SJA_42_2_898-915

Introduction

For millions of people, rice (Oryza sativa L.) is a source of life, culture, tradition, and a living, making it the most valuable commodity in the world (Mohapatra et al., 2025). It is eloquently upheld the need to heighten awareness of the role in alleviating poverty and malnutrition (Asha et al., 2025). Rice is the staple food for about 156 million people of Bangladesh. With a population growth rate of two million people year, Bangladesh’s overall population is expected to reach 238 million by 2050 (Rahman et al., 2025; Soma et al., 2023). Rice cultivation in Bangladesh thrives due to the country’s predominantly low-lying terrain, humid climate, and abundant monsoon rainfall (Imran et al., 2025). Rice is cultivated year-round in Bangladesh across three distinct growing seasons: aus (summer), aman (monsoon), and boro (winter) (Jesia et al., 2025). To feed this constantly growing population, more rice must be produced overall. Bangladesh produces 33.80 million tons of rice annually, using around 84.67% of the country’s planted land. According to BBS (2022) aus and aman rice production are 30,00,857 and 149,58,390 tons respectively. According to BBS (2021) boro production is 4.154-ton hectare-1. So, we need to increase per unit rice production.

The demand for rice is rising in response to the continuous population growth. Rice production is predicted to need to rise by 50–60% by 2025 in order to meet population growth-related demand (Mia et al., 2024; Jamal et al., 2023). It’s possible that conventional fertilizer management alone won’t be sufficient to supply the world’s enormous food needs. The time to look for novel methods that can ensure greater yields with less degradation of natural resources has come (Islam et al., 2024; Shiferaw et al., 2009). Due to inadequate management techniques and nutrient imbalances, rice yield per hectare is steadily declining (Mia et al., 2023; Tan et al., 2005). Producing healthy and vigorous seedlings is the most important factors for obtaining high yield in rice (Salam et al., 2022; Mahajan et al., 2017).

An essential component of transplanted rice is nursery management. The standard procedure for growing rice is to raise it in a nursery before relocating it to the main field (Choudhary & Machavaram, 2022; Ferdous et al., 2018). Although it has been done for many years, farmers often do not use any fertilizer in rice nurseries, and there are no appropriate fertilizer recommendations for rice nurseries (Hasan et al., 2018; Sarangi et al., 2015). Only the primary field management of rice has received significant study attention thus far, with little focus on nursery management. Effective nursery management will influence how the crop behaves in field (Paul et al., 2017; Grossnickle and MacDonald, 2018). Rice nursery management is generally less costly than primary field management (Sarwar et al., 2014).

A balanced diet in the nursery is crucial for producing healthy rice seedlings, among other aspects of nursery management (Singh et al., 2005). The management of nutrients in the nursery bed affects rice yield, growth, and development. By investing a little money to raise robust and healthy seedlings in the nursery, the farmer can reap an extra 1 to 2 t ha-1 in benefits from the rice crop (Hossen et al., 2025; Adhikari et al., 2013). Increasing the nursery’s fertility level results in a considerable rise in seedling length, root length, dry weight, root number, and seedling growth (Singh et al., 2005).

Another crucial element that affects the healthy growth and vigor of rice seedlings is the seeding density in the nursery. Weaker seedlings are produced in nursery beds with high seeding densities because of competition between the plants for light, nutrients, and water (Niraula and Timilsina, 2020). The ideal seed rate in a nursery bed allows the plant to use more nutrients and sunlight to grow its aerial and subsurface parts more effectively (Islam et al., 2024; Jana et al., 2016). When a larger seed rate is utilized at the nursery level, the rice crop’s yield and yield components suffer, whereas when a lower seeding rate is used at the nursery, the yield increases (Sidddika et al., 2024; He et al., 2018). Accordingly, to improve rice development and yields, the right seeding density and nutrient management in the nursery bed are required (Mondal et al., 2021).

Nutrient management and seeding density approaches have a significant impact on creating superior, healthy, and quality seedlings as well as enhancing the crop yield of winter rice (Sarangi et al., 2014). Low nursery planting density combined with improved fertilizer management can result in higher seedling vigor. The yield of robust seedlings was noticeably higher (Forhad et al., 2023). However, despite these findings, limited research has systematically examined the combined effects of nursery seeding density and integrated nutrient management on seedling quality and subsequent yield of winter rice under local agro-ecological conditions. Moreover, the optimum interaction between seeding density and nutrient supply for producing vigorous seedlings remains insufficiently documented.

In order to ensure optimal growth and yield of rice, it is therefore preferable to use an integrated method for satisfying the nutrient need of the rice seedling plants. This would essentially comprise applying nutrients by maintaining a proper seeding density in the nursery bed. Based on the above facts the present study was undertaken withe following objectives: to evaluate the effect of nursery seeding density and nutrient management on seedling growth and yield of winter rice and to identify the optimum nursery seeding density and suitable nutrient management for obtaining higher yield of winter rice.

Materials and Methods

Experimental site

The trial site was 18 meters over mean level of the ocean and took place at 24°42’55’’ N latitude and 90°25’47’’ E longitude. The experiment was laid out in the field no. 2 of the wetland block of Agronomy Field Laboratory, Bangladesh Agricultural University, Mymensingh from December 2021 to May 2022.

Experimental soil

Within the agro-ecological zone of the non-calcareous dark gray riverbank soil of the Old Brahmaputra Floodplain is the place (AEZ–9; UNDP and FAO, 1988). The soil belongs to the silt-loam texture class, with a pH of 6.8, an electrical conductivity (EC) of 1.23 ds m-1, 1.29% organic carbon, 0.101% N, P 26 ppm, K 0.14% meq, S 15 ppm, Zn 0.60 ppm, and B 0.25 ppm. The tested soil’s morphological, chemical, and physical characteristics (0–15 cm depth) appear in Table 1.

Climate and weather condition

The information about the weather conditions that existed during the crop growing period is displayed in Table 2. A hot, humid monsoon season with considerable rainfall from June to October, a hot, humid season with high humidity from April to June, and a comparatively chilly and dry winter season from November to March define the site’s subtropical climate. During the experimental period, the monthly mean maximum temperature ranged from 23.46°C to 32 °C and the monthly mean minimum temperature ranged from 12.6°C to 23.9°C. The relative humidity ranged from 73.61 to 84.12 percent and a total rainfall of 139.3 mm was received during the crop growth period.

Experiment treatment

Factor A- Nursery seeding density (3): Low density (100 g m-2) (S1), Medium density (150 gm-2) (S2), High density (200 gm-2) (S3). Factor B- Nursery nutrient management: No nutrient applied (N1), Nitrogen (N) @ 6 gm-2 (N2), Phosphorous(P) @5 gm-2 (N3), Potassium (K) @ 3 gm-2(N4), Nitrogen (N) @ 6 gm-2 + Phosphorous (P) @5 gm-2 + Potassium (K) @ 3
gm-2 (N5), Compost 1 kgm-2 (N6).

Table 2: Weather data regarding monthly average air temperature, rainfall, relative humidity and sunshine hours at the experimental site during November 2021 to May 2022

Month	Monthly avg. temp (0C)			Rainfall (mm)	Relative humidity (%)	Sunshine (hrs)
Month	Max	Min	Average	Rainfall (mm)	Relative humidity (%)	Sunshine (hrs)
November 2021	30.00	19.20	24.60	5.9	82.00	210.3
December 2021	26.3	14.00	20.15	4.3	83.40	189.9
January 2022	26.28	12.15	19.22	0.00	84.35	227.2
February 2022	27.03	15.54	21.28	1.17	83.00	164.8
March 2022	29.82	17.70	23.76	1.90	73.19	208.2
April 2022	31.65	22.30	27.00	2.22	77.67	193.5
May 2022	32.50	23.33	29.00	2.30	75.00	195.10

Source: Department of Water Management and Irrigation, Weather Yard, Bangladesh Agricultural University Mymensingh

Experiment design

Three replications of the randomized completely block design (RCBD) were used to set up the experiment in both the nursery and the main field.

Plant material used

The planting material for this investigation was the high-yielding winter rice cultivar BRRI dhan89. In 2018, this was published by the Bangladesh Rice Research Institute (BRRI). Somaclonal variation was used to create this variant. The variety was developed from crossing between BRRI dhan29 and Oryzarufipogon. The duration of the variety is 154-158 days and it has a yield potential of 8.0 -9.7 t ha-1. This variety is recommended for boro (winter) season. This cultivar is harvested between the middle of April and the beginning of May. It can withstand mild wilt disease. Plant height is 106 cm. Thousand grain weight is 24.4 g. Grain size is medium and slender.

Seed collection and sprouting

BRRI dhan89 rice seeds were gathered from Bangladesh Agricultural University’s Agronomy Field Laboratory in Mymensingh. The seeds spent a full day submerged in water in a bucket. After removing these seeds from the water, they were securely covered with gunny sacks. The seeds began to grow after 48 hours, and within the following 72 hours, they were ready to be sown.

Nursery raising

On December 5, 2021, a series of ploughing and laddering was used to prepare the nursery bed. Following the seeding density (SD) according to treatment, sprouted seeds were planted on elevated wet nursery beds that were 1.0 m long and 0.5 m wide. The nursery was fertilized using compost, muriate of potash (K), triple super phosphate (P), and urea (N) in accordance with the procedures. The bed was equipped with drainage channels to remove any surplus water.

Main field preparation

On January 13, 2022, the field was prepped by tractor plowing and then cross-harrowing to crush the soil. Then, on January 15, 2022, three consecutive ploughings and cross plowing were used to puddle at a suitable water level. All uprooted weeds were removed from the field after ploughing and laddering. The field layout was completed on 17 January 2022 following the final site preparation.

Fertilizer application

Fertilizers like N, P, K, and S were applied at rates of 200, 100, 70, and 60 kg ha-1 in the form of urea, TSP, MoP, and gypsum, respectively (FRG, 2018). When the area was finally prepared, all of the TSP, MoP, and gypsum was applied. At 15, 40, and 60 DAT, urea was applied in three equal amounts.

Uprooting of seedlings

The nursery bed was irrigated one day before uprooting the seedlings. The seedlings were uprooted on 17 January 2022 for avoiding much mechanical injury to the roots.

Transplanting of seedlings in the field

The seedlings of 45 days were transplanted in the main field on 17 January 2022 following the spacing 25 cm × 15 cm with 3 seedlings hill-1.

Herbicide application

The main field was sprayed with a pre-emergence herbicide, Pretilachlor @ 400 mL acre -1 at five days after transplanting.

Gap filling

Some seedlings died off after seven days of transplanting, so healthy seedlings from the nursery bed were planted to fill the void left by the dead seedlings.

Irrigation and drainage

A thin layer of water was kept at the time of transplanting for better establishment of the seedlings. From the third day onwards, 2 to 3 cm depth of water was maintained up to the panicle initiation stage except at the time of top dressing of urea, where the water was drained out and re-irrigated to maintain 5 cm depth of water. After dough stage, water was entirely drained out to enhance maturity and make harvesting easier.

Weeding

Two weeding’s of the plots were carried out with the aid of manual labor. 30 days following transplanting, the first-hand weeding was completed, and 20 days following the first, the second one.

Plant protection

Carbotaf 5G @ 2 ml L-1 (200 L ha-1) and Regent 3GR @ 2 ml L-1 (200 L ha-1) were sprayed to treat some instances of stem borer and brown plant hopper.

Sampling, harvesting and processing

When almost 80% of the seeds had become golden

Table 3: Effect of SD on RL, SL and SDW of BRRI dhan89

SD	RL (cm)		SL (cm)		SDW (cm)
SD	20DAS	40DAS	20DAS	40DAS	20DAS	40DAS
S1	2.53	5.50a	9.42	15.69 b	32.24a	86.35a
S2	2.60	5.44a	9.23	16.52a	32.18a	77.86 b
S3	2.34	5.12 b	9.55	15.67 b	30.34 b	81.53 b
Sx	0.12	0.15	0.13	0.19	0.41	1.99
Level of significance	NS	*	NS	**	**	**
CV (%)	14.13	8.27	4.17	3.66	3.86	7.32

* =Significant at 5% level of probability, NS = non-significant

In a column figure with same letter or without letter do not differ significantly whereas figure with dissimilar letter differ significantly as per DMRT. S1= Low density (100 g m-2), S2= Medium density (150 gm-2), S3= High density (200 gm-2).

Table 4: Effect of nursery NM on RL, SL and SDW of BRRI dhan89

NM	RL (cm)		SL (cm)		SDW (cm)
NM	20DAS	40DAS	20DAS	40DAS	20DAS	40DAS
N1	2.16 b	4.55 c	9.23 bc	15.86 b	27.08 e	68.22 d
N2	2.28 b	4.66 c	9.17 c	15.37 bc	29.94 d	76.35 c
N3	2.50ab	5.27 b	9.55ab	16.83a	30.77 cd	81.98 bc
N4	2.62a	5.42 b	9.13 c	15.71 bc	31.63 c	82.53 b
N5	2.77a	6.26a	9.89a	16.83a	35.92a	93.63a
N6	2.63a	5.96a	9.43 bc	15.17 c	34.16 b	88.77a
Sx	0.17	0.21	0.18	0.28	0.58	2.83
Level of significance	**	**	**	**	**	**
CV (%)	14.13	8.27	4.17	3.66	3.86	7.32

N1= No nutrient applied, N2= Nitrogen (N) @ 6 gm-2, N3= Phosphorous(P) @5 gm-2, N4= Potassium (K) @ 3 gm-2, N5= Nitrogen (N) @ 6 gm-2 + Phosphorous (P) @5 gm-2 + Potassium (K) @ 3gm-2, N6= Compost 1 kgm-2.

yellow, the crop was fully mature and ready for harvest. In order to collect the required data on different plant characteristics and yield metrics, five hills (apart from border hills) were preselected at random from each plot and uprooted before to harvest. The crops were harvested manually on 11 May 2022 from each plot to record the yields of grain and straw yields. Each plot’s harvested crop was taken to the threshing floor after being individually wrapped and appropriately marked. A pedal thresher was used to thresh the crops. Grains were washed and sun-dried. Straws were appropriately sun-dried as well. Lastly, the grain yield was translated to tons per hectare after being corrected to 14% moisture.

Data collection parameters

The following data were recorded. Seedling growth parameters: Shoot Length (SL), Root Length (RL), Seedling dry weight (SDW). Crop characteristics, yield parameters and yield: Plant height (PH), Total tillers hill-1 (TT), Effective tillers hill-1 (ET), Non-effective tillers hill-1 (NET), Total spikelets panicle-1 (SPP), Grains panicle-1 (GPP), 1000-grain weight (TGW), Grain yield (GY), Straw yield (SY), Harvest Index (HI)

Harvest index

It represents the grain-to-biological yield ratio and was computed using the formula below:

Statistical analysis

They compiled and tabulated the information collected for statistical analysis. For the Analysis of Variance (ANOVA), the statistical software tool MSTAT-C was utilized. The mean differences between the treatments were ascertained using Duncan’s Multiple Range Test (Gomez and Gomez, 1984). RStudio was used for data visualization, while Microsoft Excel was employed to organize the dataset for analysis (R Core Team, 2022).

Table 5: Interaction effects of SD and nursery NM on RL, SL and SDW of BRRI dhan89

SD x NM	RL (cm)		SL (cm)		SDW (cm)
SD x NM	20DAS	40DAS	20DAS	40DAS	20DAS	40DAS
S1N1	2.20	4.13 f	9.22	15.15	28.88 ij	69.19
S1N2	2.40	4.22 ef	9.18	15.37	30.09 ghi	80.64
S1N3	2.50	5.93abc	9.53	17.34	30.96 fgh	86.07
S1N4	2.60	5.96ab	8.88	15.04	31.43 efg	86.96
S1N5	2.83	6.43a	10.09	16.55	36.93 a	99.74
S1N6	2.68	6.32a	9.61	14.72	35.14ab	95.50
S2N1	2.46	4.80 def	8.98	16.42	26.18 k	65.76
S2N2	2.53	4.96 d	9.05	15.90	32.58 def	73.64
S2N3	2.56	4.96 d	9.20	17.18	32.16 ef	74.66
S2N4	2.66	5.22 cd	9.34	16.63	31.97 efg	75.26
S2N5	2.80	6.40a	9.76	17.43	35.96ab	91.80
S2N6	2.63	6.33a	9.04	15.58	34.23 bcd	86.06
S3N1	1.83	4.72 def	9.48	16.03	26.19 k	69.70
S3N2	1.90	4.81 def	9.27	14.84	27.17 jk	74.78
S3N3	2.45	4.93 de	9.92	15.96	29.19 hi	85.20
S3N4	2.60	5.09 d	9.18	15.46	31.50 efg	85.38
S3N5	2.69	5.96ab	9.81	16.52	34.88 bc	89.36
S3N6	2.58	5.23 bcd	9.66	15.20	33.10 cde	84.76
Sx	0.29	0.36	0.32	0.48	0.99	4.89
Level of significance	NS	**	NS	NS	**	NS
CV (%)	14.13	8.27	4.17	3.66	3.86	7.32

Results

Seedling growth parameters

At 20 DAS, seedling RL of BRRI dhan89 was not significantly affected by SD. But, numerically the maximum seedling RL (2.60 cm) was recorded with 150 g m-2 SD and the minimum one (2.34 cm) was found in 200 g m-2 SD (Table 3).Nutrient management (NM) had a significant effect on the RL, and N+P+K treatment produced the highest RL (2.77cm) followed by N, P, K and compost, each these particular treatments were discovered to be much more higher than the control treatment (Table 4). Interaction between SD and NM did not produce any significant effect on RL at all (Table 5).

At 40 DAS, seedling RL was significantly affected by nursery SD. The RL (5.50 cm) was recorded with 100 g seeds m-2 followed by 150 g seeds m-2 and 200 g seeds m-2 (Table 3). had a significant effect on the RL. The N+P+K treatment resulted in the highest RL (6.26 cm) followed by N, P, K and compost; and each these particular treatments were discovered to be much higher than the control treatment (Table 4). Interaction between SD and NM produced significant effect on RL. SD of (100 gm-2) and N+P+K treatment combination produced the highest RL (6.43 cm) followed by each these particular treatments were discovered to be much higher than the control treatment (Table 5).

At 20 DAS, no significant effect of SD was found on seedling SL of BRRI dhan89. But, numerically the maximum SL (9.55 cm) was recorded with 200 g m-2 SD and the minimum one (9.23 cm) was obtained from 150 g m--2 SD (Table 3). NM had a significant effect on the SL and N+P+K treatment produced the highest SL (9.89 cm) followed by N, P, K and compost (Table 4). Interaction between SD and NM did not produce any significant effect on SL (Table 5).

At 40 DAS, SD had significant effect on seedling SL. SD of 150 g m-2 performed the best in terms of SL (16.52 cm) on the other hand, 100 g m-2 performed the worst (15.69 cm) (Table 3). NM exerted a significant effect on the SL of rice seedling. The maximum SL

Table 6: Effect of SD on yield contributing characters and yield of BRRI dhan89

SD	PH (cm)	TT (no.)	ET (no.)	NET (no.)	SPP (no.)	GPP (no.)	TGW (g)	SY (t ha-1)	HI (%)
S1	110.03	9.65a	8.87a	0.78	106.34	99.07	26.18	6.04	49.31
S2	107.52	8.98 b	8.21 c	0.76	104.60	98.48	26.38	5.94	48.88
S3	106.61	9.11 b	8.30 b	0.80	104.42	98.02	26.11	5.87	48.93
Sx	1.66	0.12	0.05	0.09	2.39	0.64	0.16	0.16	0.71
Level of significance	NS	**	**	NS	NS	NS	NS	NS	NS
CV (%)	4.62	3.88	1.69	6.09	6.83	1.94	1.83	8.25	4.32

(16.83 cm) was recorded with P and N+P+K followed by N, P and which was at per with K and compost and each these particular treatments were discovered to be much higher than the control treatment (Table 4). Interaction between SD and NM did not produce significant effect on SL either. However, numerically the longest SL (17.43 cm) was found when 150 g m-2 SD was used with N+P+K applied in nursery (Table 5).

SDW

At 20 DAS, the SDW of BRRI dhan89 was significantly affected by SD. SD of 100 g m-2 produced the highest SDW (32.18 mg) which was statistically similar with 150 g m-2 and the lowest SDW (30.34 mg) was produced by 200 g m-2 (Table 3). The NM had a significant effect on SDW. Application of N+P+K resulted in the highest SDW (35.92 mg), followed by N, P which was at par with K and compost and all these treatments performed better than to the control treatment (Table 4). The interplay between SD and NM had significantly influenced on the SDW of BRRI dhan89. Best SDW (36.93 mg) was recorded at 100 g m-2 SD with application of N+P+K in nursery bed (Table 5).

At 40 DAS, rice SDW was significantly affected by SD. The highest SDW (86.35 mg) was observed with SD of 100 g m-2, the lowest one (77.86 mg) was recorded at 150 g m-2 which was statistically similar with 200 gm-2 (Table 3). NM exhibited a significant effect on the SDW and N+P+K produced the highest seedling weight (93.63mg) whereas application of cowdung resulted the statistically similar SDW (Table 4). There was no significant effect of the interaction between SD and NM of SDW. However, numerically the highest SDW (99.74 mg) was produced when SD of 100 g m-2 interacted with N+P+K (Table 5).

Crop characteristics, yield parameters and yield

Effect of SD

Different seeding densities had no significant effect on PH of BRRI dhan89. However, numerically the tallest plant (110.03 cm) was obtained from the 100 g m-2 treatment, whereas the shortest one (106.61 cm) was recorded with the 200 g m-2 treatment (Table 6). The effect of SD on TT was significant. The highest TT (9.65) was produced in 100 g m-2 treatment, while the lowest one (8.98) was recorded in 150 g m-2 treatment (Table 6). ET, one of the yield parameters, was significantly affected by SD. The highest ET (8.87) were obtained with 100 g m-2 SD and the minimum ET (8.21) were obtained with 150 g m-2 SD (Table 6). The SD treatments can be categorized as 100 g m-2>150 g m-2> 200 gm-2depending on the decreasing ET. SD showed no significant effect on NET of rice. SD of 100 g m-2produced the highest number of NET (0.80) and the lowest NET (0.76) was obtained in the 150 g seeds m-2 (Table 6). SD had no significant effect on GPP of BRRI dhan89. SD performed the highest (99.07) with 100 g m-2 while performed the lowest with (98.02)200g m-2 (Table 6). The number of total SPP was not significantly affected by SD. But, numerically the maximum number of total SPP (106.34) was observed in 100 g m-2 treatment and the minimum number of total SPP (104.42) was produced by 150 g m-2 treatment (Table 6). Effect of SD on TGW of BRRI dhan89 was not significantly affected by SD. However, numerically the highest TGW (26.38 g) of winter rice was obtained when seed were sown with 150 g m-2 SD and the lowest TGW (26.11 g) was recorded when the seed were sown with 200 g m-2 SD (Table 6). The data on GY reveal significant differences due to SD used in the rice nursery bed. The GY of rice was increased significantly, with the decreased SD. The highest GY (5.86 t ha-1) was recorded when 100 g m-2 seeds were sown while, 150 g m-2 and 200 g m-2 seeding densities resulted in the yield of 5.67 t ha-1 and 5.61 t ha-1, respectively (Figure 1A). Effect of SD the effect of SD on SY was not significant of BRRI dhan89. But, numerically the highest SY (6.04 t ha-1) was observed in 100g m-2 treatment and the lowest SY (5.87 t ha-1)

Table 7: Effect of nursery NM on yield contributing characters and yield of BRRI dhan89

NM	PH (cm)	TT (no.)	ET (no.)	NET (no.)	SPP (no.)	GPP (no.)	TGW (g)	SY (t ha-1)	HI (%)
N1	105.79	8.90 c	8.04 d	0.85	103.56	95.54 c	26.13	5.78	48.87
N2	108.41	9.18 bc	8.37 c	0.81	105.16	98.05 b	26.16	5.96	49.11
N3	107.57	9.13 bc	8.37 c	0.76	104.58	98.71 b	26.18	5.96	49.00
N4	107.97	9.22 bc	8.47 bc	0.74	104.82	98.60 b	26.08	5.95	49.04
N5	110.31	9.71a	8.94a	0.77	106.94	100.82a	26.49	6.11	49.09
N6	108.30	9.34 b	8.57 b	0.77	105.65	99.45ab	26.30	5.94	49.14
Sx	2.35	0.17	0.07	0.13	3.39	0.90	0.23	0.23	0.99
Level of significance	NS	**	**	NS	NS	**	NS	NS	NS
CV (%)	4.62	3.88	1.69	6.09	6.83	1.94	1.83	8.25	4.32

found in 200 g m-2 treatment (Figure 1B). It was found that the HI was not statistically significant in all the treatments. However, numerically the highest HI (49.31%) was found in 100 g m--2 and a minimum one (48.88%) was found in 150 g m-2 (Table 6).

Effect of NM

Effect of NM on rice PH was not significant. However, numerically the tallest plant (110.31 cm) was found in N+P+K treatment and the shortest one (105.79 cm) was obtained from the control treatment (Table 7). TT was not significantly affected by different NM. However, numerically the highest TT (9.71) was produced when N+P+K was applied and the lowest one (8.90) was produced when no nutrient was applied (Table 7). NM had significant effect on ET of winter rice variety of BRRI dhan89. The highest ET (8.94) were recorded with N+P+K and the lowest one (8.04) was obtained from control (Table 7). The treatments for NM like K, Compost, P and N showed statistically similar effect on the ET. NM had no significant effect on NET production of winter rice variety of BRRI dhan89. However, numerically the highest number of NET (0.85) was recorded with control and lowest number of NET (0.74) was recorded for K (Table 7). NM had significant effect on GPP of rice. The N+P+K treatment performed the best (100.82) and the control treatment performed the poorest (95.54); the effects of the other treatments on GPP were statistically comparable (Table 7). The effect of different NM on the number of total SPP was not significant. However, numerically the maximum number of total SPP (106.94) was obtained with N+P+K treatment and the minimum one (103.56) was recorded with no nutrient (Table 7). NM had no significant effect on TGW of BRRI dhan89. However, numerically the highest TGW (26.49 g) of winter rice was observed when N+P+K was applied and the lowest TGW (26.08 g) was recorded for K treatment applied in nursery bed (Table 7). GY of BRRI dhan89 was significantly affected by NM. The various nutrient treatments given to the rice nursery resulted in notable variances, according to the GY statistics (Table 7). With increased fertilizer, the GY significantly increased. The highest GY (5.89 t ha-1) was seen when N+P+K was given, which was at par with that of N (5.73 t ha--1), followed by statistically similar (5.72t ha-1) treatment but significantly superior

Table 8: Interaction effects of SD and nursery NM on yield contributing characters and yield of BRRI dhan89

SD x NM	PH (cm)	TT (no.)	ET (no.)	NET (no.)	SPP (no.)	GPP (no.)	TGW (g)	GY (t ha-1)	SY (t ha-1)	HI (%)
S1N1	107.38	9.45	8.53 ef	0.91	104.74	94.26	26.05	5.68	5.97	48.77
S1N2	110.48	9.40	8.65 de	0.75	106.57	99.89	26.00	5.80	6.00	49.13
S1N3	109.48	9.50	8.83 cd	0.66	105.89	99.96	26.45	5.93	6.03	49.67
S1N4	109.15	9.60	8.85 cd	0.75	106.78	98.90	26.21	5.86	5.98	49.57
S1N5	112.20	10.10	9.26a	0.83	107.79	101.64	26.32	6.06	6.33	48.89
S1N6	111.50	9.88	9.11ab	0.76	106.27	99.81	26.04	5.85	5.91	49.85
S2N1	105.83	8.60	7.83 jk	0.76	103.00	96.44	26.31	5.43	5.68	48.84
S2N2	108.73	9.13	8.30 fgh	0.83	105.32	96.68	26.59	5.67	5.91	49.13
S2N3	107.32	8.85	8.05 ij	0.80	103.24	97.79	26.16	5.56	5.95	48.44
S2N4	107.95	9.01	8.25 ghi	0.76	104.40	99.79	26.25	5.71	6.00	48.71
S2N5	109.23	9.41	8.66 cde	0.75	106.92	100.71	26.49	5.85	6.05	49.30
S2N6	106.08	8.86	8.16 hi	0.70	104.72	99.52	26.52	5.80	6.08	48.85
S3N1	104.15	8.65	7.76 k	0.88	102.95	95.93	26.02	5.46	5.69	48.99
S3N2	106.00	9.03	8.18 hi	0.85	103.58	97.59	25.90	5.72	5.98	49.05
S3N3	105.92	9.05	8.23 ghi	0.81	104.61	98.38	25.93	5.65	5.90	48.89
S3N4	106.80	9.05	8.33 fgh	0.71	103.28	97.10	25.79	5.58	5.86	48.85
S3N5	109.48	9.63	8.90 bc	0.73	106.11	100.10	26.67	5.74	5.96	49.06
S3N6	107.32	9.28	8.43 efg	0.85	105.97	99.03	26.36	5.50	5.83	48.73
Sx	4.07	0.29	0.12	0.23	5.86	1.56	0.39	0.08	0.40	1.73
Level of significance	NS	NS	*	NS	NS	NS	NS	NS	NS	NS
CV (%)	4.62	3.88	1.69	6.09	6.83	1.94	1.83	1.76	8.25	4.32

to the rest of the treatments under this study (Figure 2A). SY of BRRI dhan89 rice was not significantly affected by the NM. Numerically, the highest SY (6.11 t ha-1) was found when N+P+K was applied and the lowest one (5.78 t ha-1) was obtained when control treatment was applied in the nursery (Figure 2B). The data show that the HI was not significantly affected by NM. Numerically the highest HI (49.14%) was found in compost and a minimum one (48.87%) was found in control treatment (Table 7).

Interaction effect of SD and NM

The interactions between SD and NM that significantly failed to affect PH of rice. Numerically the tallest plant (112.20 cm) was obtained from the interaction of S1N5 and the shortest one (104.15 cm) was found in the interaction of S3N1 (Table 8). Interaction between SD and NM had no significant effect on total tiller per hill. However, numerically the highest TT (10.10) was produced in the interaction of S1N5 and the lowest one (8.60) was found in treatment combination of S2N1 (Table 8). The interaction between SD and NM exerted significant effect on ET. The best performance (9.26) was recorded with the combination of S1N5 and the worst performance (7.76) was obtained from the interaction of S3N1 (Table 8). NET was not significantly affected by the interaction between SD and NM. Numerically the highest (0.91) and lowest (0.66) NET were recorded for S1N1 and S1N3 (Table 8). The interaction between SD and NM had no significant effect on grain panicle-1 of winter rice. However, numerically the maximum grains per panicle (101.64) were recorded for the S1N5 treatment combination and the minimum GPP (105.03) were recorded for the S3N1 combination (Table 8). The interaction effect of SD and NM on the number of total SPP was not significant. Numerically, the highest number of total SPP (107.79) was observed in S1N5 and the lowest number of total SPP (103) was obtained from the interaction of S2N1 (Table 8). The interaction between SD and NM had no significance on grains per panicle of BRRI dhan89 either. However, the results revealed that numerically the highest TGW (26.67 g) of BRRI dhan89 was obtained from the combination of S3N5 and the lowest one (25.79 g) was recorded from interaction of S3N4 (Table 8). The interaction effect of SD and NM on GY of BRRI dhan89 was not significant. However, numerically the maximum GY (6.06 t ha-1) was recorded with the combination of S1N5 and the minimum one (5.43 t ha-1) was recorded by S2N1 (Table 8). No significant variation in SY was found due to interaction between SD and NM practices. However, numerically the highest straw (6.33 t ha-1) yield was recorded with the combination of S1N5 and the lowest one (5.68 t ha-1) was obtained from the interaction of S2N1 (Table 8). No significant variation in HI was found due to interaction between SD and NM practices. But, numerically the highest HI (49.85 %) was recorded with the combination of S1N6 and the lowest one (48.88 %) was obtained from S2N3 (Table 8).

PC, correlation and heatmap analysis

The characteristics that best capture the effect of SD on winter rice seedling growth and yield were identified using Principal Component Analysis. Figure 3 displays the bi-plot of the first two principal components along with the variable loadings. With 76.1% of the variation explained by the first principal component (PC1), represented by the x-axis, and 23.9% by the second principal component (PC2), represented by the y-axis. PC1 is highly positively correlated with the TT, ET, HI, SPP, GY, PH, SY, and GPP variables. They have a high positive link with one another, as seen by their clustering. TGW and NET are more aligned with PC2. NET and TGW have negative correlation with TT, ET, HI, SPP, GY, PH, SY, GPP.

The characteristics that best characterize the influence of NM on the growth and yield of winter rice seedlings were identified using Principal Component Analysis. Figure 4 displays the bi-plot of the first two principal components along with the variable loadings. First principal component (PC1), which makes up 83.7% of the overall variance, is represented by the x-axis, while second principal component (PC2), which makes up 9.5% of the total variance, is represented by the y-axis. The variables GY, SY, GPP, HI, PH, ET, TT, and SPP are strongly positively correlated with PC1. NET is more aligned with PC2 and have a negative correlation with GY, SY, GPP, HI, PH, ET, TT, SPP.TGW is closer to origin. Treatment 3 and 4 are strongly influenced by PC2 and is distinct from other observations and treatment 2 and 6 is strongly influenced by PC1 and is distinct from other observations while treatment 1 and 5 are closer to the origin, indicating they have more average values for the principal components.

A correlation was performed to identify the impact of SD on seedling growth and yield of winter rice (Figure 5). The analysis revealed that PH was strongly positively correlated with GY (P < 0.05, r = 1.00), GPP showed a strong positive correlation with SY (P < 0.05, r = 1.00), and ET was strongly correlated with both HI and TT (P < 0.05, r = 1.00). PH was associated with higher GY, while GPP corresponded to increased SY. Moreover, higher ET was linked to improved HI and TT, indicating that these traits are closely interrelated and contribute to overall productivity.

A correlation was performed to identify the impact of NM on seedling growth and yield of winter rice (Figure 6). GY showed a strong positive correlation (P < 0.01) with GPP (r = 0.95), SPP (r = 0.94), ET (r = 0.96), TT (r = 0.93) and PH (P < 0.001 and r = 0.98). Similarly, SY exhibited a strong positive correlation (P < 0.01) with, GPP (r = 0.94), SPP (r = 0.92), ET (r = 0.97), PH (r = 0.94), TT (r = 0.92), and GY (P < 0.001 and r = 1.00). Moreover, GPP was strongly correlated (P < 0.01) with SPP, PH, and TT (r = 0.93) as well as with ET (r = 0.96). Likewise, SPP showed strong positive correlations (P < 0.001) with PH and ET (r = 0.98), TT (r = 0.99), and TGW (P < 0.05 and r = 0.87), while PH was strongly correlated (P < 0.01) with ET and TT (r = 0.96). In addition, ET exhibited strong positive correlations with TT (P < 0.001 and

r = 0.99) and TGW (P < 0.05 and r = 0.83). Finally, TT was strongly correlated (P < 0.05) with TGW (r = 0.87). GY showed strong positive correlations with PH, GPP, SPP, ET, and TT, indicating that taller plants with more grains and spikelets per panicle and higher numbers of tillers produce higher grain yield,

and SY was similarly associated with these traits. GPP and SPP were strongly correlated with PH, ET, TT, and TGW, reflecting that panicle structure, plant vigor, and grain weight are closely linked. Overall, all these traits are highly interrelated, and improvements in one are likely to enhance others, contributing to higher grain and straw yields.

To determine which traits best illustrate how nursery SD and NM affect winter rice seedling growth and yield, a heat map was employed. The y-axis shows the various treatments, and the x-axis shows the variables associated with vegetative and yield parameters (Figure 7). TT, ET, HI, GPP, SY, SPP, PH, and GY variables had a high contribution with Low density (100 g m-2). TT, ET, HI, GPP, SY, SPP, PH, and GY variables represented the lowest contribution with High density (200 gm-2). Light, moderate, and neutral associations between all the parameters were maintained by other treatments.

The variables that contribute to growth and yield are represented on the x-axis, and the various treatments are represented on the y-axis (Figure 8). Growth and yield contributing character like SY, GY, GPP, SPP, PH, ET, TT, and TGW demonstrated the highest contribution when Nitrogen (N) @ 6 gm-2 + Phosphorous (P) @5 gm-2 + Potassium (K) @ 3gm-2 was applied. No nutrient applied treatment showed the lowest SY, GY, GPP, SPP, PH, ET, TT, and HI while NET was highest. The associations between all the factors were light, moderate, and neutral in other treatments.

Discussion

Proper nursery management is essential for seedling growth and crop performance transplant rice. SD and NM are crucial factors for producing healthy seedling to survive in the main field. As proper SD and NM in nursery bed trigger the growth and quality of rice seedlings and ultimately increase rice yield.

The interaction between two SD and NM did not significantly influence the root height, shoot height and dry weight of the seedlings but, SD and NM solely influenced the SL and SDW. As SD decreased, a declining trend in seedling SL was noted. Because there was more competition for sunlight in the nursery bed, the tallest seedlings were generated at greater SD than at medium and low seeding densities. Significantly more emerging seedlings and dry matter were produced per unit area as a result of high-density sowing. There were more seedlings per unit area because there were more seeds sown per unit area, and more seedlings meant more dry matter was produced per unit area. Low density seeding, however, appears to have a greater per SDW. This resulted from healthy, strong seedlings in a low-density nursery that were free from competition.

Additionally, it was noted that the seedlings from the low-density nursery were more robust and had a darker green hue, particularly when N+P+K was applied in the nursery bed. However, Pathania et al. (2016) found higher SL in nursery at low SD compared to high SD. But according to Patra et al. (2014), larger SD promotes intra-seedling competition, producing tall, lanky seedlings with low vigor and no tillers. Before and after transplanting, rice plants’ above- and below-ground traits differed according to seedling rates (Sasaki, 2004). Low nursery SD combined with better NM can result in better seedling vigor.

Manandhar et al. (2020) found that NM levels profoundly affected PH. A similar outcome on PH was also demonstrated by Roy et al. (2018). However, the PH of BRRI dhan89 did not exhibit any relevance in this investigation.

The interaction between the components resulted in a significant change in the amount of ET produced per square meter. The number of ET with low SD was considerably increased in the nursery by the application of N+P+K. Furthermore, compared to high SD, ET per square meter for lower density seeding was noticeably higher. Forhad et al. (2023) also noted that ET in the nursery increased as the SD decreased.

The interaction results revealed that under higher densities, application of N+P+K in nurseries had no much effect on ET per square meter, but had significant effect with low SD. Rani (2012) states that the most prolific tillers m-2 were produced in the nursery bed that received the proper amounts of nitrogen, FYM, and phosphorus.

There was no discernible difference in the number of grains per panicle between the SD and NM interactions. However, the characteristic was only impacted by SD and NM. Compared to high SD, low SD generated noticeably more grains per panicle. Similarly, Mustari (2013) claimed that the highest GPP was obtained from lower seed rate in nursery bed.

Depending on genetic composition, TGW is a significant yield contributor that is least impacted by growing conditions (Ashraf et al., 1999). Neither of the factors viz., SD and nursery NM of had substantial differences for 1000 grain weight among each other. Similarly, interaction effect between the factors on TGW was non-significant. Nevertheless, compared to high density unfertilized nurseries, Forhad et al. (2023) saw higher TGW from low density fertilized nurseries.

The GY was only controlled by the SD and NM of the seedlings; the interactions between the two parameters had no discernible effect. Low SD produced the highest yield (5.86 t ha-1) in the experiment comparing to medium and high SD. Forhad et al. 2023 opined that low SD at nursery bed resulted in a higher number of productive tillers and GY. Nursery fertilization raises yields by 23–30% in comparison to the control, claim Kyalo et al. (2020). Rice’s GY and grain protein content are also enhanced by the prudent use of nitrogen fertilizer (Ray et al., 2015).

The results clearly indicated that higher seedling density (SD) can substantially reduce grain yield (GY), suggesting that intense competition in the nursery adversely affects seedling growth. In the early stages of crop growth, De Datta (1980) found that an increase in SD was associated with an increase in intra-plant and inter-plant competition.

This present research shows no notable effect of either SD or NM on HI. Rani (2012) reported same concept for HI. However, Kyalo et al. (2020) showed HI were considerably different across the treatments.

Conclusions and Recommendations

The current research concludes that SD and NM in nursery beds have the ability to boost crop output and seedling growth. With the application of N 6 g m-2+ P 5 g m-2+ K 3 g m-2, the SD of 100 g m-2 produced superior seedling growth, which in turn improved the yield parameters and ultimately enhanced yield among the various seeding densities and NM procedures used in the nursery. Confirmation and establishment of appropriate nursery management practices for winter rice require more thorough research.

Acknowledgements

We acknowledge the Agronomy Farm Lab, Department of Agronomy, BAU for providing logistics support to this research.

Novelty Statement

This study introduces a novel integration of seeding density and nursery nutrient management to evaluate their combined influence on the growth and yield of BRRI dhan89, a widely cultivated high-yielding rice variety in Bangladesh. While previous research has often examined these factors separately, this experiment uniquely demonstrates how optimized seedling density, when paired with balanced N-P-K fertilization in the nursery stage, can significantly enhance seedling vigor and subsequent field performance. The findings reveal that low seeding density combined with enriched nursery nutrition produces more robust seedlings, which translate into superior tillering capacity and grain yield. This integrated approach highlights an efficient, practical strategy for improving rice productivity, offering farmers a refined method to maximize returns through precise seed and nutrient management at the nursery level.

Author’s Contribution

Md. Parvez Anwar: Conceptualization, Data Curation, Writing - review and editing, Project Administration.

Projna Roy Kongkon: Methodology, Formal Analysis, Writing - original draft.

Md. Liton Mia: Formal Analysis.

Sumona Akter Jannat: Methodology, Writing - original draft.

Md. Omar Ali: Validation.

Md. Shafiqul Islam: Writing - review & editing.

Ahmed Khairul Hasan: Supervision, Conceptualization, Validation.

Sabina Yeasmin: Data Curation, Writing - review & editing.

AKM Mominul Islam: Conceptualization, Validation.

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 have declared no conflict of interest.

References

Adhikari, B.B., B. Mehera and S. Haefele. 2013. Impact of rice nursery nutrient management, seeding density and seedling age on yield and yield attributes. Am. J. Plant Sci., 4(12): 146-155. https://doi.org/10.4236/ajps.2013.412A3017

Asha, U.H., T. Ferdous, R. Ahmed, M. Khatun, M. Haque, M. Nuruzzaman, M.S. Islam, N. Rahman, N.J. Sunny, M.L. Mia and K. Hossen. 2025. Optimizing Productivity and Nutritional Composition of Boro Rice (BRRI dhan97) through Vermicompost Application. Asian Plant Res. J., 13(2): 1–13. https://doi.org/10.9734/aprj/2025/v13i2296

Ashraf, M., A. Khalid and K. Ali. 1999. Effect of seedling age and density on growth and yield of rice in saline soil. Pak. J. Biological Sci., 2(3): 860-862.

BBS (Bangladesh Bureau of Statistics). 2021. Bangladesh Bureau of Statistics Division, Government of the People’s Republic of Bangladesh.

BBS (Bangladesh Bureau of Statistics). 2022. Bangladesh Bureau of Statistics Division, Government of the People’s Republic of Bangladesh.

Choudhary, V. and R. Machavaram. 2022. Need of automation in paddy nurseries for raising paddy seedlings in India: A Review. J. Biosyst. Eng., 47(2): 209-222. https://doi.org/10.1007/s42853-022-00139-9

De Datta, S.K. 1980. Principles and practices of rice production. Wiley and Sons, Singapore.

Ferdous, S.A., M.N.H. Miah, M. Hoque, S. Hossain and A.K. Hasan. 2018. Enhancing rice yield in acidic soil through liming and fertilizer management: Lime and fertilizer maximize rice yield in acid soil. J. Bangladesh Agric. Univ., 16(3): 357-365. https://doi.org/10.3329/jbau.v16i3.39393

Forhad, F.B., M.P. Anwar, H.O. Rashid, A.K. Hasan, S.A. Kheya, S. Afrin and A.K.M.M. Islam. 2023. Improving seedling growth and yield of transplant Boro rice through nursery management. SAARC J. Agric., 21(1): 153-166. https://banglajol.info/index.php/SJA/article/view/65886

Gomez, K. and A. Gomez. 1984. Statistical Procedure for Agricultural Research. John Wiely and sons, New York, USA.

Grossnickle, S.C. and J.E. MacDonald. 2018. Why seedlings grow: influence of plant attributes. New For., 49: 1-34. https://doi.org/10.1007/s11056-017-9606-4

Hasan, M.R., M.R. Rahman, A.K. Hasan, S.K. Paul and A.H.M.J. Alam. 2018. Effect of variety and spacing on the yield performance of maize (Zea mays L.) in old Brahmaputra floodplain area of Bangladesh. Arch. Agric. Environ. Sci., 3(3): 270-274. https://doi.org/10.26832/24566632.2018.0303010

He, H., C. You, H. Wu, D. Zhu, R. Yang, Q. He and L. Wu. 2018. Effects of nursery tray and transplanting methods on rice yield. Agron. J., 110(1): 104-114. https://doi.org/10.2134/agronj2017.06.0334

Hossen, M.T., M.L. Mia, U.K. Sarker, A.K. Hasan, M.A. Salam and M.S. Islam. 2025. Impacts of Dryopteris serrato-dentata (Beddome) Hayata (Dhekishak) and Blumealacera DC. (Shialmutra) on rice and weed as growth inhibitor: an evaluation through the use of manure and fertilizer. Discov. Agric., 3(1): 19. https://doi.org/10.1007/s44279-025-00169-9

Islam, M.S., M.L. Mia and M.S.U. Bhuiya. 2024. Allelopathic effect of Albizia lebbeck (Koroi) leaf residues on the weed growth performance of Boro rice. Int. J. Multidiscip. Res. Growth Eval., 5(03): 665-671. https://doi.org/10.54660/.IJMRGE.2024.5.3.665-671

Islam, M.S., M.L. Mia and M.S.U. Bhuiya. 2024. Field assessment of Echinochloa crusgalli (L.) residues for allelopathic effects on both crops and weeds. Int. J. Multidiscip. Res. Growth Eval., 5(03): 657-664. https://doi.org/10.54660/.IJMRGE.2024.5.3.657-664

Jamal, M.R., P. Kristiansen, M.J. Kabir and L. Lobry de Bruyn. 2023. Challenges and adaptations for resilient rice production under changing environments in Bangladesh. Land., 12(6): 1217. https://doi.org/10.3390/land12061217

Jana, K., S.K. Das, G. Moinuddin, G.K. Mallick and B. Biswas. 2016. Grain yield of aerobic rice as influenced by seed rate and row spacing in aerobic situation under changed climate. J. Appl. Nat. Sci., 8(4): 2164.

Kyalo, G., G. Chemining’wa, N. Nhamo and F. Mussgnug. 2020. Effects of improved nursery management, seedling transplanting age and split nitrogen fertilizer application on growth and yield of lowland rice in Eastern Uganda. J. Agric. Sci., 12: 144-151. https://doi.org/10.5539/jas.v12n8p144

Mahajan, G., V. Kumar and B.S. Chauhan. 2017. Rice production in India. Rice prod. Worldw., 53-91. https://doi.org/10.1007/978-3-319-47516-5_3

Manandhar, S., B. Chaudhary, A.K. Srivastava, S. Singh, U.S. Singh and S.M. Haefele. 2020. Nutrient Management for Higher Productivity of Swarna Sub1 under Flash Floods Areas. J. Nepal Agric. Res. Counc., 6: 101-114. https://doi.org/10.3126/jnarc.v6i0.28121

Mia, M.L., M. Begum, I.J. Riza, M.H. Kabir, F.A. Neshe, S. Monira, F. Zaman and M.S. Islam. 2023. Effect of integrated nutrient management on the yield performance of inbred and hybrid rice. Int. J. Sustain. Crop Prod., 18(1): 10-18. https://ggfjournals.com/content/papers/MIN-857

Mia, M.L., M.R. Hossain, S. Chandro, A.K. Sarker, M.S. Zahedi, N.H. Bappy M.S. and Islam. 2024. Allelopathic Effects of Residues of Fimbristylis dichotoma Along with Manures and Fertilizers on the Weed Growth in Boro Rice. Asian J. Res. Agricultur For., 10(4): 101–111. https://doi.org/10.9734/ajraf/2024/v10i4320

Mohapatra, P.K., R.K. Sarkar, D. Panda and E. Kariali. 2025. Origin and Evolution of Rice as Domesticated Staple Food Crop. In Tillering Behavior of Rice Plant (pp. 1-17). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-97-5235-5_1

Mondal, M., B. Biswas, S. Garai, S. Adhikary, P.K. Bandyopadhyay, S. Sarkar and A. Hossain. 2021. Raising climate-resilient embolden rice (Oryza sativa L.) seedlings during the cool season through various types of nursery bed management. Sustain., 13(22): 12910. https://doi.org/10.3390/su132212910

Mustari, S., M.S. Hossain, M.S. Islam, M.A. Salam and M.A. Kader. 2013. Performance of BRRI hybrid dhan4 as Influenced by Seed Rate in Nursery Bed and Age of Seedling During Transplanting. Eco-friendly Agric., 6(8): 160-163.

Niraula, N. and A. Timilsina. 2020. Effects of seeding density on growth attributes of broadleaf mustard in nursery bed. Int. J. Appl. Sci. Biotechnol., 8(1): 65-70. https://doi.org/10.3126/ijasbt.v8i1.28257

Pathania, R., J. Shekhar, S.S. Rana and S. Sharma. 2016. Evaluation of seeding rates of rice nursery on seedling vigour and its effect on crop productivity under system of rice intensification. J. Appl. Nat. Sci., 8(4): 2224-2230.

Patra, S.R., M. Ray, S. Das, P. Halder, S.K. Hembram and C. Majumder. 2014. Seeding density, seedling age and nutrient management for wet season rice production. ORYZA- Int. J. Rice., 51(3): 213-218.

Paul, S.K., B. Roy, A.K. Hasan and M.A.R. Sarkar. 2017. Yield and yield components of short duration transplant Aus rice (cv. Parija) as influenced by plant spacing and nitrogen level. Fundam. Appl. Agric., 2(2): 233-236. https://f2ffoundation.org/faa/index.php/home/article/view/148

Rahman, A., R. Ahammed, J. Roy, M.L. Mia, M.A. Kader, M.A. Khan and M.S. Islam. 2025. Investigating the impact of oligo-chitosan on the growth dynamics and yield traits of Oryza sativa L. ‘BRRI dhan29’under subtropical conditions. Heliyon., 11(1): e41552. https://doi.org/10.1016/j.heliyon.2024.e41552

Rani, A.S. 2012. Rice Yield as Affected by Seedling Vigour Through Nutrient Management Techniques in Nursery, MS Thesis, Department of Agronomy, Acharya NG Ranga Agricultural University, India. http://krishikosh.egranth.ac.in/handle/1/67686

Ray, S., M.A.R. Sarkar, S.K. Paul, A.K.M.M. Islam and S. Yeasmin. 2015. Variation of growth, yield and protein content of transplant Aman rice by three agronomic practices. Agril. Biol. Sci. J., 1(4): 167-176.

Roy, A., M.A.R. Sarkar and S.K. Paul. 2018. Effect of age of seedlings at staggered transplanting and nutrient management on yield performance of aromatic fine rice (cv. BRRI dhan38). SAARC J. Agric., 16(1): 49-59. https://doi.org/10.3329/sja.v16i1.37422

Salam, M.A., M.D. Hossain, M.L. Mia, K.A.M. Onna and M. Begum. 2022. Effect of crop establishment method and weed management practices on the performance of T. aman rice. J. Agric. Rural Dev., 14(1 &2): 1-11.

Sarangi, S.K., B. Maji, S. Singh, D. Burman, S. Mandal, D.K. Sharma and S.M. Haefele. 2015. Improved nursery management further enhances the productivity of stress-tolerant rice varieties in coastal rainfed lowlands. Field Crops Res., 174: 61-70. https://doi.org/10.1016/j.fcr.2015.01.011

Sarangi, S.K., B. Maji, S. Singh, D.K. Sharma, D. Burman, S. Mandal and S.M. Haefele. 2014. Crop establishment and nutrient management for dry season (boro) rice in coastal areas. Agron. J., 106(6): 2013-2023. https://doi.org/10.2134/agronj14.0182

Sarwar, N., H. Ali, M. Maqsood, A. Ahmad, E., Ullah, T. Khaliq and J.E. Hill. 2014. Influence of nursery management and seedling age on growth and economic performance of fine rice. J. Plant Nutr., 37(8): 1287-1303. https://doi.org/10.1080/01904167.2014.881490

Sasaki, R. 2004. Characteristics and seedlings establishment of rice nursling seedlings. Jpn. Agric. Res. Q., 38(1): 7–13. https://doi.org/10.6090/jarq.38.7

Shiferaw, B.A., J. Okello and R.V. Reddy. 2009. Adoption and adaptation of natural resource management innovations in smallholder agriculture: reflections on key lessons and best practices. Environ. Dev. Sustain., 11: 601-619. https://doi.org/10.1007/s10668-007-9132-1

Siddika, M.S., M.L. Mia, N. Salsabil, A. Alam, M.R. Hasan, M.H. Rashid, M.R. Rahman, M.S. Islam and F. Zaman. 2024. Allelopathic Potential of Amrul Shak (Oxalis europea) Residues on the Yield Performance of T. Aman Rice. Int. J. Adv. Multidiscip. Res. Stud., 4(5): 81-86. https://doi.org/10.62225/2583049X.2024.4.5.3194

Singh, K.N., B., Hasan, B.A. Kanday and A.K. Bhat. 2005. Effect of nursery fertilization on seedling growth and yield of rice (Oryza sativa). Indian J. Agron., 50(3): 187-189.

Soma, K., W. Hennen and S. van Berkum. 2023. Can Domestic Food Production Provide Future Urban Populations with Food and Nutrition Security? —Insights from Bangladesh, Kenya and Uganda. Sustain., 15(11): 9005. https://doi.org/10.3390/su15119005

Tan, Z.X., R. Lal and K.D. Wiebe. 2005. Global soil nutrient depletion and yield reduction. J. Sustain. Agric., 26(1): 123-146. https://doi.org/10.1300/J064v26n01_10

UNDP (United Nations Development Programme) and FAO (Food and Agriculture Organization of the United Nations). 1988. Land Resources Appraisal of Bangladesh for Agricultural Development. Report 2. Agroecological Regions of Bangladesh. Bangladesh Agricultural Research Council, Dhaka.

Imran-Hossain-Sohag, M., M.L. Mia, M.S. Islam and M. A. Salam. 2025. Yield of transplant aman rice cultivars as influenced by nitrogen management and seeding density in the nursery. Plant Physiol. Soil Chem., 5(2): 80-85. http://doi.org/10.26480/ppsc.02.2025.80.85

Jesia, N.S., M.L. Mia, F.A. Pinkya, F. Zaman, A.K. Hasan, M.R. Uddin and M.S. Islam. 2025. Effect of Age and Depth of Seedlings of Kasturi Aromatic Rice on Fodder and Yield Performance during Aman Season in Bangladesh. Asian Plant Res. J., 13(5): 36-50. https://doi.org/10.9734/aprj/2025/v13i5329

FRG. 2018. Fertilizer Recommendation Guide. Bangladesh Agricultural Research Council, Ministry of Agriculture, Government of the People’s Republic of Bangladesh, Farmgate, Dhaka-1215.

R Core Team. 2022. “R: a language and environment for statistical computing (Vienna, Austria: R Foundation for Statistical Computing).” Available at: https://www.r-project.org/.