Assessment of Nitrogen Sources and Management for Sustainable Nitrogen Use in Subtropical Conditions: A Varietal Performance Study on Rice and Weed Growth

Abstract

When growing rice, nitrogen (N) is the most vital component since it determines how much grain the crop will produce. Among the many causes of rice’s low productivity, improper nitrogen supply and inefficient nitrogen use are thought to be an important issue. In order to ensure sustainable N-management as well as to lower N-losses, it was decided to investigate how various rice cultivars react to both organic and inorganic nitrogen sources, as well as how weed infestation behavior changes with these sources in boro rice fields. Five distinct fertilizer combinations, including the control, were tested in an entirely block design that was randomized and had three replications. Each of the following sources of N: urea (prilled) at 100% of the RD (N_100PU), poultry manure at 100% of the RD (N_100PM), urea at 50% of the RD + poultry manure at 50% of the RD (N_50PU+50PM), and urea super granule at 100% of the RD (2.7 g per 4 hills) (N_USG) were administered on BRRI dhan29, BRRI dhan88, BRRI dhan92 and BRRI dhan96, respectively. It is observed that N_50PU+50PM was beneficial for weed among the nitrogen sources. But comparatively less weed invasion was noticed in the N_USG treated plots next to control specially in BRRI dhan29. With the use of N_USG, all of the varieties tested in this experimentation generated greater grain yield than they did with the use of the other nitrogen sources. When treated with N_USG, BRRI dhan29 showed a rise in grain yield around 64.34% and a greater nitrogen utilization efficiency compared to control. When benefit cost ratio (BCR) is considered, both the BCR and net income were the highest for the BRRI dhan29 variety while N_USG was employed. Finally, it can be inferred that, in comparison to other sources, the combination of BRRI dhan29 with application of N_USG appeared as the promising combination in order to increase grain production while improving nitrogen use efficiency, greatest BCR and to lessen the infestation of weeds throughout the boro season.

1. Introduction

Nitrogen (N) management continues to be a problem for the long-term viability of many farming systems across the world. Farmers who want to maximize output, frequently over-supply nitrogen to field crops. Although higher nitrogen rates frequently result in good yields, the efficacy of nitrogen utilization is still poor. N is the most important limiting element that has an impact on grain yield and is required by rice in greater amounts than any other nutrient [1]. Nitrogen absorption of rice crops differs depending on the kind of soil and its pH, irrigation water management, water temperature, sources, dosages, modalities of delivery, sources of nitrogen and varieties [2].

When it comes to fighting for resources, different crop species—and even subspecies—have distinct capabilities. Variety is often the most crucial element in achieving higher yields of rice due to their differences in genotypic features, the quantity of input required, and the climate for the entire harvesting period [3]. It is possible that nutrient efficient genotypes may produce greater harvests per unit of input nutrients when grown in comparable agro-ecologies as conventional genotypes [4]. Possible explanations for the disparity between nutrient absorption and utilization include altered root anatomy, enhanced nutrient solubilization in the rhizosphere, enhanced transport, dispersion, and usage, and a more balanced relationship between nutrients’ sources and sinks [5]. Recent years have seen the development of numerous new rice cultivars by the Bangladesh Rice Research Institute (BRRI). Some of these are also being grown by farmers. Therefore, it is crucial to examine how much more sensitive the rice types are to nitrogen management and sources.

Urea, poultry manure, and urea super granule (USG), among other types of nitrogen fertilizers, may all help rice grow more productively [6]. When using these N fertilizer sources with different rice cultivars, there are noticeable variations in the factors that contribute to rice yield [7]. Even so, weeds can outcompete rice plants in nitrogen absorption rates, especially in fields with an abundance of N [8]. One of the most significant pests is weed, which cut grain yield in aus rice by 70–80%, transplant aman rice by 30–40%, and boro rice by 22–36% [9]. It was discovered that when nitrogen rates increased, Echinochloa crus-galli’s growth and seed production also did so, which reduced the amount of nitrogen available to rice plants [10]. As a result, effective nitrogen management is crucial for determining how nitrogen affects the behavior of weed infestations as well as the development and productivity of rice.

Over the last half-century, nitrogenous fertilizer use has increased in cropping systems [11], yet crop yield response and N fertilizer use efficiency have been steadily declining [12]. The most important factor contributing to this poor productivity is thought to be inefficient nitrogen use. Low nitrogen usage efficiency, higher agricultural costs, and environmental deterioration are all connected to the improper utilization of nitrogen fertilizer [13]. Numerous experts have noted that nowadays, crops lose over 50% of the nitrogen fertilizer they apply [14,15]. For instance, research by Lassaletta et al. [12] demonstrated that, in contrast to 68% in the 1960s [16], only 47% of nitrogen applied on a global scale is transformed into harvest products.

To increase the effectiveness of crop plants’ usage of N, it’s crucial to utilize N fertilizer from the right sources. This approach not only enhances output but also lowers manufacturing costs and pollution levels. If it is feasible to boost nitrogen use efficiency to a level of more than 50% by using deep placement of nitrogen as modified urea materials, this would not only help rice production, but it would also lean toward boosting nitrogen use efficiency at the farm level. Spreading urea that has been pumped up is often performed by broadcasting in a spreading way. The nitrogen loss is decreased when split urea fertilizer is used, making it an effective method. Nitrogen loss can reach 50% when applied to the soil surface via broadcast urea, but it may be negligible when applied by point spread at a depth of 10 cm [17]. Chemical fertilizers can provide nutrients for plant development, but they cannot enhance the quality of the soil, as has been demonstrated. In this regard, the addition of organic materials is a tried-and-true tool for delivering nutrients and regaining the health of the soil. The addition of organic matter to soil can be achieved by applying poultry manure (PM). Grain yield and protein content in rice can both be enhanced by mixing fertilizers with PM [18]. Thus, it is clear that rice benefits in diverse ways from different sources of nitrogen and that nitrogenous fertilizers are necessary to satisfy the nutritional requirements of rice plants. The question of which source will help rice plants use nitrogen more efficiently and generate more of it still has to be resolved. It is essential that N-supply and N-demand correspond in time and space in order to maximize agronomic nitrogen use efficiency (NUE). So, to enable rice plants to utilize nitrogen more economically and effectively than weeds, a sufficient supply of nitrogen must be optimized.

With these considerations in mind, this study set out to employ a comprehensive plan to optimize suitable nitrogen source for boro rice varieties with minimum weed infestation. In this regard, we describe an experimental investigation to assess the rice varietal performance and weed growth under different nitrogen sources and optimization of suitable N sources for boro rice varieties.

2. Materials and Methods

2.1. Attributes of Plants and the Setting

We grew four different kinds of boro rice: BRRI dhan29, BRRI dhan88, BRRI dhan92, and BRRI dhan96 during the month of November 2020 to May 2021 at Bangladesh Agricultural University’s (BAU) Agronomy Field Laboratory (AFL) in Mymensingh. Experimental conditions were based on the subtropical humid monsoon environment of the Old Brahmaputra Floodplain (AEZ-9). Figure 1 shows the research period’s agro-climatic conditions. The soils at the experimental site are mostly silty loam, which is rather infertile, has a low organic matter content, and is mostly dark grey in color (

Table 1. Chemical characteristics of the experimental soil.

1. pH	6.8
2. Organic matter (OM) (%)	0.93
3. Total nitrogen (%)	0.13
4. Available sulphur (ppm)	13.90
5. Available phosphorus (ppm)	16.3
6. Exchangeable potassium (meq%)	0.28

Source: The findings were derived from the examination of the initial soil sample completed at the Dept. of Soil Science, BAU, Bangladesh.

).

2.2. Setup for the Experiment

The four different types of boro rice mentioned earlier were fertilized with five distinct combinations using RCBD. The fertilizer combinations are summarized in

Table 2. Nitrogenous fertilizer sources and combinations used in the experiment.

Nitrogen Treatment	Total Nutrient Supplied (kg ha−1)						Time of Application of Nitrogenous Fertilizer	Place of Collection
	N	P	K	Ca	Zn	S
Control (N0)	0	21.12	59.76	19.8	3.6	12.6	-	-
N100PU (300 kg ha−1)	138	21.12	59.76	19.8	3.6	12.6	Three equal splits at 15, 30, 45 DAT	Registered fertilizer dealer of Mymensingh District, Bangladesh
N100PM (14.5 t ha−1)	138	238.62	183	0	0	12.6	During final land preparation	Poultry Farm, Dept. of Poultry Science, Bangladesh Agricultural University (BAU)
N50PU+50PM (150 kg ha−1 + 7.25 t ha−1)	138	129.87	168.51	0	0	12.6	Final soil preparation included the application of poultry manure, and urea was spread in three equal portions at DATs of 15, 30, and 45.	Registered fertilizer dealer, Poultry Farm, BAU
NUSG (2.7 g per 4 hills)	138	21.12	59.76	19.8	3.6	12.6	Eight centimeters below the surface in the center of four hills separating two nearby rows, seven days after transplanting.	Local registered fertilizer dealer of Khulna District, Bangladesh

All plots were treated with recommended dose of TSP, MoP, Gypsum and ZnSO₄ during final land preparation. The recommended doses are urea (300 kg ha⁻¹), triple super phosphate (100 kg ha⁻¹), muriate of potash (120 kg ha⁻¹), gypsum (60 kg ha⁻¹⁾ and zinc sulphate (10 kg ha⁻¹) [19].

. Three independent times of the experiment were carried out. There were 60 plots in all. Each plot measured 5 m² (2.5 m × 2.0 m). In every plots, treatments were assigned randomly.

2.3. Crop Husbandry

The land was first given two ploughs utilizing a power tiller, followed by more ploughing, cross-ploughing, and leveling with a ladder. Following leveling, the experimental plots were set up in accordance with the chosen treatments and designs. With three seedlings per hill, maintaining line-to-line distance of 25 cm and plant–plant distance of 15 cm, the uprooted seedlings were planted in the main field on 29 December 2020. The age of the seedlings was 45 days. Nitrogen and all the fertilizer and manure were applied as per treatment specification and. For ensuring vigorous growth and development of crop intensive care like gap filling, weeding and irrigation was given during growth period of the crop. At 20 and 40 days following transplanting, hand-weeding was performed twice. The harvesting times varied throughout the cultivars since they reached maturity at various times. On 19 April 2021, BRRI dhan96 was harvested. On 5 May 2021, BRRI dhan29, BRRI dhan88, and BRRI dhan92 were harvested. The crop from each plot was meticulously bagged and tagged before being moved to the concrete threshing floor. Grain cleaning and drying yielded 14% moisture.

2.4. Sampling and Measurement

2.4.1. Collection of Weed Data

Using weed parameters (density and dry weight (DW) as metrics, we determined the rate of weed infestation. Two separate sampling dates were used to harvest weed samples using a 25 cm² quadrate: 20 DAT and 40 DAT. The weeds within each quadrant were pulled out and cleaned after the density of weeds had been assessed. Prior to processing, the weeds were allowed to air dry for a certain times (6–8 h). After that in the next step, the specimens received labels and put inside a bag (brown paper). They were then dried in an electric oven that was heated at 80 degrees Celsius for seventy-two hours, or until a steady weight had been achieved. The dry weight of the weeds was measured after they had dried using an electric balance.

2.4.2. Collection of Data after Harvesting

From three randomly chosen hills in each unit plot, data on specific plant attributes were obtained. All of the necessary conversions were made from the yield (grain straw yield, and biological) and harvest index (HI), which were recorded from one square meter of a plot.

2.4.3. Nitrogen Use Efficiency (NUE)

Grain yield in nitrogen-treated plots and control (N0) plots were used to calculate the NUE. PFP (partial factor productivity) and AE_N (agronomic efficiency) showed the NUE.

(A)

PFP

PFP measured crop yield per nutrient unit [20].

$$\mathrm{PFP}= \frac{\mathrm{Yield} \mathrm{of} \mathrm{harvested} \mathrm{portion} \mathrm{of} \mathrm{crop} \mathrm{with} \mathrm{N} \mathrm{applied} \left(\mathrm{kg} \mathrm{grain}\right)}{\mathrm{Amount} \mathrm{of} \mathrm{applied} \mathrm{N} \left(\mathrm{kg}\right)}$$

(B)

AE_N

It is measured in terms of the amount of yield gain that occurs for each unit of nutrient that is administered. It shows more accurately how a fertilizer directly affects production and has a direct link to economic gain [20,21].

$${\mathrm{AE}}_{\mathrm{N}}= \frac{\mathrm{Grain} \mathrm{yield} \mathrm{at} \mathrm{applied} \mathrm{N} \mathrm{dose} -\mathrm{grain} \mathrm{yield} \mathrm{at} \mathrm{N}0}{\mathrm{Amount} \mathrm{of} \mathrm{applied} \mathrm{N}} \mathrm{kg} {\mathrm{kg}}^{-1}$$

2.5. Economic Analysis

Individual spending categories were costed, and a partial budget analysis was carried out. All costs, both material and non-material, made up the variable cost. To find the gross return, the market prices of the grain and straw yields were added together. The following methodologies calculated net income and benefit cost ratio (BCR):

Net income = gross income − variable cost

$$\mathrm{BCR}= \frac{Gross income}{Total cost of production}$$

2.6. Analytical Statistics

For statistical analysis, weed and all parameters that effects grain yield were gathered and tabulated correctly. The computer application MSTAT C version 2.10 (MSU, East Lansing, MI, USA) was used to statistically evaluate the acquired data using the “Analysis of Variance” method. The mean difference between the treatments was checked with DMRT [22]. The correlation matrix and principal component analysis were done by using R version 4.3.1 (RStudio, Boston, MA, USA).

3. Results

3.1. Weed Dynamics

We detected seven different kinds of weeds in the field, representing four different families viz. Shama (Echinochloa crusgalli; Poaceae family), Anguli (Digitaria sanguinalis; Poaceae), Pani chaise (Eleocharis atropurpurea; Cyperaceae family), Amrul shak (Oxalis corniculate; Oxalidaceae family), Arail (Leersia hexandra; Poaceae), Topapana (Pistia stratiotes; Araceae family), and Angta (Paspalum scrobiculatum; Poaceae family). Both the quantity of weeds and their dry weight varied greatly among rice varieties when different nitrogen management techniques were used. All the varieties responded differently to different nitrogen sources. It was noticed that among the nitrogen sources, N_50PU+50PM treatment was more favorable for weed in case of all varieties. But comparatively minimum weed infestation was noticed in the USG treated plots next to control. At 20 DAT, weed density fluctuated from 16.33 to 32.33 plant m², where BRRI dhan92 with control treatment had minimum weed infestation and BRRI dhan88 with N_50PU+50PM had the highest. At 40 DAT, BRRI dhan88 when treated with N_50PU+50PM permitted maximum weed growth (30.66 plant m²) and the minimum weed population (19.00 plant m²) was found in BRRI dhan29 with N_USG. Weed biomass, a way to see how plants and weeds compare, was between 3.06 g m² (BRRI dhan29 with N₀) to 6.16 g m² (BRRI dhan88 with N_50PU+50PM) at 20 DAT. At 40 DAT, the lowest and highest weed biomass was documented in BRRI dhan29 with N_USG (5.96 g m²) and BRRI dhan96 withN_50PU+50PM (40 g m²), respectively (

Table 3. Weed parameters found in boro rice as affected by interactions between variety and nitrogen management.

Interaction		Density (no. m−2)		DW (g m−2)
		20 DAT	40 DAT	20 DAT	40 DAT
BRRI dhan29	N0	18.66 fgh *	21.00 g–j	3.06 i	6.03 h
	N100PU	22.33 def	24.00 d–h	4.10 d–g	7.76 efg
	N100PM	29.33 ab	25.66 b–f	4.86 cde	8.93 cde
	N50PU+50PM	31.33 a	28.66 abc	5.33 abc	8.50 c–g
	NUSG	22.66 def	19.00 j	3.96 fgh	5.96 h
BRRI dhan88	N0	20.66 efg	22.66 e–j	4.06 d–g	7.93 d–g
	N100PU	26.00 bcd	25.00 c–g	4.76 c–f	8.70 c–f
	N100PM	29.00 ab	28.00 a–d	5.76 ab	9.80 bc
	N50PU+50PM	32.33 a	30.66 a	6.16 a	10.66 ab
	NUSG	22.00 def	23.33 e–i	4.73 c–f	9.00 cde
BRRI dhan92	N0	16.33 h	19.33 ij	3.20 hi	5.76 h
	N100PU	22.00 def	20.00 hij	4.03 e–h	6.96 gh
	N100PM	22.66 def	23.66 e–h	4.90 cd	7.76 efg
	N50PU+50PM	27.00 bc	26.66 a–e	5.43 abc	8.70 c–f
	NUSG	20.33 e–h	24.66 c–g	3.63 ghi	7.10 fgh
BRRI dhan96	N0	17.33 gh	21.66 f–j	4.40 d–g	8.63 c–f
	N100PU	23.66 cde	23.66 e–h	4.83 cde	9.40 bcd
	N100PM	27.00 bc	26.33 b–e	5.46 abc	10.63 ab
	N50PU+50PM	29.33 ab	29.66 ab	6.10 ab	11.53 a
	NUSG	20.66 efg	23.66 e–h	5.30 bc	9.33 b–e
Level of significance		**	**	**	**
CV%		10.79	10.55	10.77	11.55

* Note that DMRT claims column numbers with the same letter or no letter cannot vary greatly, but those with different letters do. ** = Significant at 1% level of probability. N₀ = No N (nitrogen); N_100PU = 100% of the RD (recommended dose) of N from urea (300 kg per ha); N_100PM = 100% of the RD (recommended dose) of N (nitrogen) from poultry manure (14.5 t ha⁻¹); N_50PU+50PM = 50% of the RD (recommended dose) of N (nitrogen) from urea (150 kg per ha) + 50% of the RD (recommended dose) of N (nitrogen) from poultry manure (7.25 t ha⁻¹); N_USG = 100% of RD (recommended dose) of the N from USG (2.7 g).

).

3.2. Yield and Yield Components

Rice output and characteristics varied by nitrogenous fertilizer source. However, the variable TGW (1000-grain weight) did not differ substantially. PH (plant height) of different rice varieties ranges from 74.33 cm to 100.66 cm where the longest plant was found in BRRI dhan92 variety with N_50PU+50PM and the shortest one was documented in BRRI dhan96 with N₀ treatment. Comparatively, all the rice varieties produced more TT and ET when treated with USG but BRRI dhan88 had the highest TT (15.11) and ET (14.00) which was identical with the TT and ET of BRRI dhan29 with N_USG and BRRI dhan96 had the lowest TT (6.57) and ET (5.82) with N₀ treatment. Panicle length varied from 19.87 cm (BRRI dhan92 with N_100PM) to 25.02 cm (BRRI dhan96 with N₀). Compared to the control, about 30.02%, 25.18%, 24.57% and 17.43% increase in GP was noticed in BRRI dhan29, BRRI dhan88, BRRI dhan92 and BRRI dhan96 when this varieties were cultivated under N_USG, respectively. Between 2.00 t ha⁻¹ and 6.03 t ha⁻¹, the GY significantly varied, and in that case also N_USG performed well irrespective to all varieties. GY increased due to the application of USG and BRRI dhan29 yielded more in this treatment compared to others and as usual BRRI dhan96 with N₀ treatment yielded less. However, highest SY (6.76 t ha⁻¹) was documented in BRRI dhan29 with N_100PU, whereas BRRI dhan96 with N₀ produced the lowest straw yield (4.43 t ha⁻¹). The highest HI (48.51%) was noticed in BRRI dhan88 with N_100PU, whereas the lowest one (27.20%) was obtained from BRRI dhan88 when no N was applied (

Table 4. Yield components of boro rice as affected by interaction between variety and nitrogen management.

Interaction		PH (cm)	TT (no.)	ET (no.)	PL (cm)	GPP (no.)	TGW (g)	GY (t ha−1)	SY (t ha−1)	HI (%)
BRRI dhan29	N0	83.66 e–h *	7.12 i	6.12 g	22.36 bcd	82.90 fg	19.42	2.15 h	5.05 d–g	30.06 c
	N100PU	87.78 de	14.99 a	12.77 abc	19.87 ef	115.84 ab	19.06	5.40 abc	6.76 a	44.35 b
	N100PM	85.44 efg	10.55 h	9.44 f	23.10 abc	109.50 abc	19.18	4.38 ef	5.47 c–f	44.48 b
	N50PU+50PM	86.55 ef	12.44 d–h	11.43 cde	23.14 abc	114.05 ab	19.09	4.66 c–f	5.54 c–f	45.65 ab
	NUSG	93.22 c	14.66 ab	13.48 ab	23.93 ab	118.47 a	19.26	6.03 a	6.53 ab	48.03 ab
BRRI dhan88	N0	87.00 de	6.62 i	5.88 g	23.16 abc	84.74 d–g	21.26	2.03 h	5.44 c–f	27.20 c
	N100PU	91.89 cd	14.33 a–d	13.33 ab	23.16 abc	105.66 abc	21.1	5.33 abc	5.66 b–e	48.51 a
	N100PM	85.22 e–h	11.21 gh	10.29 def	22.42 bcd	105.45 abc	21.05	4.74 b–f	5.30 c–g	47.13 ab
	N50PU+50PM	86.55 ef	12.22 e–h	11.22 c–f	22.31 bcd	102.46 a–e	21.32	4.45 def	5.12 d–g	46.52 ab
	NUSG	87.00 de	15.11 a	14.00 a	22.48 bcd	113.26 ab	21.21	5.52 ab	6.58 ab	45.56 ab
BRRI dhan92	N0	94.00 bc	7.12 i	6.14 g	23.72 ab	70.32 g	23.03	2.19 h	5.25 c–g	29.44 c
	N100PU	101.44 a	13.55 a–f	12.00 bcd	24.36 ab	93.21 c–f	23.18	5.38 abc	5.98 a–d	47.48 ab
	N100PM	99.00 ab	12.88 b–g	11.88 b–e	25.02 a	92.50 c–f	23.19	5.00 b–e	5.88 a–d	45.98 ab
	N50PU+50PM	100.66 a	13.99 a–e	13.06 abc	24.66 a	84.05 efg	23.23	5.20 bcd	6.09 abc	46.07 ab
	NUSG	100.44 a	14.52 abc	13.45 ab	24.73 a	93.22 c–f	23.18	4.94 b–e	6.09 abc	44.55 b
BRRI dhan96	N0	74.33 j	6.57 i	5.82 g	18.13 f	84.85 d–g	19.26	2.00 h	4.43 g	30.18 c
	N100PU	81.66 f–i	11.99 fgh	10.88 def	21.49 cde	100.97 a–f	19.21	4.86 b–e	5.50 c–f	46.93 ab
	N100PM	77.33 ij	11.22 gh	10.10 ef	20.02 ef	97.52 b–f	19.25	3.55 g	4.66 fg	44.61 b
	N50PU+50PM	81.11 ghi	11.33 gh	10.41 def	20.07 ef	100.93 a–f	19.16	3.97 fg	4.89 efg	44.74 ab
	NUSG	80.11 hi	12.55 c–g	11.33 cde	20.59 de	102.76 a–d	19.04	4.91 b–e	5.69 b–e	46.25 ab
Level of significance		**	**	**	**	**	ns	**	**	**
CV%		3.55	10.19	10.50	5.77	11.45	1.16	10.94	10.38	5.38

* Note that DMRT claims column numbers with the same letter or no letter cannot vary greatly, but those with different letters do. ** = Significant at 1% level of probability, ns = Non-significant Here, N₀ = No N (nitrogen); N_100PU = 100% of the RD (recommended dose) of N from urea (300 kg per ha); N_100PM = 100% of the RD (recommended dose) of N (nitrogen) from poultry manure (14.5 t ha⁻¹); N_50PU+50PM = 50% of the RD (recommended dose) of N (nitrogen) from urea (150 kg per ha) + 50% of the RD (recommended dose) of N (nitrogen) from poultry manure (7.25 t ha⁻¹); N_USG = 100% of RD (recommended dose) of the N from USG (2.7 g), HI = Harvest index, SY = Straw yield, GY = Grain yield, TGW = 1000-grain weight, GP = grains panicle⁻¹, PL = Panicle length, ET = Effective tillers hill⁻¹, TT = Total tillers hill⁻¹, PH = Plant height.

).

3.3. Nitrogen Use Efficiency

Figure 2 and Figure 3 indicates that the application of USG significantly increased nitrogen use efficiency in case of all selected varieties. About 75.37 kg, 68.96 kg, 61.76 kg and 61.34 kg grain produced in BRRI dhan28, BRRI dhan88, BRRI dhan92 and BRRI dhan96, respectively, against one kg of applied nitrogen. The PFP was higher in N_USG treatment followed by N_100PU, N_50PU+50PM, and N_100PM in almost all varieties. Conversely, the lowest PFP observed in BRRI dhan96 with N_100PM. From these results, it could be said that PFP was always higher in case of USG as compared to prilled urea and poultry manure. Similarly, about 48.55 kg, 43.56 kg, 34.39 kg, and 36.35 kg increase in grain production against 1 kg of applied nitrogen was noticed in BRRI dhan28, BRRI dhan88, BRRI dhan92, and BRRI dhan96, respectively, due to the application of USG. But the lowest AE (11.27) observed in BRRI dhan96 with N_100PM. So PFP and AE were always higher in case of USG as compared to prilled urea and poultry manure in this study.

3.4. Economics of Different Nitrogen Management and Variety

Partial budget analysis was carried out to assess the financial performance of four boro rice varieties. The variation BRRI dhan29 yielded the highest net income when USG was applied (Tk. 66,001 ha⁻¹); BRRI dhan88 yielded the second-highest net income when NUSG was applied (56,126 Tk ha⁻¹); and BRRI dhan96 yielded the lowest net income (Tk. −23,970 ha⁻¹). According to

Table 5. Various nitrogen management strategies and their impact on the cost-effectiveness of boro rice varieties.

Variety	N0		N100PU		N100PM		N50PU+50PM		NUSG
	Net Income (Taka)	BCR	Net Income (Taka)	BCR	Net Income (Taka)	BCR	Net Income (Taka)	BCR	Net Income (Taka)	BCR
BRRI dhan29	20,295	0.80	52,880	1.53	14,395	1.13	25,300	1.24	66,001	1.68
BRRI dhan88	−18,300	0.81	44,330	1.45	20,490	1.19	18,370	1.18	56,126	1.58
BRRI dhan92	−16,335	0.83	47,410	1.48	29,460	1.27	39,675	1.38	41,341	1.43
BRRI dhan96	−23,970	0.75	33,890	1.34	−8965	0.92	7275	1.07	38,141	1.39

N₀ = No N (nitrogen); N_100PU = 100% of the RD (recommended dose) of N from urea (300 kg per ha); N_100PM = 100% of the RD (recommended dose) of N (nitrogen) from poultry manure (14.5 t ha⁻¹); N_50PU+50PM = 50% of the RD (recommended dose) of N (nitrogen) from urea (150 kg per ha) + 50% of the RD (recommended dose) of N (nitrogen) from poultry manure (7.25 t ha⁻¹); N_USG = 100% of RD (recommended dose) of the N from USG (2.7 g).

, the variety BRRI dhan29 with the application of USG had the highest benefit–cost ratio (1.68), whereas the variety BRRI dhan96 with N₀ treatment had the lowest BCR (0.75).

3.5. Relationship among Studied Yield and Yield Contributing Components

In Figure 4, we can see the correlation matrix of the measured traits, which lets us see how they are linked. PH showed a significant and positive correlation with TT, ET, PL, TGW, and SY, while it showed non-significant positive correlation with GY and HI and non-significant negative correlation with GPP. The relationship among TT, ET, GPP, GY, SY, and HI was also significant and positive but also showed non-significant positive correlation with PL and TGW. At the same time, ET had very strong correlation with GY and HI and moderate correlation with GPP and SY but non-significant correlation with PL and TGW. GPP had positive correlation with GY and HI but non-significant negative correlation with PH, PL and TGW. TGW had only significant positive correlation with PH and PL. GY is strongly correlated with TT, ET, GPP, SY, and SY but non-significant with PH, PL and TGW. SY showed significant positive correlation with PH, PL, TT, ET and GY. Finally, HI had significant correlation with TT, ET, GPP and GY.

The range between the highest and lowest values is 1 to −1. Blue and red ellipses represent positive and negative associations, correspondingly. The greater color intensity reflects stronger co-efficient, whilst lower coefficient is reflected by lower color intensity. Here, PH = Plant height, TT = Total tillers hill⁻¹, ET = Effective tillers hill⁻¹, PL = Panicle length, GP = grains panicle⁻¹, TGW = 1000-grain weight, GY = Grain yield, SY = Straw yield, HI = Harvest index.

3.6. Principal Component Analysis

PCA was performed to decrease data heterogeneity and discover possible correlations between varieties, nitrogen management, and measured parameters with the trial dataset which contained four boro rice varieties, five nitrogen management, and nine distinct parameters (Figure 5). According to principal component analysis (PCA), 87.8 percent of the total heterogeneity was explained by the first two PCs. Because the first and second PCs generated 58% and 29.8% of the entire divergence, correspondingly, a PCA biplot was constructed with only the first two components. From the figure it was clear that TT, ET, GPP and HI was closely related with GY and these parameters were found highest in V₁N₁, V₁N₃, V₁N₄, V₂N₁, V₂N₄, and V₄N₁. On the other hand, PH, PL, SY, TGW, and SY were not closely related with grain yield and was found in V₃N₁, V₃N₂, V₃N₃, and V₃N₄.

4. Discussion

To satisfy the crop’s needs, nitrogen fertilizer application is crucial. However, the effectiveness of additional fertilizer relies on the sources of nitrogen, the method of administration, the amount of nitrogen used and management [23]. For boro rice fields, to enhance production with the least amount of weed infestation, appropriate nitrogen sources must be optimized. According to the experimental results, there was less weed infestation in the USG-treated plot than the plots with other nitrogen sources for all varieties, especially for BRRI dhan29. Different crop species have different capacities to compete for resource. Of the weed species, five (shama, anguli, amrul shak, arail and angta) exhibited grass-like morphology and one (pani chaise) was sedge- and one (topapana) was broadleaf-type morphology. All of the weed species included in the experiment were annuals. The four most significant weeds in fields of rice comprised Shama (Echinochola crusgali), Anguli (Digitaria sanguinalis), Topapana (Pistia stratiotes), and Angta (Paspalum scrobiculatum), as determined by investigations carried at AFL by Rahman et al. [24] and Khatun et al. [25].

The varieties with lower weed densities were able to limit weeds because they were better capable of competing with weeds for resources. Many weeds are heavy nitrogen consumers, which means they can reduce the amount of nitrogen available for crop development. In addition to reducing the quantity of N accessible to crops, weeds can also develop more quickly in areas with greater soil N concentrations [26]. Worst-case scenarios might result from this, when N-fertilizer boosts weeds’ capacity for competition more than it does for crops [27]. The fact that rice plants develop quickly when USG is applied and have a higher nitrogen availability than weeds may be the reason why there is a decrease in weed biomass. In contrast to poultry manure and prilled urea, USG released nitrogen slowly, ensuring that the weed had less access to it throughout its development phase and favoring the growth of the rice plant. Due to variations in nitrogen sources, Nandan et al. [28] noted fluctuation in weed density and dry weight.

Grain yields were higher after N-fertilizer treatments compared to zero-N. Grain output rose mostly as an outcome of rising ET and GP. The correlation matrix also indicates that GY is strongly correlated with ET and GP. So, it seems that boro rice had an increase in the yield when the ET and GP were raised. According to Paudel et al. [29], grain yield and ET are positively correlated. A substantial positive correlation between grain yield and GP was observed by Sarker et al. [30]. Fertilizers rich in nitrogen (N) boost cytokinin levels inside tiller nodes, which in turn speeds up primal emergence., it is the most popular and efficient strategy to expand the tiller population under field settings [31]. Therefore, it makes sense to hypothesize that N was engaged either directly or indirectly in the growth, division and synthesis of new cells and tissues, which in turn led the rice cultivars to have more tillers. The highest GP, however, is generated with the optimal amount of nitrogen fertilizer. A sufficient nitrogen supply is necessary for rice grain growth and to enhance the GP. One possible explanation for the rise in GP could be a more efficient nitrogen uptake by the plant. Increased availability of nitrogen during the plant’s growth season from the nitrogen sources is crucial for increased nitrogen uptake. With the use of USG, all of the types in this trial generated greater grain yield than they did with the use of the other nitrogen sources. USG, which is more effective than PU and PM, caused the grain yield of BRRI dhan29 to rise by around 64.34%. Generally, increased NH₄⁺-N concentration in soil was facilitated by frequent application of PU. However, rice grown with PU fertilizer did not have similar N dynamics over the whole growth period. Due to deep installation of USG, it produced NH₄⁺-N gradually and steadily by maintaining the majority of its urea nitrogen close to plant roots in the soil. As a consequence, the rice plant had a constant supply of nitrogen during its growth cycle, which eventually led to a more grain yield. The aforementioned findings are in line with those of presented in [32], who found that deep insertion of USG was superior than PU and N loss was likewise negligible.

By retaining most of the nitrogen in the soil, near plant roots, and away from irrigation water, the use of USG also improved the efficiency of nitrogen usage [33]. To evaluate the efficiency of applied nitrogen and its contribution to increase maximum economic production through effective absorption or usage by the plant, the assessment of nitrogen use efficiency (NUE) in crop plants is absolutely necessary. Rice output and nitrogen absorption both rise when there is less nitrogen loss from the soil. Using USG in the root zone is the most efficient way to raise rice output and nitrogen usage efficiency. The denitrification process is stopped by deep installation of USG. As a consequence, nitrogen loss is decreased and nitrogen utilization efficiency is increased by 20–25% for greater grain production [17]. Point of placement of USG to 10 cm depth can save 30% nitrogen as PU urea, increase absorption rate, improve soil health, reduce leaching loss, surface run-off, de-nitrification and volatilization [34]. As compared to broadcast application of prilled urea, deep placement of USG greatly increased the efficiency of N-usage, according to Kapoor et al. [35].

In comparison to USG application, which was applied once at seven days after rice seedling transplantation, the broadcast application of PU fertilizer at 15, 30, and 45 days included additional labor costs. Even though PM was also treated once, it could not have been able to provide the plant with the necessary nitrogen both singularly and in combination, which prevented it from producing a suitable net income. Due to the three times more applications, PU had a higher labor expense but a reasonable net income. The minimum labor cost and maximum net income with BCR was observed in BRRI dhan29 with USG treated plots due to single application and more efficient utilization of nitrogen. Consequently, USG could be an economical substitute to PU fertilizer and could result in saving cash over the long run, particularly for the farming of rice.

5. Conclusions

Without a doubt, nitrogenous fertilizers are crucial to supply the nitrogen that rice plants need, but their inadequate availability to the crop owing to leaching and other losses results in a low yield. This experimental investigation discloses that the tested boro rice varieties specially BRRI dhan29 received a maximum benefit of nitrogen from USG than other nitrogen sources, which was reflected in its yield performance and efficiency of nitrogen use and also in lower weed infestation. Using USG in lieu of PU and PM could be economically viable as well as advantageous for rice production, owing to economic analysis. Therefore, it may be advisable to optimize the use of USG fertilizer for getting higher yield with minimum weed infestation and keeping the cost of production at sustainable level. Investigation into the impact of various nitrogenous fertilizers and their application strategies across different rice-growing regions is also strongly suggested. If the current study’s results are to be verified, additional investigations in multiple agro-ecological zones might turn out to be indispensable.