Field-Based Evaluation of Rice Genotypes for Enhanced Growth, Yield Attributes, Yield and Grain Yield Efficiency Index in Irrigated Lowlands of the Indo-Gangetic Plains

Abstract

Nitrogen (N) fertilizers are widely used worldwide to increase agricultural productivity. However, significant N losses contributing to air and water pollution ultimately reduce the nitrogen use efficiency (NUE) of crops. Numerous research studies have emphasized the use of a low dose of N fertilizer, but few have focused on screening N-efficient rice genotypes. This study aimed to identify and screen ten rice genotypes that are N-use-efficient under different N fertilization treatments using the surface placement of neem-oil-coated urea: N₀ (control), N₆₀ (½ of recommended N), and N₁₂₀ (recommended N) for two consecutive years (2020 and 2021) under a split plot design. In both growing seasons, the application of N₁₂₀ yielded the highest panicles m⁻² (PAN = 453), filled grains panicle⁻¹ (FGP = 133), leaf area index (LAI = 5.47), tillers m⁻² (TILL = 541), grain yield t ha⁻¹ (GY = 5.5) and harvest index (HI = 45.4%) by the genotype ‘Nidhi’, being closely followed by the genotype ‘Daya’. Four genotypes (‘Nidhi’, ‘Daya’, ‘PB 1728’ and ‘Nagina 22’), out of the ten genotypes evaluated, responded well to different fertilization treatments with N with respect to the grain yield efficiency index (GYEI ≥ 1). Regarding N fertilization, N₆₀ and N₁₂₀ recorded the highest increase in PAN (28.5%; 41.4%), FGP (29.5%; 39.3%), test weight (29.5%; 45.3%), LAI at 30 days after transplanting (DAT) (143.7%; 223.3%), and LAI at 60 DAT (61.6%; 70.1%) when compared with N₀. Furthermore, the application of N₆₀ and N₁₂₀ improved GY and HI by 47.6% and 59.4%, and 3.4% and 6.2%, respectively, over N₀. Nitrogen addition (N₆₀ and N₁₂₀) also significantly increased the chlorophyll content at 60 DAT (8.8%; 16.3%), TILL at 60 DAT (22.9%; 46.2%), TILL at harvest (28%; 41.4%), respectively, over N₀. Overall, our research findings clearly indicate that ‘Nidhi’ and ‘Daya’ could be efficient candidates for improved nitrogen use, grain yield and GYEI in the Indo-Gangetic plains of India.

1. Introduction

Rice is a heavy user of nitrogen (N) fertilizers. In India, to feed the growing populations, it has been suggested that N fertilizer consumption would need an increase of approximately 24 million tons in 2030 compared with 2022; the current total N fertilizer consumption (year 2022) is around 18.86 million tons [1,2]. India’s production of rice (milled rice) increased from 53.6 million tons in the fiscal year 1980–1981 to 120 million tons in the fiscal year 2020–2021 [3]. In soil, more than 40–50% of the applied N is lost through different mechanisms, such as ammonia (NH₃) volatilization, denitrification to nitrous oxide (N₂O) and dinitrogen (N₂), leaching and runoff [4,5,6]. These losses not only reduce the yield and economic efficiency of applied N [7], but also cause grave environmental consequences [8,9].

Due to the expansion of cultivation areas, the introduction of new cultivars, and the use of chemical fertilizers, rice yield has increased during the past 50 years, keeping pace with the world’s population growth [10]. Nevertheless, the N use efficiency (NUE) of applied N is still low [11,12,13], which not only causes climate-change-related issues, air, and water pollution, but also causes increases in the cost of production, given the waste of N as a valuable resource [11,14]. Therefore, it is important to reduce the loss of N from agricultural land [11], and there is a need for more attention to the identification and performance of N-efficient genotypes. Rice is the key staple food for the world’s poorest and undernourished people living in Asia and Africa as they cannot afford—or do not have access to—nutritious foods [15]. In the next 20 years, the world population is expected to increase by about two billion, and in Asia alone, to increase by around half of the world population [16]. A report by the CGIAR System [17] notes that with the expected growth in population and income and a decline in rice acreage, global demand for rice will continue to increase from 479 million tons of milled rice in 2014 to between 536 million and 551 million tons in 2030, with little scope for the easy expansion of agricultural land or irrigation. Furthermore, rice is a semi-aquatic plant and generally grows under flooded conditions, which makes it unique [18,19]. Special difficulties in managing N arise from this preferred habitat, including significant losses of N to water.

Numerous studies were conducted before the 21st century to improve rice nitrogen use efficiency (NUE) and yield [20,21,22]. Their findings showed that nutrient-efficient cultivars under field conditions can help design selection regimes and identify useful traits that are important for screening N-efficient genotypes. The knowledge of the genotypes’ traits that increase NUE can be combined with the best N management practices, which would help contribute to economically viable and environmentally sustainable systems globally [23].

Different levels of N input (low, medium, high) in experimental studies have shown that significant variability is present for the use, uptake, and utilization efficiency of N. Hence, these aspects are the main areas where researchers can evaluate the response of existing genotypes at various levels of N. A number of agronomic factors in crop growth cycles affect performance and overall NUE, including the optimum N rate, appropriate N source, and timing of N application [11]. Thus, the combination of N-efficient genotype development with the best management practices is therefore an important path for various stressed ecosystems around the world. It has often been shown that rice NUE, which integrates physiological and soil N supply capacities, decreases with increasing N supply in the soil [24].

To identify the appropriate breeding strategies, the germplasm must be evaluated for physiological variability in NUE [25], genotype interaction with N inputs, and different levels of N based on precise selection. Therefore, in the present study, we assessed the response of rice genotypes with different levels of N for several rice genotypes, where rice was fertilized with neem-oil-coated urea according to the regulatory requirements of India. Our experimental trials were based on the new idea of screening rice genotypes for a higher NUE. The main objectives were to: (i) evaluate the growth and yield components of rice genotypes under control versus half and recommended N supplies; (ii) investigate the differences between rice cultivars in terms of economic yield and harvest index; (iii) screen the rice genotypes based on the grain yield efficiency index.

2. Materials and Methods

2.1. Study Site and Soil Characteristics

A two-year field experiment (2020 and 2021) was carried out at the research farm of the ICAR (Indian Agricultural Research Institute, New Delhi, 28°38′ N 77°10′ E) during the Kharif (rainy) season. During the rice growing season, the annual maximum and minimum temperatures ranged from 20 °C to 31.6 °C and 4 °C to 28 °C, respectively. Before transplanting rice genotypes, undisturbed soil samples were collected from the upper 0.02 m soil profile. The physico-chemical properties of the soil are available in Supplementary Materials Table S1.

2.2. Experimental Design and Treatments

The experiments were designed in a split plot design with three levels of N in the main plot, viz. N₀ (control), N₆₀ (½ of the recommended N), N₁₂₀ (recommended N), and ten rice genotypes in sub-plots, viz. G₁ (‘Tella Hamsa’), G₂ (‘Vasumati’), G₃ (‘VL Dhan 209’), G₄ (‘Daya’), G₅ (‘PB 1728’), G₆ (‘Anjali’), G₇ (‘Heera’), G₈ (‘Birupa’), G₉ (‘Nagina 22’) and G₁₀ (‘Nidhi’), replicated thrice. The characteristics and features of the rice genotypes are available in Supplementary Materials Table S2. Irrigation was performed using channels and hand-hoeing was performed to keep the crop weed-free. Nitrogen was supplied through the neem-oil-coated urea containing 46.6% N, and one ton of neem-coated urea contained 0.5 kg neem oil [26]. The N was applied in three splits: 1/2 at ten days after transplanting, 1/4 at tillering, and 1/4 at the panicle initiation stage. In addition to N, for the application of the recommended phosphorus and potassium, a one-time basal dose of single superphosphate and a muriate of potash was applied (

Table 1. The timing and amount of N fertilization levels in two years’ experimental trial.

Treatment	Crop Stages for N Fertilization			Amount of N Fertilizer Applied through Neem-Oil-Coated Urea (46.6% N)				SSP * (16% P2O5)	MOP ** (60% K2O)
	Basal	Tillering	Panicle Initiation	Basal (Kg ha−1)	Tillering (Kg ha−1)	Panicle Initiation (Kg ha−1)	Total N (Kg ha−1)	Basal (Kg P2O5 ha−1)	Basal (Kg K2O ha−1)
N0	-	-	-	-	-	-	-	60	40
N60	10 days after transplanting (50% of N60)	25% of N60	25% of N60	30	15	15	60	60	40
N120	10 days after transplanting (half of N120)	25% of N120	25% of N120	60	30	30	120	60	40

* SSP: single superphosphate; ** MOP: muriate of potash.

).

2.3. Sampling and Measurements

Four plants (hills) from each plot were randomly selected and cut to ground level at 30 and 60 DAT (days after transplanting), and physiological maturity for dry matter accumulation (DMA). The collected samples were sun-dried for 5–7 days and then oven-dried for 24 h at a temperature of 70 °C. Five rice plants were randomly selected from each plot for tiller counting at 30 DAT, 60 DAT and the harvesting stage. To determine the leaf area of the rice genotypes, the LICOR-3100 leaf area meter was used at 30 and 60 DAT. The chlorophyll content of leaves was measured indirectly using the SPAD 502 (Konica Minolta, Inc., Tokyo, Japan) chlorophyll meter. To ensure accuracy, fully expanded and youngest leaves were chosen, and 10 readings were taken from 10 different hills plant⁻¹ plot⁻¹ to represent the SPAD value. The readings were collected from the midpoint of the leaf blade, specifically between the leaf base and the tip. To obtain yield data, rice plants were harvested from the central 4 m² region of each plot at maturity. The harvest index, 1000-grain weight, filled grains panicle⁻¹ (nos.) and panicles m⁻² were measured.

2.4. Calculation of Related Indicators

The grain yield efficiency index (GYEI) was used for classifying the genotypes as efficient or inefficient nitrogen users. Grain yield is the best measure of genotype evaluation in screening experiments. The GYEI helps to separate genotypes into high-yielding, stable, nutrient-efficient genotypes and low-yielding, unstable, nutrient-inefficient genotypes [27].

GYEI = (grain yield of rice genotype for a low-level N input/average grain yield of 10 rice genotypes for a low-level N input) × (grain yield of rice genotype for a high-level N input/average grain yield of rice genotypes for a high-level N input) [27].

2.5. Data Analysis

The data obtained from two consecutive years of the study were analyzed using the available, open-access software R studio (agricolae package of R Version), and a probability level of <0.05 was used to determine statistical significance. Two years of mean data of the crop traits recorded were used to make Pearson’s correlation coefficient matrix or a diagram using the package ‘Metan’ (multi-environment trial analysis) in R studio [28]. The ANOVA details are provided in Supplementary Tables S3–S17.

3. Results

3.1. Growth

3.1.1. Number of Tillers

Among all the genotypes, ‘Nidhi’ had the highest number of tillers at 30 and 60 days after transplanting (DAT) and at the harvesting stage, and the next best genotype was ‘Daya’ (

Table 2. Effect of nitrogen fertilization on tillers and chlorophyll index (30 DAT) of rice.

Nitrogen/Genotype	SPAD Values at 30 DAT		Tillers m−2 at 30 DAT
	2020	2021	2020	2021
N0	25.3 b	24.4 b	89.0 c	88.0 c
N60	31.3 a	30.4 a	112 b	111 b
N120	33.5 a	32.5 a	129 a	128 a
‘Tella Hamsa’	31.1 bc	30.2 bc	103 ef	102 ef
‘Vasumati’	33.2 ab	32.2 ab	109 cde	111 bcd
‘VL Dhan 209’	27.6 de	26.7 de	107 de	107 de
‘Daya’	34.9 a	34.0 a	118 ab	116 abc
‘PB 1728’	33.3 ab	32.5 ab	116 abc	113 bcd
‘Anjali’	29.2 cd	28.1 cd	99 f	99 f
‘Heera’	31.1 bc	30.3 bc	102 ef	103 e
‘Birupa’	23.8 f	22.6 f	111 bcd	118 ab
Nagina22	30.9 bc	30.1 bc	113 bcd	109 cde
‘Nidhi’	25.4 ef	24.1 ef	122 a	122 a
N	11.8	11.79	2.25	2.38
V	7.0	6.97	3.05	2.87
N × V	ns	ns	ns	ns

ns = non-significant; DAT = days after transplanting. Values in a column followed by different letters are significantly different at p < 0.05 as determined by LSD among the genotypes; Letters indicate the comparison among genotypes at different N levels; N = nitrogen, V = genotype.

and

Table 3. Effect of nitrogen × genotype interaction on chlorophyll index (SPAD) at 60 days after transplanting (DAT) and number of tillers at 60 DAT and harvesting stage of rice.

Nitrogen × Genotype		‘Tella Hamsa’	‘Vasumati’	‘VL Dhan 209’	‘Daya’	‘PB 1728’	‘Anjali’	‘Heera’	‘Birupa’	‘Nagina 22’	‘Nidhi’	Mean
SPAD value (60 DAT)
2020	N0	38.7 mn	42.3 ij	33.6 pq	40.9 kl	43.9 efgh	38.0 n	42.6 hij	24.6 s	37.5 n	34.8 op	37.7 c
	N60	44.9 de	42.9 ghi	38.8 mn	46.5 ab	45.1 cde	41.4 jk	44.0 efg	30.3 r	39.9 lm	36.0 o	41.0 b
	N120	46.3 bc	47.1 ab	46.1 bcd	47.8 a	44.9 de	43.5 fghi	44.7 ef	32.7 q	46.3 bc	38.8 mn	43.8 a
	Mean	43.3 d	44.1 bc	39.5 f	45.1 a	44.6 ab	40.9 e	43.8 cd	29.2 h	41.2 e	36.5 g
	* N × G = 1.32/* G × N = 1.30
2021	N0	38.3 p	41.4 l	32.6 u	40.0 n	43.0 i	37.0 q	41.6 kl	23.7 x	36.5 r	33.6 t	36.8 c
	N60	43.7 h	41.8 jp	38.4 p	45.6 cd	44.1 g	40.0 m	43.2 i	29.4 w	39.0 o	35.0 s	40.1 b
	N120	45.3 d	46.1 b	44.8 e	46.7 a	44.5 ef	42.0 j	44.1 fg	31.1 v	45.9 bc	37.3 q	42.8 a
	Mean	42.4 d	43.1 c	38.6 g	44.1 a	43.9 b	40.0 f	43.0 c	28.1 i	40.4 e	35.3 h
	* N × V = 0.38/* V × N = 0.37
Tillers (60 DAT)
2020	N0	292 op	307 no	280 pq	395 k	399 jk	239 r	268 q	311 n	368 m	413 j	327 c
	N60	365 m	385 kl	378 lm	538 e	504 f	297 nop	361 m	390 kl	501 f	564 d	428 b
	N120	454 h	474 g	451 h	620 b	590 c	375 lm	433 i	464 gh	583 c	647 a	509 a
	Mean	370 f	388 e	369 f	517 b	498 c	304 h	354 g	388 e	484 d	541 a
	* N × G = 16.94/* G × N = 16.21
2021	N0	268 t	310 q	280. st	399 jk	368 nop	238 u	292 rs	393 kl	305 qr	411 j	326 c
	N60	361 p	389 klm	378 lmno	505 f	501 f	295 qrs	365 op	536 e	383 klmn	562 d	428 b
	N120	432 i	464 gh	450 h	589 c	582 c	375 mnop	453 h	620 b	473 g	647 a	508 a
	Mean	354 g	387 e	369 f	4976 c	484 d	303 h	370 f	516 b	387 e	540 a
Tillers at harvest
2020	N0	269 pqr	302 nop	277 opq	392 fghi	375 ghij	238 r	251 qr	320 lmn	379 ghij	409 fg	321 c
	N60	361 hijk	375 ghij	346 jkl	509 cd	489 de	303 nop	340 klm	396 fgh	461 e	530 c	411 b
	N120	408 fg	391 fghi	359 ijk	595 ab	569 b	309 mno	381 ghij	416 f	492 de	621 a	454 a
	Mean	346 fg	356 f	328 gh	499 b	478 c	284 i	324 h	377 e	444 d	520 a
	* N × G = 34.9/* G × N = 34.5
2021	N0	250 qr	319 lmn	276 opq	374 ghij	378 ghij	238 r	268 pqr	391 fghi	301 nop	408 fg	320 c
	N60	338 klm	395 fgh	346 jkl	488 de	460 e	303 nop	360 hijk	508 cd	374 ghij	529 c	410 b
	N120	380 ghij	415 f	358 ijk	568 b	492 de	308 mno	407 fg	593 ab	391 fghi	619 a	453 a
	Mean	323 h	376 e	327 gh	477 c	443 d	283 i	345 fg	497 b	355 f	519 a
	* N × G = 34.6/* G × N = 34.2

* LSD (p = 0.05) for nitrogen means at same or different level of genotypes; * LSD (p = 0.05) for genotypes means at same or different level of nitrogen. DAT = days after transplanting; SPAD = soil plant analysis development. Values in a column followed by different letters are significantly different at p < 0.05 as determined by LSD. Letters indicate the comparison among genotypes at different N levels.

). In the first and second years at 30 DAT, the number of tillers m⁻² decreased significantly in the order of ‘Nidhi’ = ‘PB 1728’ = ‘Daya’ > ‘Nagina 22’ = ‘Birupa’ > ‘Vasumati’ > ‘VL Dhan 209’ > ‘Tella Hamsa’ = ‘Heera’ > ‘Anjali’. All the genotypes responded up to N₁₂₀ and the highest numbers of tillers were observed at 60 DAT. During the first and second years, the order of genotypes with respect to the significantly decreasing tiller numbers at 60 DAT was ‘Nidhi’ > ‘Daya’ > ‘PB 1728’ > ‘Nagina 22’ > ‘Birupa = ‘Vasumati’ > ‘VL Dhan 209’ = ‘Tella Hamsa’ > ‘Heera’ > ‘Anjali’, and ‘Nidhi’ = ‘Birupa’ = ‘Daya’ > ‘PB 1728’ > ‘Vasumati’ > ‘Nagina 22’ = ‘VL Dhan 209’ > ‘Heera’ = ‘Tella Hamsa’ > ‘Anjali’, respectively. Only four genotypes (‘Daya’, ‘Heera’, ‘Nidhi’, and ‘Tella Hamsa’) responded up to N₁₂₀ at the harvesting stage. The order of genotypes with respect to the significant decrease in the number of tillers at the harvesting stage was ‘Nidhi’ = ‘Daya’ = ‘PB 1728’ > ‘Nagina 22’ > ‘Birupa’ > ‘Vasumati’ = ‘Tella Hamsa’ > ‘VL Dhan 209’ = ‘Heera’ > ‘Anjali’ in the first season and ‘Nidhi’ > ‘Birupa’ > ‘Daya’ > ‘PB 1728’ > ‘Vasumati’ > ‘Nagina 22’ = ‘Heera’ = ‘VL Dhan 209’ ≥ ‘Tella Hamsa’ > ‘Anjali’ in the second season.

3.1.2. Chlorophyll

SPAD (soil plant analysis development) values were measured in order to determine the status of chlorophyll in leaves at different growth stages. The genotype ‘Daya’ had a higher chlorophyll content at 30 and 60 DAT, which was on par with the genotype ‘PB 1728’ (Table 2 and Table 3). With the N level N_120, the genotypes ‘Birupa’ and ‘VL Dhan 209’ reported the highest chlorophyll content. In both years at 30 DAT, the significantly decreasing chlorophyll content in different genotypes followed the order ‘Daya’ = ‘PB 1728’ = ‘Vasumati’ > ‘Heera’ = ‘Tella Hamsa’ = ‘Nagina 22’ > ‘Anjali’ > ‘VL Dhan 209’ > ‘Nidhi’ = ‘Birupa’. Similarly the order of the genotypes with respect to the significantly decreasing chlorophyll content at 60 DAT was ‘Daya’ = ‘PB 1728’ = ‘Vasumati’ = ‘Heera’ > ‘Tella Hamsa’ > ‘Nagina 22’ = ‘Anjali’ > ‘VL Dhan 209’ > ‘Nidhi’ > ‘Birupa’ in the first year, and ‘Daya’ > ‘PB 1728’ > ‘Vasumati’ = ‘Heera’ > ‘Tella Hamsa’ > ‘Nagina 22’ > ‘Anjali’ > ‘VL Dhan 209’ > ‘Nidhi’ > ‘Birupa’ in the second year.

3.1.3. Dry Matter Accumulation

In the first and second years at 30 days after transplanting (DAT), the dry matter accumulation (DMA) at 30 DAT, 60 DAT and the harvesting stage was 31.1% and 42.4%, 41% and 46.4%, and 32.9% and 38% higher with N₆₀ and N₁₂₀, respectively, over N₀ (

Table 4. Effect of nitrogen × genotype interaction on dry matter accumulation (g m⁻²) at 30, 60 DAT (days after transplanting) and at harvest of rice.

Nitrogen × Genotype		‘Tella Hamsa’	‘Vasumati’	‘VL Dhan 209’	‘Daya’	‘PB 1728’	‘Anjali’	‘Heera’	‘Birupa’	‘Nagina 22’	‘Nidhi’	Mean
30 DAT	N0	88 no	104 m	103 m	117 ijk	115 jkl	84 o	92 n	114 kl	104 m	119 ijk	104 c
(2020)	N60	123 hi	131 fg	121 hij	152 d	160 c	118 ijk	108 lm	134 ed	149 d	166 bc	136 b
	N120	132 fg	140 e	136 ef	164 c	172 ab	126 gh	121 hij	149 d	160 c	178 a	148 a
	Mean	114 h	125 f	120 g	145 c	149 b	109 i	107 i	132 e	137 d	154 a
	* N × G = 7.1/* G × N = 7.8
30 DAT	N0	84 n	106 lm	102 m	116 ij	113 jkl	91 n	87 n	118 ij	100 m	113 jk	103 c
(2021)	N60	117 ij	148 d	121 hi	151 d	133 ef	107 klm	122 hi	165 bc	130 fg	159 c	135 b
	N120	125 gh	159 c	135 ef	163 bc	149 d	120 hij	131 fg	177 a	140 e	170 ab	147 a
	Mean	109 g	138 c	120 e	144 b	131 d	106 g	114 f	153 a	123 e	147 b
	* N × G = 7.3/* G × N = 7.7
60 DAT	N0	311 l	456 hij	331 l	578 e	579 e	245 m	239 m	489 ghi	447 ijk	588 e	426 c
(2020)	N60	473 ghi	561 ef	498 gh	763 bc	773 ab	399 k	396 k	673 d	689 d	783 ab	601 b
	N120	491 ghi	582 e	523 fg	789 ab	800 ab	414 jk	408 jk	697 d	716 cd	820 a	624 a
	Mean	425 d	533 c	451 d	710 a	717 a	352 e	348 e	620 b	617 b	731 a
	* N × G = ns/* G × N = ns
60 DAT	N0	245 v	514 m	330 t	577 k	488 o	238 v	310 u	587 j	387 s	577 k	425 c
(2021)	N60	397 r	688 h	498 n	763 e	672 i	395 rs	472 p	782 c	560 l	772 d	600 b
	N120	413 q	715 f	522 m	788 c	697 g	407 q	491 no	819 a	582 jk	799 b	623 a
	Mean	352 i	639 d	450 g	709 c	619 e	347 i	424 h	730 a	510 f	716 b
	* N × G = 8.5/* G × N = 8.2
Harvest	N0	523 l	692 i	552 kl	862 f	860 f	444 m	439 m	788 gh	699 i	923 e	678 c
(2020)	N60	709 i	841 fg	748 hi	1145 b	1160 b	597 jk	594 jk	1009 d	1033 cd	1175 ab	901 b
	N120	737 hi	873 ef	784 gh	1183 ab	1100 ab	620 j	611 jk	1046 cd	1073 c	1230 a	936 a
	Mean	656 f	802 d	695 e	1063 b	1073 b	554 g	548 g	948 c	935 c	1109 a
	* N × G = 60.1/* G × N = 60.4
Harvest	N0	444 v	769 o	551 t	862 kl	787 n	438 v	522 u	922 j	620 r	858 l	677 c
(2021)	N60	596 s	1032 h	747 p	1144 e	1008 i	593 s	708 q	1174 c	840 m	1159 d	900 b
	N120	619 r	1072 f	783 n	1182 c	1045 g	610 r	736 p	1228 a	873 k	1198 b	935 a
	Mean	553 i	958 d	694 g	1062 c	947 e	547 i	655 h	1108 a	778 f	1072 b
	* N × G = 12.1/* G × N = 24.5

* LSD (p = 0.05) for nitrogen means at same or different level of genotypes; * LSD (p = 0.05) for genotypes means at same or different level of nitrogen; ns = non-significant; DAT = days after transplanting. Values in a column followed by different letters are significantly different at p < 0.05 as determined by LSD. Letters indicate the comparison among genotypes at different N levels.

). The highest DMA was produced by the ‘Nidhi’ and ‘Birupa’ genotypes at all the stages of growth. The order of genotypes with respect to the significantly decreasing DMA at 30 DAT was ‘Nidhi’ > ‘PB 1728’ > ‘Daya’ > ‘Nagina 22’ > ‘Birupa’ > ‘Vasumati’ > ‘VL Dhan 209’ > ‘Tella Hamsa’ > ‘Anjali’ = ‘Heera’ in 2020, and ‘Birupa’ > ‘Nidhi’ = ‘Daya’ > ‘Vasumati’ > ‘PB 1728’> ‘Nagina 22’ = ‘VL Dhan 209’ > ‘Heera’ > ‘Tella Hamsa’ = ‘Anjali’ in 2021. Regarding the DMA at 60 DAT, only two genotypes, ‘VL Dhan 209’ and ‘Daya’, responded up to N₁₂₀ in the first year, but in the second year all the genotypes responded up to N₁₂₀. The order of the genotypes with respect to the significantly decreasing DMA at 60 DAT, in the first and second years, was ‘Nidhi’ = ‘PB 1728’ = ‘Daya’ > ‘Birupa’ = ‘Nagina 22’ > ‘Vasumati’ > ‘VL Dhan 209’ = ‘Tella Hamsa’ > ‘Anjali’ = ‘Heera’, and ‘Birupa’ > ‘Nidhi’ > ‘Daya’ > ‘Vasumati’ > ‘PB 1728’ > ‘Nagina 22’ > ‘VL Dhan 209’ > ‘Heera’ > ‘Tella Hamsa’ = ‘Anjali’, respectively. Similarly, the order of the genotypes with respect to the significantly decreasing DMA at the harvesting stage was ‘Nidhi’ > ‘PB 1728’ = ‘Daya’ > ‘Birupa’ = ‘Nagina 22’ > ‘Vasumati’ > ‘VL Dhan 209’ > ‘Tella Hamsa’ > ‘Anjali’ = ‘Heera’ in the first year, and ‘Birupa’ > ‘Nidhi’ > ‘Daya’ > ‘Vasumati’ > ‘PB 1728’ > ‘Nagina 22’ > ‘VL Dhan 209’ > ‘Heera’ > ‘Tella Hamsa’ = ‘Anjali’ in the second year.

3.1.4. Leaf Area Index

During the two years of the study, the genotype ‘Nidhi’ recorded the highest leaf area index (LAI) at 30 days after transplanting (DAT) and 60 DAT (

Table 5. Effect of nitrogen × genotype interaction on leaf area index and number of panicles of rice.

Nitrogen × Genotype		‘Tella Hamsa’	‘Vasumati’	‘VL Dhan 209’	‘Daya’	‘PB 1728’	‘Anjali’	‘Heera’	‘Birupa’	‘Nagina 22’	‘Nidhi’	Mean
	LAI (30)
2020	N0	0.44 r	0.51 q	0.32 s	0.78 lmn	0.72 no	0.16 t	0.21 t	0.62 p	0.67 op	0.81 lm	0.52 c
	N60	1.12 j	1.21 i	1.00 k	1.66 de	1.50 f	0.77 mn	0.84 l	1.36 h	1.49 fg	1.69 d	1.26 b
	N120	1.54 f	1.62 e	1.42 g	2.06 a	1.91 b	1.18 ij	1.25 i	1.76 c	1.89 b	2.11 a	1.67 a
	Mean	1.03 e	1.11 d	0.91 f	1.5 a	1.37 b	0.7 h	0.77 g	1.25 c	1.35 b	1.54 a
	* N × G = 0.07/* V × N = 0.11
2021	N0	0.15 r	0.60 o	0.31q	0.66 no	0.70 mn	0.19 r	0.42 p	0.77 lm	0.49 p	0.80 l	0.51 c
	N60	0.76 lm	1.34 h	0.99 k	1.47 fg	1.48 f	0.83 l	1.11 j	1.64 de	1.20 i	1.68 cd	1.25 b
	N120	1.17 ij	1.75 c	1.40 gh	1.87 b	1.89 b	1.24 i	1.52 f	2.05 a	1.61 e	2.09 a	1.66 a
	Mean	0.69 h	1.23 c	0.9 f	1.33 b	1.36 b	0.75 g	1.01 e	1.49 a	1.1 d	1.52 a
	* N × G = 0.07/* G × N = 0.12
	LAI (60)
2020	N0	3.07 r	3.27 q	2.71 s	3.66 n	3.57 no	2.06 u	2.42 t	3.37 pq	3.46 op	3.86 m	3.15 b
	N60	4.66 j	4.96 i	4.36 k	5.97 c	5.67 e	4.07 l	4.17 l	5.17 h	5.47f	6.20 a	5.08 a
	N120	5.02 i	5.32 g	4.98 i	6.11 b	5.91 cd	4.13 l	4.58 j	5.41 fg	5.83 d	6.36 a	5.35 a
	Mean	4.25 g	4.52 f	4.02 h	5.25 b	5.05 c	3.42 j	3.72 i	4.65 e	4.92 d	5.47 a
	* N × G = 0.13/* G × N = 0.34
2021	N0	2.05 u	3.35 pq	2.70 s	3.45 op	3.55 no	2.40 t	3.05 r	3.65 n	3.25 q	3.85 m	3.13 b
	N60	4.05 l	5.15 h	4.35 k	5.45 f	5.65 e	4.15 l	4.65 j	5.95 c	4.95 i	6.20 a	5.07 a
	N120	4.10 l	5.40 fg	4.95 i	5.80 d	5.90 cd	4.55 j	5.00 i	6.10 b	5.30 g	6.35 a	5.33 a
	Mean	3.4 j	4.63 e	4 h	4.9 d	5.03 c	3.7 i	4.23 g	5.23 b	4.5 f	5.47 a
	* N × G = 0.14/* G × N = 0.35
	Panicles m−2
2020	N0	213 p	293 n	246 o	363 fghi	297 n	228 op	236 op	366 fghi	249 o	385 ef	288 c
	N60	302 mn	379 efgh	334 jkl	428 d	384 efg	315 lmn	317 lmn	436 d	341 ijkl	463.96 c	370 b
	N120	326 klm	395 e	352 hijk	478 bc	491 ab	335 jkl	331 jkl	492 ab	357 ghij	511 a	407 a
	Mean	281 f	356 d	311 e	423 b	391 c	293 f	294 f	431 b	316 e	453 a
	* N × G = 26.9/* G × N = 30.8
2021	N0	213 o	294 m	246 n	363 fgh	297 m	228 no	236 no	366 fgh	249 n	385 ef	287 c
	N60	302 lm	379 efg	334 ijk	428 d	384 efg	315 klm	317 klm	436 d	341 hijk	464 c	370 b
	N120	325 jkl	394 e	351 hij	477 bc	491 ab	335 ijk	330 jk	491 ab	357 ghi	510 a	406 a
	Mean	280 f	356 d	310 e	423 b	390 c	292 f	294 f	431 b	315 e	453 a
	* N × G = 26.9/* G × N = 30.8

* LSD (p = 0.05) for nitrogen means at same or different level of genotypes; * LSD (p = 0.05) for genotypes means at same or different level of nitrogen; DAT = days after transplanting. Values in a column followed by different letters are significantly different at p < 0.05 as determined by LSD. Letters indicate the comparison among genotypes at different N levels.

). With respect to LAI, all the genotypes responded up to N₁₂₀ at 30 DAT, but at 60 DAT, the genotypes had responses on par with N₆₀ and N₁₂₀. The N response order of the genotypes to the significantly decreasing LAI at 30 DAT was ‘Nidhi’ = ‘Daya’ > ‘PB 1728’ = ‘Nagina 22’ > ‘Birupa’ > ‘Vasumati’ > ‘Tella Hamsa’ > ‘VL Dhan 209’ > ‘Heera’ > ‘Anjali’ in the first year, and ‘Nidhi’ = ‘Birupa’ > ‘Daya’ = ‘PB 1728’ > ‘Vasumati’ > ‘Nagina 22’ > ‘Heera’ > ‘VL Dhan 209’ > ‘Anjali’ > ‘Tella Hamsa’ in the second year. With the exception of ‘Anjali’, ‘Nidhi’ and ‘Tella Hamsa’, all the genotypes responded up to N₁₂₀ after 60 DAT. The response of the genotypes to N with respect to LAI at 60 DAT was ‘Nidhi’ > ‘Daya’ > ‘PB 1728’ > ‘Nagina 22’ > ‘Birupa’ > ‘Vasumati’ > ‘Tella Hamsa’ > ‘Daya’ > ‘Heera’ > ‘Anjali’ in the first year, and ‘Nidhi’ > ‘Birupa’ > ‘PB 1728’ > ‘Daya’ > ‘Vasumati’ > ‘Nagina 22’ > ‘Heera’ > ‘VL Dhan 209’ > ‘Anjali’ > ‘Tella Hamsa’ in the second year.

3.2. Yield-Attributing Characteristics

3.2.1. Number of Panicles

In both years of the study, the genotype ‘Nidhi’ had the highest number of panicles m⁻², and the next best genotype was ‘Birupa’ (Table 5 and

Table 6. Effect of nitrogen fertilization on yield attributes of rice crop.

Treatment	Filled Grains Panicle−1		1000-Grain Weight (g)
	2020	2021	2020	2021
N0	92 c	91 c	20.5 b	19.5 b
N60	119 b	118 b	26.4 ab	25.4 ab
N120	128 a	127 a	29.6 a	28.6 a
‘Tella Hamsa’	96 d	95 d	24.0 c	23.1 cd
‘Vasumati’	115 bc	114 bc	25.5 abc	24.5 abcd
‘VL Dhan209’	102 cd	102 cd	24.9 bc	24.1 abcd
‘Daya’	129 a	128 a	27.3 ab	26.3 ab
‘PB 1728’	121 ab	120 ab	24.6 bc	23.9 abcd
‘Anjali’	98 d	97 d	23.6 c	22.5 d
‘Heera’	101 d	100 d	25.5 abc	24.7 abcd
‘Birupa’	131 a	130 a	27.9 a	26.8 a
‘Nagina 22’	105 cd	104 cd	24.4 bc	23.6 bcd
‘Nidhi’	133 a	132 a	27.0 ab	25.8 abc
Interaction	ns	ns	ns	ns

ns = non-significant; Values in a column followed by different letters are significantly different at p < 0.05 as determined by LSD. Letters indicate the comparison among genotypes at different N levels.

). Only four genotypes, viz. ‘Daya’, ‘PB 1728’, ‘Birupa’ and ‘Nidhi’, recorded a significant increase in panicles m⁻² up to N₁₂₀. The genotypes with significantly decreasing numbers of panicles m⁻² were ordered as follows: ‘Nidhi’ > ‘Birupa’ = ‘Daya’ > ‘PB 1728’ > ‘Vasumati’ > ‘Nagina 22’ = ‘VL Dhan 209’ > ‘Tella Hamsa’ = ‘Heera’ = ‘Anjali’.

3.2.2. Filled Grains

The highest number of filled grains panicle⁻¹ was obtained by the genotype ‘Nidhi’, and it was on par with ‘Birupa’ and Daya’ (Table 6). Genotypes with the significant decrease in the filled grains panicle⁻¹ followed the order: ‘Nidhi’ = ‘Birupa’ = ‘Daya’ = ‘PB 1728’ = ‘Vasumati’ > ‘Nagina 22’ = ‘VL Dhan 209’ > ‘Tella Hamsa’ = ‘Anjali’ = ‘Heera’.

3.2.3. 1000-Grain Weight

Similar values of the 1000-grain weight were recorded with N₆₀ and N_120, but both of these N levels recorded significantly higher 1000-grain weights over the control, i.e., N₀ (Table 6). The genotypic difference in significantly decreasing 1000-grain weight followed the order ‘Birupa’ ≥ ‘Daya’ = ‘Nidhi’ = ‘Heera’ = ‘Vasumati’ ≥ ‘VL Dhan 209’ = ‘PB 1728’ = ‘Nagina 22’ ≥ ‘Tella Hamsa’ = ‘Anjali’.

3.3. Grain Yield and Harvest Index

In both years, all the genotypes with N₁₂₀ application produced 8% and 2.7% higher grain yields and harvest indexes over N₆₀, and the genotypes ‘Nidhi’ and ‘Daya’ produced the highest grain yield and harvest index (

Table 7. Grain yield of rice (t ha⁻¹) as influenced by years, nitrogen and genotypes (main effect table).

Treatment	Grain Yield of Rice (t ha−1)
Year
2020	3.86 a
2021	3.77 a
Nitrogen
N0	2.82 c
N60	4.15 b
N120	4.48 a
Genotype
‘Tella Hamsa’	2.85 e
‘Vasumati’	3.86 d
‘VL Dhan209’	2.94 e
‘Daya’	5.06 b
‘PB 1728’	4.40 c
‘Anjali’	2.47 f
‘Heera’	2.84 e
‘Birupa’	4.40 c
‘Nagina 22’	3.81 d
‘Nidhi’	5.54 a

Values in a column followed by different letters are significantly different at p < 0.05 as determined by LSD. Letters indicate the comparison among years, nitrogen and genotypes.

,

Table 8. Effect of years × nitrogen × genotype interaction on grain yield (t ha⁻¹) of rice.

Year/Genotype	2020				2021
	N0	N60	N120	Mean	N0	N60	N120	Mean
‘Tella Hamsa’	2.80 kl	3.60 gh	3.80 fg	3.40 f	1.93 o	2.81 kl	2.19 mno	2.31 f
‘Vasumati’	2.60 lm	3.90 fg	4.05 f	3.52 ef	3.10 jk	4.35 f	5.15 e	4.20 c
‘VL Dhan209’	2.40 mn	3.20 ij	3.15 ijk	2.92 g	2.15 no	3.45 ghij	3.28 hij	2.96 e
‘Daya’	3.60 gh	5.30 c	6.60 b	5.17 b	3.45 ghij	5.33 de	6.06 abc	4.95 b
‘PB 1728’	3.40 hi	4.90 d	5.30 c	4.53 c	3.14i jk	4.53 f	5.13 e	4.27 c
‘Anjali’	1.95 o	2.90 jkl	3.05 ijk	2.63 f	2.01 no	2.62 lm	2.28 mno	2.30 h
‘Heera’	2.20 no	2.90 jkl	3.00 jk	2.70 gh	2.05 no	3.32 hij	3.56 ghi	2.98 e
‘Birupa’	3.10 ijk	3.75 fgh	4.05 f	3.63 e	3.38 ghij	5.62 cd	6.48 a	5.16 ab
‘Nagina 22’	3.15 ijk	4.45 e	5.20 cd	4.27 d	2.42 lmn	3.82 g	3.79 g	3.34 d
‘Nidhi’	3.80 fg	6.25 b	7.35 a	5.80 a	3.68 gh	5.91 bc	6.24 ab	5.28 a
Mean	2.90 c	4.12 b	4.56 a		2.73 c	4.18 b	4.41 a
* N:G:Y = 0.41/** N:G = 0.29/*** Y:G = 0.24/**** Y:N = 0.20

N = nitrogen; G = genotype; Y = year; *, **, ***, **** = LSD (p = 0.05). Values in a column followed by different letters are significantly different at p < 0.05 as determined by LSD. Letters indicate the comparison among genotypes at different N levels.

and

Table 9. Effect of nitrogen × genotype interaction on harvest index (%) of rice.

Nitrogen × Genotype		‘Tella Hamsa’	‘Vasumati’	‘VL Dhan 209’	‘Daya’	‘PB 1728’	‘Anjali’	‘Heera’	‘Birupa’	‘Nagina 22’	‘Nidhi’	Mean
	Harvest index (%)
2020	N0	35.5 jkl	36.5 ijk	28.5 p	40.5 ef	36.0 ijk	30.5 op	32.5 mno	35.5 jkl	39.0 fgh	44.0 bcd	35.8 c
	N60	36.5 ijk	37.5 hij	30.5 op	43.0 cd	37.5 hij	31.5 no	32.5 mno	36.5 ijk	40.0 efg	45.5 ab	37.1 b
	N120	36.5i jk	34.5 klm	32.5 mno	45.0 v	38.0 ghi	33.0 mn	33.5 lmn	37.5 hij	42.0 de	47.5 a	38.0 a
Mean	Mean	36.2 d	36.2 d	30.5 f	42.8 b	37.2 d	31.7 ef	32.8 e	36.5 d	40.3 c	45.7 a
	* N × G = 2.08/* G × N = 2.08
2021	N0	37.3 ij	39.7 efghij	37.0 j	41.2 cdefghi	40.1 efghij	37.6 hij	37.9 hij	42.0 bcdefg	37.9 hij	43.2 abcde	39.4 c
	N60	38.1 ghij	40.3 defghij	38.9 fghij	42.6 bcdef	41.4 bcdefgh	38.4 ghij	39.1 fghij	43.2 abcde	39.2 fghij	45.3 ab	40.7 b
	N120	39.1 fghij	40.9 cdefghij	40.8 cdefghij	44.2 abcd	42.7 bcdef	39.2 fghij	40.3 defghij	44.4 abc	40.5 cdefghij	46.8 a	41.9 a
	Mean	38.2 d	40.0 cd	38.9 d	42.7 b	41.4 bc	38.4 d	39.1 d	43.2 ab	39.2 cd	45.1 a
	* N × G = ns/* G × N = ns

* LSD (p = 0.05) for nitrogen means at same or different level of genotypes; * LSD (p = 0.05) for genotypes means at same or different level of nitrogen; ns = non-significant. Values in a column followed by different letters are significantly different at p < 0.05 as determined by LSD. Letters indicate the comparison among genotypes at different N levels.

). The pooled analysis revealed a significant interaction effect between years, N levels, and genotypes on grain yield (GY). No significant interaction effect of years and N levels on GY was observed. The grain yields were statistically similar during the two years of the study. GY increased successively with increasing N levels up to N₁₂₀. The interaction effect of years and varieties on GY was significant, with only five genotypes (‘Tela Hamsa’, ‘Daya’, ‘PB 1728’, ‘Nagina 22’, and ‘Nidhi’) showing no significant differences between years. The genotypes ‘Vasumati’ and ‘Anjali’ produced significantly higher grain yields in the year 2020 as compared to 2021. Contrary to the above, the genotypes ‘VL Dhan 209’, ‘Heera’ and ‘Birupa’ recorded significantly higher grain yields during the year 2021 over the year 2020. ‘Nidhi’ had the highest GY at N₁₂₀, but in the second year, it was on par with ‘Birupa’. The pooled results over two years showed that the ‘Nidhi’ genotypes had a significantly higher GY than all other genotypes. ‘Daya’ had the second-highest GY among all the genotypes and was significantly different from the others. ‘Anjali’ had the lowest GY and was significantly lower than all the other genotypes. ‘Nidhi’ recorded the highest yield at all N levels, indicating that it was the most efficient genotype for grain production across low, medium, and high N levels. The order of the genotypes with respect to the significantly decreasing grain yield was ‘Nidhi’ > ‘Daya’ > ‘Birupa’ ≥ ‘PB 1728’ > ‘Vasumati’ ≥ ‘Nagina 22’ > ‘Tella Hamsa’ ≥ ‘VL Dhan 209’ ≥ ‘Heera’ > ‘Anjali’. The grain yield response (pooled) with N₁₂₀ application was only achieved in six genotypes: ‘Vasumati’ ‘Daya’, ‘PB 1728’, ‘Birupa’, ‘Nagina 22’, and ‘Nidhi’. Regarding the significantly decreasing HI, the sequence of genotypes was ‘Nidhi’ > ‘Daya’ > ‘Nagina22’ > ‘PB1728’ = ‘Birupa’ = ‘Vasumati’ = ‘Tella Hamsa’ > ‘Heera’ ≥ ‘Anjali’ ≥ ‘VL Dhan 209’ in the first year, and ‘Nidhi’ ≥ ‘Birupa’ ≥ ‘Daya’ = ‘VL Dhan 209’ = ‘Vasumati’ = ‘Nagina 22’ ≥ ‘Heera’ = ‘VL Dhan 209’ = ‘Anjali’ = ‘Tella Hamsa’ in the second year.

3.4. Grain Yield Efficiency Index

Based on the grain yield efficiency index (GYEI), the lowland rice genotypes were classified as efficient (GYEI ≥ 1), moderately efficient (GYEI < 1–0.5), and inefficient (GYEI ≤ 0.5) with respect to the responses to N₆₀ and N₁₂₀ (

Table 10. Grain yield efficiency index (GYEI) of 10 rice genotypes.

Genotype	2020	2021
‘Tella Hamsa’	0.73	0.45
‘Vasumati’	0.84	1.09
‘VL Dhan 209’	0.54	0.68
‘Daya’	1.87	1.63
‘PB 1728’	1.38	1.17
‘Anjali’	0.47	0.39
‘Heera’	0.46	0.63
‘Birupa’	0.81	1.81
‘Nagina 22’	1.24	0.84
‘Nidhi’	2.46	2.02

, Figure 1). In this study, four rice genotypes (‘Daya’, ‘PB 1728’, ‘Nagina 22’, ‘Nidhi’) were classified as efficient N users, but in the second year, ‘Vasumati’ and ‘Birupa’ also became efficient N users and ‘Nagina 22’ became a moderately efficient N utilizer. Four genotypes (‘Tella Hamsa’, ‘Vasumati’, ‘VL Dhan 209’, ‘Birupa’) were grouped as moderately efficient N utilizers, and two genotypes (‘Anjali’, ‘Heera’) were inefficient N utilizers in 2020. In 2021, the genotype ‘Tella Hamsa’ was an inefficient N user.

3.5. Correlation Analysis among Agro-Morphological Traits or Parameters of Rice

The relationship among all plant traits such as leaf area index at 30 DAT (days after transplanting), 60 DAT (LAI-30 and LAI-60), chlorophyll index at 30 and 60 DAT (SPAD-30 and 60), tiller numbers at 30 and 60 DAT (TILL-30 and 60) and the harvesting stage (TILL-H), dry matter accumulation at 30 and 60 DAT (DMA-30 and 60) and harvest (DMA-H), panicles m⁻² (PAN), filled grains panicle⁻¹ (FGP), test weight (TW), grain yield (GY), straw yield (SY), biological yield (BY), and harvest index (HI) were analyzed using Pearson’s correlation coefficient (Figure 2, Figure 3 and Figure 4). With respect to different levels of N₀, N₆₀ and N₁₂₀, SPAD-30 was positively correlated with SPAD-60, but all remaining traits had a negative and non-significant correlation. Similarly, SPAD-60 showed a non-significant correlation with all traits. With the increase in N levels to N₆₀, the test weight showed a positive correlation with TILL-30 (p < 0.01) and PAN (p < 0.05), but with all remaining traits there was a negative, non-significant correlation. At the full dose of N application, the test weight showed a positive correlation with FGP, HI (p < 0.05), and TILL-30 (p < 0.01). The remaining agro-morphological traits or parameters of rice showed a significant positive correlation with each other at the three levels of N fertilization.

4. Discussion

Overall, the yearly variations in the significance of the data were quite similar, mainly due to the normal weather and identical field conditions across the two years. Though the grain yields were statistically similar during the two years of the study, the interaction of years, N levels and genotypes had a significant effect on the grain yield of rice.

4.1. Effect of Nitrogen

The application of N significantly increased all the agro-morphological performance measures including number of tillers, leaf area index (LAI), and chlorophyll content at all stages of growth. Conversely, each growth characteristic showed a different response to the application of N because each genotype had a unique genetic makeup. The better growth characteristics with increased N levels may be attributable to some genotypes being more N responsive and others being less N responsive, which may have reduced N loss and promoted better crop growth and development. The values recorded in the various performance indicators increased significantly from N₀ to N₆₀ at all stages of growth, but as the levels of N increased from N₆₀ to N₁₂₀, the data showed a significant difference between genotypes. The number of tillers m⁻², LAI and chlorophyll content showed a similar trend. As growth consistently increased with N input, the number of tillers steadily increased until reaching the peak at 60 DAT. With N₁₂₀ application, the genotypes showed a significant increase in tiller numbers compared to N₆₀ and N₀. This could have been the result of higher levels of LAI and chlorophyll in the leaf at all stages of growth, which improved the source-to-sink ratio and caused the plant to assimilate more photosynthates.

Nitrogen input regulates several plant hormones (e.g., auxin, cytokinin) and the expression of genes, all of which affect the emergence and growth of tillering [29,30,31,32]. Furthermore, Sui et al. [33] found that increasing the levels of N application at the recommended rate led to higher N absorption and utilization, which increased photosynthetic activity, so that N was quickly assimilated by rice plants to accelerate their growth. Generally, additional nitrogen considerably improved vegetative development, which ultimately led to better overall vegetative growth, including taller plants and more tillers m⁻². Different levels of N had a pronounced effect on the dry matter accumulation (DMA) of the rice genotype. Nitrogen fertilization in the first and second years with N₆₀ and N₁₂₀ increased the DMA at 30 days after transplanting (DAT) (30.8–31.4% and 42.2–42.5%), DMA-60 (40.9–4% and 46.3–46.4%) and DMA at harvest (32.8–32.9% and 37.9–38%) over N₀, respectively, which can be attributed to the split application of N in the genotypes and the maximum growth rate occurring at all these stages. A well-balanced application of N can increase the dry matter content by producing photo-assimilates in the leaves, which are the main center of plant growth during the vegetative stage, and later distributing the assimilates to the reproductive organs [34,35]. The yield attributes of different genotypes, such as the number of panicles m⁻², the number of filled grains panicle⁻¹ and the test weight, were significantly influenced by different levels of N.

The glutamate synthetase enzyme serves as a mobilizer of N during senescence and determines the number of grains, grain size, and grain filling in rice genotypes [36]. Nitrogen levels from N₀ to N₁₂₀ showed significant increases in panicles m⁻². This increase was possibly the reason behind the increase in tillers m⁻² with successive increases in N levels. In rice production, N is generally applied during the early vegetative stages to promote the number of panicles plant⁻¹. The application of N to rice plants in the early vegetative stages promotes panicles m⁻², while topdressing with N during the initiation stage of panicles increases the filled grains panicle⁻¹ [37]. Generally, greater tiller numbers m⁻² and panicle numbers are produced in genotypes that have higher N uptake [38]. The number of panicle-filled grains increased significantly at the N levels N₆₀ and N₁₂₀. The genotypes remained green for a longer time with increased N levels to some extent. This is applicable to genotypes based on genetic characteristics and their physiology. In both years, the application of N₆₀ and N₁₂₀ recorded the highest LAI, so this could have been the reason behind the higher number of filled grains panicle⁻¹ and the higher levels of source–sink ratio, which resulted in high carbohydrate formation from high photosynthesis.

The grain yield and genotype harvest index are, in general, significantly and positively correlated with N levels, soil fertility, weather, and environmental condition of a given area. The average grain yield increased for N₆₀ and N₁₂₀ over the control by 42.1% and 57.2% in the first season and 53.1% and 61.5% in the second season. N₁₂₀ produced 10.7% and 5.5% higher economic yields of the rice genotypes over N₆₀ in 2020 and 2021, respectively. Higher levels of N led to greater uptake, which was correlated with a greater sink in the genotype. However, it differed from genotype to genotype because of the genetic potential. A possible strategy to use energy resources efficiently and improve yield performance is to use suitable N-efficient genotypes to increase the rice harvest index. Several studies reported that leaf photosynthetic capacity and grain yield were closely associated with N fertilization, and a deficiency of N decreased both the photosynthetic capacity and grain yield [39,40]. Therefore, the rice genotype efficiency of using N is directly and positively governed by the genetic makeup of the cultivar.

4.2. Rice Genotype

Rice genotypes with different growth parameters, such as tiller numbers m⁻², leaf area, and chlorophyll content, were significantly influenced by the genetic characteristics of the cultivar. The genotypes ‘Nidhi’ and ‘Daya’ were significantly superior in tiller numbers m⁻². In particular, the genotypes ‘Nidhi’ and ‘PB 1728’ showed the highest LAI, while genotypes ‘Vasumati’ and ‘Daya’ showed the highest chlorophyll content compared to the other rice genotypes. The genetic characteristics of the genotype may be the cause of the different growth behavior of rice cultivars [41,42]. The ‘Nidhi’ genotype showed a higher leaf area index due to a higher tiller number m⁻² and a longer leaf width of the genotype. Rice plant tiller numbers are influenced by LAI [43]. In rice genotypes, other factors such as plant height [44,45], panicle size [46], and hormones [47] may be responsible for differences in tillering capacity between rice genotypes.

Genotypes such as ‘Nidhi’ and ‘Birupa’ had higher yield attributes such as panicle number m⁻², filled grains panicle⁻¹, and test weight. The higher yield attributes of this genotype were due to the greater surface area of the rice roots, the better overall growth and development of the plant, and the greater photosynthetic efficiency of the leaf area index at flowering and physiological maturity. The genotypes ‘Nidhi’ and ‘Daya’ produced a significantly greater grain yield and harvest index than the other genotypes. The highest grain yield and harvest index in this genotype was due to the better formation of yield-attributing characteristics. The number of panicles m⁻² was directly associated with grain yield (Table 6 and Table 7), also according to a previous study [48]. A higher yield is the result of an increased yield sink capacity, and the number of panicles per unit area is primarily responsible for the differential responses of grain yield to N rates [49,50,51]. Counce et al. [52] reported that rice grain yields increased mainly by increasing the tiller numbers. Similarly, in our study, the highest grain yield of ‘Nidhi’ and ‘Daya’ under the N₁₂₀ treatment was mainly attributed to the greater number of tillers, panicles m⁻², harvest index and increased biomass accumulation.

The grain yield efficiency index (GYEI) had a direct positive correlation with the grain yields of the genotypes. Grain yield is the best way to assess a genotype in screening experiments. Regarding GYEI, the four genotypes ‘Nidhi’, ‘Daya’, ‘PB 1728’, and ‘Nagina 22’ responded well to different levels of N, and these genotypes had a GYEI ≥ 1 (Table 10). The genotypes ‘Daya’, ‘PB 1728’, ‘Nagina 22’ and ‘Nidhi’ were efficient N utilizers. Our results indicated that the efficiency of N use was different in all rice genotypes. There is a lot of information on how different cultivars and species of rice use nitrogen differently [53,54]. Plant characteristics such as the shape of the root system and the density of root hairs have been linked to differences in the plants’ ability to thrive in low-nitrogen soils.

4.3. Interaction of Years, Nitrogen and Genotypes

The interaction of years, nitrogen levels and genotypes was found to have a significant effect on grain yield. The genotypes ‘Tela Hamsa’, ‘Daya’, ‘PB 1728’, ‘Nagina 22’, and ‘Nidhi’ showed no significant differences between years. These former genotypes produced similar yields during both years and can be considered stable genotypes. However, the grain yields of the genotypes ‘Vasumati’, ‘Anjali’, ‘VL Dhan 209’, ‘Heera’ and ‘Birupa’ differed significantly during the two years, and can thus be considered unstable genotypes. The pooled yield across the two years also showed that, in general, the most stable genotypes, viz. ‘Nidhi’, ‘Daya’ and ‘PB 1728’, produced significantly higher grain yields over the most unstable genotypes, viz., ‘Anjali’, ‘VL Dhan 209’ and ‘Heera’. Grain yield is a quantitative parameter that is determined by the additive main effect of environment (E) and genotype (G) in addition to the non-additive effect of the G X E interaction (GEI) [55]. Breeders focus on the GEI effect to identify the yield stability of genotypes across different conditions and environments, which cannot be determined separately [56,57]. The GY heritability is exposed to variability across different environments [58], which hinders the accuracy of superior varietal selection processes [59]. Thus, widely adapted genotypes with the ability to produce stable, high yields across diversified environments constitute a major objective for rice breeders [60].

4.4. Relationship between Agro-Morphological Traits or Parameters of Rice

Our results showed that the chlorophyll index at 30 DAT (SPAD-30) and 60 DAT (SPAD-60), test weight (TW), and the response of other plant traits in various rice genotypes were dramatically different when the levels of N increased. With successive increases in N levels, all traits except SPAD-30, SPAD-60, and test weight (TW) were significantly and positively correlated with each other (see Figure 2, Figure 3 and Figure 4) [61,62,63]. In the correlation analysis, panicles m⁻² (PAN) did not change with different levels of N. Harvest index had a positive correlation with GY [64]. Several studies reported that researchers could produce a high yield and nitrogen use efficiency (NUE) by increasing the distribution of biomass into the grain, rather than simply increasing its concentration [20]. On the basis of these results, it can be concluded that the application of low, medium, and high doses of N alone is not sufficient to improve the NUE, and that the selection of a genotype with the genetic potential for N accumulation is also an important factor for improving the NUE. Therefore, improving N metabolism and identifying genetic pathways involved in N metabolism may be the key to improving the NUE in rice genotypes.

5. Conclusions

The results obtained here are from field experiments conducted over two years that considered the overall performance of rice genotypes at different N levels and broadly based on multiple performance indicators. In particular, with respect to the grain yield, harvest index and grain yield efficiency index, the genotypes ‘Nidhi’ and ‘Daya’ can be considered N-efficient genotypes. Furthermore, with respect to the grain yield, the most stable genotypes were found to be ‘Nidhi’, ‘Daya’ and ‘PB 1728’, and the most unstable genotypes were ‘Anjali’, ‘VL Dhan 209’ and ‘Heera’. Thus, the genotypes ‘Nidhi’, ‘Daya’ and ‘PB 1728’ could be used to enhance the nitrogen use efficiency and to breed nitrogen-efficient genotypes.