Soil quality and health under different tree species in an urban university campus: A multidimensional study

doi:10.21203/rs.3.rs-4304253/v1

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Research Article

Soil quality and health under different tree species in an urban university campus: A multidimensional study

https://doi.org/10.21203/rs.3.rs-4304253/v1

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published 28 Oct, 2024

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Soil is vital to food security and ecosystem nutrient recycling. Rapid infrastructure development projects requiring mineral resource extraction have led to an overall decrease in soil quality. Due to a higher environmental footprint, cities' soil quality has declined quickly, which requires continuous monitoring and evaluation. Educational institutions are traditionally not taken into account for such monitoring. Thus, the present study investigated the soil health status under 10 different plantations in Maharshi Dayanand University located in Rohtak city. Using different digital and volumetric methods, 20 physicochemical parameters and nutrients viz. sand, silt, clay, pH, electrical conductivity (EC), organic matter (OM), macronutrients and micronutrients. Sampling was performed at four depths (0-10, 10.1-20, 20.1-30 and 30.1-40 cm) to collect 40 samples. Repeated measures of one-way analysis of variance (ANOVA) and pairwise comparison were used to detect significant differences. Different tree plantations affected soil parameters significantly (p<0.05). The soil nutrient index value (SNIV) classified sand (3), pH (2.62), Ca²⁺ (2.82), Cu (2.60) and Fe (2.65) in the high fertility class. Network analysis demonstrated an effect of physicochemical parameters on OM and nutrients. The structural stability index (SSI) appropriated 50 % of the samples as thoroughly degraded (SSI<5 %). Principal component analysis (PCA) produced five significant components and designated N, P, Cu and OM as the most critical soil chemistry variables. Hierarchical cluster analysis (HCA) produced 3 clusters for tree species with similar soil properties. Overall, the campus's soil is alkaline, non-saline and nutrient deficient, and surface layers are more fertile. The soil under F. virens is the most productive. The results obtained and customized solutions provided in this article may help to improve soil health on the campus ans aid in sustainable soil use, conservation and management. This may also motivate other campuses around the globe to assess their soil health status.

Soil health

soil fertility

soil and trees

soil nutrients

Over the past decade, a steady land expansion for human interests has occurred. This has adversely affected the forests, biodiversity and nutrient cycling in soils and is mainly due to fragmentation and contamination of the ecosystems (Domingos et al. 2015; Tariq et al. 2024b). More severe negative impacts are observed concerning soil contamination, as it takes a long time for the soil to renew itself. Environmental pollution has changed the pattern of nutrient cycling in soils on the planet (Bussotti and Pollastrini 2015; Chia et al. 2023). In cities across the globe, the natural process has been altered more noticeably. Recent trends have projected that soils in all parts of the world have a nutrient deficiency. This nutrient deficiency occurs due to differences in land use type, land management practices and soil type (Kumar et al. 2021; Molina et al. 2024). Globally, after oceans and geological pools, soils store the most carbon (C) (Lindén et al. 2020). Soil plays a vital role in the terrestrial C cycle (Nair et al. 2015). On-land soils store approximately 75% of the C, three times more than living plants and animals. Soil organic matter (SOM) or organic matter (OM) is the crucial regulator of biogeochemical cycling and soil fertility (Asabere et al. 2018; Brown et al. 2024). The OM is the primary source of nutrients like C, nitrogen (N), and potassium (K). The OM also helps maintain soil structure, thereby improving or restricting water movement and increasing soil resilience (Brock et al. 2017). Compaction from heavy vehicles and routine litter clearing from gardens, lawns, and parks make urban soil C and OM poor (Pouyat et al. 2002; Vodyanitskii 2015).

Through biomass decomposition, carbon dioxide (CO₂) escapes into the soil and due to the porous nature of the soil, it gets trapped there. The OM stored in the soil is a potential food source for microorganisms. Additionally, more C increases soil fertility and decreases soil erosion, two factors directly related to increased tree growth and crop productivity. The top layer of the soil has more OM than the bottom layer of soil in forests (Chia et al. 2017). Also, soil in primary forests has more C than the topsoil layer in secondary forests, except for some secondary forests in Brazil and Sumatra (Vashum et al. 2016). The higher amount of OM in the soil top layer may be attributed to factors like litterfall activity and the decomposition of vegetation. The C storage in soil is one of the many ecosystem services by soils and high C soils can be a source of monetary value (through C credits) (Nandal et al. 2023b). Soil is a source of both macro- and micronutrients. The requirement of these nutrients is mainly for primary production and but the excess availability of N, phosphorus (P) and calcium (Ca²⁺) can limit the net primary productivity (Townsend et al. 2011). Nutrient deficiency also increases the root-to-shoot ratio, while increased nutrient content decreases the root-to-shoot ratio and increases C storage in a plant (Andrews et al. 2001; Tariq et al. 2024a).

Tree growth is not only dependent on the availability of nutrients but depends upon the competition among various trees. Tree growth is sluggish in high-density places (Zheng et al. 2024). Slow growth also makes trees susceptible to pest attacks and low C sequestration rates. Urban soils differ from non-urban soils due to their direct influence on mixing and excavation, anthropogenic activities, and the indirect influence of urban heat and local atmospheric composition (Mónok et al. 2021). Human activities can alter soil physical parameters like pH, bulk density (BD) and clay content (Nehls et al. 2013; Asabere et al. 2018). Environmental factors like the deposition of various substances like nitric acid and sulphuric acid may alter the soil chemistry (Lopes et al. 2015).

The soil nutrient deficiency is measured in terms of soil fertility. Soil fertility is the primary regulator of soil productivity, which refers to the capacity of soil to impart nutrients to vegetation (Singh et al. 2021). Sap solution is one way the plants take up soil nutrients for growth and development. Depending upon the amount needed by plants, nutrients are either macro (needed in large amounts) or micro (required in smaller amounts). The three primary macronutrients are N, P, and K. Protein synthesis and energy metabolism rely heavily on these nutrients. Secondary macronutrients include sulphur (S), Ca²⁺ and magnesium (Mg²⁺); they help maintain the plant's structural integrity. The elements copper (Cu), iron (Fe), manganese (Mn), and zinc (Zn) are examples of micronutrients; they play a significant role in enzyme activation by acting as a co-factor.

Around 60% of India's total geographical land area is occupied by arid and semi-arid soils (mostly nutrient-poor) and the accurate status of nutrients in these soils is still lacking (Karthika et al. 2021). The Indian soils, especially in the urban areas, are subjected to adverse conditions like monoculture plantation, erosion by wind and water, water logging, drought and excess moisture (Singh et al. 2014b), the data availability on soil health status here also remains a serious issue. Most research investigations are undertaken by educational institutions such as universities and research institutes. Universities have earned the monikers "mini-cities" or "small-scale cities" because of many similar conditions like cities and the location of all necessary amenities within their campuses (Wibowo et al. 2019). Yet, these "mini-cities" have neglected ecological and environmental monitoring of natural resources. Worldwide, regarding soil analysis, only a few studies (Gunamantha et al. 2021; Sahu et al. 2021; Saha and Handique 2022; Mwendwa et al. 2022) have been undertaken on campuses or academic premises. Educational institutions can be crucial in limiting soil degradation in urban environments, promoting sustainability and realizing one health concept. Thus, this study has been undertaken to evaluate the soil health under 10 tree species at Maharshi Dayanand University (MDU), Rohtak, with the following research queries: which will not only but will also take the campus a step ahead toward the

a) What is the status of various physicochemical parameters, OM and nutrients in the soil of different tree species? b) How do the soil parameters vary at different depths and under different tree species? c) What is the correlation among different soil parameters? d) What are the soil stability and fertility levels measured using different indices? e) What are the most critical parameters affecting soil chemistry and clustering of different trees based on soil characteristics (through statistical methods)? Concerning the objectives mentioned above, we hypothesized that a) the soil characteristics are significantly different at varying depths and b) the soil would be structurally stable and have high fertility.

Study site

MDU lies between 28° 52′ 49.1″ N and 76° 36′ 59.3″ E in Rohtak district of Haryana state (Fig. 1). The varsity spreads around 622 acres of area. The university campus was designated the cleanest and greenest government university campus in India in 2018. As the study site is in a sub-tropical climate, it experiences scorching summer heat and chilly winter winds. This area gets an annual rainfall of 550–600 mm. The study site has both monospecific as well as mixed plantations. There are approximately 60 tree species on the campus. The soil in the area is mainly of sandy loam-type (Sethi et al. 2012).

Soil sampling and data analysis

For soil sampling, a core sampler with dimensions having a radius (r) of 4.5 cm and a height (H) of 10 cm was used. Soil samples along different depths (cm) viz. 0–10 (layer 1 [L1]), 10.1–20 (layer 2 [L2]), 20.1–30 (layer 3 [L3]), 30.1–40 (layer 4 [L4]), under ten different tree species viz. Eucalyptus globulus Labill. (25), Pongamia pinnata (L.) Pierre (13), Ficus benjamina L. (14), Azadirachta indica A. Juss. (15), Ficus virens Aiton (11), Terminalia arjuna (Roxb. ex DC.) Wight & Arn. (18), Polyalthia longifolia (Sonn.) Thwaites (16), Callistemon lanceolatus D.C. (23), Syzygium cumini (L.) Skeels (10) and Dalbergia sissoo Sensu Miq. (15) were collected (numbers after tree species in brackets indicate age of plantations). These are the dominant tree species on the campus, covering around 85% of the planted area (Nandal et al. 2023a). Different tree management practices such as pruning, coppicing and thinning are regularly undertaken to regulate tree growth. Forty composite samples [10 from each depth (4 total depths) under different tree species] were collected. Three sub-samples constituted one composite sample, as shown in Fig. 2 (Kokkora et al. 2022). The soil samples were sieved through a 2 mm sieve. The samples were oven-dried at 60 ºC till the constant weight was obtained. The dried soil samples were used for analysis. Apart from OM, seven physical and 12 chemical parameters were analyzed. The samples were analyzed in triplicates.

Physical parameters

Soil texture (ST)

It was estimated using the hydrometric method and classified according to United States Department of Agriculture (USDA) triangle (USDA-Soil Survey Staff 1999). For measuring Soil moisture (SM), Soil water holding capacity (SWHC), BD and Soil porosity (SP) a Hilgard soil cup was utilized (Baruah and Borthakur 1997).

SOM

The SOM was calculated from soil organic carbon (SOC). The SOC was estimated by standard procedure propounded by Walkley and Black (1934). The SOC (%) was converted to SOM (%) by multiplying it with a value of 1.724 (Van Bemmelen factor). The SOM (%) was converted to SOM (Mg ha^− 1) using the equation as given below:

$$SOM \left(Mg {ha}^{-1}\right)=SOM \left(\%\right) \times sampling depth \left(m\right) \times BD \left({g cm}^{-3}\right)\times 10000 \mathbf{E}\mathbf{q} \left(1\right)$$

Chemical parameters

pH and electrical conductivity (EC): For pH, water and soil suspension in a ratio of 2:1 was dipped with a pH-measuring electrode of a portable pH meter (ESICO–1615) (Schofield and Taylor 1955). For EC, water and soil suspension in a ratio of 2:1 was dipped with an EC-measuring electrode of an EC meter (EI–7200) (Smith and Doran 1997).

Macro and micronutrients

The soil samples' nutrient status was determined using standard methods available in the literature. Available N was determined using Subbiah (1955), available P using Olsen (1954), available K using Leaf (1958), available S by Chesnin and Yien (1951) and extractable Ca²⁺ and Mg²⁺ using Cheng and Bray (1951). All the DTPA extractable micronutrients were analyzed by following the standard method of Lindsay and Norvell (1969). Different soil properties analyzed along with key steps involving statistical analysis have been depicted in Fig. 3.

Soil nutrient index value (SNIV) and Soil stabilization index (SSI)

Different soil parameters (physical and chemical) were classified as low, medium and high based on the concentrations available from existing literature, described in the following part. Physical parameters like sand, silt and clay and SOM classification were adopted from Khosravi Aqdam et al. (2023). Chemical parameters, including pH, EC and K, were categorized according to the method followed by Ravikumar (2013). The macronutrients N, P and S from Choudhury et al. (2021). The secondary macronutrients were classified by the criterion followed by Sachan and Krishna (2022). We followed Ravikumar (2013) for all the micronutrients. The SNIV was computed through the standard method already available in the literature (Parker et al. 1951). The SNIV was then categorized into three classes, viz. low (< 1.67), medium (1.67–2.33) and high (> 2.33). The following equation was used for calculating SNIV:

$$SNIV= \frac{1\times {S}_{l}+2\times {S}_{m}+3{\times S}_{h}}{{S}_{l}+{S}_{m}+{S}_{h}} \mathbf{E}\mathbf{q} \left(2\right)$$

Where,

$${S}_{l}=\text{S}\text{a}\text{m}\text{p}\text{l}\text{e}\text{s} \text{i}\text{n} \text{t}\text{h}\text{e} \text{l}\text{o}\text{w} \text{c}\text{a}\text{t}\text{e}\text{g}\text{o}\text{r}\text{y}$$

$${S}_{m}=\text{S}\text{a}\text{m}\text{p}\text{l}\text{e}\text{s} \text{i}\text{n} \text{t}\text{h}\text{e} \text{m}\text{e}\text{d}\text{i}\text{u}\text{m} \text{c}\text{a}\text{t}\text{e}\text{g}\text{o}\text{r}\text{y}$$

$${S}_{h}=\text{S}\text{a}\text{m}\text{p}\text{l}\text{e}\text{s} \text{i}\text{n} \text{h}\text{i}\text{g}\text{h} \text{c}\text{a}\text{t}\text{e}\text{g}\text{o}\text{r}\text{y}$$

The SSI, propounded by Pieri (2012), provides information on soil structure. The SSI was calculated using the formula given in Eq. (3).

$$SSI= \frac{SOM}{{S}_{c}+{C}_{c}}\times 100 \mathbf{E}\mathbf{q} \left(3\right)$$

Where,

$${S}_{c}=\text{S}\text{i}\text{l}\text{t} \text{c}\text{o}\text{n}\text{t}\text{e}\text{n}\text{t}$$

$${C}_{c}=\text{C}\text{l}\text{a}\text{y} \text{c}\text{o}\text{n}\text{t}\text{e}\text{n}\text{t}$$

Based on the value, SSI can be divided into four categories viz. SSI > 9% (structurally stable soils), 9 − 7% (low risk of soil degradation), 7 − 5% (high risk of soil degradation) and < 5% (degraded soils).

Graphing, Spatial mapping and Statistical analysis

For graphing, we used Origin Pro software 2022. QGIS software version 3.16 was used for the construction of maps. An interpolation function with an inverse distance weight method having a coefficient value of 2.0 was used for the spatial distribution of SSI on the campus. The statistical analysis involved the use of repeated one-way analysis of variance (ANOVA), network analysis, principal component analysis (PCA) and hierarchical cluster analysis (HCA). Repeated ANOVA was used to find significant differences among the physicochemical parameters of soil at different depths and among the different tree species (as used previously by Chia et al. 2020). Tukey's grouping letters denote significant differences among different soil depths. We also performed pairwise tests to look for significant differences among the soil parameters (both between different layers and between different tree species). Based upon the Pearson's correlation coefficient (r) values, network analysis was used to project the relationship among various soil properties. The PCA was used to determine the most critical parameters that mainly drive the soil fertility and structure. This multivariate technique also provided a new set of principal components (PCs) instead of original values by dimension reduction. Based on the similarity between soil properties, different tree species were clubbed together with the help of HCA. The Ward's linkage method was used to perform HCA.

The results were presented with minimum, maximum, mean and standard deviation values. One-way ANOVA was used to compare the mean value at different soil layers and between different tree species; multiple pairwise comparisons among means were also displayed.

Physicochemical parameters and OM

The findings for physicochemical characteristics, OM (Fig. 4a) and nutrients (Fig. 4b) of various soil samples are presented as box plots. The USDA soil triangle presented that most of the soil samples had loamy sand texture, followed by some samples in sandy loam class and few samples under C. lanceolatus and T. arjuna were sandy (Fig. 5). The average amount of sand was 84.05 ± 4.05%. Under F. virens and T. arjuna, the sand content's minimum (30.1–40 cm) and maximum (surface) values were reported, respectively. The maximum sand % was in L1. The mean sand content between different soil layers was not significantly different (Fig. 6a). The mean values (highest under P. pinnata) under different tree species were markedly different (p < 0.001).

The amount of silt varied from 4.80–8.89%. The lowest and highest silt percentages were found under F. virens at 10.1–20 cm and F. benjamina at 10.1–20 cm, respectively. The highest silt % was recorded in L2. Statistically, there was no overall significant difference among different soil depths (Fig. 6b). Like sand content, the mean silt (%) was also highest under P. pinnata, with a significant difference among different tree species (p < 0.001). The clay values had a mean value of 9.70 ± 4.69% and a range of 2.10–19.65%. For F. virens and T. arjuna, respectively, the highest and lowest clay contents were found. The clay values peaked in L4. Overall, no significant difference was observed between various soil layers for clay content (Fig. 6c). The highest clay (%) was recorded under T. arjuna samples and, with a statistical difference among tree species at p < 0.001. In the case of D. sissoo, surface soil had a pH value of 7.10 (the lowest).

The highest pH value, i.e., 8.40, was recorded under C. lanceolatus (30.1–40 cm) and T. arjuna (20.1–30 cm). The soils in L3 were the most alkaline. Among the different layers, the mean pH values were not significantly different (Fig. 6d). A mean pH value of 7.96 was recorded in the soil samples. The mean values suggested that soil under T. arjuna was most alkaline (8.20) and almost neutral (7.43) in the case of D. sissoo. Overall, there was a significant difference between the pH values of different tree species (p < 0.01). The soil under E. globulus had a minimum (0.11 dS/m) EC while F. benjamina had a maximum (1.04 dS/m) EC. The highest EC was for L4. The EC means of soil layers were not significantly different from each other (Fig. 6e). The mean EC values were 0.41 ± 0.27 (dS/m). C. lanceolatus soil had maximum (mean) values. A significant difference (p < 0.001) among different species was observed. P. longifolia (1.51 Mg ha^− 1) and D. sissoo (22.73 Mg ha^− 1), at 30.1–40 cm and surface levels, respectively, had minimum and maximum values for soil OM. L1 had the most OM. In general, the means for different soil layers were different at p < 0.005 (Fig. 6f). A mean value of was 9.88 ± 4.74 (Mg ha^− 1) observed for OM. This mean value was highest under F. benjamina, closely followed by D. sissoo. A significant difference between various average values was observed among different soil samples from distinct trees (p < 0.0005).

The SM was highest under F. virens (13.00%) and lowest under S. cumini (1.74%). L4 had the most SM (%). ANOVA revealed no significant differences among different layers (Fig. 6g). SM had an average value of 5.55 ± 2.80%. It was maximum in soil samples under F. virens. The means were significantly different among different tree species at p < 0.00001. The SWHC showed a wide variation, ranging from 20.61% under T. arjuna to 48.23% under F. virens. The SWHC peaked in L4. Different soil layers had similar means (Fig. 6h). The mean value for SWHC was 30.62%. It was highest under F. virens (43.84%). Like other physicochemical parameters, SWHC means differed among trees (p < 0.000001). F. virens 1.38 (g cm^− 3) and T. arjuna 1.55 (g cm^− 3) soils, respectively, had minimum and maximum BD values. Contrary to SM and SWHC, the BD peaked in L1 with no significant difference among layers (Fig. 6i). The BD had a mean value of 1.49 ± 0.04 (g cm^− 3) with a maximum value under P. pinnata and closely followed by P. longifolia. The tree species significantly differed among mean values (p < 0.00001). The SP was maximum under F. virens (50.75%) and minimum under T. arjuna (45.10%). Similar to SM and SWHC, the SP levels were highest in L4 and significant differences were observed (Fig. 6j). A mean value of 47.25 (%) was recorded for SP. F. virens soil samples had a maximum (mean) SP of 49.41%. The SP means significantly differed under different tree species (p < 0.00001). The mean values of physicochemical parameters and OM for different tree species are depicted in Online Resource 1.

Primary Macronutrients

N concentrations in the soil ranged from 14.25 to 219.75 Kg ha^− 1. Under D. sissoo surface soil (0–10 cm), the level of N was highest, and under P. longifolia soil (30.1–40 cm), it was lowest. Significant differences existed between the N values at various depths (p < 0.01), with peak values in L1 and also the source of variation (Fig. 7a). F. virens soil had the highest average N values, while P. longifolia had the lowest. The mean N values varied significantly throughout tree species. The P values ranged from 10.01 to 12.32 Kg ha^− 1. T. arjuna soil samples had maximum P content at 0–10 cm and S. cumini had minimum P at 20.1–30 cm depth. The P content was maximum in L1 and significantly different (p < 0.05) at various depths (Fig. 7b) and between trees as well (p < 0.005). Among all the trees, the highest mean P content was reported under T. arjuna and the lowest under S. cumini. A wide range from 41.20 to 778.27 Kg ha^− 1 was observed for K. E. globulus soil had the lowest K content at 30.1–40 cm depth, while T. arjuna soil had the highest K content at the surface (0–10 cm). The K values peaked in L1 with significant variation between layers (maximum between L1 and L4) (Fig. 7c). The mean K values were identical to the abovementioned species. The mean of K was also different (p < 0.05) for tree species.

Secondary Macronutrients

Among the secondary micronutrients like S, Ca²⁺ and Mg²⁺, S and Ca²⁺ had similar mean values, while Mg²⁺ had low values. The S had values ranging from 1.57 to 16.67 ppm. T. arjuna soil had the maximum of S at 30.1–40 cm depth and A. indica had a minimum S value at 30.1–40 cm depth. The mean S content topped in L4 with no significant differences among layers (Fig. 7d). The mean values for S were highest under C. lanceolatus and lowest under A. indica. The trees' average S values differed significantly (p < 0.05). The range for Ca²⁺ was 2.70-11.07 cmol kg^− 1. The maximum Ca²⁺ was recorded at surface soil (0–10 cm) under T. arjuna and the minimum under E. globulus (30.1–40 cm). The means of Ca²⁺ in different layers were significantly different (p < 0.005), with the highest values in L1 (Fig. 7e). T. arjuna and E. globulus soils had the highest and lowest mean Ca²⁺ values, respectively. The means had significant differences at p < 0.000001. Mg²⁺ concentrations ranged from 0.47 to 2.67 cmol kg^− 1. T. arjuna soil had the highest Mg²⁺ reported from surface soil, whereas E. globulus soil had the lowest Mg²⁺ reported from soil at lower depths, i.e., 30.1–40 cm. The levels of Mg²⁺ were maximum in L1 with significant differences (p < 0.05) among layers (Fig. 7f). The trees mentioned above also contained the mean maximum and minimum values, though the means varied by tree species.

Micronutrients

The analysis also included micronutrients like Cu, Fe, Mn, and Zn. Cu concentrations ranged from 0.13 to 0.93 ppm. P. longifolia soil had the lowest Cu levels at 30.1–40 cm depth, while F. benjamina soil had the highest Cu levels. The means of Cu were not significantly different along the depths and values peaked at L1. However, the means of L1 and L4 were significantly different (p < 0.05) (Fig. 7g). The differences among tree species were statistically significant (p < 0.001). Fe concentrations ranged from 2.23 to 25.35 ppm. The soil under F. benjamina, the amount of Fe was greatest at the surface soil, and under S. cumini, the amount was lowest at a depth of 30.1–40 cm. Similar to Cu, the mean values of Fe soil layers were not significantly different and maximum Fe content was recorded in L1 (Fig. 7h). The average values followed the same trend noted for the Cu levels. Different tree species had variability in means at p < 0.0005. The Mn content ranged from 0.28 to 4.14 ppm. The highest and lowest Mn values were found under D. sissoo at the surface and S. cumini at a depth of 30.1–40 cm, respectively. The means of Mn varied significantly (p < 0.05) with soil depths and L1 had the most Mn content (Fig. 7i). D. sissoo soil had the highest average value, while P. longifolia soil had the lowest average value. Significant differences among different tree species were observed. Zn values were between 0.14 and 2.12 ppm. The maximum Zn content was found in the surface soil under D. sissoo, and the lowest Zn content was found in the soil (10.1–20 cm) under C. lanceolatus. The means of soil layers were significantly different (p < 0.05), with a very high amount of Zn in L1 (Fig. 7j). The mean values were again highest under D. sissoo and lowest under of A. indica. Differences among the means of tree species were significant at p < 0.0005. The mean values of nutrients for different tree species are depicted in Online Resource 2. The results suggested that the 1st hypothesis of the study has two different scenarios; depending on soil characteristics, it can be conditionally accepted (in OM and most nutrients) and rejected (in physical properties, EC and some nutrients) as well.

SNIV, soil fertility and SSI

A composite SNIV was calculated from all 40 samples. Of all the 20 parameters, 16 were used for SNIV computation (Table 1). Among the three classes of ST, only sand had category III SNIV (3), indicating high soil fertility status. Silt and clay had SNIV of 1 each and low soil fertility. The pH for most samples was in the medium to high category, with an overall SNIV of 2.62 and increased soil fertility. EC, OM, N and P each had SNIV of 1 (low soil fertility). The SNIV for K was 2.15, suggesting medium levels of soil fertility. Among the secondary macronutrients, S and Mg²⁺ were in category I (low fertility), while Ca²⁺ was in category III (SNIV of 2.82 and high fertility). Cu and Fe had high SNIV (high fertility) among the micronutrients. The medium fertility of Mn (SNIV of 1.75) and the low fertility of Zn (SNIV of 1.62) were identified. The 2nd hypothesis of the study was rejected due to the degraded and low fertility of soils, and an alternative hypothesis was accepted. The SSI values were only in two ranges (< 5.0 and 5.0–7.0%). The SSI values from the samples collected under five tree species viz. Eucalyptus globulus, Ficus virens, Pongamia pinnata, Syzygium cumini and Terminalia arjuna were at high risk of degradation (5.0–7.0%). The remaining soil samples were structurally degraded with SSI values < 5%. More than 80% of the area on the campus has structurally destabilized soils (Fig. 8).

Table 1

SNIV and soil fertility status for different soil properties
S. No.	Parameter	Category			SNIV	Category	Soil fertility status
S. No.	Parameter	Low	Medium	High	SNIV	Category	Soil fertility status
1.	Sand (%)	< 16.6	16.6–39.2	> 39.2	3	III	High
2.	Silt (%)	< 28	28–47	> 47	1	I	Low
3.	Clay (%)	< 22.7	22.7–46	> 46	1	I	Low
4.	pH	< 6.0	6.0–8.0	> 8.0	2.62	III	High
5.	EC (dS/m)	< 1.0	1.0–2.0	> 2.0	1.07	I	Low
6.	OM (%)	< 0.64	0.64–1.45	> 1.45	1.60	I	Low
7.	N (Kg ha^− 1)	< 280	280–560	> 560	1	I	Low
8.	P (Kg ha^− 1)	< 11	11-24.6	> 24.6	1.32	I	Low
9.	K (Kg ha^− 1)	< 123	123–296	> 296	2.15	II	Medium
10.	S (Kg ha^− 1)	< 10	––––	> 10	1.60	I	Low
11.	Ca²⁺ (cmol kg^− 1)	< 1.5	1.5–4.5	> 4.5	2.82	III	High
12.	Mg²⁺ (cmol kg^− 1)	< 1.5	1.5–4.5	> 4.5	1.32	I	Low
13.	Cu (ppm)	< 0.20	––––	> 0.20	2.60	III	High
14.	Fe (ppm)	< 2.5	2.5–4.5	> 4.5	2.65	III	High
15.	Mn (ppm)	< 1.0	––––	> 1.0	1.75	II	Medium
16.	Zn (ppm)	< 0.5	0.5-1.0	> 1.0	1.62	I	Low

Network Analysis

The colour band (based on 'r' values) in the network among different soil properties depicts the strength of association (Fig. 9). The broader lines indicate a strong correlation (both positive and negative) and narrow lines suggest a weak correlation. The age of tree plantations did not affect any of the soil parameters. Sand content was only positively associated with silt content. It was almost perfectly and negatively correlated with clay and SWHC. A significant negative correlation of sand with parameters like OM, N and Cu. Like sand, silt content also had the exact correlation with the parameters mentioned above except Cu (where the correlation was negative but insignificant). The clay content was negatively associated with sand and silt. The clay content and SWHC were precisely and positively associated with each other. OM, N and Cu also had a positive correlation with clay. The pH and EC values had no significant bearing on other soil properties. The OM values were positively associated with SWHC, N, P and Cu. The SM was positively and negatively correlated with SP and BD, respectively. A positive association of SWHC with N and Cu was also observed. Apart from SM, the BD negatively correlated with SP as well. P and Cu constructively accompanied the N. The P was positively coupled with K, Ca²⁺, Mg²⁺ and Cu. The K also showed a positive coupling with Ca²⁺ and Mg²⁺. Nutrients like S, Fe and Mn were not linked to physical or chemical properties. The Ca²⁺ and Mg²⁺ also showed a mutual positive correlation. A significant positive linkage between Cu and Zn was also observed. The 'r' values for the network are in Online Resource 3.

PCA and HCA

All the estimated soil parameters and sampling sites were put through PCA to further confirm and determine the most critical parameters affecting soil chemistry. The PCA yielded five major PCs having Eigenvalues of more than 1. These 5 PCs explained more than 83% of the variance in data, with maximum variance provided by PC1 (37.65%) and reduced variance by four other PCs. Due to significantly less dimension reduction and less variance explanation, the rest of the 12 PCs were not considered (Online Resource 4). Out of the five significant PCs, the 1st three were used for presenting a 3D PCA biplot with loadings for soil parameters and scores for sampling sites (Fig. 10). Initially, the loading values were obtained from a correlation matrix (Online Resource 5). For plotting, the loadings were rescaled to the level of scores from sampling sites (Online Resource 6).

The N, P, Cu and OM significantly contributed to the 1st PC. Apart from these, K, Ca²⁺ and Mg^2+, in decreasing order of their significance, were loaded positively in 1st PC. Sand, silt and BD loaded negatively in the 1st PC. The 2nd PC explained a 20.21% variance with SWHC (negatively correlated with factor) as a dominant parameter. The Mn and sand loaded positively with the 2nd factor. Clay loaded in the 2nd PC with the same strength as sand but with an effect in the opposite direction. The 3rd PC accounted for 12.19% of the variance. In this PC, S, SM, SP, pH and silt were the prominent variables that loaded constructively, with S being the dominant one. Around 7% of the variance was explained by 4th PC. The variables that positively affected this factor were Zn, Cu and S, while Mg²⁺, Ca²⁺ and K were loaded in opposite directions. The 5th primary PC showed a 6.49% of the variance. The pH, S and EC were this PC's primary and positive loaders, while SP, N and OM were the negative loaders. For sampling sites at different depths, Ds1, Fv1, Fv2 and Ta1 had a significant (positive) impact on PC1, while Eg2, Pl4 and Pp4 were on the negative axis. In 2nd PC, Ai1, Ds3 and Ta1 loaded constructively and Fv4 (maximum loading), Fb4 and Fv3 were loaded in opposite directions. The 3rd PC for sites was attributed to Ta1, Cl4 (positive) and Ds1 (negative). Unlike 1st three PCs, the variables in 4th and 5th PCs had less bearing on factors. Apart from correlation among sites at different depths, the affinity between overall (mean) soil samples was measured with HCA. A total of 3 significant clusters and two minor clusters were obtained from HCA. The 1st and 2nd major clusters had no minor clusters, while the 3rd cluster had two minor ones. The 1st cluster consisted of A. indica, F. benjamina and F. virens. D. sissoo and T. arjuna were part of 2nd cluster. The rest of the tree species were part of 3rd primary cluster. Tree species like P. pinnata and S. cumini were part of 1st minor cluster, while, C. lanceolatus, E. globulus and P. longifolia were contained in 2nd cluster (Fig. 11).

Physicochemical parameters and nutrient distribution

A total of 40 soil samples under different tree species at four depths were analyzed for various physicochemical properties, OM and nutrients. Our results demonstrate an effect of tree species on the distribution of soil nutrients. The effect of different variables on soil structure and fertility is explained in the following section.

The mean value of all parameters among different tree species did not exhibit a uniform or typical pattern across the four depths. Contrary to these results, the soil properties in natural forests may follow an increasing or decreasing trend (Sharma et al. 2021) or maybe non-uniform (Zhu et al. 2021). The non-uniform parameter variation on the campus may be attributed to anthropogenic activities. Water conditions, dumping, filling, regular planting and uprooting of trees are some of the anthropogenic activities that regulate soil properties (Santorufo et al. 2021). Long-term soil structure changes are a result of such activities. Unlike natural forests where mainly rain and groundwater are water sources, the soils in the urban areas receive water from houses, sewage and industries and artificial watering. The stability of the soil is impacted by these activities, and the soil may be enriched with nutrients through the artificial application of various fertilizers.

Depending upon the texture of the soil, the water may remain on the surface (clayey soils) or seep down (sandy soils) to further layers and disturb the soil ion balance. Due to the high sand content in the samples, more than 70% of the soils were sandy loam or loamy sand in texture. This is because soils in semi-arid areas have less water and often are sandier in texture. A few soil samples were sandy in texture. This could be due to the transportation of soils from other sources (de Nijs and Cammeraat 2020). The variations could also arise due to different parent rock material, vegetation and micro-climatic conditions (Silver et al. 2000). The maximum sand % in L1 may be attributed to weak soil aggregate structure (Sadiq et al. 2021). Eluviation and Illuviation processes significantly affect the accumulation of clay at lower depths and higher clay content at subsoils (L4), indicating rapid chemical weathering at surface soils (Akinbola et al. 2009). The pH range of all the soil samples suggests that soils in the study area are either neutral or highly alkaline. This minor variation in the pH values could be due to differences in the parent material of the soils and the dissolution of other bases in water (Kumar 2022). Although inconclusive, the pH values increased with depth up to L3 and then decreased. The accumulation of basic cations and quick replacement of H⁺ by Ca²⁺ may increase soil alkalinity (Ogbodo 2011). Low rainfall is another factor that may result in high pH (Singh et al. 2014a). The results of EC indicate that soils are non-saline and the concentration of soluble salts is less, similar to the results obtained in other studies (Katsube et al. 2003). Highly saline soils are the common hindrance to the growth of plants (Drake et al. 2016). A few samples had high EC attributed to soil pollution and leachate (Kumar et al. 2023a). Also, due to dense habitation, the point sources on the national highway (NH-9) may contributed to elevated EC levels.

Irrespective of soil layers and tree species, the OM is relatively low on campus. The litter fall from trees in the form of branches, twigs and leaves continuously adds OM to surface soils (Xu et al. 2022), but the regular cleaning of litter does not allow it to leach further, thus disturbing internal biogeochemical cycles (Yinga et al. 2022). As reported previously, the clay content directly affected OM levels (Mondal et al. 2021). The presence of herbs (Deb et al. 2019) and less disturbed sites tend to have more OM. Higher soil erosion rates (Baligh et al. 2021) and increased OM mineralization (Luo et al. 2017) also decrease OM in soil. The positive correlation of clay with SWHC also presented it as a significant regulator of OM.

The SM in the sandy soils ranges from 3–10% and most of the samples in the study were in this range only. As some samples had more clay content, the SM increased to 14%. Also, none of the samples were very high in clay content, so the SM levels remained below 20% (lower limit of SM for clayey soils) (Brandt et al. 2017). Further, biomass and composition of soil microbes also affect SM (Ma et al. 2015). The SWHC increased with depth and clay content. The larger macropores in soil can significantly contribute to enhanced levels of SWHC (Debnath et al. 2012). Except for F. virens, SWHC for soil samples was between 25–35%. Similar results have been obtained by Dhindsa et al. (2016) in the wheat cropping system. The BD levels were attributed to SM and SP. Both these affected the BD negatively. The higher sand content in soil contributes to larger values of BD (Deb et al. 2019). The lower OM content may also increase soil BD (Awad and Al-Soghir 2023). The results are close to other findings (Dutta 2022). Except for one sample, the SP content was less than 50%. Samples with high clay % have more SP (Yu et al. 2022). Ali et al. (2010) demonstrated a positive effect of OM on SP.

The soil samples under different depths were also analyzed for ten nutrients (mentioned in the results). N is the primary element in plants after C, Hydrogen (H) and Oxygen (O₂), as it helps the plant in development and reproduction (Ren et al. 2014). In our study, all the analyzed samples were categorized as low in N content. The maximum N, Mn and Zn content (at surface level) in the case of D. sissoo was attributed to very high litterfall from the tree on the surface soils. Moreover, the sampling site was much less disturbed, i.e., no planting, uprooting, or cultivation of plants. These factors may increase soil SOC, ultimately increasing soil N availability (Mandal 2022). Further, a similar positive association of N with OM has been reported by (Mehraj et al. 2022) and (Wang et al. 2013). The N content may also increase with the increase in the clay content of the soil (Mathewos et al. 2023).

Further, soils with higher clay content have more extractable Fe levels (Kumar et al., 2020). The N content positively correlated with Cu could be explained based on previous studies that involved elevation in the amount of Cu levels upon application of N (Snowball et al. 1980). The P and K, both primary macronutrients and mobile in nature, shared a positive correlation. The P content in most of the samples was in the low category. The maximum P concentration was recorded in T. arjuna and the minimum in the case of S. cumini. The semi-arid environmental conditions and use of fertilizers may lead to the build-up of the P in soil and may result in high P concentration in the soil (Satish et al. 2018a; Sashikala et al. 2021), but the regular disturbance of soil from human activities and transportation of soil from one place to another may reduce soil P content. The medium P content in some samples may also be attributed to the decomposition of SOM that releases organically bound P (Kumar et al., 2021). The N and P had a positive effect on each other, as observed by (Kumar et al. 2023b). In T. arjuna, the K content was highest, whereas in E. globulus, it was lowest. All samples—aside from E. globulus, which has a low K content—were either rated as medium (P. pinnata, C. lanceolatus, P. longifolia, and S. cumini) or high (the remaining tree species). For horticultural purposes, a significant amount of K is needed (Kumar et al. 2020) and on campus, regular ornamental plantation takes place, which may increase the K availability (Kenney et al. 2002) in the soil. Further, minerals like Illite, Biotite and Microline, K-rich feldspars and mica in the rock material may add K to the soil (Pandey et al. 2020).

The secondary macronutrients like S, Ca²⁺ and Mg²⁺ are pivotal in protein and oil formation, promoting nodules, maintaining structural integrity through the cell wall and cell membrane stabilization, chlorophyll formation and enzyme activation. The tree species like C. lanceolatus (highest S content), F. benjamina and T. arjuna had sufficient levels of S. In contrast, the remaining tree species were categorized as S deficient. The mean S content was reported as lowest in the case of A. indica. The low amount of S under most of the tree species may be attributed to higher fractions of sand in soils, lower SOM and a consistent decrease in the use of organic manures (Pandey et al. 2020). The S in soil was negatively correlated with N, K, Mg and Mn. The primary reason for this inverse relationship may be due to the mobilization of heavy metals due to increased S levels in the soil (Skwierawska et al. 2012). The Ca²⁺ and Mg²⁺ were in high and medium concentrations; a few tree species had very high levels of Ca²⁺ (T. arjuna). The amount of Ca²⁺ and Mg²⁺ in the soil is affected by leaching (Dutta 2022). The soil erosion and harvesting process may also affect Ca²⁺ and Mg²⁺ (Sharma 2022). The nutrients like Mg²⁺, Ca²⁺ and K had a robust positive correlation. This could be due to the mobile nature of Mg²⁺ and K. Also, Mg²⁺ and Ca²⁺ closely compete with each other during absorption in the soil.

Mn is a crucial micronutrient in the soil that takes part in plant photosynthesis. The majority of the samples were medium in Mn content. The tree species such as P. longifolia and S. cumini were Mn deficient. The probable reason might be the presence of concrete roads that may have hindered the Mn uptake by plants. The Mn concentration in the case of P. longifolia might be low due to less litterfall under the tree. Soil pH, aeration, SOM and other soil physical properties influence the Mn concentration. The chelation process and biomass recycling may also lead to higher levels of Mn in the soil (Kumar 2022; Keskinen et al. 2023). Manganiferous minerals can contribute to high Mn levels in the soil (Dhaliwal et al. 2023).

Though most of the tree species had an excellent Zn amount in the soil, P. longifolia and S. cumini had low Zn content for the above reasons. C. lanceolatus (low in Zn concentration) was sampled in a park, where regular surface crushing of the soil may have impacted the Zn levels in the soil beneath the tree species. The higher amount of Zn in most soil samples may be attributed to higher SOC (Singh et al. 2014c). The low Zn content in some soils may be due to the precipitation of Zn in the form of hydroxides and carbonates (Satish et al. 2018b).

Micronutrients like Cu and Fe regulate the plant redox chemistry and help in mitochondrial respiration and various other metabolic pathways. All the soil samples (except soil under P. longifolia, classified as deficient) were sufficient in Cu levels. The maximum Cu content was reported in F. benjamina. The variability in the Cu concentration on the study site could be due to different SOC values, the nature of parent rock material, soil management and filling and erosion of soil layers due to wind and water (Kumar 2022). The majority of the soil samples were classified as sufficient in Fe levels. Some soil samples collected under tree species such as A. indica, F. virens and S. cumini were deficient in Fe levels. The lower amount of Fe in some samples may be linked to higher precipitation rates of Fe by CaCO_3, which may have decreased the Fe availability in the soil (Sashikala et al. 2021). The higher rates of litter decomposition are also linked with higher availability of Fe in soil (Chakrawal et al. 2024).

Soil fertility, SSI and PCA

According to SSI, the soils are structurally degraded. The low OM in soil may be attributed to the degradation of soil structure because OM improves soil cohesion, porosity, water retention and overall soil structure (Walsh and McDonnell 2012). By reducing the water filtration rate, the degraded soils decrease plant water availability (AbdelRahman et al. 2022). The structurally altered soils also face a high erosion risk (Piaszczyk et al. 2019). Five soil parameters, sand, pH, Ca²⁺, Cu and Fe, had high fertility index in the present study. These parameters were compared to some recent and similar studies (Table 2). Sand, Cu and Fe (wherever computed) were precisely similar to our results, suggesting similar soils in different regions. In different regions, the pH and Ca²⁺ varied from low to high. The major loaders in 1st PCA viz. N, P, Cu and OM were also compared with other findings (Table 3). Except in a study by (Sharma et al. 2021), all the variables of the present investigation were presented as major loaders but in a different order of significance. The N, P and OM were present as major loaders in two studies.

Table 2

Comparison of high soil fertility parameters with other findings
S. No.	Study site	Soil fertility or quality					Reference
S. No.	Study site	Sand	pH	Ca²⁺	Cu	Fe	Reference
1.	Suketi Basin (Himachal) India	–––	Medium	Medium	High	High	Kumar et al. (2023)
2.	Upper Kabete Campus coffee farm, University of Nairobi, Kenya	–––	Low	High	High	High	(Mwendwa et al., 2022)
3.	Compound Farms in Nsukka Campus, Nigeria	High	Medium	Medium	–––	–––	(Okorie et al., 2022)
4.	Universitas Pendidikan Ganesha campus (Singaraja) Indonesia	High	Medium	–––	–––	–––	(Gunamantha et al., 2021)
5.	College of agriculture and research station Kurud (Chhatisgarh) India	–––	Medium	–––	High	High	(Sahu et al., 2021)
6.	Agroecological regions (Punjab) India	High	High	Low	High	High	(Garnaik et al., 2020)
7.	Rajiv Gandhi South Campus (Uttar Pradesh) India	–––	Medium	–––	High	High	(Kashiwar et al., 2018)
8.	Paulownia fortunei plantations, China	–––	Low	Medium	High	High	Tu et al. (2017)

Table 3

First PCA variables comparison with other studies
S. No.	Study site	1st PCA variables^†				Reference
1.	MDU	N	P	Cu	OM	Present study
2.	Suketi basin (Himachal) India	N	OC	Mg²⁺	pH	Kumar et al. (2023)
3.	Agro-Climatic Zones (Karnataka) India	Cu	Mn	OC	Fe	(Lalitha et al., 2022)
4.	Shiwalik range, India	clay	silt	sand	Fe	(H. Sharma et al., 2021)
5.	Beni-Amir (Tadla plain) Morocco	silt	sand	Ca²⁺	pH	(Oumenskou et al., 2019)
6.	Paulownia fortunei plantations, China	Mg²⁺	Ca²⁺	P	K	Tu et al. (2017)
7.	Qinghai-Tibetan Plateau, China	N	pH	P	Ca²⁺	(Li et al., 2013)
^†From left to right, the variables are in decreasing order of their significance

The present study investigated the status of various physicochemical parameters and soil nutrients at MDU, Rohtak. Different type of plantations exhibits varied soil physicochemical properties. The USDA soil triangle revealed that most analyzed samples had a loamy sand texture. The SSI suggested that the soil on the campus is structurally degraded (due to extremely low levels of clay). Further, the soils are alkaline and non-saline. The SNIV indicated an urgent need to address problems with soil fertility. The SNIV was high for sand (toxic), pH, Ca²⁺, Cu and Fe; only K and Mn were categorized as medium. The rest of the physicochemical parameters and nutrients had lower values of SNIV, indicating low fertility. The soil under tree species like D. sissoo, T. arjuna and F. benjamina had high OM and mean nutrient content, while A. indica, E. globulus and S. cumini had a lower concentration of nutrients. The variation in the soil parameters with increasing depth was non-uniform. Except for OM, no other physicochemical property showed a significant difference among different layers. The soil nutrient values varied significantly along the various depths. All the 20 soil parameters demonstrated a marked difference among different tree species. This difference was more pronounced in the case of soil nutrients. The soil's physical properties remarkably affected the nutrient status in the soil and to some extent, nutrients showed a correlation among themselves as well. The PCA revealed that N, OM, P and Cu are major soil parameters affecting soil chemistry. The HCA produced three primary clusters for the grouping of tree species. Overall, the soils on the campus are almost degraded structurally and nutrient-poor. These soils having a nutrient deficiency can be made fertile again with proper management practices. We identified some of the local, easy-to-use and eco-friendly solutions to these problems, as depicted in Fig. 12. The culmination of the recommended practices and the present state of efforts by the university administration can help the authorities upgrade the status of soil's health on the campus. This may also help other institutions worldwide to undertake similar research and join hands in the coveted process of campus sustainability.

C	Carbon
SOM	Soil organic matter
OM	Organic matter
N	Nitrogen
K	Potassium
CO₂	Carbon dioxide
P	Phosphorus
Ca²⁺	Calcium
BD	Bulk density
S	Sulphur
Mg²⁺	Magnesium
Cu	Copper
Fe	Iron
Mn	Manganese
Zn	Zinc
MDU	Maharshi Dayanand University
L	Layer
ST	Soil texture
SM	Soil moisture
SWHC	Soil water holding capacity
SP	Soil porosity
SOC	Soil organic carbon
EC	Electrical conductivity
SNIV	Soil nutrient index value
SSI	Structural stability index
ANOVA	Analysis of variance
PCA	Principal component analysis
HCA	Hierarchical cluster analysis
PCs	Principal components
OC	Organic carbon

Funding: No funding was received to assist with the preparation of this manuscript.

Conflicts of interests: The authors have no competing interests to declare that are relevant to the content of this article.

Ethical responsibility: All authors have read, understood, and have complied as applicable with the statement on "Ethical responsibilities of Authors" as found in the Instructions for Authors and are aware that with minor exceptions, no changes can be made to authorship once the paper is submitted.

Data availability: Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Author Contributions Statement: Abhishek Nandal: Conceptualization, Investigation, Data curation, Experimentation, Written original draft and Statistical Analysis. Sunita Rani: Conceptualization, Review, Editing and Statistical Analysis. Surender Singh Yadav: Conceptualization, Supervision, Validation, Review & Editing. Naveen Kaushik: Review, Editing and Statistical Analysis. Naveen Kataria: Experimentation, Review and Editing. Pritam Hasanpuri: Experimentation, Review and Editing. Rattan Lal: Conceptualization, Review & Editing.

Acknowledgements

The Council of Scientific & Industrial Research (CSIR), New Delhi is gratefully acknowledged by the first author for its financial support. The University Grants Commission (UGC), New Delhi, SERB-DST, Government of India, New Delhi, DST-FIST, New Delhi, and Haryana State Council for Science and Technology (HSCST) are all acknowledged for their financial support of the corresponding author.

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Soil quality and health under different tree species in an urban university campus: A multidimensional study

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Journal Publication

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Abstract

Figures

INTRODUCTION

MATERIAL AND METHODS

Physical parameters

SOM

Chemical parameters

Soil nutrient index value (SNIV) and Soil stabilization index (SSI)

Graphing, Spatial mapping and Statistical analysis

RESULTS

Physicochemical parameters and OM

Primary Macronutrients

Secondary Macronutrients

Micronutrients

SNIV, soil fertility and SSI

Network Analysis

PCA and HCA

DISCUSSION

Physicochemical parameters and nutrient distribution

Soil fertility, SSI and PCA

CONCLUSIONS

Abbreviations

Declarations

References

Additional Declarations

Status:

Journal Publication

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