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Article

Study on the Distribution and Quantification Characteristics of Soil Nutrients in the Dryland Albic Soils of the Sanjiang Plain, China

by
Jingyang Li
1,2,
Huanhuan Li
3,4,*,
Qiuju Wang
1,2,
Yiang Wang
5,
Xu Hong
6 and
Chunwei Zhou
1
1
Heilongjiang Academy of Agricultural Sciences, Harbin 150086, China
2
Heilongjiang Provincial Key Laboratory of Soil Environment and Plant Nutrition, Harbin 150086, China
3
Department of Hydraulic Engineering, Hebei University of Water Resources and Electric Engineering, Cangzhou 061001, China
4
Hebei Technology Innovation Center for Coastal Wetland Water Resources Allocation and Ecological Protection, Cangzhou 061001, China
5
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
6
Agricultural Technology Center, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Harbin 150081, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(8), 1857; https://doi.org/10.3390/agronomy15081857
Submission received: 30 May 2025 / Revised: 28 July 2025 / Accepted: 29 July 2025 / Published: 31 July 2025
(This article belongs to the Section Soil and Plant Nutrition)
Browse Figures
Versions Notes

Abstract

The main soil type in the Sanjiang Plain of Northeast China, dryland albic soil is of great significance for studying nutrient distribution characteristics. This study focuses on 852 Farm in the typical dryland albic soil area of the Sanjiang Plain, using a combination of paired t-test, geostatistics, correlation analysis, and principal component analysis to systematically reveal the spatial differentiation of soil nutrients in the black soil layer and white clay layer of dryland albic soil, and to clarify the impact mechanism of plow layer nutrient characteristics on crop productivity. The results show that the nutrient content order in both the black and white clay layers is consistent: total potassium (TK) > organic matter (OM) > total nitrogen (TN) > total phosphorus (TP) > alkali-hydrolyzable nitrogen (HN) > available potassium (AK) > available phosphorus (AP). Both layers exhibit a spatial pattern of overall consistency and local differentiation, with spatial heterogeneity dominated by altitude gradients—nutrient content increases with decreasing altitude. Significant differences exist in nutrient content and distribution between the black and white clay layers, with the comprehensive fertility of the black layer being significantly higher than that of the white clay layer, particularly for TN, TP, TK, HN, and OM contents (effect size > 8). NDVI during the full maize growth period is significantly positively correlated with TP, TN, AK, AP, and HN, and the NDVI dynamics (first increasing. then decreasing) closely align with the peak periods of available nitrogen/phosphorus and crop growth cycles, indicating a strong coupling relationship between vegetation biomass accumulation and nutrient availability. These findings provide important references for guiding rational fertilization, agricultural production layout, and ecological environmental protection, contributing to the sustainable utilization of dryland albic soil resources and sustainable agricultural development.

1. Introduction

Albic soil is a restrictive, low-productivity soil type characterized by a unique three-layer profile consisting of a black soil layer, an albic soil layer, and an illuvial layer. It is distributed across agricultural regions in multiple countries, including France [1], China [2,3,4], Russia [5,6], and Malaysia [7]). The Sanjiang Plain in Northeast China is the core distribution area, where albic soil accounts for up to 25% of the total cultivated land [8,9]. This typical three-layer structure creates a distinct “nutrient-rich top and nutrient-poor bottom” gradient, resulting in a shallow plow layer with poor fertility. Key nutrients such as available phosphorus and alkali-hydrolyzable nitrogen exhibit pronounced vertical heterogeneity. This restrictive profile not only limits root access to deeper soil nutrients but also exacerbates the risks of soil erosion and fertility degradation. In the USDA Soil Taxonomy system, the albic horizon typically occurs in Alfisols or Spodosols, characterized by a light-colored eluvial layer depleted in clay, iron, or organic matter. According to the World Reference Base for Soil Resources (WRB), albic materials are often associated with Albic Luvisols or Albic Stagnosols, both commonly observed in agricultural areas of the Sanjiang Plain. It is widely recognized in the international soil science community as a representative low-yield soil type that severely constrains regional food production and sustainable agricultural development [10,11,12]. As a result, albic soil has been listed among the top ten most challenging soils to remediate globally by the international soil science community.
In recent years, under the combined stress of global climate change and intensive agricultural development, albic soil regions have widely experienced topsoil thinning, nutrient imbalance, and a decline in water and nutrient retention capacity. These issues have led to noticeable soil fertility degradation and increasing fluctuations in crop yield, becoming critical constraints on productivity improvement in major grain-producing areas. Existing research on dryland albic soil has primarily focused on the physicochemical characteristics of the albicization process [13,14,15,16], improvement technologies [17,18,19,20,21], and the optimization of crop productivity [22,23,24]. Xiu et al. conducted a two-year field trial to systematically assess the combined effects of biochar and straw return on the alleviation of the restrictive albic layer and soybean growth. They found that both amendments significantly improved soil physical permeability by reducing bulk density and increasing porosity in the albic soil layer [25]. Havryshko et al. demonstrated that different fertilization regimes and periodic topdressing under various crop rotation conditions significantly affected the size distribution and stability of soil aggregates, along with the physicochemical properties of albic soil. It proposed that the transformation from forest ecosystems to agricultural ecosystems effectively improved the pH characteristics of the albic soil layer [26]. Chen et al. investigated the effects of chemical fertilizers, straw, and biochar on the tillage characteristics of albic soil. The results showed that the use of amendments significantly improved soil workability, as evidenced by a reduced plasticity index (PI) and an increased friability index (FI) [27]. Zang et al. developed a probabilistic mixture model based on remote sensing imagery targeting the transitional zone between albic and non-albic soil layers, which accurately reflected the content and spatial distribution of soil organic matter in the albic layer of the Sanjiang Plain [28]. However, most existing studies focus on a single soil layer or a limited set of nutrient indicators, lacking systematic comparisons of spatial nutrient heterogeneity between the black soil layer and the albic soil layer. As a core component of material cycling in agroecosystems, the spatial distribution of soil nutrients directly influences crop productivity and the sustainable use of soil resources. In addition, vegetation indices such as NDVI provide an effective and non-destructive approach to monitor crop growth dynamics and assess the coupling between nutrient availability and biomass accumulation throughout the growing season. Integrating NDVI observations with soil nutrient analysis offers valuable insights for evaluating nutrient use efficiency and supports precision agriculture practices. Therefore, it is imperative to investigate the restrictive mechanisms of albic soil from the perspective of nutrient spatial heterogeneity and crop growth monitoring, to provide a scientific foundation for precision soil amelioration and crop productivity improvement.
Based on this, the present study focuses on 852 Farm, a typical albic soil region in the Sanjiang Plain of Northeast China. A combination of paired t-tests, geostatistical analysis, correlation analysis, and principal component analysis was employed to achieve the following objectives: (1) to determine the spatial distribution patterns of key soil nutrients in the black soil layer and albic soil layer; (2) to identify the nutrient differentiation characteristics between the black soil layer and the albic soil layer; and (3) to elucidate the mechanisms by which the albic soil layer influences maize growth throughout the full growing season. The novelty and significance of the study is that, while previous studies have investigated soil nutrient distribution and crop yield relationships in various regions [29], this study provides several novel contributions specific to dryland albic soils of the Sanjiang Plain. Unlike previous studies that focused on single soil layers, the study systematically compares nutrient heterogeneity between the black soil layer and albic soil layer, extending the vertical nutrient distribution to the unique albic soil system, with the aim of providing precise data support for fertilization strategies and soil improvement measures, thereby improving agricultural productivity, reducing fertilizer waste and environmental pollution, and ultimately promoting sustainable agriculture in dryland albic soil regions.

2. Materials and Methods

2.1. Study Area and Data Source

The studied area was in the 852 Farm, which is located in the core region of the Sanjiang Plain in Heilongjiang Province, China. It represents a typical dryland albic soil area, situated between 46°06′–46°37′ N and 132°18′–132°54′ E (Figure 1) [30]. The study area features relatively flat topography, with a slight elevation gradient from the Northwest (higher) to the Southeast (lower). The soil parent material is primarily Quaternary fluvio-lacustrine deposits, rich in various minerals, providing the essential material foundation for the formation of albic soil [31]. The farm is located in a temperate continental monsoon climate zone, characterized by short, warm summers and long, cold winters, with an average annual temperature of approximately 2.3 °C. Annual precipitation ranges from 500 to 600 mm, with about 65% occurring between June and August. The region receives 2300–2500 h of sunshine annually [32].
Favorable hydrothermal conditions have made this region one of China’s major grain-producing bases, with approximately 84,000 hectares (equivalent to 840 km2) of cultivated land. The area has a long history of agriculture, with maize, soybean, and wheat as the main crops [33,34]. The soil profiles in the study area exhibit a typical three-layer structure, including a dark surface horizon, a bleached albic horizon, and an illuvial layer enriched in clay and iron oxides. The classification of these soils was determined based on field morphological characteristics and laboratory soil taxonomy procedures following international standards.

2.2. Experimental Design

This study first analyzed the characteristics and spatial distribution patterns of seven key soil nutrients, including total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (HN), available phosphorus (AP), available potassium (AK), and organic matter (OM). In addition, soil pH was examined as a chemical indicator of acidity or alkalinity due to its indirect influence on nutrient availability. Second, it compared nutrient differences between soil layers and among different plots. Finally, it explored the mechanisms by which soil nutrients affect crop development throughout the full growing season. The 852 Farm maintains uniform agricultural management practices across its cultivated areas. Key environmental parameters were verified as homogeneous through consistent fertilization, minimal spatial variation in meteorological data distribution and uniform tillage regime. This environmental uniformity ensures observed nutrient patterns primarily reflect intrinsic soil layer characteristics rather than external factors. Therefore, a randomized block design was adopted, and two representative locations within 852 Farm were selected as comparative sampling sites. At each sampling point, one black soil sample and one white slurry soil sample were taken, respectively providing inherent control for spatial variability. A total of 40 undisturbed sampling points were established across the two localities, with 20 points in Location 1 (L1) and 20 in Location 2 (L2). All points were evenly distributed across representative agricultural plots to ensure adequate spatial coverage and comparability between the two locations (Figure 2). Figure 3 illustrates the boundary delineation between the black soil layer and albic soil layer in Location 1 (L1) and Location 2 (L2). The soil profile, from top to bottom, consists of the black soil layer, albic soil layer, and illuvial layer. The black soil layer is located at the surface, with a depth of approximately 20 cm. The albic soil layer lies beneath the black soil layer and is a grayish-white horizon formed through albicization and other pedogenic processes, with a thickness of approximately 20–35 cm (see Figure 4); below it is the illuvial layer. The stratification between soil layers was clear and relatively continuous, indicating that the selected plots effectively represent the diversity and complexity of the black soil layer and albic soil layer within the farm.

2.3. Measurement Items and Methods

To analyze the characteristics and differences in soil nutrients across different layers and plots, descriptive statistics were first used to evaluate nutrient ranges and variability. Geostatistical methods were then applied to study spatial distribution patterns. Finally, paired t-tests [35] were used to assess nutrient differences between soil layers. To quantify the influence of soil nutrients on crop development throughout the growing season, Pearson correlation analysis and principal component analysis (PCA) [36] were employed to reveal underlying mechanisms. In parallel, NDVI values were used to reflect crop biomass dynamics and were aligned with key phenological stages (seedling, vegetative growth, tasseling, and maturity) to assess the temporal coupling between nutrient availability and crop growth.
Soil samples were collected in the spring of 2023. At each sampling point, soils were stratified by profile into the black soil layer (average depth 5–15 cm) and albic soil layer (average depth 25~35 cm). Approximately 500 g of soil was collected per layer, air-dried, ground, and sieved through 2 mm and 0.25 mm mesh. The following indicators were measured: total nitrogen (TN), total phosphorus (TP), total potassium (TK), alkali-hydrolyzable nitrogen (HN), available phosphorus (AP), available potassium (AK), organic matter (OM), and pH. TN was determined using the Kjeldahl method; TP by molybdenum antimony colorimetry; TK by sodium hydroxide fusion; HN by alkali hydrolysis diffusion; AP using the hydrochloric acid–ammonium chloride method; AK by ammonium acetate extraction followed by flame photometry; OM by external heating potassium dichromate oxidation; and pH by potentiometry.

2.4. Data Processing

This study integrated statistical and geostatistical methods to examine soil nutrient characteristics and their impact on crops. Kriging interpolation in ArcGIS 10.2 (Esri, Red- 199 lands, CA, USA) was used to construct spatial distribution maps of soil nutrients in the black soil layer and albic soil layer. To support the reliability of this interpolation method, sampling points in both L1 and L2 were arranged in a regular grid pattern, with approximate spacing of 100–120 m. The layout was adapted to the dimensions of each agricultural plot to ensure spatial continuity and minimize boundary effects in geostatistical modeling. IBM SPSS Statistics 26.0 (IBM Corp., Armonk, NY, USA) was employed for basic statistics, coefficient of variation analysis, and correlation analysis. MATLAB (Math-206 Works, Natick, MA, USA) was used for principal component analysis of soil nutrients. Prior to PCA and statistical analysis, all nutrient data were followed by standardization for dimensional uniformity. NDVI data were obtained from Sentinel-2 satellite imagery (10 m spatial resolution) with a 5-day temporal resolution during the 2023 growing season (Emergence stage–Maturity stage), covering the entire study area shown in Figure 2.

2.5. Agronomic Management Practices

The productive plots within the 852 Farm follow a uniform agronomic management regime based on conventional mechanized farming practices. Crop rotation is practiced primarily among maize, soybean, and wheat. Standardized operations include mechanized plowing, rotary tillage, fertilization, sowing, pesticide application, and harvesting, typically performed using medium- to large-scale agricultural machinery. Fertilizer application consists of base fertilization with compound fertilizer before sowing and top-dressing with nitrogen fertilizer at the stem elongation stage. Straw return and stubble retention are commonly adopted in recent years to enhance soil organic matter content and improve soil structure. No conservation tillage or organic farming techniques were applied in the study plots during the observation period. All sampling plots were located in rainfed agricultural lands with no irrigation or differential treatment. Field management remained consistent across the two localities, minimizing anthropogenic variability, and ensuring that observed differences primarily reflected intrinsic soil properties.
All experimental plots in both L1 and L2 locations followed a uniform fertilization regime in accordance with standard regional maize cultivation practices in the Sanjiang Plain. Prior to sowing, a compound fertilizer (N–P2O5–K2O, 20–15–10) was applied at a rate of 450 kg/ha to ensure baseline nutrient availability. During the stem elongation stage, urea (46% N) was top-dressed at a rate of 150 kg/ha to support vegetative growth. These fertilization measures were consistently implemented across all sampling plots to minimize management-induced variability, ensuring that the observed differences in soil nutrients primarily reflected inherent soil profile characteristics. This consistency in field management allows the study to focus on the intrinsic relationship between nutrient spatial distribution and crop response.

3. Results and Discussion

3.1. Spatial Variability of Soil Nutrients in the Black Soil Layer

3.1.1. Statistical Characteristics of Soil Nutrients in the Black Soil Layer

To understand the nutrient characteristics of the black soil layer in 852 Farm, data from different plots were compiled and analyzed. As shown in Figure 5 and Table 1, there were significant differences among different nutrients within the black soil layer, while nutrient content differences between the two plots were relatively minor. The descending order of nutrient contents in the black soil layer was TK (g/kg) > OM (%) > TN (g/kg) > TP (g/kg) > HN (mg/kg) > AK (mg/kg) > AP (mg/kg). Among them, TK, AP, and AK showed low coefficients of variation across both plots, indicating relatively uniform spatial distribution. TN, TP, HN, and OM had significantly higher coefficients of variation in L1 than in L2, suggesting more pronounced spatial variability in L1. Soil pH ranged from 4.87 to 5.44 with a low coefficient of variation, indicating slightly acidic soil conditions with relatively uniform spatial distribution.

3.1.2. Spatial Distribution Patterns of Soil Nutrients in the Black Soil Layer

Spatial distribution maps of soil nutrients in the black soil layer were generated using ArcGIS 10.2. As shown in Figure 6, nutrient distributions exhibited general consistency with localized differences. Overall consistency was reflected in a southwest-to-northeast gradient, where nutrient contents increased with decreasing elevation. Localized differences were attributed to spatial heterogeneity in soil nutrient supply capacity. HN, AP, and AK exhibited relatively wide spatial ranges and moderate spatial variability, indicating uneven distribution. TN, TP, TK, OM, and pH showed narrower variation ranges and lower spatial variability, suggesting more homogeneous distribution patterns.
As shown in the nutrient correlation heatmap (Figure 7), there is a strong degree of correlation among soil nutrients in the black soil layer. OM exhibited high correlation coefficients with all other nutrients, indicating that organic matter decomposition is one of the primary sources of nutrient availability. Enhancing OM levels can effectively improve nutrient supply capacity. TN showed a very strong positive correlation with HN and OM (R > 0.901 ***), suggesting that nitrogen mineralization is highly active in the soil. HN, as a directly available nitrogen source for crops, is closely linked to TN content. Additionally, OM decomposition releases substantial amounts of TN, providing a continuous nitrogen supply to the soil. TN was moderately positively correlated with TP, AK, and AP (R > 0.752), indicating some degree of synergistic interaction between nitrogen, phosphorus, and potassium in nutrient cycling. TK was significantly negatively correlated with AK, TP, AP, and OM (R = −0.70), suggesting that available potassium is supplied primarily from sources other than total potassium, and that potassium fixation in the soil competes with available AK supply.

3.2. Spatial Variation in Soil Nutrients in the Albic Soil Layer

3.2.1. Statistical Characteristics of Soil Nutrients in the Albic Soil Layer

As summarized in Figure 8 and Table 2, nutrient contents within the albic soil layer exhibited evident variability among indicators, though differences between the two plots remained relatively small based on descriptive statisticcs. The order of nutrient content in the albic soil layer was TK > OM > TN > TP > HN > AK > AP, consistent with that of the black soil layer. TK, AP, AK, TN, and TP showed small coefficients of variation across the two plots, suggesting relatively uniform spatial distribution. OM had a significantly higher coefficient of variation in L1 than in L2, indicating greater spatial heterogeneity in L1. HN exhibited a higher coefficient of variation in L2, suggesting more uneven spatial distribution in that plot. Soil pH ranged from 4.67 to 5.48 with a low coefficient of variation, indicating a generally acidic condition with uniform spatial distribution, consistent with the overlying black soil layer.

3.2.2. Spatial Distribution Patterns of Soil Nutrients in the Albic Soil Layer

As illustrated in the spatial distribution maps of the albic soil layer (Figure 9), nutrient contents increased from southwest to northeast as elevation decreased, showing a pattern similar to that observed in the black soil layer. The overall trend demonstrated increasing nutrient concentrations from higher to lower elevations along the southwest–northeast direction. The spatial uniformity of nutrient distribution was comparable to that of the black soil layer. HN, AP, and AK exhibited broader spatial variation, indicating uneven distribution. TN, TP, TK, OM, and pH showed narrower variation ranges, suggesting relatively uniform spatial patterns.
As further shown in the correlation heatmap (Figure 10), nutrient concentrations in the albic soil layer were highly interrelated. OM showed strong positive correlations with other nutrients, indicating its important role in supporting overall nutrient availability through decomposition. TN was positively correlated with HN, OM, TP, AK, and AP, suggesting a tendency for co-accumulation of nitrogen and phosphorus within the albic soil layer. TK was significantly negatively correlated with AK, TP, AP, and OM (R = −0.70), indicating a competitive relationship between potassium and phosphorus fixation and release processes in the soil, along with regional differentiation in nutrient accumulation.

3.3. Comparison of Soil Nutrient Differences Between the Black Soil Layer and Albic Soil Layer

Significant differences were observed in nutrient contents between the black soil layer and the albic soil layer (p < 0.001, Cohen’s d > 0.1) (Table 3). Key nutrients such as TN, TP, TK, HN, and OM were significantly higher in the black soil layer compared to the albic soil layer (effect size > 8), largely due to its higher organic matter content and microbial activity, highlighting the superior cycling of nitrogen, phosphorus, and potassium in the black soil layer. Although the overall fertility of the black soil layer was higher, no significant difference in pH was observed between the two layers (p > 0.05), indicating similar acidity–alkalinity conditions. These findings underscore the ecological function of the black soil layer as a fertile substrate, where nutrient reserves and cycling dynamics play a key role in supporting plant growth and biogeochemical processes.
The clustering heatmap (Figure 11) reveals that the spatial distribution of nutrients in the black and albic soil layers exhibits both heterogeneity and homogeneity. In the black soil layer (Figure 11a), TN, OM, and HN showed strong positive correlations and clustered together, indicating co-regulation by organic matter accumulation and decomposition processes. TP, AP, AK, and TK were grouped together, suggesting that long-term fertilization management has enhanced the spatial consistency of available nutrients. A weak correlation was observed between pH and OM, implying that pH stability in the black soil layer is partially regulated by the buffering capacity of organic matter. In the albic soil layer (Figure 11b), TN, OM, and TP exhibited strong homogeneity, closely linked to the overall low organic matter content, which strongly influences their spatial distribution. AP, AK, and TK were clustered together, indicating that available nutrients are similarly affected by tillage and management practices. In contrast, pH was distinctly independent from other variables, suggesting that its strong response to single factors is due to high acidity and weak buffering capacity in the albic soil layer.
PCA was used to identify the dominant factors influencing soil nutrients in both layers. As shown in Table 4, two principal components were extracted for the black soil layer, explaining a cumulative variance of 87.275%. The first principal component was dominated by AP, AK, TP, and TN, while the second component was characterized by OM and HN (Figure 12a). This indicates that nutrient characteristics in the black soil layer are primarily influenced by total nitrogen and available phosphorus and potassium, followed by contributions from organic matter and HN. The high levels of available nutrients reflect the strong fertility of the black soil layer. For the albic soil layer, three principal components were extracted, accounting for 96.324% of the total variance. The first component was dominated by TN, TP, HN, and OM; the second by AK; and the third by AP and pH (Figure 12b). These results indicate that nutrient dynamics in the albic soil layer are primarily driven by nitrogen, phosphorus, and organic matter, though their overall levels are lower than in the black soil layer. Notably, potassium activity was relatively higher in the albic soil.

3.4. Mechanisms by Which Albic Soil Affects Crop Productivity

As shown in Figure 13, the NDVI values for maize throughout the full growing cycle in both plots followed a distinct phase-based pattern: an overall rise followed by a decline, with minor differences between plots during the same growth stages. In May, during the seedling stage, NDVI values were low, with L1 slightly higher than L2 due to more exposed bare soil. In June, during stem elongation, NDVI values increased significantly, indicating greater variability and the onset of rapid vegetative growth. In August, during the tasseling stage, NDVI values peaked, suggesting nearly saturated vegetation cover. In September, NDVI values declined as the crop entered senescence and vegetation coverage reduced. Notably, L2 exhibited slightly higher NDVI values and denser vegetation cover, likely due to differences in soil nutrient availability or field management practices, highlighting the significant influence of soil fertility and management on maize productivity.
As shown in Figure 14, the maize yield in both plots was significantly and positively correlated with NDVI values (R2 = 0.98 and 0.90, respectively). The data were evenly distributed and exhibited a high degree of regression fit, confirming a strong association between NDVI and yield. Therefore, NDVI is an effective proxy for maize productivity in dryland albic soil regions. Its dynamic pattern—rising from emergence to tasseling and declining at maturity—mirrors yield trends, suggesting that biomass accumulation and nutrient use efficiency are closely linked. As a rapid and non-destructive monitoring tool, NDVI can provide vital technical support for precision fertilization and ecological management in dryland albic soil areas.
PCA was conducted separately for NDVI and soil nutrients in each plot. As shown in Figure 15, the first two principal components in L1 explained 90.9% of the total variance. NDVI was significantly positively correlated with TP, TN, AK, AP, and HN, indicating these nutrients as key drivers of maize growth, where increased levels contribute to higher NDVI values. In L2, the first two principal components explained 90% of the variance, with NDVI again positively correlated with TP, TN, AK, AP, and HN, further validating their integrated regulatory role in maize development.

3.5. Discussion

This study systematically investigated the spatial distribution characteristics of soil nutrients in the dryland albic soil of Sanjiang Plain. The nutrient content order in both soil layers (TK > OM > TN > TP > HN > AK > AP) reflects the unique geochemical cycling patterns in this region. The exceptionally high TK accumulation (15.03–19.01 g/kg) suggests potassium’s relative abundance compared to nitrogen and phosphorus limitations, likely originating from Quaternary fluvio-lacustrine deposits rich in potassium-bearing minerals (Figure 6c and Figure 9c). This finding extends Montagne et al. [1]’s theory of albic soil mineralogy by quantifying the potassium dominance in Chinese dryland systems. The southwest–northeast nutrient gradient (Figure 6 and Figure 9) demonstrates elevation-dependent spatial patterns, where decreasing altitude correlates with increasing nutrient concentrations. This topographic control mechanism complements Zhang et al. [9]’s phosphorus distribution model, while our PCA results (Figure 12) further reveal that TN, TP, and OM collectively explain 57.3–62.7% of nutrient variance in both layers, highlighting their co-regulation role. The black soil layer’s significantly higher fertility (effect size > 8 for TN, TP, TK, HN, OM) confirms its critical function as the primary nutrient reservoir, supporting Dong et al. [16]’s assertion about albic soil’s vertical nutrient stratification. The strong OM-TN-HN correlations (R > 0.901, Figure 7 and Figure 10) unveil a self-reinforcing cycle: organic matter decomposition enhances nitrogen mineralization, which subsequently promotes biomass production and further OM accumulation. This mechanism explains why L2 exhibited higher NDVI values (Figure 13b) despite similar management practices, as its slightly elevated OM (5.13% vs. 4.97%) triggered stronger nutrient cycling. The negative TK-AK correlation (R = −0.70) indicates potassium fixation processes competing with availability, suggesting that TK’s abundance does not necessarily translate to plant-accessible forms.
Recent studies by Tiruneh et al. have demonstrated the effectiveness of spectral vegetation indices for monitoring crop productivity under different soil conditions [37]. While their work focused on teff and finger millet in Ethiopian highlands using Sentinel-2 data, these findings in the Sanjiang Plain albic soils similarly confirm the strong correlation between NDVI dynamics and maize yield (R2 = 0.98 and 0.90). This indicates that the vegetation index approach shows consistent applicability across diverse soil types (from Ethiopian highlands to Chinese albic soils). Meanwhile, this study found a significant positive correlation between maize NDVI during the full growth period and TP, TN, AK, AP, and HN, consistent with Wang et al. [4], asserting that crop yield is positively correlated with the availability of rapidly available nutrients. Furthermore, this study revealed a seasonal coupling pattern in nutrient use efficiency. Crop yield and planting structure are significantly influenced by integrated soil nutrient availability. NDVI dynamics (Figure 13 and Figure 14) demonstrate tight coupling between nutrient availability and crop growth stages. The phenological alignment—NDVI peaks coinciding with tasseling-stage HN/AP maxima (August)—provides operational guidance for precision fertilization timing. However, the L1-L2 NDVI differences (Figure 15) caution against universal threshold applications, emphasizing location-specific calibration as proposed by Zhou et al. [14]. Three practical implications emerge from these findings: (1) the elevation–nutrient relationship supports terrain-adjusted fertilization strategies; (2) OM management should be prioritized given its central role in nutrient cycling; and (3) NDVI monitoring requires albic-layer-specific interpretation due to its filtering effect on root-zone nutrients. These recommendations align with but substantially refine Wang et al. [4]’s framework for albic soil management. The acidic conditions (pH 4.87–5.48) persisted uniformly across layers, corroborating Lu et al. [38]’s findings about Sanjiang Plain’s soil chemistry. This acidity may exacerbate phosphorus fixation, explaining the relatively low AP levels (5.40–6.09 mg/kg) despite high TP, suggesting potential benefits from pH-adjusted phosphorus fertilizers. Unexpectedly, the albic layer showed higher AK variability (CV = 0.09 vs. 0.06 in black soil), possibly indicating more active potassium dynamics in the mineral-rich subsoil. This challenges conventional assumptions about the albic layer’s nutrient inertia and warrants further investigation into subsoil potassium mobilization mechanisms.
In the study area, conventional tillage was adopted in both L1 and L2 plots, including moldboard plowing to a depth of 25 cm and rotary tillage before sowing. Maize was grown under rainfed conditions without straw return, and fertilization followed local agronomic practices. No conservation tillage or organic amendments were applied. In addition, agricultural productivity may also be influenced by external factors such as crop variety, production technology, pest pressure, and market-driven decisions [39,40,41,42,43]. Moreover, soil functionality may be further affected by various anthropogenic pressures, including contamination, long-term land use, or soil degradation processes, which require comprehensive assessment [44,45]. Therefore, future work will further explore the internal relationships between soil nutrients, crop variety selection, and production conditions, aiming to provide robust theoretical and practical guidance for optimizing agricultural management in the Sanjiang Plain albic soil region and enhancing resilience to external environmental and economic changes.

3.6. Limitations and Future Directions

This study has certain limitations that should be acknowledged. First, the nutrient data were obtained from a single field survey, which may not reflect seasonal variability or year-to-year fluctuations in soil nutrient dynamics. Multi-temporal monitoring would provide more robust insights into nutrient cycling processes. Second, while NDVI served as a useful indicator of crop vigor, integrating additional vegetation indices or UAV-based imagery could improve spatial detail and real-time monitoring capacity. Third, this research focused primarily on macronutrients (e.g., TN, TP, AK, and AP), but did not include micronutrients or biological indicators such as enzyme activity or microbial communities, which are also crucial for comprehensive soil fertility evaluation. Future research should aim to incorporate long-term, multi-seasonal data, explore fine-scale interactions between soil properties and crop responses, and assess management practices tailored to the specific characteristics of dryland albic soils.

4. Conclusions

This study systematically analyzed the spatial distribution patterns of soil nutrients and their impact on maize productivity in a dryland albic soil region, focusing on the black soil layer and albic soil layer. The main conclusions are as follows:
(1)
Nutrient characteristics of the black soil layer and albic soil layer
Statistical and geostatistical analyses showed that the nutrient content order in both layers was TK > OM > TN > TP > HN > AK > AP, with potassium exhibiting the highest accumulation. Both soil types exhibited a spatial distribution pattern of general consistency with localized differences. Nutrient levels increased with decreasing elevation, reflecting spatial heterogeneity in nutrient supply capacity.
(2)
Comparison of nutrient differences between the two soil layers
Paired t-tests revealed significant differences and consistent stratification between the two layers, with the black soil layer showing a marked fertility advantage over the albic soil layer. In particular, TN, TP, TK, HN, and OM levels in the black soil layer were significantly higher (effect size > 8). While the albic soil layer showed higher activity of available nutrients, it lacked long-term fertility reserves. No significant difference in pH was found between the two layers, confirming the widespread acidic environment of dryland albic soil regions and its common regulatory role in nutrient dynamics.
(3)
Mechanism of soil nutrient influence on maize growth
PCA results showed that NDVI was significantly positively correlated with TP, TN, AK, AP, and HN, and its temporal trend (rising then declining) aligned with nutrient use efficiency and the crop growth cycle, with peak NDVI values corresponding to peaks in available nitrogen and phosphorus. This indicates a strong coupling between biomass accumulation and nutrient availability. Higher NDVI values were generally associated with areas of greater total nitrogen and organic matter content, further confirming the spatial linkage between crop productivity and soil fertility in the study area.
These findings enhance the understanding of fertility formation and degradation processes in albic soils. They also provide both theoretical and empirical support for the development of differentiated fertilization strategies and soil improvement practices. By integrating these insights into field management, the study offers practical guidance for improving nutrient use efficiency and promoting sustainable agricultural development in dryland albic soil regions.

Author Contributions

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

Funding

This study was supported by the National Key Research and Development Program (2022YFD1500800), the Outstanding Youth Project of Heilongjiang Provincial Agricultural Science and Technology Innovation Leap Project (CX25JC15), the Doctoral Fund Project of Hebei University of Water Resources and Electric Engineering (SYBJ2402), and the Scientific Research Project of Hebei Provincial Department of Education (BJK2024018).

Data Availability Statement

Dataset available on request from the authors. The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

The authors gratefully acknowledge Beidahuang Group Heilongjiang 852 Farm Co., Ltd., for their strong support in soil sampling and related research.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Figure 1. Geographic location map of the study area: (a) China’s administrative divisions; (b) location of 852 Farm.
Figure 1. Geographic location map of the study area: (a) China’s administrative divisions; (b) location of 852 Farm.
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Figure 2. Soil sampling areas in 852 Farm.
Figure 2. Soil sampling areas in 852 Farm.
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Figure 3. Soil profile stratification of the black soil layer and albic soil layer in different locations: (a) L1; (b) L2.
Figure 3. Soil profile stratification of the black soil layer and albic soil layer in different locations: (a) L1; (b) L2.
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Figure 4. Frequency distribution histogram of the occurrence depth of albic soil layer.
Figure 4. Frequency distribution histogram of the occurrence depth of albic soil layer.
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Figure 5. Box plot of soil nutrient contents in the black soil layer of different locations: (a) L1; (b) L2.
Figure 5. Box plot of soil nutrient contents in the black soil layer of different locations: (a) L1; (b) L2.
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Figure 6. Spatial distribution maps of soil nutrients in the black soil layer: (a) TN; (b) TP; (c) TK; (d) HN; (e) AP; (f) AK; (g) OM; (h) pH.
Figure 6. Spatial distribution maps of soil nutrients in the black soil layer: (a) TN; (b) TP; (c) TK; (d) HN; (e) AP; (f) AK; (g) OM; (h) pH.
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Figure 7. Heatmap of soil nutrient correlations in the black soil layer.
Figure 7. Heatmap of soil nutrient correlations in the black soil layer.
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Figure 8. Boxplots of soil nutrient contents in the albic soil layer of different locations: (a) L1; (b) L2.
Figure 8. Boxplots of soil nutrient contents in the albic soil layer of different locations: (a) L1; (b) L2.
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Figure 9. Spatial distribution maps of soil nutrients in the albic soil: (a) TN; (b) TP; (c) TK; (d) HN; (e) AP; (f) AK; (g) OM; (h) pH.
Figure 9. Spatial distribution maps of soil nutrients in the albic soil: (a) TN; (b) TP; (c) TK; (d) HN; (e) AP; (f) AK; (g) OM; (h) pH.
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Figure 10. Heatmap of soil nutrient correlations in the albic soil.
Figure 10. Heatmap of soil nutrient correlations in the albic soil.
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Figure 11. Cluster heatmap of soil nutrient contents in different locations: (a) black soil layer; (b) albic soil layer.
Figure 11. Cluster heatmap of soil nutrient contents in different locations: (a) black soil layer; (b) albic soil layer.
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Figure 12. PCA loading score plots of soil nutrients in different soil layers: (a) black soil layer; (b) albic soil layer.
Figure 12. PCA loading score plots of soil nutrients in different soil layers: (a) black soil layer; (b) albic soil layer.
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Agronomy 15 01857 g013
Figure 13. Violin plots of NDVI values during the whole growth period of maize in different locations: (a) L1; (b) L2.
Figure 13. Violin plots of NDVI values during the whole growth period of maize in different locations: (a) L1; (b) L2.
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Figure 14. Relationship curves between NDVI values and crop yields in different locations: (a) L1; (b) L2.
Figure 14. Relationship curves between NDVI values and crop yields in different locations: (a) L1; (b) L2.
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Figure 15. PCA plots of NDVI values and soil nutrients in different locations: (a) L1; (b) L2.
Figure 15. PCA plots of NDVI values and soil nutrients in different locations: (a) L1; (b) L2.
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Table 1. Statistical results of soil nutrient parameters in the black soil layer.
Table 1. Statistical results of soil nutrient parameters in the black soil layer.
Nutrient ContentLocationMeanMinimumMaximumStandard DeviationVarianceCoefficient of Variation
TN (g/kg)L12.792.293.200.340.120.12
L22.752.602.870.110.010.04
TP (g/kg)L11.120.931.230.120.010.11
L21.251.191.300.050.010.04
TK (g/kg)L115.0314.7115.450.360.130.02
L213.6813.2013.980.330.110.02
HN (mg/kg)L1313.54264.34353.6932.85109.430.10
L2305.49295.14319.3610.22104.360.03
AP (mg/kg)L122.0519.7025.812.395.730.11
L225.5521.1831.183.8114.530.15
AK (mg/kg)L1113.08101.05121.887.9162.560.07
L2140.58130.79146.787.1951.710.05
OM (%)L14.974.055.590.580.340.12
L25.134.955.370.180.030.04
pHL15.225.005.440.190.040.04
L25.144.875.260.160.030.03
Table 2. Statistical results of soil nutrient parameters in the albic soil layer.
Table 2. Statistical results of soil nutrient parameters in the albic soil layer.
Nutrient ContentLocationMeanMinimumMaximumStandard DeviationVarianceCoefficient of Variation
TN (g/kg)L10.640.600.710.050.010.07
L20.700.580.770.080.010.11
TP (g/kg)L10.450.410.530.050.010.11
L20.470.380.520.050.010.12
TK (g/kg)L119.0118.1320.060.800.640.04
L216.9616.6717.240.230.050.01
HN (mg/kg)L166.9063.0472.243.3911.460.05
L267.7353.9076.929.2685.730.14
AP (mg/kg)L15.404.746.090.550.300.10
L25.914.986.600.620.390.11
AK (mg/kg)L168.6162.0277.135.8534.200.09
L277.7671.0082.484.4019.350.06
OM (%)L11.391.051.730.310.090.22
L21.511.231.790.230.050.15
pHL14.774.674.960.130.020.03
L25.204.975.480.240.060.05
Table 3. Results of paired t-test for differences between different soil layers.
Table 3. Results of paired t-test for differences between different soil layers.
Nutrient ContentResearch SubjectMeanStandard DeviationRegression Statistic
Mean DifferencetpCohen’s d
TNBlack soil layer2.770.242.125.5740.000 **8.087
Albic soil layer0.670.07
TPBlack soil layer1.190.110.7319.3980.000 **6.134
Albic soil layer0.460.05
TKBlack soil layer17.981.213.6316.530.000 **6.227
Albic soil layer14.360.78
HNBlack soil layer309.5223.33242.232.3060.000 **10.216
Albic soil layer67.326.59
APBlack soil layer23.83.5218.1516.7050.000 **5.283
Albic soil layer5.650.61
AKBlack soil layer126.8316.1553.6412.4930.000 **3.951
Albic soil layer73.196.86
OMBlack soil layer5.050.413.5921.0060.000 **6.643
Albic soil layer1.450.27
pHBlack soil layer5.180.170.21.9160.0880.606
Albic soil layer4.980.29
The ** indicate statistical significance at the 0.01 levels, respectively.
Table 4. Statistical table of PCA eigenvalues and variance contribution rates of different soil layers.
Table 4. Statistical table of PCA eigenvalues and variance contribution rates of different soil layers.
Factor CodingChernozem LayerKaolinite Clay Layer
EigenvalueExplained Variance Ratio/%Cumulative/%EigenvalueExplained Variance Ratio/%Cumulative/%
14.58457.29757.2975.02062.74562.745
22.39829.97887.2751.42317.78580.530
30.7098.85996.1331.26415.79496.324
40.1551.93398.0660.1982.47698.801
50.0931.16099.2260.0690.86599.666
60.0480.59699.8220.0150.18399.848
70.0140.17899.9150.0080.09699.944
8001000.0040.056100
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Li, J.; Li, H.; Wang, Q.; Wang, Y.; Hong, X.; Zhou, C. Study on the Distribution and Quantification Characteristics of Soil Nutrients in the Dryland Albic Soils of the Sanjiang Plain, China. Agronomy 2025, 15, 1857. https://doi.org/10.3390/agronomy15081857

AMA Style

Li J, Li H, Wang Q, Wang Y, Hong X, Zhou C. Study on the Distribution and Quantification Characteristics of Soil Nutrients in the Dryland Albic Soils of the Sanjiang Plain, China. Agronomy. 2025; 15(8):1857. https://doi.org/10.3390/agronomy15081857

Chicago/Turabian Style

Li, Jingyang, Huanhuan Li, Qiuju Wang, Yiang Wang, Xu Hong, and Chunwei Zhou. 2025. "Study on the Distribution and Quantification Characteristics of Soil Nutrients in the Dryland Albic Soils of the Sanjiang Plain, China" Agronomy 15, no. 8: 1857. https://doi.org/10.3390/agronomy15081857

APA Style

Li, J., Li, H., Wang, Q., Wang, Y., Hong, X., & Zhou, C. (2025). Study on the Distribution and Quantification Characteristics of Soil Nutrients in the Dryland Albic Soils of the Sanjiang Plain, China. Agronomy, 15(8), 1857. https://doi.org/10.3390/agronomy15081857

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