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Article

Determining the Critical Nitrogen Application Rate for Maximizing Yield While Minimizing NO3−-N Leaching and N2O Emissions in Maize Growing on Purple Soil

by
Yuanyuan Liu
†,
Yuanxue Chen
†,
Dandan Peng
,
Huabin Lu
,
Ting Shuai
,
Ying Quan
,
Chunling Zeng
and
Kaiwei Xu
*
College of Resources, Sichuan Agricultural University, Chengdu 611130, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(6), 1358; https://doi.org/10.3390/agronomy15061358
Submission received: 29 March 2025 / Revised: 4 May 2025 / Accepted: 29 May 2025 / Published: 31 May 2025
(This article belongs to the Section Soil and Plant Nutrition)
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Abstract

Optimization of nitrogen (N) fertilizer application is essential to achieve higher crop yields at lower environmental costs. This study investigated the impacts of N application rates (0, 180, and 360 kg N ha−1) on maize productivity, N use efficiency (NUE), NO3−-N leaching, cumulative N2O emissions, and N surplus in maize growing in the purple soil of Southwest China through a 2-year field study. The critical N rate balancing yield optimization with reduced NO3−-N leaching and N2O emissions was identified. The results showed that grain yield initially increased linearly and then stabilized with increasing N rates, while NUE significantly decreased. NO3−-N leaching, N2O emissions, and N surplus exhibited quadratic increase. Regression analysis indicated that 158–163 kg N ha−1 achieved optimal yield while maintaining acceptable NO3−-N leaching and N2O emissions compared to 360 kg N ha−1. This range also enhanced NUE and minimized soil N residue.

1. Introduction

As a vital cereal crop globally, maize cultivation area has reached 1.94 × 108 ha, accounting for 27% of total cereal cultivation in 2017 [1], with its production systems facing dual challenges of food security and ecosystem sustainability. From 1980 to 2015, continuous nitrogen (N) input increased maize yield from 3.15 t ha−1 to 5.64 t ha−1 [1], yet this growth incurred significant environmental costs. In China, the average N application level in maize fields is 249 kg ha−1 [2]. This value considerably surpasses the global average of 169 kg ha−1, as reported by [3]. Although excessive N application minimally affects crop yield, it substantially reduces N use efficiency [4]. Globally, only 41% of applied N is effectively absorbed by maize [3]. Unutilized N enters the environment through nitrate (NO3−-N) leaching, nitrous oxide (N2O) emissions, and ammonia volatilization [4,5]. Data indicate that in 2020, global maize systems emitted 1.80 × 105 t of N2O and lost 1.47 × 106 t of NO3−-N due to fertilizer overuse [6]. These losses exacerbate water eutrophication and groundwater pollution, while their profound climate impacts have become a global concern, underscoring the critical need for optimizing N application rates to balance agricultural productivity with environmental sustainability [4,5].
Optimizing N fertilization to balance food security and environmental sustainability has become a research hotspot in agriculture [7]. The extensive application of N fertilizers has triggered a cascade of environmental issues, including eutrophication of surface water bodies and excessive NO3−-N concentrations in groundwater [8]. Under this context, aquatic N emission indicators such as runoff N concentration, soil NO3−-N residue, and NO3−-N leaching intensity have become critical parameters for determining optimized N management strategies [4,7,8]. Consequently, current research primarily focuses on identifying optimal application rates or thresholds by integrating NO3−-N leaching dynamics with crop yield optimization or economic objectives [4,7,8]. For example, [7] confirmed that 180–200 kg N ha−1 in wheat–maize rotations on China’s Loess Plateau maintains high maize profitability while reducing environmental costs. Field trials in Northwest China by [9] revealed that split application with 28.5% N reduction boosted economic returns by 397 CNY ha−1, outperforming conventional high-rate single applications by 1675 CNY ha−1. Similarly, [4] demonstrated that 180–196 kg N ha−1 achieves maximum yield while minimizing NO3−-N leaching in radish-production regions of Northern China. However, critical N rates balancing yield targets with NO3−-N leaching in maize growing in purple-soil regions remain underexplored.
NO3−-N, as a key substrate for N2O production, directly drives soil denitrification through its accumulation [10]. With a 100-year global-warming potential 273 times higher than carbon dioxide, N2O contributes 7% to total radiative forcing due to its long atmospheric lifetime (114 years) [11,12]. Even minor concentration increases can intensify greenhouse effects and accelerate biodiversity loss [13]. Current N2O emission growth rates exceed IPCC projections, potentially driving global warming to 3 °C [14], surpassing the Paris Agreement’s 1.5 °C safety threshold [15] and threatening human health. Agriculture, as the largest anthropogenic N2O source [5], contributes over 60% of global human-induced emissions through continuous N inputs [14], making emission reduction imperative. Existing mitigation measures include organic fertilizer substitution, straw return, reduced tillage, and optimized fertilization timing/placement [16,17]. However, reducing excessive chemical fertilization remains the most promising pathway [14].
Farmland N2O emissions exhibit regional heterogeneity in response to N application: linear increases in Canadian maize and vegetable systems [18,19] versus exponential or quadratic relationships in Southwestern China’s open-field vegetables and garlic–maize rotations [20,21]. Global meta-analyses confirm exponential emission increases with N input [22], creating high uncertainty in regional inventories [23]. This spatial variability underscores the necessity of soil–crop system-specific studies.
As a key maize production hub in China, the southwest maize region contributed 11.7% of national output over recent three years [24]. Its purple soil systems exhibit unique N cycling characterized by high background N loads, elevated leaching risks, and complex terrain that collectively intensify N losses [25]. Current regional research predominantly focuses on singular environmental impact assessments [26,27], lacking systematic exploration of “yield–environment” dual-objective optimization. Although process-based models (e.g., denitrification–decomposition models) can simulate crop yield, NO3−-N leaching, and N2O emissions concurrently—providing a framework to determine environmentally and agronomically balanced N recommendations [2,28]—regional-scale variability in topography, soil properties, climatic conditions, and management practices critically influences crop growth and N cycling, thereby complicating the development of universal management strategies [29].
To this end, we conducted a 2-year trial with different N application rates based on a 12-year long-term locational trial, which included (1) the effects of different N application rates on maize yield, N accumulation, and N fertilizer utilization; (2) the response of NO3−-N leaching, N2O emissions, and N surplus under different N application rates; and (3) the determination of a critical N input rate that can maintain crop yield and high N fertilizer utilization while minimizing adverse environmental impacts, with a view to providing data support for the development of N fertilizer management strategies according to local conditions.

2. Materials and Methods

2.1. Study Site and Design

The agronomic trials were executed in the subtropical monsoon region of Ya’an City, Southwestern China (29°58′ N, 102°58′ E), characterized by distinct seasonal variations in temperature and precipitation. Meteorological records indicate that this area maintains an average annual temperature of 21.0 °C, with seasonal extremes from 1.7 °C (January) to 30.0 °C (July), coupled with 1365 mm mean annual rainfall. Based on the World Reference Base (FAO Classification), the soil at the experimental site is categorized as Luvic Xerosols, and its texture is sandy clay loam. Prior to this study, spring maize (‘Zhongyu No.3′, Sichuan Academy of Agricultural Sciences, Chengdu, China) had been continuously cultivated as a single-season crop for 12 consecutive years at this long-term positioning experiment site. This high-yielding cultivar demonstrates stable productivity, strong stress resistance, superior grain quality, and broad adaptability across diverse environments. The maize was sown in a “90 cm: 50 cm” wide–narrow row configuration with an in-row spacing of 23 cm, achieving a planting density of approximately 67,500 plants per hectare. Before the commencement of the experiment, the soil was extracted with 1:1 (weight/volume), and then pH was determined using a PHS-3E pH meter (INESA, Shanghai, China) according to [30]. Organic carbon (SOC) content was analyzed using the potassium dichromate volumetric method (K2Cr2O7, Sinopharm, Shanghai, China). Total N (TN) was quantified through the semi-micro Kjeldahl method (Kjeltec 8400, FOSS, Hillerød, Denmark). Available potassium (AK) was measured via NH4OAc extraction coupled with flame photometry (FP640, INESA, Shanghai, China). Available phosphorus (AP) was assessed using NaHCO3 extraction with molybdenum–antimony colorimetric spectrophotometry (Multiskan GO, Thermo Fisher, MA, USA) [30,31]. The analytical results are presented in Table 1.
The field experiment began in 2010 and consisted of three different N treatments (kg N ha−1 y−1): 0 (control, N0), 180 (moderate N, N180), and 360 (farmer’s practice N, N360) [26,27]. We used urea as the N fertilizer and split the treatment into two applications: 50% in early April (as basal fertilizer at sowing) and 50% in early June (during the mid-growth). P (super single phosphate, 75 kg P2O5 ha−1) and K (potash of sulfate, 105 kg K2O ha−1) were all applied at sowing in early April; these applications were based on soil analyses [26,27]. Prior to planting, the basal fertilizers were integrated into the upper 0–25 cm soil layer through rotary tillage. The top dressings were applied with water to the sides of the maize. Each treatment consisted of three replicated plots, which were arranged in a completely randomized design. Individual plot dimensions measured 22.8 m2, comprising two pairs of 9.5 m long wide–narrow rows for maize cultivation.

2.2. Sampling and Analysis

2.2.1. Determination of Maize Plant Biomass and Yield, and Calculation of N Use Efficiency

Plant sampling was conducted during maize harvest seasons in 2022 and 2023. From each plot, five uniformly growing plants were selected, severed at the stem base, and separated into distinct components: stems, leaves, husks, cobs, grains, and roots. The samples underwent enzymatic deactivation at 105 °C for 30 min a DHG-9140 forced-air oven (Yiheng, Shanghai, China), followed by drying at 80 °C to constant weight for biomass determination and subsequent nutrient analysis. Yield assessment involved harvesting two central rows per plot. Total ear count and fresh weight were recorded, with grains threshed for moisture-content determination performed using a PM-8188 grain moisture meter (NewSans, Shenzhen, Guangdong, China). Final grain yield was standardized to 14% moisture content [9].
Plant samples were digested using the sulfuric acid–hydrogen peroxide (H2SO4-Sinopharm, Shanghai, China; H2O2-Sinopharm, Shanghai, China) method, N content in each plant component was determined through steam distillation titration employing a K1100 automatic Kjeldahl distillation unit (INESA, Shanghai, China) coupled with a DL50 autotitrator (Mettler Toledo, Zurich, Switzerland). N accumulation was calculated as the product of tissue-specific N concentration multiplied by corresponding biomass [7].
The recovery efficiency (REN), partial factor productivity (PFPN), and agronomic efficiency (AEN) of N were calculated as follows [4]:
REN (%) = (UN − U0)/N × 100
PFPN (kg kg−1) = Yg/N
AEN (kg kg−1) = (Yg − Y0)/N
where UN and U0 are the total N uptake (kg ha−1) by grain, leaf, husk, cob, and stem in the fertilizer treatments and the control treatment, respectively; Yg and Y0 are the grain yield (kg ha−1) in the fertilizer treatments and the control treatment, respectively; and N is the total N fertilizer inputs (kg ha−1).

2.2.2. Calculation of NO3−-N Leaching

Prior to spring maize sowing (3 April 2022 and 4 April 2023) and after harvest (16 August 2022 and 24 August 2023), five profile soil samples (0–60 cm depth) were collected from each experimental plot. These samples were stratified at 20 cm intervals and composited by layer. After removing roots and gravel, fresh soil was sieved through a 2 mm mesh (Retsch, Haan, Germany) and thoroughly homogenized. Soil NO3−-N was extracted with 2 M KCL solution (Sinopharm, Shanghai, China) and determined following the method described by [31]. Bulk density (BD) was measured using a ring shear test [30]. NO3−-N accumulation (kg ha−1) in each layer was expressed as the product of the BD of the corresponding soil layer, NO3−-N content, soil thickness, and a conversion factor of 0.1, and NO3−-N leaching was characterized by analyzing changes in NO3−-N accumulation in the 0–60 cm soil layer of spring maize before sowing and after harvesting [32,33].

2.2.3. Calculation of N2O Emissions

N2O emissions from purple soil during the 2022–2023 maize growing seasons were monitored using the static chamber–gas chromatography method. Following maize sowing and basal fertilizer application, stainless-steel bases (50 cm × 50 cm × 15 cm; Beijing Dongfang Yienong Sci-Tech, Beijing, China) were installed at the centers of both wide and narrow interrows in each plot. Gas collection involved positioning static chambers (50 cm × 50 cm × 50 cm; polycarbonate material, Patent ZL2021102345.6, Beijing Dongfang Yienong) onto these pre-fixed bases. The sampling frequency, analytical techniques, and calculation formulas for N2O fluxes and cumulative emissions followed the methods outlined in our previous work [26].

2.2.4. Calculation of N Surplus

N surplus is defined as the difference between N input and output, calculated as follows [32]:
N input (kg N ha−1) = Nf + Ns
N output (kg N ha−1) = Ngra + Nnit + NN2O
where Nf is the N fertilizer rate (kg N ha−1), Ns is the straw N returned to the field in the previous season (kg N ha−1), Ngra is the grain N uptake (kg N ha−1) in the plot, Nnit is the NO3−-N leaching (kg N ha−1) in the plot, and NN2O is the N2O emissions (kg N ha−1) in the plot.

2.3. Statistical Analysis

All data were first assessed for normality using OriginPro 2021 (OriginLab Corporation, Northampton, MA, USA), and subsequently analyzed using two-way ANOVA in SPSS 16.0 (IBM, Armonk, NY, USA). This analysis focused on maize yield, plant biomass, N content, N accumulation, and N fertilizer-use efficiency under each fertilizer treatment across the two-year period. Simultaneously, data on NO3−-N accumulation, NO3−-N leaching, and soil N surplus for each fertilizer treatment were analyzed using one-way ANOVA. Differences were assessed for significance at the 0.05 level using the LSD method. Additionally, the relationships between N application and grain yield, NO3−-N accumulation, NO3−-N leaching, N2O emissions, and N surplus were modeled using OriginPro 2021.

3. Results

3.1. Crop Productivity

On average, for both years, spring maize grain yield was 4.20 and 4.31 times higher than N0 under N180 and N360 treatments, respectively (Figure 1a). Further, the fitting of N application to grain yield revealed a linear–platform relationship between N application and grain yield, with equations y = 44.0x + 2213.3 (0 < x < 162.8), y = 9698.3 (x ≥ 162.8), and R2 = 0.99***. The platform yield was 162.8 kg N ha−1 when N was applied at a rate of 9698.3 kg N ha−1, and it continued increase in N fertilizer had no significant effect on yield (Figure 2).
The aboveground and root biomass of spring maize exhibited a consistent pattern of initial increase followed by stabilization with N application across both experimental years. When N application exceeded 180 kg ha−1, plant biomass showed no statistically significant variations. Compared to the control treatment (N0), the N180 and N360 treatments demonstrated substantial biomass enhancement, with aboveground biomass averaging 4.03-fold and 4.06-fold increases, respectively. Similarly, root biomass in these N-amended treatments reached 2.51-fold (N180) and 2.46-fold (N360) of the control values during the two-year study period (Figure 1b).
Spring maize exhibited consistent N concentration gradients across plant components at harvest, following the order of grain > root > leaf > husk > cob > stem, irrespective of N application rates during both growing seasons. The N contents of different parts of spring maize increased gradually with the increase in N application and reached the maximum value when the N application rate was 180–360 kg N ha−1. On average, the N contents of stem, leaves, husk, cob, grains, and roots increased 0.51–3.95, 0.24–0.93, 0.43–4.51, 0.39–1.60, 0.10–0.63, and 0.22–2.64 times, respectively, in each of the two-year N treatments compared with that of the no-N treatment (Figure 1c).
Maize exhibited a distinct N partitioning hierarchy at physiological maturity, with grains serving as the predominant N reservoir (46.2% of total accumulation), followed sequentially by leave (18.7%), root (12.4%), stem (9.80%), cob (7.10%), and husk (5.80%). N fertilization (180–360 kg ha−1) significantly enhanced N allocation across all plant compartments, demonstrating 86.9–127.3% mean elevation relative to the non-fertilized control (N0) during the biennial observation period (Figure 1d).
PFPN and AEN exhibited significant responses to the year, N application treatments, and their interactive effects (p < 0.05), whereas REN remained insensitive to year–N application treatment interactions. The elevated N input (N360) induced marked reductions in all efficiency metrics relative to N180, with biennial mean decreases of 44.2% (REN), 48.6% (PFPN), and 48.1% (AEN) (Table 2).

3.2. NO3−-N Leaching

N application significantly enhanced soil NO3−-N content across all soil layers (9.19–257% increase) compared to the N0 treatment, regardless of sampling timing (pre-sowing or post-harvest). Under identical N application treatments, soil NO3−-N content exhibited a decreasing trend with increasing soil depth. Furthermore, all soil layers demonstrated significant temporal depletion of NO3−-N content from pre-sowing to post-harvest phases, irrespective of fertilization status (Figure A1).
N fertilization significantly enhanced soil NO3−-N accumulation across all soil layers compared to the N0 treatment, regardless of sampling timing (pre-sowing or post-harvest) over the two-year period. Vertical stratification analysis revealed a consistent decline in NO3−-N accumulation with increasing soil depth under both fertilized and unfertilized conditions (Table 3). Post-harvest NO3−-N accumulation was markedly reduced by 28–63% relative to pre-sowing levels across all treatments. Profile-scale (0–60 cm) analysis demonstrated that N180 and N360 treatments increased NO3−-N accumulation by 18.8–84.3% and 43.8–152%, respectively, compared to N0 treatment. A significant exponential relationship emerged between N input and NO3−-N accumulation (y = 40e0.002x; R2 = 0.61***) (Figure 3).
NO3−-N leaching were quantified based on differentials in NO3−-N accumulation between pre-sowing and post-harvest measurements. During the 2022 spring maize growing season, leaching values reached 113 (N0), 136 (N180), and 156 kg N ha−1 (N360), while the 2023 season showed reduced losses at 46.5 (N0), 87.1 (N180), and 110 kg N ha−1 (N360). A significant quadratic relationship emerged between N application rate and NO3−-N leaching: y = 46.5 + 0.48x − 6.65 × 10−4x2 (R2 = 0.64***, Figure 3).

3.3. N2O Emissions

N2O emissions exhibited a positive correlation with N application rates, with observed values ranging from 0.13 to 3.61 kg N ha−1 across both growing seasons. The N360 treatment generated N2O emissions 13.4-fold that of N0 and 2.35-fold that of N180. A robust quadratic relationship was established between N input and N2O flux: y = 0.34 + 0.003x + 1.26 × 10−5x2 (R2 = 0.92***, Figure 4).

3.4. N Surplus

N inputs showed no significant interannual variation during the 2022 and 2023 spring maize growing seasons. In contrast, N outputs (sum of grain N uptake, cumulative N2O emissions, and NO3−-N leaching) demonstrated significant interannual differences. Specifically, the 2022 season exhibited 13.6% higher grain N uptake and 0.67-fold greater NO3−-N leaching compared to 2023, whereas cumulative N2O emissions were significantly lower in 2022 (26.8% reduction). Consequently, total N outputs were substantially higher in 2022 (131–270 kg N ha−1) compared to 2023 (61.4–214 kg N ha−1). Both N180 and N360 treatments significantly elevated N outputs relative to N0, with increases exceeding 69.0% and 89.5%, respectively. N surplus values for N0, N180, and N360 were −131–42.0, −20.1–56.5, and 143–209 kg N ha−1 for the two years, respectively (Table 4).
Furthermore, the relationship between N application rate and soil N surplus was investigated by establishing a quadratic regression model (y = 9.72 × 10−4x2 + 0.37x − 82.9; R2 = 0.906***) through coordinate analysis with N input as the abscissa (x-axis) and N surplus as the ordinate (y-axis). Notably, a breakeven point (zero N surplus) was identified at an application rate of 158.8 kg N ha−1 (Figure 5).

4. Discussion

4.1. Impact of N Fertilization Gradients on Grain Yield in Spring Maize and N Use Efficiency

Numerous studies have shown that N application exerts a significant influence in promoting an increase in crop yields [7,34]. Field experiments conducted over two consecutive growing seasons revealed that N fertilization boosted spring maize grain yields by 2.41- to 5.44-fold compared to unfertilized controls (Figure 1a). While demonstrating significant yield enhancement within optimal application ranges, the growth rate of crop productivity diminished substantially when N inputs surpassed critical threshold levels [4,7,35]. The prevalent linear-plateau/quadratic regression–response patterns observed between N inputs and crop productivity under field conditions are primarily mediated through tripartite mechanisms: (1) suboptimal canopy photosynthesis caused by mutual shading; (2) high respiratory loss resulting from high tissue N concentration; and (3) inefficient carbohydrate translocation from straw to grain [36,37]. In addition, excessive N fertilization induces multifaceted detrimental impacts, manifested through soil structural degradation and pH decline, while synergistically constraining plant-accessible nutrient/water reserves and photosynthetic efficiency [38,39]. In the present research, for spring maize, no remarkable differences in maize yield were observed between the N180 and N360 treatments, as depicted in Figure 1a and Figure 2. This finding indicates that the application of excess N (N360) did not result in a substantial enhancement of crop yield when compared to the N180 treatment. Consequently, local farmers could potentially apply approximately 180 kg N ha−1 in a single season, thereby reducing N input by approximately 50% without compromising maize yield. Concurrently, [28] discovered that when N application was near crop requirements, additional N fertilizer application did not effectively translate into maize yield.
Studies using the 15N isotope tracer technique have shown a notable threshold effect between N fertilizer application and environmental risk. Once N fertilizer inputs surpass the critical level, the dissipation capacity of the system reaches saturation. Subsequently, soil N residue enters a stable phase, accompanied by a substantial rise in fertilizer N losses [37]. For example, part of the excess N fertilizer accumulates in the soil profile [7,40], and the rest enters the environment via ammonia volatilization, nitrate leaching, and N2O emission [3,40]. Moreover, it has been reported that as the quantity of N applied rises, the utilization rate of fertilizer N typically declines [4,7,28]. This finding is consistent with the trend observed in this study, where the REN, PFPN, and AEN decrease with an increase in N application amount (Table 2). It indicates that, compared with N360 treatment, moderate N application (i.e., N180) can more effectively improve the utilization efficiency of N fertilizer.

4.2. Impact of N Fertilization Gradients on NO3−-N Leaching and N2O Emissions in Purple Soils

Excessive N application leads to a large accumulation of NO3−-N in the soil profile [7,40]. In a 7-year field-rotation experiment of winter wheat and summer maize conducted in the Guanzhong Plain, it was found that when the N application rate was 165–495 kg N ha−1, the average accumulation of NO3−-N in the 0–100 cm soil layer was 63.6–220.4 kg N ha−1 [7]. In this study, after the spring maize harvest, when the N application rate was 360 kg N ha−1, the average accumulation of NO3−-N in the 0–60 cm soil layer was 97.7 kg N ha−1. Compared with the conventional N fertilization level, a 50% reduction (i.e., N180) decreased NO3−-N accumulation in the 0–60 cm soil profile by 30.3% (Table 3). The observed exponential correlation between fertilization intensity and NO3−-N accumulation suggests that elevated fertilizer inputs have the potential to augment the proportion of residual N fertilizer within the soil matrix (Figure 3) [4,7]. When the amount of N applied exceeds the crop demand, the accumulated NO3−-N cannot be fully absorbed and utilized by the next crop. When heavily irrigated or subjected to heavy rainfall, this NO3−-N will gradually migrate to deeper soil layers [40]. Soil texture also significantly affects NO3−-N leaching; if the texture is loose, with large inter-particle pores and weak capillary effects, nutrients are easily leached. The purple soil in this experimental area has low clay particle content [41]. Similarly, [2] pointed out that NO3−-N leaching was more serious in areas dominated by silt sand, light, and fluvo-aquic soil after comparative analysis.
A notable variation in leaching was observed across years when analyzing leaching based on NO3−-N accumulation, with average NO3−-N leaching recorded as 135 and 81.1 kg N ha−1 in 2022 and 2023, respectively (Table 4). This discrepancy can be ascribed to the occurrence of moderate rainfall (12.3 mm) on the subsequent day of sowing in 2022, and the cumulative rainfall during the seedling stage (April–May) in that year was 418 mm, which was significantly higher than the 234 mm recorded in 2023. Additionally, the N demand of maize during the seedling stage was not high [42], and the substantial precipitation enabled the soil N that had not been absorbed by the crop to percolate, thereby exerting a direct effect on the extent of NO3−-N leaching [9,40]. The range of NO3−-N leaching was 41.1–106.4 kg N ha−1 in 2023, which is comparable to the range of NO3−-N leaching reported by [41] in their study in the district (12.3–110 kg N ha−1 yr−1). The analysis revealed that NO3−-N leaching exhibited an average increase of 28.6% in response to the N360 treatment in comparison to the N180 treatment. Quadratic regression analysis of NO3−-N leaching versus N fertilization intensity revealed a positive correlation between the N input escalation and leaching coefficient (Figure 3). These findings align with documented N dynamics in wheat–maize systems across Northern China [7]. In summary, the application of excessive N has been demonstrated to increase the proportion of NO3−-N leaching. Furthermore, the leaching of NO3−-N has been identified as one of the primary routes through which N is lost in agricultural areas.
NO3−-N as a substrate for N2O production through denitrification: Excessive N application has been shown to not only affect the leaching of NO3—N; it also has an impact on the emission of N2O [2]. The quadratic relationship between N application and N2O emission clearly reflects this phenomenon (Figure 4). A comparison of the N180 treatment with the N360 treatment reveals that the former results in an average of 1.35 times the emission of N2O (Table 4). The following reasons have been posited to explain this phenomenon: when the N applied to a crop exceeds its requirements, the excess N can, in principle, provide abundant energy to the nitrifying microorganisms in the soil. NO3−-N produced by nitrification has been demonstrated to facilitate denitrification, which in turn increases N2O production [43]; conversely, the excess N application lowers the pH of the purple soil, and the pH of the soil treated with a N application rate of 360 kg N ha−1 treatment had a soil pH of 5.97 [26]. Ref. [44] demonstrated that the N2O: N2 ratio during denitrification decreased exponentially when the soil pH increased from 4 to 7, with the highest N2O emissions occurring at pH values of 5.6–6. Furthermore, an increase in the quantity of N added to the soil resulted in greater root biomass in the maize plants (Figure A1). This, in turn, led to an increase in soil porosity, which also contributed to the emission of N2O [33].

4.3. Impact of N Fertilization Gradients on N Surplus in Purple Soils

The objective of sustainable agriculture is to satisfy the food requirements of a growing population with efficient crop productivity and nutrient utilization while minimizing negative environmental impacts [45]. A substantial body of research has demonstrated that the judicious application of fertilizer, in suitable amounts, is the most efficacious strategy to mitigate the environmental impacts of NO3−-N leaching and N2O emissions [28]. The N180 treatment has been shown to result in soil N surplus values that are well below the N surplus benchmarks for Dutch and EU cropping systems (Table 4) [46,47]. The N180 treatment demonstrated a substantial benefit in terms of N harvesting of crop grain when compared to the N0 and N360 treatments (Figure 1a). The enhanced uptake of N by the crop resulted in enhanced economic efficiency in agriculture, whilst simultaneously averting significant N environmental pollution. Furthermore, the application of 180 kg N ha−1 holds the possibility of further diminishing the N surplus in terms of NO3−-N leaching and N2O emissions. This assertion is substantiated by the findings that demonstrated the soil N surplus value was zero at 159 kg N ha−1 fertilization intensity (Figure 5). Nevertheless, it was noted that the maximum maize yield of 9,698 kg ha−1 was attained when the N application rate reached 163 kg N ha−1 (Figure 2). In summary, the optimal N application range was identified as (158–163 kg N ha−1), achieving a balance between maize yield, NO3−-N leaching, and N2O emissions.

5. Conclusions

In conclusion, the findings of this research indicated that the yield of spring maize reached an optimal state when N was applied at a rate ranging from 158 to 163 kg N ha−1. Simultaneously, the leaching of NO3−-N and the emissions of N2O were kept within acceptable limits. This rate of N fertilizer application led to enhanced N fertilizer utilization efficiency. In comparison with the conventional N application rates employed by farmers, it notably reduced the leaching of NO3−-N and the emissions of N2O. Moreover, it also decreased soil N residues. Therefore, a critical N fertilization rate of 158–163 kg N ha−1 is recommended for spring maize in Southwest China. This study provides theoretical guidance for local farmers to improve their fertilizer management and environmental protection practices.

Author Contributions

Conceptualization, Y.L. and Y.C.; methodology, Y.L., D.P., H.L. and C.Z.; investigation, H.L., T.S., Y.Q. and C.Z.; software, Y.L., D.P. and T.S.; formal analysis, Y.L., D.P., H.L., T.S., Y.Q. and C.Z.; writing—original draft, Y.L. and K.X.; visualization, Y.C. and K.X.; supervision, Y.C. and K.X.; project administration, K.X.; resources and validation, D.P.; data curation, H.L., T.S., Y.Q. and C.Z.; funding acquisition, Y.C.; writing—review and editing, K.X. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Ministry of Agriculture and Rural Affairs, China (Grant No. 2022YFD1901402), and the Science and Technology Department of Sichuan Province (Grant Nos. 2025YFHZ0115 and 2024YF0502138SN).

Data Availability Statement

Data are available upon request due to limitations imposed by the contract of the project that supported the research. The data presented in this study are available upon request from the corresponding author to protect the results of the research project.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
NNitrogen
N2ONitrous oxide
NO3−-NNitrate
RENRecovery efficiency of nitrogen
PFPNPartial factor productivity of nitrogen
AENAgronomic efficiency of nitrogen

Appendix A

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Figure A1. Soil BD (a,b) and NO3−-N content (c,d) before sowing and after harvest in spring maize in 2022 and 2023. Different letters above columns indicate statistically significant differences (p < 0.05). N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
Figure A1. Soil BD (a,b) and NO3−-N content (c,d) before sowing and after harvest in spring maize in 2022 and 2023. Different letters above columns indicate statistically significant differences (p < 0.05). N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
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Figure 1. The grain yield (a), plant biomass (b), N content (c), and N accumulation (d) in various plant compartments of spring maize were systematically analyzed across the 2022 and 2023 growing seasons under different N fertilization regimes. Different letters above columns indicate statistically significant differences (p < 0.05). N stands for N application treatment, Y stands for year, and N × Y stands for the interaction between N application treatment and year. *** and * indicate significant differences at p < 0.001 and p < 0.05 levels, respectively. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
Figure 1. The grain yield (a), plant biomass (b), N content (c), and N accumulation (d) in various plant compartments of spring maize were systematically analyzed across the 2022 and 2023 growing seasons under different N fertilization regimes. Different letters above columns indicate statistically significant differences (p < 0.05). N stands for N application treatment, Y stands for year, and N × Y stands for the interaction between N application treatment and year. *** and * indicate significant differences at p < 0.001 and p < 0.05 levels, respectively. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
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Figure 2. Spring maize grain yields in 2022 and 2023. Different letters above columns indicate statistically significant differences (p < 0.05). *** p < 0.001 indicates the significance of the regression model. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
Figure 2. Spring maize grain yields in 2022 and 2023. Different letters above columns indicate statistically significant differences (p < 0.05). *** p < 0.001 indicates the significance of the regression model. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
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Figure 3. Regression analysis of nitrogen application with NO3−-N accumulation and NO3−-N leaching from post-harvest soil in 2022 and 2023. *** p < 0.001 indicates the significance of the regression model. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
Figure 3. Regression analysis of nitrogen application with NO3−-N accumulation and NO3−-N leaching from post-harvest soil in 2022 and 2023. *** p < 0.001 indicates the significance of the regression model. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
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Figure 4. Regression analysis of N level with cumulative N2O emissions in 2022 and 2023. *** p < 0.001 indicates the significance of the regression model. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
Figure 4. Regression analysis of N level with cumulative N2O emissions in 2022 and 2023. *** p < 0.001 indicates the significance of the regression model. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
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Figure 5. Regression analysis of N level with N surplus in 2022 and 2023. *** indicates a significant difference at the p < 0.001 level. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
Figure 5. Regression analysis of N level with N surplus in 2022 and 2023. *** indicates a significant difference at the p < 0.001 level. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
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Table 1. Chemical properties of the soil before the commencement of the experiment.
Table 1. Chemical properties of the soil before the commencement of the experiment.
Soil TypepHSOC (g kg−1)TN (g kg−1)AP (mg kg−1)AK (mg kg−1)
Purple soil6.3320.40.9140.573.1
Table 2. Nitrogen fertilizer utilization efficiency in 2022 and 2023.
Table 2. Nitrogen fertilizer utilization efficiency in 2022 and 2023.
YearTreatmentREN (%)PFPN (kg kg−1)AEN (kg kg−1)
2022N0///
N18071.1 ± 2.1 a44.4 ± 1.7 a36.7 ± 1.7 a
N36038.3 ± 0.4 b22.1 ± 0.7 b18.3 ± 0.7 b
2023N0/ /
N18079.1 ± 4.2 a60.3 ± 0.7 a43.4 ± 0.7 a
N36045.4 ± 0.6 b31.7 ± 0.7 b23.3 ± 0.7 b
Y********
N*********
Y × Nns*****
Recovery efficiency of nitrogen (REN), partial factor productivity of nitrogen (PFPN), agronomic efficiency of nitrogen (AEN). Data are means ± standard error. Different letters above columns indicate statistically significant differences within years (p < 0.05). *** and ** indicate significant differences at p < 0.001 and p < 0.01 levels, respectively, and ns indicates non-significant. N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
Table 3. Distribution of NO3−-N accumulation in 0–60 cm soil layer during spring maize harvest in 2022 and 2023.
Table 3. Distribution of NO3−-N accumulation in 0–60 cm soil layer during spring maize harvest in 2022 and 2023.
Sampling TimeTreatmentNO3−-N Distribution in the 0–60 cm Soil Layer
(kg ha−1)		NO3−-N Accumulated in the 0–60 cm Soil Layer (kg ha−1)
0–20 cm20–40 cm40–60 cm
Before spring maize sowed in 2022N053.0 ± 3.06 c48.6 ± 1.58 c40.8 ± 2.68 b142 ± 7.33 c
N18063.8 ± 1.98 b61.7 ± 2.13 b61.1 ± 2.79 a187 ± 1.33 b
N36081.6 ± 0.12 a71.3 ± 0.59 a68.5 ± 1.58 a221 ± 1.04 a
After spring maize harvested in 2022N015.9 ± 0.03 c6.70 ± 0.13 c6.50 ± 0.06 c29.1 ± 0.20 c
N18024.3 ± 0.07 b16.9 ± 0.11 b9.80 ± 0.03 b51.0 ± 0.10 b
N36027.2 ± 0.11 a23.2 ± 0.11 a14.6 ± 0.14 a64.9 ± 0.28 a
Before spring maize sowed in 2023N035.9 ± 1.18 c28.3 ± 0.86 c24.3 ± 1.39 c88.5 ± 3.36 c
N18050.9 ± 0.44 b50.1 ± 0.54 b48.6 ± 1.22 b150 ± 1.42 b
N36076.0 ± 2.74 a67.9 ± 0.78 a66.4 ± 1.43 a210 ± 1.86 a
After spring maize harvested in 2023N020.6 ± 1.05 c13.0 ± 0.22 c8.40 ± 0.32 c42.0 ± 1.24 c
N18028.5 ± 2.06 b19.9 ± 0.41 b14.1 ± 0.16 b 62.4 ± 2.54 b
N36045.0 ± 1.31 a29.8 ± 0.75 a25.7 ± 0.80 a100 ± 1.37 a
Data are means ± standard error. Different letters above columns indicate statistically significant differences within years (p < 0.05). N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
Table 4. Soil N residues under different N application treatments during the spring maize growing season in 2022 and 2023.
Table 4. Soil N residues under different N application treatments during the spring maize growing season in 2022 and 2023.
N FlowsSpring Maize in 2022 (kg N ha−1)Spring Maize in 2023 (kg N ha−1)
N0N180N360N0N180N360
N inputs
N fertilization rate01803600180360
Straw N returned14.1 ± 0.53 c49.4 ± 1.29 b57.5 ± 0.51 a12.8 ± 0.52 c48.8 ± 1.85 b57.3 ± 1.77 a
Total N inputs14.1 ± 0.53 c229 ± 1.29 b418 ± 0.51 a12.8 ± 0.52 c229 ± 1.85 b417 ± 1.77 a
N outputs
Grain N removal17.9 ± 0.61 c110 ± 2.58 b111 ± 2.06 a14.7 ± 0.26 c94.1 ± 5.18 b102 ± 2.59 a
Cumulative N2O emissions0.09 ± 0.00 c0.64 ± 0.03 b1.83 ± 0.07 a0.22 ± 0.01 c1.07 ± 0.04 b2.21 ± 0.05 a
NO3−-N leaching113 ± 7.16 c136 ± 1.27 b156 ± 1.06 a46.5 ± 3.58 c87.1 ± 1.15 b110 ± 2.10 a
Total N outputs131 ± 7.63 c246 ± 3.06 b270 ± 3.01 a61.4 ± 3.83 c182 ± 5.6 b214 ± 2.19 a
N surplus−117 ± 7.76 c−16.8 ± 1.98 b148 ± 3.35 a−48.6 ± 4.34 c46.5 ± 5.34 b203 ± 2.68 a
Data are means ± standard error. Different letters above columns indicate statistically significant differences within years (p < 0.05). N0, N180, and N360 represent 0, 180, and 360 kg N ha−1 yr−1, respectively.
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Liu, Y.; Chen, Y.; Peng, D.; Lu, H.; Shuai, T.; Quan, Y.; Zeng, C.; Xu, K. Determining the Critical Nitrogen Application Rate for Maximizing Yield While Minimizing NO3−-N Leaching and N2O Emissions in Maize Growing on Purple Soil. Agronomy 2025, 15, 1358. https://doi.org/10.3390/agronomy15061358

AMA Style

Liu Y, Chen Y, Peng D, Lu H, Shuai T, Quan Y, Zeng C, Xu K. Determining the Critical Nitrogen Application Rate for Maximizing Yield While Minimizing NO3−-N Leaching and N2O Emissions in Maize Growing on Purple Soil. Agronomy. 2025; 15(6):1358. https://doi.org/10.3390/agronomy15061358

Chicago/Turabian Style

Liu, Yuanyuan, Yuanxue Chen, Dandan Peng, Huabin Lu, Ting Shuai, Ying Quan, Chunling Zeng, and Kaiwei Xu. 2025. "Determining the Critical Nitrogen Application Rate for Maximizing Yield While Minimizing NO3−-N Leaching and N2O Emissions in Maize Growing on Purple Soil" Agronomy 15, no. 6: 1358. https://doi.org/10.3390/agronomy15061358

APA Style

Liu, Y., Chen, Y., Peng, D., Lu, H., Shuai, T., Quan, Y., Zeng, C., & Xu, K. (2025). Determining the Critical Nitrogen Application Rate for Maximizing Yield While Minimizing NO3−-N Leaching and N2O Emissions in Maize Growing on Purple Soil. Agronomy, 15(6), 1358. https://doi.org/10.3390/agronomy15061358

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