hectare) rate of phosphorus fertilizer in the form of TSP (Triple supper phosphate) was applied on both sets of experimental plots to avoid the confounding effect of other nutrients. The genotypes were assigned to plots at random within each block. All other crop management practices were employed uniformly to all genotypes as per the recommendations for the crop.

Table 2. Pre-planting experimental sites soil pH, total nitrogen, available phosphorus and texture

Soil properties	Experimental sites
	Debre Zeit	Chefe Donsa	Minjar
pH	6.8	6.9	6.8
Total N (%)	0.1	0.08	0.12
Available phosphorus (mg kg ^−1)	15.2	5.9	10.1
Texture	Clay	clay	clay

2.3. Data Collection

The genotypes were evaluated for the following 16 phenological (days to heading, days to grain filling and days to physiological maturity), agronomic (plant height, number of fertile tillers, spike length, number of spikelet per spike, number of seed per spike, biomass yield, grain yield, harvest index and thousand seed weight), physiological (normalized difference vegetative index and chlorophyll contents) and quality (protein content and hectoliter weight) traits. Days to heading (DH) was recorded by counting the number of days from sowing to the stage at which 50% of the plants heads within a plot and days to physiological maturity (DM) was recorded by counting the number of days from sowing to 90% physiological maturity on plot basis, while grain filling period (GFP) was obtained by subtracting DH from DM. Plant height (PH), number of fertile tillers per plant (NFT), spike length (SL), spikelet per spike (SPS), and number of seeds per spike (NSPS) were recorded from ten randomly sampled plants from the four central rows and their average data were taken for analysis. After plants were mechanically harvested, data on above-ground biomass yield (BM) and grain yield (GY) were collected and converted to a hectare basis. BM was measured in the field using a hanging (spring) balance during harvesting, whereas GY was calculated by weighing the threshed grain on an analytical balance and adjusting to 12.5 percent moisture content. Harvest index (HI) was determined as the ratio of GY to BM. Thousand seed weight (TSW) was obtained by counting thousand grains using seed counter and weighing on analytical balance. On the other hand, normalized difference vegetative index (NDVI) was measured using a handheld green seeker optical sensor and a SPAD-502 plus chlorophyll analyzer was used to obtain chlorophyll content (CHO). Grain protein content (PC) was analyzed using Pertien protein analyzer, and hectoliter weight (HLW) was measured by a portable hectoliter test weight kit.

2.4. Data Analyses

2.4.1. Analysis of variance (ANOVA)

The F-max ratio test for homogeneity of variance was carried out to determine the validity of the experiment and to combine the data over locations. Because the error variances for all traits were homogeneous, the data were pooled and analyzed across locations using different softwares. ANOVA was performed by the SAS software version 9.4 for Alpha lattice design. A liner mixed model (REML) including location, replication, block, genotype and genotype × location interaction effects was performed using the software to generate the adjusted mean as the best linear unbiased predictor (BLUPs) for all traits. The combined ANOVA model used was:

$Yijk=\hspace{0.17em}\mu \hspace{0.17em}+\hspace{0.17em}gi\hspace{0.17em}+\hspace{0.17em}Li\hspace{0.17em}+GLij\hspace{0.17em}+\hspace{0.17em}Bk\left(j\right)\hspace{0.17em}+Lijk$

where Yijk= observed value of genotype i in block k of location j; µ= grand mean; gi= effect of genotype i; Li=location effect; GLi=the interaction effect of genotype i with location j; Bk(j)=effect of block k in location j and Lijk=random error or residual effect of genotype i in block k of location j.

2.4.2. Estimation of variance components, heritability and genetic advance

Estimation of variance components for the data combined over locations was performed by R-software version 4.1.3 (R Core team, 2013). Variability parameters such as genotypic coefficient of variation (GCV), phenotypic coefficient of variation (PCV), broad sense heritability (H²), and genetic advance (GA) and genetic advance as a percentage of mean (GAM) were estimated for the traits evaluated to assess the extent of variation, determine whether the observed variation was due to genotypes or environment, and determine the significance for selection.

2.4.3. Genetic divergence analysis

The multivariate hierarchical cluster analysis was performed based on the unweighted pair group with the arithmetic mean (UPGMA) clustering method from the Euclidean distance matrix following the average linkage method by SAS software. Genetic distances between the genotypes were estimated using the Euclidean distance calculated for the studied traits after standardization as established by (Mohammadi & Prasanna, 2003). Principal component analysis (PCA) was employed using a correlation matrix to detect traits that explained the most variability in the data set. As suggested by (Johnson et al., 2014), only principal components (PCs) with eigenvalues greater than one were considered as important, and for these PCs eigenvectors greater than half divided by the square root of the respective eigenvalues were used as a measure to identify the traits contributing to each of the selected PCs. For both the cluster and principal component analysis, the data were as pre-standardized to a mean of zero and a variance of one in order to avoid bias due to measurement units.

3. Results and discussion

3.1. Phenotypic Variability under Optimum N

Analysis of variance was carried out for the 16 traits and there was a significant (P < 0.01) difference among durum wheat genotypes for all characters evaluated under optimum nitrogen conditions (Table S 2). This shows the presence of conducive environment to express their genetic potential. Hence, selection would be effective for different quantitative characters and the right genotypes that meet the breeding objectives can be used for future hybridization. The finding of the study was found comparable with the work of (Yohannes & Abebe, 2020) and (Zemede et al., 2019) on durum wheat.

The genotype x location  interactions were significant (P < 0.01) on DH, DM, GFP, NDVI, NFT, SPS, BM, GY, HI, PC, and TSW and was significant (P < 0.05) for NDVI, HLW, and CHO, indicating that the phenotypic performance of durum wheat genotypes varied across locations (Table S 2). The genotypes variations to different environmental conditions suggest the need to select specifically adapted genotypes to diverse environments. This finding is in agreement with the results of (Alemu et al., 2019) who reported variations among bread wheat genotypes for most of the traits.

3.2. Phenotypic Variability under Low N

The combined analysis of variance indicated that the mean squares of genotypes was highly significant (P < 0.01) for all traits except DM which showed significant variation at (P < 0.05), indicating sufficient opportunity for genetic improvement under low N. The genotype × location interactions were highly significant (P < 0.01) for DH, DM, NFT, SL, NSPS, BM, GY, TSW and CHO and NDVI, PH, SPS, and significant (P < 0.05) for HI, while significant genotype x location  interactions were not observed for GFP, PC and HLW (Table S 2). Lemma et al. (2021) noted significant variation among durum wheat genotypes under drought stress except for DH and days to anthesis. In contrast, (Ayadi et al., 2012) found no significant variations among durum wheat genotypes for GY under low N condition.

3.3. Performance of Genotypes under Optimum N

The extent of variability of a character is very important for the improvement of any crop through breeding. The variability of the characters were measured by range, mean, GCV and PCV. The mean and ranges of the 16 traits tested under optimum N are presented in Table 3. The results indicated that the presence of significant differences between genotypes for all traits. The values of DH, DM and GFP in that order ranged from 60–92, 107–129 and 35–49 days with the mean values of 71.6, 114.1 and 42.5 days. As such, 47.5%, 38% and 55.5% of total number of genotypes were found late and showed more days compared to the overall average number of days for DH, DM and GFP, respectively. The minimum NDVI value was 50 and the maximum was 80 with the mean value of 65.81, thus exhibiting large variations among the genotypes. The plant height (PH) varied from 51.37 to 83.67 cm with a grand mean of 63.49 cm. The ranges for NFT were 2 to 7 with a mean of 3.69. The minimum and maximum values of SL, SPS and NSPS were 3.87 and 9.6 cm, 14 and 21, and 27 and 78 with over all mean of 5.58, 17.15 and 47.41, respectively. Longer spikes produced lower numbers of seeds as the loner spikes are very lax in their spike density.

Table 3. Ranges, means, estimated variance components, broad sense heritability and genetic advance for 16 variables of 200 durum wheat genotypes tested at three locations under optimum nitrogen soil conditions

Variables	N levels	Ranges	Mean ± SE	GCV (%)	PCV (%)	H^2 (%)	GA	GAM (%)
DH	ON	62–92	71.6±0.73	6.35	6.52	95.05	9.13	12.76
	LN	64–99	75.91±1.02	7.69	7.92	94.30	11.68	15.39
DM	ON	107–129	114.14±0.86	2.78	2.98	87.07	6.10	5.34
	LN	111–133	119.18±0.97	3.20	3.40	88.48	7.39	6.20
GFP	ON	35–49	42.51±0.91	5.46	6.25	76.41	4.18	9.84
	LN	31–50	43.24±1.21	5.46	6.74	65.56	3.94	9.10
NDVI	ON	50–80	65.81±2.09	5.44	7.07	59.38	5.69	8.64
	LN	39–62	49.48±2.08	5.88	8.36	49.41	4.21	8.51
PH	ON	51.37–83.67	63.49±1.82	10.29	11.05	86.62	12.52	19.73
	LN	45.17–66.63	52.93±1.63	6.52	7.84	69.13	5.91	11.17
NFT	ON	2–7	3.69±0.36	21.71	25.74	71.12	1.39	37.71
	LN	1–5	2.58±0.33	21.02	27.73	57.45	0.85	32.82
SL	ON	3.87–9.60	5.58±0.24	18.30	19.25	90.38	1.20	35.85
	LN	3.43–8.27	5.12±0.24	20.29	21.31	90.71	2.04	39.82
SPS	ON	14–21	17.15±0.51	5.57	6.97	63.82	1.57	9.17
	LN	12–19	15.69±0.59	5.99	7.99	56.38	1.46	9.28
NSPS	ON	27–78	47.41±2.45	21.38	22.59	89.52	19.75	41.66
	LN	25–69	42.21±2.67	18.96	20.97	81.77	14.91	35.32
BM	ON	6.5–15	10.61±0.46	10.59	12.22	75.23	2.01	18.94
	LN	3.5–9.3	5.94±0.33	11.82	14.23	69.06	1.20	20.24
GY	ON	1.83–5.2	3.83±0.23	10.40	13.30	61.08	0.64	16.74
	LN	0.77–3.17	1.98±0.18	14.62	19.25	57.64	0.45	22.86
HI	ON	0.24–0.48	0.36±0.01	9.15	10.69	73.33	0.06	16.15
	LN	0.21–0.46	0.33±0.02	10.54	13.26	63.16	0.06	17.25
PC	ON	9.4–14.03	11.13±0.28	3.88	5.27	54.25	0.66	5.89
	LN	9.53–13.50	11.28±0.28	4.46	5.68	61.74	0.81	7.23
HLW	ON	41.07–82.80	69.19±3.98	5.29	9.70	29.70	4.11	5.93
	LN	35.53–71.17	49.60±4.03	5.26	12.65	17.32	2.24	4.51
TSW	ON	28.93–51.90	39.58±0.94	10.11	10.66	89.99	7.82	19.76
	LN	28.60–52.73	38.58±1.20	10.44	11.33	84.85	7.64	19.80
CHO	ON	30–50	40.62±1.26	7.55	8.73	74.70	5.46	13.44
	LN	26.20–45.03	36.60±1.29	5.47	7.40	54.73	3.05	8.34

The difference between the maximum and minimum values in BM, GY and HI was 8.5 t/ha, 3.37 t/ha and 0.24, respectively. GY varied from 1.83 to 5.2 t/ha with a mean of 3.83 t/ha, while BM ranged from 6.5 to 15 t/ha with a grand mean of 10.61 t/ha. Wide variation (9.4 to 14.03%) in PC was observed among genotypes with a mean of 11.13%. The minimum and maximum values for HLW, TSW and CHO ranged from 41.07 to 82.80 g, 29.93 to 51.90 g and 30 to 50, with a grand mean of 69.19 g, 35.50 g, and 40.62, respectively. In general, the highest variation was exhibited by NFT (71.4%), followed by NSPS (65.4%), GY (64.8%), SL (59.7%) and BM (56.7%), while DM (17.1%), GFP (28.6%) and DH (32.6%) showed the lowest diversity. Thus, the presence of sufficient variation among genotypes in GY and important yield components and physiological traits offers the possibility for selecting high yielding durum wheat varieties. The variations observed in mean values for traits studied were almost comparable with the values reported by (Tesfaye et al., 2016) and (Abinasa et al., 2011) but higher than the findings of (Tegenu et al., 2021), except for NDVI and PC on durum wheat. Some differences observed in values of the traits between the current study and the previous studies might be related to variations in genotypes and test environments.

3.4. Phenotypic Performance of Genotypes under Low N

Ranges and mean performance values of the 200 durum wheat genotypes for the 16 response variables evaluated based on the average data over the three locations under low N conditions indicated in Table 3. The genotypes exhibited high phenotypic diversity for the traits NFT (80%), GY (75.7%), NSPS (63.8%), BM (62.4%), SL (58.5%) and HI (54.3%), while DM (16.5%), HLW (24.8%) and PC (29.4%) had low variations. The mean values of DH, DM and GFP were 75.91, 119.18 and 43.24 days and ranged from 64 to 99, 111 to 133 and 31 to 50 days, respectively. As such, 28%, 50% and 62% of the genotypes required more days above the average values for DH, DM and GFP, respectively. The locations mean for NDVI, PH, NFT, SL, SPS, and NSPS ranged from 39 to 62, 45.17 to 66.63, 1 to 5, 3.43 to 8.27, 12 to 19 and 25 to 69 with mean values of 49.48, 52.93, 2.58, 5.12, 15.69 and 42.21, respectively. The difference between the highest and lowest values for BM, GY and HI were 5.8 t/ha, 2.4 t/ha and 0.25 which varied from 3.5 to 9.3 t/ha, 0.77 to 3.17 t/ha and 0.21 to 0.46 with the mean values of 5.94 t/ha, 1.98 t/ha and 0.33, respectively, indicating the presence of substantial variation among durum wheat genotypes.

The variations between durum wheat genotypes in PC under low nitrogen varied from 9.53 to 13.50% with a mean of 11.28%. The lowest and highest values for HLW, TSW and CHO ranged from 35.53 to 71.17 g, 28.60 to 52.73 g and 26.20 to 45.03 with the mean values of 69.19 g, 38.58 g and 36.60. The results indicated the inherent genetic difference among the genotypes had substantial influences on traits assessed besides the effect of environments. Martre et al. (2011) reported significant variation among winter wheat genotypes for GY, PH, HI and DH under low N. Similarly, (Tegenu et al., 2021) found variation among durum wheat genotypes under high rainfall areas in Ethiopia which are characterized by low soil nitrogen. Kubota et al. (2018) also reported differential performances of spring wheat cultivars for all traits under low N.

3.5. Estimation of Variance Components under Optimum N

Genotypic and phenotypic coefficients of variation (GCV and PCV) measure the magnitude of variability present in a given population. Estimates of GCV, PCV, broad sense heritability (H²), genetic advance (GA) and genetic advance as a percent of mean (GAM) estimated based on selection intensity of the best 5% of the genotypes are shown in Table 3. The PCV were slightly higher than GCV for all of the traits indicating the role of environment in the expression of characters, which also supported by the findings of (Nukasani et al., 2013). Among the traits studied, high values (>20%) of GCV and PCV were recorded for NFT and NSPS as similar findings were made by (Tesfaye et al., 2016).

The traits NDVI, SL, BM, GY, and TSW had moderate GCV and PCV values (10–20%), while the remaining traits had low GCV and PCV values (<10%), with the exception of HI, which had moderate PCV under optimum nitrogen. Similar findings showing low PCV and GCV values were reported by (Muhder et al., 2020) on bread wheat and (Yohannes & Abebe, 2020) on durum wheat. In contrast, (Tegenu et al., 2021) and (Tesfaye et al., 2016) reported high GCV and PCV for GY, BM, and HI under optimum N.

Heritability (H²) estimates ranged from moderate to very high for all characters except HLW, which had the lowest heritability (29.70%), indicating a high environmental influence on the character’s expression. The highest heritability of 95% was observed for DH followed by for SL, TSW and NSPS each with about 90%, and then DM and NDVI with 87%, indicating that these traits were governed by additive genes. High heritability values for DH and TSW reported by (Tsegaye et al., 2012) and (Bhushan et al., 2013) were found consistent with current findings. Moreover, (Tegenu et al., 2021) reported high heritability of all attributes studied with the exception of NDVI and moisture content. With estimates ranging from 61 to 75%, GFP, BM, CHO, HI, NFT, SPS and GY had moderately high heritability values (60–79%), but NDVI and PC had moderate heritability values (Table 3).

Genetic advance as percentage of the mean (GAM) was 5.34 for DM and 41.66 for NSPS (Table 3). In comparison, low GAM values were in bread wheat noted for NFT and NSPS (Meles et al., 2017) and for DM (Saini, 2017), whereas (Kumar et al., 2018) observed high GAM for NSPS and NFT in wheat. The current study revealed high heritability estimates for NSPS, SL and NFT along with high GAM, demonstrating the existence of additive gene effects. Selection based on these traits would be effective in the breeding for N use efficiency under optimum N. In contrast, the heritability and GAM estimates of HLW were low standing at 29.70% and 5.93%, respectively, indicating non-additive gene action and a stronger environmental influence on this trait. On the other hand, the high heritability and low GAM estimates found for DM suggested that, this trait is primarily regulated by a combination of both genetic and environmental effects.

3.6. Estimation of Variance Components under Low N

Response to selection for quantitative traits is directly proportional to the function of its heritability, genetic advance and genotypic variance. Heritability permits to distinguish the genetic dissimilarity among traits and genetic variance reveals the potential for improvement of a particular trait. Under low N, PCV were higher than GCV for all the traits. The GCV and PCV for NFT and SL were high. Similarly, PCV was high for NSPS. Estimates of GCV and PCV were moderate for BM, GY, HI and TSW, NSPS and HLW, while both PCV and GCV were low for the other traits (Table 3). Similar findings were reported by (Hossain et al., 2021), who found moderate GCV and PCV for GY and TSW and low values for DH, DM, PH, SPS and CHO under heat stress conditions. Moderate GCV and PCV for HI and low for PC results were witnessed by (Muhder et al., 2020).

Estimates of broad sense heritability and genetic advance as percentage of mean for 16 traits of durum wheat genotypes are given on Table 3. Accordingly, DH, DM, SL, NSPS and TSW showed very high heritability estimate (>80%), indicating low influences of the environment and selection based on these traits might be highly effective. Similarly, moderately high heritability values (60–79%) were recorded for GFP, PH, BM, HI and PC. On the other hand, medium heritability estimates were observed for NDVI, NFT, SPS, GY and CHO. However, HLW showed very low broad sense heritability (17.32%). The results of this study revealed high (>20%) GAM values for NFT, SL, NSPS, BM and GY. Traits such as DH, PH, HI and TSW showed moderate GAM values (10–20%). Low GAM values (<10%) were obtained for DM, GFP, NDVI, SPS, PC, CHO and HLW. In line with these findings (Gerema, 2020) reported high heritability and GAM for NSPS and low GAM for DM in durum wheat. High broad sense heritability for DH, DM, NSPS and TSW and moderately high heritability for GY and PC (Malbhage et al., 2020) was reported in durum wheat. Moderate heritability and high GAM for GY of bread wheat under drought stress condition was reported by (Milkessa et al., 2021).

SE=standard error, GCV=genotypic coefficient of variation, PCV= phenotypic coefficient of variation, H²= broad sense heritability, GA=genetic advance, GAM=genetic advance as percentage of mean. ON=optimum nitrogen; LN=low nitrogen. DH=Days to heading, DM=Days to maturity, GFP=Grain filling period (days), NDVI=Normalized vegetative index, PH=plant height, NFT=Number of fertile tillers, SL=Spike length, SPS=Number of spikelet per spike, NSPS= Number of seed per spike, BM=Biomass Yield, GY=Grain yield, HI=Harvest index, PC=Grain protein content, HLW=Hectoliter weight, TSW=Thousand seed weight and CHO=Chlorophyll content.

3.7. Clustering and Genetic Divergence under Optimum and Low N

In order to produce good heterotic crossings, breeding programs need genetically distinct and desirable genotypes for hybridization. Cluster analysis is frequently used to determine the level of genetic variation and group genotypes based on their similarities into one cluster. Understanding of genetic relationships between genotypes offers valuable information to tackle selective breeding and germplasm resources management. The cluster and genetic divergence analysis results for each of the optimum and low N experiments are presented below.

3.7.1. Clustering of genotypes under optimum N

Cluster analysis using the means of the sixteen variables evaluated across three locations grouped the 200 durum wheat genotypes into 13 distinct clusters. Twelve of the thirteen clusters are actual clusters, and just one is a solitary cluster with only one genotype (Table 4 and Figure S 1). According to the distribution pattern, cluster I comprised of more than 50% of the genotypes (112), followed by clusters VII, VI, VIII, III, and II with 48, 8, 6, 5 and 4 genotypes, respectively. Each of the clusters IV, V, X, and XIII included three genotypes, whereas clusters IX and XI contained only two genotypes each. Cluster-XII contained small number of genotype (Table 4 and Figure S 1).

Table 4. Cluster numbers and genotypes grouped in each clusters for 200 genotypes tested for 16 traits based on averages of three locations under optimum nitrogen conditions

Clusters	No. of genotypes	Percentage	Genotypes in the clusters
I	112	56	1, 86, 148, 98, 87, 113, 128, 89, 132, 96, 145, 126, 127, 139, 114, 111, 130, 165, 90, 112, 189, 118, 135, 133, 134, 107, 144, 137, 103, 106, 110, 102, 151,117, 171, 122, 104, 115, 108, 5, 159, 12, 185, 109, 152, 91, 93, 125, 119, 162, 15, 92, 161, 88, 95, 116, 141, 100, 195, 97, 124, 94, 188, 147, 158, 155, 30, 182, 142, 181, 143, 101, 164, 136, 154, 140, 191, 146, 157, 163, 166, 2, 41, 3, 169, 177, 7, 18, 20, 186, 170, 190, 198, 197, 176, 183, 194, 173, 19, 178,187, 168, 129, 105, 121, 149, 150, 156, 167, 175, 179 and 14
II	4	2	180, 184, 172, 10
III	5	2.5	193, 192, 138, 123 and 99
IV	3	1.5	200, 160 and 120
V	3	1.5	196, 174 and 131
VI	8	4	47, 199, 39, 37, 25, 71, 57 and 4
VII	48	24	67, 83, 45, 74, 35, 16, 65, 42, 73, 48, 40, 52, 44, 56, 54, 38, 79, 50, 72, 33, 60, 46, 36, 32, 70, 43, 22, 80, 62, 64, 51, 82, 49, 55, 34, 26, 81, 69, 153, 13, 53, 75, 68, 77, 63, 21, 8 and 17
VIII	6	3	78, 66, 24, 76, 61 and 9
IX	2	1	27 and 28
X	3	1.5	11, 23 and 84
XI	2	1	58 and 29
XII	1	0.5	6
XIII	3	1.5	85, 59 and 31

Of the 200 durum wheat genotypes grown under optimum N conditions, about 12.5 % of the high yielding genotypes were grouped into clusters I (14), II (3), V (3), VII (3), IX (1), and X (1). Genotypes in Clusters VIII and III had medium GY values which varied from 3.85 to 3.90 t/ha. In contrast, the genotypes in cluster IV, VI, XI, XII, and XIII were low in grain yield. This indicates the possibility of producing desired recombinants for the development of high yielding varieties by crossing the superior genotypes of the aforementioned diverse cluster pairs. Several authors reported comparable and similar findings on durum wheat (Yohannes & Abebe, 2020) found 12 cluster using a total of 64 genotypes, (Batu, 2019), obtained four cluster from 100 durum wheat genotypes, (Birkneh, 2021), reported six clusters using 45 durum wheat genotypes and the work of (Mengistu et al., 2016) showed eleven distinct clusters produced from a total of 289 durum wheat landraces in the study of genetic diversity.

3.7.2. Clustering of genotypes under low N

The cluster analysis grouped the 200 durum wheat genotypes into eight clusters using the average clustering method based on morphological, some quality, and physiological traits of durum wheat genotypes grown under low nitrogen conditions in three locations (Table 5 and Figure S 2). Of these clusters six were real clusters and two of them (Cluster V and VIII) were solitary with each comprising only one genotype. The distribution pattern of genotypes showed that cluster I had the maximum number of genotypes (117) followed by cluster IV with 62 genotypes, cluster II with seven genotypes and cluster VI having six genotypes, whereas clusters III and VII had three genotypes. Selection of durum wheat genotypes as parents from the eight clusters recognized in this study for hybridization might result in segregates with best combinations of superior alleles for various traits.

Table 5. Cluster numbers and genotypes grouped in each clusters for the 200 genotypes tested at three locations under low nitrogen

Clusters	No of genotypes	Percentage	Genotypes in the clusters
I	117	58.5	1, 2, 3, 5, 7, 10, 12, 15, 18, 19, 20, 30, 41, 86–104, 106–120, 122–149, 151, 152,154–173, 176–191, 194, 195, 197, 198 and 200
II	7	3.5	174, 175, 150, 196, 121, 105 and 14
III	3	1.5	167, 192 and 193
IV	62	31	55, 48, 9, 67, 83, 69, 45, 33, 57, 61, 27, 85, 68, 63, 43, 42, 28, 60, 52, 44, 76, 71, 40, 54, 59, 56, 38, 66, 73, 39, 153, 51, 46, 36, 32, 50, 26, 78, 81, 72, 65, 24, 64, 62, 82, 80, 84, 49, 34, 23, 11, 75, 53, 17, 13, 77, 21, 8, 25, 4, 199 and 37
V	1	0.5	70
VI	6	3	29, 35, 74, 47, 58 and 16
VII	3	1.5	22, 31 and 79
VIII	1	0.5	6

In this study, the top 25 high yielding genotypes under low N conditions grouped into three clusters with 14 of them in cluster I, six in cluster IV and five in cluster II. Genotypes grouped in cluster III, VI, VII, and VIII were relatively low yielding while genotype in cluster V was medium in GY. In line with this result, (Zemede et al., 2019) grouped 64 durum wheat genotypes exposed to drought stress at anthesis in to five clusters. Zarei et al. (2013) grouped 410 durum wheat F5 lines in to four clusters under drought stress. Ahmadizadeh et al. (2011), (Naghavi & Khalili, 2017; Soleymanifard et al., 2012) and (Belete et al., 2020) classified durum wheat grown under drought stress in to three clusters. Nouri et al. (2011) categorized 14 durum wheat genotypes in to three clusters under drought. The difference in number of clusters among this finding and previous scholars could be due to the variation in source and number of genotypes, climatic and edaphic environments.

3.7.3. Analysis of cluster means under optimum N

The cluster means for 16 quantitative characters of durum wheat genotype grown under optimum N are given in Table 6. The results showed that advanced lines from CIMMYT, ICARDA and DZARC were grouped into clusters I, III and IV. These clusters were distinguished by high values of GFP, NSPS, BM, HI, PC, HLW, TSW, CHO, and GY compared to the average means, whereas DH, DM, PH, NFT, SL, HI and CHO had lower values. Ethiopian released varieties with greater HLW, HI, GY and CHO were included in Cluster II and can be chosen for these traits. Cluster V included CIMMYT materials, released varieties, and DZARC breeding lines that were distinguished by high HI and GY as well as early heading and maturation. This suggests that genotypes in cluster V may be selected for their early maturity and high yield performance. Since most of the Ethiopian breeding lines were derived from CIMMYT, they were grouped with CIMMYT sources and released varieties into the same clusters (I, III, IV, and V) thereby indicating high gene flow from CIMMYT to the Ethiopian breeding block.

Table 6. Mean values of the character in the clustering of durum wheat genotypes grown under optimum N over three locations during 2020 cropping season

Traits	Cluster means													Overall mean
	C I	C II	C III	CIV	CV	C VI	C VII	C VIII	C IX	C X	C XI	C XII	C XIII
DH	69.46	69.37	69.19	69.18	69.20	69.64	71.16	71.39	71.47	71.45	71.59	70.00	71.60	70.36
DM	113.17	113.10	113.04	113.01	113.04	113.41	113.86	114.05	114.12	114.09	114.20	111.00	114.16	113.40
GFP	43.81	43.83	43.95	43.92	43.92	43.85	42.77	42.75	42.74	42.73	42.69	41.00	42.63	43.12
NDVI	64.05	64.17	64.06	63.88	64.04	64.08	65.63	65.72	65.79	65.94	65.92	56.00	65.82	64.24
PH	59.48	59.51	59.66	59.56	59.59	60.29	62.88	63.22	63.23	63.41	63.39	66.35	63.38	61.84
NFT	3.25	3.27	3.26	3.25	3.27	3.29	3.65	3.65	3.66	3.69	3.70	3.00	3.71	3.43
SL	4.89	4.89	4.93	4.91	4.94	5.00	5.47	5.51	5.51	5.54	5.56	4.65	5.56	5.18
SPS	16.79	16.83	16.92	16.89	16.92	16.95	17.16	17.23	17.22	17.23	17.25	18.00	17.23	17.12
NSPS	53.38	53.72	54.21	54.22	54.19	53.41	48.68	48.42	48.27	48.05	47.94	47.00	47.70	50.71
BM	10.10	10.15	10.14	10.10	10.14	10.12	10.52	10.55	10.59	10.62	10.63	6.58	10.59	10.06
GY	3.87	3.90	3.90	3.89	3.91	3.86	3.85	3.85	3.86	3.86	3.86	2.07	3.84	3.73
HI	0.38	0.38	0.38	0.38	0.38	0.38	0.37	0.37	0.36	0.36	0.36	0.32	0.36	0.37
PC	11.12	11.11	11.10	11.10	11.11	11.13	11.15	11.13	11.12	11.14	11.14	9.72	11.13	11.02
HLW	69.52	69.86	69.80	69.61	69.78	69.28	69.42	69.43	69.47	69.54	69.47	50.50	69.21	68.07
TSW	39.58	39.46	39.33	39.23	39.39	39.54	39.44	39.63	39.64	39.67	39.65	31.13	39.58	38.87
CHO	42.38	42.50	42.53	42.43	42.47	42.30	41.05	40.95	40.89	40.86	40.83	33.00	40.73	40.99

Note: DH=Days to heading, DM=Days to maturity, GFP=Grain filling period (days), NDVI=Normalized vegetative index, PH=plant height, NFT=Number of fertile tillers, SL=Spike length, SPS=Number of spikelet per spike, NSPS= Number of seed per spike, BM=Biomass Yield, GY=Grain yield, HI=Harvest index, PC=Grain protein content, HLW=Hectoliter weight, TSW=Thousand seed weight and CHO=Chlorophyll content.

The advanced lines and Ethiopian landrace genotypes grouped in cluster VI had higher means than the overall average for all traits except DH, SPS, SL, NFT, and NDVI. All genotypes in clusters VII, VIII, IX, X, XI, and XIII were Ethiopian landraces with the exception of genotype 153 in cluster VII, which came from CIMMYT. Genotypes in these clusters had lower mean values than the overall means for NSPS, CHO, GFP, and HI; however, the other characteristics had higher mean values above the average. These genotypes were also taller and had longer spike genotypes that matured later, suggesting that these genotypes were selected for NFT, SL, SPS, and BM. Similar results were found by (Mengistu et al., 2016), who obtained high genetic diversity among Ethiopian farmers’ cultivars with significant agronomic and phenological features. Only one genotype from ICARDA was found in Cluster XII, and it characterized by low mean values for all traits except PH and SPS. The genotypes grouped under the different clusters have some special traits that could yield advantageous genetic recombinants in a hybridization program. The current study found that durum wheat genotypes examined under various environmental conditions and optimal N conditions exhibited substantial variations, which provide opportunity for genetic improvement through selection and hybridization.

3.7.4. Analysis of clusters means under low N

The cluster mean for the 200 genotypes tested at three locations are presented in Table 7. The majority of CIMMYT and ICARDA materials and few Ethiopian landraces and released varieties were grouped in cluster I. Genotypes in this cluster were characterized with lower SPS, shorter plants, short SL, early maturity and highest HI. This cluster had the second highest NSPS next to cluster III. Cluster II consisted of ICARDA and CIMMYT materials, Ethiopian released varieties and DZARC advanced lines with the highest mean values of GY, HI and HLW. The CIMMYT materials and DZARC advanced lines were contained in cluster III, and are characterized by earliest heading and maturity, high GY, NSPS, HI and CHO. The majority of Ethiopian landraces were grouped in Cluster IV with the highest means for TSW. Genotypes in this cluster showed moderate performance in most yield related traits and phenological traits compared to the remaining clusters. Cluster V comprised a single Ethiopian landrace with delayed DH and DM. It had the second earlier genotypes in GFP next to cluster VIII.

Table 7. Mean values of the characters in the clustering of durum wheat genotypes under low N

Traits	CI	CII	CIII	CIV	CV	CVI	CVII	CVIII	Overall mean
DH	72.44	72.44	72.32	75.43	86.00	75.93	75.95	79.00	76.19
DM	116.97	117.03	117.01	118.90	128.00	119.23	119.23	118.00	119.30
GFP	44.65	44.70	44.79	43.56	43.00	43.41	43.37	39.00	43.31
NDVI	48.06	48.22	48.13	49.56	56.00	49.61	49.60	44.00	49.15
PH	50.91	50.92	50.96	52.80	58.60	52.83	52.87	58.93	53.60
NFT	2.26	2.28	2.28	2.59	4.00	2.62	2.63	2.00	2.58
SL	4.39	4.43	4.45	5.06	6.83	5.12	5.12	4.35	4.97
SPS	15.24	15.27	15.32	15.72	17.00	15.75	15.75	16.00	15.76
NSPS	47.09	47.02	47.41	42.92	33.00	42.53	42.39	44.00	43.30
BM	5.67	5.71	5.70	5.97	7.83	5.97	5.95	3.92	5.84
YD	2.00	2.03	2.03	2.01	1.97	2.00	1.99	0.92	1.87
HI	0.35	0.35	0.35	0.33	0.24	0.33	0.33	0.22	0.31
PC	11.12	11.15	11.15	11.29	13.08	11.29	11.29	9.87	11.28
HLW	50.07	50.48	50.36	49.98	45.67	49.74	49.66	40.43	48.30
TSW	37.51	37.87	37.78	38.71	33.95	38.61	38.61	30.47	36.69
CHO	37.26	37.35	37.39	36.82	35.00	36.73	36.67	27.00	35.53

The genotypes in this cluster were also characterized by high SL, SPS, NFT, PC, NDVI and BM. However, they are least in NSPS compared to the other clusters and low in CHO, HLW, TSW and HI next to cluster VIII. Clusters VI and VII consisted of Ethiopian landrace durum wheat genotypes having similar mean values for most of the traits indicating genetically close relationship among the genotypes found in these clusters. The genotypes in these clusters had the second highest PC and NDVI next to cluster V and TSW next to cluster IV. The mean values of all traits were greater than the average values of clusters except for DH, DM, GFP, SPS and PH. Genotype 6 was the single ICARDA genotype grouped in cluster VIII alone. This genotype was characterized by having lower mean values for most of the traits with the exception of DM, PH, SPS and NSPS than the overall mean. Among the eight clusters, three of them (I, II and IV) can be selected for GY and cluster V can be selected for PC. This result was in agreement with the findings of earlier studies of (Ali et al., 2021; Mohammad et al., 2013; Naghavi & Khalili, 2017 and Mahpara et al., 2022). In general, the results suggested that crossing among distant genotypes from different clusters would increase the probability of getting large variability in segregating generations.

3.7.5. Distance among clusters under optimum N

Highly significant genetic diversity was found among the genotypes as illustrated by high inter-Euclidean (estimated genetic distance) cluster distances than intra-cluster genotype distances. The results of the intra- and inter-cluster distances between the 13 clusters are displayed in Table 8. The inter-cluster distance between the genotypes ranged from 18.22 to 220.99. Clusters X and XII had the largest inter-cluster distance followed by clusters IV and X, II and XI, and clusters V and XII, suggesting that these clusters were genetically more distinct from one another than any other pairs of clusters. Hybridization between genotypes using these clusters would produce high heterotic values in the F1 generation, and result in greater variability in segregating populations. The genotypes in Cluster I and II were closer to one another followed by clusters VI and VIII, indicating that hybridization between them would be unlikely to produce high variability than those genotypes found in different clusters.

Table 8. Average intra (Bold) and inter (off-diagonal) cluster distance (D²) among thirteen clusters of 200 durum wheat genotypes grown under optimum nitrogen soil conditions over three locations

Clusters	I	II	III	IV	V	VI	VII	VIII	IX	X	XI	XII	XIII
I	4.21	18.22	24.48	18.99	24.37	49.38	83.87	74.21	101.08	132.44	126.93	99.65	70.19
II		1.79	29.64	57.82	29.13	94.09	115.49	112.56	133.82	153.06	180.71	151.29	122.17
III			1.86	36.77	47.71	84.03	132.50	107.33	161.85	170.97	163.10	123.67	127.46
IV				1.85	71.71	75.60	124.77	110.86	142.82	187.58	159.85	83.87	80.96
V					2.64	67.99	82.50	75.74	110.48	100.74	122.28	177.06	101.58
VI						3.12	29.49	18.28	66.83	88.56	45.87	70.22	31.27
VII							4.36	18.68	38.46	32.12	50.55	109.21	27.63
VIII								2.93	42.65	71.30	35.73	95.36	45.21
IX									2.07	85.17	67.39	143.61	58.19
X										2.37	108.52	220.99	93.24
XI											2.64	158.25	72.58
XII												0.00	53.22
XIII													2.06

Note: Chi-square value=21.03 at 5% and 26.03 at P≤0.01

Intra-cluster distances ranged from 0 to 4.36 indicating that the genotypes in the cluster were homogeneous. Cluster VII has the largest intra-cluster distance followed by clusters I and VI. In contrast, the smallest intra-cluster distance was recorded for cluster XII followed by clusters II, IV, and III. The genotypes within the clusters having high intra-cluster distance were found different than the genotypes in the clusters with the smallest intra-cluster distance where selection is inefficient. Similar findings were reported by (Arya et al., 2017; Gashaw et al., 2007) and (Yohannes & Abebe, 2020) who used cluster distance (D²) statistics to determine inter- and intra-cluster distance for the study of genetic variability in wheat.

3.7.6. Distance among clusters under low N

The intra-cluster average distance between genotypes varied from 0 and 4.71 indicating close relationship of genotypes within clusters. The highest intra-cluster distance was found in cluster IV followed by cluster I, while no distance was found in clusters V and VII (Table 9). The variation in inter-cluster distance, on the other hand, ranged from 13.79 to 241.78, indicating significant genetic divergence among genotypes in clusters. The maximum inter-cluster distance was displayed between clusters V and VIII followed by clusters III and V and clusters II and VIII, while the minimum distance was recorded between cluster I and II (Table 9).

Table 9. Average intra (Bold) and inter (off-diagonal) cluster distance (D²) among eight clusters of 200 durum wheat genotypes grown under low nitrogen soil conditions

Clusters	CI	CII	CIII	CIV	CV	CVI	CVII	CVIII
CI	4.50	13.79	28.77	53.75	140.58	91.34	40.92	118.15
CII		2.83	40.25	53.22	142.73	98.11	60.97	167.73
CIII			2.59	112.29	196.09	154.13	99.83	162.13
CIV				4.71	45.14	19.41	23.46	135.36
CV					0.00	58.87	102.46	241.78
CVI						3.47	35.05	129.29
CVII							0.00	62.28
CVIII								2.67

Note: Chi-square value=14.07 at 5% and 18.47 at P≤0.01

Thus, parents for hybridization can be selected from clusters V and VIII, which may provide diverse and useful recombinants in segregating generations. Rahman et al. (2013) identified diversity among spring wheat genotypes grown under drought stress using their intra- and inter-cluster distances.

3.8. Principal Components Analysis under Optimum N

Principal components analysis (PCA) is used to construct patterns and relationships between the genotypes and their quantitative attributes, which can partially validate the findings from cluster analysis. It is useful to identify the traits contributing much to the overall diversity among the test genotypes. Under Optimum N conditions, the first five principal components (PCs) with eigenvalues greater than one explained 81.29% of the total variation. The PC1 explained about 39.63% of the variation and the PCs 2, 3, 4, and 5 explained 18.31%, 8.92%, 7.91%, and 6.51% of the gross variation, respectively (Table 10).

Table 10. Eigenvectors, eigenvalues and percentage of total variance explained by the first five principal components for 16 variables of 200 durum wheat genotypes grown under optimum N over three locations

Variables	Eigenvectors
	PC1	PC2	PC3	PC4	PC5
Days to heading	0.82	−0.20	−0.12	0.32	−0.26
Days to maturity	0.58	−0.11	0.08	0.59	−0.42
Grain filling period (days)	−0.72	0.24	0.33	0.16	−0.06
Normalized vegetative index	0.72	0.50	0.07	−0.05	0.11
Plant height (cm)	0.75	0.08	0.38	0.02	0.16
No. of fertile tillers	0.70	0.14	−0.07	−0.43	0.003
Spike length (cm)	0.85	0.06	−0.01	0.01	0.30
No. of spikelet’s/spike	0.44	0.12	−0.06	0.57	0.63
No. of seeds/spike	−0.83	0.09	−0.13	0.33	0.27
Biomass yield (t/ha)	0.67	0.65	−0.14	−0.02	−0.03
Grain yield (t/ha)	−0.06	0.94	−0.22	0.00003	−0.08
Harvest index (%)	−0.81	0.43	−0.10	0.03	−0.06
Protein content (%)	0.09	−0.01	0.63	−0.32	0.25
Hectoliter weight (%)	−0.02	0.91	−0.13	−0.04	−0.11
Thousand seed weight (g)	0.11	0.34	0.74	0.19	−0.29
Chlorophyll content	−0.71	0.27	0.28	0.16	0.17
Eigenvalue	6.34	2.93	1.43	1.27	1.04
Difference	3.41	1.5	0.16	0.22	0.13
Variance explained (%)	39.63	18.31	8.92	7.91	6.51
Cumulative variance explained (%)	39.63	57.94	66.86	74.78	81.29

In similar study, (Yohannes & Abebe, 2020) reported five principal components accounting for 81.58% of the overall variation among 64 durum wheat genotypes evaluated for 12 characters. Likewise, (Kandel et al., 2018) found that the first six principal components based on 17 quantitative attributes of 41 wheat genotypes accounted for 77.5% of the variability. The work of (Adilova et al., 2020) indicated that the first three principal components explained about 90.8% of the variability among 25 bread wheat genotypes evaluated for 10 traits, whereas (Devesh et al., 2019) found that seven principal components explained about 66.22% of the variability among 60 wheat genotypes assessed for 19 characters.

Significant contributions to PC1 were made by DH, PH, NFT, SL, BM and NDVI where SL among others had the highest proportions. High contributors to the observed variation in PC2 were BM, GY, and HLW, whereas PC and TSW exhibited the maximum value on PC3. In PC4, DM and NSPS imposed high component loading, and the latter caused the highest fluctuation in PC5. PCA quantifies how much independent effect of a character contributes to the overall variation seen in a particular population. The primary variables observed to discriminate the genotypes were DH, DM, NDVI, SL, NFT, SPS, NDVI, GY, BM, TSW, HLW, and PC. Thus, the use of characters that showed significant contribution in each PC’s might be important for selection and genetic improvement in the population.

3.9. Principal component analysis under low N

The principal components analysis (PCA) of the quantitative characters of 200 durum wheat genotypes grown under low N yielded four principal components with eigenvalue greater than one that together accounted for 73.63% of the total variations (Table 11). The first principal component explained about 40.12 % the gross variation and it was due chiefly to variations in DH, DM, PH, NFT, SL, SPS, BM, NDVI, PC and TSW where, the highest contributor trait was DH. The traits GY, HLW, BM, NDVI, HI and TSW were the most contributors to explain about 17.84% of the variation in second principal component where GY had the highest loading to PC2. The third principal component accounted for 8.39 % of the variation and it was due mainly to variations in SPS, NSPS, GY, BM and DH.

Table 11. Eigenvectors, eigenvalues and percentage of total variance explained by the first three principal components for 16 variables of 200 durum wheat grown under low N genotypes grown under low N

Variables	PC1	PC2	PC3	PC4
Days to heading	0.910	−0.110	0.210	−0.110
Days to maturity	0.860	−0.020	0.120	−0.020
Grain filling period (days)	−0.760	0.190	−0.310	0.230
Normalized vegetative index	0.600	0.530	−0.002	−0.004
Plant height (cm)	0.680	0.060	−0.310	0.340
No. of fertile tillers	0.730	0.140	0.089	−0.030
Spike length (cm)	0.850	0.070	0.088	0.240
No. of spikelet/spike	0.550	0.010	0.400	0.590
No. of seeds/spike	−0.800	−0.050	0.390	0.280
Biomass yield (t/ha)	0.470	0.740	0.230	−0.120
Grain yield (t/ha)	−0.195	0.919	0.255	−0.045
Harvest index (%)	−0.740	0.520	0.120	0.110
Protein content (%)	0.330	0.220	−0.380	0.220
Hectoliter weight (%)	−0.230	0.750	0.000	−0.310
Thousand seed weight (g)	0.274	0.417	−0.700	0.113
Chlorophyll content	−0.453	0.232	0.059	0.562
Eigenvalue	6.419	2.855	1.342	1.165
Difference	3.564	1.512	0.178	0.269
Variance explained (%)	40.120	17.840	8.390	7.280
Cumulative variance explained (%)	40.120	57.960	66.350	73.630

The fourth principal component accounted for 7.29% of the total variation as a result of variations related to SPS and CHO. The result of PCA revealed that BM, SPS, NSPS, TSW, GFP, HI and GY were the most contributor traits to all principal components. Previously, (Mehdi & Mostafa, 2012) reported that about 69.30% of the total variation among 140 bread wheat genotypes evaluated for 15 variables under stress conditions was explained by five principal components. Mahpara et al. (2022) also found that the first four principal components accounted for 98.34% of the total variability among 40 wheat genotypes evaluated for 12 characters. Using 64 wheat genotypes evaluated for nine traits, (Ali et al., 2021) found that five principal components explained approximately 86.95% of the variability. Likewise, (Sangi et al., 2022) identified two principal components using eight physiological and yield traits of 23 durum wheat genotypes.

4. Conclusions and recommendations

A breeding program aimed at improving durum wheat should focus on genetic variation. The presence of significant variations among genotypes for all studied characters under both N conditions demonstrated adequate opportunity for genetic enhancement. The interaction of genotypes and locations was also significant for most of the traits except SL and NSPS under optimum N and GFP, PC and HLW under low N conditions. This implies that for traits not significantly affected by genotype by location interaction, selection can be made at any one specific environment. But for the traits on which genotype and environment interact significantly, selection must be based on diverse environmental conditions. All quantitative traits except DH, DM, GFP and PC were higher under optimum N than low N conditions which attributed to N stress.

Higher GCV, PCV and heritability (H²) estimates coupled with higher values of GAM were observed for NFT and NSPS under optimum N and for SL and NSPS under low N. Hence, NSPS can be used as selection criterion under both optimum and low N conditions, while NFT and SL can be used as selection criterion under optimum and low N conditions, respectively. Moreover, medium to high estimates of GCV, PCV, H² and GAM computed for GY under both N conditions suggested selection based on phenotypic expression of durum wheat genotypes is possible to improve the trait. The lower values of GCV, PCV, H² and GAM for HLW and PC under both N conditions indicated high environmental impact for the improvement of these traits by selection.

In this study, nitrogen stress narrows down genetic divergence of durum wheat genotypes as confirmed by cluster and principal components analyses, demonstrating that a large number of genotypes are required to achieve significant genetic diversity under N stress marginal conditions. The cluster analysis classified the 200 durum wheat genotypes into thirteen and eight groups under optimum and low N conditions, respectively. The highest grain yield was exhibited by genotypes found in cluster V under optimum N and clusters II and III under low N environments; this implies parallel selection of genotypes under both N conditions. The highest intra-cluster distance was detected among genotypes in cluster VII and IV in optimum and low N conditions, respectively. The maximum inter-cluster distance was recorded between cluster X and XII under optimum N and between cluster V and VIII under low N condition. The principal components analysis resulted in five and four PCs which explained 81.29% and 73.63% of the total variations in optimum and N stress conditions, respectively. Under optimum N condition, phenological traits, GY and BM, TSW and PC, DM and NSPS were the highest contributor traits to the variations accounted for by PC1, PC2, PC3, PC4 and PC5, respectively. However, under low N condition, most phenological traits, BM, GY and HLW, SPS and NSPS, and CHO and SPS had more contribution to the variation explained by PC1, PC2, PC3 and PC4, respectively. Therefore, durum wheat genotypes found in distant clusters with highest contributor traits for the existing variation under both N conditions can be employed in crossing blocks to improve desired characters.

In conclusion, the existence of wide range of genetic variation in the study paves high possibility for durum wheat improvement through selection and hybridization for improving grain yield and adaptation to N stress conditions. Moreover, these findings should be supported by marker assisted selection for identification of pertinent traits in durum wheat to improve nitrogen efficiency. Additionally, further studies on the physiological traits and nitrogen use efficiency components of these diverse durum wheat genotypes are paramount.

Author contributions

Conceptualization, T.G; Methodology, T.G., F.A., K.A., T.B., and N.G.; Data curation, T.G, K.A., and T.B.; Formal analysis and software, T.G., T.B. and K. A.; Funding acquisition, T.G.; Resources, T.G.; Supervision, T.G., F.A., T.B., K.A., B.A. and N.G.; Visualization: T.G.; Writing-original draft, T.G.; Writing—review and editing, T.G., F.A., T.B., K.A., B.A. and N.G. All authors have read and agreed to the published version of the manuscript.

Acknowledgments

The authors are grateful to the Ethiopian Institute of Agricultural Research (EIAR) and the International Maize and Wheat Improvement Center (CIMMYT) for financial support, as well as the Debre Zeit Agricultural Research Center for providing an experimental field.

Disclosure statement

No conflict of interest was reported by the authors.

Data availability statement

The data presented in this study are available upon request from the corresponding author

Supplementary material

Supplemental data for this article can be accessed online at https://doi.org/10.1080/23311932.2023.2197758.

Additional information

Funding

This research was funded by Ethiopian Institute of Agricultural Research (EIAR)

Notes on contributors

Tesfaye Geleta Aga

Tesfaye Geleta Aga is a researcher at the Ethiopian Institute of Agricultural Research; Debre Zeit Agricultural Research Center in Ethiopia for the last 10 years. He is currently pursuing a PhD in plant breeding and seed systems at Mekele University. He had conducted research on the adaptation of durum wheat to nutrient deficiency and acid soil tolerance. He is interested in studies that involve testing genotypes for nutrient use efficiency, abiotic stress tolerances and the generation of genetic variability using modern breeding tools. He is member of Ethiopian crop science society.