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Article

Soil Nutrient Variability Analysis of Typical Planting Patterns in Agricultural Reclamation Areas of the Southern Dianchi Lake Basin

by
Zhuojun Miao
1,2,
Junen Wu
1,2,*,
Lei Zhao
1,
Feng Cheng
1 and
Yuchen Zhang
1
1
Faculty of Geography, Yunnan Normal University, Kunming 650500, China
2
GIS Technology Research Center of Resource and Environment in Western China, Ministry of Education, Yunnan Normal University, Kunming 650500, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(11), 2566; https://doi.org/10.3390/agronomy15112566
Submission received: 27 September 2025 / Revised: 2 November 2025 / Accepted: 4 November 2025 / Published: 7 November 2025
(This article belongs to the Section Agroecology Innovation: Achieving System Resilience)
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Abstract

This study investigates the effects of typical planting patterns on soil nutrient accumulation and associated environmental impacts in agricultural reclamation areas of the southern Dianchi Lake Basin. Taking the cut flower cultivation area in Dahewei Village, Jinning District, Yunnan Province, as the research site, we compared soil physicochemical properties, nutrient contents, and their correlations with environmental factors under open-field and greenhouse cultivation, and analyzed the characteristics of soil fertility changes and non-point-source pollution risks in greenhouses. We found that greenhouse cultivation is associated with altered soil physicochemical properties, including smaller aggregate sizes, increased soil moisture content (from 30.15% to 32.20%), elevated pH values (from 7.11 to 7.23), and 79% higher electrical conductivity compared to open-field conditions (620.82 vs. 347.60 μS cm−1, p < 0.01). Compared with open-field systems, greenhouse cultivation exhibited greater nutrient accumulation, particularly for total nitrogen (TN) and available potassium (AK) in the 0–10 cm topsoil layer, demonstrating pronounced surface enrichment. Additionally, greenhouse conditions showed weaker correlations between soil nutrients and meteorological factors but stronger inter-nutrient coupling. Enhanced soil moisture and temperature conditions were associated with reduced nutrient leaching but simultaneously increased surface nutrient enrichment and salinization risks. These findings provide quantitative evidence for precision fertilization strategies, optimized irrigation management, and targeted soil health interventions in intensive greenhouse systems. The results have practical applications for preventing surface nutrient accumulation and long-term salinization in protected agriculture.

1. Introduction

Greenhouse cultivation is a modern agricultural practice that enables intensive cropping under controlled environments, using facilities such as plastic greenhouses to regulate environmental factors and achieve year-round harvests [1,2]. As an important pathway for ensuring food security and promoting farmers’ income, greenhouse agriculture has developed rapidly in China. However, high-intensity fertilizer and pesticide inputs pose significant risks of agricultural non-point source pollution [3,4,5]. Excessive nitrogen (N) and phosphorus (P) loss can trigger eutrophication and associated ecological problems in water bodies [6,7,8]. Soil, as both a source and sink of pollutants, plays a critical role: its physicochemical properties determine nutrient migration and transformation [9,10]. It serves as the direct receptor of agricultural inputs and a reservoir regulating pollutant behavior through adsorption–desorption processes [11,12,13]. Therefore, accurately understanding soil nutrient status and its relationship with environmental factors is fundamental to revealing the mechanisms of agricultural non-point source pollution. Most studies on soil nutrients and non-point source pollution in greenhouse systems have focused on vegetables and food crops, whereas investigations on cut-flower production remain scarce.
Dianchi Lake has experienced severe eutrophication and entered a stable turbid state since 1993, with current total nitrogen concentrations (1.6 mg L−1) still exceeding the ecological threshold (1.2 mg L−1) despite recent policy interventions [14,15]. Agriculture has been identified as an important source of nitrogen and phosphorus loading in the southern sub-basins of the lake [16]. Jinning District, located in the southern Dianchi Lake Basin of Yunnan Province, is an important cut flower production area in China, where the combination of intensive greenhouse cultivation and high ecological sensitivity of the eutrophic lake basin provides a relevant context for studying agricultural nutrient accumulation and pollution risks [16]. In recent years, the area under cut flower greenhouse cultivation in this district has continuously expanded, with corresponding increases in fertilizer and pesticide application intensity, making agricultural non-point source pollution risks increasingly prominent [17,18]. Different cultivation methods exhibit significant differences in fertilizer and pesticide application, irrigation management, and other aspects, potentially leading to variations in soil nutrient accumulation characteristics and non-point source pollution risks.
This study takes cut flower production bases in Jinning District as the research object, systematically investigating soil nutrient status under two cultivation modes: open-field and greenhouse cultivation. By measuring soil nutrient indicators including total nitrogen (TN), ammonium nitrogen (AN), available phosphorus (AP), and available potassium (AK), as well as physicochemical property indicators such as total carbon (TC), moisture content (SWC), pH value (pH), and electrical conductivity (EC), we analyze soil nutrient accumulation characteristics under different cultivation modes and assess agricultural non-point source pollution risks. The research aims to: (1) clarify soil nutrient accumulation characteristics in cut flower cultivation; (2) elucidate the differential effects of different cultivation modes on soil nutrients and pollution risks; (3) analyze the synergistic relationships between soil nutrients and environmental factors, providing theoretical basis and practical guidance for sustainable development of the regional cut flower industry and agricultural non-point source pollution prevention and control.

2. Materials and Methods

2.1. Study Site Description

The study area is located in Dahewei Village, Jinning District, Kunming City, Yunnan Province (24°43′10″ N, 102°36′3″ E), adjacent to the southwestern shore of Dianchi Lake. The region is situated at the edge of the collision zone between the Eurasian and Indian plates, in the western part of the Yangtze Plate, with relatively complex geological structures [19]. The landform is an alluvial-lacustrine basin, with limestone as the main parent rock material. The soil type is classified as red soil, with clay and clay loam as the predominant textures [20]. The region is characterized by a northern subtropical plateau monsoon climate, featuring mild climate year-round with distinct wet and dry seasons.
During the study period, annual average wind speed was approximately 3 m s−1, with obvious seasonal variations and higher values in spring and winter (Figure 1a). Annual average atmospheric pressure was around 80 kPa, being relatively higher in summer and autumn (Figure 1b). Annual average relative humidity was approximately 75%, higher from May to August and lower from September to October (Figure 1c). Solar radiation was strongest in April and June throughout the year and weakest from December to March of the following year, with daily average radiation of approximately 20 MJ m−2, being stronger in spring, summer, and autumn, and weaker in winter (Figure 1d). Precipitation was mainly concentrated in the rainy season from May to October, with less rainfall during the dry season from November to April of the following year (Figure 1e). Annual average temperature was 14.8 °C, with July being the hottest month and large temperature differences between winter and spring (Figure 1f).
Jinning District, Yunnan Province, is an important cut flower production base in China, adjacent to the Dounan International Flower Market. The cut flower cultivation area in this district reaches 3693.33 ha, accounting for more than 90% of the total flower cultivation area (4060 ha) in the district, with an annual output value exceeding 3.2 billion yuan [21]. The main land use types in the study area are farmland and rural construction land, with farmland primarily used for rose cut flower and vegetable cultivation [22]. Rose cut flowers are mainly cultivated under greenhouse cultivation, while vegetable crops (corn, Chinese kale, etc.) are primarily grown in open fields, forming a typical “protected cut flower-open field crop” planting pattern.

2.2. Experimental Design and Sampling Methods

This study employed a comparative observational design to investigate soil nutrient dynamics under these two contrasting cultivation systems—greenhouse rose and open-field crop (primarily for vegetable and corn) rotation—representative of dominant agricultural practices in the southern Dianchi Lake Basin.
Study Design Rationale and Limitations: We recognize that randomized controlled experiments provide the strongest basis for causal inference. However, such a design was not feasible in this context for three primary reasons: (1) Both cultivation systems represent long-established agricultural infrastructure—including multi-year greenhouse structures, perennial rose plantings, and established irrigation networks—managed by independent farm households. Plot-level randomization and artificial treatment assignment would be impossible without destroying these existing production systems; (2) Our research objective was to characterize soil conditions as they exist under actual farming practices in this region, rather than to isolate specific management effects under controlled conditions; (3) The observational approach enables investigation of long-term cumulative effects (years to decades) of these contrasting systems that would be difficult to replicate in short-term manipulative experiments. Consequently, while our study describes soil differences associated with these cultivation systems, definitive causal attribution is limited by the absence of pre-treatment randomization, as discussed further in Section 4.6.
To minimize confounding effects from pre-existing soil variability and maximize comparability between cultivation systems, we implemented multiple design and analytical controls: (1) Geographic co-location: All sampling sites were located within a compact watershed area (<2 km2) along the same irrigation canal network, ensuring shared water sources, uniform climatic conditions, and similar hydrological regimes. The area is underlain by uniform parent material (alluvial-lacustrine sediments from Dianchi Lake) with minimal topographic variation (<3% slope). (2) Baseline soil characterization: Soil texture analysis revealed no significant differences between greenhouse and open-field sites across the 0–80 cm profile (p > 0.05; Figure 2b), with both systems exhibiting clay loam texture (approximately 25% clay, 25% silt, 50% sand and gravel). This consistency indicates minimal pedogenic variability and confirms comparability of baseline soil conditions. (3) Spatial replication: We established 3 greenhouse sites and 5 open-field sites, each managed by independent farm households (Figure S1). The unequal replication reflected the contrasting spatial scales of the two systems (larger contiguous open-field areas vs. smaller distributed greenhouse plots). (4) Statistical accounting for spatial structure: We employed mixed-effects models (Section 2.5) that explicitly partition variance into between-site and within-site components, treating sampling sites as random effects nested within cultivation mode. This hierarchical structure accommodates the unequal replication and yields valid inference about cultivation mode effects while accounting for spatial heterogeneity.
This observational design characterizes soil conditions under established farming systems but cannot definitively establish causality, as treatment assignment was not randomized. Observed differences may reflect cultivation mode effects, uncontrolled confounding factors, or their interactions. Nevertheless, the consistency of findings across multiple spatially replicated sites, temporal stability over 13 months, baseline soil uniformity (Figure 2b), and biological plausibility of proposed mechanisms (Section 4) suggest that cultivation mode is meaningfully associated with observed soil patterns. Results should be interpreted as representative of these specific agricultural systems in this region, rather than as generalizable universal effects of protected vs. open-field cultivation in all contexts.
Rose cut flowers were cultivated in greenhouse facilities using year-round perennial multi-harvest cultivation, while corn and vegetables were grown in open fields under an annual rotation system. Both cultivation modes adopted local traditional management practices. In greenhouse cultivation facilities, irrigation timing was determined based on manual experience judgment, with approximately 6–7 mm per event applied every 2–3 days. Open-field cultivation mainly relied on natural precipitation, with supplemental irrigation during drought periods. Fertilization followed the local conventional “organic fertilizer + compound fertilizer” model. This comparison involves systematic differences in crop biology (perennial rose vs. annual vegetables/corn), management practices (drip fertigation vs. rainfall-dependent cultivation), and input intensity. These differences are integral components of the two contrasting agricultural systems, not confounding factors to be controlled. Soil samples were collected monthly from March 2022 to March 2023, yielding 39 temporal observations for greenhouse (3 points × 13 months) and 65 for open-field (5 points × 13 months). Sampling was conducted using a soil auger at five depths: 0–10 cm, 10–20 cm, 20–30 cm, 30–50 cm, and 50–80 cm. After thorough mixing of soil from each layer, approximately 200 g was placed in sealed plastic bags and brought back to the laboratory. Soil samples were divided into three parts: one part (approximately 100 g) was air-dried naturally for determination of physicochemical properties (pH value, electrical conductivity, particle size, soil texture, etc.), another part was sieved through a 60-mesh sieve for nutrient content determination (total carbon, total nitrogen, ammonium nitrogen, available phosphorus, available potassium), and the remaining part was used to determine soil water content (SWC, %) using the oven-drying method (105 °C, 48 h).

2.3. Test Items and Analytical Methods

Soil total carbon was determined using the K2Cr2O7 volumetric method with external heating; total nitrogen was determined using the Kjeldahl method; ammonium nitrogen was determined using 2 mol L−1 KCl extraction-indophenol blue method; available phosphorus was determined using NaHCO3 extraction-molybdate-antimonyl method; available potassium was determined using NH4OAc extraction-flame photometry.
pH value was determined using the potentiometric method (soil:water ratio of 1:2.5); electrical conductivity was measured using electrical conductivity meter (Shanghai INESA Scientific Instrument Co., Ltd., Shanghai, China) measurement (soil:water ratio of 1:5); water content was determined using the oven-drying method (105 °C, 48 h), calculated using the following formula:
SWC   %   =   W 1 − W 2   /   W 2
where W1 is the fresh soil weight (g) and W2 is the dry soil weight (g).

2.4. Environmental Factor Monitoring

Small automated weather stations (Insentek (Zhejiang) Technology Co., Ltd., Hangzhou, China) were deployed in the study area to continuously monitor environmental factors including relative humidity, solar radiation, atmospheric pressure, wind speed, rainfall, and temperature, with hourly data collection.

2.5. Data Processing and Analysis

Excel 2019 was used for data entry, verification, and preprocessing, removing outliers and calculating basic statistics.
A mixed-effects repeated measures analysis of variance (ANOVA) was conducted to evaluate the effects of cultivation mode, soil depth, and time on soil nutrients and physicochemical properties. Fixed factors included cultivation mode (greenhouse vs. open-field) and soil depth (five levels: 0–10, 10–20, 20–30, 30–50, and 50–80 cm), while sampling time (13 monthly periods from March 2022 to March 2023) served as the repeated measure. Sampling sites were treated as random factors nested within cultivation mode, with individual sampling points as experimental units. This hierarchical structure directly addresses the unequal replication (3 vs. 5 sampling points) by modeling site-level variance, ensuring valid statistical inference while preserving all individual observations.
Prior to analysis, data were examined for normality and homogeneity of variance to ensure compliance with ANOVA assumptions. When variance analysis results were significant (p < 0.05), the least significant difference (LSD) method was used for multiple comparisons. Pearson correlation analysis was used to evaluate correlations between soil nutrients and environmental factors and physicochemical properties.
R 4.3.3 and SPSS 22.0 software were used for experimental data processing, statistical analysis, and graphing.

3. Results

3.1. Differences in Soil Physical Properties Under Different Cultivation Methods

No significant difference in soil bulk density was observed between greenhouse cultivation and open-field cultivation (p > 0.05). Soil bulk density under greenhouse cultivation showed no significant difference between dry and rainy seasons (p > 0.05), while open-field soil bulk density was significantly higher in the dry season than in the rainy season (p = 0.019; Figure 2a). Although bulk density showed no significant difference between the two cultivation systems, soil aggregate composition exhibited notable differences (Figure 2c), reflecting the differential effects of contrasting tillage regimes. Under greenhouse no-tillage conditions, soil remains undisturbed by mechanical operations, maintaining relatively stable bulk density; while open-field tillage periodically disrupts soil structure, natural settling after tillage allows bulk density to recover. However, no-tillage does not imply complete soil structural stability: high-intensity drip fertigation continuously alters soil moisture and chemical environments, with root activities and microbial processes promoting aggregate reorganization, leading to transformation from large to small aggregates. Soil texture in the 0–80 cm soil layers was similar between the two cultivation methods, predominantly clay, with approximately 25% clay particles, 25% silt, and 50% sand and gravel, showing no significant differences among soil layers (p > 0.05; Figure 2b). Overall, the content of <0.005 mm aggregates was similar between the two cultivation methods, but in the 0–10 cm soil layer, greenhouse cultivation had higher 0.05–0.25 mm aggregate content than open-field cultivation, and lower 0.25–2 mm aggregate content than open-field cultivation (Figure 2c).

3.2. Soil Nutrient Content

Except for available potassium, different cultivation methods and seasons had significant effects on soil nutrient content (p < 0.001). Nutrient content showed a decreasing pattern with increasing soil depth, with significant differences among soil layers (p < 0.001). Vertical nutrient variation was more pronounced under greenhouse cultivation (Figure 3).
Total nitrogen content in open-field cultivation soil (2.459 ± 0.040 g kg−1) was slightly higher than in greenhouse cultivation (2.385 ± 0.094 g kg−1; Table 1). Significant differences between the two cultivation methods were observed in the surface layer (0–10 cm; p < 0.05), no significant difference in the 10–20 cm layer (p > 0.05), significant difference in the 20–30 cm layer (p < 0.01), and significant differences in the deep soil layers (30–80 cm; p < 0.001; Figure 4a).
Average ammonium nitrogen content in open-field cultivation (0.023 ± 0.001 g kg −1) exceeded that in greenhouse cultivation soil (0.021 ± 0.001 g kg−1; Table 1). However, ammonium nitrogen in greenhouse cultivation surface soil (0–10 cm) reached its annual maximum of 0.025 ± 0.001 g kg−1 in summer, exceeding open-field cultivation during the same period (Figure 3c,d). No significant differences were observed between the two methods in shallow layers (0–30 cm; p > 0.05); significant differences in the 30–50 cm layer (p < 0.01); and significant differences in the 50–80 cm layer (p < 0.001; Figure 4b).
Available phosphorus content in open-field cultivation (0.159 ± 0.006 g kg−1) was higher than in greenhouse cultivation (0.137 ± 0.012 g kg−1; Table 1). No significant differences were observed between the two methods in the 0–30 cm soil layers (p > 0.05), while open-field cultivation was significantly higher than greenhouse cultivation in the deep layers (30–80 cm; p < 0.01; Figure 4c).
Available potassium content in greenhouse cultivation (0.327 ± 0.043 g kg−1) was slightly higher than in open-field cultivation (0.318 ± 0.031 g kg−1; Table 1). Only in the deep layer (50–80 cm) was open-field cultivation significantly higher than greenhouse cultivation (p < 0.05), with no significant differences in other soil layers (p > 0.05; Figure 4d).

3.3. Soil Physicochemical Properties

Soil physicochemical properties (total carbon, water content, pH, and electrical conductivity) showed significant differences among different seasons, sites, and soil depths (p < 0.001). Compared with open-field cultivation, vertical differentiation of physicochemical properties in all soil layers was more pronounced under greenhouse cultivation, with only soil water content showing the opposite trend (Figure 5).
Soil total carbon content was similar between the two cultivation methods, with average values of 21.381 ± 0.252 g kg−1 and 21.024 ± 0.484 g kg−1 for open-field and greenhouse cultivation, respectively (Table 1). Regarding seasonal dynamics, soil carbon content under greenhouse cultivation showed greater amplitude of fluctuation, while open-field cultivation showed relatively stable performance (Figure 5a,b). Vertical distribution characteristics showed: no significant difference in surface soil (0–10 cm) total carbon between the two cultivation methods (p > 0.05); soil total carbon in open-field cultivation was significantly lower than greenhouse cultivation in the subsurface layer (10–20 cm; p < 0.05); while in deep soil layers of 20–30 cm, 30–50 cm, and 50–80 cm, total carbon content in open-field cultivation was significantly higher than greenhouse cultivation (p < 0.01; Figure 4e).
Soil water content under greenhouse cultivation (32.203 ± 0.500%) was higher than open-field cultivation (30.151 ± 1.230%; Table 1). Spatial variability analysis showed that soil water content in open-field cultivation exhibited greater variability across vertical profiles and seasons than greenhouse cultivation (Figure 5c,d). Among these, water content under greenhouse cultivation was significantly higher than open-field cultivation in the 10–20 cm and 30–50 cm soil layers (p < 0.001; Figure 4f).
Greenhouse cultivation soil showed higher alkalinity than open-field cultivation, with average pH values of 7.234 ± 0.124 and 7.105 ± 0.028, respectively (Table 1). Greenhouse cultivation soil pH showed an increasing trend with depth, displaying obvious alkalization characteristics (Figure 5e); in contrast, pH values in open-field cultivation showed relatively small variation among soil layers (Figure 5f). Statistical analysis showed significant differences in pH values between the two cultivation methods in the 10–20 cm soil layer (p < 0.05), while differences in pH values in the deep soil layers of 30–50 cm and 50–80 cm reached significant levels (p < 0.001; Figure 4g).
Vertical variation of soil electrical conductivity in open-field cultivation was relatively uniform. Soil electrical conductivity under both cultivation methods reached peak values in September. Under greenhouse cultivation, electrical conductivity increased with soil depth in the surface to 30 cm depth range; it showed a decreasing trend in the 30–80 cm depth range (Figure 5g,h). Soil electrical conductivity under greenhouse cultivation (620.820 ± 59.028 μS cm−1) was significantly higher than open-field cultivation (347.601 ± 10.267 μS cm−1; Table 1), and this difference reached significant levels in all soil layers (p < 0.01; Figure 4h).

3.4. Correlation Analysis of Soil Nutrients and Physicochemical Properties

Compared with greenhouse cultivation, meteorological factors showed closer relationships with open-field cultivation, mostly exhibiting negative correlations. Under both cultivation methods, soil depth showed highly negative correlations with total carbon, total nitrogen, and available potassium; however, compared with open-field cultivation, soil nutrients under greenhouse cultivation showed higher correlations with soil physicochemical properties (Figure 6).
Under greenhouse conditions, soil nutrients exhibited close relationships with depth and pH, demonstrating strong inter-correlations among different nutrient indicators. In contrast, EC showed higher correlations with environmental factors such as wind speed and pH than with soil nutrients (Figure 6a). Compared to greenhouse cultivation, open-field conditions displayed weaker inter-correlations among soil nutrients but stronger associations with meteorological factors. Moreover, EC was closely related to minimum temperature, relative humidity, and precipitation, beyond its association with wind speed (Figure 6b).

4. Discussion

This study compared soil fertility dynamics between greenhouse rose production and open-field vegetable/corn cultivation—the dominant agricultural systems in the Dianchi Lake Basin. The observed differences reflect integrated effects of cultivation environment, crop type, and management practices (Section 4.6), providing insights into soil nutrient accumulation under intensive facility agriculture and its environmental implications for this ecologically sensitive region.

4.1. Soil Physical Structure Characteristics

Soil aggregates are fundamental units of soil structure, and their quantity and stability directly affect soil organic matter retention, nutrient cycling, and water movement [23]. In this study, soil aggregate composition changed markedly under both cultivation systems, but the driving mechanisms fundamentally differed (Figure 2). In the 0–10 cm surface layer, greenhouse cultivation exhibited higher proportions of 0.05–0.25 mm aggregates but lower proportions of 0.25–2 mm aggregates compared to open-field cultivation, indicating a shift toward smaller aggregate size classes. This structural transformation aligns with patterns observed in intensive greenhouse vegetable systems, where fertilization management significantly influences aggregate size distribution and water-stable aggregate formation [24].
In the greenhouse no-tillage system, aggregate composition changed significantly despite the absence of mechanical disturbance, showing increased small aggregates and decreased large aggregates. This transformation was primarily driven by: (1) high-intensity drip fertigation continuously altering soil chemical environments, in which base cations from fertilizers (e.g., K+, Ca2+) affect aggregate binding agents; (2) intensive root activities and exudates promoting micro-aggregate reorganization; (3) microbial decomposition of organic binding agents within large aggregates, transforming them into smaller forms [23,25]. Notably, this process is gradual and biochemically driven, rather than mechanically induced.
In contrast, aggregate evolution in the open-field tillage system was predominantly governed by periodic mechanical disruption [25]. Frequent tillage operations directly fragment large aggregates, but root growth, microbial activities, and wetting-drying cycles promote reformation during tillage intervals. This cyclical “disruption-rebuilding” process maintains open-field soil aggregates in dynamic equilibrium, with lower water-fertilizer input intensity causing more moderate chemical changes than greenhouse conditions.
Despite different driving mechanisms, both systems exhibit similar outcomes: increased proportions of small aggregates. Although small aggregates facilitate rapid nutrient release, the depletion of large aggregates weakens long-term soil structural stability. Large aggregates contain higher organic carbon than small aggregates; therefore, the shift from large to small aggregates promotes soil organic carbon loss [26].
Regarding moisture conditions, soil water content under greenhouse cultivation was generally higher than in open-field cultivation (Figure 4f). This elevated moisture results from regular irrigation combined with reduced evaporative losses due to the enclosed environment, which stabilizes diurnal temperature fluctuations and benefits root growth [27]. Although greenhouse cultivation reduces evaporation losses, over-irrigation remains common and can lead to excessive soil water accumulation and nutrient leaching [28,29,30], creating a trade-off: while elevated moisture promotes salt leaching, it simultaneously accelerates nutrient losses and increases risks of soil compaction and root diseases.

4.2. Vertical Distribution Patterns of Soil Nutrients

Except for available potassium, nutrient differences between greenhouse cultivation and open-field cultivation were significant (p < 0.001). Total nitrogen, ammonium nitrogen, and available potassium contents in the 0–10 cm surface soil layer under greenhouse cultivation were higher than those in open-field cultivation. Soil nutrient content showed a decreasing trend with increasing depth, exhibiting significant surface accumulation characteristics (Table 1).
Covering materials blocked the leaching effects of natural precipitation, thereby reducing the migration rate of nutrients to deeper layers [31]. In intensive production, high-frequency drip fertigation delivers dissolved chemical fertilizers directly to the surface layer, creating continuous nutrient pulses where root activity concentrates. This irrigation-mediated application maintains elevated soil moisture that enhances nutrient dissolution and mobilization without corresponding downward leaching. Since root systems distribute primarily in shallow layers, nutrient input, transformation, and uptake are all concentrated in the surface soil layer [32], further strengthening this vertical differentiation pattern. Moreover, the no-tillage regime in greenhouse rose cultivation is a key factor contributing to surface nutrient enrichment. In contrast, in open-field tillage systems, annual tillage operations mix the 0–20 cm soil layer, promoting more uniform nutrient distribution within the plow layer.
Surface nutrient enrichment facilitates short-term crop uptake but presents ecological risks. During heavy rainfall or excessive irrigation, high-concentration nutrients can readily be transported through surface runoff, increasing agricultural diffuse pollution loads [33]. Depleted deeper layers limit downward root development, creating nutrient supply imbalance with surface excess and deep-layer deficiency [34]. Active nutrients such as nitrogen and phosphorus require deep fertilization techniques and increased organic matter application to improve their vertical distribution. Potassium is relatively stable but surface over-enrichment should be controlled to avoid salt accumulation.

4.3. Changes in Soil Chemical Environment

Overall, greenhouse cultivation showed higher soil pH and electrical conductivity than open-field cultivation, with more dispersed distribution across soil layers (Figure 4g,h). High-intensity chemical fertilizer application introduced abundant alkaline cations, while irrigation water salts gradually accumulated under limited evaporation.
Soil pH deviation from the optimal range affects nutrient availability, while increased electrical conductivity directly threatens crop osmotic regulation capacity. These changes in chemical indicators reflect the readjustment process of soil chemical balance under greenhouse cultivation conditions. However, shallow soil (0–20 cm) under greenhouse cultivation showed lower pH values than open-field cultivation (Table 1). This is consistent with previous studies, indicating that soil acidification occurs under greenhouse cultivation environments, which can further alter soil microbial community structure and potentially expose soil to phthalate (PAEs) contamination [35].
Soil electrical conductivity under greenhouse cultivation (620.820 ± 59.028 μS cm−1) was significantly higher than open-field cultivation (347.601 ± 10.267 μS cm−1; Table 1), representing a 1.8-fold increase. The fertilization regime in greenhouse rose production typically combines organic manure with synthetic fertilizers containing high water-soluble salt concentrations (N-P-K compounds). Since both cultivation systems shared the same irrigation water source and were closely located, the observed EC difference primarily reflects fertilization management rather than water quality.
This elevated EC reflects salt accumulation resulting from intensive fertilization practices under protected cultivation conditions. In greenhouse vegetable production systems, excessive fertilizer application leads to progressive salt accumulation in surface soil layers, with the plastic covering preventing natural precipitation from leaching salts out of the root zone. Studies on intensive greenhouse systems have demonstrated that irrigation amount and frequency significantly affect soil EC, with insufficient leaching leading to continued salt build-up [36]. Although the current EC level in our study (620.820 μS cm−1) is relatively low compared to typical salinity stress thresholds for most horticultural crops, the 1.8-fold increase compared to open-field conditions indicates an ongoing accumulation trend. Implementing appropriate leaching practices during non-growing seasons and optimizing fertigation management are essential for preventing long-term salinization and maintaining sustainable soil conditions in intensive greenhouse production [37].

4.4. Reconstruction of Nutrient Cycling Relationships

Greenhouse cultivation altered the relationships between soil nutrients and environmental factors (Figure 6). The correlations between nutrients and meteorological factors were weakened under greenhouse cultivation because plastic covering created a relatively enclosed microenvironment, significantly reducing the direct influence of external climatic conditions [38]. The internal microclimate conditions were relatively stable and more subject to artificial control. Therefore, the linear relationships between soil nutrient status and natural factors were no longer evident. Conversely, the coupling degree among nutrient elements was enhanced, with stronger interactions among components in the enclosed environment [39]. Therefore, when evaluating soil fertility under greenhouse cultivation, emphasis should be placed on characterizing endogenous nutrient supply and cycling mechanisms rather than simply applying indicator systems from natural conditions. The observed high coupling among nutrient elements (Figure 6) necessitates balanced fertilization that accounts for interactive effects among nitrogen, phosphorus, potassium, and micronutrients to promote efficient nutrient cycling. Understanding these cycling patterns is crucial for optimizing fertilization regimes and improving fertilizer use efficiency, as nutrient cycling constitutes the core process of soil fertility formation.
The strong nutrient interactions observed also provide a mechanistic basis for combined organic-inorganic fertilizer application. Organic matter decomposition supplies carbon and energy for microorganisms while activating soil enzyme systems [40], whereas inorganic fertilizers provide mineral nutrients for microbial growth, and their integration creates a more efficient nutrient cycling system than either component alone. In practice, optimizing the ratios and timing of both fertilizer types is essential for maximizing these synergistic benefits. Based on the coupling characteristics of soil nutrients, constructing a modern soil and fertilizer science theory and technology system of “nutrient synergy, fertilizer combination, organic matter enhancement, and precision management” is a key direction that urgently needs a breakthrough in scientific management of greenhouse cultivation soils. Additionally, greenhouse cultivation conditions not only directly affect soil nutrient cycling processes and characteristics, but also indirectly influence soil nutrient cycling by regulating plant root growth and plant–soil interactions [41].
The observed changes in soil nutrient dynamics have important implications for microbial communities and enzymatic activities that mediate critical nutrient transformation. Greenhouse cultivation creates distinct environmental conditions—elevated temperature, stable moisture, and concentrated nutrient inputs—collectively reshaping soil microbial community structure and functional diversity [42]. Surface nutrient enrichment (Table 1) provides abundant substrates for microbial metabolism, potentially enhancing nitrogen and phosphorus cycling enzymes such as urease, phosphatase, and β-glucosidase [43]. However, elevated soil salinity (EC = 620.820 μS cm−1; Table 1) and altered pH may simultaneously impose osmotic and biochemical stress on sensitive microbial groups, potentially reducing overall microbial diversity and functional redundancy [42]. The balance between nutrient substrate availability and environmental stress determines whether greenhouse soils maintain robust microbial-mediated nutrient transformations or experience functional degradation. Future research should quantify microbial biomass, community composition, and enzyme activity profiles to establish mechanistic links between management practices and soil biological health, thereby providing a biological foundation for optimizing nutrient management strategies.

4.5. Environmental Impact Assessment

Excessive irrigation and fertilization resulted in over-enrichment of surface nutrients, increasing the potential risk of agricultural diffuse pollution. Although this study did not directly measure nutrient losses (e.g., leaching, runoff), the observed soil conditions can be contextualized using findings from comparable intensive systems. Global meta-analyses of greenhouse vegetables report mean nitrogen leaching rates of approximately 297 kg N ha−1 yr−1 [44], while Chinese open-field vegetable studies document total nitrogen and phosphorus runoff losses of 16.5 and 3.45 kg ha−1, respectively [45]. While our greenhouse rose system differs from vegetable systems in crop type and management practices, comparable fertilization intensity and irrigation regime suggest similar environmental loading magnitudes, though crop-specific nutrient uptake patterns should be considered.
The proximity of the study area to Dianchi Lake heightens the environmental significance of these findings. Dianchi Lake has experienced severe eutrophication in recent decades, with agricultural diffuse pollution identified as a major contributing factor. Irrigation return flow can easily carry high concentrations of nitrogen and phosphorus into water systems, causing eutrophication problems [46]. Meanwhile, soil salt accumulation and structural degradation are more prominent under greenhouse cultivation conditions. These changes not only affect current crop growth but may also damage the long-term productive capacity of soil. To address these issues, greenhouse soil fertility enhancement and nutrient management should adopt total quantity control principles, combining source control, process interception, and end-point strengthening to integrate diffuse pollution prevention throughout soil management. On the one hand, source reduction is a key measure for controlling diffuse pollution. Fertilizer application rates should be strictly determined according to crop requirements and soil supply capacity, avoiding precautionary over-application [47]. Given the observed surface nutrient enrichment (surface ammonium nitrogen of 0.025 g kg−1 vs. 0.018–0.021 g kg−1 in deeper layers), reducing total fertilizer inputs while maintaining productivity is feasible and would directly reduce environmental loading.
On the other hand, deep application techniques and slow-release fertilizers can reduce surface nutrient accumulation and decrease runoff loss risks. Additionally, field ecological interception systems should be strengthened by properly establishing plant buffer strips and sedimentation ponds to intercept nitrogen and phosphorus carried by runoff and reduce nutrient losses from farmland. Moreover, proper water supply and irrigation management have significant effects on both plant quality and environmental outcomes under greenhouse cultivation [48]. Given the elevated soil moisture (32.2% vs. 30.2%) and intensive irrigation volumes (around 6–7 mm per event applied every 2–3 days), there is substantial potential to optimize water use while simultaneously reducing nutrient leaching risks. Therefore, implementing precision agriculture and combining quantitative fertilization with irrigation will effectively address agricultural pollution problems and improve flower quality [37].

4.6. Research Limitations and Application Value

Different crop types (greenhouse rose vs. open-field vegetables/corn) were inherently coupled with contrasting cultivation regimes (perennial no-tillage vs. annual tillage), meaning observed soil properties reflect integrated effects rather than isolated variables. Fertilization practices varied among farmers, introducing additional treatment variation, and the one-year monitoring period may not fully capture long-term soil evolution. Most importantly, as noted in Section 2.2, this observational design cannot establish definitive causal relationships without pre-treatment randomization. Potential confounding factors could contribute to observed patterns. Nevertheless, cultivation system appears to be a primary driver based on baseline soil uniformity (Figure 2b, p > 0.05), spatial consistency across independent sites, temporal stability over 13 months, biological plausibility (Section 4.1, Section 4.2, Section 4.3 and Section 4.4), and concordance with controlled experiments. While causal claims warrant caution, our findings provide a robust characterization of actual farming systems in the Danchi Lake Basin. This characterization, though observational, yields several actionable management insights.
Despite these limitations, this study offers actionable management insights. For greenhouse systems: focus on deep soil improvement through organic fertilizer application to enhance aggregate stability [49]; adopt precision irrigation (drip/micro-sprinkler) to mitigate water accumulation and leaching risks [50]; implement deep-layered fertilization while controlling total inputs to address surface enrichment. For open-field systems: prioritize surface nutrient retention, tillage layer maintenance, and precision fertilization. Environmental risk management requires stratified monitoring aligned with the National Agricultural Diffuse Pollution Monitoring Plan (2022–2025) [51]. These recommendations target greenhouse rose and open-field crop systems in our study area; adaptation and validation are needed for other regions, crops, or conditions.

5. Conclusions

In the greenhouse rose and open-field vegetable/corn production systems of the southern Dianchi Lake Basin, greenhouse cultivation was associated with enhanced N, P, and K accumulation in the 0–10 cm topsoil layer and altered physicochemical properties, including elevated EC and increased salinization risk. The altered microclimate was associated with reduced nutrient exchange with meteorological factors but stronger internal nutrient coupling. These findings highlight the need for precision fertilization, improved irrigation management, and organic-inorganic nutrient balance to sustain soil fertility while reducing diffuse pollution.
For the studied agricultural systems in the southern Dianchi Lake area, these findings deepen understanding of soil nutrient accumulation characteristics and their environmental effects in greenhouse cut-flower and open-field crop production systems, providing important theoretical basis and practical guidance for sustainable development of greenhouse rose cultivation and agricultural diffuse pollution prevention in this region. As an observational study, these results describe soil patterns associated with these cultivation practices; experimental validation would further strengthen causal understanding. Future research should conduct comprehensive studies over longer time scales and in more regions, incorporating indicators such as soil microorganisms and enzyme activities to further reveal the mechanisms of greenhouse cultivation impacts on soil quality and the agricultural environment. This would provide a scientific basis for developing precision fertilization strategies and environmental management measures.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/agronomy15112566/s1, Figure S1: Spatial distribution of greenhouse and open-field sampling sites in the study area. Yellow stars indicate greenhouse sampling locations (n = 3), and red circles indicate open-field sampling locations (n = 5). The base map shows high-resolution satellite imagery. The map includes a scale bar (100 m), north arrow, and coordinate system in decimal degrees (WGS 84).

Author Contributions

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

Funding

This research was funded by the National Natural Science Foundation of China, grant numbers 32160280 and 42361065; the Yunnan Province “Xingdian Talent Support Program” Young Talent Special Projects, grant numbers XDYC-QNRC-2022-0012 and XDYC-QNRC-2024-483; the Yunnan Province “Xingdian Talent Support Program” Innovation Team Special Project, grant number 202305AS350003; the Open Fund of the Key Laboratory of Tropical Forest Ecology, Chinese Academy of Sciences, grant number 22-CAS-TFE-04; and the Young and Middle-Aged Academic and Technological Leaders of Yunnan, grant number 202405AC350070.

Data Availability Statement

The datasets presented in this article are not readily available because the data are part of an ongoing study. Requests to access the datasets should be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Changes of environmental factors from March 2022 to March 2023. (a) Wind speed; (b) Atmospheric pressure; (c) Relative humidity; (d) Solar radiation; (e) Rainfall; (f) Temperature.
Figure 1. Changes of environmental factors from March 2022 to March 2023. (a) Wind speed; (b) Atmospheric pressure; (c) Relative humidity; (d) Solar radiation; (e) Rainfall; (f) Temperature.
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Figure 2. Soil physical properties across sampling sites and seasons. (a) Seasonal variation of soil bulk density at different depths (S1: 0–10 cm, S2: 10–20 cm, S3: 20–30 cm, S4: 30–50 cm, S5: 50–80 cm). (b) Soil particle size distribution across depths and sites showing clay, silt, and sand fractions. (c) Soil aggregate size distribution at different depths with diameter classes ranging from <0.005 mm to 2 mm. GH: greenhouse; OF: open-field. Error bars represent standard error. ns = not significant.
Figure 2. Soil physical properties across sampling sites and seasons. (a) Seasonal variation of soil bulk density at different depths (S1: 0–10 cm, S2: 10–20 cm, S3: 20–30 cm, S4: 30–50 cm, S5: 50–80 cm). (b) Soil particle size distribution across depths and sites showing clay, silt, and sand fractions. (c) Soil aggregate size distribution at different depths with diameter classes ranging from <0.005 mm to 2 mm. GH: greenhouse; OF: open-field. Error bars represent standard error. ns = not significant.
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Figure 3. Seasonal dynamics of soil nutrient contents under greenhouse and open-field conditions. (a,b) Total nitrogen content under greenhouse and open-field cultivation; (c,d) Ammonium nitrogen content under greenhouse and open-field cultivation; (e,f) Available phosphorus content under greenhouse and open-field cultivation; (g,h) Available potassium content under greenhouse and open-field cultivation. Error bars represent standard error. *** p < 0.001.
Figure 3. Seasonal dynamics of soil nutrient contents under greenhouse and open-field conditions. (a,b) Total nitrogen content under greenhouse and open-field cultivation; (c,d) Ammonium nitrogen content under greenhouse and open-field cultivation; (e,f) Available phosphorus content under greenhouse and open-field cultivation; (g,h) Available potassium content under greenhouse and open-field cultivation. Error bars represent standard error. *** p < 0.001.
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Figure 4. Vertical distribution of soil properties at different depths under greenhouse and open-field cultivation. (a) Total nitrogen content; (b) Ammonium nitrogen content; (c) Available phosphorus content; (d) Available potassium content; (e) Total carbon content; (f) Soil water content; (g) Soil pH values; (h) Soil electrical conductivity. Error bars represent standard error. Asterisks indicate significant differences between cultivation methods at the same depth: * p < 0.05, ** p < 0.01, *** p < 0.001.
Figure 4. Vertical distribution of soil properties at different depths under greenhouse and open-field cultivation. (a) Total nitrogen content; (b) Ammonium nitrogen content; (c) Available phosphorus content; (d) Available potassium content; (e) Total carbon content; (f) Soil water content; (g) Soil pH values; (h) Soil electrical conductivity. Error bars represent standard error. Asterisks indicate significant differences between cultivation methods at the same depth: * p < 0.05, ** p < 0.01, *** p < 0.001.
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Figure 5. Seasonal dynamics of soil physicochemical properties under greenhouse and open-field conditions. (a,b) Total carbon content under greenhouse and open-field cultivation; (c,d) Soil water content under greenhouse and open-field cultivation; (e,f) Soil pH values under greenhouse and open-field cultivation; (g,h) Soil electrical conductivity under greenhouse and open-field cultivation. Error bars represent standard error. *** p < 0.001.
Figure 5. Seasonal dynamics of soil physicochemical properties under greenhouse and open-field conditions. (a,b) Total carbon content under greenhouse and open-field cultivation; (c,d) Soil water content under greenhouse and open-field cultivation; (e,f) Soil pH values under greenhouse and open-field cultivation; (g,h) Soil electrical conductivity under greenhouse and open-field cultivation. Error bars represent standard error. *** p < 0.001.
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Figure 6. Correlation matrix showing strong and statistically significant relationships between environmental factors, soil nutrients and physicochemical properties. (a) Greenhouse cultivation; (b) Open-field cultivation. Color intensity indicates correlation strength and direction (blue = positive, red = negative), with correlation coefficients displayed as numbers. Only strong correlations (|r| > 0.5, p < 0.05) are shown to highlight the most meaningful relationships. TC = total carbon; TN = total nitrogen; AN = ammonium nitrogen; AP = available phosphorus; AK = available potassium; SWC = soil water content; EC = electrical conductivity.
Figure 6. Correlation matrix showing strong and statistically significant relationships between environmental factors, soil nutrients and physicochemical properties. (a) Greenhouse cultivation; (b) Open-field cultivation. Color intensity indicates correlation strength and direction (blue = positive, red = negative), with correlation coefficients displayed as numbers. Only strong correlations (|r| > 0.5, p < 0.05) are shown to highlight the most meaningful relationships. TC = total carbon; TN = total nitrogen; AN = ammonium nitrogen; AP = available phosphorus; AK = available potassium; SWC = soil water content; EC = electrical conductivity.
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Table 1. Mean values of soil nutrient contents and physicochemical properties in different soil layers.
Table 1. Mean values of soil nutrient contents and physicochemical properties in different soil layers.
SiteDepth (cm)TN
(g kg−1)		AN
(g kg−1)		AP
(g kg−1)		AK
(g kg−1)		TC
(g kg−1)		SWC
(%)		pHEC
(μS cm−1)
OF0~102.540 ± 0.0100.024 ± 0.0010.170 ± 0.0070.401 ± 0.01421.913 ± 0.06428.858 ± 0.6357.035 ± 0.055385.592 ± 18.736
10~202.527 ± 0.0100.023 ± 0.0010.169 ± 0.0060.364 ± 0.01321.841 ± 0.05927.967 ± 0.6347.124 ± 0.046349.511 ± 17.418
20~302.489 ± 0.0100.022 ± 0.0010.164 ± 0.0050.329 ± 0.01421.588 ± 0.05833.508 ± 0.4137.174 ± 0.033343.223 ± 18.859
30~502.408 ± 0.0100.024 ± 0.0010.153 ± 0.0070.263 ± 0.01020.842 ± 0.06027.706 ± 0.7277.145 ± 0.039327.496 ± 18.744
50~802.329 ± 0.0110.021 ± 0.0010.139 ± 0.0050.233 ± 0.00920.719 ± 0.05732.715 ± 0.4537.046 ± 0.047332.185 ± 18.152
average2.459 ± 0.0400.023 ± 0.0010.159 ± 0.0060.318 ± 0.03121.381 ± 0.25230.151 ± 1.2307.105 ± 0.028347.601 ± 10.267
GH0~102.578 ± 0.0270.025 ± 0.0010.159 ± 0.0070.436 ± 0.01821.964 ± 0.16830.465 ± 0.5506.966 ± 0.079519.670 ± 57.715
10~202.543 ± 0.0240.023 ± 0.0010.157 ± 0.0070.392 ± 0.02422.095 ± 0.17032.022 ± 0.5296.989 ± 0.088737.047 ± 64.536
20~302.443 ± 0.0280.021 ± 0.0010.150 ± 0.0060.350 ± 0.02021.234 ± 0.19133.489 ± 0.4507.174 ± 0.057740.942 ± 62.782
30~502.295 ± 0.0290.020 ± 0.0010.127 ± 0.0060.255 ± 0.01620.168 ± 0.17932.318 ± 0.7837.466 ± 0.035658.998 ± 40.778
50~802.063 ± 0.0490.018 ± 0.0010.093 ± 0.0070.203 ± 0.01319.657 ± 0.19632.723 ± 0.4737.574 ± 0.039447.442 ± 15.286
average2.385 ± 0.0940.021 ± 0.0010.137 ± 0.0120.327 ± 0.04321.024 ± 0.48432.203 ± 0.5007.234 ± 0.124620.820 ± 59.028
Note: GH = greenhouse; OF = open-field. TN, TC, AN, AP, AK, SWC, pH, and EC represent total nitrogen, total carbon, ammonium nitrogen, available phosphorus, available potassium, soil water content, pH value, and electrical conductivity, respectively. Values are means ± SE (n = 5 depths for average values).
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Miao, Z.; Wu, J.; Zhao, L.; Cheng, F.; Zhang, Y. Soil Nutrient Variability Analysis of Typical Planting Patterns in Agricultural Reclamation Areas of the Southern Dianchi Lake Basin. Agronomy 2025, 15, 2566. https://doi.org/10.3390/agronomy15112566

AMA Style

Miao Z, Wu J, Zhao L, Cheng F, Zhang Y. Soil Nutrient Variability Analysis of Typical Planting Patterns in Agricultural Reclamation Areas of the Southern Dianchi Lake Basin. Agronomy. 2025; 15(11):2566. https://doi.org/10.3390/agronomy15112566

Chicago/Turabian Style

Miao, Zhuojun, Junen Wu, Lei Zhao, Feng Cheng, and Yuchen Zhang. 2025. "Soil Nutrient Variability Analysis of Typical Planting Patterns in Agricultural Reclamation Areas of the Southern Dianchi Lake Basin" Agronomy 15, no. 11: 2566. https://doi.org/10.3390/agronomy15112566

APA Style

Miao, Z., Wu, J., Zhao, L., Cheng, F., & Zhang, Y. (2025). Soil Nutrient Variability Analysis of Typical Planting Patterns in Agricultural Reclamation Areas of the Southern Dianchi Lake Basin. Agronomy, 15(11), 2566. https://doi.org/10.3390/agronomy15112566

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