Exogenous Regulators Enhance Physiological Recovery and Yield Compensation in Maize Following Mechanical Leaf Damage

Abstract

To elucidate how exogenous regulators mitigate the impact of mechanical leaf damage on maize, field experiments were conducted on two sowing dates (S1, S2) using two cultivars (XY335, ZD958). Severe leaf damage at the six-leaf stage significantly reduced kernel number, ear number, and 100-kernel weight, causing yield losses of 21.9–48.9%. Foliar application of melatonin (MT), brassinolide (BR), and urea (UR) substantially alleviated these losses, increasing yield by 14.1–52.2% compared to damaged controls, with UR and BR being most effective, especially in ZD958. These regulators restored leaf area index (LAI) by promoting leaf width and delaying senescence, improved photosynthetic performance (Pn, Gs, Ci, and Tr), enhanced post-silking dry matter accumulation by up to 31%, and accelerated grain filling through increased maximum and mean filling rates. Structural equation modeling confirmed that kernel number and 100-kernel weight were the primary yield determinants. These findings reveal the physiological mechanisms underlying damage recovery and demonstrate the potential of targeted regulator applications—urea as a cost-effective option, brassinolide for improving kernel number under sustained stress, and melatonin for broad resilience. This study provides not only theoretical evidence but also a feasible strategy for mitigating yield loss in maize production under field conditions where leaf damage commonly occurs.

1. Introduction

Maize (Zea mays L.) is one of the most important staple crops worldwide, playing an irreplaceable role in ensuring food security and promoting agricultural economic development [1]. In recent years, with the intensification of global climate change and the increasing complexity of agricultural production environments, maize faces multiple challenges during its growth and development [2]. Among various biotic and abiotic stresses, leaf mechanical damage has emerged as a significant factor limiting maize yield and quality. Leaf damage caused by natural factors such as hail, wind lodging, and pest feeding is frequently observed in maize production and results in significant yield loss. However, effective agronomic strategies to mitigate such losses are limited. As the main photosynthetic organ of all plants, leaves are crucial for efficient light capture, energy conversion, and dry matter accumulation [3].

Leaf mechanical damage directly affects the structural integrity of chloroplasts and reduces photosynthetic pigment content, leading to a significant decline in photosynthetic rate [4,5]. Moreover, such damage can alter hormonal balance within the plant, thereby influencing growth and development [6]. For instance, damage induces a surge in ethylene production [7], which primarily initiates a wound-healing process crucial for survival. This process involves synergistic actions with auxins to reprogram cells. However, a concomitant effect of this ethylene burst may be the acceleration of leaf senescence and abscission in surrounding tissues, which can secondarily compromise photosynthesis and biomass accumulation. In addition, mechanical damage can disrupt water and nutrient transport, affect nutrient uptake and utilization efficiency, and thereby exert multifaceted negative effects on plant growth [8].

Exogenous treatments have recently gained widespread attention for their role in mitigating plant stress [9]. Melatonin (MT), as an important signaling molecule and antioxidant, can scavenge reactive oxygen species (ROS) and reduce oxidative damage to plant cells [10]. It also regulates plant growth and development, including seed germination, root growth, and seedling development [11]. Under stress conditions, MT enhances plant resistance to drought [12], salt [13], high temperature [14], and other adverse environments by modulating signaling pathways and gene expression, strengthening the antioxidant defense system, and improving osmotic regulation [15]. Moreover, numerous studies have shown that exogenous MT can enhance photosynthetic efficiency, improve antioxidant capacity, delay leaf senescence, and promote root and shoot growth, thereby contributing to greater biomass accumulation [16,17]. In addition, MT has been found to facilitate assimilate translocation and improve dry matter partitioning to reproductive organs, which ultimately enhances yield formation under stress conditions [18,19]. These results indicate that MT plays a crucial role in sustaining plant growth dynamics and optimizing dry matter allocation when plants are challenged by external disturbances.

Urea (UR), a major source of nitrogen fertilizer, is widely used in agricultural production [20]. Nitrogen is an essential macronutrient involved in various physiological and biochemical processes in plants [21]. Urea provides available nitrogen to promote the synthesis of amino acids, proteins, and nucleic acids, thereby facilitating plant growth and development [22]. Regarding photosynthesis, nitrogen is a key precursor for chlorophyll synthesis, and sufficient nitrogen supply can enhance chlorophyll content and photosynthetic efficiency [23]. Additionally, urea regulates carbon–nitrogen metabolism, promotes the absorption and utilization of other nutrients, and improves stress resistance and growth vigor [24]. Recent studies further indicate that optimized urea application strategies significantly contribute to crop resilience and productivity. For example, foliar application of urea ammonium nitrate has been shown to increase dry matter accumulation, improve nitrogen translocation, and enhance yield formation in summer maize under stress conditions [25]. Similarly, controlled-release urea effectively boosts yield and nitrogen use efficiency while reducing N losses in wheat–maize rotations [26]. Field experiments also demonstrated that applying controlled-release urea at optimal soil depths (10–15 cm) increased kernel number per ear and 1000-grain weight, thereby improving final yield [27]. Moreover, combined foliar application of urea and fulvic acid enhanced aboveground biomass and grain yield by 5–17%, while improving nitrogen use efficiency by 17–24 percentage points [28]. These findings highlight that exogenous urea not only enhances photosynthetic capacity and leaf area development but also facilitates dry matter allocation and yield formation, particularly under stress conditions [29].

Brassinosteroids (BR) are a class of plant hormones with multiple physiological activities, playing crucial roles throughout plant growth and development [30]. They promote cell elongation and division, increase cell size, and thereby enhance overall plant growth [31]. In terms of photosynthesis, BRs can increase chlorophyll content and photosynthetic enzyme activity, enhancing photosynthetic efficiency [32]. Furthermore, they regulate nutrient allocation and transport, facilitating the movement and accumulation of photosynthetic products to important organs such as grains, ultimately improving yield and quality [33]. Under stress conditions, BRs enhance plant resistance to drought [34], salinity [35], and heavy metals [36] by modulating the antioxidant defense system and hormonal balance, maintaining normal growth and development. Recent studies have further demonstrated that exogenous application of BRs can improve root and shoot growth, promote sucrose redistribution, and alleviate stress-induced growth inhibition, thereby supporting dry matter accumulation and yield formation [37,38]. For example, foliar sprays of BR analogues improved drought tolerance by enhancing water relations and leaf gas exchange [39], while BRs promoted carbohydrate translocation from stems to reproductive organs under low nitrogen stress, mitigating floret degeneration and enhancing yield [40,41]. These findings highlight the potential of BRs to mitigate stress-induced growth inhibition and support the physiological traits relevant to the present study.

Although previous studies have explored the roles of melatonin, urea, and brassinosteroids in alleviating plant stress, systematic research on their effects under leaf mechanical damage remains limited. Therefore, this study focuses on maize at the six-leaf stage experiencing severe mechanical leaf damage, and investigates the effects of exogenous melatonin, urea, and brassinosteroid treatments on maize yield, dry matter accumulation, photosynthetic parameters, grain-filling traits, leaf area, and agronomic characteristics. The aim is to elucidate the physiological mechanisms by which these treatments mitigate the effects of leaf mechanical damage and to translate these insights into practical, cultivar-specific strategies for post-damage management, thereby providing actionable recommendations for farmers to stabilize yield under such adversities.

2. Materials and Methods

2.1. Overview of the Experimental Site

Field experiments were conducted in 2022 at the Qingyuan Agricultural Experimental Station of Hebei Agricultural University, Baoding, Hebei Province, China (38.76° N, 115.49° E), located on the North China Plain. The site has a semi-arid warm temperate climate, with a mean annual temperature of 12.6 °C, a frost-free period of about 200 days, annual sunshine duration of 2216 h, and mean annual precipitation of 510 mm, approximately 80% of which falls between June and September. The soil is classified as heavy loam, with the 0–40 cm layer containing 19.65 g·kg⁻¹ organic matter, 0.94 g·kg⁻¹ total nitrogen, 14.0 mg·kg⁻¹ available phosphorus, and 58.6 mg·kg⁻¹ available potassium. Meteorological conditions during the maize growing season are summarized in

Table 1. Meteorological data during the maize growing seasons under different sowing dates.

Sowing Date	Hybrid	Treatment	Precipitation (mm)			Sunshine Duration (h)			Daily Mean Temperature (°C)			Evapotranspiration (mm)
			Sd-V6	V6-R1	R1-HT	Sd-V6	V6-R1	R1-HT	Sd-V6	V6-R1	R1-HT	Sd-V6	V6-R1	R1-HT
S1	XY	CK	135.1	175.6	201.9	176.4	133.5	273.1	787.6	658.6	1317.8	79.7	72.0	181.5
		SH	169.2	141.5	201.7	185.2	142.0	272.2	841.7	685.4	1288.3	84.4	76.7	184.1
		Re	135.4	175.3	202.0	180.8	146.4	272.2	814.7	712.4	1288.3	82.0	79.1	184.1
	ZD	CK	135.1	175.6	202.0	176.4	139.5	272.6	787.6	684.4	1313.0	79.7	75.0	183.1
		SH	169.2	141.5	202.0	185.2	148.7	271.0	841.7	713.9	1275.5	84.4	80.3	183.8
		Re	135.4	175.3	202.0	180.8	153.0	271.0	814.7	740.9	1275.5	82.0	82.7	183.8
S2	XY	CK	153.6	169.0	147.9	147.7	123.3	260.6	762.9	641.2	1190.9	76.6	74.9	182.6
		SH	153.6	169.0	143.4	165.3	115.1	259.3	843.8	610.5	1158.7	86.7	70.8	181.5
		Re	153.6	173.5	143.4	154.7	125.7	259.3	790.9	663.5	1158.7	80.4	77.1	181.5
	ZD	CK	153.6	169.0	147.9	147.7	129.9	257.7	762.9	666.0	1174.2	76.6	78.3	181.5
		SH	153.6	174.2	142.7	165.3	117.3	261.9	843.8	637.4	1143.5	86.7	73.4	181.6
		Re	153.6	169.0	143.4	154.7	125.7	259.3	790.9	663.5	1158.7	80.4	77.1	181.5

Note: Growth stages are defined as: Sd (Sowing date), V6 (six-leaf stage), R1 (silking), HT (Harvest date). ‘Re’ represents the recovery treatment group (SH + exogenous regulators: MT, BR, or UR). The delayed development in SH treatments is evident, particularly in the late-sown (S2) plots.

.

2.2. Experimental Design

The experiment was conducted across two sowing dates. In the first (S1) and second (S2) sowings, artificial leaf damage at the six-leaf stage (70–80% leaf area loss) was imposed to replicate the leaf-damaged injury. Two widely cultivated hybrids in the region were used: Xianyu 335 (XY: Dupont Pioneer, Changchun, China), characterized by a faster grain-filling rate and higher yield potential under optimal conditions, and Zhengdan 958 (ZD: China National Seed Group Co., Ltd., Beijing, China), known for its slower but sustained grain-filling process, greater stress tolerance, and wider adaptability. By comparing their differential recovery mechanisms under leaf removal stress and exogenous regulator treatments, we can more comprehensively reveal the compensatory effects of these regulators across different genetic backgrounds. A split-plot design was adopted, with maize variety as the main plot and treatments as subplots: control (CK, all leaves intact), simulated leaf damage (plants were clipped approximately 15 cm above the soil surface, and all leaves above the clipping point were removed, SH), and SH combined with foliar application of melatonin (MT: Shanghai Yuanye Bio-Technology Co., Ltd., Shanghai, China), brassinolide (BR: Beijing Solarbio Science & Technology Co., Ltd., Beijing, China), or urea (UR: Tianjin Kaitong Chemical Co., Ltd., Tianjin, China) as recovery measures. Foliar sprays were applied at 24 h after damage, the V12 stage, silking, and 10 days after silking. Application rates were 100 μmol·L⁻¹ for MT [42], 0.1 mg·L⁻¹ for BR [43], and 2 g·L⁻¹ for UR [44], delivered using a manual backpack sprayer at 50 L·mu⁻¹, ensuring full coverage of both leaf surfaces. Each subplot consisted of 12 rows (including four border rows), with a row length of 15 m, 0.6 m row spacing, and a plant density of 7.5 plants·m⁻², oriented north–south. Sowing and harvest dates are summarized in

Table 2. Calendar dates (month–day) of key growth stages for two maize hybrids under early (S1) and late (S2) sowing dates.

Sowing Date	Hybrid	Treatment	Sowing	Emergence	V6	V12	R1	R3	R6
S1	XY	CK	6.4	6.10	7.3	7.16	7.28	8.22	9.21
		SH	6.4	6.10	7.5	7.22	7.31	8.25	9.24
		Re	6.4	6.10	7.4	7.19	7.31	8.25	9.24
	ZD	CK	6.4	6.10	7.3	7.14	7.29	8.23	9.22
		SH	6.4	6.10	7.5	7.20	8.1	8.26	9.25
		Re	6.4	6.10	7.4	7.17	8.1	8.26	9.25
S2	XY	CK	6.20	6.25	7.17	7.30	8.10	9.4	10.4
		SH	6.20	6.25	7.20	8.2	8.12	9.6	10.6
		Re	6.20	6.25	7.18	7.31	8.12	9.6	10.6
	ZD	CK	6.20	6.26	7.17	7.29	8.11	9.5	10.5
		SH	6.20	6.26	7.20	8.2	8.13	9.7	10.7
		Re	6.20	6.26	7.18	7.31	8.12	9.6	10.6

Note: Similar to Table 1.

. S1 (sown 4 June) represented early sowing, while S2 (sown 20 June) represented late sowing. Based on climatic data (Table 1), the two sowing dates differed in temperature and precipitation distribution, providing distinct environments to evaluate recovery responses. Prior to sowing, fields were irrigated, rotary-tilled, and rolled to ensure uniform emergence.

2.3. Field Management

A compound fertilizer (N:P₂O₅:K₂O = 25:8:12; Mosaic Agricultural Resources (Beijing) Co., Ltd., Beijing, China) was applied as the basal fertilizer at rates of 150 kg N·ha⁻¹, 48 kg P₂O₅·ha⁻¹, and 72 kg K₂O·ha⁻¹. Field management practices, including irrigation, weed control, and pest and disease management, were implemented in accordance with local agronomic recommendations. Fertilizer application rates were equal across all treatments during the growth period.

2.4. Methods

2.4.1. Meteorological Data Collection

Daily meteorological data for 2022 were obtained from the China Meteorological Data Service Center (China Meteorological Data Service Center (http://data.cma.cn/, Beijing, China, accessed on 30 October 2022)). The variables included local air temperature, precipitation, wind speed, relative humidity, and sunshine duration. Reference crop evapotranspiration (ET₀) was calculated according to the FAO-56 Penman–Monteith equation:

$${ET}_0={\displaystyle \frac{0.408△\left(R_n-G\right)+\gamma \frac{900}{T+273}u2(e_s-e_a)}{△+\gamma (1+0.34u_2)}}$$

where R_n is the net radiation at the crop surface (MJ·m⁻²·d⁻¹), G is the soil heat flux density (MJ·m⁻²·d⁻¹), T is the mean daily air temperature at 2 m height (°C), e_s is the saturation vapor pressure (kPa), e_a is the actual vapor pressure (kPa), Δ is the slope of the saturation vapor pressure–temperature curve (kPa·°C⁻¹), and γ is the psychrometric constant (kPa·°C⁻¹).

2.4.2. Morphological and Leaf Area Measurements

At silking, 10 plants per plot were selected to measure plant height, ear height, and the length and diameter of the third basal internode (between nodes 3 and 4 from the base). Plant height was recorded as the distance from the soil surface to the tassel tip, and ear height as the distance from the soil surface to the node bearing the uppermost ear. Internode length was measured as the distance between the lower and upper nodes, and internode diameter was determined at the mid-point of the internode using a vernier caliper (Guilin Guanglu Measuring Instrument Co., Ltd., Guilin, China) [45]. The following indices were calculated:

Ear height ratio (%) = Ear height/Plant height × 100

At silking, mid-grain filling, and maturity, three representative plants from the central row of each plot were harvested. For each green leaf, leaf length (l) and maximum width (w) were measured to calculate leaf area. Leaf area index (LAI) was calculated as:

LA = L × W × 0.75

LAI = (Total leaf area per plant × n)/s

where n is the plant density (plants·m⁻²) and s is the land area (m²).

2.4.3. Photosynthetic Parameters

During silking, the net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO₂ concentration (Ci), and stomatal conductance (Gs) of the ear leaf were measured using an LI-6800 portable photosynthesis system (LI-COR, Lincoln, NE, USA) on clear days between 09:00 and 11:30. Three plants were randomly selected per plot, each measurement was repeated three times, and the mean values were used for analysis [46].

2.4.4. Dry Matter Accumulation and Translocation

At silking (R1), five representative plants per treatment were tagged for subsequent sampling. At mid-grain filling (R3) and maturity (R6), these tagged plants were harvested and separated into stems, leaves, sheaths, tassels, husks, cobs, and grains. Samples were oven-dried at 105 °C for 30 min to inactivate enzymes and then dried at 70 °C to constant weight. The following indices were calculated:

Pre-silking dry matter accumulation ratio (%) = Dry matter at silking/Dry matter at harvest × 100

Post-silking dry matter accumulation = Dry matter at harvest − Dry matter at silking

Post-silking dry matter accumulation ratio (%) = Post-silking dry matter accumulation/Dry matter at harvest × 100

Dry matter translocation = Dry matter at silking − Dry matter at harvest (same organ)

Dry matter translocation rate (%) = Dry matter translocation/Dry matter at silking (same organ) × 100

Contribution of post-silking translocation to yield (%) = Post-silking dry matter translocation/Grain yield × 100

Harvest index (%) = Grain yield/Aboveground biomass × 100%

2.4.5. Grain Filling Parameters

At silking (R1), three representative ears per treatment were tagged for grain-filling analysis. At 10, 15, 25, 35, 45, and 55 days after silking (DAP), these tagged ears were sampled in triplicate. Kernels were classified as dominant (middle two-thirds of the ear) or inferior (apical one-third). Kernel number was recorded, and samples were oven-dried to determine the hundred-kernel dry weight. The grain filling process was described by fitting the logistic equation [47]:

W = k/((1 + ae^(−bt)))

where t is days after silking (t₀ = 0), W is hundred-kernel dry weight, k is the maximum hundred-kernel weight, and a and b are shape parameters.

The derived parameters were:

T_max = lna/b

W_max = k/2

G_max = b × W_max × (1 − W_max/k)

Gmean = W/T

P = 6/b

Grain–leaf ratio was calculated as:

Grain–leaf ratio (grain·cm⁻²) = Grains per plant/Green leaf area per plant

2.4.6. Yield Determination and Ear Trait Measurement

At physiological maturity (R6), plants were hand-harvested from a central area of each plot (excluding border rows) to determine grain yield. Ears were counted, shelled, and grain weight was recorded and adjusted to 14% moisture content. Yield components were determined from 10 representative plants per plot, including ear number, kernel number per ear, and 100-kernel weight (measured after oven-drying at 70 °C to constant weight) [48].

2.5. Data Processing and Analysis

Data were processed in Microsoft Excel (version 2508; Microsoft Corporation, Redmond, WA, USA). One-way analysis of variance (ANOVA) and independent-samples t-tests were performed in IBM SPSS Statistics (version 22.0; IBM Corp., Armonk, NY, USA). Figures were generated with Microsoft Excel (version 2508; Microsoft Corporation, Redmond, WA, USA) and GraphPad Prism (version 9.0; GraphPad Software, Boston, MA, USA). Multiple comparisons among treatments were conducted using the least significant difference (LSD) test. During the preparation of this work, the authors used ChatGPT-3.5 (OpenAI, San Francisco, CA, USA) to assist with language polishing and ideation. Specifically, the tool was used to improve the clarity, grammar, and fluency of the English text, and to assist in organizing ideas for the literature review and discussion sections. Following the use of this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published work.

3. Results

3.1. Effects of Exogenous Regulators on Yield and Its Components

To examine the effects of simulated leaf damage and exogenous regulators on maize yield and its components, variance and path analyses were performed. Leaf damage at V6 markedly inhibited ear development, reducing kernel number per ear, increasing barren stalk rate, and decreasing effective ear number and 100-kernel weight, ultimately leading to a 21.9–48.9% yield loss. Application of MT, BR, and UR significantly mitigated these effects, enhancing yield by 14.1–52.2% compared with the SH, with UR and BR being the most effective, and XY showing greater recovery than ZD (

Table 3. Effects of exogenous application of MT, UR, and BR on the constituents of maize yield machines.

Variety	Treatment	100-Grain Weight (g)		Kernel Numbers Per Ear		Ear Density (104 hm−2)		Yield (kg·hm−2)		Empty Bar Rate (%)
		S1	S2	S1	S2	S1	S2	S1	S2	S1	S2
XY	CK	37.5 a	34 ab	544.2 a	542.5 a	6.49 b	6.53 b	13.23 a	12.04 a	3.9 b	3.3 b
	SH	34.0 c	32.0 b	394.8 c	461.2 c	6.32 c	6.38 c	8.48 c	9.41 b	6.4 a	5.4 a
	MT	36.1 b	33.4 c	473.4 b	523.4 ab	6.36 c	6.50 b	10.87 b	11.36 a	5.8 a	3.8 b
	BR	36.2 b	34.5 a	479.8 b	487.8 bc	6.49 b	6.51 b	11.28 b	10.95 a	3.9 b	3.6 b
	UR	36.5 b	33.2 b	441.1 bc	497.1 abc	6.66 a	6.64 a	10.72 b	10.96 a	1.3 c	1.7 c
ZD	CK	32.2 a	31.7 a	496.4 a	611.4 a	6.35 a	6.19 b	10.16 a	12.01 a	6.0 d	8.3 c
	SH	30.9 c	26.4 d	294.9 c	445.8 c	5.70 d	6.00 d	5.19 c	7.04 d	15.6 a	11.2 a
	MT	31.2 bc	27.6 c	393.7 b	472.9 bc	6.05 c	6.13 c	7.42 b	7.98 c	10.4 b	9.2 b
	BR	31.5 b	28.9 b	406.5 b	494.9 b	6.16 d	6.16 bc	7.91 b	8.81 b	8.7 c	8.8 bc
	UR	31.4 bc	28.9 b	394.6 b	465.3 bc	6.39 a	6.24 a	7.91 b	8.38 bc	5.4 d	7.5 d

Note: Within each column, values followed by different lowercase letters are significantly different (p < 0.05) according to Duncan’s multiple range test.

).

The contributions of yield components to the total crop yield varied across sowing periods. In S1, 100-kernel weight had the largest direct effect, while kernel number per ear exhibited a notable indirect effect via 100-kernel weight (indirect coefficient = 0.70). In S2, the effective ear number became the primary direct contributor to the total crop yield, with kernel number per ear showing a significant indirect effect through effective ear number. (indirect coefficient = 0.79) (

Table 4. The path analysis of yield and yield components.

Sowing	Independent Variable	Simple Correlation Coefficient with Yield	Direct Path Coefficient	Indirect Path Coefficient			Total
				Kernel Numbers Per Row	100-Grain Weight	Ear Density
S1	Kernel numbers per row	0.817	0.647	-	0.70	0.63	1.330
	100-grain weight	0.954	0.365	0.60	-	0.59	1.190
	Ear density	0.866	0.078	0.60	0.66	-	1.260
S2	Kernel numbers per row	0.717	0.443	-	0.16	0.79	0.950
	100-grain weight	0.804	0.571	0.14	-	0.38	0.520
	Ear density	0.880	0.208	0.65	0.35	-	1.000

). Path analysis revealed that the contribution of yield components to final yield was strongly influenced by environmental conditions. Under favorable S1 conditions, hundred-kernel weight served as the primary direct determinant of yield, while kernels per ear mainly exerted indirect effects by influencing kernel weight. Conversely, under stressful S2 conditions, productive ear number became the dominant direct contributor to yield, with kernels per ear largely functioning through its effect on ear number. These results suggest that ensuring adequate ear number is the foremost priority for yield stability under late-sowing stress, whereas maximizing kernel weight is more important under optimal growing conditions.

3.2. Effects of Exogenous Regulators on Plant Traits

To assess the effects of simulated leaf damage and exogenous regulators on maize plant architecture, the data presented in Figure 1 were analyzed, including plant height, ear height, stem morphology, and leaf traits. To assess the effects of simulated leaf damage and exogenous regulators on maize plant architecture, plant height, ear height, stem morphology, and leaf traits were analyzed. In S1, the plant height of both varieties was significantly higher in the control (CK) than in other treatments; XY showed no differences among other treatments, whereas ZD in SH was significantly lower than MT, BR, and UR. In S2, plant height differences among treatments were not significant.

Ear height was consistently highest in CK. In XY, SH was significantly lower than MT, BR, and UR, while in ZD (S1), MT and UR exceeded SH significantly. Stem analysis indicated that the third basal internode length was greatest in CK (XY) and in CK and SH (ZD). Stem diameter was largest in CK; MT and UR showed a non-significant thickening trend compared with SH, and all regulators significantly reduced the internode length-to-diameter ratio. Leaf morphology showed that the mean length and width of five leaves above and below the ear were highest in CK and lowest in SH.

Overall, leaf damage markedly inhibited maize growth, reducing plant height, ear height, internode length, and leaf size. Although exogenous regulators had limited effects on stem elongation, they promoted internode thickening and reduced the length-to-diameter ratio, partially mitigating damage effects.

3.3. Restorative Effects of Exogenous Regulators on LAI

The results indicated, as shown in Figure 2, that the leaf area index (LAI) of both maize varieties peaked at the silking stage and gradually declined thereafter, showing similar trends across the two sowing periods. The SH consistently exhibited significantly lower LAI than the control (CK) at all growth stages, whereas application of MT, BR, and UR markedly enhanced LAI recovery following leaf damage. In XY335, LAI increased by 1.9–19.9% at the R1, R3, and R6 stages in S1, and by 4.3–31.5% in S2. For ZD958, the corresponding increases were 3.8–21.8% in S1 and 2.7–44.0% in S2. Analysis at the fifth leaf position indicated that exogenous regulators primarily enhanced LAI by increasing leaf width rather than leaf length, and significantly delayed leaf senescence. Comparisons between varieties showed that XY335 had lower LAI than ZD958 at the R1 and R3 stages but surpassed ZD958 at R6. The regulators had a more pronounced effect on leaf length in XY335, whereas in ZD958 they mainly increased leaf width, suggesting a stronger LAI recovery effect in ZD958. Additionally, leaf length, leaf width, and LAI were generally higher in S1 than in S2, with regulators exerting more obvious effects on leaf length in S2, while their influence on leaf width did not differ significantly between sowing periods. Overall, exogenous regulators significantly improved LAI, particularly during the silking and grain-filling stages in S1 and the maturity stage in S2.

3.4. Effects of Exogenous Regulators on Photosynthetic Parameters

Simulated leaf damage and the application of exogenous regulators significantly affected maize leaf photosynthetic parameters. As shown in Figure 3, compared with the control (CK), the severely damaged treatment (SH) decreased net photosynthetic rate (Pn), intercellular CO₂ concentration (Ci), stomatal conductance (Gs), and transpiration rate (Tr) by 21.3%, 10.7%, 13.0%, and 10.3% in XY, respectively, with significant differences; similar trends were observed in ZD. Following treatment with exogenous regulators, all photosynthetic parameters exhibited an increasing trend. Compared with SH, Pn increased by 14.6–22.0%, Ci by 4.9–8.1%, Gs by 10.2–12.6%, and Tr by 7.5–10.8% under MT, BR, and UR treatments, with differences reaching significance.

3.5. Effects of Exogenous Regulators on the Dynamic Changes in Post-Silking Dry Matter Accumulation

3.5.1. Recovery of Dry Matter and Kernel Allocation by Exogenous Regulators

As shown in

Table 5. Dynamic changes in dry matter accumulation per plant under different treatments.

Sowing	Variety	Treatment	Post-Silking DM Accumulation (g/Plant)	Before-Silking DM Accumulation Rate (%)	Post-Silking DM Accumulation Rate (%)	Post DM/Grain (%)
S1	XY	CK	263.14 a	35.69 a	64.31 c	135.03 b
		SH	230.17 b	29.98 c	70.02 a	137.52 b
		MT	265.99 a	30.53 bc	69.47 ab	140.58 ab
		BR	253.69 a	29.75 c	70.25 a	147.73 a
		UR	234.13 b	31.91 b	68.09 b	138.76 b
	ZD	CK	250.03 a	33.84 b	66.16 d	124.25 a
		SH	194.31 c	28.58 e	71.42 a	123.62 a
		MT	200.79 c	35.45 a	64.55 e	121.98 a
		BR	220.23 b	30.06 d	69.94 b	123.37 a
		UR	201.14 c	31.67 c	68.33 c	123.48 a
S2	XY	CK	214.67 a	39.45 a	60.55 b	123.24 ab
		SH	159.05 c	36.54 b	63.46 a	106.34 d
		MT	208.13 a	34.12 b	65.88 a	125.42 a
		BR	175.71 b	35.49 b	64.51 a	116.78 c
		UR	206.23 a	34.12 b	65.88 a	120.34 bc
	ZD	CK	191.09 a	38.78 ab	61.22 bc	115.09 a
		SH	131.07 c	36.05 bc	63.95 ab	101.76 b
		MT	139.70 c	39.17 a	60.83 c	101.77 b
		BR	135.39 c	38.21 ab	61.79 bc	101.43 b
		UR	176.07 b	34.76 c	65.24 a	113.24 a

Note: Within each column, values followed by different lowercase letters are significantly different (p < 0.05) according to Duncan’s multiple range test.

, sowing date, cultivar, and post-damage treatment significantly affected dry matter (DM) accumulation per plant before and after silking, as well as the proportion of post-silking DM allocated to kernels. Overall, S1 had higher DM accumulation than S2, and XY consistently showed greater post-silking DM accumulation and kernel allocation than ZD under the same treatment. In both sowing dates, SH treatment markedly reduced post-silking DM accumulation, with reductions of 10–15% in S1 and 14–27% in S2 (p < 0.05). The inhibitory effect was more pronounced in S2. The proportion of post-silking DM accumulation also decreased under SH in most cases, with reductions of 5–8 percentage points in S2, although in S1, a slight increase was observed in XY due to a sharper decline in pre-silking accumulation. Application of MT significantly improved post-silking DM accumulation in both cultivars and sowing dates, in some cases exceeding CK levels (e.g., S1 XY). Compared with SH, MT increased post-silking accumulation by 25–31% in S2 and restored kernel allocation proportion to CK levels, showing a consistently greater recovery effect than BR and UR. Both BR and UR treatments also alleviated SH-induced reductions, but their effects were smaller and less stable, particularly in S2 ZD, where post-silking DM accumulation and kernel allocation remained below CK levels. The variation trend of the proportion of post-silking DM allocated to kernels was largely consistent with changes in post-silking DM accumulation. SH significantly reduced this proportion in both sowing dates (p < 0.05), while MT exhibited the most stable and significant alleviation effect across all conditions.

3.5.2. Effects of Exogenous Regulators on Dry Matter Accumulation Dynamics

As illustrated in Figure 4, from R1 to R6, stem dry matter showed an increasing trend in S1, whereas in S2 it initially increased and then declined; leaf dry matter peaked at R1 and gradually decreased thereafter, while sheath dry matter reached its maximum at R3 in both sowing periods. Across all stages, CK consistently exhibited the highest values and SH, the lowest. Compared with SH, MT, BR, and UR treatments significantly enhanced dry matter accumulation in XY, with stem increasing by 11.5–46.8%, 3.3–21.5%, and 4.2–44.0%, leaf by 3.9–57.3%, 3.5–34.5%, and 5.9–66.3%, and sheath by 5.7–102.6%, 6.3–62.1%, and 7.1–67.0% across the three stages, respectively. Notably, MT and UR exhibited a more pronounced recovery effect on organ dry matter than BR.

3.6. Effects of Exogenous Regulators on Grain Filling Parameters

Simulated leaf damage and post-damage foliar application of three regulators differently affected maize grain filling parameters (

Table 6. Changes in grain filling parameters under different treatments and their correlations with final hundred-kernel weight.

Sowing Date	Variety	Treatment	Equation Parameters			Grain-Filling Parameters
			a	b	c	Gmax (g·d−1)	Gmean (g·d−1)	Wmax (g)	Tmax (d)	P(d)	Coefficient of Determination (R2)
S1	XY	CK	39.04	41.46	0.14	1.37	1.20	19.52	25.72	44.03	0.988
		SH	34.71	14.69	0.11	0.95	0.84	17.35	26.93	56.69	0.987
		MT	39.19	16.20	0.11	1.08	0.94	19.59	25.02	55.88	0.988
		BR	39.62	14.60	0.10	0.99	0.87	19.81	24.96	58.77	0.991
		UR	39.20	16.99	0.11	1.08	0.95	19.60	25.04	54.81	0.989
	ZD	CK	37.15	33.17	0.13	1.21	1.06	18.58	25.57	47.28	0.973
		SH	31.34	16.36	0.11	0.86	0.76	15.67	27.97	52.72	0.971
		MT	37.40	14.87	0.10	0.94	0.82	18.70	25.16	59.61	0.976
		BR	35.89	16.11	0.11	0.99	0.87	17.94	25.95	55.25	0.986
		UR	37.70	16.12	0.10	0.94	0.83	18.85	25.05	58.00	0.980
S2	XY	CK	39.65	84.73	0.14	1.39	1.22	19.82	25.37	41.52	0.988
		SH	34.04	27.01	0.11	0.94	0.82	17.02	26.47	53.00	0.997
		MT	39.67	21.56	0.10	0.99	0.87	19.84	25.35	58.32	0.999
		BR	39.90	21.59	0.10	1.00	0.87	19.94	25.33	57.54	0.992
		UR	40.63	19.33	0.10	1.02	0.89	20.32	25.27	60.45	0.997
	ZD	CK	40.28	70.41	0.13	1.31	1.15	20.14	25.30	46.22	0.987
		SH	34.47	18.29	0.09	0.78	0.68	17.24	27.27	63.94	0.986
		MT	40.63	13.48	0.08	0.81	0.71	20.31	25.10	73.20	0.987
		BR	40.31	15.47	0.09	0.91	0.80	20.16	25.18	69.13	0.980
		UR	39.19	19.78	0.10	0.98	0.86	19.59	25.33	62.40	0.984
Correlation coefficient (r)			--	--	--	0.715 **	0.712 **	0.807 **	−0.821 **	−0.353

Note: The asterisks indicate the significance level of the regression model (or correlation coefficient). **, p < 0.01.

), with R² ranging from 0.971 to 0.999, indicating that the fitted equations accurately reflected kernel dry matter accumulation. Leaf damage reduced W_max, G_max, and Gmean and prolonged T_max. Compared with SH, exogenous regulator treatments increased G_max by 4.2–15.1%, Gmean by 3.6–14.5%, and W_max by 12.9–20.3%, and advanced T_max by 1.89–2.92 days in S1; in S2, G_max increased by 3.9–25.6%, G_mean by 4.4–26.5%, W_max by 16.2–19.4%, and T_max was advanced by 1.12–2.17 days. These results suggest that foliar application of exogenous regulators after leaf damage can enhance the maximum grain filling rate and kernel weight at peak filling, thereby improving yield. Correlation analysis showed that final hundred-kernel weight was strongly positively correlated with G_max, G_mean, and W_max, and strongly negatively correlated with T_max.

3.7. Effects of Exogenous Regulators on the Kernel-to-Leaf Ratio

As shown in Figure 5, the kernel-to-leaf ratio (KLR) of all treatments in both sowing periods generally increased with growth, reaching the maximum at maturity. In XY, UR showed the highest KLR at the early grain-filling stage in S1, whereas CK and BR were highest at maturity, and SH was consistently lower than the other treatments; in S2, CK was significantly lower at all stages. In ZD, SH had the highest KLR at the early grain-filling stage in S1, while CK was highest at maturity; in S2, CK remained the lowest, with the other treatments increasing KLR to varying degrees. These results suggest that simulated leaf damage elevated KLR during early grain filling, enhancing the source-to-sink ratio, but reduced source capacity in later stages, limiting nutrient supply to sink organs, impairing their growth, and reducing kernel number per ear. Foliar application of the three exogenous regulators effectively promoted kernel development.

3.8. Construction of the Structural Equation Model Among Traits

As shown in Figure 6, the structural equation model (SEM) fit the hypothesized model well (χ² = 8.144, p = 0.148, df = 5; GFI = 0.973; SRMR = 0.035; RMSEA < 0.001). Under simulated leaf damage and regulator treatments, kernel number per ear and hundred-kernel weight together explained 99.1% of the variation in yield, with kernel number exerting the greater influence (standardized path coefficient = 0.700). Leaf and sheath dry weights at silking and mid-grain-filling stages significantly affected kernel number and hundred-kernel weight, accounting for 59.4% and 82.3% of their variation, respectively. Specifically, sheath dry weight at silking (R1SW) mainly influenced kernel number (path coefficient = 0.642), leaf dry weight at silking (R1LW) affected both kernel number and hundred-kernel weight (path coefficients = 0.444 and −0.525), and sheath and leaf dry weight at mid-grain-filling (R3SW and R3LW) significantly affected hundred-kernel weight (path coefficients = 0.690 and 0.335).

As shown in

Table 7. Effects of exogenous regulator application on leaf area recovery, grain-filling dynamics, and yield compensation in two maize hybrids under mechanical leaf damage across two sowing dates.

Sowing Date	Variety	Treatment	LAI_Recovery_R1 (%)	LAI_Recovery_R3 (%)	∆Tmax (Days)	Yield_Loss (%)	Yield_Recovery (%)
S1	XY	CK	100.0	100.0	1.2	35.9	100.0
		SH	0.0	0.0	0.0	35.9	0.0
		MT	51.4	53.5	1.9	35.9	50.3
		BR	26.1	54.6	2.0	35.9	59.0
		UR	23.2	21.2	1.9	35.9	47.2
	ZD	CK	100.0	100.0	2.4	48.9	100.0
		SH	0.0	0.0	0.0	48.9	0.0
		MT	39.9	31.3	2.8	48.9	44.9
		BR	31.3	30.6	2.0	48.9	54.7
		UR	45.4	54.2	2.9	48.9	54.7
S2	XY	CK	100.0	100.0	1.1	21.8	100.0
		SH	0.0	0.0	0.0	21.8	0.0
		MT	16.0	14.1	1.1	21.8	74.1
		BR	11.2	10.4	1.1	21.8	58.6
		UR	39.0	40.0	1.2	21.8	58.9
	ZD	CK	100.0	100.0	2.0	41.4	100.0
		SH	0.0	0.0	0.0	41.4	0.0
		MT	7.0	20.1	2.2	41.4	18.9
		BR	9.5	12.9	2.1	41.4	35.6
		UR	15.2	22.4	1.9	41.4	27.0

, comprehensive analysis showed that SH significantly reduced the leaf area index (LAI) at both the R1 and R3 stages, extended the grain-filling duration (T_max) by 1.1–2.4 days, and caused a yield reduction of 21.8–48.9%. The application of exogenous regulators effectively reversed these changes. Among them, UR demonstrated the strongest ability to restore LAI, especially in ZD958-S1, achieving a recovery rate of 45.4–54.2%. BR provided the best compensation for yield loss under most conditions—for example, a recovery rate of 58.9% in XY335-S1. Meanwhile, although MT showed inconsistent effects across scenarios, it achieved a high recovery rate of 74.1% in certain environments such as XY335-S2, indicating its genotype-specific potential. All three regulators enhanced yield recovery by synergistically improving photosynthetic source capacity (via increased LAI) and sink strength (by shortening T_max), with their efficacy influenced by both cultivar and sowing environment.

4. Discussion

4.1. Source–Sink Balance Restoration and Enhancement of Canopy Photosynthetic Efficiency: Core Pathways of Exogenous Regulators

Numerous studies have demonstrated that exogenous MT can regulate root growth [49], shoot and explant development [50], and activate rhizome formation [51]; BR can enhance source strength, promote phloem loading and assimilate transport, and facilitate mineral element translocation [52], thereby increasing dry matter accumulation and fruit size and weight [53]; foliar application of UR can promote nutrient uptake in maize and improve grain nutrient content and yield [54]. In this experiment, simulating 70% leaf damage caused a significant reduction in maize yield (21.9–48.9%). However, treatments with MT, BR, and UR effectively alleviated the stress induced by leaf damage, increasing yield by 13.4–43.1%, 16.4–52.4%, and 16.5–52.5%, respectively, compared with the damaged control (Table 3). These results indicate that severe leaf damage at the six-leaf stage can disrupt certain growth points, causing developmental stagnation, while even mild damage can inhibit the normal development of female ears, leading to an increase in barren stalks and a reduction in kernel number. Overall, the simulated leaf damage significantly suppressed single-ear kernel number and hundred-kernel weight and increased the barren stalk rate; in contrast, exogenous application of MT, BR, and UR effectively improved these yield components, thereby promoting the recovery of grain yield.

Before discussing the compensatory effects of exogenous regulators on yield formation, it is important to recognize the inherent yield penalty caused by severe leaf damage at the six-leaf stage. In our experiment, simulated leaf loss at V6 significantly reduced assimilate supply and kernel formation, leading to pronounced decreases in grain yield components. This observation is consistent with previous reports that complete defoliation at the five-leaf stage reduced maize yield by approximately 10–13% owing to decreases in leaf area and ear weight [55]. Similarly, under high planting density, removal of four to six top leaves reduced canopy apparent photosynthesis by 26.2–46.2%, thereby lowering yield potential [56]. These findings highlight that early leaf loss should not be considered as the removal of redundant leaves but rather as a critical stress event constraining carbon assimilation and grain filling. Against this background of substantial yield reduction, the role of MT, BR, and UR becomes particularly evident, as each regulator compensates for the lost assimilative capacity and contributes to yield recovery through distinct physiological and morphological pathways.

Over 95% of maize grain yield is derived from photosynthetic products, primarily from post-silking assimilates [57]. As the main photosynthetic organ, leaves play a critical role in post-flowering photosynthesis and in maintaining green leaf area, which are essential for dry matter accumulation and yield formation [58]. Commonly used indicators include LAI and Pn. The present study showed that exogenous application of the three regulators effectively maintained higher LAI in defoliated maize and significantly enhanced photosynthetic parameters of ear leaves (Figure 2 and Figure 3). This suggests that the regulators optimized light distribution within the canopy and delayed leaf senescence, thus sustaining high photosynthetic efficiency. Notably, post-silking dry matter accumulation is significantly positively correlated with grain yield [59]. In this experiment, simulated leaf damage significantly reduced plant height. Application of MT, BR, and UR accelerated vegetative growth, resulting in a notable recovery in plant height (Figure 1). Since the first application coincided with the six-leaf stage (a period of vigorous vegetative growth), the regulators may have promoted nutrient allocation to vegetative organs, which could have partially delayed floral differentiation, leading to increased ear height in treated plants. Whether floral differentiation was accelerated in the later stage under regulatory treatment requires further investigation.

Post-flowering dry matter accumulation directly reflects kernel filling capacity and is closely related to yield components [60]. Our study confirmed that leaf damage sharply reduced plant dry matter accumulation, indicating severe inhibition of growth under stress. Exogenous application of MT, BR, and UR increased post-damage dry matter, the amount and rate of assimilate translocation to grains, and the contribution of post-silking translocation to grain yield (Table 5, Figure 4). Although the regulators could not completely compensate for early-stage damage, these results demonstrate that exogenous application of melatonin and other regulators can serve as an effective field intervention following leaf damage.

The coordination between source and sink is fundamental for achieving high yield and efficiency. Foliar application of growth regulators is an effective approach to modulate the source-to-sink ratio in high-density populations [61]. In this study, simulated leaf damage increased the grain-to-leaf ratio (i.e., relatively higher sink demand) during early grain filling, but the ratio was significantly lower than the control during later stages (Figure 5). This variation may be associated with delayed reproductive development caused by leaf damage: the tasseling and silking stages were postponed by 2–4 days in the damaged plants. It is speculated that high temperatures (observed during the V8–R1 period) and reduced rainfall (Table 2) during this period may have affected floret development, pollination, fertilization, and kernel set, resulting in kernel abortion. Meanwhile, the decline in leaf source capacity and assimilate supply limited the full development of kernels. Exogenous application of MT, BR, and UR effectively mitigated leaf source degradation and kernel damage, thereby improving source–sink balance.

Furthermore, the significant delay in silking date observed in the SH treatment (Table 2) is a strong indicator of a prolonged anthesis–silking interval (ASI), a common response to abiotic stress in maize that severely compromises pollination success and kernel set [62,63]. The ability of exogenous regulators, particularly UR and BR, to mitigate this developmental delay suggests an additional mechanism by which they improve yield: by enhancing reproductive synchrony and ensuring better pollination under stress conditions.

Grain size was a major contributor to yield variation between the two hybrids. XY335, which has larger kernels, compensated for yield reduction primarily through increased hundred-kernel weight. In contrast, ZD958, characterized by smaller kernels but greater stability in seed set, relied mainly on maintaining ear number and kernel number per ear. These distinct strategies demonstrate the close interdependence among ear density, kernel number, and kernel weight in determining final yield (Table 3). Furthermore, sowing date influenced the recovery pathway: favorable conditions in S1 supported yield compensation through kernel weight, whereas terminal stress in S2 increased dependence on kernel set stability. Exogenous regulators functioned in complementary manners to alleviate defoliation-induced losses—BR increased kernel number, UR enhanced kernel weight, and MT improved both ear density and grain filling. Collectively, the interplay among genotype, environment, and regulator highlights the diverse mechanisms underlying yield recovery.

4.2. Differential Mechanisms and Practical Implications of MT, BR, and UR

Our findings offer a clear quantitative assessment of the recovery effects achieved using exogenous regulators (Table 7). Yield restoration was driven by two key mechanisms: the rapid recovery of photosynthetic capacity—where UR most consistently restored LAI, thereby improving assimilate supply for yield formation—and enhanced sink strength during grain filling, as all regulators, especially MT and BR, significantly shortened the time to maximum grain-filling rate (T_max), promoting dry matter accumulation in kernels. Notably, the strong yield recovery mediated by BR likely stems from its dual function of moderately maintaining source activity (through LAI preservation) and substantially strengthening sink performance (via reduced T_max and increased kernel number).

This functional divergence highlights the need to evaluate the relative contributions of ear number, kernel number per ear, and hundred-kernel weight to final yield under both defoliation and regulator treatments. In our study, defoliation primarily reduced kernel number and kernel weight, while productive ear number remained relatively stable. Applications of regulators partially reversed these reductions: BR and MT were more effective in restoring kernel weight (especially in XY335), whereas UR helped stabilize ear number and kernel number (particularly in ZD958) (Table 3).

Based on these mechanistic insights, we further examined the distinct contributions of MT, BR, and UR in the following aspects:

In terms of morphological regulation, the three regulators exhibited different emphases: MT enhanced structural stability and assimilate supply by promoting stem thickening (by 15.2–18.6%) (Figure 1) and increasing photosynthetic rate at silking (by 19.2–22.0%) (Figure 3), which is consistent with previous findings showing that exogenous melatonin can increase chlorophyll content and photosynthetic efficiency, thereby maintaining leaf area and photosynthetic performance under drought stress [64]. BR mainly prolonged the leaf stay-green duration by 5–7 days, increased leaf width (by 8.7–12.4%), raised the leaf area index by 21.8–44.0% (Figure 2), and promoted floret differentiation, leading to an increase in kernel number per ear by 11.2–14.6% (Table 3). These results agree with reports that BR improves plant morphological traits (e.g., leaf width, tissue expansion) to enhance stress resistance [65]. UR optimized stem structure (reducing the length-to-diameter ratio by 18.2–22.3%) (Figure 1) and regulated assimilate partitioning (increasing the grain allocation ratio by 12.5–15.3%) (Table 5), thereby significantly improving 100-kernel weight by 9.8–12.3% (Table 3).

In terms of physiological repair, the functional differentiation of the three regulators was evident. The key advantage of MT treatment was the rapid and significant increase in photosynthetic rate, particularly maintaining higher photosynthetic efficiency during the mid- and late-grain-filling stages, thus ensuring assimilate production. This aligns with reports showing that melatonin enhances the antioxidant system and regulates carbon and nitrogen metabolism to sustain photosynthetic capacity [66]. BR treatment was the most effective in delaying leaf senescence, significantly increasing chlorophyll content, and enhancing light-use efficiency, thereby maintaining the continuous productivity of source organs, consistent with findings that BR delays leaf senescence and improves antioxidant activity [67]. UR treatment showed the greatest efficacy in regulating dry matter translocation and distribution, increasing the proportion of post-anthesis assimilates allocated to kernels by 12.5–15.3% (Table 5).

In terms of yield formation, the three regulators compensated for yield loss through distinct pathways. Our path analysis (Table 4) and SEM results (Figure 6) underscored that yield recovery was primarily driven by enhancements in kernel number per ear and hundred-kernel weight. MT treatment contributed to yield compensation by significantly increasing the number of effective ears (by 13.5–15.8%). BR treatment was most effective in increasing kernel number per ear (by 11.2–14.6%), which was associated with its physiological role in promoting floret differentiation. UR treatment exhibited the greatest advantage in significantly improving 100-kernel weight (by 9.8–12.3%), primarily by optimizing the grain-filling process and extending the active grain-filling period by 2–3 days (Table 6). The recovery efficacy of exogenous regulators was governed by a significant genotype-by-environment interaction. Compensatory effects were more pronounced in S2, where distinct cultivar-specific preferences emerged. For the hybrid XY335, both BR and UR were highly effective in S2, elevating yield to 90.9% of non-damaged controls. In contrast, for ZD958, UR and BR were the optimal choices, restoring yields to 69.7% and 73.3% of controls in S2, respectively. Although MT consistently enhanced recovery across all conditions, its effects were not cultivar-specific. These results highlight that the selection of an optimal remedial strategy must account for both the genetic background of the cultivar and the prevailing environmental conditions.

In summary, foliar application of MT, BR, or UR can effectively alleviate the stress caused by simulated mechanical leaf damage through distinct morphological and physiological pathways, restoring source–sink balance, enhancing dry matter accumulation and partitioning, and compensating for yield losses. These findings highlight the potential of exogenous regulators to improve crop resilience in the face of mechanical damage, a common challenge for maize growers caused by environmental stressors such as hail, strong winds, and mechanical operations.

In practical terms, the use of these regulators can be tailored to specific stages of growth and types of damage: MT should be applied after significant mechanical leaf damage, such as from hail or strong winds, to reduce oxidative stress and enhance recovery. This application can be particularly beneficial in the early vegetative or reproductive stages when the plant is still building its photosynthetic capacity. A foliar spray 24 h after damage, followed by subsequent applications during critical growth stages, can maximize recovery; BR is especially effective in the later stages of growth, particularly during reproductive development when kernel formation and grain-filling are key to yield. BR can be applied in the mid-to-late stages after mechanical damage to help promote kernel number and size, ensuring a more complete recovery of yield potential; UR offers an affordable and effective solution for early-stage recovery. Application during the vegetative growth phase, especially shortly after mechanical damage, can help restore photosynthetic efficiency and promote dry matter accumulation, boosting overall plant recovery. UR can be applied as a foliar spray or through fertigation to quickly supply nitrogen and stimulate leaf regrowth.

While this study primarily reveals the regulators’ effects at the physiological–ecological scale, further investigation integrating hormonal profiling, transcriptomics, and proteomics is required to elucidate the underlying molecular mechanisms, including gene expression regulation and hormone signaling interactions. This will enable more precise recommendations for the timing, dosages, and application methods of these regulators, ensuring that their use is both effective and cost-efficient for maize growers.

5. Conclusions

Overall, this study clarifies the physiological mechanisms through which exogenous regulators—MT, BR, and UR—alleviate yield loss caused by mechanical leaf damage in maize. While based on controlled artificial defoliation, the significant recovery observed supports the potential effectiveness of these regulators under field conditions. MT rapidly enhances photosynthetic efficiency and antioxidant capacity, offering prompt stress mitigation; BR effectively delays leaf senescence and improves kernel number by promoting floret development; UR optimizes dry matter allocation, boosting grain-filling rate and hundred-kernel weight.

The study provides a practical strategy for maize growers: UR is recommended as the most cost-effective solution for early-stage recovery, rapidly supplying nitrogen and ensuring consistent yield recovery. BR is ideal for maximizing kernel number and maintaining leaf area under prolonged stress, making it the preferred choice for later-stage damage. MT is particularly valuable for improving overall plant resilience and reducing oxidative damage, especially when integrated with other agronomic practices. These findings, therefore, offer both theoretical insights and field-level recommendations for maize production.