SJA_42_2_584-600

Introduction

After rice, bread wheat (Triticum aestivum L.) is the second most vital cereal crop in Bangladesh, significantly contributing to the country’s food supply to satisfy consumer demand and bolster food security (Ahmed et al., 2025). Wheat production in Bangladesh peaked in the early 2000s and has since declined due to increasing challenges from erratic weather patterns, heightened water scarcity, and variable climatic conditions, resulting in import dependency (Figure 1). During the past decade, Bangladesh has experienced a pronounced escalation in import volumes, culminating in 6.9 million metric tons in 2024. This quantity, financed by the Government of Bangladesh, corresponded to an expenditure of approximately 2 billion USD (FAO, 2025). Although Bangladesh’s winter season (Rabi) offers ideal agroclimatic conditions for its production, the country still depends significantly on wheat imports because of a lack of domestic production for several reasons, including genotypes, soil conditions, and environmental factors, especially irrigation and sowing time (Islam et al., 2019; Rahman et al., 2021). Therefore, there is an urgent necessity to enhance domestic wheat output by employing current wheat genetic resources and implementing appropriate agronomic practices to reduce import dependency.

Soil moisture levels at critical growth stages have been reported to be highly sensitive to phenological development in wheat, particularly at heading, anthesis, and maturity time (Ihsan et al., 2016, Pantha et al., 2024). Water stress both in vegetative and reproductive stages, especially at tillering, booting, flowering, or grain-filling periods, can lead to inevitable yield losses owing to decreased grain number, shriveled kernels, and poor development of spikes (Ali et al., 2022; Nyaupane et al., 2024). The fast and sporadic reduction in groundwater levels, winter precipitation, and increasing competition from alternative crops can obstruct the water supply throughout the wheat-growing season (Mojid, 2019). Consequently, optimal irrigation systems for wheat cultivation are crucial for realizing potential yields (Islam et al., 2019; Rahman et al., 2024). Over its complete cultivation cycle, wheat requires an estimated 350-450 mm of water, which is aptly distributed among critical phenological stages such as crown root initiation, tillering, flowering, and grain filling (Ahmad and Kumar, 2015; Meena et al., 2019). However, vegetative growth and reproductive development of wheat are severely hampered by several components, like timing, frequency, and the amount of watering (Si et al., 2023; Zhao et al., 2024; Wang et al., 2025). Because both excessive or insufficient irrigation can hamper a plant’s usual growth and development, as well as waste valuable resources, which encourages the plant to be vulnerable to diseases, namely leaf rust and root rot (Lidwell-Durnin & Lapthorn, 2020; Yan et al., 2024). Therefore, it is important to understand how the wheat plant responds to diverse irrigation for developing site-specific water management techniques in changing climatic conditions.

Many studies have reported that wheat productivity severely depends on the frequency and timing of irrigations. For instance, Rahman et al. (2024) found that three distinct irrigations at the CRI, heading, and grain filling stages ensure proper vegetative growth and development in Bangladesh’s climatic conditions. On the other hand, wheat production can be reduced by up to 30% when low irrigation is combined with sensitive growth and developmental stages (Islam et al., 2019; Cebi et al,. 2023; Wang et al., 2024). These results are consistent with studies conducted in other South Asian countries that demonstrated that water scarcity at different critical growth stages can reduce the grain yield (Baloch et al., 2014; Anjum et al., 2021; You et al., 2022; Mobeen et al., 2025). The IPCC (2019) stated that regulating irrigation systems can be adaptive to environmental conditions and genotype can reduce groundwater depletion. Additionally, study aimed at discovering water-efficient wheat cultivars has been undertaken using various bread wheat genotypes in Bangladesh (Acharjee & Shariot-Ullah, 2021; Ahmed et al., 2023). However, genotype-specific irrigation needs throughout crucial growth phases remain little investigated, especially across the many edaphic and climatic conditions of the nation.

Recent studies in South Asia have highlighted the benefits of precision irrigation techniques, including deficit irrigation and alternate furrow irrigation, in intensifying water productivity without losing grain yield (Sarker et al., 2020; Mobeen et al., 2025). Little but growing evidence from Bangladesh recommends that adaptive irrigation systems in different agro-ecological climatic zones can extensively promote wheat productivity (Islam and Nursey-Bray, 2017; Hasan et al., 2019). Nevertheless, few location-specific factual studies have assessed how strategic irrigation schedules influence the growth, phenology, harvest index, accumulation of biomass, and ultimately grain yield (Islam et al., 2019; Rahman et al., 2024). Moreover, wheat cultivation has expanded expeditiously in the non-traditional areas of Bangladesh because of the lower profitability of Boro rice cultivation and increased demand for diversified cereal options (Islam and Nursey-Bray, 2017; Mian and Begum, 2020). Divergent soil textures, water-retention capacities, and microclimatic factors are frequently found in these regions, in regarding how the crop responds to irrigation (Hossain and Teixeira da Silva, 2013; Monshi and Malek, 2013; Tabassum et al., 2015; Yesmin et al., 2022). So, it is essential to assess wheat performance under various irrigation regimes in different environments to produce context-specific insights and policy suggestions.

The present study aims to fill this knowledge gap by addressing the impact of distinct irrigation schedules on the agronomical growth patterns, phenological development, and yield attributes of wheat genotypes cultivated under field conditions in Mymensingh, Bangladesh. Regarding this, we evaluated 7 wheat cultivars in four different irrigation regimes to find out how plants behave under different irrigation schedules, after subsequent evaluation of growth, phenology, and yield traits. Specifically, identifying the most crucial stages of irrigation that impact yield, and suggesting useful advice for the best irrigation practices that wheat farmers can implement in diverse agro-ecological zones. The findings help to inform policymakers and extension officers about the irrigation timing for wheat, guiding farmers to optimize water use for strengthening wheat productivity in a water-constrained environment.

Materials and Method

Experimental site

The experiment was performed at a farmer’s field in Muktagacha, Mymensingh, Bangladesh, from November 2024 to March 2025 to assess the morphological, phenological, and yield performance of seven selected wheat genotypes at four distinct irrigation conditions. Geographically, the experimental site (24°25”N latitude and 90°50”E longitude) is situated in the northwest part of Mymensingh district in Bangladesh. The experimental area exhibits a subtropical climate, characterized by elevated temperatures and substantial rainfall during the kharif season (April to September) and limited rainfall coupled with moderately low temperatures and immense light intensity during the rabi season (October to March) (Figure 2). The studied location comprised silty clay loams with substantial water-retention capacity and nutrient-rich, non-calcareous gray alluvial floodplain soil, efficiently drained by the Brahmaputra River and its tributaries (Sheel et al., 2016). Ratul et al. (2021) and Tabassum et al. (2023) previously provided valuable information about the studied area where the soil pH ranged from 5.5 to 6.5, with a substantial organic matter content viz. total nitrogen at 0.15%, available phosphorus at 3.25 ppm, available sulfur at 4.48 ppm, and exchangeable potassium at 0.12 ppm.

Plant materials

Seven wheat varieties (Protiva, BARI Gom-19, BARI Gom-20, BARI Gom-22, BARI Gom-25, BARI Gom-30, and BARI Gom-32) were used in the present study. Among them, six namely BARI Gom-19, BARI Gom-20, BARI Gom-22, BARI Gom-25, BARI Gom-30, and BARI Gom-32 were collected from the Bangladesh Agricultural Research Institute (BARI), Gazipur, Bangladesh while the Protiva variety was collected from the Bangladesh Wheat and Maize Research Institute (BWMRI), Dinajpur, Bangladesh.

Experimental design

The experiment employed a split-plot design with three replications to evaluate the effect of wheat genotypes on their morphological, phenological, and yield-related traits under different irrigation levels. We designed the present experiment as two factors: Factor A (variety) and Factor B (irrigation). Factor A consisted of seven wheat varieties: V1 = Protiva, V2 = BARI Gom-19, V3 = BARI Gom-20, V4 = BARI Gom-22, V5 = BARI Gom-25, V6 = BARI Gom-30, and V7 = BARI Gom-32. Conversely, four irrigation levels were included in Factor B during the growth period: I0 = no irrigation, I1 = one irrigation at the crown root initiation (CRI) stage, I2 = two irrigations at the CRI and grain filling stages, and I3 = three irrigations at the CRI, booting, and grain filling phases (Figure 3).

The main plots were assigned irrigation treatments, whereas the sub-plots were randomly allocated wheat genotypes, resulting in twenty-eight treatment combinations (7 genotypes × 4 irrigation levels) assessed across eighty-four plot units. Each plot measured 2.5 m × 2.0 m (5.0 m²), with a spacing of 20 cm between rows and 5 cm between plants. To keep plots from interfering with each other, there was a 1.0 m space between major plots, a 0.6 m space between sub-plots, and a 0.5 m space between replications were maintained.

Data collection

The agronomic and yield-related trait performance for several wheat varieties was recorded under different irrigation conditions.

Plant height (cm)

Ten randomly selected plants per plot were measured for plant height (in cm) from the base to the top of the tallest spikelet, and an average was measured.

Number of effective tiller hill-1

Tillers with a spike that produced grain were considered as an effective tiller. Ten hills were chosen at random for each plot, and the quantity of productive tillers on each hill was noted. Number of effective tiller hill-1 was calculated by using the formula outlined by Gomez and Gomez (1984) as follows:

Number of non-effective tiller hill-1 was calculated utilizing the following formula:

Number of non-effective tiller hill-1 = Total tillers hill-1 − Effective tillers hill-1

Days to heading:

Days to heading were calculated by counting the number of days from the date of sowing to the date when approximately 50% of the plants in each plot showed visible panicle emergence. The plots were checked every day starting 45 days after planting. The date on which the 50% threshold was met was recorded, and the number of days from the sowing date to this observed date was taken as the days to heading for each plot.

Days to maturity

The number of days to maturity was calculated by counting the number of calendar days from the planting date to the date when approximately 90% of the spikes in each plot had turned golden yellow. This stage was confirmed by manually threshing a sample of spikes to ensure the grains had reached physiological maturity. The date of this confirmed maturity was then subtracted from the planting date to determine the total days to maturity for each plot.

Length of spike (cm)

The spike length of the selected samples was measured, and the average was considered.

Number of filled grains spike-1

Ten spikes were chosen at random from ten distinct plants in each plot to calculate the grain count per spike. The mean grain number spike-1 was determined by manually counting the number of grains in each spike and averaging them.

Thousand grain weight (g)

One thousand clean, dry, and undamaged seeds were chosen at random from each plot’s collected sample. A precise electronic balance was used to measure the total weight of these seeds.

Grain yield (tha-1)

The grains from each unit plot were sun-dried to a consistent moisture content and then carefully weighed using an electronic balance. This weight was then increased by the dry grain weight of the selected sample plants. The total grain yield of each plot was calculated and converted in tons per hectare (tha-1).

Straw yield (tha-1)

The complete biomass of straw from each experimental unit, including the plants sampled, was harvested and weighed to calculate the straw yield per plot. The ultimate yield, measured in tons per acre, was determined by sun-drying the harvested straw until it reached a consistent weight.

Biological yield (tha-1)

Biological yield was calculated using the following known formulas: Grain yield + straw yield = biological yield (tha-1)

Statistical analysis

Treatments variation

RStudio version R.4.3.2 was used to conduct statistical analysis. The study employed a one-way analysis of variance (ANOVA) to assess the significance of treatment effects. The replication, treatment, and error components’ mean square values were computed using the “agricolae” program.

Mean performance

The package “agricolae” was used to calculate the mean performance with lettering, and MS-excel was used to design the graph that compares various performances across (a) variety, (b) irrigation schedule, and (c) interaction of variety and irrigation schedule for yield, where SY stands for straw yield and BY for biological yield.

Correlation estimation

Pearson’s correlation coefficients among traits were calculated using the “corrplot” package.

PCA and heatmap

A multivariate principal component analysis (PCA) was performed using the “factoextra” and “factoMineR” packages to generate a scree plot and calculate the variance contributions (%). PCA biplots were made using the “ggplot2” and “ggrepel” programs. The “heatmaply” software was used to create the PCA heatmap. The “pheatmap” program was used to build heatmap.

Results

ANOVA of agronomic traits under irrigation regimes

The analysis of variance (ANOVA) provided in Table 1 revealed highly significant impacts of both wheat genotypes and irrigation schedules on 12 major morphological, phenological, and yield parameters. The key traits revealed significant variation from

Table 1: Analysis of Variances (ANOVA) for 12 agronomic and phenological traits of wheat varieties and irrigation schedule.

Source of variation

NET

NGS

TGW

Yield

Genotypes

Replication

2.33**

0.19***

0.04***

4.14***

13.37***

0.92***

0.76

15.60***

0.01***

0.01

1.64

Treatment

27.45***

0.74***

0.16***

15.56***

46.63***

9.50***

67.69***

98.49***

2.09***

0.50***

3.36***`

73.01***

Error

0.46

0.01

0.31

0.78

0.07

0.47

0.44

0.01

0.02

1.29

Irrigation schedule

Replication

2.34**

0.18***

0.05***

4.15***

13.40***

0.91***

0.75

15.55***

0.01***

0.01

1.65

Treatment

59.31***

7.60***

1.85***

35.22***

13.37***

39.34***

63.25***

32.59***

1.34***

0.13**

1.41***

65.65***

Error

0.46

0.01

0.33

0.77

0.07

0.47

0.44

0.01

0.03

0.02

1.29

Interaction of wheat genotypes and irrigation schedule

Replication

2.33*

0.19***

0.05**

4.12***

13.48***

0.91***

0.75

15.59***

0.01***

0.01

1.65

Treatment

12.69***

1.01***

0.24***

7.37***

11.85***

6.48***

22.07***

25.51***

0.61***

0.18***

0.96***

26.36***

Error

0.62

0.01

0.31

0.79

0.09

0.45

0.46

0.01

0.02

0.03

1.31

Note: PH=Plant height (cm), ET=Number of effective tiller hill-1, NET=Number of non-effective tiller hill-1, DH=Days to heading, DM=Days to maturity, LS=Length of spike (cm), NGS=Number of grain spike-1, TGW=Thousand grain weight (g), Yield=Grain yield (tha-1), SY=Straw yield (tha-1), BY=Biological yield (tha-1), HI=Harvest index (%).

Table 2: Impact of varietal differences and irrigation scheduling on growth, phenology, and yield of different wheat varieties.

Treatments	PH	ET	NET	DH	DM	LS	NGS	TGW	HI
Genotypes
V1	90.94d	5.58a	1.47a	54.39f	102.92b	10.95e	40.37d	44.25e	49.13d
V2	92.98c	5.49b	1.36b	55.92c	104.25a	11.17e	44.27c	44.71e	48.76d
V3	94.56ab	5.43b	1.34bc	55.15d	103.05b	11.69d	45.72b	45.41d	49.22d
V4	94.13b	5.01de	1.31cd	57.65a	104.08a	12.55c	48.01a	44.60e	50.17c
V5	94.89a	4.98e	1.27d	56.44b	102.58b	12.45c	46.05b	50.04b	53.18b
V6	94.99a	5.07d	1.15e	55.12de	99.57c	12.98b	46.15b	46.87c	53.89ab
V7	95.11a	5.30c	1.14e	54.67ef	99.52c	13.25a	45.81b	51.39a	54.38a
LSD (0.05)	0.55	0.08	0.04	0.45	0.72	0.22	0.56	0.54	0.93
CV (%)	0.72	1.78	4.06	1.01	0.86	2.25	1.53	1.43	2.22
Irrigation schedule
I0	91.70d	4.52d	1.68a	53.93d	101.17c	10.54d	42.80d	45.08c	49.08c
I1	93.72c	5.06c	1.35b	55.32c	102.27a	11.67c	44.83c	46.58b	50.96b
I2	94.77b	5.62b	1.14c	56.38b	102.75ab	12.62b	45.88b	47.46a	51.56b
I3	95.59a	5.87a	0.99d	56.85a	102.96a	13.76a	46.85a	47.90a	53.37a
LSD (0.05)	0.42	0.06	0.03	0.34	0.45	0.17	0.42	0.41	0.70
CV (%)	0.73	1.78	4.06	1.01	0.86	2.25	1.53	1.43	2.22

Note: Here, PH=Plant height (cm), ET=Number of effective tiller hill-1, NET=Number of non-effective tiller hill-1, DH=Days to heading, DM=Days to maturity, LS=Length of spike (cm), NGS=Number of grain spike-1, TGW=Thousand grain weight (g), HI=Harvest index (%).

variety treatments (V1 to V7) at the 1% significance level, including number of effective tillers hill-1 (0.74***), days to heading (15.56***), days to maturity (46.63***), length of spike (9.50***), number of grain spike-1 (67.69***), thousand grain weight (98.49***), grain yield (2.09***), and harvest index (73.01***). Similarly, all traits exposed highly significant effects (p < 0.001) from irrigation treatments (I0 to I3), where the significant variation in plant height (59.31***), number of effective tiller hill-1 (7.60***), length of spike (39.34***), number of grain spike-1 (63.25***), thousand grain weight (32.59***), grain yield (1.34***), and harvest index (65.65***) indicate that water availability has a dominant effect on morphological, phenological, and yield traits. Furthermore, there is a significant difference among genotype × irrigation interactions of yield traits, including number of grain spike-1 (22.07***), thousand grain weight (25.51***), grain yield (0.61***), straw yield (0.18***), biological yield (0.96***), and harvest index (26.36***).

Influence of genotypes and irrigation regimes on wheat performance

The growth, phenological development, and yield-contributing traits of wheat genotypes were greatly influenced by the irrigation schedule and varietal differences, displayed in Table 2. The highest plant height (95.11 cm), length of spike (13.25 cm), thousand grain weight (51.39 g), and harvest index (54.38%) were recorded in BARI Gom-32 (V7), while Protiva (V1) revealed low performance across the traits. Yield attributes increased gradually from no irrigation (I0) to the maximum irrigation level (I3). Irrigation treatment I3, produced the highest values in plant height (95.59 cm), number of effective tillers hill-1 (5.87), length of spike (13.76 cm), thousand grain weight (47.90 g), and harvest index (53.37%). In contrast, the I0 treatment led to decreased performance, especially in effective tillers (4.52), spike length (10.54 cm), number of grain spike-1 (42.80),

Table 3: Interaction effect of variety and irrigation scheduling on growth, phenology, and yield of different wheat varieties.

Interaction	PH	ET	NET	DH	DM	LS	NGS	TGW	HI
V1I0	88.70l	4.84k	1.86a	51.88n	101.88f-h	9.35q	38.08n	42.57o	45.25q
V2I0	90.73k	4.75k	1.75b	54.67k-m	103.13b-g	9.57q	41.98kl	43.04no	46.07pq
V3I0	92.32j	4.69kl	1.73b	53.37mn	101.93fg	10.09p	43.43ij	43.73m-o	46.88op
V4I0	91.88jk	4.26m	1.70b	55.37f-h	102.97d-g	10.95m-o	45.71e-g	42.93no	48.29mn
V5I0	92.65h-j	4.23m	1.66b	54.17j-l	101.47g-i	10.85no	43.77hi	49.72d	52.18ef
V6I0	92.87h-j	4.32m	1.54c	53.00mn	98.45k	11.38lm	43.87hi	45.19i-l	52.37ef
V7I0	92.76h-j	4.56l	1.53c	52.48n	98.40k	11.65j-l	42.80i-k	48.36e	52.57de
V1I1	90.72k	5.37hi	1.53c	53.18lm	102.98d-g	10.48op	40.12m	44.08l-n	47.53no
V2I1	92.75h-j	5.29hi	1.42d	55.97g-j	104.23a-e	10.70o	44.02hi	44.54j-m	48.95lm
V3I1	94.33fg	5.23ij	1.40de	54.67i-l	103.03d-g	11.22l-n	45.47fg	45.23i-l	49.26kl
V4I1	93.90gh	4.80k	1.37de	56.67cd	104.07a-e	12.08i-k	47.74b-d	44.43k-m	49.70i-l
V5I1	94.67d-g	4.77k	1.33de	55.47e-h	102.57e-g	11.98jk	45.80e-g	51.23bc	53.41cd
V6I1	94.88d-g	4.86k	1.21f	54.30j-l	99.55jk	12.51hi	45.90e-g	46.69fg	53.81c
V7I1	94.77d-g	5.09j	1.20f	53.78k-m	99.50jk	12.78gh	44.83gh	49.87d	54.09c
V1I2	91.77jk	5.94b-d	1.32e	54.65h-k	103.46a-f	11.43lm	41.17lm	44.95i-m	49.47j-l
V2I2	93.80g-i	5.85d	1.21f	57.43c-f	104.72a-c	11.64kl	45.07gh	45.42h-k	49.96i-k
V3I2	95.38b-f	5.79de	1.19f	56.13f-h	103.51a-f	12.16ij	46.52d-f	46.11g-i	50.48hi
V4I2	94.95c-g	5.36hi	1.16fg	58.13ab	104.55a-d	13.02e-g	48.79ab	45.31h-l	51.21gh
V5I2	95.72a-e	5.33hi	1.12f-h	56.93cd	103.05c-g	12.92f-h	46.85c-e	52.10ab	51.79e-g
V6I2	95.93a-d	5.42gh	1.00i	55.77f-i	100.03i-k	13.45de	46.95c-e	47.57ef	53.98c
V7I2	95.82a-e	5.66ef	0.99i	55.25g-j	99.98i-k	13.73cd	45.88e-g	50.75cd	54.03c
V1I3	92.59ij	6.18a	1.17f	55.05g-j	103.66a-e	12.57g-i	42.13j-l	45.40h-k	54.30c
V2I3	94.62e-g	6.10ab	1.06g-i	57.83c-e	104.92a	12.78gh	46.03e-g	45.86g-i	50.04i-k
V3I3	96.21a-c	6.03a-c	1.04hi	56.53d-g	103.71a-e	13.31d-f	47.48cd	46.55f-h	50.25ij
V4I3	95.77a-e	5.61f	1.01i	58.53a	104.75ab	14.16bc	46.85c-e	45.76g-j	51.47fg
V5I3	96.54ab	5.58fg	0.97i	57.33bc	103.25a-f	14.06c	47.82b-d	51.19bc	55.33b
V6I3	96.76a	5.67ef	0.85j	56.17d-g	100.23h-j	14.59ab	47.92bc	48.01e	55.42b
V7I3	96.65ab	5.91cd	0.84j	55.65f-i	100.18ij	14.87a	49.76a	52.55a	56.83a
LSD (0.05)	1.28	0.17	0.09	1.05	1.67	0.51	1.31	1.26	0.91
CV (%)	0.84	2.05	4.69	1.15	0.99	2.59	1.77	1.65	1.08

thousand-grain weight (45.08 g), and harvest index (49.08%). Additionally, phenological traits differed significantly between varieties and irrigation levels. BARI Gom-22 (V4) showed the longest durations to heading (57.65 days) and maturity (104.08 days), whereas V6 (BARI Gom-30) and V7 (BARI Gom-32) exposed the shortest durations to heading (54.39 days) and maturity (99.52 days), respectively. Finally, significant genotype × irrigation responses were further confirmed by the interaction effects (Table 3), where maximum plant heights were shown by V6I3 (96.76 cm), while the lowest were found in V1I0 (88.70 cm). Non-effective tillers were reduced in V7I3 (0.84), while effective tillers peaked in V1I3 (6.18). The earliest heading and maturity periods under stress were displayed by V1I0 (51.88 days) and V6I0 (98.45 days), while the longest values were uncovered by V4I3 (58.53 days) and V2I3 (104.92 days). The genotype × irrigation also improved the components of grain yield at full irrigation level (I3), with the longest length of spike (14.87 cm), the most grains spike-1 (49.76), the thousand grain weight (52.55 g), and the harvest index (56.83%) for V7I3.

Differences in yield traits

The production of wheat grain yield, straw yield, and biological yield are influenced by variety, irrigation schedule, and their interactions, illustrated in Figure 4, where variety V1 (Protiva) and V2 (BARI Gom-19) exhibited diminished productivity, suggesting restricted adaptability and inefficient resource utilization, whereas V7 (BARI Gom-32) and V6 (BARI Gom-30) consistently surpassed the other genotypes, achieving the highest grain yields (4.66 tha-1 and 4.42 tha-1, respectively), reflecting significant stress tolerance and robust genetic potential (Figure 4a). Similarly, the impact of irrigation was equally profound, with a gradient of improvement observed from zero irrigation (I0) to the fully irrigated regime (I3). Among various irrigation conditions, I3 demonstrated a notable increase in grain yield (4.34 tha-1), straw yield (3.17 tha-1), and biological yield (7.44 tha-1), all of which highlighted the importance of water availability at crucial points (Figure 4b). Among these variety and irrigation interactions, the V7 × I3 combination highlighting the maximum

grain yield (4.82 tha-1), straw yield (3.65 tha-1), and biological yield (8.47 tha-1), closely followed by V6 × I3 (Figure 4c).

Pearson correlation analysis

Twelve important agronomic traits were analyzed using Pearson correlation under three distinct conditions: (a) variety; (b) irrigation schedules; and (c) the interaction between wheat variety and irrigation schedule. Across the varieties, the most important trait, yield, was positively correlated with the number of effective tiller hills-1 (0.98***), length of spike (0.99***), number of grain spike-1 (0.98***), and thousand grain weight (0.99***), straw yield (0.98***), biological yield (1.00***) and harvest index (1.00***). but negatively connected with the number of non-effective tiller hills-1 (-0.96***) Figure 5a. In irrigation schedule, yield was strongly influenced by the thousand-grain weight (0.98*), number of grain spike-1 (0.98*), and length of spike (0.98*) Figure 5b. Three irrigations (I3) strengthened these relationships, while no irrigation (I0) weakened them. Significantly, correlations between yield and important traits were strengthened by irrigation during critical stages (CRI, booting, and grain filling), highlighting the importance of strategic water management. Finally, interaction of variety and irrigation provided stronger correlation compare to variety and irrigation (Figure 5c),

where yield showed strong positive correlations with effective tillers (1.00***), spike length (0.98*), number of grain spike-1 (0.98*), thousand grain weight (0.99**), straw yield (0.99***), biological yield (1.00***), and harvest index (1.00***). This pattern intensified with nearly perfect correlation across all major yield determinants. These findings indicate how optimal irrigation schedule and higher genetic potential combine to improve traits that contribute to wheat production.

PCA analysis

Principal Component Analysis (PCA) was utilized to perceive the multivariate interactions between wheat genotypes and irrigation regimes across the assessed traits (Figure 6). The first two principal components (PC1 and PC2) together explained 76.6% of the combined variation, with PC1 accounting for 57.7% and PC2 for 19.2% (Figure 6a). The results of the interactions between irrigation and varieties were displayed in the PCA biplot (Figure 6b). In first quadrant, days to heading, number of effective tillers hill-1, and number of grain spike-1 plotted positive values in both PC1 and PC2 with the longer distance for V4I3, indicating BARI Gom-22 provided suitable values for these traits at irrigation level I3. In the second quadrant, V1I1 showed a longer distance for the trait days to maturity, indicating that the variety Protiva exhibited the highest days to maturity at I1 irrigation level. Positioned in the third quadrant, V1I0 provided longer distance for number of non-effective tiller hill-1. Finally, in forth quadrant, V7I3 exhibited longer distance with near traits length of spike, thousand grain weight, yield, biological yield, and harvest index, underscoring that BARI Gom-32 is suitable for these important traits at irrigation level I3. The principal component scores (Figure 6c) supported this pattern, with the highest values corresponding to

the I3 regime when combined with high-performing varieties. Grain yield (0.35), biological yield (0.32), thousand grain weight (0.27), length of spike (0.37), harvest index (0.31), and number of grain spike-1 (0.33) were contributed positively to PC1.

Heatmap analysis

Agronomic, phenological, and yield traits provided clear varietal diversity triggered by irrigation, according to the heatmap analysis (Figure 7). The highest values for length of spike, thousand-grain weight, and number of grain spike-1, grain yield, biological yield, and harvest index were consistently found in V7I3. This result indicated that under I3 irrigation conditions, V7 (BARI Gom-32) performed better than the other varieties. Similarly, V5 (BARI Gom-25) had the most effective tillers hill-1 under I3, highlighting the significance of irrigation at crucial points. On the other hand, V1I0 exhibited the lowest values for length of spike, thousand grain weight, and number of grain spike-1, grain yield, biological yield, and harvest index. This finding implied that V1 (Protiva) produced the lowest yield-related traits compared to other varieties at irrigation level I0. Overall, the heat map demonstrated that water stress significantly decreased the growth and yield components, but the optimum irrigation schedule combined with high-performing varieties increased productivity.

Discussion

This study reveals that the use of precision-based irrigation control systems substantially impacts the morphological, phenological, and yield characteristics of wheat, particularly when adapted to genotype-specific responses to various agroecological regions in Bangladesh. The significant influences of irrigation schedules and genotypic variation on all measured parameters (p < 0.001) illustrate the significance of genotype × irrigation interactions in enhancing wheat yield.

The ANOVA revealed significant variances (p < 0.001)

across all evaluated traits, indicating that both irrigation and genotype exert considerable effects on twelve traits. These findings corroborate previous studies highlighting the essential influence of genetic composition and water availability on wheat performance (Mutanda et al., 2025; Zhao et al., 2025). Irrigation at three distinct growth stages (I3), specifically CRI, booting, and grain filling, markedly improves several measures, including plant height, effective tillers hill-1, thousand grain weight, and spike length, hence influencing grain output. The highest growth sensitivity in the critical stages of wheat, such as CRI, heading, and grain filling, was also observed in the previous report by Monteleone et al. (2023); Mu et al. (2021). The enhanced performance under I3 corroborates the findings of Liu et al. (2024), who found that irrigation in a timely manner profoundly enhances biomass distribution and harvest index. The observed phenological variation among the tested wheat genotypes under differential irrigation levels reflects fundamental genetic regulatory mechanisms. BARI Gom-32 (V7) is an early maturing cultivar, while BARI Gom-22 (V4) requires extended growth period. The phenological variety between BARI Gom-32 (V7) and BARI Gom-22 (V4) reflects agronomically important traits that can be effectively employed in adaptable climate-resilient plant breeding amidst evolving climatic conditions (Ihsan et al., 2016). This temporal flexibility enables crops to evade critical stress windows caused by increasingly shorter winters and late-season aridity. Protiva (V1) and other genotypes exhibit enhanced heading and maturity under extreme moisture conditions (I0), underscoring the presence of drought resistance mechanisms. However, the absence of correlation with increased yield aligns with Sewore et al. (2024) and recommends no connection between phenological escape and yield potential. On the other hand, delayed phenology under higher irrigation levels (I3) suggests that more water availability extends vegetative growth and delays senescence. This promotes uninterrupted nutrient absorption and biomass accumulation across the grain-filling period, resulting in increased grain weight and improved yield stability (Du et al., 2022; Monteleone et al., 2023; Zhuang et al., 2024). These findings are aligned with Liu et al. (2024), who provided the valuable insight of keeping sufficient water content during the grain-filling stage enhances sink strength, which reinforce the significance of combining phenological adaptability, water-use precision, and yield stability in breeding programs to create suitable wheat cultivars to encounter future challenges of ever-changing climatic conditions (Mu et al., 2021).

The notable genotype × irrigation correlations for yield-related parameters (e.g., grain spike-1, thousand grain weight, biological yield, and harvest index) affirm that optimal irrigation is inadequate without genetic compatibility. BARI Gom-32 (V7) consistently surpassed other genotypes across all irrigation regimes, particularly under I3, in metrics such as thousand-grain weight (52.55), length of spike (14.87 cm), yield (4.82 tha-1) and harvest index (56.83%). Such superior performance is probably an outcome of their genetic potential for resource-use efficiency and better physiological adaptation, consistent with reports by Bapela et al. (2022), who observed genotypic difference under water use efficiency in high-yielding wheat cultivars in South Asia. Protiva’s (V1) poor performance under drought (I0) is another reflection of the vulnerability of poorly adapted cultivars to water stress.

Pearson correlation showed strong and positive correlations of grain yield with most influential physiological and morphological traits. These include the number of effective tillers hill-1 (r = 1.00***), length of spike (r = 0.98***), and thousand-grain weight (r = 0.99***). These results underscore how these attributes function collectively to improve yield under full irrigation level (I3), harmonious with the result of Liu et al. (2024), who identified that improved sink-source regulation is crucial for expanding wheat yield. The consistent associations under diverse irrigation at different stages propose a balanced response to water supply that helps with prominent growth, biomass accumulation, and yield improvement. Additionally, the negative relation between yield and the number of non-effective tillers hill-1 shows how unproductive biomass distribution can reduce total grain yield. This is likely due to increased spikelet sterility and floret abortion under suboptimal conditions, as noted by Rahman et al. (2024). In these results, Zhuang et al. (2024) reported the importance of spike-related traits and grain weight for predicting wheat yield, particularly with optimal irrigation conditions in different growth stages. Together, these results imply that improving watering practices and increasing the use of productive tillers can profoundly enhance the potential of wheat yield.

The Principal Component Analysis (PCA) provides an extensive perception of the intricate interactions among the measured genotypes and irrigation level which influence wheat yield production through numerous mechanisms. The first two principal components (PC1 and PC2) simultaneously described 76.6 percent of the total variation confirming PCA’s strength in simplifying complex trait interdependences. With adequate water supply, the full irrigation regime (I3), BARI Gom-32 and BARI Gom-22 exhibited significant correlations with key yield-attributing characteristics, as they were positioned furthest along the positive axes of PC1 and PC2, respectively. BARI Gom-32 exhibited superior adaptation to optimal moisture availability, correlating with thousand-grain weight, spike length, grain production, biological yield, and harvest index (Anjum et al., 2021; Islam et al., 2019; Wang et al., 2025). These traits are key identifiers of source-sink efficiency, known to improve with enough irrigation facilities, enhancing efficient grain filling and biomass allocation (Ahmed et al., 2023; Çebi et al., 2023). Conversely, Protiva’s (V1I0) placed in the negative quadrant of the biplot, along with traits like the number of non-effective tillers hill-1, suggests an inappropriate response to water stress. This is likely caused by reduced translocation of photosynthate, higher floret abortion, and development of impaired spikelet (Si et al., 2023; Wang et al, 2024; Zhao et al., 2024). These findings are consistent with previous research on wheat physiology under drought stress, emphasizing the detrimental impacts of water shortage on source (photosynthesis and biomass) and sink (grain) growth (Liu et al., 2024; Yan et al., 2024). In addition, spike length, spike grain number, thousand-grain weight, and yield-related indices had strong positive correlations with each other, particularly under I3, according to the directed loading vectors. High-performing genotypes harness water availability to boost yield components synergistically, an expression of coordinated physiological reaction (Monteleone et al., 2023; Zhuang et al., 2024). Finally, the multivariate approach of PCA confirmed the advantage of specific genotype and irrigation interactions (specifically V7I3 and V5I3) and demonstrated the effectiveness of PCA analysis in trait-based selection techniques in selecting high-yielding, climate-stable wheat genotypes (Rahman et al., 2024; Wang et al., 2025).

The heat map analysis helps in interpreting the differential adaptation of wheat genotypes to different water regimes by providing an extensive description of genotype × irrigation interactions across several agronomic, phenological, and yield traits. BARI Gom-32 (V7) consistently performed well in important yield components such as length of spike (LS), thousand grain weight (TGW), number of grains spike-1 (NGS), grain yield (Yield), biological yield (BY), and harvest index (HI) under the ideal irrigation schedule (I3), highlighting its agronomic stability and physiological robustness under well-watered conditions (Islam et al., 2019; Rahman et al., 2021; Wang et al., 2024). Similarly, BARI Gom-25 (V5) demonstrated a marked increase in effective tillers hill-1 (ET), confirming the importance of timely irrigation during sensitive phenophases like booting, grain filling, and CRI, which are essential for improving assimilate allocation and tiller fertility (Bapela et al., 2022; Zhao et al., 2025). Concurrently, Protiva (V1) exhibited the lowest performance among the studied genotypes under the water stress condition (I0). This occurred because insufficient water stress encourages reduced transport of assimilates and excessive floret abortion through reduction of photosynthetic activity, sink capacity, and spike length elongation (Liu et al., 2024; Mutanda et al., 2025). These findings corroborate previous reports that irrigation-induced morphological and physiological trait control directly governs wheat yield development and resource-use efficiency (Baloch et al., 2014; Islam et al., 2019; Çebi et al., 2023). Overall, well-irrigation management along with high-yielding genotypes like BARI Gom-32 and BARI Gom-25 improves the stability against environmental variations and maximizes production potential in irrigated and stressed environments. Recent achievements in breeding and agronomic research have highlighted the essential role of the synergy between genotype and environment in developing climate-adaptive wheat genotypes (Ihsan et al., 2016; Sen et al., 2017; Mu et al., 2021; Monteleone et al., 2023).

Recent climate change settings in South Asian agriculture impacted the wheat cultivation and its production trends (Mobeen et al., 2025; Sarker et al., 2020), as documented by Rehman et al. (2024), revealing the urgent requirement for adopting fruitful strategies in wheat farming in Bangladesh. This study underscores the necessity of implementing climate-resilient agriculture practices that incorporate genotypic selection with precision irrigation approaches. The favorable outcomes of BARI Gom-32 and BARI Gom-25 at appropriate soil moisture levels indicated significant yield advantages from the promotion of genetically robust and high-yielding wheat genotypes. In stage-specific irrigation, particularly during the critical stages of CRI, booting, and grain filling, these varieties can substantially improve crop productivity (Rahman et al., 2021; Ali et al., 2022; Nyaupane et al., 2024). The findings amply support that genotype selection synchronized with an appropriate irrigation schedule enhances growth, phenology, and grain yield.

Conclusions and Recommendations

The present research illustrates the importance of optimizing the irrigation time and genotype performance in improving wheat production under the subtropical climate of Bangladesh. Among the varieties tested, BARI Gom-32 and BARI Gom-25 produced the highest yield and agronomic performance under full irrigation conditions (I3), while Protiva performed the lowest yield and its related traits. Strategic irrigation during crown root initiation, booting, and grain filling stages was crucial for increasing yield-related traits such as spike length, thousand-grain weight, and harvest index. Strong correlations between yield and its attributing traits, such as effective tiller number, spike length, and thousand-grain weight, affirm their value as yield indicators. Multivariate analyses ensured the interaction between genotype and irrigation, providing a strong basis for selecting varieties and irrigation regimes. These findings offer valuable guidance for developing water-efficient, high-yielding wheat to decrease import dependency and strengthen food security in Bangladesh.

Acknowledgments

The authors are thankful to the National Science and Technology (NST) fellowship, Ministry of Science and Technology, Government of Bangladesh, for the financial support.

Novelty of statement

This research provides new insights into the interactive effects of genotype and irrigation scheduling on wheat performance under subtropical agro-ecological conditions of Bangladesh. The study demonstrates that three-stage irrigation substantially enhances growth, phenology, and grain yield, particularly in BARI Gom-32, thereby contributing to climate-resilient and sustainable wheat production strategies.

Author’s Contribution

Muntarina Hussan Mouri: Conceptualization, Investigation, Methodology, Project administration, Software, Data curation, Formal analysis, Resources, Visualization, Writing- original draft.

Mahadi Hasan Monshi: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Resources, Writing- original draft.

Rehenuma Tabassum: Supervision, Formal analysis, Methodology, Conceptualization, Data curation, Visualization, Writing- original draft, review & editing.

Bright Gyamfi Adu: Formal analysis, Investigation, Methodology, Software, Writing- review & editing.

Hasan Mehraj: Data curation, Formal analysis, Resources, Methodology, Writing- review & editing.

Md. Shakhawat Hossain: Resources, Validation, Writing- review & editing.

Jahidul Hassan: Conceptualization, , Formal analysis, Investigation, Methodology, Writing- original draft.

Ahmed Khairul Hasan: Formal analysis, Resources, Validation, Writing- review & editing.

Fakhrul Islam Monsh: Fund acquisition, Conceptualization, Supervision, Investigation, Methodology, Project administration, Resources, Validation, Writing- original draft, Writing- review, editing, and finalizing the manuscript.

Generative AI or AI assisted technology statement

The authors declare that no generative AI and AI assisted technology was used in the creation of this manuscript.

Conflict of interest

The authors declare no competing interests.

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