Canopy-Level Regulation of Within-Boll Cotton Yield and Fiber Quality Under Staged Saline Water Supplemental Irrigation in Xinjiang

Abstract

Freshwater scarcity severely limits sustainable cotton production in arid regions. This study aimed to establish the optimal salinity threshold for staged saline water supplemental irrigation (SWSI) and elucidate its canopy-level mechanisms in optimizing within-boll yield components and fiber quality. A two-year field trial (2023–2024) was conducted in Awati County, Xinjiang, using mulched drip irrigation at five SWSI levels (3.5–9.5 g L⁻¹) and a freshwater control (CK). Compared with CK, 3.5 g L⁻¹ treatment significantly increased lint yield by 31.4%, boll number per plant by 22.45%, and fibers per seed by 6.01–10.59%, while fiber length and strength rose by 6.98–10.38% and 2.69–6.00%, respectively. When salinity reached 8.0 g L⁻¹, yield declined by 8.5%, and a salinity of 9.5 g L⁻¹ reduced yield by 24.52%. Spatially, mid-fruiting branches (nodes 4–6) remained stable, maintaining high lint mass per seed even under high salinity, whereas upper branches (≥node 7) were most sensitive; at 9.5 g L⁻¹, the boll number (0.36) was 56.6% lower than at 3.5 g L⁻¹ (0.83), and the Q-score decreased by 6.7%. These results demonstrate that SWSI with ≤5.0 g L⁻¹ salinity (optimum 3.5 g L⁻¹) simultaneously enhances lint yield and fiber quality, providing a practical strategy for efficient saline water use in arid cotton regions.

1. Introduction

Water scarcity is the primary constraint facing agricultural development in global arid regions [1,2]. As the pillar industry of Xinjiang, cotton comprises more than 90% of China’s arid region production [3]. Its sustainable cultivation is intimately tied to the security of the national textile supply chain and the livelihoods of local farmers; consequently, balancing water conservation with cotton yield protection has become a central goal of regional water governance. Notably, Xinjiang possesses approximately 34.82 billion tons of exploitable saline water resources, accounting for about 13.4% of China [4], with a distribution area of about 233,000–267,000 hectares [5]. Amid the rigid constraint of severe freshwater shortage, the scientific exploitation of saline water hinges on pinpointing the critical thresholds where cotton yield and quality remain coordinated and on unveiling the intrinsic mechanisms of saline water regulation; this constitutes a key scientific frontier that must be crossed to guarantee safe cotton production.

Research has indicated that, when compared with freshwater, saline water irrigation (EC: 3.0–15.7 dS m⁻¹) results in an average crop yield reduction of 17.3%; yet among crops such as maize, wheat, pepper, and tomato, cotton exhibits the least yield penalty under such conditions [6]. Grieve et al. proposed that optimal seed cotton yield could be achieved when the soil saturation extract salinity threshold is 7.7 dS m⁻¹ [7]. Yang et al. reported that when irrigation water salinity (EC is below 9 g L⁻¹), cotton yield and water use efficiency show no significant changes [8]; moreover, irrigation with water at 3 g L⁻¹ salinity can enhance cotton yield. Similarly, through a decade-long furrow irrigation experiment with saline water, Zheng et al. found that EC ≤ 4 g L⁻¹ (approximately 7.1 dS m⁻¹) had no significant effects on cotton plant height, leaf area index, or yield (p ≥ 0.05) [9]. However, it is particularly noteworthy that once irrigation water salinity exceeds the established threshold, it markedly inhibits cotton growth and development, leading to yield reductions alongside diminished fiber quality [10,11,12,13,14]. In an effort to clarify the impact mechanisms of saline water irrigation on yield, research attention has been largely directed toward yield components including boll density, boll weight, and lint percentage [14]. As lint cotton yield represents a complex composite trait, a more refined dissection of “within-boll” yield components is essential for elucidating the microscopic mechanisms underlying cotton yield formation [15,16].

The within-boll components provide a deeper physiological foundation for revealing fiber biomass accumulation in individual bolls. Smith and Coyle emphasized that bolls per unit area serve as the primary determinant of lint cotton yield, followed by seed number per boll and lint mass per seed [17,18]. More precisely, single-boll yield is collectively determined by a series of foundational components, including seed number per boll, fiber number per seed, and seed surface area. Among these, seed surface area, as the physical substrate for fiber attachment, acts as an effective factor influencing fiber density per unit seed surface and ultimate lint mass per seed [19]. In recent years, the within-boll yield analytical framework has been successfully applied to uncover the intrinsic mechanisms by which factors such as planting density, sowing date, irrigation volume, and phosphorus fertilization influence yield [20,21,22]. However, the effects of saline water irrigation on these critical within-boll traits remain underexplored.

Of greater importance, cotton plants exhibit pronounced phenotypic plasticity, whereby environmental influences lead to inherent spatial variations in boll yield and fiber quality contributions across different fruiting branch positions [23]. This intra-canopy spatial heterogeneity implies that salinity stress may not uniformly affect all bolls. Previous studies have largely overlooked this spatial dimension from a canopy structure perspective, failing to elucidate the patterns of variation in saline effects within the plant, thereby limiting our comprehensive understanding of the mechanisms by which salinity influences cotton yield and quality formation.

Furthermore, existing research on saline water irrigation has primarily concentrated on continuous irrigation regimes, extensively exploring their overall effects [6]. In practical production, particularly in arid regions like southern Xinjiang, where freshwater competition is acute during the squaring stage and seasonal droughts are prone during the flowering and boll stage, implementing phased saline water supplemental irrigation represents a more feasible and realistic water management strategy. The impacts of such phased supplemental irrigation on cotton yield—especially its influence on the spatially heterogeneous within-boll yield components—remain unknown.

Therefore, to address the aforementioned research gaps, we hypothesize that phased saline water supplemental irrigation during key growth stages exerts position-specific effects; moreover, supplementing with saline water of appropriate mineralization can optimize within-boll yield components at critical fruiting branch positions and improve comprehensive fiber quality, thereby maintaining or enhancing overall lint cotton yield at the plant level. The objectives of this study are (1) to quantify the optimal and threshold salinity levels of staged saline water supplemental irrigation for co-improving cotton lint yield and fiber quality, and (2) to elucidate the position-specific regulation mechanism of salinity on boll retention and within-boll yield components across different canopy positions. This research will provide a scientific basis for strategies enabling efficient utilization of saline water in arid cotton-growing regions.

2. Materials and Methods

2.1. Experimental Site

A two-year field experiment was conducted with cotton cultivar Xinlu Zhong 88 at the Awati Comprehensive Cotton Experimental Station of the Xinjiang Academy of Agricultural Sciences (40°06′ N, 80°44′ E; elevation 1025 m), located in Awati County, Aksu Prefecture, Xinjiang, China, during the cotton-growing seasons from April to October in 2023 and 2024. The region has a typical warm temperate continental arid climate, characterized by low precipitation, abundant sunshine, and high thermal resources. According to the China Meteorological Data Service Center, the long-term average annual precipitation is 46.4 mm, with 2679 h of sunshine, an annual accumulated temperature of 3987.7 °C, an average annual temperature of 10.4 °C, a frost-free period of 211 days, an annual temperature range of 34.0 °C, and a daily temperature range of 15.0 °C. Basic soil samples were collected from the 0 to 80 cm soil layer prior to sowing in 2023, as shown in

Table 1. Soil basic fertility and total salt of soil at a depth of 0–60 cm before cotton planting in this experiment.

Year	Soil Layer (cm)	Organic Matter (g·kg−1)	Alkaline Nitrogen (mg·kg−1)	Available Phosphorus (mg·kg−1)	Available Potassium (mg·kg−1)	Total Salt (%)
2023	0–20	7.94	18.59	16.89	132.83	0.72
	20–40	7.19	19.86	13.63	147.00	0.68
	40–60	7.27	14.89	12.12	159.00	0.60
2024	0–20	8.81	38.14	23.41	144.33	0.97
	20–40	8.90	36.93	24.64	126.00	0.93
	40–60	4.78	15.66	2.80	170.00	0.84

. Daily temperature and precipitation data during the cotton growth periods in both years were recorded and are presented in Figure 1.

2.2. Experimental Design

The experiment was arranged in a randomized complete block design with six irrigation treatments and three replications, using drip irrigation under plastic mulch. The control (CK) received conventional local freshwater irrigation without any supplementary irrigation, with a total irrigation quota of 3600 m³·ha⁻¹. The other five treatments involved supplementary irrigation using saline water with different salinity levels: 3.5 g·L⁻¹ (S3.5), 5.0 g·L⁻¹ (S5), 6.5 g·L⁻¹ (S6.5), 8.0 g·L⁻¹ (S8), and 9.5 g·L⁻¹ (S9.5). The saline water was sourced from a natural groundwater brine at the Awati Comprehensive Cotton Experimental Station of the Xinjiang Academy of Agricultural Sciences. The different salinity levels of irrigation water (3.5, 5.0, 6.5, 8.0, and 9.5 g·L⁻¹) were obtained by volumetrically blending a concentrated natural saline groundwater (12.5 g·L⁻¹) with freshwater (0.3 g·L⁻¹) in specific ratios. The mixing ratio was calculated using the following formula for the saline water volume fraction (x):

$$\mathrm{x} = {\displaystyle \frac{\mathrm{C}\mathrm{t}-\mathrm{C}\mathrm{f} }{\mathrm{C}\mathrm{s}-\mathrm{C}\mathrm{f}}}$$

(1)

where

• Ct is the target irrigation water salinity (e.g., 3.5, 5.0 g·L⁻¹, etc.),
• Cs is the saline water salinity (12.5 g·L⁻¹),
• Cf is the freshwater salinity (0.3 g·L⁻¹).

The ionic composition of the base saline water (mg·L⁻¹) was as follows: Na⁺, 2720.00; Ca²⁺, 416.79; Mg²⁺, 454.80; K⁺, 44.00; Cl⁻, 29.51; SO₄²⁻, 2951.31; HCO₃⁻, 0.24; and CO₃²⁻, 0.02.

Saline water was applied once at the squaring stage (375 m³·ha⁻¹), 60 days after sowing (DAS) on 13 June 2023, and 63 DAS on 9 June 2024, and twice during the flowering and boll setting stages, depending on high-temperature conditions (375 m³·ha⁻¹ and 450 m³·ha⁻¹, respectively) at 97 DAS (20 July) and 112 DAS (5 August) in 2023, as well as 99 DAS (15 July) and 123 DAS (8 August) in 2024. The total irrigation quota for the saline treatments was 4800 m³·ha⁻¹, including 1200 m³·ha⁻¹ of saline water. Each plot measured 27.6 m² (9.2 m × 3.0 m), and a buffer row of cotton (2.3 m wide) was left between plots to avoid treatment interference.

In this experiment, cotton seedlings were hand-thinned to a final density of approximately 237,000 plants ha⁻¹ at the three-leaf stage. Sowing was carried out on 14 April 2023 and 7 April 2024, respectively, and manual topping was performed on 12 July 2023 and 10 July 2024, while harvesting took place on 15 September in both years. Before sowing, fertilizer was applied as 3.0 t ha⁻¹ organic fertilizer, 225 kg ha⁻¹ urea (46.4% N), and 300 kg ha⁻¹ primary calcium phosphate (46% P₂O₅). The chemical fertilizers used included urea (N, 46.0%), monoammonium phosphate (N, 12.0%; P₂O₅, 61.0%), and potassium sulfate (K₂O, 50.0%). Nitrogen and phosphorus fertilizers were applied at rates of 240 and 130 kg ha⁻¹, respectively. All fertilizers were delivered through drip fertigation, and other agronomic practices followed local standard management for cotton production.

2.3. Sampling and Measurements

2.3.1. Within-Boll Yield Components

After all the bolls matured, within-boll yield determination was conducted on 2 October 2023 and 29 September 2024. The bolls were categorized into three groups based on their location on the fruit branch (FB): FBs1–3, FBs4–6, and FBs ≥ 7. A total of 90 plants (comprising 3 replicates of 30 plants each) were selected from each treatment to assess spatial boll distribution. The boll setting rate (BSR) for different FBs was calculated as the number of setting bolls divided by the total number of fruit nodes.

At a different plot, 20 bolls were randomly selected from the FB1–3, FB4–6, and FB ≥ 7 positions, respectively, and total boll weight on different FBs’ position were recorded. After drying the bolls to a constant weight in the sun, individual boll weights (BWs) for each FB position were calculated as follows:

$$\mathrm{Individual} \mathrm{boll} \mathrm{weights} (\mathrm{BWs}, \mathrm{g} \mathrm{per} \mathrm{boll}) ={\displaystyle \frac{\mathrm{Boll} \mathrm{weight} }{\mathrm{Boll} \mathrm{numbers}}}$$

(2)

Seed cotton was then ginned to obtain lint yield and lint percentage. The fuzzy seeds were collected and underwent acid-delinting and air drying for 24 h, after which the total seed count for each FB position was recorded. Then, the seed number per boll and the seed index (g per 100 seeds) were determined. Additionally, the number of fibers per seed, seed surface area (SSA), and lint mass per unit SSA were estimated for each FB position, according to the methods outlined by Groves et al. [24] and Li et al. [25]. The following formulas were used.

$$\mathrm{Lint} \mathrm{percentage} (\mathrm{LP}, \%)={\displaystyle \frac{\mathrm{Sampled} \mathrm{seed} \mathrm{weight}}{\mathrm{Sampled} \mathrm{lint} \mathrm{weight}}}\times 100$$

(3)

$$\mathrm{Seed} \mathrm{number} \mathrm{per} \mathrm{boll} (\mathrm{SPB}, \mathrm{no}.)= {\displaystyle \frac{\mathrm{Seeds} \mathrm{number} }{\mathrm{Sampled} \mathrm{bolls} \mathrm{number}}}$$

(4)

$$\mathrm{Seed} \mathrm{index} (\mathrm{SI}, \mathrm{g} \mathrm{per} 100\mathrm{seeds})= {\displaystyle \frac{\mathrm{Sampled} \mathrm{seed} \mathrm{weight}-\mathrm{Lint} \mathrm{weight}}{\mathrm{Seeds} \mathrm{number}}}\times 100$$

(5)

$$\mathrm{Lint} \mathrm{mass} \mathrm{per} \mathrm{seed} (\mathrm{LMS}, \mathrm{mg})={\displaystyle \frac{\mathrm{Sampled} \mathrm{lint} \mathrm{weight}\left(\mathrm{g}\right) }{\mathrm{Seeds} \mathrm{number}}}\times 1000$$

(6)

$$\mathrm{Fiber} \mathrm{per} \mathrm{seed} (\mathrm{no}.)={\displaystyle \frac{\mathrm{LMS}}{\mathrm{BPS}}}\times {10}^6$$

(7)

$$\mathrm{Seed} \mathrm{surface} \mathrm{area} (\mathrm{SSA}, {\mathrm{mm}}^2)=35.74+6.59\times \mathrm{SI}$$

(8)

$$\mathrm{Lint} \mathrm{mass} \mathrm{per} \mathrm{unit} \mathrm{SSA} (\mathsf{\mu }\mathrm{g})={\displaystyle \frac{\mathrm{LMS} (\mathrm{mg})}{\mathrm{SSA}}}\times 1000$$

(9)

2.3.2. Yield, Yield Components, and Fiber Quality

Cotton bolls from an area of 3.33 m² of each plot were hand harvested for cotton yield estimation. The ginned lint samples were sent to the Cotton Quality Testing Center, Ministry of Agriculture, Anyang, Henan, to obtain high-volume instrument (HVI) index data (uniformity (%), length (mm), strength (cN tex⁻¹), and micronaire value). The Q-score was calculated as follows [25]:

$$\mathrm{Q}-\mathrm{score} = 0.15 \times \mathrm{Uniformity} + 0.5 \times \mathrm{Fiber} \mathrm{length} + 0.1 \times \mathrm{Fiber} \mathrm{strength} + 0.25 \times \mathrm{Micronaire}$$

(10)

2.4. Statistical Analysis

All measured indicators were subjected to analysis of variance (ANOVA). A one-way ANOVA followed by least significant difference (LSD) post hoc tests (α = 0.05) was performed to assess the effects of saline irrigation treatments at each specific fruit branch position and year. Furthermore, a multifactorial ANOVA within the framework of the General Linear Model (GLM) was conducted to examine the main effects of treatment (T), fruiting branch position (FP), year (Y), and their interactions. When significant main effects were detected, mean comparisons were conducted using the LSD test at the 0.05 significance level.

Pearson’s correlation analysis was performed using all measured indicators to evaluate the relationships among variables. All statistical analyses were conducted using IBM SPSS Statistics 25.0 (SPSS Inc., Chicago, IL, USA), and figures were generated using Origin 2021 (Origin Lab Inc., Northampton, MA, USA). Post hoc comparisons were performed using the least significant difference (LSD) test at p < 0.05. Significant differences are indicated by different lowercase letters following the means at p < 0.05. Notation: ns, not significant (p > 0.05); * significant at p < 0.05.

3. Results

3.1. Cotton Yield and Yield Components

Water salinity treatment significantly affected all yield components (

Table 2. Effects of different treatments on boll density, boll weight, lint percentage, and lint yield from 2023 to 2024.

Year	Treatment	Boll Density (No./ha)	Boll Weight (g)	Lint Percentage (%)	Lint Yield (kg/ha)
2023	CK	95.11 d	5.41 ab	44.78 ab	2304.65 c
	S3.5	127.83 a	5.67 a	45.94 a	3329.27 a
	S5	115.89 b	5.42 ab	45.14 ab	2837.57 b
	S6.5	108.78 bc	5.11 bc	44.59 b	2470.88 c
	S8	101.81 cd	4.89 cd	44.00 bc	2194.92 cd
	S9.5	93.04 d	4.73 d	43.18 c	1903.84 d
2024	CK	106.75 bc	5.51 b	45.53 bc	2674.78 bc
	S3.5	119.34 a	5.71 a	47.25 a	3216.34 a
	S5	109.21 ab	5.72 a	46.31 ab	2892.21 b
	S6.5	100.63 bc	5.34 c	46.27 b	2487.21 c
	S8	97.52 bc	5.25 c	46.11 b	2361.82 cd
	S9.5	93.26 c	5.03 d	44.69 c	2095.10 d
Source of variance	T	*	*	*	*
	Y	ns	*	*	ns
	T × Y	ns	ns	ns	ns

Boll density, boll weight, lint percentage, and lint yield were measured under five saline supplementary irrigation treatments with varying mineralization levels (S3.5, S5, S6.5, S8, and S9.5), along with a control treatment (CK, no saline water irrigation). All data are means (n = 4). Post hoc comparisons were performed using the least significant difference (LSD) test at p < 0.05, and significant differences are indicated by different lowercase letters following the means. The effects of water treatment (T), year (Y), and their interaction were analyzed by univariate analysis of variance (ANOVA) within the framework of the General Linear Model. The significance of the interaction effect is indicated in the table (* for * p < 0.05; ns for not significant).

). Boll density, boll weight, lint percentage, and lint yield all showed marked responses to salinity level, whereas the effects of year were significant only for lint percentage (the overall pattern remained stable). No significant treatment × year interaction was observed for any variable, confirming consistent treatment responses between the years.

Compared with the CK, saline water supplementary irrigation markedly increased yield formation. Across the years, boll densities under S3.5 and S5 were 22.45% and 11.51% higher than CK, respectively, while S8 and S9.5 showed reduced boll values of 1.25% and 7.71% below CK. Boll weight exhibited a similar pattern, ranging from 4.73 g (S9.5) to 5.67 g (S3.5) in 2023 and 4.88–5.69 g in 2024. The increase in boll weight under S3.5 was about 3.6–4.8% higher than CK and 13.51–19.91% higher than S9.5. Lint percentage was slightly higher under mild–moderate salinity, reaching 45.14–47.25% (S3.5–S5) versus 43.18–45.53% in CK and S9.5. Lint yield followed the same trend, ranging from 1903.8 to 2095.10 kg ha⁻¹ under S9.5 to 3216.34–3329.27 kg ha⁻¹ under S3.5. On average, lint yield under S3.5 and S5 treatments increased by 31.45% and 15.07%, respectively, when compared with CK, whereas S8 and S9.5 treatments decreased yield by 8.49% and 24.52%, respectively.

3.2. Boll Setting Rate and Shading Rate

The boll setting rate demonstrated a consistent spatial pattern across all treatments, being highest in the FPs4–6 (39.32–62.72%), followed by the FPs1–3 (32.43–49.48%) and lowest in the FPs ≥ 7 (2.52–39.17%) (Figure 2). The S3.5 and S5 treatments significantly outperformed the CK treatment at all positions, with S3.5 increasing the rate by 14.63%, 9.10%, and 50.56% in the FPs1–3, FPs4–6, and FPs ≥ 7, respectively. The S3.5 treatment had the highest boll setting rate, which subsequently declined with increasing supplement irrigation water salinity. Over the two years, no significant difference was found between S3.5 (61.67%, 48.68%) and S5 (59.46%, 47.69%) in the FPs4–6 and FPs1–3, but both were significantly higher than all other treatments by 8.54–40.89% at FPs1–3 and 10.293–34.31% at FPs4–6. Significant differences existed among all treatments in the FPs ≥ 7, where the high-salinity treatments S8 and S9.5 resulted in the lowest rates (two-year averages: 12.83% and 7.18%, respectively), representing reductions of 58.19% and 76.60% compared to S3.5.

In a contrasting trend, the boll shedding rate exhibited an inverse pattern (FPs ≥ 7 > FPs1–3 > FPs4–6). The S3.5 treatment consistently resulted in the lowest shedding rate, while the S9.5 treatment had the highest, with S3.5 being 21.59%, 29.13%, and 25.32% higher than S9.5 in the FPs1–3, FPs4–6, and FPs ≥ 7, respectively. Except for the FPs4–6 in 2024, the shedding rate under the S9.5 treatment was significantly higher than that under the CK, S3.5, S5, and S6.5 treatments.

3.3. Boll Number

Consequently, influenced by the boll setting rate, the number of bolls per plant also exhibited a distinct spatial pattern: FPs 4–6 (2.05–2.76) > FPs1–3 (1.34–2.47) > FPs ≥ 7 (0.08–1.10) (Figure 3). In the FPs 4–6 and FPs ≥ 7, the CK treatment produced a boll number intermediate between the S6.5 and S8 treatments, while in the FPs1–3, it was higher than that of the S6.5, S8, and S9.5 treatments. The number of bolls per plant in all fruit positions decreased with increasing supplemental irrigation water salinity, with the S3.5 treatment consistently having the highest values (2.29, 2.66, and 0.8 for FPs1–3, FPs4–6, and FPs ≥ 7, respectively). Compared to the S9.5 treatment, S3.5 significantly increased the boll number by 32.90%, 23.19%, and 121.54% (two-year average) in the FPs1–3, FPs4–6, and FPs ≥ 7. A quantifiable negative linear relationship was established, indicating that for every 1.5 g L⁻¹ increase in salinity, the number of bolls per plant decreased by an average of 0.2, 0.1, and 0.02 in the FPs1–3, FPs4–6, and FPs ≥ 7, respectively.

3.4. Within-Boll Yield Component

3.4.1. Seed Number per Boll, Seed Surface Area and Seed Index

Seed Number

The seed number was highest in the FPs4–6 (29.25–33.71), followed by the FPs1–3 (28.35–32.85), and lowest in the FPs ≥ 7 (25.04–30.51) (

Table 3. Effects of different treatments on seed number per boll, seed surface area, and seed index across fruiting branch positions from 2023 to 2024.

Year	Treatment	Seed Number per Boll (Number)			Seed Surface Area (mm2)			Seed Index (g)
		FPs1–3	FPs4–6	FPs ≥ 7	FPs1–3	FPs4–6	FPs ≥ 7	FPs1–3	FPs4–6	FPs ≥ 7
2023	CK	29.64 cd	32.54 b	28.54 b	101.59 a	98.17 b	103.37 a	9.99 a	9.47 b	10.26 a
	S3.5	31.38 a	33.71 a	30.51 a	95.44 a	95.55 c	107.05 ab	9.06 ab	9.08 c	10.82 a
	S5	31.04 ab	32.41 b	28.06 b	97.65 a	98.35 b	104.42 ab	9.39 a	9.50 b	10.42 a
	S6.5	29.94 bc	30.66 c	27.45 b	96.25 a	100.95 a	100.03 ab	9.18 a	9.90 a	9.76 ab
	S8	28.85 cd	30.79 c	26.09 c	95.26 a	101.18 a	99.03 ab	9.03 ab	9.93 a	9.60 b
	S9.5	28.50 d	29.56 c	25.04 c	94.08 a	97.82 bc	97.50 b	8.85 b	9.42 b	9.37 b
2024	CK	29.95 bc	32.03 a	28.65 b	105.49 a	98.28 a	100.04 a	10.58 a	9.49 ab	9.76 a
	S3.5	32.85 a	33.25 a	30.35 a	96.53 b	96.8 a	99.06 a	9.22 bc	9.27 ab	9.61 a
	S5	32.70 a	32.85 a	30.24 a	99.79 b	97.51 a	100.24 a	9.72 b	9.37 ab	9.79 a
	S6.5	30.70 b	32.50 a	29.30 ab	100.59 ab	95.48 a	95.49 b	9.84 b	9.07 b	9.07 b
	S8	29.24 cd	30.50 b	28.95 ab	95.17 b	98.67 a	94.95 b	9.02 c	9.55 ab	8.98 b
	S9.5	28.35 d	29.25 b	27.18 c	100.43 ab	100.89 a	96.37 b	9.82 b	9.89 a	9.20 b
Source of variance	T	*			ns			ns
	FPs	ns			ns			ns
	Y	ns			ns			ns
	T × FPs	*			ns			*
	T × FPs × Y	*			ns			ns

Seed number per boll, seed surface area, and seed index for five saline water supplementary irrigation treatments at varying mineralization levels (S3.5, S5, S6.5, S8, and S9.5), along with a control treatment (CK, no saline water supplementary irrigation). Values present means of four replicates (n = 4). The effects of water treatment (T), fruiting branch positions (FPs), year (Y), and their interactions were analyzed using univariate analysis of variance (ANOVA) within the framework of the General Linear Model. Significant differences are indicated by different lowercase letters following the values at p < 0.05. Notation: ns, not significant (p > 0.05); * significant at p < 0.05.

). Over the two-year period, there were no significant differences between the S3.5 and S5 treatments in the middle and lower fruit branches, but both were generally significantly higher than the S8, S9.5, and CK treatments. At the FPs1–3, the two-year mean seed number was 29.80 for CK, while saline irrigation treatments increased to 32.15 (S3.5) and 31.87 (S5), corresponding to 7.89% and 6.96% increases, respectively. Whereas higher salinity levels (S8 and S9.5) reduced seed number to 29.0 and 28.43, 2.52–4.60% lower than CK. At the FPs4–6, CK averaged 32.29, and both S3.5 (33.48) and S5 (32.63) exceeded it by 3.7% and 1.07%, while S6.5 and S8 decreased to 31.58 and 30.65, 2.18–5.08% lower, and S9.5 further declined to 29.41. At the FPs ≥ 7, CK averaged 28.60, S3.5 and S5 remained slightly higher (30.43 and 29.15, 6.42% and 1.94% higher than CK), whereas S6.5 and S8 averaged 28.38 and 27.52, and S9.5 reached 26.11, 8.69% lower than CK.

Seed Surface Area

The SSA was not significantly influenced by the treatment, fruit branch position, year, nor any of their interactions (Table 3). SSA increased gradually with fruiting position, averaging 98.19 mm² at FPs1–3, 98.30 mm² at FPs4–6, and 99.80 mm² at FPs ≥ 7, showing an overall increase of 1.63% from the FPs1–3 to FPs ≥ 7 (Table 3). At the FPs1–3, CK recorded a mean surface area of 103.54 mm², S3.5 and S5 maintained 96.00 and 98.72 mm², whereas S9.5 decreased to 97.26 mm², about 4.66–8.04% lower than CK. At the FPs4–6, CK averaged 98.30 mm², S3.5 and S5 were 96.18–97.93 mm², whereas S8 and S9.5 increased to 99.36–100.0 mm². At the FPs ≥ 7, CK averaged 101.71 mm², S3.5 and S5 remained similar (103.06 and 102.33 mm²), S6.5 decreased to 97.76 mm², while S8 and S9.5 were 97.00 and 96.94 mm², 4.64% and 4.69% lower than CK, respectively.

Seed Index

Treatment, fruiting branch position, and year—or any of their interactions—did not have a significant effect on the SI (Table 3). The two-year mean values of 9.48 g, 9.50 g, and 9.72 g correspond to that of the FPs1–3, FPs4–6, and FPs ≥ 7, respectively. At the FPs1–3 and FPs4–6, the CK exhibited a higher SI, averaged 10.30 g and 9.548 g at the FPs1–3 and FPs4–6, respectively. While S8 and S9.5 treatments resulted in a significant reduction in SI. CK, at the FPs1–3, S3.5 and S5 treatment recorded values of 9.14 and 9.56 g, which were 9.24–12.25% lower than CK. At FPs ≥ 7, the mean SI under CK was 10.01 g, while S3.5 and S5 were slightly higher (10.22 g and 10.11 g, respectively), no significant differences were observed among these three treatments. However, all three were significantly higher than the S6.5–S9.5 treatments. Compared with S3.5, both S8 and S9.5 showed a 9.05% reduction in SI.

3.4.2. Number of Fibers per Seed, Lint Mass per Seed and Lint Mass per Unit Seed Surface Area

Fiber Number per Seed

FPS exhibited distinct spatial variation across fruiting branch positions (

Table 4. Effects of different treatments on fibers per seed, lint mass per seed and lint mass per unit SSA across fruiting branch positions from 2023 to 2024.

Year	Treatment	Fibers per Seed (Number)			Lint Mass per Seed (mg)			Lint Mass per Unit SSA (μg)
		FPs1–3	FPs4–6	FPs ≥ 7	FPs1–3	FPs4–6	FPs ≥ 7	FPs1–3	FPs4–6	FPs ≥ 7
2023	CK	18,142.62 ab	19,794.17 bc	21,996.35 a	78.45 a	80.42 a	81.18 ab	771.60 ab	819.01 ab	785.43 ab
	S3.5	20,587.02 a	21,968.19 a	22,576.26 a	77.91 a	80.41 a	86.95 a	816.67 a	841.53 a	812.94 a
	S5	17,809.52 ab	21,004.81 ab	21,182.83 ab	76.30 a	82.05 a	82.66 ab	781.36 ab	834.44 a	791.61 ab
	S6.5	17,651.98 ab	21,790.22 a	20,607.42 ab	73.73 ab	83.86 a	75.59 bc	766.23 ab	830.50 a	753.4 bc
	S8	17,173.82 ab	21,194.24 ab	20,412.65 ab	69.43 ab	83.24 a	74.06 bc	726.87 bc	822.91 a	747.55 bc
	S9.5	15,945.860 b	18,892.26 c	18,529.96 b	65.67 b	73.79 b	70.59 c	695.64 c	754.30 b	726.22 c
2024	CK	20,944.48 ab	21,412.73 a	23,309.52 a	81.95 a	80.89 b	86.27 a	776.92 bc	823.22 ab	862.34 ab
	S3.5	22,639.92 a	21,716.38 a	22,013.06 a	82.15 a	82.55 ab	87.00 a	851.15 a	852.86 a	878.19 a
	S5	19,417.34 bc	22,343.59 a	24,952.82 a	79.2 ab	82.27 ab	87.78 a	794.04 b	843.93 a	875.55 a
	S6.5	19,617.05 bc	21,088.86 a	23,255.46 a	80.35 a	80.31 b	79.94 b	798.79 b	841.23 a	837.14 b
	S8	17,840.47 c	22,748.20 a	21,618.42 a	73.20 c	83.42 a	79.29 b	769.10 bc	845.53 a	835.32 b
	S9.5	19,149.58 bc	22,082.99 a	21,092.62 a	75.41 bc	80.73 b	77.31 b	750.21 c	801.78 b	802.28 c
Source of variance	T	*			*			*
	FPs	*			ns			ns
	Y	*			ns			ns
	T × FPs	ns			*			*
	T × FPs × Y	ns			ns			ns

Fibers per seed (FPS), lint mass per seed (LMS), and lint mass per unit SSA (LMSSA) for five saline water supplementary irrigation treatments at varying mineralization levels (S3.5, S5, S6.5, S8, and S9.5), along with a control treatment (CK, no saline water supplementary irrigation). Values present means of four replicates (n = 4). The effects of water treatment (T), fruiting branch position (FPs), year (Y), and their interactions were analyzed using univariate analysis of variance (ANOVA) within the framework of the General Linear Model. Significant differences are indicated by different lowercase letters following the values at p < 0.05. Notation: ns, not significant (p > 0.05); * significant at p < 0.05.

). CK produced 19,543.55 (FPs1–3), 20,603.47 (FPs4–6), and 22,652.94 (FPs ≥ 7) fibers per seed, while S3.5 treatment increased this number to 21,613.47, 21,842.29 and 22,294.66, respectively, representing increases of 10.60%, 6.01%, and −1.58%, respectively. Conversely, the S9.5 exhibited a significant inhibitory effect, consistently reducing FPS across all positions to 17,547.72, 20,487.63, and 19,811.29 from lower to upper, respectively. The suppression was most pronounced at the FPs1–3 and FPs ≥ 7, showing reductions of 10.21% and 12.54% versus CK, and 18.81% and 11.14% versus S3.5.

Lint Mass per Seed

LMS was significantly influenced by the water treatment and its interaction with fruiting branch position Overall, LMS across all fruiting branch positions showed a general inverse relationship with increasing water mineralization degree, with peaks at S3.5/S5 and the lowest values at S9.5 (Table 4). Pronounced inhibition was observed at the FPs1–3 and FPs ≥ 7, where S9.5 reduced LMS by 12.04% and 11.68%, respectively, relative to CK. Conversely, at the FPs4–6, the inhibitory effect was negligible. The medium- to high-salinity treatments (S5–S8), with LMS ranging from 82.15 to 83.31 mg, even slightly outperformed CK (80.66 mg) by an average of 2.50%.

Lint Mass per Unit Seed Surface Area

The effects of different treatments on LMSSA exhibited considerable spatial heterogeneity across fruiting branch positions (Table 4). Over the two-year period, the FPs4–6 demonstrated the greatest stability, showing no significant differences (p > 0.05) between the CK and treatments ranging from S3.5 to S8. In contrast, supplemental irrigation with low- to medium-salinity water (S3.5–S5) significantly promoted LMSSA, with their two-year mean values exceeding those of CK at all positions. Specifically, the S3.5 treatment achieved mean values of 842.22 μg, insignificantly outperforming CK (806.42 μg) by an average of all FPs representing increases of 7.70%, 3.18%, and 2.63% over CK at FPs1–3, FPs4–6 and FPs ≥ 7, respectively, and was significantly higher than the S9.5 treatment. Even when the mineralization degree increased to S6.5, slight increases in LMSSA were still observed at the lower and middle branches compared to CK. Conversely, the S9.5 treatment resulted in substantial reductions at all positions compared to CK, with reductions ranging from 5.25% to 7.24%. When compared to the S3.5 treatment, the suppressive effect of S9.5 was even more pronounced, with the reduction in LMSSA widening to a range of 8.16% to 13.31%.

3.5. Fiber Quality

3.5.1. Fiber Length

The fiber length across all fruiting positions exhibited a general trend of decreasing as the salinity levels increased (

Table 5. Effects of different treatments on cotton fiber quality across fruiting branch positions from 2023 to 2024.

Year	Fruit Position (FPS)	Treatment	Fiber Length (mm)	Uniformity (%)	Fiber Strength (CN·Tex−1)	Micronaire	Q-Score
2023	FPs1–3	CK	30.10 b	86.60 c	28.35 a	5.05 a	32.14 b
		S3.5	31.95 a	88.10 a	29.30 a	4.10 b	33.15 a
		S5	31.35 a	87.50 ab	29.50 a	4.80 b	32.95 a
		S6.5	30.00 b	86.85 bc	29.35 a	4.85 b	32.18 b
		S8	29.40 b	86.40 c	29.25 a	4.80 b	31.79 b
		S9.5	29.70 b	86.50 c	28.90 a	4.90 b	31.94 b
	FPs4–6	CK	29.60 b	86.50 ab	28.60 d	4.85 a	31.85 b
		S3.5	31.65 a	87.30 a	31.65 a	4.05 b	33.10 a
		S5	30.65 ab	86.45 ab	30.80 ab	4.50 ab	32.50 ab
		S6.5	30.15 ab	85.55 b	30.15 bc	4.55 ab	32.06 b
		S8	30.30 ab	86.10 ab	29.55 bcd	4.60 a	32.17 ab
		S9.5	30.05 b	85.10 b	29.20 cd	4.65 a	31.87 b
	FPs ≥ 7	CK	28.90 a	83.95 a	28.20 b	4.65 ab	31.03 ab
		S3.5	30.10 a	85.75 a	29.05 ab	4.55 a	31.96 a
		S5	29.75 ab	85.20 a	28.50 a	4.70 ab	31.68 a
		S6.5	28.40 b	85.55 a	28.65 b	4.62 c	31.05 ab
		S8	28.60 ab	84.40 a	28.45 ab	4.60 bc	30.96 ab
		S9.5	28.30 b	82.90 a	26.90 ab	4.95 ab	30.51 b
2024	FPs1–3	CK	28.35 b	83.77 b	29.55 a	5.06 a	30.96 c
		S3.5	32.57 a	85.06 a	30.16 a	4.00 d	33.06 a
		S5	31.86 a	86.29 a	30.26 a	4.51 c	33.03 a
		S6.5	30.97 ab	85.96 a	30.10 a	4.69 bc	32.56 ab
		S8	29.88 ab	85.50 a	29.63 a	4.89 ab	31.95 abc
		S9.5	28.78 b	84.49 ab	29.64 a	4.93 ab	31.26 bc
	FPs4–6	CK	28.95 c	84.78 a	30.65 a	4.71 a	31.44 c
		S3.5	32.64 a	84.79 a	31.16 a	4.17 c	33.19 a
		S5	30.26 b	84.60 a	30.81 a	4.39 bc	32.00 b
		S6.5	30.11 b	84.94 a	30.79 a	4.53 abc	32.01 b
		S8	28.48 c	83.70 ab	30.35 a	4.73 ab	31.01 d
		S9.5	27.93 c	83.21 b	30.38 a	4.80 a	30.68 d
	FPs ≥ 7	CK	28.32 bc	82.98 b	28.76 b	4.75 a	30.67 c
		S3.5	31.12 a	84.95 a	30.13 a	4.56 a	32.45 a
		S5	29.21 b	83.86 ab	29.73 a	4.39 a	31.25 b
		S6.5	27.92 bc	83.65 ab	28.33 ab	4.48 a	30.46 c
		S8	27.90 bc	83.51 ab	28.91 ab	4.80 a	30.57 c
		S9.5	27.39 c	80.72 c	27.65 b	5.06 a	29.83 d
Source of variance		T	ns	**	*	ns	ns
		FPs	ns	ns	ns	*	ns
		Y	ns	ns	**	ns	ns
		T × FPs	*	*	ns	*	*
		T × FPs × Y	*	ns	ns	ns	ns

Five supplemental irrigation treatments with saline water supplementary irrigation at varying mineralization levels (S3.5, S5, S6.5, S8, and S9.5), along with a control treatment (CK, no saline water supplementary irrigation), were applied to assess their impact on cotton fiber quality traits across different fruiting branch positions. Each value represents the mean of four replicates (n = 4). The effects of water treatment (T), fruiting branch position (FPs), year (Y), and their interactions were analyzed using univariate analysis of variance (ANOVA) within the framework of the General Linear Model. Additionally, one-way ANOVA was employed to examine the differences among FPs under the various treatments. Significant differences are indicated by different lowercase letters following the values at p < 0.05. Notation: ns, not significant (p > 0.05); * significant at p < 0.05; ** significant at p < 0.01.

). As the salinity of the supplemental saline water irrigation increased, the fiber length at different fruit branch positions basically showed a decreasing trend. Except for the FPs4–6 in 2024, there were no significant differences among S6.5, S8, and S9.5 at all other positions in both years. Regarding the lower positions, no significant difference was found between the S3.5 and S5 treatments, and both values were significantly higher than those of CK and S9.5. The S3.5 treatment increased fiber length by 10.38% compared to CK and by 10.34% compared to S9.5. For the FPs4–6 and FPs ≥ 7, while internal variations were noted, the S3.5 (31.65 mm) treatment consistently and significantly surpassed both CK and S9.5 in both years. The respective increases were 9.81%, 10.89%, and 36.98%, 9.92% under S3.5, significantly surpassed both CK and S9.5 in both years in either the FPs4–6 or FPs ≥ 7. However, no significant difference was found between the CK and S9.5 treatments for either the FPs4–6 or FPs ≥ 7 positions.

3.5.2. Uniformity

Uniformity showed a trend of improvement with moderate salinity treatments but decreased significantly with high-salinity treatments (Table 5). S3.5 and S5 improved uniformity compared to CK, while high-salinity treatments (S8 and S9.5) caused a slight decline, especially at the FPs4–6 and FPs ≥ 7 fruiting positions, and S9.5 treatments led to a decrease in uniformity of 1.7% and 2.0% in FPs4–6 and FPs ≥ 7 compared to CK. Moreover, the reduction in the S9.5 treatment compared to S3.5 further increased from 1.27% at the lower position to 2.24% and 4.32% at the FPs4–6 and FPs ≥ 7 positions, respectively.

3.5.3. Fiber Strength

The fiber strength in all fruiting positions with S3.5–S6.5 were all enhanced compared to the CK treatment (Table 5), with average increases over two years ranging from 0.04% to 6.0%; notably, the FPs4–6 section under S3.5 treatment exhibited the highest increase relative to CK, reaching 6.00%. As the degree of mineralization increased, the overall relative breaking strength displayed a declining trend, while the increases for S3.5 relative to the S9.5 treatment progressively enlarged across sections, demonstrating increases of 1.58%, 5.42%, and 8.48% in the FPs1–3, FPs4–6, and FPs ≥ 7, respectively.

3.5.4. Micronaire

Analysis of micronaire values revealed a consistent spatial gradient across the different fruiting branch positions, with values highest in the FPs1–3, intermediate in the FPs ≥ 7, and lowest in the FPs4–6 (Table 5). The S3.5–S6.5 treatments consistently had lower micronaire values than the CK, S8, and S9.5 treatments in all positions. Notably, the S3.5 treatment with the lowest values in the FPs4–6 and lower, with two-year averages of 4.11 and 4.05, respectively representing reductions of 14.00% and 12.91% relative to CK and S9.5 in the FPs4–6, and 19.85% and 17.56% in the FPs1–3. Although these reductions narrowed to 3.01% and 9.00% in the FPs ≥ 7, no significant differences were detected among the micronaire values of the CK, S8, and S9.5 treatments at any position.

3.5.5. Q-Score

The S3.5 treatment achieved the highest Q-score at all fruit positions, with two-year averages of 31.60 (FPs1–3), 31.28 (FPs4–6), and 30.17 (FPs ≥ 7), representing an average increase of 4.69% over CK (Table 5). As salinity increased, the Q-score declined. Compared to S3.5, the reductions for S5–S9.5 were 0.34–4.76% (FPs4–6), 2.72–5.98% (FPs4–6), and 2.30–6.73% (FPs ≥ 7). The highest reduction was 6.73% for the S9.5 treatment in the FPs ≥ 7, and the difference between S3.5 and S9.5 at the FPs ≥ 7 position was statistically significant across both years (Table 5).

3.6. Correlation Analysis

Correlation analysis revealed that BSR, SR, BNPP, BD, SNBP, FPS, LMS, and LMSSA were all significantly or nearly significantly positively correlated with LY (r ranged from 0.829 to 0.992) (Figure 4). In contrast, the boll shedding rate was significantly negatively correlated with LY, with r > 0.90 in both years. Q-score was significantly positively correlated with FL and Uni. It also showed some positive correlation with BNPP, BD, and LY, but this was significant only in 2023.

4. Discussion

4.1. Regulatory Mechanism of Staged Saline Water Supplementary Irrigation on Cotton Yield Components

Cotton is considered one of the most salinity-tolerant crops, and its yield is generally sustained when salinity remains within an appropriate range throughout the entire growth period [24,25]. This study applied periodic saline water supplemental irrigation during the bud-stage drought spell and the high-temperature interval spanning flowering and boll development. Relative to the non-irrigated control, lint yield rose by 31.4% under 3.5 g L⁻¹ saline water and by 15.1% under 5 g L⁻¹ saline water (p < 0.05) (Table 2). These results indicate that, within the specific staged, low-proportion (approximately 25%) saline water supplemental irrigation regime established here, salinity levels of 3.5–5.0 g L⁻¹ imposed no yield penalty; instead, they elicited a compensatory increase in yield production. This aligns with Soares et al., who reported that saline water applied only during vegetative and flowering stages did not significantly reduce yield [26]. We attribute this to the staged saline water supplemental irrigation that helped maintain a higher soil–water potential in the root zone, which mitigates bud and boll shedding triggered by high temperature or drought and consequently increases seed cotton production.

However, at 6.5 g L⁻¹, saline water supplemental irrigation reduced lint yield by 0.4% relative to the control; above 8 g L⁻¹, both yield and fiber quality declined concurrently (p < 0.05). This indicates that, even under periodic saline water supplemental irrigation, the threshold–slope model remains valid, exhibiting a distinct salinity threshold [27,28,29].Whereas our earlier work identified 3 g L⁻¹ as the optimum for continuous saline irrigation [30], the present threshold rose to 6.5 g L⁻¹, reflecting differences in water management and the spatio-temporal redistribution of salts. Staged supplementary irrigation supplied saline water only during bud-stage drought and peak flowering heat, so the total ionic load per season remained low and root zone salt accumulation was gradual [31]; intermittent freshwater pulses through subsurface drip further redistributed moisture and leached salts downward, buffering the salinity shock [32]. The staged supplementary irrigation not only expanded the suitable salinity window but also provided a quantitative basis for the safe utilization of saline water resources in arid regions.

4.2. Salinity Tolerance Differences in Fruit Branch Positions and Site-Specific Boll Retention Mechanisms

The spatial heterogeneity within the cotton canopy leads to varying sensitivities of different fruit branch positions to stress [33]. When the salinity exceeds 6.5 g L⁻¹, the boll drop rate in the upper fruit branches (FPs ≥ 7) significantly increases (Figure 2), and the number of bolls formed is significantly lower than in the middle and lower branches (p < 0.05) (Figure 3). This suggests that cotton may adopt a site-specific fruiting strategy that avoids allocating resources to vulnerable young bolls in the upper canopy. This likely reflects salt-induced osmotic stress and ionic toxicity, which elevate abscisic acid (ABA) and ethylene levels, restrict carbohydrate supply and thereby accelerate young-boll abscission [34,35]. Conversely, under moderate salinity (3.5–5 g L⁻¹) and within the favorable light–temperature regime of the mid-canopy, we propose that the observed response, potentially involving osmotic adjustment and antioxidant responses preserved leaf function and carbon flow [35], so that retention of the more vulnerable early fruiting positions at the base and the late positions at the top benefited most from this regulation.

4.3. Salt-Driven Seed Tradeoffs Govern Boll Level Fiber Yield

The seed is the sole carrier of cotton fibers, and both seed number and individual size determine total lint mass per boll [36]. Reproductive processes can be indirectly supported under moderate salt stress through the optimization of resource allocation and the activation of mild defense signaling [37]. Under low-level salt stress (S3.5 and S5), cotton reallocates assimilates toward ovules, significantly increasing seed number per boll (p < 0.05) but reducing the carbon allocated to individual seed coats. Consequently, SSA declined (Table 3). However, with increasing irrigation water salinity, the negative effects of salt stress intensified, particularly on the upper fruit branches. High salinity imposes osmotic stress that dehydrates the pollen tube and, through ionic toxicity, directly disrupts the key physiological events at the tube apex, resulting in fertilization failure or a higher incidence of malformed seeds [21]. Although the overall ANOVA showed no significant treatment effect, SSA under S9.5 was on average 4.7% lower than under CK, indicating a mild but consistent suppression of seed surface expansion under high-salinity conditions. SI remained stable across treatments, positions and years, yet averaged over both years it declined by 9.1% from S3.5 to S9.5, reflecting salt-imposed restriction of seed dry-matter accumulation [38].

4.4. Mid-Canopy Boll Fiber Traits Confer Salinity Tolerance Advantage

Intraboll lint yield is governed by three seed-based metrics, FPS, LMS, and LMSSA, which together connect the seed substrate to the final lint biomass. Salt stress downregulates genes controlling fiber initiation and elongation and disturbs the auxin ethylene balance, thereby suppressing early fiber development [39]. In this study, 3.5–5 g L⁻¹ salinity increased FPS and LMSSA, and maintained or slightly raised LMS, whereas at 9.5 g L⁻¹ all fiber related traits declined markedly, as shown in Table 4.

Across all treatments, mid-canopy bolls FPs 4–6 remained the most stable and superior; even under high salinity their LMS exceeded that of the control. Upper FPs ≥ 7 and lower FPs 1–3 bolls were disproportionately sensitive, with FPS and LMS falling far more than in mid-canopy positions. A possible explanation is that salt stress suppresses the expression of genes related to both cotton fiber cell elongation and secondary cell wall synthesis [40].

4.5. Site-Specific Fiber Quality Gains Under Moderate Salinity

Our results demonstrate a clear threshold response of fiber quality to irrigation water salinity: low–moderate mineralization (3.5–6.5 g L⁻¹) significantly increased fiber length and strength while reducing micronaire value (Table 5). This benefit likely arises from a re-optimized resource allocation triggered by the timely supplementation of low-salinity saline water: it accelerates primary-wall elongation [41], promotes the deposition of cell wall components [42], and changed the duration of the fiber development, thereby producing finer, longer and stronger fibers with less intracellular water [43]. At S9.5, however, fiber length and strength declined and micronaire rose, presumably because severe osmotic stress and ionic toxicity collapsed fiber turgor, restricted cell expansion, and disrupted division/elongation processes [44]. Furthermore, irrigation with high-salinity water also disrupted the synchrony of fiber elongation, thus leading to a decrease in uniformity.

Quality gains under S3.5 were not uniform along the stem. Mid-canopy bolls (FPs 4–6) registered the largest strength, 31.65 cN/tex in 2023 and 31.16 cN/tex in 2024, representing increases of 5.6% over the lower and 6.1% over the upper positions. High-mineralization water disrupted elongation synchrony and reduced uniformity; upper bolls (FPs ≥ 7) suffered the greatest Q-score loss (6.7%), coinciding their late developmental phase with peak salt exposure and limited assimilate supply [45].

5. Conclusions

The results indicate that the salinity threshold for staged saline supplemental irrigation at the squaring and flowering–boll stages is approximately 6.5 g L⁻¹. When salinity remains below this threshold (3.5–5 g L⁻¹), both lint yield and the Q-score are enhanced; the 3.5 g L⁻¹ treatment performs best, increasing lint yield by 31.45% and raising the Q-score by 4.7% relative to the control. Once salinity exceeds 8.0 g L⁻¹, however, yield and fiber quality decline significantly. Based on within-boll yield components and fiber quality metrics, supplemental irrigation with 3.5–5.0 g L⁻¹ saline water significantly increased boll retention, seeds per boll, fibers per seed, and fiber mass per unit seed surface area in the lower-middle fruiting positions (FPs 4–6). These gains preserved single-boll weight while enhancing fiber length, strength, and uniformity and reducing micronaire, resulting in a coordinated yield quality improvement; in contrast, high-salinity treatment (≥8.0 g L⁻¹) markedly suppressed within-boll performance and fiber quality in the upper fruiting positions.

This study confirms the feasibility of staged saline water supplemental irrigation for cotton production in Xinjiang, extends the safe utilization ceiling to 6.5 g L⁻¹, and provides an operational salinity range of 3.5–5.0 g L⁻¹ for large-scale application. However, continuous monitoring of root zone salt dynamics was lacking, and regulatory evidence for fiber development under salt stress remains absent. Future work should integrate in situ soil water–salt sensors with multi-omics technologies to elucidate the molecular–soil–plant mechanisms governing fiber quality formation under saline irrigation, thereby refining the theoretical framework for brackish-water use.