Tap Maize Yield Productivity in China: A Meta-Analysis of Agronomic Measures and Planting Density Optimization

Abstract

Maize is a staple crop in China, playing a crucial role in agriculture and food security. However, current planting densities are suboptimal, leading to lower yields and unrealized potential. This study explores the potential to maximize maize yields by optimizing planting density and implementing region-specific agronomic measures across China’s diverse agro-ecological zones. We compiled a dataset consisting of 1974 independent field trials from 720 publications across China’s main maize-growing areas, spanning the period from 2000 to 2023, to assess the impact of optimal planting density and agronomic practices on China’s maize production. Our findings reveal that increasing the planting density to optimal levels—49.34% higher than current farmer practices—can significantly boost national maize yields by 16.28%. Furthermore, adopting agronomic techniques like precision irrigation, soil tillage, and plant growth regulators enhances this effect, raising planting density by 69.91% and yield by 27.26%. Notably, the irrigated maize-growing areas in Northwest China showed the highest yield potential, whereas the southern hilly regions had the lowest. This underscores the significance of tailoring optimal density and agronomic practices to each region. Combining agronomic measures with adjusted planting densities can reduce this disparity. Precision irrigation, soil tillage, and plant growth regulators were particularly effective in optimizing planting density and maximizing yield potential, especially in Northwest China and the North China Plain. In contrast, plant growth regulators proved most effective in Southwest China and Southern China. This study underscores the potential of integrating optimized planting density with agronomic measures to significantly improve maize productivity, thereby supporting sustainable agriculture. It provides a scientific basis for regionalized agricultural management.

1. Introduction

One of the main challenges for agriculture worldwide is to reconcile the increasing food demand with more viable production practices to achieve the relevant Sustainable Development Goal. Global food demand is expected to increase by 35.00–56.00% from 2010 to 2050 [1], while yields have stagnated in 24.00–39.00% of food-producing regions [2]. Although China has made substantial strides in agricultural productivity [3,4,5], it faces the dual pressures of feeding a growing population and supporting industrial expansion [6]. Maize (Zea mays L.), a staple crop with critical importance in food security and industrial applications, occupies a central role in this context [6]. As the second largest producer globally, China accounts for 21% of the world’s maize acreage [7]. However, unlike wheat and rice, over 90% of China’s maize is allocated to feed, industrial applications, and energy production [8]. Projections suggest that China will continue as a net importer of maize in the upcoming decades [9,10]. As maize demand is growing by the day, exploring effective planting strategies is key to ensuring maize production.

Strategies to enhance maize yields have focused on the complex interactions between environmental factors, agronomy, and genotype improvements [11,12]. Evidence shows that in the United States, the world’s largest maize producer, maize yields have increased steadily over the past several decades due to the synergistic interaction of improved genetics and agronomy [13]. However, studies also show that climate and agronomy, rather than genetics, have driven yield growth in recent years in high-quality United States maize production areas [14]. Similarly, a higher planting density is believed to be one of the main reasons the United States achieves higher maize yields than other countries [15].

Increasing planting density is an efficient agronomic strategy. In China, the current maize planting densities remain significantly below optimal levels, leading to suboptimal yields. Maize planting densities in China range from 49,850 to 65,180 plants ha⁻¹ [16], which is much lower than the average density in the United States in past decades [17], and also lower than the European Union, where soil and climate conditions vary among countries [18]. This discrepancy between actual and potential yields presents a substantial opportunity for improvement. For specific varieties, through the optimal planting density (OPD), resources such as light, temperature, and water can be reasonably allocated to the maximum extent, thus promoting the formation of corn photosynthetic products, which is crucial for establishing a high-yielding corn population [19,20]. Current OPD calculations focus primarily on limited field trials, setting up a limited number of density gradient trials or measuring yield potential through the highest recorded checks and simulations [21,22]. These methods do not fully represent the range of maize yield potential. The application of quadratic function fitting to process density gradient trials for obtaining OPD offers an improved solution [17], providing a more accurate reflection of maize yield potential for each test site and region.

In addition to increasing planting density, other strategies to enhance maize yield have also been extensively studied. As maize yields have stagnated in recent years, strategies to improve them have focused more on agronomy improvements and future-oriented climate projections. Several studies have demonstrated these strategies. Optimization practices, for example, through different strategies as nutrients, cultivars, establishment, or pest management in different climatic regions, are expected to have a positive impact on maize yields in South Africa [23]. Similarly, in the Kenyan region, adjustments in agronomic factors can offset the impacts of climate change on maize in rainfed areas [24]. In short, agronomic measures are undoubtedly important strategies for maize yield formation. Common agronomic measures include soil tillage, row spacing adjustment, and so on. Studies now mainly focus on the effects of agronomic measures on yield enhancement, such as adjustments to planting density and irrigation depth [25], plant growth regulators in response to density [26], and integrated management practices [27] on yield enhancement effects. However, there are fewer studies on the effects of these agronomic measures on maize yield potential enhance.

The impact of agronomic strategies on the yield potential of maize requires further investigation, especially in different production regions. Crop yield potential is defined as the yield of a crop in a suitable environment where nutrients and water are not limited, and pests and diseases are controlled [28,29]. This implies that, for a given crop variety, yield potential is determined by light and temperature conditions and planting density [29]. The use of agronomic strategies may enhance the utilization efficiency of these natural resources, thereby improving the yield potential [30,31]. However, selecting appropriate agronomic strategies is challenging, due to China’s vast and diverse hydrothermal resources and varying cropping conditions across different ecological regions [32]. As is well understood, maize growth and phenological responses vary across different climatic regions due to disparities in temperature, rainfall, light, and other conditions [28]. Consequently, identifying regionally appropriate agronomic strategies to maximize the use of natural resources for yield enhancement is a valuable area of research.

Based on above, the objectives of this study were to: (i) evaluate the optimal planting density and yield gains across maize production ecoregions in China; (ii) investigate the efficacy of agronomic measures—including row spacing adjustment, plant growth regulators, soil tillage, plastic film mulching, and precision irrigation—in boosting maize optimal planting density and yield potential; (iii) analyze regional disparities in yield potential improvements attributable to these agronomic measures and density optimizations. This study’s findings can serve as a scientific reference for custom agricultural strategies aimed at enhancing maize yield potential, addressing China’s growing demand for maize.

2. Materials and Methods

2.1. Data Collection and Preparation

The literature database was primarily compiled from maize planting density trials conducted in China. The sources of these papers include the China National Knowledge Infrastructure (http://www.cnki.net/) and the Web of Science, encompassing relevant studies published between 2000 and 2023. The database includes journal articles as well as master’s and doctoral dissertations. The search keywords employed were: “yield*” AND “density*” AND (“maize*” OR “corn*”) AND (“China*” OR “Chinese*”). Each study included in the database was reviewed based on the following criteria: (1) The study was based on field experiments conducted in China, with greenhouse and pot trials excluded; (2) The trial evaluated more than three levels of plant density, and each density level contains three or more repetitions; (3) Specific yield data were provided, excluding any yield data derived from model simulations; (4) Information on the trial locations was available; (5) Trials conducted under stress conditions were excluded from the analysis; and (6) The publication should clearly provide information on the treatment, sample size, mean, and standard deviation (SD) or standard error (SE); this data should be available for direct extraction or accessible through the use of software.

Most of the data were extracted directly from tables within the publications. For data presented solely in graphical form, values were obtained using the GetData Graph Digitizer software 2.26 (http://getdata-graph-digitizer.com (accessed on 5 December 2023)). For each independent experiment, the parameters obtained include location information, variety, planting density, yield, and the use of agronomic measures. The field trial treatments were classified into two agronomic strategies: without agronomic measures (NM) and with agronomic measures (AM). The AM strategy was further categorized into five specific agronomic measures: (1) row spacing adjustment (RA); (2) use of plant growth regulators (GR); (3) soil tillage (ST); (4) plastic film mulching (FM); and (5) precision irrigation (PR). Through subgroup analysis of these agronomic measures, it was found that there were either no significant differences or significant increases in both OPD and yield among the corresponding agronomic measures (Supplementary Materials Figure S1). Therefore, further subdivision into individual subgroups was not conducted, and only the overall effects of the major agronomic measures on planting density and yield were considered. Other measures beyond these five specific agronomic measures were excluded. As a control, the farmer planting (FP) density was determined using data collected from 402 sites across the published studies, representing the regional maize planting density practices of local farmers [17].

China’s geographic regions are widely dispersed, and its maize-growing areas can be broadly classified into five major ecoregions as shown in Figure 1: Northeast China (NE), North China Plain (NCP), Northwest China (NW), Southwest China (SW), and Southern China (SC). Of these, Northeast China, the North China Plain, and Southwest China account for 91% of the maize planting area in the country, contributing nearly 90% of the total maize production [8,33]. However, the climatic and geographic conditions vary significantly across these regions, leading to diverse agricultural practices, making regional studies highly significant for optimizing maize production.

2.2. Data Analysis

OPD was determined from independent trials. For each trial, the OPD and the corresponding yield were derived by fitting a quadratic function of the density yield, with the resulting curve representing the density–yield relationship. Exclude data with quadratic function opening upwards (a > 0). The Z-score method and Interquartile Range method are used in the data cleaning stage to remove outliers and noise [34,35]. The cleaned data conforms to a normal distribution. The final compiled dataset, comprising 1974 independent experiments’ OPD from 720 publications, was conducted across the main maize production areas in China. This includes 1508 NM and 466 AM experiments, involving more than 640 corn varieties, with the distribution illustrated in Figure 1.

To estimate the yield potential under FP density, we employed a nearest neighbor assignment approach [36]. Planting density data from 402 farmer sites were geographically matched to the 1508 NM strategy points, generating 1508 initial pairs. For each pair, the FP density was substituted into the NM density–yield potential curve (Figure 2) to calculate the theoretical yield. To maintain analytical rigor, data points with FP densities exceeding the original density gradient range defined in the NM strategy were excluded. This filtering step retained 512 valid FP-NM pairs, each containing paired density and yield potential values. The regional distribution of NM, AM, and FP study sites is summarized in

Table 1. Number of NM, AM, and FP study sites in five regions ¹.

Maize Planting Area	Study Sites of NM	Study Sites of AM	Study Sites of FP
NE	714	245	244
NCP	607	156	225
NW	20	3	14
SW	120	25	15
SC	47	37	14

¹ Three treatments including farmer planting density (FP), optimal planting density without agricultural measures (NM), and optimal planting density with agricultural measures (AM). Study sites in five regions including Northeast China (NE), North China Plain (NCP), Northwest China (NW), Southwest China (SW), and Southern China (SC).

. All calculations were processed using Python 3.12 software.

To evaluate the effect sizes of NM and AM relative to FP, spatially overlapping or proximate data points were selected across the three management strategies. The FP densities for these points were derived from the aforementioned proximity-based matching method, which linked 402 farmer sites to NM and AM points via density–yield potential relationships. After excluding mismatched densities, 512 FP-NM pairs and 96 FP-AM pairs were retained, ensuring FP densities aligned with the experimental density ranges (Figure 2). Crucially, the consistency in FP density across NM and AM comparisons minimized spatial confounding, enabling valid cross-strategy evaluations. The magnitude of the effect of NM and AM on FP was then quantified using the natural logarithm of the response ratio [37,38]. The effect size was calculated using Formula (1):

$$\mathit{ln}\left(RR\right)=ln({\displaystyle \frac{X_t}{X_c}})$$

(1)

for each location, the effect size was computed as the mean of the logarithmic ratios, converted to a percentage, using Formula (2):

$$\mathrm{E}\mathrm{f}\mathrm{f}\mathrm{e}\mathrm{c}\mathrm{t} \mathrm{S}\mathrm{i}\mathrm{z}\mathrm{e}\left(\mathrm{\%}\right)=\left({\displaystyle \frac{\sum \mathit{ln}\left(RR\right)}{n}}\right)\times 100\%$$

(2)

where $X_t$ and $X_c$ represent: (1) The OPD in agronomic strategies and density in farmer density, respectively; (2) The OPD yield in agronomic strategies and yield in farmer density, respectively. $n$ represents the number of treatments.

3. Results

3.1. Effect of Agronomic Strategies on Planting Density and Yield in Different Regions of China

3.1.1. Regional Variations in Maize Yield and Planting Density Under Different Agronomic Strategies

The magnitudes of maize OPD and its yields under various agronomic strategies are illustrated in Figure 3. Generally, NM enhances maize planting density by 49.34%, from 4.80 × 10⁴ plants ha⁻¹ to 7.17 × 10⁴ plants ha⁻¹, when compared with FP. Moreover, AM escalates the planting density by 69.91% to 8.08 × 10⁴ plants ha⁻¹ relative to FP, and by 13.81% relative to NM. The yields of OPD exhibit a similar trend: NM increases maize yields by 16.28%, from 9.57 Mg ha⁻¹ to 11.13 Mg ha⁻¹, when compared to FP. Meanwhile, AM further elevates the yields by 27.26% to 12.18 Mg ha⁻¹ compared to FP, and by 9.43% compared to NM.

The OPD and OPD yields varied across five maize growing regions. Regarding planting density, on the one hand, the OPD of NM increased by 33.00% to 54.78%, ranging from 6.32 × 10⁴ plants ha⁻¹ to 8.36 × 10⁴ plants ha⁻¹, when compared to FP density across the five regions. Among these regions, the North China Plain exhibited the highest increase, while Southern China showed the lowest increase. On the other hand, the OPD of AM increased by 45.18% to 78.06%, ranging from 6.24 × 10⁴ plants ha⁻¹ to 9.92 × 110⁴ plants ha⁻¹, relative to the FP density in the five regions. Furthermore, compared to NM, AM increased OPD by 9.38% to 20.10% in all regions except Southwest China. The highest increase was observed in Northwest China, while Southwest China showed nearly the same OPD values in AM as in NM.

Similarly, the OPD yields varied among regions, with the OPD yields demonstrating a pattern of AM > NM > FP across all five regions. Compared to FP, NM increased the OPD yields by 5.76% to 23.98%, ranging from 8.94 Mg ha⁻¹ to 12.90 Mg ha⁻¹, while AM increased the OPD yields by 14.32% to 31.66%, ranging from 9.43 Mg ha⁻¹ to 13.97 Mg ha⁻¹ in the five regions. Moreover, compared to NM, AM increased the OPD yields by 1.23% to 13.22% in the five regions, with Northeast China exhibiting the highest rate of increase in OPD yields and Southwest China the lowest.

3.1.2. Effect Sizes of Agronomic Strategies on Regional Maize Planting Density and Yield

Overall, the OPD and OPD yields effects of AM were significantly higher than those of NM by 6.32% and 6.67%, respectively (Figure 4). The impact of AM and NM on density was positive across all regions. The effect size of NM on OPD ranged from 22.00% to 41.27% in different regions, with an overall effect of 38.14%, while AM demonstrated a positive effect size of 18.47% to 52.05% in OPD, totaling an overall effect size of 44.46% (Figure 4a). A similar trend was observed in yields response (Figure 4b), where AM exhibited a positive effect size of 8.74% to 26.84% in yields, totaling an overall effect size of 20.62%. In contrast, NM showed lower effect sizes, ranging from 7.24% to 17.20% across different regions, with a total effect size of 13.95%. Notably, AM displayed higher yield improvements in regions such as the North China Plain and Northwest China compared to NM. However, the effect sizes of AM on OPD yields were lower in Southern China, and the effect sizes of NM on OPD yields were lower in Northwest China.

3.2. Effect of Different Types of Agronomic Measures on OPD and OPD Yield of Maize

In general, all types of agronomic measures demonstrated an increase in OPD compared to NM, as shown in Figure 5a. Precision irrigation exhibited the highest density increase effect, with an increase of 28.21% in OPD, equivalent to 9.19 plants ha⁻¹. Plant growth regulators, soil tillage, precision irrigation, and plastic film mulching all revealed an increase in OPD of more than 10%. In contrast, row spacing adjustment showed a lower increase of 8.88%.

The trends in OPD yields were consistent across different agricultural measures compare to NM. However, there were notable differences in their impacts. Soil tillage had the most significant effect, with an increase in OPD yield of 20.39%, reaching 13.40 Mg ha⁻¹, as shown in Figure 5b. Precision irrigation also led to a substantial increase, though slightly less pronounced at 12.05%. Plastic film mulching ranked second highest, contributing to an OPD yield increase of 16.87%, reaching 13.00 Mg ha⁻¹. In comparison, plant growth regulators and adjustments in row spacing resulted in more modest increases ranging from 3.16% to 8.74%, which were less than those achieved by the other mentioned measures.

Certainly, the improvement rate in OPD yields is not equivalent to the improvement in OPD. For instance, plastic film mulching results in a greater enhancement in OPD yield even with a lesser improvement in OPD when compared to precision irrigation.

3.3. Effect of Agronomic Measures on OPD and OPD Yield of Maize in Different Regions

3.3.1. Effect of Agronomic Measures on OPD of Maize in Different Regions

As shown in Figure 6, the impact of agronomic measures on OPD varies across regions. In both Northeast China and the North China Plain, agronomic measures have been found to positively influence OPD when compared to NM. The effects of adjusting row spacing and using plant growth regulators were similar in these two regions, with an increase ranging from 11% to 13%. However, Northeast China experienced more significant enhancements from soil tillage, plastic film mulching, and precision irrigation, with respective increases of 21.47%, 20.36%, and 35.60%. In contrast, the North China Plain saw much smaller increases due to these agronomic measures, at 4.66%, 6.54%, and 16.00%, respectively. Nonetheless, it is evident that precision irrigation is the agronomic measure that contributes most significantly to increasing OPD in these two regions.

There were relatively few study sites about soil tillage, plastic film mulching, and precision irrigation in Southwest China and Southern China. The effects of row spacing adjustment and plant growth regulators on OPD were relatively small in Southwest China, with increases of 1.50% and 1.38%, respectively. In contrast, in Southern China, row spacing adjustment led to a reduction in OPD by 12.51%, and plant growth regulators had the most significant impact on increasing OPD among the four regions except Southwest China, with an increase of 18.8%.

3.3.2. Effect of Agronomic Measures on OPD Yield of Maize in Different Regions

Figure 7 illustrates that the OPD yields response varies across different regions. In Northeast China, measures such as precision irrigation, soil tillage, row spacing adjustment, and plastic film mulching enhanced the OPD yields by 17.08%, 16.69%, 15.09%, and 12.05%, respectively, constituting the primary yield enhancement strategies. Among them, soil tillage measures have resulted in a OPD yield of 14.12 Mg ha⁻¹. In contrast, the impact of plant growth regulators on increasing OPD yield was relatively lower, at 5.37%. Meanwhile, in the North China Plain, soil tillage, plant growth regulators, and row spacing adjustment boosted the OPD yields by 7.56%, 6.37%, and 5.10%, respectively, showing a modest improvement compared to Northeast China. Plant growth regulators proved to be the most effective method for enhancing OPD yield in Southwest China and Southern China, increasing yields by 7.24–8.05%. However, row spacing adjustments did not contribute to yield enhancement in these regions, with changes of −1.97% and −0.66%, respectively.

4. Discussion

4.1. Yield Gains Achieved Through Increased Planting Density

Increasing plant density is key to maximizing maize yield. Our research shows optimizing plant density to OPD can boost maize yield by 16.28%, which corresponds to a 49.34% increase in planting density. This significant yield enhancement highlights the importance of precise planting density adjustments in agricultural management.

Under suboptimal planting densities, common in current farming practices, resources such as light, water, and nutrients are underutilized, leading to restricted biomass accumulation and yield [39]. Transitioning to OPD enhances resource capture, enabling more efficient light interception, improved photosynthetic activity, and higher water use efficiency [40,41], collectively contributing to increased biomass and grain yield [42]. However, the magnitude of these benefits varies across regions due to differences in climatic conditions and resource availability.

Regional disparities in yield potential are driven largely by climatic factors, including temperature and solar radiation during the growing season [14]. For example, Northwest China, benefiting from abundant solar radiation, maintains a high yield potential even at increased planting densities [40,43]. In this region, selecting varieties tolerant to high planting densities is essential to maximize productivity [40]. Conversely, in Southwest and Southern China, where rainfed farmland predominates and resource limitations constrain plant carrying capacity, tailored planting density strategies are crucial for achieving yield gains [44].

While breeding efforts have produced cultivars with improved architectural traits to support higher planting densities [45,46,47], the full yield potential of these advancements remains underutilized due to the significant gap between current planting densities and OPD. Our analysis of ‘Zhengdan 958’, a widely cultivated variety, reveals that even a single genotype exhibits varying performance in OPD and yield across different production regions (Supplementary Materials Figure S2). These findings align with our broader dataset of over 400 varieties, highlighting the critical importance of region-specific management practices in maximizing yield potential (Figure 3).

In addition, despite extensive research on the relationship between planting density and yield [17,43,48,49], key questions remain. Our study differentiates between OPD achieved through agronomic measures versus OPD without such interventions. This distinction is vital because prior research often conflated these scenarios, which may lead to less accurate calculations of yield gains attributed to increased planting density.

In addition, while technological and environmental constraints are significant, economic factors also play a pivotal role in farmers’ reluctance to adopt optimal planting densities. For example, high upfront costs of precision irrigation equipment, variable maize market prices, and smallholder farmers with decentralized operations receive limited subsidies. In addition, agricultural production is mainly smallholder farmers in China, and the efficiency of technology use is still low [50]. This is mainly because the recommended practical measures with benefits are less adopted by smallholder farmers, and the planting density is considered to be one of the main reasons for the yield gap in maize production [50]. The reasons for this phenomenon are understood as farmers’ lack of knowledge, risk aversion tendency and market chao [50,51]. These economic barriers align with global patterns [23]. Of course, the government and research institutions are narrowing this gap through policy intervention and technical guidance [50,52], and the development of more effective technical research is still an important link to give full play to productivity.

4.2. Application of Agronomic Measures Further Taps OPD and Yield Potential

While numerous studies have highlighted the role of field management techniques in improving maize yields [53,54,55], most focus on single planting densities or field-scale experiments, with limited investigation into their combined effects under OPD across regions. This study addresses this gap by demonstrating that integrating agronomic measures such as precision irrigation, soil tillage, plant growth regulators, row spacing adjustment, and plastic film mulching significantly enhances OPD and yield potential, providing robust insights for region-specific agricultural strategies.

Precision irrigation is a critical agronomic measure that supports high-density planting by addressing water limitations during critical growth stages. By ensuring adequate moisture levels, precision irrigation reduces water stress, enhances plant health, and promotes uniform crop growth, thereby increasing yield potential [56,57]. This strategy also improves water use efficiency by minimizing the risks of over- or under-watering, making it particularly effective in water-limited regions [58]. However, increased planting density does not necessarily demand proportional increases in irrigation, as excessive water application may lead to waterlogging and reduce yield efficiency [59]. Therefore, the success of precision irrigation depends on its careful calibration to regional conditions and crop requirements.

Soil tillage enhances OPD and yield potential by improving soil structure, facilitating root development, and increasing nutrient and water uptake. By reducing soil compaction, tillage allows better infiltration and retention of water, creating conditions conducive to high-density planting [60]. However, the effectiveness of tillage practices varies with regional soil characteristics and management systems. For example, long-term shallow tillage has degraded soil structure in parts of eastern Inner Mongolia, reducing its efficacy in supporting maize yields [61]. As such, regionally tailored tillage practices are essential to maximize its benefits while preventing soil degradation.

In high density maize cultivation, plant growth regulators address the trade-off between lodging and yield through distinct mechanisms. Growth promoters, such as gibberellins, enhance biomass accumulation and kernel number per ear by activating endogenous hormone signaling pathways, promoting cell elongation, and expanding photosynthetic area [62]. These effects are particularly beneficial during the seedling stage and under stress conditions, where they can compensate for growth delays. However, excessive use of growth promoters may lead to excessive stem elongation, reducing mechanical strength and increasing lodging risk. In contrast, growth inhibitors, such as chlormequat chloride and paclobutrazol, mitigate lodging by suppressing gibberellin biosynthesis, shortening internode length, and significantly increasing lignin and cellulose content in stems [63,64]. Additionally, growth inhibitors optimize canopy light penetration by reducing plant height, alleviating light competition among lower leaves, and synergistically improving the leaf area index (LAI) and light interception efficiency [65]. They also prioritize the allocation of photosynthetic assimilates to the ear, thereby increasing the harvest index. In our study, both growth promoters and inhibitors demonstrated yield improvements, but growth inhibitors exhibited higher OPD and yield, making them more suitable for high-density, lodging-prone regions (Supplementary Materials Figure S1). Based on these characteristics, plant growth regulators such as gibberellins were predominantly applied in Northeast China to stimulate early biomass accumulation under high-density conditions, while chlormequat chloride and paclobutrazol—growth inhibitors that enhance stem lignin content—were prioritized in Southwest China to mitigate lodging in hilly terrains. Practical applications should dynamically adjust plant growth regulators use based on planting density, varietal traits, and environmental stresses—for example, promoting growth during the seedling stage and controlling excessive growth during the jointing stage—to achieve risk-controlled yield maximization.

Row spacing adjustment optimizes spatial arrangements within the field, reducing root competition and improving plant adaptability to high-density planting. Narrow row spacing minimizes both inter-row and intra-row competition, allowing for better root distribution and resource use efficiency [66,67]. This strategy improves the structural integrity of maize populations and reduces lodging risks, resulting in higher yields [68,69]. However, its effectiveness is highly dependent on environmental factors such as rainfall and soil fertility, underscoring the need for localized implementation.

Plastic film mulching demonstrates strong regional specificity and is particularly beneficial in arid and semi-arid regions like the Loess Plateau. By improving soil moisture retention and heat preservation, it enhances water use efficiency and promotes photosynthesis, leading to higher yields [70,71]. Furthermore, plastic film mulching increases leaf stomatal density and photosynthetic rates, further supporting yield gains in dryland agriculture [72]. Its targeted application in water-scarce regions highlights its potential as a regionally adapted strategy.

Integrated management practices, combining multiple agronomic measures, often result in synergistic effects that surpass the contributions of individual practices. For example, the combination of precision irrigation, soil tillage, and plant growth regulators optimizes water, nutrient, and light resources simultaneously, creating more resilient and productive cropping systems [73,74]. However, the complexity of measuring these interactions introduces variability, making it challenging to develop universal recommendations. Tailoring integrated practices to suit local conditions is crucial to achieving higher yields and improving sustainability.

However, although agronomic measures combined with high-density planting have increased yield potential, their long-term impacts on soil health and water use must be carefully managed. Increased planting density can exacerbate soil nutrient depletion [75]. Precision irrigation, though effective in optimizing water use, may lead to over-extraction of groundwater in arid regions like Hebei Plain, China [76]. In addition, high density planting requires deeper topsoil, while traditional tillage often exacerbates soil degradation [77]. Future research should explore sustainable strategies to balance yield gains with environmental stewardship.

In summary, agronomic measures enhance OPD and yield potential by addressing region-specific constraints and optimizing resource use. Precision irrigation, soil tillage, plant growth regulators, row spacing adjustment, and plastic film mulching each contribute unique benefits, while integrated approaches offer additional synergies. Future research should focus on refining these strategies for localized application and exploring their combined effects to further unlock maize yield potential across diverse agro-ecological regions.

4.3. Regional Effectiveness of Agronomic Measures

The effectiveness of agronomic measures in improving OPD and tapping yield potential varies across different regions in China, due to the diverse climatic and geographic conditions. Furthermore, our analysis, based on the widely cultivated variety ‘Zhengdan 958’ (Supplementary Materials Figure S2), demonstrates the critical role of agronomic strategies in enhancing yield potential across key regions, including the North China Plain, Northeast China, and Southern China. These findings underscore the importance of tailored strategies that address regional challenges and leverage local advantages.

In Northeast China and the North China Plain, precision irrigation and soil tillage have proven highly effective in enhancing both OPD and yield potential. These regions benefit from favorable conditions such as gently sloping landscapes, synchronized rain and heat periods, and extended growing seasons characterized by optimal light and temperature conditions [43,78]. Soil tillage is particularly impactful in these areas, contributing to significant yield improvements with moderate increases in OPD by enhancing root growth, nutrient uptake, and water retention. Precision irrigation further complements these benefits by reducing water stress and improving water use efficiency, particularly during critical growth stages [79]. Notably, Northeast China predominantly cultivates spring maize, which has a longer growth cycle and greater potential for optimizing resource use efficiency [30], while the North China Plain focuses on summer maize, benefiting from mechanized farming and robust infrastructure [80,81,82].

Northwest China presents a contrasting scenario, where abundant solar radiation and thermal resources create ideal conditions for high-density planting. This region maintains some of the highest maize yield records in China [43]. However, limited rainfall restricts the areas suitable for maize cultivation, which are primarily used for seed production and constitute only 5% of the total maize planting area [83]. Precision irrigation is critical in this arid region, ensuring sufficient water availability to support high-density planting and sustain yield potential. Tailored agronomic practices that optimize water use and leverage the region’s solar advantages are essential for maximizing productivity.

In contrast, Southwest and Southern China face unique challenges due to demanding climatic conditions, variable rainfall, and diverse topography. These regions are characterized by frequent rainfall variability, which can lead to waterlogging and drought within a single growing season, complicating water management strategies [33,84]. Steep terrains and heterogeneous soil types further hinder the uniform application of agronomic measures. For instance, row spacing adjustments, which are effective in flatter regions, prove less suitable here due to irregular plant stands and inconsistent root distribution [85]. In Southern China, despite the high clay content of soils, low nutrient availability and poor permeability limit root growth and respiration, reducing maize productivity [86]. Plant growth regulators have demonstrated effectiveness in these regions by enhancing stalk strength, improving lodging resistance, and mitigating nutrient deficiencies, making them a key strategy for addressing these environmental challenges.

The regional variations in the effectiveness of agronomic measures highlight the need for strategies tailored to local climatic and soil conditions, particularly in light of future climate uncertainties [17]. For regions with relatively favorable water availability, such as Northeast China and the North China Plain, precision irrigation and soil tillage should be prioritized to maximize yield potential. Conversely, in areas facing more complex challenges, such as Southwest and Southern China, plant growth regulators may offer higher benefits [27]. Combining agronomic measures with high planting density in Northeast China, where conditions are most favorable, holds the greatest potential for significant yield improvements. In comparison, regions like Southwest China show relatively modest responses, emphasizing the need for targeted interventions to address specific regional constraints.

Developing region-specific guidelines for agronomic measures is crucial to assist farmers in adjusting planting densities and management practices based on local conditions. Training and support programs that empower farmers to efficiently implement these measures are equally important. By aligning agronomic strategies with the distinct environmental and socio-economic contexts of each region, it is possible to boost productivity, foster sustainable agricultural systems, and enhance the resilience of maize production in China.

4.4. Study Limitations and Future Research Perspectives

This study has demonstrated the significant potential of optimizing planting density and implementing agronomic measures to improve maize yields across diverse regions in China. However, limitations in data availability and scope highlight areas for further investigation.

First, the scarcity of data from Northwest China limits our understanding of the combined effects of agronomic measures and planting density optimization in this high-potential region. Future efforts should prioritize data collection and collaboration with local institutions to enhance regional recommendations. Similarly, agronomic measures such as soil tillage were underrepresented in southern Chian, constraining a comprehensive evaluation of their effectiveness. Expanding field experiments to include a broader range of practices across regions is needed in future work. Second, the influence of genotype on yield potential remains underexplored. While widely cultivated varieties like ‘Zhengdan 958’ were included, detailed data on genotype-specific responses to planting densities and agronomic measures are needed to refine recommendations. This includes evaluating the interaction between genotypes and local environmental conditions, particularly in high- and low-density planting scenarios. The density tolerance of corn largely depends on the type of cultivated variety [45]. Modern hybrids usually achieve high-density tolerance by improving plant structure [46,47]. However, for some other varieties, the plants will not tolerate crowding and respective shading, and thus production will be affected. Therefore, when considering dense planting, it is necessary to take into account both the selection of varieties and environmental adaptability.

Third, environmental factors such as soil fertility, water retention, and climate variability play a critical role in determining yield potential but were only partially addressed in this study. Future research should adopt a systems approach to assess the interactions among soil, climate, and genotype, ensuring that recommendations are robust and regionally adaptable. Finally, integrated management practices were excluded due to their complexity and variability across trials. Future studies should develop robust designs to evaluate the combined effects of multiple measures, such as precision irrigation and soil tillage, to identify synergistic strategies for optimizing yields. Set goals for environmental friendliness, resource conservation, and efficiency. Additionally, the economic feasibility of these practices remains underexplored. Developing cost-benefit models tailored to regional socio-economic constraints will provide actionable insights for optimizing both productivity and profitability.

In conclusion, addressing these limitations requires interdisciplinary research that integrates agronomic, environmental, and economic factors. Expanding data collection, exploring genotype-specific responses, and refining economic models will enable more accurate and region-specific recommendations. Such efforts are essential to sustainably maximize maize productivity and support resilient agricultural systems in China.

5. Conclusions

In this study, we compiled a dataset focusing on the optimal planting density and yield potential of maize. We discovered that combining high-density planting with appropriate agronomic measures in various regions can further tap yield potential. Currently, the planting density employed by Chinese farmers is relatively low. Transitioning from their current practices to the optimal planting density, which involves increasing the planting density by 49.34%, could significantly boost the maize yield by 16.28%. This increase is particularly noticeable in Northeast China and the North China Plain. By optimizing agronomic measures along with planting density, the yield of each region was further elevated by 1.23–13.22% (with an overall increase of 9.43%) compared to optimizing only the planting density. The application of agronomic strategies such as precision irrigation, soil tillage, plant growth regulators, row spacing adjustment, and plastic film mulching generally demonstrated promising yield potentials, and the suitability of these agricultural tactics varied across regions. Based on these findings, this study offers recommendations for optimal planting densities and intensified agronomic strategies in different regions, offering significant advantages for sustainable regional agricultural development. To ensure successful implementation, region-specific extension programs should prioritize precision irrigation and soil tillage in water-sufficient areas like Northeast China, coordinated with strategies to increase planting density, while promoting plant growth regulators in regions with climatic constraints. Policymakers and agricultural institutions must collaborate to provide training, subsidies, and infrastructure support for farmers adopting these practices. Future research should focus on genotype-specific responses under varying densities, cost-benefit analyses of integrated strategies, and climate-resilient agronomic frameworks to sustain yield gains amid global change.