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Article

Variations in Soil Biological and Biochemical Indicators under Different Grazing Intensities and Seasonal Changes

by
Nadia Kamali
1,*,
Ahmad Sadeghipour
2,
Mahshid Souri
1 and
Andrea Mastinu
3,*
1
Rangeland Research Division, Research Institute of Forests and Rangelands, AREEO, Tehran 13185-116, Iran
2
Desert Studies Faculties, Semnan University, Semnan 35131-19111, Iran
3
Department of Molecular and Translational Medicine, Division of Pharmacology, University of Brescia, 25123 Brescia, Italy
*
Authors to whom correspondence should be addressed.
Land 2022, 11(9), 1537; https://doi.org/10.3390/land11091537
Submission received: 29 August 2022 / Accepted: 6 September 2022 / Published: 11 September 2022
Browse Figures
Versions Notes

Abstract

:
Knowledge of variations in soil biological and biochemical indicators with grazing and seasonal changes is important for assessing soil quality and required management. Lack of proper management could induce irreversible damage to the soil structure; therefore, a seasonal experiment was carried out in Salook National Park, Iran; arranged in a factorial-based randomized complete block design (RCBD) in all seasons of a year. The study area had three plots including a no-hunting area, national park, and protected area. Our data showed that overgrazing has altered the chemical–physical components of the soil with effects on the soil microbiome. The most affected areas are those subjected to the hunting ban and in the summer season. It could be concluded that low grazing intensity while protecting the soil ecosystem structure would increase the biochemical and biological characteristics of the soil and provide adequate conditions for providing forage to the natural herbivores living in this area. In order to preserve the biological resources of the region, it is strongly recommended that the level of protection be increased, especially in areas where hunting is prohibited.

1. Introduction

Natural ecosystems are threatened by natural and human-induced changes, such as drought, overgrazing, overcultivation, etc. [1,2,3]. The harsh climatic conditions of Iran have caused the arid and semi-arid areas to have little vegetation cover. On the other hand, traditionally, people’s lives in arid and semi-arid areas of Iran have benefited from animal husbandry. In most places, due to high stocking rate and traditional free grazing systems, severe destruction of vegetation is observed [2,3]. The study of Iran’s climatic conditions in a period of fifty years shows that the conditions have become difficult (increasing temperature, changes in the pattern of precipitation, changes in the type of precipitation, etc.) [4], which can have a great effect on the soil biological characteristics and vegetation cover [5,6,7,8,9,10,11,12,13].
In order to protect soil, vegetation, and water resources, different protection levels have been defined in Iran’s environment. In the study area, three levels of protection are observed, including the national park, the protected area, and no-hunting area. A national park is an area in which no human exploitation takes place due to its national and global importance and is only grazed by wildlife. A protected area is an area where the only human exploitation that takes place is 60 days of grazing by nomadic livestock. A no-hunting zone is an area where human exploitation, such as agriculture and mining, is allowed and it is grazed by nomadic and rural livestock throughout the year and only wildlife hunting is prohibited [14].
An ecosystem is the result of the activity of all living and non-living components, and Eldridge and colleagues reported that grazing reduced ecosystem structure (by 35%), function (24%), and composition (10%) [15]. Grazing exerts important effects on plant biodiversity [16,17]. It could change the composition of dominant plants and reduce the quality and quantity of inputs to the soils [18,19]. At the same time, grazing can act as an input in the production of organic matter that allows an increase in the soil’s organic carbon stocks [20]. However, overgrazing can drastically change soil chemistry. Indeed, it was observed that in semi-arid environments the concentrations of organic carbon, total nitrogen, and total sulfur decreased significantly with increasing grazing intensity [21]. According to many authors, this chemical–physical alteration is attributable to the combined effect of the trampling of animals, the reduction in the input of organic matter above and below the soil, and the growth and erosion of the roots because of grazing [21,22,23,24,25,26,27,28].
The decrease in vegetation cover due to excessive grazing causes soil erosion and rapid evaporation of moisture [29]. Soil can become compacted by trampling, resulting in higher bulk density which reduces the pore space of the soil and restricts the movement of water and oxygen [30]. Good soil oxygenation can help improve the microbiological and inorganic components [31,32]. Indeed, reduced aeration increases the risk of anaerobic conditions, modifying biogeochemical cycles and increasing greenhouse gas emissions [33]. Microbial biomass carbon (MBC) is another factor affected by grazing intensity. Indeed, soil microbes, bacteria and fungi, are vital to the sustainable functioning of natural and managed ecosystems, as they exist in enormous numbers and have an immense cumulative mass and activity [34,35,36]. Soil microbial biomass, as a living part of soil organic matter (SOM), functions as a transient nutrient sink and is responsible for decomposition and transformation of organic materials, which are mostly derived from above- and below-ground plant residues, and releasing nutrients from organic matter which are used by plants [37]. The microbial compartment is responsible of more than 80% of the degradation of soil organic matter and plays an essential role in soil function and soil C storage. Microbial biomass carbon as an active component of soil organic carbon regulates biochemical processes and is very sensitive to environmental changes [38,39]. Grazing-induced changes in litter input and erosion could influence soil organic matter content and a higher MBC concentration suggests an enormous potential for carbon sequestration in national parks and protected area ecosystems. The volume of MBC and other soil biochemical characteristics is altered by seasons, which results from the changes in soil and air temperature variations [40,41,42].
Regarding the above-mentioned issues, the goal of this paper is to evaluate the soil biological and biochemical indicators affected by different grazing intensities and seasonal changes. It is already known that different levels of protection affect the biological and biochemical properties of the soil and vegetation. However, given the ability of governments to increase the level of soil protection when defaced by human activity, it is essential to define the phases of soil improvement in an ecologically fragile area such as the one studied. Loss of plant biodiversity often results in nutrient deficiencies that cannot be restored with urine and livestock manure.

2. Materials and Methods

2.1. Study Area

The study area, namely the Salook region, is located 20 km northwest of the city of Esfarayen, North Khorasan province, Iran. The geographical location of this region is 37°15′ to 38°8′ north latitude and 57°16′ to 57°6′ east longitude. Salook includes a national park (6317 ha), protected area (7654 ha), and no-hunting area (11,677 ha) (Figure 1). This region has been used by humans and livestock for many years, but in recent decades, restrictions have been established to prevent destruction. So, the protected and no-hunting areas were established 49 years ago, and a part of them was separated as a national park 44 years ago. All three regions have the same geomorphological structure and pedological characteristics; each is divided into two units: mountain and plain, of which more than 60% is in the mountainous region and the rest in the plain region. The mountain unit includes mountains with medium height and rounded to sharp tips, with a slope of 8 to 40%, a height of about 800 to 1500 m and sometimes more, consisting of conglomerate with less than 50% rock outcrop, with semi-deep soils and good vegetation cover, and surface erosion is moderate. The classification of the soil according to the US soil taxonomy classification system is: sandy, mixed, mesic, Lithic Torriorthents. The plain unit includes plateaus and upper terraces with a low to medium height with a slope of 5 to 8% and sometimes more, which is formed from young alluvium. The soil of these lands is deep and has little gravel, good vegetation, and moderate water erosion. The classification of the plain’s soil according to the US soil taxonomy classification system is: sandy, mixed, mesic, Typic Xerorthents. Some of the landscapes of this area are shown in Figure 2. Livestock that graze in the region is Kurdish native Khorasan sheep. This region has a large diversity of plant and animal species and the extent of this diversity has prompted distinct levels of conservation management in the region. The climate of this region is more affected by subtropical high-pressure systems, Azores high-pressure, western winds, Siberian, and Mediterranean high-pressure systems. The mean annual precipitation of the case study is 283 mm, and the mean temperature is 12 °C; the mean of the minimum and maximum temperature is 6.5 and 18 °C, respectively.

2.2. Experimental Design

The studied sites were supposed as the first factor and divided into three sections, including a no-hunting area (just free grazing of nomadic livestock takes place in all seasons), national park (only grazed by wild animals), and protected area (grazed by nomadic livestock for 60 days of the year; about 22 May to end of July). General information of the study area is shown in Table 1. The sections were marked at five points and the entrance of the nomadic livestock was strictly controlled. The second factor was the variations during four seasons.

2.3. Soil Sampling

Seasonal soil sampling was regularly carried out on the fifteenth of the second month of each season and biological characteristics were recorded. At each management level (no-hunting area, national park, and protected area), three large plots were considered in homogeneous units (with the same physiographic conditions (aspect, slope, elevation)), and in each large plot, five profiles were randomly drilled (15 profiles) to a depth of 30 cm. The soil samples were transferred to the laboratory for measurement of physical, chemical, and biological properties.

2.4. Laboratory Measurements

The soil samples were stored in two ways: part of each sample was air-dried, powdered, and passed through a 2 mm sieve to measure physical and chemical properties. The other part was placed at 4 °C for measurement of the biological properties of the soil. Soil physical and chemical properties were measured once during this study while biological properties were studied in each season of the year.

2.5. Physical Properties

The clod method was used for soil bulk density measurement [43]. Using bulk density and soil particle density (2.65 g/cc), soil porosity was calculated [44]. Mean weight diameter (MWD) was calculated for each soil using a sieve and shaker and the following formula by [45]:
M W D = ∑ i = 1 n x i w i ,
where xi is the mean diameter of each size range of aggregates separated by sieving, and wi is the weight of aggregates in that size range as a fraction of the total dry weight of soil used.

2.6. Chemical Properties

Soil electrical conductivity (EC) and soil pH were measured using an Orion Ionalyzer Model 125 901 in a 1/2.5 soil/water solution. Soil organic carbon and total nitrogen were determined by the Walkley–Black [46] and the Kjeldahl acid digestion methods [47], respectively. Soil potassium and total calcium were determined using the normal ammonium acetate [48] and EDTA titrometry methods [49], respectively. Additionally, available phosphorus was measured by the Olsen method [50].

2.7. Biological Properties

2.7.1. Fungal Biomass

Fungal biomass was determined by the volume of ergosterol. In this way, ergosterol of soil fungi was soaped with KOH, then extracted by N-hexane in a separating funnel and dried at 40 °C on a rotary evaporator and finally dissolved in methanol. The amount of ergosterol was measured at 282 nm by HPLC [51].

2.7.2. Basal Soil Respiration

To do this, 50 g of wet soil was transferred to sealed containers. Then, by adding 20 mL of 0.5 M sodium hydroxide, the samples were kept separately in containers for 24 h at 25 °C. Finally, the amount of released carbon dioxide was measured by titration with 0.25 normal acid and the amount of C-CO2 in mg/kg of dry soil was calculated [52].

2.7.3. Microbial Biomass Carbon

A fumigation–extraction method was used to measure the microbial biomass carbon [53]. For this purpose, 25 g of moist soil from each sample was fumigated with chloroform for 24 h. The chloroforms of the samples were then removed by vacuum and the fumigated and non-fumigated soils (one part) were shaken separately with 0.5 M potassium sulfate solution (5 parts extract) and then shaken for 30 min. After adjusting the pH of the samples, their organic carbon was estimated by a wet oxidation method using sodium and hydrochloric acid in the range of 6.5 to 6.8.

2.7.4. Microbial Biomass Carbon to Microbial Biomass Nitrogen Ratio (MBC/MBN)

Microbial biomass nitrogen was also measured by fumigation with chloroform and ammonium and nitrate were measured by a colorimetric method. The results were presented as weight (mg.kg−1soil) and, finally, the ratio of carbon to nitrogen of the microbial biomass was obtained [53].

2.7.5. Microbial Metabolic Quotient

This index was calculated from the division of microbial biomass carbon into organic carbon. Soil organic carbon was calculated by the wet oxidation method [54].

2.7.6. Metabolic Coefficient (qCO2)

This index was calculated as the carbon dioxide released from the respiration of each living microbial unit per unit of time.

2.7.7. Arbuscular Mycorrhizal Spore Number (AMSN)

At the beginning and end of the growing season, soil sampling of the dominant rhizosphere (Artemisia aucheri) was performed in all studied areas. Fifteen samples from each treatment were taken to the laboratory to count the number of spores. To count the total number of arbuscular mycorrhizal fungi in the soil, samples were dried in air and then sieved at 2 mm. Then, the number of spores in a certain amount of soil was counted using the Gerdemann–Nicolson method (1963) under a microscope [55].

2.8. Substrate-Induced Respiration (SIR)

To measure the substrate-induced respiration (SIR), 100 g of soil was poured into a one-liter plastic container. Then, 2 cc of 1% glucose solution was added as a substrate to each container and 10 cc of sodium hydroxide 0.5 normal was immediately placed in the 1 L containers with soil, then, the lid was tightly closed and containers were incubated for 2 h at a temperature of 25 degrees Celsius. After 6 h, based on the method described for soil respiration, the titration was performed, the amount of CO2 was calculated, and the amount of substrate-induced respiration was recorded and expressed in milligrams of C-CO2 per kilogram of dry soil per hour [56].

2.9. Data Analysis

The soil physical and chemical properties were statistically analyzed by one-way ANOVA. However, the biological factors were arranged in a factorial-based randomized complete block design (RCBD). Data on soil biological properties and seasonal changes were analyzed as a factorial experiment in a completely randomized design with fifteen replications in each treatment. Data on physical and chemical properties of soil were subjected to analysis of variance (ANOVA) using the general linear model (GLM) procedure (SPSS 19.0). The means were compared by the Duncan method at p < 0.05. Graphs were drawn by Excel software 2016 and the PCA analysis was run by XLSTAT software.

3. Results and Discussion

3.1. Physicochemical Properties

Effect of the grazing intensity on the soil physicochemical properties, including bulk density, porosity, MWD, pH, EC, P, K, and Ca, are presented in Table 2. The ANOVA results showed a significant effect of grazing on bulk density, porosity, MWD, pH, and P content. In the no-hunting area, due to severe trampling of livestock and loss of vegetation cover on the soil surface, bulk density increased, and soil porosity decreased, and this was in line with our expectations. The results of other research confirmed this issue [57,58].
In terms of salinity, there was no significant variation in EC and pH in response to different grazing intensities. This data confirm that livestock grazing has no negative impact on soil pH and EC as observed by several authors [59,60].
To understand the level of soil nutrients with different grazing intensities, we studied phosphorus (P), potassium (K), and calcium (Ca) content in soil of unlimited and managed grazing. Although the contents of K and Ca were stable among the treatments, P content was affected by the grazing intensity. Decreased soil litter due to overgrazing and loss of vegetation may have reduced phosphorus in the no-hunting zone [61]. On the other hand, intense livestock grazing leads to decrease plant cover and SOM which accelerates the soil erosion; due to the strong association of P with soil particles, soil erosion is one of the most important processes causing P loss [62]. The finding showed that the lowest values of porosity, phosphorous, and MWD occurred in the no-hunting site when compared with the others. In contrast, the maximum values were seen in soils sampled from the national park (Table 3). Heavy grazing and livestock trampling, especially when the soil is moist (like the areas with free grazing system = no hunting), cause soil porosity reduction and aggregates collapse.

3.2. Fungal Biomass

The main and interaction effects of season and grazing were meaningful on the fungal biomass (p < 0.01) (Table 4). The national park and protected area had a higher fungal biomass than the no-hunting area (Table 5). Comparison of means for seasons showed no significant differences between seasons and it seems that the differences are more relevant to the controlling practices than the seasons.
The low entrance of livestock and conservation of the soil ecosystem led to the high volume of this trait. Reducing soil disturbance prepared a suitable condition for fungi to colonize and would subsequently increase the soil carbon storage [63]. Increasing soil temperature and soil organic carbon in the soil protected from overgrazing or human activities makes a suitable condition for fungi and microbial communities to make more nutrients available to the plants [64,65,66,67,68], thereby, a significant increase in forage yield of this area is expected.

3.3. Microbial Biomass Carbon (MBC)

Microbial biomass carbon was affected by the main effects of site and season treatments; however, their interaction was not significant (Table 6). With regard to low grazing intensity in controlled grazing areas (national park and protected area), the plant cover should be more than in the no-hunting area, thereby the colonization of the microbial community in the rhizosphere area is high. These results agree with Table 6 which shows high microbial biomass carbon in moderate- and low-grazing areas. Moreover, the lowest microbial biomass carbon was measured in the winter season and the highest was recorded in the summer season as observed by Xu and colleagues [69]. The decline in soil MBC in response to grazing was probably a result of the decrease in carbon input from litter decomposition [70].

3.4. Basal Soil Respiration (BSR)

Basal soil respiration was affected by variation in sites and seasons (Table 4). Unlike the fungal biomass, the highest value of BSR is found in the no-hunting region during season 1–3 treatments (Table 5). This is related to the negative relationship of BSR and fungal biomass (Table 7). Based on Strebel et al. (2010), the lowest BSR is achieved in the absence of grazing which agrees with our results [71]. According to Table 7, the highest value of microbial metabolic quotient is seen in the no-hunting treatment, thereby, a high microbial metabolic quotient could also confirm the high volume of BSR in the no-hunting treatment. Kamali and colleagues reported that the disturbance of the total organic carbon and N of the soil surface causes the rise in BSR [14].

3.5. Microbial Biomass Carbon to Microbial Biomass Nitrogen Ratio (MBC/MBN)

The microbial biomass consists mostly of bacteria and fungi, which decompose crop residues and organic matter in the soil. This process releases nutrients, such as nitrogen (N), phosphorous, and others, into the soil environment, which are available for plant uptake. Generally, up to 5% of the total organic carbon and N in soil is in the microbial biomass. When microorganisms die, these nutrients are released in forms that can be available to the plants. The MBC/MBN ratio could be affected by environmental conditions and in this regard water availability and temperature are the most effective factors. Our results showed significant differences between treatments (Table 4). Based on the results of Table 5, sites that had controlled grazing, especially the national park, had the highest volume of MBC/MBN, and the lowest amount was seen in the winter season in all treatments. In addition, high microbial activities in high temperatures led to a high MBC/MBN ratio in the spring and summer seasons.

3.6. Microbial Metabolic Quotient (qCO2)

The microbial metabolic quotient trait was affected by the main effects of site and season treatments; however, the interaction effect of these was not significant (Table 4). Based on results shown in Table 6, the maximum values were obtained from the no-hunting treatment and warm seasons. As this index is related to the released CO2 from the respiration, like BSR, the highest values were recorded in the no-hunting treatment (Table 6).

3.7. Arbuscular Mycorrhizal Spore Number (AMSN)

Spore quantification has been very useful for evaluating the level and diversity of mycorrhizae because spores are highly resistant to adverse conditions and may reflect the previous history of mycorrhizal symbiosis in the soil [72]. In lowland humid tropics, spore abundance varies with the season, with the highest abundance in the dry season [73], and it is related to low nutrient availability and plant phenology, among other factors [74]. Our results showed meaningful effects of studied factors on arbuscular mycorrhizal spore number (Table 4). As shown in Table 5, there is no remarkable behavior of soil disturbance that resulted from grazing on spore number, however, its volume was high in dry seasons in all treatments. Similarly, Guadarrama and Álvarez-Sánchez (1999) showed that the spore number of arbuscular mycorrhiza was higher in dry seasons than wet seasons [75]. In addition, they showed that the abundance and richness of mycorrhizal spores are affected by season changes which completely agrees with our results.

3.8. Total N and MBN/N

Plant species and number of microbial functional groups are directly responsible for the cycling of nitrogen (N). Nitrifiers, denitrifiers, and N2 fixers largely determine N availability in soils [76] and play important roles in intensive grazing systems. A greater total N volume in dry seasons, especially in summer (Table 4), is related to the greater enzyme activities in intensive grazing treatment (Table 5). The larger amount of N available with intensive grazing than light grazing was also reported by Patra et al. (2005) which agrees with our results [77]. With respect to microbial functional genes involved in N cycling, which have important implications for soil biogeochemical processes, free grazing was found to increase abundances of N mineralization and nitrification genes but decrease denitrification gene abundance in an alpine meadow [78]. Temperature also plays an important role in the availability of N in the soil; by reducing the temperature, the volume of total N decreased. Previously, Tscherko and colleagues found limited effects of water stress and soil nitrogen on soil biomass population growth and activity rates during the early part of a model ecosystem’s development [79].

3.9. Substrate-Induced Respiration (SIR)

The substrate-induced respiration (SIR) was introduced to measure fungal, bacterial, and total microbial contributions in response to glucose-induced respiration and the potentially active microbial biomass in plant residue decomposition [80]. These substrates are clearly important sites of microbial activity and nutrient mineralization and immobilization [81]. Accordingly, areas having proper management and grazing control should have a desirable pool of C and microbial biomass. Thereby, our results truly showed the highest volume of SIR in protected areas having the lowest grazing intensity. It seems that, in low grazing intensity treatment, many plant densities with low soil destruction led to increasing the rhizosphere activity with a high substrate-induced respiration rate.

3.10. Principal Component Analysis (PCA) Results

Principal component analysis (PCA) is a technique for dimensionality reduction in large datasets, increasing interpretability and simultaneously minimizing information loss. As the name suggests, PCA can identify principal components and helps us to analyze a series of features that are more valuable instead of examining all features. In fact, PCA extracts those features that provide more value to us. In this study, the quality of the data was evaluated using Bartlett’s and Kaiser–Meyer–Olkin (KMO) tests before the PCA analysis. The results showed a meaningful correlation between the variables (Bartlett = 741.667) and the adequate number of observations (KMO = 0.677) for the PCA test according to the ability of PCA in visually displaying the correlation of the components as well as plots. From the results of the variables having significant differences in season and site interaction effects, 23 pairs out of 28 pairs of the data were meaningful (p < 0.05) (Table 7). Moreover, the volume of eigenvalues was used as a criterion for interpreting the relationships between soil variables and components. PCA results showed that the two factors F1 and F2 could almost characterize 70% of the total variance and the other six factors did not have a remarkable effect on the results. In this way, having a high eigenvalue of factor 1 (45.38%) showed that this component estimates more variance than an individual variable. The order of importance of these components is shown in Table 8 based on their eigenvalues.
Accordingly, factor 1 had the highest positive factor loading with MBN, SIR, and fungal biomass, and the highest negative factor loading with basal soil respiration and AMSN. However, factor 2 had the highest positive and negative factor loading with MBC/MBN and MBN/N, respectively (Table 9).
Figure 3 shows the distribution of the observations where the national park and protected area are categorized in the positive site of factor 1 and the no-hunting site is on the negative side of the graph containing AMSN, basal soil respiration, and CAI criteria (Figure 4).
Briefly, the biplot in Figure 4 graphically shows the abundance and correlations of the criteria in each site and factor, in which the national park site had more correlates with fungal biomass, SIR, and MBC/MBN criteria, the protected area had more correlates with MBN and MBN/N, and, finally, the no-hunting site had more correlates with AMSN, basal soil respiration, and CAI. Figure 5 shows the weight and importance of the criteria in the results, in which variables that have vectors longer than this radius make a higher contribution than the average and can be interpreted with confidence. In this regard, criteria further from the center of the radius had a higher weight in the model than the others. Therefore, in terms of the biological characteristics of the PCA test, AMSN, basal soil respiration, CAI, and MBC/MBN had the highest contribution and importance.
Similarly, in terms of soil physical characteristics, the bulk density, porosity, and MDW were categorized in the F1 group, in which, due to the negative correlation of bulk density with other criteria (Table 10 and Figure 6), F1 had a positive factor loading with bulk density (0.967) and negative factor loading with porosity and MDW (−0.967 and −0.761, respectively). In this way, bulk density, porosity, and MDW were determined as the determining factors in the no-hunting, national park, and protected areas, respectively. Moreover, the criteria having no significant effect on the results were removed from the model previously.

4. Conclusions

Proper grazing management based on the potential of the region and the number of livestock could increase land productivity. In this study, it was observed that overgrazing reduced class I and II plants and increased therophytes belonging to class III. These aspects significantly reduce the productivity of the pastures and change the trend of the conditions of the pastures in a negative way. Furthermore, we observed that grazing and seasonal changes significantly affected fungal biomass, basal soil respiration, microbial biomass carbon, MBN, MBC/MBN, microbial metabolic quotient, MBN/N, SIR, and CAI volumes.
The study areas are among the hotspots of biodiversity and preserving them is important from a national and international point of view. Considering that soil is the substrate for plant growth and plays a very important role in carbon exchanges, it is very important to maintain its quality. The results of this study showed that soil conditions in restricted grazing areas are better than in free grazing areas. It is useful for governments to pay more attention to these natural environments characterized by rare and unique species that are threatened by incorrect and unstable management.

Author Contributions

Conceptualization, N.K. and A.S.; methodology, N.K. and M.S.; formal analysis, N.K. and A.S.; investigation, N.K. and A.S.; data curation, N.K., M.S. and A.M.; writing—original draft preparation, N.K.; writing—review and editing, A.M.; supervision, A.M.; funding acquisition, A.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors would like to express their great appreciation to Khosro Sagheb-Talebi and Mehrdad Zarafshar for their valuable and constructive suggestions during the development of this research.

Conflicts of Interest

The authors declare no conflict of interest.

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Land 11 01537 g001 550
Figure 1. The location of the study area.
Figure 1. The location of the study area.
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Figure 2. The views of the study area.
Figure 2. The views of the study area.
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Figure 3. Soil biological characteristics distribution in PCA. (N: no hunting; P: protected area; NP: national park).
Figure 3. Soil biological characteristics distribution in PCA. (N: no hunting; P: protected area; NP: national park).
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Figure 4. PCA biplot result of soil biological characteristics.
Figure 4. PCA biplot result of soil biological characteristics.
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Figure 5. Varimax rotation biplot for soil biological characteristics.
Figure 5. Varimax rotation biplot for soil biological characteristics.
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Figure 6. Varimax rotation biplot for soil physical characteristics.
Figure 6. Varimax rotation biplot for soil physical characteristics.
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Table
Table 1. General characteristics of the study areas.
Table 1. General characteristics of the study areas.
Conservative Management LevelSite DescriptionGrazing TimeSheep ha−1Dominant Rangeland Species (Canopy Cover, %)Associated Plant Species Biomass, (kg ha−1)Associated Plant Species (Canopy Cover, %)Soil Texture (0–30 cm Depth)
National ParkOnly grazed by wild animals for 19 years, dominated by rangeland species (cover: ~51%)0 days-Artemisia aucheri Boiss. (8.96),
Cousinia umbrosa Bunge. (5.40)
Artemisia aucheri Boiss. (98.3),
Cousinia umbrosa Bunge.
(12.3)		Stipa barbata Desf. (2.8),
annual grasses (2.67) 		Sandy loam (sand: 58.30%, silt: 22.18%, clay: 18.9%)
Protected AreaGrazed by nomadic livestock for 60 days of the year for 48 years, dominated by rangeland species (cover: ~43%)60 days of the year (about 22 May to end of July)18–20Artemisia aucheri Boiss. (6.66),
Cousinia umbrosa Bunge (4.55)		Artemisia aucheri Boiss. (73.42),
Cousinia umbrosa Bunge. (9.8)		Lactuca orientalis (Boiss.) Boiss. (3.2),
Rosa persica Michx. ex Juss. (3.28)		Sandy loam (sand: 58.77%, silt: 21.74%, clay: 19.48%)
No HuntingJust free grazing of nomadic livestock takes place in all seasons, dominated by woody species (cover: ~24%)Free grazing in all seasons35–50Artemisia aucheri Boiss. (5.23),
Rosa persica Michx. ex Juss. (4.90)		Artemisia aucheri Boiss. (57.46),
Rosa persica Michx. ex Juss. (10.5)		Hulthemia persica (Michx. ex Juss.) Bornm. (3.22),
Lactuca orientalis (Boiss.) Boiss. (2.75)		Sandy loam (sand: 59.12%, silt: 22.093%, clay: 18.78%)
Table
Table 2. Analysis of variances for the soil physical–chemical properties.
Table 2. Analysis of variances for the soil physical–chemical properties.
dfSandSiltClayPotassiumBulk DensitypH
Between Groups20.4800.7882.0861371.2120.348 **0.002
Within Groups4215.04512.28015.6282044.6780.0180.068
Total44
dfPhosphorusECCaT.N.VMDWPorosity
Between Groups2350.688 **0.0460.55016.6130.919 **411.764 **
Within Groups4219.9140.1450.53469.4770.02622.555
Total44
** represent significant differences at the probability level (p ≤ 0.01).
Table
Table 3. Comparison of means for the soil physical–chemical properties.
Table 3. Comparison of means for the soil physical–chemical properties.
TreatmentsBulk DensityPorosityPhosphorusMWD
No hunting1.483 a46.33 b26.866 c0.781 b
National park1.202 b56.326 a36.533 a1.246 a
Protected area1.232 b54.06 a31.933 b1.171 a
In each column same letter(s) indicate no significant difference at 5% probability level.
Table
Table 4. Analysis of variances for the soil biological characteristics.
Table 4. Analysis of variances for the soil biological characteristics.
SourcedfFungal BiomassBasal Soil RespirationMicrobial Biomass CarbonMBNMBC/MBNMicrobial Quotient
Rep140.2200.020118.5740.7987.9860.613
Site282.148 **2.549 **105,690.617 **181.103 **579.191 **21.207 **
Season34.040 **0.869 **5002.696 **14.694 **370.855 **4.310 **
Site × Season60.894 **0.300 **164.6241.832 *60.539 **1.389
Error1540.1990.026179.4290.74012.1940.813
CV% 29.8141.5914.7314.7822.4237.5
SourcedfQCo2Mycorrhiza CorgMBN/NSIRCAI
Rep140.00299.810.380.060.30.31
Site20.3 **4788.74 **352.69 **4.28 **29.36 **31.68 **
Season30.01 **8897.14 **10.4 **6.75 **4.23 **5.69 **
Site × Season60.004363.12 **0.820.34 **3.75 **6.7 **
Error1540.000471.850.590.070.180.06
CV% 32.7918.1817.626.2528.4742.67
** represent significant differences at the probability level (p ≤ 0.01). * represent significant differences at the probability level (p ≤ 0.05).
Table
Table 5. Comparison of means for interaction effects of grazing managements and season treatments.
Table 5. Comparison of means for interaction effects of grazing managements and season treatments.
TreatmentsFungal BiomassBasal Soil RespirationMBNMBC/MBNAMSNN TotalMBN/NSIRCAI
N × season 10.253 d0.544 b2.589 f17.404 ab59.53 b0.444 fg0.6 e1.083 de0.567 c
N × season 20.293 d0.965 a4.335 d14.057 cd77.73 a0.728 a0.6 e0.673 f1.552 b
N × season 30.256 d0.579 b3.613 e11.7 d57.07 b0.739 a0.48 e1.071 e0.578 c
N × season 40.231 d0.235 cde4.76 d5.352 e33.27 def0.306 h1.612 b0.137 g2.926 a
P × season11.849 b0.266 cd6.7 bc17.329 ab40.8 c0.627 bc1.08 cd1.661 c0.175 cd
P × season21.883 b0.308 c7.213 ab17.232 ab60.87 b0.582 cd1.268 c1.117 de0.282 cd
P × season31.945 b0.231 d6.767 abc16.995 ab37.4 cd0.617 bc1.112 cd1.411 cd0.172 cd
P × season41.025 c0.119 e7.4 a14.102 cd29.93 f0.4 g1.921 a2.165 b0.066 d
NP × season 12.761 a0.228 cde6.105 c19.542 a38 cd0.536 de1.145 cd2.219 b0.108 cd
NP × season 22.796 a0.265 cd6.678 bc19.795 a57.4 b0.641 bc1.055 d1.333 de0.202 cd
NP × season 32.847 a0.237 cde6.441 c18.164 a36.73 cde0.657 b1.016 d2.113 b0.116 cd
NP × season 41.914 b0.163 de7.181 ab15.168 bc30.73 ef0.474 ef1.568 b2.849 a0.062 d
N = No hunting; NP = National park; P = Protected area. In each column same letter(s) indicate no significant difference at 5% probability level.
Table
Table 6. Comparison of means for the main effects of grazing managements and season treatments.
Table 6. Comparison of means for the main effects of grazing managements and season treatments.
TreatmentMicrobial Biomass CarbonMicrobial QuotientqCO2
Site
No hunting 42.55 b3.076 a0.142 a
National park112.72 a2.01 b0.02 b
Protected area117.53 a2.085 b0.018 b
Season
192.11 b2.695 a0.062 a
2104.16 a2554 a0.07 a
388.91 b1.965 b0.068 a
478.56 c2.38 a0.043 b
In each column same letter(s) indicate no significant difference at 5% probability level.
Table
Table 7. Correlation between the soil biological characteristics.
Table 7. Correlation between the soil biological characteristics.
VariablesFungal BiomassBasal Soil Respiration MBN MBC/MBNAMSNMBN/NSIRCAI
Fungal biomass1
Basal soil respiration −0.473 *1
MBN 0.603 *−0.510 *1
MBC/MBN0.479 *−0.1000.0431
AMSN−0.285 *0.637 *−0.378 *0.0751
MBN/N0.189 *−0.556 *0.648 *−0.251 *−0.557 *1
SIR0.517 *−0.413 *0.480 *0.333 *−0.405 *0.286 *1
CAI−0.481 *0.366 *−0.344 *−0.513 *0.1080.026−0.604 *1
* represent significant differences at the probability level (p ≤ 0.05).
Table
Table 8. Categorizing the eigenvalues of the meaningful biological characteristics.
Table 8. Categorizing the eigenvalues of the meaningful biological characteristics.
F1F2F3F4F5F6F7F8
Eigenvalue3.6301.8940.7130.5900.4410.3640.2330.135
Variability (%)45.38123.6728.9127.3755.5104.5442.9171.689
Cumulative %45.38169.05377.96685.34090.85095.39498.311100.000
Table
Table 9. Factor loading of the studied biological characteristics.
Table 9. Factor loading of the studied biological characteristics.
F1F2F3F4F5F6F7F8
Fungal biomass0.7540.3300.261−0.3470.137−0.2910.067−0.161
Basal soil respiration−0.7870.2410.2740.2190.3700.019−0.232−0.098
MBN (mg kg−1)0.789−0.2000.5170.049−0.023−0.014−0.1530.210
MBC/MBN0.3190.793−0.092−0.3140.1100.379−0.0530.060
AMSN−0.6450.4550.4910.106−0.2220.1000.255−0.011
MBN/N0.597−0.6710.1730.042−0.0040.3510.023−0.194
SIR0.7600.242−0.1180.4450.3180.0040.2210.046
CAI−0.608−0.5850.093−0.3310.3490.0380.1860.111
Table
Table 10. Correlation matrix of the soil physical characteristics.
Table 10. Correlation matrix of the soil physical characteristics.
VariablesBulk Density (gr/C3)MWD (mm)Porosity (%)
Bulk density (gr/C3)1
MWD (mm)−0.570 *1
porosity (%)−1.000 *0.570 *1
* represent significant differences at the probability level (p ≤ 0.05).
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Kamali, N.; Sadeghipour, A.; Souri, M.; Mastinu, A. Variations in Soil Biological and Biochemical Indicators under Different Grazing Intensities and Seasonal Changes. Land 2022, 11, 1537. https://doi.org/10.3390/land11091537

AMA Style

Kamali N, Sadeghipour A, Souri M, Mastinu A. Variations in Soil Biological and Biochemical Indicators under Different Grazing Intensities and Seasonal Changes. Land. 2022; 11(9):1537. https://doi.org/10.3390/land11091537

Chicago/Turabian Style

Kamali, Nadia, Ahmad Sadeghipour, Mahshid Souri, and Andrea Mastinu. 2022. "Variations in Soil Biological and Biochemical Indicators under Different Grazing Intensities and Seasonal Changes" Land 11, no. 9: 1537. https://doi.org/10.3390/land11091537

APA Style

Kamali, N., Sadeghipour, A., Souri, M., & Mastinu, A. (2022). Variations in Soil Biological and Biochemical Indicators under Different Grazing Intensities and Seasonal Changes. Land, 11(9), 1537. https://doi.org/10.3390/land11091537

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