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
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) |
|
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) |
|
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- |
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, |
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 |
.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) |
|
.4 to .5 |
55.65 to 69.55 |
Terraced forest soil in Spain |
the top |
3.4 SMAF Scores
Scott et al. (2010) proposed the following equation to calculate BG activity scores for particular soils:
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
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.
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.
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:
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.
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. Constructed wetlands (14 to 28 μg p-nitrophenol g−1 soil h−1)
Figure 3 Source: Zhang et al. (2010). 
-
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) 
-
3. Blue Cypress Marsh Conservation Area(BCMCA)
(56 μg MUF g−1 soil h−1)
Figure 5 Source: Corstanje and Reddy (2007) 
-
4. Constructed Mangrove Wetland (14 to 28 μg p-nitrophenol g−1 soil h−1)
Figure 6 Source: 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. Common soil series found in lowa (43 to 283 μg p-nitrophenol g−1 soil h−1)
Figure 7 Source: 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. Florida Everglades (.02 to .08 μmol MUFg−1 ash-free dry mass (AFDM) h−1)
Figure 8 Source: Pentona and Newman (2008).
Enzyme-Based Resource Allocated Decomposition and Landscape Heterogeneity in the Florida Everglades
-
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) 
Enzyme-Based Resource Allocated Decomposition and Landscape Heterogeneity in the Florida Everglades
References
Allison SD, Vitousek PM (2005) Responses of extracellular enzymes to simple and complex nutrient
inputs. Soil Biology and Biochemistry 37: 937-944
Acosta-Martinez V, Cruz L, Sotomayor-Ramirez D, Perez-Alegria L (2007) Enzyme activities as affected by
soil properties and land use in a tropical watershed. Applied Soil Ecology 35: 35-45
Andrews SS, Karlen DL, Cambardella CA (2004) The soil management assessment framework: A
quantitative soil quality evaluation method. Soil Sci. Soc. Am. J. 68:1945–1962
Bastida F, Moreno JL, Hernandez T, Garcia C (2007) The long-term effects of the management of a forest
soil on its carbon content, microbial biomass and activity under a semi-arid climate. Applied Soil Ecology. J.apsoil 37: 53-62
Browman MG, Tabatabai MA (1978) Phosphodiesterase activity of soils. Soil Science Society of
America Journal 42, 284-290
Corstanje R, Reddy KR (2007) Soil microbial ecophysiology of a wetland recovering from phosphorus
eutrophication. Wetlands 27: 1046-1055
Costa AL, Carolin M, Caçador I (2007) Microbial activity profiles in Tagus estuary salt marsh sediments.
Hydrobiologia 587: 169-175
Dick R P, Tabatabai MA (1987) Polyphosphates as sources of phosphorus for plants. Ferriker
Research 12, 103-108
Dilly O, Nannipieri P (2004) Response of ATP content, respiration rate and enzyme activities in an arable
and a forest soil to nutrient additions. Biology and Fertility of Soils 34: 64-72
Dinesh R, Chaudhury SG, Ganeshamurthy AN, Pramanik SC (2005) Biochemical properties of soils of
undisturbed and disturbed mangrove forests of South Andaman. Wetlands Ecology and Management 12: 309-320
Eivazi F, Tabatabai MA (1988) Glucosidases and galactosidases in soils. Soil Biology and Biochemistry 20:
601-606
Garcıa-Gil JC, Plaza C, Soler-Rovira P, Polo A (2000) Long-term effects of municipal solid waste compost
application on soil enzyme activities and microbial biomass. Soil Biology and Biochemistry 32:1907-1913
Hill BH, Elonen CM, Jicha TM, Cotter AM, Trebitz AS, Danz NP (2006) Sediment microbial enzyme activity
as an indicator of nutrient limitation in Great Lakes coastal wetlands. Freshwater Biology Freshwater Biology 51:1670–1683
Jones RD (1998) Numerical Interpretation of Class III Narrative Nutrient Water Criteria for Everglades
Wetlands. Apr 20 2010. <http://www2.fiu.edu/~serp/jrpp/flume1.pdf>
Kang H, Freeman C (2009) Soil Enzyme Analysis for Leaf Litter Decomposition in Global Wetlands 40:
3323 - 3334
Makoi, Joachim HJR, Ndakidemi, PA (2008) Selected soil enzymes: Examples of their potential roles in
the ecosystem. African Journal of Biotechnology 7:181-191
Masciandaro, G et al. (2008) Comparison of extraction methods for recovery of extracellular β-
glucosidase in two different forest soils. Soil Biology and Biochemistry 40:2156-2161
Pentona, CR, Newman S(2008) Enzyme-Based Resource Allocated Decomposition and Landscape
Heterogeneity in the Florida Everglades. J Environ Qual 37:972-976
Prenger JP, Reddy KR (2004) Microbial Enzyme Activities in a Freshwater Marsh after Cessation of
Nutrient Loading. Soil Sci. Soc. Am. J. 68:1796-1804
Pulford ID, Tabatabai MA (1988) Effect of water logging on enzyme activities in soil. SoilBiol. Biochem.
20:215–219
Reddy KR, White JR, Wright A, Chua T (1999) Influence of phosphorus loading on microbial processes in
the soil and water column of wet lands. In:Reddy, K.R., O’Connor,G.A., Schelske, C.L.(Eds.), Phosphorus Biogeochemistry in Subtropical Ecosystems. LewisPubl., NewYork, pp.249–273
Reddy KR, Osborne T Z, Inglett KS, Corstanje R (2006) Influence of Water Levels on Subsidence of
Organic Soils in the Upper St. Johns River Basin. Final Report Submitted to: St. Johns River Water Management District. Apr 20 2010. <http://www.sjrwmd.net/technicalreports/pdfs/SP/SJ2007-SP5.pdf>
Stott DE, Andrews SS, Liebig MA, Wienhold BJ, Karlen DL (2010) Evaluation of β-Glucosidase Activity as a
Soil Quality Indicator for the Soil Management Assessment Framework. Soil Sci Soc Am J 74:107-119
Wright AL, Reddy KR (2001) Phosphorus Loading Effects on Extracellular Enzyme Activity in Everglades
Wetland Soils. Soil Science Society of America Journal 65:588-595
Yang Q, Tam NFY, Wong YS, Luan TG, Su WS, Lan CY, Shin PKS, Cheung SG (2008) Potential use of
mangroves as constructed wetland for municipal sewage treatment in Futian, Shenzhen, China, Marine Pollution Bulletin, Volume 57, Issues 6-12, 5th International Conference on Marine Pollution and Ecotoxicology, 2008, Pages 735-743
Zhang CB, Wang J, Liu WL, Zhu SX, Liu D, Chang SX, Chang J, Ge Y (2010) Effects of plant diversity on
nutrient retention and enzyme activities in a full-scale constructed wetland. Bioresource Technology, 101: 1686-1692