Grain-Filling Characteristics and Yield Formation of Rice at Saline Field

Abstract

It is of great interest to utilize saline fields to promote rice production in China. It has still not been established how salinity stress affects grain-filling characteristics and the relationships with yield formation of rice in a saline field. This experiment was conducted with Ningjing 7 (salinity-tolerant rice variety) and Wuyunjing 30 (salinity-susceptible rice variety) in a non-saline field and a high-saline field in 2021 and 2022. The grain yields of Ningjing 7 and Wuyunjing 30 in a high-saline field were 37.7% and 49.8% lower (p < 0.05) than in a non-saline field across two years. Ningjing 7 exhibited a higher (p < 0.05) grain yield than Wuyunjing 30 in a high-saline field. The reductions in filled-grain percentage and grain weight in inferior grains were greater than in superior grains of Ningjing 7 and Wuyunjing 30. For Ningjing 7 and Wuyunjing 30, the total starch contents in superior and inferior grains at 15, 30, and 45 days after heading were reduced (p < 0.05) in a high-saline field compared to a non-saline field. The ADP–glucose pyrophosphorylase, granule-bound starch synthase, and starch synthase activities after heading in superior and inferior grains in a high-saline field were lower (p < 0.05) than those in a non-saline field, and the reductions were more pronounced for Wuyunjing 30. The maximum grain-filling rate and mean grain-filling rate were decreased, while the time to achieve the maximum grain-filling rate was increased in a high-saline field compared to a non-saline field, especially for Wuyunjing 30. The mean grain-filling rate and grain-filling amount in superior and inferior grains during the early, middle, and late stages were lower in a high-saline field than in a non-saline field. For Ningjing 7 and Wuyunjing 30, the reductions in the grain-filling amount in the inferior grains during the early, middle, and late stages in a high-saline field were greater than those in superior grains. Our results suggest that salinity stress inhibited the grain-filling rate, reduced the total starch content and affected key enzyme activities, which led to the poor sink-filling efficiency and yield performance of rice in a saline field, especially for the salinity-susceptible variety.

1. Introduction

Rice, one of the most important staple cereal crops, plays a pivotal role in ensuring food security worldwide [1]. Globally, achieving food security is increasingly challenged by rapid population growth, rises in food demand, and continuous reductions in arable land area [2]. Therefore, it is of great interest to utilize reserve land resources including saline lands to boost crop production, especially in China [3,4]. Although considered a salinity-sensitive crop, rice can adapt and grow well in a saline field under flood irrigation, which can lower soil salinity and increase the contents of soil carbon and nutrients of saline soils [5,6,7].

Rice grain-filling is a key physiological process determining sink-filling efficiency (filled-grain percentage and grain weight), thus influencing grain yield [8]. The sink-filling efficiency in rice greatly depends on the positions of spikelets on the panicle. Generally, spikelets located at different positions in a panicle present unsynchronized grain-filling dynamics and quality characteristics. Superior grains (SG) are usually located in the upper part of the panicle with characteristics of early flowering, fast grain-filling rate, and high grain weight, while inferior grains (IG) have the opposite characteristics [9,10]. The Richards equation is widely used to analyze rice grain-filling characteristics, which can provide a deeper understanding of the growth patterns of rice grains during the grain-filling process and the relationships with rice yield formation under abiotic stress [11,12,13]. For example, Kimbembe et al. [12] concluded that soil drying decreased the rice grain-filling rate, which resulted in a lower grain weight and grain yield of rice. The rice grain yield and the related morpho-physiological traits under salinity stress have been widely reported [14,15,16,17]. Salinity stress can easily damage the leaf chloroplast structure, and reduce chlorophyll content and net photosynthetic rate, thereby inhibiting plant photosynthesis and accumulation of assimilates [17,18]. Generally, the above studies on responses of rice yield formation to salinity were mostly conducted with pots under controlled conditions, with a relatively stable salinity concentration in the soils. The existing literature reported that the results observed in saline pots with controlled conditions may not correspond to those observed under saline field conditions, in which the soil salinity level varies greatly due to dynamics changes in soil water and salt content [4,19,20,21]. Thus, it is more practical to explore how salinity stress affects rice grain-filling characteristics and the relationships with yield formation of rice under saline conditions.

Starch is the major storage carbohydrate and constitutes approximately 90% of rice grains; therefore, the development of rice grains mainly depends on the starch biosynthesis of grains [22]. Previous studies reported that the starch content varied with the grains being located at different positions in a panicle, and SG would always present a faster starch accumulation rate than IG [23,24]. Starch accumulation in rice grains is a complex process that requires the coordinated participation of key enzymes including ADP–glucose pyrophosphorylase (AGPase), granule-bound starch synthase (GBSS), starch synthase (SSS), and starch branching enzyme (SBE) [25]. To date, few studies have analyzed the effects of salinity on enzyme activities involved in starch accumulation in grains [26,27]. For example, Chen et al. [26] reported that salinity stress significantly inhibited the activities of AGPase, SSS, and SBE, and resulted in a great decrease in the accumulation of amylose and amylopectin in grains after heading. To date, information on the starch content and its related enzyme activities of rice grains under salinity stress is still limited.

The main objectives of the study were to (1) investigate the effects of salinity stress on the grain-filling process and starch formation of rice, and (2) determine the relationships between the grain-filling characteristics and grain yield formation of rice when subjected to salinity stress at a field scale.

2. Materials and Methods

2.1. Experimental Site, Soil Properties, and Meteorological Information

The field experiment was conducted at Linhai Farm (120.38° E, 33.94° N) and Jinhaidao (120.44° E, 33.81° N), Yancheng, Jiangsu, China in 2021 and 2022. The Linhai Farm and Jinhaidao are approximately 41 km and 7 km from the Yellow Sea and share similar climatic conditions (Supplementary Materials: Figure S1). Linhai Farm has been engaged in rice-based rotation for nearly 60 years, which has helped to greatly decrease the soil salinity level. The experimental field at Jinhaidao is a relatively new reclaimed mudflat with a 2-year rice-planting history before our study. The soil’s physicochemical properties, such as soil organic carbon (SOC) and the electrical conductivity of soil-saturated paste extract (ECe), were determined following the method of Tejada et al. [28]. Soil was collected from a 0–20 cm depth and analyzed before the rice season across two study years. The two experimental sites differed mainly in their soil salinity concentration (the ECe of the experimental field at Linhai Farm ranged from 0.6 to 0.8 ds m⁻¹, and the ECe of the experimental field at Jinhaidao ranged from 8.1 to 8.6 ds m⁻¹), and other soil characteristics were relatively similar in the two fields across two years (Supplementary Materials: Table S1). Therefore, the experimental fields at Linhai Farm and Jinhaidao were regarded as a non-saline field (NSF) and a high-saline field (HSF), respectively, according to the saline soil classification by the USDA [29].

2.2. Rice Cultivar, Experimental Design, and Field Management

Two rice cultivars currently planted in local production, Ningjing 7 (NJ 7, salinity-tolerant rice) and Wuyunjing 30 (WYJ 30, salinity-susceptible rice), were grown in the paddy fields. Ningjing 7 produces higher (p < 0.01) grain yield and lower (p < 0.01) yield loss than Wuyunjing 30 under salinity stress [30,31]. The two cultivars are both japonica inbred rice, and similar in terms of the growth period from sowing to heading and from heading to maturity, as well as their total growth durations (Supplementary Materials: Table S2). Seeds of the two cultivars were kindly provided by Kingearth Seed Company (Yangzhou, Jiangsu, China).

The field experiments adopted a randomized block design with three replications for two years. Each plot covered 32 m² (8 m × 4 m) in 2021 and 36 m² (9 m × 4 m) in 2022. Seeds of the rice cultivars were sown on 20 May and transplanted on 9 June at a hill spacing of 0.3 m × 0.12 m with four seedlings per hill. The fertilizer management was the same in the two fields for the two study years. The total nitrogen (N) application rate was 270 kg ha⁻¹, with a ratio of 3:3:2:2 at 1 day (d) before transplanting, 7 d after transplanting, panicle initiation, and penultimate-leaf-appearance stage, respectively. In addition, 150 kg ha⁻¹ of phosphorus fertilizer and 180 kg ha⁻¹ of potassium fertilizer were applied at 1 d before transplanting. Irrigation methods during the rice-growing periods followed local recommendations; a water layer (1–2 cm) on the soil surface was maintained from rice transplanting to heading, and alternate wetting and drying irrigation was applied from heading to maturity. Weed and pest management were consistent across the experimental plots and followed local recommendations.

2.3. Sampling and Measurement

We used the same sampling and measurement methods at NSF and HSF for two years. The plants of 5 hills in each plot were collected and divided into leaves and stems at jointing, and panicles, leaves, and stems at heading and maturity; consecutively, the dry weights of the rice parts were determined after oven-drying (Memmert UF 260, Memmert GmbH, Büchenbach, Germany) at 75 °C for 80 h.

The leaf photosynthetic rate and relative chlorophyll content of the flag leaf were determined at 20 and 40 days after heading (DAH). The flag leaf photosynthetic rate was measured using two portable photosynthetic instruments (LI-6400, Li-Cor, Lincoln, NE, USA) with three replications. The photosynthetic instrument was calibrated following the instructions of the manufacturer, and measurements were conducted between 9:30 and 11:30 in the field. The relative chlorophyll content of the flag leaf was measured with the SPAD chlorophyll meter (SPAD-502, Konica Minolta, Osaka, Japan), and such measurements were performed between 15:00 and 16:30, with six replications in the field.

Plants heading and flowering on the same date were labeled as single stems with similar growing conditions, and 600 single stems were selected in each plot. The single plants were collected from heading to maturity, and 15 samples were collected at 4 d intervals to determine the grain-filling dynamics of SG and IG. The total sampling times of two rice cultivars planted at NSF and HSF were consistently 14, based on the grain-filling period (Supplementary Materials: Table S2). The SGs were grains flowering on the first 2 d, and the IGs were those flowering on the last 2 d. At each sampling, SG and IG were harvested and dried in an oven at 105 °C for 30 min, and then at 75 °C until constant weight.

Furthermore, labeled plants were collected at 15, 30, and 45 DAH to determine the total starch content and key enzyme activities involved in the starch synthesis of SG and IG. The total starch content was determined using a commercial kit (BC0700, Beijing Solarbio Science & Technology, Beijing, China). The key enzymes involved in the starch synthesis of rice included AGPase, GBSS, SSS, and SBE. The four enzymes’ activities were determined using commercial assay kits (BC0430, BC3295, BC1850, BC1865, Beijing Solarbio Science & Technology, Beijing, China). The detailed procedure of determining total starch content and the key enzyme activities followed the instructions of the manufacturer.

Rice plants from 200 hills were hand-harvested at maturity for grain yield measurement with a moisture content of 14% in each plot. Rice plants from 100 hills were collected to determine panicles per m², and 20 hills were collected for measuring spikelets per panicle, filled-grain percentage, and grain weight in each plot. In addition, rice plants from 10 hills were collected to determine the filled-grain percentage and grain weight of SG and IG in each plot.

2.4. Model Analysis of Grain Filling

In this study, Richards’s equation with a better-fit coefficient was adopted to simulate rice grain-filling dynamics. Richards’s equation was expressed as:

$$W=A{(1+B{\mathrm{e}}^{-kt})}^{-{\displaystyle \frac{1}{N}}}$$

(1)

where W represents the grain weight, A represents the final grain weight, and t represents the days after heading. The parameters B, K, and N were computed by the regression equation. The deviation of Equation (1) is the grain-filling rate Equation (2).

$$G={\displaystyle \frac{{AKBe}^{-Kt}}{{N(1+{Be}^{-Kt})}^{\frac{(N+1)}{N}}}}$$

(2)

After the parameters of Richards’s equation were estimated, the maximum grain-filling rate (G_max), mean grain-filling rate (G_mean), the time to achieve the maximum grain-filling rate (T_max), and effective grain-filling period (EP) were calculated as follows:

$${\mathrm{G}}_{\max }={\displaystyle \frac{\mathrm{A}\mathrm{K}}{{(1+\mathrm{N})}^{\frac{\mathrm{N}+1}{\mathrm{N}}}}},{\mathrm{G}}_{\mathrm{mean}}={\displaystyle \frac{\mathrm{A}\mathrm{K}}{2(\mathrm{N}+2)}},{\mathrm{T}}_{\max }={\displaystyle \frac{\mathrm{I}\mathrm{n}\mathrm{B}-\mathrm{I}\mathrm{n}\mathrm{N}}{\mathrm{K}}},\mathrm{EP}=-{\displaystyle \frac{\mathrm{I}\mathrm{n}\frac{{\left(\frac{100}{99}\right)}^{\mathrm{N}}-1}{\mathrm{B}}}{\mathrm{K}}}$$

(3)

The rice grain-filling process can be divided into early (0–t₁), middle (t₁–t₂), and late stages (t₂–t₃). The t₁, t₂, t₃, and G_mean during such three stages were calculated as follows:

$${\mathrm{t}}_1=-{\displaystyle \frac{\mathrm{I}\mathrm{n}\frac{{\mathrm{N}}^2+3\mathrm{N}+\mathrm{N}{({\mathrm{N}}^2+6\mathrm{N}+5)}^{1/2}}{2\mathrm{B}}}{\mathrm{K}}},{\mathrm{t}}_2=-{\displaystyle \frac{\mathrm{I}\mathrm{n}\frac{{\mathrm{N}}^2+3\mathrm{N}-\mathrm{N}{({\mathrm{N}}^2+6\mathrm{N}+5)}^{1/2}}{2\mathrm{B}}}{\mathrm{K}}},{\mathrm{t}}_3=-{\displaystyle \frac{\mathrm{I}\mathrm{n}\frac{{\left(\frac{100}{99}\right)}^{\mathrm{N}}-1}{\mathrm{B}}}{\mathrm{K}}}$$

(4)

$$\begin{gathered} {\mathrm{G}}_{\mathrm{mean}}\mathrm{during}\mathrm{early}\mathrm{stage}={\displaystyle \frac{W_1}{t_1}},{\mathrm{G}}_{\mathrm{mean}}\mathrm{during}\mathrm{middle}\mathrm{stage}={\displaystyle \frac{W_2-W_1}{t_2{-t}_1}}, \\ {\mathrm{G}}_{\mathrm{mean}}\mathrm{during}\mathrm{late}\mathrm{stage}={\displaystyle \frac{W_3-W_2}{t_3{-t}_2}} \end{gathered}$$

(5)

2.5. Statistical Analysis

The data were processed using analysis of variance (ANOVA) with the least significant difference (LSD) in SPSS 17.0 Software (SPSS Inc., Chicago, IL, USA). The figures were drawn with Sigmaplot 11 (Systat Software, Inc., Richmond, CA, USA).

3. Results

3.1. Grain Yield and Yield Components

When averaged across two years, the grain yields of NJ 7 and WYJ 30 at HSF were 37.7% and 49.8% lower (p < 0.05) than those at NSF. For NJ 7 and WYJ 30, the yield reductions at HSF were both attributable to the decreased (p < 0.05) panicles per m², spikelets per panicle, filled-grain percentage, and grain weight. NJ 7 and WYJ 30 shared a similar grain yield at NSF, while NJ 7 exhibited a 24.8% higher (p < 0.05) grain yield than WYJ 30 at HSF. NJ 7 had consistently higher (p < 0.05) spikelets per m² due to the presence of more spikelets per panicle, a greater filled-grain percentage, and greater grain weight than WYJ 30 at HSF (

Table 1. Grain yield and yield components of rice at two fields across two years.

Year	Field Type	Cultivar	Panicles per m2	Spikelets per Panicle	Spikelets per m2 (×103)	Filled-Grain Percentage (%)	Grain Weight (mg)	Actual Grain Yield (t ha−1)
2021	NSF	NJ 7	313 a	148 a	46.3 a	87.5 b	26.2 a	10.0 a
		WYJ 30	320 a	143 b	45.8 a	88.4 a	25.4 b	9.9 a
	HSF	NJ 7	274 b	121 c	33.2 b	80.8 c	24.6 bc	6.4 b
		WYJ 30	278 b	112 d	31.1 c	74.3 d	23.1 c	5.0 c
2022	NSF	NJ 7	324 a	145 a	47.0 a	86.9 a	25.9 a	10.2 a
		WYJ 30	332 a	140 b	46.5 a	87.2 a	25.1 b	10.2 a
	HSF	NJ 7	293 b	115 c	33.7 b	80.5 b	24.1 c	6.2 b
		WYJ 30	286 b	109 d	31.2 c	74.1 c	22.9 d	5.1 c
Analysis of variance (ANOVA)
Year			ns	ns	ns	ns	ns	ns
Field type			**	**	**	**	**	**
Cultivar			ns	*	ns	**	**	**
Year × Field type			ns	ns	ns	ns	ns	ns
Year × Cultivar			ns	ns	ns	ns	ns	ns
Field type × Cultivar			ns	ns	ns	**	ns	**
Year × Field type × Cultivar			ns	ns	ns	ns	ns	ns

NSF, non-saline field; HSF, high-saline field. NJ 7, Ningjing 7; WYJ 30, Wuyunjing 30. Values followed by different letters indicate statistical significance at the 0.05 probability level within the same year and the same column. In the ANOVA, ns is not significant at the 0.05 probability level, * is significant at the 0.05 probability level, and ** is significant at the 0.01 probability level, according to the LSD test.

).

For NJ 7 and WYJ 30, the numbers of SG and IG on the panicle were both lower (p < 0.05) at HSF than those at NSF across two years. The filled-grain percentage and grain weight in SG and IG of NJ 7 and WYJ 30 were decreased (p < 0.05) at HSF, compared with those at NSF. The reductions in filled-grain percentage and grain weight in IG were more extensive than those in SG of NJ 7 and WYJ 30 at HSF. For example, the grain weights in SG and IG of WYJ 30 at HSF were 10.4% and 20.9% lower than those at NSF when averaged across two years. Compared with WYJ 30, NJ 7 had a consistently higher (p < 0.05) number of SGs and IGs on the panicle, and a higher filled-grain percentage and grain weight in SG and IG, especially at HSF (

Table 2. Number of SGs and IGs on the panicle, and filled-grain percentage and grain weight in SG and IG of rice at two fields across two years.

Year	Field Type	Cultivar	Number of SGs on the Panicle	Number of IGs on the Panicle	SG		IG
					Filled-Grain Percentage (%)	Grain Weight (mg)	Filled-Grain Percentage (%)	Grain Weight (mg)
2021	NSF	NJ 7	17.2 a	33.0 a	91.7 b	28.1 a	82.7 b	25.5 a
		WYJ 30	16.1 b	30.7 b	92.6 a	27.2 b	83.8 a	25.0 b
	HSF	NJ 7	14.3 c	26.5 c	84.8 c	26.4 c	75.5 c	22.5 c
		WYJ 30	12.4 d	22.8 d	77.7 d	24.6 d	68.9 d	20.4 d
2022	NSF	NJ 7	16.9 a	32.4 a	90.9 a	27.8 a	84.4 a	25.2 a
		WYJ 30	15.8 b	29.8 b	90.5 a	26.9 b	84.8 a	24.7 b
	HSF	NJ 7	13.6 c	25.2 c	85.4 b	25.8 c	75.9 b	22.6 c
		WYJ 30	12.1 d	22.2 d	78.5 c	24.4 d	67.3 c	20.7 d
Analysis of variance (ANOVA)
Year			ns	ns	ns	ns	ns	ns
Field type			**	**	**	**	**	**
Cultivar			*	**	**	**	**	**
Year × Field type			ns	ns	**	ns	*	ns
Year × Cultivar			ns	ns	ns	ns	ns	ns
Field type × Cultivar			ns	ns	**	ns	**	**
Year × Field type × Cultivar			ns	ns	ns	ns	ns	ns

NSF, non-saline field; HSF, high-saline field. SG, superior grains; IG, inferior grains. NJ 7, Ningjing 7; WYJ 30, Wuyunjing 30. Values followed by different letters indicate statistical significance at the 0.05 probability level within the same year and the same column. In the ANOVA, ns is not significant at the 0.05 probability level, * is significant at the 0.05 probability level, and ** is significant at the 0.01 probability level, according to the LSD test.

).

3.2. Dry Matter Weight, Leaf Photosynthetic Rate, and SPAD Values

The dry matter weight at jointing, heading, and maturity, as well as dry matter accumulation from jointing to heading and from heading to maturity, of NJ 7 and WYJ 30 were reduced (p < 0.05) at HSF, compared with those at NSF. For example, the values of dry matter accumulation from heading to maturity of NJ 7 and WYJ 30 at HSF were 73.7% and 130.8% lower than those at NSF across the two study years, respectively. For NJ 7 and WYJ 30, the harvest index was higher (p < 0.05) at HSF than that at NSF. Compared with WYJ 30, NJ 7 had a consistently higher (p < 0.05) dry matter weight at jointing, heading, and maturity, and greater dry matter accumulation from heading to maturity at HSF. There was no significant difference in the harvest index between NJ 7 and WYJ 30 at NSF, while NJ 7 exhibited a higher (p < 0.05) harvest index than WYJ 30 at HSF (

Table 3. Dry matter weight and accumulation, and harvest index of rice at two fields across two years.

Year	Field Type	Cultivar	Dry Matter Weight (t ha−1)			Dry Matter Accumulation (t ha−1)		Harvest Index
			Jointing	Heading	Maturity	Jointing-Heading	Heading-Maturity
2021	NSF	NJ 7	4.1 a	10.6 a	17.2 a	6.5 a	6.6 a	0.497 c
		WYJ 30	4.0 a	10.7 a	16.9 b	6.7 a	6.2 b	0.501 c
	HSF	NJ 7	2.4 b	6.4 b	10.3 c	4.0 b	3.9 c	0.531 a
		WYJ 30	2.0 c	5.6 c	8.2 d	3.6 c	2.6 d	0.521 b
2022	NSF	NJ 7	3.8 a	10.8 a	17.4 a	7.0 a	6.6 a	0.501 c
		WYJ 30	3.9 a	10.8 a	17.6 a	6.9 a	6.8 a	0.498 c
	HSF	NJ 7	2.2 b	6.3 b	10.0 b	4.1 b	3.7 b	0.531 a
		WYJ 30	1.7 c	5.8 c	8.4 c	4.1 b	2.6 c	0.520 b
Analysis of variance (ANOVA)
Year			ns	ns	ns	ns	ns	ns
Field type			**	**	**	**	**	**
Cultivar			*	*	**	ns	**	*
Year × Field type			ns	ns	ns	ns	ns	ns
Year × Cultivar			ns	ns	ns	ns	ns	ns
Field type × Cultivar			*	*	**	ns	**	**
Year × Field type × Cultivar			ns	ns	ns	ns	ns	ns

NSF, non-saline field; HSF, high-saline field. NJ 7, Ningjing 7; WYJ 30, Wuyunjing 30. Values followed by different letters indicate statistical significance at the 0.05 probability level within the same year and the same column. In the ANOVA, ns is not significant at the 0.05 probability level, * is significant at the 0.05 probability level, and ** is significant at the 0.01 probability level, according to the LSD test.

).

For NJ 7 and WYJ 30, the flag leaf photosynthetic rate and SPAD values at 20 and 40 DAH at HSF were lower (p < 0.05) than those at NSF across the two years. For example, the flag leaf photosynthetic rate and SPAD values of NJ 7 at 20 DAH at HSF were 20.8% and 24.8% lower than those at NSF across two years. Compared with WYJ 30, NJ 7 had a 12.8% higher (p < 0.05) flag leaf photosynthetic rate at 20 DAH and a 35.4% higher rate at 40 DAH at HSF across two years. Similarly, NJ 7 had higher (p < 0.05) flag leaf SPAD values at 20 and 40 DAH than WYJ 30 at HSF (Figure 1).

3.3. Total Starch Content and Related Enzyme Activities

Total starch content in SG and IG of NJ 7 and WYJ 30 at 15, 30, and 45 DAH were reduced (p < 0.05) at HSF, compared with those at NSF. For example, the total starch content in SG at 15, 30, and 45 DAH of WYJ 30 at HSF was decreased by 42.6%, 16.3%, and 9.1% across the two years, compared with the values at NSF, respectively. There were no significant differences in total starch content in SG and IG between NJ 7 and WYJ 30 at NSF. The total starch content in SG at 15, 30, and 45 DAH was higher (p < 0.05) for NJ 7 than for WYJ 30 at HSF. Similar results were observed for total starch content in IG between NJ 7 and WYJ 30 at HSF (Figure 2).

The AGPase, GBSS, and SSS activities after heading in SG and IG of NJ 7 and WYJ 30 at HSF were all lower (p < 0.05) than those at NSF, while such a trend was not detected for the SBE activity. The reductions in AGPase, GBSS, and SSS activities after heading in SG and IG of WYJ 30 at HSF were more pronounced than those of NJ 7. For example, the AGPase activity in SG at 15 30, and 45 DAH of NJ 7 was decreased by 53.4%, 42.7%, and 78.5% at HSF, while the AGPase activity in IG at 15, 30, and 45 DAH of NJ 7 was decreased by 98.4%, 56.8%, and 105.4% at HSF, respectively. Generally, NJ 7 had higher (p < 0.05) AGPase, GBSS, and SSS activities after heading than WYJ 30 at NSF and HSF (

Table 4. AGPase, GBSS, SSS, and SBE activities after heading in SG of rice at two fields across two years.

Year	Field Type	Cultivar	AGPase Activity in SG (mol min−1 mg−1)			GBSS Activity in SG (mol min−1 mg−1)			SSS Activity in SG (mol min−1 mg−1)			SBE Activity in SG (U g−1)
			15 DAH	30 DAH	45 DAH	15 DAH	30 DAH	45 DAH	15 DAH	30 DAH	45 DAH	15 DAH	30 DAH	45 DAH
2021	NSF	NJ 7	15.8 a	43.8 a	14.1 a	6.4 a	16.8 a	4.5 a	3.4 a	5.3 a	1.8 a	3.8 a	6.5 a	2.3 a
		WYJ 30	12.5 b	35.6 b	11.5 b	5.1 b	13.6 b	3.7 b	2.9 b	4.3 b	1.7 a	3.0 b	5.3 b	1.9 a
	HSF	NJ 7	10.3 c	30.7 c	7.9 c	4.2 c	11.8 c	2.5 c	2.3 c	4.1 b	1.1 b	2.5 b	4.3 c	1.3 b
		WYJ 30	6.3 d	22.7 d	5.6 d	2.5 d	8.9 d	1.8 d	1.3 d	3.1 c	0.8 b	2.5 b	3.5 d	1.9 a
2022	NSF	NJ 7	15.7 a	42.7 a	13.5 a	6.3 a	16.3 a	4.3 a	3.9 a	5.4 a	1.9 a	3.8 a	6.3 a	2.2 a
		WYJ 30	11.3 b	32.9 b	10.5 b	4.6 b	12.6 b	3.4 b	2.9 b	4.3 b	1.5 b	3.7 a	5.9 a	2.3 a
	HSF	NJ 7	10.7 b	30.8 c	7.3 c	4.3 b	11.9 b	2.3 c	2.5 b	3.5 c	1.3 b	2.6 b	5.4 a	1.2 b
		WYJ 30	5.6 c	21.9 d	5.4 d	2.3 c	8.8 c	1.7 d	1.4 c	2.5 d	0.7 c	1.3 c	3.4 b	0.9 b
Analysis of variance (ANOVA)
Year			ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns
Field type			**	**	**	**	**	**	**	**	**	**	**	**
Cultivar			**	**	**	**	**	**	**	**	*	*	**	ns
Year × Field type			ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns
Year × Cultivar			ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns
Field type × Cultivar			ns	**	ns	ns	*	ns	ns	ns	ns	ns	ns	ns
Year × Field type × Cultivar			ns	ns	ns	ns	ns	ns	ns	ns	ns	*	ns	ns

AGPase, ADP–glucose pyrophosphorylase; GBSS, granule-bound starch synthase; SSS, starch synthase; SBE, starch branching enzyme. NSF, non-saline field; HSF, high-saline field. SG, superior grains. NJ 7, Ningjing 7; WYJ 30, Wuyunjing 30. DAH, days after heading. Values followed by different letters indicate statistical significance at the 0.05 probability level within the same year and the same column. In the ANOVA, ns is not significant at the 0.05 probability level, * is significant at the 0.05 probability level, and ** is significant at the 0.01 probability level, according to the LSD test.

and

Table 5. AGPase, GBSS, SSS, and SBE activities after heading in IG of rice in two fields across two years.

Year	Field Type	Cultivar	AGPase Activity in IG (mol min−1 mg−1)			GBSS Activity in IG (mol min−1 mg−1)			SSS Activity in IG (mol min−1 mg−1)			SBE Activity in IG (U g−1)
			15 DAH	30 DAH	45 DAH	15 DAH	30 DAH	45 DAH	15 DAH	30 DAH	45 DAH	15 DAH	30 DAH	45 DAH
2021	NSF	NJ 7	11.8 a	37.4 a	7.6 a	5.6 a	14.8 a	3.3 a	3.1 a	4.9 a	1.4 a	3.2 a	4.8 a	1.7 a
		WYJ 30	8.7 b	30.4 b	6.2 b	4.1 b	12.1 b	2.7 b	2.5 b	4.0 b	1.1 b	3.3 a	4.9 a	1.4 a
	HSF	NJ 7	7.7 b	25.4 c	4.3 c	3.6 b	10.2 c	1.8 c	2.2 b	3.7 c	0.8 c	2.1 b	3.1 b	1.0 a
		WYJ 30	4.3 c	19.7 d	3.0 d	2.0 c	7.8 d	1.3 d	1.2 c	2.6 c	0.6 d	1.2 c	3.6 b	0.7 a
2022	NSF	NJ 7	12.8 a	36.5 a	7.3 a	6.1 a	14.5 a	3.1 a	3.8 a	4.8 a	1.3 a	3.5 a	4.7 a	2.1 a
		WYJ 30	8.4 b	28.1 b	5.7 b	4.0 b	11.1 b	2.4 b	2.2 b	3.7 b	0.9 b	2.3 b	3.6 b	1.3 b
	HSF	NJ 7	8.0 b	26.2 b	3.9 c	3.8 b	10.9 b	1.7 c	2.4 b	3.3 c	0.9 b	2.2 b	3.3 b	1.3 b
		WYJ 30	4.3 c	19.1 c	2.9 d	2.0 c	7.5 c	1.1 d	1.0 c	2.2 d	0.4 c	2.2 b	3.5 b	1.7 ab
Analysis of variance (ANOVA)
Year			ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	**
Field type			**	**	**	**	**	**	**	**	**	**	**	**
Cultivar			**	**	**	**	**	**	**	*	**	*	ns	ns
Year × Field type			ns	ns	ns	ns	ns	ns	ns	ns	ns	*	ns	ns
Year × Cultivar			ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns	ns
Field type × Cultivar			ns	*	ns	ns	ns	ns	ns	ns	ns	ns	ns	*
Year × Field type × Cultivar			ns	ns	ns	ns	ns	ns	ns	ns	ns	*	ns	*

AGPase, ADP–glucose pyrophosphorylase; GBSS, granule-bound starch synthase; SSS, starch synthase; SBE, starch branching enzyme. NSF, non-saline field; HSF, high-saline field. IG, inferior grains. NJ 7, Ningjing 7; WYJ 30, Wuyunjing 30. DAH, days after heading. Values followed by different letters indicate statistical significance at the 0.05 probability level within the same year and the same column. In the ANOVA, ns is not significant at the 0.05 probability level, * is significant at the 0.05 probability level, and ** is significant at the 0.01 probability level, according to the LSD test.

).

3.4. Grain-Filling Dynamics and Characteristics

For NJ 7 and WYJ 30, the grain weight in SG increased first and then stabilized, while the grain weight in IG increased throughout the measurement period (Figure 3).

Generally, the differences in grain weight between SG and IG of NJ 7 and WYJ 30 were widened at HSF, compared with those at NSF. The grain-filling dynamics in SG and IG of NJ 7 and WYJ 30 at NSF and HSF were well fitted by the Richards equation (R² ≥ 0.960,

Table 6. Parameters of the Richards equation in SG and IG of rice at two fields across two years.

Year	Field Type	Cultivar	SG					IG
			A	B	N	K	R2	A	B	N	K	R2
2021	NSF	NJ 7	23.201	7222.224	4.300	0.507	0.964	19.741	12,351.510	4.000	0.285	0.978
		WYJ 30	22.410	7586.711	4.000	0.460	0.965	19.389	9851.484	3.727	0.279	0.981
	HSF	NJ 7	21.632	4867.788	3.100	0.409	0.990	17.128	11,967.810	3.455	0.266	0.985
		WYJ 30	20.212	4417.228	3.128	0.388	0.986	15.274	12,351.480	3.346	0.280	0.985
2022	NSF	NJ 7	23.006	7222.230	4.235	0.493	0.969	19.591	10,031.540	3.727	0.281	0.978
		WYJ 30	22.122	7951.202	4.000	0.463	0.969	19.204	9669.743	3.782	0.277	0.980
	HSF	NJ 7	21.190	4677.106	3.128	0.404	0.990	17.642	9467.813	3.237	0.264	0.985
		WYJ 30	20.049	4417.229	3.128	0.388	0.986	15.202	12,351.480	3.400	0.280	0.986

NSF, non-saline field; HSF, high-saline field. SG, superior grains; IG, inferior grains. NJ 7, Ningjing 7; WYJ 30, Wuyunjing 30.

).

The G_max and G_mean in SG and IG were both lower at HSF than those at NSF, especially for WYJ 30. For example, the G_max and G_mean values in IG of NJ 7 were 11.3% and 10.3%, respectively, while for WYJ 30 they were 18.3% and 17.4% lower at HSF than those at NSF across two years. The T_max of NJ 7 was increased by 21.6% in SG and 8.2% in IG, while it was 14.0% in SG and 3.7% in IG for WYJ 30 across two years at HSF, compared with those at NSF. Generally, the EP values in SG of NJ 7 and WYJ 30 were increased at HSF; however, such a trend was not detected for EP in IG. NJ 7 had consistently higher G_max and G_mean in SG and IG, but lower T_max and EP than WYJ 30 at NSF and HSF (

Table 7. Grain-filling parameters in SG and IG of rice at two fields across two years.

Year	Field Type	Cultivar	SG				IG
			Gmax (mg grain−1 d−1)	Gmean (mg grain−1 d−1)	Tmax (d)	EP (d)	Gmax (mg grain−1 d−1)	Gmean (mg grain−1 d−1)	Tmax (d)	EP (d)
2021	NSF	NJ 7	1.51	0.934	14.6	23.7	0.752	0.469	28.2	44.3
		WYJ 30	1.38	0.860	16.4	26.3	0.755	0.473	28.2	44.6
	HSF	NJ 7	1.37	0.868	18.0	29.2	0.663	0.417	30.7	47.9
		WYJ 30	1.21	0.764	18.7	30.5	0.635	0.401	29.3	45.6
2022	NSF	NJ 7	1.47	0.910	15.1	24.4	0.768	0.481	28.1	44.4
		WYJ 30	1.37	0.853	16.4	26.3	0.736	0.460	28.3	44.8
	HSF	NJ 7	1.32	0.834	18.1	29.5	0.704	0.445	30.2	47.6
		WYJ 30	1.20	0.758	18.7	30.5	0.625	0.394	29.3	45.7

NSF, non-saline field; HSF, high-saline field. SG, superior grains; IG, inferior grains. NJ 7, Ningjing 7; WYJ 30, Wuyunjing 30. G_max, maximum grain-filling rate; G_mean, mean grain-filling rate; T_max, the time to achieve the maximum grain-filling rate; EP, effective grain-filling period.

).

The duration values (days) in SG during the early, middle, and late stages of NJ 7 and WYJ 30 were higher at HSF than at NSF, while these values were not consistent with those in IG during the early, middle, and late stages. The G_mean and GFA in SG and IG during the early, middle, and late stages of NJ 7 and WYJ 30 were lower at HSF than those at NSF. For NJ 7 and WYJ 30, the reductions in GFA in IG during the early, middle, and late stages at HSF were greater than those in SG. When averaged across two years and two cultivars, the GFA values in SG during the early, middle, and late stages at HSF were decreased by 57.0%, 28.4%, and 14.6%, respectively, while they were decreased by 66.0%, 51.9%, and 44.3% for IG. NJ 7 exhibited higher GFA values due to G_mean in SG and IG during the early, middle, and late stages than WYJ 30 at NSF and HSF (

Table 8. Grain-filling characteristics of early, middle and late stages in SG and IG of rice in two fields across two years.

Year	Field Type	Cultivar	Early Stage			Middle Stage			Late Stage
			Duration Days (d)	Gmean (mg grain−1 d−1)	GFA (mg)	Duration Days (d)	Gmean (mg grain−1 d−1)	GFA (mg)	Duration Days (d)	Gmean (mg grain−1 d−1)	GFA (mg)
			SG
2021	NSF	NJ 7	10.8	0.965	179	7.8	1.342	179	5.1	0.422	37.4
		WYJ 30	12.2	0.795	156	8.4	1.228	165	5.8	0.383	35.6
	HSF	NJ 7	13.6	0.610	119	8.7	1.216	151	6.8	0.369	36.1
		WYJ 30	14.1	0.553	97	9.2	1.071	122	7.2	0.325	29.1
2022	NSF	NJ 7	11.1	0.919	172	7.9	1.306	175	5.3	0.410	36.7
		WYJ 30	12.3	0.782	151	8.3	1.219	160	5.7	0.380	34.5
	HSF	NJ 7	13.7	0.597	111	8.8	1.169	141	6.9	0.355	33.5
		WYJ 30	14.1	0.549	93.4	9.2	1.062	118	7.2	0.323	28.1
			IG
2021	NSF	NJ 7	21.5	0.399	282	13.5	0.669	299	9.3	0.209	64.3
		WYJ 30	21.5	0.379	250	13.5	0.672	278	9.7	0.208	61.6
	HSF	NJ 7	23.7	0.292	184	13.8	0.589	216	10.3	0.181	49.4
		WYJ 30	22.8	0.267	139	13.0	0.564	167	9.8	0.173	38.7
2022	NSF	NJ 7	21.4	0.384	266	13.4	0.683	296	9.6	0.211	65.6
		WYJ 30	21.5	0.377	242	13.7	0.655	266	9.7	0.203	58.6
	HSF	NJ 7	23.4	0.296	174	13.7	0.625	215	10.5	0.190	50.5
		WYJ 30	22.7	0.268	136	13.1	0.556	161	9.8	0.170	37.1

NSF, non-saline field; HSF, high-saline field. SG, superior grains; IG, inferior grains. NJ 7, Ningjing 7; WYJ 30, Wuyunjing 30. G_mean, mean grain-filling rate; GFA, grain-filling amount.

).

3.5. Correlation Analysis

Total starch contents in SG and IG at 15, 30, and 45 DAH were positively (p < 0.05, p < 0.01) correlated with actual grain yield and dry matter weight at maturity. G_max and G_mean in SG and IG were positively (p < 0.05, p < 0.01), while T_max was negatively (p < 0.05, p < 0.01), correlated with actual grain yield and dry matter weight at maturity. G_mean and GFA during the early, middle, and late stages in SG and IG were positively (p < 0.05, p < 0.01) correlated with actual grain yield and dry matter weight at maturity (

Table 9. Correlation analysis between actual grain yield and dry matter weight at maturity and grain-filling characteristics of rice in a saline field.

Parameter			Actual Grain Yield	Dry Matter Weight at Maturity
SG		Total starch content at 15 DAH	0.83 *	0.82 *
		Total starch content at 30 DAH	0.90 **	0.89 **
		Total starch content at 45 DAH	0.81 *	0.80 *
IG		Total starch content at 15 DAH	0.84 **	0.83*
		Total starch content at 30 DAH	0.90 **	0.89 **
		Total starch content at 45 DAH	0.80 *	0.79 *
SG		Gmax	0.84 **	0.83 *
		Gmean	0.80 *	0.79 *
		Tmax	−0.92 **	−0.92 **
		EP	−0.94 **	−0.94 **
IG		Gmax	0.94 **	0.93 **
		Gmean	0.93 **	0.92 **
		Tmax	−0.78 *	−0.80 *
		EP	−0.69	−0.71 *
SG	Early stage	Duration days	−0.92 **	−0.92 **
		Gmean	0.93 **	0.93 **
		GFA	0.96 **	0.96 **
	Middle stage	Duration days	−0.92 **	−0.91 **
		Gmean	0.85 **	0.84 **
		GFA	0.92 **	0.91 **
	Late stage	Duration days	−0.96 **	−0.96 **
		Gmean	0.89 **	0.88 **
		GFA	0.78 *	0.76 *
IG	Early stage	Duration days	−0.86 **	−0.87 **
		Gmean	0.99 **	0.99 **
		GFA	0.97 **	0.96 **
	Middle stage	Duration days	0.41	0.38
		Gmean	0.94 **	0.94 **
		GFA	0.96 **	0.95 **
	Late stage	Duration days	−0.59	−0.62
		Gmean	0.96 **	0.95 **
		GFA	0.95 **	0.94 **

SG, superior grains; IG, inferior grains. DAH, days after heading. G_max, maximum grain-filling rate; G_mean, mean grain-filling rate; T_max, the time to achieve the maximum grain-filling rate; EP, effective grain-filling period. GFA, grain-filling amount. *, p < 0.05; **, p < 0.01.

).

4. Discussion

4.1. Grain Yield Formation of Rice at Saline Field

Salinity stress is one of the major abiotic stresses causing yield loss in rice [14,15,16]. The present study was conducted in a saline field, with great differences especially in soil salinity level between NSF and HSF; besides this, soil organic carbon, total nitrogen, and available phosphorus at NSF were consistently higher than those at HSF (Supplementary Materials: Table S1), which indicates the poor soil nutrient availability of saline lands [32,33]. Here, our results clearly show that salinity stress affected (p < 0.01) rice grain yield and yield components, with a rice yield reduction of 36.1–50.1% for two varieties grown in saline soil compared to non-saline soil (Table 1). Previous studies reported that the yield loss of rice under salinity was attributable to decreased spikelets per panicle and grain weight [34], spikelets per panicle and filled-grain percentage [35], panicles per m², spikelets per panicle, filled-grain percentage, and grain weight [4,36]. In this study, panicles per m², spikelets per panicle, filled-grain percentage, and grain weight of rice cultivars were all decreased, and this resulted in a yield penalty of rice at HSF (Table 1), consistent with the studies of Meng et al. [4] and Zhu et al. [36].

Rice yield loss was closely associated with poor leaf photosynthetic characteristics under salinity stress [17]. It was reported that salinity stress undermined leaf protective enzyme systems, and resulted in poor leaf photosynthetic functions and premature leaf senescence in plants [37,38]. In this study, the flag leaf photosynthetic rate and SPAD values at 20 and 40 DAH of rice were decreased (p < 0.05) under salinity (Figure 1), which might lead to poor leaf assimilation capacity and lower biomass accumulation (Table 3). For cereals, grain yield is a function of total shoot biomass and harvest index. Previous studies have reported that salinity lowered the shoot biomass weight of rice, yet the effect on the harvest index remained controversial [4,34,39]. For example, Zeng and Shannon [34] reported that total shoot biomass and harvest index were both reduced under salinity stress; Meng et al. [4] concluded that salinity stress decreased total shoot biomass while increasing harvest index. Here, our results demonstrate that dry matter weight at maturity was reduced, while the harvest index was increased, for NJ 7 and WYJ 30 under saline field conditions. This result indicates that the yield loss of rice under salinity stress can mainly be attributed to the lower shoot biomass weight rather than the harvest index. The increased harvest index under salinity stress was closely associated with the plant senescence of rice. As mentioned above, leaf photosynthetic rate and SPAD values after heading were decreased under salinity stress (Figure 1), and it has been shown that the accelerated plant senescence of rice could enhance non-structural carbohydrates’ (NSCs’) remobilization and increase the harvest index [40,41,42]. Besides this, the dry matter weight and harvest index were affected (p < 0.01, p < 0.05) by rice cultivar; NJ 7 (salinity-tolerant rice) produced a higher (p < 0.05) dry matter weight at maturity and a higher harvest index than WYJ 30 (salinity-susceptible rice) at HSF (Table 3), indicating that the improved assimilate accumulation and assimilate transportation efficiency both contributed to the higher grain yield of NJ 7 under saline field conditions.

4.2. Effects of Salinity Stress on Grain-Filling Characteristics of Rice at Saline Field

For rice, the different responses of filled-grain percentage and grain weight between SG and IG to abiotic stresses (such as elevated temperature and drought) have been reported previously [11,12,13,43]. For example, Dou et al. [11] concluded that elevated temperature decreased the grain weight of SG while increasing the grain weight of IG. Our results demonstrate that the number of SGs and IGs, filled-grain percentage and grain weight in SG and IG were decreased under soil salinity. However, the decreases in filled-grain percentage and grain weight in IG were more pronounced than those in SG when subjected to salinity stress (Table 2). This result is not fully consistent with the report of Dou et al. [11]. The disagreement might be due to the different abiotic stresses and their duration. In comparison, salinity stress applied in this study is a long-term stress, while the elevated temperature in the study of Dou et al. [11] is a relatively short-term stress; therefore, salinity stress inhibited both filled-grain percentage and grain weight in rice grains, especially for IG.

Rice grain-filling is a process of starch biosynthesis and accumulation [22]. In this study, salinity stress greatly affected starch accumulation and lowered total starch content in SG and IG (Figure 2). The synthesis and accumulation of starch in rice grains are regulated by changes in AGPase, GBSS, SSS, and SBE activities [25]. Our results indicate that salinity stress affected (p < 0.01) the AGPase, GBSS, SSS, and SBE activities in SG and IG; the AGPase, GBSS, and SSS activities after heading in the SGs and IGs of two rice cultivars were all reduced (p < 0.05) with the increase in soil salinity level, while such a trend was not detected for the SBE activity (Table 4 and Table 5). AGPase is proven to be an important enzyme promoting the production of the crucial precursor substance ADP–glucose (ADPG) for starch synthesis in grains, and the decrease in the activity of AGPase implies that less sucrose is converted into ADPG, lowering the rate of starch synthesis [25]. GBSS, SSS, and SBE are key enzymes involved in both amylose and amylopectin synthesis [22]. The effects of salinity stress on rice grain starch content, amylose content and amylopectin content, as well as the related enzyme activities, were not fully consistent, which might be attributed to the differences in salinity level and rice variety sensitivity to salinity [44,45,46]. For example, Chen et al. [44] reported that salinity stress significantly decreased the GBSS activity, while not for AGPase, SBE, and SSS activities in rice; the salinity-induced repression of GBSS activity was the most significant factor reducing the starch accumulation of rice. Sangwongchai et al. [46] reported that the GBSS and SBE activities of salinity-tolerant rice cultivars were increased significantly under salinity stress, leading to the increase in starch. The present study suggests that the activities of key enzymes involved in starch synthesis of SG and IG were inhibited under salinity stress and led to lower total starch contents in rice grains. Our results also show that the activities of key enzymes involved in starch synthesis were affected (p < 0.01, p < 0.05) by rice cultivar; NJ 7 (salinity-tolerant rice) had higher (p < 0.05) AGPase, GBSS, and SSS activities after heading (Table 4 and Table 5), which contributed to higher total starch content in SG and IG than in WYJ 30 (salinity-susceptible rice) (Figure 2). These findings indicate that higher activities of key enzymes involved in starch synthesis are beneficial for starch accumulation in grains. Starch accumulation in turn might be an important characteristic underlying the salinity tolerance of NJ 7 (Table 9).

Previous studies adopted the Richards equation to simulate the grain-filling of rice under the stresses of higher and lower temperatures and drought [11,12,13]. In this study, the grain-filling dynamics in SG and IG of two rice cultivars at NSF and HSF were well fitted by the Richards equation (R² ≥ 0.960, Table 6). This result suggests that the Richards equation was well fitted to simulate grain-filling dynamics of rice under abiotic stresses. The present study has shown that NJ 7 and WYJ 30 were rice cultivars of synchronous grain-filling type under both NSF and HSF, based on the time difference between the points at which the SG and IG reached their G_max (Table 7). This result indicates that salinity stress did not change rice grain-filling patterns. Similar to the other abiotic stresses [11,12], salinity stress reduced the G_max and G_mean while increasing the T_max in SG and IG; salinity stress also decreased the G_mean and GFA in SG and IG during the early, middle, and late stages of rice (Table 7 and Table 8). Such results indicate that salinity stress lowered the grain-filling rate and resulted in the significant deterioration of sink-filling efficiency and grain yield (Table 1 and Table 9). Besides this, our results show that the reductions in GFA in IG during the early, middle, and late stages at HSF were greater than those in SG, suggesting that the inhibitory effects of salinity stress on the grain-filling rate and amount of IG were stronger than those of SG (Table 8). It was reported that IG in the panicle generally has a poor physiological structure, which might limit its ability to compete for assimilates [47,48]; meanwhile, the key enzyme activities involved in the starch synthesis of IG were inhibited under salinity stress (Table 4 and Table 5). Therefore, salinity stress greatly influenced rice starch synthesis and accumulation (Figure 2, Table 4 and Table 5).

5. Conclusions

Salinity stress negatively affected rice the grain-filling characteristics, especially for the salinity-susceptible variety. Across the two varieties, rice yield was decreased by 36.1–50.1% in a saline field compared to non-saline field conditions. Salinity stress inhibited the leaf photosynthetic characteristics and reduced the biomass accumulation while increasing the harvest index. Salinity stress decreased total starch content, as well as the AGPase, GBSS, and SSS activities after heading in SG and IG. Salinity stress greatly lowered the grain-filling rate and resulted in the significant deterioration of grain-filling efficiency and grain yield. Salinity stress had a greater inhibitory effect on the grain-filling characteristics of IG than the effect it had on those of SG. Further studies may focus on investigating the sink–source relationship, grain nutrients’ dynamic transport processes, and the hormonal action and biochemical processes of salinity-affected rice, which can provide guidance for developing salinity-tolerant varieties and management strategies for rice under saline conditions.