agricultural management effects on soil microbial dynamic under peanut cropping systems in semi

Agricultural Management Effects on Soil Microbial Dynamic Under Peanut Cropping Systems in Semi-Arid Soil

By

Brandon Melester, B.S.

A Thesis In

MICROBIOLOGY

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

MASTER OF SCIENCE

Approved

Dr. John C. Zak Chair of Committee

Dr. Veronica Acosta-Martinez

Dr. Michael San Francisco

Dr. Fred Hartmeister Dean of the Graduate School

August, 2010

Texas Tech University, Brandon Melester, August 2010

ii

TABLE OF CONTENTS

ABSTRACT .......................................................................................................................... iv

LIST OF TABLES ............................................................................................................... vi

LIST OF FIGURES ............................................................................................................ vii

I. INTRODUCTION TO MICROBIAL DYNAMICS, MICROBIAL EVALUATION AND AGRICULTURAL PRACTICES .......................................... 1

Introduction ......................................................................................................................... 1 Arbuscular Mycorrhiza ................................................................................................. 1

Microbial Communities ................................................................................................ 4

Microbial Biomass ........................................................................................................ 5 FAME ........................................................................................................................... 5 Soil Enzymes ................................................................................................................ 6 Tillage ........................................................................................................................... 7 Objectives and Hypothesis ............................................................................................ 8 References ......................................................................................................................... 11

II. EFFECTS OF MANAGEMENT PRACTICES IN A SEMI-ARID AGROECOSYSTEM ON SOIL MICROBIAL BIOMASS AND MICROBIAL COMPOSITION ...................................................................................................... 16

Introduction ....................................................................................................................... 16 Methods............................................................................................................................. 18

Plot Design ........................................................................................................................................ 18 Crop Management Treatments .................................................................................... 20 Sampling ..................................................................................................................... 20 Microbial Biomass Carbon ......................................................................................... 20 Microbial Community Structure ................................................................................. 21 Data Analysis .............................................................................................................. 22


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Results ............................................................................................................................... 23 Effects of Tillage on MBC .......................................................................................... 23 Effects of Irrigation on MBC ...................................................................................... 23 Effects of Nitrogen on MBC ....................................................................................... 24 Changes in field conditions ......................................................................................... 24 Influences between MBC, nutrients, and enzymatic activities .......................................... 25 Temporal changes in MBC ......................................................................................... 25 FAME ......................................................................................................................... 26 Discussion ......................................................................................................................... 28

Nitrogen ...................................................................................................................... 28 Tillage ......................................................................................................................... 29 Irrigation ..................................................................................................................... 30 Soil characteristics and seasonal variation .................................................................. 30 References ......................................................................................................................... 32

III. EFFECTS OF MANAGEMENT PRACTICES IN A SEMI-ARID AGROECOSYSTEM ON SOIL MICROBIAL ENZYMATIC ACTIVITY ........................................................................................................ 50

Introduction ....................................................................................................................... 50 Methods............................................................................................................................. 54

Plot Design ........................................................................................................................................ 54 Crop Management Treatments .................................................................................... 56 Sampling ..................................................................................................................... 56 Enzyme Assays ........................................................................................................... 57 Data Analysis .............................................................................................................. 57 Results ............................................................................................................................... 58

Effect of Irrigation ...................................................................................................... 58 Effects of Nitrogen ...................................................................................................... 60 Tillage Effects ............................................................................................................ 62 Combined enzymatic and nutrient effect .................................................................... 64 Effect of nutrients ....................................................................................................... 65 Correlations between enzyme activity levels .............................................................. 65 Changes in key nutrient levels .................................................................................... 65 Discussion ......................................................................................................................... 67

Effects of Irrigation ..................................................................................................... 67 Response to nitrogen application ................................................................................ 68 Response to tillage type .............................................................................................. 69 Influence of nutrient and other enzymes ..................................................................... 70 Conclusion ........................................................................................................................ 70 References ......................................................................................................................... 73


iv

ABSTRACT

Recent agricultural practices have been shifting towards low-input sustainable

systems as it’s becoming more evident that the cost and negative side effects associated

with current agricultural practices are becoming increasingly too great. As much of our

current agriculture occurs in semi-arid regions of the United States increasing attention is

being focused on management practices that decrease water consumption along with

decreasing overall energy inputs.

West Texas produces more peanuts than any other area of the Unite States outside

of Georgia. Currently, over 320,000 tons are produced annually in the south plains. In

order to improve those numbers without increasing input, it will be imperative to

understand how agricultural management affects soil quality and microbial dynamics.

The soil microbial biota has tremendous influence on the overall soil quality. Soil quality

and the sustainability of agroecosystems are greatly reliant on the activity and diversity of

the soil microbial community and the processes they facilitate. Understanding the

interactions between management practices and the soil microbiota will be a key

component in developing the best system to maximize crop productivity while decreasing

soil inputs.

The effects of tillage, nitrogen, and irrigation management within a peanut

cropping system in West Texas were examined over a two year study to determine the

effects they had on structural and functional microbial dynamics. Microbial Biomass

Carbon and Fatty Acid methyl Ester analysis were used to examine how these

managements practices affected the soil microbe structure and composition. Enzyme


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assays of phosphodiesterase, β-glucosaminidase, β-glucosidase, alkaline phosphatase, and

α-galactosidase were made over the two year period to determine the effect of

agricultural management on soil microbe functioning.

Tillage was not shown to have any effect on the structural or functional microbial

dynamics. It was concluded that strip-tillage would be ideal for these systems due to

lower cost and less disturbance while producing the same benefit. Full Irrigation

significantly increases β-glucosidase activity, bacterial abundance, and MBC. Irrigation

reductions were shown to produce “hangover effects” in which microbial activity,

biomass and abundance not only decreased during the irrigation reduction but were also

lower in subsequent seasons. Nitrogen had differential effects on microbial dynamics.

The results indicated that while there was no significant effect on microbial biomass

carbon, nitrogen addition did have differential effects on enzymatic activity. Reduction

in bacterial abundance was shown to be associated with nitrogen addition. These results

indicate that crop management decisions will have consequences that either maintain the

activity and diversity of the soil microbial community or alter composition and activity

such that great inputs of energy are needed to compensate for the loss of microbial

dynamics.


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LIST OF TABLES

2.1. Correlation matrix comparing soil extractable nutrients to Microbial Biomass Carbon ......................................................................................................................... 35

2.2. Correlation matrix comparing soil extracellular enzymes to Microbial Biomass

Carbon ......................................................................................................................... 35

3.1. P-values for comparisons of the three different irrigation treatments effects on

soil extracellular enzyme activity ............................................................................. 76

3.2. Correlation matrix between interactions of soil enzymes and soil nutrients ............... 77

3.3. Correlation matrix between all extracellular soil enzymes effects on each other ........ 78


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LIST OF FIGURES

2.1. Plot design of study site (2007) .................................................................................................... 36

2.2. The effect of strip tillage compared to conventional tillage on Microbial Biomass

Carbon (a) 2007-2008 (b) Seasons ............................................................................. 37

2.3. The effect of late season drought compared to full irrigation on Microbial Biomass

Carbon (a) 2007-2008 (b) Seasons ............................................................................ 38

2.4. The effect of all irrigation levels on Microbial Biomass Carbon

(a) 2008 (b) Seasons ................................................................................................... 39

2.5. The effect of nitrogen on Microbial Biomass Carbon

(a) 2007 (b) Seasons ................................................................................................... 40

2.6. Monthly changes in nutrient dynamics and microbial functionality

(a) Discriminate function analysis .............................................................................. 41

2.6. Monthly changes in nutrient dynamics and microbial functionality

(b) Vector correlations ................................................................................................ 42

2.7. Changes in Microbial Biomass Carbon across growing seasons 2007-2008

(a) Seasonal (b) Monthly (c) Extended Months ......................................................... 43

2.8. Relative bacterial and fungal abundances

(a) Seasonal totals for bacteria and fungi abundance (b) Seasonal Fungi:Bacteria ratio ............................................................................................................................. 44

2.9. Relative bacteria and fungi groups abundances

(a) Fungal groups (b) Bacterial groups ...................................................................... 45

2.10. Effects of nitrogen on relative bacteria abundances

(a) 2007 (b) Seasonal ................................................................................................. 46

2.11. Effects of nitrogen on relative fungi abundance

(a) 2007 (b) Seasonal ................................................................................................. 47

2.12. Effects of irrigation on relative Gram-positive bacteria abundance

(a) 2007-2008 (b) Seasonal ........................................................................................ 48

2.13. Effects of irrigation on relative Gram-negative bacteria abundance

(a) 2007-2008 (b) Seasonal ....................................................................................... 49


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3.1. Study site (2007) ............................................................................................................................... 79

3.2. Effects of irrigation of phosphodiesterase activity

(a) 2007-2008 (b) Seasonal .......................................................................................................... 80

3.3. Effect of irrigation on β-glucosaminidase activity (a) 2007-2008 (b) Seasonal .................................................................................................................. 81

3.4. Effects of irrigation on β-glucosidase activity

(a) 2007-2008 (b) Seasonal .................................................................................................................. 82

3.5. Effects of irrigation on alkaline phosphatase activity

(a) 2007-2008 (b) Seasonal .......................................................................................................... 83

3.6. Effects of irrigation on α-galactosidase activity

(a) 2007-2008 (b) Seasonal ................................................................................................................... 84

3.7. Comparison of 3 irrigation levels on collective enzyme activity

(a) Discriminate function analysis (b) Vector correlations ................................................. 85

3.8. Discriminate function analysis on effect of nitrogen on collective enzyme activity ..... 86

3.9. Effect of nitrogen on phosphodiesterase activity

(a) 2007 (b) Seasonal ..................................................................................................................... 87

3.10. Effect of nitrogen on β-glucosaminidase activity

(a) 2007 (b) Seasonal ................................................................................................. 88

3.11. Effect of nitrogen on β-glucosidase activity

(a) 2007 (b) Seasonal ................................................................................................. 89

3.12. Effect of nitrogen on alkaline phosphatase activity

(a) 2007 (b) Seasonal ................................................................................................. 90

3.13. Effect of nitrogen on α-galactosidase activity

(a) 2007 (b) Seasonal ................................................................................................. 91

3.14. Seasonal nitrogen effect on collective enzyme activity

(a) Discriminate function analysis ............................................................................................... 92

3.14. Seasonal nitrogen effect on collective enzyme activity

(b) Vector correlations .................................................................................................................... 93


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3.15. Effect of tillage on phosphodiesterase activity

(a) 2007-2008 (b) Seasonal .......................................................................................................... 94

3.16. Effect of tillage on β-glucosaminidase activity

(a) 2007-2008 (b) Seasonal ................................................................................................................... 95

3.17. Effect of tillage on β-glucosidase activity

(a) 2007-2008 (b) Seasonal ................................................................................................................... 96

3.18. Effect of tillage on alkaline phosphatase activity

(a) 2007-2008 (b) Seasonal .......................................................................................................... 97

3.19. Effect of tillage on α-galactosidase activity

(a) 2007-2008 (b) Seasonal ................................................................................................................... 98

3.20. Yearly tillage effect on collective enzyme activity and nutrient dynamics

(a) Discriminate function analysis ............................................................................................... 99

3.20. Yearly tillage effect on collective enzyme activity and nutrient dynamics

(b) Vector correlations .................................................................................................................. 100

3.21. Season variation in soil extractable nitrate .............................................................. 101

3.22. Seasonal variation in soil extractable ammonium ................................................. 102

3.23. Season variation in soil phosphorus ........................................................................ 103

3.24. Seasonal variation in soil organic matter .............................................................. 104


1

CHAPTER 1

INTRODUCTION TO SOIL MICROBIAL DYNAMICS, MICROBIAL EVALUATION METHODS AND AGRICULTURAL PRACTICES

Introduction

The interactions between management practices and the soil biota and subsequent

effects on the performance of crops are of great agricultural importance. Moreover

there is a large potential for increased productivity in understanding these interactions

and managing them effectively (Watt et al. 2006). However, despite the important roles

of the soil microbiota in agroecosystem functioning (e.g.,Verhoef and Brussaard 1990)

very little is known of their activities, diversities, composition, and abundance under

semi-arid conditions in a peanut-based cropping systems (e.g., Acosta-Martinez et al.

2003). Soil quality and the sustainability of agroecosystems are reliant on the activity

and diversity of the soil microbial community and the processes they facilitate (Pankhurst

et al. 1996). Soil microbes are sensitive to disturbances from management decisions and

will alter activity rates, biomass, and community structure accordingly to the severity and

duration of the disturbance (e.g., Bossio et al. 1998, Schloter et al. 2003). Understanding

how to manipulate environmental conditions to fully utilize the microbial potential will

be invaluable in moving forward in developing more sustainable agroecosystems.

Arbuscular Mycorrhiza

Microorganisms are vital to all terrestrial ecosystems by performing essential

functions such as decomposition, mineralization and nutrient transfer to plants (Prescott


2

et al. 2005). Nitrogen and phosphorous are two essential macro-nutrients that

microorganisms use to complete key steps in cycling and uptake (Cardon and Gage

2006). Plant productivity in natural and managed ecosystems is often limited by nitrogen

and phosphorous (Chapin et al. 1986), and interactions between the soil microflora and

plants to increase nutrient availability become crucial (Richardson 2001, Biro et al.

2000).

Arbuscular mycorrhizae (AM) are the largest group of mycorrhizae (Wright 2005)

and are one of the most important symbioses in terrestrial ecosystems (Smith and Read,

1997). AM fungi (AMF) influence soil carbon fluxes as well as nutrient dynamics of

plants, soils, and the atmosphere (Treseder and Cross 2006). There are some 150 known

AMF species (Vandenkoornhuyse 2002) that form symbiotic relationships with

approximately 80-85% of all terrestrial plant species (Vandenkoornhuyse 2002) including

various cropping systems (Zak and McMichael 2001). AM fungi are key mediators

between above-ground plant biomass and available soil nutrients (Read et al. 1992).

Arbuscular mycorrhizae are intracellular symbiontes that form arbusculars, which

are intricately branched haustoria (hyphal tips) in the cortex cells of a plant root (Smith

and Read, 2006).Arbuscules are the site of nutrient exchange between the plant and the

fungus. Arbuscular mycorrhiza benefit the host plant by increasing uptake of phosphorus

(P), zinc (Zn), copper (Cu), and nickel (Ni) and may increase resistance to soil-borne

pathogens, insect herbivores, and drought (Smith and Read 1997, Wright 2005).

The primary benefit of forming arbuscular mycorrhiza for most plants occurs

through the increased P uptake as phosphorus is one of the most limiting nutrients of


3

plants (Tressier and Raynal 2003). The necessity to form this symbiotic relationship with

the AM fungi arises from the morphological and physiological characteristics of root

systems combined with low levels of soil P and the immobility of P in the soil matrix

(Wright 2005). Many plant roots exhibit slow growth rates that make it extremely

difficult to react to nutrient changes in the environment, and a large number of the roots

also have insufficient branching, lateral roots, and root hairs to fulfill their P demands

(Wright 2005). Plants that form the symbiotic relationship with AM fungi depend on the

AM hyphae as an extension of the roots to access a greater nutrient pool (Leyval et al.

2002, Wright 2005). Furthermore, research has shown that mycorrhizal plants are more

effective at using organic P than nonmycorrhizal plants (Wright 2005). The AMF

mycelium receives 3-20% net photosynthate produced by host and 37-47% of carbon (C)

delivered below ground (Treseder and Cross 2006). This substantial allocation of C

demonstrates how valuable this symbiosis is to plant nutrient uptake; otherwise the plant

would not invest such a large percentage of net photosynthate to sustain the arbuscular

mycorrhizal relationship. AM fungi also increase the aggregate stability of soil reducing

wind and soil erosion (Liang et al. 2008, Wright 2005). The production of the

hydrophobic, insoluble, recalcitrant C glomalin protein by the AMF is the means by

which they are able to aggregate soil particles.

AM fungi can be increasingly important in agroecosystems to compensate for

lower overall microbial activity and nutrient cycling resulting from high input of nutrients

and disturbance from plowing (Dick 1994). Most agricultural plants form arbuscular

mycorrhizae and as a result there has been a greater effort made to understand how AM


4

fungi can improve crop productivity and reduce fertilizer inputs (Smith and Read 1997,

Zak et. al. 1998). However, there is been very little research into the establishment and

maintenance of AMF within annual agricultural ecosystems under arid and semi-arid

conditions (Smith and Read 1997, Zak and McMichael 2001).

Microbial Communities

Bacteria are estimated to be the most numerous group of microorganisms found in

soil even though they account for less than half of the microbial biomass due to their

small size (Kennedy 1999). Bacterial community composition and diversity in

agricultural systems has a key role in the cycling of nutrients within these systems. These

influences include key roles in the cycling of N, S, C, methane and other nutrients

decomposition, and compound transformation (Zak et al. 1994, Kennedy 1999). Soil

bacteria also play a large role in the formation of soil structure (Tisdall 1991). A vast

array of bacteria is present and affects agroecosytems and has the potential to be altered

due to environmental stresses and management practices (Kennedy 1999).

Soil fungi comprise the largest portion of the soil microbial biomass and in many

terrestrial systems are responsible for the majority of the ecosystem processes.

Saprophytic fungi are extremely important in soil dynamics because of their roles in

decomposition, C and N storage and sources, soil stabilization, and biochemical

processes (Bills et al. 2004, Went and Stark 1968). Fungi have been shown to be

biological control agents in some agricultural systems (Hall and Papierok 1982) and


5

important decomposers and nutrient supplier to agroecosystems (Lodge 1997, Miller and

Lodge 2007, Hietschmidt et al. 1996). Fungal abundance and diversity have been shown

to be susceptible to changes in agricultural management practices such as tillage and in

turn these changes can influence the nutrient cycles of that system (Miller and Lodge

1997).

Microbial Biomass

The microbial biomass is an important regulator of nutrient transformation and

storage of many vital nutrients including N, P, and S and is also a key component of

organic C mineralization (Nunan et. al. 1998, Horwarth and Paul 1994). Previous

research has demonstrated that microbial biomass is a sensitive indicator of differences in

sustainable cropping systems, including examining effects of tillage, crop rotations, and

soil type on nutrient turnover and organic C (Anderson and Domsch 1989, Horwath and

Paul 1994) and also considered to be the most reliable indicator of soil quality (Gil-Sotres

et al. 2004).

FAME

Fatty Acid Methyl Ester (FAME) analysis is a technique that relies on the

different fatty acids among organisms to indentify species or groups present using gas

chromatography (GC) of the volatile fatty acid methyl esters extracted from

microorganisms (Kunitsky et. al. 2006, Sasser 1990). The MIDI Sherlock Microbial

Identification System developed procedure is a cheap, quick, and reliable system that has


6

helped create libraries with over 1,500 bacterial species and over 200 species of yeast for

pure systems (Kunitsky et al. 2006). The FAME analysis has been shown to be an

effective tool in estimating of AM fungi biomass, as well as monitoring the degree to

which microbes are affected by management practices in agricultural soils (Olsson et al.

1999). FAME analysis has many advantages in describing microbial community

structure over culture-based methods in that FAME avoids selectivity toward fast

growing organisms on media. However, the technique can extract fatty acids from clay-

organic matter complexes and may include fatty acid profiles that are no longer present in

the soils in the community structure assessment (Acosta-Martinez et al. 2004). To date

16:1ω5c has been identified as an indicative fatty acid marker for AM fungi (Madan et al.

2001, Olsson et al. 1999) while 18:1ω9c, 20:1ω9c, 20:2ω6c, and 22:1ω9c have the

potential to be additional AMF fatty acid markers (Madan et al. 2001). Pankhurst et al.

1997 showed that FAME profiles can be used as an effective indicator to detecting

changes microbial compositions in response to agricultural management practices.

Soil Enzymes

Microorganisms are the main source of enzymes in the soils and thus the

composition and abundance of the soil microbial community controls soil processes (e.g.,

Acosta-Martinez et al. 2004). These microbial enzymes play vital roles in critical soil

processes such as decomposition, nutrient cycling, decomposition of xenobiotics, and soil

aggregation (Barea et al. 1996, Reynolds et al. 2003). The products from the reactions


7

from the microbial enzymes are extremely important in driving soil nutrient dynamics

(Acosta-Martinez et al. 2004).

Previous research has shown that soils from the rhizosphere region have greater

enzymatic activity than that of non-rhizosphere soils (Dick 1994) and this can be

explained by the greater species diversity associated with the rhizosphere. Roots can

excretes carbon that can stimulate microbial activity increasing nutrient cycling within

the root –region and benefitting the plant directly (Dick 1994). Different agricultural

management practices can increase or decrease the soil enzyme production (Bandick and

Dick 1999) and thereby can have significant effects on nutrient availability and soil

organic matter levels. A decrease in microbial enzyme production will result in a

decrease in mineralization and cycling of nitrogen and phosphorous necessary to sustain

productivity. (Mina et al 2001). Understanding the specific interaction between

management practices and microbial dynamics in the rhizosphere of annual cropping

systems will be important in developing techniques to maximize efficiency of

agroecosystems while reducing inputs.

Tillage

Strip-tillage (or minimal tillage) combines the benefits of conventional tillage and

no-till agriculture. The process disturbs the row but leaves the furrow undisturbed with a

complete residue cover. The undisturbed soil will retain moisture more effectively than

the disturbed soils with this moisture available to the crop plants (Musick et al. 1977). In

a semi-arid system conventional tillage provides advantages over no-till agriculture


8

through reduction in wind erosion but also can lead to soil compaction, loss of organic

matter and disruption of soil aggregates (Deng and Tabatabai 1996, Dick 1984, Doran

1980). Strip-tillage is a management technique that combines the benefits associated

with conventional tillage while reducing the negative effects. The effects of tillage will

have strong impacts on microbial diversity (Kennedy 1999). The minimal tillage

approach used in agricultural systems has been shown to increase the soil microbial

biomass and soil enzymatic activity (Dalal 1989, Spedding et al. 2004, Follett and

Schimel 1989). As agriculture shifts towards minimal tillage it will be important to know

exactly what affect it will have on the microbial dynamics of the soil.

Objectives and Hypotheses

Objective1: Evaluate the response of the microbial biomass to tillage, irrigation, and nitrogen under

a peanut cropping system in semi-arid soils.

Hypotheses

1) Tillage, irrigation, and nitrogen fertilizer will each have significant negative

effects on the size of the microbial biomass.

As water is limiting in semi-arid environments differences in irrigation schedules

should have the largest negative impact on the size of the microbial biomass. An increase

in the microbial biomass is expected to be correlated with increases in irrigation.

2) Traditional tillage during the growing season will negatively impact the

amount of microbial biomass that develops.

Strip-tillage should have a measureable positive effect on soil microbial biomass

due to the residue cover in the furrows and a decrease in disturbance. It was expected


9

that the decrease in disturbance of soil aggregates in strip-tillage practices as compared to

conventional tillage would contribute to increased microbial biomass carbon C as the

result of keeping the fungal mycelia network intact.

3) Nitrogen application should decrease the microbial biomass by changing soil

microbial community structure. Increased soil nitrogen will allow fast growing microbes

to become dominant during period of optimum soil moisture.

Objective 2: Evaluate the response of the microbial community structure to tillage, irrigation, and

nitrogen of a peanut cropping system in semi-arid soils.

Hypotheses

1) Irrigation will increase the bacterial contributions to microbial community

structure.

As fungi have the ability to translocate water through their mycelium and survive

at lower water potentials, irrigation should favor a shift towards a bacterial dominated

system.

2) Tillage will change the microbial community structure to a bacterial dominated

system. Soils under strip-tillage are expected to have greater relative fungi abundance

associated with them. Conventional tillage should result in more disruption on mycelial

networks and negatively affect the fungal abundances.

3) Nitrogen fertilization will alter the structure of the soil microbial community

and reduce complexity.


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Objective 3: Evaluate the response of microbial enzyme production to tillage, irrigation, and nitrogen

of a peanut cropping system in semi-arid soils.

Hypotheses

1) Reduction in irrigation should result in reduced microbial enzyme production

for measured enzymes.

2) As irrigation will increase microbial activity, those enzymes associated with

the bacterial assemblages should increase accordingly.

3) Conventional-tillage should have a negative effect on the enzyme activity

levels as this practice is most disruptive to the soil microbial community particularly

fungi populations through disruption of hyphae.

4) Nitrogen was expected to have negatives effect on phosphodiesterase and

alkaline phosphatase activity levels and to positively affect β-glucosamindase, β-

glucosidase, and α-galactosidase.

Phosphodiesterase and alkaline phosphatase activity levels are expected to decline

with nitrogen inputs because these enzymes are associated with P cycling and are

expected to have an inverse relationship with N. The other enzymes are involved in C

cycling and would be expected to increase as a limiting nutrient such as nitrogen is

added.


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Schloter, M., Dilly, O., Munch, J.C. 2003. Indictors for evaluating soil quality. Agriculture, Ecosystems, and Environment. 98: 255-262. Sen, R. 2003. The root-microbe interface: new tools for sustainable plant production. New Phytologist. 157: 391-394. Smith, S. E. and D. J. Red 1997. Mycorrhizal Symbiosis, Academic Press. Smith, S. E., Smith, F. A, et al. 2003. Mycorrhizal fungi can dominate phosphate supply to plants irrespective of growth response. Plant Physiology 133(16-20). Souza, F. d., G. A. Kowalchuk, et al. 2004. PCR-Denaturing Gradient Gel Electrophoresis Profiling of Inter- and Intraspecies 18S rRNA Gene Sequence Heterogeneity Is an Accurate and Sensitive Method To Assess Species Diversity of Arbuscular Mycorrhizal Fungi of the Genus Gigaspora. Applied and Environmental Microbiolgy 70(3): 1413-1424. Spedding, T.A., Hamel, C. Mehuys, Madramootoo, C.A. 2004. Soil microbial dynamics in maize-growing soil under different tillage and residue management system. Soil Biology and Biochemistry. 36: 499-512. Tabatabai, M. 1994. Soil Enzymes. Methods of soil analysis. Part 2. Microbiological and biochemical properties. R. Weaver, J. Angle and P. Bottomley. Madison, Wisconson, SSSA: 775-833. Tisdall, J.M. 1991. Fungal hyphae and structural stability of soil. Aust. J. Soi Res. 29: 729-743. Treseder, K. K. and A. Cross 2006. Global Distrubtions of Arbuscular Mycorrhizal Fungi. Ecosystems 9: 305-316. Watt, M., J. A. Kirkegard, et al. 2006. Rhizosphere biology and crop productivity-a review. Australian Journal of Research 44: 299-317. Went, F.W., Stark, N. 1968. The biological and mechanical role of soil fungi. Proceedings of the National Academy of Sciences of the United States of America. 60: 497-504. Wright, S. F. 2005. Management of Arbuscular Mycorrhizal Fungi. Roots and Soil Management: Interactions Between Roots and the Soil. R. W. Zobel and S. F. Wright. Madison, WI, American Society of Agronomy: 183-197. Wynne, J. C., G. H. Elkan, et al. 1980. Greenhouse evaluationsof strains of Rhizobium for peanuts. Agronomy Journal 72: 645-649.


15

Vainio, E. J. and J. Hantula 2000. Direct analysis of wood-inhabiting fungi using denaturing gradient gel electrophoresis of amplified ribosomal DNA. Mycological Research 104(8): 927-936. Vandenkoornhuyse, P., R. Husband, et al. 2002. Arbuscular mycorrhizal community composition associated with two plant species in a grassland ecosystem. Molecular Ecology 11: 1555-1564. Verhoef, H. A. and Brussaard, L. 1990. Decomposition and nitrogen mineralization in natural and agroecosystems: the contribution of soil animals. Biochemistry 11: 175-211. Zak, J. C. 1992. Responses of soil fungal communities to disturbances. The Fungal Community (2nd Edition). G. Carroll and D. T. Wicklow. New York, Marcel Dekker: 403-425. Zak, J. C., Willig, M.R., Moorhead, D.L., Lildman, H.G., 1994. Functional diversity of bacterial communities: a quantitative approach. Soil Biol. Biochem. 26: 1101-1108. Zak, J. C. and B. McMichael 2001. Agroecology of Arbuscular Mycorrhizal Activity. Structure and Function in Agrecosystem Design and Management. Boca Raton, FL, CRC Press: 145-156. Zak, J. C., B. McMichael, et al. 1998. Arbuscular-mycorrhizal colonization dynamics of cotton (Gossypium hirsutum L.) growing under several production systems on the Southern High Plains, Texas. Agriculture, Ecosystems & Environment 68(3): 245-254. Zhang, Q. and J. C. Zak 1998. Effects of water and nitrogen mendment on soil microbial biomass and fine root production in a semi-arid environment in West Texas. Soil Biology and Biochemistry 30(1): 39-45.


16

CHAPTER 2

EFFECTS OF MANAGEMENT PRACTICES IN A SEMI-ARID AGROECOSYSTEM ON SOIL MICROBIAL BIOMASS & MICROBIAL COMPOSITION

Introduction

Agro-management practices such as tillage, irrigation, herbicide and pesticide

applications, and fertilizer applications can have substantial negative impacts on

agricultural systems (Welbaum et al. 2004). These production decisions directly affect

soil quality by influencing the chemical and physical make-up of the soil, the activity and

diversity of the soil microflora and the soil macro and micro fauna and their activity

(Watt et al. 2006, Bossio et al. 1998, Yeates et al. 1997, Lupwayi et al. 1998). Greater

understandings of the negative impacts of traditional agricultural practices are needed if

there is to be a shift towards developing more sustainable agricultural systems (Pankhurst

et al. 1996). In order for this to occur, it is important to understand the affects of the

different management practices used and to determine which of these practices supports a

sustainable agro ecosystem.

Watt et al. (2006) emphasized that there is a large potential for increasing

productivity by managing properly the interactions between management practices and

the soil microflora Moreover understanding the interactions between microbial activity

and management practices may be key to developing sustainable agricultural systems that

maximize crop productivity while reducing inputs.

Saprophytic and symbiotic soil bacteria and fungi have large impacts on the

functioning of agroecosystems through their contributions to decomposition by regulating


17

nutrient cycles (Miller and Lodge 2007). The inputs of carbon and nutrients from plants

influences the abundance of fungi and bacteria in the rhizosphere by driving nutrient

cycles (Acosta-Martinez et al. 2004, Zak et al. 1994, Kennedy 1999). In highly managed

agroecosystems, however, the most common response to management decisions is a

reduced responses in microbial activity resulting from high input of nutrients and

disturbance from plowing (Dick 1994).

A key indicator to how the agricultural soil management practices are affecting

microbial dynamics is through the evaluation of microbial biomass. The microbial

biomass is an important regulator of nutrient transformation and storage of many vital

nutrients including N, P, and S and is also a key component of organic C mineralization

(e,g., Nunan et. al. 1998, Horwarth and Paul, 1994). Previous research has demonstrated

that microbial biomass is a sensitive indicator of differences in sustainable cropping

systems, including the effects of tillage, crop rotations, and soil type on nutrient turnover

and organic C (e,g., Anderson and Domsch 1989, Horwath and Paul 1994). Moreover

the amount and dynamics of soil microbial biomass also considered to be the most

reliable indicator of soil quality (Gil-Sotres et al. 2004).

For a semi- arid agroecosystem, the dynamics of the soil microbial biomass is

regulated in part by the seasonal distribution of precipitation, which can impose a major

stress on the soil microbial community in addition that as a result of management

practices. To maximize sustainability of cropping system in semi-arid environments and

to reduce costs associated with irrigation, tillage practices and fertilizer application this


18

study was conducted within a peanut cropping system to evaluate key production impacts

on soil microbial biomass.

Methods Plot Design

Field plots for this two year study (2007 and 2008) were established at the USDA-

ARS Cropping System Research Laboratory in Lubbock, Texas USA (101⁰ 47’ west

longitude; 33⁰ 45’ north latitude; 993m elevation. The field soil is an Olton clay loam

(mixed, superactive, thermic and aridic soil) and are classified as Paleustolls (Bronson et

al. 2004). Previous to the initiation of this study, the field was under conventional cotton

production for 6 growing seasons. In November 2006, the whole circular field was

portioned into 36 concentric rows. Approximately 1.3 hectares (3.3 acres), one half of a

circular field was prepared for peanut production to assess the impacts of irrigation,

nitrogen, and tillage on soil microbial dynamics and microbial functionality.

The sample site was divided into six equally sized triangular plots that are labeled

plots 1-6. Every two plots receive the same imposed conditions so that there were two

replicates for each treatment. Within each of the plots, different tillage techniques were

applied alternating between conventional tillage and strip tillage zones. In the

conventional tillage rows there was rye planted in the furrow only and in the strip tillage

portions rye was planted across the bed and the furrow. Within the plots zones of strip

and conventional tillage rows alternated respectively starting from the outside of the

plots. There were two strip and two conventional tillage sections within each zone. Two


19

alternating conventional and strip tillage sections of six rows each were sampled in each

plot.

Plots 1 and 2 served as the control and did not receive nitrogen fertilizer in year

2007. The remaining Plots 3, 4, 5, and 6 received 30lb/acre (33.6 kg/Hectare) of URAN

(Urea Ammonium Nitrate) on July 25th, August 9th, August 27th, September 24th and

September 24th in 2007. In 2008, Plots 1 through 6 received URAN 35 lb/acre (39.2

Kg/Ha) on July 8th, August 1st and September 2nd. Thus, all Plots received nitrogen in

2008.

There were 2 levels of irrigation applied in 2007. Plots 1, 2, 5 and 6 received full

irrigation (100-100-100% EvapoTranspiration (ET)), where 100% ET irrigation refers to

the amount of water supplemented to the crop that equals the total loss of water through

evaporation from the soil and the total transpiration form the aerial parts of the plant. For

2007 full irrigation was determined to be 1.5 inches of water. The late growing seasons

last from beginning of August till the end of October. Plots 3 and 4 were kept under late

deficit treatment (100-100-50%ET), applying only 0.75 inch of irrigation (half) during

the last 90 days while maintain full irrigation prior to. There were 3 levels of irrigation in

year 2008. Plots 1 and 2 received full irrigation (100-100-100%ET). Plots 3 and 4 were

kept under late deficit treatment (100-100-50%ET). Plots 5 and 6 were under early deficit

irrigation. They were irrigated with half of the regular irrigation 90.75 inch quantity in

initial 45 days (beginning of May to mid-June).


20

Crop Management Treatments

In 2007, the treatments applied to the field were: 1) Full Irrigation and no

Nitrogen (plots 1 & 2), 2) Nitrogen and a Late Season Drought (Plots 3 &4), and 3) Full

Irrigation and added Nitrogen (Plots 5 & 6). In 2008, the applied treatments were: 1) Full

Irrigation and added Nitrogen (Plots 1 & 2), 2) Late Season Drought and added Nitrogen

(Plots 3 & 4), and 3) an Early Season Drought and added Nitrogen (Plots 5 & 6). As with

2007 there were alternating conventional and strip tillage rows that were sampled (Figure

2.1).

Sampling

Four soil samples were taken within each plot; two from conventional tillage rows

and two strip tillage rows. For each sample, soil was collected from three different

locations across the plot to account for any variations that may be present within each

section of the zone and combined per row. Soil samples were collected from a depth of

15cm, with approximately 330g of samples taken for each replicate sample. Once

samples were taken, they were kept in a cooler and stored at 4⁰ C until they were

processed for enzymatic analysis.

Microbial Biomass Carbon

The chloroform fumigation and extraction method (Nunan et al. 1998, Vance et

al. 1987) was used to assess microbial biomass carbon (MBC) in response to

management practices. For each sample collection a 10 g dry weight equivalent was


21

measured out for each soil sample. A 10 g dry-weight equivalent is determined by drying

5.0 g soil for 48 hrs at 60°C and measuring the % soil moisture. Two 10 g dry-weight

equivalent sub-samples of each field sample were placed in a dessicator with a moist

paper towel at the bottom to impede the desiccation of soil samples during the

fumigation. Twenty five ml of ethanol-free chloroform with anti-bumping granules will

be added along with the moist paper towel in a dessicator for the fumigation. A second

sub-set of samples were placed within a dessicator without chloroform to serve as a

control. All samples were fumigated for 48 hrs. Soil samples were then extracted using

50ml of a 0.5 M K2SO4 solution, shaken for 1 hours and then filter through Whatman #32

150mm filter paper. To determine the microbial biomass present in each sample, a

subsample of the filtered K2SO4 was read at 280nm according to the procedure described

by Nunan et. al. (1998). To obtain the amount of microbial biomass carbon, the control

absorbance values are subtracted from the fumigated absorbance values and multiplied by

the KEC.

Microbial Community Structure

To evaluate the impacts of management decisions on the structure of the soil microbial

community fatty Acid Methyl Ester (FAME) analyses were conducted (referenced

needed) To conduct the FAME analyses a 3 gram dry weight equivalents were collected

from each treatment plot. The soil samples were first subjected to a saponification step

(45g sodium hydroxide, 150ml methanol, and 150ml distilled water) and boiled in a

100ºC water bath for 30 minutes. Samples were then methylated using a 325ml 6.0N


22

hydrochloric acid, and 275ml methyl alcohol reagent. Subsequently, 200ml hexane,

200ml methyl tert-butyl ether reagent were used to extract the methyl esters into the

organic phase. Lastly, samples were run through a gas chromatography (6890 GC Series

II Hewlett Packard, Wilmington, DE, USA) with a temperature program that increased

the temperature 5ºC per minute starting at 170ºC and ending at 270ºC. A GC detector

measured the peaks from the volatile methyl esters and the Sherlock MIS Software

(MIDI, Inc. Newark, DE) was used to obtain the types and proportional abundances of

each peak.

Data Analysis

Changes in microbial biomass levels in response to different management

practices across seasons and across years were evaluated using one-way ANOVA. The

overall impacts of management affects on microbial were evaluated using mANOVA.

Influences of soil nutrients levels on microbial biomass and the influence of the enzyme

levels on each other were evaluated using bivariate correlations. Statistical analyses

including one-way ANOVA, mANOVA, bivariate correlations were performed using

SPSS 14.0 for windows. Each data set underwent the Least Significant Difference (LSD)

post hoc test when significance (p ≤ 0.05) was indicated on the one-way ANOVA and

mANOVA.

Discriminant function analysis (DFA) was employed to visualize microbial

responses to managanement practices in multivariate space, using MATLAB (Natick,

Massachusetts). DFA analyzed microbial responses to 2007 and 2008 separately. For


23

each DFA, 1000 randomized (bootstrap) iterations were performed in order to build null

distributions from which to compare actual sampling distribution.

Results

Effects of Tillage on MBC

There was no significant difference (p= 0.732) between the overall MBC levels

between the conventional and strip tillage treatments across seasons and the two years of

the study (Figure 2.2a). There was also no significant (p ≤ 0.05) difference seen in MBC

levels between the two treatments within any of the seasons. The overall MBC was

greater in 2008 than in 2007 for both tillage types (Figure 2.2b).

Effects of Irrigation on MBC

Full irrigation vs. late season drought 2007-2008

There was a significant difference in irrigation’s affect on MBC. Overall MBC

was highest across both years and across seasons under full irrigation (p = 0.01) than that

of reducing irrigation resulting in a late season drought (LSD) across both 2007 and

2008(Figure 2.3a). MBC was greater in the full irrigation treatments in every season

compared to LSD. MBC was statistically (p ≤ 0.05) greater fall 2007 and summer 2008

and partially separated in fall 2008 (Figure 2.3b).


24

Full irrigation vs. late season drought vs. early season drought

During 2008, full irrigation produced greater MBC than LSD and early season

drought (ESD) but difference were only significantly different (p ≤ 0.05) from MBC

associated with the “late season drought.” While MBC from the ESD plots appeared

greater than MBC from LSD treatment plots, the values were not significance (p = 0.858)

(Figure 2.4a). Full irrigation produced a consistent pattern of greater MBC than LSD and

ESD within seasons but was significantly greater than LSD in summer 2008 only. The

greatest difference in MBC between LSD and ESD was seen in summer 2008, although

the difference was not considered significant (Figure 2.4b).

Effects of Nitrogen on MBC

Nitrogen fertilization of a peanut cropping system did not have an overall effect

on MBC across years and seasons (Figure 2.5a). There were statistical differences

between fertilized and no fertilizer plots within seasons. There was a trend of the MBC

being higher in both treatments for the summer season compared to the fall, thought this

pattern was only significant (p ≤ 0.05) for the no-nitrogen treatments (Figure 2.5b).

Changes in field conditions

There was a clear separation in microbial structure among all sample dates and

October 2008 among (Figure 2.6a). Note that 1 is also different from 6, 5, 3 and 2. The

mANOVA confirmed that there was a significant (p ≤ 0.05) difference in what between

this month from the rest of what –expand your comparisons. The DFA also showed that


25

microbial community structure in July 2007 separated out from all of the other months

other than October 2007 and that October 2007 separated out from all other months

except July 2007 (Figure 2.6a). Microbial community structure for July and October

2007 was shown to be different from all other months by mANOVA (p ≤ 0.05). Vector

analysis demonstrated that nutrient dynamics and soil organic matter were the driving

force for this temporal separation (Figure 2.6b).

Influences between MBC, nutrients, and enzymatic activity

All significant correlations between MBC and soil nutrients across both growing

seasons were negative (Table 2.1). Some of these negative correlations with MBC

included phosphorus (-28%), potassium (-36%), magnesium (-35%), Boron (-31%) and

manganese (-37%). There were no overall significant relationships between MBC and

levels of extractable NO3 and NH4. No strong correlations were detected between MBC

and any of the measured microbial enzymes with the highest being 17% with α-

galactosidase (Table 2.2).

Temporal changes in MBC

Seasonally, MBC did not change significantly as there was only a significant

difference in MBC between fall 2007 and fall 2008 with MBC in 2008 being significantly

greater at that time period (p ≤ 0.05). There were no differences in MBC levels within

either year (Figure 2.7a). levels of MBC were the highest in October of 2008 and it’s the

lowest in October 2007 (Figure 2.7b & 2.7c).


26

FAME

The relative abundances of fungal FAMES were greater than the relative bacteria

abundances % within every season (Figure 2.8a). The fungi:bacteria ratio of relative

abundance was the highest in the fall season of 2007 and the lowest in the summer of

2008 (Figure 2.8b). In both sampling periods the changes in the fungi:bacterial ratios

were the result of fluctuations in the relative fungal abundances and not those of the soil

bacteriaFungal abundance was significantly greater in fall 2007 and decreased

significantly in summer 2008. Both years showed a significant increase in the

fungi:bacteria raito from the summer to fall (Figure 2.8a & 2.8b).

The relative abundance of Arbuscular Mycorrhizal Fungal FAME profiles and

saprophytic fungi FAME profiles remained consistent and almost identical in all seasons

except during summer 2008. The relative abundance of saprophytic fungi FAMEs

decreased significantly during this season (Figure 2.9a). There was a significant and

positive correlation between MBC levels and relative abundance of saprophytic fungal

FAME levels (R=.361).

The relative abundances of Gram-Positive bacteria FAME profiles remained

consistent throughout all the growing seasons and were significantly higher than the

relative abundances for Gram-Negative bacteria and actinomycetes (Figure 2.9b).

Nitrogen Effects

Nitrogen addition had a significant negative effect (p ≤ 0.05) on the relative

bacteria abundances across 2007 when compared to those plots that received no


27

additional nitrogen (Figure 2.10a). There were seasonal effects with no significant

difference between the relative bacterial abundances in the summer. However, the level

of bacterial FAMES within nitrogen application was significantly less in the fall season

that year.

Nitrogen did not have a significant effect on the relative fungi abundances for the

2007 data combined (Figure 2.11a). However, when looking within the seasons fungal

abundances increased positively to nitrogen addition in the fall season 2007. There were

no differences in the relative fungi abundance % between the nitrogen and no nitrogen

plots in summer 2007 (Figure 2.11b).

Effects of Irrigation

Overall Gram -Positive FAME levels were lower under late season drought

compared to full irrigation (p ≤ 0.05). The overall Gram -Positive relative abundance

levels decreased across both years under late Season Drought when compared to Full

Irrigation (Figure 2.12a). The relative abundances Gram-Positive FAME levels were

greater under full irrigation in every season but the differences were only significant for

fall 2007 (2.12b). Gram-Negative relative abundances exhibited a positive response to the

LSD treatment when compared to Full irrigation (Figure 2.13a). However, this response

to LSD by the Gram-Negative bacteria was not seen in every season. The relative

abundance was greater under LSD in summer 2007 and fall 2008 but only significantly

greater in fall 2008. In summer 2008, the relative Gram-Negative abundance was greater

under full irrigation and no response was able to be measured for fall 2007 as a result of

insufficient amounts of relative Gram-Negative abundance in that season (Figure 2.13b)


28

Discussion

Nitrogen

One of the goals of this study was to understand the impact that nitrogen

application had on the soil microbiological dynamics in a semi-arid agroecosystem.

Previous research examining nitrogen inputs effect on MBC have produced contradictory

results of both positive and negative responses of soil MBC to nitrogen (Moore et al.

2000). The results from this study indicated that nitrogen did not have any significant

effect on the soil MBC.

Nitrogen did elicit a significant response from the soil bacteria associated with the

peanut cropping system. The relative bacteria abundances of FAME profiles responded

negatively to short-term nitrogen application. It is hard to speculate to the true

relationship between added N and relative bacteria abundance due to short-term data

collected. This response could be the result of N inhibition on soil microbial

communities through repression of soil enzyme activity (Bardgett et al. 1999).

Alternatively, the increased N could be reducing the need for N-fixing bacteria in the soil.

Previous research yielding similar results concluded increased N inputs could promote

the growth of certain nematodes which in turn decrease a certain population of the

sustained bacteria (Sarathchandra et al. 2001).

The relative fungal abundance showed an opposite but lesser response to soil

nitrogen addition. The N application effect was not considered significant for the

duration of the study but was considered significant in the fall season when the relative

fungi abundance was at its greatest. The relatively small amount of data collected in the


29

short-term portion of this study makes it is difficult to speculate whether N did have an

effect on fungal abundance or not. Because N fertilization has been shown to cause

chances in the abundance of fungal species (Sarathchandra et al. 2001) and the result was

near significant we feel comfortable to at least suggest that there is a small effect.

The effects seen as result of N application reflect short term results. The data

obtained is only valuable when evaluating short-term management strategies. The effects

of long-term N application can have dramatically different effects than that of short-term

application (Marchner et al. 2003) and such long-term evaluation of N application and its

effect on relative microbial abundance are needed to fully understand the consequences

of this management practice.

Tillage

The two tillage methods compared in this study did not differ in result from one

another. The level of MBC remained almost exactly the same, as did all the relative

microbial abundance within the conventional and strip tillage samples. MBC has often

been shown as a sensitive indicator to changes in tillage practices (Anderson and

Domsch, 1989, Horwath and Paul 1994, Jackson and Calderon 2003) but it has also been

shown to be non-responsive to tillage management changes (Calderon and Jackson 2002,

Aslam et al. 1999). The lack of difference between the two tillage treatments is probably

a result of the similarity between them as strip tillage is a reduction of conventional

tillage. These results indicate that when strategizing between tillage treatments, effects

other than microbial responses such as soil aggregation or erosion should be considered


30

when trying to determine which method will be more beneficial for that particular

agroecosystem.

Irrigation

Full irrigation had a significantly positive effect on the soil MBC and Gram-

Positive bacteria abundance. These results are not surprising as water is necessary for

any successful agroecosystem. These results further demonstrate the necessity of water

for crop productivity. Further research will be needed to determine ways to minimize

irrigation without reducing microbial dynamics. One short-term method evaluated in this

study was timing of irrigation. The effects of late season irrigation and early season

irrigation were inclusive in this study but the results suggest that further investigation

may lead to promising results. Although there were no significant differences between

the LSD and ESD irrigation treatments, variables measured all followed a pattern of

higher levels associated with ESD indicating that irrigation is more influential in the later

part of the growing season. Although this should be evaluated over more than one

growing season, it would appear if water is limited irrigating in the later months will help

to maximize the soil microflora potential.

Soil characteristics and seasonal variation

FAME analysis indicated that the soil in this study was fungal dominated, with

the fungi:bacteria ratios being significantly greater in the fall seasons compared to the

summers. The relative fungi abundance were almost exactly the same between


31

saprophytic fungi and mycorrhizal fungi in all seasons with the exception of the summer

2008 season in which saprophytic fungi displayed a significant decrease in abundance.

Gram-Positive bacteria made up the highest concentration of bacteria in the soil, a result

consistent with other research (Ibekwe and Kennedy 1999). Actinomycetes and Gram-

Negative bacteria abundance were both relatively low and did not display much variation.

This information can be used in future research to help focus on the microbial groups that

could have the most influence on agroecosystems due to their abundance.

DFAs showed strong monthly variation in the plots mainly as the result of

nutrient fluxes. The October 2008 month separate out most significantly followed by the

October 2007 month. Interestingly enough, these two months also represented the

extreme high and low in MBC months with October 2008 displaying the highest MBC

and October 2007 resulting in the lowest MBC. Although, there were no significant

correlations between individual nutrients and soil MBC levels, it would certainly appear

that there may be some interaction between the combined levels of soil nutrients and the

MBC. There results also indicated that there may be some key shifts going on between

the September and October months as each year the MBC between these two months

were the greatest. More detailed examination of trends in these months may provide

insight into soil nutrient shifts and their effects.


32

References Acosta-Martinez, v., D. R. Upchurch, et al. 2004. Early impacts of cotton and peanut cropping systems on selected soil chemical, physical, microbiological and biochemical properties. Biol Fertil Soils 40: 44-54. Anderson, T.H., and Domsch, K.H. 1989. Ratios of microbial biomass carbon to total organic carbon in arable soils. Soil Biology and Biochemistry. 21: 471-479. Aslam, T., Choudhary, M.A., Saggar, S. 1999. Tillage impacts on soil mictobial biomass C, N, and P, earthworms and agronomy after two years of cropping following permanent pasture in New Zealand. Soil and Tillage Research. 51: 103-111. Bardgett, R.D., Mawdsley, J.L., Edwards, S., Hobbs, P.J., RodwellJ.S., Davies, W.J. 1999. Plant species and nitrogen effects on soil biological properties of temperate upland grasslands. Functional Ecology 13: 650-660. Bossio, D.A., Scow, K.M., Gunapala, N., Graham, K.J. 1998. Determinants of soil microbial communities: effects of agricultural management, season, and soil type on phospholipid fatty acid profies. Microbial Ecology 36: 1-12. Calderόn, F.J., Jackson, L.E. 2002. Rototilage, disking and subsequent irrigation:effects on soil nitrogen dynamics, microbial biomass, and carbon dioxide efflux. J. Environ Qual. 31: 752-758. Dick, R. P. 1994. Soil Enzyme Activities as Indicators of Soil Quality. Defining Soil Quality for a Sustainable Environment. J. W. Doran, D. C. Coleman, D. F. Bezdicek and B. A. Stewart. Madison, WI, AGRIS: 107-124. Gil-Sotres, F., C. Trasar-Cepeda, et al. 2004. Different approaches to evaluating soil quality using biochemical properties. Soil Biology and Biochemistry 37: 877-887. Gilbert, D. Amblard, C., Bourdier, G., Francez, A.J. 1998. Short-term effect of nitrogen enrichment on the microbial communities of a peatland. Hydrobiologia. 373/374: 111-119. Horwath, W. R. and Paul E. A. 1994. Microbial Biomass. Methods of Soil Analysis, Part 2. Microbolgical and Biochemical Properties. J. M. Bigham. Madison, WI, Soil Society of America: 753-772. Ibekwe, A.M., Kennedy, A.C. 1999. Fatty acid methyl ester (FAME) profiles as a tool to investigate structure of two agricultural soils. Plant and Soil. 206: 151-161.


33

Jackson, L.E., F. J. Calderon, K. L. Steenwerth, K. M. Scow and D. E. Rolston. 2003. Responses of soil microbial processes and community structure to tillage events and implications for soil quality Geoderma. 114: 305-315. Kennedy, A.C. 1999. Bacterial diversity in agroecosystems. Agriculture, Ecosystems and Environment. 74: 65-76. Lupwayi, N.Z., Rice, W.A., Clayton, G.W. 1998. Soil microbial diversity and community structure under wheat as influenced by tillage and crop rotation. Soil Biology and Biochemistry. 30: 1733-1741. Marchner, P., Kandeler, E., Marschner, B. 2003. Structure and function of the soil microbial community in a long-term fertilizer experiment. Soil Biology and Biochemistry. 35: 453-461. Mendes, I.C., Bandick, A.K., Dick,. R.P., and Bottemley. P.J. 1999. Microbial Biomass and Activities in Soil Aggregates Affected by Winter Cover Crops. Soil Science Society of American Journal. 63: 873-881. Miller, R.M., Lodge, D.J. 2007. Fungal Responses to Disturbance: Agriculture and Forestry. Environmental and Microbial Relationships. Christian P. Kubicek and Irina S. Druzhinina. Springer Berlin Heidelberg: 47-68. Moore, J.M., Klose, S., Tabatabai, M.A. 2000. Soil microbial biomass carbon and nitrogen as affected by cropping systems. Biol Fertl Soils. 30: 200-210. Nunan, N., M. A. Morgan, et al. 1998. Ultraviolet absorbance (280 nm) of compounds released from soil during chloroform fumigation as an estimate of the microbial biomass. Soil Biology and Biochemistry 30: 1599-1603. Pankhurst, C.E., Oohel-Keller, K., Doube, B.M., Gupta, V.V.S.R. 1996. Biodiversity of soil microbial communities in agricultural systems. Biodiversity and Conservation. 5: 197-209. Rodriquez-Kabana, R. 1986. Organic and inorganic nitrogen amendments to soil as nematode suppressants. J Nemato. 8: 129-134. Sarathchandra, S.U., Ghani, A., Yeates, G.W., Burch, G., Cox, N.R. 2001. Effect of nitrogen and phosphate ferilisers on microbial and nematode diversity in pasture soil. Soil Biology and Biochemistry. 33: 953-964. Vance, E.D., Brookes, P.C., Jenkinson, D.S. 1987. An extraction method for measuring soil microbial biomass C. Soil Biology and Biochemistry. 19: 703-707.


34

Verhoef, H. A. and L. Brussaard 1990. Decomposition and nitrogen mineralization in natural and agroecosystems: the contribution of soil animals. Biochemistry 11: 175-211. Welbaum, G.E., Sturz, A.V., Dong, Z., Nowak, J. 2004. Managing Soil Microorganisms to Improve Productivity of Agro-Ecosystems. Critical Reviews in Plant Sciences. 23: 175-193. Watt, M., J. A. Kirkegard, et al. 2006. Rhizosphere biology and crop productivity-a review. Australian Journal of Research 44: 299-317. Yeates, G.W., Bardgett, R.D., Cook, R., Hobbs, P.J., Bowling, P.J., Potter, J.F. 1997. Faunal and microbial diversity in three Welsch grassland soils under conventional and organic management regimes. Journal of Applied Ecology. 34: 453-470. Zak, J. C., Willig, M.R., Moorhead, D.L., Lildman, H.G., 1994. Functional diversity of bacterial communities: a quantitative approach. Soil Biology and Biochemistry.. 26: 1101-1108.


35

Table 2.1. Correlation matrix comparing soil nutrients and edaphic characteristics to Microbial Biomass Carbon from soil samples collected across the growing seasons of 2007-2008. All numbers represent r calculated using Pearson Correlation. The r values range from 1 to -1 with being a 100% positive correlation and -1 a 100% negative correlation. Significant r values are indicated in bold. Note [MBC= microbial biomass C, NO3 = soil extractable nitrate, NH4= soil extractable ammonium, P= soil extractable phosphorus, K= soil extractable potassium, Mg= soil extractable magnesium, Ca= soil extractable calcium, S= soil extractable sulfur, B= soil extractable boron, Zn= soil extractable zinc, Mn= soil extractable manganese, Fe= soil extractable iron, Cu= soil extractable copper, CEC= cation exchange].

NO3 NH4 pHw P K Mg Ca S B Zn Mn Fe Cu CEC

MBC 0.12 0.04 0.03 0.28 0.36 0.35 0.09 0.03 0.31 0.09 0.37 0.27 0.25 0.03

Table 2.2. Correlation matrix comparing soil extractable nutrients to Microbial Biomass Carbon from soil samples collect across the growing seasons of 2007-2008. All numbers represent r calculated using pearson correlation. The r values range from 1 to -1 with being a 100% positive correlation and -1 a 100% negative correlation. Significant r values are indicated in bold. Note [MBC= microbial biomass, Phosph= phosphodiesterase, Glucomini= β-glucosaminidase, Glucosidase= β-glucosidase, Alkaline= alkaline phosphatase, Alpha= α-galactasidase].

Phospho Gucomini Glucosi Alk Alpha

MBC -0.03 0.07 0.04 0.13 0.17


36

Figure 2.1. Plot design established to study the effects of irrigation, nitrogen fertilization, and tillage on soil microbial dynamics and community structure associated with a peanut cropping system in semi-arid west-Texas in year 2007. The numbers indicated refer to the different Plots. Tillage treatment and Plot alignment remained the same in 2008. Irrigation and nitrogen treatments differed in Plots 1,2,5, and 6 in 2008.


37

Figure 2.2. The effect of strip tillage compared to conventional tillage treatments on Microbial Biomass Carbon (MBC) associated with a peanut cropping system in semi-arid West Texas. (a)Total MBC from 2007-2008. Values are means± S.E., n= 96. (b) MBC in each of the summer and fall seasons in both 2007 and 2008. Values are means± S.E., n=24. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Mic

rob

ial B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600Conventional

Strip

a a

b. Summer 07 Fall 07 Summer 08 Fall 08

Mic

rob

ial B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600

700Conventional

Strip

b

abb b

abab

a

ab


38

Figure 2.3. The effect of late season drought (LSD) compared to full irrigation (Full) on Microbial Biomass Carbon (MBC). (a)Total MBC from 2007-2008. Values are means± S.E., n= 64. (b) MBC in each of the summer and fall seasons in both 2007 and 2008. Values are means± S.E., n=16. Groups statistically different are indicated with different letters above mean bars.

a.

Mic

rob

ial B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600 Full

LSD a

b


Mic

robia

l B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600

700Full

LSD

bc bc

ab

c

a

bc

a

ab


39

Figure 2.4. The effect of late season drought (LSD) and early season drought (ESD) compared to full irrigation (Full) on Microbial Biomass Carbon (MBC). (a)Total MBC from 2007. Values are means± S.E., n= 32. (b) MBC in each of the summer and fall seasons in both 2007 and 2008. Values are means± S.E., n=16. Groups statistically different are indicated with different letters above mean bars.

a.

Mic

rob

ial B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600

700Full

LSD

ESD a

bab

b. Summer 08 Fall 08

Mic

robia

l B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600

700Full

LSD

ESD a

b

ab

a

ab ab


40

Figure 2.5. The effect of nitrogen application compared to nitrogen application treatments on Microbial Biomass Carbon (MBC). (a)Total MBC from 2007-2008. Values are means± S.E., n= 32. (b) MBC in each of the summer and fall seasons in both 2007 and 2008. Values are means± S.E., n=16. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Mic

rob

ial B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600No nitrogen

nitrogen

a a


Mic

robia

l B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600

700 No nitrogen

nitrogen

a

ab

c

bc


41

Figure 2.6. (a) Discriminate function analysis (DFA) examining microbial and nutrient dynamics associated with different management programs associated with a peanut production system in semi-arid West Texas conditions in the various months in 2007 and 2008 using different enzyme activity levels, nutrient levels, and MBC as the parameters. Note [1= June 07, 2= July 07, 3= Sept. 07, 4= Oct. 07, 5= June 08, 6=July 08, 7=Sept. 08, 8= Oct. 08, Phosph= phosphodiesterase, Glucomini= β-glucosaminidase, Glucosidase= β-glucosidase, Alkaline= alkaline phosphatase, Algal= α-galactasidase, NO3 = soil extractable nitrate, NH4= soil extractable ammonium, P= soil extractable phosphorus, K= soil extractable potassium, Mg= soil extractable magnesium, Ca= soil extractable calcium, S= soil extractable sulfur, B= soil extractable boron, Zn= soil extractable zinc, Mn= soil extractable manganese, Fe= soil extractable iron, Cu= soil extractable copper, CEC= cation exchange, MBC= microbial biomass C].

a.


42

Figure 2.6. (b) Vector correlations indicating correlations between the different nitrogen treatments in summer and fall and enzyme activities. Correlations are indicated by the smallest angle between two vectors. Ex 0°= 100% positive correlation, 90°= no correlation, and 180°= 100% negative correlation. Note [1= June 07, 2= July 07, 3= Sept. 07, 4= Oct. 07, 5= June 08, 6=July 08, 7=Sept. 08, 8= Oct. 08, Phosph= phosphodiesterase, Glucomini= β-glucosaminidase, Glucosidase= β-glucosidase, Alkaline= alkaline phosphatase, Algal= α-galactasidase, NO3 = soil extractable nitrate, NH4= soil extractable ammonium, P= soil extractable phosphorus, K= soil extractable potassium, Mg= soil extractable magnesium, Ca= soil extractable calcium, S= soil extractable sulfur, B= soil extractable boron, Zn= soil extractable zinc, Mn= soil extractable manganese, Fe= soil extractable iron, Cu= soil extractable copper, CEC= cation exchange, MBC= microbial biomass C].

b.


43

Figure 2.7. Changes in Microbial Biomass Carbon (MBC) during the growing seasons from 2007-2008. (a)MBC during the summer and fall seasons of 2007-2208. Values are means± S.E., n= 48. (b) MBC in months corresponding to enzyme measurements. Values are means± S.E., n=24. (c) MBC in all measured months. Values are means± S.E., n=24. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a. Summer 07 Fall 07 Summer 08 Fall 08

Mic

rob

ial B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600

b

aab

ab

b. June 07 July 07 Sept 07 Oct 07 June 08 July 08 Sept 08 Oct 08

Mic

rob

ial B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600

700

bc

ab ab

c

ab b

bc

a

c. M

ay 0

7

June

07

July 0

7

Aug

07

Sep

t 07

Oct 0

7

May

08

June

08

July 0

8

Aug

08

Sep

t 08

Oct 0

8

Mic

robia

l B

iom

ass C

(

g/

g s

oil)

100

200

300

400

500

600

700

c

bc

abab

ab

c

ab ab bc

ab

bc

a


44

Figure 2.8. Relative bacteria and fungi abundances measured by fatty acid methyl ester (FAME) analysis across two growing seasons in a peanut production system in West Texas. (A) Relative FAME abundance (%) of bacteria and fungi. (B) Change in the Fungal:Bacterial ratio during the growing seasons 2007-2008. Means were calculated by relative fungi abundance (%) over relative bacteria abundance (%). Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.


Re

lative

Mic

rob

ial A

bu

nd

an

ce (

%)

10

15

20

25

30

Bacteria

Fungi


Re

lative

Mic

rob

ial A

bu

nd

an

ce (

%)

Fu

ngal:B

acte

rial R

atio

0.5

1.0

1.5

2.0

2.5

b

a

c

b


45

Figure 2.9. Relative bacteria and fungi abundances measured by fatty acid methyl ester (FAME) analysis across two growing seasons in a peanut production system in West Texas.. (A) Relative abundance (%) of FAMEs associated with AMF and Saprophytic fungi. (B) Changes in the relative abundance (%) of FAMEs associated with Gram Positive, Gram Neagitive, and Actinomycete bacteria.


Re

lative

Fu

ngi A

bun

da

nce (

%)

2

4

6

8

10

12

14

16 AMF

Saprophytes


Re

lative

Ba

cte

ria

Ab

un

da

nce (

%)

2

4

6

8

10

12

14 Gram +

Gram -

Actinomycetes


46

Figure 2.10. Effects of nitrogen on relative bacteria abundances associated with a peanut production system in West Texas. (a) Relative bacteria abundance % in year 2007. Values are means± S.E., n=48. (b) Relative bacteria abundance % in growing seasons in year 2007. Values are means± S.E, n=24. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Re

lative

Ba

cte

ria

Ab

un

da

nce

%

2

4

6

8

10

12

14

16 No nitrogen

nitrogen a

b


Rela

tive B

acte

ria A

bunda

nce (

%)

2

4

6

8

10

12

14

16 No nitrogen

nitrogen

a

ab

a

b


47

Figure 2.11. Effects of nitrogen on relative fungi abundance associated with a peanut production system in West Texas. (a) Relative fungi abundance % in year 2007. Values are means± S.E., n=48. (b) Relative fungi abundance % in growing seasons in year 2007. Values are means± S.E, n=24. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Re

lative

Fu

ngi A

bu

nd

an

ce

(%

)

5

10

15

20

25

30No nitrogen

nitrogen

a

a


Rela

tive F

ungi A

bunda

nce (

%)

10

20

30

No nitrogen

nitrogen

c c

b

a


48

Figure 2.12. Effects of irrigation on relative Gram-Positive bacteria abundance associated with a peanut production system in West Texas. (a) Relative gram-positive bacteria abundance % in year 2007 & 2008. Values are means± S.E. (b) Relative Gram-Positive abundance % in growing seasons in years 2007 & 2008. Values are means± S.E. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Re

lative

Gra

m-P

ositiv

e B

acte

ria

Ab

un

da

nce

(%

)

2

4

6

8

10

12Full Irrigation

LSD a

b


Rela

tive G

ram

-Positiv

e B

acte

ria A

bunda

nce (

%)

2

4

6

8

10

12

14 Full Irrigation

LSD

abcbc

a

c

ab

abc ab

abc


49

Figure 2.13. Effects of irrigation on relative Gram-Negative bacteria abundance %. (a) Differences In relative gram-negative bacteria abundance % in year 2007 & 2008. Values are means± S.E. (b) Differences in relative Gram-Negative abundance % in growing seasons in years 2007 & 2008. Values are means± S.E. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars. * indicates values too low to be displayed on graph.

a.

Re

lative

Gra

m-N

ega

tive

Ba

cte

ria

Ab

un

da

ce

(%

)

0.2

0.4

0.6

0.8

1.0

1.2Full Irrigation

LSD

b

a


Rela

tive G

ram

-Negative B

acte

ria A

bunda

nce (

%)

0.5

1.0

1.5

2.0

2.5 Full Irrigation

LSD a

bb

bc

cc

*


50

CHAPTER 3

EFFECTS OF MANAGEMENT PRACTICES IN A SEMI-ARID AGROECOSYSTEM

ON SOIL MICROBIAL ENZYMATIC ACTIVITY

Introduction

Enzymes are biological catalysts that facilitate reactions without undergoing

permanent chemical changes and are necessary for any biochemical reaction to take place

under most natural conditions. In all terrestrial ecosystems, microbial enzymes are

essential to perform necessary ecosystem processes by breaking down organic and

inorganic compounds into useable forms for subsequent biomass production by plants

and soil microbes. While the production of enzymes have multiple levels of regulation,

from their synthesis to their reactivity, for microbial enzymes excreted into the soil,

activity is influenced by soil pH, ionic strength, soil temperature, and the presence and

absence of clay and organic matter that can bind soil enzymes (Dick and Tabatabai

1993).

For the soil ecosystem, enzymes can be found both in the form of intracellular

enzymes and extracellular enzymes. While soil enzymes originate mainly from plants

and microorganisms, the largest portion originates from the microbial biomass (Tabatabai

1994). As bacteria and fungi are the main source of enzymes in the soils the composition

and abundance of microbes that produce these degradative enzymes controls soil

processes (Acosta-Martinez et al. 2004). Decomposition rates, nutrient cycling,

decomposition of xenobiotics, and soil aggregation are critical soil processes that are

controlled by the microbial enzyme activities (Barea et al. 1996, Reynolds et al. 2003).


51

Once extracellular enzymes are produced the activity of these molecules can become

uncoupled from biological activity. While extracellular enzymes can be degraded over

time, a significant portion becomes stabilized by binding to clay particles and forming

complexes (Boyd and Mortland 1990). The enzymes in these enzyme-clay complexes

still have activity but their activity is significantly reduced, thus directly impacting rates

of critical ecosystem processes. Formation of these complexes with clay can also causes

the enzymes to be immobilized, thus completely decreasing activity (Dick and Tabatabai

1987). For most terrestrial ecosystems it is known that the activity and production of

extracellular enzymes are influenced by vegetation composition, agricultural practices,

such as tillage and chemical applications, and levels of organic matter (Tabatabai 1994).

It is not unreasonable to expect the management practices used in agriculture soils can

have affects on the activity of the enzymes in the soils (Bandick and Dick 1999).

Changes in concentration and activity of certain extracellular enzymes in the soil

can have strong impacts on the nutrient cycles of those soils. Substantial changes in the

dynamics of extracellular enzymes will often be seen in the rhizosphere region where

previous research has shown greater enzymatic activity than that of non-rhizosphere soils

(e.g. Dick 1994). This can be explained by the greater amount of plant material and

microorganisms and species diversity associated with the rhizosphere compared to the

bulk soil. The roots themselves excrete carbon to stimulate microbial activity that can

increase the production of microbial enzymes (Dick 1994). Soil enzymes can lead to

higher levels of crop yields when the byproducts of their reactions including increased

mineralization are utilized by the roots in the rhizosphere (Dick and Tabatabai 1993).


52

The activity of many soil enzymes has been found to correlate with the fertility status of

soils (Dick and Tabatabai 1993) and enzyme assays give an estimate of the microbial

activity of the soil in relationship to the management practices that are employed.

For agricultural systems, enumerating the seasonal levels of hydrolases,

phosphatases, phosphodiesterase and β-glucosidase (e.g., Acosta Martinez et al. 2009)

provide key insight to the soil quality of the system. Hydrolases are a class of enzymes

that catalyze the hydrolysis of proteins, nucleic acids, starches, fats, phosphoric acids,

and other macromolecule substances. Included in the hydrolases are the phosphatase

enzymes which catalyze the hydrolysis of esters and anhydrides of phosphoric acids.

Phosphatses play important roles in P and N cycles in soils making them valuable

enzymes to examine when trying to understand microbial dynamics (Dick and Tabatabai

1993). Acid phosphatase or alkaline phosphatase, phosphodiesterase, β-glucosidase, α-

galactosidase, and β-glucosaminidase all contribute to P and N dynamics in soils.

Acid phosphatase and alkaline phosphatase are two important

phosphomonoesterases that have been shown to be pivotal in soil organic P

mineralization and have been seen to be specifically involved in hydrolysis in soils of β-

glycerophosphate, phenylphosphate, β-napthyl phosphate, and p-nitrophenyl (Tabatabai

1994). The two enzymes differ in pH ranges of their optimum activity. Acid

phosphatases have optimum activity in the acidic pH ranges and are usually most active

in soils with a pH of around 6. Alkaline phosphatases have optimum activity in the

alkaline pH ranges and have been shown to be most active with a pH of around 8 (Dick

and Tabatabai 1993). The activity of acid and alkaline phosphatase has been shown to


53

become more active in certain cropping systems as organic P becomes more limited

(Baligar et al. 1988).

Phosphodiesterase has been the least studied phosphatase in soils and is believed

to potentially be an important component of the P cycle and contributes to the

degradation of nucleic acids in the soil (Tabatabai 1994). Moreover, Phosphodiesterase

has been suggested to be an extracellular enzyme utilized by Arbuscular Mycorrhizal

fungi in their symbiosis with plants (Lindahl et al. 2005).

β-glucosidase is an important extracellular soil enzyme that provides a major

energy source for soil microbes and has been positively correlated to crop yields (Dick

1997). β-glucosaminidase is a key extracellular enzyme involved in N mineralization

that hydrolyzes C-N bonds of organic sugars free NH3 that can be uptaken by plants

(Dick 1997, Ekenler and Tabatabai 2002). α-galactosidase is another enzyme involved in

C dynamics by hydrolyzing melibiose and lactose (Dick 1997).

Tillage and crop-residue management can have significant impacts on microbial

mediated biochemical reactions in the soil (Dalal 1989, Spedding et al. 2004). These

impacts are the result of the effects the tillage practices on altering the distribution, and

activities of soil microbial communities and in turn an effect on the soil microbial

enzymes (Dick 1984). Organic C and N have been shown to accumulate at the soil

surface (0-2. cm) under reduced tillage involving crop residue placement (Havlin et al.

1990). Extracellular soil enzymes have been shown to be significantly affected by tillage

(Deng and Tabatabai 1996) and in general have lower activity in no-tillage management

compared to conventional tillage (Doran 1980, Angers et al. 1993).


54

The objective of this component of the overall project was to evaluate the response of the

microbial activity (enzyme production) to tillage, irrigation, and nitrogen of a peanut

cropping system in semi-arid soils.

Methods Plot Design

Field plots for this two year study (2007 and 2008) were established at the USDA-

ARS Cropping System Research Laboratory in Lubbock, Texas USA (101⁰ 47’ west

longitude; 33⁰ 45’ north latitude; 993m elevation. The field soil was a mixed superactive,

thermic and aridic soil (Olton clay loam) and are classified as Paleustolls (Bronson et al.

2004). Previous to the initiation of this study, the field was used for continuous cotton

production under conventional tillage for a duration of 6 growing seasons. The circular

field was portioned into 36 concentric rows prior to the initiation of this study in

November 2006. Approximately 1.3 hectares (3.3 acres), one half of a circular field was

prepared for peanut production to assess the impacts of irrigation, nitrogen, and tillage on

soil microbial dynamics and microbial functionality.

The sample site was divided into six equally sized triangular plots that are labeled

plots 1-6. Every two plots receive the same imposed conditions so that there were two

replicates for each treatment. Within each of the plots, different tillage techniques were

applied alternating between conventional tillage and strip tillage zones. In the

conventional tillage rows there is rye planted in the furrow only and in the strip tillage

portions rye is planted across the bed and the furrow. The plots alternate between zones


55

of strip and conventional tillage rows respectively starting from the outside of the plots.

There are two strips and two conventional tillage sections within each zone. Two

alternating conventional and strip tillage sections of six rows each were sampled in each

plot.

Zone 1 and 2 served as the control and did not receive nitrogen fertilizer in year

2007. The remaining zones 3, 4, 5, and 6 received 30lb/acre (33.6 kg/Hectare) of URAN

(Urea Ammonium Nitrate) on July 25th, August 9th, August 27th, September 24th and

September 24th in 2007. In 2008, zones 1 through 6 received URAN 35 lb/acre (39.2

Kg/Ha) on July 8th, August 1st and September 2nd. All 6 zones received nitrogen

fertilization in 2008.

In 2007, two levels of irrigation were implemented. Zone 1, 2, 5 and 6 served as

controls and received full irrigation (100-100-100% EvapoTranspiration (ET)). 100% ET

irrigation is defined as the amount of water supplemented to the crop equaling the total

loss of water through evaporation (water lost from the soil to the atmosphere) and the

total transpiration (subsequent water lost from aerial parts of the plant). For 2007 full

irrigation was determined to be 1.5 inches of water. The late growing seasons last from

beginning of August till the end of October. Zones 3 and 4 were kept under late deficit

treatment (100-100-50%ET), applying only 0.75 inch of irrigation (half) during the last

90 days while maintain full irrigation prior to. There were 3 levels of irrigation in year

2008. Zones 1 and 2 received full irrigation (100-100-100%ET). Zones 3 and 4 were

kept under late deficit treatment (100-100-50%ET). Zones 5 and 6 were under early


56

deficit irrigation. They were irrigated with half of the regular irrigation 90.75 inch

quantity in initial 45 days (beginning of May to mid-June).

Crop Management Treatments

In 2007, the treatments applied to the field were: 1) Full Irrigation and no

Nitrogen (plots 1 & 2), 2) Nitrogen and a Late Season Drought (plots 3 &4), and 3) Full

Irrigation and added Nitrogen (plots 5 & 6). In 2008, the applied treatments were: 1) Full

Irrigation and added Nitrogen (plots 1 & 2), 2) Late Season Drought and added Nitrogen

(plots 3 & 4), and 3) an Early Season Drought and added Nitrogen (plots 5 & 6). As with

2007 there were alternating conventional and strip tillage rows that were sampled (Figure

3.1).

Sampling

Four soil samples were taken within each plot; two from conventional tillage rows

and two strip tillage rows. For each sample, soil was collected from three different

locations across the plot to account for any variations that may be present within each

section of the zone and combined per row. Soil samples were collected from depth of

15cm, with approximately 330g of samples taken for each replicate sample. Once

samples were taken, they were kept in a cooler and stored at 4⁰ C until they were

processed for enzymatic analysis.


57

Enzyme Assays

Alkaline phosphatase, phosphodiesterase, β-glucosaminidase, β-glucosidase, and

α-galactosidase were measured for activity in all samples following the procedures

described by Tabatabai (1994) at their optimal pH value. Results were expressed in

milligrams of p –nitrophenol released per kilogram soil per hour (Acosta-Martinez et. al.

2003). Absorbance was measured at 400 nm, the optimal wavelength of p –nitrophenol

(Eril and Avci 2002).

Data Analysis

Differences in individual enzymatic activities in response to different

management practices were evaluated using one-way ANOVA. The overall impacts of

management affects on the total microbial enzymatic activities of the soil and total

nutrient influence of the soil were evaluated using mANOVA. Influences of soil

nutrients levels on enzymatic activities and the influence of the enzyme levels on each

other were evaluated using bivariate correlations. All analyses were performed using

SPSS 14.0 for windows. Each data set underwent the Least Significant Difference (LSD)

post hoc test when significance (p ≤ 0.05) was indicated on the one-way ANOVA and

mANOVA.

Discriminant function analysis (DFA) was employed to visualize microbial

responses to managanement practices in multivariate space, using MATLAB (Natick,

Massachusetts). DFA analyzed microbial responses to 2007 and 2008 separately. For

each DFA, 1000 randomized (bootstrap) iterations were performed in order to build null

distributions from which to compare actual sampling distribution.


58

Results

Effect of Irrigation

Full Irrigation vs. Late Season Drought (LSD) for the 2007-2008 growing seasons

Phosphodiesterase Activity

There was not a significant (p = 0.065) effect of the LSD treatment on

phosophodiesterase activity when compared to the full irrigation control (Figure 3.2a)

over the two year period. However, there did appear to be the trend of higher levels of

phosphodiesterase activity in the full irrigation treatment when compared to the levels

seen in the LSD treatment. This pattern was further revealed when the phosphodiesterase

levels between the two treatments were compared throughout the individual seasons.

Phosphodiesterase activities were consistently greater (p ≤ 0.05) under full irrigation

treatment than when exposed to late season drought in the fall months of 2007 (Figure

3.2b).

β-Glucosaminidase Activity

β-glucosaminidase activity did not differ in response to irrigation (p= 0.137)

(Figure 3.3a). There was a noticeable pattern of higher β-glucosaminidase under full

irrigation than LSD β-glucosaminidase levels. This was seen in the summer months of

2008 when the β-glucosaminidase levels of the Full Irrigation treatment were

significantly (p ≤ 0.05) higher than that of the β-glucosaminidase levels of the LSD

treatment (Figure 3.3b).


59

β-Glucosidase Activity

The effect of the full irrigation treatment was overall significantly (p ≤ 0.05)

greater than that of the LSD treatment on the levels of β-glucosidase activity across both

sampling seasons (Figure 3.4a). The effect of irrigation treatment resulted from the fall

months of 2007, in which the full irrigation treatment produced significantly (p ≤ 0.05)

higher β-glucosidase levels than the LSD treatment for this same period (Figure 3.4b).

Alkaline Phosphatase Activity

There was no significant (p= 0.44) difference seen between the full irrigation

treatment and the LSD treatment on the overall levels of alkaline phosphatase activity

across both sampling seasons (Figure 3.5a). This trend of no difference between the two

treatments was observed in each of the summer and fall seasons in 2007 and 2008

respectively (Figure 3.5b).

α-Galactosidase Activity The activity levels of α-galactosidase did not exhibit any overall difference (p=

0.754) between the full and LSD irrigation treatments (Figure 3.6a). Furthermore, no

difference could be seen in the summer and fall months in both 2007 and 2008 (Figure

3.6b).

Full Irrigation vs. Late Season Drought (LSD) vs. Early Season Drought (ESD) during

growing season of 2008

The mANOVA showed that there was not a significant difference between the

three irrigation treatments when all enzymes are used. Furthermore, DFAs showed that


60

the irrigation treatment groups do not separate out for both years combined (Figure 3.7a).

However, if either phosphodiesterase or α-galactosidase data are removed as parameters

the mANOVA detected significant differences across irrigation treatments (p ≤ 0.05).

Phosphodiesterase and α-galactosidase activity levels were shown to be affected the least

by the three irrigation treatments (Figure 3.7b). One-way ANOVA showed that there

were significant (p ≤ 0.05) differences between the LSD and ESD treatments for β-

glucosaminidase and alkaline phosphatase activity levels with full higher levels in

response to ESD treatments. A significant difference between full vs. LSD for β-

glucosidase activity levels was seen with full irrigation producing higher enzymatic

activity levels (Tale 3.1).

Effects of Nitrogen

No nitrogen vs. Nitrogen Application: 2007

Effect on combined enzyme activity

The mANOVA indicated that the addition of nitrogen did not have a significant

effect on the combined enzyme activity compared to the no nitrogen treatment. There

were patterns seen on the levels of enzyme activities for the individual enzymes and the

effect of the two treatments varied depending on the Individual enzyme examined across

the growing season (Figure 3.8).


There were no significant (p = 0.263) effect on phosphodiesterase levels between

the nitrogen and no nitrogen treatment (Figure 3.9a). There were also no seasonal


61

differences seen between the phosphodiesterase activity levels in the two treatments

(Figure 3.9b).

β-Glucosaminidase Activity

There were no significant (p= 0.086) effects of nitrogen on the overall levels of β-

glucosaminidase activities. However there did appear to be a trend of lower levels

associated with the nitrogen treatments across the growing seasons (Figure 3.10a).

When looking at the summer and fall seasons, there are no statistical significant

differences between the two treatments (Figure 3.10b).


The nitrogen and no nitrogen treatments showed no significant (p= 0.685)

differences in the overall β-glucosidase activity levels (Figure 3.11a). The nitrogen

treatment did display significantly (p ≤ 0.05) greater β-glucosidase activity levels in the

fall than that of the no nitrogen treatment (Figure 3.11b).


The overall levels of alkaline phosphatase activity appeared to be greater with the

no nitrogen treatment compared to treatment with nitrogen (Figure 3.12a) but there

wasn’t a significant (p= 0.111) difference. The no nitrogen treatment was statistically no

greater than the nitrogen treatment in both the summer and fall season even though it

appeared to have a pattern of greater alkaline phosphates activity (Figure 3.12b).


62

α-Galactosidase Activity

There were no significant differences (p= 0.652) between the nitrogen and no

nitrogen treatment on the overall α-galactosidase activity levels (Figure 3.13a). The

activity of this enzyme did not differ across season irrespective of the addition of nitrogen

(Figure 3.13b).

Combined enzymatic effect across growing seasons in 2007

The mANOVA found that the there were no significant (p≤ 0.05) differences

between the no nitrogen and nitrogen treatments for all enzymes combined for either the

summer or fall seasons. However, enzyme activities under nitrogen addition during

summer sampling approached significant differences in comparison to no-nitrogen

summer (p= 0.092) and no nitrogen fall (p= 0.082). There was a significant seasonal

difference (p ≤ 0.001) difference seen between the summer and fall nitrogen treatments.

This was further seen in DFA which displayed a separation between the nitrogen summer

treatments from the rest of the treatments (Figure 3.14a). This separation was driven

most by the levels of β-glucosidase activity (Figure 3.14b).

Tillage Effects: Conventional vs. Strip (2007-2008)


Levels of phosphodiesterase activity were overall higher under conventional

tillage than activity levels across years under strip-tillage (Figure 3.15a) but the levels

were not significantly different (p= 0.77). Moreover, the enzyme activity levels between

the two treatments were not significantly different during any of the summer or fall


63

seasons in 2007 and 2008. However, the fall season of 2008 had significantly lower

levels of phosphodiesterase activity than all the other seasons for both tillage treatments

(Figure 3.15b).

β-Glucosaminidase Activity There was no significant (p= 0.424) difference in β-glucosaminidase between the

conventional and tillage treatments on the overall levels of β-glucosaminidase activity

across all seasons (Figure 3.16a). Within seasons, β-glucosaminidase levels differed only

during the 2008 season when the summer levels were significantly higher for the

conventional summer and the strip fall of 2008 (Figure 3.16b).


There was no significant difference (p= 0.412) between the overall levels of β-

glucosidase activity between the conventional and strip tillage treatments across both

growing seasons (Figure 3.17a). There were no significant (p ≤ 0.05) differences in β-

glucosidase activity levels between the two tillage treatments within any of the seasons.

However, there was an observed seasonal difference as the summer seasons had

significantly lower levels of activity than the fall seasons for both the conventional and

strip tillage treatments (Figure 3.17b).


There was no significant difference (p= 0.736) between the overall levels of

alkaline phosphatase activity between the conventional and strip tillage treatments across

both years (Figure 3.18a). While there were no significant (p ≤ 0.05) difference observed


64

in alkaline phosphatase levels between the two treatments within any of the seasons,

alkaline phosphatase levels for the strip tillage treatments in 2007 were lower than those

observed under conventional tillage (Figure 3.18b). However, for the 2008 growing

season, the levels of alkaline phosphatase activities were higher during the fall under the

strip tillage treatment.

α-Galactosidase Activity

Across both growing seasons, α-galactosidase activity was not significantly

different between the conventional and strip tillage treatments (p= 0.373) (Figure 3.19a).

There were no significant (p ≤ 0.05) differences between the two tillage treatments within

any of the seasons, however the α-galactosidase levels were significantly less in 2007

than 2008 for both treatments (Figure 3. 19b).

Combined enzymatic and nutrient effect

The mANOVA indicated that tillage did not impact the total soil nutrient and

extracellular enzyme dynamics (p= 0.575). However, when examining the annual tillage

effect there was a separation of the different groups (p ≤ 0.001). Discriminate function

analysis (Figure 3.20) displayed the groups separating out by different years. There was

a complete separation among the nutrient and enzymatic effects of the conventional

treatment between 2007 and 2008. The DFA did not show a complete separation

between nutrient and enzyme dynamics between the strip 2007 treatment and strip 2008

treatment but the subsequent ANOVA indicated that overall nutrient and enzymatic

levels were significantly different between the two growing years (p ≤ 0.001).


65

Effect of nutrients

Correlation between nutrient levels and enzyme levels

Ammonium (NH4) and organic matter (OM) levels had the highest correlations

with individual enzyme. Phosphodiesterase activities showed correlated (-0.43) with

NH4 levels. OM was shown to have the strongest and largest number of significant

correlations with the most enzymes with including phosphodiesterase (-0.21), β-

glucosaminidase (0.33), alkaline phosphatase (0.29), along with α-galactosidase (0.43).

The levels of β-glucosaminidase was the least related to any of the soil nutrient

parameters measured (Table 3.2).

Correlations between enzyme activity levels

Alkaline phosphatase and α-galactosidase showed the strongest significant

correlation (36%) of activity levels followed by β-glucosaminidase and α-galactosidase

(33%). Activity of β-glucosaminidase had the most significant correlations with other

(Table 3.3).

Changes in key nutrient levels (growing seasons of 2007-2008)

Soil Extractable Nitrate

NO3 levels were highly variable across all treatments in 2007 compared to the

levels in 2008 (Figure 3.21). There was no statistical difference between NO3 levels in

the summer and fall of 2008. There was much more variability in the NO3 levels in the

different seasons during 2007. The levels in the summer of 2007 were statistically


66

greater than levels seen in any other season in any of the two years. Conversely, the

levels in the fall of 2007 were statistically lower than any other season in either of the

two years (Figure 3.21).

Soil Extractable Ammonium (NH4) Dynamics

NH4 levels across all treatments remained very consistent from the summer of

2007 through the summer of 2008 (Figure 3.22). In the fall 2008 the levels showed a

significant increase. NH4 levels in the fall of 2008 exhibited 15 fold increase when

compared to the NH4 levels from the summer 2008 growing season (Figure 3.22).

Soil Phosphorus (P) dynamics

P levels showed no statistical (p ≤ 0.05) differences between the seasons within an

individual year but exhibited significant differences between the two years irrespective of

season (Figure 3.23).

% Soil Organic Matter (OM) dynamics

OM levels remained almost completely consistent throughout the 2007 and 2008

growing season with one exception. The levels of soil organic matter (OM) were

significantly (p ≤ 0.05) greater in the summer of 2008 than either season of 2007 or the

fall of 2008 (Figure 3.24).


67

Discussion

The worlds’ population is continually proliferating and as a result so must the

worlds’ food supply. Increasing the efficiency of modern agriculture will be monumental

in helping to meet the ever rising food demands of a growing human population. Current

agricultural practices such as chemical inputs and irrigation are becoming more

expensive and are not as sustainable as low-input systems (Clark et al. 1998, Zak and

McMichael 2001). Furthermore, global climate changes may present even more

difficulties in maintaining and improving on current productivity (Adam et al. 1990).

Finding low input solutions to maximizing agricultural production will not only cut down

on cost and potentially increase productivity but will also help create more sustainable

agroecosystems even in a more unpredictable climate (Stainforth et al. 2005).

Understanding how agricultural practices affect the microbial dynamics of the soil will be

essential to developing the most effective and sustainable agroecosystems. Determining

which tillage technique, irrigation strategies, and fertilizer application are the best for a

particular agroecosystem will be the key to developing the most efficient system. The

semi-arid conditions in West Texas impose unique challenges to developing sustainable

production systems under increasing costs of fertilizers and greater demands for limited

water supplies.

Effects of Irrigation

As full irrigation maintained higher plant growth and provided consistently

optimum conditions for microbial activity, one would expect the highest levels of enzyme


68

activities under these conditions thin in either early of late season drought. The latter two

treatments were established to determine if a reduction in irrigation at specific periods in

crop growth would not have detrimental effects. Full irrigation only produced

significantly (p ≤ 0.05) greater activity levels for β-glucosidase suggesting that having

consistent moisture alone will not provide for maximum microbial activity.

β-glucosaminidase and alkaline phosphatase did not show any differences to the

overall response to the different irrigation treatments but the ANOVA confirmed that

they did show significant differences between the late season drought treatment and the

early season drought treatment within particular seasons. This seasonal pattern may

suggest that β-glucosaminidase and alkaline phosphatase production is highly dependent

on when adequate soil moisture available. Both enzymes displayed significantly greater

activity levels in the early season drought treatment than they did in the late season

drought treatment in 2008 suggesting that seasonal differences in either production or the

densities of microbes producing these enzymes may be contributing to the observed

pattern.

Response to nitrogen application

Levels of β-glucosaminidase showed the strongest response to nitrogen

significantly increasing with its input. This result was to be expected due to its role in N

cycling by breaking C-N bonds of amides and releasing NH3 in the process used in N

mineralization which provides a useable form of N for plants (Dick, 1997). Moreover β-

glucosaminidase can contribute 5-10% of the organic N in the soil surface by hydrolyzing


69

amino sugars (Ekenler and Tabatabai, 2002). This result suggest that β-glucosaminidase

is aiding in N cycling.

β-glucosidase is involved in the hydrolysis of glycosidic molecules releasing

glucose to be used for and energy source for the micro flora (Dick 1997) and would not

be expected to be strongly affected by N inputs. It indeed did not display an overall

response to the nitrogen application but did show significantly greater levels of activity

with nitrogen application in the fall season. One explanation for this observation is a lag

response to altered C:N ratios in the soil from plant growth during the summer. Soil

enzymes have been shown to respond to changing C:N ratios in soil (Sinsabaugh et al.

2005) and β-glucosidase is directly involved in soil C dynamics.

Alkaline phosphatase showed consistently lower levels of activity in the nitrogen

treated plots compared to the no nitrogen treatment. This trend may be a result of

nitrogen addition creating a competitive disadvantage to organisms that secrete alkaline

phosphatase.

Response to tillage type

Soil enzymes can be accurate and sensitive indicators to agricultural management

treatments (Deng & Tabatabai, 1996, Dick 1997, Burns and Dick 2002) including the

short-term effects of tillage practices (Kandeler et al. 1999). However, this hasn’t always

been shown to be the case. Conversely, studies have shown that soil enzymes do not

always respond to changes in tillage (Mina et al. 2008). In this study, tillage had no

impact on the activity of any of the extracellular soil enzymes measured within seasons or


70

overall in this study. The lack of a functional response to tillage treatments could have

been affected by the soil composition of the agricultural plots in this study or the

cropping system studied. Tietjen showed that clay-enzyme complexes can reduce the

activity of alkaline phosphatase but increased the activity of β-glucosidase. Clay

particles can also indirectly affect the activity of various soil enzymes forming bonds to

other cationic particles which can alter the pH affecting the optimal activity or interact

with the enzymes themselves (Boyd and Mortaland 1990). The affects of tillage

treatment on the functional responses in the soil measured by enzymatic activity may

indeed be there but outweighed by the effect of the formation of clay-enzyme complexes.

Influence of nutrients and other enzymes on enzymatic activity levels

Soil organic matter (OM) had the broadest correlation with enzyme activity

levels, significantly correlating with phosphodiesterase, β-glucosaminidase, alkaline

phosphatase, and α-galactosidase, the most notable being β-glucosaminidase (33%) and

α-galactosidase (43%). This result is consistent with other research which has shown

OM to be a driving force in N mineralization and C cycling (Fontaine et al. 2003),

processes directly affected by β-glucosaminidase and α-galactosidase (Dick 1997). This

indicates that OM levels may be an appropriate measure of microbial functionality

irrespective of cropping management.

Conclusion

Irrigation had an affect on enzymatic activity. phosphodiesterase and β-

glucosaminidase activity was greater under constant irrigation while others did not


71

exhibit any response to irrigation schedule. The differential response suggests that

composition of the soil microbial community changed in response to irrigation. Nitrogen

fertilization directly impacted the activity of β-glucosaminidase, an enzyme directly

involved in N mineralization and cycling dynamics. Nitrogen inputs significantly

increased short-term β-glucosidase activity in the fall seasons. This is mostly likely due

to a secondary effect due to the fact that β-glucosidase is involved in proving C for soil

microbes and not N cycling. Tillage was not found to significantly impact the activity

levels of the measured soil enzymes. This contradicts most of the previous research.

This result may be due to other variables having stronger affects on enzymatic activity

levels than tillage or that the impacts of tillage in this system were short-lived. As seen in

other studies, soil organic matter appears to be the best measure of the level of soil

functionality and microbial dynamics.

The outcome of this study was the determination of the importance of irrigation to

peanut cropping systems in a semi-arid environment. Full irrigation is needed to

maximize the activity and abundance of the soil microbial community. Furthermore,

attempts to conserve water and reduce water quantities will not only hinder the soil

microbial activity and abundance in that particular growing season but will also

negatively affect them in subsequent growing seasons even if full irrigation is re-

implemented. Strip-tillage was not shown to have any negative impacts on microbial

dynamics in comparison and therefore should be considered a better tillage option

because of the reduced soil erosion and energy required associated with this practice.


72

Nitrogen addition had minimal effects on the system and appears to be an unnecessary

expenditure when growing peanut crops in this particular system.


73

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Dick, P.R. 1994. Soil enzyme activities as indicators of soil quality. In Defining Soil

Quality for a Sustainable Environment. Soil Sci. Soc. Am.. Edited by Doran et al. Madison, WI: Special Publication. 107-124. Dick, P.R. 1997. Soil Enzymes as Intergative Indictors of Soil Health. Biological Indictors of Soil Health. Edited by Pankhurst et al. CAB International, Wallingford, England 121-157 Dick, R.P. and M.A. Tabatabai. 1987. Polyphosphates as sources of phosphorus for plants. Fert. Res.. Vol. 12: 107-118. Dick, P.R. and Tabatabai, M.A. 1993. Significane and Potential Uses of Soil Enzymes. Soil Microbiolgy Ecology: Applications in Agricultural and Environmental Management. Edited by F. Blaine Metting, Jr. Marcel Dekker, Inc. New York, New York 10016: 95-127. Dick, P.R., Tabatabai, M.A. 1993. Potential uses of soil enzymes. Soil Microbial Ecology. B.Metting. Marcel Dekker Inc. New York, New York, pp. 95-127. Ekenler, M. and Tabatabai, M.A. 2002. β-Glucosaminidase activity of soils: effect of cropping systems and its relationship to nitrogen mineralization. Biol Fertil Soils. 36:

367-376. Erel, O. & Avci, S. 2002. Semi-automated enzymatic measurement of serum zinc concentration. Clinical Biochemistry. 35: 41-47. Fontaine, S., Mariotti, A., Abbadie, L. 2003. The priming effect of organic matter: a question of microbial competition. Soil Biology and Biochemistry. 35: 837-843. Havlin J.L., Kissel, D.E., Maddux, L.D., Claasseen, M.M. and Long, J.H., 1990. Crop rotation and tillage effects on soil organic carbon and nitrogen. Soil Sci. Soc. Am. J. 54, pp. 448–452. Kandeler, E., Tscherko D., Spiegel, H. 1999. Long-term monitoring of microbial biomass, N minerlisation and enzyme activities of a Chernozem under different tillage management. Biol Fertil Soils. 28: 343-351. Lindahl, B.D., Finlay, R., Cairney, J.W. 2005. Ezymatic Activities of Mycelia in Mycorrhizal Fungal Communities. The Fungal Community: Its Organization and Role in the Ecosystem Third Edition. J. Dighton, J.F. White, P. Oudemans. Taylor & Francis Group, LLC, Boca Raton, FL 33487-2742: 331-348.


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Mina, B.L., Saha, S., Kumar, N., Srivastva, A.K., Gupta, H.S. 2008. Changes in soil nutrient content and enzymatic activity under conventional and zero-tillage practices in an Indian sandy clay loam soil. Nutr Cycl Argoecosyst. 82: 273-281.

Reynolds, H. L., A. Packer, et al. 2003. Grassroots Ecology: Plant-Micorbe-Soil Interactions As Drivers Of Plant Community Structure And Dynamics. Ecology 84(9): 2281-2291. Sinsabaugh, R.L., Gallo, M.E., Lauber, C., Waldrop, M.P., Zak, D.R. 2005. Extracellular enzyme activities and soil organic dynamics for northern hardwood forest receiving stimulated nitrogen deposition. Biogeochemisty. 75: 201-215. Spedding, T.A., Hamel, C. Mehuys, Madramootoo, C.A. 2004. Soil microbial dynamics in maize-growing soil under different tillage and residue management system. Soil Biology and Biochemistry. 36: 499-512. Stainforth, D.A., Aina, T., Christensen, C., Collins. M., Faull, N., Frame, D.J., Kettleborough, J.A., Knight. S., Martin, A., Murphy, J.M., Piani, C., Sexton, D., Smith, L.A., Spicer, R.A., Thorpe, A.J., Allern, M.R. 2005. Uncertainty in predictions of the climate response to rising levels of greenhouse gases. Nature. 433: 403-406 Tabatabai, M. (1994). Soil Enzymes. Methods of soil analysis. Part 2. Microbiological and biochemical properties. R. Weaver, J. Angle and P. Bottomley. Madison, Wisconson, SSSA: 775-833 Tietjen, T. and Wetzel, R.G. 2003. Extracellular enzyme-clay mineral complexes: Enzyme absorption, alteration of enzyme activity, and protection from photodegradation. Aquatic ecology. 37: 331-339. Verhoeven, J.T.A., Maltby, E., Schmitz, M.B. 1990. Nitrogen and phosphorus mineralization in fens and bogs. Journal of Ecology. 78: 713-726. Zak, J.C., McMichael, B. 2001. Argoecology of Arbuscular Mycorrhizal Activity. Structure and Function in Agroecosystem Design and Management. M. Shiyomi, H. Koizumi. CRS Press: 145-166.


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Table 3.1. P-values for comparisons of the three different irrigation treatments imposed in 2008 for a peanut cropping system. Values were considered significant at p ≤ 0.05. Significant values are indicated in bold. Note [Phospho= phosphodiesterase, Glucosamini= β-glucosaminidase, Glucosidase= β-glucosidase, Alkaline= alkaline phosphatase, Alpha= α-galactosidase, Full= full irrigation, LSD= late season drought, ESD= early season drought].

Phospho Glucosamini Glucosidase Alkaline Alpha

Full vs. LSD 0.55 0.07 0.08 0.44 0.91 Full vs. ESD

0.88

0.89

0.05

0.16

0.81

LSD vs.ESD

0.45

0.05

0.82

0.03

0.90


77

Table 3.2. Correlation matrix comparing soil extractable nutrients to extracellular soil enzyme activity from soil samples collect under peanut cultivation for two growing seasons of 2007-2008. All numbers represent r calculated using Pearson Correlations. The r values range from 1 to -1 with being a 100% positive correlation and -1 a 100% negative correlation. Significant r values are indicated in bold. Note [Phosph= phosphodiesterase, Glucomini= β-glucosaminidase, Glucosidase= β-glucosidase, Alkaline= alkaline phosphatase, Alpha= α-galactasidase, NO3 = soil extractable nitrate, NH4= soil extractable ammonium, P= soil extractable phosphorus, K= soil extractable potassium, Mg= soil extractable magnesium, Ca= soil extractable calcium, S= soil extractable sulfur, B= soil extractable boron, Zn= soil extractable zinc, Mn= soil extractable manganese, Fe= soil extractable iron, Cu= soil extractable copper, CEC= cation exchange, OM= soil organic matter].

NO3

NH4 P K Mg Ca S B Zn Mn Fe Cu

CEC OM

Phospho 0.04 0.43 0.02 0.13 0.19 0.29 0.24 0.24 0.04 0.22 0.13 0.18 0.26 0.21

Gucomi 0.02 0.11 0.03 0.10 0.02 0.10 0.11 0.01 0.06 0.05 0.01 0.10 0.08 0.33

Glucosi

d 0.16 0.13 0.04 0.01 0.07 0.08 0.18 0.25 0.09 0.03 0.01 0.08 0.04 0.01

Alkaline 0.06 0.18 0.09 0.13 0.15 0.08 0.08 0.10 0.16 0.11 0.22 0.16 0.03 0.29

Alpha 0.05 0.15 0.11 0.18 0.27 0.25 0.03 0.18 0.22 0.36 0.24 0.27 0.17 0.43


78

Table 3.3. Correlation matrix between all extracellular soil enzymes collected under peanut cultivation throughout the growing seasons of 2007-2008. All numbers represent r calculated using Pearson Correlations. The r values range from 1 to -1 with being a 100% positive correlation and -1 a 100% negative correlation. Significant r values are indicated in bold. Note [Phosph= phosphodiesterase, Glucomini= β-glucominidase, Glucosidase= β-glucosidase, Alkaline= alkaline phosphatase, Alpha= α-galactasidase].

Phospho Gucomini Glucosidase Alk Alpha

Phospho 1.00 Gucomini 0.03 1.00

Glucosidase 0.18 0.23 1.00 Alk -0.06 0.24 0.17 1.00

Alpha -0.16 0.34 0.16 0.36 1.00


79

Figure 3.1. Plot design for established to study the effects of irrigation, nitrogen fertilization, and tillage on soil microbial dynamics and community structure associated with a peanut cropping system in semi-arid west-Texas in year 2007. The numbers indicated refer to the different zones. Tillage treatment and zone alignment remained the same in 2008. Irrigation and nitrogen treatments differed in plots 1, 2, 5, and 6 in 2008.


80

Figure 3.2. The effect of late season drought (LSD) compared to full irrigation (Full) on phosphodiesterase activity levels associated with a peanut cropping system. (a)Total phosphodiesterase activity levels across two years, 2007-2008. Values are means± S.E., n=64. (b) Phosphodiesterase levels in each of the summer and fall seasons in both 2007 and 2008. Values are means ± S.E, n= 16. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Ph

osp

ho

die

ste

rase

Activity

(mg P

n k

g-1

so

il h

-1)

20

40

60

80

Full

LSD a

a

b.

Summer 07 Fall 07 Summer 08 Fall 08

Ph

osp

ho

die

ste

rase

Activity

(m

g k

g-1

so

il h

-1)

20

40

60

80

100

120Full

LSD

abc

bc

ab

c

a

ab

dd


81

Figure 3.3. The effect of late season drought (LSD) compared to full irrigation (Full) on β-glucosaminidase activity levels within a peanut cropping system. (a)Total β-glucosaminidase activity across two growing seasons, 2007-2008. Values are means± S.E., n= 64. (b) β-glucosaminidase levels in each of the summer and fall seasons in both 2007 and 2008. Values are means± S.E., n=16. Groups statistically different are indicated with different letters above mean bars.

a.

-G

luco

sa

min

ida

se

Activity

(mg P

N k

g-1

so

il h

-1)

2

4

6

8

10

12

14

16

18Full

LSD

a

a


-G

lucoscam

inid

ase

(m

g P

N k

g-1

soil-1

)

5

10

15

20

25

Full

LSD

ab

bc bc

ab

a

bc

c

c


82

Figure 3.4. The effect of late season drought (LSD) compared to full irrigation (Full) on β-glucosidase activity levels within a peanut cropping system. (a)Total β-glucosidase activity levels across two growing seasons, 2007-2008. Values are means± S.E., n= 64. (b) β-glucosidase levels in each of the summer and fall seasons in both 2007 and 2008. Values are means± S.E., n=16. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

-G

luco

sid

ase

Activity

(mg P

N k

g-1

so

il h

-1)

20

40

60

80 Full

LSD

a

b


-G

lucosid

ase A

ctivity

(mg P

N k

g-1

soil

h-1

)

20

40

60

80

100

120Full

LSD

c c

a

bc bc

c

ab

bc


83

Figure 3.5. The effect of late season drought (LSD) compared to full irrigation (Full) on alkaline phosphatase activity levels from a peanut cropping system. (a)Total alkaline phosphatase activity levels from 2007-2008. Values are means± S.E., n= 64. (b) Alkaline phosphatase levels in each of the summer and fall seasons in both 2007 and 2008. Values are means± S.E., n=16. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Alk

alin

e P

ho

sp

ha

tase

Activity

(mg P

n k

g-1

so

il h

-1)

20

40

60

80

100

120

140Full

LSD

aa


Alk

alin

e P

hosphata

se A

ctivity

(mg P

N k

g-1

soil

h-1

)

30

60

90

120

150

180

210Full

LSD

aa

a aa

a

aa


84

Figure 3.6. The effect of late season drought (LSD) compared to full irrigation (Full) on α-galactosidase activity levels from a peanut cropping system. (a) Total α-galactosidase activity levels from 2007-2008. Values are means± S.E., n= 64. (b) α-galactosidase levels in each of the summer and fall seasons in both 2007 and 2008. Values are means± S.E., n=16. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

-G

ala

cto

sid

ase

Activity

(mg P

N k

g-1

so

il h

-1)

2

4

6

8

10

12

14

16 Full

LSD a

a


-G

ala

cto

sid

ase

(mg P

N k

g-1

soil

h-1

)

5

10

15

20Full

LSD

abc

bc

cbc

aa

aab


85

Figure 3.7. (a) Discriminate function analysis (DFA) comparing the differences of the three different irrigation treatment imposed in 2008 on a peanut cropping system using different enzyme activity (phosphodiesterase, β-glucosaminidase, β-glucosidase, alkaline phosphatase, α-galactosidase) levels as the parameters. (b) Vector correlations indicating correlations between irrigation treatments and enzyme activities. Correlations are indicated by the smallest angle between two vectors. Ex 0°= 100% positive correlation, 90°= no correlation, and 180°= 100% negative correlation. Note [1= full irrigation, 2= late season drought, 3= full season drought, Phos= phosphodiesterase, Gmini= β-glucosaminidase, Gdase= β-glucosidase, AlkPho= alkaline phosphatase, Agal= α-galactosidase].

a.

b.


86

Figure 3.8. Discriminate function analysis (DFA) comparing no nitrogen application and nitrogen application to a peanut cropping system using phosphodiesterase, β-glucosaminidase, β-glucosidase, alkaline phosphatase, and α-galactosidase activity levels as the parameters.


87

Figure 3.9. The effects of nitrogen application on phosphodiesterase activity levels from a peanut cropping system. (a)Total phosphodiesterase activity levels across two growing years, 2007-2008. Values are means± S.E., n= 32. (b) Phosphodiesterase levels in each of the summer and fall seasons in both 2007 and 2008. Values are means± S.E., n=16. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Ph

osp

ho

die

ste

rase

Activity

(mg P

N k

g-1

so

il h

-1)

20

40

60

80

100

No nitrogen

nitrogen a

a


Phosphodie

ste

rase A

ctivity

(mg P

n k

g-1

soil

h-1

)

20

40

60

80

100

120 No nitrogen

nitrogen

a

b

a

aa


88

Figure 3.10. The effect of no nitrogen application compared to nitrogen application treatments on β-glucosaminidase activity levels from a peanut cropping system. (a)Total β-glucosaminidase activity levels across two growing years, 2007-2008. Values are means± S.E., n= 32. (b) β-glucosaminidase levels in each of the summer and fall seasons in both 2007 and 2008. Values are means± S.E., n=16. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

-G

luco

sa

min

ida

se

Activity

(mg P

N k

g-1

so

il h

-1)

5

10

15

20 No nitrogen

nitrogen

a

a


-G

lucosam

inid

ase A

ctivity

(mg P

n k

g-1

soil

h-1

)

5

10

15

20

25 No nitrogen

nitrogen

a

a

a a


89

Figure 3.11. The effect of nitrogen on β-glucosidase activity levels from a peanut cropping system. (a)Total β-glucosidase activity levels from 2007-2008. Values are means± S.E., n= 32. (b) β-glucosidase levels in each of the summer and fall seasons across two growing seasons, 2007-2008. Values are means± S.E., n=16. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

-G

luco

sid

ase

Activity

(mg P

N k

g-1

so

il h

-1)

20

40

60

80

No nitrogen

nitrogen

a

a


-G

lucosid

ase A

ctivity

(mg P

N k

g-1

soil

h-1

)

20

40

60

80

100 No nitrogen

nitrogen

bc

c

b

a


90

Figure 3.12. The effect of no nitrogen application compared to nitrogen application treatments on alkaline phosphatase activity levels from a peanut cropping system. (a)Total alkaline phosphatase activity levels from 2007-2008. Values are means± S.E., n= 32. (b) Alkaline phosphatase levels in each of the summer and fall seasons across two growing years, 2007- 2008. Values are means± S.E., n=16. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Alk

alin

e P

ho

sp

ha

tase

Activity

(mg P

N k

g-1

so

il h

-1)

20

40

60

80

100

120

140

160No nitrogen

nitrogen

a

a


Alk

alin

e P

hosphata

se A

ctivity

(mg P

N k

g-1

soil

h-1

)

30

60

90

120

150

180

210 No nitrogen

nitrogen

a

aa

a


91

Figure 3.13. The effect of nitrogen application compared to nitrogen application treatments on α-galactosidase activity levels in a peanut cropping system. (a)Total α-galactosidase activity levels from 2007-2008. Values are means± S.E., n= 32. (b) α-Galactosidase levels in each of the summer and fall seasons across two growing years, 2007 -2008. Values are means± S.E., n=16. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

-G

ala

cto

sid

ase

Activity

(mg P

N k

g-1

so

il h

-1)

2

4

6

8

10

12

14 No nitrogen

nitrogen

a

a


-G

ala

cto

sid

ase A

ctivity

(mg P

N k

g-1

soil

h-1

)

2

4

6

8

10

12

14

16

18 No nitrogen

nitrogen

a

a

aa


92

Figure 3.14. (a) Discriminate function analysis (DFA) comparing the differences of nitrogen and no nitrogen application in different seasons in 2007 using different enzyme activity (phosphodiesterase, β-glucosaminidase, β-glucosidase, alkaline phosphatase, α-galactosidase) levels as the parameters. Note [1= no nitrogen summer, 2= nitrogen summer, 3= no nitrogen fall, 4= no nitrogen fall, Phos= phosphodiesterase, Gmini= β-glucosaminidase, Gdase= β-glucosidase, AlkPho= alkaline phosphatase, Agal= α-galactosidase].

a.


93

Figure 3.14. (b) Vector correlations indicating correlations between the different nitrogen treatments in summer and fall and enzyme activities. Correlations are indicated by the smallest angle between two vectors. Ex 0°= 100% positive correlation, 90°= no correlation, and 180°= 100% negative correlation. Note [1= no nitrogen summer, 2= nitrogen summer, 3= no nitrogen fall, 4= no nitrogen fall, Phos= phosphodiesterase, Gmini= β-glucosaminidase, Gdase= β-glucosidase, AlkPho= alkaline phosphatase, Agal= α-galactosidase].

b.


94

Figure 3.15. The effects of strip tillage compared to conventional tillage on phosphodiesterase activity levels in a peanut cropping system. (a) Total phosphodiesterase activity levels from 2007-2008. Values are means± S.E., n= 96. (b) Phosphodiesterase levels in each of the summer and fall seasons across two growing years, 2007- 2008. Values are means± S.E., n=24. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Ph

osp

ho

die

ste

rase

Activity

(mg P

N k

g-1

so

il h

-1)

20

40

60

80

Conventional

Strip a

a


Phosphodie

ste

rase A

ctivity

(mg P

N k

g-1

soil

h-1

)

20

40

60

80

100

Conventional

Strip

a

a

a

a

aa

b

b


95

Figure 3.16. The effect of strip tillage compared to conventional tillage treatments on β-glucosaminidase activity levels in a peanut cropping system. (a) Total β-glucosaminidase activity levels from 2007-2008. Values are means± S.E., n= 96. (b) β-glucosaminidase levels in each of the summer and fall seasons across two growing years, 2007- 2008. Values are means± S.E., n=24. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

-G

luco

sa

min

ida

se

Activity

(mg P

N k

g-1

so

il h

-1)

2

4

6

8

10

12

14

16

18 Conventional

Srip a

a


-G

lucosam

inid

ase A

ctivity

(mg P

N k

g-1

soil

h-1

)

2

4

6

8

10

12

14

16

18

20

22 Conventional

Strip

ab

ab

ab

ab

a

abab

b


96

Figure 3.17. The effect of strip tillage compared to conventional tillage on β-glucosidase activity levels in a peanut cropping system. (a) Total β-glucosidase activity levels from 2007-2008. Values are means± S.E., n= 96. (b) β-glucosidase levels in each of the summer and fall seasons across two growing years, 2007- 2008. Values are means± S.E., n=24. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

-G

luco

sid

ase

Activity

(mg P

N k

g-1

so

il h

-1)

20

40

60

80 Conventional

Strip a

a


-G

lucosid

ase A

ctivity

(mg P

N k

g-1

soil

h-1

)

20

40

60

80

Conventional

Strip

b b

aa

b

b

a

ab


97

Figure 3.18. The effect of strip tillage compared to conventional tillage on alkaline phosphatase activity levels in a peanut cropping system. (a) Total alkaline phosphatase activity levels from 2007-2008. Values are means± S.E., n= 96. (b) Alkaline phosphatase levels in each of the summer and fall seasons across two growing years, 2007- 2008. Values are means± S.E., n=24. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

Alk

alin

e P

ho

sp

ha

tase

Activity

(mg P

N k

g-1

so

il h

-1)

20

40

60

80

100

120

140

160Conventional

Strip a a


Alk

alin

e P

hosphata

e A

ctivity

(mg P

N k

g-1

soil

h-1

)

20

40

60

80

100

120

140

160

180

200 Conventional

Strip

ab

bc abcbc

cbc

abc

a


98

Figure 3.19. The effect of strip tillage compared to conventional tillage on α-galactosidase activity levels in a peanut cropping system. (a) Total α-galactosidase activity levels from 2007-2008. Values are means± S.E., n= 96. (b) α-Galactosidase levels in each of the summer and fall seasons across two growing years, 2007- 2008. Values are means± S.E., n=24. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.

a.

-G

ala

cto

sid

ase

Activity

(g P

N k

g-1

so

il h

-1)

2

4

6

8

10

12

14

16 Conventional

Strip

aa


-G

ala

cto

sid

ase A

ctivity

(mg P

N k

g-1

soil

h-1

)

2

4

6

8

10

12

14

16

18Conventional

Strip

bcbc

c c

a

ab

aab


99

Figure 3.20. (a) Discriminate function analysis (DFA) comparing the differences of conventional and strip tillage in 2007 and 2008 using different enzyme activity levels and nutrient levels as the parameters. Note [1= conventional 07, 2= strip 07, 3= conventional 08, 4= strip 08, Phosph= phosphodiesterase, Glucomini= β-glucosaminidase, Glucosidase= β-glucosidase, Alkaline= alkaline phosphatase, Alpha= α-galactasidase, NO3 = soil extractable nitrate, NH4= soil extractable ammonium, P= soil extractable phosphorus, K= soil extractable potassium, Mg= soil extractable magnesium, Ca= soil extractable calcium, S= soil extractable sulfur, B= soil extractable boron, Zn= soil extractable zinc, Mn= soil extractable manganese, Fe= soil extractable iron, Cu= soil extractable copper, CEC= cation exchange].

a.


100

Figure 3.20. (b) Vector correlations indicating correlations between the different nitrogen treatments in summer and fall and enzyme activities. Correlations are indicated by the smallest angle between two vectors. Ex 0°= 100% positive correlation, 90°= no correlation, and 180°= 100% negative correlation. Note [1= conventional 07, 2= strip 07, 3= conventional 08, 4= strip 08, Phosph= phosphodiesterase, Glucomini= β-glucosaminidase, Glucosidase= β-glucosidase, Alkaline= alkaline phosphatase, Alpha= α-galactasidase, NO3 = soil extractable nitrate, NH4= soil extractable ammonium, P= soil extractable phosphorus, K= soil extractable potassium, Mg= soil extractable magnesium, Ca= soil extractable calcium, S= soil extractable sulfur, B= soil extractable boron, Zn= soil extractable zinc, Mn= soil extractable manganese, Fe= soil extractable iron, Cu= soil extractable copper, CEC= cation exchange].

b.


101

Figure 3.21. Season variation in soil extractable nitrate (NO3) levels across all treatments associated with a peanut cropping system. Values are means± S.E., n= 48. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.


Soil

extr

acta

ble

N0

3 (

pp

m)

5

10

15

20

25

30 a

c

b

b


102

Figure 3.22. Seasonal variation in soil extractable ammonium (NH4) levels across all treatments associated with a peanut cropping system. Values are means± S.E., n= 48. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.


So

il e

xtr

acta

ble

NH

4 (

pp

m)

1.0

1.5

2.0

10.0

12.0

14.0

16.0

18.0

b

b

b

a


103

Figure 3.23. Seasonal variation in soil phosphorus (P) levels associated with a peanut cropping system. Values are means± S.E., n= 48. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.


Soil

P (

pp

m)

10

20

30

40

50

60

70a

a

b

b


104

Figure 3.23. Seasonal variation in soil organic matter (OM) levels associated with a peanut cropping system. Values are means± S.E., n= 48. Groups statistically (p ≤ 0.05) different are indicated with different letters above mean bars.


% S

oil

Org

an

ic M

att

er

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

b

b

a

b

agricultural management effects on soil microbial dynamic under peanut cropping systems in semi

Documents

soil microbial biomass

soil microbial dynamic

microbial dynamics

microbial evaluation

microbial composition

microbial communities

microbial biomass carbon

effects of tillage