Exploring relationship among nitrogen fertilizer, yield and nitrogen use efficiency in modern wheat varieties under subtropical condition

Abstract

Due to variations in the length of crop growth and the dynamics of soil nitrogen, the nitrogen (N) requirements of different wheat varieties may change. In this relation, during two successive wheat-growing seasons in 2018 and 2019, pot experiments were conducted to assess relationship among N fertilizer, yield and efficiency of N use in contemporary wheat varieties. Ten varieties of wheat viz. BARI Gom -24, BARI Gom -25, BARI Gom -26, BARI Gom -27, BARI Gom -28, BARI Gom -29, BARI Gom-30, BARI Gom-31, BARI Gom-32, and BARI Gom-33 and four N levels e.g. N 0, N 45 kg ha⁻¹, N 90 kg ha⁻¹ and N 135 kg ha⁻¹ were used in the study. Completely randomized design (CRD) was followed with three replications. Wheat yield and yield-contributing elements like spike length, 1000- grain weight, and grain yield enhanced at N levels up to N 90 kg ha⁻¹. The principal component analysis illustrated that the range BARI -32 had the most grain manufacturing and N use efficiency with N 90 kg ha⁻¹ application. Correlation depicted that there is a robust relation amongst N use, yield and N use efficiency. Dendrogram organized primarily based totally at the resemblance via way of means of Euclidean distance among wheat varieties and it was confirmed that varieties allocated in clusters II had been greater diverse. The end consequence found out big hereditary difference in wheat which could doubtlessly be used for the wide range cultivation and breeding plan.

1. Introduction

In Bangladesh, winter season is appropriate for wheat (Triticum aestivum L.) cultivation because the most favorable temperature (10–20 °C) prevails during this period. The average wheat yield in 2020 was 3.098 t ha⁻¹ and that was 0.65% superior to 2019 (BBS, 2020). It accounts for 11% of the cultivated area during winter period which produced 7% of the total food grains. Wheat cultivation could solve the food problem on a larger scale and save the country an enormous amount of foreign currency. A comparative study comparing wheat in Bangladesh with many other wheat producing countries showed that average yields in Bangladesh are very low, unfortunately even half the wheat productivity of other developed countries (FAO, 2020).

Despite its low yield, wheat faces numerous biotic and abiotic stresses. Inconsistency and poor use of fertilizers, a lack of knowledge about variations, edaphic characteristics, improper management of farmers' field operations and technology are also present (Meena et al., 2013, Ali et al., 2018). The need for wheat fertilizer relies on how easily the crops can access the soil (Krentos and Orphanos, 1979). It is crucial to understand the nutritional status of the soil and plant nutrient uptake prior to applying fertilizer. In addition, different plant species and genotypes have different capacities for absorbing water, nutrients, and responding to stress. A cultivar that thrives in one kind of soil may struggle in another kind of soil, and and vice versa (Adhikari et al., 2019).

N is a yield restrictive nutrient in cereals crop (Sun et al., 2012). Farmers are probably used to broadcasting more N fertilizer than required for increasing the grain yield. Average nitrogen use in crop production is surprisingly high, and above a certain amount, nitrogen utilizing effectiveness decreases (Peng et al., 2006, Zhang et al., 2006). Declining nitrogen utilization effectiveness of irrigated crops augmented contamination due to quick loss of spent nitrogen through volatilization, denitrification, and leaching of nitrogen from fields (Liu et al., 2008). About 15 kg N ha⁻¹ losses at optimal nitrogen levels, while conventional nitrogen application by farmers exceeded 100 kg N ha⁻¹. There are some key factors of genotypes like root system and N application rate etc. are reponsible for the effectiveness of N uptake and utilization (Timsina et al., 2001, Deng et al., 2012). Total N accumulation at the grain filling stage can significantly contribute to yield enhancement (Lin et al., 2006). In addition, increasing nitrogen recovery efficiency and optimizing nitrogen uptake can increase crop yields (Walker Timothy et al., 2008, Thakuria et al., 2009). Therefore, N losses may be reduced by judicious use of N without negotiating yield of crop (Cui et al., 2006, Zheng et al., 2007).

For this reason and potential environmental impact, Nitrogen use efficiency (NUE) plays an important function in optimizing grain yield. NUE denotes the relation between output and the quantity of N used in the field. Inorganic nitrogen fertilizers can bring significant costs to wheat production and negatively impact the environment through the leaching process and N₂O emissions. Management practices need to be researched so that farmers can increase yields, reduce production costs and ensure agricultural sustainability. From this perspective, the development and cultivation of high NUE wheat varieties can reduce the amount of nitrogen applied as fertilizer without reducing grain yield. Wheat NUE is estimated as poorer than 60% (Haile et al., 2012, Hawkesford, 2012, Duan et al., 2014). Based on the genotype used, the N application at the rate of 80–120 kg N ha⁻¹ consequences in a range of 28.8–40.0 kg of grain with 1 kg of N uses (Rahman et al., 2011). Phytomass accumulation and leaf chlorophyll content are associated with NUE. It can therefore be used to indirectly select cultivars that can use N proficiently.

Therefore, the application of appropriate amount of N fertilizer can be regarded as the main means to increase grain yield, improve uptake of N and utilization effectiveness of wheat. Nevertheless, superior varieties are sometimes produced without consideration for viability and yield on less fertile soils. They were selected for their ability to produce higher yields under fertilizer-intensive conditions. Therefore, to meet the foodstuff needs of emerging people, we continue to evaluate varieties with high NUE, breeds should be selected based on genetic variation. Therefore, the key purpose of this research was to examine cultivars based on yield, NUE and genetic variation for large-scale assortment cultivation and use in breeding programs in Bangladesh.

2. Materials and methods

2.1. Experimental site

The study was conducted at net house, Department of Agronomy, Bangladesh Agricultural University, Mymensingh, Bangladesh. The experimental site falls between the latitude of 24°42^΄55″ and longitude of 90°25΄47″ and from the sea level the elevation is of 19 m.

2.2. Soil and climate

A composite sample (0–20) of topsoil was collected from the plot before the start of the experiment. Soil texture, pH, total organic carbon, whole N, existing P and K were analyzed. pH was extracted using a distilled glass electrode method (1:2.5, soil: water). Soil nitrogen was estimated by globally standardized Kjeldahl method. Available phosphorus was extracted using the Olsen method (Olsen et al., 1982). This method has been done by extracting phosphate from soil using solution 0.5 N sodium bicarbonate solution and accustomed to pH 8.5. Air-dried soil samples were shacked in a 0.5 M ammonium acetate solution for 30 min and Potassium was extracted. Jenway PFP7 flame photometer was used to calculate K content. Physical and chemical properties of the soil are shown in Table 1.

Table 1.

Physical characteristics and chemical composition of the experimental soil.

Properties	Results	Composition	Results
Sand (0.0–0.02 mm) %	21.95	Soil pH	6.80
Silt (0.02–0.002 mm) %	66.75	Organic matter (%)	1.29
Clay (<0.002 mm) %	11.30	Total nitrogen (%)	0.101
Soil textural class	Silt loam	Available phosphorus (ppm)	26.00
Consistency	Granular	Exchangeable potassium (me/100 g soil)	0.13

The experimental site is located in a subtropical climate, with high rainfall during April to September and low rainfall during October to March (Fig. 1). The winter season has plenty of sunshine, followed by relatively cool temperatures. Temperatures begin to rise as the season progresses towards summer from February. Fig. 1 showed the monthly maximum, minimum, regular temperature, precipitation, relative humidity, and hours of sunshine for the test area throughout the test period.

Fig. 1.

Monthly average temperature, rainfall, relative humidity and sunshine hours of the experimental site during 2018 and 2019.

2.3. Experimental design and treatments

Ten wheat varieties viz. BARI Gom-24 (V₁), BARI Gom-25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom-31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀) and four nitrogen levels i.e. 0 nitrogen (N₁), N 45 kg ha⁻¹ (N₂), N 90 kg ha⁻¹ (N₃) and N 135 kg ha⁻¹ (N₄) were used in the research. The detail information of mentioned varieties are included in Table 2. The seeds were collected from Bangladesh Agricultural Research Institute (BARI). The pot experiment followed a 3-replicate completely randomized design (CRD). The whole amount of pots was 240 (40 × 6) because vicious sampling was performed at three growth stages (25 days, 50 days and 75 days). Each set had 40 (10 × 4) pots, arranged side by side at intervals of 10 to 25 cm.

Table 2.

List of the ten wheat varieties, with their genetic origins and sources that were used in the experiment.

Sl. no	Cultivar	Genetic Origin	Parental Source/Accession Number	Source
1.	BARI Gom-24	Inbred	BAW 1008	BARI
2.	BARI Gom-25	Inbred	BAW 1059	BARI
3.	BARI Gom-26	Inbred	BAW 1064	BARI
4.	BARI Gom-27	Inbred	BAW 1120	BARI
5.	BARI Gom-28	Inbred	BAW 1059	BARI
6.	BARI Gom-29	Inbred	BAW 1151	BARI
7.	BARI Gom-30	Inbred	BAW677 x BIJOY (BAW 1161)	BARI
8.	BARI Gom-31	Inbred	BAW1182	BARI
9.	BARI Gom-32	Inbred	SOTABDI x GOURAB (BAW1202)	BARI
10.	BARI Gom-33	Inbred	KACHU x SOLALA (BAW1260)	BARI

2.4. Pot preparation and fertilizer application

Experiments were conducted in pots to avoid interfering effects of various environmental factors (Schneider et al., 2014) and to adequately and accurately estimate N content, uptake, utilization and recovery efficiencies. With 12.5 kg ha⁻¹ of 2% kasugamycin WP from Meibang Pesticide Co., Ltd. in Shaanxi, China, the soil was sanitized. After that, the soil was irrigated and covered with plastic film. After seven days, the plastic was removed. Each plastic pot was filled with 8 kg of soil and placed in his net house of the department of Agronomy at BAU in Mymensingh. Fertilizer rate was maintained at 68-66-20 kg ha⁻¹ in the form of P₂O₅-K₂O-S. Urea, a nitrogen fertilizer, was applied according to the treatment instructions. This fertilizer is mostly available and widely used by farmers. Due to practical considerations, urea was used as the N source. Fertilizers, with the exception of urea, were all applied at final pot preparation. The mentioned dose of urea was applied in 3 equal splits at 25, 50, 75 days after sowing (DAS).

2.5. Crop husbandry

Four seeds were sown at 5 cm intervals in pot⁻¹. Intercultural operations have been made to ensure that plants continue to grow normally. Two times weeding were done at 20 and 50 DAS. After weeding, the pots were watered two times at 25 and 55 DAS. Plant height (cm) and total tiller plant⁻¹ data were measured at 25, 50, and 75 DAS. Leaf area index (LAI) was also calculated on the days mentioned using a leaf area meter (LI 3100, Licor Inc., Lincoln NE, USA). Crops are harvested in pots when they are fully mature and taken to the threshing floor to be threshed and dried. Data on seed yield, yield-determining factors, and growth were all collected separately.

2.6. Calculation of N uptake (kg ha⁻¹) and NUE indicators

N uptake by plant (kg ha⁻¹)$=$ N uptake in grain (kg ha⁻¹)$+$ N uptake in straw (kg ha⁻¹

$$\text{Nitrogen use efficiencey}(\text{NUE})=\frac{\text{N uptake by plant with N application}-\text{N uptake by without N application}}{\text{The amount of N application}}$$

N uptake efficiency was determined with the following formula (Dobermann and Fairhurst, 2000)

$${\text{UE}}_{\text{N}}=\frac{{\text{UN}}_{+\text{N}}-{\text{UN}}_{0\text{N}}}{\text{FN}}\times 100$$

where, UE_N is the uptake efficiency of N, UN_+N is the total plant N uptake measured in the aboveground biomass (Kg ha⁻¹) and UN_0N is the total N uptake without the addition of N (Kg ha⁻¹), FN is the amount of fertilizer applied in kg ha⁻¹.

$$\mathrm{N}\mathrm{u}\mathrm{t}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}\mathrm{e}\mathrm{f}\mathrm{f}\mathrm{i}\mathrm{c}\mathrm{i}\mathrm{e}\mathrm{n}\mathrm{c}\mathrm{y}=\frac{\mathrm{G}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{N}\mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{d}\mathrm{p}\mathrm{l}\mathrm{o}\mathrm{t}-\mathrm{G}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n}\mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{p}\mathrm{l}\mathrm{o}\mathrm{t}}{\mathrm{N}\mathrm{u}\mathrm{p}\mathrm{t}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{r}\mathrm{p}\mathrm{l}\mathrm{o}\mathrm{t}-\mathrm{N}\mathrm{u}\mathrm{p}\mathrm{t}\mathrm{a}\mathrm{k}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{l}\mathrm{p}\mathrm{l}\mathrm{o}\mathrm{t}}$$

2.7. Data analysis

Data were analyzed with probability P ≤ 0.05 using analysis of variance and Duncan's multiple range test (Gomez and Gomez, 1984). Different letters within a treatment show significant changes at the 0.05 probability level, but values ​​followed by the same letter are not statistically different. Principal component analysis (PCA) was performed using SigmaPlot version 14 from Systat Software, Inc., San Jose, CA, USA (https://www.systatsoftware.com) and Pearson correlation analysis of variables was done with R (heatmap package) for Windows 4.1.2, R: A Language and environment for statistical computing. R Foundation for Statistical Computing Core Team R (2020) based in Vienna, Austria. https://www.R-project.org/ (accessed 4 June 2022).

3. Results

3.1. Growth parameters

The results exposed that variety had a considerable (P ≤ 0.001) impact on plant height (PH). Increased N levels also result in increased PH. The highest PH (27.25 cm) at 25 DAS, (47.91 cm) at 50 DAS and (71.58 cm) at 75 DAS was obtained from BARI Gom-32 whereas, the lowest PH (24.25 cm) at 25 DAS, (39 cm) at 50 DAS and (58 cm) at 75 DAS was obtained from BARI Gom-24 (Fig. 2). Similarly, the highest PH (26.51 cm) at 25 DAS, (44.43 cm) at 50 DAS and (69.45 cm) at 75 DAS was at N 90 kg ha⁻¹ (Fig. 3). The interaction of BARI Gom -32 and N 90 kg ha⁻¹ produced the highest PH (30 cm) at 25 DAS, (52.66 cm) at 50 DAS, and (78 cm) at 75 DAS, whereas BARI Gom-24 and 0 N produced the smallest plant (21.50 cm) at 25 DAS, (31.33 cm) at 50 DAS, and (54.33 cm) at 75 DAS (Table 7).

Fig. 2.

PH of wheat as influenced by variety (bar represent standard deviation of the mean). Ten varieties viz., BARI Gom-24 (V₁), BARI Gom-25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom-31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀).

Fig. 3.

Effect of N levels on PH of wheat (bar represent standard deviation of the mean). Four levels of nitrogen viz., 0 nitrogen (N₁), N 45 kg ha⁻¹ (N₂), N 90 kg ha⁻¹ (N₃), and N 135 kg ha⁻¹ (N₄).

Table 7.

Interaction effect between variety and nitrogen on PH, TPP and LAI of wheat.

Interactions	PH (cm)			TPP			LAI
	25 DAA	50 DAS	75 DAS	25 DAS	50 DAS	75 DAS	25 DAS	50 DAS	75 DAS
V1N1	21.50mn	31.33o	54.33l	1.00	2.33	3.00	0.32d-h	0.65ij	1.05ij
V2N1	24.00h-l	38.33i-n	72.66bc	1.00	2.00	3.00	0.34b-e	0.70e-h	1.10e-h
V3N1	24.66gh-l	42.33d-l	76.00ab	1.00	2.66	3.00	0.29h-l	0.70e-h	1.10e-h
V4N1	26.66c-g	48.33a-e	63.00e-h	1.00	1.66	3.00	0.28j-p	0.56mn	0.96mn
V5N1	23.16j-m	43.00c-k	65.33d-f	1.00	2.33	4.00	0.33c-f	0.68hi	1.08hi
V6N1	28.33a-d	52.33a	73.33a-c	1.00	2.33	4.00	0.25q-s	0.56mn	0.96mn
V7N1	20.66n	48.33a-e	75.00ab	1.00	3.00	4.00	0.29i-n	0.61kl	1.01kl
V8N1	24.83f-l	42.33d-l	62.00e-i	1.00	2.33	4.00	0.26o-r	0.64j	1.04j
V9N1	23.16j-m	38.33i-n	63.33e-h	1.00	2.66	5.00	0.26m-r	0.68hi	1.08hi
V10N1	22.66k-n	36.00l-o	60.33g-k	1.00	2.00	5.00	0.30f-k	0.72d-f	1.12d-f
V1N2	25.00f-k	41.00f-m	65.33d-f	1.00	2.00	5.00	0.31-i	0.69f-h	1.09f-h
V2N2	29.66a	44.00b-j	62.33e-h	1.00	2.66	5.00	0.28j-p	0.63jk	1.03jk
V3N2	24.16h-l	33.00no	54.33l	1.00	2.33	6.00	0.30f-k	0.60kl	1.00kl
V4N2	29.33ab	40.33g-m	63.33e-h	1.00	2.00	6.00	0.34b-d	0.70e-h	1.10ef-h
V5N2	28.33a-d	39.33h-n	59.00h-l	1.00	2.66	6.00	0.26n-r	0.60l	1.00l
V6N2	27.16b-f	43.33b-k	74.00a-c	1.00	2.00	6.00	0.23s	0.59lm	0.99lm
V7N2	22.83k-n	37.00k-o	56.66j-l	1.00	2.33	7.00	0.25p-s	0.55n	0.95n
V8N2	25.00f-k	48.33a-e	75.33ab	1.00	2.66	7.00	0.25rs	0.64j	1.04j
V9N2	29.66a	40.00h-m	70.00cd	1.00	2.33	7.00	0.33b-f	0.70d-h	1.10de-h
V10N2	26.00d-h	39.33h-n	63.33e-h	1.00	2.66	7.00	0.28i-o	0.69f-h	1.09f-h
V1N3	25.50e-j	47.66a-f	75.33ab	1.00	3.00	8.00	0.26op-r	0.70e-h	1.10e-h
V2N3	28.16a-d	46.00a-h	69.66cd	1.00	2.66	8.00	0.31-i	0.72c-e	1.12c-e
V3N3	25.00f-k	49.33a-c	78.00a	1.00	2.66	8.00	0.32c-g	0.68gh	1.08gh
V4N3	29.00a-c	44.00b-j	66.66de	1.00	3.00	8.00	0.36ab	0.71d-g	1.11d-g
V5N3	25.66e-i	40.00h-m	65.83de	1.00	2.33	9.00	0.35a-c	0.78b	1.18b
V6N3	26.33d-h	37.66j-o	62.16e-i	1.00	3.00	9.00	0.30f-k	0.73c-e	1.13c-e
V7N3	26.00d-h	50.00ab	69.83cd	1.00	3.00	9.00	0.31-i	0.75bc	1.15bc
V8N3	28.33a-d	49.00a-d	77.00ab	1.00	2.66	9.00	0.33c-f	0.77b	1.17b
V9N3	30.00a	52.66a	78.00a	1.00	3.33	10.00	0.37a	0.82a	1.22a
V10N3	23.50i-m	44.66b-i	63.00e-h	1.00	3.00	10.00	0.31f-j	0.77b	1.17b
V1N4	22.50l-n	47.33a-f	73.00bc	1.00	2.00	10.00	0.31-i	0.73cd	1.13cd
V2N4	25.00f-k	47.00a-g	72.33bc	1.00	2.33	10.00	0.28k-q	0.72d-f	1.12d-f
V3N4	24.66g-l	35.00m-o	55.66kl	1.00	3.00	11.00	0.27l-r	0.60l	1.00l
V4N4	23.33i-m	42.00e-l	57.33i-l	1.00	2.66	11.00	0.27l-r	0.59lm	0.99lm
V5N4	27.66a-e	44.33bc-j	64.00e-g	1.00	2.66	11.00	0.29h-m	0.71d-h	1.11d-h
V6N4	27.00b-g	41.00f-m	55.00l	1.00	3.00	11.00	0.32c-g	0.72c-e	1.12c-e
V7N4	24.16h-l	40.33g-m	64.00e-g	1.00	2.66	12.00	0.31f-j	0.70e-h	1.10e-h
V8N4	22.83k-n	43.33bc-k	60.66f-j	1.00	2.33	12.00	0.25p-s	0.68hi	1.08hi
V9N4	24.83f-l	42.33d-l	64.33e-g	1.00	2.66	12.00	0.30g-k	0.63jk	1.03jk
V10N4	23.33i-m	41.66e-m	63.33e-h	1.00	2.33	9.33	0.30f-k	0.69f-h	1.09f-h
F-test (0.05)	**	**	**	NS	NS	NS	**	**	**
CV%	5.72	9.96	4.57	2.30	9.25	9.13	5.70	2.89	1.82

**, Significant at 1% level of probability; NS, Non-significant. Ten varieties viz., BARI Gom-24 (V₁), BARI Gom-25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom-31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀) and four levels of nitrogen viz., 0 nitrogen (N₁), N 45 kg ha⁻¹ (N₂), N 90 kg ha⁻¹ (N₃), and N 135 kg ha⁻¹ (N₄).

Based on total tillers plant⁻¹ (TPP), the higher TPP (2.83) at 50 DAS and 11.0 at 75 DAS was recorded from BARI Gom-32 and the lowest TPP was 2.16 at 50 DAS and 3.00 at 75 DAS, 4.50 at 100 DAS was recorded from BARI Gom-24 (Table 3). The higher number of TPP (2.83) at 50 DAS and 11.0 at 75 DAS was recorded from N 90 kg ha⁻¹ and the lowest number of TPP 2.16 at 50 DAS and 3.00 at 75 DAS was recorded from applying 0 nitrogen (Table 4). The interaction effect of variety and N on the number of TPP was statistically not significant (Table 7). However, the higher number of TPP (3.33) at 50 DAS, and 10.0 at 75 DAS was recorded from the interaction of BARI Gom-32 and N 90 kg ha⁻¹ and the lowest TPP 2.00 at 50 DAS and 3.00 at 75 DAS was recorded from the interaction of BARI Gom-25 and 0 N.

Table 3.

Effect of variety on numbers of TPP of wheat.

Variety	TPP
	25 DAS	50 DAS	75 DAS
V1	1.00	2.16d	3.00i
V2	1.00	2.50a-d	4.00h
V3	1.00	2.33b-d	5.00g
V4	1.00	2.25cd	6.00f
V5	1.00	2.50a-d	7.00e
V6	1.00	2.83a	8.00d
V7	1.00	2.75ab	9.00c
V8	1.00	2.66a-c	10.00b
V9	1.00	2.83a	11.00a
V10	1.00	2.50a-d	11.33a
F-test (0.05)	NS	*	**
CV%	2.30	9.25	9.13

**, Significant at 1% level of probability; *, Significant at 5% level of probability NS, Non-significant, Ten varieties viz., BARI Gom-24 (V₁), BARI Gom-25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom-31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀).

Table 4.

Effect of nitrogen levels on numbers of TPP of wheat.

N rates	TPP
	25 DAS	50 DAS	75 DAS
N1	1.00	2.63	7.50
N2	1.00	2.46	7.50
N3	1.00	2.56	7.50
N4	1.00	2.46	7.23
F-test (0.05)	NS	NS	NS
CV%	2.30	9.25	9.13

NS, Non-significant, Four levels of nitrogen viz., 0 nitrogen (N₁), N 45 kg ha⁻¹ (N₂), N 90 kg ha⁻¹ (N₃), and N 135 kg ha⁻¹ (N₄).

The highest LAI (0.31) at 25 DAS, (0.76) at 50 DAS and (1.16) at 75 DAS was obtained from BARI Gom-32 (Table 5). The highest LAI (0.30) at 25 DAS, (0.69) at 50 DAS and (1.09) at 75 DAS was obtained from N 90 kg ha⁻¹(Table 6)^. LAI varied between variety and nitrogen treatments from 0.25 to 1.22. Higher N rates increased LAI in different DAS compared without to control. The highest LAI (0.37) at 25 DAS, (0.82) at 50 DAS and (1.22) at 75 DAS was obtained from the interaction of BARI Gom-32 and N 90 kg ha⁻¹ while the lowest LAI (0.23) at 25 DAS, (0.591) at 50 DAS and (0.991) at 75 DAS was attained from BARI Gom−28 with N 45 kg ha⁻¹ (Table 7).

Table 5.

Effect of variety on LAI of wheat.

Variety	LAI
	25 DAS	50 DAS	75 DAS
V1	0.31b	0.65d	1.05d
V2	0.28c	0.62e	1.02e
V3	0.29c	0.68c	1.08c
V4	0.29c	0.62e	1.02e
V5	0.28c	0.64d	1.04d
V6	0.31ab	0.70b	1.10b
V7	0.32a	0.76a	1.16a
V8	0.29c	0.65d	1.05d
V9	0.31ab	0.76a	1.16a
V10	0.29c	0.67c	1.07c
F-test (0.05)	**	**	**
CV%	5.70	2.89	1.82

**, Significant at 1% level of probability; Ten varieties viz., BARI Gom-24 (V₁), BARI Gom-25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom-31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀).

Table 6.

Effect of nitrogen levels on leaf area index of wheat.

N rates	LAI
	25 DAS	50 DAS	75 DAS
N1	0.28b	0.67	1.07
N2	0.29ab	0.68	1.08
N3	0.30a	0.69	1.09
N4	0.30ab	0.67	1.07
F-test (0.05)	NS	NS	NS
CV%	5.70	2.89	1.82

NS, Non-significant, Four levels of nitrogen viz., 0 nitrogen (N₁), N 45 kg ha⁻¹ (N₂), N 90 kg ha⁻¹ (N₃), and N 135 kg ha⁻¹ (N₄).

3.2. Yield and yield components

On the number of TPP, the interaction impact of variety and N treatment was statistically insignificant (Table 8). However, the interaction with BARI Gom-32 and N 90 kg ha⁻¹ resulted in a larger number of TPP (5.00). The interaction of BARI Gom-24 and 0 nitrogen produced the lowest number of effective tillers plant⁻¹ (ETP) (4.33). The interaction effect of variety and N treatment on the number of grains spike^-1 (GS) was statistically not significant (Table 8). Spike length (SL) is one of the wheat yield components that affects grain yield. Grain yields (GY) may be better in crops with longer SL. The variety and source of nitrogen fertilizer had a significant (P < 0.01) impact on SL. BARI Gom-32 with N 90 kg ha⁻¹ produced the longest SL (11.00 cm), while BARI Gom-30 with 0 nitrogen produced the shortest SL (8.36 cm). When plots treated with N were compared to control plots, the differences were greater. An analysis of variance revealed that nitrogen rates and sources had a significant impact on thousand seed weight (TSW) (Table 8).

Table 8.

Interaction effect between variety and nitrogen on yield attributes and straw yield of wheat.

Interactions	PH (cm)	TPP	ETP	GS	SL(cm)	TGW (g)	SY (t ha−1)
V1N1	84.33h-k	4.33	2.66d	38.33	9.13g-j	33.33r-u	2.78kl
V2N1	86.33g-j	4.66	3.33bcd	37.66	9.40e-i	32.33u	2.36m
V3N1	91.00b-f	4.66	3.33bcd	34.66	8.66h-j	37.00l-r	2.90j-l
V4N1	78.00n-p	4.33	2.66d	36.00	9.16f-j	34.00q-u	3.06jk
V5N1	83.00i-m	4.33	3.00cd	38.00	9.50d-h	33.00s-u	3.06jk
V6N1	82.00k-n	4.33	3.00cd	40.33	8.56i-j	32.66tu	3.23j
V7N1	93.00a-c	5.00	4.00b	36.66	8.36j	34.33p-u	2.90j-l
V8N1	80.00l-o	4.33	2.66d	35.00	9.33e-i	33.33r-u	2.56lm
V9N1	83.33i-l	4.66	3.33bcd	41.00	9.53d-h	35.00o-u	2.81kl
V10N1	82.33j-m	4.00	2.66d	39.33	9.66c-g	32.33u	2.84j-l
V1N2	77.33op	4.66	3.33bcd	42.33	9.66c-g	42.66e-i	4.06i
V2N2	79.00m-p	4.66	3.66bc	43.66	10.00b-g	45.00b-e	4.26hi
V3N2	85.66g-k	4.33	2.66d	43.00	9.33e-i	41.00f-k	4.40f-i
V4N2	88.00e-h	4.66	3.66bc	44.00	10.33a-d	41.00f-k	4.78ef
V5N2	78.00n-p	4.66	3.00cd	42.66	9.33e-i	39.33i-n	4.46f-i
V6N2	91.00bc-f	4.66	3.00cd	45.00	9.33e-i	40.33h-m	4.36g-i
V7N2	82.66j-m	4.33	3.33bcd	44.00	9.16f-j	40.66g-l	4.73e-g
V8N2	89.00c-g	4.66	3.33bcd	44.66	9.70c-g	43.33d-h	4.36g-i
V9N2	92.00b-e	4.33	3.00cd	46.66	9.75b-g	42.66e-i	4.71-g
V10N2	87.00f-i	4.66	3.33bcd	44.66	9.70c-g	45.33b-e	4.45f-i
V1N3	89.00c-g	5.00	4.00b	47.33	10.66ab	50.00a	5.88d
V2N3	92.00b-e	4.66	3.33bcd	48.33	10.50a-c	49.66a	5.96d
V3N3	94.33ab	4.66	3.66bc	47.66	10.66ab	45.33b-e	6.06d
V4N3	93.00a-c	5.00	4.00b	48.33	10.33a-d	47.00a-d	6.53a-c
V5N3	92.33b-d	4.33	3.00cd	47.00	10.66ab	44.33d-g	6.60ab
V6N3	83.66i-l	5.00	4.00b	47.00	10.33a-d	46.66a-d	6.15cd
V7N3	93.00a-c	5.00	3.66bc	47.66	10.66ab	48.33a-c	6.63a
V8N3	92.66b-d	4.66	4.00b	50.66	10.00b-g	49.33a	6.55a-c
V9N3	97.00a	5.66	5.00a	49.00	11.00a	49.33a	6.83a
V10N3	83.00i-m	5.00	4.00b	47.66	10.33a-d	48.66ab	6.20b-d
V1N4	92.00b-e	4.33	3.66bc	45.66	10.00b-g	44.66c-f	4.43f-i
V2N4	92.00b-e	4.33	3.33bcd	47.66	9.66c-g	41.66e-j	4.35g-i
V3N4	88.66d-g	5.00	3.66bc	46.00	10.33a-d	41.00f-k	4.56f-h
V4N4	85.66g-k	4.66	3.33bcd	47.66	10.08a-f	38.00j-p	4.22hi
V5N4	88.66d-g	4.66	3.33bcd	46.00	10.00b-g	37.00l-r	4.16hi
V6N4	81.66k-n	5.00	3.66bc	49.00	9.50d-h	36.66m-s	4.10i
V7N4	75.66p	4.66	3.00cd	46.66	10.16a-e	38.33j-o	5.03e
V8N4	75.66p	4.33	3.33bcd	49.00	9.66c-g	37.33k-q	4.80ef
V9N4	83.00i-m	4.66	3.00cd	46.00	10.00b-g	39.00i-n	4.53f-h
V10N4	86.33g-j	4.33	3.33bcd	47.00	10.16a-e	36.33n-t	4.26hi
F-test (0.05)	**	NS	*	NS	*	**	**
CV%	2.93	9.11	7.88	4.79	5.81	5.63	5.46

**, Significant at 1% level of probability; NS, Non-significant. Ten varieties viz., BARI Gom-24 (V₁), BARI Gom-25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom-31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀) and four levels of nitrogen viz., 0 nitrogen (N₁), N 45 kg ha⁻¹ (N₂), N 90 kg ha⁻¹ (N₃), and N 135 kg ha⁻¹ (N₄).

The maximum TSW (49.33 gm) was recorded for plots treated at N 90 kg ha⁻¹ and BARI Gom-32. The highest GY (4.30 t ha⁻¹) was obtained from BARI Gom-32 whereas the lowest GY (1.69 t ha⁻¹) was obtained from BARI Gom-24 (Fig. 4). The highest GY (3.03 t ha⁻¹) was obtained from N 90 kg ha⁻¹ (Fig. 5). Similar trends of results were recorded for straw yield (SY). GY was statistically significant regarding the interaction effect of variety and nitrogen (Fig. 6). The highest GY (4.55 t ha⁻¹) was obtained from BARI Gom-32 and N 90 kg ha⁻¹ whereas the lowest GY (1.47 t ha⁻¹) was obtained from BARI Gom 24 with 0 N.

Fig. 4.

Effect of variety on GY and SY of wheat (bar represent standard deviation of the mean). Ten varieties viz., BARI Gom-24 (V₁), BARI Gom-25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom-31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀).

Fig. 5.

Effect of N levels on GY and SY of wheat (bar represent standard deviation of the mean). Four levels of nitrogen viz., 0 nitrogen (N₁), N 45 kg ha⁻¹, (N₂), N 90 kg N ha⁻¹, (N₃), and N 135 kg ha⁻¹, (N₄).

Fig. 6.

Effect of variety and nitrogen on GY (t ha⁻¹) of wheat (bar represent standard deviation of the mean). Ten varieties viz., BARI Gom-24 (V₁), BARI Gom-25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom−31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀) and four levels of nitrogen viz., 0 nitrogen (N₁), N 45 kg N ha⁻¹, (N₂), N 90 kg N ha⁻¹, (N₃), and N 135 kg ha⁻¹, (N₄).

3.3. N content, uptake in grains and straw and NUE indicators

The interaction effect of variety and nitrogen treatment on% N content in grains (NCG), straw (NCS) and N uptake in the plant (NUP) was statistically significant (Table 9).

Table 9.

Interaction effect between variety and nitrogen on % N content and N uptake of wheat.

Interactions	% NCG	% NCS	NUP (kg ha−1)
V1N1	1.91a	0.52 p	41.65mn
V2N1	1.80b	0.48 pq	39.43no
V3N1	1.70cd	0.54 o	45.98lm
V4N1	1.70cd	0.53 o	45.20lm
V5N1	1.55fg	0.47 pq	36.71n-p
V6N1	1.55fg	0.44 q	32.58p
V7N1	1.45hij	0.48 pq	35.58op
V8N1	1.50gh	0.46 pq	34.90op
V9N1	1.54fg	0.45 pq	36.19op
V10N1	1.39j-l	0.46 pq	32.47p
V1N2	1.45h-j	0.71 lm	53.80jk
V2N2	1.38k-m	0.72 k-m	52.76jk
V3N2	1.42i-k	0.74 j-l	56.59ij
V4N2	1.34l-n	0.76 k	56.08ij
V5N2	1.26o-r	0.75 l-n	49.080kl
V6N2	1.29n-r	0.68 n	46.02lm
V7N2	1.25qr	0.76 l-n	48.947kl
V8N2	1.31n-q	0.71 mn	47.28l
V9N2	1.30n-r	0.74 l-n	50.120kl
V10N2	1.25qr	0.73 n	45.08lm
V1N3	1.32m-p	0.85 c-e	67.80c-e
V2N3	1.33lm-o	0.87 a-c	70.54a-d
V3N3	1.42jk	0.91 ab	75.43a
V4N3	1.38j-m	1.01 a	75.65a
V5N3	1.31n-q	0.98 a-c	68.03c-e
V6N3	1.26p-r	0.81 d-g	62.62fgh
V7N3	1.25qr	0.84 b-d	66.00defg
V8N3	1.24r	0.86 c-e	64.90e-h
V9N3	1.25qr	0.88 a-c	68.26c-e
V10N3	1.24r	0.83 fg	60.18hi
V1N4	1.65d	0.79 h-j	64.43e-h
V2N4	1.65d	0.80 f-h	68.96c-e
V3N4	1.73c	0.83 d-f	65.48defg
V4N4	1.81b	0.82 e-g	74.18ab
V5N4	1.68cd	0.74 f-h	69.36b-e
V6N4	1.55fg	0.71 ij	60.86g-i
V7N4	1.58ef	0.77 f-h	67.13def
V8N4	1.65d	0.69 g-i	67.46d-f
V9N4	1.63de	0.77 d-f	72.84a-c
V10N4	1.48ghi	0.65 h-j	61.24g-i
F-test	**	**	**
CV%	2.72	4.96	5.68

**, Significant at 1% level of probability. Ten varieties viz., BARI Gom-24 (V₁), BARI Gom-25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom-31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀) and four levels of nitrogen viz., 0 nitrogen (N₁), N 45 kg ha⁻¹ (N₂),N 90 kg ha⁻¹ (N₃), and N 135 kg ha⁻¹ (N₄).

The maximum NUP (75.65 kg ha⁻¹) was attained from BARI Gom-27 with N 90 ha⁻¹and the lowest NUP (32.58 kg ha⁻¹) was recorded from BARI Gom-29 with 0 N. On NUE, the interaction impact of variety and nitrogen treatment was statistically significant (Table 10). BARI Gom-32 with N 90 ha⁻¹ had the highest NUE (35.63), while BARI Gom-26 with N 135 ha⁻¹ had the lowest (14.44). UE_N and N utilization efficiency significantly varied due to the application of variety and N application. The maximum UE_N (36.64%) was recorded from BARI Gom-32 along with N 90 ha⁻¹ whereas the highest N utilization efficiency was obtained from BARI Gom-27 with N 45 Kg ha⁻¹ (Table 10).

Table 10.

Interaction effect between variety and N on NUE, UE_N (%) and N utilization efficiency (%) of wheat.

Interactions	NUE (%)	UEN (%)	N utilization efficiency (%)
V1N1	not computable	not computable	not computable
V2N1	not computable	not computable	not computable
V3N1	not computable	not computable	not computable
V4N1	not computable	not computable	not computable
V5N1	not computable	not computable	not computable
V6N1	not computable	not computable	not computable
V7N1	not computable	not computable	not computable
V8N1	not computable	not computable	not computable
V9N1	not computable	not computable	not computable
V10N1	not computable	not computable	not computable
V1N2	27.01b-f	27.99i	11.19bc
V2N2	29.63a-e	30.62efg	11.17bc
V3N2	23.57c-g	24.58jk	10.84bcd
V4N2	24.19c-g	25.16j	13.32a
V5N2	27.47a-f	28.49hi	11.69b
V6N2	29.87a-d	30.87ef	10.02def
V7N2	29.69a-e	30.69efg	10.38cde
V8N2	27.52a-f	28.51hi	9.52efg
V9N2	30.94a-c	31.97de	9.97def
V10N2	28.00a-f	29.02ghi	9.26fgh
V1N3	29.06a-f	30.05fgh	10.03def
V2N3	34.56ab	35.52abc	8.79ghi
V3N3	32.72ab	33.72cd	7.43k
V4N3	33.83ab	34.82abc	7.81jk
V5N3	34.79ab	35.85ab	8.21ijk
V6N3	33.38ab	34.38bc	8.48hij
V7N3	33.80ab	35.81bc	8.32ijk
V8N3	33.33ab	35.34bc	8.76ghi
V9N3	35.63a	36.64a	8.76ghi
V10N3	30.78a-c	31.74ef	8.11ijk
V1N4	16.87gh	17.87m	5.83l
V2N4	21.87d-h	22.86kl	5.05lmn
V3N4	14.44h	15.46n	5.56lm
V4N4	21.46d-h	22.48l	3.22q
V5N4	24.18c-g	25.19j	3.61pq
V6N4	20.95f-h	21.94l	4.78mno
V7N4	23.37c-g	24.37jk	3.91opq
V8N4	24.12c-g	25.17j	3.68pq
V9N4	27.14a-f	28.16i	3.98opq
V10N4	21.31e-h	22.33l	4.48nop
F-test	**	**	**
CV%	5.33	5.43	9.48

**, Significant at 1% level of probability. Ten varieties viz., BARI Gom-24 (V₁), BARI Gom-25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom-31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀) and four levels of nitrogen viz., 0 nitrogen (N₁), N 45 kg ha⁻¹ (N₂), N 90 kg ha⁻¹ (N₃), and N 135 kg ha⁻¹ (N₄).

3.4. Genetic divergence analysis

The ten wheat varieties were divided into four clusters (Table 11). Cluster II has the greatest number of variations (4). Cluster IV had the greatest mean values for GY, TGW, SL, ETP and PH (Table 12). It showed that this cluster can be used to select varieties for GY, TGW, SL, ETP and PH. Cluster III also had the highest mean values for TPP and GS, indicating that variety for breeding of the TPP and GS may be accomplished from this cluster.

Table 11.

Cluster membership of wheat varieties with percentage.

Cluster number	Number of variety	Percent (%)	Name of variety
I	2	20.00	V1 and V2
II	4	40.00	V3, V4, V5 and V10
III	2	20.00	V6 and V8
IV	2	20.00	V7 and V9

Table 12.

Cluster analysis of various traits of wheat varieties.

Characters	I	II	III	IV
PH (cm)	82.50	84.60	88.58	91.25
TPP	4.50	4.52	4.83	4.79
ETP	3.08	3.17	3.63	3.83
GS	37.08	44.35	47.71	47.62
SL	9.02	9.72	10.11	10.48
TGW	33.75	39.98	42.12	47.58
GY	2.87	3.03	2.92	3.16
SY	4.26	4.54	4.52	4.77

A dendrogram was prepared from data set of mean values of diverse traits of the ten wheat varieties and showed four super clusters; I, II, III and IV, as shown in Fig. 7. Dendrogram distributed the variety based on number among the clusters, which depicted that members of cluster II was more dissimilar and hybridization between their members could generate a significant diversity for the selection process.

Fig. 7.

Dendrogram based on summarized data on differentiation among 10 varieties of wheat according to Ward’s method. Clusters (I-IV) denotes the dissimilarity among clusters and numbers (1–9) on the X-axis represent individual experts.

3.5. Principle component analysis (PCA)

The principal components of morpho-physiological and yield parameters that best describe the efficiency of different doses N in wheat varieties were identified using PCA. Fig. 8 illustrates the bi-plot of the first two principal components (PCs) and variable loadings. A variation of 46.7 % and 13.5 % was explained by the first two principle components among the wheat varieties. Varieties showing the highest values for all parameters for PC 1 and PC 2 are located in the upper right section of the bi-plot. Varieties with moderate values for PC 1 and PC 2 are located in the upper left and lower right section. On the other hand, varieties showing the lowest values among the all parameters for PC 1 and PC 2 are located in the lower left section of the bi-plot. The figure revealed that, leaf area (LA), LAI, PH, NCS, SL and ETP were positively associated with each other while these parameters showed a strong negative association with number of non-effective tiller (Non-ETP).In addition, nitrogen in plant (NP), straw weight (SW), biological yield (BY), number of grain (NG), thousand-grain weight (TGW), grain weight (GW), number of seed (SN), number of tiller (TN), harvest index (HI) and nitrogen use efficiency (%NUE) showed a moderate positive relationship with each other. In considering the interaction effect of different varieties of wheat and nitrogen treatments, it is evident that BARI Gom-32 (V₉), BARI Gom-28 (V₅) and BARI Gom-30 (V₇) were the best wheat varieties in response to N 90 kg ha⁻¹ (N₃) (Fig. 8).

Fig. 8.

Bi-plot of principal component analysis (PCA) showing the first two principal components (PC 1 and PC 2). For PCA, different symbols at the right denotes ten varieties viz., BARI Gom-24 (V₁), BARI Gom−25 (V₂), BARI Gom-26 (V₃), BARI Gom-27 (V₄), BARI Gom-28 (V₅), BARI Gom-29 (V₆), BARI Gom-30 (V₇), BARI Gom-31 (V₈), BARI Gom-32 (V₉), BARI Gom-33 (V₁₀) and color code denotes four levels of nitrogen viz., 0 nitrogen (N₁), N 45 kg ha⁻¹ (N₂), N 90 kg ha⁻¹ (N₃), and N 135 kg ha⁻¹ (N₄). PH, plant height; % NG, % N in grain; % NS, % N in straw; ET, effective tiller; LA, leaf area; LAI, leaf area index; SL, spike length; NP, N in plant; SW, straw weight; BY, biological yield; NG, N in grain; NS, N in straw; GW, grain weight; SN, number of seed; TN, number of tiller; NUE, nitrogen use efficiency.

3.6. Correlations among different growth, yield and physiological attributes

To evaluate the relationships between various growth, yield and physiological parameters, we conducted a correlation analysis (Fig. 9). The following figure represents the positive and negative contribution among all the growth, yield and physiological parameters with each other. It is observed that, there was a strong significant positive correlation (p < 0.001 and p < 0.01) among LA, LAI, ETP, GW, SW, BY, TGW, NS and SL. In addition, nitrogen in grain (NG), nitrogen in plant (NP) and nutrient use efficiency also positively correlated (p < 0.001 and p < 0.01) with GW, SW, BY, HI, SL, SN, TGW, NG and NP. However, we found a negative correlation (p < 0.001, p < 0.01 and p < 0.05) % nitrogen in grain (PNG), % nitrogen in straw (PNS) and TGW. In particular, non-effective tiller showed a negative correlation with almost all parameters (p < 0.01 and p < 0.05).

Fig. 9.

Pearson correlation matrix for different growth, yield and physiological parameters. PH, plant height; LA, leaf area; LAI, leaf area index; TN, number of tiller; ET, number of effective tiller; Non-ET, number of non-effective tiller GW, grain weight; SW, straw weight; BY, biological yield; HI, harvest index; SL, spike length; SN, number of seed/spike; TGW, thousand grain weight; PNG, % N in grain; PNS, % N in straw; NG, N in grain; NS, N in straw; NP, N in plant; NUE, nitrogen use efficiency. Heatmap at the bottom indicates positive and negative correlations.

4. Discussion

Crop growth and yield attributes characterize the real-time nutritional value of the soil and the results can provide information on potential crop nutrient management. Our study results showed that using different N rates significantly improves wheat growth, yield and NUE indices. In our study, PH augmented with rising N rate. An optimal rate of nitrogen was found to significantly increase plant height (Mengel and Kirkby, 2001). Nitrogen fertilization has been observed to increase overall tiller numbers (Abdullatif et al., 2010). Nitrogen fertilizers have also been observed to have a significant impact on the total number of wheat tillers (Abdollahi Gharekand et al., 2012, Haileselassie et al., 2014). LAI is an indicator of the photosynthetic system and is associated with increased yield (Singh et al., 2009). In our study, the LAI reached at peak with BARI Gom-28 and N 45 kg ha⁻¹. Due to the large variation in LAI as a result of production systems, it is recommended to take this into account in forecasting and modeling winter wheat growth (Bavec et al., 2007).

This study revealed that GY was closely related ETP, GS and TGW. These types of responses are consistent with studies of cereal crops in which grain amount m⁻² is the most important yield factor driving yield variability due to hereditary and administrative factors (Slafer et al., 2005). It has also been reported that GY are highly dependent on ETP (Kariali and Mohapatra, 2007). TGW of wheat has also been described to affect yield (Khan et al., 2013, Dargie et al., 2020). In the current study, the highest GY was observed from 90 kg N ha⁻¹ and the lowest was associated with 0 kg N ha⁻¹. Increasing nitrogen fertilization rates have also been reported to increase wheat GY (Abdollahi Gharekand et al., 2012), with the highest grain yield of N 64 kg ha⁻¹ (5.46 t ha ⁻¹). The lowest yield in controls was due to the absence of N, resulting in decreased yield attributes and other physiological parameters. The maximum GY of BARI Gom-32 can be explained by the excellent N concentration in grain. The results of the correlation analysis showed a strong association between N uptake and N content, chlorophyll, and dry biomass, indicating that NUE improved and ultimately increased the winter wheat yield (Kubar et al., 2022). In contrast to cereals, the N content of straw was relatively low. A stable correlation between NCG, NCS, and GY pointed out that increment of N in grain and straw can progress wheat yield.

Regarding the N effect on grain yield, diverse studies designated that grain yield significantly augmented up to a definite amount (Zhang et al., 2008, Rezaee et al., 2009, Lampayan et al., 2010). Excessive use of nitrogen fertilizers has been documented to lead to decreased physiological nitrogen utilization efficiency and yield. As a result, we found that the nitrogen utilization efficiency improved when the N content increased to a level of N 90 kg ha⁻¹. Furthermore, increasing N decreased the efficiency of nitrogen utilization, as increased concentrations of N exceeded absorption over use (Singh et al., 2014). Nitrogen uptake has been reported to increase at rates up to N 64 kg ha⁻¹ and then decrease (Belete et al., 2018). The overall raise in N uptake during growth stages of wheat can increase grain N mobilization during the grain filling stage (Kayan and Adak, 2012). This can be explained by the divide appliance of the N (360 kg N ha⁻¹).

Several researchers have conducted a range of research on the utilize of N and its efficiency of use. This directly impacted physiological progress and yield (Quanbao et al., 2007, Fageria et al., 2010, Singh et al., 2014). The response of N on wheat yield and N consumption persisted in wheat production. Adequate nitrogen application rate is related between nitrogen utilization and wheat production, and thus can increase nitrogen utilization which ultimately increases NUE and improves wheat grain yield. In our study, varietal performance was wide-ranging under dissimilar N applications. Performance discrepancies may be related to deviations in growth and NUE metrics. Evaluation of NUE indices in culture is significant for evaluating the consequences of used N and their function in enhancing peak yields through competent plant utilization. In our study, the uptake efficiencies of NUE, UE_N and N uptake efficiency increased up to N 90 ha⁻¹ and reduced thereafter. The declining movement of the NUE index at superior N rates suggests that wheat plants may not use N at superior rates, or that the rate of N uptake by plants exceeds the rate at which N loss cannot be sustained (Fageria and Baligar, 2005). Similar outcomes were seen in a research as well (Jiang, 2018). Our findings showed that the NUE decreased as the nitrogen rate rose (Table 10). Additionally, it was discovered that, generally speaking, an increase in nitrogen fertilizer rate decreased the NUE but increased yield and N loss (Gupta et al., 2004, Kearney, 2010, Jiang et al., 2019). Besides, volatilization, denitrification, and runoff are major pathways of nitrogen loss from soils, which can lead to pollution, higher production prices, lower grain yields, and even global warming (Li et al., 2012). However, the extent of nitrogen loss depends on nitrogen supply, weather situation and crop type (Zhu, 1997). A decrease in N uptake effectiveness also occurred at elevated N rates (Mae et al., 2006). There was a contribution to the genetic diversity of pod plant⁻¹ (Kayan and Adak, 2012). The contribution of pod number plant⁻¹ and 1000 seed weight to overall diversity was very high (Malik et al., 2010).

5. Conclusions

In summary, variety BARI Gom-32 along with N 90 kg ha⁻¹ exhibited the highest LAI, SL, TGW, GY and nitrogen utilization competence. As a result, the current study helped identify the most efficient cultivars that can be strongly recommended to wheat farmers in Bangladesh. Furthermore, wheat varieties have a lot of genetic heterogeneity in terms of grain production, HI, and PH, which is very useful in breeding programs. Therefore, the present study suggests that these cultivars can be used in wheat breeding programs to produce genetically enhanced wheat varieties.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.