**4. Results and discussion**

#### **4.1 Site characteristics**

*Agrometeorology*

the freezer (−20°C) until ready for assay.

ratio of fungi to bacteria biomass in the soil.

**3. Statistical analysis**

livestock being grazed on the pasture field. ECa is a product of dynamic soil factors (e.g., soil moisture) and static measurements (e.g., bulk density, clay type) [39] and is generally stable throughout the growing season. Sampling following the soil ECa gradient (described above) was repeated at the 12 selected points, while soil sampling following the transect method were taken at points about 15–25 m apart along a SE-NW and NE–SW transects during August and October sampling, respectively. The GPS coordinates of locations where soil samples were collected were obtained via the GPS app locator of a smartphone to identify transect sampling points on the pasture site map. Approximately, 50 g of soil core taken at a depth of 10–15 cm was transferred to plastic ziplock bag and kept in an icebox. Between samples, the soil corer was cleaned with alcohol (70%) to prevent cross contamination of samples. Soil samples were transported to the laboratory where they were processed using 2 mm sieves to remove pebbles and any plant material and immediately stored in

For the assessment of microbial diversity and abundance, the total microbial fatty acids (FA) were extracted following the procedure described by Schutter and Dick [30]. Briefly, total microbial lipids in 5 g soil samples were extracted in 10 ml of 0.2 M methanolic potassium hydroxide and the mixture heated at 37°C for 1 h with intermittent shaking. The solution was then neutralized by adding 1 N acetic acid and the lipids dissolved in hexane. The mixture was centrifuged at 6000 rpm for 10 min and the supernatant was carefully recovered, filtered, and further processed before fatty acids were quantified using gas chromatography with 0.05 mg/ml nonadecanoic acid (C19:0) as an internal standard. A total of 19 FAMEs were retained and used to determine microbial community composition following FAMEs nomenclature of the IUPAC-IUB Commission on Biochemical Nomenclature (IUPAC-UIB, 1987). Specific fatty acids with 14–20 carbon composition were used to represent fungal, bacterial groups, and micro-eukaryotes. Bacterial biomass was represented by the sum of 10 FAMEs: iC14:0, iC15:0, aC15:0, C15:0, iC16:0, iC17:0, aC17:0, C17:0, cyC17:9, cyC19:9,10, and cyC19:11,12 [16]. Actinomycetes bacteria were quantified by 10Me fatty acids: 10MeC18:0 and 10MeC19 [22, 40], while saprophytic fungal biomass was represented by C18:2cis9,12 [41]. In addition, micro-eukaryotic biomass was represented by the sum of C20:3, C20:4 and C20:5 [19, 42]. Finally, the fatty acid C16:1cis11 was used as a biomarker for arbuscular mycorrhizal fungi (AMF) [43]. Total microbial biomass was estimated by summing up FAMEs representing bacteria, actinomycetes, saprophytic fungi, and AMF. In addition, total FAMEs for bacterial (bacteria and actinomycetes) and fungi (AMF and saprophytic fungi) were used to calculate the

The resulting datasets from FAMEs analysis on the microbial groups were analyzed using generalized linear mixed models procedure (GLIMMIX Procedure) in SAS® 9.4 software package and means separated at p < 0.05. This analysis involved testing sampling methods (ECa-directed vs. transect-based), the effect of sampling date/time as well as any interactions between sampling method and time. To further investigate any impacts of plant (brome grass monoculture) as well as the effect of soil physical and chemical parameters on microbial diversity and abundance, principal component analysis (PCA) was conducted. The PCA was performed to determine the contribution of soil characteristics (e.g., ECa, pH) in the variation and separation of both temporal and sampling method. Soil chemical, physical and biological attributes were used in the PCA to elucidate the effects of

**176**

The physical and chemical characteristics of soil in the brome grass pasture are summarized in **Table 1**. Soil organic matter contents were relatively high, averaging 4.14%. High organic matter content in the top 20 cm of the pasture soil can be attributed to dense rhizomatous roots of brome grass root biomass and shoots [32]. Soil nutrient availability to crops is influenced by soil pH. The pasture site exhibited a slightly acidic soil pH (5.98) and was positively correlated (R2 0.89, p < 0.05) with calcium but negatively correlated to percent H+ (R2 0.95, p < 0.05). Available N (Nitrates-N ppm) of soils sampled from the pasture site averaged 4.42, which can be attributed to the cow dung manure, N fertilizer, as well as the high biomass from the brome grass shoots and roots. 1:1 soil soluble salts (mmho cm−1) was strongly and positively correlated to measured available soil nitrates (R2 0.79, p < 0.05). Available P in the pasture site was measured at 18.07 ppm P. Hydrogen (H+ ) cations contributed significantly to the total sum of cations me 100 g−1 (CEC) of the pasture site.

Values for ECa ranged from 21 to about 44 mS m−1 (**Figure 1**), which indicated a low to moderate level of spatial site heterogeneity. The mean ECa value was 32 mS m−1 with ECa of 30.1–35 and 25.1–30 mS m−1 being more common (the two combined covered nearly the entire the pasture). Regions with lowest and highest ECa values mainly constituted small pockets that were randomly distributed across the pasture with no clear pattern that could be discerned (**Figure 1**). As a result, soil in this pasture was considered to be less variable and the ECa values fell within normal range of 0–150 mS m−1 for grass pasture [35] but lower than in a fertilized maize field [46] within Eastern Nebraska.

#### **4.2 Soil microbial community**

General composition of microbial communities, namely, total microbial biomass, diversity, and composition of soil microbes detected using FAMEs assay in soils sample collected over two seasons and methods is presented in **Figures 2** and **3**. Each sector of the pie chart (**Figure 3**) represents individual microbial composition as a percentage of total recovered microbial fatty acid. Microbial biomass was dominated by bacteria (55–65%), followed in declining order by arbuscular mycorrhizae (15–25%) saprophytic fungi (8–9%) actinomycetes (8%) micro-eukaryotes (4%).

**Figure 2.**

*Total microbial biomass (A) and the ratio of fungi to bacteria (B). Individual bars represent the mean and standard error collected following ECa (clear bars) and random method (dark bars).*

Bacteria was found to be highly correlated (R2 0.84, p < 0.05) with actinomycetes. This was consistent across the two sampling durations. Bacteria correlation with saprophytic fungi (R2 0.53–0.6, p < 0.05) and micro-eukaryotes (R2 0.56–0.59, p < 0.05) was only significant during August and October sampling, respectively. The abundance of soil micro-eukaryotes showed significant correlation with AMF (R2 0.74–0.77, p < 0.05) for both soil sampling methods while the correlation with saprophytic fungi (R2 0.64, p < 0.05) and actinomycetes (R2 0.72, p < 0.05) was observed only in August and October, respectively. Furthermore, the total recovered microbial FA was found to be strongly correlated with all microbial groups ranging between R2 0.59 and 0.89 and was statistically significant across all microbial groups and sampling times except for AMF (p = 0.59) in October samples. The high correlation of bacteria with actinomycetes are similar to that of Grigera et al. [16] who established a high level of correlation (R2 0.88, p < 0.05) in an agricultural field in Buffalo county, Nebraska, continuously cropped with corn. These high correlations highlight similarities in the edaphic conditions (e.g., pH and organic matter content) in which the microbes coexist in complementary biogeochemical functions in recycling both N and C [47].

**179**

**Table 2.**

*Nebraska.*

**Figure 3.**

*Spatio-Temporal Dynamics of Soil Microbial Communities in a Pasture: A Case Study…*

*DOI: http://dx.doi.org/10.5772/intechopen.93548*

**4.3 Comparison of sampling methods**

*percentage of total recovered microbial fatty acid.*

Statistical analysis did not show any significant differences in soil microbial biomass of soil samples collected based on ECa stratification or transect sampling methods. The sampling method did not result in any significant differences (**Table 2.**) in the abundance of bacteria, actinomycetes, saprophytes, mycorrhizae, or micro-eukaryotes. Transect sampling technique has the same sensitivity and reliability as an ECa-based method in capturing the spatial and temporal dynamics of soil microbiota and can thus be used as a method of choice for sites with a relatively low range of ECa variability indicative of similar soil chemical,

**Type III tests of fixed effects**

**Effect Bacteria Actinomycetes Saprophytes Mycorrhizae Micro-eukaryotes** Date (D) <.0001 0.002 0.239 0.076 0.740 Sampling (S) 0.583 0.650 0.660 0.413 0.589 DxS 0.257 0.274 0.656 0.318 0.951

*Summary table of date (D) and sampling methodology (S) and their interactive effects of D and S on bacteria, actinomycetes, saprophytes, mycorrhizae, and micro-eukaryotes sampled at the PR-HPA site in Mead,* 

*Pie charts showing diversity and composition of soil microbes detected using FAMEs assay in soils sample collected in august (a and C) and October (B and D). Soil samples were collected following ECa-based method (A and B) and random method (C and D). Each sector represents individual microbial composition as a* 

*Spatio-Temporal Dynamics of Soil Microbial Communities in a Pasture: A Case Study… DOI: http://dx.doi.org/10.5772/intechopen.93548*

#### **Figure 3.**

*Agrometeorology*

**178**

(R2

**Figure 2.**

between R2

Bacteria was found to be highly correlated (R2

established a high level of correlation (R2

recycling both N and C [47].

saprophytic fungi (R2

saprophytic fungi (R2

This was consistent across the two sampling durations. Bacteria correlation with

*Total microbial biomass (A) and the ratio of fungi to bacteria (B). Individual bars represent the mean and* 

*standard error collected following ECa (clear bars) and random method (dark bars).*

p < 0.05) was only significant during August and October sampling, respectively. The abundance of soil micro-eukaryotes showed significant correlation with AMF

0.64, p < 0.05) and actinomycetes (R2

and sampling times except for AMF (p = 0.59) in October samples. The high correlation of bacteria with actinomycetes are similar to that of Grigera et al. [16] who

Buffalo county, Nebraska, continuously cropped with corn. These high correlations highlight similarities in the edaphic conditions (e.g., pH and organic matter content) in which the microbes coexist in complementary biogeochemical functions in

0.74–0.77, p < 0.05) for both soil sampling methods while the correlation with

observed only in August and October, respectively. Furthermore, the total recovered microbial FA was found to be strongly correlated with all microbial groups ranging

0.59 and 0.89 and was statistically significant across all microbial groups

0.53–0.6, p < 0.05) and micro-eukaryotes (R2

0.84, p < 0.05) with actinomycetes.

0.88, p < 0.05) in an agricultural field in

0.56–0.59,

0.72, p < 0.05) was

*Pie charts showing diversity and composition of soil microbes detected using FAMEs assay in soils sample collected in august (a and C) and October (B and D). Soil samples were collected following ECa-based method (A and B) and random method (C and D). Each sector represents individual microbial composition as a percentage of total recovered microbial fatty acid.*

#### **4.3 Comparison of sampling methods**

Statistical analysis did not show any significant differences in soil microbial biomass of soil samples collected based on ECa stratification or transect sampling methods. The sampling method did not result in any significant differences (**Table 2.**) in the abundance of bacteria, actinomycetes, saprophytes, mycorrhizae, or micro-eukaryotes. Transect sampling technique has the same sensitivity and reliability as an ECa-based method in capturing the spatial and temporal dynamics of soil microbiota and can thus be used as a method of choice for sites with a relatively low range of ECa variability indicative of similar soil chemical,


#### **Table 2.**

*Summary table of date (D) and sampling methodology (S) and their interactive effects of D and S on bacteria, actinomycetes, saprophytes, mycorrhizae, and micro-eukaryotes sampled at the PR-HPA site in Mead, Nebraska.*

physical and microbial properties. The two soil sampling techniques used in this research (i.e., ECa- and transect-based) captured comparable soil microbial communities and abundance in both space and time highlighting the significant role of vegetation on soil microbial communities as highlighted by others like Grigera et al.; Lauber et al.; Pereira e Silva et al.; and Segal et al. [16–19].

#### **4.4 Effects of temperature on soil microbes**

There were statistically significant seasonal differences in the total soil microbial biomass irrespective of soil sampling technique with a considerably higher abundance in August compared to October. Total soil microbial mass in August and October soil samples had means of 130.2 and 137.7 nmol g−1 in August compared to 117.7 and 119.8 nmol g−1 in October for soil samples collected via ECa-directed and transectbased sampling methods. Despite the observed decline in October, which was cooler, no statistical difference was observed in space and time for microbial biomass.

When examining the effect of sampling date (i.e., August vs. October) on the abundance of individual microbial groups, results demonstrated a significant shift in soil biota with temporal changes affecting selective groups. Specifically, saprophytic fungi, actinomycetes, and micro-eukaryotes remained seasonally stable and constituted about 8–9 and 3–4% of the total microbial biomass, respectively (**Figure 2**). Bacteria and AMF abundance exhibited a significant temporal variability. In particular, a significant decline of bacterial biomass ranging from 4 to 10% observed in August and October, respectively, was observed irrespective of method of soil sampling. In contrast to bacteria, a significant increase (4–10%) in AMF abundance was noted in soil sampled in October compared to August (**Figure 4**). Bacterial abundance declined by up to 10% from August to October with a corresponding 10% increase in AMF observed during the same period.

Fungi to bacterial (F:B) ratio which is indicative of the changes in soil microbial communities [48] was calculated in August (0.30–0.39) and noted to have increased (0.45–0.53) in October, representing a 20 and 32% increase for ECa and transect soil samples, respectively. Although there was a general increase in F:B in October samples, statistical significance was only observed in soil sampled via the transect

#### **Figure 4.**

*Monthly average temperature and precipitation from Mead weather station (41.17° N, 96.47° W) closest to the pasture study site (4 km) at the East Nebraska research and Experimental Station (ENREC). Bars indicate total monthly precipitation while stars show mean monthly temperature.*

**181**

**Table 3.**

*standard deviation.*

higher (20.8°

*Spatio-Temporal Dynamics of Soil Microbial Communities in a Pasture: A Case Study…*

method (**Figure 3B**). Increase in F:B ratio during the cooler month reflected the shift in compositional abundance of fungi and bacteria that was observed in October (**Figure 2**). Commonality in trend in total microbial biomass and the ratio of fungi to bacteria as observed along time illustrates the comparable sensitivity of

Shifts in soil microbial communities are affected by seasonality and specifically temperature and moisture. Temporal changes in soil microbial abundance observed in our work has also been demonstrated elsewhere in soil under controlled environment [3, 50] as well as in different ecosystems including forests [51–54], deserts [55], cultivated land, [17–19] as well as grasslands [17]. While fluctuation in microbial abundance across the soil types are common, and the key drivers tend to vary according to the location and soil type, with environmental factors such as temperature and precipitation being the most dominant factors [17, 50, 54, 55]. Our results concur with those of several researchers such as Papatheodorou, Argyropoulou, and Stamou [56] who conducted studies on soils from a grassland in Mediterranean Greece. They detected a linear decline in bacterial diversity, evenness, richness, and mean oxidation, especially of carbohydrate and carboxylic substrates over a 6-month (July to December) study. Substrate use efficiency and specifically carbon use efficiency have also been found to decrease with nutrient availability and increasing temperature [57]. Our study's August and October environmental conditions showed that the monthly mean temperature was

C) in August compared to October (12.2°C). Cumulative monthly

**(mm)**

C

**Gravimetric water content (%)**

precipitation measured 78.89 mm and 110.99 mm for August and October, respectively, which for this area is about average for August but double the average for the month of October (**Figure 2**). In addition, we examined in detail these two environmental parameters recorded 4 days preceding the soil sampling dates. The results summarized in **Table 3** showed that average temperature was 20.5°

in August and dropped to 10.7oC in October. With respect to precipitation, a cumulative 1.27 mm of rain was received 3 days prior to the August sampling and none prior to the October sampling. While there were differences in precipitation both monthly (August and October) and the days preceding the aforementioned sampling dates, this variation did not have a significant effect on soil moisture as revealed by computed soil gravimetric water content (**Table 3**). This implies that, changes observed in bacterial and fungal composition (**Figure 4**) may possibly have resulted from factor(s) other than soil moisture which are discussed in the next section below. These results concur with those of [58] who characterized soil microbial communities and their conditioning by varied plant species. They noted that soil bacterial communities are primarily influenced by abiotic conditions; namely temperature and ECa (**Figure 5**). Fungal communities on the other hand are determined by biotic conditions such, as plant species [58] as seen with the

**Date Sample type Temperature (°C) Precipitation** 

August 31 EC 20.5 ± 2.2 0.3 ± 0.4 18.7 ± 5.9

October 27 EC 10.7 ± 2.5 0 17.84 ± 2.4

*temperature and precipitation recorded 5 days before soil samples were collected. Gravimetric soil water content was calculated as the difference between fresh and dry soil of a unit of soil and the values indicate mean and* 

*Summary of weather data and soil water content. Values indicate means and standard deviation of* 

Random 19.8 ± 6.2

Random 17.39 ± 2.1

*DOI: http://dx.doi.org/10.5772/intechopen.93548*

the two soil sampling methods [16, 22, 49].

method (**Figure 3B**). Increase in F:B ratio during the cooler month reflected the shift in compositional abundance of fungi and bacteria that was observed in October (**Figure 2**). Commonality in trend in total microbial biomass and the ratio of fungi to bacteria as observed along time illustrates the comparable sensitivity of the two soil sampling methods [16, 22, 49].

Shifts in soil microbial communities are affected by seasonality and specifically temperature and moisture. Temporal changes in soil microbial abundance observed in our work has also been demonstrated elsewhere in soil under controlled environment [3, 50] as well as in different ecosystems including forests [51–54], deserts [55], cultivated land, [17–19] as well as grasslands [17]. While fluctuation in microbial abundance across the soil types are common, and the key drivers tend to vary according to the location and soil type, with environmental factors such as temperature and precipitation being the most dominant factors [17, 50, 54, 55].

Our results concur with those of several researchers such as Papatheodorou, Argyropoulou, and Stamou [56] who conducted studies on soils from a grassland in Mediterranean Greece. They detected a linear decline in bacterial diversity, evenness, richness, and mean oxidation, especially of carbohydrate and carboxylic substrates over a 6-month (July to December) study. Substrate use efficiency and specifically carbon use efficiency have also been found to decrease with nutrient availability and increasing temperature [57]. Our study's August and October environmental conditions showed that the monthly mean temperature was higher (20.8° C) in August compared to October (12.2°C). Cumulative monthly precipitation measured 78.89 mm and 110.99 mm for August and October, respectively, which for this area is about average for August but double the average for the month of October (**Figure 2**). In addition, we examined in detail these two environmental parameters recorded 4 days preceding the soil sampling dates. The results summarized in **Table 3** showed that average temperature was 20.5° C in August and dropped to 10.7oC in October. With respect to precipitation, a cumulative 1.27 mm of rain was received 3 days prior to the August sampling and none prior to the October sampling. While there were differences in precipitation both monthly (August and October) and the days preceding the aforementioned sampling dates, this variation did not have a significant effect on soil moisture as revealed by computed soil gravimetric water content (**Table 3**). This implies that, changes observed in bacterial and fungal composition (**Figure 4**) may possibly have resulted from factor(s) other than soil moisture which are discussed in the next section below. These results concur with those of [58] who characterized soil microbial communities and their conditioning by varied plant species. They noted that soil bacterial communities are primarily influenced by abiotic conditions; namely temperature and ECa (**Figure 5**). Fungal communities on the other hand are determined by biotic conditions such, as plant species [58] as seen with the


#### **Table 3.**

*Agrometeorology*

**180**

**Figure 4.**

*Monthly average temperature and precipitation from Mead weather station (41.17° N, 96.47° W) closest to the pasture study site (4 km) at the East Nebraska research and Experimental Station (ENREC). Bars indicate* 

physical and microbial properties. The two soil sampling techniques used in this research (i.e., ECa- and transect-based) captured comparable soil microbial communities and abundance in both space and time highlighting the significant role of vegetation on soil microbial communities as highlighted by others like Grigera

There were statistically significant seasonal differences in the total soil microbial biomass irrespective of soil sampling technique with a considerably higher abundance in August compared to October. Total soil microbial mass in August and October soil samples had means of 130.2 and 137.7 nmol g−1 in August compared to 117.7 and 119.8 nmol g−1 in October for soil samples collected via ECa-directed and transectbased sampling methods. Despite the observed decline in October, which was cooler, no statistical difference was observed in space and time for microbial biomass.

When examining the effect of sampling date (i.e., August vs. October) on the abundance of individual microbial groups, results demonstrated a significant shift in soil biota with temporal changes affecting selective groups. Specifically, saprophytic fungi, actinomycetes, and micro-eukaryotes remained seasonally stable and constituted about 8–9 and 3–4% of the total microbial biomass, respectively (**Figure 2**). Bacteria and AMF abundance exhibited a significant temporal variability. In particular, a significant decline of bacterial biomass ranging from 4 to 10% observed in August and October, respectively, was observed irrespective of method of soil sampling. In contrast to bacteria, a significant increase (4–10%) in AMF abundance was noted in soil sampled in October compared to August (**Figure 4**). Bacterial abundance declined by up to 10% from August to October with a corre-

Fungi to bacterial (F:B) ratio which is indicative of the changes in soil microbial communities [48] was calculated in August (0.30–0.39) and noted to have increased (0.45–0.53) in October, representing a 20 and 32% increase for ECa and transect soil samples, respectively. Although there was a general increase in F:B in October samples, statistical significance was only observed in soil sampled via the transect

et al.; Lauber et al.; Pereira e Silva et al.; and Segal et al. [16–19].

sponding 10% increase in AMF observed during the same period.

**4.4 Effects of temperature on soil microbes**

*total monthly precipitation while stars show mean monthly temperature.*

*Summary of weather data and soil water content. Values indicate means and standard deviation of temperature and precipitation recorded 5 days before soil samples were collected. Gravimetric soil water content was calculated as the difference between fresh and dry soil of a unit of soil and the values indicate mean and standard deviation.*

#### **Figure 5.**

*Cluster heat map showing relationship between soil microbiota, soil physicochemical attributes, and environmental variables. Soil properties and microbes are indicated on the bottom, while soil ECa and temperature are to the left of the image. The rows representing soil samples characteristics are clustered based on hierarchical cluster analysis of the values of the measured soil variables represented in the columns. These soil variables are sorted based on physical, chemical, and biological characteristics. Letters "E" and "e" represent high and low field ECa, respectively, and classification was based on the median ECa value of 32.5 mS m−1. The ECa values greater than 32.5 mS m−1 units are represented by "E," while those below the media are denoted by "e." Letters "T" and "t" represent the warmer and cooler month of August and October, respectively. The z values are represented by the blue color, while the color intensity shows the level of significance.*

increased flush of Brome grass root growth during the late fall season. The brome plant-AMF microbial feedback elicits subsequent biases toward the development of brome grass monoculture.

#### **4.5 Impacts of chemical, biological, and physical properties on soil microbial communities**

The overall relationship between the soil microbes and soil physiochemical characteristics was computed and summarized in https://prhpa.unl.edu/supplementary-materials-ltar-pasture-soil-characteristics-0. The results show significant correlations between soil physical, chemical, and biological characteristics. In general, there were 30 and 24 statistically significant correlations among the measured soil parameters sampled in August and October, respectively. A total of 15 of these correlations were consistent across the two sampling time points (https://prhpa.unl. edu/supplementary-materials-ltar-pasture-soil-characteristics-0).

Concerning microbial groups and their abundance as impacted by soil physicochemical characteristics, bacteria was negatively correlated with bulk density (BD) and NO3 − at −0.74 and − 0.66, respectively, while being positively correlated (R<sup>2</sup> 0.73) with C:N. In addition, saprophytic fungi were strongly correlated with ECa, EClab, and NO3 − at R<sup>2</sup> 0.6, 0.84, and 0.88, respectively. On the other hand, in soil sampled in October, statistical significance was solely observed between AMF and soil NO3 − (R<sup>2</sup> –0.61). These results are in agreement with the direct effect of BD on soil drainage and its negative influence on bacteria populations similarly observed in crop-livestock studies [59]. Additionally, soils with high available NO3 − demonstrate a lower population of bacteria necessary in nitrification processes

**183**

**Figure 6.**

*Spatio-Temporal Dynamics of Soil Microbial Communities in a Pasture: A Case Study…*

populations. A highly positive association of ECa, EClab and NO3

phytic fungi is attributable to the cationic byproducts as well as NO3

−

−

[47]. Soils with low N content, indicative of high C:N ratios, have higher bacterial

cellular breakdown of organic matter by the saprohytic fungi's enzymes. Seasonal variations that lead to lower soil temperatures influence bacteria populations and

regrowth brome grass roots resulting in their higher prevalence and abundance in October soil samples. Our findings concur with those of [58] who determined that plant species determined the relative abundance of AMF fungi in comparison to saprotrophs (e.g., saprophytic fungi and bacteria) which were influenced by soil

The relationship between soil microbes and soil physicochemical characteristics as impacted by ECa variability of individual sampling point was examined using PCA (**Figure 6**). The first two PCA axes explained a total of 57.4% of the variability with PC1 and PC2 explaining 32.3 and 27.4%, respectively, of the total variation between several of the soil physicochemical characteristics, temperature, and microbial diversity (**Figure 5**). Specifically, PC1and PC2 largely explained variability in microbial groups and soil characteristics, respectively, (**Figure 6**). A consistent discrimination of soils was evident in the sum of microbial fatty acids which was noted as being negatively associated with relatively low ECa levels. High

abundance which was similarly observed by [16] in a corn field in Buffalo county in Nebraska. This observation is also noted in this study as shown in the right hand corner of the heatmap which generally depicts soils of 32.5 mS m−1 exhibiting z-scores of above 0 across microbial types (**Figure 5**). High P levels on the other hand decreased AMF root colonization and spore density thereby decreasing microbial abundance and diversity in soils that are relatively high in P from sources such as fertilizers [60, 61]. Sources of P in this pasture site included effluents from

*Principal component analysis (PCA) of microbial groups in soil sampled via ECa method during the warm (August) and cooler (October) temperature. Abbreviations containing a combination of letters and numerals denote the seasons temperature (warm "T" and cool temperature "t") followed by soil ECa (high "E" or low "e" soil ECa values relative to the median value of 32.5 mS m−1), while the numeral (1–12) indicates the point on* 

−

. On the other hand, AMF colonize roots of

where associated with soils of high microbial

−

with sapro-

from extra-

*DOI: http://dx.doi.org/10.5772/intechopen.93548*

nitrification processes reducing NO3

abiotic factors such as pH.

levels of ECfield, EClab, and NO3

the livestock in the form of manure and urine.

*the field where soil samples were collected following ECa gradient.*

*Spatio-Temporal Dynamics of Soil Microbial Communities in a Pasture: A Case Study… DOI: http://dx.doi.org/10.5772/intechopen.93548*

[47]. Soils with low N content, indicative of high C:N ratios, have higher bacterial populations. A highly positive association of ECa, EClab and NO3 − with saprophytic fungi is attributable to the cationic byproducts as well as NO3 − from extracellular breakdown of organic matter by the saprohytic fungi's enzymes. Seasonal variations that lead to lower soil temperatures influence bacteria populations and nitrification processes reducing NO3 − . On the other hand, AMF colonize roots of regrowth brome grass roots resulting in their higher prevalence and abundance in October soil samples. Our findings concur with those of [58] who determined that plant species determined the relative abundance of AMF fungi in comparison to saprotrophs (e.g., saprophytic fungi and bacteria) which were influenced by soil abiotic factors such as pH.

The relationship between soil microbes and soil physicochemical characteristics as impacted by ECa variability of individual sampling point was examined using PCA (**Figure 6**). The first two PCA axes explained a total of 57.4% of the variability with PC1 and PC2 explaining 32.3 and 27.4%, respectively, of the total variation between several of the soil physicochemical characteristics, temperature, and microbial diversity (**Figure 5**). Specifically, PC1and PC2 largely explained variability in microbial groups and soil characteristics, respectively, (**Figure 6**). A consistent discrimination of soils was evident in the sum of microbial fatty acids which was noted as being negatively associated with relatively low ECa levels. High levels of ECfield, EClab, and NO3 − where associated with soils of high microbial abundance which was similarly observed by [16] in a corn field in Buffalo county in Nebraska. This observation is also noted in this study as shown in the right hand corner of the heatmap which generally depicts soils of 32.5 mS m−1 exhibiting z-scores of above 0 across microbial types (**Figure 5**). High P levels on the other hand decreased AMF root colonization and spore density thereby decreasing microbial abundance and diversity in soils that are relatively high in P from sources such as fertilizers [60, 61]. Sources of P in this pasture site included effluents from the livestock in the form of manure and urine.

#### **Figure 6.**

*Agrometeorology*

**Figure 5.**

of brome grass monoculture.

**on soil microbial communities**

increased flush of Brome grass root growth during the late fall season. The brome plant-AMF microbial feedback elicits subsequent biases toward the development

*Cluster heat map showing relationship between soil microbiota, soil physicochemical attributes, and environmental variables. Soil properties and microbes are indicated on the bottom, while soil ECa and temperature are to the left of the image. The rows representing soil samples characteristics are clustered based on hierarchical cluster analysis of the values of the measured soil variables represented in the columns. These soil variables are sorted based on physical, chemical, and biological characteristics. Letters "E" and "e" represent high and low field ECa, respectively, and classification was based on the median ECa value of 32.5 mS m−1. The ECa values greater than 32.5 mS m−1 units are represented by "E," while those below the media are denoted by "e." Letters "T" and "t" represent the warmer and cooler month of August and October, respectively. The z* 

*values are represented by the blue color, while the color intensity shows the level of significance.*

The overall relationship between the soil microbes and soil physiochemical characteristics was computed and summarized in https://prhpa.unl.edu/supplementary-materials-ltar-pasture-soil-characteristics-0. The results show significant correlations between soil physical, chemical, and biological characteristics. In general, there were 30 and 24 statistically significant correlations among the measured soil parameters sampled in August and October, respectively. A total of 15 of these correlations were consistent across the two sampling time points (https://prhpa.unl.

Concerning microbial groups and their abundance as impacted by soil physicochemical characteristics, bacteria was negatively correlated with bulk density

0.73) with C:N. In addition, saprophytic fungi were strongly correlated with

soil sampled in October, statistical significance was solely observed between AMF

BD on soil drainage and its negative influence on bacteria populations similarly observed in crop-livestock studies [59]. Additionally, soils with high available NO3

demonstrate a lower population of bacteria necessary in nitrification processes

at −0.74 and − 0.66, respectively, while being positively correlated

–0.61). These results are in agreement with the direct effect of

0.6, 0.84, and 0.88, respectively. On the other hand, in

−

**4.5 Impacts of chemical, biological, and physical properties** 

edu/supplementary-materials-ltar-pasture-soil-characteristics-0).

**182**

(R<sup>2</sup>

(BD) and NO3

and soil NO3

ECa, EClab, and NO3

− (R<sup>2</sup>

−

− at R<sup>2</sup>

*Principal component analysis (PCA) of microbial groups in soil sampled via ECa method during the warm (August) and cooler (October) temperature. Abbreviations containing a combination of letters and numerals denote the seasons temperature (warm "T" and cool temperature "t") followed by soil ECa (high "E" or low "e" soil ECa values relative to the median value of 32.5 mS m−1), while the numeral (1–12) indicates the point on the field where soil samples were collected following ECa gradient.*

The pasture site comprised of a monoculture of brome grass, a cool season grass species with extensive below ground rhizomes and a unique a capacity to maintain active growth during cooler weather. Brome grass has been reported to yield high root biomass [62] with approximately 1014 g m−2 root mass measured in 0–10 cm depth of the soil. The seasonal shifts impacts in root biomass production impacted soil microbial communities specifically increasing AMF abundance [63] by influencing C availability [52, 64]. We speculate that as these plants were undergoing late season growth, facilitated by their inherent ability to withstand low soil temperatures (can withstand temperatures as low as −28°C); C allocation to the rhizomes (storage organs) increased as a survival strategy thereby affecting root exudate production. Exudates acting as cues coupled with changes in the production of these compounds have been shown to impact soil microbial community and composition [65]. Thus, elevated production of these signal molecules may have triggered a surge in species of AMF that preferentially associate or benefits from this grass species [66–68]. Bacteria in turn are critical for C nutrient cycling [69, 70]. Some bacteria species are also known to interact with plants symbiotically while fixing nitrogen and also externally in root zones, decomposing organic matter and releasing nutrients to plant roots [71]. Their abundance is largely influenced by the substrate quality of the roots and their exudates.
