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BioGeoChemistry

Soil β-glucosidase (BG) activity

November 13, 2006 at 2:14am

Evaluation of a Scoring Equation for Soil β-glucosidase (BG) activity for the Soil Management Assessment Framework

Yashkirat Singh

Apr 20 2010

Abstract

This study was carried out on the campus of the University of Florida in Gainesville, FL. Four different sites (Forest, Garden, Grass, Pond) were picked to analyze their respective β-glucosidase (BG) activities. Additionally a wetland soil from an unknown location was analyzed as well. Triplicate samples were prepared and a fourth sample was used as the control. In all there were 20 samples that were analyzed for β-glucosidase (BG) activity. The BG activity values were in line with those reported in the literature. When these BG values were fed into the SMAF-BG activity score equation, they produced a profile of the Gainesville soil. The steepness of the curve (BG score vs. BG activity) demonstrated that this soil cannot support high levels of BG activity.

1. Introduction

1. 1 Soil enzymes

Soil enzymes mediate and catalyze a number of soil biochemical and nutrient-cycling processes involved in soil functions and are considered to be the most likely candidates for determining early responses to changes in soil management (Dick et al., 1987). Enzyme activities increase as a response to increases in soil microbial populations. β-glucosidase (BG) , an enzyme, plays a major role in the degradation of soil organic matter and plant residues. It catalyzes the hydrolysis of β-d-glucopyranosides in the final, rate-limiting step in the degradation of cellulose, the most abundant polysaccharide in the earth, providing simple sugars for the soil microbial population.

1.2 β-glucosidase (BG)

β-glucosidase is characteristically useful as a soil quality indicator, and may give a reflection of past biological activity, the capacity of soil to stabilise the soil organic matter, and can be used to detect management effect on soils (Eivazi and Tabatabai, 1988). This has facilitated its adoption for soil quality testing (Scott et al., 2010). Generally, -glucosidase activities can provide advanced evidence of changes in organic carbon long before it can be accurately measured by other routine methods.

1.3 Soil Quality & SMAF

The Soil Management Assessment Framework (SMAF) was developed to assess conservation effects on soil, and uses multiple soil quality indicator measurements to compare soil functioning. Soil quality and its assessment is soil and site specific and depends on a variety of factors, including inherent soil characteristics, environmental influences such as climate, and human values such as intended land use, management goals, and environmental protection, all of which are considered (and can be manipulated by the user) in the SMAF tool (Andrews et al., 2004).

1.4 Problem Definition

Currently the SMAF includes two microbial or biochemical indicators, PMN and MBC, both being represented by more-is-better curves (Andrews et al., 2004). Neither of these parameters addresses microbial activity or the potential metabolic activity of the soil. The inclusion of β-glucosidase (BG) activity within the SMAF framework will solve the problem.

1.5 Justification for Including BG Activity in SMAF

Within the structure of the SMAF, relative BG activity would relate to the following soil functions due to its importance in C cycling and providing simple sugars to support a diverse microbial population: nutrient cycling (for plant growth), biodiversity and habitat (within the soil and the plants and animals sustained by the soil), filtering and buffering (excess nutrients and toxic chemicals from the water), and physical stability and support soil structure (Scott et al., 2010). An increasing BG activity, which usually increases with increasing soil microbial biomass, would reflect on a soil’s ability to break down plant residues and improve the availability of nutrients for subsequent crops.

A SMAF compatible scoring equation for soil β-glucosidase (BG) activity was developed by Scott et al (2010) using published data sets representing diferent soils and management. The resulting equation was an S-shaped curve: y = a/[1 + bexp(−cx)], where x is the measured BG activity (mg p-nitrophenol [PNP] released kg−1 soil h−1), a and b are constants, and c is a factor modified by soil classification, texture, and climate.

Data from this study and others was used to validate the scoring equation for soil β-glucosidase (BG) activity.

2. Methods

2.1 Soil Sampling and Processing

The study was carried out on the campus of the University of Florida in Gainesville, FL. Four different sites (Forest, Garden, Grass, Pond) were picked to analyze their respective β-glucosidase (BG) activities. The samples were air dried, gently ground with a wooden roller, and sieved through a 2-mm sieve. Additionally wetland soil from an unknown location was analyzed as well. Triplicate samples were prepared and a fourth sample was used as the control. In all there were 20 samples that were analyzed for β-glucosidase (BG) activity. Moisture content of the soil was determined by drying the soil at 70ºC for 72 h.

2.2 Protocol (Garcıa-Gil et al., 2000)

β-glucosidase activities were determined by using p-nitrophenyl-b-d-glucopyranoside (PNG) as a substrate which is broken down by the BG enzyme present in soils. This assay is based on the release and detection of p-nitrophenol (PNP). The colorimetric procedure was used for estimation of p-nitrophenol (PN) released by the breakdown PNG.

Ten ml of 0.1 M maleate buffer pH 6.5 and 0.5 ml of substrate were added to 0.5 g of sample and incubated at 37.8C for 60min. The reaction was stopped by cooling rapidly to 28C for 15min; 0.5 ml of .5M CaCl2 and 2 ml of 0.5M NaOH were then added and the mixture centrifuged at 2000g for 10 mins (Garcıa-Gil et al., 2000). To stop the b-glucosidase assay, trishy droxymethylaminomethane (THAM) was used according to Tabatabai (1982). The amount of PNP was determined using a spectrophotometer at 398nm (Tabatabai and Bremner, 1969).

2.3 PNP Standards

The colorimetric procedure used for estimation of PN depends upon the fact that alkaline solutions of this phenol have a yellow color, whereas acid solutions of PN are colorless. Addition of NaOH to the incubated soil-buffer-substrate mixture to develop the yellow color of the PN released showed that the substrate, hydrolyzed with time in the presence of excess NaOH.

2.4 Control

The control was designed to correct for the presence of trace amounts of PN in the substrate (PNG) used and for extraction of trace amounts of colored soil material by the CaCl2-NaOH treatment. No chemical hydrolysis of PNG could be detected when the buffered PNG was incubated as described but without soil.

2.5 Buffer

Used of 0.05M THAM buffer, along with CaCl2-THAM pH 12 as an extractant for the PN released, produced the least color in the extracts. Choice of pH was based on studies showing that maximum activity occurs when using 0.05M THAM buffer, pH 8.0.

3. Results

3.1 PNP Standards

Table 2: Curve Fitting (Linear) for PNP Standards Data

PNP Standards Curve

Table 1: PNP Standard Curve Data

PNP Concentration (mg/L)

Absorbace (λ=390 nm)

0

0.002

0.1

0.012

0.2

0.023

0.5

0.058

0.75

0.087

1

0.113

3.2 BG Activity Analysis

Grass Lib West Grass Soil

Sample

Weight (mg)

Dry Wt. (mg)

PNG Consumed (mg /gm soil)

PNG Consumed (mg /gm soil)Corrected for Control

Average (mg /gm soil)

PNG Consumed (μg /gm soil)

Average (μg /gm soil)

#1

0.578

0.46818

0.22289504

0.115263461

115.2634614

#2

0.527

0.42687

0.157337761

0.049706182

49.70618196

#3

0.532

0.43092

0.158878759

0.05124718

51.24718045

#C

0.513

0.41553

0.107631579

 

 

 

 

0.072072275

 

72.07227461

McCarty Forest Soil

Sample

Weight (mg)

#1

0.583

0.51304

0.167881647

0.063052101

63.0521012

#2

0.507

0.44616

0.175073964

0.070244419

70.24441904

#3

0.526

0.46288

0.16936247

0.064532924

64.5329243

#C

0.54

0.4752

0.104829545

 

 

 

 

0.065943148

 

65.94314818

UF Dairy Pond Soil

Sample

Weight (mg)

#1

0.589

0.42997

0.238621299

0.147537037

147.5370375

#2

0.581

0.42413

0.229175017

0.138090755

138.0907554

#3

0.577

0.42121

0.244289072

0.15320481

153.2048103

#C

0.589

0.42997

0.091084262

 

 

 

 

0.146277534

 

146.2775344

Garden Soil

Sample

Weight (mg)

#1

0.585

0.47385

0.34045584

0.161821865

161.821865

#2

0.524

0.42444

0.308937659

0.130303684

130.3036836

#3

0.553

0.44793

0.311332128

0.132698152

132.6981523

#C

0.571

0.46251

0.178633975

 

 

 

 

0.1416079

 

141.6079003

Wetland Soil# 25

Sample

Weight (mg)

#1

0.496

0.19344

1.007056452

0.764086552

764.0865519

#2

0.745

0.29055

0.444656686

0.201686786

201.6867859

#3

0.596

0.23244

1.289945792

1.046975893

1046.975893

#C

0.575

0.22425

0.2429699

 

 

 

 

0.67091641

 

670.9164102

3.3 Data-Set from Literature

Wetland Soils: BG Activity (in PNP)

References

Original Units

Range

Converted (all units to μg)

Estimated       (PNP Equivalent) ^

Place

Depth

Dinesh et al. (2005)


μmol p-Nitrophenol  g−1 soil  h−1

3.6 to 6.1

500.8 to 848.7

SAME

Undisturbed Mangrove Forests

Soils were collected from the upper 0–30 cm

Pulford and Tabatabai (1988).

μg p-nitrophenol  g−1 soil  h−1

43 to 283

43 to 283

SAME

Common  soil  series  found  in  lowa (Wetland inluded).

The  10 soils  used  were  surface  samples  (0-15 cm).

Costa et al. (2007)


ηmol p-Nitrophenol  g−1 soil  h−1

1200

166.93

SAME

Estuary salt marsh

depth (8–10cm)

Yang et al. (2008)

μg p-nitrophenol  g−1 dry wt. soil  h−1

35 to 80

35 to 80

SAME

Mangrove Wetland

Surface soil sam-
ple (0–5cm).

Zhang et al. (2010)

μg p-nitrophenol  g−1 dry wt. soil  h−1

14 to 28

14 to 28

SAME

Constructed wetlands

Rhizosphere

Acosta-Martinez et al. (2007)

mg p-nitrophenol  Kg−1 dry wt. soil  h−1

1.04 to 63.4

1.04 to 63.4

SAME

 Willamette silt loam,
Walla Walla silt loam,
Newberg loam ,
Jory silty clay loam ,
Bashaw clay.

A total of 103 soil samples were taken at 0–15 cm

Reddy et al. (1999)

μg MUF g−1 soil  h−1

562 to 2055

562 to 2055 μg MUF g−1 soil  h−1

713.74 to 2609.85

Water Conservation Area 2A (WCA2A), Everglades,Florida (Nutrient Enriched & Natural Sites)

 

Reddy et al. (2006)

µg MUF g-1 dwsoil min-1

3 to 12; 9 (mean)

180 to 720 μg MUF g−1 soil  h−1

228.6 to 914.4

Blue Cypress Marsh (BCM) of USRB

drained 7.6 cm from surface

Jones (1998)

μmol MUF-C g-1
h-1

.52 to 1.66

91.61 to 292.4 μg MUF gC-1  h−1

116.34 to 371.4

Everglades Wetlands

 

Prenger and Reddy (2004).

μmol MUF/g dry wt. soil/hr

.4 to .8

70.468 - 140.936 μg MUFg−1 dry wt. soil  h−1

89.5 to 179

Freshwater Marsh (Blue Cypress Marsh Conservation Area).

Intact soil cores(0–10cm)

Hill et al. (2006).

nmol MUF /gC/hr

68 to 663

11.979 to 116.8  μg MUF gC-1  h−1

NA

Coastal wetlands of the Laurentian Great Lakes

Surface sediments (top 5 cm)

Kang and Freeman (2009)

nmol MUF /cm3/min

1.13-6.68

11.94 to 70.60 μg MUF cm3 soil h−1

NA

Global Welands

Soil samples were collected to 10 cm deep from the surface, with the surface vegetation removed.

Corstanje and Reddy (2007)

μg MUF g−1 soil  h−1

56

56 μg MUF g−1 soil  h−1

71.2

Blue Cypress Marsh Conservation Area (BCMCA)

Soil cores were sectioned in 0–5 and 5–10 cm depth

Pentona and Newman (2008).

μmol MUFg−1 ash-free dry mass (AFDM) h−1

.02 to .08

3.524 to 14.0936 μg MUFg−1 soil ash-free dry mass (AFDM)* h−1

NA

Florida Everglades

Soil cores were obtained in triplicate to a depth of approximately 30 cm at each habitat–site

^Conversion of MUF to PNP: Multiply the MUF value by 1.27 (Molecular Weight of MUF/Molecular Weight of PNP)

Forest Soils: BG Activity (in PNP)

References

Original Units

Range

Converted (all units to μg)

Place

Depth

Dilly and Nannipieri (2004)

μg p-nitrophenol  g−1 dry wt. soil  h−1

60 to 90

60 to 90

Landscape consists of  hills and lakes

 

Bastida et al. (2007)


μmol p-Nitrophenol  g−1 soil  h−1

.4 to .5

55.65 to 69.55

Terraced forest soil in Spain

the top
15 cm of soil

3.4 SMAF Scores

Scott et al. (2010) proposed the following equation to calculate BG activity scores for particular soils:

Equation 1 Source: Scott et al. (2010). SMAF equation where the a is a constant equal to 1.01; b is a constant set to 48.4; and c is a factor that is equal to SMAF equation Coefficients here c1 is the SOM class factor, c2 is the texture class factor, and c3 is the climate class factor (See Appendix B, Tables 1–3). The following data was used to calculate the parameters for the above equation (that is, c value in conjunction with Tables 1-3, Appendix B):

Gainesville Mean Precipitation:

1342.1 mm

Gainesville Mean Temperature:

69.7 deg F

Gainesville Growing Season:

255 days

SOM Factor Class (c1) :

1 (Aquods)

Texture Class (c2) :

1

Climate Designation (c3) :

1

c= c1x c2 + c1x c2x c3 :

2

3. Discussion

The following BG activities were observed in Gainesville soils:

Sample

Average BG Activity

McCarty Forest Soil

65.9

Library West Grass Soil

72.1

Garden Soil

141.6

UF Dairy Pond Soil

146.3

Wetland Soil# 25

670.9

3.1 BG Activity in Wetland Soil Sample # 25

The high BG activity observed in Garden and UF Dairy pond soil can be explained on the basis of the diversity of plants growing in those locations. Soil with greater diversity has higher beta-glucosidase activity. The b-glucosidase activities are a result of a variety of organisms such as plants, animals, fungi and bacteria, and catalyze the final steps of degradation of cellulose in to sugar. The b-glucosidase activities tend to increase with species richness, indicating that increase in the richness improves the decomposition of carbohydrates.

BG Activity

3.2 BG Activity of McCarty Forest Soil Sample

Forest soil in this study was defined as undisturbed land with trees and shrubs. The small forest behind McCarty hall was picked to study this kind of soil. The value for BG activity is in line with what other researchers have reported. McCarty Forest soil had the lowest BG activity among the other samples. This could be because of the cooler temperature in the forest and low quality of litter (less diversity). Moreover the moisture content was the lowest in Forest soil. Forest Soil Activity

3.3 SMAF Scores

Scott et al. tested the BG activity equation (1) against several data sets. They obtained sigmoid curves. What is to be noted is that the if the SMAF score is high (say .8) compared to BG activity (< 200 μg PNP g−1 dry wt. soil h−1) then the curve has a steep slope. This can be seen from the figures below: Figure 1 Source: Sigmoid Curves for particular Soils. The numbers (e.g 1-4-1) designate the classes (texture, climate, etc.) of that soil. Source: Scott et al. (2010). b-Glucosidase Scores

Figure 2 Source: Close up of soils that reach a score of .8 with comparatively less BG activity. Scott et al. (2010). b-Glucosidase Scores less than .8

As can be seen from the figure below, the data on Gainesville soils (Forest, Garden, Grass, Pond), corresponds to a steep sigma curve. It is therefore highly unlikely for Gainesville soil to exhibit high BG activity. This conclusion is supported by the fact that Gainesville soils are generally considered to be poor for growing crops. It should be noted that more samples are required to build a true sigmoid curve for Gainesville soils. It was unfortunate that the four samples selected tended to cluster and hence were not enough the build the complete curve.

Gainesville soils (Forest, Garden, Grass, Pond) b-Glucosidase Scores less than .8

Conclusion

This study was carried out on the campus of the University of Florida in Gainesville, FL. Four different sites (Forest, Garden, Grass, Pond) were picked to analyze their respective β-glucosidase (BG) activities. Additionally a wetland soil from an unknown location was analyzed as well. Triplicate samples were prepared and a fourth sample was used as the control. In all there were 20 samples that were analyzed for β-glucosidase (BG) activity. The BG activity values were in line with those reported in the literature. When these BG values were fed into the SMAF-BG activity score equation, they produced a profile of the Gainesville soil. The steepness of the curve (BG score vs. BG activity) demonstrated that this soil cannot support high levels of BG activity. It should be noted, however, that more samples are required to build a true sigmoid curve for Gainesville soils. It was unfortunate that the four samples selected tended to cluster and hence were not enough the build the complete curve.

Appendix A

Glucosidase Activity In Various Soils

  1. 1. Constructed wetlands (14 to 28 μg p-nitrophenol g−1 soil h−1) Figure 3 Source: Zhang et al. (2010). Zhang et. al.,2010
  2. 2. Wetlands (bog, fen, marsh and swamp) covering a latitudinal range of 5 deg to 60deg N. (11.94 to 70.60 μg MUF cm3 soil h−1) Figure 4 Source: Kang and Freeman (2009) Kang and Freeman, 2009
  3. 3. Blue Cypress Marsh Conservation Area(BCMCA) (56 μg MUF g−1 soil h−1) Figure 5 Source: Corstanje and Reddy (2007) Corstanje and Reddy, 2007
  4. 4. Constructed Mangrove Wetland (14 to 28 μg p-nitrophenol g−1 soil h−1) Figure 6 Source: Yang et al. (2008) Yang et al. (2008)

    Glucosidase activity in a northern Typha marsh receiving farmland drainage ranged from 200 mg/gm/hr at the inflowsite to 5000 mg/gm/hr at an out flow site, corresponding to increases in particle size (Jacksonetal.,1995).

  5. 5. Common soil series found in lowa (43 to 283 μg p-nitrophenol g−1 soil h−1) Figure 7 Source: Pulford and Tabatabai (1988). Pulford and Tabatabai (1988)

    The activities of eight enzymes involved in C, N, P and S cycling were assayed in soils before and after waterlogging for times ranging from 0 to 10 days at room temperature (22°C).

  6. 6. Florida Everglades (.02 to .08 μmol MUFg−1 ash-free dry mass (AFDM) h−1) Figure 8 Source: Pentona and Newman (2008). Pentona and Newman (2008)

    Enzyme-Based Resource Allocated Decomposition and Landscape Heterogeneity in the Florida Everglades

  7. 7. Rainforest soil derived from a 300-year-old volcanic tephra substrate at 1200m elevation on the Island of Hawaii. (2.5 μmol p-Nitrophenol g−1 soil h−1) Figure 9 Source: Allison and Vitousek (2005) Allison and Vitousek  (2005)

    Enzyme-Based Resource Allocated Decomposition and Landscape Heterogeneity in the Florida Everglades

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