The Impact of Split Nitrogen Fertilizer Applications on the Productivity and Nitrogen Use Efficiency of Rice

Abstract

The application of nitrogenous fertilizer in reduced (“split”) doses of its total is suggested as a means to increase nitrogen use efficiency and rice productivity whilst reducing its environmental impact. Field trials conducted in 2022 and 2023 aimed to assess the impact of split nitrogen fertilizer applications on the productivity and nitrogen use efficiency of rice. This experiment included three nitrogen treatments (N1: control (no nitrogen); N2: 50% basal + 25% at tillering stage + 25% at panicle initiation stage (conventional method); N3: 33.33% basal + 33.33% at tillering stage + 33.33% at panicle initiation stage (equal split of nitrogen)) and four high-yielding rice varieties (V1: Super Gold 2019; V2: Super Basmati 2019; V3: Noor Basmati 2017; V4: Kissan Basmati 2016). The results indicated that the N3 treatment, with an equal split of nitrogen, combined with the V4 variety (Kissan Basmati 2016) produced the most favorable outcomes. The results indicated that the N3 treatment, particularly when applied to Kissan Basmati (V4), produced, statistically, the highest leaf area index (32.98%, 29.59%), 1000-grain weight (32.84%, 46.97%), grain yield (30.02%, 38.09%), agronomic nitrogen use efficiency (9.21%, 11.63%), and partial factor productivity (29.98%, 38.11%) compared to the control for the study periods of 2022 and 2023, respectively. Moreover, the grain yield demonstrated a strong positive correlation with growth traits and other yield components, except for plant height. The results showed that the application of three equal nitrogen doses significantly increases rice production, and therefore, in this yield context, improves nitrogen use efficiency.

1. Introduction

Rice, as one of the key cereal crops worldwide, plays a crucial role in meeting the nutritional needs of billions of people. However, achieving optimal rice yields in a sustainable manner remains a challenge due to the complex interactions between its agronomic practices and environmental factors. Among these practices, nitrogen fertilization is particularly critical, as it directly influences the growth, development, and productivity of rice plants. Rice cultivation presents significant challenges in terms of environmental impact. Nitrogen management not only focuses on rice productivity as a pivotal determinant but also involves in nitrogen use efficiency (NUE) and greenhouse gas (GHG) emissions. To improve rice production and feed over a billion people globally over the next 30 years, nitrogen fertilizer management is a practical solution [1]. Fertilizer application, especially nitrogenous fertilizers, will rise in response to increases in rice demand, and this will result in higher emissions of greenhouse gases [2].

Nitrogen is an essential plant nutrient that is often in deficit and serves as the principal nutrient limiting rice crop yield. One way to boost rice grain yield is through the application of nitrogen fertilizer [3]. The total nitrogen in a soil is a significant factor influencing crop yield; its effectiveness depends on its availability in forms that plants can readily absorb and utilize. The proper management of nitrogen levels is essential for the optimization of crop productivity and ensuring sustainable agricultural practices [4]. Achieving a balanced application of nutrients is of great importance for plant growth and development [5]. Greenhouse gas (GHG) emissions significantly increase upon the application of more N fertilizer. A limited amount of the applied reactive nitrogen is utilized for food production, and the leftover part is lost through a variety of processes, including ammonia volatilization, nitrate leaching, and denitrification [1]. The inadequate recovery of nitrogen is caused by applying fertilizers indiscriminately without accounting for agro-climatic factors [6]. Nitrogen recovery may be affected by differences in soil nitrogen supply capacity, crop nitrogen uptake efficiency, and soil moisture conditions [7]. Conventional techniques require flooding the soil continuously during the growing season of rice. This contributes to nitrogen losses through various mechanisms [8]. Kim et al. [9] found that when soils have a large percentage of water-filled pore space (80%), denitrification is the predominant pathway for N₂O emission.

The concept of nitrogen use efficiency (NUE) illustrates how efficiently nitrogen is absorbed and used by the rice plant [10]. Nitrogen use efficiency serves as a standard metric for assessing nitrogen management. Nitrogen use efficiency helps in meeting both environmental and economic targets by decreasing nutrient losses, minimizing the impact on greenhouse gases, and lowering the costs associated with excessive fertilizer applications [11]. The definition of NUE is the share of applied nitrogen that is absorbed by the plants, generally not exceeding 30% in lowland rice [7].

Although nitrogen is the major input for rice production, unequal fertilization at inappropriate stages does not result in a higher yield; rather, it decreases nitrogen use efficiency [12]. Thus, the timely and split application (divided application) of nitrogen allows for the more efficient use of nitrogen by rice throughout the growing season as this practice provides specific amounts of nutrients to the crop during peak periods of growth and reduces N losses. Unequally splitting nitrogen applications is commonly practiced at the farmer’s level for transplanted rice. However, the application of N into three split(s) at the sowing, tillering, and panicle initiation stages is most beneficial for achieving a higher grain yield from rice varieties [12,13]. The inappropriate split of N can cause vigorous vegetative growth, resulting in the lodging of plants, and can also increase susceptibility to insects, pests, and diseases, which ultimately reduces yield [14].

The determination of the crop maturation duration, yield, and nitrogen use efficiency of different basmati rice varieties is imperative to enhance the output [15]. There is considerable variation among rice varieties in terms of their nitrogen use efficiency and their physiological responses to nitrogen availability. This variation impacts how effectively each variety utilizes nitrogen for growth and yield formation, making the choice of variety crucial for the optimization of nitrogen management and the achievement of the most effective possible yield [16]. Rice productivity enhancement and greenhouse gas emission reduction is possible through high-yielding cultivar selection and the application of N in proper split(s) [17]. Increases in plant biomass and tiller number increase the amount of aerenchyma and improves O₂ transfer from the atmosphere to the rhizosphere, which in turn increases CH₄ oxidization activity [18]. Methane fluxes and rice biomass have been discovered to have a substantial positive association [19], and various outcomes have been obtained while comparing different rice varieties. Among the various agronomic practices, nitrogen management plays a pivotal role in influencing rice productivity and environmental sustainability. However, the inefficiencies associated with conventional nitrogen application methods often lead to nutrient losses through leaching and volatilization, contributing to environmental degradation and a reduction in nitrogen use efficiency. Split nitrogen applications will improve nitrogen use efficiency and productivity in rice by reducing nitrogen losses to the environment and enhancing the uptake of nitrogen by the plants compared to unequal split(s). In this context, the strategic application of nitrogen in split doses has emerged as a promising approach to enhance nitrogen uptake, improve yield, and minimize environmental impacts. The present study aimed to evaluate the effects of different split nitrogen application strategies on the productivity and nitrogen use efficiency of high-yielding rice varieties.

2. Materials and Methods

2.1. Experimental Site and Design

A two-year field trial was carried out in the summer season of 2022 and 2023 at the Agronomy Research Farm, University of Agriculture, Faisalabad, Punjab, Pakistan (184.4 m altitude from sea level, 31° N, 73° E), to evaluate the impact of split nitrogen fertilizer applications on the productivity and nitrogen use efficiency of rice. The net plot size was 5 m × 1.8 m and the transfer of nitrogen fertilizer between the experimental plots was avoided by the establishment of bunds between the plots.

Table 1. Physico-chemical attributes of experimental soil over two years (2022 and 2023).

Characteristics	2022	2023	Unit	Status
	(0–15 cm)	(15–30 cm)
Chemical analysis
pH	7.75	7.65	–	Medium alkaline
EC	1.30	1.68	dS m−1	Non-saline
Exchangeable sodium (Na)	0.41	0.39	mmol 100 g−1	Normal
Total nitrogen (N)	0.05	0.04	%	Low
Available phosphorus (P)	15.40	11.55	mg kg−1	Low
Exchangeable potassium (K)	187.50	149.00	mg kg−1	Medium
Organic matter	0.92	0.68	%	Low
Boron	0.83	0.66	mg kg–1	Deficient
Zinc	2.16	1.38	mg kg–1	Deficient
Ferrous	8.41	5.03	mg kg–1	Adequate
Bulk density	1.48	1.53	g m–3	Low
Texture	Sandy Clay Loam

illustrates the findings of the physico-chemical examination of the experimental soil. Furthermore, weather information for this study was obtained from the Meteorological Cell of the University of Agriculture Faisalabad and presented in Figure 1. The rice seedlings were transplanted into the experimental plots in July in both years’ experiments. The crop was harvested in November of both years. The randomized complete block design under factorial arrangement was used as the experimental design. Two factors, (i) three nitrogen splits and (ii) four high yielding rice varieties, were studied. Three nitrogen split treatments were used in the experiments, namely N₁: control (no nitrogen); N₂: 50% of the recommended dose was applied as basal + 25% of remaining 50% at tillering stage and 25% at panicle initiation stage (conventional method); N₃: the recommended nitrogen fertilizer was split in three equal doses (33.33% was applied as basal + 33.33% at tillering stage + 33.33% at panicle initiation stage), while four rice varieties, V1: Super gold 2019; V2: Super basmati 2019, V3: Noor basmati 2017; V4: Kissan basmati 2016, were part of the experiments. The recommended dose of nitrogen fertilizer was 137.5 kg ha⁻¹. This recommended dose was split according to the treatment. The source of nitrogen fertilizer was urea manufactured by Fauji Fertilizer Company Limited (FFC), Sadiqabad, Pakistan. Urea was applied using the broadcast method, where granules are evenly spread over the soil surface. This technique ensures uniform distribution and allows for effective nitrogen uptake by plants. The rice varieties were obtained from the Rice Research Institute, Kala Shah Kaku, Lahore. Topsin M^® 70% wettable powder fungicide and Oxadiargyl (pre-emergence) and Ethoxysulfuron (post-emergence) weedicides from Bayer Crop Science^® (Pakistan) were used for plant protection.

2.2. Crop Husbandry

The data on crop husbandry operations for each season are summarized in

Table 2. Detailed information on crop season field management techniques used in the experiments.

Management Practices	Year
	2022	2023
Previous crop stubble management	9 May	12 May
Soaking (Rauni) irrigation	4 July	5 July
Land preparation	5 July	7 July
Nursery sowing for transplanted rice	7 June	10 June
Seed rate	12 kg ha−1	12 kg ha−1
Transplanting	7 July	10 July
Fertilizers application	P:K and Zn @ 90: 62.5 and 12 kg ha−1	P:K and Zn @ 90: 62.5 and 12 kg ha−1
Weed management	Oxadiargyl 80% WP (100 g ha−1) Ethoxy sulfuron 60 WG (50 g ha−1)	Oxadiargyl 80% WP (100 g ha−1) Ethoxy sulfuron 60 WG (50 g ha−1)
Harvesting	8 November	14 November

N: Nitrogen; P: phosphorous; K: potassium; Zn: zinc; WP: wettable powder; WG: wettable granules.

. Before sowing, the seeds were soaked in tap water for 24 h and then incubated for a duration of 48 h. The nursery was sown with pre-germinated seeds on 7th June and 10th June in 2022 and 2023, respectively. The transplantation of 30 days old seedling (2 seedling hill⁻¹) was carried out with a space of 22.5 cm × 22.5 cm between the hills and rows. After 10 days of transplanting, zinc was applied at a rate of 15 kg ha⁻¹ in the form of zinc sulfate, which contains 33% Zn. All plots were kept at around 5 cm water depth during the growing season and bunds were made to avoid the transfer of nitrogen between the experimental plots. The total phosphorus and potassium fertilizers were applied at the time of sowing while nitrogen was applied in split(s) according to its treatment. Weeds were controlled using the stale seedbed method combined with both pre-emergence and post-emergence herbicides. To suppress rice stem borers and other chewing pests, Regent^® GR was used at a rate of 75 g ha⁻¹. Using a knapsack sprayer, Nativa was used for spraying during both years at 45 and 60 days after transplanting (DAT) to protect the crop from glume discoloration, brown leaf spot, neck blast, and leaf blast. For the protection of rice plants against bacterial leaf blight, Aliette^® was applied at 55 and 65 DAT in 2022 and 2023, respectively.

2.3. Observations

Leaf area index (LAI) was measured seven times during each growing season using Sunscan Canopy Analysis. Total dry matter (TDM) (g m⁻²) was also monitored seven times along the growing season during both years. TDM was obtained by harvesting 10 cm from the row. The fresh weight of the sample was recorded just after harvest. Then, the sample was separated into components (leaf, stem, etc.) and weighed. Then, 20 g out of each component of the fresh sample was taken as a subsample, sun-dried, and later oven-dried to ensure moisture removal and weighed. Later on, all dry-weight samples were converted into g m⁻².

Grain yields were calculated at physiological maturity by harvesting a (1 × 1 m) area from each field’s center, leaving the border rows. To achieve a consistent dry weight, a manual method was used to harvest the rice plants in each plot, which were then threshed and sun-dried for two to three days. A grain moisture meter (8988N Xiamen Hyhoo Imp. & Exp. Co., Ltd., Xiamen, China) was used to measure the moisture content of the dried grains. For rice storage, a standard moisture of 14% was used to calculate rice grain yields.

Agronomic nitrogen use efficiency (ANUE) was measured according to the following equation described by Fageria [20].

$$\mathrm{A}\mathrm{N}\mathrm{U}\mathrm{E}={\displaystyle \frac{\mathrm{G}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n} \mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d} \mathrm{o}\mathrm{f} \mathrm{N} \mathrm{f}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d} \mathrm{p}\mathrm{l}\mathrm{o}\mathrm{t}\mathrm{s}-\mathrm{G}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n} \mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d} \mathrm{o}\mathrm{f} \mathrm{u}\mathrm{n}\mathrm{f}\mathrm{e}\mathrm{r}\mathrm{t}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{z}\mathrm{e}\mathrm{d} \mathrm{p}\mathrm{l}\mathrm{o}\mathrm{t}\mathrm{s}}{\mathrm{Q}\mathrm{u}\mathrm{a}\mathrm{n}\mathrm{t}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{o}\mathrm{f} \mathrm{N} \mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{d} }}$$

Partial factor productivity (PFP_N) is the simplest way to measure nitrogen use efficiency and reveals units of yield per unit of nitrogen applied. PFP_N was calculated using the formula given below [21].

$${PFP}_N={\displaystyle \frac{\mathrm{Y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d}}{\mathrm{A}\mathrm{m}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t} \mathrm{o}\mathrm{f} \mathrm{n}\mathrm{i}\mathrm{t}\mathrm{r}\mathrm{o}\mathrm{g}\mathrm{e}\mathrm{n} \mathrm{a}\mathrm{p}\mathrm{p}\mathrm{l}\mathrm{i}\mathrm{e}\mathrm{d}}}$$

2.4. Statistical Analysis

The collected traits were statistically analyzed using two-way ANOVA, and the HSD test was applied to distinguish between treatment means with a 95% confidence interval. The paired comparison plot technique was used to generate graphs of interactive effects, and analyses were conducted with Origin-Pro software V2021 (Originlab, Northampton, MA, USA). Pearson correlation analysis was carried out with two-tailed tests (df–2) using the aforementioned software.

3. Results

3.1. Impact of Nitrogen Split(s) on the Growth and Yield Parameters of Various Rice Varieties (Main Effects)

According to the analyzed data, the leaf area index (LAI) revealed that the main effects were significant for both factors over the years, excluding the LAI 3, 4, and 5 of the varieties in the year 2022, as demonstrated in

Table 3. Impact of nitrogen split(s) at various stages on leaf area index of rice varieties in 2022 and 2023. The standard error of treatments is indicated by the ± symbol.

Treatments	Parameters
	LAI 1		LAI 2		LAI 3		LAI 4		LAI 5		LAI 6		LAI 7
	2022	2023	2022	2023	2022	2023	2022	2023	2022	2023	2022	2023	2022	2023
N1	0.74 ± 0.06 b	0.64 ± 0.06 c	1.44 ± 0.07 b	1.74 ± 0.11 c	2.15 ± 0.15 b	2.91 ± 0.23 b	3.62 ± 0.21 b	4.11 ± 0.29 b	2.58 ± 0.19 b	2.94 ± 0.20 c	1.79 ± 0.21 b	1.87 ± 0.13 c	0.62 ± 0.34 b	0.84 ± 0.11 c
N2	0.98 ± 0.12 a	1.22 ± 0.12 b	1.96 ± 0.10 a	2.48 ± 0.12 b	3.25 ± 0.17 a	3.98 ± 0.17 a	4.72 ± 0.19 a	5.18 ± 0.20 a	3.68 ± 0.18 a	4.05 ± 0.08 b	2.62 ± 0.29 a	2.98 ± 0.05 b	1.49 ± 0.24 a	1.95 ± 0.07 b
N3	1.08 ± 0.05 a	1.38 ± 0.04 a	2.15 ± 0.17 a	2.64 ± 0.06 a	3.42 ± 0.22 a	4.14 ± 0.21 a	4.88 ± 0.28 a	5.34 ± 0.26 a	3.85 ± 0.21 a	4.21 ± 0.21 a	2.88 ± 0.39 a	3.14 ± 0.15 a	1.68 ± 0.41 a	2.11 ± 0.12 a
HSD (α 5%)	0.1246	0.1465	0.2538	0.1524	0.3213	0.1622	0.3878	0.1622	0.3878	0.1493	0.4155	0.1493	0.3789	0.1493
V1	0.93 ± 0.07 ab	1.08 ± 0.08 ab	1.78 ± 0.07 ab	2.29 ± 0.10 ab	2.91 ± 0.19	3.68 ± 0.21 ab	3.37 ± 0.17	4.88 ± 0.26 ab	3.34 ± 0.13	3.73 ± 0.18 ab	2.46 ± 0.13 ab	2.67 ± 0.11 ab	1.26 ± 0.19 ab	1.63 ± 0.10 ab
V2	0.98 ± 0.08 a	1.12 ± 0.07 ab	1.87 ± 0.16 ab	2.33 ± 0.09 ab	2.93 ± 0.10	3.72 ± 0.20 ab	4.40 ± 0.14	4.92 ± 0.25 ab	3.36 ± 0.18	3.78 ± 0.16 ab	2.49 ± 0.20 ab	2.71 ± 0.11 ab	1.29 ± 0.23 ab	1.68 ± 0.11 ab
V3	0.80 ± 0.07 b	0.95 ± 0.06 b	1.70 ± 0.12 b	2.16 ± 0.08 b	2.75 ± 0.15	3.55 ± 0.19 b	4.21 ± 0.21	4.75 ± 0.24 b	3.18 ± 0.15	3.60 ± 0.17 b	2.05 ± 0.41 b	2.54 ± 0.10 b	0.99 ± 0.38 b	1.08 ± 0.50 b
V4	1.02 ± 0.08 a	1.16 ± 0.08 a	2.05 ± 0.11 a	2.37 ± 0.11 a	3.17 ± 0.29	3.76 ± 0.21 a	4.64 ± 0.38	4.96 ± 0.25 a	3.60 ± 0.31	3.82 ± 0.15 a	2.73 ± 0.45 a	2.74 ± 0.11 a	1.53 ± 0.52 a	1.72 ± 0.11 a
HSD (α 5%)	0.1591	0.1870	0.3240	0.1945	NS	0.2070	NS	0.2070	NS	0.1905	0.5303	0.1905	0.4835	0.1905
Interaction	NS	NS	NS	NS	NS	NS	NS	NS	NS	NS	NS	NS	NS	NS

Here LAI = leaf area index; N1 = control (no nitrogen); N2 = 50% as basal + 25% at tillering stage + 25% at panicle initiation stage; N3 = 33.33% as basal + 33.33% at tillering stage + 33.33% at panicle initiation stage; V1 = Super Gold 2019; V2 = Super Basmati 2019; V3 = Noor Basmati 2017; V4 = Kissan Basmati 2016; NS = non-significant; HSD = honestly significant difference. Variable small alphabets showing statistically significance among treatment means.

. However, the highest LAI was observed after 75 days of transplanting in LAI4, where equal split(s) of nitrogen performed effectively (34.80%, 29.92%) in both years, respectively, as compared to the control. On the other side, the variety Kissan basmati showed a higher LAI as compared to the other varieties. Over the course of both seasons, a congruent trend in LAI progression was observed, with values ascending after sowing, peaking at 75 days after transplanting, then diminishing. On the other side, total dry matter (TDM) was significantly affected by the nitrogen split(s) and varieties during 2022, excepting TDM 1, 5, 6, and 7 for the varieties, which were suggested to be non-significant. A similar pattern was noted during the 2023 period, as illustrated in

Table 4. Impact of nitrogen split(s) at various stages on total dry matter of rice varieties in 2022 and 2023. The standard error of treatments is indicated by the ± symbol.

Treatments	Parameters
	TDM 1		TDM 2		TDM 3		TDM 4		TDM 5		TDM 6		TDM 7
	2022	2023	2022	2023	2022	2023	2022	2023	2022	2023	2022	2023	2022	2023
N1	75.5 ± 4.93 b	83.0 ± 5.37 b	223.3 ± 5.13 b	232.5 ± 19.35 b	398.63 ± 22.21 c	399.39 ± 10.80 b	600.84 ± 35.25 b	607.08 ± 25.62 b	863.01 ± 19.47 b	890.70 ± 18.43 b	997.10 ± 50.42 b	991.50 ± 49.66 b	1113.60 ± 49.50 b	1121.20 ± 48.78 b
N2	108.75 ± 8.47 a	125.29 ± 9.15 a	265.30 ± 8.53 a	283.37 ± 24.56 a	469.41 ± 17.69 b	479.84 ± 16.76 a	711.71 ± 35.67 a	730.12 ± 18.10 a	978.48 ± 38.61 a	1012.80 ± 39.40 a	1236.20 ± 45.12 a	1242.80 ± 87.56 a	1452.60 ± 42.61 a	1471.20 ± 55.53 a
N3	123.68 ± 17.91 a	142.15 ± 16.83 a	279.74 ± 10.42 a	299.70 ± 22.80 a	483.77 ± 14.72 a	495.55 ± 10.61 a	731.94 ± 43.32 a	750.37 ± 36.82 a	997.72 ± 57.97 a	1041.20 ± 34.11 a	1257.40 ± 17.83 a	1271.30 ± 51.64 a	1462.20 ± 60.65 a	1484.30 ± 52.51 a
HSD (α 5%)	16.892	20.049	14.945	16.889	13.125	22.787	21.756	29.551	40.551	48.355	71.482	86.606	82.275	90.918
V1	101.30 ± 8.22	115.22 ± 8.32	252.87 ± 9.41 ab	267.94 ± 23.34 ab	446.84 ± 19.92 b	454.95 ± 15.51 ab	680.34 ± 33.10 ab	694.73 ± 27.06 ab	941.34 ± 37.62	972.20 ± 12.15	1159.5 ± 15.51	1162.30 ± 61.64	1347.70 ± 32.83	1356.30 ± 21.03
V2	105.39 ± 13.83	119.54 ± 12.71	265.27 ± 7.81 a	281.59 ± 22.86 a	457.14 ± 18.17 ab	464.75 ± 6.57 ab	689.22 ± 38.55 a	710.59 ± 27.52 a	950.54 ± 36.32	990.50 ± 30.20	1170.90 ± 35.63	1180.90 ± 55.44	1360.80 ± 42.75	1368.50 ± 57.62
V3	92.94 ± 10.28	107.34 ± 10.83	236.09 ± 8.04 b	251.78 ± 21.51 b	428.80 ± 19.94 c	435.86 ± 8.72 b	659.94 ± 43.26 b	672.71 ± 27.70 b	930.89 ± 44.06	960.60 ± 43.68	1137.70 ± 59.92	1139.40 ± 64.14	1289.20 ± 57.54	1330.50 ± 79.91
V4	110.95 ± 9.42	125.09 ± 9.92	270.24 ± 6.86 a	286.07 ± 21.24 a	469.63 ± 14.79 a	477.49 ± 20.09 a	696.49 ± 38.75 a	705.39 ± 25.11 ab	962.85 ± 36.72	1003.20 ± 36.54	1186.30 ± 40.10	1191.70 ± 70.60	1373.50 ± 70.57	1380.20 ± 50.52
HSD (α 5%)	NS	NS	19.073	21.555	16.750	29.082	27.766	37.715	NS	NS	NS	NS	NS	NS
Interaction	NS	NS	NS	NS	NS	NS	NS	NS	NS	NS	NS	NS	NS	NS

Here TDM = total dry matter; N1 = control (no nitrogen); N2 = 50% as basal + 25% at tillering stage + 25% at panicle initiation stage; N3 = 33.33% as basal + 33.33% at tillering stage + 33.33% at panicle initiation stage; V1 = Super Gold 2019; V2 = Super Basmati 2019; V3 = Noor Basmati 2017; V4 = Kissan Basmati 2016; NS = non-significant; HSD = honestly significant difference. Variable small alphabets showing statistically significance among treatment means.

. The lowest TDM was observed after 30 days of transplanting during the entire experiments, and in TDM 1, the equal nitrogen split with Kissan basmati led to more dry matter (63.79%, 71.26%) in both experimental years, respectively, as compared to the control using Noor basmati. TDM 7 showed more dry matter than the previous ones because its trend increases with time duration. The equal split of nitrogen with Kissan basmati performed effectively (31.30%, 32.38%) in all trends in total dry matter across the years, correspondingly, compared to the control.

The yield components are highlighted in

Table 5. Impact of nitrogen split(s) at various stages on yield components of rice varieties in 2022 and 2023. The standard error of treatments is indicated by the ± symbol.

Treatments	Parameters
	Plant Height (cm)		Productive Tillers (m−2)		Number of Branches per Panicle		Number of Grains per Panicle
	2022	2023	2022	2023	2022	2023	2022	2023
N1	96.67 ± 3.34 b	98.75 ± 2.65 b	349.34 ± 8.46 c	352.91 ± 15.63 b	10.55 ± 0.30	10.25 ± 0.47 b	117.73 ± 3.01 b	124.70 ± 6.20 b
N2	101.92 ± 1.6 a	103.42 ± 1.47 a	402.17 ± 9.24 b	407.08 ± 14.64 a	11.07 ± 0.42	11.24 ± 0.28 a	161.33 ± 4.11 a	169.08 ± 4.80 a
N3	102.83 ± 1.25 a	103.75 ± 1.27 a	427.74 ± 14.32 a	434.39 ± 14.77 a	11.20 ± 0.40	11.52 ± 0.33 a	164.75 ± 4.68 a	166.92 ± 6.35 a
HSD (α 5%)	4.2123	3.6243	19.342	29.490	NS	0.6652	8.0981	11.052
V1	103.89 ± 1.86 b	105.33 ± 1.75 b	393.10 ± 12.10	397.34 ± 18.89	11.02 ± 0.29	10.94 ± 0.36	148.81 ± 3.23	153.61 ± 7.81
V2	110.00 ± 2.84 a	111.78 ± 1.79 a	397.61 ± 10.22	402.10 ± 14.96	10.85 ± 0.49	11.04 ± 0.48	149.21 ± 2.39	155.06 ± 5.65
V3	95.44 ± 2.27 c	96.89 ± 2.15 c	379.71 ± 9.80	385.79 ± 15.79	10.43 ± 0.30	10.81 ± 0.16	144.11 ± 5.39	148.71 ± 4.84
V4	92.56 ± 1.03 c	93.89 ± 1.50 c	401.93 ± 10.58	407.28 ± 10.42	11.45 ± 0.42	11.22 ± 0.44	149.81 ± 4.73	156.89 ± 4.83
HSD (α 5%)	5.3759	4.6255	NS	NS	NS	NS	NS	NS
Interaction	NS	NS	NS	NS	NS	NS	NS	NS

Here N1 = control (no nitrogen); N2 = 50% as basal + 25% at tillering stage + 25% at panicle initiation stage; N3 = 33.33% as basal + 33.33% at tillering stage + 33.33% at panicle initiation stage; V1 = Super Gold 2019; V2 = Super Basmati 2019; V3 = Noor Basmati 2017; V4 = Kissan Basmati 2016; NS = non-significant; HSD = honestly significant difference. Variable small alphabets showing statistically significance among treatment means.

and

Table 6. Impact of nitrogen split(s) at various stages on yield components, agronomic nitrogen use efficiency, and partial factor productivity of rice varieties in 2022 and 2023. The standard error of treatments is indicated by the ± symbol.

Treatments	Parameters
	1000-Grain Weight (g)		Grain Yield (t ha−1)		Agronomic Nitrogen Use Efficiency		Partial Factor Productivity
	2022	2023	2022	2023	2022	2023	2022	2023
N1	19.27 ± 1.68 b	19.56 ± 1.19 b	3.70 ± 0.21 b	3.78 ± 0.18 b	-	-	26.90 ± 1.55 b	27.51 ± 1.31 b
N2	25.50 ± 0.91 a	27.45 ± 0.87 a	4.95 ± 0.23 a	5.17 ± 0.25 a	9.09 ± 1.93 a	10.12 ± 1.91 a	35.99 ± 1.74 a	37.63 ± 1.82 a
N3	25.69 ± 1.13 a	28.80 ± 0.59 a	4.98 ± 0.20 a	5.26 ± 0.28 a	9.33 ± 1.75 a	10.78 ± 2.02 a	36.24 ± 1.52 a	38.30 ± 2.07 a
HSD (α 5%)	1.7923	1.5939	0.3309	0.4207	2.9690	2.8403	2.5066	3.0596
V1	23.67 ± 1.40	25.47 ± 0.53	4.36 ± 0.20 bc	4.67 ± 0.35 b	6.06 ± 0.50	6.86 ± 1.51	31.75 ± 1.47 bc	34.02 ± 2.55 b
V2	23.71 ± 1.06	25.64 ± 0.89	4.73 ± 0.22 ab	4.98 ± 0.22 ab	6.54 ± 1.06	6.70 ± 1.05	34.42 ± 1.62 ab	36.28 ± 1.63 ab
V3	22.46 ± 1.46	24.07 ± 1.27	4.01 ± 0.10 c	4.06 ± 0.08 c	5.89 ± 0.77	6.78 ± 0.84	29.17 ± 0.77 c	29.57 ± 0.64 c
V4	24.11 ± 1.05	25.91 ± 0.82	5.06 ± 0.35 a	5.23 ± 0.29 a	6.06 ± 2.57	7.51 ± 1.83	36.84 ± 2.55 a	38.06 ± 2.12 a
HSD (α 5%)	NS	NS	0.4223	0.5369	NS	NS	3.0714	3.9049
Interaction	NS	NS	NS	NS	NS	NS	NS	NS

Here N1 = control (no nitrogen); N2 = 50% as basal + 25% at tillering stage + 25% at panicle initiation stage; N3 = 33.33% as basal + 33.33% at tillering stage + 33.33% at panicle initiation stage; V1 = Super Gold 2019; V2 = Super Basmati 2019; V3 = Noor Basmati 2017; V4 = Kissan Basmati 2016; NS = non-significant; HSD = honestly significant difference. Variable mall alphabets showing statistically significance among treatment means.

. The highest plant height was counted for the variety Super basmati under the equal split of nitrogen (6.37%, 5.06%), followed by the conventional method (5.43%, 4.72%) compared to the control in the years 2022 and 2023, respectively. Meanwhile, during both years of study, the equal split of nitrogen achieved the highest number of productive tillers (22.44%, 23.08%), respectively, relative to the control. When measured against the control, the equal nitrogen split yielded the statistically greatest number of branches per panicle (6.16%, 12.39%), followed by the conventional method (4.92%, 9.6%), for the study periods in 2022 and 2023, accordingly. Nonetheless, equal nitrogen split(s) significantly boosted the number of grains per panicle (39.93%) in the 2022 study period, which is statistically similar to the conventional method (37.03%), and under the control treatment, the lowest value was computed. In examining the 2023 period relative to the previous year, the conventional method yielded the highest number of grains per panicle (35.58%), followed by the equal split of nitrogen (33.85%), compared to the control. The 1000-grain weight during 2022 and 2023 was highest (33.31%, 47.23%) when equal nitrogen splits were applied, and statistically comparable results (32.33, 40.33%) were noted with the conventional method. Similarly, in 2022, there was a higher grain yield (34.59%) under equal splits of nitrogen with Kissan basmati, which was statistically at par with the conventional method (33.78%), whereas during the 2023 period, the highest grain yield (39.15%) was recorded by equal split of nitrogen under Kissan basmati, followed by the conventional method (36.77%). In terms of agronomic nitrogen use efficiency, both years 2022 and 2023 showed that the equal split of nitrogen had a higher efficiency (9.33%, 10.78%), which was statistically on par with the conventional method (9.09%, 10.12%), respectively, as compared to the control. During both years of study, as compared to the control, the equal split of nitrogen resulted in the highest partial factor productivity (34.72%, 39.22%), which was statistically on par with the conventional method (33.79%, 36.78%), correspondingly.

3.2. Impact of Nitrogen Split(s) on Growth and Yield Parameters of Various Rice Varieties (Interactive Effects)

As depicted in Figure 2, nitrogen split(s) had a considerable effect on the leaf area index (LAI) of rice varieties throughout the experiment. At 75 days after transplanting, the leaf area index achieved its highest (32.98%, 29.59%) value with equal nitrogen split(s) under Kissan basmati and showed a gradual increase during both years, respectively. At the same time, the lowest value (33.00%, 26.51%) was recorded under the control treatment with Noor basmati compared to the higher-performing interactive treatments. After 30 days of transplanting, the first LAI was taken with minute values, then, in 15-day intervals, at 45 days, 60 days, 75 days, and so on, subsequent LAI values were taken. The maximum leaf area was suggested at LAI4 after 75 days of transplanting, whereas at 75 days, a peak was depicted at which the plants produced more photosynthates, proceeding to move downward until maturity. The dry matter produced by the rice plants showed a gradual increase with successive growth stages and reached its maximum value at maturity, as summarized in Figure 3. The dry matter accumulation in rice demonstrated slow exponential growth, a linear growth phase, and a subsequent phase of constant weight. TDM 1 was taken after 30 days of transplanting; then, 15-day intervals were imposed to collect TDM values, and their maximum was demonstrated at 120 days of transplanting. The equal split of nitrogen under Kissan basmati showed the highest TDM (29.22%, 29.90%), and the lowest (27.85%, 39.23%) was discovered in the control with Noor basmati in 2022 and 2023, respectively.

Regarding the results of the interaction effects, Figure 4 demonstrates the variation in plant height, productive tillers, and number of productive tillers per panicle in response to the three treatments of nitrogen split under four rice varieties over the years. During the study periods of 2022 and 2023, statistically, the leading plant height (26.68%, 25.92%) was depicted in the equal nitrogen split with Super basmati, followed by the conventional method under Super basmati (25.93%, 25.55%), as compared to the control under Kissan basmati, which decreased by 21.06% and 20.58%, correspondingly. The productive tillers were impacted by the subject factors, and in both years’ trials, the crop achieved its maximum yield (32.81%, 32.82%) using equal nitrogen split with Kissan basmati followed by equal nitrogen split under Super basmati (31.42%, 31.67%) and equal nitrogen split with Super gold (31.01%, 30.79%), whereas during both years, the lowest productive tillers (24.70%, 24.71%) were obtained when the control with Noor basmati was employed, respectively, as shown in Figure 4. Similarly, during the 2022 period, statistically, the highest number of branches per panicle (24.30%) was calculated with the treatment interaction of the conventional method with Kissan basmati and the lowest (19.55%) was declared under the control with Noor basmati, whereas during the 2023 period, the peak number of branches per panicle (18.06%) was documented by employing an equal nitrogen split with Kissan basmati, and following this, the rest of the interactive treatments displayed similar statistical results. The lowest value (15.30%) was calculated under the control with Noor basmati (Figure 4).

Regarding the traits under study, the interaction between the nitrogen splits and the rice varieties displayed differences in both years of experimentation, as presented in Figure 5. Throughout the year 2022, the treatment interaction between nitrogen split with Kissan basmati boosted the number of grains per panicle (53.70%), comparable to the remaining treatments and higher than what was depicted in the control with Noor basmati (34.93%). In the next year, the conventional method with Kissan basmati depicted the highest number of grains per panicle (51.29%), and the smallest value (33.90%) was observed in the control with Noor basmati. Similarly, in the first year of experimentation, the conventional method with Kissan basmati had the highest thousand-grain weight (44.05%), and the lowest figure (30.57%) was noted under the control with Noor basmati. During the next year, the equal nitrogen split with Kissan basmati performed most effectively (59.30%), and the weakest performance (37.22%) was demonstrated in the control with Kissan basmati. The interactive treatment of equal split under Kissan basmati reflected the highest grain yield (71.87%, 85.30%), followed by the conventional method under Kissan basmati (70.62%, 82.10%), and the lowest grain yield (41.81%, 46.03%) was calculated where the interactive treatment control with Noor basmati was used in the 2022 and 2023 periods, respectively.

3.3. Agronomic Nitrogen Use Efficiency and Partial Factor Productivity

The agronomic nitrogen use efficiency was influenced by employing nitrogen split(s) and rice varieties over the years, exhibited in Figure 6. During the 2022 period, the equal split of nitrogen under Super basmati yielded higher results (9.94%), followed by the rest of the statistically similar interactions, which shared the same letter except for all interactions of N1 with the four varieties. In the next year, the equal split of nitrogen under Kissan basmati exhibited the maximum agronomic nitrogen use efficiency (11.63%), whereas the interaction of the control with all varieties depicted a value of zero. However, the equal nitrogen split with Kissan basmati contributed to more partial factor productivity (71.89%, 85.16%), followed by the conventional method under Kissan basmati (70.82%, 81.95%), and the value reached its lowest point (41.82%, 45.99%) under the control with Noor basmati for the years 2022 and 2023, respectively, as detailed in Figure 6.

3.4. Pearson Correlation Analysis

To examine the relationships among growth traits, yield components, and nitrogen use efficiencies during both years of the study, a correlation analysis was performed, which is charted in Figure 7. In both years, there is a strong positive correlation among these various indicators. These correlations among the coefficients, close to a maximum of 1.00, suggest a direct relationship where an increase in the LAI may correspond to greater biomass production. During the 2022 period, total dry matter (TDM) had a highly positive correlation with leaf area index (LAI). Productive tillers (PT) had a strong positive correlation with leaf area index and total dry matter, but a negative correlation with plant height. Additionally, thousand-grain weight (1000 GW) and grain yield (GY) had a strong positive correlation with leaf area index (LAI), total dry matter (TDM), productive tillers (PT), and number of grains per panicle (NGPP). This demonstrates that these parameters made a notable contribution to the thousand-grain weight and grain yield. Moreover, agronomic nitrogen use efficiency and partial factor productivity also had a strong positive correlation with leaf area index, total dry matter, productive tillers (PT), number of grains per panicle (NGPP), thousand-grain weight (1000 GW), and grain yield (GY), as well as a negative correlation with plant height (PH) and number of branches per panicle (NBPP). On the other hand, partial factor productivity (PFP_N) had a positive correlation with agronomic nitrogen use efficiency (ANUE). In addition, during the year 2023, a similar relationship was exhibited between the growth attributes, yield components, and nitrogen use efficiencies. Productive tillers (PT) had a strong positive correlation with leaf area index and total dry matter, and a negative correlation with plant height. In contrast, thousand-grain weight (1000 GW) and grain yield (GY) had a strong correlation with leaf area index (LAI), total dry matter (TDM), productive tillers (PT), number of branches per panicle (NBPP), and number of grains per panicle (NGPP). The nitrogen use efficiencies followed the same trend suggested in the previous year.

4. Discussion

In current agricultural practices, high yield usually depends on excessive N inputs; on the other side, nitrogen fertilizer is costly to the farmer, and reduced, omitted, or excessive N can influence soil health, NUE, and rice productivity. Reduced NUE is linked to nitrogen loss by leaching, denitrification, immobilization, and uneven soil distribution. Nitrogen management in rice production systems has received considerable attention over the past few years. Optimizing nitrogen application management in relation to rice varieties is essential for achieving sustainable rice yields.

Nitrogen supports plant growth by enlarging and increasing the number of meristematic cells, which in turn facilitates the formation of new shoots [22]. In addition, the application of nitrogen enhances cytokinin levels, which affects cell wall extensibility [23]. Therefore, it is logical to suggest that nitrogen directly or indirectly plays a role in promoting cell enlargement, division, and tissue production, thereby enhancing growth characteristics. These findings align with the results reported by Pathan et al. [24]. For every plant, the leaves serve as key organs that engage actively in photosynthesis. For high yields, it is essential to focus on maximizing the leaf area of the plant [25]. The growing trend in LAI with balanced nitrogen applications can be attributed to the positive impact of nitrogen on leaf development and leaf area duration [26]. The observed increase in LAI until a certain period may be due to nitrogen’s role in enhancing both the number of leaves per plant and the expansion of each leaf. The enhancement in leaf number and size due to adequate nutrition might be due to improved nutrient absorption through better root development and the increased translocation of carbohydrates to the developing grains. In contrast, the lowering of LAI after flowering might be caused by the shedding of leaves from the lower parts of the plants. These patterns are in line with the results of Azarpour et al. [27]. Providing a balanced amount of nitrogen can increase dry matter content by facilitating the production of photoassimilates in leaves, the main growth centers during the vegetative phase, and later reallocating these assimilates to the reproductive organs [27]. In addition, the dry matter yield in rice is notably related to the interception of photosynthetically active radiation. The presence of low nitrogen levels in plant leaves is a limiting factor that reduces radiation use efficiency and biomass productivity, which in turn lowers dry matter production in rice.

Our results show that there was a considerable variation in the outcomes of yield components and rice productivity under different nitrogen split(s) and rice varieties. In both years, higher plant height was the result of split N supplies, but the potential of each variety remained dominant. Equal N applications caused cell division, which in turn caused stem elongation [28]. The observations on plant height are consistent with the outcomes of Abbasi et al. [29], who documented that with equal N split during critical phases, plants showed remarkably improved height. Previous research has shown that productive tillers increased with the management of nitrogen split(s) [30]. Because split application contributes to cell division, there may be a correlation between the rise in tillers and increased N availability during the tillering stage. Wang et al. [31] shown that the nitrate transporter is regulated by N availability, which in turn affects the quantity of rice tillers. Tiller bud outgrowth and tiller numbers increase when the nitrogen content of rice plants rises [32]. Jahan et al. [33] identified that an increase in nitrogen availability resulted in more tillers m⁻² due to improved cell division. Effective tillers contribute more to rice plant productivity compared to the total number of tillers. The application of equally split nitrogen fertilizer improved the number of grains per panicle. Increased nitrogen absorption from the equally split nitrogen applications contributed to a higher number of grains per panicle by encouraging more branches per panicle [34]. Variability in thousand-grain weight among the treatments was low, as it is a characteristic primarily controlled by genetics. Other studies reported similar results in the context of nitrogen fertilizer management and concluded that agronomic practices could lead to better grain size [35]. The productivity of rice plants is largely determined by the number of panicle-bearing tillers, the amount of filled grains, and the weight of the grains [36]. An increase in grain yield with equal nitrogen applications could be due to more effective nitrogen and nutrient absorption, which would support greater dry matter production and its translocation [37]. In our investigation, there was an increase in grain production with the effective timing of each nitrogen split at the critical stages of the different rice varieties. Zhang et al. [30] found that equal N splits considerably raised grain yield. Chen et al. [32] further observed that the application of nitrogen had a good impact on rice grain yield. Increases in plant biomass by managing nitrogen splits under different rice varieties indicated the better performance of biomass-contributing traits. This increase in plant biomass after nitrogen application was also observed in a rice study conducted by Jahan et al. [33].

Our study suggests that the higher agronomic nitrogen use efficiency (ANUE) with nitrogen applied in three equal splits could be associated with lower runoff, NH₃ volatilization, denitrification, and nitrification [38]. By using split applications of nitrogen, it is possible to increase its uptake and efficiency, which in turn enhances NUE and yield [39]. As the nitrogen fertilizer rates increased in the split application treatments, nitrogen use efficiency (NUE) decreased, likely due to higher nitrogen losses associated with increased NH₃ volatilization [40]. Achieving higher ANUE in paddy rice fields is a challenging problem affected by a range of factors including nitrogen application rate and timing, the type of nitrogen fertilizer, plant variety, growth stages, soil conditions, crop management, and environmental factors. Nitrogen flow in plants includes a series of processes such as acquisition, assimilation, re-mobilization, and utilization for yield formation [41]. Maximizing NUE in rice production requires the acceleration of these processes through improved agronomic practices. The current experiment indicates that the ANUE is higher when N fertilizer is applied in an equal split. This makes the split application method for N fertilizer key for reducing denitrification and losses of nitrogen fertilizer, which is necessary for plants to use it efficiently. ANUE is usually higher when equal splits of nitrogen are applied at critical growth stages than when applied at unequal rates [42]. The accurate estimation of nitrogen use efficiency (NUE) in crops is key to understanding the fate of applied nitrogen and its effectiveness in maximizing economic yield through optimal plant uptake and utilization. The reduction in NUE at higher nitrogen levels indicates that rice plants are ineffective in absorbing or utilizing the added nitrogen. Ammonia volatilization, denitrification, surface runoff, and leaching are typical pathways for nitrogen loss in the soil floodwater systems, leading to serious issues including environmental pollution, higher costs of production, reduced grain yields, and potential impacts on global warming [43]. The impact and nature of nitrogen losses depend on variables such as the timing, quantity, and method of nitrogen application, as well as the type of nitrogen source, soil conditions, weather, and crop health. Decreased efficiency in nitrogen uptake at higher application rates has also been reported by Mae et al. [44]. The application and uptake of nitrogen have a major impact on rice physiological processes. Synchronizing the nitrogen requirements with the supply is critical for better nitrogen management in rice. The dynamics between nitrogen intake and loss influence the development, biomass, and growth of plants [45]. The impact of nitrogen was found as variations in N uptake under different rice varieties at different times of split nitrogen applications. Greater N uptake is a sign that a crop’s N requirements have been met under ideal N availability and ideal conditions. According to some research, adding more N to underperforming rice varieties would not boost grain N uptake. The primary goal of applying nitrogen fertilizer is to maximize nitrogen use efficiency in rice production. We noticed that effective nitrogen splits at the proper stages enhanced N use efficiency. Out results corroborate the outcomes of Yousaf et al. [46], who showed that optimal nitrogen efficiencies are attained when nitrogen levels in rice are aligned with crop requirements. The findings of Hameed et al. [47] suggested that nitrogen applied in split doses improves PFP_N. Depending on the soil moisture content, nitrogen dynamics vary, which might lead to variable N fertilizer recovery rates, which depend on N supply from several sources, application rates, splits, and timings to improve PFP_N [48]. Furthermore, existing N recommendations for application amount and timing are frequently followed all over broad rice production areas, limiting efficient N utilization due to temporal variability. The use of split nitrogen fertilizer applications can enhance rice productivity and PFP_N, while simultaneously reducing nitrogen losses [49]. Applying nitrogen in multiple stages can improve nitrogen absorption and its effectiveness, which in turn enhances PFP_N and yield [39]. The outcomes of this study highlighted how key it is to properly control nitrogen (N) in accordance with different rice varieties to produce rice. According to our research, rice productivity outcomes were noticeably enhanced by the equal split of nitrogen under different rice varieties. Thus, N application practices, as well as changes and improvements, require the inclusion of recommendations for nitrogen fertilizer aligned with soil nutrient status, analysis, and location [50]. Although soil moisture was consistently and adequately supplied in the current study, climatic conditions and rainfall variability vary throughout the entire growing seasons in the field conditions; therefore, more research in this area is necessary to attain a more accurate optimization of rice N and varieties. Moreover, the uptake and utilization of nitrogen is affected by the use of different varieties of rice, in addition to the current meteorological conditions, soil moisture status, and availability of nitrogen [51]. It was found that agronomic traits exhibited a high degree of genetic variability and variation [52].

5. Conclusions

Recent global trends indicate increasing pressure on food production systems due to population growth and climate change, necessitating efficient agricultural practices. Studies show that optimized nitrogen management enhances crop yield and resource efficiency, aligning with sustainable agriculture goals. Furthermore, the rise in fertilizer costs underscores the need for precise application methods to maximize productivity. Tailoring nitrogen practices, as demonstrated in this study, can mitigate environmental impacts while supporting food security. This study highlights the critical role of optimized split nitrogen applications in enhancing rice productivity and nitrogen use efficiency. Among the nitrogen treatments evaluated, the equal split application (N3: 33.33% basal + 33.33% at tillering stage + 33.33% at panicle initiation stage) demonstrated superior performance, particularly when combined with the Kissan Basmati 2016 variety. This combination (N3V4) resulted in the highest leaf area index, total dry matter, 1000-grain weight, and grain yield, along with improved agronomic nitrogen use efficiency and partial factor productivity. The findings reveal that an equal nitrogen split not only specifically maximizes growth and yield components but also contributes to more efficient nitrogen utilization because equal doses of nitrogen at critical growth stages are used more efficiently than more doses at a single stage. This resulted in the strong positive correlation observed between grain yield and other growth traits, except for plant height.

Moreover, the results from 2023 outperformed those of 2022, indicating the potential for even greater benefits under optimal growing conditions. Overall, this study highlights the importance of tailoring nitrogen management practices to specific rice varieties, with equal nitrogen split(s) showing particular promise for the Kissan Basmati variety. These insights can inform future agronomic practices aimed at achieving sustainable rice production with enhanced resource use efficiency. This study shows that three equal splits of nitrogen at the critical growth stages of basmati rice varieties have the potential to improve yields and nitrogen use efficiency. However, further assessments of these equal split(s) on coarse and hybrid rice are necessary to determine the feasibility of these practices and ecofriendly site-specific nitrogen management. Future studies should incorporate direct measurements of N leaching to evaluate the environmental impacts of split nitrogen applications. This will enable a more comprehensive assessment of nitrogen sustainability and ecological benefits. Future trials should employ larger sample sizes and more advanced statistical models to enhance the robustness of the findings. This will improve the precision and reliability of the results in the implementation of split nitrogen applications for sustainable crop production.