Enhancing Hybrid Maize Performance and Yield Through Potassium Sulfate Fertilization: A Field-Based Assessment

Abstract

Maize is the third most important cereal crop in the world due to its exceptional productivity and adaptability. The study was performed to evaluate the effects of potassium sulfate fertilizer on growth, physiological traits, yield, and its components of three single crosses of maize over two growing seasons. A field experiment was conducted at the agronomy experimental farm, Assiut University, Egypt, using a strip-plot design with three replications. Treatments included four potassium sulfate rates (0, 60, 120, and 180 kg ha⁻¹) and three maize hybrids (SC2031, SC2036, SC168). The results revealed significant combined analysis of variance for potassium sulfate levels and hybrids on most of the studied traits. The hybrid SC2036, when fertilized with 120 kg K/ha, is especially suitable for achieving high productivity under the tested agro-environmental conditions. The path and principal component analysis results highlight that ear diameter and leaf number are the most influential traits for grain yield improvement for all tested crosses. Traits such as chlorophyll content and 1000-grain weight contributed mainly through indirect path effects. The path analysis also underlines hybrid-specific differences in how yield components affect grain yield per plant. These results highlight that the integration of nutrient management, hybrid selection, and multivariate analysis provides a comprehensive strategy for improving maize productivity.

1. Introduction

Maize (Zea mays L.) is the third most important cereal crop in the world due to its exceptional productivity and adaptability [1]. Worldwide, it is recognized as a strategic food and feed crop that provides an enormous amount of protein and energy for humans and livestock. In Egypt, approximately 950,000 hectares are dedicated to maize cultivation, resulting in an annual grain production of approximately 7.13 million tons [2].

Several factors, including fertilizer application, soil moisture, biotic factors (weeds, insects, and diseases), and poor agronomic practices, such as inadequate seedbed preparation, inappropriate plant density, late planting, and poor management practices, affect both yield and productivity. Mineral fertilizers are used to satisfy the nutritional demands of crops with high yield potential, compensating for nutrients lost by the removal of plant products and by leaching. As a result, providing balanced nutrition is crucial for managing nutrients and has a significant impact on increasing crop yield and quality.

Potassium (K) facilitates water uptake into plant roots through strong osmotic potential, aiding in the synthesis of proteins and chlorophyll. In maize, potassium reduces lodging and produces strong and sturdy stems. The rate of gas exchange and transpiration is regulated by potassium, which also affects the rate at which leaf stomata open [3]. It is an essential nutrient that affects most physiological and biochemical processes, which consequently influences plant growth and metabolism [4]. Potassium nutrition is essential to improve the lodging resistance, quality, and yield of maize [5]. The use of potassium fertilizer to boost maize yield has been the main subject of recent research, reflecting its essential role in improving maize productivity and physiological performance under various environmental conditions [6,7,8].

The selection of an appropriate cultivar is a critical factor that significantly impacts both the quantity and quality of maize yield [9]. The measurable production benefits that translate into increased yield quantity can be obtained without incurring additional expenditure. Recent advancements in breeding have led to the development of numerous new hybrids and have significantly influenced the criteria used in selecting breeding techniques [10]. Thus, the components of crop yield, structured by the number of ears, 1000-grain weight, and the number of grains per ear, depending on hybrid type, may influence production to varying extents.

Path coefficient analysis, first introduced by [11], is a statistical technique that partitions the correlation between variables into direct and indirect effects. In crop science, particularly in maize breeding, it has been extensively used to understand the contribution of yield components to the final crop yield [12]. This approach provides deeper insights and helps quantify their relative influence on yield formation and productivity [13,14]. However, due to the complex interrelationships among these traits, simple correlation analysis may not fully explain their actual contribution to yield. Consequently, path coefficient analysis helps breeders prioritize traits with the most substantial direct effects on crop yield, making it a powerful tool for indirect selection [15]. Although both path coefficient analysis and principal component analysis (PCA) have been applied individually in maize studies, their combined use provides complementary insights into the interrelationships among yield components and the main factors driving yield variation, thereby offering a more comprehensive understanding of maize productivity.

The present study was primarily driven by the need to optimize potassium nutrition to improve maize growth, yield, and physiological performance under local conditions, thereby providing practical fertilization recommendations for farmers. Additionally, the inclusion of three maize hybrids allowed us to assess their differential responses to potassium fertilization, which may also guide maize breeders in selecting hybrids with higher K-use efficiency. This study hypothesizes that different levels of potassium sulfate fertilization will significantly affect the growth, yield components, and grain yield of maize hybrids, primarily through enhancing photosynthetic activity, chlorophyll synthesis, and assimilate translocation that promote ear development and grain filling. The main objectives of this study were: (i) evaluate the effects of different potassium sulfate fertilization levels on the growth parameters of three maize hybrids, (ii) assess the impact of these fertilization rates on yield components and grain yield per plant across the selected hybrids, (iii) perform path coefficient analysis to quantify the direct and indirect effects of yield components on grain yield, thereby identifying traits with the most substantial contribution to yield enhancement, and (iv) apply principal component analysis (PCA) to identify the key variables driving variation in maize performance under different potassium treatment conditions.

2. Materials and Methods

2.1. Description of the Experiment Area and Design

The present study was carried out in the experimental farm of the Agronomy Department (27° 08′ 20.8″ N, 31° 19′ 40.5″ E), Faculty of Agriculture, Assiut University, Egypt, during the 2023 and 2024 growing seasons. Soil samples were collected at the beginning of each field experiment and analyzed in the laboratory to determine the physicochemical characteristics of the experimental site (

Table 1. Physical and chemical properties of the experimental soil. Means of air temperature and relative humidity and precipitation obtained from etiological station at Assiut, Egypt, during the two growing seasons.

Property (Unit)	Year 2023	Year 2024	Property	Year 2023	Year 2024
Particle size distribution			Soluble cations
Sand (%)	26.40	26.80	Ca2+ (meq 100 g−1)	9.5	9.4
Silt (%)	25.40	25.50	Mg2+ (meq 100 g−1)	3.0	3.0
Clay (%)	48.20	47.70	Na+ (meq 100 g−1)	6.1	6.0
Soil texture class	Clay	Clay	K+ (meq 100 g−1)	2.0	2.0
Bulk density (g cm−3)	1.21	1.20	Soluble anions
Field capacity (%)	40.27	40.20	Cl− (meq 100 g−1)	4.0	4.0
Wilting point (%)	21.00	21.00	HCO3− + CO32− (meq 100 g−1)	7.0	6.9
Infiltration rate (cm h−1)	0.13	0.12	SO42− (meq 100 g−1)	10.5	10.4
CaCO3 (%)	1.22	1.18	Total nitrogen (%)	0.08	0.09
pH (1:2.5)	7.77	7.78	Available phosphorous (mg kg−1)	11.2	11.2
Electrical conductivity (dS m−1)	2.03	2.03	Available potassium (mg kg−1)	240.0	235.0
Organic matter (%)	1.70	1.72

) according to [16]. Soil samples were collected prior to establishing each field experiment to assess the initial physicochemical properties of the experimental site and to ensure field uniformity. Sampling was conducted from the topsoil layer (0–15 cm depth), which represents the standard depth for evaluating soil fertility, nutrient availability, and pH.

Table 2. Means of air temperature and relative humidity and precipitation obtained from the etiological station at Assiut, Egypt, during the two growing seasons.

Month	Temperature (°C)		Relative Humidity (%)	Precipitation (m3)
	Min	Max
2023
May	21.0	35.5	29	0
June	25.0	38.0	30	0
July	25.0	39.0	33	0
August	24.0	38.0	38	0
September	23.0	37.2	36	0
2024
May	20.8	35.5	26	0
June	25.1	40.4	27	0
July	25.0	39.9	28	0
August	26.5	39.4	30	0
September	24.0	36.4	40	0

Source: Meteorological authority, Assiut, Egypt.

presents the mean values of air temperature, relative humidity, and precipitation recorded at the meteorological station in Assiut, Egypt, during the two growing seasons.

The experiment design was a strip-plot arranged in a randomized complete block design (RCBD) with three replications. Each plot consisted of six ridges, each 3 m long and 0.6 m wide. Two seeds were planted per hill, spaced 20 cm apart, on one side of the ridge (plot size = 10.5 m²). The three single crosses (SC2031, SC2036, and SC168) were obtained from High Tech Seeds Company during the two growing seasons. These three maize hybrids were distributed horizontally, and the potassium sulfate (K₂SO₄, equal to 54% K₂O) was applied as soil fertilization at rates of 0, 60, 120, and 180 kg ha⁻¹, which were randomly arranged vertically in the experimental design. After 30 days of planting, the treatments were applied in one dose. The selected rates were based on previous recommendations for maize production in Upper Egypt and earlier field studies. The field experiment included 12 treatments in each replication. The sowing dates were 30th and 28th May in 2023 and 2024, respectively. The agricultural practices for the maize crop, including fertilization, irrigation, weed and disease control, and harvesting, were carried out as recommended. The recommended nitrogen dose is 280 nitrogen units per hectare in two doses, while phosphorus is added during land preparation for planting at a rate of 72 phosphorus units per hectare.

2.2. Measured Characters

2.2.1. Growth Parameters

At the maturity stage, five plants from each plot were taken to determine the following characters:

• Plant height (PH, cm): the length of the main stem from the soil surface to the plant apex has been measured using a ruler.
• Leaf number (LN, leaves plants⁻¹): the total number of fully expanded leaves per plant.
• Flag leaf area (FLA, cm²): calculated using the method [17], it involved measuring the length of the leaf blade from its base to the tip of the leaf (leaf L) and the width of the leaf at its widest point (leaf W) and multiplying them by a correction factor (0.75), derived to account for the natural curvature and shape of maize leaves, which are not perfect rectangles as illustrated in the equation:

  Flag leaf area (FLA) = Leaf L × Leaf W × 0.75
• Stem diameter (SD, cm): measured at the base of the stem using a digital caliper.
• Ear height (EH, cm): the height from the soil surface to the top-most node bearing an ear.

2.2.2. Chlorophyll Content (Chl.)

Chlorophyll content was determined in the flag leaves of five plants from each plot at 60 days after planting by chlorophyll meter SPAD-502 plus, as reported [18]. SPAD calibration equations:

Y = 0.118x² + 0.919x + 7.925

where Y represents the chlorophyll concentration in mg/m² and x represents SPAD value. The calibration equation was used to convert SPAD readings into actual chlorophyll concentration (mg m⁻²), providing a more quantitative and physiologically meaningful estimate than SPAD units alone. The calibration equation was used to convert SPAD meter readings (x) into actual chlorophyll concentration (Y), expressed in mg m⁻² of leaf area.

2.2.3. Grain Yield and Its Attributes

At the maturity stage, five plants from each plot were taken to determine the following characters:

• Ear diameter (ED, cm): measured at the midpoint of the ear using a digital caliper.
• Ear length (EL, cm): measured from the base to the tip of the ear.
• Grains yield per plant (GYP, g plants⁻¹): the weight of grains per sampled plant.
• Shelling percentage (SP, %): $=\left({\displaystyle \frac{\mathrm{G}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n} \mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}}{\mathrm{E}\mathrm{a}\mathrm{r} \mathrm{w}\mathrm{e}\mathrm{i}\mathrm{g}\mathrm{h}\mathrm{t}}}\right)\times 100$
• 1000-grain weight (SI, g): the weight of 1000 randomly sampled grains.
• Grain yield (GY, ton ha⁻¹): the weight of grain yield of each plot adjusted to 15.5% moisture content was recorded.

2.2.4. Grain Quality

• Protein content (PP, %): determined from grain nitrogen (N) concentration measured using the micro Kjeldahl method and expressed as N × 6.25 [19].
• Oil content (OP, %): extracted by Soxhlet apparatus using petroleum ether (boiling range of 60–80 °C) according to [19].
• Protein yield (kg ha⁻¹): $=\left({\displaystyle \frac{\mathrm{G}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n} \mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d} (\mathrm{k}\mathrm{g} {\mathrm{h}\mathrm{a}}^{-1})\times \mathrm{P}\mathrm{r}\mathrm{o}\mathrm{t}\mathrm{e}\mathrm{i}\mathrm{n} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\left(\mathrm{\%}\right)}{100}}\right)$
• Oil yield (kg ha⁻¹): $=\left({\displaystyle \frac{\mathrm{G}\mathrm{r}\mathrm{a}\mathrm{i}\mathrm{n} \mathrm{y}\mathrm{i}\mathrm{e}\mathrm{l}\mathrm{d} (\mathrm{k}\mathrm{g} {\mathrm{h}\mathrm{a}}^{-1})\times \mathrm{O}\mathrm{i}\mathrm{l} \mathrm{c}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{e}\mathrm{n}\mathrm{t}\left(\mathrm{\%}\right)}{100}}\right)$

2.3. Statistical Analysis

All statistical analyses were conducted using R software (version 4.x; R Core Team, Vienna, Austria). The combined analysis of variance for data obtained over the two seasons was carried out according to [20], after testing the homogeneity using Bartlett’s test [21] and verifying normality of residuals using the Shapiro–Wilk test [22]. Differences between means were compared using the least significant differences (LSD) value at 5% level according to [23].

Path analysis is a statistical method used to partition the correlation coefficients between grain yield per plant (dependent variable) and other traits (number of leaves per plant, flag leaf area, chlorophyll content, ear diameter, ear length, 1000-grain weight) into direct and indirect effects, according to [24]. PCA is a widely used statistical technique to assess genetic variation among plant genotypes and to identify key traits contributing to overall diversity [25]. The first two principal components (PC1 and PC2), which had the highest eigenvalues, were selected according to the criterion proposed by [26]. A biplot of PC1 and PC2 was constructed to group treatment combinations and to visualize the relationships among the studied traits and treatments.

3. Results

3.1. Analysis of Variance

The combined analysis of variance revealed significant effects of potassium sulfate fertilization and maize single crosses on several yield and quality traits across two growing seasons (

Table 3. Combined analysis for yield and quality traits of three maize hybrids (SC2031, SC2036, and SC2055) grown under four potassium sulfate application rates (0, 60, 120, and 180 kg K₂SO₄ ha⁻¹) over two growing seasons (2022 and 2023).

S.O.V	D.F	Ear Diameter (cm)	Ear Length (cm)	Grain Yield (g plant−1)	1000-Grain Weight (g)	Shelling Percentage (%)	Grain Yield (t ha−1)	Protein Content (%)	Oil Content (%)	Protein Yield (kg ha−1)	Oil Yield (kg ha−1)
S	1	3.34 **	12.17 **	1063.4 **	11.9 *	1.36	2.80 **	1.04 **	0.07	72,014.0 **	10,336.6 **
Error a	4	0.03	0.42	21.2	0.94	0.4	0.08	0.02	0.01	1989.8	542.1
PS	3	5.02 **	21.2 **	1346.8 **	72.9 **	5.46 **	5.61 **	6.4 **	2.36 **	213,784.8 **	51,327.0 **
S × PS	3	0.07 **	0.24 **	7.8 **	0.02	3.05 **	0.05 **	0.07 *	0.01	2331.2 **	296.7 **
Error b	12	0.001	0.01	0.18	0.01	0.1	0.001	0.01	0.003	125.2	27.4
SC	2	5.15 **	13.7 **	1009.5 **	88.1 **	71.1 **	3.92 **	16.0 **	4.80 **	244,611.9 **	59,068.4 **
S × SC	2	0.02 *	0.12	4.1 **	0.22 *	3.13 **	0.21 **	0.11 **	0.002	1828.4 **	466.1 **
Error c	8	0.004	0.03	0.29	0.03	0.22	0.002	0.01	0.002	88.6	26.7
PS × SC	6	0.12 **	0.35 **	58.2 **	2.8 **	3.67 **	0.10 **	0.53 **	0.13 **	6033.2 **	815.4 **
S × PS × SC	6	0.06 **	0.08 **	12.2 **	0.12 **	1.67 **	0.05 **	0.04 *	0.01	300.0 *	172.3 **
Error d	24	0.002	0.01	0.17	0.01	0.11	0.001	0.01	0.003	103.1	21.7

S.O.V. = Source of Variation; D.F. = Degrees of Freedom; S = Seasons; PS = Potassium sulfate; SC = Single cross; * and ** represent significance at 5 and 1% probability levels, respectively.

). The Season × Potassium Sulfate × Single Cross (S × PS × SC) interaction shows statistical significance for all yield and quality traits, except oil content, which indicates that the combined influence of seasonal conditions, genotype, and potassium sulfate levels plays a crucial role in determining both yield and quality parameters in maize.

The combined analysis across two seasons revealed significant effects of potassium sulfate (PS) fertilizer and maize single crosses (SC) on various growth and physiological traits (

Table 4. Combined analysis for the growth and physiological traits of three maize hybrids (SC2031, SC2036, and SC2055) grown under four potassium sulfate application rates (0, 60, 120, and 180 kg K₂SO₄ ha⁻¹) over two growing seasons (2022 and 2023).

S.O.V	D.F	Plant Height (cm)	Leaf Number (n plant−1)	Flag Leaf Area (cm2)	Chlorophyll Content (mg m−2)	Stem Diameter (cm)	Ear Height (cm)
S	1	125.9	23.5 **	80.5 **	8881.2 **	0.003	393.7 **
Error a	4	30.8	1.24	4.32	316.9	0.01	5.61
PS	3	1106.2 **	28.4 **	2.79 **	147,215.5 **	1.89 **	1603.0 **
S × PS	3	1.20	0.17	1.14	434.7 **	0.02	3.73 *
Error b	12	1.95	0.07	0.37	24.4	0.02	1.04
SC	2	5618.9 **	25.5 **	23.2 **	22,647.6 **	0.41 **	427.1 **
S × SC	2	0.04	0.01	0.39	1087.7 *	0.03	17.0 *
Error c	8	14.4	0.14	0.70	211.8	0.01	4.15
PS × SC	6	8.3 **	0.02	0.21	1467.2 **	0.13 **	17.8 **
S × PS × SC	6	0.14	0.04	0.35	262.1 **	0.07 *	3.92 **
Error d	24	0.76	0.06	0.28	63.7	0.02	0.76

S.O.V. = Source of Variation; D.F. = Degrees of Freedom; S = Seasons; PS = Potassium sulfate; SC = Single cross; * and ** represent significance at 5 and 1% probability levels, respectively.

). The three-way interaction (S × PS × SC) showed no significant effect across growth traits (number of leaves and flag leaf area), while a significant effect was observed on the studied physiological traits (plant height, chlorophyll content, stem diameter, and ear height). This suggests that these physiological measurements are more sensitive to the combined influence of environment, nutrition, and genetics, compared to growth measurements. This also highlights the importance of matching fertilizer strategies to specific genotypes for optimal oil yield.

3.2. Means of Characteristics over Two Seasons

The interaction of hybrid × potassium sulfate level significantly influenced almost all growth, yield, and quality traits across the hybrids (

Table 5. Means ± standard error (n = 3) of plant height, leaf number, flag leaf area, chlorophyll, stem diameter, and ear height of maize as affected by potassium sulfate fertilizer, single crosses, and interaction over two seasons.

Variable		Plant Height (cm)	Leaf Number (n plant−1)	Flag Leaf Area (cm2)	Chlorophyll (mg m−2)	Stem Diameter (cm)	Ear Height (cm)
K2SO4 fertilizer
0 kg ha−1		214.0 ± 3.6 d	14.97 ± 0.29 d	35.9 ± 0.47 a	306.3 ± 4.2 d	2.16 ± 0.02 d	96.5 ± 1.06 d
60 kg ha−1		218.8 ± 3.1 c	15.91 ± 0.26 c	35.8 ± 0.36 a	343.0 ± 5.3 c	2.51 ± 0.02 c	103.5 ± 1.34 c
120 kg ha−1		226.9 ± 3.2 b	16.91 ± 0.24 b	35.3 ± 0.30 b	414.1 ± 10.7 b	2.70 ± 0.02 b	110.8 ± 1.21 b
180kg ha−1		231.4 ± 2.9 a	17.88 ± 0.25 a	35.1 ± 0.32 b	511.7 ± 8.8 a	2.93 ± 0.09 a	118.4 ± 0.74 a
Single cross
SC2031		229.3 ± 1.29 b	16.47 ± 0.27 b	35.5 ± 0.27 b	394.3 ± 16.4 b	2.56 ± 0.07 b	107.0 ± 1.94 b
SC2036		233.8 ± 1.57 a	17.42 ± 0.26 a	36.5 ± 0.31 a	424.3 ± 18.4 a	2.71 ± 0.08 a	111.6 ± 1.79 a
SC168		205.3 ± 1.64 c	15.36 ± 0.27 c	34.6 ± 0.26 c	362.8 ± 15.6 c	2.45 ± 0.05 c	103.2 ± 1.71 c
Interaction
0 kg ha−1	SC2031	222.1 ± 0.55 h	15.03 ± 0.35 a	35.8 ± 0.74 a	311.6 ± 3.8 i	2.17 ± 0.01 g	95.5 ± 2.22 j
	SC2036	225.1 ± 1.17 f	15.97 ± 0.34 a	35.7 ± 0.56 a	337.6 ± 4.4 g	2.18 ± 0.01 g	100.2 ± 1.22 h
	SC168	232.9 ± 0.83 d	16.95 ± 0.28 a	35.3 ± 0.51 a	420.3 ± 5.3 e	2.14 ± 0.05 g	93.8 ± 0.84 k
60 kg ha−1	SC2031	236.9 ± 0.65 c	17.93 ± 0.24 a	35.1 ± 0.34 a	507.5 ± 12.5 b	2.52 ± 0.01 ef	102.7 ± 1.67 g
	SC2036	224.0 ± 0.85 g	16.05 ± 0.38 a	34.7 ± 0.71 a	321.6 ± 2.6 h	2.59 ± 0.01 def	109.5 ± 1.35 e
	SC168	230.1 ± 0.90 e	16.87 ± 0.32 a	35.0 ± 0.48 a	366.3 ± 3.8 f	2.43 ± 0.03 f	98.2 ± 0.84 i
120 kg ha−1	SC2031	238.8 ± 1.25 b	17.88 ± 0.25 a	34.5 ± 0.45 a	460.9 ± 6.2 d	2.70 ± 0.02 cd	111.8 ± 0.91 d
	SC2036	242.1 ± 1.28 a	18.87 ± 0.28 a	34.1 ± 0.47 a	548.2 ± 8.4 a	2.79 ± 0.02 bc	115.7 ± 1.03 c
	SC168	195.8 ± 1.08 i	13.82 ± 0.32 a	37.2 ± 0.79 a	285.8 ± 4.8 j	2.62 ± 0.02 de	104.9 ± 1.06 f
180 kg ha−1	SC2031	201.2 ± 0.98 k	14.88 ± 0.31 a	36.7 ± 0.70 a	325.1 ± 8.8 h	2.87 ± 0.19 b	118.1 ± 1.17 b
	SC2036	209.0 ± 1.59 j	15.89 ± 0.18 a	36.3 ± 0.39 a	361.1 ± 9.8 f	3.30 ± 0.02 a	121.2 ± 0.95 a
	SC168	215.2 ± 1.22 i	16.83 ± 0.30 a	36.0 ± 0.60 a	479.3 ± 8.9 c	2.62 ± 0.09 de	116.0 ± 0.75 c

Means followed by the same letter or letters in the same column are not significant at the p < 0.05 level according to the least significant difference (LSD) test.

,

Table 6. Means ± standard error (n = 3) of ear diameter, ear length, grain yield, 1000-grain weight, shelling percentage, and grain yield of maize as affected by potassium sulfate (K₂O) fertilizer, single crosses, and their interaction over two seasons.

Variable		Ear Diameter (cm)	Ear Length (cm)	Grain Yield (g plant−1)	1000-Grain Weight (g)	Shelling Percentage (%)	Grain Yield (t ha−1)
K2SO4 fertilizer
0 kg ha−1		4.02 ± 0.09 d	17.50 ± 0.13 d	168.6 ± 1.21 d	328 ± 0.23 d	70.9 ± 0.46 b	6.57 ± 0.08 d
60 kg ha−1		4.64 ± 0.11 c	18.29 ± 0.17 c	179.2 ± 2.00 c	348 ± 0.43 c	71.6 ± 0.28 a	7.03 ± 0.09 c
120 kg ha−1		4.99 ± 0.12 b	19.13 ± 0.21 b	184.1 ± 1.80 b	362 ± 0.44 b	71.1 ± 0.45 b	7.49 ± 0.10 b
180 kg ha−1		5.23 ± 0.12 a	20.02 ± 0.25 a	188.7 ± 1.79 a	375 ± 0.50 a	70.3 ± 0.39 c	7.86 ± 0.12 a
Single cross
SC2031		4.86 ± 0.11 b	18.65 ± 0.22 b	181.4 ± 1.92 b	357 ± 0.42 b	71.1 ± 0.15 b	7.27 ± 0.13 b
SC2036		5.10 ± 0.13 a	19.53 ± 0.26 a	185.9 ± 2.12 a	370 ± 0.47 a	72.6 ± 0.22 a	7.63 ± 0.11 a
SC168		4.20 ± 0.09 c	18.03 ± 0.18 c	173.2 ± 1.44 c	332 ± 0.26 c	69.2 ± 0.25 c	6.82 ± 0.11 c
Interaction
0 kg ha−1	SC2031	4.08 ± 0.09 i	18.17 ± 0.19 e	181.7 ± 1.25 e	350 ± 0.19 e	70.5 ± 0.15 d	6.95 ± 0.08 g
	SC2036	4.23 ± 0.14 h	19.02 ± 0.17 c	186.4 ± 1.85 d	370 ± 0.18 c	71.9 ± 0.24 bc	7.68 ± 0.08 d
	SC168	3.74 ± 0.15 j	17.42 ± 0.12 g	167.6 ± 1.57 j	330 ± 0.29 i	71.5 ± 0.11 c	6.51 ± 0.10 j
60 kg ha−1	SC2031	4.79 ± 0.02 e	18.97 ± 0.17 c	186.0 ± 2.02 d	337 ± 0.21 h	70.7 ± 0.26 d	7.49 ± 0.06 e
	SC2036	5.09 ± 0.07 d	20.02 ± 0.20 b	189.9 ± 1.64 c	379 ± 0.31 b	69.0 ± 0.25 e	7.93 ± 0.11 b
	SC168	4.05 ± 0.12 i	18.07 ± 0.15 e	171.3 ± 2.30 h	368 ± 0.24 d	70.7 ± 0.51 d	6.91 ± 0.10 h
120 kg ha−1	SC2031	5.15 ± 0.08 c	20.00 ± 0.28 b	190.5 ± 1.21 b	379 ± 0.30 b	68.7 ± 0.24 e	7.83 ± 0.04 c
	SC2036	5.42 ± 0.13 b	21.08 ± 0.31 a	196.0 ± 2.06 a	397 ± 0.17 a	68.3 ± 0.24 f	8.28 ± 0.05 a
	SC168	4.39 ± 0.06 g	17.02 ± 0.11 h	166.8 ± 2.21 k	317 ± 0.18 k	73.4 ± 0.31 a	6.30 ± 0.10 k
180 kg ha−1	SC2031	5.43 ± 0.15 b	18.37 ± 0.25 d	175.4 ± 1.96 g	338 ± 0.15 g	72.2 ± 0.49 b	6.97 ± 0.14 g
	SC2036	5.64 ± 0.13 a	18.97 ± 0.17 c	180.3 ± 0.76 f	348 ± 0.18 f	73.0 ± 0.26 a	7.36 ± 0.22 f
	SC168	4.63 ± 0.06 f	17.75 ± 0.25 f	170.0 ± 2.74 i	326 ± 0.20 j	71.8 ± 0.36 bc	6.64 ± 0.08 i

Means followed by the same letter or letters in the same column are not significant at the p < 0.05 level according to the least significant difference (LSD) test.

and

Table 7. Means ± standard error (n = 3) of protein %, oil %, protein yield, and oil yield of maize as affected by potassium sulfate fertilizer, single crosses, and their interaction over two growing seasons.

Variable		Protein (%)	Oil (%)	Protein Yield (kg ha−1)	Oil Yield (kg ha−1)
K2SO4 fertilizer
0 kg ha−1		11.10 ± 0.12 d	4.49 ± 0.11 d	731.0 ± 16.1 d	296.3 ± 10.2 d
60 kg ha−1		11.36 ± 0.13 c	4.91 ± 0.05 c	799.5 ± 18.5 c	345.8 ± 7.8 c
120 kg ha−1		11.94 ± 0.22 b	5.16 ± 0.09 b	896.8 ± 24.7 b	387.2 ± 10.3 b
180kg ha−1		12.43 ± 0.21 a	5.33 ± 0.11 a	979.4 ± 28.7 a	419.8 ± 13.6 a
Single cross
SC2031		11.41 ± 0.07 b	4.81 ± 0.07 b	808.3 ± 21.2 b	338.2 ± 10.4 b
SC2036		12.63 ± 0.17 a	5.48 ± 0.07 a	967.0 ± 25.9 a	419.4 ± 11.3 a
SC168		11.08 ± 0.11 c	4.63 ± 0.07 c	779.6 ± 16.8 c	329.4 ± 9.1 c
Interaction
0 kg ha−1	SC2031	10.75 ± 0.02 j	4.74 ± 0.02 g	747.1 ± 9.42 h	329.7 ± 4.60 g
	SC2036	11.10 ± 0.04 h	4.79 ± 0.02 g	851.9 ± 12.0 f	368.0 ± 5.57 e
	SC168	10.60 ± 0.01 k	4.06 ± 0.01 i	690.0 ± 10.8 i	264.2 ± 3.83 j
60 kg ha−1	SC2031	11.90 ± 0.05 d	5.12 ± 0.02 d	902.7 ± 9.6 d	388.8 ± 4.08 c
	SC2036	12.05 ± 0.04 c	5.19 ± 0.01 c	944.1 ± 17.7 c	390.6 ± 10.00 c
	SC168	11.73 ± 0.09 e	4.92 ± 0.05 f	810.5 ± 17.2 g	353.6 ± 5.99 f
120 kg ha−1	SC2031	13.17 ± 0.08 b	5.66 ± 0.02 b	1031.2 ± 10.2 b	443.2 ± 3.64 b
	SC2036	13.57 ± 0.20 a	5.94 ± 0.06 a	1123.7 ± 20.6 a	491.9 ± 7.00 a
	SC168	10.98 ± 0.11 i	4.30 ± 0.02 h	692.4 ± 18.1 i	271.2 ± 5.95 i
180 kg ha−1	SC2031	11.57 ± 0.05 f	5.02 ± 0.03 e	807.2 ± 18.8 g	350.5 ± 8.14 f
	SC2036	11.81 ± 0.07 de	5.12 ± 0.02 d	870.3 ± 29.3 e	377.0 ± 12.2 d
	SC168	11.27 ± 0.04 g	4.80 ± 0.01 g	748.6 ± 10.9 h	319.0 ± 4.40 h

Means followed by the same letter or letters in the same column are not significant at the p < 0.05 level according to the least significant difference (LSD) test.

). In general, the application of potassium sulfate improved growth, yield, and quality parameters compared with the unfertilized control. The two hybrids SC2031 and SC2036 consistently outperformed SC168 at many fertilizer levels, especially at 120 kg ha⁻¹ potassium sulfate, which proved optimal for most traits.

Among the three tested hybrids, SC2036 at 120 kg ha⁻¹ potassium sulfate showed the highest performance in most of the measured traits, i.e., chlorophyll content (548.2), plant height (242.1), ear length (21.08 cm), grain yield (196 g plant⁻¹), 1000-grain weight (397.0 g), protein content (13.57%), oil content (5.94%), and grain yield (196 g per plant and 8.28 tons per ha). SC168 consistently recorded the lowest means for chlorophyll content (285.8), plant height (195.8), ear length (17.02), grain yield (166.8 g per plant and 6.30 tons per ha), and 1000-grain weight (317.0 g). SC2031 had intermediate values across all traits. Shelling percentage remained relatively stable (~70–71%).

Furthermore, the interaction between potassium sulfate and single crosses revealed important cross-specific responses to potassium sulfate. SC2031 and SC2036 exhibited high chlorophyll readings at 120 kg ha⁻¹ K, while SC168 showed high chlorophyll readings at 180 kg ha⁻¹ K. SC2031 also showed a high chlorophyll content at a potassium sulfate rate of 60 kg ha⁻¹ K. The morphometric traits (plant height, stem diameter, ear height, ear diameter, ear length) were also maximal in SC2031 and SC2036 at both 120 kg ha⁻¹ K and 180 kg ha⁻¹ K, indicating stronger vegetative and reproductive growth under this higher K rate.

Regarding grain yield (per plant and per hectare), again, SC2031 and SC2036 peaked under 120 kg ha⁻¹ K. SC2036 also showed high grain yield values at 60 kg ha⁻¹ potassium sulfate. Increasing the K rate to 180 kg ha⁻¹ generally did not further increase yield; in some cases, there was a slight decline. Additionally, the highest values for the quality/yield component traits (1000-kernel weight, shelling %, protein yield, oil yield) were found in SC2031 and SC2036 under 120 kg ha⁻¹. SC2036 also showed higher values for these traits at 60 kg ha⁻¹ potassium sulfate. Overall, two hybrids (SC2031 and SC2036) consistently recorded the highest values for most traits when supplied with 120 kg potassium sulfate ha⁻¹, whereas increasing the rate to 180 kg potassium sulfate ha⁻¹ did not result in further improvement and, in some cases, led to a decline. SC2036 showed a weaker response to potassium application and remained inferior across most measured traits.

These results demonstrated that potassium sulfate application is important for increasing the growth and yield of maize, as many studied traits showed strong responses to increasing potassium fertilizer. Moreover, the responses of different single crosses of maize varied according to the genetic composition of the single cross. The results clearly demonstrate that maize crosses responded differently to varying potassium fertilizer levels. Consequently, it is important to identify the optimal cultivar response to the optimum level of potassium sulfate application.

3.3. Path Coefficient Analysis

Path coefficient analysis was used to detect the direct and indirect effects of six traits, i.e., LN, FLA, CHL, ED, EL, and SI, via the partition of their phenotypic correlation into both effects upon grain yield per plant⁻¹ for each single maize cross (

Table 8. Partitioning of phenotypic correlation into direct and indirect effects by path coefficient analysis for grain yield per plant⁻¹ in each single cross separately over the two-year average.

Effect		SC2031	SC2036	SC168
1—Leaf number and grain yield/plant	r17 =	0.94	0.944	0.992
Direct effect	p17 =	1.373	0.453	0.799
Indirect effect via flag leaf area	r12p27 =	−0.169	−0.47	−0.654
Indirect effect via chlorophyll	r13p37 =	−0.268	0.822	0.006
Indirect effect via ear diameter	r14p47 =	0.944	0.347	0.624
Indirect effect via ear length	r15p57 =	−0.948	−0.126	−0.294
Indirect effect via 1000-grain weight	r16p67 =	0.007	−0.082	0.512
2—Flag leaf area and grain yield/plant	r27 =	−0.883	−0.616	−0.964
Direct effect	p27 =	0.173	0.56	0.661
Indirect effect via leaf number	r12p17 =	−1.336	−0.38	−0.79
Indirect effect via chlorophyll	r23p37 =	0.266	−0.734	−0.006
Indirect effect via ear diameter	r24p47 =	−0.901	−0.224	−0.619
Indirect effect via ear length	r25p57 =	0.921	0.105	0.292
Indirect effect via 1000-grain weight	r26p67=	−0.007	0.057	−0.502
3—Chlorophyll and grain yield/plant	r37 =	0.85	0.908	0.963
Direct effect	p37 =	−0.273	0.826	0.007
Indirect effect via leaf number	r13p17 =	1.344	0.45	0.755
Indirect effect via flag leaf area	r23p27 =	−0.169	−0.497	−0.589
Indirect effect via ear diameter	r34p47 =	0.879	0.333	0.581
Indirect effect via ear length	r35p57 =	−0.937	−0.125	−0.277
Indirect effect via 1000-grain weight	r36p67 =	0.006	−0.079	0.487
4—Ear diameter and grain yield/plant	r47 =	0.993	0.997	0.991
Direct effect	p47 =	0.97	0.371	0.624
Indirect effect via leaf number	r14p17 =	1.336	0.424	0.798
Indirect effect via flag leaf area	r24p27 =	−0.161	−0.338	−0.655
Indirect effect via chlorophyll	r34p37 =	−0.248	0.742	0.006
Indirect effect via ear length	r45p57 =	−0.912	−0.118	−0.294
Indirect effect via 1000-grain weight	r46p67 =	0.007	−0.083	0.512
5—Ear length cm and grain yield/plant	r57 =	0.921	0.947	0.989
Direct effect	p57 =	−0.95	−0.126	−0.294
Indirect effect via leaf number	r15p17 =	1.37	0.453	0.799
Indirect effect via flag leaf area	r25p27=	−0.168	−0.467	−0.655
Indirect effect via chlorophyll	r35p37 =	−0.27	0.822	0.006
Indirect effect via ear diameter	r45p47 =	0.932	0.347	0.623
Indirect effect via 1000-grain weight	r56p67 =	0.007	−0.082	0.511
6—1000-grain weight and grain yield/plant	r67 =	0.966	0.995	0.998
Direct effect	p67 =	0.007	−0.084	0.513
Indirect effect via leaf number	r16p17 =	1.354	0.438	0.796
Indirect effect via flag leaf area	r26p27 =	−0.169	−0.378	−0.648
Indirect effect via chlorophyll	r36p37 =	−0.258	0.776	0.006
Indirect effect via ear diameter	r46p47 =	0.959	0.366	0.623
Indirect effect via ear length	r56p57 =	−0.927	−0.122	−0.293

). The relationships among yield components and their direct and indirect contributions to grain yield per plant are illustrated in Figure 1. The results revealed that the leaf number possessed powerful direct and indirect effects on grain yield/plant in two single maize crosses, i.e., SC2031 and SC168. The leaf number recorded the highest direct effect upon the grain yield/plant in values of 1.373 and 0.799 for both previous crosses, respectively.

Moreover, the highest indirect effects were observed on grain yield/plant via chlorophyll (1.344 and 0.755), ear diameter (1.336 and 0.798), ear length (1.370 and 0.799), and 1000-grain weight (1.354 and 0.796) for the same crosses SC2031 and SC168, respectively. Otherwise, the leaf number comes in the third ranking for the direct effect after chlorophyll and flag leaf area, and second ranking after chlorophyll on the cross SC2036. Furthermore, chlorophyll content was the first trait affecting grain yield per plant for the cross SC2036, either directly (0.826) or indirectly via leaf number (0.822), ear diameter (0.742), ear length (0.822), and 1000-grain weight (0.776).

3.4. PCA Analysis

The principal component analysis (PCA) revealed that the first two principal components (PCs) explained a substantial proportion of the total variance among the studied traits. The PCA biplot (Figure 2) indicates that most of the variation (89.3%) is explained by PC1, while PC2 explains an additional 7.20%. This indicates that most of the variability among the evaluated traits can be effectively summarized by the first component.

The biplot showed clear associations among traits and their contributions to the variation. Plant height (PH), ear length (EL), number of leaves per plant (LN), and 1000-grain weight (SI) were positively associated with PC1, clustering together and indicating strong correlations. Conversely, ear height (EH) showed a negative association with PC1, suggesting its contrasting influence on the variation compared with yield-related traits. Chlorophyll content (Chl.), and stem diameter (SD) were positioned moderately along PC1 and PC2, indicating their intermediate contribution to both components.

4. Discussion

Understanding how maize hybrids respond to potassium sulfate under varying environmental conditions is essential for developing fertilizer recommendations that improve yield stability. Prior to analysis, data were checked for normality and homogeneity of variances, and all assumptions were satisfied, supporting the validity of the ANOVA, path, and PCA results. Although the three-way interaction (season × potassium sulfate × single cross) was statistically significant for almost all the studied traits, the practical objective of this study is to provide a clear recommendation for fertilizer management to farmers across different growing seasons. Therefore, the discussion focuses primarily on the potassium sulfate × single cross (PS × SC) interaction averaged over seasons. This approach emphasizes the consistent responses of the evaluated hybrids to potassium sulfate application, irrespective of seasonal variation, thereby allowing the formulation of more reliable and broadly applicable recommendations for maize production.

In this study, potassium sulfate has a consistent positive effect on maize growth and yield. It enhanced chlorophyll content, plant height, ear development, grain filling, and protein and oil content. Potassium regulates key processes, including photosynthesis, stomatal conductance, enzyme activation, and assimilate transport, thereby enhancing overall plant vigor and yield potential [27,28]. Moreover, potassium is closely interlinked with nitrogen metabolism, enhancing nitrogen uptake and assimilation by improving root growth, activating nitrate reductase, and facilitating the translocation of nitrogenous compounds within the plant [29]. This synergistic relationship between potassium and nitrogen is well established, as adequate potassium improves root development and membrane transport capacity, thereby enhancing nitrate uptake and the xylem–phloem translocation of nitrogen compounds [29,30]. At the biochemical level, potassium activates key enzymes involved in nitrogen metabolism, such as nitrate reductase and the GS–GOGAT system, which promote amino acid and protein synthesis and improve nitrogen use efficiency [31,32]. Furthermore, by enhancing photosynthetic rate and chlorophyll formation, potassium provides more carbon skeletons for amino acid and protein biosynthesis [4,27,33].

Furthermore, potassium is fundamental for protein synthesis and lipid metabolism, explaining the linear increase observed in grain quality traits such as protein and oil content with increasing K₂OS₄ levels. It also plays a crucial role in carbohydrate translocation, supporting grain filling and contributing to greater grain weight and ear size [34]. These results are consistent with earlier studies demonstrating that potassium enhances grain filling, nitrogen metabolism, and lipid biosynthesis [27,31]. These results highlight potassium’s multifunctional role in maize physiology, including enhancing photosynthate transport to grains, improving enzyme activation for protein synthesis, and promoting kernel development and grain filling [27,28]. Although our study focused on potassium supplied as sulfate, previous work in maize (e.g., with KCl, K₂SO₄ and KNO₃) showed that the form of the potassium salt can affect growth, physiology, and yield responses [35,36]. Consequently, including additional K sources (e.g., KCl, KNO₃) would clarify whether the responses observed are specific to the sulfate-based form.

The highly significant differences among single hybrids across all traits reflect substantial genetic variability. This indicates that hybrids differ in nutrient uptake capacity, nutrient use efficiency, and photosynthetic performance, as reported by [37,38]. Such variability underscores the importance of selecting hybrids that combine strong genetic potential with efficient nutrient utilization. For instance, SC2036 showed superior performance across potassium levels, particularly at 120 kg K₂SO₄ ha⁻¹, while SC168 was less responsive and may be better suited to low-input production systems. These results agree with [28,39]. Furthermore, path coefficient analysis confirmed that the influence of yield components on grain weight is genotype dependent. For example, enhancing ear diameter appears crucial in SC2031, SC168 responds more strongly to grain weight, while chlorophyll content had strong direct effects for SC2036. Traits like ear length and 1000-grain weight had mixed or negative direct effects but contributed indirectly. Such differential trait contributions have been reported in other cereals, where the emphasis on yield components is shaped by genetic background and environmental conditions [40,41].

Path coefficient analysis is a powerful tool used to partition correlation coefficients into direct and indirect effects, thereby providing a clear understanding of potential associations among yield components and their impact on grain yield [42]. Ear diameter and leaf number had the strongest and most consistent direct positive effects on grain yield across hybrids. These findings support earlier work by [43,44], who identified ear diameter as a significant contributor to maize grain yield. Similarly, they [45] underlined positive effects of ear diameter and grain weight, which align with the present findings. Moreover, ear diameter appeared as a useful trait for distinguishing stress-tolerant maize genotypes [46]. Additionally, leaf number also showed substantial direct and indirect associations with grain yield, particularly through its interactions with ear diameter and chlorophyll content [13]. Similarly, ear length exhibited negative direct effects across hybrids but contributed positively through indirect effects, particularly via ear diameter and leaf number. This indicates that longer ears alone may not be associated with higher yield unless they are coupled with efficient grain filling [47].

The results of PCA analysis highlighted that the major contributing traits to PC1 (yield-related) such as grain yield per plant (GYP), leaf number (LN), ear diameter (ED), ear length (EL), and 1000-grain weight (SI) are strongly correlated and are the main drivers of yield performance. Similar findings were reported by [48,49], who observed that ear traits and kernel weight were the most important contributors to yield variation among maize genotypes. While traits contributing to PC2 (growth differentiators) such as flag leaf area (FLA), chlorophyll content (Chl.), stem diameter (SD), and plant height (PH) help differentiate growth patterns and adaptive responses among treatments. The intermediate positioning of chlorophyll content and stem diameter along PC1 and PC2 suggests that these traits play a secondary role in differentiating treatment responses. This observation is supported by findings that SPAD and stem diameter often contribute indirectly to yield through improved photosynthetic efficiency and lodging resistance, rather than direct yield determination [50,51]. The negative association of ear height with PC1 reflects its contrasting influence, in line with earlier reports that excessive ear height can compromise plant stability and does not necessarily translate into higher yield [52].

Treatment distribution further confirmed the role of potassium sulfate in enhancing trait performance. SC2031 and SC2036 at 120 kg K₂SO₄ ha⁻¹ clustered with yield-associated traits, indicating that this fertilization level optimizes the physiological and morphological characteristics required for high productivity. In contrast, treatments without potassium (0 K₂SO₄) or those receiving excessive levels (180 K₂SO₄) were positioned away from yield traits, suggesting either nutrient limitation or possible nutrient imbalance at higher rates. This aligns with reports by [53], who underlined the synergistic role of potassium in improving nitrogen use efficiency, assimilating partitioning and yield stability in cereals. These results underscore the value of PCA as a tool to reveal trait inter-relationships and guide both hybrid selection and nutrient management strategies.

5. Conclusions

This study confirms that potassium sulfate significantly enhances maize growth, yield, and grain quality, with optimal performance at 120 kg K₂SO₄ ha⁻¹. Genotypic differences were pronounced, with SC2036 showing superior nutrient-use efficiency and yield potential. Path and PCA analyses identified ear diameter and leaf number as key predictors of grain yield. These findings highlight the importance of integrating potassium management with genotype-specific trait selection to improve maize productivity. Future research should validate these strategies across diverse environments to support stable, high-yielding, and nutrient-efficient hybrid development.

6. Practical Recommendations

The application of 120 kg K₂SO₄ ha⁻¹ proved optimal under the study conditions, maximizing growth and yield without economic loss from excessive input. Higher rates did not yield proportional benefits, indicating that moderate, balanced K fertilization is both agronomically and economically efficient.

Genotypic differences in potassium responsiveness were evident. SC2036 is highly responsive and efficient, suitable for intensive production systems. SC2031 is moderately responsive, adaptable under variable nutrient conditions. SC168 is less responsive, suitable for low-input or resource-limited systems.

The combined use of path and PCA analyses provides breeders with reliable criteria (ear diameter, leaf number) for selecting high-yielding, K-efficient hybrids.