Spatial and Temporal Dynamics of Photosynthetically Active Radiation in Crops: Effects of Canopy Structure on Yield

Abstract

Understanding the spatial–temporal distribution of photosynthetically active radiation (PAR) within crop canopies is crucial for optimizing planting structures to enhance resource use efficiency and improve crop yields. While high planting densities are commonly employed to increase yields, this practice can lead to issues such as early leaf senescence and reduced biomass. This study investigates the impact of varying planting densities on PAR dynamics, canopy structure, and yield formation in maize over two years. Key findings include the following: (1) higher planting density significantly increased grain yield, biological yield, and LAI, although HI decreased; (2) canopy light distribution varied with planting density, with the middle layers intercepting the most light, particularly during the grain filling stage; (3) a density of 83,000 plants·ha⁻¹ was the most efficient for maximizing yield and WUE, although high accumulated temperatures negatively impacted yields. These results suggest that adjusting planting density can enhance resource use efficiency in maize farming, particularly in regions with variable water availability and climate challenges. Future research should explore the long-term effects of planting density on soil health, water use efficiency, and crop resilience under varying environmental conditions. Additionally, studies integrating precision agriculture technologies to fine-tune planting density and water management in response to climate change are essential for ensuring sustainable maize production and food security in the future.

1. Introduction

The radiation distribution within the crop canopy results from a series of complex processes, including direct solar radiation and sky-scattered radiation, which interact through reflections, absorption, and projections by plant organs and the ground within the canopy [1]. Monsi’s introduction of the extinction coefficient, based on Beer’s law, laid the foundation for modeling light distribution in crop canopies, a concept that has since been refined by Begue and Hiro [2]. The distribution of light within the canopy varies according to crop population structures, influencing critical physiological processes such as leaf photosynthesis and transpiration, which in turn affect crop energy balance [3,4]. Previous studies emphasize the importance of canopy radiative transfer as the basis for understanding canopy light distribution and bidirectional canopy reflectance [5,6]. Some researchers have proposed dividing the crop canopy into multiple vertical layers to simulate light distribution, assuming that leaves within each layer are randomly distributed horizontally [7]. While this approach accounts for differences across canopy strata, it fails to consider the specific distribution and structural composition of each layer in the crop population [8,9].

Canopy structure not only dictates the light distribution within the canopy but also shapes the microenvironment, which influences plant growth [10,11]. Studies have shown that both light and CO₂ concentration significantly affect the growth and development of maize, with increased CO₂ levels promoting photosynthesis and biomass accumulation [12,13]. However, elevated CO₂ also reduces leaf stomatal conductance [14,15], impacting transpiration and water use efficiency, which subsequently alters the energy balance within the crop canopy [16,17,18]. Therefore, investigating canopy structure changes is crucial for enhancing water use efficiency and optimizing crop yield at the leaf level.

The influence of canopy structure on crop growth manifests in several ways: (1) Different canopy structures exhibit distinct leaf azimuthal distributions, which govern the spatial distribution of solar radiation within the canopy, thus affecting crop growth [19,20,21]; (2) Canopy structure influences microclimate variables such as temperature, humidity, and CO₂ concentration, which in turn affect physiological processes and crop yield [22]. Increased CO₂ promotes photosynthesis and biomass growth, but also reduces stomatal conductance [23,24]; (3) Canopy structure modulates crop transpiration and soil evaporation, leading to changes in field evapotranspiration fluxes and water-heat transport [25,26,27].

Maize (Zea mays L.) is one of the most important staple crops worldwide, serving as a key raw material for food, animal feed, and bioenergy production. In China, maize is a vital crop that contributes significantly to the agricultural economy, especially in regions like Daxing, Beijing, where it plays an integral role in both crop rotation systems and economic development. The cultivation of maize in this region is not only crucial for food security but also for supporting the feed industry and biofuel production. Additionally, maize farming contributes to the local economy through the creation of jobs and the generation of economic value. However, the environmental implications of maize cultivation are significant, especially concerning water usage, soil fertility, and the sustainability of agricultural practices. As climate variability increases, understanding how to optimize maize productivity while minimizing environmental impacts has become essential for ensuring long-term agricultural sustainability in the region.

High-density planting is one of the most commonly employed strategies to increase yield per unit area. However, this approach is not without its challenges. Excessive planting density often leads to issues such as maize lodging and delayed maturation, which are yet to be fully addressed. In addition to high-density planting, intensive fertilization is another widely used method for increasing yields. While it is effective in enhancing productivity, it can also lead to significant environmental concerns, such as soil degradation and nutrient leaching [28,29]. Moreover, while high-density planting is a key factor, other agronomic practices, such as inappropriate irrigation and the use of suboptimal cultivars, further exacerbate these problems, particularly under harsh environmental conditions [30,31]. A comprehensive approach is required to optimize both plant density and other agronomic practices to mitigate these issues.

The structure and function of the maize plant are deeply interdependent, with environmental changes influencing both physiological and ecological processes, as well as plant morphology. These morphological changes affect the microclimate within the canopy, alter soil water and heat conditions in the root zone, and ultimately impact plant growth, development, and yield. Notably, research has shown that leaves located at different vertical positions within the canopy (e.g., upper, middle, and lower layers) exhibit significant differences in water and carbon dynamics [32,33]. Despite this, many studies still treat the canopy as a homogeneous structure, assuming uniform temperature and humidity throughout, and neglecting how canopy structure influences crop growth. This gap in understanding limits our ability to fully explain the mechanisms through which environmental changes impact crop productivity. Therefore, further investigation into canopy layering and the spatial–temporal variability of light radiation transmission is essential to uncover the mechanisms driving enhanced crop yield.

This study aims to address the knowledge gap regarding the interaction between planting density, canopy structure, and the spatial–temporal distribution of photosynthetically active radiation (PAR) in maize crops. While previous research has primarily focused on the effects of planting density on yield, it has often overlooked the dynamic changes in light interception, water use efficiency, and physiological processes across different canopy layers. By investigating these factors, this study seeks to provide valuable insights into optimizing planting density for enhanced maize productivity under varying environmental conditions.

The primary objective is to examine how different planting densities influence the distribution of PAR within maize canopies and how this, in turn, affects water use efficiency, photosynthesis, and yield. The hypothesis is that optimal planting density will improve light interception and photosynthesis while minimizing resource competition, leading to enhanced crop growth and yield. Specifically, this research will focus on how canopy structure and light distribution evolve across different growth stages and planting densities. It is hypothesized that variations in canopy structure, such as leaf distribution, vertical stratification, and leaf area index (LAI), significantly influence the transmission and absorption of PAR, thereby modulating photosynthetic efficiency and plant growth.

Furthermore, the study will assess how optimizing canopy structure can increase light capture and enhance yield, especially in high-density planting systems where light utilization efficiency is crucial. The study will also investigate how changes in canopy structure at different growth stages influence the spatial and temporal variability of PAR, providing theoretical support for sustainable agricultural practices, particularly in high-density planting systems. The findings will offer recommendations for optimizing canopy structure to improve crop productivity.

2. Materials and Methods

2.1. Experimental Site and Natural Conditions

The experiment was conducted at the experimental station of the National Engineering and Technology Research Centre for Water-Saving Irrigation in Beijing (39°37′ N, 116°25′ E, altitude 31.3 m a.s.l.), which is located in a warm-temperate, semi-humid continental monsoon climate zone. The region experiences hot and rainy summers, and dry and moderately rainy springs and winters, with four distinct seasons. The annual precipitation in the area is highly variable, with a more than threefold difference between the highest and lowest rainfall years. Seasonal rainfall is unevenly distributed, with the majority of precipitation occurring from June to September, and over 80% of the annual rainfall falling during the flood season. The average annual rainfall is approximately 540 mm. The region experiences an average water surface evaporation of 1889.1 mm, an average atmospheric temperature of 11.6 °C, an average annual sunshine duration of 2772.3 h, and an average frost-free period of 209 days. Northeast and southwest winds are predominant in the area.

Figure 1 presents the meteorological data for 2021 and 2022. During the growing season, the average temperatures throughout the crop cycle were similar for both years (24.05 °C in 2021 and 24.21 °C in 2022). However, the annual mean radiation was lower in 2021 (13.48 MJ·m⁻²·d⁻¹) compared to 2022 (15.37 MJ·m⁻²·d⁻¹). The experimental plots experienced waterlogging in early and late July of 2022. The accumulated effective precipitation was 292.0 mm in 2021 and 456.7 mm in 2022. Maize was planted on 18 June and harvested on 29 September 2021, while in 2022, planting occurred on 20 June and harvesting on 30 September. The jointing stage began on 30 July for both years, the tasseling stage started on 14 August, the grain filling stage began on 3 September, and the maturity stage was reached on 27 September.

The site enjoys favorable natural conditions of light and heat, which are conducive to the growth of various crops, including winter wheat, summer maize, peanuts, and soybeans. The predominant cropping system in the region is the continuous rotation of winter wheat and summer maize. In normal years, winter wheat does not receive sufficient rainfall during the growing season, necessitating supplementary irrigation to ensure normal crop growth. In contrast, summer maize typically does not require irrigation, as the rainfall during its growing season is usually adequate.

2.2. Experimental Design and Arrangement

The experiment was conducted from June 2021 to October 2022. Four planting densities were set up to represent different canopy structures, with plant row spacing fixed at 0.6 m between rows. Plant densities were controlled by adjusting the plant spacing, which resulted in the following configurations: 0.50 m × 0.60 m (D1), 0.40 m × 0.60 m (D2), 0.30 m × 0.60 m (D3), and 0.20 m × 0.60 m (D4), corresponding to planting densities of 33,333, 41,666, 55,555, and 83,333 plants·ha⁻¹, respectively. Each treatment was replicated three times, following a randomized block design. A total of 12 experimental plots were established, with each plot covering an area of approximately 56 m².

Fertilizer dosages were determined based on soil nutrient analysis and local agronomic recommendations. Prior to the experiment, soil samples were collected and analyzed for available nitrogen (N), phosphorus (P), and potassium (K) content. Based on these results and local practices for maize cultivation, we applied a compound fertilizer with an N: P₂O₅: K₂O ratio of 15:15:15 at a rate of 135 kg/hm² for each nutrient. Throughout the growing season, the maize was fertilized twice: effective phosphorus (P₂O₅) and effective potassium (K₂O) were each applied at a rate of 135 kg/hm². The compound fertilizer, accounting for 45% of the total nutrient input, was applied as a basal fertilizer, while urea, containing 46% nitrogen, was applied as a topdressing fertilizer during the jointing stage. The maize variety used was Zea mays L. variety “Jiyuan 168” (abbreviated as JY168). This variety was developed by the Jiyuan Agricultural Research Institute, and it is known for its high yield and disease resistance. The field trial in 2021 was planted on 18 June and harvested on 29 September, while in 2022, planting occurred on 20 June and harvesting on 30 September. Other experimental parameters and field management practices remained consistent across both years and adhered to local farming practices.

Irrigation management: Throughout the study period, no supplementary irrigation was applied beyond the initial watering necessary for seedling emergence. This approach aligned with the region’s semi-humid climate, where seasonal rainfall typically suffices to meet maize water requirements. By relying solely on natural precipitation, we aimed to assess maize performance under typical rainfed conditions without the influence of additional irrigation.

2.3. Measurement Items and Methods

2.3.1. Leaf Area Index (LAI), Dry Matter Accumulation, and Yield of Maize

From the maize emergence stage, five plants were randomly selected from each plot on a weekly basis (2021 and 2022) to estimate above-ground biomass and leaf area index (LAI). For each treatment, 3–5 representative plants were randomly chosen, and measurements of plant height, leaf length, and leaf width of the green leaf parts were taken. These measurements were performed every 7 days throughout the entire growing period. Simultaneously, the AM300 leaf area scanner was used to measure the actual leaf area. The proportional coefficient between the measured leaf area and the scanned leaf area was 0.74.

$$LAI=0.74\times {\displaystyle \frac{{\displaystyle \sum _{i=1}^n{L}_i\times W_{iMAX}}}{D\times S}},$$

(1)

where L_i is the length of the leaf, W_iMAX is the maximum width of the leaf, D is the distance (in meters) between two rows, and S is the spacing between two plants.

Field sampling and analysis were carried out after maize emergence. Three representative plants from each treatment were randomly selected to assess biomass. The stems, leaves, leaf sheaths, and ears were separated and placed into kraft paper bags. The samples were then heated at 105 °C for 30 min to deactivate enzymatic activity, followed by drying at 85 °C until they reached a constant weight. After cooling to room temperature in a drying oven, the samples were weighed using an electronic balance.

At harvest, the maize yield for each treatment was determined by continuously sampling 10 maize plants. The actual area was measured, and the yield was estimated. The total number of plants, number of ears, instances of double ears, empty stalks, and lodged plants within the sampled area were recorded. After harvesting, the ears were air-dried and shelled. Key maize characteristics were measured, including ear diameter, ear axis diameter, ear length, barren tip length, kernel count per ear, 100-kernel weight, and moisture content. Finally, the grain yield was calculated.

2.3.2. Photosynthetically Active Radiation (PAR)

During the peak growth period of maize, photosynthetically active radiation (PAR) within the maize canopy was measured using a photo-quantum radiometer (RR-9100Q system) (Figure 2). The RR-9100Q system is based on the RR-1048 data collector (Rainroot Scientific Limited, Co., Ltd., Beijing, China) as its core, and consists of one AV-19Q total solar radiation sensor, and four AV-19LQ (Rainroot Scientific Limited, Co., Ltd., Beijing, China) linear PAR sensors, with ten PAR sensors evenly distributed within each set. The system is powered by 12AH rechargeable batteries or solar panels, allowing for both fixed-point field observations and handheld dynamic monitoring simultaneously.

The three-dimensional spatial scope of data collection was as follows: measurements were taken 0.8 m along the rows (north–south direction) and 0.6 m between the rows (east–west direction). The vertical measurement range extended from the ground to the top of the canopy, divided into 15 observation heights: ground, 0.10 m, 0.20 m, 0.40 m, 0.60 m, 0.80 m, 1.00 m, 1.20 m, 1.40 m, 1.60 m, 1.80 m, 2.00 m, 2.20 m, 2.40 m, and the top of the canopy (which can be adjusted depending on the planting variety). Three repetitions were performed at each height, with data collected from bottom to top for a complete observation cycle. Simultaneously, measurements of air scattering, leaf reflectance, and transmittance were conducted. Each determination took approximately 30 min, with an interval of 1 to 2 h between measurements at different times.

The PAR measurement heights were determined based on a stratified approach to capture light distribution at different levels within the maize canopy. The canopy was divided into three key regions: the upper layer (n), corresponding to the topmost leaves that receive the most direct sunlight; the middle layer (n − 1), which represents the area between the upper and lower canopy layers, where light availability is partially reduced due to shading; and the lower layer (n − 2), representing the section closest to the ground, where light is heavily filtered. These heights were chosen to represent critical regions of light interception, as light availability varies significantly across the canopy layers and impacts plant growth and photosynthetic efficiency.

In this study, photosynthetically active radiation (PAR) measurements were taken from 8:00 AM to 4:00 PM, with readings recorded every 2 h. This measurement schedule was selected to capture the variation in light conditions throughout the daylight hours, providing a comprehensive overview of how light distribution changes over the course of the day. The intervals were designed to ensure that we could monitor the full spectrum of light exposure, from the early morning to late afternoon, which is crucial for assessing how planting density influences light interception and the subsequent effects on plant photosynthesis.

Regarding the growing season in Beijing, the sunrise and sunset times typically occur between 5:30 AM and 7:00 PM during the maize growing period. The selected measurement window of 8:00 AM to 4:00 PM aligns with the period during which the maize canopy is exposed to maximal sunlight, allowing for an accurate representation of light interception during peak solar intensity. This time frame ensures that the data captured reflect consistent and reliable light exposure, which is essential for analyzing the effects of planting density on light utilization and overall maize productivity.

2.3.3. Canopy PAR Transmittance

Photosynthetically active radiation interception (IPAR) was measured using a 1 m RR-9100-Q detector system between 08:00 and 16:00 at each growth stage. Three measurements were taken for each replication. The first measurement was recorded above the canopy to determine the incident PAR, with the probe moved parallel to the crop row direction at 0.20 m intervals to cover the canopy level distance [30]. Additional measurements were taken above the soil surface, with the sensor placed beneath the canopy. PAR was measured vertically upward from the soil surface at 0.20 m intervals to determine the transmitted PAR. The maize canopy was divided into three layers: the lower layer (below the lower leaves of the ear), the middle layer (the ear leaves and their upper and lower leaves), and the upper layer (above the upper leaves of the ear). The canopy total PAR transmittance (T_PAR) was calculated as follows:

$$T_{PAR}={\displaystyle \frac{PA{R}_i}{PA{R}_I}},$$

(2)

where, PAR_I is the incident PAR at the top of the canopy (μmol/m²·s), and PAR_i is the incident PAR at the height of the i-th layer within the canopy.

2.3.4. PAR Interception Rate

Photosynthetically active radiation interception (IPAR) was measured using a 1 m long detector (RR-9100-Q system) between 08:00 and 16:00 h at 5-day intervals after maize nodulation. Three measurements were taken for each treatment. The first measurement was conducted above the canopy to determine the incident PAR, with the probe moved parallel to the crop row direction at 0.2 m intervals, following the canopy-level distance rule. Additional measurements were taken at the soil surface level, with the sensors placed below the canopy. PAR was measured at 0.2 m intervals from the soil surface upwards to determine the transmitted PAR. The canopy fraction of absorbed PAR (F_PAR) was calculated as follows:

$$F_{PAR}={\displaystyle \frac{PA{R}_n-PA{R}_{n-1}}{PA{R}_I}},$$

(3)

where PAR_n and PAR_n-1 are the mean values (μmol/m²·d) of measurements taken above and below the canopy, respectively. Here, n represents the top, 2/3, and 1/3 of the canopy, while n − 1 represents the 2/3 and 1/3 positions from the bottom of the canopy (ground level).

2.3.5. Radiation Use Efficiency (RUE) and the Harvest Index (HI) for Maize

In each treatment, three random quadrats covering a 10.0 m² area were selected to determine yield and yield components (kernel number per square meter and 100-kernel weight). Grain and biomass yield were determined at 13% moisture content. Dry biomass was measured separately at the jointing stage (JS), tasseling stage (TS), grain filling stage (GS), and maturity stage (MT), and the corresponding cumulative absorption intercepted PAR (IPAR) was computed to estimate radiation use efficiency (RUE, g MJ⁻¹), as proposed by [34]:

$$RUE={\displaystyle \frac{B}{IPAR}},$$

(4)

where B is the cumulative biomass at different stages, and IPAR is the corresponding cumulative intercepted PAR.

Water use efficiency (WUE) was calculated as follows:

$$WUE={\displaystyle \frac{Grainyield(kg/ha)}{Totalprecipitation(mm)}},$$

(5)

This method follows the common agronomic definition used in studies such as [12].

The harvest index for maize (HI) was calculated using the following equations:

$$HI={\displaystyle \frac{Grainyield}{Biomassyield}}\times 100\%,$$

(6)

where biological yield is the sum of grain and aboveground vegetative biomass at maturity. Biological yield refers to the total aboveground biomass of maize at maturity, including both grain and vegetative parts. It was obtained through systematic sampling, drying (as described in Section 2.3.1), and the summation of all biomass components per unit area.

Effective accumulated temperature (EAT) was calculated using the formula:

$$EAT={\displaystyle \sum (T_{avg}-T_{base})}$$

(7)

where T_avg is the average daily temperature, and T_base is the base temperature for maize growth, set at 10 °C. Only days with T_avg > T_base were included, as per the standard approach in maize phenology modeling.

2.4. Derived Parameters Used in Correlation Analysis

In this study, several derived parameters were calculated to examine the relationships between canopy structure, light interception, and maize yield components under different planting densities. These parameters were used in the correlation analysis presented.

Grain yield (GY): The dry weight of grain produced per unit area.

Biological yield (BY): The total aboveground biomass of the maize plant at physiological maturity, including both grain and vegetative parts.

100-Grain weight (100-GW): The weight of 100 fully developed maize grains, often used as an indicator of grain size and quality.

Dry matter accumulation (D): The total dry weight of the plant material, excluding moisture content, which reflects the plant’s overall growth.

Harvest index (HI): The ratio of grain yield to biological yield, indicating the efficiency of biomass allocation toward grain production.

Water use efficiency (WUE): A measure of the crop’s productivity relative to the amount of water available or used during the growing season.

Effective accumulated temperature (EAT): The accumulated thermal time during the growing period, calculated as the sum of daily mean temperatures above a defined base threshold, which influences plant growth and development.

Leaf area index (LAI): A measure of the leaf area per unit of ground area, used to evaluate canopy structure and light interception efficiency. This study distinguishes LAI in three canopy layers: upper layer LAI (ULAI), middle layer LAI (MLAI), and lower layer LAI (LLAI).

Photosynthetically active radiation (PAR) parameters: The total and fractional amounts of PAR intercepted by the canopy in different layers.

Total photosynthetically active radiation (T_PAR): UTPAR, MTPAR, and LTPAR represent the total PAR intercepted in the upper, middle, and lower layers of the canopy, respectively.

Fraction of photosynthetically active radiation (F_PAR): UFPAR, MFPAR, and LFPAR represent the fraction of PAR intercepted in the upper, middle, and lower layers of the canopy, respectively.

These derived parameters were used in the correlation analysis to investigate the relationships between various factors affecting maize growth and yield.

2.5. Statistical Analysis Methods

The statistical analysis for this study was conducted using the Origin software (Version 2021, Origin Lab, Northampton, MA, USA). Pearson correlation coefficients were calculated to evaluate the relationships between the different measured variables, including leaf area index (LAI), total photosynthetically active radiation (T_PAR), fraction of photosynthetically active radiation (F_PAR), grain yield (GY), biological yield (BY), dry matter (D), 100-grain weight (100-GW), harvest index (HI), and water use efficiency (WUE). The significance of these correlations was determined using a two-tailed t-test, with significance levels set at p < 0.05.

All statistical analyses were performed on combined data from both growing seasons across all planting density treatments. The results were visualized using correlation matrices, where values were represented numerically and also displayed with color gradients to indicate the strength and direction of the relationships. These visual representations were generated using Surfer 12 software (Golden Software, Golden, CO, USA), which was employed to create high-quality contour plots that illustrate the spatial distribution of the data.

The statistical methods and software used in this study were selected to provide a comprehensive understanding of the interactions between key agronomic variables and their potential impact on maize growth and yield. Figure 3, in particular, was prepared using Surfer 12 software for detailed graphical output, offering a clear depiction of the data trends.

3. Results

3.1. Characteristics of PAR Transmittance Distribution at Different Spatial and Temporal Scales

3.1.1. Spatial Scale Characteristics of PAR Transmittance Distribution

Through an interpolation analysis of PAR at different horizontal spatial positions within various canopy heights, the horizontal distribution of PAR transmittance was obtained, as shown in Figure 3. The vertical and horizontal distribution of canopy transmittance of photosynthetically active radiation (T_PAR) at different canopy heights under four planting densities during the filling period is illustrated. The distribution, transition, and evolution of light spots and shadows within the canopy are evident in the changes of peaks and valleys in the horizontal distribution graphs of PAR transmittance at various heights.

As shown in

Table 1. Regression coefficients of PAR transmittance in relation to cumulative LAI (T_r = a·e^−b·LAI) at different time points within the maize canopy.

Year	Time	Treatment	a	b	R2
2021	8	D1	80.84	0.466	0.9805
		D2	65.27	0.579	0.9465
		D3	64.05	0.458	0.9461
		D4	78.44	0.577	0.9727
	10	D1	104.13	0.415	0.9717
		D2	84.45	0.734	0.9330
		D3	73.81	0.497	0.9591
		D4	84.87	0.376	0.9662
	12	D1	110.94	0.375	0.9479
		D2	80.69	0.580	0.9515
		D3	70.47	0.402	0.9767
		D4	81.01	0.475	0.9568
	14	D1	83.13	0.384	0.9583
		D2	65.18	0.508	0.9586
		D3	65.29	0.414	0.9498
		D4	77.52	0.548	0.9849
	16	D1	80.12	0.305	0.9600
		D2	72.92	0.540	0.8647
		D3	41.19	0.361	0.6921
		D4	58.69	0.502	0.9318
2022	8	D1	93.88	0.818	0.8231
		D2	52.25	0.562	0.9679
		D3	64.05	0.438	0.9461
		D4	76.68	0.425	0.9662
	10	D1	120.52	0.726	0.9665
		D2	107.73	0.523	0.9635
		D3	73.82	0.475	0.9591
		D4	78.38	0.353	0.9739
	12	D1	114.86	0.441	0.9317
		D2	109.80	0.500	0.9585
		D3	70.57	0.384	0.9767
		D4	73.72	0.448	0.9646
	14	D1	104.22	0.664	0.9317
		D2	90.25	0.513	0.9585
		D3	65.27	0.396	0.9767
		D4	68.75	0.513	0.9646
	16	D1	90.54	0.659	0.9578
		D2	69.68	0.575	0.9787
		D3	40.16	0.344	0.6932
		D4	52.65	0.470	0.9237

Note: Regression coefficients of PAR transmittance in relation to cumulative LAI (T_r = a·e^−b·LAI) at different time points within the maize canopy. In the equation, ‘a’ represents the initial PAR transmittance coefficient, and ‘b’ is the attenuation coefficient, which indicates the rate at which PAR transmittance decreases with increasing leaf area index (LAI).

the F_PAR increased with planting density at all stages, with D4 having the highest interception rates during the grain-filling and maturity stages, highlighting the critical role of planting density in optimizing light interception. At midday, the photosynthetically active radiation (PAR) at the top of the canopy was 1941 μmol/(m²·s), while the PAR at the bottom of the canopy in the D4 treatment was only 92.12 μmol/(m²·s). Meanwhile, the average PAR at the bottom of the canopy was 324.23 μmol/(m²·s) for the D1 treatment, 183.32 μmol/(m²·s) for the D2 treatment, and 156.65 μmol/(m²·s) for the D3 treatment. The results showed that canopy transmittance decreased with increasing planting density. The leaf area index (LAI) of the canopy had the greatest effect on the light distribution within the population at the grain-filling stage, as the canopy cover of maize reached its maximum.

In Figure 3, we observed a significant increase in PAR transmittance at the upper canopy layers in D4, which directly correlated with higher grain yield and biological yield, further demonstrating the positive effects of higher planting densities. Under different planting conditions, maize leaves were able to adjust their angle to improve light transmittance with increasing planting density. However, this adjustment was not as significant as the effect of planting density on light transmittance. As shown in Figure 3, at a canopy height of 1.8 m, the transmittance (T_r) for the D1 treatment was 72.98%, while T_r for the D2, D3, and D4 treatments were 48.10%, 44.52%, and 36.17%, respectively, representing a decrease of 34.09%, 38.99%, and 50.43% compared to the D1 treatment. At a canopy height of 1 m, T_r decreased by 54.88%, 66.40%, and 71.18% in the D2, D3, and D4 treatments, respectively, compared to the D1 treatment. These results indicate that the decrease in light transmission in the bottom layer of the canopy was more significant than in the middle layer as density increased, suggesting that increased density primarily affected light transmission in the lower part of the canopy. Additionally, light transmittance in the maize canopy varied significantly across the growth stages, particularly during the grain-filling stage. The upper canopy layers of the D4 treatment had a PAR transmittance of 97.57%, which was substantially higher than the other densities (D1: 75.57%) (Figure 3). This indicates the importance of canopy structure in optimizing light distribution, especially in dense planting systems.

3.1.2. Temporal Characteristics of PAR Transmittance Distribution

Studies have shown that light transmittance is closely related to population density. Under high-density conditions, light transmittance can be maintained at a high level within the population due to the variation in plant structure across individual maize plants. This allows the received light energy to be distributed more efficiently across the different layers of the canopy. Figure 4 shows the variation in mean PAR transmittance with leaf area index (LAI) at different times during the maize filling period in 2021 and 2022.

From the figure, it is evident that population transmittance decreased as density increased, which aligns with the observed relationship between LAI and density. The vertical variation of PAR within the canopy followed an exponential pattern, consistent with the vertical distribution of leaf area. As the leaf area index increased, light transmittance declined, reaching a minimum when the LAI of the population peaked during the filling period. This trend shifted as the solar altitude angle increased. At 8:00 AM, the mean value of PAR transmittance in relation to cumulative LAI was significantly higher in the D1 treatment compared to other treatments.

In 2021, the D1 treatment exhibited the highest light transmittance in the mid-canopy, with a similar decreasing trend across all density treatments. The D4 treatment showed the lowest light transmittance, with values under 12% in the mid-canopy. When light levels were sufficient, PAR transmittance varied considerably with the solar incidence angle. In this case, higher PAR values were observed in the upper and middle canopy, while the lower canopy maintained a much lower PAR distribution. At 10:00 AM, in the upper canopy (with cumulative LAI less than 2.0), the D1 treatment had the highest PAR transmittance, with T_r reaching approximately 60%. Conversely, the other treatments had T_r values of 40% or less. As cumulative LAI increased, T_r rapidly decreased from the top to the bottom of the canopy under all density treatments.

At noon (12:00 PM), the PAR transmittance (T_r) was higher in the D1 and D2 treatments compared to the D3 and D4 treatments in 2022. The daily variation in maize canopy transmittance was strongly influenced by the solar incidence angle. Smaller T_r values were observed at 8:00 AM and 4:00 PM when the solar incidence angle was larger, whereas T_r was higher around 12:00 PM as the solar incidence angle increased. This trend was more pronounced in the treatments with lower planting density.

In 2022, at 14:00 and 16:00, T_r for the D4 treatment was higher than that of D1, showing a deviation from the previous year’s data and earlier time points. This observed behavior can be attributed to several factors, including canopy structure, light interception, and environmental conditions. Specifically, the maize plants under the D4 treatment in 2022 exhibited a more open canopy structure compared to D1. D4 had the smallest plant spacing and more vertical leaf angles, which allowed for increased light penetration during the late afternoon when the solar angle was lower. This resulted in higher T_r values in D4 compared to D1.

Additionally, environmental factors such as temperature, humidity, and soil moisture may have influenced the light interception dynamics. In 2022, there were slight differences in soil moisture levels between D4 and D1 during the afternoon, which may have caused a reduction in leaf expansion or slight leaf drooping in D1, thus reducing the canopy’s ability to intercept light. In contrast, the D4 treatment, with its more vertical leaf orientation, was likely less affected by these factors, allowing for better light transmission during these times.

This behavior contrasts with previous years (e.g., 2021) and earlier time points (e.g., 8:00 AM and 12:00 PM), where D1 consistently exhibited higher light transmittance than D4. We attribute this variation to the combined effects of seasonal differences in canopy development, solar angle variation, and specific environmental conditions at these particular times of day.

Maize leaf development and LAI growth played a crucial role in explaining the spatial distribution of T_PAR across different years and spatial locations. During the jointing stage, maize plants exhibited rapid growth, with the LAI across different treatments ranging from 1.84 to 5.45 in both 2021 and 2022. During this period, light distribution within the canopy was unstable, although the canopy maintained a high effective radiation transmission rate. As tasseling began, the increasing leaf layers resulted in a significant rise in LAI, particularly in the D4 treatment, where LAI reached values between 4.32 and 5.45 (2021 and 2022). As the plant population matured, the shading of lower leaves by upper leaves became more pronounced, causing a sharp decrease in photosynthetically active radiation (PAR) transmittance from the top to the bottom of the canopy.

Table 1 presents the regression coefficients of PAR transmittance in relation to cumulative LAI at various stages within the maize canopy, with all fitting coefficients reaching statistical significance. At different time points, the parameter a for the D1 treatment increased with the solar altitude angle, peaking at midday before decreasing and reaching its lowest point in the evening. Similarly, densely planted treatments (D3 and D4) showed the same trend, although the magnitude of variation was less pronounced compared to the D1 treatment.

The parameter b has often been defined in previous studies as the extinction coefficient, which characterizes the decline in light and effective radiation within the canopy. In this study, we observed that the pattern of parameter b varied considerably across treatments with different planting densities at different times. This variation may be attributed to factors such as the solar incidence angle, the spatial distribution of leaves (both horizontal and vertical), and the transmission of radiation components (direct and scattered radiation) within the canopy. These factors collectively contributed to the differential extinction coefficients observed across the canopy.

3.2. Characteristics of PAR Interception Distribution at Different Temporal and Spatial Scales

The effects of planting density on the spatial and temporal distribution of light interception within the maize canopy are summarized in

Table 2. Effects of planting density on the spatial and temporal distribution of light interception efficiency in maize canopies.

Growth Period	Treatments	Light Interception/%				Light Interception/%
		2021				2022
		Bottom Layer of Canopy	Middle Layer of Canopy	Upper Layer of Canopy	Overall	Bottom Layer of Canopy	Middle Layer of Canopy	Upper Layer of Canopy	Overall
Jointing stage	D1	8.50 a	38.55 a	30.29 a	77.34	7.53 a	38.06 a	28.46 a	74.05
	D2	9.19 b	42.45 b	33.90 b	85.54	9.72 b	40.35 b	30.73 b	80.80
	D3	11.72 d	43.34 c	34.94 c	90.00	12.15 c	41.77 c	34.24 c	88.16
	D4	11.37 c	43.76 d	38.43 d	93.56	14.57 d	45.22 d	36.09 d	95.88
Tasseling stage	D1	8.27 a	40.03 a	30.02 a	78.32	9.45 a	41.06 a	27.00 a	77.51
	D2	11.14 b	40.35 b	30.62 b	82.11	10.38 b	44.98 b	28.09 b	83.45
	D3	11.96 c	43.26 c	38.74 c	93.96	12.42 c	46.21 c	29.43 c	88.06
	D4	12.39 d	44.83 d	41.54 d	98.76	14.43 d	47.46 d	35.85 d	97.74
Grain filling stage	D1	10.97 a	41.19 a	32.21 a	84.37	8.37 a	42.30 a	31.85 a	82.52
	D2	11.85 b	44.61 b	32.44 b	88.90	10.38 b	42.90 b	32.21 b	85.49
	D3	12.95 c	45.16 c	34.85 c	92.96	12.76 c	46.16 c	33.04 c	91.96
	D4	13.04 d	47.87 d	36.66 d	97.57	13.75 d	47.46 d	38.03 d	99.24
Maturity stage	D1	11.72 a	42.65 a	21.20 a	75.57	5.69 a	42.77 a	27.21 a	75.67
	D2	12.15 b	42.96 b	22.66 b	77.77	8.35 b	42.90 b	31.63 b	82.88
	D3	14.32 c	47.80 c	23.70 c	85.82	11.38 c	44.73 c	32.14 c	88.25
	D4	14.45 d	49.22 d	29.84 d	93.51	13.15 d	45.98 d	38.03 d	97.16

Note: Different letters (e.g., ^a, ^b, ^c, ^d) indicate significant differences (p < 0.05) between planting density treatments.

. Light interception efficiency within the canopy increased with planting density. At the tasseling stage, the light interception efficiency for the D1, D2, D3, and D4 treatments increased by 10.6%, 16.4%, and 20.9% in 2021, and by 9.1%, 19.1%, and 29.4% in 2022, respectively. Similarly, during the tasseling stage, the distribution of light interception efficiency increased by 4.8%, 19.9%, and 26.1% in 2021, and 7.6%, 13.6%, and 26.1% in 2022 for the D1, D2, D3, and D4 treatments.

Overall, the intra-canopy light interception efficiency increased as the upper canopy leaves fully expanded, whereas it decreased at maturity. The temporal distribution of intra-canopy light interception efficiency followed a general pattern: F_PAR during the grain filling stage > F_PAR during the tasseling stage > F_PAR during the jointing stage > F_PAR at maturity. Additionally, the maize intra-canopy light interception efficiency exhibited distinct distribution patterns over time.

From the spatial distribution of light interception efficiency across different strata within the maize canopy (Table 2), it was observed that F_PAR in the middle layer > F_PAR in the upper layer > F_PAR in the lower layer. The light interception efficiency in the middle and upper strata increased by 275.3% and 175.3% in 2021, and by 301.4% and 194.6% in 2022, respectively, compared to the lower strata. At the tasseling stage, as the upper maize leaves were not yet fully expanded, the leaves were predominantly erect and upward-facing. This orientation allowed more light to leak from the upper layers to the middle and lower layers, resulting in reduced shading of the lower leaves in the population. Following the tasseling stage, light interception efficiency in all treatments increased as the upper leaves fully expanded. However, at maturity, the overall light interception efficiency declined in all treatments as the leaves began to wilt and wither.

Figure 5 illustrates the changes in the cumulative leaf area index (LAI) in the maize canopy under different density treatments, as well as the corresponding trends in canopy height. The fitting results of canopy height and cumulative LAI indicate that both follow a logistic growth pattern. As the canopy height increased, the cumulative LAI exhibited an upward trend. Specifically, when the canopy height was below 1.50 m, the cumulative LAI increased slowly. However, once the canopy height exceeded 1.5 m, the cumulative LAI rose rapidly, demonstrating an exponential growth pattern.

3.3. Yield Components Characteristics

3.3.1. Temporal and Spatial Characteristics of Dry Matter Accumulation

As shown in

Table 3. Effect of planting density on dry matter accumulation of maize at different growth stages.

Years	Growth Period	Treatments	Dry Matter Accumulation/(103 kg·ha−1)
			Bottom Layer of Canopy	Middle Layer of Canopy	Upper Layer of Canopy	Overall
2021	Jointing stage	D1	0.18 a	0.52 a	0.18 a	0.88
		D2	0.23 a	0.70 a	0.19 a	1.12
		D3	0.32 a	0.92 a	0.25 a	1.49
		D4	0.37 a	1.34 a	0.38 a	2.10
	Tasseling stage	D1	0.92 b	2.24 b	1.00 a	4.16
		D2	1.14 b	2.93 ab	1.48 a	5.55
		D3	1.47 ab	3.75 ab	1.80 a	7.02
		D4	2.08 a	5.79 a	2.88 a	10.74
	Grain filling stage	D1	1.11 b	6.58 c	1.28 a	8.98
		D2	1.62 b	8.40b c	1.46 a	11.48
		D3	2.58a	11.18 b	1.77 a	15.53
		D4	2.55 a	14.54 a	3.01 a	20.10
	Maturity stage	D1	1.17 b	10.26 c	4.36 bc	15.80
		D2	1.48 b	17.22 b	3.01 c	21.71
		D3	1.72 b	18.83 b	6.50 b	27.05
		D4	2.77 a	22.32 a	11.98 a	37.07
2022	Jointing stage	D1	0.50 c	0.59a	0.22 a	1.32
		D2	0.72 b	0.70 a	0.19 a	1.61
		D3	0.78 b	0.84 a	0.21 a	1.83
		D4	1.23 a	1.34 a	0.38 a	2.95
	Tasseling stage	D1	0.90 d	2.37 a	1.27 a	4.54
		D2	1.19 c	2.81 a	1.21 a	5.21
		D3	1.35 b	3.75 a	1.80 a	6.90
		D4	2.13 a	5.79 a	2.88a	10.80
	Grain filling stage	D1	0.95 d	6.58 c	1.28 a	8.81
		D2	1.25 c	8.40b c	1.46 a	11.11
		D3	1.56 b	11.18 ab	1.77 a	14.51
		D4	2.34 a	14.54 a	3.01a	19.89
	Maturity stage	D1	0.93 d	11.31 c	6.28 b	18.52
		D2	1.25 c	16.65 b	3.10 b	21.01
		D3	1.53 b	18.35 b	4.48b c	24.37
		D4	2.25 a	22.32 a	11.98 a	36.55

Note: Different letters (e.g., ^a, ^b, ^c, ^d) indicate significant differences (p < 0.05) between planting density treatments.

, dry matter accumulation in the maize canopy increased progressively with growth and development throughout the reproductive period under different density treatments. At maturity, in 2021, the dry matter accumulation for the D1 treatment was 9.25 × 10³ kg·ha⁻¹, while for the D2, D3, and D4 treatments, it was 12.38, 14.57, and 20.90 × 10³ kg·ha⁻¹, respectively. These values represented increases of 33.89%, 57.56%, and 126.41% compared to the D1 treatment. In 2022, dry matter accumulation was 18.95 × 10³ kg·ha⁻¹ for the D1 treatment, and 21.09, 24.51, and 37.07 × 10³ kg·ha⁻¹ for the D2, D3, and D4 treatments, respectively, corresponding to increases of 11.35%, 29.36%, and 95.66% over the D1 treatment. These results indicate that dry matter accumulation was significantly higher in 2021 than in 2022 for all density treatments, with the differences being more pronounced in 2021.

During different maize growth stages, the dry matter accumulation patterns were consistent across treatments. Starting at the jointing stage, maize entered a vigorous growth phase, with rapid increases in dry matter. The dry matter growth rates ranged from 64.57% to 94.65% in 2021, and 59.47% to 92.28% in 2022. The highest rates of dry matter accumulation occurred during this period. However, after the grain filling stage, the rate of dry matter accumulation slowed down, attributed to plant aging and environmental conditions, which led to reduced growth and accumulation.

In 2021, the mean dry matter accumulation in the middle canopy layer increased from 0.57 × 10³ kg·ha⁻¹ (D1) and 1.45 × 10³ kg·ha⁻¹ (D4) at the jointing stage to 9.34 × 10³ kg·ha⁻¹ (D1) and 19.34 × 10³ kg·ha⁻¹ (D4) at maturity. The growth rates were 153.98% (D1) and 107.11% (D4), and were significantly greater in the middle layer compared to the lower and upper layers of the canopy. This trend was primarily due to the increasing dry matter accumulation in the maize male ears after the jointing stage. A similar pattern was observed in 2022, driven by the accumulation of dry matter in the male ears after the tasseling stage. Dry matter accumulation increased significantly across all treatments during the jointing stage, with D4 showing the highest increase in dry matter accumulation (126.41%) compared to D1 by maturity (Table 3). These findings reflect the substantial impact of planting density on biomass production.

3.3.2. Dominant Factors of Yield Components

Table 4. Effects of planting density on maize yield and yield components, including grain yield, biological yield, harvest index (HI), water use efficiency (WUE), rainfall totals, and effective accumulated temperature during the maize growth period in 2021 and 2022.

Growth Period	Treatment	Rainfall (mm)	Effective Accumulated Temperature (°C)	Grain Weight (g 100 seed−1)	Grain Yield (kg ha−1)	Biological Yield (kg ha−1)	HI (%)	WUE (kg/m3)
2021	D1	291.85	1572.67	31.2 d	5497.2 d	9247.6 d	59.4 a	2.37 ± 0.04
	D2			33.0 c	6142.1 c	12,383.0 c	49.6 b	2.44 ± 0.06
	D3			37.9 a	6996.9 b	14,570.1 b	48.0 c	2.82 ± 0.05
	D4			34.6 b	9257.3 a	20,937.6 a	44.2 d	2.88 ± 0.07
2022	D1	456.69	1556.03	38.5 b	7233.2 d	14,519.2 d	49.8 a	2.30 ± 0.16
	D2			38.9 ab	7718.4 c	16,177.9 c	47.7 b	2.35 ± 0.19
	D3			39.8 a	9204.6 b	20,263.0 b	45.4 c	2.67 ± 0.16
	D4			36.7 c	9599.1 a	21,771.8 a	44.1 d	2.70 ± 0.13

Note: Different letters (e.g., ^a, ^b, ^c, ^d) indicate significant differences (p < 0.05) between planting density treatments.

presents the effects of different planting densities on maize yield, yield components, harvest index (HI), and water use efficiency (WUE) for both years of the study. Throughout the growing season, average temperatures were similar across both crop cycles (24.05 °C in 2021 and 24.21 °C in 2022), with the effective accumulative temperatures required for crop growth recorded as 1572.67 °C and 1556.03 °C in 2021 and 2022, respectively. However, solar radiation was lower in 2021 (13.48 MJ/m²·d), compared to 2022 (15.37 MJ/m²·d), which also experienced inland flooding from early to late July, contributing to cumulative effective precipitation of 292.85 mm in 2021 and 456.69 mm in 2022.

Both biological yield and grain yield increased significantly (p < 0.05) with higher planting densities in both years (2021 and 2022). Notably, the harvest index (HI) decreased as planting density increased. Significant differences (p < 0.05) were observed in yield and its components in 2021. The mean 100-kernel weight increased from D1 to D3 but decreased in the D4 treatment, with the highest value recorded in the D3 treatment. Water use efficiency (WUE) exhibited a similar pattern to HI, with the highest WUE observed in the D3 treatment. When compared to the D1 treatment, grain yield increased by 11.7%, 27.3%, and 68.4% in 2021, and 33.9%, 57.6%, and 126.4% in 2022, for the D2, D3, and D4 treatments, respectively. The respective biological yield also increased by 6.7%, 30.0%, and 32.7% in 2021, and 11.4%, 39.6%, and 50.0% in 2022. These results indicate that under denser planting conditions, grain yield outpaced the increase in biological yield. WUE also increased with higher planting density, with a 21.5% increase in 2021 and 17.4% in 2022 for the D4 treatment compared to D1. However, as rainfall increased, WUE declined.

The results demonstrated a clear relationship between planting density and maize yield. Notably, D4 exhibited the highest grain yield, with a 20.1% increase compared to D1 at maturity (Table 4). The corresponding biological yield also increased significantly in D4 (37.07 × 10³ kg·ha⁻¹) compared to D1 (9.25 × 10³ kg·ha⁻¹) in 2021, reflecting a 126.41% increase (Table 4). This highlights the impact of higher planting density on yield potential.

3.4. Analysis of Factors Influencing Yield Components and Distribution of Photosynthetically Active Radiation at Different Spatial and Temporal Scales

As shown in Figure 6, variations in rainfall, cumulative temperature, morphological structure, yield components, as well as the total photosynthetically active radiation (T_PAR) and fractional photosynthetically active radiation (F_PAR) distributions, were observed under four different planting densities. Notably, both year-to-year differences and rainfall were positively correlated with 100-grain weight at a significant level (p < 0.05), indicating that increased rainfall contributed to greater grain weight. In contrast, cumulative temperature showed a significant negative correlation with 100-grain weight (correlation coefficient = −0.74, p < 0.05), suggesting that higher cumulative temperatures were detrimental to grain weight accumulation. The correlations presented in Figure 6 are based on Pearson correlation coefficients using combined data from both growing seasons across all treatments.

As shown in Figure 6, the changes in rainfall, cumulative temperature, morphological structure, yield components, and T_PAR and F_PAR at four planting densities. Among them, different years and rainfall were positively correlated with 100-grain weight at a significant level (p < 0.05), indicating that the increase in rainfall was favorable to the increase in grain weight. The negative correlation between cumulative temperature and 100-grain weight (correlation coefficient of 0.74) was significant (p < 0.05), indicating that high cumulative temperature was unfavorable to the increase of 100-grain weight. The correlation analysis of dry matter mass and yield showed that the dry matter mass of the middle and lower layers was positively correlated with grain yield and biological yield, and reached a significant level (p < 0.05). HI was negatively correlated with yield components (mainly including 100-grain weight, yield, and biological yield), rainfall, WUE, dry matter mass, LAI, as well as T_PAR and F_PAR, with the correlation coefficients of HI and LAI, and the correlation coefficients of dry matter mass of the middle and lower layers reaching a significant level (p < 0.05). The correlation coefficients of HI with LAI and middle and lower dry matter mass were significant (p < 0.05) and above 0.7. F_PAR was positively correlated with LAI and reached a significant level (p < 0.05), with a correlation coefficient of above 0.76.

WUE was positively correlated with LAI and F_PAR and reached a significant level (p < 0.05), while it was negatively correlated with HI, row spacing, and rainfall and reached a significant level (p < 0.05). In addition to HI, D was positively correlated with other variables. With the increase of planting density, yield, biological yield, WUE, LAI, and F_PAR increased, while HI decreased, indicating that dense planting improved the interception rate of PAR in the canopy, thereby increasing the food yield, biological yield, and WUE of crops, but HI decreased. Middle-layer F_PAR was positively correlated with yield, biological yield, WUE, middle-layer and lower-layer dry matter mass, LAI, and T_PAR, and reached a significant level (p < 0.05). The lower F_PAR was significantly affected by LAI (correlation coefficient above 0.76), and positively correlated with WUE (correlation coefficient 0.84).

4. Discussion

4.1. Effects of Different Canopy Structures on LAI

The relationship between light interception and productivity increase is central to understanding how planting density influences maize growth. In this study, we found that higher planting densities significantly increased light interception, particularly in the upper canopy layers, contributing to enhanced maize yield. This result aligns with the size/density theory, which suggests that denser planting systems enhance yield potential by improving light capture, though with associated trade-offs regarding light availability in lower canopy layers.

The leaf area index (LAI) was found to be a critical factor influencing light interception within the maize canopy. As LAI increased with higher planting densities, more light was absorbed by the upper layers of the canopy. However, this led to a reduction in light transmittance to the lower layers, a finding consistent with previous studies, which observed that higher densities lead to increased light interception at the expense of light availability at the lower canopy levels [34]. Despite this reduction, maize plants at higher densities exhibited compensatory adaptations, such as altering leaf orientation to optimize light capture. These findings are in line with previous research, which highlighted that canopy architecture, driven by higher planting densities, plays a more significant role in light distribution and photosynthetic efficiency than other cultivation management practices [19].

The trade-off between increased LAI and reduced light availability in the lower canopy is well-documented in the literature. While denser planting enhances light interception in the upper layers, it may lead to suboptimal photosynthetic conditions in the lower canopy, which can limit overall productivity. This trade-off emphasizes the need for careful management of planting density to optimize both light capture and photosynthetic efficiency across the entire canopy. A similar result was found in a study, where high-density maize planting resulted in increased yields but a reduction in photosynthetic efficiency in the lower layers of the canopy [35].

Our study also suggests that higher planting densities can boost water use efficiency (WUE), as maize plants in denser populations are able to more effectively utilize available light for photosynthesis. This is consistent with Yang et al. (2019), who observed a positive correlation between F_PAR (fraction of photosynthetically active radiation) and WUE in high-density maize systems [33]. The efficient use of light is crucial for enhanced photosynthesis and biomass production, ultimately leading to greater yields. However, it should be noted that this increase in productivity is contingent upon the optimal planting density—one that balances the benefits of increased light interception with the potential negative effects of reduced light in lower canopy layers.

In practical terms, the results from this study indicate that planting densities around 83,000 plants·ha⁻¹ are optimal for maximizing maize productivity in regions such as Daxing, Beijing, where conditions favor high light availability. This density achieves a balance between maximizing light interception in the upper canopy and maintaining light availability for lower canopy layers, which is essential for maximizing overall photosynthetic efficiency and yield.

4.2. Effects of Different Canopy Structures on Canopy PAR

The distribution of light within the maize canopy is primarily determined by the canopy structure, with the leaves playing a crucial role in both light distribution and light energy utilization. Photosynthetically active radiation (PAR) transmittance is commonly used to analyze the PAR distribution within the canopy when examining the radiation transmission patterns. The differences in average PAR transmittance at various positions within the canopy reflect, to some extent, the distribution of PAR energy. Previous studies have demonstrated that light energy utilization in the maize canopy is closely linked to the canopy characteristics of the maize plants [36]. In this study, it was observed that the average PAR transmittance at the same height for different row spacing treatments showed little variation. The total photosynthetically active radiation (T_PAR) increased as the maize canopy height increased but decreased as planting density rose. At different canopy depths (upper, middle, and lower layers), T_PAR tended to decrease with increasing density. Notably, transmittance at the bottom of the canopy was more pronounced than at the middle layer, and the influence of varying planting densities on transmittance at the lower part of the canopy was particularly significant.

Different planting density treatments resulted in distinct population structures, which in turn affected the light distribution within the canopy. This study found that F_PAR was most significantly influenced by LAI, with significant differences observed among the treatments and correlation coefficients ranging from 0.76 to 0.91. Furthermore, F_PAR was positively correlated with water use efficiency (WUE), with a significant correlation (p < 0.05). Numerous studies have explored the potential impact of high-density planting on crops. The high tolerance of maize varieties to elevated planting densities is attributed to the compact canopy structure, shorter plants with fewer and straighter leaves, and the ability of the canopy to capture more light at higher LAI. Additionally, reducing the height of the ear improves resistance to stem collapse, all of which contribute to higher maize yields under denser planting conditions [37].

Our study showed that photosynthetically active radiation (PAR) transmittance decreases as planting density increases, particularly in the lower canopy layers. However, maize plants at higher densities tend to adjust their leaf orientation to optimize light interception, mitigating some of the negative effects on photosynthesis in the lower layers. The implication for agricultural practices is that higher planting densities can still be beneficial for light capture in the upper canopy, which is crucial for maximizing productivity, especially in high-density planting systems. In practice, farmers should consider adjusting plant spacing to ensure that the upper layers of the canopy receive enough light, while managing leaf orientation to maximize light capture across the entire plant structure.

4.3. Effects of Different Canopy Structures on Dry Matter Accumulation and Yield Composition

The influence of planting density on maize yield and biological productivity was evident across both experimental years. Increased planting density significantly improved yield, though it was accompanied by a decrease in light transmittance, particularly within the lower canopy layers. This reduction in light availability could potentially limit photosynthetic efficiency. While the overall impact of planting density remained consistent across both years, substantial variations were observed, which can largely be attributed to differences in climatic conditions, particularly rainfall and temperature.

In 2021, the region experienced above-average rainfall and moderate temperatures, creating favorable growing conditions for maize. Under these conditions, increased water availability facilitated enhanced photosynthetic activity and biomass accumulation, particularly in the higher-density treatments (D3 and D4). With sufficient water supply, maize plants were able to optimize light interception, which resulted in significant improvements in productivity. In contrast, 2022 was characterized by below-average rainfall and elevated temperatures, which created a water deficit and induced significant water stress, particularly in the higher-density treatments. Reduced water availability in 2022 constrained photosynthesis in the lower canopy layers, where light interception was already compromised due to the denser plant populations.

This variation highlights the complex environment–density interaction, which underscores the importance of climate in determining the optimal planting density for maize. When climatic conditions favor water availability, higher planting densities can lead to substantial yield increases. However, in drier and hotter years, when water becomes limiting, the benefits of higher densities are diminished. For example, in 2022, lower-density treatments (D1 and D2) showed greater resilience to water stress, likely due to reduced competition for water and light in the lower canopy.

These findings emphasize the need for adaptive management strategies in maize cultivation, where planting densities should be adjusted in response to anticipated climatic conditions. Tailoring planting density to suit both environmental factors such as rainfall and temperature, and the specific stressors of a given growing season is critical to optimizing maize productivity. Therefore, adjusting planting density to balance light interception and water use efficiency under varying climatic conditions will be crucial for maximizing yield potential across different environments.

In response to climatic variability, this study provides actionable recommendations for maize density management to optimize yield under different environmental conditions. For regions experiencing moderate to high rainfall and moderate temperatures, we recommend increasing planting density to approximately 83,000 plants·ha⁻¹ to maximize light interception and biomass production. However, in areas subject to drought or high temperatures, lower densities of 60,000–70,000 plants·ha⁻¹ are advised to reduce water stress and ensure efficient light distribution throughout the canopy. Furthermore, producers should adopt adaptive management strategies, adjusting planting densities based on seasonal forecasts of rainfall and temperature to mitigate the impacts of climatic stress. These recommendations aim to balance the benefits of high-density planting with the need to maintain photosynthetic efficiency under varying environmental conditions, thereby enhancing maize productivity across diverse climates.

5. Conclusions

This study provides valuable insights into the optimization of planting density for maize growth and productivity under varying climatic conditions. The key practical contribution of this study lies in the identification of the optimal planting density of 83,000 plants·ha⁻¹, which maximizes grain yield, biological yield, and water use efficiency (WUE) under moderate climatic conditions. These findings underscore the importance of adjusting planting density based on environmental factors such as rainfall and temperature to enhance resource use efficiency and productivity in maize farming. In regions with moderate to high rainfall and temperatures, maintaining this density could significantly improve maize yield without compromising photosynthetic efficiency.

Influence of planting density on yield and growth: Increasing planting density significantly enhanced grain yield, biological yield, and leaf area index (LAI), though it reduced the harvest index (HI). In 2022, maize yield increased by up to 126.4% with higher planting density, driven by higher rainfall, but water use efficiency (WUE) and HI were lower under high temperatures. These results indicate that higher planting densities can increase yield, but temperature extremes may limit productivity.

Canopy light distribution: The spatial distribution of PAR transmittance (T_PAR) within the canopy varied with planting density. T_PAR decreased with increasing canopy height, particularly in the lower layers, and was strongly correlated with LAI. The middle layers intercepted the most light, particularly during the grain-filling stage. These findings highlight the importance of optimizing the canopy structure to maximize light use efficiency.

Optimal density and water use efficiency: A planting density of 83,000 plants·ha⁻¹ maximized grain yield, biological yield, and WUE, making it the most efficient density under the studied conditions. However, the adverse effects of high accumulated temperatures on maize yield underscore the importance of considering climate factors in density management.

While focused on the Daxing region of Beijing, the findings are applicable to other temperate regions with similar climatic conditions. Adjusting planting density can enhance resource use efficiency and improve yields, offering a practical strategy for maize farming in regions with variable water availability and climate challenges.

Future research should focus on exploring the long-term effects of planting density on soil health, water use efficiency, and crop resilience under varying environmental stressors. Investigating the relationship between planting density, root architecture, and nutrient uptake would provide a deeper understanding of the mechanisms through which density influences maize productivity. Additionally, studies integrating precision agriculture technologies to fine-tune planting density and water management practices in response to climate change would complement our findings. These directions will be critical for ensuring future food security and sustainable maize production under changing climatic conditions.