Effects of Photovoltaic Array Configurations on the Soil Environment and Salinization Dynamics in a Degraded Saline-Alkaline Grassland

Effects of Photovoltaic Array Configurations on the Soil Environment and Salinization Dynamics in a Degraded Saline-Alkaline Grassland | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Photovoltaic Array Configurations on the Soil Environment and Salinization Dynamics in a Degraded Saline-Alkaline Grassland Ma Nannan¹, Huang Ruoxi¹, Wang Feng¹, Wang Yiran¹, Ren Yuxuan¹, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9140332/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study aimed to investigate the effects of different photovoltaic (PV) array support structures on soil physicochemical properties and salinization dynamics in a saline-alkali grassland of the Songnen region, to propose an optimized strategy for the "PV-ecological" synergistic model. A randomised block design was employed to evaluate plant community and soil parameters under eight typical PV support structures, with a comprehensive assessment conducted using the membership function method. Results showed that the horizontal single-axis tracker (P) recorded the highest plant diversity beneath panels, with a Shannon-Wiener index of 2.14 and a Pielou evenness index of 0.85. Soil under this configuration exhibited a relatively high available potassium content (196.21 mg/kg), and a significant positive correlation was found between total phosphorus and available potassium (P < 0.05), suggesting enhanced nutrient cycling. The horizontal single-axis tracker achieved the highest composite score (0.805) in the comprehensive membership function assessment. The findings demonstrate that this support structure, through its dynamic shading, creates a favorable micro-environment in the saline-alkali grassland. The habitat beneath its panels showed minimal divergence from the non-PV control area and supported the most stable ecosystem. This model effectively harmonizes ecological conservation with power generation, and is thus recommended as the optimal configuration for "grass-solar complementarity" in degraded saline-alkali grasslands, with potential for application in similar cold, high-altitude saline-alkali regions. photovoltaic support structure model grassland vegetation soil physicochemical properties land cover flat single-axis tracker Figures Figure 1 Figure 2 Figure 3 1. Introduction Driven by the dual carbon goals, photovoltaic (PV) power generation has expanded rapidly as a vital component of clean energy [Yin,2014]. China's cumulative installed PV capacity has exceeded 110 gigawatts, with large-scale photovoltaic bases in desert regions becoming key development targets [Cui,2019; Fortunel, 2009]. However, the extensive construction of PV power stations has also raised ecological concerns, particularly within grassland ecosystems. Photovoltaic panels alter surface light and heat distribution through shading effects [Li,2024], which influences soil water-salt transport and nutrient cycling [Kirchhoff, 2021], thereby modifying plant community structure and ecosystem functions [Bao,2024]. In the ecologically fragile Songnen saline-alkali grasslands, the impact of PV installations on local habitats requires urgent quantitative assessment. Existing research indicates that the installation of PV arrays creates heterogeneous microhabitats. The shaded areas beneath panels form low-light zones with elevated soil moisture, though insufficient light may inhibit plant growth [Zheng, 2023; Li, 2023 ; Ding, 2021 ; Yue,2022]. Different PV array support configurations also significantly alter the plant-growth microenvironment, primarily affecting light intensity [Li,2024], soil conditions [Kirchhoff,2021], air humidity [Damo, 2023], and air temperature [Hu,2019]. The installation of PV panels influences surface soil hydrological conditions and nutrient cycling, thereby regulating plant community composition, species diversity, and ecosystem functions [Bao,2024]. In desert PV zones, flat single-axis tracking systems enhance soil moisture content beneath panels, whereas fixed mounting structures better facilitate vegetation recovery in inter-panel areas [Wang,2025]. In desert grasslands, vegetation diversity and soil organic carbon content are markedly higher under flat single-axis tracking systems compared to fixed structures [Wu,2025]. However, existing research predominantly focuses on arid or desert ecosystems, with limited studies conducted in high-latitude, cold, saline-alkali grasslands. Systematic comparisons of the ecological effects of multiple support structures are scarce, and analyses often examine vegetation or soil responses in isolation. This lack of a comprehensive examination of ‘plant-soil system synergistic responses’ hinders the identification of key pathways through which PV support structures influence ecosystems. Furthermore, differing support configurations—varying in mounting height, tilt angle, and shading dynamics—create distinct shading conditions. These conditions may exert differential impacts on ecosystems by regulating factors such as light, heat, water, and salinity. Comparative research addressing these interactions remains underdeveloped. This study utilized the National Photovoltaic Energy Storage Demonstration Platform (Daqing Base) as its experimental site—a facility operational for five years—and selected eight representative PV array mounting configurations. It systematically investigated how these structures drive synergistic changes in plant community structure and soil physicochemical properties in the Songnen saline-alkali grassland by regulating microenvironmental factors such as moisture and salinity. The eight support configurations represent prevailing technologies and exhibit significant variations in mounting height, tilt angle, and shading dynamics, thereby providing an ideal gradient for comparing ecological effects. Through synchronous analysis of vegetation indicators (e.g., diversity, biomass) and soil parameters (e.g., pH, moisture content, salinity, nutrients), this study aims to elucidate: (1) the differentiation patterns of plant-soil systems under different PV support configurations; (2) the coupling relationship between vegetation responses and soil water-salinity-fertility dynamics; and (3) which support configuration most favours achieving synergistic ‘grass-solar complementarity’ development. By innovatively focusing on evaluating vegetation-soil synergistic responses and employing a multi-indicator comprehensive assessment, this research reveals the ecological regulation mechanisms of support structures. It provides a theoretical foundation and practical pathways for ensuring ecological security and promoting the sustainable development of PV power stations in high-latitude saline-alkali grassland regions. 2. Materials and Methods 2.1 Study Area Overview The National Photovoltaic and Energy Storage Demonstration Platform (Daqing Base), hereafter referred to as the study site, is located in Gaotaizi Town, Datong District, Daqing City, Heilongjiang Province, China (124°54′20″E, 46°10′17″N). Situated on the Songnen Plain, it is the northernmost and coldest national photovoltaic base in China, covering an area of more than 100,000 mu (approximately 6,667 hectares). As the largest photovoltaic experimental and demonstration base in Asia and worldwide, it began operation on August 11, 2021, with a total investment of 6 billion CNY. The site is established on a warm-temperate meadow grassland, which represents a typical degraded saline-alkaline grassland with perennial hay yields as low as 30–85 kg per mu. The region has a temperate continental monsoon climate, featuring distinct seasons and considerable temperature variation. The mean annual temperature is about 4.2°C, with extreme highs of 38.9°C and lows of − 39.2°C. Annual sunshine duration ranges from 2,600 to 2,842 hours, and the relatively high total annual solar radiation provides favorable conditions for solar energy utilization. Precipitation is moderate, with an annual average of approximately 427.5 mm, which supports both forage growth and photovoltaic power generation. Common plant species within the experimental area include Leymus chinensis, Suaeda glauca, Suaeda corniculata, Puccinellia tenuiflora, Phragmites australis, and Aster pekinensis. Eight photovoltaic mounting configurations were investigated: (1)Flexible Fixed Mounting (R), (2) Flat Single-axis Tracker (P), (3) full-dimensional tracker (Q), (4) seasonally adjustable mount (J), (5) tilted single-axis tracker (X), (6) dual-axis tracker (S), (7) vertical single-axis mount (C), (8) fixed mount (G). (9) Control group – non-PV area within the photovoltaic field (CK) ( Table 1 ). 2.2 Plant Community Survey Methodology 2.2.1 Plot Design This study employed a plot design to systematically compare plant community characteristics under eight typical photovoltaic (PV) array mounting systems, within the zone directly shaded by the PV panels. For each mounting system, three representative sub-arrays were randomly selected from all eligible, numbered sub-arrays using a random number table. All study areas, including those beneath panels and control plots, were protected from grazing and other human disturbances.For each of the eight mounting system types, three replicate sub-arrays were established. Within each sub-array, three 1 m × 1 m quadrats were placed for herbaceous plant community surveys. This resulted in a total of 8 treatments × 3 replicates × 3 quadrats = 72 survey quadrats under PV panels.Control plots (CK) were established in uninterrupted open areas outside the perimeter of the PV arrays. Nine control quadrats were randomly distributed within an undisturbed zone located approximately 100 meters from the power station fence, ensuring their uniform distribution around the site. This yielded a total of 9 control quadrats.Therefore, the total number of survey quadrats in this study was 72 (under panels) + 9 (control) = 81. 2.2.2 Survey Methodology The plant community survey was conducted on August 31, 2024, during the local peak growing season to adequately reflect interannual community characteristics. Within each 1m × 1m plot, all present plant species were first recorded. Vegetation height was then determined by measuring the height of five randomly selected individuals per species (or all individuals if fewer than five were present) using a tape measure, calculating the average height per species, and then deriving the overall average height for the plot. Total vegetation cover was estimated visually as the percentage of ground area covered by the vertical projection of all above-ground plant parts. Following these measurements, all above-ground vegetation within the plot was harvested at ground level. The samples were transported to the laboratory, oven-dried at 105°C to constant weight (approximately 48 hours), and weighed to determine dry above-ground biomass with an accuracy of 0.01g. 2.2.3 Diversity Index Calculation Methods Species diversity in this study was assessed using the Shannon-Wiener index, Pielou index, and Margalef index. These indices were calculated using Equations (1), (2), and (3) respectively: Shannon-Wiener Index: $$\:\text{H}\text{=}\sum\:_{\text{i}\text{=}\text{1}}^{\text{n}}{\text{P}}_{\text{i}}\text{ln}{\text{P}}_{\text{i}}$$ (1) Pielou index: $$\:\text{E=N/}\text{ln}\text{S}$$ (2) Margalef index: $$\:\text{M=}\left(\text{N}\text{−}\text{1}\right)\text{/}\text{ln}{\text{N}}_{\text{i}}$$ (3) Where S denotes the number of species within the sample plot, Pi = Ni/N, where Ni represents the number of individuals of species i, and N denotes the total number of individuals of all plants within the sample plot. 2.3 Soil Characterisation Methods 2.3.1 Soil Sampling Soil samples were collected on 31 August 2024, immediately following the completion of vegetation surveys to ensure spatio-temporal consistency between plant and soil data. Sampling locations were selected within plots where the substrate surface was relatively uniform. Soil sampling adhered to the principles of random selection, equal quantities, and multi-point mixing. Within each 1m×1m plant survey plot, five sampling points were selected using a staggered grid pattern. A ring knife was employed to collect soil samples from the 0–10cm layer at each point. Soil samples from all five points within the same plot were equally mixed to form a composite sample. Excess soil was discarded using the quartering method, ultimately retaining approximately 1kg of soil sample for chemical analysis. Simultaneously, undisturbed soil cores were extracted in situ using a ring knife for soil moisture content determination. Based on this methodology, soil samples were collected from beneath the eight support structures and the control plots in quantities matching the vegetation survey plots, yielding 72 + 9 = 81 samples. 2.3.2 Soil Sample Analysis All soil samples were properly labelled (indicating sampling number, location, depth, date, etc.) and transported back to the laboratory refrigerated and protected from light. Following pretreatment involving air-drying, grinding, and sieving, the following parameters were determined: soil moisture content was measured using the oven-drying method, while pH and electrical conductivity were assessed using a pH meter and conductivity meter respectively. Total soluble salts in soil were measured using the conductivity method. Soil nutrient analysis encompassed soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and available potassium (AK). Chemical analysis methods were employed: potassium dichromate oxidation for organic matter, Kjeldahl nitrogen determination for total nitrogen, molybdenum-antimony colorimetry for total phosphorus, and flame photometry for available potassium. 2.4 Data Statistics and Analysis Quantitative characteristics of plant communities—Shannon-Wiener index, Pielou index, Margalef index, height, cover, density, and biomass—were compared using the Least Significant Difference (LSD) test for multiple comparisons at a significance level of P < 0.05. Data were organised using Excel 2021, subjected to LSD tests via SPSS 26.0, and plotted using Origin 2024. Figures present mean values ± standard deviation. Formulas for membership function values (Wj), membership function values µXja, and composite index values (Da) are as follows: $$\:{\text{W}}_{\text{j}}\text{=}{\text{P}}_{\text{j}}\sum\:_{\text{i}\text{=}\text{1}}^{\text{n}}{\text{P}}_{\text{j}}$$ 4 $$\:\text{µ}\left({\text{X}}_{\text{ja}}\right)=\left({\text{X}}_{\text{ja}}\text{−}{\text{X}}_{\text{jmax}}\right)\text{/}\left({\text{X}}_{\text{jmin}}\text{−}{\text{X}}_{\text{jmax}}\right)$$ 5 $$\:{\text{D}}_{\text{a}}\text{=}\sum\:_{\text{i}\text{=1}}^{\text{n}}\left[\left({\text{X}}_{\text{ja}}\right)\text{×}{\text{W}}_{\text{j}}\right]$$ (6) In the formula: Pj denotes the contribution rate of the jth principal component for each indicator; the weight coefficient (Wj) indicates the prominence of the jth principal component among all principal components; Xja represents the value of the jth principal component for the ath support structure; Xjmin and Xjmax denote the minimum and maximum values of the jth principal component respectively; µXja is the membership function value for the transformed principal component value; Da denotes the comprehensive evaluation value of the a-th support structure for the grassland vegetation and soil characteristic indicators of the national photovoltaic base. 3. Results 3.1 Effects of Eight Photovoltaic Support Configurations on Grassland Vegetation in PV Fields 3.1.1 Impact of Eight Photovoltaic Panel Support Configurations on Plant Community Species Richness As shown in Fig. 1 -A, the non-PV control area (CK) exhibited the highest species richness at 5 species. This was followed by the fixed support (G) at 4.33 species, showing no significant difference from the control (CK) ( P > 0.05).. The vertical single-axis (C) system exhibited the lowest species richness at 2.11 species, significantly lower than the control ( P < 0.05). This was followed by the inclined single-axis tracking (X) system at 2.22 species. 3.1.2 Effects of Eight Photovoltaic Panel Mounting Configurations on Plant Community Height As shown in Fig. 1 -B, the inclined single-axis tracking mount (X) yielded the highest average plant community height at 58.84 cm, followed by the Full-Dimensional Tracking Mount (Q) at 58.79 cm; The fixed support (G) exhibited the lowest average plant height beneath the panels at merely 36.97 cm, followed by the control group (CK) at 39.94 cm. The differences between the fixed support (G) and the control group were not statistically significant ( P > 0.05). The plant heights beneath the flexible support (R), Single-axis Tilt Mounting System (X),Seasonally Adjustable Mount (J), flat single-axis support (P), and full-dimensional tracking support (Q) were all significantly higher than those of the control group (CK) ( P < 0.05). 3.1.3 Impact of Eight Photovoltaic Panel Mounting Configurations on Total Plant Cover As shown in Fig. 1 -C, the Vertical Single-Axis Mount(C) group exhibited the highest total plant cover under panels at 79%, followed by the horizontal single-axis support (P) group at 74.44%. The Single-axis Tilt Mounting System (X) group recorded the lowest total plant cover under panels at merely 40.93%, followed by the full-axis tracking support (Q) group at 57.78%. The total canopy cover beneath Flexible Fixed Mountings (R), vertical single-axis supports (C),Seasonally Adjustable Mounts (J), horizontal single-axis tracking supports (P), and fixed supports (G) exceeded that of the control group (67.78%), though the differences were not statistically significant ( P > 0.05). The total canopy cover under the Single-axis Tilt Mounting System (X) and full-range tracking support (Q) was significantly lower than the control group ( P 0.05). 3.1.4 Effect of Eight Photovoltaic Panel Support Configurations on Plant Biomass As shown in Fig. 1 -D, the horizontal single-axis bracket (P) group exhibited the highest biomass at 613.59 g/m², followed by the non-PV control group (CK) at 586.27 g/m². The fixed bracket (G) group recorded the lowest biomass at 251.71 g/m², followed by the inclined single-axis tracking bracket (X) at 273.66 g/m². The Flexible Fixed Mounting (R), Single-axis Tilt Mounting System (X), Vertical Single-Axis Mount(C),Seasonally Adjustable Mount (J), full-dimensional tracking support (Q), dual-axis tracking support (S), and fixed 3.1.5 Effects of Eight Photovoltaic Panel Support Modes on Plant Diversity Table 2 indicates that the Pielou index was highest for the horizontal single-axis support (P) at 0.85, followed by the control group (CK) at 0.83. The seasonal adjustable support (J) had the lowest index at 0.58, followed by the Flexible Fixed Mounting (R) at 0.60. The fixed support (G) exhibited the highest species richness with a Margalef index of 2.10, followed by the flat single-axis support (P) at 1.90. The Flexible Fixed Mounting (R) showed the lowest species richness with a Margalef index of merely 0.95, followed by theSeasonally Adjustable Mount (J) at 1.00. The Shannon-Wiener index was highest for the flat single-axis support (P) at 2.14, followed by the control group (CK) at 1.95. The Shannon-Wiener index was lowest for the Flexible Fixed Mounting (R) at merely 0.653, followed by theSeasonally Adjustable Mount (J) group at 1.22. Comparing diversity indices across the eight support configurations, the flat single-axis support (P) group demonstrated relatively superior species diversity, species richness, and evenness. The Flexible Fixed Mounting (R) andSeasonally Adjustable Mount (J) groups performed comparatively poorer. Table 2 Analysis of Community Diversity Indices under Eight Photovoltaic Support Scenarios in the PV Field Area No. Mounting System Code Diversity Index Pielou Margalef Shannon-Wiener 1 Flexible Fixed Mounting R 0.60b 0.95bc 1.10c 2 Single-axis Tilt Mounting System X 0.70ab 1.39bc 1.55b 3 Vertical Single-Axis Mount C 0.76ab 1.32bc 1.63ab 4 Seasonally Adjustable Mount J 0.58bc 1.00bc 1.22bc 5 Flat Single-axis Mounting P 0.85a 1.90a 2.14a 6 Full-Dimensional Tracking Mount Q 0.61b 1.44b 1.37b 7 Dual-axis Tracking Mounting System S 0.75ab 1.33bc 1.58b 8 Fixed Mount G 0.78ab 2.10a 1.77ab 9 Control Group CK 0.83a 1.52b 1.95a 3.2 Effects of Eight Photovoltaic Mounting Configurations on Grassland Soil Characteristics in PV Fields 3.2.1 Impact of Eight Photovoltaic Panel Mounting Configurations on Soil pH As shown in Fig. 2 -A, analysis of soil pH across the eight mounting configurations reveals that the vertical single-axis mount (C) group exhibited the highest pH at 9.47, followed by the Full-Dimensional Tracking Mount (Q) at 9.25. The lowest pH was recorded under the fixed support (G) at 8.78, followed by the dual-axis tracking support (S) at 9.15. Soil pH values beneath the inclined single-axis tracking (X), vertical single-axis (C), and seasonally adjustable (J) supports were higher than the control group (CK). Conversely, the Flexible Fixed Mounting (R), horizontal single-axis support (P), full-axis tracking support (Q), dual-axis tracking (S), and fixed (G) structures exhibited lower pH than the control (CK). No significant differences in soil pH were observed among the eight support types ( P > 0.05). 3.2.2 Effects of Eight PV Panel Support Configurations on Soil Moisture Content As shown in Fig. 2 -B, the highest soil moisture content (39.59%) was recorded beneath the Flexible Fixed Mounting (R), followed by the fixed support (G) at 38.44%. The lowest soil moisture content (32.73%) was observed in the non-PV control area (CK); followed by theSeasonally Adjustable Mount (J) at 33.34%. All eight PV array support configurations exhibited higher soil moisture content beneath panels than the control group (CK), with Flexible Fixed Mountings (R), flat single-axis supports (P), and fixed supports (G) showing significantly higher values than the control (CK) ( P < 0.05). The soil moisture content under the inclined single-axis tracking (X), vertical single-axis (C), seasonally adjustable (J), full-tracking (Q), and dual-axis tracking (S) support systems was slightly higher than that of the control group (CK), with no significant difference ( P > 0.05). 3.2.3 Effect of Eight Photovoltaic Panel Support Configurations on Soil Electrical Conductivity As shown in Fig. 2 -C, the full-tracking support (Q) exhibited the highest conductivity at 2.15 ms/cm, followed by the horizontal single-axis support (P) at 1.94 ms/cm. The lowest conductivity was observed in the dual-axis tracking support (S) at merely 1.20 ms/cm, followed by the Single-axis Tilt Mounting System (X) at 1.26 ms/cm. Among the eight PV support configurations, the conductivity beneath the panels of the Flexible Fixed Mounting (R), flat single-axis support (P), and full-axis tracking support (Q) was significantly higher than that of the control group (CK) ( P 0.05). The fixed support (G) group exhibited the highest soil total base content at 5.46 g/kg, followed by the dual-axis tracking support (S) at 5.37 g/kg. The Flexible Fixed Mounting (R) group exhibited the lowest total soil base content at 4.64 g/kg, followed by the vertical single-axis (C) group at 4.63 g/kg. The total soil base content in the Single-axis Tilt Mounting System (X), dual-axis tracking support (S), and fixed support (G) groups was slightly higher than the control group (CK), with no significant difference compared to the control group (CK) ( P > 0.05). Flexible fixed (R), vertical single-axis (C), seasonally adjustable (J), horizontal single-axis (P), and full-axis tracking (Q) mounts exhibited lower total soil base content than the control (CK), with no significant difference compared to the control (CK) ( P > 0.05). 3.2.5 Effects of Eight Photovoltaic Panel Support Structures on Soil Nutrient Content As shown in Table 3 , the Flexible Fixed Mounting (R) exhibited the highest soil organic carbon content at 20.16%, while theSeasonally Adjustable Mount (J) recorded the lowest at 12.59%. The Flexible Fixed Mounting (R) group exhibited the highest soil total nitrogen content at 0.78 g/kg. The Vertical Single-Axis Mount(C) group had the lowest soil total nitrogen content at merely 0.48 g/kg. TheSeasonally Adjustable Mount (J) group showed the highest soil total phosphorus content at 0.35 g/kg, followed by the Flexible Fixed Mounting (R) at 0.34 g/kg. The dual-axis tracking support (S) group exhibited the lowest total phosphorus content at merely 0.25 g/kg. Soil available potassium content was highest in the parallel single-axis support (P) group, reaching 196.21 mg/kg, followed by the Flexible Fixed Mounting (R) group at 195.21 mg/kg. Overall, the soil nutrient composition beneath the Flexible Fixed Mounting (R) exhibited superior quality, with significantly higher levels of organic carbon, total nitrogen, total phosphorus, and available potassium compared to the control group (CK) ( P < 0.05). Table 3 Analysis of soil nutrient content under eight PV support scenarios in the photovoltaic field No. Mounting System Code Soil organic carbon (%) Total nitrogen (g/kg) Total phosphorus (g/kg) Available potassium (mg/kg) 1 Fixed Mount G 16.49b 0.65b 0.30ab 194.44a 2 Flat Single-axis Mounting P 16.05bc 0.60b 0.25b 196.21a 3 Single-axis Tilt Mounting System X 15.27bc 0.65b 0.24b 173.30c 4 Dual-axis Tracking Mounting System S 14.88bc 0.60b 0.23b 165.93c 5 Flexible Fixed Mounting R 20.16a 0.78a 0.34a 195.21a 6 Full-Dimensional Tracking Mount Q 13.43c 0.57 0.25b 180.89bc 7 Seasonally Adjustable Mount J 12.59c 0.57 0.35a 191.84a 8 Vertical Single-Axis Mount C 15.51bc 0.48c 0.28b 176.88bc 9 Control Group CK 15.52bc 0.63bc 0.28b 185.52b 3.3 Correlation Analysis of Grass-Soil Interface Factors Across Eight Support Structures in Photovoltaic Fields As illustrated in Fig. 3 , at P ≤ 0.05, the Pielou evenness index and Shannon-Wiener diversity index exhibited significant positive correlations; the Margalef diversity index showed significant positive correlation with soil moisture content; and total soil phosphorus and available potassium demonstrated significant positive correlations. Species richness and soil pH showed a significant negative correlation, while soil electrical conductivity and total base cation content exhibited a significant negative correlation. In the Pearson correlation coefficient plot, both the x-axis and y-axis represent respective indicators, with colour intensity indicating the magnitude of the correlation coefficient between two indicators. Closer to red (coefficient closer to 1) indicates stronger positive correlation; closer to white indicates weaker correlation; closer to blue indicates stronger negative correlation. 3.4 Comprehensive evaluation of membership functions for grass-soil interface indicators across eight support models in the photovoltaic field As individual indicators cannot accurately reflect the impact of the eight support models on vegetation and soil parameters in the national photovoltaic base, membership functions were employed. Higher membership function values indicate lesser adverse effects of the supports on grassland. Analysis of the total evaluation D-value changes across indicators (Table 4 ) indicates that among the eight PV array support configurations, the horizontal single-axis support (P) achieved the highest comprehensive evaluation D-value of 0.805. This was followed by the Flexible Fixed Mounting (R) group at 0.475 and the Single-axis Tilt Mounting System (X) group at 0.469. Table 4 Membership Function Values for Various Indicators in the PV Field Area No. Mounting System Code Comprehensive Evaluation D Value Sorting 1 Flexible Fixed Mounting R 0.475 3 2 Single-axis Tilt Mounting System X 0.469 4 3 Vertical Single-Axis Mount C 0.306 8 4 Seasonally Adjustable Mount J 0.227 9 5 Flat Single-axis Mounting P 0.805 1 6 Full-Dimensional Tracking Mount Q 0.408 7 7 Dual-axis Tracking Mounting System S 0.454 5 8 Fixed Mount G 0.409 6 9 Control Group CK 0.528 2 4. Discussion 4.1 Effects of Eight Photovoltaic Array Support Configurations on Plant Community Characteristics The installation of photovoltaic (PV) panels exerts differential impacts on various plant traits within grassland ecosystems [Luo, 2023]. The most immediate effect is the obstruction of sunlight, which creates varying degrees of shading. This reduction in light availability directly influences photosynthetic efficiency and consequently plant growth [Adeh, 2018]. In this study, the non-PV control area (CK) exhibited the highest species richness. By maintaining the stability and continuity of the natural habitat, this area preserved the regional species pool most effectively. In contrast, the establishment of PV arrays altered the microenvironment through shading, which selectively filtered plant species and led to a reduction in species richness within the array zones [Jiang, 2023]. Except for the fixed support structure, plants beneath other panel types showed greater average height than those in the control. This is likely a shade-avoidance response, where plants under panels elongate their stems to reach limited light resources [Han, 2025]. The area and intensity of ground shading varied with different support configurations, thereby influencing plant height. Plants under fixed support panels were slightly shorter than the control group ( P > 0.05), possibly because the fixed tilt angle and contiguous panel arrangement resulted in light levels below the threshold required for positive photomorphogenesis, strongly limiting photosynthesis and overall growth, thus preventing the enhanced growth observed under other support types and even underperforming relative to the natural control [Lu, 2025 ]. In this study, the total canopy cover of plants under the Single-axis Tilt Mounting System was significantly lower than that of the control. This may be attributed to the fixed tilt angle of this configuration, which positions the front edge of the panels too close to the ground, resulting in prolonged shading that negatively affects canopy development. Biomass under the horizontal single-axis tracker (P) was slightly higher than the control ( P > 0.05). This may result from the dynamic, moderate shading created by this mounting system, which avoids strong light inhibition while providing a relatively uniform light distribution. This environment allows plants to optimize photosynthesis through moderate height increase, thereby enhancing biomass accumulation [Tan, 2023]. Combined with the observation of lower plant height under these panels, it suggests superior nutrient allocation to biomass production rather than to excessive vegetative growth. Analysis of plant diversity under different PV arrays reveals that the horizontal single-axis tracker (P) supported superior species diversity, richness, and evenness. Its moderate shading significantly reduces surface temperature, thereby curbing water evaporation and better preserving soil moisture [Zhao, 2024]. In the Songnen saline-alkali grassland, this moisture retention helps mitigate salt accumulation at the surface through evaporation, alleviating the 'surface salt crust' phenomenon. Furthermore, the ground clearance and array spacing of the horizontal single-axis tracker provide essential space for plant growth and air circulation, fostering a microclimate conducive to plant community development. This relatively balanced microenvironment also supports greater biomass accumulation [Liu, 2025]. 4.2 Effects of Eight Photovoltaic Array Support Configurations on Substrate Soil Characteristics Different photovoltaic (PV) array mounting configurations create distinct microenvironments by redistributing localized light, moisture, and temperature, which fundamentally drives their differential impacts on grassland vegetation and soil properties [Yu,2025]. In this study, soil moisture content beneath all eight mounting configurations was higher than in the non-PV control area. This aligns with findings by Du et al. ( 2025 ), who reported significantly greater soil moisture within PV arrays compared to adjacent open areas. This phenomenon is attributed to the shading effect of PV panels, which significantly lowers surface temperature and reduces soil water evaporation [Wu,2025]. The altered shading regime substantially modifies soil water-salt dynamics and nutrient availability. By mitigating wind exposure and evaporation, it enhances soil water retention beneath the panels and significantly affects solute transport pathways. The reduction in evaporation under the panels creates a low-evaporation zone, thereby alleviating surface salt accumulation [Liu,2025; Yu,2025]. Changes in soil moisture further influence nutrient mobility. The accumulation of readily available potassium in the surface layer beneath the panels is likely linked to these modified moisture dynamics and associated runoff processes, consistent with Liu's(2013) observation that "moisture and topography influence the spatial distribution of soil nutrients." In this study, both the horizontal single-axis tracker and the rigid fixed mount created relatively stable and pronounced shaded zones. These conditions reduced soil temperature and evaporation, allowing the sub-panel areas to maintain higher soil moisture. The suitable moisture levels coupled with relatively lower temperatures fostered favorable conditions for soil microbial activity while potentially slowing litter decomposition rates, thus promoting organic matter accumulation. No significant differences in soil pH or total base saturation were detected among the eight PV mounting systems ( P > 0.05) (Fig. 2 -A). This lack of significant variation may be due to the relatively short operational history of the PV field and the spatially concentrated distribution of the eight array types within a confined area, resulting in low overall soil spatial heterogeneity and thus minimal measurable differences between treatments. 4.3 Relationship between plant community characteristics and soil physicochemical properties This study revealed a close association between plant community diversity and soil physicochemical properties. The Margalef richness index showed a significant negative correlation with soil pH ( P < 0.05), indicating that salt accumulation significantly inhibits plant growth and reduces community diversity. This finding is consistent with the observation by Cao et al. (2019) that in high-salinity environments, the growth of non-halophytic plants is constrained, leading to community succession dominated by salt-tolerant species and a consequent decline in diversity. Li et al. ( 2017 ) demonstrated that high saline-alkali stress increases root osmotic pressure and reduces water-use efficiency, thereby impairing plant growth and reproduction. Liu et al. ( 2021 ) further confirmed in the saline-alkali soils of northern China that soil salinization not only reduces community diversity but also affects species evenness, identifying salinity as a key ecological factor limiting vegetation restoration. In this study, the horizontal single-axis tracker (P) significantly alleviated salt stress through dynamic shading: soil moisture content beneath the panels increased by approximately 12%, and available potassium content reached 196.21 mg/kg. Simultaneously, both the Shannon-Wiener index (2.14) and aboveground biomass (613.59 g/m²) were higher than under other configurations, indicating that its optimized microenvironment for water-salt redistribution provides niche space for diverse plant communities. Different support structures created distinct heterogeneous microenvironments through shading, exerting varying impacts on plants and soil. Shading does not necessarily induce negative effects; moderate shading may even benefit degraded grasslands in saline-alkali habitats. The horizontal single-axis tracker optimizes the allocation of light and heat resources through dynamic shading, promoting water infiltration and salt leaching to alleviate saline-alkali stress. This moderate distribution of resources not only mitigates stress but also effectively promotes primary productivity, as evidenced by higher aboveground biomass. Concurrently, increased plant biomass beneath the panels enhances soil organic carbon input through litterfall, further promoting soil aggregate formation and nutrient sequestration. This positive plant-soil feedback resulted in the horizontal single-axis tracker achieving the highest membership function evaluation score (0.805). The minimal habitat variation beneath and between its panels (coefficient of variation < 10%) indicated significantly better ecosystem stability compared to other configurations. The superior performance of the horizontal single-axis tracker stems from a synergistic mechanism involving “shading–moisture–salinity–plants–microbes.” Its dynamic shading structure (with ± 60° horizontal-axis tracking) reduces surface evaporation, promotes water infiltration and salt leaching, and mitigates surface salt accumulation. Concurrently, balanced light and thermal conditions support the coexistence of shade-tolerant and light-demanding species (Pielou index of 0.85), enhancing community stability. Litter input from the plant community feeds back into the soil, forming a positive loop with microbial activity: higher biomass increases litter input, while stable moisture and moderate shading delay organic matter mineralization, promoting soil aggregation and nutrient sequestration [Shuai, 2026]. In this study, soil total phosphorus and available potassium beneath the horizontal single-axis tracker showed a significant positive correlation ( P < 0.05), indicating high microbial-driven nutrient transformation efficiency. In summary, the horizontal single-axis support system achieves synergistic enhancement of plant diversity and soil fertility through microenvironmental regulation, providing an ecological basis for “grass-solar complementarity” in saline-alkali grasslands. Future research should further elucidate the response pathways of microbial community structure and key enzyme activities to refine the model of this synergistic mechanism. 5. Conclusions This study systematically evaluated the effects of different photovoltaic (PV) array mounting systems on soil and vegetation in the degraded saline-alkali grasslands of the Songnen region. The results reveal that the shading effect of PV arrays can act as a potential tool for mitigating soil salinization and promoting ecosystem restoration by regulating local hydrothermal conditions. In these grasslands, the dynamic shading environment generated by the horizontal single-axis tracker (P) demonstrated significant soil improvement benefits. It effectively moderated the near-surface microclimate by reducing soil temperature and evaporation, thereby promoting moisture retention and salt leaching, which alleviated saline-alkali stress. Concurrently, this configuration significantly enhanced soil nutrient availability: available potassium content beneath the panels reached 196.21 mg/kg and showed a significant positive correlation with total phosphorus, indicating more active soil nutrient cycling. These optimized soil conditions further facilitated vegetation recovery, as reflected in higher plant diversity (Shannon–Wiener index = 2.14) and aboveground biomass (613.59 g/m²). Through litter return and microbial activity, a preliminary positive feedback loop of ‘soil improvement–vegetation restoration’ was established. Therefore, this research demonstrates that by adopting an optimized PV array structure such as the horizontal single-axis tracker, the physical shading of a PV power plant can be transformed into an ‘ecological regulation’ measure for saline-alkali soils. Owing to its dynamic tracking capability, the horizontal single-axis tracker achieved the highest score (0.805) in the membership function-based comprehensive evaluation, confirming its optimal balance between power generation needs and soil environmental improvement. This model provides a feasible technical pathway for implementing PV development while achieving soil salinization control and ecological restoration in cold, saline-alkali regions such as the Daqing area. Future studies should further quantify the long-term effects of different PV configurations on soil salt transport, potential heavy metal migration, and soil microbial functions. This will deepen the understanding of PV power plants as multifunctional environmental interventions and help optimize their ecological sustainability. Declarations Ethical Approval: Ethical approval is not applicable to this study. Funding: This research was funded by the Daqing New Energy Field "Unveiling the List" Science and Technology Research Project of China (Grant No. 2021BD05), the Heilongjiang Provincial Department of Science and Technology, and the Provincial Academy Cooperation Project of China (Grant No. YS16B12). The APC was not funded. Author Contribution Author Contributions: Conceptualization, MNN ; methodology, MNN; software, MNN; validation, HRX and WF; formal analysis,MNN; investigation,M NNand RYX; data curation, KDCand W YR; writing—original draft preparation, MNN; writing—review and editing,WGL and QSM; visualization, MNN; supervision, WGL and QSM; project administration, QSM; funding acquisition, QSM. All authors have read and agreed to the published version of the manuscript. Data Availability The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. The raw geographic location data of the sampling plots are not publicly available to protect the integrity of the long-term research site. Generalized site descriptions and all other analyzed data are included in this published article. References Adeh, E. H., Selker, J. S., & Higgins, C. W. (2018). Remarkable agrivoltaic influence on soil moisture, micrometeorology and water-use efficiency. PLoS ONE, 13(11), e0203256. https:/ /doi.org/10.1371/journal.pone.0203256 Bao, P. A., Ji, B., Sun, G., et al. (2024). Effects of photovoltaic power station construction on plant communities and soil characteristics. Journal of Grassland Science, 33(12), 23–33. Cao, G., & Zhang, Q. (2019). The impact of runoff and salt dynamics on vegetation restoration in arid regions. Environmental Science Research, 32(6), 245–252. Cui, Y., Fang, L., Guo, X., et al. (2019). Natural grassland as the optimal pattern of vegetation restoration in arid and semi-arid regions: Evidence from nutrient limitation of soil microbes. Science of the Total Environment, 648, 388–397. https://doi.org/10.1016/j.scitotenv.2018.08.206 Damo, U. M., Ozoegwu, C. G., Ogbonnaya, C., et al. (2023). Effects of light, heat and relative humidity on the accelerated testing of photovoltaic degradation using Arrhenius model. Solar Energy, 250, 335–346. https://doi.org/10.1016/j.solener.2022.12.019 Ding, C. X., & Liu, Y. (2021). Impact of photovoltaic park construction on soil prokaryotic microbial communities in alpine desert grasslands of the Qinghai-Tibet Plateau. Journal of Grassland Science, 29(5), 1061–1069. Du, X. D., Li, W., Qi, X. Y., et al. (2025). Analysis of soil physicochemical properties and stoichiometric characteristics under heterogeneous conditions in photovoltaic areas. Journal of Grassland Science, 33(1), 181–188. Fortunel, C., Garnier, E., Joffre, R., et al. (2009). Leaf traits capture the effects of land use changes and climate on litter decomposability of grasslands across Europe. Ecology, 90(3), 598–611. https://doi.org/10.1890/08-0418.1 Han, P., Xie, Y. C., Ye, L., et al. (2025). A review of the impacts of photovoltaic projects on desert ecosystems. Acta Ecologica Sinica, 36(12), 3871–3878. https://doi.org/10.13287/j.1001-9332 . 2025 12.004 Hu, H., Yuan, D., Wang, T., et al. (2019). Dynamic performance of high concentration photovoltaic/ thermal system with air temperature and humidity regulation system (HCPVTH). Applied Thermal Engineering, 146, 577–587. https://doi.org/10.1016/j.applthermaleng.2018.10.033 Jiang, L. Z., Luo, J. F., Wu, S. N., et al. (2023). Effects of habitat heterogeneity under photovoltaic arrays on plant leaf functional traits in karst desertification-prone areas. Chinese Journal of Solar Energy, 44(6), 252–259. https://doi.org/10.19912/j.0254-0096.tynxb.2022-0122 Kirchhoff, M., Romes, T., Marzolff, I., et al. (2021). Spatial patterns of argan-tree influence on soil quality of inter-tree areas in open woodlands of South Morocco. Soil, 7(2), 511–524. https://doi.org/10.5194/soil-7-511-2021 Li, A. Z., Hou, H. J., Wang, X., et al. (2024). Study on the influence of photovoltaic module layout on temperature and light environment in photovoltaic greenhouses. Journal of Solar Energy, 45(9), 285–294. Li, Y. (2023). Effects of photovoltaic panel arrays on soil hydrolytic enzyme activity and enzyme stoichiometric characteristics in degraded grasslands of the Songnen Plain[Master’s thesis]. Northeast Normal University. Li, Z. Y., Cong, R. C., Yang, Q. S., et al. (2017). Effects of saline-alkali stress on growth and osmotic adjustment substances in willow seedlings. Acta Ecologica Sinica, 37(24), 8511–8517. Liu, Q., Ma, H. Y., La, B., et al. (2025). Impact of large-scale photovoltaic arrays on microclimate in Northwest China’s cold desert regions under dual carbon context. Journal of Qinghai Normal University (Natural Science Edition), 41(1), 74–83, 112. Liu, X. H., Zhang, Q. Q., Xu, H. L., et al. (2021). Spatial distribution and species diversity of plant communities in saline-alkali lands of Northern Xinjiang. Acta Ecologica Sinica, 41(4), 1501–1513. Liu, Z. P. (2013). Spatial distribution of soil nutrients and influencing factors in the Loess Plateau region[Doctoral dissertation]. Graduate University of the Chinese Academy of Sciences. Lu, M. H. (2025). Effects of photovoltaic construction on surface microhabitats and vegetation in desertified areas[Master’s thesis]. Ningxia University. Luo, T., Yin, X. D., Qu, S. M., et al. (2023). Effects of photovoltaic panels on quantitative characteristics of plant functional groups in meadow grasslands. Grassland and Lawn, 43(6), 32–37. Shuai Zhengfeng, Lei Xiandao, Wang Changming, et al. A Study on the Mechanisms of the Impact of Photovoltaic Power Plant Construction on Plant Diversity in Northern Desert Grassland Areas [J/OL]. Journal of Grassland Science, 1–17 [2026-03-16]. https://link.cnki.net/urlid/ 11.3362.S.20251011. 0911.002. Tan, Z. X., Chen, X. C., Xiao, S., et al. (2023). Impact of the Tala Tan photovoltaic power station on vegetation diversity in Qinghai Province. Qinghai Science and Technology, 30(6), 10–18. Wang, X. M., Gao, S., Kan, X. B., et al. (2025). Research on ‘Light-Temperature’ environment and energy management in single-axis tracking photovoltaic greenhouses. Journal of Yunnan Normal University (Natural Science Edition), 45(5), 9–14. Wu, T., Duan, Y. Y., Li, J., et al. (2025). Effects of different photovoltaic array constructions on natural recovery of desert grassland plant communities and soil physicochemical properties. Grassland Science, 9(8), 1–13. Wu, Z. X., Yuan, T. Y., Bao, E. C., et al. (2025). Effect of photovoltaic module density on microenvironment and sweet potato yield in photovoltaic-agricultural systems. Transactions of the Chinese Society of Agricultural Engineering, 41(22), 256–264. Yin, R., Deng, H., Wang, H. L., et al. (2014). Vegetation type affects soil enzyme activities and microbial functional diversity following revegetation of a severely eroded red soil in subtropical China. Catena, 115, 96–103. https://doi.org/10.1016/j.catena.2013.11.015 Yu, Y. X., Ji, T., He, G. X., et al. (2025). Impact of photovoltaic power station construction on temperate desert vegetation and soil. Journal of Grassland Science, 11(21), 1–12. Yue, S. J. (2022). Ecological and environmental effects of large-scale photovoltaic development in Qinghai desert regions[Doctoral dissertation]. Xi’an University of Technology. Zhao, C. L., Ma, W. Y., Zhang, Y. J., et al. (2024). Ecological effects of photovoltaic power stations at different scales. Acta Ecologica Sinica, 44(23), 10964–10973. Zheng, J. Q., Luo, Y., Chang, R., et al. (2023). Study on the local climate and ecological impacts of large-scale photovoltaic development. Journal of Solar Energy, 44(8), 253–265. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9140332","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":633172230,"identity":"e43d3420-c9e7-43b9-a9ed-06a4be4d4c3c","order_by":0,"name":"Ma Nannan¹","email":"","orcid":"","institution":"Heilongjiang Bayi Agricltral niversity","correspondingAuthor":false,"prefix":"","firstName":"Ma","middleName":"","lastName":"Nannan¹","suffix":""},{"id":633172231,"identity":"7c132fba-678e-4c76-98d8-2fb0ffd3422e","order_by":1,"name":"Huang Ruoxi¹","email":"","orcid":"","institution":"Heilongjiang Bayi Agricltral niversity","correspondingAuthor":false,"prefix":"","firstName":"Huang","middleName":"","lastName":"Ruoxi¹","suffix":""},{"id":633172232,"identity":"3b6b8062-86b3-45ec-81ca-a2b2889d0648","order_by":2,"name":"Wang Feng¹","email":"","orcid":"","institution":"Heilongjiang Bayi Agricltral niversity","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Feng¹","suffix":""},{"id":633172233,"identity":"68fa89d4-e523-4de0-b630-2c453fd288ad","order_by":3,"name":"Wang Yiran¹","email":"","orcid":"","institution":"Heilongjiang Bayi Agricltral niversity","correspondingAuthor":false,"prefix":"","firstName":"Wang","middleName":"","lastName":"Yiran¹","suffix":""},{"id":633172234,"identity":"bb537786-c585-492d-a7d3-660ca8c77de8","order_by":4,"name":"Ren Yuxuan¹","email":"","orcid":"","institution":"Heilongjiang Bayi Agricltral niversity","correspondingAuthor":false,"prefix":"","firstName":"Ren","middleName":"","lastName":"Yuxuan¹","suffix":""},{"id":633172235,"identity":"ac0ffc21-dba4-4989-9d50-ac492c33b812","order_by":5,"name":"Kong Dechao¹","email":"","orcid":"","institution":"Heilongjiang Bayi Agricltral niversity","correspondingAuthor":false,"prefix":"","firstName":"Kong","middleName":"","lastName":"Dechao¹","suffix":""},{"id":633172236,"identity":"2bd1314c-d5f1-4101-915a-28cbb626adfe","order_by":6,"name":"Wu Guiling²","email":"","orcid":"","institution":"State Key Laboratory of Sanjiangyuan Ecology","correspondingAuthor":false,"prefix":"","firstName":"Wu","middleName":"","lastName":"Guiling²","suffix":""},{"id":633172237,"identity":"4f3c9579-83bb-4bde-a97c-fe680425484c","order_by":7,"name":"Qu Shanmin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIie3RsQrCMBCA4ZTCTcWu6dJnCBQqvk1CwcnBsZsRQRfBtY9REZyvCHbJAwgOZnJ3q4tandx63QTzz/dxHMeYy/WDhaxGVA+IQzKJ5kahhUESaSoRC0wqC7EqkUwAJcog9XZ1VrIm33eTYVAhSj72U3Odemtz7iaj4r1FHCE9TYTvLQlEXKxAKZ9BUpAJYksQuOBUEmkjUWkQvL2lIt3SvvJwu2uYbVbZ1jY5gXwlBfaa/5C+wuVyuf6kF9sARgleJmayAAAAAElFTkSuQmCC","orcid":"","institution":"Heilongjiang Bayi Agricltral niversity","correspondingAuthor":true,"prefix":"","firstName":"Qu","middleName":"","lastName":"Shanmin","suffix":""}],"badges":[],"createdAt":"2026-03-16 16:38:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9140332/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9140332/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108978240,"identity":"f5415a78-27c2-4cb9-9977-daeaab65f920","added_by":"auto","created_at":"2026-05-11 11:35:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":172664,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVegetation response under eight photovoltaic mounting scenarios in the PV field area (A: number of species, B: plant height, C: total coverage, D: biomass)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNote: Different lowercase letters on data bars indicate statistically significant differences (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05). The same applies below.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9140332/v1/470886fde167cdddf2d17d65.jpg"},{"id":108978146,"identity":"1dc593e9-4d22-47e3-bfdf-7c7a80e3ce05","added_by":"auto","created_at":"2026-05-11 11:34:20","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":195423,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSoil responses under eight PV support configurations in the photovoltaic field (A: soil pH, B: soil moisture content, C: electrical conductivity, D: total base content)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9140332/v1/c4c8868bf2ce0e02eda0ca97.jpg"},{"id":108945844,"identity":"7e3b60fb-3639-45a5-a223-6208b88b1b51","added_by":"auto","created_at":"2026-05-11 06:17:30","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":829463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation Analysis between Photovoltaic Field Plant Communities and Soil Physicochemical Characteristics\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9140332/v1/71c514bfa4f07c683123f2d5.png"},{"id":108979949,"identity":"49fe17ca-18b1-4898-b4dd-6b625c9b8062","added_by":"auto","created_at":"2026-05-11 12:02:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1411276,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9140332/v1/409af255-4afb-47dc-a4e3-0856bd5bfb24.pdf"},{"id":108945842,"identity":"489d05bf-cde3-4d66-8ad7-c4c1fb8a3d27","added_by":"auto","created_at":"2026-05-11 06:17:29","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":72453,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9140332/v1/6b9121e8883c5281523ea7ba.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Photovoltaic Array Configurations on the Soil Environment and Salinization Dynamics in a Degraded Saline-Alkaline Grassland","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eDriven by the dual carbon goals, photovoltaic (PV) power generation has expanded rapidly as a vital component of clean energy [Yin,2014]. China's cumulative installed PV capacity has exceeded 110 gigawatts, with large-scale photovoltaic bases in desert regions becoming key development targets [Cui,2019; Fortunel, 2009]. However, the extensive construction of PV power stations has also raised ecological concerns, particularly within grassland ecosystems. Photovoltaic panels alter surface light and heat distribution through shading effects [Li,2024], which influences soil water-salt transport and nutrient cycling [Kirchhoff, 2021], thereby modifying plant community structure and ecosystem functions [Bao,2024]. In the ecologically fragile Songnen saline-alkali grasslands, the impact of PV installations on local habitats requires urgent quantitative assessment.\u003c/p\u003e \u003cp\u003eExisting research indicates that the installation of PV arrays creates heterogeneous microhabitats. The shaded areas beneath panels form low-light zones with elevated soil moisture, though insufficient light may inhibit plant growth [Zheng, 2023; Li, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Ding, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Yue,2022]. Different PV array support configurations also significantly alter the plant-growth microenvironment, primarily affecting light intensity [Li,2024], soil conditions [Kirchhoff,2021], air humidity [Damo, 2023], and air temperature [Hu,2019]. The installation of PV panels influences surface soil hydrological conditions and nutrient cycling, thereby regulating plant community composition, species diversity, and ecosystem functions [Bao,2024]. In desert PV zones, flat single-axis tracking systems enhance soil moisture content beneath panels, whereas fixed mounting structures better facilitate vegetation recovery in inter-panel areas [Wang,2025]. In desert grasslands, vegetation diversity and soil organic carbon content are markedly higher under flat single-axis tracking systems compared to fixed structures [Wu,2025]. However, existing research predominantly focuses on arid or desert ecosystems, with limited studies conducted in high-latitude, cold, saline-alkali grasslands. Systematic comparisons of the ecological effects of multiple support structures are scarce, and analyses often examine vegetation or soil responses in isolation. This lack of a comprehensive examination of \u0026lsquo;plant-soil system synergistic responses\u0026rsquo; hinders the identification of key pathways through which PV support structures influence ecosystems. Furthermore, differing support configurations\u0026mdash;varying in mounting height, tilt angle, and shading dynamics\u0026mdash;create distinct shading conditions. These conditions may exert differential impacts on ecosystems by regulating factors such as light, heat, water, and salinity. Comparative research addressing these interactions remains underdeveloped.\u003c/p\u003e \u003cp\u003eThis study utilized the National Photovoltaic Energy Storage Demonstration Platform (Daqing Base) as its experimental site\u0026mdash;a facility operational for five years\u0026mdash;and selected eight representative PV array mounting configurations. It systematically investigated how these structures drive synergistic changes in plant community structure and soil physicochemical properties in the Songnen saline-alkali grassland by regulating microenvironmental factors such as moisture and salinity. The eight support configurations represent prevailing technologies and exhibit significant variations in mounting height, tilt angle, and shading dynamics, thereby providing an ideal gradient for comparing ecological effects. Through synchronous analysis of vegetation indicators (e.g., diversity, biomass) and soil parameters (e.g., pH, moisture content, salinity, nutrients), this study aims to elucidate: (1) the differentiation patterns of plant-soil systems under different PV support configurations; (2) the coupling relationship between vegetation responses and soil water-salinity-fertility dynamics; and (3) which support configuration most favours achieving synergistic \u0026lsquo;grass-solar complementarity\u0026rsquo; development. By innovatively focusing on evaluating vegetation-soil synergistic responses and employing a multi-indicator comprehensive assessment, this research reveals the ecological regulation mechanisms of support structures. It provides a theoretical foundation and practical pathways for ensuring ecological security and promoting the sustainable development of PV power stations in high-latitude saline-alkali grassland regions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Area Overview\u003c/h2\u003e\n \u003cp\u003eThe National Photovoltaic and Energy Storage Demonstration Platform (Daqing Base), hereafter referred to as the study site, is located in Gaotaizi Town, Datong District, Daqing City, Heilongjiang Province, China (124\u0026deg;54\u0026prime;20\u0026Prime;E, 46\u0026deg;10\u0026prime;17\u0026Prime;N). Situated on the Songnen Plain, it is the northernmost and coldest national photovoltaic base in China, covering an area of more than 100,000 mu (approximately 6,667 hectares). As the largest photovoltaic experimental and demonstration base in Asia and worldwide, it began operation on August 11, 2021, with a total investment of 6\u0026nbsp;billion CNY. The site is established on a warm-temperate meadow grassland, which represents a typical degraded saline-alkaline grassland with perennial hay yields as low as 30\u0026ndash;85 kg per mu. The region has a temperate continental monsoon climate, featuring distinct seasons and considerable temperature variation. The mean annual temperature is about 4.2\u0026deg;C, with extreme highs of 38.9\u0026deg;C and lows of \u0026minus;\u0026thinsp;39.2\u0026deg;C. Annual sunshine duration ranges from 2,600 to 2,842 hours, and the relatively high total annual solar radiation provides favorable conditions for solar energy utilization. Precipitation is moderate, with an annual average of approximately 427.5 mm, which supports both forage growth and photovoltaic power generation. Common plant species within the experimental area include Leymus chinensis, Suaeda glauca, Suaeda corniculata, Puccinellia tenuiflora, Phragmites australis, and Aster pekinensis.\u003c/p\u003e\n \u003cp\u003eEight photovoltaic mounting configurations were investigated: (1)Flexible Fixed Mounting (R), (2) Flat Single-axis Tracker (P), (3) full-dimensional tracker (Q), (4) seasonally adjustable mount (J), (5) tilted single-axis tracker (X), (6) dual-axis tracker (S), (7) vertical single-axis mount (C), (8) fixed mount (G). (9) Control group \u0026ndash; non-PV area within the photovoltaic field (CK) ( Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Plant Community Survey Methodology\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Plot Design\u003c/h2\u003e\n \u003cp\u003eThis study employed a plot design to systematically compare plant community characteristics under eight typical photovoltaic (PV) array mounting systems, within the zone directly shaded by the PV panels. For each mounting system, three representative sub-arrays were randomly selected from all eligible, numbered sub-arrays using a random number table. All study areas, including those beneath panels and control plots, were protected from grazing and other human disturbances.For each of the eight mounting system types, three replicate sub-arrays were established. Within each sub-array, three 1 m \u0026times; 1 m quadrats were placed for herbaceous plant community surveys. This resulted in a total of 8 treatments \u0026times; 3 replicates \u0026times; 3 quadrats\u0026thinsp;=\u0026thinsp;72 survey quadrats under PV panels.Control plots (CK) were established in uninterrupted open areas outside the perimeter of the PV arrays. Nine control quadrats were randomly distributed within an undisturbed zone located approximately 100 meters from the power station fence, ensuring their uniform distribution around the site. This yielded a total of 9 control quadrats.Therefore, the total number of survey quadrats in this study was 72 (under panels)\u0026thinsp;+\u0026thinsp;9 (control)\u0026thinsp;=\u0026thinsp;81.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Survey Methodology\u003c/h2\u003e\n \u003cp\u003eThe plant community survey was conducted on August 31, 2024, during the local peak growing season to adequately reflect interannual community characteristics. Within each 1m \u0026times; 1m plot, all present plant species were first recorded. Vegetation height was then determined by measuring the height of five randomly selected individuals per species (or all individuals if fewer than five were present) using a tape measure, calculating the average height per species, and then deriving the overall average height for the plot. Total vegetation cover was estimated visually as the percentage of ground area covered by the vertical projection of all above-ground plant parts. Following these measurements, all above-ground vegetation within the plot was harvested at ground level. The samples were transported to the laboratory, oven-dried at 105\u0026deg;C to constant weight (approximately 48 hours), and weighed to determine dry above-ground biomass with an accuracy of 0.01g.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.3 Diversity Index Calculation Methods\u003c/h2\u003e\n \u003cp\u003eSpecies diversity in this study was assessed using the Shannon-Wiener index, Pielou index, and Margalef index. These indices were calculated using Equations (1), (2), and (3) respectively:\u003c/p\u003e\n \u003cp\u003eShannon-Wiener Index:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:\\text{H}\\text{=}\\sum\\:_{\\text{i}\\text{=}\\text{1}}^{\\text{n}}{\\text{P}}_{\\text{i}}\\text{ln}{\\text{P}}_{\\text{i}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e(1)\u003c/p\u003e\n \u003cp\u003ePielou index:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:\\text{E=N/}\\text{ln}\\text{S}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e(2)\u003c/p\u003e\n \u003cp\u003eMargalef index:\u003c/p\u003e\n \u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:\\text{M=}\\left(\\text{N}\\text{\u0026minus;}\\text{1}\\right)\\text{/}\\text{ln}{\\text{N}}_{\\text{i}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e(3)\u003c/p\u003e\n \u003cp\u003eWhere S denotes the number of species within the sample plot, Pi\u0026thinsp;=\u0026thinsp;Ni/N, where Ni represents the number of individuals of species i, and N denotes the total number of individuals of all plants within the sample plot.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Soil Characterisation Methods\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.1 Soil Sampling\u003c/h2\u003e\n \u003cp\u003eSoil samples were collected on 31 August 2024, immediately following the completion of vegetation surveys to ensure spatio-temporal consistency between plant and soil data. Sampling locations were selected within plots where the substrate surface was relatively uniform. Soil sampling adhered to the principles of random selection, equal quantities, and multi-point mixing. Within each 1m\u0026times;1m plant survey plot, five sampling points were selected using a staggered grid pattern. A ring knife was employed to collect soil samples from the 0\u0026ndash;10cm layer at each point. Soil samples from all five points within the same plot were equally mixed to form a composite sample. Excess soil was discarded using the quartering method, ultimately retaining approximately 1kg of soil sample for chemical analysis. Simultaneously, undisturbed soil cores were extracted in situ using a ring knife for soil moisture content determination.\u003c/p\u003e\n \u003cp\u003eBased on this methodology, soil samples were collected from beneath the eight support structures and the control plots in quantities matching the vegetation survey plots, yielding 72\u0026thinsp;+\u0026thinsp;9\u0026thinsp;=\u0026thinsp;81 samples.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.3.2 Soil Sample Analysis\u003c/h2\u003e\n \u003cp\u003eAll soil samples were properly labelled (indicating sampling number, location, depth, date, etc.) and transported back to the laboratory refrigerated and protected from light. Following pretreatment involving air-drying, grinding, and sieving, the following parameters were determined: soil moisture content was measured using the oven-drying method, while pH and electrical conductivity were assessed using a pH meter and conductivity meter respectively. Total soluble salts in soil were measured using the conductivity method. Soil nutrient analysis encompassed soil organic matter (SOM), total nitrogen (TN), total phosphorus (TP), and available potassium (AK). Chemical analysis methods were employed: potassium dichromate oxidation for organic matter, Kjeldahl nitrogen determination for total nitrogen, molybdenum-antimony colorimetry for total phosphorus, and flame photometry for available potassium.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Data Statistics and Analysis\u003c/h2\u003e\n \u003cp\u003eQuantitative characteristics of plant communities\u0026mdash;Shannon-Wiener index, Pielou index, Margalef index, height, cover, density, and biomass\u0026mdash;were compared using the Least Significant Difference (LSD) test for multiple comparisons at a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n \u003cp\u003eData were organised using Excel 2021, subjected to LSD tests via SPSS 26.0, and plotted using Origin 2024. Figures present mean values\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation.\u003c/p\u003e\n \u003cp\u003eFormulas for membership function values (Wj), membership function values \u0026micro;Xja, and composite index values (Da) are as follows:\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{\\text{W}}_{\\text{j}}\\text{=}{\\text{P}}_{\\text{j}}\\sum\\:_{\\text{i}\\text{=}\\text{1}}^{\\text{n}}{\\text{P}}_{\\text{j}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\:\\text{\u0026micro;}\\left({\\text{X}}_{\\text{ja}}\\right)=\\left({\\text{X}}_{\\text{ja}}\\text{\u0026minus;}{\\text{X}}_{\\text{jmax}}\\right)\\text{/}\\left({\\text{X}}_{\\text{jmin}}\\text{\u0026minus;}{\\text{X}}_{\\text{jmax}}\\right)$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv format=\"TEX\" class=\"mathdisplay\" name=\"EquationSource\"\u003e$$\\:{\\text{D}}_{\\text{a}}\\text{=}\\sum\\:_{\\text{i}\\text{=1}}^{\\text{n}}\\left[\\left({\\text{X}}_{\\text{ja}}\\right)\\text{\u0026times;}{\\text{W}}_{\\text{j}}\\right]$$\u003c/div\u003e\n \u003cp\u003e(6)\u003c/p\u003e\n \u003cp\u003eIn the formula: Pj denotes the contribution rate of the jth principal component for each indicator; the weight coefficient (Wj) indicates the prominence of the jth principal component among all principal components; Xja represents the value of the jth principal component for the ath support structure; Xjmin and Xjmax denote the minimum and maximum values of the jth principal component respectively; \u0026micro;Xja is the membership function value for the transformed principal component value; Da denotes the comprehensive evaluation value of the a-th support structure for the grassland vegetation and soil characteristic indicators of the national photovoltaic base.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Effects of Eight Photovoltaic Support Configurations on Grassland Vegetation in PV Fields\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.1.1 Impact of Eight Photovoltaic Panel Support Configurations on Plant Community Species Richness\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-A, the non-PV control area (CK) exhibited the highest species richness at 5 species. This was followed by the fixed support (G) at 4.33 species, showing no significant difference from the control (CK) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).. The vertical single-axis (C) system exhibited the lowest species richness at 2.11 species, significantly lower than the control (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This was followed by the inclined single-axis tracking (X) system at 2.22 species.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e3.1.2 Effects of Eight Photovoltaic Panel Mounting Configurations on Plant Community Height\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-B, the inclined single-axis tracking mount (X) yielded the highest average plant community height at 58.84 cm, followed by the Full-Dimensional Tracking Mount (Q) at 58.79 cm; The fixed support (G) exhibited the lowest average plant height beneath the panels at merely 36.97 cm, followed by the control group (CK) at 39.94 cm. The differences between the fixed support (G) and the control group were not statistically significant (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05). The plant heights beneath the flexible support (R), Single-axis Tilt Mounting System (X),Seasonally Adjustable Mount (J), flat single-axis support (P), and full-dimensional tracking support (Q) were all significantly higher than those of the control group (CK) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.1.3 Impact of Eight Photovoltaic Panel Mounting Configurations on Total Plant Cover\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-C, the Vertical Single-Axis Mount(C) group exhibited the highest total plant cover under panels at 79%, followed by the horizontal single-axis support (P) group at 74.44%. The Single-axis Tilt Mounting System (X) group recorded the lowest total plant cover under panels at merely 40.93%, followed by the full-axis tracking support (Q) group at 57.78%. The total canopy cover beneath Flexible Fixed Mountings (R), vertical single-axis supports (C),Seasonally Adjustable Mounts (J), horizontal single-axis tracking supports (P), and fixed supports (G) exceeded that of the control group (67.78%), though the differences were not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The total canopy cover under the Single-axis Tilt Mounting System (X) and full-range tracking support (Q) was significantly lower than the control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The total canopy cover under the dual-axis tracking support (S) was lower than the control group, but the difference was not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.1.4 Effect of Eight Photovoltaic Panel Support Configurations on Plant Biomass\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e-D, the horizontal single-axis bracket (P) group exhibited the highest biomass at 613.59 g/m\u0026sup2;, followed by the non-PV control group (CK) at 586.27 g/m\u0026sup2;. The fixed bracket (G) group recorded the lowest biomass at 251.71 g/m\u0026sup2;, followed by the inclined single-axis tracking bracket (X) at 273.66 g/m\u0026sup2;. The Flexible Fixed Mounting (R), Single-axis Tilt Mounting System (X), Vertical Single-Axis Mount(C),Seasonally Adjustable Mount (J), full-dimensional tracking support (Q), dual-axis tracking support (S), and fixed\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.1.5 Effects of Eight Photovoltaic Panel Support Modes on Plant Diversity\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e indicates that the Pielou index was highest for the horizontal single-axis support (P) at 0.85, followed by the control group (CK) at 0.83. The seasonal adjustable support (J) had the lowest index at 0.58, followed by the Flexible Fixed Mounting (R) at 0.60. The fixed support (G) exhibited the highest species richness with a Margalef index of 2.10, followed by the flat single-axis support (P) at 1.90. The Flexible Fixed Mounting (R) showed the lowest species richness with a Margalef index of merely 0.95, followed by theSeasonally Adjustable Mount (J) at 1.00. The Shannon-Wiener index was highest for the flat single-axis support (P) at 2.14, followed by the control group (CK) at 1.95. The Shannon-Wiener index was lowest for the Flexible Fixed Mounting (R) at merely 0.653, followed by theSeasonally Adjustable Mount (J) group at 1.22.\u003c/p\u003e \u003cp\u003eComparing diversity indices across the eight support configurations, the flat single-axis support (P) group demonstrated relatively superior species diversity, species richness, and evenness. The Flexible Fixed Mounting (R) andSeasonally Adjustable Mount (J) groups performed comparatively poorer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of Community Diversity Indices under Eight Photovoltaic Support Scenarios in the PV Field Area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMounting System\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eDiversity Index\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePielou\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMargalef\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eShannon-Wiener\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlexible Fixed Mounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.60b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.95bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle-axis Tilt Mounting System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.70ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.39bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.55b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVertical Single-Axis Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.76ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.32bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.63ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeasonally Adjustable Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.00bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.22bc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlat Single-axis Mounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.85a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.90a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.14a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull-Dimensional Tracking Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.61b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.44b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.37b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDual-axis Tracking Mounting System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.75ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.58b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.78ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.10a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.77ab\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.83a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.52b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.95a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Effects of Eight Photovoltaic Mounting Configurations on Grassland Soil Characteristics in PV Fields\u003c/h2\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Impact of Eight Photovoltaic Panel Mounting Configurations on Soil pH\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A, analysis of soil pH across the eight mounting configurations reveals that the vertical single-axis mount (C) group exhibited the highest pH at 9.47, followed by the Full-Dimensional Tracking Mount (Q) at 9.25. The lowest pH was recorded under the fixed support (G) at 8.78, followed by the dual-axis tracking support (S) at 9.15. Soil pH values beneath the inclined single-axis tracking (X), vertical single-axis (C), and seasonally adjustable (J) supports were higher than the control group (CK). Conversely, the Flexible Fixed Mounting (R), horizontal single-axis support (P), full-axis tracking support (Q), dual-axis tracking (S), and fixed (G) structures exhibited lower pH than the control (CK). No significant differences in soil pH were observed among the eight support types (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Effects of Eight PV Panel Support Configurations on Soil Moisture Content\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-B, the highest soil moisture content (39.59%) was recorded beneath the Flexible Fixed Mounting (R), followed by the fixed support (G) at 38.44%. The lowest soil moisture content (32.73%) was observed in the non-PV control area (CK); followed by theSeasonally Adjustable Mount (J) at 33.34%. All eight PV array support configurations exhibited higher soil moisture content beneath panels than the control group (CK), with Flexible Fixed Mountings (R), flat single-axis supports (P), and fixed supports (G) showing significantly higher values than the control (CK) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The soil moisture content under the inclined single-axis tracking (X), vertical single-axis (C), seasonally adjustable (J), full-tracking (Q), and dual-axis tracking (S) support systems was slightly higher than that of the control group (CK), with no significant difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Effect of Eight Photovoltaic Panel Support Configurations on Soil Electrical Conductivity\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-C, the full-tracking support (Q) exhibited the highest conductivity at 2.15 ms/cm, followed by the horizontal single-axis support (P) at 1.94 ms/cm. The lowest conductivity was observed in the dual-axis tracking support (S) at merely 1.20 ms/cm, followed by the Single-axis Tilt Mounting System (X) at 1.26 ms/cm. Among the eight PV support configurations, the conductivity beneath the panels of the Flexible Fixed Mounting (R), flat single-axis support (P), and full-axis tracking support (Q) was significantly higher than that of the control group (CK) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Effect of Eight Photovoltaic Panel Support Configurations on Soil Total Base Content\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-D, differences in total base content were not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The fixed support (G) group exhibited the highest soil total base content at 5.46 g/kg, followed by the dual-axis tracking support (S) at 5.37 g/kg. The Flexible Fixed Mounting (R) group exhibited the lowest total soil base content at 4.64 g/kg, followed by the vertical single-axis (C) group at 4.63 g/kg. The total soil base content in the Single-axis Tilt Mounting System (X), dual-axis tracking support (S), and fixed support (G) groups was slightly higher than the control group (CK), with no significant difference compared to the control group (CK) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Flexible fixed (R), vertical single-axis (C), seasonally adjustable (J), horizontal single-axis (P), and full-axis tracking (Q) mounts exhibited lower total soil base content than the control (CK), with no significant difference compared to the control (CK) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 Effects of Eight Photovoltaic Panel Support Structures on Soil Nutrient Content\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the Flexible Fixed Mounting (R) exhibited the highest soil organic carbon content at 20.16%, while theSeasonally Adjustable Mount (J) recorded the lowest at 12.59%. The Flexible Fixed Mounting (R) group exhibited the highest soil total nitrogen content at 0.78 g/kg. The Vertical Single-Axis Mount(C) group had the lowest soil total nitrogen content at merely 0.48 g/kg. TheSeasonally Adjustable Mount (J) group showed the highest soil total phosphorus content at 0.35 g/kg, followed by the Flexible Fixed Mounting (R) at 0.34 g/kg. The dual-axis tracking support (S) group exhibited the lowest total phosphorus content at merely 0.25 g/kg. Soil available potassium content was highest in the parallel single-axis support (P) group, reaching 196.21 mg/kg, followed by the Flexible Fixed Mounting (R) group at 195.21 mg/kg. Overall, the soil nutrient composition beneath the Flexible Fixed Mounting (R) exhibited superior quality, with significantly higher levels of organic carbon, total nitrogen, total phosphorus, and available potassium compared to the control group (CK) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of soil nutrient content under eight PV support scenarios in the photovoltaic field\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMounting System\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoil organic carbon\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal nitrogen\u003c/p\u003e \u003cp\u003e(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal phosphorus\u003c/p\u003e \u003cp\u003e(g/kg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAvailable potassium (mg/kg)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.49b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.30ab\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e194.44a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlat Single-axis Mounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.05bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e196.21a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle-axis Tilt Mounting System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.27bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.65b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e173.30c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDual-axis Tracking Mounting System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.88bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.60b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.23b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e165.93c\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlexible Fixed Mounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.16a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.78a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.34a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e195.21a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull-Dimensional Tracking Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.43c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.25b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e180.89bc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeasonally Adjustable Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.59c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e191.84a\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVertical Single-Axis Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.51bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.48c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e176.88bc\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.52bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.63bc\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.28b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e185.52b\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Correlation Analysis of Grass-Soil Interface Factors Across Eight Support Structures in Photovoltaic Fields\u003c/h2\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, at P\u0026thinsp;\u0026le;\u0026thinsp;0.05, the Pielou evenness index and Shannon-Wiener diversity index exhibited significant positive correlations; the Margalef diversity index showed significant positive correlation with soil moisture content; and total soil phosphorus and available potassium demonstrated significant positive correlations. Species richness and soil pH showed a significant negative correlation, while soil electrical conductivity and total base cation content exhibited a significant negative correlation. In the Pearson correlation coefficient plot, both the x-axis and y-axis represent respective indicators, with colour intensity indicating the magnitude of the correlation coefficient between two indicators. Closer to red (coefficient closer to 1) indicates stronger positive correlation; closer to white indicates weaker correlation; closer to blue indicates stronger negative correlation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e3.4 Comprehensive evaluation of membership functions for grass-soil interface indicators across eight support models in the photovoltaic field\u003c/p\u003e \u003cp\u003eAs individual indicators cannot accurately reflect the impact of the eight support models on vegetation and soil parameters in the national photovoltaic base, membership functions were employed. Higher membership function values indicate lesser adverse effects of the supports on grassland. Analysis of the total evaluation D-value changes across indicators (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) indicates that among the eight PV array support configurations, the horizontal single-axis support (P) achieved the highest comprehensive evaluation D-value of 0.805. This was followed by the Flexible Fixed Mounting (R) group at 0.475 and the Single-axis Tilt Mounting System (X) group at 0.469.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMembership Function Values for Various Indicators in the PV Field Area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMounting System\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eComprehensive Evaluation D Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSorting\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlexible Fixed Mounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSingle-axis Tilt Mounting System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVertical Single-Axis Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSeasonally Adjustable Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eJ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFlat Single-axis Mounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.805\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFull-Dimensional Tracking Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDual-axis Tracking Mounting System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFixed Mount\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Effects of Eight Photovoltaic Array Support Configurations on Plant Community Characteristics\u003c/h2\u003e \u003cp\u003eThe installation of photovoltaic (PV) panels exerts differential impacts on various plant traits within grassland ecosystems [Luo, 2023]. The most immediate effect is the obstruction of sunlight, which creates varying degrees of shading. This reduction in light availability directly influences photosynthetic efficiency and consequently plant growth [Adeh, 2018]. In this study, the non-PV control area (CK) exhibited the highest species richness. By maintaining the stability and continuity of the natural habitat, this area preserved the regional species pool most effectively. In contrast, the establishment of PV arrays altered the microenvironment through shading, which selectively filtered plant species and led to a reduction in species richness within the array zones [Jiang, 2023]. Except for the fixed support structure, plants beneath other panel types showed greater average height than those in the control. This is likely a shade-avoidance response, where plants under panels elongate their stems to reach limited light resources [Han, 2025]. The area and intensity of ground shading varied with different support configurations, thereby influencing plant height. Plants under fixed support panels were slightly shorter than the control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05), possibly because the fixed tilt angle and contiguous panel arrangement resulted in light levels below the threshold required for positive photomorphogenesis, strongly limiting photosynthesis and overall growth, thus preventing the enhanced growth observed under other support types and even underperforming relative to the natural control [Lu, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2025\u003c/span\u003e]. In this study, the total canopy cover of plants under the Single-axis Tilt Mounting System was significantly lower than that of the control. This may be attributed to the fixed tilt angle of this configuration, which positions the front edge of the panels too close to the ground, resulting in prolonged shading that negatively affects canopy development. Biomass under the horizontal single-axis tracker (P) was slightly higher than the control (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). This may result from the dynamic, moderate shading created by this mounting system, which avoids strong light inhibition while providing a relatively uniform light distribution. This environment allows plants to optimize photosynthesis through moderate height increase, thereby enhancing biomass accumulation [Tan, 2023]. Combined with the observation of lower plant height under these panels, it suggests superior nutrient allocation to biomass production rather than to excessive vegetative growth. Analysis of plant diversity under different PV arrays reveals that the horizontal single-axis tracker (P) supported superior species diversity, richness, and evenness. Its moderate shading significantly reduces surface temperature, thereby curbing water evaporation and better preserving soil moisture [Zhao, 2024]. In the Songnen saline-alkali grassland, this moisture retention helps mitigate salt accumulation at the surface through evaporation, alleviating the 'surface salt crust' phenomenon. Furthermore, the ground clearance and array spacing of the horizontal single-axis tracker provide essential space for plant growth and air circulation, fostering a microclimate conducive to plant community development. This relatively balanced microenvironment also supports greater biomass accumulation [Liu, 2025].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Effects of Eight Photovoltaic Array Support Configurations on Substrate Soil Characteristics\u003c/h2\u003e \u003cp\u003eDifferent photovoltaic (PV) array mounting configurations create distinct microenvironments by redistributing localized light, moisture, and temperature, which fundamentally drives their differential impacts on grassland vegetation and soil properties [Yu,2025]. In this study, soil moisture content beneath all eight mounting configurations was higher than in the non-PV control area. This aligns with findings by Du et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), who reported significantly greater soil moisture within PV arrays compared to adjacent open areas. This phenomenon is attributed to the shading effect of PV panels, which significantly lowers surface temperature and reduces soil water evaporation [Wu,2025].\u003c/p\u003e \u003cp\u003eThe altered shading regime substantially modifies soil water-salt dynamics and nutrient availability. By mitigating wind exposure and evaporation, it enhances soil water retention beneath the panels and significantly affects solute transport pathways. The reduction in evaporation under the panels creates a low-evaporation zone, thereby alleviating surface salt accumulation [Liu,2025; Yu,2025]. Changes in soil moisture further influence nutrient mobility. The accumulation of readily available potassium in the surface layer beneath the panels is likely linked to these modified moisture dynamics and associated runoff processes, consistent with Liu's(2013) observation that \"moisture and topography influence the spatial distribution of soil nutrients.\"\u003c/p\u003e \u003cp\u003eIn this study, both the horizontal single-axis tracker and the rigid fixed mount created relatively stable and pronounced shaded zones. These conditions reduced soil temperature and evaporation, allowing the sub-panel areas to maintain higher soil moisture. The suitable moisture levels coupled with relatively lower temperatures fostered favorable conditions for soil microbial activity while potentially slowing litter decomposition rates, thus promoting organic matter accumulation. No significant differences in soil pH or total base saturation were detected among the eight PV mounting systems (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e-A). This lack of significant variation may be due to the relatively short operational history of the PV field and the spatially concentrated distribution of the eight array types within a confined area, resulting in low overall soil spatial heterogeneity and thus minimal measurable differences between treatments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Relationship between plant community characteristics and soil physicochemical properties\u003c/h2\u003e \u003cp\u003eThis study revealed a close association between plant community diversity and soil physicochemical properties. The Margalef richness index showed a significant negative correlation with soil pH (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that salt accumulation significantly inhibits plant growth and reduces community diversity. This finding is consistent with the observation by Cao et al. (2019) that in high-salinity environments, the growth of non-halophytic plants is constrained, leading to community succession dominated by salt-tolerant species and a consequent decline in diversity. Li et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) demonstrated that high saline-alkali stress increases root osmotic pressure and reduces water-use efficiency, thereby impairing plant growth and reproduction. Liu et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) further confirmed in the saline-alkali soils of northern China that soil salinization not only reduces community diversity but also affects species evenness, identifying salinity as a key ecological factor limiting vegetation restoration. In this study, the horizontal single-axis tracker (P) significantly alleviated salt stress through dynamic shading: soil moisture content beneath the panels increased by approximately 12%, and available potassium content reached 196.21 mg/kg. Simultaneously, both the Shannon-Wiener index (2.14) and aboveground biomass (613.59 g/m\u0026sup2;) were higher than under other configurations, indicating that its optimized microenvironment for water-salt redistribution provides niche space for diverse plant communities.\u003c/p\u003e \u003cp\u003eDifferent support structures created distinct heterogeneous microenvironments through shading, exerting varying impacts on plants and soil. Shading does not necessarily induce negative effects; moderate shading may even benefit degraded grasslands in saline-alkali habitats. The horizontal single-axis tracker optimizes the allocation of light and heat resources through dynamic shading, promoting water infiltration and salt leaching to alleviate saline-alkali stress. This moderate distribution of resources not only mitigates stress but also effectively promotes primary productivity, as evidenced by higher aboveground biomass. Concurrently, increased plant biomass beneath the panels enhances soil organic carbon input through litterfall, further promoting soil aggregate formation and nutrient sequestration. This positive plant-soil feedback resulted in the horizontal single-axis tracker achieving the highest membership function evaluation score (0.805). The minimal habitat variation beneath and between its panels (coefficient of variation\u0026thinsp;\u0026lt;\u0026thinsp;10%) indicated significantly better ecosystem stability compared to other configurations.\u003c/p\u003e \u003cp\u003eThe superior performance of the horizontal single-axis tracker stems from a synergistic mechanism involving \u0026ldquo;shading\u0026ndash;moisture\u0026ndash;salinity\u0026ndash;plants\u0026ndash;microbes.\u0026rdquo; Its dynamic shading structure (with \u0026plusmn;\u0026thinsp;60\u0026deg; horizontal-axis tracking) reduces surface evaporation, promotes water infiltration and salt leaching, and mitigates surface salt accumulation. Concurrently, balanced light and thermal conditions support the coexistence of shade-tolerant and light-demanding species (Pielou index of 0.85), enhancing community stability. Litter input from the plant community feeds back into the soil, forming a positive loop with microbial activity: higher biomass increases litter input, while stable moisture and moderate shading delay organic matter mineralization, promoting soil aggregation and nutrient sequestration [Shuai, 2026]. In this study, soil total phosphorus and available potassium beneath the horizontal single-axis tracker showed a significant positive correlation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating high microbial-driven nutrient transformation efficiency.\u003c/p\u003e \u003cp\u003eIn summary, the horizontal single-axis support system achieves synergistic enhancement of plant diversity and soil fertility through microenvironmental regulation, providing an ecological basis for \u0026ldquo;grass-solar complementarity\u0026rdquo; in saline-alkali grasslands. Future research should further elucidate the response pathways of microbial community structure and key enzyme activities to refine the model of this synergistic mechanism.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study systematically evaluated the effects of different photovoltaic (PV) array mounting systems on soil and vegetation in the degraded saline-alkali grasslands of the Songnen region. The results reveal that the shading effect of PV arrays can act as a potential tool for mitigating soil salinization and promoting ecosystem restoration by regulating local hydrothermal conditions. In these grasslands, the dynamic shading environment generated by the horizontal single-axis tracker (P) demonstrated significant soil improvement benefits. It effectively moderated the near-surface microclimate by reducing soil temperature and evaporation, thereby promoting moisture retention and salt leaching, which alleviated saline-alkali stress. Concurrently, this configuration significantly enhanced soil nutrient availability: available potassium content beneath the panels reached 196.21 mg/kg and showed a significant positive correlation with total phosphorus, indicating more active soil nutrient cycling. These optimized soil conditions further facilitated vegetation recovery, as reflected in higher plant diversity (Shannon\u0026ndash;Wiener index\u0026thinsp;=\u0026thinsp;2.14) and aboveground biomass (613.59 g/m\u0026sup2;). Through litter return and microbial activity, a preliminary positive feedback loop of \u0026lsquo;soil improvement\u0026ndash;vegetation restoration\u0026rsquo; was established.\u003c/p\u003e \u003cp\u003eTherefore, this research demonstrates that by adopting an optimized PV array structure such as the horizontal single-axis tracker, the physical shading of a PV power plant can be transformed into an \u0026lsquo;ecological regulation\u0026rsquo; measure for saline-alkali soils. Owing to its dynamic tracking capability, the horizontal single-axis tracker achieved the highest score (0.805) in the membership function-based comprehensive evaluation, confirming its optimal balance between power generation needs and soil environmental improvement. This model provides a feasible technical pathway for implementing PV development while achieving soil salinization control and ecological restoration in cold, saline-alkali regions such as the Daqing area. Future studies should further quantify the long-term effects of different PV configurations on soil salt transport, potential heavy metal migration, and soil microbial functions. This will deepen the understanding of PV power plants as multifunctional environmental interventions and help optimize their ecological sustainability.\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthical Approval:\u003c/strong\u003e \u003cp\u003eEthical approval is not applicable to this study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research was funded by the Daqing New Energy Field \"Unveiling the List\" Science and Technology Research Project of China (Grant No. 2021BD05), the Heilongjiang Provincial Department of Science and Technology, and the Provincial Academy Cooperation Project of China (Grant No. YS16B12). The APC was not funded.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAuthor Contributions: Conceptualization, MNN ; methodology, MNN; software, MNN; validation, HRX and WF; formal analysis,MNN; investigation,M NNand RYX; data curation, KDCand W YR; writing\u0026mdash;original draft preparation, MNN; writing\u0026mdash;review and editing,WGL and QSM; visualization, MNN; supervision, WGL and QSM; project administration, QSM; funding acquisition, QSM. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author on reasonable request. The raw geographic location data of the sampling plots are not publicly available to protect the integrity of the long-term research site. 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Journal of Solar Energy, 44(8), 253\u0026ndash;265.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"photovoltaic support structure model, grassland vegetation, soil physicochemical properties, land cover, flat single-axis tracker","lastPublishedDoi":"10.21203/rs.3.rs-9140332/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9140332/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study aimed to investigate the effects of different photovoltaic (PV) array support structures on soil physicochemical properties and salinization dynamics in a saline-alkali grassland of the Songnen region, to propose an optimized strategy for the \"PV-ecological\" synergistic model. A randomised block design was employed to evaluate plant community and soil parameters under eight typical PV support structures, with a comprehensive assessment conducted using the membership function method. Results showed that the horizontal single-axis tracker (P) recorded the highest plant diversity beneath panels, with a Shannon-Wiener index of 2.14 and a Pielou evenness index of 0.85. Soil under this configuration exhibited a relatively high available potassium content (196.21 mg/kg), and a significant positive correlation was found between total phosphorus and available potassium (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting enhanced nutrient cycling. The horizontal single-axis tracker achieved the highest composite score (0.805) in the comprehensive membership function assessment. The findings demonstrate that this support structure, through its dynamic shading, creates a favorable micro-environment in the saline-alkali grassland. The habitat beneath its panels showed minimal divergence from the non-PV control area and supported the most stable ecosystem. This model effectively harmonizes ecological conservation with power generation, and is thus recommended as the optimal configuration for \"grass-solar complementarity\" in degraded saline-alkali grasslands, with potential for application in similar cold, high-altitude saline-alkali regions.\u003c/p\u003e","manuscriptTitle":"Effects of Photovoltaic Array Configurations on the Soil Environment and Salinization Dynamics in a Degraded Saline-Alkaline Grassland","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-11 06:17:21","doi":"10.21203/rs.3.rs-9140332/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b70e2c48-7e72-4220-ae5a-a6cc194af2e2","owner":[],"postedDate":"May 11th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"12","date":"2026-05-01T08:08:41+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T06:17:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-11 06:17:21","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9140332","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9140332","identity":"rs-9140332","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}