Synergistic Regulation of Planting Density and Mepiquat Chloride on Yield and Plant Architecture of Short-Season Cotton in the Yangtze River Basin, China

Abstract

Optimizing planting density and mepiquat chloride (MC) is essential for simplified, machine-harvestable cotton production in the Yangtze River Basin. A two-year field experiment was conducted to explore the synergistic regulatory mechanisms of MC and planting density on plant architecture, physiology, and yield in short-season direct-seeding cotton. A split-plot design was employed with varying gradients of MC dosage and planting density. The results indicate that density and MC function complementarily in shaping plant architecture: MC primarily controls vertical growth (“dwarfing”), while density elevates the initial fruiting node (“elevation”), with no antagonistic interaction between the two. Regarding canopy structure, increasing density is the primary driver for improving the leaf area index (LAI), while MC optimizes light distribution during the critical boll stage. In terms of yield formation, high density significantly enhances seed cotton yield by increasing the number of bolls per unit area, which effectively overcompensates for the reduction in bolls per plant. Notably, a dose-dependent synergistic effect was observed where high MC dosage maximized the yield potential of high-density populations. Furthermore, fiber quality remained stable across treatments, driven primarily by interannual climate factors rather than agronomic regulation. Consequently, an independent synergistic optimization strategy is recommended, combining high density to secure population yield with medium-to-high MC dosage to shape an ideal machine-harvestable architecture. This approach provides a theoretical basis and technical pathway for high-yield and efficient cotton cultivation in the region.

1. Introduction

The Yangtze River Basin is one of China’s primary cotton-producing regions, and optimizing planting patterns and advancing mechanization are essential for ensuring the stability of China’s cotton industry [1]. Historically, high-density planting systems (such as narrow-row cotton) have been widely explored in major cotton belts like the United States and Australia to maximize light interception and yield per unit area [2]. In China, this concept has evolved to adapt to local labor shifts. In recent years, the migration of rural labor to urban areas and increasing competition for land between food crops and cotton have led to high production costs and limited benefits for traditional sparse-planting cotton (with a density of 20,000–30,000 plants·ha⁻¹), due to its extended growing period, multiple management steps, and poor mechanization adaptability [3]. The ‘short-season direct-seeding cotton’ model integrates early-maturing variety breeding, late sowing (from late May to early June), and single-machine sowing and harvesting technologies, shortening the growing period to 125 days and achieving a maturity rate exceeding 85%. This model not only effectively mitigates the crop rotation conflict between cotton and oilseed crops but also reduces production steps and management costs (with 2–3 fewer chemical regulations and a 60% reduction in overall production costs), positioning it as the core strategy for simplified cotton production in the Yangtze River Basin [4].

Planting density and chemical regulation by mepiquat chloride (MC) are two fundamental agronomic measures for regulating the balance between individual cotton plant growth and overall population development [5]. Traditional cotton fields in the Yangtze River Basin rely on low density and high fertilization to sustain yield, which results in low resource utilization efficiency and large, sprawling plants with drooping fruit branches, making them challenging to harvest mechanically [6,7]. However, increasing density can compensate for insufficient boll number per plant by enhancing the number of bolls per unit area, which is a key strategy for improving yield in short-season direct-seeding cotton [8]. Previous studies specifically focusing on the Yangtze River region have suggested increasing planting density to 60,000–90,000 plants·ha⁻² to optimize canopy closure and yield stability [9]. However, increasing density also intensifies competition between plants, leading to issues such as reduced canopy light transmittance and elevated shedding rates [9].

MC acts as an inhibitor of gibberellin biosynthesis, specifically targeting the conversion of geranylgeranyl diphosphate to ent-kaurene, thereby reducing endogenous gibberellic acid (GA) levels and inhibiting cell elongation [10,11]. Through this molecular mechanism, MC can alleviate the negative effects of high-density planting by inhibiting vertical growth and optimizing canopy structure. However, its dynamic regulatory mechanism under high-density conditions in short-season direct-seeding cotton remains poorly understood.

Current research primarily focuses on the individual effects of density or MC, overlooking their interaction under the specific ecological conditions of short-season direct-seeding cotton. On one hand, seasonal irregular precipitation in the Yangtze River Basin may affect the regulatory efficiency of MC and the response of density to the environmental conditions of the cotton population [12]. On the other hand, the condensed growing period of short-season direct-seeding cotton and the concentrated development of buds, flowers, and bolls raise the question of whether regulating density and MC during the critical photosynthetic period (flowering and boll stage) affects the material basis for yield formation, an area still lacking systematic research. Moreover, whether high density and high doses of MC lead to deterioration in fiber quality remains a critical issue that has yet to be clarified in production systems. Previous studies have indicated that MC may alter quality by affecting the distribution of photosynthetic products to fiber, but the effects under the short-season direct-seeding model remain debated [13,14,15]. To address these issues, a two-year field experiment was conducted in Liuyang, Hunan, from 2023 to 2024, using the early-maturing variety “JX0010”. Multiple gradients of MC dosage and planting density were applied to systematically assess agronomic traits, canopy structure (specifically leaf area index, LAI), photosynthetic physiology, yield, and fiber quality. The objectives of this study are to elucidate: ① the synergistic regulatory effects of density and MC on the yield of short-season direct-seeding cotton and their dominant factors; ② the interaction mechanism between density and MC in shaping the ideal machine-harvestable plant type (reducing plant height and increasing the first fruit branch height); and ③ whether a safety threshold exists for the effects of density and MC on photosynthetic physiology and fiber quality. The findings of this study can provide scientific support for the synergistic optimization of density and chemical control in short-season direct-seeding cotton in the Yangtze River Basin, promoting the sustainable development of the regional cotton industry toward higher yield, greater efficiency, and mechanization.

2. Materials and Methods

2.1. Experimental Materials

The cotton seeds used in this experiment were from the conventional early-maturing variety JX0010, with a growing period of approximately 105 days, provided by the Cotton Research Institute of Hunan Agricultural University. MC was purchased from Zhongmian Xiaokang Biotechnology Co., Ltd. (Anyang, China) as a water-soluble formulation with an effective ingredient concentration of 250 g/L (Mix thoroughly before use).

2.2. Experimental Site Overview

The experiment was conducted at the Liuyang Base of Hunan Agricultural University from 2023 to 2024. The experimental soil was sandy loam, with its basic fertility listed in

Table 1. Soil base nutrients for field trials in 2023 and 2024.

Year	Organic Material (g/kg)	pH Value	Total Nitrogen (g/kg)	Alkali Hydrolyzed Nitrogen (mg/kg)	Effective Phosphorus (mg/kg)	Quick Acting Potassium (mg/kg)
2023	29.57	5.64	1.93	163.65	132.57	118
2024	32.84	6.21	2.04	153.34	126.13	126

. The base is located in Yanxi Town, Liuyang City, Hunan Province (28°18′ N, 113°49′ E). The region has a typical subtropical monsoon climate, with abundant light and heat resources and ample precipitation, meeting the growth and development needs of cotton.

The daily average temperature and rainfall from 0 to 130 days after cotton sowing in 2023 and 2024 are shown in Figure 1. While the overall temperature trends were consistent with the seasonal patterns of the Yangtze River Basin, precipitation distribution exhibited significant interannual variability. In 2024, rainfall was notably frequent and concentrated during the seedling and bud stages (0–40 days after sowing), which created a wetter environment favoring initial vegetative growth. In contrast, 2023 was characterized by relatively lower rainfall during the early vegetative phase but experienced a significant intense precipitation event during the late boll-opening stage (around 115 days after sowing). These distinct rainfall patterns likely contributed to the observed interannual variations in canopy development and the efficacy of chemical regulation reported in this study.

2.3. Experimental Design

The experiment was conducted using a split-plot design arranged in randomized complete blocks with three replicates. MC management served as the main plot factor, while planting density acted as the subplot factor. The main plots consisted of three MC total dosage levels: MC1 (180 g·ha⁻¹), MC2 (270 g·ha⁻¹), and MC3 (360 g·ha⁻¹). MC applications were split according to growth stages: all treatments received a constant dose of 45 g·ha⁻¹ at both the seedling and bud stages. Subsequently, during the flowering and boll stages, dosages were adjusted to 45, 90, and 135 g·ha⁻¹, respectively, to achieve the target total gradients. The subplots featured three planting density levels: D1 (6.0 × 10⁴ plants·ha⁻¹), D2 (9.0 × 10⁴ plants·ha⁻¹), and D3 (12.0 × 10⁴ plants·ha⁻¹). Each subplot covered an area of 16 m² (5 m × 3.2 m), consisting of four rows with a row spacing of 76 cm.

Other field management practices followed the guidelines for short-season cotton cultivation in the Yangtze River Basin [16]. The field water regime was primarily rain-fed. To prevent waterlogging stress given the high precipitation in the region (as shown in Figure 1), deep drainage ditches were maintained around the plots throughout the growing season. No supplemental irrigation was applied during the experiment.

2.4. Measurement Indicators and Methods

2.4.1. Agronomic Traits

All agronomic traits were measured using a fixed-plant method. During the seedling stage, five cotton plants with consistent growth were selected and tagged in each subplot, and these tagged plants were measured at the boll opening stage.

Plant height: The vertical distance from the cotyledon node to the apical growth point of the main stem was measured using a ruler.

Number of fruiting branches: The total number of fruiting branches with visible buds, flowers, or bolls was counted.

Initial node position: The position of the first true leaf node was used as the starting point (labeled as node 1). The counting continued upward until the first true leaf node where the first fruiting branch emerged at the leaf axil.

Initial node height: The height between the cotyledon node and the first fruiting branch node was measured.

2.4.2. LAI

LAI of the cotton population was measured using a plant canopy analyzer (AccuPAR model LP-80, METER Corporation, San Francisco, CA, USA) at the bud stage, flowering stage, boll stage, and boll opening stage. During each measurement, data collection was performed at selected positions in the front, middle, and rear of each subplot.

2.4.3. Photosynthetic Characteristics

Consistent cotton plants were selected from each subplot for measurement. The measurement site was initially the fourth functional leaf from the main stem apex. Following the topping operation, the measurement site was adjusted to the third functional leaf from the top. A portable photosynthesis system (LI-6800, LI-COR, Lincoln, NE, USA) was used for measurements, with red and blue light as the light source, and light intensity set at 1400 μmol·m⁻²·s⁻¹. Measurements were taken during the cotton bud stage, flowering stage, boll stage, and boll opening stage. For each measurement, three cotton plants with consistent growth were randomly selected from each subplot. Three random points on the functional leaf of each plant were selected to measure photosynthetic parameters, including net photosynthetic rate (Pn), stomatal conductance (Gs), intercellular CO₂ concentration (Ci), and transpiration rate (Tr).

2.4.4. Yield Indicators

Number of bolls per plant: At harvest, 10 consecutive cotton plants were randomly selected from each subplot. The number of effective bolls per plant (excluding rotten and damaged bolls) was recorded and the average was calculated.

Boll weight: At the boll opening stage, 50 fully opened bolls were harvested from the upper and middle parts of each subplot. After drying thoroughly, the total seed cotton weight was measured, and the average boll weight was calculated.

Seed cotton yield (kg·ha⁻¹) = average number of bolls per plant × average boll weight (g) × planting density (plants·ha⁻¹)/1000 × 0.85 (correction factor) [4].

Lint yield (kg·ha⁻¹) = seed cotton yield (kg·ha⁻¹) × lint percentage.

2.4.5. Quality Indicators

Fifty fully opened bolls were harvested from the upper and middle parts of each subplot, naturally dried, and then ginned to obtain lint. All lint samples were sent to the Cotton Research Institute of the Chinese Academy of Agricultural Sciences for testing, including various fiber quality indicators such as upper half mean length, uniformity index, fibre strength, micronaire value, and elongation rate.

2.5. Data Processing and Statistical Analysis

The experimental data were organized and preliminarily processed using Microsoft Excel 2010. Statistical analysis was performed using SPSS 25.0 software (IBM Corp., Armonk, NY, USA). The individual subplot was defined as the experimental unit, with three biological replicates for each treatment (n = 3). Analysis of Variance (ANOVA) was conducted to assess the effects of MC, planting density, and their interactions. Duncan’s multiple range test was selected for multiple comparisons and significance testing due to its high statistical power in detecting significant differences among multiple agronomic treatment means. Differences were considered statistically significant at p < 0.05 and highly significant at p < 0.01. Figures were generated using Origin 2021 (OriginLab Corp., Northampton, MA, USA).

3. Results

3.1. Agronomic Traits of Cotton

As shown in

Table 2. Effects of different treatments on agronomic traits of cotton.

Variable		2023				2024
		Plant Height (cm)	Number of Fruit Branches	Initial Node Height (cm)	Initial Node Position (cm)	Plant Height (cm)	Number of Fruit Branches	Initial Node Height (cm)	Initial Node Position (cm)
MC1	D1	114.93 ± 3.28 a	15.73 ± 1.23 ab	26.63 ± 4.33 cd	4.27 ± 0.66 a	107.07 ± 4.09 a	13.73 ± 0.57 a	29.53 ± 2.85 a	7.33 ± 0.13 a
	D2	115.10 ± 4.26 a	16.07 ± 1.41 ab	33.43 ± 3.92 abc	5.33 ± 0.29 a	104.53 ± 3.35 ab	12.93 ± 0.24 a	35.00 ± 1.45 a	7.13 ± 0.27 a
	D3	106.40 ± 1.67 ab	11.80 ± 1.62 c	41.63 ± 1.49 a	5.40 ± 0.12 a	104.80 ± 5.00 ab	12.60 ± 0.31 a	38.53 ± 3.60 a	7.33 ± 0.33 a
MC2	D1	100.47 ± 1.70 bc	17.67 ± 0.13 a	23.87 ± 3.12 d	4.53 ± 0.48 a	97.33 ± 2.02 abc	13.87 ± 0.90 a	30.80 ± 2.80 a	7.00 ± 0.95 a
	D2	105.27 ± 6.17 b	18.47 ± 1.97 a	32.07 ± 2.76 bcd	5.00 ± 0.35 a	99.07 ± 2.44 abc	13.80 ± 0.61 a	30.13 ± 1.81 a	7.27 ± 0.55 a
	D3	107.33 ± 1.77 ab	15.13 ± 0.85 abc	40.27 ± 1.00 ab	5.40 ± 0.23 a	100.67 ± 1.75 abc	13.47 ± 0.55 a	36.13 ± 2.26 a	7.27 ± 0.24 a
MC3	D1	93.47 ± 2.67 cd	14.87 ± 0.52 abc	26.27 ± 2.07 cd	4.73 ± 0.37 a	88.47 ± 1.70 c	13.47 ± 1.20 a	28.73 ± 1.27 a	6.73 ± 0.41 a
	D2	89.67 ± 0.66 d	12.93 ± 0.48 bc	35.73 ± 1.44 ab	5.33 ± 0.07 a	90.80 ± 7.04 c	12.73 ± 0.55 a	36.07 ± 6.04 a	7.60 ± 0.23 a
	D3	94.33 ± 1.78 cd	13.73 ± 0.41 bc	39.93 ± 3.67 ab	5.47 ± 0.33 a	92.20 ± 2.57 bc	12.07 ± 0.94 a	35.47 ± 1.85 a	7.67 ± 0.41 a
MC1		112.14 ± 1.79 a	14.53 ± 0.65 b	33.90 ± 1.66 a	5.00 ± 0.21 a	105.47 ± 2.22 a	13.09 ± 0.41 a	34.36 ± 1.73 a	7.27 ± 0.26 a
MC2		104.36 ± 1.79 b	17.09 ± 0.65 a	32.07 ± 1.66 a	4.98 ± 0.21 a	99.02 ± 2.22 b	13.71 ± 0.41 a	32.36 ± 1.73 a	7.18 ± 0.26 a
MC3		92.49 ± 1.79 c	13.84 ± 0.65 b	33.98 ± 1.66 a	5.18 ± 0.21 a	90.49 ± 2.22 c	12.76 ± 0.41 a	33.42 ± 1.73 a	7.33 ± 0.26 a
D1		102.96 ± 1.79 a	16.09 ± 0.65 a	25.59 ± 1.66 c	4.51 ± 0.21 b	97.62 ± 2.22 a	13.69 ± 0.41 a	29.69 ± 1.73 b	7.02 ± 0.26 a
D2		103.34 ± 1.79 a	15.82 ± 0.65 a	33.74 ± 1.66 b	5.22 ± 0.21 a	98.13 ± 2.22 a	13.16 ± 0.41 a	33.73 ± 1.73 ab	7.33 ± 0.26 a
D3		102.69 ± 1.79 a	13.56 ± 0.65 b	40.61 ± 1.66 a	5.42 ± 0.21 a	99.22 ± 2.22 a	12.71 ± 0.41 a	36.71 ± 1.73 a	7.42 ± 0.26 a
MC		0 **	0.006 **	0.661	0.764	0.001 **	0.275	0.72	0.915
D		0.967	0.025 *	0 **	0.016 *	0.874	0.269	0.033 *	0.534
MC × D		0.106	0.247	0.945	0.925	0.928	0.959	0.671	0.784

MC = Mepiquat Chloride, D = Density. Different lowercase letters within the same column indicate significant differences at the 0.05 level for the same year. ** denotes significance at the 0.01 level, while * denotes significance at the 0.05 level.

, in 2023, the plant height in the MC3 treatment was significantly reduced by 19.65 cm and 11.87 cm compared to MC1 and MC2, respectively (a decrease of 17.5% and 11.4%; p < 0.01). In 2024, the plant height in the MC3 treatment was also significantly reduced by 14.98 cm and 8.53 cm compared to MC1 and MC2, respectively (a decrease of 14.2% and 8.6%; p < 0.01). In terms of fruiting branches, the MC2 treatment had the highest number in 2023 (17.09), while the MC3 treatment had the lowest (13.84). The MC3 treatment significantly decreased the number of fruiting branches by 19.0% compared to MC2 (p = 0.006). In 2024, however, the differences between treatments were not statistically significant (p = 0.275). This diminished response in 2024 may be attributed to the frequent precipitation during the seedling and bud stages (Figure 1), which promoted vigorous vegetative growth and likely mitigated the suppressive effect of chemical control on branch differentiation.

For initial node height and position, the primary effect of MC was not significant in both years (p > 0.05), suggesting that MC primarily regulates the vertical growth of cotton plants without significantly affecting the initiation of fruiting branches.

The primary effect of density (D) on initial node height showed a linear increase with increasing density (D1 < D2 < D3). In 2023, the initial node height in the D3 treatment was significantly higher than in D1 and D2 by 15.02 cm (58.7%; p < 0.01) and 6.87 cm (20.4%), respectively. In 2024, the initial node height in the D3 treatment was significantly higher than D1 by 7.02 cm (23.7%; p = 0.033) and 2.98 cm (8.8%) higher than D2. For initial node position, it shifted upward with increasing density, with D3 having the highest and D1 the lowest in both years. In 2023, the difference between treatments was significant, with D3 having a 20.2% higher initial node position than D1 (p = 0.016). However, in 2024, the difference was not significant (p = 0.534). The reduced sensitivity of node initiation to density in 2024 further suggests that the abundant early-season moisture created a favorable environment that partially masked the shade-avoidance response typically triggered by high density. regarding the number of fruiting branches, a linear decrease was observed with increasing density in 2023, with D3 significantly reducing the number by 2.53 (15.7%; p = 0.025) compared to D1. In 2024, however, there were no significant differences between density treatments (p = 0.269).

3.2. Cotton Leaf Area Index

As shown in

Table 3. Effects of different treatments on leaf area index of cotton.

Variable		2023				2024
		Bud Stage	Flowering Stage	Boll Stage	Boll Opening Stage	Bud Stage	Flowering Stage	Boll Stage	Boll Opening Stage
MC1	D1	0.80 ± 0.40 ab	1.73 ± 0.11 b	5.50 ± 0.10 bc	1.98 ± 0.17 ab	1.54 ± 0.13 b	1.95 ± 0.09 ab	5.13 ± 0.01 bcd	2.09 ± 0.18 a
	D2	1.62 ± 0.15 abc	2.32 ± 0.40 ab	5.76 ± 0.18 abc	1.86 ± 0.07 ab	1.60 ± 0.10 a	2.50 ± 0.38 ab	5.51 ± 0.22 ab	1.86 ± 0.15 a
	D3	1.44 ± 0.27 abc	3.06 ± 0.27 a	6.41 ± 0.35 a	1.71 ± 0.31 ab	1.70 ± 0.09 ab	3.26 ± 0.28 a	6.18 ± 0.28 a	1.83 ± 0.13 a
MC2	D1	1.44 ± 0.16 a	1.98 ± 0.36 ab	4.27 ± 0.32 d	1.35 ± 0.11 b	1.40 ± 0.23 b	1.93 ± 0.38 ab	4.47 ± 0.24 de	1.56 ± 0.30 a
	D2	1.22 ± 0.14 bc	2.32 ± 0.51 ab	5.31 ± 0.29 bc	1.62 ± 0.30 b	1.31 ± 0.10 b	2.35 ± 0.49 ab	5.22 ± 0.24 bc	1.68 ± 0.29 a
	D3	1.80 ± 0.21 a	2.62 ± 0.13 ab	5.94 ± 0.03 ab	2.49 ± 0.40 b	1.96 ± 0.22 a	2.85 ± 0.20 ab	5.82 ± 0.01 ab	2.20 ± 0.66 a
MC3	D1	0.87 ± 0.21 bc	1.62 ± 0.53 b	4.16 ± 0.52 d	1.49 ± 0.08 b	1.51 ± 0.04 b	1.67 ± 0.59 b	4.01 ± 0.53 e	1.50 ± 0.04 a
	D2	1.37 ± 0.14 abc	1.71 ± 0.17 b	4.97 ± 0.01 cd	1.70 ± 0.14 a	1.42 ± 0.09 b	1.86 ± 0.17 b	4.70 ± 0.07 cde	1.68 ± 0.08 a
	D3	1.57 ± 0.21 abc	2.37 ± 0.36 ab	5.47 ± 0.41 bc	1.80 ± 0.23 b	1.64 ± 0.09 ab	2.66 ± 0.45 ab	5.09 ± 0.24 b cd	1.89 ± 0.17 a
MC1		1.29 ± 0.13 a	2.37 ± 0.20 a	5.59 ± 0.17 a	1.85 ± 0.13 a	1.61 ± 0.08 a	2.57 ± 0.21 a	5.61 ± 0.15 a	1.93 ± 0.17 a
MC2		1.49 ± 0.13 a	2.31 ± 0.20 a	5.17 ± 0.17 b	1.82 ± 0.13 a	1.56 ± 0.08 a	2.38 ± 0.21 a	5.17 ± 0.15 b	1.81 ± 0.17 a
MC3		1.27 ± 0.13 a	1.90 ± 0.20 a	4.87 ± 0.17 b	1.67 ± 0.13 b	1.52 ± 0.08 a	2.07 ± 0.21 a	4.60 ± 0.15 c	1.69 ± 0.17 b
D1		1.03 ± 0.13 b	1.78 ± 0.20 b	4.64 ± 0.17 c	1.61 ± 0.13 b	1.48 ± 0.08 b	1.85 ± 0.21 b	4.54 ± 0.15 c	1.72 ± 0.17 b
D2		1.40 ± 0.13 ab	2.11 ± 0.20 ab	5.34 ± 17 b	1.73 ± 0.13 a	1.44 ± 0.08 b	2.24 ± 0.21 b	5.14 ± 0.15 b	1.74 ± 0.17 a
D3		1.60 ± 0.13 a	2.69 ± 0.20 a	5.94 ± 0.17 a	2.00 ± 0.13 a	1.77 ± 0.08 a	2.93 ± 0.21 a	5.70 ± 0.15 a	1.97 ± 0.17 a
MC		0.418	0.228	0.002 **	0.574	0.704	0.265	0.001 **	0.602
D		0.016 *	0.016 *	0 **	0.125	0.016 *	0.007 **	0 **	0.5
MC × D		0.207	0.836	0.672	0.066	0.28	0.969	0.919	0.601

MC = Mepiquat Chloride, D = Density. Different lowercase letters within the same column indicate significant differences at the 0.05 level for the same year. ** denotes significance at the 0.01 level, while * denotes significance at the 0.05 level.

, both MC and D have a highly significant effect on the LAI at the boll stage. The primary effect of MC was significant only at the boll stage, where LAI significantly decreased with increasing MC dosage (2023: p = 0.002; 2024: p = 0.001). Specifically, in 2023, the LAI in the MC3 treatment was significantly reduced by 12.9% compared to MC1, and in 2024, it was reduced by 18.0%.

The primary effect of D was significant at the bud, flowering, and boll stages (p < 0.05), with LAI showing a significant positive correlation with D, and reaching a highly significant level at the boll stage (p < 0.01). In 2023, the LAI in the D3 treatment was significantly higher by 28.0% compared to D1, and in 2024, the increase was 25.6%. By the boll opening stage, the effect of density on LAI was no longer significant (p > 0.05).

Regarding the interaction, the MC × D split-plot interaction was not significant at any growth stage, suggesting that the effects of MC and D on LAI are independent of each other. At the boll stage, LAI was significantly influenced by both MC dosage and D, but no significant interaction effect was observed between the two. Specifically, low-dose (MC1) and high density (D3) can increase LAI at the boll stage by 18–22% and 25–28%, with this effect being highly consistent across both 2023 and 2024. Therefore, both factors can be independently optimized in production without the need to consider the risk of synergistic or antagonistic interactions.

3.3. Photosynthetic Parameters at the Flowering and Boll Stage

As shown in

Table 4. Effects of different treatments on photosynthetic parameters during the flowering and boll stage of cotton.

Variable		2023				2024
		Pn (μmol m−2 s−1)	Tr (mmol m−2 s−1)	Ci (ppm)	Gs (mol m−2 s−1)	Pn (μmol m−2 s−1)	Tr (mmol m−2 s−1)	Ci (ppm)	Gs (mol m−2 s−1)
MC1	D1	31.20 ± 0.11 ab	18.32 ± 0.25 a	307.04 ± 2.44 ab	1.26 ± 0.03 a	31.85 ± 1.27 a	16.80 ± 0.53 ab	313.51 ± 1.59 ab	0.87 ± 0.06 a
	D2	30.62 ± 1.45 ab	17.87 ± 0.51 ab	312.79 ± 2.93 a	1.22 ± 0.01 a	30.71 ± 1.08 a	16.98 ± 0.36 ab	316.41 ± 3.33 a	0.84 ± 0.02 a
	D3	29.89 ± 1.27 b	17.04 ± 0.38 abc	310.75 ± 4.51 ab	1.26 ± 0.06 a	31.70 ± 0.30 a	16.19 ± 0.24 ab	317.63 ± 2.46 a	0.81 ± 0.00 a
MC2	D1	29.57 ± 1.29 b	17.58 ± 0.21 abc	295.65 ± 7.91 b	1.19 ± 0.05 a	32.57 ± 2.28 a	18.15 ± 1.23 a	300.87 ± 4.48 c	0.94 ± 0.06 a
	D2	31.02 ± 0.31 ab	17.33 ± 0.43 abc	302.52 ± 4.27 ab	1.31 ± 0.01 a	31.89 ± 1.46 a	18.01 ± 0.45 a	298.14 ± 3.57 c	1.04 ± 0.09 a
	D3	30.51 ± 0.48 b	17.46 ± 0.49 abc	306.30 ± 2.61 ab	1.27 ± 0.04 a	31.43 ± 0.66 a	17.30 ± 0.25 ab	302.79 ± 3.39 bc	1.02 ± 0.13 a
MC3	D1	33.52 ± 0.05 a	17.78 ± 0.12 abc	314.60 ± 4.06 a	1.18 ± 0.03 a	32.06 ± 0.61 a	15.40 ± 0.04 b	314.25 ± 4.73 ab	0.80 ± 0.02 a
	D2	31.89 ± 1.30 ab	16.73 ± 0.26 bc	307.82 ± 3.63 ab	1.20 ± 0.08 a	32.46 ± 1.34 a	16.71 ± 0.40 ab	310.24 ± 5.62 abc	0.94 ± 0.07 a
	D3	31.67 ± 0.56 ab	16.35 ± 0.67 c	314.38 ± 2.99 a	1.16 ± 0.01 a	32.54 ± 1.52 a	17.32 ± 1.05 ab	315.18 ± 3.33 a	1.01 ± 0.11 a
MC1		30.57 ± 0.43 b	17.74 ± 0.27 a	310.19 ± 1.90 a	1.24 ± 0.02 a	31.42 ± 0.52 a	16.66 ± 0.23 b	315.85 ± 1.42 a	0.84 ± 0.02 b
MC2		30.37 ± 0.46 b	17.46 ± 0.20 a	301.49 ± 3.12 b	1.26 ± 0.03 a	31.96 ± 0.82 a	17.82 ± 0.41 a	300.60 ± 2.04 b	0.99 ± 0.05 a
MC3		32.36 ± 0.50 a	16.95 ± 0.30 a	312.27 ± 2.11 a	1.18 ± 0.03 a	32.35 ± 0.62 a	16.48 ± 0.43 b	313.22 ± 2.45 a	0.92 ± 0.05 ab
D1		31.43 ± 0.68 a	17.89 ± 0.15 a	305.77 ± 3.83 a	1.21 ± 0.02 a	32.16 ± 0.78 a	16.78 ± 0.55 a	309.54 ± 2.91 a	0.87 ± 0.03 a
D2		31.17 ± 0.45 a	17.31 ± 0.26 b	307.71 ± 2.35 a	1.24 ± 0.03 a	31.69 ± 0.70 a	17.23 ± 0.28 a	308.26 ± 3.44 a	0.94 ± 0.04 a
D3		30.69 ± 0.50 a	16.95 ± 0.31 b	310.48 ± 2.09 a	1.23 ± 0.03 a	31.89 ± 0.52 a	16.94 ± 0.37 a	311.87 ± 2.77 a	0.95 ± 0.06 a
MC		0.013 *	0.079	0.014 *	0.082	0.683	0.035 *	0 **	0.057
D		0.532	0.032 *	0.409	0.718	0.903	0.678	0.51	0.394
MC × D		0.34	0.457	0.435	0.411	0.945	0.244	0.913	0.5

MC = Mepiquat Chloride, D = Density. Different lowercase letters within the same column indicate significant differences at the 0.05 level for the same year. ** denotes significance at the 0.01 level, while * denotes significance at the 0.05 level.

, the photosynthetic parameters exhibited significant variations between the two growing seasons, likely driven by the distinct meteorological conditions (Figure 1). Generally, Gs and Tr were lower in 2024 compared to 2023. For instance, under the D1 treatment, Gs significantly decreased from ~1.26 mol·m⁻²·s⁻¹ in 2023 to ~0.87 mol·m⁻²·s⁻¹ in 2024. This reduction may be attributed to the continuous high precipitation during the seedling and bud stages in 2024, which likely hindered early root development and subsequent hydraulic conductivity.

Regarding specific treatment effects, in 2023, MC had a significant effect on the net Pn and Ci (Pn: p = 0.013; Ci: p = 0.014), but its effects on Tr and Gs were not significant (Tr: p = 0.079; Gs: p = 0.082). For Pn, the treatments followed the order MC3 > MC1 > MC2, with the Pn value in the MC3 treatment significantly increasing by 5.8% and 6.5% compared to MC1 and MC2, respectively (p < 0.05). For Ci, in 2023, the order was also MC3 > MC1 > MC2, with the Ci value in the MC3 treatment significantly higher by 3.6% compared to MC2 (p < 0.05).

The primary effect of Density had a significant impact on Tr only in 2023 (p = 0.032), with the trend being D1 > D2 > D3. The Tr value in the D1 treatment was significantly increased by 5.5% compared to D2 and D3 (p < 0.05).

In 2024, MC significantly affected Tr and Ci (Tr: p = 0.035; Ci: p < 0.001) but had no significant effect on Pn and Gs (Pn: p = 0.683; Gs: p = 0.057). For Tr, the MC2 treatment significantly increased Tr by 8.1% compared to MC3 (p < 0.05). Compared to 2023, Tr values for all treatments decreased by 3% to 8%. For Ci, the order was MC1 > MC3 > MC2, with the Ci value in the MC1 treatment significantly higher by 5.1% compared to MC2 (p < 0.001).

The MC × D interaction did not significantly affect any photosynthetic parameters (p > 0.05), indicating that the effects of MC and density on cotton photosynthesis are relatively independent, allowing for independent optimization.

3.4. Cotton Yield and Yield Components

As shown in

Table 5. Effects of different treatments on cotton yield and yield composition.

Variable		2023				2024
		Seed Cotton Yield (kg/ha−1)	Lint Yield (kg/ha−1)	Boll Number per Plant	Boll Weight (g)	Seed Cotton Yield (kg/ha−1)	Lint Yield (kg/ha−1)	Boll Number per Plant	Boll Weight (g)
MC1	D1	5500.05 ± 328.26 abcd	2218.23 ± 204.22 ab	17.67 ± 1.05 a	5.20 ± 0.18 a	5064.16 ± 182.38 bc	1787.05 ± 54.67 b	17.93 ± 0.72 b	4.71 ± 0.05 a
	D2	5810.60 ± 359.97 abc	2133.24 ± 125.00 bc	12.20 ± 0.70 c	5.29 ± 0.04 a	5801.16 ± 430.308 abc	2019.56 ± 147.01 ab	13.70 ± 1.04 c	4.71 ± 0.01 a
	D3	6258.06 ± 372.46 a	2259.40 ± 197.48 ab	9.73 ± 0.50 e	5.35 ± 0.04 a	6628.54 ± 85.05 a	2295.18 ± 9.69 a	11.27 ± 0.48 de	4.91 ± 0.16 a
MC2	D1	4924.63 ± 315.17 cd	1728.45 ± 46.50 cd	15.27 ± 0.84 b	5.37 ± 0.06 a	5824.57 ± 162.34 abc	1797.40 ± 233.44 ab	20.97 ± 0.41 a	4.63 ± 0.05 a
	D2	5752.13 ± 307.24 abc	2134.33 ± 150.25 bc	12.07 ± 0.62 cd	5.30 ± 0.02 a	6130.51 ± 450.15 ab	2156.28 ± 174.08 ab	14.27 ± 0.93 c	4.77 ± 0.05 a
	D3	6190.56 ± 345.40 ab	2372.18 ± 87.42 ab	9.90 ± 0.47 e	5.21 ± 0.08 a	6179.94 ± 322.37 ab	2141.41 ± 103.43 ab	10.90 ± 0.67 de	4.73 ± 0.11 a
MC3	D1	4538.01 ± 339.54 d	1606.77 ± 62.99 d	14.37 ± 1.04 b	5.27 ± 0.10 a	4856.70 ± 331.11 c	1711.59 ± 121.00 b	16.90 ± 0.96 b	4.78 ± 0.07 a
	D2	5132.45 ± 325.90 bcd	2020.21 ± 167.26 abc	11.00 ± 0.47 cde	5.18 ± 0.11 a	5147.58 ± 358.04 bc	1772.90 ± 108.08 b	12.27 ± 0.64 cd	4.66 ± 0.13 a
	D3	6169.82 ± 291.97 ab	2639.76 ± 70.41 a	10.20 ± 0.20 de	5.04 ± 0.14 a	5578.08 ± 488.01 abc	1868.02 ± 166.58 ab	9.57 ± 0.69 e	4.85 ± 0.08 a
MC1		5856.236 ± 5192.05 a	2203.623 ± 82.82 a	13.20 ± 0.41 a	5.28 ± 0.06 a	5831.29 ± 195.75	2033.93 ± 80.34 a	14.30 ± 0.44 a	4.78 ± 0.05 a
MC2		5622.44 ± 192.05 a	2078.32 ± 82.82 a	12.41 ± 0.41 ab	5.29 ± 0.06 a	6045.01 ± 195.75	2031.69 ± 80.34 a	15.38 ± 0.44 a	4.71 ± 0.05 a
MC3		5280.09 ± 192.05 a	2088.91 ± 82.82 a	11.86 ± 0.41 b	5.16 ± 0.06 a	5194.12 ± 195.75	1784.17 ± 80.34 a	12.91 ± 0.44 b	4.76 ± 0.05 a
D1		4987.56 ± 192.05 c	1851.1 ± 82.82 c	15.77 ± 0.41 a	5.28 ± 0.06 a	5248.48 ± 195.75 c	1765.35 ± 80.34 b	18.60 ± 0.44 a	4.71 ± 0.05 a
D2		5565.06 ± 192.05 b	2095.93 ± 82.82 b	11.76 ± 0.41 b	5.25 ± 0.06 a	5693.08 ± 195.75 ab	1982.91 ± 80.34 ab	13.41 ± 0.44 b	4.71 ± 0.05 a
D3		6206.14 ± 192.05 a	2423.78 ± 82.82 a	9.94 ± 0.41 c	5.20 ± 0.06 a	6128.85 ± 195.75 a	2101.54 ± 80.34 a	10.58 ± 0.44 c	4.83 ± 0.05 a
MC		0.131	0.508	0.092	0.22	0.017 *	0.065	0.003 *	0.641
D		0.001 **	0 **	0 **	0.62	0.018 *	0.026 *	0 **	0.184
MC × D		0.689	0.037 *	0 **	0.133	0.501	0.638	0.236	0.525

MC = Mepiquat Chloride, D = Density. Different lowercase letters within the same column indicate significant differences at the 0.05 level for the same year. ** denotes significance at the 0.01 level, while * denotes significance at the 0.05 level.

, for the primary effect of MC on seed cotton yield, no significant difference was observed between treatments in 2023 (p = 0.131); however, in 2024, the yield showed a trend of initially increasing and then decreasing as the MC dosage increased (p = 0.017), with MC2 (6045 kg·ha⁻¹) > MC1 (5831 kg·ha⁻¹) > MC3 (5194 kg·ha⁻¹). MC2 significantly increased yield by 16.4% compared to MC3, but no significant difference was observed between MC2 and MC1.

For the number of bolls per plant, a decreasing trend with increasing MC dosage was observed in both years, but the ranking varied between years. In 2023, the order was MC3 > MC2 > MC1, with MC3 showing a 10.2% decrease compared to MC1 (p = 0.092). In 2024, the order was MC2 > MC1 > MC3, with MC3 significantly decreasing by 16.0% and 9.7% compared to MC2 and MC1, respectively (p = 0.003). No significant differences in boll weight were observed between treatments in either year (p > 0.05).

For the primary effect of D on seed cotton yield, in 2023, D3 (6206 kg·ha⁻¹) significantly increased by 24.4% compared to D1 (4988 kg·ha⁻¹) (p < 0.001). In 2024, D3 (6129 kg·ha⁻¹) significantly increased by 16.8% compared to D1 (5248 kg·ha⁻¹) (p = 0.018). For lint yield, a significant increase with density was observed in both years, with D3 increasing by 30.9% compared to D1 in 2023 (p < 0.001) and by 19.1% in 2024 (p < 0.01). The number of bolls per plant decreased significantly with increasing density in both years (p < 0.001), with a decrease of approximately 37% in 2023 and 43% in 2024 (p < 0.001). This indicates that the increase in seed cotton yield was primarily driven by the compensatory effect of the population boll number. No significant differences in boll weight were observed between density treatments in either year (p > 0.05).

Regarding the interaction, the MC × D interaction did not significantly affect seed cotton yield. Across all MC levels, seed cotton yield consistently followed the order D3 > D2 > D1, indicating that the yield-enhancing effect of density was stable across different MC treatments. However, in 2023, a significant MC × D interaction effect on lint yield was observed (p = 0.037). Specifically, under low MC (MC1), increasing density from D1 to D3 resulted in a minor lint yield increase of 2.1–15% (p > 0.05). In contrast, under high MC (MC3), the same increase in density boosted yield by 64.0%. This indicates that high MC dosage is essential for unlocking the full yield potential of high-density populations. Regarding yield components, the number of bolls per plant decreased with increasing density across all MC levels, but the magnitude of this reduction varied by dosage: the reduction from D1 to D3 was 31% under MC1, 25% under MC2, and 37% under MC3. A similar trend was observed in 2024, though not statistically significant. This suggests that while moderate MC (MC2) helps alleviate inter-plant competition (lowest reduction), high MC (MC3) combined with high density exerts a stronger suppression on individual plant growth, which is then overcompensated by the population density to achieve maximum yield.

3.5. Cotton Fiber Quality

The primary effect of MC on fiber quality was limited (

Table 6. The influence of different treatments on cotton fiber quality.

Variable		2023					2024
		Upper Half Mean Length (mm)	Uniformity Index (%)	Fiber Strength (cN/tex)	Micronaire Value	Elongation Rate (%)	Upper Half Mean Length (mm)	Uniformity Index (%)	Fiber Strength (cN/tex)	Micronaire Value	Elongation Rate (%)
MC1	D1	29.73 ± 0.52 a	83.53 ± 0.58 a	29.63 ± 0.52 a	4.77 ± 0.07 a	6.83 ± 0.03 a	29.63 ± 0.30 a	84.67 ± 0.80 a	28.00 ± 0.38 a	4.50 ± 0.19a	6.73 ± 0.07 a
	D2	29.73 ± 0.72 a	83.80 ± 0.25 a	29.07 ± 0.48 a	4.70 ± 0.06 a	6.83 ± 0.03 a	29.97 ± 0.29 a	85.20 ± 0.35 a	28.43 ± 0.35 a	4.60 ± 0.06a	6.70 ± 0.00 a
	D3	29.97 ± 0.15 a	83.93 ± 0.29 a	29.53 ± 0.38 a	4.70 ± 0.06 a	6.80 ± 0.00 a	29.40 ± 0.36 a	85.37 ± 0.32 a	27.97 ± 0.19 a	4.40 ± 0.12a	6.70 ± 0.06 a
MC2	D1	30.90 ± 0.55 a	83.60 ± 0.51 a	29.40 ± 0.60 a	4.77 ± 0.24 a	6.87 ± 0.03 a	29.37 ± 0.23 a	84.70 ± 0.26 a	28.60 ± 0.50 a	4.50 ± 0.06a	6.67 ± 0.03 a
	D2	30.40 ± 0.59 a	83.77 ± 0.03 a	29.10 ± 0.53 a	4.87 ± 0.03 a	6.80 ± 0.00 a	29.17 ± 0.03 a	84.57 ± 0.43 a	28.13 ± 0.48 a	4.53 ± 0.03a	6.70 ± 0.06 a
	D3	30.27 ± 0.22 a	84.00 ± 0.59 a	29.47 ± 0.32 a	4.60 ± 0.06 a	6.80 ± 0.00 a	29.97 ± 0.23 a	85.47 ± 0.44 a	28.37 ± 0.22 a	4.33 ± 0.12a	6.67 ± 0.03 a
MC3	D1	31.03 ± 0.52 a	82.83 ± 1.01 a	29.90 ± 0.36 a	4.53 ± 0.07 a	6.83 ± 0.30 a	28.20 ± 0.40 b	84.90 ± 0.51 a	27.43 ± 0.37 a	4.77 ± 0.18a	6.60 ± 0.00 a
	D2	30.73 ± 0.46 a	82.93 ± 0.65 a	29.00 ± 0.80 a	4.67 ± 0.07 a	6.80 ± 0.00 a	29.83 ± 0.15 a	85.13 ± 0.09 a	28.03 ± 0.44 a	4.40 ± 0.00a	6.67 ± 0.03 a
	D3	30.87 ± 0.35 a	84.13 ± 0.19 a	29.40 ± 0.15 a	4.60 ± 0.12 a	6.80 ± 0.00 a	29.57 ± 0.03 a	85.33 ± 0.07 a	27.87 ± 0.15 a	4.50 ± 0.10a	6.70 ± 0.00 a
MC1		29.81 ± 0.23 b	83.76 ± 0.27 a	29.41 ± 0.22 a	4.72 ± 0.08 a	6.82 ± 0.00 a	29.67 ± 0.20	85.08 ± 0.28 a	28.13 ± 0.15 a	4.52 ± 0.08 a	6.71 ± 0.01 a
MC2		30.52 ± 0.16 ab	83.79 ± 0.33 a	29.32 ± 0.22 a	4.74 ± 0.09 a	6.82 ± 0.02 a	29.50 ± 0.23	84.91 ± 0.15 a	28.37 ± 0.21 a	4.46 ± 0.08 a	6.68 ± 0.02 a
MC3		30.88 ± 0.21 a	83.30 ± 0.21 a	29.43 ± 0.19 a	4.60 ± 0.06 a	6.81 ± 0.02 a	29.20 ± 0.15	85.12 ± 0.19 a	27.78 ± 0.34 a	4.56 ± 0.05 a	6.66 ± 0.02 a
D1		30.56 ± 0.21 a	83.32 ± 0.24 a	29.64 ± 0.15 a	4.69 ± 0.11 a	6.84 ± 0.02 a	29.07 ± 0.15 b	84.76 ± 0.17 a	28.01 ± 0.31 a	4.61 ± 0.10 a	6.67 ± 0.02 a
D2		30.29 ± 0.24 a	83.50 ± 0.19 a	29.06 ± 0.26 a	4.74 ± 0.03 a	6.81 ± 0.02 ab	29.66 ± 0.29 a	84.97 ± 0.29 a	28.20 ± 0.22 a	4.51 ± 0.07 a	6.69 ± 0.01 a
D3		30.37 ± 0.24 a	84.02 ± 0.24 a	29.47 ± 0.17 a	4.63 ± 0.07 a	6.80 ± 0.01 b	29.64 ± 0.15 a	85.39 ± 0.19 a	28.07 ± 0.19 a	4.11 ± 0.04 a	6.69 ± 0.01 a
MC		0.043	0.471	0.959	0.209	0.781	0.107	0.812	0.163	0.544	0.258
D		0.789	0.274	0.348	0.435	0.062	0.017	0.199	0.808	0.116	0.739
MC × D		0.933	0.83	0.973	0.603	0.651	0.006	0.876	0.587	0.357	0.493

MC = Mepiquat Chloride, D = Density. Different lowercase letters within the same column indicate significant differences at the 0.05 level for the same year. The unit of fiber strength is centinewtons per tex (cN/tex).

). In 2023, only the upper half mean length showed a significantly increasing trend with increasing MC dosage (p = 0.043), while no significant differences were observed for other quality indicators (p > 0.05). In 2024, no significant MC effects were observed for any of the tested quality indicators.

The primary effect of Density and the MC × D interaction did not significantly affect any of the quality traits surveyed (p > 0.05). The only exception was the upper half mean length in 2024, with a significant difference of 0.59 mm between D2 (29.66 mm) and D1 (29.07 mm) treatments (p = 0.017), though this difference did not meet the 1 mm agronomic significance threshold.

Furthermore, the coefficient of variation (CV) for interannual variability of the micronaire value, fiber strength, and uniformity index (CV interannual = 5.8–9.4%) was greater than the CV for variation between treatments (CV treatment < 3.1%).

4. Discussion

4.1. Synergistic Mechanism of MC Dwarfing and Density-Induced Elevation of First Fruit Branch

An ideal machine-harvestable cotton plant type typically needs to meet the dual requirements of “dwarfing” (plant height ≤ 100 cm) and “elevation” (first fruit branch height ≥ 18 cm) [17]. The results from both years of the experiment consistently show that MC and density exhibit a synergistic pattern of “functional complementarity and non-antagonism” in regulating cotton plant morphology.

MC primarily regulates the “dwarfing” of cotton plants, with plant height decreasing linearly as its dosage increases [18]. The MC3 treatment reduced plant height by 15–20 cm compared to MC1, stabilizing the plant height around 92 cm, without significantly reducing the number of fruiting branches (≥12 branches), thus meeting the height threshold required for machine harvesting [19]. MC had no significant effect on initial node height or initial node position (p ≥ 0.05), ensuring that its “dwarfing” effect did not compromise the initial position of fruiting branches, consistent with previous findings [20].

Density primarily exerts the “elevation” effect, where initial node height increases linearly with density, driven by a shading avoidance response due to intraspecific competition (p < 0.001). In 2023, the D3 treatment increased initial node height by 15.02 cm (58.7%) compared to D1, and in 2024, it increased by 7.02 cm (23.7%), raising the first fruit branch height to over 40 cm. This helps reduce the risk of lower bolls rotting, consistent with the “density-shading-elevation” pathway previously proposed [21,22]. D did not significantly affect plant height (2023: p = 0.967; 2024: p = 0.874), indicating that its effect on plant height is independent of MC’s dwarfing effect.

There were no significant interactions between MC and D for any of the agronomic traits examined (p > 0.05), which aligns with some previous research findings. Therefore, in practical production, MC dosage and planting density can be optimized independently, synergistically shaping the ideal machine-harvestable plant type with “dwarfing and elevated fruiting branches,” without concerns about antagonistic or excessive synergistic risks. In this study, the combination of “D3 (12.0 × 10⁴ plants·ha⁻¹) + MC2/MC3 (270–360 g·ha⁻¹)” achieved the ideal machine-harvestable plant structure with a plant height of around 90 cm and an initial node height of around 40 cm, while ensuring a sufficient number of fruiting branches (≥12 branches).

4.2. Effects on Canopy Structure and Photosynthetic Physiology

Canopy light energy utilization is a critical factor for cotton yield formation. Regarding canopy structure, planting density (D) plays a key role in yield formation by regulating the LAI. In this study, LAI at the boll stage increased linearly with density (D3 increased by 25–28% compared to D1), and this effect was highly consistent across both years, providing a solid ‘source’ basis for achieving high yield in high-density populations. These findings align with the conclusion of Chen et al. [23] that “high density can maintain an appropriate LAI range, ensuring effective photosynthetic interception”. Furthermore, our results confirm that under the short-season direct-seeding model in the Yangtze River Basin, the D3 treatment maintains LAI within the optimal range of 3.0–3.5 at the boll stage. This corresponds to the LAI threshold necessary for high yields in densely planted cotton, providing structural support for maximizing canopy photosynthetic efficiency.

The suppressive effect of MC on LAI was significant only at the boll stage, indicating that its regulatory effect is growth-stage-specific. In this study, at the boll stage, LAI in the MC3 treatment was reduced by 12.9% and 18.0% compared to MC1 in 2023 and 2024, respectively (p < 0.01), while no significant changes were observed in LAI during the bud and flowering stages. This result aligns with the concept that ‘MC regulation is stage-selective, primarily affecting the period of vigorous vegetative growth’ [24]. This stage-specific regulation might explain why MC does not affect the final yield, and even increases yield under the MC2 treatment, as it primarily suppresses ineffective vegetative growth during the later growth stages, thereby optimizing the canopy’s light distribution.

In 2023, the MC3 treatment enhanced the net Pn and Ci without significant changes in Gs, indicating that the increase in photosynthesis was likely due to improvements in mesophyll conductance or photochemical efficiency, rather than being driven by stomatal factors. This phenomenon aligns with the ‘mesophyll conductance limitation’ model proposed by Zhai et al. [25], suggesting that MC might primarily enhance Pn by improving the photosynthetic biochemical efficiency of mesophyll cells. In 2024, possibly due to meteorological conditions, the photosynthetic enhancement effect of MC did not appear, and Gs decreased by 20–30%. This result is highly consistent with the conclusion by Zhao et al. [26] that ‘under summer sowing and high-density conditions, the MC effect is more easily diluted by environmental factors’.

Regarding density, the D3 treatment primarily optimized water use efficiency by significantly reducing Tr, a trait that will be more advantageous in drought years. No significant MC × D interaction was found for any photosynthetic parameters, further confirming at the physiological level that MC and density can be independently regulated [27].

4.3. Effects of Density and MC on Yield and Yield Components

In the short-season direct-seeding cultivation model, “increasing density to compensate for delayed growth” and effective chemical control are critical strategies for boosting cotton yield and efficiency. MC application can effectively regulate cotton plant height, optimize fruiting branch structure, balance source–sink relationships, and resolve individual-plant and population conflicts, leading to higher yields [28].

In this study, increasing planting density (D3) was the most effective method for achieving higher yields. This was achieved by significantly increasing the number of bolls per unit area, effectively compensating for the substantial decrease in the number of bolls per plant (decreased by 37–43%), resulting in a significant increase in seed cotton yield by 16.8–24.4%. This finding aligns closely with the conclusions of previous research [29]. However, population expansion has limits; excessively high planting density can intensify competition between plants, potentially resulting in decreased boll weight and an increased risk of boll rot [30]. In this study, the D3 treatment did not result in a significant decrease in boll weight, suggesting that under current climate and soil fertility conditions, a density of 12.0 × 10⁴ plants·ha⁻¹ remains within the reasonable range for dense cotton planting in the Yangtze River Basin’s short-season direct-seeding system.

The effect of MC exhibited a typical “dose–effect” relationship and showed annual variation. In 2024, the medium dose (MC2) treatment resulted in the highest yield, whereas the high dose (MC3) led to a yield reduction. This suggests that MC should be applied carefully in this model, as excessive chemical control could overly suppress the photosynthetic capacity of source organs (leaves), disrupting the source–sink balance and negating the yield benefits provided by high density.

The MC × D interaction significantly affected lint yield (p = 0.037). At the MC3 (high-dose MC) level, the yield potential of D3 was fully realized, with lint yield significantly increasing by 64.0% compared to D1. However, at MC1 and MC2 levels, the yield differences between density treatments were not significant (increase < 15%). This indicates that high-dose MC, by creating a more compact canopy structure, effectively alleviates the shading problem commonly encountered in high-density populations, improving the light environment in the lower and middle canopy, thus fully unlocking the yield potential of high-density planting [31]. These results elucidate the physiological basis underlying the synergy between high planting density and intensive chemical regulation, confirming that optimized canopy structure is central to realizing yield potential. However, considering the yield reduction risk of MC3 in 2024, this strategy should be applied with caution in practice.

4.4. Effects on Fiber Quality

The results of this study indicate that within the treatment range of MC (180–360 g·ha⁻¹) and density (6.0–12.0 × 10⁴ plants·ha⁻¹), no significant effects were observed on any of the tested fiber quality indicators, including fiber length, strength, and micronaire value. The coefficient of variation (CV) for interannual variation in fiber quality traits (CV = 5.8–9.4%) was significantly higher than the CV for variation between treatments (CV < 3.1%), suggesting that the genetic characteristics of the variety and interannual climate fluctuations are the dominant factors influencing fiber quality formation [32]. This finding aligns with the conclusions of Wang et al. [33] and Qi et al. [34], who reported that “fiber quality can remain stable within an optimal range of density and chemical control”.

In 2023, the upper half mean length in the MC3 treatment was significantly increased by 3.6% compared to MC1 (p = 0.043), possibly due to high-dose MC promoting the allocation and transport of photosynthetic products to fibers [35]. However, this effect was not observed in 2024, implying that the response of fiber quality to chemical control is also influenced by environmental conditions [36,37]. This phenomenon offers important insights for future research, suggesting the need to incorporate the “genotype × environment × management” interaction model for further analysis.

Our results indicate that fiber quality remained largely stable within the agronomic ranges tested in this study, suggesting that significant quality trade-offs were not a limiting factor under these high-yield conditions. Consequently, agronomic management can prioritize the synergistic optimization of yield components and plant architecture with fewer constraints regarding fiber quality deterioration.

5. Conclusions

This study systematically clarifies the synergistic regulatory mechanisms of mepiquat chloride (MC) and planting density in short-season direct-seeding cotton in the Yangtze River Basin. Our findings demonstrate that MC and density function in a complementary, non-antagonistic manner to shape an ideal plant architecture, characterized by effective height control from MC and the elevation of fruiting nodes from high density. In terms of yield formation, increasing planting density serves as the primary driver for yield enhancement through population compensation, while MC plays a critical role in maximizing the yield potential of high-density populations via dose-dependent regulation. Importantly, these agronomic adjustments operate distinct physiological pathways without negative interactions and maintain fiber quality stability within the tested ranges. Consequently, we propose an independent synergistic regulation strategy centered on “high density (12.0 × 10⁴ plants·ha⁻¹) to secure population yield, coupled with medium-dose MC (270 g·ha⁻¹) to optimize plant type.” Future research should expand on these findings by investigating the molecular networks underlying these interactions across diverse genotypes and ecological zones to enhance precision management.