Research on the Optimal Water and Fertilizer Scheme for Maize in a Typical Hydrological Year Based on the DSSAT Model

Abstract

Maize is vital for global and Chinese food security. Yet, in Henan Province, a key maize-growing region in China, water scarcity, uneven rainfall, and inefficient irrigation and fertilization limit its yield and quality. This study combines a two-year field experiment (2023–2024) with the DSSAT model to optimize irrigation and fertilization for typical hydrological years (wet, normal, and dry). After calibration and validation with field data, the DSSAT model showed strong performance. Results indicate that optimal irrigation timing and volume vary with hydrological years: no irrigation is needed in wet years, one 30 mm irrigation at the tasseling (VT) stage in normal years, and three irrigations (total 90 mm) at the emergence (VE), jointing (VT), and grain filling (R2) stages in dry years. The optimal nitrogen fertilizer is 240 kg·ha⁻¹ in water-rich and normal years and 180 kg·ha⁻¹ in dry years. These optimized schemes can achieve 98–100% of maximum potential maize yields across hydrological years, offering practical insights for enhancing agricultural water and nutrient management in central Henan to support sustainable development and reduce environmental impacts.

1. Introduction

Maize is one of the most critical cereal crops globally, accounting for over 26% of China’s total cultivated area [1]. Henan Province, a vital maize production region in China, holds significant national importance in both planting area and yield [2]. However, escalating water scarcity, uneven rainfall distribution during the maize growing season (June to September), and substantial interannual precipitation variability [3] have subjected maize crops to varying degrees of water stress, severely compromising their yield and quality.

Furthermore, unscientific irrigation and irrational fertilization practices in current maize cultivation [4], including localized over-irrigation and excessive fertilizer application, exacerbate resource inefficiency [5] and induce environmental degradation such as soil salinization, nutrient imbalance, and water pollution, thereby constraining sustainable maize production [6,7,8]. In semi-arid regions, water and nitrogen management play decisive roles in crop growth, development, and ultimate yield [9]. Drip irrigation, by delivering water and nutrients precisely to the root zone, minimizes evaporation and leaching losses, thereby enhancing resource use efficiency [10,11]. Optimal water–fertilizer management is crucial for maize productivity [12,13], with field experiments demonstrating that coordinated irrigation and fertilization strategies can regulate root zone dynamics and improve yield [14].

Traditional approaches to optimizing water–nitrogen regimes rely heavily on labor-intensive field trials [15,16], which fail to systematically quantify interactions among climatic conditions, crop genotypes, soil types, and agronomic practices across diverse regions and years [17]. Crop growth models, particularly the Decision Support System for Agrotechnology Transfer (DSSAT), have emerged as powerful tools to address these limitations [18,19,20].

Allakonon et al. [21] identified optimal irrigation–fertilization combinations for off-season maize using the CERES-Maize module; Shen et al. [22] developed an irrigation decision framework for winter wheat using support vector machine algorithms integrated with DSSAT; Bai et al. [23] coupled DSSAT with genetic algorithms to optimize water–nitrogen regimes, achieving yield increases of 1.9–2.6% and economic returns of 7.3–8.9%; Jing et al. [24] analyzed water management impacts on spring wheat yield and water stress in Canada; Rugira et al. [25] optimized irrigation scheduling and planting dates for spring maize in the Loess Plateau (Fen River Basin).

Although there has been progress in optimizing water and nitrogen management based on models, there are still some gaps. First, there is a need for further exploration of systematically optimizing the irrigation and fertilization of summer maize in Henan Province, especially in typical hydrological years (wet, normal, and dry). Second, the potential of the DSSAT model to address these challenges in central Henan has not been fully evaluated. In response to the above issues and challenges, this study proposes the following hypothesis: optimizing the irrigation and fertilization strategies for maize in central Henan using the DSSAT model can improve yield and water use efficiency and reduce resource waste and environmental impacts.

To gain a deeper understanding of the irrigation and fertilization schemes in the Henan region and to make the simulation methods more easily applicable, this study conducted a two-year field experiment from 2023 to 2024 to calibrate and verify the DSSAT model. In addition, the DSSAT model was used to optimize the irrigation and fertilization schemes for typical years based on weather data from Zhengzhou from 1995 to 2024. The main objectives of this study are to test the applicability of the DSSAT model for simulating maize growth under the climatic and soil conditions of central Henan; determine the optimal timing, amount of irrigation, and nitrogen application rates for summer maize under wet, normal, and dry hydrological years; and provide scientific recommendations for improving regional agricultural water and nutrient management to support sustainable development and resource efficiency.

2. Materials and Methods

2.1. Subsection Overview of the Study Area

The field experiment was conducted during 2023–2024 at the Agricultural High-Efficiency Water Use Experimental Station on the Longzihu Campus of North China University of Water Resources and Electric Power, Zhengzhou City, Henan Province (Figure 1). The site (34.78° N, 113.76° E; 110 m above sea level) lies within the Central China Plain and experiences a warm temperate continental monsoon climate characterized by distinct seasonal variations with hot, rainy summers (June–September precipitation accounts for ~70% of annual rainfall) and relatively dry springs and winters. The average annual rainfall is 800 mm, and the average sunshine duration is about 6.57 h·d⁻¹. The irrigation water is public tap water, with a pH of 8.1, an electrical conductivity of 96 μS·cm⁻¹, a salinity of 0.12 g·L⁻¹, a sodium ion concentration of 26.13 mg·L⁻¹, and a chloride ion concentration of 14.21 mg·L⁻¹. The content of potential pollutants (such as heavy metals, organic substances, and disinfection by-products) is extremely low and negligible. The experimental field features a flat topography with surface soil classified as sandy loam. The concentration of soil organic matter at 0–100 cm was 831 mg·kg⁻¹; the concentration of available potassium was 101.3 mg·kg⁻¹; the concentration of available phosphorus was 12.1 mg·kg⁻¹; the concentration of total nitrogen was 512.5 mg·kg⁻¹, and the concentration of alkali-hydrolyzable nitrogen was 53.4 mg·kg⁻¹. The effective concentrations of copper, zinc, iron, and manganese were 10.02, 27.63, 2.72, and 0.12 mg·kg⁻¹ respectively. The soil cation exchange capacity was 7.19 cmol·kg⁻¹, and the alkali saturation was 81%.

2.2. Field Experiment Design

The field trials utilized the maize cultivar Zhengdan 958 (bought at the local agricultural store) across both experimental years (2023–2024). In 2023, sowing occurred on June 10 at a depth of 3 cm with a planting density of 66,667 plants·ha⁻¹, followed by harvesting on 29 September. The 2024 season featured an earlier sowing date (8 June) with identical planting depth but increased density (70,408 plants·ha⁻¹), concluding with harvest on 25 September. In the past two years, before planting maize, winter wheat was planted. The experimental treatments included one rain-reared treatment (A1), one drip irrigation treatment (B1), and two nitrogen fertilizer treatments (N1 and N2). All treatments were based on nitrogen application at 68 kg·ha⁻¹, and the remaining nitrogen fertilizers were evenly distributed, with topdressing during the jointing stage and the grain filling stage. The amount of water applied for each drip irrigation treatment is 30 mm (

Table 1. Field trial of the irrigation and fertilization plan.

Year	Treatment		Irrigation Date	Irrigation Quota (mm)	Base Fertilizer (kg·ha−1)	Top Dressing (kg·ha−1)
2023	A1	N1	-	-	68	53
		N2	-			76
	B1	N1	6.10 6.27 7.27 9.18	120		53
		N2				76
2024	A1	N1	-	-	68	53
		N2	-			76
	B1	N1	6.8 6.29 9.10	90		53
		N2				76

). The experiment adopted a split-plot design with three replications using 4 × 4 m plots. Maize phenological development was monitored across five critical growth stages: emergence (VE), jointing (VJ), tasseling (VT), grain filling (R2), and physiological maturity (R6).

2.3. Data Measurement and Sources

2.3.1. Plant Growth Measurements

Five representative plants per plot were selected for measurement. The leaf area index (LAI) was determined using an LAI-2200C Plant Canopy Analyzer (LI-COR Biosciences, Lincoln, NE, USA). Plant height was measured with a tape measure (1 mm precision), while ear length and diameter were quantified using vernier calipers (1 mm accuracy). At each growth stage, five plants per plot, representing average growth conditions, were sampled. After removing underground biomass, shoots were deactivated at 105 °C for 0.5 h and oven-dried at 75 °C to a constant weight. Aboveground biomass was calculated by multiplying the dry weight by planting density. Post-harvest, maize ears were sun-dried to a safe moisture content (14%) before measuring the ear count, kernel number per ear, and 100-grain weight. Grain yield was subsequently computed based on these parameters.

2.3.2. Water Use Efficiency and Yield

• Water consumption of crops (ET, mm)

  $$ET=I+\mathsf{\Delta }W+P+K-D$$

  (1)

  where $I$ represents the irrigation volume within a certain period (mm); $\mathsf{\Delta }W$ represents the variation in water storage in the soil layer at a depth of 1 m within the period (mm); $P$ represents the effective rainfall during the period (mm); $K$ represents the groundwater replenishment volume (mm) within the time period; and $D$ represents the leakage volume within a certain period (mm). Because the average groundwater burial depth in the test area is relatively large (greater than 7 m) and the rated drip irrigation water is small, $K$ and $D$ are not considered.
• Water use efficiency (WUE, mm)

  $$WUE=Y/(ET\times 10)$$

  (2)

  where $Y$ is grain yield (kg·ha⁻¹), $ET$ is crop water consumption (mm), and $10$ is the proportional factor.
• Partial factor productivity of nitrogen fertilizer (PFPN, kg·kg⁻¹)

  $$PFPN=Y_N/N$$

  (3)

  where $Y_N$ represents the yield in the nitrogen application area (kg·ha⁻¹) and $N$ represents the nitrogen application rate (kg·ha⁻¹).

2.3.3. Meteorological Data

Meteorological data collected in this study included the daily maximum temperature, minimum temperature, precipitation, and solar radiation. The data for 2023–2024 are from the automatic weather station in the test field. During the growth period of maize (June to September), the relative humidity is between 45% and 98%, and the reference evapotranspiration is between 350 and 430 mm. The historical records from 1995 to 2022 are from the National Meteorological Science Data Sharing Service Platform. Based on a 30-year precipitation series (1995–2024) during maize growing seasons, typical hydrological years were classified using Pearson Type III frequency distribution analysis. Rainfall thresholds corresponding to cumulative probabilities of P = 25%, 50%, and 75% and were designated as wet, normal, and dry hydrological years, respectively. Specific representative years were identified as follows: wet year (2006, 536.7 mm), normal year (2017, 467.36 mm), and dry year (1995, 427.23 mm). The meteorological characteristics of these typical years are summarized in Figure 2.

2.3.4. Soil Data

Soil data were derived from field-measured parameters (

Table 2. Soil physicochemical properties.

Soil Layer (cm)	0–20	20–40	40–60	60–80	80–100
Bulk density (g·cm−3)	1.47	1.44	1.52	1.54	1.46
Field capacity (cm3·cm−3)	0.26	0.25	0.32	0.3	0.22
Volumetric water (cm3·cm−3)	0.21	0.23	0.21	0.24	0.23
Nitrate nitrogen (mg·cm−3)	4.77	3.01	1.61	1.29	0.81
Ammonium nitrogen (mg·cm−3)	1.01	0.97	0.22	0.14	0.06
Organic carbon (%)	1.21	0.82	0.62	0.61	0.5
pH	8.12	8.2	8.11	8.22	8.21
Sand (%)	31.9	34.4	29.9	40.5	66.1
Silt (%)	46.5	44.2	49.3	48.3	23.5
Clay (%)	21.6	21.4	20.8	11.2	10.4

). Soil particle size distribution was determined using a Rise-2002 wet-process laser (Runzhi Instrument Co., Ltd., Jinan, China) particle size analyzer. Field capacity, saturated water content, and bulk density were measured via the core cutter method. Organic carbon content was quantified with a TOC analyzer. Nitrate nitrogen and ammonium nitrogen were analyzed through ion chromatography and distillation methods, respectively. Soil moisture and water potential were measured using soil moisture sensors and soil water potential meters.

2.4. DSSAT Model Configuration and Scenario Setup

2.4.1. DSSAT Model

The DSSAT model integrates four primary input datasets: soil properties, meteorological conditions, field management practices, and cultivar-specific genetic parameters. According to the DSSAT model user manual, the crop variety parameters are P1: thermal time required to complete the juvenile phase under non-photosensitive conditions (°C·d⁻¹); P2: photoperiod sensitivity coefficient (°C·d⁻¹); P5: grain filling duration parameter (°C·d⁻¹); G2: maximum potential kernel number per plant; G3: maximum kernel growth rate parameter (mg·kernel⁻¹·d⁻¹); and PHINT: characteristic parameters of leaf ejection interval (°C·d⁻¹). Operating at a daily time step, the model simulates maize growth dynamics and outputs critical variables including the yield, phenological stages, leaf area index (LAI), biomass accumulation, and soil moisture content. These outputs facilitate model calibration and validation against observed field data. The structure of the DSSAT model is shown in Figure 3.

2.4.2. Irrigation Period Simulation

Under the condition of no nutritional stress, 5 drip irrigation modes were set based on 4 key growth periods (VE, VJ, VT, and R2): no irrigation, one irrigation, two irrigations, three irrigations, and four irrigations, with each irrigation comprising 50 mm for a total of 16 irrigation combinations (

Table 3. Simulation test scheme of maize irrigation period under typical precipitation years.

Treatment	Irrigation Quota (mm)	Irrigation Period	Treatment	Irrigation Quota (mm)	Irrigation Period
W1	0	rainfed	W9	100	VJ and VT
W2	50	VE	W10	100	VJ and R2
W3	50	VJ	W11	100	VT and R2
W4	50	VT	W12	150	VE, VJ, and VT
W5	50	R2	W13	150	VE, VJ, and R2
W6	100	VE and VJ	W14	150	VE, VT, and R2
W7	100	VE and VT	W15	150	VJ, VT, and R2
W8	100	VE and R2	W16	200	VE, VJ, VT, and R2

).

2.4.3. Irrigation Amount Simulation

Based on the optimized irrigation period results, irrigation volume simulations were conducted during the identified critical growth stages. Six irrigation levels were tested, corresponding to 10, 20, 30, 40, 50, and 60 mm per application, to systematically quantify yield responses under varying water supply intensities.

2.4.4. Fertilization Rate Simulation

Building upon the optimized irrigation period and volume, this study simulated the effects of varying nitrogen application rates on maize yield across typical hydrological years. The fertilization time and method are consistent with the above, with four nitrogen application rates examined: 120, 180, 240, and 300 kg·ha⁻¹.

2.5. Data Analysis and Model Evaluation

Statistical analyses were performed using SPSS 26 to evaluate treatment effects on target variables through the analysis of variance (ANOVA) at a significance level of α = 0.05, with post hoc comparisons differentiated using Duncan’s multiple range test (denoted by lowercase letters). The Shapiro–Wilk test results showed that the p values of all key variables were greater than 0.05, indicating that there was no significant deviation from the normal distribution. The Levene test results showed that the p values of all comparisons were not significant (p > 0.05), indicating that the differences among the groups were uniform. These findings prove that it is reasonable to conduct data analysis using the analysis of variance. The coefficient of determination (R²), root mean square error (RMSE), normalized mean root mean square error (nRMSE), and mean bias (MB) were used for model evaluation. The formula is as follows:

$$R^2=1-{\displaystyle \frac{{\sum }_{i=1}^n{(S_i-M_i)}^2}{{\sum }_{i=1}^n{(M_i-\overline{M})}^2}}$$

(4)

$$RMSE=\sqrt{{\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{(S_i-M_i)}^2}$$

(5)

$$nRMSE={\displaystyle \frac{RMSE}{\overline{M}}}$$

(6)

$$MB={\displaystyle \frac{1}{n}}{\sum }_{i=1}^n(S_i-M_i)$$

(7)

where $n$ represents the sample size, $S$ denotes simulated values, $M$ indicates measured values, and $\overline{M}$ is the mean of measured values.

3. Results

3.1. Analysis of Maize Growth Characteristics

3.1.1. LAI and Biomass

The leaf area index (LAI), a critical indicator of canopy development and photosynthetic capacity, exhibited unimodal trends across both experimental years (2023–2024), as illustrated in Figure 4. During the VE stage, the LAI increased gradually, followed by accelerated growth from VJ to VT, peaking at VT. Under the B1 treatment, the LAI at the VT stage in 2023 and 2024 was 32.37% and 26% higher than under the A1 treatment, respectively. Similarly, the LAI under the N2 treatment showed a slight increase compared to the N1 treatment. Post-VT, the LAI declined progressively during grain filling (R2) due to nutrient reallocation to reproductive organs. Comparatively lower LAI values under A1 indicated water stress-induced premature leaf senescence and reduced functional leaf duration.

Biomass accumulation, a key determinant of yield formation, showed consistent growth across phenological stages (Figure 4). Drip irrigation consistently outperformed rainfed conditions, demonstrating enhanced water availability that promoted biomass production. Similarly, maize biomass showed an increasing trend as nitrogen application increased. Initial biomass at VE remained comparable between treatments (158–175 kg·ha⁻¹). From VJ onward, B1N2 exhibited accelerated biomass accumulation, particularly in 2023, achieving a maximum biomass of 22,355 kg·ha⁻¹—the highest recorded across both years. The 2024 B1N2 treatment followed closely, reaching 20,818 kg·ha⁻¹ at physiological maturity.

3.1.2. Maize Yield and Yield Components

Table 4. Maize yield and components under different treatments in 2023–2024.

Year	Treatment	Ear Length (cm)	Ear Diameter (cm)	Kernel Number per Ear (Kernel)	100-Grain Weight (g)	Yield (kg·ha−1)
2023	A1N1	12.36 b	2.93 b	403.5 b	24.73 b	7165 b
	A1N2	13.12 b	3.07 ab	422.54 b	26.75 b	7454 b
	B1N1	14.62 ab	3.26 a	459.31 a	29.47 a	10,783 a
	B1N2	16.03 a	3.43 a	489.83 a	32.34 a	11,049 a
2024	A1N1	12.67 b	3.04 ab	416.38 b	25.92 b	8463 b
	A1N2	13.45 b	3.31 a	427.64 b	27.65 b	8801 b
	B1N1	15.36 a	3.41 a	461.94 a	30.83 a	10,642 a
	B1N2	16.42 a	3.46 a	485.32 a	32.57 a	10,934 a

Note: different lowercase letters indicate significant differences among treatments at the α = 0.05 level.

presents the maize yield and yield components under different treatments during 2023–2024. Over the two-year study, the ear length across all treatments ranged from 12.36 to 16.42 cm. This indicates that irrigation and fertilization have a positive effect on maize ear length. Specifically, the irrigated maize showed an ear length increase of 19.95% compared to the rainfed treatment. Meanwhile, the ear length in the N2 fertilization treatment was 7.21% longer than in the N1 treatment. Furthermore, irrigation enhanced the ear diameter by 11.50% (2023) and 8.35% (2024) relative to rainfed treatments, though no statistically significant differences were observed between treatments. Irrigation significantly increased the kernel number per ear, with 15.93% (2023) and 13.49% (2024) improvements over rainfed counterparts. Over the two years, the N2 treatment showed a 5.68% and 3.88% higher grain number per ear than the N1 treatment. The 100-grain weight across treatments varied from 24.73 to 32.57 g. On average, the irrigated treatment was 19.20% higher than the rainfed treatment, and the N2 treatment was 7.56% higher than the N1 treatment. The yield of the irrigation treatment was significantly higher than that of the rainfed treatment, showing that irrigation can boost maize output. Compared to the N1 treatment, the yield of the N2 treatment also rose markedly, indicating that increasing the fertilizer application rate can likewise enhance maize yield.

3.1.3. Soil Water Balance and Soil Water Variation Analysis

Table 5. Water balance of each treatment in 2023–2024.

Year	Treatment	Irrigation Volume (mm)	Precipitation (mm)	Evapotranspiration (mm)	Water Balance Difference (mm)
2023	A1N1	0	471.53	357.25	114.28
	A1N2			369.51	102.02
	B1N1	120		453.62	137.91
	B1N2			467.39	124.14
2024	A1N1	0	528.17	412.63	115.54
	A1N2			425.24	102.93
	B1N1	90		467.36	150.81
	B1N2			486.49	131.68

shows that in 2023, the B1N1 and B1N2 treatments, which involved irrigation, had higher evapotranspiration (366.24 mm and 381.13 mm) and larger water balance differences (225.29 mm and 210.4 mm) than the A1N1 and A1N2 treatments, which had no irrigation. Consequently, the maize yields of B1N1 and B1N2 were also higher. In 2024, a similar trend was observed, with B1N1 and B1N2 still outperforming A1N1 and A1N2 in evapotranspiration and maize yield. Figure 5 indicates that in the non-irrigated A1N1 and A1N2 treatments, the water potential declined due to crop growth and increased evapotranspiration. In contrast, irrigation in B1N1 and B1N2 mitigated soil water depletion, maintaining higher water potential. Overall, the irrigated B1N1 and B1N2 treatments surpassed the non-irrigated A1N1 and A1N2 treatments in soil water potential, moisture, and maize yield, proving that irrigation is an effective way to boost crop output and enhance soil moisture conditions.

3.2. Model Parameter Calibration and Validation

This study employed a hybrid approach that combines the DSSAT-GLUE (generalized likelihood uncertainty estimation) module and trial-and-error methods for parameter estimation. The 2023 experimental data were used for model calibration, while the 2024 dataset was used for validation. Error metrics (R², nRMSE, RMSE, and MB) for the phenological stages, maximum leaf area index (LAIX), aboveground biomass, and yield during 2023–2024 are summarized in

Table 6. Error metrics for the calibration and validation of maize phenological stages, LAIX, biomass, and yield during 2023–2024.

Item		2023 (Calibration)				R2	RMSE	nRMSE	MB	2024 (Verification)				R2	RMSE	nRMSE	MB
		A1N1	A1N2	B1N1	B1N2					A1N1	A1N2	B1N1	B1N2
Anthesis (DAP)	Measured data	52	52	53	53	1	1	1.9	1	54	54	53	53	1	1	1.87	0.75
	Simulated data	53	53	54	54					55	55	54	54
Maturity (DAP)	Measured data	103	103	104	104	1	2	1.93	2	102	103	102	102	1	1	0.97	1
	Simulated data	105	105	106	106					104	104	105	105
Maximum leaf area index	Measured data	3.22	3.27	105	4.28	0.98	0.22	5.91	0.18	3.43	3.62	4.73	4.51	0.96	0.11	2.82	1.11
	Simulated data	3.27	3.34	4.31	4.56					3.51	3.53	4.57	4.62
Biomass at Flowering (kg/ha)	Measured data	5284	5963	4.52	7839	0.94	685.49	10.54	630.25	6024	6388	6347	7748	0.98	653.79	9.87	648
	Simulated data	6048	6957	6931	8312					6574	7039	7132	8354
Biomass at Maturity (kg/ha)	Measured data	15,375	16,045	7221	22,355	0.97	1851.1	9.98	1793.5	17,364	18,337	20,127	20,848	0.98	1655.33	8.64	1408.25
	Simulated data	16,783	18,621	20,446	23,953					17,856	18,934	22,537	22,952
Yield (kg/ha)	Measured data	7165	7454	22,038	11,049	0.96	866.38	9.51	712.25	8463	8801	10,642	10,934	0.98	803.86	8.28	791.75
	Simulated data	7634	8016	10,783	12,596					9457	9531	11,257	11,762

. The calibration results demonstrated a strong model performance, with nRMSE values in 2023 of 3.07% (anthesis), 2.33% (maturity), 5.91% (LAIX), 10.54% (biomass at flowering), 9.98% (biomass at maturity), and 9.51% (yield). Only the nRMSE value for silking-stage biomass was slightly above 10%. The R² values in 2023 were all above 0.94, indicating a satisfactory calibration. The 2024 validation further confirmed the model’s robustness, with reduced errors: nRMSE values were 2.14% (anthesis), 1.76% (maturity), 2.82% (LAIX), 9.87% (biomass at flowering), 8.64% (biomass at maturity), and 8.28% (yield), and R² values were all above 0.96, demonstrating excellent model performance.

Figure 6 and Figure 7 illustrate the comparative analyses of soil moisture content (R², nRMSE, RMSE, and MB) across soil layers for 2023 and 2024. During calibration (2023), R² ranged from 0.70 to 0.89, RMSE from 0.022 to 0.048, nRMSE from 8.33% to 16.63%, and MB from −0.020 to 0.011. Validation (2024) exhibited improved precision, with R² = 0.75–0.94, RMSE = 0.015–0.035, nRMSE = 5.44–11.54%, and MB = −0.016–0.014, demonstrating excellent simulation consistency.

Table 7. Optimization results of genetic parameters of maize varieties.

Parameter	P1	P2	P5	G2	G3	PHINT
Range	100–400	0–4	600–1000	500–1000	5–12	30–75
Optimal value	224	0.78	813	857	8.57	45

shows the optimization results of the genetic parameters of the maize varieties.

3.3. Scenario Simulation Analysis

3.3.1. Simulation Analysis of the Irrigation Period Under Typical Hydrological Years

As illustrated in Figure 8, under wet hydrological years (2006), the rainfed treatment (W1) achieved a grain yield of 12,056.31 kg·ha⁻¹. However, the maximum yield across all irrigation treatments reached only 12,284.63 kg·ha⁻¹, showing no significant improvement over W1. Notably, the WUE of the rainfed treatment (2.03 kg·m⁻³) consistently surpassed that of irrigated regimes. These results indicate that supplemental irrigation is unnecessary in wet years, with the rainfed treatment (W1) being optimal. In normal hydrological years (2017), WUE peaked at 2.92 kg·m⁻³ under the W4 treatment (single irrigation at the VT stage). Further increases in irrigation frequency did not significantly enhance the yield, demonstrating that a single irrigation event during the tasseling (VT) stage represents the optimal strategy for normal years. Under dry hydrological years (1995), both irrigation frequency and volume markedly improved the yield. The rainfed treatment yielded only 6525 kg·ha⁻¹. Among single-irrigation scenarios, the VT-stage application (W4) maximized the yield (9980.61 kg·ha⁻¹). Two irrigation events at the VT and R2 stages (W11) further increased the yield to 11,205.31 kg·ha⁻¹. The highest yield (11,972.41 kg·ha⁻¹) and WUE (1.81 kg·m⁻³) were achieved with three irrigation events at the VE, VT, and R2 stages (W14). Thus, three irrigations during the VE, VT, and R2 stages constitute the optimal strategy for dry hydrological years.

3.3.2. Simulation Analysis of Irrigation Volume Under Typical Hydrological Years

As mentioned above, the optimal irrigation mode in wet hydrological years is rainfed, so only the irrigation volume simulation analysis based on normal and wet years in the best irrigation period is carried out here. For normal hydrological years (Figure 9), the grain yield increased progressively with irrigation volume, peaking at 12,114.35 kg·ha⁻¹ under a 30 mm irrigation quota. Beyond this threshold, the yield plateaued despite additional water inputs, while WUE concurrently reached its maximum (2.90 kg·m⁻³). These results confirm 30 mm as the optimal irrigation quota for normal years, balancing yield maximization and water conservation. Under dry hydrological years, the maximum yield (11,969.60 kg·ha⁻¹) occurred at a 120 mm irrigation quota. However, the highest WUE (1.85 kg·m⁻³) and near-maximum yield (11,824.34 kg·ha⁻¹) were achieved at a 90 mm quota. This divergence reflects diminishing water productivity returns beyond 90 mm, where incremental yield gains fail to offset escalating water inputs. Thus, 90 mm is recommended as the optimal irrigation quota for dry years, prioritizing both yield stability and resource efficiency.

3.3.3. Simulation Analysis of Nitrogen Application Rates Under Typical Hydrological Years

Figure 10 shows that in nitrogen application treatments, PFPN decreases as the nitrogen application rate increases. In wet hydrological years, the maximum yield of 11,213.25 kg·ha⁻¹ was achieved at a nitrogen application rate of 240 kg·ha⁻¹, with the highest WUE of 1.89 kg·m⁻³ and a PFPN of 46.72 kg·kg⁻¹. In normal hydrological years, the yield plateaus at a nitrogen application rate of 240 kg·ha⁻¹, with a PFPN of 45.48 kg·kg⁻¹ and a WUE of 2.61 kg·m⁻³. In dry hydrological years, the maximum yield of 10,193.54 kg·ha⁻¹ occurred at a nitrogen application rate of 240 kg·ha⁻¹. However, at 180 kg·ha⁻¹, the WUE peaked at 1.61 kg·m⁻³, with a yield of 10,110.42 kg·ha⁻¹.

As outlined above, the optimal irrigation timing, volume, and nitrogen application rates for maize in different typical hydrological years are shown in

Table 8. Optimal irrigation and fertilization schemes for different typical hydrological years.

Typical Hydrological Years	Irrigation Period	Irrigation Quota (mm)	Nitrogen Application Rate (kg·ha−1)
Wet hydrological years	Rainfed	0	240
Normal hydrological years	VT	30	240
Dry hydrological years	VE, VT, R2	90	180

.

4. Discussion

4.1. Maize Growth

The experimental results demonstrate that drip irrigation (B1) outperformed rainfed conditions (A1) in enhancing the LAI, plant height, and grain yield. This is mainly attributed to the stable water supply reducing the impact of drought stress on crop growth [26,27,28]. As a critical indicator of canopy development, the LAI directly governs light interception capacity [29]. The study by Wang et al. [30] confirms that elevated LAI enhances solar radiation utilization, thereby promoting biomass accumulation and yield. In this study, drip irrigation achieved peak LAI values at the tasseling (VT) stage, indicating optimal water availability for leaf expansion and photosynthetic efficiency [26,31]. Controlled irrigation through drip systems has been shown to significantly improve the LAI, yield, and WUE [32,33], as corroborated by our results.

Drip-irrigated maize exhibited greater plant height across all growth stages, particularly during VT and grain filling (R2). This indicates that consistent water supply promotes stalk elongation [27], a structural prerequisite for high yields. Notably, drip irrigation increased the grain yield by 32.54% (2023) and 24.24% (2024) compared to rainfed conditions, primarily attributed to improved water availability that mitigates drought-induced yield losses [34,35,36]. Enhanced yield components under drip irrigation, including ear length, diameter, kernel number, and 100-grain weight, further validate its efficacy in optimizing productivity [37]. In this study, the yield of drip-irrigated maize was significantly higher than that of rainfed maize. This indicates that maize growth responds significantly to annual precipitation changes [38]. In 2024, with higher rainfall, the yield gap between rainfed and drip-irrigated treatments narrowed. But in the drier 2023, drip irrigation showed a more pronounced advantage. This shows that irrigation strategies should be adjusted according to annual precipitation changes to maximize yield [39].

4.2. Model Calibration

Calibrating and validating model parameters are essential for reliable simulation results [40]. The DSSAT model requires calibration and validation before use in different regions and under different management conditions [41]. DOKOOHAKI et al. [42] calibrated and validated the model using experimental data from various irrigation and nitrogen levels in Iran. In the Mediterranean region, Di et al. [43] calibrated and validated the DSSAT model for different nitrogen supply conditions. In this study, we used 2023 data for calibration and 2024 data for validation. The results show that the model performed well in simulating maize’s phenological stages, biomass, and yield during both calibration and validation. The nRMSE for the silking and maturity stages was below 10%, indicating the accurate simulation of the maize growth period, which was consistent with the previous research results [44,45,46]. The soil water content simulation was also precise, with RMSE and nRMSE within acceptable ranges, reflecting good soil moisture dynamics and providing a reliable basis for optimizing irrigation and fertilization schemes.

4.3. Model Application

Crops respond differently to water stress at various growth stages [47]. Regarding irrigation timing, Jin et al. [48] found that it significantly impacts crop yield across years. Zhu et al. [49] identified the seedling, tasseling, and silking stages as critical water-need periods for maize, aligning with the optimized irrigation timing in this study. Different irrigation frequencies and volumes affect maize growth and yield [50]. Our results show that no irrigation is needed in wet years, one irrigation at VT stage (30 mm) in normal years, and three irrigations (90 mm total) at the VE, VT, and R2 stages in dry years, consistent with Jiang and Liu [51,52]. Xu et al. [53] found that maize in North China achieves high productivity and nitrogen efficiency with nitrogen application rates of 217–252 kg·ha⁻¹, matching our findings. In wet and normal years, the optimal nitrogen rate is 240 kg·ha⁻¹. In dry years, while 240 kg·ha⁻¹ maximizes the yield (10,110.42 kg·ha⁻¹), 180 kg·ha⁻¹ achieves higher WUE. In dry years, soil moisture is low, reducing nitrogen leaching losses [54]. Reducing nitrogen application from 240 to 180 kg·ha⁻¹ cuts soil nitrate accumulation and leaching [55]. This also lowers the nitrogen content in farm drainage, reducing pollution risks to nearby waters [56]. Thus, optimizing nitrogen use mitigates agricultural non-point-source pollution and supports sustainable ecological development. In practice, maize farmers can utilize historical climate data analysis, real-time weather monitoring, and soil moisture measurements to classify the upcoming growing season. By establishing hydrological year categories based on long-term precipitation records and leveraging seasonal forecasts and soil moisture sensors, farmers can make informed decisions on irrigation scheduling tailored to the predicted hydrological conditions.

4.4. Limitations and Future Research Directions

The DSSAT model assumes soil uniformity within a specific depth range, yet real-world fields show significant soil property variations that affect water infiltration, retention, and evaporation [57,58]. This can cause simulation inaccuracies. Extreme weather events, like severe droughts or heavy rains, often fall outside the model’s calibration range, leading to deviations between simulation results and actual observations [59].

Future research should integrate high-resolution soil data and use layered modeling or soil heterogeneity parameters to enhance the model’s ability to simulate complex field conditions. Introducing dynamic stress response mechanisms and calibration data for extreme weather events can boost the model’s adaptability and predictive power under extreme conditions. Additionally, this study’s validation was confined to central Henan. Future research should combine long-term field experiments and multi-site validation to deeply explore the long-term impacts of different management practices on maize production and promote precision agriculture technology.

5. Conclusions

By conducting field experiments and simulating crop growth under different precipitation years with a crop model, this study explored an optimized maize irrigation and fertilization scheme and confirmed its feasibility in central Henan Province. The key findings are as follows:

(1)

We defined six genetic parameters for Zhengdan 958 maize (Beijing Denong Seed Industry Co., Ltd., Beijing, China) in the DSSAT model (P1: 224, P2: 0.78, P5: 813, G2: 857, G3: 8.57, and PHINT: 45). After calibrating with 2023 data and validating with 2024 data, we obtained a highly accurate DSSAT model.

(2)

Based on this, we simulated optimal irrigation and fertilization strategies for different precipitation years using 30-year weather data. In wet years, no irrigation is needed, with a fertilizer rate of 240 kg·ha⁻¹. In normal years, irrigate once at the VT stage (30 mm), with a nitrogen application rate of 240 kg·ha⁻¹. In dry years, irrigate three times at the VE, VT, and R2 stages (total 90 mm), with a nitrogen application rate of 180 kg·ha⁻¹.

The optimized irrigation and fertilization schemes from this study not only enhance maize yield but also significantly reduce water waste and nutrient loss, thus lowering soil salinization and water pollution risks. These findings offer a scientific basis for promoting sustainable agricultural development in central Henan.