Managing microbially-mediated nitrogen cycling to decrease ...€¦ · Managing microbially-mediated nitrogen cycling to decrease risk of loss . ... bacteria was greater in surface

Managing microbially-mediated nitrogen cycling to

decrease risk of loss

from semi-arid rainfed agricultural soils

Louise Marjorie Fisk

Bachelor of Science (Technology), University of Waikato

This thesis is presented for the degree of

Doctor of Philosophy

(Soil Science and Plant Nutrition)

of The University of Western Australia

School of Earth and Environment, Faculty of Science

2015

ii

iii

Abstract

More efficient management of nitrogen (N) in agricultural soils is vital to maximise

food supply and minimise losses of N to the environment. Nitrification is a key pathway

of detrimental N loss, as nitrate and gaseous nitrous oxide are produced. In semi-arid

soils, N cycling and nitrification is not well understood during summer fallow, an

important period for N loss, as most research has instead focussed on N fertiliser

management during the growing season. In order to better understand and manage N

cycling in cropped semi-arid soils, this thesis investigated factors contributing to risk of

N loss, as well as possible solutions to decrease the risk of loss. The close link between

soil N and carbon (C) cycling suggested that solutions might be found through

management of soil organic matter. Soil was used from a long-term field site in the

northern grainbelt of Western Australia with a range of crop residue and tillage

treatments (no tillage; no tillage with burnt stubble; tillage; tillage plus additional crop

residue inputs; and tillage plus crop residues run-down) that altered soil organic matter

content since 2003, allowing examination of N transformation pathways without

confounding effects of differing soil types or climate.

Firstly, steady-state N transformations and risk of N loss (defined as gross nitrification:

immobilisation ratio) were examined, in response to a range of soil temperatures, root

exudate C and field treatment (tilled soil and tilled soil plus crop residues), using 15N

isotopic pool dilution and turnover of 14C-labelled substrates. Tilled soil plus crop

residues had 76% more total C than tilled soil. Root exudates were effective at

decreasing risk of N loss by stimulating microbial N immobilisation over nitrification.

Abstract

iv

In comparison, management of N loss through additional crop residue inputs was

unlikely to be effective, as increased soil organic matter enhanced the supply of both C

and N substrates and N cycling overall. At temperatures above 30 °C, net N

mineralisation was associated with decreased microbial C use efficiency, likely

contributing to increases in inorganic N pools during summer fallow.

Seasonal variation in ammonia-oxidising microorganisms and their relationships to

other soil biogeochemical properties (microbial biomass C, dissolved organic C,

ammonium, nitrate and potentially mineralisable N) were then investigated in all five

field treatments that were sampled on ten occasions over a two year period. Bacterial

and archaeal amoA gene abundances were measured by quantitative real-time

polymerase chain reaction at these time points. Ammonia-oxidising bacteria regulated

nitrification in the surface of this soil rather than ammonia-oxidising archaea.

Collaborative research supported this conclusion: abundance of ammonia-oxidising

bacteria was greater in surface soil (0–10 cm) than in subsoil (10–90 cm) and was

correlated to gross nitrification, while abundance of ammonia-oxidising archaea was

greater in subsoil than in surface soil and had no relationship to gross nitrification.

Growth of ammonia-oxidising bacteria was correlated to increased nitrate pools during

summer fallow, potentially increasing the risk of N loss by leaching in the first rains of

the following growing season.

A nitrification inhibitor, nitrapyrin, was evaluated for its ability to control nitrification

of N released from soil organic matter mineralisation at high soil temperature (20 and

40 °C), in tilled soil or tilled soil plus crop residues. The soil was wet-up from dry and

subsequently either held at optimal water content, or allowed to dry. Nitrapyrin

Abstract

v

successfully inhibited nitrification by 86% at 40 °C, illustrating the potential to decrease

risk of N loss outside the cropping period.

The thesis findings highlight that risk of N loss from this semi-arid rainfed agricultural

soil is primarily related to variation in rainfall, temperature, supply of C and N

substrates (especially in relation to crop rhizodeposition or lack thereof during fallow

periods), and abundance of ammonia-oxidising bacteria. Ammonia-oxidising archaea

were not important regulators of nitrification. Further investigation is required to find

other methods of controlling the risk of N loss from semi-arid rainfed agricultural soils,

including whether nitrapyrin is effective at inhibiting nitrification under field conditions

during summer fallow. Nevertheless, the findings of this thesis imply that there are

limited options for management of N loss during summer in the current annual cropping

system, due to the predominant influence of climate on N cycling and lack of plants at

the time of maximum inorganic N production.

vi

For Robbie

We did not come to remain whole.

We came to lose our leaves like the trees,

The trees that are broken

And start again, drawing up on great roots

Robert Bly

vii

Acknowledgements

“Data, things are only impossible until they’re not.”

Captain Jean-Luc Picard, Star Trek Next Generation 1.17

They say that doing a PhD is 10% intelligence and 90% persistence, but I would say

that somewhere in there at least half depends on other people’s kindness and support. I

gratefully thank my supervisors, Prof. Dan Murphy, Assoc. Prof. Louise Barton and Dr

Linda Maccarone. I appreciate collaboration from Prof. Davey Jones and Dr Helen

Glanville of Bangor University, Wales. Thank you to Yoshi Sawada, Richard Bowles,

Ian Waite, Chris Swain, Hazel Gaza, Xiaodi Li, Michael Smirk and Darryl Roberts for

laboratory and field assistance, Laura Firth and Marty Firth for statistical advice, and

members of the Soil Biology and Molecular Ecology Group and Soil Science admin

staff for thoughts and support.

Special thanks to the UWA Graduate Education Officers, particularly Krystyna Haq, for

skills and inspiration. And finally, thank you to my Australian friends and family for

perspective, encouragement and sanity: Jane, Jane, Linda and Ken, Bel, Scott, Glenn,

Stuart, Heath, Malin, Katrina, Emielda, Georgie, Rupy, Nian, Bede, Andy, Tony, Dan,

Stuart, Leon, Steve, Grace, Andy, Kikki and Jack.

This research was part of the Grains Research and Development Corporation’s Soil

Biology Initiative II (UWA00139) and was also funded by the Australian Research

Council and the Australian Government. Financial support for my candidature was

Acknowledgements

viii

provided by an Australian Postgraduate Award, The University of Western Australia,

and the School of Earth and Environment, UWA. Greg Wells and Dow AgroSciences

donated a sample of nitrapyrin and CSIRO provided rainfall and temperature data. This

research was made possible by the support of the Liebe Group, who manage the Long-

Term Soil Biology Trial site.

ix

Statement of Candidate Contribution

In accordance with The University of Western Australia’s regulations regarding

Research Higher Degrees, this thesis is presented as a series of journal papers, some of

which have been co-authored. The bibliographic details of the papers and where they

appear in the thesis are outlined below, with the contribution of the candidate and co-

authors in parentheses.

(i) Fisk, L.M. (70%), Barton, L. (10%), Jones, D.L. (5%), Glanville, H.C. (5%) &

Murphy, D.V. (10%), 2015. Root exudate carbon mitigates nitrogen loss in a semi-arid

soil. Soil Biology & Biochemistry, 88, 380-389. doi:10.1016/j.soilbio.2015.06.011

Appears as Chapter 3.

(ii) Fisk, L.M. (70%), Barton, L. (10%), Maccarone, L.D. (10%) & Murphy, D.V.

(10%). Seasonal dynamics of ammonia-oxidising bacteria but not archaea influence risk

of nitrogen loss in a semi-arid agricultural soil. Manuscript in preparation.


(iii) Fisk, L.M. (60%), Maccarone, L.D. (20%), Barton, L. (10%), & Murphy, D.V.

(10%), 2015. Nitrapyrin decreased nitrification of nitrogen released from soil organic

matter but not amoA gene abundance at high soil temperature. Soil Biology &

Biochemistry, 88, 214-223. doi:10.1016/j.soilbio.2015.05.029


Statement of Candidate Contribution

x

(iv) Banning, N.C. (30%), Maccarone, L.D. (30%), Fisk, L.M. (10%) & Murphy, D.V.

(30%), 2015. Ammonia-oxidising bacteria not archaea dominate nitrification activity in

semi-arid agricultural soil. Scientific Reports, 5. doi:10.1038/srep11146

Appears as Appendix A.

We hereby declare that all authors have granted permission to the candidate (Louise

Fisk) to use the results presented in these publications.

Candidate

Signature _________________________________________ Date _______________

Name ________________________________

Coordinating Supervisor

Signature _________________________________________ Date _______________

Name ________________________________

xi

Table of Contents

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .vii

Statement of candidate contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xvii

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi

Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxiii

Chapter 1.

Introduction 1.1. Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1

1.2. Research gaps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .3

1.3. This work: Objectives and outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .6

Chapter 2.

Nitrogen loss from semi-arid rainfed agricultural soils 2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .11

2.2. Rainfed arable agriculture in semi-arid regions . . . . . . . . . . . . . . . . . . . . . . . . . . .12

2.3. Rainfed arable agriculture in the semi-arid region of south-western Australia . . .14

2.4. Soil nitrogen cycle and environmentally detrimental loss . . . . . . . . . . . . . . .16

2.4.1. The soil nitrogen cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16

2.4.2. Nitrate in semi-arid soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19

2.4.3. Nitrous oxide emissions from semi-arid soils . . . . . . . . . . . . . . . . . . . . .20

2.4.4. Tools to predict the risk of nitrogen loss . . . . . . . . . . . . . . . . . . . . .26

2.5. Factors controlling risk of nitrogen loss through microbially-mediated soil

nitrogen transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27

2.5.1. Soil water and wetting-drying cycles . . . . . . . . . . . . . . . . . . . . . . . . . . .29

2.5.2. Soil temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .33

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xii

2.5.3. Availability of carbon and nitrogen substrates . . . . . . . . . . . . . . . . . . . . .35

2.5.4. Soil pH and liming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38

2.5.5. Relationships between plants and nitrogen cycling microorganisms . . .40

2.6. Molecular ecology of nitrifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

2.6.1. Ammonia-oxidising bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

2.6.2. Ammonia-oxidising archaea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43

2.6.3. Niche differentiation between ammonia-oxidising bacteria and archaea

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45

2.6.4. Nitrite-oxidising bacteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .46

2.6.5. Heterotrophic nitrification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47

2.7. Approaches to limiting nitrogen losses from semi-arid soils . . . . . . . . . . . . . . .48

2.7.1. Nitrification inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .50

2.7.2. Nitrapyrin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .51

2.8. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .55

Chapter 3.

Root exudate carbon was more effective than soil organic carbon at

decreasing the risk of nitrogen loss in a semi-arid soil 3.1. Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57

3.2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59

3.3. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61

3.3.1. Study site and field soil collection . . . . . . . . . . . . . . . . . . . . . . . . . . .61

3.3.2. Laboratory experimental design . . . . . . . . . . . . . . . . . . . . . . . . . . .63

3.3.3. Peptide and amino acid turnover . . . . . . . . . . . . . . . . . . . . . . . . . . .65

3.3.4. Modelling 14C dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .66

3.3.5. Gross N transformation rates and inorganic N . . . . . . . . . . . . . . . . . . . . .67

3.3.6. Modelling N transformation rates . . . . . . . . . . . . . . . . . . . . . . . . . . .70

3.3.7. Nitrous oxide analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70

3.3.8. Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .70

3.4. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .71

3.4.1. Soil organic matter turnover (N supply) . . . . . . . . . . . . . . . . . . . . .71

3.4.2. Nitrogen transformation rates and inorganic N pools . . . . . . . . . . . . . . .73

3.4.3. Fate of inorganic N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77

3.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .78

Table of Contents

xiii

3.5.1. Sources of soil organic C to decrease the risk of N loss . . . . . . . . .78

3.5.2. Inorganic N accumulates at high soil temperature . . . . . . . . . . . . . . .80

3.5.3. Differences between amino acid and peptide turnover . . . . . . . . . . . . . . .81

3.5.4. Implications for semi-arid environments . . . . . . . . . . . . . . . . . . . . .82

3.6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .83

Chapter 4.

Seasonal dynamics of ammonia-oxidising bacteria but not archaea

influence risk of nitrogen loss in a semi-arid agricultural soil 4.1. Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .85

4.2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87

4.3. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .90

4.3.1. Study site and soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .90

4.3.2. Experimental design and soil collection . . . . . . . . . . . . . . . . . . . . .93

4.3.3. Microbial biomass C and dissolved organic C . . . . . . . . . . . . . . . . . . . . .94

4.3.4. Inorganic N analysis and potentially mineralisable N . . . . . . . . . . . . . . .95

4.3.5. Nucleic acid extraction and qPCR . . . . . . . . . . . . . . . . . . . . . . . . . . .95

4.3.6. Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .97

4.4. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .98

4.4.1. Environmental conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .98

4.4.2. Microbial biomass C and dissolved organic C . . . . . . . . . . . . . . . . . . . . .98

4.4.3. Inorganic N and potentially mineralisable N . . . . . . . . . . . . . . . . . . . .100

4.4.4. Ammonia oxidiser gene abundance . . . . . . . . . . . . . . . . . . . . . . . . . .102

4.4.5. Relationships between bacterial amoA gene abundance and other

variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .103

4.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .108

4.6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .113

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xiv

Chapter 5.

Nitrapyrin decreased nitrification of nitrogen released from soil organic

matter but not amoA gene abundance at high soil temperature 5.1. Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .115

5.2. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .117

5.3. Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .120

5.3.1. Soil and soil collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .120

5.3.2. Laboratory experimental design . . . . . . . . . . . . . . . . . . . . . . . . . .121

5.3.3. Gross N transformation rates and inorganic N analysis . . . . . . . .122

5.3.4. Calculation of gross N transformation rates . . . . . . . . . . . . . . . . . . . .124

5.3.5. Nucleic acid extraction and qPCR . . . . . . . . . . . . . . . . . . . . . . . . . .124

5.3.6. Statistical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .125

5.4. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .126

5.4.1. Recovery of 15N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .126

5.4.2. Water-filled pore space . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .127

5.4.3. Labelled ammonium and nitrate-N . . . . . . . . . . . . . . . . . . . . . . . . . .127

5.4.4. Unlabelled inorganic N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .130

5.4.5. Gross N transformation rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .133

5.4.6. Bacterial and archaeal amoA gene abundance . . . . . . . . . . . . . . . . . . . .135

5.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .136

5.6. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .140

Chapter 6.

General discussion 6.1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .141

6.2. Contributing factors to variation in risk of nitrogen loss from semi-arid

rainfed agricultural soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .142

6.2.1. Variation in rainfall and temperature . . . . . . . . . . . . . . . . . . . . . . . . . .142

6.2.2. Root exudate carbon inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .144

6.2.3. The importance of surface soil layers . . . . . . . . . . . . . . . . . . . . . . . . . .147

6.3. Management of semi-arid soils to decrease risk of nitrogen loss outside the

growing season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .150

6.3.1. Crop residue inputs and increased soil organic matter . . . . . . . . . . . . . .150

6.3.2. Nitrapyrin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151

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6.4. Future research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .153

6.4.1. Further unravelling the interactions between ammonia oxidisers and

N loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .153

6.4.2. Field application of nitrapyrin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .158

6.4.3. Other methods to manage risk of nitrogen loss . . . . . . . . . . . . . .160

6.5. Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .162

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .167

Appendices Appendix A. Ammonia-oxidising bacteria not archaea dominate nitrification

activity in semi-arid agricultural soil . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .201

Supplementary information . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .222

Appendix B. Data not shown in Chapter 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .225

Appendix C. Data not shown in Chapter 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .227

Appendix D. Data not shown in Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .229

Appendix E. Publications arising from this thesis . . . . . . . . . . . . . . . . . . . . . . . . . .231

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xvi

xvii

List of Figures

Chapter 2.

2.1. Arid regions of the world . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12 2.2. World distribution of Mediterranean-type climates . . . . . . . . . . . . . . . . . . . . .13 2.3. A simplified soil nitrogen cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17 2.4. Conceptual nutrient release from high quality (High Q), low quality (Low Q)

and a mixture of organic materials in relation to plant uptake . . . . . . . . .50

2.5. Structure of nitrapyrin, 2-chloro-6-(trichloromethyl)-pyridine . . . . . . . . .52

Chapter 3.

3.1. Daily maximum and minimum soil temperatures at 5 cm depth and daily

rainfall for 2011 at the research site . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62

3.2. Influence of temperature on microbial carbon use efficiencies of (a) 14C-

labelled peptides without root exudates; (b) 14C-labelled peptides with root

exudates; (c) 14C-labelled amino acids without root exudates; and (d) 14C-

labelled amino acids with root exudates . . . . . . . . . . . . . . . . . . . . . . . . . . .72

3.3. Influence of temperature on half-lives of pool a1 of (a) 14C-labelled peptides

without root exudates; (b) 14C-labelled peptides with root exudates; (c) 14C-

labelled amino acids without root exudates; and (d) 14C-labelled amino acids

with root exudates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .73 3.4. Influence of temperature after seven days of incubation on (a) gross N

mineralisation without root exudates; (b) gross N mineralisation with root

exudates; and influence of temperature over seven days of incubation on (c)

net N mineralisation without root exudates and (d) net N mineralisation with

root exudates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .75

List of Figures

xviii

3.5. Influence of temperature on (a) gross nitrification without root exudates; (b)

gross nitrification with root exudates; (c) gross N immobilisation without root

exudates; (d) gross N immobilisation with root exudates; (e) N:I ratio without

root exudates; and (f) N:I ratio with root exudates . . . . . . . . . . . . . . . . . . . . .76 3.6. Influence of temperature after seven days of incubation on (a) NH4

+-N; and

(b) NO3--N . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77

3.7. Influence of temperature on (a) N2O flux over 24 h; and (b) N2O 15N

enrichment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .78

Chapter 4.

4.1. (a) Daily rainfall (bar graph, left y-axis) and daily soil minimum and

maximum temperature at 5 cm depth (line graph, right y-axis) measured at

the study site. (b) Soil water content at time of sample collection . . . . . . . . .91 4.2. Change in (a) microbial biomass carbon; and (b) dissolved organic carbon

through time . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .99

4.3. Change in (a) total inorganic nitrogen; (b) NH4+-N; (c) NO3

--N; and (d)

potentially mineralisable nitrogen through time . . . . . . . . . . . . . . . . . . . .101 4.4. Change in bacterial amoA gene abundance (AOB) through time . . . . . . . .102 4.5. Significant linear regression relationships between logged bacterial amoA

gene abundance (logAOB) and (a) logged dissolved organic carbon

(logDOC); (b) logged microbial biomass carbon (logMBC); and (c) square

root transformed nitrate concentration (sqrtNO3-) . . . . . . . . . . . . . . . . . . . .105

4.6. Principal component analysis biplot of principal components 1 (PC1) and 2

(PC2) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .106

Chapter 5.

5.1. Change in total recovery of 15N (% of applied 15N) through time from soil

applied with (a) 15N-labelled NH4+ and (b) 15N-labelled NO3

- . . . . . . . .128 5.2. Change in water-filled pore space (% WFPS) through time (a) at 20 °C; and

(b) at 40 °C . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .128

List of Figures

xix

5.3. Change in 15N-labelled nitrate (NO3-) above natural abundance through time

with added 15(NH4)2SO4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .129 5.4. Change in unlabelled inorganic N through time at 40 °C . . . . . . . . . . . . . .131 5.5. Change in unlabelled inorganic N through time at 20 °C . . . . . . . . . . . . . .132 5.6. Change in gross N mineralisation and nitrification rates through time . .134 5.7. Change in bacterial amoA gene abundance (AOB) through time . . . . . . . .136

List of Figures

xx

xxi

List of Tables

Chapter 1.

1.1. Research aims and hypotheses addressed in each thesis chapter . . . . . . . . . .8

Chapter 2.

2.1. Nitrate leaching rates observed in semi-arid climates with winter dominant

rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22 2.2. Nitrous oxide emissions observed in semi-arid climates with winter dominant

rainfall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24 2.3. Common agricultural nitrification inhibitors . . . . . . . . . . . . . . . . . . . . . . . . . . .51

Chapter 3.

3.1. Properties of field trial soils (0–10 cm depth) collected eight years after soil

organic carbon management treatments were imposed . . . . . . . . . . . . . . .64

Chapter 4.

4.1. Properties of field organic matter treatments (0–10 cm depth) at start of

present study, seven years after treatments were imposed . . . . . . . . . . . . . . .92

4.2. Linear regression results for response of logged bacterial amoA gene

abundance to each soil and environmental variable separately . . . . . . . .104

4.3. Eigenvector loadings of principal components 1–6 . . . . . . . . . . . . . . . . . . . .107 4.4. Loadings matrix (eigenvectors) for principal components 1–6. . . . . . . . .108

List of Tables

xxii

Chapter 5.

5.1. Properties of field soils (0–10 cm depth), collected nine years after soil

organic matter (OM) treatments were imposed . . . . . . . . . . . . . . . . . . . .121

Chapter 6.

6.1. Specific thesis questions as set out in Chapter 1, the chapter in which each

question was answered, related hypotheses and answers . . . . . . . . . . . . . .164

xxiii

Abbreviations

ANOVA analysis of variance

AMO ammonia monooxygenase

AOA ammonia-oxidising archaea

AOB ammonia-oxidising bacteria

BSA bovine serum albumin

C carbon

CARD-FISH catalysed reporter deposition-fluorescent in situ hybridisation

CO2 carbon dioxide

DCD dicyandiamide

DGGE denaturing gradient gel electrophoresis

DMPP 3, 4-dimethylpyrazole phosphate

DNA deoxyribonucleic acid

DNRA dissimilatory nitrate reduction to ammonium

DOC dissolved organic carbon

DRY soil wet-up to 45% water-filled pore space from dry, then subsequently

allowed to dry (laboratory treatment)

EC electrical conductivity

FISH fluorescent in situ hybridisation

HAO hydroxylamine oxidoreductase

HNO nitroxyl

IRMS isotope ratio mass spectrometer

KNO3 potassium nitrate

K2SO4 potassium sulphate

LFOM light fraction organic matter

LMWOM low molecular weight organic matter

MBC microbial biomass carbon

MIT mineralisation-immobilisation turnover

mRNA messenger ribonucleic acid

N nitrogen

Abbreviations

xxiv

N2 dinitrogen gas

NanoSIMS nano-scale secondary ion mass spectrometry

NaOH sodium hydroxide

NH3 ammonia

NH4+ ammonium

(NH4)2SO4 ammonium sulphate

N:I ratio ratio of gross nitrification to gross nitrogen immobilisation

NO nitric oxide

N2O nitrous oxide

NO2- nitrite

NO3- nitrate

NOB nitrite-oxidising bacteria

No RE no synthetic root exudates (laboratory treatment)

NXR nitrite oxidoreductase

OM organic matter

OWC soil wet-up to 45% water-filled pore space from dry, then subsequently

held at optimal water content (45% water-filled pore space) (laboratory

treatment)

PC1 principal component 1

PC2 principal component 2

PCA principal component analysis

PCR polymerase chain reaction

P/Etp ratio of precipitation to potential evapotranspiration

PMN potentially mineralisable nitrogen

qPCR quantitative real-time polymerase chain reaction

+RE plus synthetic root exudates (laboratory treatment)

RNA ribonucleic acid

rRNA ribosomal ribonucleic acid

SEM standard error of the mean

SIP stable isotope probing

SOC soil organic carbon

TukeyHSD Tukey’s honest significant difference test

WFPS water-filled pore space

1

Chapter 1.

Introduction

1.1. Background

Present-day agriculture is experiencing unprecedented pressure to provide food for the

growing global population, without degradation of the environment. Anthropogenic

harnessing of the nitrogen (N) cycle, largely for food production, annually converts

around 210 million tonnes of N from the atmosphere into reactive N forms, or half of

the total estimated N fixation by all biogeochemical processes combined (Fowler et al.,

2013; Rockström et al., 2009). This reactive N is also a major source of environmental

pollution, contributing to problems ranging from eutrophication and acidification to

global warming (Gruber and Galloway, 2008). Efficient management of N in agriculture

is therefore essential for sustainable food production while minimising harmful effects

on the environment. The central relationship that determines whether N is helpful or

harmful is ‘synchrony’: how well N supply in soil matches crop N demand (Crews and

Peoples, 2005). Nitrogen supply from fertiliser application or organic matter (OM)

mineralisation that does not match either the timing or amount of crop N demand can

accumulate in soil and has potential to be lost.

Environmentally detrimental N losses from semi-arid rainfed agricultural soils occur

primarily by nitrate (NO3-) leaching or gaseous N emissions such as nitrous oxide (N2O)

Ch. 1: Introduction

2

and nitric oxide (NO). Other losses that may occur include soil erosion by water or wind

and ammonia (NH3) volatilisation, although this is not usually a major loss pathway

from acidic soil (Ryan et al., 2009). Nitrate leaching negatively impacts groundwater

and surface water by encouraging growth of algae and causing eutrophication,

acidification and decreased water quality (Di and Cameron, 2002; Peoples et al., 2004).

Nitrous oxide and NO are also detrimental to the environment because N2O is a

greenhouse gas that contributes to global warming and depletion of stratospheric ozone,

while NO is highly reactive in the troposphere, causing acid rain and forming ozone

(Galbally et al., 2008; Peoples et al., 2004). Nitrate leaching, N2O and NO emissions are

a consequence of nitrification either directly or indirectly: directly, because nitrification

is the pathway whereby inorganic ammonium (NH4+) is converted to NO3

-, with N2O

and NO sometimes formed as a by-product; and indirectly, because N2O and NO can

also be formed during denitrification, when NO3- is further converted to dinitrogen gas

(N2; Medinets et al., 2015; Wrage et al., 2001). Microbially-mediated nitrification

therefore plays a key role in N loss processes, and can be considered as the ‘gatekeeper’

between internal soil cycling and external N loss (Schimel et al., 2005).

Management of N in rainfed agricultural soils of semi-arid regions presents particular

challenges. Rainfed agriculture relies on natural rainfall patterns to provide water to

crops without irrigation. In semi-arid regions, annual precipitation is only 20–50% of

potential evapotranspiration and is highly variable between seasons and years, which

makes rainfed crop production inherently risky and uncertain (Harrington and Tow,

2011; UNESCO, 1979). Nutrient cycling and biological activity in semi-arid soils is

governed by pulses in soil water availability due to sporadic rainfall events, and

microbial activity can be particularly intense when temperatures are elevated (Austin et

al., 2004; Belnap et al., 2005; Noy-Meir, 1973). Emerging research indicates that N

Ch. 1: Introduction

3

cycling in water-limited soil behaves differently to when moisture is plentiful and

predictable (Collins et al., 2008). For example, N2O emissions worldwide are

predominantly in response to N fertiliser applications (Reay et al., 2012), but a large

proportion of N2O and NO emissions in semi-arid regions are due to wetting of hot, dry

soil, as occurs during summer and autumn fallow (Galbally et al., 2008). In addition,

microbial biomass, net N mineralisation and potential nitrification and denitrification

rates were greater in a semi-arid Californian shrubland soil during the dry summer than

during the winter growing season (Parker and Schimel, 2011). Plant productivity is

often more limited by lack of water than is microbial activity, so crop uptake is often

uncoupled from N supply by OM mineralisation (Angus, 2001; Collins et al., 2008).

This asynchrony is particularly pronounced in rainfed annual cropping systems: most

crop plants are only present for 12–16 weeks, and take up N in large amounts for only

about three to four weeks of this period (Olson and Kurtz, 1982; Robertson, 1997).

Maximum crop N uptake though can be very rapid, at approximately 4 kg N ha-1 day-1

for wheat during the grand period of vegetative growth (Olson and Kurtz, 1982).

However, OM mineralisation still occurs outside the cropping period: gross N

mineralisation rates can be as high as 6.8 kg N ha-1 in the 24 hours after wetting of dry

soil (Murphy et al., 1998b). This inorganic N that is produced outside the growing

season does not match the timing of crop N demand, so is at risk of loss.

1.2. Research Gaps

The challenge is to find management practices that can reduce the risk of N loss outside

the growing season in semi-arid soils. This might be achieved by management of

microbial N transformations to decrease N supply or prevent nitrification, or to capture

excess NO3- before it is lost (Crews and Peoples, 2005). In order to find techniques for

Ch. 1: Introduction

4

preventing N loss from semi-arid soils, we need to better understand the influence of

different environmental and biogeochemical factors on N transformation rates and

microbial populations. These factors include soil water and temperature, carbon (C) and

N substrate availability and soil pH (Booth et al., 2005; Sahrawat, 2008).

Variation in populations of ammonia-oxidising bacteria (AOB) and archaea (AOA) is

likely a factor regulating risk of N loss in soil. Ammonia oxidation is the first, limiting

step of nitrification, and is carried out by AOA and AOB that convert ammonia (in

equilibrium with NH4+) to nitrite (NO2

-; Hatzenpichler, 2012; Kowalchuk and Stephen,

2001). The key enzyme of ammonia oxidation in bacteria is ammonia monooxygenase

(AMO), the active site of which is coded for by the amoA gene (Kowalchuk and

Stephen, 2001). Chemolithoautotrophic bacteria were thought to be the sole organisms

carrying out ammonia oxidation, until the discovery of homologous gene sequences for

amoA in marine Crenarchaeota (Treusch et al., 2005; Venter et al., 2004). Since then

AOA have been found to be ubiquitous in marine, freshwater and terrestrial

environments and have been placed in the newly-described archaeal phylum,

Thaumarchaeota (Brochier-Armanet et al., 2008; Pester et al., 2011). Relative

abundances of AOA and AOB vary widely between soils in different environments, as

does evidence for their contributions to nitrifying activity (for example Adair and

Schwartz, 2008; Di et al., 2009; Gubry-Rangin et al., 2010; Jia and Conrad, 2009).

Niche specialisation and differentiation between these two groups of microorganisms

however has not been clearly defined, despite the ongoing search for regulating factors

(that might include soil pH or NH4+ availability; Prosser and Nicol, 2012). Nor is it well

understood what factors regulate seasonal variation in AOA and AOB abundances and

activity, particularly in semi-arid environments (Adair and Schwartz, 2008; Sher et al.,

Ch. 1: Introduction

5

2013). These factors that regulate ammonia-oxidising microorganisms may have

consequent effects on nitrification rates and risk N loss.

One way to capture excess inorganic N is to stimulate microbial N immobilisation by

increasing C availability in soil. Soil N cycling is tightly coupled to C cycling, as a

direct result of living organisms requiring both of these elements to build biomass at a

molecular level (Sterner and Elser, 2002). The majority of nitrifiers in agricultural soils

are considered to be autotrophs, which use inorganic N substrates from soil to generate

energy but fix their own C from carbon dioxide (CO2; De Boer and Kowalchuk, 2001;

Kurakov et al., 2001). Mineralising and immobilising microorganisms on the other hand

are heterotrophs: they need organic C from soil as an energy source. Soil OM and C

availability is generally low in semi-arid soils, due to low plant productivity, soil loss by

erosion, low rainfall and elevated temperatures (Archibold, 1995; Jenny, 1941; Ryan,

2011). Carbon in soil can be altered by long-term agricultural practices such as tillage

and OM inputs, or by short-term C inputs from plant root exudates (Dick, 1992; Jones

et al., 2004a). However, the impact of increased C availability on risk of N loss in semi-

arid agricultural soils has not been fully investigated.

Nitrification inhibitors also have potential to control the risk of N loss in semi-arid

agricultural soils. These chemicals inhibit nitrification, often by deactivating one of the

enzymes involved (McCarty, 1999). Nitrification inhibitors such as nitrapyrin have been

used successfully for many years to decrease nitrification and N loss of applied

fertiliser, particularly in cooler climates (Slangen and Kerkhoff, 1984; Wolt, 2004).

However, the applicability of nitrification inhibitors under semi-arid conditions has not

been investigated, particularly for the purpose of controlling nitrification of NH4+

Ch. 1: Introduction

6

released by OM decomposition at elevated temperatures (as occurs during summer

fallow).

In summary, management of N cycling in semi-arid rainfed agricultural soils presents a

challenge, as asynchrony of N supply is inherent in the water-limited environment.

Mineralisation of OM and crop residues outside the growing season is a major source of

inorganic N, at a time of minimal plant uptake. Therefore, tools to decrease the risk of N

loss need to be identified for this period, and might be discovered through an increased

understanding of N cycling and microbial population dynamics.

1.3. This Work: Objectives and Outline

The objective of this thesis is to gain a better understanding of N cycling in semi-arid

rainfed agricultural soils, through investigating the factors contributing to variation in

risk of N loss, and possible management solutions to decrease the risk of N loss.

In order to address this objective, this research specifically aimed to answer the

following questions:

1. What environmental and biochemical factors contribute to temporal variation in

risk of N loss? (Chapters 3 and 4)

2. How do total soil C and root exudate C affect N cycling and risk of loss? (Chapter

3)

3. How do ammonia-oxidising populations vary with season, depth and agricultural

management? (Chapters 4 and 5, Appendix A)

4. How are ammonia-oxidising populations related to other soil environmental and

biochemical factors? (Chapters 4, Appendix A)

Ch. 1: Introduction

7

5. Does increasing soil total C through additional crop residue inputs decrease the risk

of N loss? (Chapter 3)

6. Does the nitrification inhibitor nitrapyrin decrease risk of loss of NH4+ produced by

OM mineralisation under temperature and water availability conditions that may

occur during summer? (Chapter 5)

Overarching hypotheses for this thesis in relation to the aims listed above are:

1. Temporal variation in risk of N loss in semi-arid rainfed agricultural soil is related

to water availability (rainfall and soil water content) soil temperature and soil C

availability (total soil organic C and labile root exudate C).

2. Increasing total soil organic C and root exudate C will increase soil C availability

and enable heterotrophic microorganisms to compete more successfully over

autotrophic nitrifiers, thus increasing N immobilisation and decreasing the risk of N

loss.

3. Ammonia-oxidising population abundance (both AOA and AOB) will be greater

during the winter growing season than during summer fallow; will decrease with

depth as N substrates diminish; will be enhanced by additional crop residue inputs

and no tillage; but will be decreased by tillage and stubble burning.

4. Ammonia-oxidising population abundance will be positively related to rainfall, soil

water content, microbial biomass C (MBC) and NO3- concentrations, negatively

related to soil temperature, dissolved organic C (DOC) and total soil C but not

related to NH4+ concentrations.

5. Increasing total soil C through additional crop residue inputs will decrease the risk

of N loss.

6. The nitrification inhibitor nitrapyrin will decrease risk of loss of NH4+ produced by

OM mineralisation under conditions that may occur during summer.

Tabl

e 1.

1. R

esea

rch

aim

s and

hyp

othe

ses a

ddre

ssed

in e

ach

thes

is ch

apte

r.

The

sis C

hapt

er a

nd T

itle

Res

earc

h A

ims

Hyp

othe

ses

Cha

pter

3.

Roo

t exu

date

car

bon

was

mor

e ef

fect

ive

than

soil

orga

nic

carb

on a

t dec

reas

ing

the

risk

of n

itrog

en lo

ss in

a se

mi-a

rid so

il.

To u

nder

stand

how

diff

eren

t sou

rces

of C

alte

r N tr

ansf

orm

atio

ns in

arab

le se

mi-a

rid so

il.

Spec

ifica

lly, h

ow to

tal s

oil o

rgan

ic C

ver

sus r

oot e

xuda

te C

affe

cted

:

N

dec

ompo

sitio

n pa

thw

ays;

and

th

e su

bseq

uent

fate

of N

and

risk

of N

loss

as d

efin

ed b

y th

e

nitri

ficat

ion

to im

mob

ilisa

tion

(N:I

) rat

io,

unde

r con

ditio

ns re

flect

ive

of b

oth

sum

mer

and

win

ter c

ondi

tions

in

sem

i-arid

soils

.

Incr

easin

g so

il C

ava

ilabi

lity

(bot

h to

tal s

oil o

rgan

ic C

and

root

exud

ate

C) w

ill d

ecre

ase

the

pote

ntia

l for

N lo

ss (i

.e.

decr

ease

the

N:I

ratio

), es

peci

ally

at t

empe

ratu

res g

reat

er

than

30

°C.

Cha

pter

4.

Seas

onal

dyn

amic

s of a

mm

onia

-oxi

disin

g

bact

eria

but

not

arc

haea

influ

ence

risk

of

nitro

gen

loss

in a

sem

i-arid

agr

icul

tura

l

soil.

To im

prov

e un

ders

tand

ing

of te

mpo

ral p

opul

atio

n dy

nam

ics o

f

amm

onia

oxi

dise

rs so

as t

o be

tter u

nder

stand

N lo

ss m

echa

nism

s in

sem

i-arid

env

ironm

ents

dom

inat

ed b

y w

inte

r rai

nfal

l. Sp

ecifi

cally

:

ho

w so

il O

M c

onte

nt a

ffect

s am

mon

ia o

xidi

ser a

bund

ance

;

if

incr

ease

d so

il O

M m

odifi

es se

ason

al v

aria

tion

in a

mm

onia

oxid

iser

abu

ndan

ce; a

nd

w

hich

soil

envi

ronm

enta

l and

bio

chem

ical

fact

ors r

egul

ate

the

rela

tive

abun

danc

e of

AO

B an

d A

OA

.

Am

mon

ia-o

xidi

sing

bact

eria

will

dom

inat

e ov

er A

OA

in th

e

surf

ace

soil

thro

ugho

ut th

e ye

ar.

Incr

ease

d so

il O

M c

onte

nt w

ill in

crea

se a

mm

onia

oxi

dise

r

abun

danc

e.

Seas

onal

var

iatio

n in

am

mon

ia o

xidi

ser a

bund

ance

will

be

rela

ted

to ra

infa

ll, so

il w

ater

con

tent

, tem

pera

ture

and

NO

3-

conc

entra

tions

but

not

to N

H4+ c

once

ntra

tions

.

Tabl

e 1.

1. c

ontin

ued

on n

ext p

age.

Tabl

e 1.

1. R

esea

rch

aim

s and

hyp

othe

ses a

ddre

ssed

in e

ach

thes

is ch

apte

r (c

ontin

ued)

. T

hesi

s Cha

pter

and

Titl

e R

esea

rch

Aim

s H

ypot

hese

s C

hapt

er 5

.

Nitr

apyr

in d

ecre

ased

nitr

ifica

tion

of

nitro

gen

rele

ased

from

soil

orga

nic

mat

ter

but n

ot a

moA

gen

e ab

unda

nce

at h

igh

soil

tem

pera

ture

.

To e

xam

ine

the

pote

ntia

l of t

he n

itrifi

catio

n in

hibi

tor n

itrap

yrin

to

cont

rol n

itrifi

catio

n at

ele

vate

d so

il te

mpe

ratu

re in

resp

onse

to a

sim

ulat

ed ra

infa

ll w

ettin

g an

d dr

ying

eve

nt. S

peci

fical

ly:

w

heth

er n

itrap

yrin

dec

reas

ed g

ross

nitr

ifica

tion

rate

s

with

out a

lterin

g ot

her N

tran

sfor

mat

ion

rate

s at 2

0 an

d 40

°C;

w

heth

er in

crea

sed

soil

OM

con

tent

dim

inish

es th

e ab

ility

of

nitra

pyrin

to in

hibi

t nitr

ifica

tion

at e

leva

ted

tem

pera

ture

;

w

heth

er d

ecre

asin

g w

ater

con

tent

with

tim

e (a

s occ

urs w

hen

soil

drie

s afte

r a su

mm

er ra

infa

ll ev

ent)

incr

ease

s the

abi

lity

of n

itrap

yrin

to in

hibi

t nitr

ifica

tion

com

pare

d to

whe

n so

il

wat

er c

onte

nt is

opt

imal

; and

if po

pula

tions

of A

OB

or A

OA

are

con

sequ

ently

affe

cted

.

Nitr

apyr

in w

ill m

ore

effe

ctiv

ely

inhi

bit n

itrifi

catio

n at

elev

ated

tem

pera

ture

s in

a lo

w O

M so

il co

mpa

red

to w

here

addi

tiona

l cro

p re

sidu

e in

puts

have

incr

ease

d so

il O

M.

Nitr

apyr

in w

ill b

e m

ore

effe

ctiv

e as

soil

drie

s and

nitr

ifica

tion

activ

ity d

ecre

ases

.

Nitr

apyr

in w

ill d

ecre

ase

amoA

gen

e ab

unda

nce

by in

hibi

ting

amm

onia

mon

ooxy

gena

se a

nd th

us d

imin

ishin

g th

e ab

ility

of

amm

onia

-oxi

disin

g m

icro

orga

nism

s to

obta

in e

nerg

y an

d to

grow

.

Ch. 1: Introduction

10

The study was conducted using soil collected from a study site established in 2003 with

a range of crop residue and tillage treatments. The research site provided a unique

opportunity to examine the effects of various treatments on the medium-term changes

(approximately 10 years) in soil OM and C, and consequent effects on N cycling and

soil biological communities and functioning, without confounding effects of soil type

and climate.

This thesis is set out as a series of experimental papers in order to answer the specific

aims (Chapters 3–5). As these chapters are either under review or are in preparation for

publication as stand alone journal articles, minor repetition exists in the introductions

and methods. Specific aims addressed in each chapter are set out in Table 1.1. A general

discussion of the overall findings follows the experimental chapters, and all references

may be found in the section following Chapter 6 at the end of the thesis.

11

Chapter 2.

Nitrogen Loss from Semi-Arid Rainfed Agricultural

Soils

2.1. Introduction

This review provides an overview of nitrogen (N) cycling and factors contributing to the

risk of N loss in semi-arid soils, particularly those which experience winter-dominant

rainfall and hot, dry summers. The review will begin by defining and describing the

particular environment and land use of the soils relevant to this thesis, including the

challenges of rainfed cropping in relation to limited water availability in these areas

worldwide and in south-western Australia. Following this is a general overview of soil

N cycling, and environmentally detrimental N loss with a specific emphasis on

microbially-mediated N transformations in semi-arid soils. A focus is then given to the

molecular ecology of nitrifiers, the microorganisms that regulate the key loss pathway

of nitrification. Finally, approaches are considered that have potential to limit N losses

from semi-arid soils, particularly nitrification inhibitors.

Ch. 2: Literature Review

12

2.2. Rainfed Arable Agriculture in Semi-Arid Regions

Semi-arid and arid regions cover approximately a third of the Earth’s surface

(Archibold, 1995) and 54% of the global agricultural area (Fig. 2.1; The World Bank,

2008). When combined with hyper-arid deserts and dry sub-humid land, these regions

support two billion people, so are considerably important for world food production

(United Nations, 2011). Semi-arid regions can be described under the climate

classification of the United Nations Educational, Scientific and Cultural Organization

(UNESCO, 1979), extended from Meigs (1953), which defines climate by the ratio of

annual precipitation to potential evapotranspiration (P/Etp). The P/Etp of semi-arid

regions ranges from 0.20–0.50, or precipitation that is only 20–50% of potential

evapotranspiration (UNESCO, 1979). The UNESCO classification further subdivides

climate regions on the basis of mean temperature of the coldest and warmest months of

the year, number of dry months with less than 20 mm precipitation, and the

precipitation regime (i.e. season of dominant rainfall; UNESCO, 1977).

Figure 2.1. Arid regions of the world. From Dregne (1983), redrawn from UNESCO

(1977).


13

Of particular interest to this thesis are semi-arid regions with winter-dominant rainfall

and hot, dry summers, some of which are also known as a Mediterranean-type climate

(Cs in the Köppen-Geiger classification; Peel et al., 2007). These regions occur between

30–45° latitude on the western side of continents, and around the Mediterranean basin

(Fig. 2.2; Harrington and Tow, 2011). As rainfall is concentrated in winter, it is

ecologically more significant compared to summer rainfall, because potential

evapotranspiration is comparatively low in winter and more rainfall is actually available

for plant growth (UNESCO, 1979).

Figure 2.2. World distribution of Mediterranean-type climates. From Ochoa-Hueso

et al. (2011).

Rainfed agriculture (i.e. agriculture without irrigation) in semi-arid regions is generally

possible for certain crops, though harvests and yields are irregular and agriculture is

intrinsically risky due to high variability in annual rainfall and the possibility of

prolonged dry periods during the cropping season (Harrington and Tow, 2011; Stewart

and Koohafkan, 2004; UNESCO, 1979). Crops that are grown in Mediterranean-type

climates range from cereals such as wheat and barley; tree crops such as olives; pulses


14

like lentils and chickpeas; and oilseed crops such as canola (Harrington and Tow, 2011).

The type of crops grown and degree of agricultural intensity and productivity depend on

the amount of annual rainfall, the degree of development of the region and population

pressure for food (Harrington and Tow, 2011).

Bowden (1979) defines four unique keys that need to be considered for agricultural

development in semi-arid lands, two of which are particularly important for

Mediterranean-type areas. These are that 1): precipitation and temperature will not be

the same in amount, range, extremes or averages between growing seasons, meaning

that each year cultivation of crops must be adjusted accordingly; and 2): crop

production is highly unpredictable and must be managed and planned differently each

growing season. The highly variable nature of rainfall and soil water availability means

that use efficiency of nutrients, such as N, is also highly variable, being related to

changes in soil organic matter (OM) mineralisation and nutrient immobilisation (Ryan,

2011). For example, wheat yields in a four-year study in northern Syria were more

dependent on seasonal rainfall than crop rotation, N fertiliser application or soil fertility

(Pala et al., 1996).

2.3. Rainfed Arable Agriculture in the Semi-Arid Region of South-

Western Australia

In Australia, almost the entire continental area is occupied by semi-arid and arid

climates, radiating gradually out from a substantial area of aridity in the interior to a

non-arid coastal fringe (Meigs, 1953; UNESCO, 1979). Seasonal precipitation in south-

western Australia is dominated by winter rainfall, with a marked summer dry period of

four to seven months, typical of a Mediterranean-type climate (Aschmann, 1973). In the


15

summer there are however occasional rainfall events, often linked with tropical

cyclones from the north-west (UNESCO, 1979).

Arable agriculture in south-western Australia is almost exclusively concentrated in the

semi-arid, winter rainfall dominated climate band known locally as the wheatbelt or

grainbelt. This area produces a significant amount of Australia’s crops: over the past

five years, the state of Western Australia has produced on average 32% of Australia’s

total winter crop yield, though depending on rainfall this proportion can range from 19–

39% (ABARES, 2014). The main winter crops grown in this region are wheat, barley,

canola and lupins (ABARES, 2014). During summer, lack of rainfall and soil water

availability limits plant growth, so most agricultural soils are left fallow.

Common arable agricultural practice in south-western Australia is no-tillage, which has

no soil disturbance other than at seeding (Roper et al., 2010). At this time, either disk

seeding without any soil throw or seeding with a knife point (5–20% disturbance) is

used (Roper et al., 2010). No-tillage usually avoids inversion and affects only the

surface 5–10 cm (Murphy et al., 2011). Compared to soil that has been rotary tilled, no-

tillage tends to have greater soil total carbon (C), light fraction OM (LFOM), dissolved

OM, microbial biomass C (MBC) and N pools and rates of C and N cycling in surface

soil than in subsurface soil due to decreased soil disturbance and mixing (Cookson et

al., 2008). Nitrogen fertiliser additions are generally low (20–100 kg N ha-1 y-1) and

targeted according to expected growing season rainfall. As a consequence, at times

more than half of N supply to crops in this environment results from mineralisation of

OM and previous crop or pasture residues (Angus, 2001).


16

Arable soils in south-western Australia are derived from highly weathered landscapes,

so the majority have coarse-textured surface soils, and are low in OM and nutrients

(Cookson et al., 2006a; McArthur, 2004; Tennant et al., 1992). Most microbial biomass

and N cycling occur in the surface 10 cm of soil and as such, changes in temperature

and rainfall have a direct effect on the microbially active surface layer (Murphy et al.,

1998a). Water availability primarily affects the timing of microbial activity, but the

magnitudes of biological processes, such as N cycle fluxes, are mostly affected by soil

temperature and texture (Cookson et al., 2006a).

2.4. Soil Nitrogen Cycle and Environmentally Detrimental Loss

2.4.1. The soil nitrogen cycle

Nitrogen cycles through a variety of inorganic and organic pools in soil, mediated by

soil microorganisms that use N for energy and growth (Fig. 2.3). The largest pool of N

in soil (greater than 90%) is the OM pool, which contains many complex forms of plant

and microbial detritus, ranging from easily decomposable and microbially available

compounds to resistant compounds (Focht and Martin, 1979). Easily decomposable

compounds include amino acids, simple sugars and organic acids, which can by broken

down by decomposers within hours to days (Behera and Wagner, 1974). Resistant N

compounds such as lignins and waxes can take months or years to decompose (Haider

et al., 1967). The majority of soil OM is a large, inactive pool that has a constant decay

rate. There is also a small, active pool of soil OM, the decay rate of which can be

influenced by the quality of plant detritus inputs (i.e. C:N ratio and chemical

constituents; Knops et al., 2002).


17

Figure 2.3. A simplified soil nitrogen cycle. Dashed red arrows show pathways of

environmentally detrimental loss. Open blue arrows show agricultural organic and

inorganic N additions. Adapted from Schimel and Bennett (2004). Abbreviations: NH4+:

ammonium; NO2-: nitrite; NO3

-: nitrate; N2O: nitrous oxide; NO: nitric oxide; N2:

dinitrogen gas.

Heterotrophic microorganisms break down soil OM in order to gain energy, C, N and

other nutrients (Robertson and Groffman, 2006). Nitrogen mineralisation occurs when

excess N is released as ammonium (NH4+) during breakdown of OM. Microorganisms

synthesise extracellular enzymes to depolymerise insoluble macromolecular OM,

producing dissolved organic N-containing compounds such as amino acids and short

chains of amino acids known as oligopeptides (Schimel et al., 2005). Many

microorganisms and plants are able to directly take up and use these simple low

molecular weight OM (LMWOM) compounds (Farrell et al., 2013; Näsholm et al.,

1998). Competition for these LMWOM compounds and their subsequent breakdown is


18

considered to be the key limiting step in the supply of N to subsequent soil N cycle

processes (Hill et al., 2012; Jones et al., 2004b; Schimel and Bennett, 2004).

Nitrification is the production of nitrate (NO3-) by the further transformation of NH4

+ to

nitrite (NO2-) and then NO3

-. Most nitrification in agricultural soils is considered to be

carried out by autotrophic microorganisms, which use nitrification in order to gain

energy as well as the building blocks for new biomass, and do not require C for

production of energy (De Boer and Kowalchuk, 2001). The molecular ecology of

nitrifiers is discussed further in Section 2.6. Nitrate can be further transformed to

dinitrogen gas (N2) by denitrification (Robertson and Groffman, 2006), or can be turned

back into NH4+ by nitrate ammonification, also known as dissimilatory nitrate reduction

to ammonium (DNRA; Giles et al., 2012). Although DNRA can be a significant NO3-

consumption pathway in some forest and rice paddy soils (Rütting et al., 2008; Templer

et al., 2008; Yin et al., 2002), DNRA is favoured by very reduced and C rich

environments, generally occurs in anaerobic conditions, and rates are expected to be

greatest in soil with high OM contents in humid temperate regions (Giles et al., 2012;

Medinets et al., 2015; Rütting et al., 2011). The soils of interest for this thesis have very

low OM contents and are well-drained, rarely becoming anaerobic, so DNRA is not

considered further in this thesis as an important N cycle process.

Inorganic forms of N (NH4+ and NO3

-) may be taken up again by soil microorganisms

and incorporated into biomass. This process is N immobilisation, and is the pathway by

which N can be retained in soil, as N becomes part of the soil OM pool again.

Nitrification on the other hand is the main pathway for environmentally detrimental

losses of N from soil, as the greenhouse gas nitrous oxide (N2O) can be formed during


19

nitrification and denitrification, and NO3- is highly mobile in groundwater or runoff

(Cameron et al., 2013; Robertson and Groffman, 2006).

Nitrogen may also be lost from soil by volatilisation of ammonia (NH3) from the

surface. Ammonium exists in soil in equilibrium with ammonia gas, and when soil pH

is high (as in calcareous soils or temporarily after urea fertiliser application), ammonia

production is favoured (Cameron et al., 2013). Ammonia in the atmosphere can be

deposited back onto the Earth’s surface, either in rainfall or attached to particulate

matter, contributing to acidification and eutrophication (Peoples et al., 2004). Ammonia

volatilisation is not considered an important N loss process in this thesis however,

because the agricultural soils of interest in the study region of south-western Australia

have predominantly acidic pH (or near neutral where lime has been applied).

2.4.2. Nitrate in semi-arid soils

Nitrate is a negatively charged ionic form of inorganic N, and as a consequence of

repellence by the positive charge of cation exchange sites in most soils, NO3- is easily

leached through drainage of soil water (McLaren and Cameron, 1996). This loss of N

can be detrimental to the environment by stimulating growth of unwanted

microorganisms and plants in receiving water bodies and thus causing eutrophication

(Smith and Schindler, 2009).

Nitrate leaching rates vary with soil type, season and climate, and are often greatest in

seasons when plant uptake of NO3- is low and soil drainage is occurring, and when

precipitation is greater than evapotranspiration and the water storage capacity of soil

(Kurtz, 1980). In semi-arid climates with winter dominant rainfall, this often occurs in

late summer and autumn, when winter crops are not yet established and taking up NO3-


20

and the first rains of the growing season cause drainage. In a sand soil from south-

western Australia that was cropped to wheat, 68% of total NO3- leached during the

growing season occurred in the first two rainfall events of autumn (Anderson et al.,

1998). Similarly, in a silty clay loam soil from Navarra, Spain, 65–80% of total N

leached occurred in the first three months of crop development (Arregui and Quemada,

2006). Soils that are conducive to water movement, such as those with poor structure

and coarse textures that promote preferential flow through macropores, are also more

susceptible to leaching losses (Cameron et al., 2013).

Few studies have measured NO3- leaching rates from soils in semi-arid climates.

Leaching rates that have been observed show that in general, native ecosystems have

very low leaching rates (less than 1 kg N ha-1 y-1), but leaching rates from cropped soils

can be significant (Table 2.1). One Australian soil leached up to 59 kg NO3--N ha-1 in

one growing season (Anderson et al., 1998), while a Spanish soil had leaching rates

greater than 75 kg NO3--N ha-1 (Arregui and Quemada, 2006).

2.4.3. Nitrous oxide emissions from semi-arid soils

Nitrous oxide is a potent greenhouse gas with 298 times the 100-year global warming

potential of carbon dioxide (CO2; Myhre et al., 2013). Nitrous oxide can be produced

from soil during nitrification by ammonia oxidisers, and during denitrification by both

denitrifiers and some ammonia-oxidising bacteria (AOB) that are also able to denitrify

(Fig. 2.3; Kool et al., 2011). Ammonia-oxidising archaea (AOA) have also been

observed to produce N2O (Löscher et al., 2012; Rasche et al., 2011; Santoro et al., 2011;

Stieglmeier et al., 2014), possibly originating from chemical reactions of intermediates

in the ammonia oxidation process, rather than through nitrifier denitrification like AOB

(Hatzenpichler, 2012). Nitrate ammonification (DNRA) can additionally form N2O as a


21

by-product, but is unlikely to be an important source of N2O in the semi-arid soils of

interest in this thesis as DNRA generally occurs under anaerobic conditions in soils

with high C content (Giles et al., 2012; Medinets et al., 2015; Rütting et al., 2011).

Agricultural sources are estimated to contribute about 60% of global anthropogenic N2O

emissions, directly from soil and from animal production (Ciais et al., 2013).

A significant proportion of N2O emissions from semi-arid soils can be produced by

wetting of dry soil when inorganic N is available (Aguilera et al., 2013). This is in

contrast to most N2O emissions from temperate soils, which appear to be in response to

N fertiliser additions (Galbally et al., 2008; Mummey et al., 1994). For example, in

south-west Australian agricultural soils, over half of annual N2O fluxes can be in

response to summer and autumn rainfall events (Barton et al., 2008; Barton et al.,

2013b). Typical N loss rates as N2O emissions are however an order of magnitude lower

on average than in irrigated semi-arid systems (Aguilera et al., 2013), where measured

annual fluxes can range from 0.08–0.69 kg N2O-N ha-1 (Table 2.2). These low

emissions in rainfed semi-arid soils compared to irrigated systems or more humid

climates may be due to typically low N fertiliser application rates, and crop growth

during the coolest season when N2O fluxes are limited by temperature (Aguilera et al.,

2013). Low temperatures below 10–12 °C can even be associated with negative N2O

fluxes (Meijide et al., 2009).

Tabl

e 2.

1: N

itrat

e le

achi

ng r

ates

obs

erve

d in

sem

i-ari

d cl

imat

es w

ith w

inte

r do

min

ant r

ainf

all.

Loc

atio

n L

and

use

Mea

n an

nual

rain

fall

(mm

) St

udy

leng

th

N a

pplic

atio

n (k

g

N h

a-1 y

-1)

Nitr

ate

leac

hing

(kg

NO

3- -N h

a-1)

Ref

eren

ce

Moo

ra, W

este

rn A

ustra

lia,

Aus

tralia

Cro

pped

[lup

in (L

upin

us

angu

stifo

lius)

, whe

at (T

ritic

um

aest

ivum

)] an

d pa

stur

e

[sub

terr

anea

n cl

over

(Tri

foliu

m

subt

erra

neum

)] ro

tatio

ns

460

98, 1

26, 5

9 da

ys

(3 g

row

ing

seas

ons)

0

2–59

#

And

erso

n et

al.

(199

8)

Dev

il C

anyo

n, S

an

Bern

ardi

no M

ount

ains

,

Cal

iforn

ia, U

SA

Fore

st (m

ixed

con

ifers

), gr

adin

g

to sh

rubl

and

(cha

parr

al)

610

4 ye

ars

0 3.

6-11

.6 §

Fenn

and

Pot

h (1

999)

NE

San

Die

go C

ount

y,

Cal

iforn

ia, U

SA

Shru

blan

d (c

hapa

rral

), po

st fi

re

530

3 ye

ars

0,

50

0.00

2–12

2.6,

‡

1.3–

130.

0

Vou

rlitis

et a

l. (2

009)

Nav

arra

, Spa

in

Cro

pped

[whe

at, b

arle

y (H

orde

um

vulg

are)

, rap

esee

d (B

rass

ica

napu

s) ro

tatio

n]

347–

492

(gro

win

g

seas

on ra

infa

ll)

120,

164

, 143

day

s

(3 g

row

ing

seas

ons)

0–13

5 5.

6–78

†

Arr

egui

and

Q

uem

ada

(200

6)

Mon

tsen

y M

ount

ains

,

Cat

alon

ia, S

pain

Fore

st [h

olm

oak

(Que

rcus

ilex

)] 90

1 10

yea

rs

0 0.

05 ¶

A

vila

et a

l. (2

002)

# M

ost f

rom

lupi

n-w

heat

rota

tion,

leas

t fro

m p

astu

re-p

astu

re, v

arie

d w

ith y

early

rain

fall.

§ 19

95-1

998

expo

rt fr

om e

ntire

cat

chm

ent,

dow

nstre

am o

f N sa

tura

ted

fore

st.

‡ H

igh

year

ly v

aria

tion

with

rain

fall.

† V

aria

tion

mai

nly

due

to d

rain

age

and

soil

min

eral

N c

onte

nt.

¶

10 y

ear a

vera

ge o

f tot

al N

exp

ort a

t cat

chm

ent o

utle

t.

Ta

ble

2.1.

con

tinue

d on

nex

t pag

e.

Tabl

e 2.

1. N

itrat

e le

achi

ng r

ates

obs

erve

d in

sem

i-ari

d cl

imat

es w

ith w

inte

r do

min

ant r

ainf

all (

cont

inue

d).

Loc

atio

n L

and

use

Mea

n an

nual

rain

fall

(mm

) St

udy

leng

th

N a

pplic

atio

n

(kg

N h

a-1 y

-1)

Nitr

ate

leac

hing

(kg

NO

3- -N h

a-1)

Ref

eren

ce

Mon

tsen

y M

ount

ains

,

Cat

alon

ia, S

pain

Fore

st (h

olm

oak

and

ald

er)

1258

1

year

0

0.7

◊ Bu

tturin

i and

Sa

bate

r (20

02)

Cat

alon

ia, S

pain

M

ainl

y fo

rest

[cor

k oa

k (Q

uerc

us

sube

r) a

nd p

ine]

, <10

%

agric

ultu

ral f

ield

s

613

3 ye

ars

0 0.

1–0.

4 ◊

Bern

al e

t al.

(200

2)

◊ St

ream

exp

ort

Tabl

e 2.

2: N

itrou

s oxi

de e

mis

sions

obs

erve

d in

sem

i-ari

d cl

imat

es w

ith w

inte

r do

min

ant r

ainf

all.

Loc

atio

n L

and

use

Rai

nfal

l (m

m)

Stud

y le

ngth

N a

pplic

atio

n ra

te

(kg

N h

a-1 y

-1)

N2O

em

issi

ons

(kg

N2O

-N h

a-1)

Ref

eren

ce

Cun

derd

in, W

este

rn

Aus

tralia

, Aus

tralia

Cro

pped

[whe

at (T

ritic

um a

estiv

um)]

368

# 1

year

0,

100

0.

09 –

0.1

1 Ba

rton

et a

l. (2

008)

Cun

derd

in, W

este

rn

Aus

tralia

, Aus

tralia

Cro

pped

[can

ola

(Bra

ssic

a na

pus)

] 36

7 #

1 ye

ar

0, 7

5 0.

08–0

.13

Barto

n et

al.

(201

0)

Cun

derd

in, W

este

rn

Aus

tralia

, Aus

tralia

Cro

pped

[nar

row

-leaf

ed lu

pin

(Lup

inus

angu

stifo

lius)

]

365

# 1

year

0

§ 0.

13

Barto

n et

al.

(201

1)

Won

gan

Hill

s, W

este

rn

Aus

tralia

, Aus

tralia

Cro

pped

(lup

in–w

heat

; whe

at–w

heat

rota

tions

)

374

# 2

year

s 0,

20

(lupi

n–w

heat

rota

tion)

,

75, 5

0 (w

heat

–whe

at

rota

tion)

0.04

–0.0

7 ‡

Barto

n et

al.

(201

3b)

Arb

uckl

e, C

alifo

rnia

,

USA

Cov

er c

rop

(legu

min

ous m

ix) b

etw

een

vine

yard

row

s (Vi

tis v

inife

ra)

42.7

(mea

sure

d

Mar

ch–O

ctob

er)

195

days

5

† 0.

07–0

.11

¶ G

arla

nd e

t al.

(201

1)

# M

ean

annu

al ra

infa

ll.

§

Lupi

n is

a g

rain

-legu

me:

no

ferti

liser

.

‡ Li

min

g de

crea

sed

N2O

em

issi

ons o

ver t

wo

year

s in

whe

at–w

heat

but

not

in lu

pin–

whe

at ro

tatio

n.

† N

ferti

liser

app

lied

once

dur

ing

study

per

iod.

¶ N

2O e

mis

sion

s are

cum

ulat

ive

for t

he g

row

ing

seas

on, c

onve

ntio

nal t

ill a

nd n

o-til

l tre

atm

ents.

Ta

ble

2.2.

con

tinue

d on

nex

t pag

e.

Tabl

e 2.

2: N

itrou

s oxi

de e

mis

sions

obs

erve

d in

sem

i-ari

d cl

imat

es w

ith w

inte

r do

min

ant r

ainf

all (

cont

inue

d).

Loc

atio

n L

and

use

Rai

nfal

l (m

m)

Stud

y le

ngth

N a

pplic

atio

n ra

te

(kg

N h

a-1 y

-1)

N2O

em

issi

ons

(kg

N2O

-N h

a-1)

Ref

eren

ce

Mon

tere

y C

ount

y,

Cal

iforn

ia, U

SA

Cov

er c

rop

[Trio

s 102

(Trit

ical

e x

Trio

seca

le) o

r Mer

ced

Rye

(Sec

ale

cere

ale)

] or c

ultiv

ated

soil

betw

een

vine

yard

row

s

460

(for w

inte

r

2005

–200

6)

1 ye

ar

0 0.

47–0

.69

Stee

nwer

th a

nd

Belin

a (2

008)

Alc

alá

de H

enar

es,

Mad

rid, S

pain

Cro

pped

[bar

ley

(Hor

deum

vul

gare

)] 43

0 13

2 da

ys (c

rop

perio

d), 3

7 da

ys

(aut

umn

fallo

w)

125

0.20

–0.3

7 ◊

Mei

jide

et a

l. (2

009)

◊ C

umul

ativ

e N

2O e

mis

sion

s for

cro

p pe

riod

(Jan

uary

–Jun

e) a

nd fi

rst r

ain

even

ts o

f aut

umn

(Oct

ober

–Nov

embe

r).


26

2.4.4. Tools to predict the risk of nitrogen loss

Experimental methods to determine actual N loss rates are often time and labour

intensive, expensive, and result in data that are not suitable for generalisation or

prediction (Buczko et al., 2010). In order to effectively manage agricultural N and

reduce N losses from soil, some method of assessing and predicting the risk and

magnitude of N losses from soil is necessary. Various N loss indicators have been used,

ranging from simple qualitative or semi-quantitative indicators such as N balances (for

example Buczko et al., 2010; Wick et al., 2012), to complex models based on a

combination of physical N transport mechanisms and N sources, which require a lot of

input data and expertise to operate (for example Delgado et al., 2008).

One tool to that can be utilised to understand the risk of N loss is the gross nitrification

to N immobilisation ratio (N:I ratio), which describes the balance between the pathway

of N loss (nitrification) and the pathway of microbial N retention (immobilisation). The

N:I ratio was first used as an indicator of N saturation and the likely fate of NO3- in

forest soils (Aber, 1992), and was used to describe the competition for NH4+ between

heterotrophic immobilising microorganisms and nitrifiers (Tietema and Wessel, 1992).

The N:I ratio has been positively correlated to leaching losses in arable and grassland

soils from temperate, humid climates of the United Kingdom and New Zealand

(Stockdale et al., 2002), but has not been examined in relation to N2O loss. The ratio has

also been compared between grassland and forested sites in a cold temperate

environment in central Canada, to suggest the risk of N loss through NO3- leaching and

N2O emissions (Cheng et al., 2012). There are few studies however that have used the

N:I ratio in semi-arid soils. In one Western Australian red earth, the N:I ratio showed an

increased risk of N loss at low (5 °C) and high temperatures (30 and 40 °C; Hoyle et al.,

2006), due to limitation of N immobilisation at these temperatures. It is unknown


27

whether the N:I ratio has a similar pattern in other semi-arid soils and under different

soil conditions.

2.5. Factors Controlling Risk of Nitrogen Loss through Microbially-

Mediated Soil Nitrogen Transformations

Variation in microbially-mediated N transformations affects the risk of N loss in semi-

arid soils through increased N loss processes such as nitrification or gaseous N

production. Generally this variation and biological activity in semi-arid soils is

controlled mainly by environmental factors of water availability and temperature (Hoyle

and Murphy, 2011; Noy-Meir, 1973). Other factors also modify microbial N cycling

and the risk of N loss such as availability of C and N substrates, soil pH, and the

relationships between microorganisms and plants (Booth et al., 2005; Sahrawat, 2008).

The factors that control the risk of N loss in soil vary spatially and with time. All soils,

by their nature, are highly spatially variable, on scales from the microscopic to the

landscape (Ettema and Wardle, 2002). For example, at the field scale, distance between

crop rows affects soil processes and functioning (Ettema and Wardle, 2002). At finer

scales, microsites provide differing degrees of substrate, water and oxygen availability,

and can protect bacteria from predation (Ettema and Wardle, 2002; Grundmann and

Debouzie, 2000; Ranjard and Richaume, 2001). Soil organisms need to locate and

access C, oxygen and nutrients within the heterogeneous soil environment, which has

differing degrees and scales of connectivity (Young et al., 1998). Spatial heterogeneity

provides high microhabitat diversity, and with limited active dispersal of most soil

microorganisms, communities are characterised by low species resource specialisation

but high species richness (Ettema and Wardle, 2002). Different microbial communities

may exist in N-rich versus N-poor microsites. In microsites that are nutrient-rich,


28

microorganisms generally mineralise N, while in nutrient poor microsites

microorganisms immobilise N, leading to intense N cycling between these microsites

with different nutrient availabilities (Schimel and Bennett, 2004; Schimel and

Hättenschwiler, 2007). This suggests that net N mineralisation measured on bulk soil is

not always a good indication of the supply of N that is at risk of loss, as N can be

translocated from high to low regions of availability and the connections (or lack

thereof) between these microsites will determine if inorganic N accumulates.

Temporal variation in the risk of N loss is also apparent on a variety of scales, from

minute to minute variations, through seasonal cycles, to annual and decadal changes.

This is especially important in semi-arid soils, where biological processes are limited by

water availability, linked to rainfall that also varies greatly with season and from year-

to-year (Harrington and Tow, 2011; Noy-Meir, 1973). For example, temporal variation

in N substrate availability is often high in semi-arid soils, with seasonal shifts in the

relative dominance of dissolved organic N and inorganic N and consequent shifts in risk

of N loss (Delgado-Baquerizo et al., 2011). In regions with winter dominant rainfall,

soil inorganic N pools are often greatest during and after summer dry periods, which

suggests that production of NH4+ and NO3

- continues to occur during seasons which are

limiting to plant growth (Delgado-Baquerizo et al., 2011; Jackson et al., 1988). The

increase in availability of N substrates for nitrification may increase the potential for N

loss, especially when there is limited plant N uptake such as during summer fallow.

This is shown by maximum NO3- leaching rates at the end of summer and in the early

growing season (Anderson et al., 1998; Avila et al., 2002).


29

2.5.1. Soil water and wetting-drying cycles

Abiotic factors of water content and soil temperature are the main controls on rates of N

cycling in semi-arid regions (Farrell et al., 2013; Hoyle and Murphy, 2011; Jones et al.,

2009; Noy-Meir, 1973). A common feature of semi-arid climates is the presence of

wetting and drying cycles in soil (Austin et al., 2004). Variations in rainfall and soil

water availability cause water pulses in the soil, which may occur over the long term in

areas with seasonally dominant rainfall and over shorter periods as wetting-drying

cycles due to individual rainfall events (Austin et al., 2004).

Microbial communities in semi-arid regions are likely adapted to the seasonal extremes

of water stress that are normal for these environments (Cookson et al., 2006a; Halverson

et al., 2000; Placella et al., 2012). Bacterial communities from these climates have

phenotypes that are both thermo- and drought-tolerant (Curiel Yuste et al., 2014).

Surprisingly, potential nitrification rates during the dry season of some semi-arid soils

can be greater than in the wet season, suggesting that the size of the ammonia-oxidising

community is also larger in the dry season, and potential for N loss is also greater

(Parker and Schimel, 2011; Sullivan et al., 2012). Microbial biomass in a semi-arid

Californian soil was also higher in the dry season than the wet, which may be because

microbial death rates are lower, as predators such as protozoa are more drought-

sensitive and reliant on hydrological connections to move about (Parker and Schimel,

2011). Maintaining the potential for metabolic activity during the dry season may be a

strategy by microbes to compete more successfully against plants for resources during

rainfall pulses (Sullivan et al., 2012). This also suggests that even though soil water

availability may be limiting for the majority of the time during dry seasons, there is still

potential for N loss at these times.


30

Soil microorganisms are faced with different challenges depending on whether soil is

drying or whether dry soil is rewet. Water films between and on soil particles decrease

and become less connected as soil dries, restricting the mobility of microorganisms that

live in water, decreasing diffusion of soluble substrates and decreasing hydrological

connectivity between different N cycle processes (Schimel et al., 2007). Decreased

movement of predators may decrease bacterial death rates, causing an apparent increase

in microbial activity (Parker and Schimel, 2011). Increased concentration of substrates

as water films contract can also stimulate microbial growth, though if the soil remains

dry and water films don’t expand again, the substrates can become depleted, causing a

drop in microbial growth after the initial increase (Schimel et al., 2007). Inorganic N

may accumulate as NH4+ when microsites of N mineralisation are not connected to

microsites of NH4+ consumption. This was shown in a semi-arid Californian soil where

the dominant N form in summer was NH4+ (Parker and Schimel, 2011).

Increases in NH4+ during drought may cause some observed differences between AOA

and AOB in their resilience to drying-rewetting stress. The abundance and community

composition of AOA appears to be less resilient to drying-rewetting stress than AOB,

possibly linked to the decreased tolerance of AOA to high NH4+ concentrations (Thion

and Prosser, 2014). Ammonia-oxidising bacteria also react more quickly to rewetting,

increasing their transcriptional activity slightly faster than AOA (within one hour

compared to nine hours; Placella and Firestone, 2013).

As soil dries, microbial activity and biomass decrease as soil microorganisms dehydrate

and either adapt to drought stress or induce survival mechanisms (Borken and Matzner,

2009). In liquid culture, mechanisms that allow microorganisms to remain active during

desiccation include accumulating solutes to maintain osmotic balance between


31

intracellular and extracellular water potentials, and changing the structure and

composition of their cell walls to resist loss of internal cell water (Schimel et al., 2007).

In the more complex soil environment however, microbial adaption to desiccation

appears to be different: microorganisms instead become dormant (Boot et al., 2013;

Kakumanu et al., 2013). This drought avoidance is likely due to C and N substrates

becoming limiting as diffusion decreases in drying soils, so microorganisms are unable

to access the resources they need to synthesise extra osmolytes (Boot et al., 2013;

Kakumanu et al., 2013). Furthermore, in environments with plants, labile C inputs to

soil from root exudates can decrease by up to 80% when soil dries, as plants decrease

their allocation of C to belowground biomass (Gorissen et al., 2004). This may affect

the availability of C to heterotrophic microorganisms, which may increase the potential

for N loss if immobilisation decreases to a greater degree than to nitrification.

When soil is wetted, microorganisms need to rapidly equilibrate to the increased water

potential, which can cause even more stress than the microorganisms experienced on

soil drying (Schimel et al., 2007). If microorganisms are unable to adjust to the

increased water potential, they can die and lyse, contributing as substrate to the initial

pulse of mineralisation often observed on wetting of dry soil (Schimel et al., 2007).

Depending on the organism, up to 26% of cell amino acids, 21% of low molecular

weight sugars (Halverson et al., 2000) and 20–70% of viable MBC (Kieft et al., 1987)

can be released on rewetting. This released C and N however is rapidly reassimilated by

microorganisms in many soils for energy and growth, so after wetting microbial

biomass can recover to the same size as in permanently moist soil, or even increase

(Bottner, 1985; Lundquist et al., 1999).


32

Wetting of dry soil has rapid short-term effects on microbial activity and C and N

cycling. Microbial activity, including N mineralisation increases within minutes to

hours after wetting (Lee et al., 2004; Murphy et al., 1998b; Sponseller, 2007). Easily

decomposable organic substrates are accumulated or exposed during the dry period,

then when dry soils are wetted these organic substrates are immediately mineralised,

causing a pulse in microbial activity, observable as a large CO2 pulse known as the

Birch effect (Jarvis et al., 2007; Kieft et al., 1987). In some soils, the substrate for the

observed N mineralisation increase can come from non-microbial organic N (Appel,

1998). Wetting of dry soil also increases the connections between water films, allowing

increased diffusion of substrates and mobility of microorganisms to access those

substrates (Borken and Matzner, 2009). Increased N mineralisation and hydrological

connectivity of microsites also increases substrate availability to microbially-mediated

N transformations such as nitrification and denitrification, leading to an increased risk

of N loss.

High water contents present different challenges for microorganisms. At high water

contents, soil oxygen is decreased and anaerobic conditions develop. Oxygen diffusion

in soil becomes limited when water-filled pore space (WFPS) is greater than 90%

(Wesseling and van Wijk, 1957). Under these conditions, aerobic microorganisms that

require oxygen as an electron acceptor for energy production may become limited, such

as autotrophic nitrifying bacteria, which may decrease risk of N loss through

nitrification. However, microorganisms that are facultative aerobes may change their

metabolism when anaerobic conditions develop, and anaerobic organisms may become

active, changing the products of N cycling. For example, denitrifiers may become more

active, or more N2O may be produced by nitrifier denitrification and denitrification

(Wrage et al., 2001).


33

2.5.2. Soil temperature

Fluctuations in temperature diurnally and with season are strong drivers of change in N

transformation rates (Frederick, 1956; Sabey et al., 1956). Microbial growth rates,

biological process rates and enzyme catalysis rates increase with increasing temperature

up to an optimum, above which they decline (Johnson et al., 1974). The pattern of

change with temperature of microbial activity is due to an intrinsic property of enzymes:

the negative change in heat capacity between the ground state of an enzyme and the

transition state of the enzyme as it facilitates the transformation of substrates into

products (Hobbs et al., 2013). Optimum temperatures for microorganisms vary

depending on their native climate. For example, optimum temperatures for nitrification

have been measured as 25 °C for cool temperate soils in Iowa (Sabey et al., 1956) and

35 °C for an Australian tropical soil (Myers, 1975).

Temperature has intrinsic and extrinsic effects on microorganisms and N cycling.

Intrinsic effects act directly on metabolic processes, while extrinsic effects are due to

environmental constraints such as changes in diffusion rates and solubility of substrates

(Davidson and Janssens, 2006). With decreasing temperature below the optimum, cell

membranes become less fluid, which decreases the efficiency of transport proteins and

enzymes located in the membrane, thus decreasing the affinity of the microorganism for

substrates which are taken up by active transport processes (Beney and Gervais, 2001;

Nedwell, 1999). Microorganisms are able to adapt to decreasing temperatures to a

certain degree, by inducing cold shock proteins, or by changing the structure of their

membranes to increase the amount of unsaturated lipids and decrease the proportion of

branched chain lipids, which helps to maintain membrane fluidity to lower temperatures

(Russell, 1990). Diffusion rates of substrates and oxygen also decrease with decreasing

temperatures (Schimel et al., 2007). Microbial growth may become limited once


34

microorganisms have used the resources in their immediate environment and if

diffusion rates are too low to replace those resources (Schimel et al., 2007).

At temperatures above optimum, microorganisms can become constrained by

irreversible protein denaturation, the physiological cost of inducing survival

mechanisms by diverting C and energy to maintenance requirements, cell lysis and

death (Corkrey et al., 2012; Johnson et al., 1974; Schimel et al., 2007). Increases in the

optimum temperature of enzymes without compromising their rate of reaction require

increases in enzyme rigidity, in order to increase the change in heat capacity for

catalysis and to provide enzyme stability at high temperatures (Hobbs et al., 2013).

Microorganisms can also acclimate to increasing temperature by increasing the

proportion of saturated and long-chain fatty acids in membrane lipids, in order to

maintain constant membrane fluidity, to decrease leakiness of the membrane and to

maintain substrate transport mechanisms (Sinensky, 1974).

Some N cycling processes may be more sensitive to temperature than others, causing

disconnections between N cycling processes that may in turn increase the risk of N loss.

Uncoupling of N mineralisation-immobilisation turnover (MIT) and increased risk of N

loss has been observed at high temperatures in semi-arid soils (above 30 °C; Hoyle et

al., 2006; Luxhøi et al., 2008), when gross N immobilisation rates were limited more

than gross N mineralisation and nitrification rates. The limitation of immobilisation at

high temperatures may be due to increased soil respiration and microbial biomass

rapidly assimilating labile C, leading to a C substrate limitation (Cookson et al., 2007).

Uncoupling of MIT has also been observed in temperate soils from Denmark at low

temperatures (below 5 °C), where limitation of immobilisation was attributed to down-


35

regulation of microbial growth and thus NH4+ consumption (Andersen and Jensen,

2001).

2.5.3. Availability of carbon and nitrogen substrates

Availability of C and N substrates are a major factor affecting rates of N cycling and

risk of N loss in semi-arid soils, as N cycling microorganisms require both C and N to

grow. The availability of N substrates may depend on production of those substrates by

previous N transformation processes, or on competition for the same N substrate

between diverse N cycling organisms. In the case of nitrification, autotrophic nitrifiers

require NH4+, so the rate at which N mineralisation produces NH4

+ affects subsequent

nitrification rates (Booth et al., 2005; Murphy et al., 2003). In addition, nitrifiers and

heterotrophic N immobilisers compete for NH4+ substrate. Autotrophic nitrifiers

generally have low specific growth rates and yields due to the low energy gain from

ammonia or nitrite oxidation, and use of most of that energy to fix CO2, so are

considered to be inefficient compared to heterotrophic microorganisms (Prosser, 1989).

Soil C availability may therefore regulate N loss processes through influencing the

competition between heterotrophic N immobilisers and autotrophic nitrifiers for NH4+.

Autotrophs fix their own C from CO2, while heterotrophs must assimilate organic

compounds for C and energy (Madigan and Martinko, 2006). Heterotrophs are generally

more active when soil C availability is high, when their higher specific growth rates

allow them to compete more successfully for NH4+ against nitrifiers. If heterotrophic

immobilisers are limited by factors such as decreased C availability, nitrifiers can

compete more effectively for NH4+ substrate, and the risk of N loss is greater (Booth et

al., 2005; Hart et al., 1994). Available or labile C is often present in the form of low

molecular weight, simple compounds, such as sugars and amino acids, obtained from


36

plant rhizodeposits and during microbial breakdown of plant residues and other soil OM

(Haynes, 2005). Available C can increase N immobilisation markedly and thus may

increase retention of N in soil (Gibbs and Barraclough, 1998). For example, inorganic N

availability and net N mineralisation was decreased after eight years of additions of

sugar (labile C) and/or sawdust (more recalcitrant C) to soil of a semi-arid shortgrass

steppe (Burke et al., 2013).

Carbon availability can also stimulate the N loss process of denitrification, which

reduces NO3- to gaseous forms (NO and N2O). The microorganisms that carry out

denitrification are predominantly heterotrophic, so depend on an organic C source

(Wrage et al., 2001). The product ratio of N2O:N2 as well as denitrification rates are

influenced by labile C: when availability of easily decomposable C substrates is high in

soil, the proportion of N2O emitted during denitrification compared to N2 decreases

compared to when there is a C limitation (Azam et al., 2002; Weier et al., 1993).

Niche differentiation between AOA and AOB may be determined by ammonia substrate

availability. Ammonia-oxidising bacteria can be limited by lack of ammonia substrate,

but stimulated by NH4+ additions, while AOA with their high affinity for ammonia

often are found to dominate in environments with low substrate concentrations

(Daebeler et al., 2015; Di et al., 2010; Di et al., 2009; Pratscher et al., 2011).

Agricultural management practices that change the availability of C and N substrates by

altering soil OM content or C and N inputs can consequently change the risk of N loss.

In semi-arid soils this is particularly important, as soil OM content is usually low

compared to soil in more humid climates due to less precipitation, greater temperatures,

low plant productivity and continual soil loss by erosion (Archibold, 1995; Jenny, 1941;


37

Ryan, 2011). Management practices that change C and N inputs and soil OM content

include addition of crop residues or other OM such as manure, additions of N fertiliser,

incorporation of surface residues and tillage, stubble burning, and growing legumes in

rotation.

Addition of chemical N fertilisers is the simplest way to provide N to semi-arid soils

that are deficient in N in order to support annual crop yields (Henderson, 1979).

However, N fertiliser additions in excess of plant demand or at times that are not

matched to plant growth can accumulate inorganic N in soil where it is at risk of loss

(Cameron et al., 2013). Accumulated N is susceptible to leaching, depending on the

amount and intensity of corresponding rainfall (Rasmussen and Collins, 1991). Loss of

N can be decreased by improving fertiliser management in order to match soil N supply

to crop demand, for example by applying the proper rate of N, split applications and

placement of fertiliser where uptake is most active (Meisinger and Delgado, 2002;

Murphy et al., 2004).

Tillage can decrease total C and N availability in soil by increasing oxidation and loss

of OM. Tillage is used for controlling weeds, preparing the physical condition of soil to

allow easy seedling establishment, improve water infiltration or to incorporate surface

residues or fertilisers (Henderson, 1979). However, loss of OM can also be significant,

depending on factors such as the intensity and duration of tillage, climate, crop and soil

type (Heenan et al., 1995; Murphy et al., 2011; Rasmussen and Collins, 1991). Greater

soil disturbance and soil-residue contact due to higher degrees of tillage can also

increase net inorganic N supply in semi-arid soils, with a consequent increase in risk of

N loss (Hoyle and Murphy, 2011). In addition to these effects, tillage redistributes


38

LFOM-C, dissolved organic C (DOC) and microbial biomass to soil locations and

depths that may not be so favourable for microbial activity (Roper et al., 2010).

Burning of crop stubble left over after harvest can also cause decreases in C and N

substrate availability and direct N losses. Burning of stubble is used to control weeds

and crop diseases (Rasmussen and Rohde, 1988), but can also affect belowground

microbial processes and N cycling in semi-arid soils by decreasing microbial biomass,

soil OM, total C and N, mineralisable N and gross N mineralisation rates (Biederbeck et

al., 1980; Hoyle et al., 2006; Rasmussen et al., 1980). High temperature burning can

also cause sizeable losses of N through volatilisation (Murphy et al., 2011).

Legumes can alter availability of N in soil and risk of N loss through several pathways.

The symbiotic association of legume crops with root-nodulating bacteria provides fixed

N to the growing crop, which consequently uses less inorganic soil N. More inorganic N

then remains in soil where it may be available for subsequent crops, or if it is not

immobilised by microorganisms it can be at risk of loss (Gupta et al., 2011).

Additionally, legume crop residues have a low C:N ratio compared to cereal residues, so

if retained after harvest and incorporated into soil, these N-rich residues can stimulate N

mineralisation as they decompose, which may also be at risk of loss if not taken up by

subsequent crops (Crews and Peoples, 2005).

2.5.4. Soil pH and liming

Soil pH affects N transformations and risk of N loss primarily through its influence on

nitrification and the equilibrium that naturally occurs between NH4+ and ammonia

(NH3) in soil. Mineralisation, NH4+ consumption and inorganic N assimilation are

generally considered to be unaffected by soil pH (Booth et al., 2005). Free ammonia,


39

not NH4+, is the substrate for ammonia oxidation, and the equilibrium between

ammonia and NH4+ is pH dependent. Each unit decrease in pH decreases the amount of

ammonia substrate by one order of magnitude (Prosser, 1989). Nitrifiers have strategies

to overcome this ammonia limitation at low pH that do allow nitrification to occur, such

as active transport of substrates, containing ammonia monooxygenase (AMO) enzymes

with high affinities for ammonia, hydrolysis of urea, carrying out heterotrophic

nitrification, making use of microsites with more neutral pH, forming biofilms by cells

attaching to surfaces and producing protective substances (Levy-Booth et al., 2014;

Prosser, 1989). Low pH is in fact the only consistent differentiating factor between

bacterial and archaeal ammonia oxidisers (Prosser and Nicol, 2012). Archaeal AMO has

an affinity for ammonia that is three to four orders of magnitude greater than bacterial

AMO (Martens-Habbena et al., 2009), allowing AOA to be more prevalent and active

than AOB at soil pH below 5.5 (Prosser and Nicol, 2012). Nitrite oxidisers on the other

hand are restricted under acid conditions due to chemical decomposition of nitrite,

which forms an equilibrium with toxic nitrous acid (Norton, 2008; Prosser, 1989).

Nitrifiers face other challenges as soil becomes more basic. As pH increases, ammonia

substrate becomes increasingly available, until pH is above the optimum for that

organism, and the advantages of increased substrate is balanced by the extra energy that

cells require to maintain their internal pH, and growth will become constrained (Prosser,

1989). As pH increases, increased ammonia also has the potential to be lost from soil by

volatilisation (Francis et al., 2008).

The effect of soil pH management on the risk of N loss is complex. Some authors have

reported that raising soil pH, for example by liming, increases nitrification rates (for

example Islam et al., 2006; Kemmitt et al., 2006), which in turn can increase N loss


40

through increased NO3- pools and as N2O emissions. This may be either directly from

nitrification (Billore et al., 1996; Goodroad and Keeney, 1984), or indirectly by

increasing N substrates for denitrification if soil water content is high (Clough et al.,

2004). However, raising soil pH can also decrease N losses from N2O emissions during

nitrification, by increasing activity of nitrite oxidisers and limiting the conversion of

nitrite to N2O (Clough et al., 2004; Feng et al., 2003). This was demonstrated in a semi-

arid soil, where increasing pH by liming decreased N2O emissions due to nitrification

compared to more acid soil (Barton et al., 2013a).

2.5.5. Relationships between plants and nitrogen cycling microorganisms

Plants predominantly affect N cycling by influencing inputs and losses of N. Plant are

both a source of N, via symbiotic associations with N fixing microorganisms, and a sink

for N, by utilising inorganic N (and thus decreasing the amount of NO3- that is available

for leaching) (Knops et al., 2002). Although plants and microorganisms might be

expected to compete for N substrates, there is in fact a temporal niche separation

between them: in the short-term microorganisms outcompete roots for N, but as

microorganisms turn over very quickly and have high C respiration losses, in the long

term N is released by microorganisms which plants are subsequently able to take up

(Jones et al., 2013; Kuzyakov and Xu, 2013). The temporal niche separation of N

uptake between microorganisms and plants suggests that microorganisms actually

cooperate rather than compete with plants, especially in soils that are N-limited.

Microorganisms store excess inorganic N and prevent N losses by leaching, helping to

maintain ecosystem stability (Kuzyakov and Xu, 2013). Plant uptake of N also

decreases the amount of N that is at risk of loss: leaching of dissolved inorganic N can

be significantly elevated in soil without roots (Lajtha et al., 2005). Agricultural systems

however often create conditions where there is a disconnect between plants and


41

microbes, so microbes become C limited and unable to immobilise excess inorganic N,

and N losses may be high (Kuzyakov and Xu, 2013). For example, in Mediterranean-

type semi-arid climates, N mineralisation occurs both in the winter growing season and

the summer non-growing season. In the winter mineralised N is more likely to be taken

up by the crop or immobilised, while in the summer mineralised N is more likely to

accumulate or be at risk of loss through leaching or gaseous N emission (Gupta et al.,

2011).

Plants also influence N cycling by regulating C inputs through rhizodeposits and root

turnover, which consequently control microbial activity and N immobilisation (Knops

et al., 2002). During the growing season, crop plant roots extend and occupy new soil

volume, during which the roots release rhizodeposits. Plants may lose 1–30% of

photosynthetically fixed C through their roots as they grow through the soil (Whipps,

1990), though basal root exudation in unstressed conditions is likely to be only 3–5% of

photosynthesised C (Dilkes et al., 2004; Jones et al., 2004a). These rhizodeposits are of

various compositions including amino acids, organic acids, sugars, polysaccharides and

phytohormones (Meharg, 1994), but most are low molecular weight solutes that are

readily available to microorganisms (Jones et al., 2004a). Within hours or days,

microorganisms utilise these available C sources for growth and maintenance

respiration. Microorganisms also use the available C to produce extracellular enzymes

that mineralise soil OM, in order to obtain N, then on microbial death, the N that was

immobilised from soil OM can be released as NH4+ and become available to plants

(Kuzyakov and Xu, 2013), or the dead microorganisms become part of soil OM again to

close the microbial N loop (Knops et al., 2002).


42

2.6. Molecular Ecology of Nitrifiers

The microorganisms that carry out nitrification, the conversion of NH4

+ to NO3-, were

originally identified as obligately aerobic chemolithoautotrophic bacteria (Lees, 1955).

Autotrophic nitrifiers use this conversion of N to produce energy as well as new

biomass, and compete for NH4+ with heterotrophic immobilising organisms, which use

N to construct biomass, but require organic C for energy production (Booth et al.,

2005). There are two steps of autotrophic nitrification: oxidation of ammonia to nitrite,

followed by oxidation of nitrite to NO3-, carried out respectively by ammonia oxidisers

(both AOB, such as the genera Nitrosomonas and AOA) and nitrite oxidisers (such as

Nitrobacter (Lees, 1955; Monteiro et al., 2014). Ammonia oxidation is the key limiting

step in nitrification, as nitrite oxidation is reliant on nitrite produced by ammonia

oxidation (Kowalchuk and Stephen, 2001). In the past 10 years, with the advent of

metagenomic techniques and identification of the functional gene amoA involved in

ammonia oxidation, it has been recognised that archaea are also capable of ammonia

oxidation and are ubiquitous in soil, marine, sediment and geothermal environments

around the world, and in a variety of climates from humid to semi-arid (Schleper and

Nicol, 2010).

2.6.1. Ammonia-oxidising bacteria

Autotrophic bacterial ammonia oxidisers belong to at least two different phylogenetic

groups, the beta-subclass of the Proteobacteria, including the Nitrosomonas and

Nitrosospira genera and Nitrosococcus mobilis, and the gamma-subclass of

Proteobacteria, including the rest of the Nitrosococcus genus (Purkhold et al., 2000).

Ammonia oxidation in bacteria involves the oxidation of ammonia to an intermediate,

hydroxylamine, catalysed by the AMO enzyme (Kowalchuk and Stephen, 2001). Two

electrons are required, which are generated by the further oxidation of hydroxylamine to


43

nitrite by the hydroxylamine oxidoreductase enzyme (HAO). Ammonia monooxygenase

has three subunits, coded for by the amoA, amoB and amoC genes (Nicol and Schleper,

2006). Ammonia-oxidising bacteria from the Betaproteobacteria have two or three

copies of the amo operon, while those from the Gammaproteobacteria contain a single

copy (Norton et al., 2002). Quantification of gene copy numbers in soil therefore is not

a direct count of the number of ammonia-oxidising microorganisms.

There is evidence that AMO in the Betaproteobacteria evolved to oxidise ammonia

specifically, while AMO in the Gammaproteobacteria evolved for both ammonia and

methane oxidation (Hooper et al., 1997). In addition, AOB in the gamma-subclass

appear to be more closely related to methane oxidisers in the same subclass than to

ammonia oxidisers in the beta-subclass (Holmes et al., 1995). The ability of AMO of

bacteria in the gamma-subclass to oxidise both ammonia and methane seems to be an

adaptation to incorporate C into cell biomass from methane, methanol or carbon

monoxide, for example when CO2 is limiting to C fixation (Jones and Morita, 1983;

Ward, 1987).

2.6.2. Ammonia-oxidising archaea

Archaeal ammonia oxidisers in soil belong to the phylum Thaumarchaeota, a new

archaeal phylum that was recognised after the discovery of AOA (Brochier-Armanet et

al., 2008; Spang et al., 2010). In many soils, AOA appear to be more abundant than

AOB (Leininger et al., 2006), and may be the reason why nitrification is so prevalent

even when NH4+ concentrations are below the predicted affinity threshold for AOB

(Monteiro et al., 2014).


44

The ammonia oxidation pathway in AOA may differ fundamentally from AOB.

Evidence for this includes: archaeal amoA sequences are shorter than and only show

about 40% similarity with bacterial amoA sequences (Nicol and Schleper, 2006); there

seems to be no equivalent of the HAO complex or cytochrome c proteins as found in

AOB (Hallam et al., 2006; Walker et al., 2010); and AOA seem to produce energy via a

copper-containing electron transfer system, as opposed to the iron-containing electron

transfer system of AOB (Hallam et al., 2006; Walker et al., 2010). Ammonia oxidation

by AOA therefore either produces an intermediate other than hydroxylamine (as in

AOB), or uses fundamentally different enzymes to produce hydroxylamine and nitrite

(Schleper and Nicol, 2010; Walker et al., 2010). Nitroxyl (HNO) has been hypothesised

as an alternative intermediate (Walker et al., 2010). However, the marine AOA

Nitrosopumilus maritimus was recently shown to produce and consume hydroxylamine

during ammonia oxidation, so AOA likely use novel uncharacterised enzymes to

catalyse ammonia oxidation (Vajrala et al., 2013). One similarity to the

Gammaproteobacteria however is that at least one AOA (Nitrosopumilus maritimus) has

only one copy of amoA in its genome (Walker et al., 2010).

Carbon fixation pathways of AOB and AOA also appear to differ. The C fixation

pathway of AOB uses ribulose biphosphate carboxylase/oxygenase in the Calvin-

Bassham-Benson Cycle. On the other hand, genome sequences suggest that AOA can

utilise acetyl coenzyme A in a modified 3-hydroxypropionate cycle, and also have

components of an oxidative tricarboxylic acid cycle, which could allow AOA to utilise

organic C and grow mixotrophically (Hallam et al., 2006; Walker et al., 2010).


45

2.6.3. Niche differentiation between ammonia-oxidising bacteria and archaea

Research is currently focussing on finding distinguishing characteristics between AOA

and AOB, specifically with regard to niche specialisation (their adaptation to abiotic and

biotic soil characteristics) and niche differentiation (their resource utilisation patterns;

Hu et al., 2014; Prosser and Nicol, 2012; Yao et al., 2013). This is because both AOB

and AOA are abundant in soil, but their relative abundances vary between regions and

often from site to site. Possible causes that have been proposed include ammonia

concentration and source (Di et al., 2010; Jia and Conrad, 2009), the ability to grow

both autotrophically and heterotrophically (Karlsson et al., 2012; Kelly et al., 2011) and

different pH sensitivities (Bru et al., 2011; Gubry-Rangin et al., 2011). Except for pH,

where acid soils with pH <5.5 are dominated by AOA, none of these factors appear to

be the sole reason for niche specialisation or differentiation between AOA and AOB,

with both bacteria and archaea having enough physiological diversity to grow under all

conditions (Prosser and Nicol, 2012).

In semi-arid regions, as in other environments, both AOB and AOA are widespread, but

the ratio of AOA:AOB can vary widely even in the same continent (Adair and

Schwartz, 2008; O’Sullivan et al., 2013). For example, in semi-arid soils from south-

eastern Australia, archaea tend to dominate the ammonia-oxidising populations, while

in south-western Australia, abundances of AOA and AOB tend to be comparable

(O’Sullivan et al., 2013). Factors that regulate the relative abundance of AOA and AOB

in semi-arid soil may include season of sampling, precipitation, temperature, soil water

content, soil OM content and C:N ratio, however there are few studies that have tested

regulating factors in semi-arid soil, or even described temporal variation in relative

abundances of ammonia oxidisers (Adair and Schwartz, 2008; O’Sullivan et al., 2013;

Sher et al., 2013). Further research also needs to examine the proportion of nitrification


46

activity that can be attributed to each of these groups of ammonia-oxidising

microorganisms, as growth rates and gene abundances may not reflect actual process

rates.

2.6.4. Nitrite-oxidising bacteria

The second step of nitrification is oxidation of nitrite to NO3-, which is carried out by

nitrite-oxidising bacteria (NOB) and involves nitrite oxidoreductase (NXR) as the main

enzyme (Prosser, 1989). The presence and diversity of NOB are more difficult to

determine using molecular techniques than ammonia-oxidising microorganisms, as

NOB belong to several phylogenetic groups (Ehrich et al., 1995; Teske et al., 1994).

Advances have been made by the design of a polymerase chain reaction (PCR)

cloning/sequencing approach (Poly et al., 2008) and denaturing gradient gel

electrophoresis (DGGE) fingerprint-type approach (Wertz et al., 2008) utilising newly

designed primers for the functional gene nxrA, which encodes the beta-subunit of NXR.

However, there are important differences between the nxrA gene sequences of different

groups of NOB. For example, the primers designed for these studies were able to target

Nitrobacter and Nitrococcus strains, but were not successful for Nitrospira (Wertz et

al., 2008). In addition, there is strong evidence that NOB have multiple copies of the

nxrA gene with different sequences in their genomes, which makes it complicated to

interpret NOB taxonomy and diversity in environmental samples (Poly et al., 2008;

Wertz et al., 2008).

Nitrite-oxidising bacteria may be less drought-tolerant than AOB, so may respond more

slowly than ammonia oxidisers to changes in water availability. This was shown by

nitrite accumulation after the first rains following the dry season in a semi-arid soil

(Gelfand and Yakir, 2008).


47

2.6.5. Heterotrophic nitrification

Some heterotrophic microorganisms are also able to oxidise ammonia to nitrite

including certain bacteria (e.g. Thiosphaera pantotropha) and fungi (e.g. Aspergillus

flavus) (De Boer and Kowalchuk, 2001; Lees, 1955). Growth of heterotrophic nitrifying

bacteria and fungi never occurs on ammonia alone: heterotrophic nitrification does not

produce energy or cell growth for the microorganism, but may have the function of

being an electron sink (Hooper et al., 1997; Killham, 1990; Robertson and Kuenen,

1990). Heterotrophic nitrifiers can use both organic and inorganic N sources (De Boer

and Kowalchuk, 2001; Zhang et al., 2014), and many are able to denitrify

simultaneously, where nitrite produced is reduced to N2 (De Boer and Kowalchuk,

2001; Robertson and Kuenen, 1990). This combined nitrification-denitrification may be

utilised to maintain high growth rates when oxygen respiration capacity is limited

(Blagodatsky et al., 2006; Stouthamer et al., 1997). Other heterotrophic nitrifiers may

use ammonia oxidation to produce N-oxides in order to inhibit the growth of competing

microorganisms (Honda et al., 1998). Fungal heterotrophic nitrification is by a different

pathway again, as a side effect of cell lysis and lignin degradation when hydroxyl

radicals react with N compounds (De Boer and Kowalchuk, 2001).

Heterotrophic nitrifiers likely use diverse ammonia-oxidising enzymes, that differ from

those of autotrophic bacterial nitrifiers. Certain species such as Thiosphaera

pantotropha have AMO and HAO enzymes like AOB, so the substrates, intermediates

and products of heterotrophic nitrification are similar to autotrophic ammonia oxidation

(Moir et al., 1996). However, the genes that encode AMO and HAO in T. pantotropha

and also in another heterotrophic nitrifier, Methylocystis capsulatus, are not

homologous to those in AOB (Hooper et al., 1997). Instead of HAO, T. pantotropha

uses another protein with iron-sulphur centres to catalyse the reaction of hydroxylamine


48

to nitrite, while M. capsulatus may use an enzyme similar to cytochrome P-460 of

Nitrosomonas for this reaction (Hooper et al., 1997; Zahn et al., 1994).

Heterotrophic nitrification may be an important source of NO3- and N2O under certain

conditions. These conditions include under native vegetation where organic N is readily

available but there is little NH4+, or in soils with high organic C concentrations and low

pH (Cai et al., 2010; Kurakov et al., 2001; Müller et al., 2014). For example, in some

forest soils, 33–46% of NO3- can be produced by heterotrophic nitrification (Kurakov et

al., 2001). Aerobic heterotrophic nitrification can produce more N2O per cell than

autotrophic nitrification, so may be a significant source of N2O in some soils (Anderson

et al., 1993; Papen et al., 1989). For example, as much as 38% of N2O emissions were

due to heterotrophic nitrification in a black arable soil with high soil organic C content

(Cai et al., 2010). Generally though, heterotrophic nitrification is considered to be

unimportant in most agricultural soils (Barraclough and Puri, 1995; De Boer and

Kowalchuk, 2001; Kurakov et al., 2001).

2.7. Approaches to Limiting Nitrogen Losses from Semi-Arid Soils

Management techniques to limit detrimental N losses due to NO3

- leaching and N2O

emissions from semi-arid soils require a good understanding of the biogeochemistry and

dynamics of the N cycle (Delgado, 2002; Meisinger and Delgado, 2002). There are two

approaches to decrease NO3- leaching: manage the leachate volume and manage the

amount of NO3- in soil (Meisinger and Delgado, 2002). In rainfed cropping systems,

management of leachate volume is difficult because the main source is precipitation, so

effective management needs to focus on managing nitrification and NO3- in soil and

synchronising plant uptake with soil NO3- production. This is also expected to decrease


49

other N losses such as N2O emissions that are in response to nitrification and

denitrification.

Increasing crop N use efficiency during the growing season might be achieved by

matching N supply to crop demand (a concept known as synchrony), by applying

fertiliser N only when crops can rapidly utilise it and in amounts that are not in excess

of expected crop yields (Meisinger and Delgado, 2002; Murphy et al., 2004). Synchrony

also takes into account N release from organic additions, and particularly in tropical

cropping systems, a lot of research has been invested in understanding how organic

materials of differing qualities show different patterns of nutrient release, and how these

patterns are modified by mixing materials of different qualities, to replace or decrease

the amount of inorganic N fertiliser (reviewed in Palm et al., 2001). Although high

quality plant materials such as legume residues have similar patterns of N availability to

and may be applied as a direct substitute for inorganic fertilisers, N release from no

single organic material can match crop N demand (Palm et al., 2001). Mineralisation of

a mixture of high and low quality plant residues generally has a pattern that is the

weighted average of the individual patterns of the two types of residue, rather than

having a period of delayed rapid N release to match plant demand (Fig. 2.4).

Matching N supply to crop demand minimises N substrates available for nitrifying

microorganisms, so that N is taken up rapidly by crop plants and is no longer available

for loss. This can be achieved by on-site monitoring of soil and plant N, and fertiliser

management techniques such as split applications, banding or deep placement and foliar

applications (Meisinger and Delgado, 2002; Subbarao et al., 2006). Other approaches to

managing soil NO3- and nitrification in semi-arid soils include having a cover crop to

take up N when soil would usually be bare, use of organic fertilisers, promoting N


50

immobilisation through additions of low-quality organic residues, improving the

precision of N application and application of nitrification inhibitors (Aguilera et al.,

2013; Meisinger and Delgado, 2002).

Figure 2.4. Conceptual nutrient release from high quality (High Q), low quality

(low Q) and a mixture of organic materials in relation to plant uptake. From Palm

et al. (2001).

2.7.1. Nitrification inhibitors

Nitrification inhibitors have been effective tools for many years to decrease nitrification

and keep fertiliser N in soil where it is accessible by crop plants, decreasing loss of N to

the environment (Wolt, 2000). Nitrification inhibitors are chemical compounds that

inhibit some part of the nitrification process (Slangen and Kerkhoff, 1984). Many

nitrification inhibitors act on AMO, the enzyme that catalyses the first step of

nitrification (McCarty, 1999). The exact mechanism differs depending on the

nitrification inhibitor, and may include acting as a substrate for AMO, thus blocking the

enzyme’s active sites and temporarily making the enzyme ineffective; reacting with

AMO and changing its configuration, thus permanently deactivating it; and creating

products that bind to other parts of the cell and inhibit normal cell metabolism

(McCarty, 1999).


51

Table 2.3. Common agricultural nitrification inhibitors (Hauck, 1980; Subbarao et

al., 2006). Common name/

Abbreviation Chemical Formula Application method

Nitrapyrin, N-serve 2-chloro-6-(trichloromethyl)-

pyridine

Injection into soil with anhydrous ammonia,

liquid fertilisers, solid fertiliser coatings

DCD dicyandiamide Mixing with solid N fertilisers, e.g. urea

DMPP 3, 4-dimethyl pyrazole phosphate Mixing with solid N fertilisers, e.g. urea

AM 2-amino-4-chloro-6-methyl

pyrimidine

Solid N fertiliser coatings

Many chemical compounds inhibit some part of the nitrification process, whether

ammonia oxidation, hydroxylamine oxidation or nitrite oxidation. These compounds

range from general inhibitors such as pesticides, herbicides and fungicides to specific

inhibitors (Hauck, 1980). Some natural compounds produced by plants (biological

nitrification inhibitors) also inhibit nitrification, such as neem seed and oil-extracted

cake, which is produced by the Indian lilac tree (Azadirachta indica; Sharma and

Prasad, 1995). Some of the chemical nitrification inhibitors which are marketed for

agricultural use are listed in Table 2.3.

2.7.2. Nitrapyrin

Nitrapyrin, or 2-chloro-6-(trichloromethyl)-pyridine, has most potential for use in semi-

arid agricultural soils because out of the three most well-known commercial nitrification

inhibitors (nitrapyrin, DCD and DMPP; Subbarao et al., 2006), nitrapyrin was the most

effective inhibitor of nitrification at 25 °C and above (Ali et al., 2008; Chen et al.,

2010). Nitrapyrin is a heterocyclic N compound, with a chlorine atom and a

trichloromethyl group substituted on either side of the ring N (Fig. 2.5; McCarty, 1999).

Properties of nitrapyrin include that it is volatile, insoluble in water, but soluble in


52

solvents such as xylene (McCarty, 1999; Slangen and Kerkhoff, 1984). Nitrapyrin has

been formulated for application with liquid fertilisers, anhydrous ammonia and as as a

solid coating on fertilisers (Nelson and Huber, 1980). The volatility of nitrapyrin

however means that it is necessary to immediately incorporate it into the soil and

surface application is not recommended (Nelson and Huber, 1980; Wolt, 2000).

Figure 2.5. Structure of nitrapyrin, 2-chloro-6-(trichloromethyl)-pyridine. Adapted

from McCarty (1999).

The mechanism by which nitrapyrin inhibits nitrification is not completely understood.

However, evidence indicates that nitrapyrin is involved with AMO (Campbell and

Aleem, 1965a; Vannelli and Hooper, 1992). Nitrapyrin is an alternative substrate for

AMO, the active site of which specifically binds and orients the aromatic ring of

nitrapyrin and forces the trichloromethyl group into the enzyme’s oxygen binding site

(Hooper et al., 1997). The trichloromethyl group of nitrapyrin is then likely reduced

instead of oxygen, producing 6-chloropicolinic acid (Vannelli and Hooper, 1992).

Nitrapyrin is only a weak substrate of AMO however, so the binding of nitrapyrin to

AMO doesn't entirely explain how nitrapyrin can so strongly inhibit nitrification

(McCarty, 1999). Inhibition of ammonia oxidation might also be partly accounted for

by the product, 6-chloropicolinic acid, which binds indiscriminately to membrane

proteins and could inhibit other membrane processes (Vannelli and Hooper, 1992). In


53

addition, nitrapyrin also appears to block electron transfer from ammonia to oxygen at

the cytochrome oxidase site involved in ammonia oxidation, by binding or chelating the

copper component of the cytochrome oxidase (Campbell and Aleem, 1965a). Nitrapyrin

has little effect on the hydroxylamine-oxidising enzyme systems or nitrite oxidation (the

subsequent steps of nitrification) compared to the ammonia-oxidising enzymes, even at

high concentrations (Campbell and Aleem, 1965a, b).

Nitrapyrin has varying effects on other biological N transformations in soil. Nitrapyrin

has no effect on microbial activity in general as measured by CO2 production, but may

inhibit N mineralisation, though only when applied in high concentrations of 20 µg g-1

(Laskowski et al., 1975; Malhi and Nyborg, 1983). Nitrapyrin has been reported to

either have no effect on N immobilisation, or to stimulate N immobilisation due to a

longer retention time of NH4+ (Aulakh and Rennie, 1984; Osiname et al., 1983).

Nitrapyrin applied with NH4+ can also stimulate heterotrophic bacterial growth

(Kangatharalingam and Priscu, 1993). Nitrapyrin can however decrease rates of N

transformations that are reliant on nitrification, such as fixation of nitrite into OM, N2O

emissions, and other gaseous N emissions (Aulakh et al., 1984; Azhar et al., 1986a;

Azhar et al., 1986b; Bremner and Blackmer, 1978; Chen et al., 2010; Smith and Chalk,

1980).

The persistence of nitrapyrin in soil after application and its subsequent bioactivity is

regulated by soil properties such as temperature, pH and OM content. Increasing

temperature decreases the effectiveness of nitrapyrin by increasing the rate of microbial

degradation of nitrapyrin, and likely increasing the recovery rate of surviving nitrifying

microorganisms (Goring, 1962). Nitrapyrin can, however, be effective at inhibiting

nitrification at soil temperatures at 25–35 °C, and also decreased N2O emissions by 96–


54

98% at 25 °C following applications of urea (Ali et al., 2008; Chen et al., 2010). With

increasing soil pH, nitrifiers are more susceptible to nitrapyrin, but surviving

microorganisms are able to recover more quickly, so overall more nitrapyrin is required

to effectively block ammonia oxidation (Goring, 1962; Hendrickson and Keeney, 1979).

Soil OM can absorb nitrapyrin, making it unavailable for inhibition, and also stimulates

the microorganisms that degrade nitrapyrin by providing an energy source (Goring,

1962; Lewis and Stefanson, 1975).

In semi-arid soils specifically, as in soils from other climates, the factors which

determine the persistence and bioactivity of nitrapyrin are those described above:

temperature, soil OM content, pH (Goring, 1962). This is demonstrated by the original

experiments describing nitrapyrin, which used a range of soils including 17 from

California, a region with a semi-arid Mediterranean-type climate (Goring, 1962). Under

cooler conditions of the winter growing season, nitrapyrin appears to be effective at

inhibiting nitrification in the field. For example, nitrapyrin decreased nitrification of

NH4+ fertilisers applied to several fallow Californian soils in the field during the winter

growing season, increasing recovery of fertiliser NH4+ by up to 77% after 21 weeks

depending on the concentration of nitrapyrin (Turner et al., 1962). Addition of

nitrapyrin to NH4+ fertilisers for irrigated cotton, sweet corn and sugar beets in

California also increased yields by up to 32%, depending on soil type and concentration

of inhibitor (Swezey and Turner, 1962).

The effectiveness of nitrapyrin at inhibiting nitrification outside the growing season has

not been investigated. For example, it is unclear if nitrapyrin can inhibit nitrification

during summer fallow when rain events encourage OM decomposition and production

of inorganic N, and when soil temperatures are elevated (above 25 °C). Although


55

nitrapyrin has been evaluated in a semi-arid soil during the summer, the soil was not

fallow and nitrapyrin was used for the purpose of retaining N fertiliser (Ali et al., 2008).

Nitrapyrin was only effective at elevated soil temperatures (35 °C) when applied at rates

greater than that normally recommended for temperate environments (i.e. >0.25–0.50

mg kg-1, or 0.56–1.12 kg ha-1; Ali et al., 2008). For example applying nitrapyrin

retained 50% of applied NH4+ in soil after 4 weeks when applied at 8.32 µg g-1, and

retained 88% of applied NH4+ after 12 weeks at 52 µg g-1 (Ali et al., 2008). This

suggests that nitrapyrin has the potential to inhibit nitrification at the elevated

temperatures experienced by soil during summer fallow in semi-arid climates.

2.8. Conclusions

The factors that ultimately influence the risk of N loss in semi-arid agricultural soils are

varied and complex. This review has described the effects of soil water, temperature, C

and N substrate availability, pH and the relationships that exist between plants and

microorganisms. Many questions remain to be answered however with regards to

interacting soil and environmental conditions on soil N supply pathways and internal

soil N cycling, especially during the hot and dry summer fallow. With the advent of

molecular techniques, new insights are being gained into the role of microorganisms in

N loss processes, particularly the influence of the relative abundances and community

structure of AOA and AOB on nitrification. What is not yet clear is what factors

influence the relative importance of AOA and AOB on nitrification rates, their seasonal

dynamics and how they can be managed to prevent N loss. The greatest risk of N loss in

semi-arid rainfed agricultural soils of south-west Australia appears to be in response to

production of inorganic N outside the growing season. Manipulation of microbially-

mediated N transformation rates to decrease the risk of loss may be possible by


56

managing C and N substrate availabilities, or by directly controlling nitrification using a

nitrification inhibitor. However, as will be explored in this thesis, it remains to be seen

whether management of risk of N loss is possible outside the growing season.

57

Chapter 3.

Root exudate carbon was more effective than soil

organic carbon at decreasing the risk of nitrogen

loss in a semi-arid soil

3.1. Abstract

The need for increased food production to support the growing global population

requires more efficient nutrient management and prevention of nitrogen (N) losses from

both applied fertiliser and organic matter (OM) decomposition. This is particularly

important in semi-arid rainfed cropped soils, where soil water and temperature are the

dominant drivers of N cycling rather than agricultural management. Here we used 14C

and 15N techniques to examine how peptide/amino acid turnover, gross and net N

transformation rates and nitrous oxide emissions responded to changes in both total and

root exudate soil organic C (SOC) pools. Soil was collected from a semi-arid rainfed

field trial with one winter crop per year followed by a summer fallow period, where

additions of straw/chaff over 10 years increased total SOC by 76% (Tilled soil

compared to Tilled+OM soil). These soils were incubated with or without synthetic root

exudate mixture to account for both sources of SOC available to microorganisms in soil.

Laboratory experimental conditions reflected soil temperatures ranging from winter

cropping (5 °C) to summer fallow (50 °C). Increased total SOC did not decrease the risk

Ch. 3: N Loss and Carbon

58

of N loss as defined by the nitrification:immobilisation (N:I) ratio at most temperatures,

so was not an effective management tool to control N losses. In comparison, root

exudate addition decreased the risk of N loss at all temperatures and for both field trial

treatments. Increased net N mineralisation and decreased microbial C use efficiency at

temperatures greater than 30 °C resulted in significant ammonium accumulation.

Microbial decomposers appeared to use amino acid-C for growth but peptide-C for

energy production. Findings indicate that the greatest risk of N loss in these semi-arid

soils will occur during the start of growing season rains, due to inorganic N

accumulation over summer fallow when there are high soil temperatures, occasional

significant rainfall events and no release of root exudates from growing plants. While

most attempts to manipulate the soil N cycle have occurred during the winter cropping

period, our findings highlight the need to manage N supply during summer fallow if we

are to minimise losses to the environment from semi-arid soils.


59

3.2. Introduction

Concerns regarding food security, low fertiliser use efficiencies and the need to

decrease greenhouse gas emissions necessitate the development of more sustainable

agricultural systems. Semi-arid and arid regions cover approximately half of the global

agricultural area (The World Bank, 2008) and are thus of major importance to food

production and associated nutrient management. Sustainable agriculture in semi-arid

regions presents unique challenges, especially in rainfed cropping systems, where

rainfall and temperature are the main drivers of microbial activity and cycling of

nutrients such as nitrogen (N; Hoyle and Murphy, 2011; Noy-Meir, 1973). Semi-arid

regions in the Southern Hemisphere have experienced a drying trend since the 1970s

predominantly at the start of the grain-growing season (April and May; Cai et al., 2012).

Although there has been a reported 15% decrease in heavy winter rainfall between 1950

and 2003 (Nicholls, 2010), summer rainfall events that occur outside of the period of

crop and annual pasture growth are increasing (Alexander et al., 2007). More summer

rainfall is expected to increase soil organic matter (OM) decomposition and N supply at

a time when there is limited or no plant N uptake (Austin et al., 2004; Murphy et al.,

1998b). Nitrogen supply in excess of microbial demand results in N release, which is at

risk of loss to the environment if nitrified.

Nitrogen losses are undesirable, potentially limiting crop yield and having detrimental

off-site environmental impacts [e.g. N leaching and emissions of nitrous oxide (N2O)].

Management practices that mitigate N losses therefore need to be developed. Losses of

N are particularly difficult to mitigate when they are not in response to N fertiliser

additions, but originate from soil OM decomposition. The timing of inorganic N release

from soil OM decomposition is difficult to change (Hoyle and Murphy, 2011) as


60

peptide and amino acid turnover is primarily regulated by water and temperature

(Farrell et al., 2013; Jones et al., 2009). Instead management options are more likely to

succeed when targeting the subsequent fate of the released inorganic N. Nitrification is

the key pathway for N loss, as nitrate (NO3-) is susceptible to leaching, and the

greenhouse gas N2O may be produced during and after nitrification (Wrage et al.,

2001). One way to decrease potential N loss is by increasing microbial N

immobilisation and thus decreasing the amount of inorganic N that is available for

nitrification and loss (Crews and Peoples, 2005). The nitrification to immobilisation

ratio, or N:I ratio, represents the balance between the N loss and retention pathways

(Aber, 1992; Tietema and Wessel, 1992). This index has been correlated with NO3-

leaching losses in temperate grassland and arable soils (Stockdale et al., 2002), but little

is known about the behaviour of the N:I ratio in cropped soils from other climates.

Increased microbial N immobilisation and decreased potential for N loss could be

achieved through manipulation of soil carbon (C) availability. Soil organic C (SOC) and

N cycles are inextricably linked: both N mineralisation and immobilisation pathways

are mediated by heterotrophic microorganisms, which require C from organic sources

for growth and production of energy. When heterotrophic immobilisers are limited by C

compared to N, net production of inorganic N occurs, which is then at risk of

nitrification and subsequent loss (Barraclough, 1997). The majority of our

understanding about soil C and N cycling processes has been gained through research in

temperate and humid environments, but N cycling appears to behave unexpectedly in

semi-arid regions, particularly in warm and dry seasons (Parker and Schimel, 2011;

Sullivan et al., 2012). For example, in Californian grassland soil, net N mineralisation

rates and nitrification potentials are greater during the warm dry summer than during the

cooler, wetter winter, resulting in greater ammonium (NH4+) pools in the summer


61

(Parker and Schimel, 2011). In addition, in south-western Australian soils, heterotrophic

N immobilisation becomes constrained at temperatures greater than 30 °C, and

mineralisation–immobilisation turnover becomes decoupled, resulting in the

accumulation of inorganic N (Hoyle et al., 2006; Luxhøi et al., 2008). This

mineralisation-immobilisation turnover decoupling at elevated temperatures was

hypothesised to be due to C substrate limitation, caused by microorganisms consuming

available C faster than C could be replaced by diffusion from nearby soil microsites

(Hoyle et al., 2006). We hypothesised therefore that increasing soil C availability will

decrease the potential of N loss (i.e. decrease the N:I ratio), especially at elevated

temperatures as occur during summer in some semi-arid soils.

Soil organic C content and availability may be changed by agricultural management

practices that build or remove C. These practices include tillage and OM inputs over the

longer term (Dick, 1992; Liu et al., 2014), or shorter term rhizosphere processes such as

inputs of labile C from root exudates and mycorrhiza (Jones et al., 2004a; Kaiser et al.,

2015). The objective of this research was to understand how different sources of C alter

N transformations in arable semi-arid soil. Specifically, we investigated how total SOC

versus root exudate C affected (a) N decomposition pathways; and (b) the subsequent

fate of N and risk of N loss as defined by the N:I ratio, under conditions reflective of

both summer and winter conditions in semi-arid soils.

3.3. Methods

3.3.1. Study site and field soil collection

Soil was collected from a field research site approximately 221 km north-northeast of

Perth in the agricultural production zone (wheatbelt) of Western Australia (30.00° S,


62

116.33° E). The soil is a sand (92% sand, 2% silt, 6% clay) and classified as a Basic

Regolithic Yellow-Orthic Tenosol (Australian soil classification; Isbell, 2002), or a

Haplic Arenosol (IUSS Working Group WRB, 2007). The area has a semi-arid climate

with cool, wet winters and hot, dry summers (Fig. 3.1). At the weather monitoring

station closest to the study site (Dalwallinu, 30.28° S, 116.67° E) the historical mean

annual rainfall is 288 mm and mean monthly temperatures range from 5.8 to 35.3 °C

(1997–2013 data; Commonwealth of Australia Bureau of Meteorology,

http://www.bom.gov.au/climate/data). Soil temperatures at 5 cm depth at the research

site ranged from 6.2–45.6 °C (2008–2012).

Figure 3.1. Daily maximum and minimum soil temperatures at 5 cm depth and

daily rainfall for 2011 at the research site.

The field site consisted of two SOC management treatments: (i) tilled soil (control;

using offset disks before seeding to 10 cm depth and seeded with knife point tines to 10

cm depth; “Tilled”), and (ii) tilled soil plus OM additions (“Tilled+OM”) of 20 t ha-1 of

barley straw, canola chaff and oaten chaff in 2003, 2006 and 2010 respectively. This

represented an additional input to soil of 27 t C ha-1, of which 7.9 t C ha-1 was retained


63

as SOC (i.e. microbial C use efficiency of 29%). This equated to 76% more SOC in

Tilled+OM soil than in Tilled soil (Table 3.1). Treatment plots (80 m by 10 m) were

randomly allocated to three replicate blocks when the experimental site was established

(2003), and have since been planted to an annual crop each winter (lupin-wheat-wheat

rotation).

Soil was collected (Ap horizon; 0–10 cm) from each of the three replicate field

treatments in May 2011 while the soil was dry (0.012 g H2O g -1 dry soil) and before

winter rain commenced. A composite soil sample of 18 cores (7 cm diameter, 10 cm

depth) was collected from each treatment plot in a zigzag sampling pattern, sieved (<2

mm) and stored at 4 °C until further analysis. Each field replicate (n = 3) was kept

separate for use in the laboratory experimental design.

3.3.2. Laboratory experimental design

The laboratory experiment consisted of soil collected from the two field trial SOC

treatments (Tilled and Tilled+OM) as described above, by three field replicates. Sub-

samples from each replicate bag of soil received one of the laboratory synthetic root

exudate treatments [plus root exudates (+RE) or no root exudates (No RE)], by four

laboratory incubation temperatures for low molecular weight OM (LMWOM) turnover

(5, 15, 30 and 50 °C) or seven temperatures for the other N transformation rate

measurements (5, 10, 15, 20, 30, 40 and 50 °C). The synthetic root exudate solution was

used to simulate conditions when plants are present in the soil. The mixture consisted of

D-glucose (6.75 mM); D-fructose and D-sucrose (1.35 mM each); succinic acid, citric

acid, L-malic acid and fumaric acid (675 µM each); and glycine, L-leucine, L-alanine,

L-valine, L-serine, L-glutamic acid, L-aspartic acid, L-lysine, L-arginine, and L-

phenylalanine (135 µM each). This solution delivered 50 µg C g-1 dry soil (C:N ratio of


64

38:1) in 250 µL for LMWOM turnover (added to 5 g soil) or 1 mL for all other N

transformation measurements (added to 20 g soil).

Table 3.1. Properties of field trial soils (0–10 cm depth) collected eight years after

soil organic carbon treatments were imposed. Values represent means ±SEM (n = 3).

Abbreviations: LFOM: light fraction organic matter; DOC: dissolved organic carbon;

MBC: microbial biomass carbon. Tilled Tilled+OM

Soil pHCaCl2 # 6.17 ± 0.19 6.30 ± 0.11

Total carbon (%) § 0.76 ± 0.08 1.36 ± 0.21*

Total nitrogen (%) § 0.07 ± 0.00 0.11 ± 0.02

Soil C:N ratio 11.2 ± 0.6 12.3 ± 0.3

LFOM carbon (mg C g-1) §‡ 0.91 ± 0.13 2.04 ± 0.39

LFOM nitrogen (mg N g-1) §‡ 0.05 ± 0.01 0.12 ± 0.03

LFOM C:N ratio 17.0 ± 0.23 16.6 ± 0.45

DOC (µg C g-1) † 120.4 ± 3.8 236.0 ± 26.3*

MBC (µg C g-1) ¶ 118.4 ± 17.3 218.9 ± 43.7

* +OM soil significantly different from No OM soil at P < 0.05.

# Soil pH measured in 0.01 M CaCl2 with a 1:5 soil:extract ratio.

§ Total C, total N, LFOM C and LFOM N determined by dry combustion of finely ground air-dry soil or

LFOM using an Elementar Vario MACRO CNS elemental analyser (Hanau, Germany).

‡ Light fraction organic matter recovered by density separation with deionised water (1.05 g cm-3).

† Dissolved organic C was extracted using 0.5 M K2SO4 (20 g soil to 80 mL extract) and analysed using

an OI Analytical Aurora 1030 Wet Oxidation TOC Analyzer (College Station, TX, USA).

¶ Microbial biomass C determined by fumigation–extraction (Brookes et al., 1985), analysed for

oxidisable C as described for DOC, and then a kEC factor of 0.45 used to convert the oxidisable C ‘flush’

into MB-C (Wu et al., 1990).

Soil was pre-incubated for 7 d at the specified temperatures to pre-condition the soil

microorganisms, as microbial community structure and function differ with incubation

temperature (Cookson et al., 2007). On day 1 and day 4 (during pre-incubation) and on

day 8 (coinciding with 14C or 15N application; see sections below) the synthetic root

exudate mixture, or an equivalent amount of deionised water (≤18.2 MΩ), was added to

the soil. In total the +RE treatment received 100 µg C g-1 dry soil and 2.64 µg N g-1 dry

soil prior to measurement of LMWOM turnover and N transformation rates. This was


65

equivalent to the mass of C contained in the microbial biomass of the Tilled soil (Table

3.1) and was thus sufficient to ensure no C limitation to the microbial population during

incubation.

3.3.3. Peptide and amino acid turnover

Mineralisation of LMWOM substrates was examined by determining 14C-labelled

peptide and amino acid turnover. Five grams of soil was placed in 50 mL polypropylene

vials and pre-incubated for 7 d, with 125 µL of either synthetic root exudate mixture or

deionised water added to the soil on days 1 and 4 as described above. To determine the

rate of 14CO2 evolution after pre-incubation, 250 µL of the synthetic root exudate

mixture (another 50 µg C g-1 dry soil and 1.32 µg N g-1 dry soil) or deionised water was

spiked with either 14C-labelled peptide (L-trialanine, ~1.5 mM, 0.28 kBq, 0.008 µCi,

American Radiochemicals Inc., USA) or 14C-labelled amino acids (~3 mM, 0.27 kBq,

0.007 µCi, American Radiochemicals Inc., USA) and added to the surface of the soil on

day 8. The amino acids were an equimolar mixture of 0.2 mM 14C-labelled L-amino

acids (alanine, arginine, aspartic acid, glutamic acid, glycine, histidine, isoleucine,

leucine, lysine, phenylalanine, proline, serine, threonine, tyrosine, valine).

To capture evolved 14CO2, a 1 M sodium hydroxide (NaOH; 1 mL) trap was placed

inside each polypropylene vial and suspended above the soil and the vial hermetically

sealed. 14CO2 evolution was monitored by replacing the NaOH trap after 0.5, 1, 2, 4, 8,

10, 24, 48, 120, 144 and 168 h. The 14C content of the 1 M NaOH traps was determined

by adding Scintisafe 3® scintillation cocktail (Fisher Scientific, Loughborough, UK) and

the 14C content was subsequently measured using a Wallac 1404 liquid scintillation

counter (Wallac EG&G, Milton Keynes, UK).


66

3.3.4. Modelling 14C dynamics

Mineralisation of LMWOM was modelled from the amount of 14C substrate remaining

in the soil using SigmaPlot 12.3 (Systat Software Inc., San Jose, CA) and confirmed

with R version 2.15.2 (R Foundation for Statistical Computing, Vienna, Austria). For

the majority of treatments a double first-order exponential decay model was fitted to the

data to represent a biphasic pattern of mineralisation:

f = ( exp–k1t) ( exp–k2t) (Eqn 3.1)

where f is the amount of 14C remaining in the soil, and describe the size of the

each mineralisation pool, and correspond to the respective rate constants for each

mineralisation phase, and t is time. The first rapid phase described by is considered

to reflect 14CO2 efflux as substrates are immediately used for catabolic processes (i.e.

respiration; Boddy et al., 2007). The remaining 14C-substrate is considered to be

immobilised in the microbial biomass via anabolic processes. The second, slower

mineralisation phase is attributed to the use of this C temporarily immobilised in

the biomass (i.e. storage-C; Boddy et al., 2007; Farrar et al., 2012).

For some treatments at 5 and 50 °C a simpler first-order exponential model with

asymptote fitted the data better:

(Eqn 3.2)

where the asymptote value describes a pool that is unavailable for microbial

mineralisation (recalcitrant C), describes the size of a single, very slowly

mineralisable pool (labile C) with k representing the exponential decay constant for this

pool.

The substrate half-life for the first mineralisation pool ( and for double and single

exponential decay models respectively) was calculated using the following equation:


67

(Eqn 3.3)

To calculate the proportion of C immobilised by the microbial biomass, we calculated

the microbial C use efficiency as follows (Manzoni et al., 2012; Sinsabaugh et al.,

2013):

(Eqn 3.4)

where is the amount of C immobilised by the microbial biomass (i.e. difference

between total 14C input minus 14CO2 evolved), and accounts for C lost through

respiration (14CO2 evolved).

For data where a single exponential decay equation was used, the following equation

was used.

(Eqn 3.5)

where is the amount of recalcitrant C and accounts for C lost through respiration

(14CO2 evolved). Since the amount of C immobilised by the microbial biomass was

determined by difference (i.e. total 14C input minus 14CO2 evolved) this could include

substrate in the microbial biomass as well as any 14C substrate remaining in the soil at

the end of the incubation period. However, residual 14C substrate is considered to be

negligible as 14C substrates such as amino acids and oligopeptides are rapidly

immobilised by the microbial biomass leaving less than a few percent of the total in

solution after a few hours (Farrell et al., 2013). In addition, transfer of 14C to humified

soil OM would be insignificant over the time courses represented in these experiments.

3.3.5. Gross N transformation rates and inorganic N

Gross N transformation rates were determined by 15N isotopic pool dilution (Kirkham

and Bartholomew, 1954). The bulk soil samples were adjusted to 45% water-filled pore


68

space (WFPS), mixed, left to equilibrate overnight at 4 °C, and then 20 g of soil was

packed into 120 mL vials to a bulk density of 1.4 g cm-3. Subsamples of each treatment

combination were then pre-incubated for 7 d at temperatures representative of field soil

conditions (5, 10, 15, 20, 30, 40 and 50 °C). Each vial was placed inside an airtight

glass jar fitted with gas septum ports in the lids to enable headspace gas analysis. Water

(10 mL) was added to the jars (but not in contact with the soil) to maintain humidity and

thus minimise soil drying. The jars were vented every 24 h to maintain an aerobic

environment. On days 1 and 4, 0.5 mL of either synthetic root exudate solution or

deionised water (≤18.2 MΩ) was added to subsample vials, for a total of 50 µg C g-1 dry

soil and 1.32 µg N g-1 dry soil during pre-incubation.

After pre-incubation, 1 mL of 15N-enriched (60 atom %) ammonium sulphate

[(15NH4)2SO4] was applied as multiple droplets to each vial of soil to obtain a

concentration of 5 µg N g-1 dry soil. The +RE treatment also received a second

application of synthetic root exudates (50 µg C g-1 dry soil and 1.32 µg N g-1 dry soil) in

the same 1 mL aliquot as the (15NH4)2SO4. The 1 mL aliquot increased the soil water

content to approximately 60% WFPS: below the WFPS at which nitrification becomes

limited by oxygen exchange in soils from this study region (Gleeson et al., 2010), and at

the upper limit of soil WFPS observed in the field (Barton et al., 2011; Barton et al.,

2013b). The vials were incubated inside airtight jars at the temperatures described above

until they were extracted. Two extraction times were selected based on a previous 15N

isotopic pool dilution study by Hoyle et al. (2006) in a similar semi-arid soil: T0 = 4–6 h

and T1 = 24 h after 15N addition. At each of these times, subsamples were shaken with

80 mL of 0.5 M potassium sulphate (K2SO4) for 30 min then filtered through Whatman

No. 42 filter paper using Buchner funnels under vacuum. The extracts were kept frozen

until further analysis for inorganic N concentration and 15N enrichment.


69

Ammonium and NO3- concentrations in soil extracts were determined by colorimetric

analysis using the modified Berthelot reaction for NH4+ (Krom, 1980; Searle, 1984) and

the hydrazinium reduction method for NO3- (Kamphake et al., 1967; Kempers and Luft,

1988) on a Skalar San Plus auto-analyser (Skalar Inc., Breda, The Netherlands). Net N

mineralisation and net nitrification rates over the 7 d pre-incubation were calculated

from the difference between NH4+ and NO3

- concentrations before and after pre-

incubation.

The soil extracts were prepared for 15N/14N isotope ratio analysis using a modified

diffusion method (Brooks et al., 1989; Sørensen and Jensen, 1991). The NH4+ and NO3

-

within the extracts was, in a two-stage process, trapped on separate acidified diffusion

disks as ammonia (NH3). The disks were subsequently analysed for %N and 15N/14N

isotope ratio by the UC Davis Stable Isotope Facility, using an Elementar Vario EL

Cube elemental analyser (Elementar Analysensysteme GmbH, Hanau, Germany)

interfaced to a PDZ Europa 20-20 isotope ratio mass spectrometer (IRMS; Sercon,

Cheshire, UK). For further details see the UC Davis website

(http://stableisotopefacility.ucdavis.edu/).

Any residual inorganic N remaining in the soil after extraction was removed by filtering

with a further 80 mL of 0.5 M K2SO4 and then two subsequent 80 mL volumes of

deionised water. The washed soil was dried at 70 °C, ground to a fine powder, and

analysed for %N and 15N/14N isotope ratio as described above. Total recovery of applied

15N after 24 h from the NH4+, NO3

- and residual soil pools was on average 98% (data

not shown).


70

3.3.6. Modelling N transformation rates

The numerical model FLUAZ (Mary et al., 1998) was used to simulate gross N

transformation rates. The lowest mean weighted errors were obtained when

mineralisation and nitrification were modelled using zero-order kinetics, immobilisation

using first-order kinetics and we assumed no denitrification. Denitrification was set to

zero in the model as measured N2O fluxes were low (see Results section 3.4.3) and a

sensitivity analysis indicated no influence of these low N2O fluxes on modelled gross N

transformation rates. The model was unable to simulate gross N cycling rates at 40 and

50 °C in some of the soils due to a convergence problem between the fitted parameters,

which may be linked to observed rapid dilution of 15N enrichment of the NH4+ pool

prior to T0 at elevated temperatures.

3.3.7. Nitrous oxide analysis

Nitrous oxide fluxes were determined by collecting headspace gas samples (15 mL)

from all treatment jars 24 h after 15N was added (i.e. before soil extraction). Three

replicate samples of the background concentration of N2O in air were also taken prior to

closing the jars. Samples were stored in 12 mL Labco Exetainers under positive

pressure before analysis for concentration and 15N atom % of N2O by the UC Davis

Stable Isotope Facility using a ThermoFinnigan GasBench + PreCon trace gas

concentration system interfaced to a ThermoScientific Delta V Plus IRMS (Bremen,

Germany). For further details see the UC Davis website (http://

stableisotopefacility.ucdavis.edu/).

3.3.8 Statistical analysis

Analysis of variance (ANOVA) with associated TukeyHSD post hoc tests were carried

out using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria) to


71

determine if there were significant differences between the soil properties of the SOC

treatments. A mixed model, PROC MIXED from SAS version 9 (SAS Institute Inc.,

Cary, NC, USA) was used to determine significant effects of SOC treatment, addition of

synthetic root exudates and temperature on 14C-labelled LMWOM substrate half-lives,

microbial C use efficiencies, inorganic N, net N cycling rates, N2O flux and 15N2O

enrichment. Gross N transformation rates were compared using the 95% confidence

intervals generated by the FLUAZ model.

3.4. Results

3.4.1. Soil organic matter turnover (N supply)

Peptide mineralisation data was best represented by a double first-order exponential

decay model (r2 = 0.9904 ± 0.0024), except for soil incubated at 50 °C and +RE soil

incubated at 5 ºC. A single-order exponential decay model with asymptote (r2 = 0.9733

± 0.0052) was fitted to the exceptions. Amino acid mineralisation data was also best

described by a double first-order exponential decay model (r2 = 0.9974 ± 0.0003).

Total SOC treatment had no significant effect on the microbial C use efficiencies of

peptides (mean of 42.8% at 5–30 °C) or amino acids (mean of 70.8% at 5–30 °C) except

at 50 °C where Tilled+OM soil was greater than Tilled soil (P<0.0001; Fig. 3.2).

Addition of synthetic root exudates (+RE) decreased peptide C use efficiencies at 5 and

50 °C by a mean of 14% (P<0.05), but increased peptide C use efficiencies at 30 °C by

a mean of 20% (P<0.0001; Fig. 3.2a–b). Root exudates also decreased amino acid C use

efficiencies at 50 °C by a mean of 14% (P<0.0001), but not at other temperatures

(P>0.05; Fig. 3.2c–d). Across all treatments, C use efficiencies were about half at 50 °C

compared to 30 °C (P<0.0001).


72

Both total SOC and root exudate treatments had minimal effect on peptide and amino

acid turnover between 15 and 30 °C (Fig. 3.3a–d). However at 5 °C, the half-lives of

both peptides and amino acids significantly decreased in the Tilled+OM treatment

(P<0.05; Fig. 3.3a–d). Addition of synthetic root exudates (+RE) only increased amino

acid half-lives at 5 °C (P<0.01; Fig. 3.3c–d).

Figure 3.2. Influence of temperature on microbial carbon use efficiencies of (a) 14C-labelled peptides without root exudates; (b) 14C-labelled peptides with root

exudates; (c) 14C-labelled amino acids without root exudates; and (d) 14C-labelled

amino acids with root exudates. Error bars are ±SEM (n = 3), and may be smaller than

the symbols. Legend is the same for all panels. Legend abbreviation: OM: organic

matter.


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Figure 3.3. Influence of temperature on half-lives of pool a1 of (a) 14C-labelled

peptides without root exudates; (b) 14C-labelled peptides with root exudates; (c) 14C-labelled amino acids without root exudates; and (d) 14C-labelled amino acids

with root exudates. Error bars are ±SEM (n = 3), and may be smaller than the symbols.

Legend is the same for all panels. Legend abbreviation: OM: organic matter.

3.4.2. Nitrogen transformation rates and inorganic N pools

Gross N transformation rates increased linearly to 30 °C, at which point gross N

mineralisation, nitrification and immobilisation rates averaged across both RE

treatments were 2-, 2.2- and 2.8-fold greater in Tilled+OM than Tilled soil, respectively

(Fig. 3.4a–b, 3.5a–d). In addition, net N mineralisation rates showed a significant

increase from 30–50 °C; Tilled+OM soil (maximum 6.3 µg N g-1 d-1) was greater than

Tilled soil (maximum 2.5 µg N g-1 d-1; P<0.001; Fig. 3.4c–d). Net N mineralisation

rates were negligible from 5–20 °C (0.02 µg N g-1 d-1) with no treatment effect (Fig.

3.4c–d). Microbial demand for NH4+ by nitrifiers (Fig. 3.5a–b) and immobilisers (Fig.


74

3.5c–d) decreased above 30 °C. As a consequence the NH4+-N pool size rapidly

increased from 3.2 µg N g-1 at 30 °C to 60.5 µg N g-1 in Tilled+OM soil and 24.9 µg N

g-1 in Tilled soil at 50 °C (Tilled+OM and Tilled soils were significantly different at

P<0.001; Fig. 3.6a).

Addition of synthetic root exudates caused a small increase in gross N mineralisation

(P<0.05; Fig. 3.4a–b), but had no effect on net N mineralisation (P=0.06; Fig. 3.4c–d).

Addition of synthetic root exudates caused a small decrease in gross nitrification at

some temperatures in both the Tilled and Tilled+OM soils (P<0.05; Fig. 3.5a–b). In

contrast root exudates caused on average a 3.9-fold increase in gross N immobilisation

from 5–30 °C in both SOC treatments (P<0.05; Fig. 3.5c–d).

Addition of synthetic root exudates slightly but significantly decreased NH4+ pool size

by a mean of 1.2 µg N g-1 (P<0.0001; combined root exudate treatments are shown in

Fig. 3.6a). Nitrate pool size in the Tilled+OM soil was approximately twice as large at

all temperatures compared to the Tilled soil (P<0.0001; combined root exudate

treatments are shown in Fig. 3.6b). Addition of synthetic root exudates had no effect on

NO3- pool size (P=0.22).

Net nitrification in the Tilled+OM soil was only greater than Tilled soil at 30 °C

(P<0.0001; data not shown). Addition of synthetic root exudates had no effect on net

nitrification rates (P=0.18). Net nitrification rates increased with increasing temperature

to a maximum at 30–40 °C then decreased substantially at 50 °C (P<0.001).


75

Figure 3.4. Influence of temperature after seven days of incubation on (a) gross N

mineralisation without root exudates; (b) gross N mineralisation with root

exudates; and influence of temperature over seven days of incubation on (c) net N

mineralisation without root exudates; and (d) net N mineralisation with root

exudates. Error bars for gross N mineralisation are 95% confidence intervals derived

from the FLUAZ model, and for net N mineralisation are ±SEM (n = 3), and may be

smaller than the symbols. Legend is the same for all panels. Legend abbreviation: OM:

organic matter.


76

Figure 3.5. Influence of temperature on (a) gross nitrification without root

exudates; (b) gross nitrification with root exudates; (c) gross N immobilisation

without root exudates; (d) gross N immobilisation with root exudates; (e) N:I ratio

without root exudates; and (f) N:I ratio with root exudates. The dashed line at 1.0 in

(e) and (f) represents the N:I ratio at which nitrification and N immobilisation rates are

equal. Error bars are 95% confidence intervals derived from the FLUAZ model, and

may be smaller than the symbols. Legend is the same for all panels. Legend

abbreviation: OM: organic matter.


77

Figure 3.6. Influence of temperature after seven days of incubation on (a) NH4+-N;

and (b) NO3--N. Synthetic root exudate treatments were combined in the figure

presented, as root exudates had no effect on NO3--N (P>0.05) and the effect of root

exudates on NH4+-N was small compared to the effects of soil organic carbon treatment

and temperature. Error bars are ±SEM (n = 6), and may be smaller than the symbols.

Legend is the same for all panels. Legend abbreviation: OM: organic matter.

3.4.3. Fate of inorganic N

Addition of synthetic root exudates decreased the N:I ratio to a greater extent and at a

wider range of temperatures than increased total SOC (Fig. 3.5e–f). Addition of

synthetic root exudates significantly decreased the N:I ratio over the temperature range

5–30 °C for the Tilled soil and between 10–30 °C for the Tilled+OM soil (P<0.05). The

N:I ratio was <1 in the presence of synthetic root exudates at all temperatures (i.e. gross

N immobilisation was greater than gross nitrification; Fig. 3.5f). In contrast, the N:I

ratio was >1 for treatments from 10–30 °C without synthetic root exudates (Fig. 3.5e).

Increased total SOC in soil without synthetic root exudates decreased the N:I ratio at

temperatures from 10–30 °C (P<0.05; Fig. 3.5e). However, total SOC additions in the

presence of root exudates slightly increased the N:I ratio but only at 15 °C (P<0.05; Fig.

3.5f).


78

Figure 3.7. Influence of temperature on (a) N2O flux over 24 h; and (b) N2O 15N

enrichment. Root exudate treatments were combined as they had no effect on N2O flux

(P>0.05) and had a small effect on 15N2O enrichment compared to soil organic carbon

and temperature treatments. Error bars are ±SEM (n = 6), and may be smaller than the

symbols. Legend is the same for both panels. Legend abbreviation: OM: organic matter.

Nitrous oxide fluxes after the 7 d incubation period were low with maximum N2O flux

at 40 °C (mean of 0.005 µg N g-1 d-1; combined root exudate treatments are shown in

Fig. 3.7a). Soil organic C treatment and addition of synthetic root exudates had no effect

on N2O flux (P=0.13; Fig. 3.7a). The 15N enrichment range of N2O (0.3–4.9 atom %;

combined root exudate treatments are shown in Fig. 3.7b) was the same as the 15N

enrichment range of the NO3- pool 24 h after 15N addition (0.4–4.3 atom %) but lower

than that of the NH4+ pool (3.0–54.2 atom %); this suggests that denitrification or nitrate

ammonification was the source of N2O.

3.5. Discussion

3.5.1. Sources of soil organic C to decrease the risk of N loss

Inputs of labile C to soil as synthetic root exudates decreased the risk of N loss to a

greater extent than long-term inputs of plant residues that increased total soil OM. Root

exudate C decreased the risk of N loss by stimulating microbial N immobilisation (on


79

average by 3.9-fold), and by slightly decreasing nitrification. By contrast, addition of

plant residues increased both immobilisation and nitrification (on average by 2.7- and

2.8-fold respectively), but had no effect on the ratio between these two competing

pathways for inorganic NH4+. Nitrification is often the principal process controlling

inorganic N consumption in semi-arid soils due to C limitation of the heterotrophic

microbial population (Hoyle et al., 2006). Root exudate C appeared to remove C

limitation of microbial heterotrophs, allowing them to compete more successfully for

NH4+. Our findings are consistent with others who have found that labile C additions

from rhizodeposition stimulate microbial N immobilisation, thus retaining N in soil

(Clarholm, 1985; Qian et al., 1997).

Addition of crop residues to soil increased both inorganic N supply and N loss pathways

and thus did not decrease the risk of N loss. Crop residue inputs increased total SOC,

dissolved organic C (DOC) and LFOM-C by 1.8-, 2.0- and 2.2-fold respectively over

eight years of treatment. Both DOC and LFOM are indicators of available C in soil, as

these soil OM fractions are transient, turn over rapidly and are microbial substrates

(Haynes, 2005; Janzen et al., 1992). Even though there was more DOC and LFOM in

the soil with crop residue inputs (Tilled+OM), overall the risk of N loss was not

decreased, and instead crop residue additions up-regulated the entire soil N cycle. This

suggests that the greater pool of soil OM from crop residue inputs did not solely

increase C availability to heterotrophic microorganisms, but also increased N supply

through mineralisation, and thus had little effect on the balance between subsequent

NH4+ retention and loss pathways. Our results are consistent with other studies with

long-term increases in soil OM due to crop residue inputs, which generally increase

microbial biomass, C and N contents and nutrient cycling (reviewed in Kumar and Goh,

2000). This is however in contrast to short-term additions of plant residues to soil, after


80

which net N immobilisation and decreased inorganic N pools are often observed (for

example Geisseler et al., 2012; Janzen and Kucey, 1988). Increasing total soil OM

through long-term additions of crop residue was not an effective management tool to

decrease N losses in this semi-arid environment.

3.5.2. Inorganic N accumulates at high soil temperature

Considerable NH4+ accumulation at soil temperatures representative of summer

conditions was a result of decreased microbial C use efficiency coinciding with

increased net N mineralisation. This NH4+ build-up was consistent with mineralisation-

immobilisation turnover decoupling observed by Hoyle et al. (2006) and Luxhøi et al.

(2008) at temperatures greater than 30 °C in similar semi-arid soils, while greater net N

mineralisation has also been observed during hot, dry summer months compared to the

cooler growing season in a Californian semi-arid grassland ecosystem (Parker and

Schimel, 2011). The two main pathways of NH4+ production in soil are either by direct

assimilation of LMWOM molecules into microbial biomass and subsequent release of

N that is not required (Jones et al., 2013), or by mineralisation of organic N to NH4+ by

extracellular enzymes (Geisseler et al., 2010). Which pathway is dominant in soil

depends on regulation of microbial enzyme production for N uptake of LMWOM or

NH4+, which in turn is hypothesised to depend on the main forms and relative

availabilities of N and C in soil (Geisseler et al., 2010). Our results suggest that at

elevated temperatures both C and N from LMWOM are less able to be incorporated into

microbial biomass, so more NH4+ is released into the soil mineral N pool, whether due

to extracellular or intracellular breakdown of LMWOM.

The mechanism for decreased microbial C use efficiency at soil temperatures greater

than 30 °C in this semi-arid soil is likely related to a shift in the balance between


81

microbial growth and respiration compared to lower temperatures. Microbial respiration

is inherently more sensitive to temperature increases than microbial growth (Allison et

al., 2010; Steinweg et al., 2008; Wetterstedt and Ågren, 2011), due to the increasing

physiological costs of respiration and heat survival mechanisms (Schimel et al., 2007).

Therefore at elevated temperatures microbes respire a larger proportion of any C they

assimilate compared to C allocated to growth. In addition, increased C cycling and

microbial metabolism at elevated temperatures can deplete readily accessible labile

substrates, requiring microorganisms to use substrates of lesser quality, and which also

lowers microbial C use efficiency (Manzoni et al., 2012; Sinsabaugh et al., 2013). In the

present study, C cycling appears to be tightly linked to N cycling, as decreased

microbial C use efficiency and assimilation of C at elevated soil temperatures is

associated with NH4+ release and thus decreased assimilation of N.

3.5.3. Differences between amino acid and peptide turnover

Measurement of peptide and amino acid turnover suggested that these LMWOM

molecules were used in differing ways by microbial decomposers in this semi-arid soil.

Peptides turned over more rapidly than amino acids, as has been previously observed in

a range of microorganisms and climates (Farrell et al., 2013; Matthews and Payne,

1980). Interestingly, we also observed that in this semi-arid soil peptide-C appeared to

be utilised for production of energy (more C was allocated to respiration), while amino

acid-C was utilised for building biomass. Peptides and amino acids are taken up actively

(i.e. using energy) by separate transport systems (Payne and Smith, 1994). Peptides may

be preferentially used for energy production over amino acids because active uptake of

one peptide molecule requires less energy than if the constituent amino acids were taken

up separately, and peptides provide more C and N per molecule than amino acids

(Farrell et al., 2013; Matthews and Payne, 1980). On the other hand, amino acids can be


82

directly used for biosynthesis of proteins, but peptides must be hydrolysed before their

constituent amino acids can be used for protein synthesis or as sources of C and N after

further breakdown (Anraku, 1980; Payne, 1980). If the patterns of N turnover of these

substrates follow C turnover, peptides may be the primary source of inorganic N after

catabolism and release of peptide-C through respiration. Amino acid-N may instead be

immobilised into microbial biomass concurrently with amino acid-C.

3.5.4. Implications for semi-arid environments

Effective management practices to decrease the risk of N loss in semi-arid soils still

need to be found. The greatest risk of N loss in this arable semi-arid soil likely occurs

from inorganic N that accumulates due to OM decomposition over summer. These

inorganic N pools are then at risk of loss by NO3- leaching during rain events that mark

the beginning of the cooler growing season (Anderson et al., 1998; Arregui and

Quemada, 2006). Risk of N loss by N2O emissions in this semi-arid soil is also likely

greatest during summer fallow: N2O emissions under steady-state water conditions in

the present study were at a maximum between 30 and 40 °C, and up to half of annual

N2O emissions in the field can be in response to summer and autumn rainfall events

(Barton et al., 2008; Barton et al., 2013b; Mummey et al., 1997).

Tools that decrease the risk of N loss may work by controlling N supply, increasing

plant N demand, or by immobilising excess inorganic N in microbial or plant biomass

(Crews and Peoples, 2005). Control of N supply in these rainfed semi-arid agricultural

soils is difficult, because the majority of inorganic N availability (up to 80% of plant N

uptake in wheat) is derived from microbial decomposition of crop residues and soil OM

(Angus, 2001; Fillery, 2001), and OM decomposition is more reliant on changes in soil

water availability and temperature than agricultural management practices (Hoyle and


83

Murphy, 2011). In the present study, increasing soil OM through long-term crop residue

inputs was not effective at decreasing the risk of N loss, because although microbial

immobilisation was increased, N supply and nitrification were also increased. The

present study however highlighted the importance of root exudates for increasing

microbial N immobilisation. Mitigation of N loss in these semi-arid soils therefore

could involve increasing the extent and duration of actively growing plant roots,

especially during late summer and early autumn. This might be achieved by

incorporation of perennials into the current annual cropping system (Crews and Peoples,

2005), or summer crops. Root growth may also be increased in the early growing season

by managing soil constraints that restrict root growth (e.g. sub-soil acidity, compaction

layers) and by selective breeding for traits such as increased root branching and early

growth vigour, which also increases root NO3- capture (Dunbabin et al., 2003; Hoad et

al., 2001; Lynch, 1995).

3.6. Conclusions

Our ability to manipulate N transformation rates and decrease the risk of N loss from

semi-arid soils will depend greatly on the C source (which is associated with the

presence or absence of root exudates), and the time of year (as high soil temperature can

cause potential disconnect in mineralisation–immobilisation turnover). Root exudate C

was more effective than long-term increased plant residue inputs at decreasing the risk

of N loss, by increasing the potential for heterotrophic microbial immobilisation relative

to nitrification. In contrast, addition of plant residues to soil increased both N supply

and N loss pathways. Therefore the greatest risk of N loss in this arable semi-arid soil

occurs from soil inorganic N that accumulates over summer. This accumulation is in

response to high net N mineralisation rates after occasional rainfall events, coupled with


84

low microbial C use efficiency at elevated soil temperatures, and limitations to active

plant growth. Potential loss of this accumulated inorganic N is subsequently greatest

following opening rains in autumn prior to crop establishment, when drainage begins

and soil temperatures cool allowing nitrification to occur. However, management

practices to mitigate this risk of N loss still need to be found.

85

Chapter 4.

Seasonal dynamics of ammonia-oxidising bacteria

but not archaea influence risk of nitrogen loss in a

semi-arid agricultural soil

4.1. Abstract

Nitrification, a key pathway of nitrogen (N) loss from soil, is regulated by ammonia-

oxidising bacteria (AOB) and archaea (AOA). Niche differentiation and seasonal

dynamics of these two groups of microorganisms may be influenced by both

environmental and soil characteristics (e.g. substrate availability, soil pH). However,

what regulates AOB and AOA in semi-arid soils is not well understood. Here the

seasonal dynamics of ammonia oxidiser gene abundances were examined in relation to

soil biogeochemical properties in a cropped semi-arid soil subjected to long-term (since

2003) tillage and crop residue management treatments. AOB regulated ammonia

oxidation in the surface soil, as AOA were undetected, potentially due to agricultural

modification of ammonium availability or soil pH. Seasonal variation in AOB was

related to dissolved organic carbon, microbial biomass carbon and nitrate concentrations

but not to ammonium concentrations, rainfall, soil water content, or temperature. Crop

residue inputs enhanced AOB abundance independent of any seasonal variation.

Increased AOB abundance during summer fallow coincided with increased soil nitrate

Ch. 4: Seasonal Dynamics

86

pools. Consequently, the growth of the AOB population likely contributes to increased

risk of N loss at the start of the following growing season.


87

4.2. Introduction

Nitrification plays a key role in nitrogen (N) loss processes, as the gatekeeper between

internal soil cycling and external N loss (Schimel et al., 2005). Ammonia oxidation, the

limiting step of nitrification, is regulated by bacterial and archaeal ammonia oxidisers,

which convert ammonia (in equilibrium with ammonium, NH4+) to nitrite

(Hatzenpichler, 2012; Kowalchuk and Stephen, 2001). Ammonia-oxidising bacteria

(AOB) belong to the beta- and gamma-subclasses of Proteobacteria while ammonia-

oxidising archaea (AOA) belong to the phylum Thaumarchaeota (Rotthauwe et al.,

1997; Spang et al., 2010). Both AOA and AOB express functionally similar genes for

the primary enzyme catalysing ammonia oxidation, ammonia monooxygenase

(Rotthauwe et al., 1997; Schleper and Nicol, 2010). However, bacterial and archaeal

ammonia oxidisers differ in their genetics, physiologies and metabolic processes, so are

likely to differ in their adaptations to abiotic and biotic soil conditions (niche

specialisation) and utilisation of resources (niche differentiation; Prosser and Nicol,

2012).

Niche specialisation and differentiation between AOA and AOB are not yet clearly

defined, but may be regulated by factors including soil pH, NH4+ supply, or relative

ability to carry out mixotrophic or heterotrophic growth (Prosser and Nicol, 2012). Low

soil pH is the only consistent differentiating factor between AOA and AOB: at pH < 5.5

acidophilic AOA appear to be the dominant ammonia-oxidising microorganisms,

particularly of the phylogenetic cluster associated with Nitrosotalea devanaterra

(Gubry-Rangin et al., 2011; Lehtovirta-Morley et al., 2011). Conditions of low NH4+

supply may allow AOA to dominate over AOB, as AOA have greater substrate affinity

for NH4+ than AOB, and are more sensitive than AOB to inhibition by high

concentrations of NH4+ (Di et al., 2010; Martens-Habbena et al., 2009). This may be


88

important in agricultural soils to which NH4+-based fertilisers are applied, selecting for

AOB populations to the detriment of AOA (Jia and Conrad, 2009; Pratscher et al.,

2011). However, some AOA seem to be tolerant to high NH4+ concentrations

(Verhamme et al., 2011), and the source of NH4+ may be important: ammonia oxidation

by archaea has been associated with mineralisation of organic N but not addition of

inorganic N sources (Levičnik-Höfferle et al., 2012; Stopnišek et al., 2010). The

metabolism of AOA is still in the early stages of investigation, but it has been suggested

that both AOB and AOA may have the ability to grow mixotrophically or

heterotrophically [i.e. are able to use organic sources of carbon (C)] (Sayavedra-Soto

and Arp, 2011; Schmidt, 2009; Walker et al., 2010). This ability may provide the

organism with a competitive advantage over obligate autotrophic ammonia oxidisers for

C assimilation or energy production under certain soil conditions such as increased C

availability.

Factors that regulate differentiation in populations between niches and sites are often

different from factors that regulate temporal fluctuations of populations at one site

(Wardle, 1998). Factors that have been proposed as regulating temporal ammonia

oxidiser gene abundance include climate and seasonal differences (e.g. temperature and

rainfall), soil organic matter (OM), and changes in NH4+ availability due to N fertiliser

additions and periods of soil OM mineralisation (Adair and Schwartz, 2008; Sher et al.,

2013; Taylor et al., 2012). Taylor et al. (2012) speculated that increases of AOA amoA

abundance (but not AOB) in spring of a fallow Oregon agricultural soil was due to their

utilisation of another energy-generating metabolism besides ammonia oxidation (i.e.

heterotrophy or mixotrophy), and that increased AOB amoA gene abundance in cropped

soil at the same site was in response to N fertiliser increasing NH4+ availability. Low

NH4+ availability due to low soil water content causing low mineralisation rates in


89

summer was hypothesised as the cause of greater AOA abundance than AOB in a semi-

arid soil in Israel, while AOB may have also been more limited by elevated temperature

than AOA: in winter AOB shifted to greater dominance than AOA in the same soil

(Sher et al., 2013). However, there appears to be no consistent effect of season on

abundance of AOB or AOA in semi-arid soils.

Drylands, including semi-arid and arid regions, are important for agriculture,

comprising one third of the world’s agriculturally productive lands, and supporting

nearly half of the global population (Harrison and Pearce, 2000; Reynolds et al., 2007).

Semi-arid agricultural soils in the grain-growing region of south-western Australia are

generally acidic, sandy, low in soil OM content, and are annually exposed to soil

temperatures greater than 40 °C during summer (Barton et al., 2013b; McArthur, 2004).

Soil NH4+ concentrations are usually less than 5 µg N g-1 dry soil except for short

periods when NH4+-based fertilisers are applied to agricultural soil during the winter

growing season (Barton et al., 2008; Barton et al., 2013b; Cookson et al., 2006b).

Archaeal ammonia oxidisers would be expected to have greater abundance than AOB in

these soils, due to their better resistance to elevated temperatures, low pH and low NH4+

availability. Studies of surface soils in this region however have shown that AOB are

similar to or more abundant than AOA (Appendix A; Gleeson et al., 2010; O’Sullivan et

al., 2013). As these studies sampled soil at one point in time, it is not yet clear whether

this dominance of AOB occurs across all seasons, and how AOB and AOA populations

vary dynamically with time in response to soil and environmental conditions.

The overall objective of this study therefore was to improve understanding of temporal

population dynamics of ammonia oxidisers so as to better understand N loss

mechanisms in semi-arid environments dominated by winter rainfall. Specifically: (i)


90

how soil OM content affects ammonia oxidiser abundance; (ii) if increased soil OM

modifies seasonal variation in ammonia oxidiser abundance; and (iii) which soil,

environmental and biochemical factors regulate the abundances of AOB and AOA were

investigated during a two year field-based study. We hypothesised that AOB will

dominate over AOA in the surface soil throughout the year, and that increased soil OM

will increase ammonia oxidiser abundance. We also hypothesised that ammonia

oxidiser abundance will be related to rainfall, soil water content, temperature and NO3-

concentrations but not to NH4+ concentrations.

4.3. Methods

4.3.1. Study site and soil

Temporal variation of ammonia-oxidising populations was examined at a field research

site with arable management treatments that aims to alter soil OM concentrations

without the confounding effects of climate and soil type. The study site is located in the

northern grainbelt of Western Australia at Buntine (30.00° S, 116.33° E), with a

Mediterranean-type semi-arid climate of cool, wet winters and hot, dry summers. Mean

annual rainfall is 285 mm and mean monthly temperatures range from 5.8–35.3 °C

[1997–2014 data, from a weather station closest to the study site at Dalwallinu (30.28°

S, 116.67° E); Commonwealth of Australia Bureau of Meteorology,

http://www.bom.gov.au/climate/data]. Rainfall and soil temperature data was collected

at the site (Fig. 4.1a) using a Tipping Bucket Raingauge Model TB4 (Hydrological

Services, Liverpool, NSW, Australia) and CS Model 107 Temperature Probes

(Campbell Scientific, Logan, Utah, USA). The soil is a sand (92% sand, 2% silt, 6%

clay), classified as a Basic Regolithic Yellow-Orthic Tenosol (Australian Soil


91

Classification; Isbell, 2002) or a Haplic Arenosol (FAO World Reference Base for Soil

Resources; IUSS Working Group WRB, 2007).

Figure 4.1. (a) Daily rainfall (bar graph, left y-axis) and daily soil minimum and

maximum temperature at 5 cm depth (line graph, right y-axis) measured at the

study site. (b) Soil water content at time of sample collection. Dashed arrows

indicate date of seeding (wheat, Triticum aestivum) and solid arrows indicate date of

harvest. Error bars are ±SEM (n = 3). Legend abbreviations: OM: organic matter; RD:

Run-Down.

Tabl

e 4.

1. P

rope

rtie

s of f

ield

org

anic

mat

ter

trea

tmen

ts (0

–10

cm d

epth

) at s

tart

of p

rese

nt st

udy,

seve

n ye

ars a

fter

trea

tmen

ts w

ere

impo

sed.

Val

ues a

re ±

SEM

(n =

3).

Org

anic

mat

ter t

reat

men

ts w

ith th

e sa

me

lette

r are

not

sign

ifica

ntly

diff

eren

t (P>

0.05

). A

bbre

viat

ion:

OM

: org

anic

mat

ter.

N

o T

ill

No

Till

B

urnt

Stu

bble

T

illed

T

illed

+OM

T

illed

+OM

R

un-D

own

Bulk

den

sity

(g c

m-3

) #

1.5

8 ±

0.04

ab

1.6

0 ±

0.01

ab

1.63

± 0

.03b

1.42

± 0

.05a

1.40

± 0

.08a

pH (C

aCl 2)

§

6.1

± 0.

1a 6.

2 ±

0.1a

6.2

± 0.

2a 6.

2 ±

0.2a

6.3

± 0.

0a

EC (d

S m

-1) ‡

0

.09

± 0.

008a

0.1

0 ±

0.00

4a 0

.08

± 0.

004a

0.1

7 ±

0.01

8b 0

.17

± 0.

013b

Tota

l car

bon

(%) †

0.

94 ±

0.0

2a 1

.05

± 0.

03ab

0.

91 ±

0.0

2a 1

.22

± 0.

15ab

1.

38 ±

0.0

7b

Tota

l nitr

ogen

(%) †

0

.09

± 0.

001a

0.

10 ±

0.0

03ac

0

.09

± 0.

002a

0.

12 ±

0.0

10bc

0

.13

± 0.

004b

C:N

ratio

11

.0 ±

0.3

0a 11

.0 ±

0.0

6a 10

.5 ±

0.0

5a 10

.1 ±

0.3

9a 10

.9 ±

0.2

6a

# Bu

lk d

ensit

y de

term

ined

usin

g th

e in

tact

cor

e m

etho

d w

ith 3

cor

es o

f 7.3

5 cm

dia

met

er b

y 10

cm

dep

th (C

ress

wel

l and

Ham

ilton

, 200

2).

§ pH

was

det

erm

ined

on

air-

dry

soil

in 0

.01M

CaC

l 2 w

ith a

1:5

soil:

extra

ct ra

tio, a

fter s

haki

ng fo

r 1 h

, and

whi

le st

irrin

g th

e so

il su

spen

sion

(Ray

men

t and

Lyo

ns, 2

011)

.

‡ El

ectri

cal c

ondu

ctiv

ity w

as d

eter

min

ed o

n ai

r-dr

y so

il in

wat

er w

ith a

1:5

soil:

wat

er ra

tio (R

aym

ent a

nd L

yons

, 201

1).

† To

tal c

arbo

n an

d ni

troge

n w

ere

dete

rmin

ed b

y hi

gh-te

mpe

ratu

re c

ombu

stio

n of

fin

ely

grou

nd a

ir-dr

y so

il us

ing

an E

lem

enta

r V

ario

MA

CRO

CN

S el

emen

tal a

naly

ser

(Han

au,

Ger

man

y; R

aym

ent a

nd L

yons

, 201

1).


93

4.3.2. Experimental design and soil collection

The site had a three-year rotation of lupin (Lupinus angustifolius) – wheat (Triticum

aestivum) – wheat since 2003, with one rainfed crop each winter. A randomised block

design was employed, with three blocks of five OM field treatment plots (80 m by 10

m). The five OM treatments were: (i) no tillage with full stubble retention, seeded with

knife point tines to 15 cm depth (No Till); (ii) no tillage with burnt stubble (Burnt); (iii)

soil tilled with offset disks to 10 cm depth prior to seeding (Tilled); (iv) tilled soil

loaded with additional OM (Tilled+OM); and (v) Tilled+OM Run-Down, where

additional OM was applied between 2003–2006 and then ceased. Organic matter was

applied at 20 t ha-1 to both Tilled+OM and Tilled+OM Run-Down plots in 2003 (barley

straw) and 2006 (canola chaff), and to Tilled+OM plots in 2010 (oat chaff). Organic

matter was applied after each lupin crop, before seeding of wheat. At the time of the

present study, seven years after commencement of the trial, soil organic carbon (SOC)

contents ranged from 0.91% in the Tilled soil to 1.38% in the Tilled+OM Run-Down

soil, while total N contents ranged from 0.09% in the Tilled soil to 0.13% in the

Tilled+OM Run-Down soil (Table 4.1).

Two crops of winter wheat were grown and harvested during the present study. The site

was fertilised at rates depending on expected growing season rainfall and projected

potential yields (1.5–2.5 t ha-1 in 2010, 3–4 t ha-1 in 2011), as is standard practice in this

region. In 2010, 60 kg ha-1 granular fertiliser (NPKS – 10.2:12.0:11.2:6.0; N as

ammonium sulphate and monoammonium phosphate) was applied at seeding (28th

May), and 40 L ha-1 of liquid fertiliser (NPKS – 32.0:0:0:0; N as urea and ammonium

nitrate) was applied at crop emergence (12th July), for a total of 23.0 kg N ha-1 y-1. In

2011, 60 kg ha-1 solid and 20 L ha-1 liquid fertiliser were applied at seeding (1st June;

NPKS and N form as above), and 50 L ha-1 liquid fertiliser was applied at emergence


94

(26th July) for a total of 35.7 kg N ha-1 y-1. Harvest in 2010 was on 16th November, and

in 2011 was on 14th November.

Soil was collected (0–10 cm depth) on ten occasions from each plot between May 2010

and November 2011. Within each plot, 25 cores (5.3 cm diameter x 10 cm depth) were

sampled using a zigzag pattern at least 1 m from the plot boundary. Subsamples of soil

from each plot were frozen for DNA analysis of AOA and AOB on return from the

field. Remaining soil was sieved to <2 mm and stored field-moist at 4 °C until further

analysis. Soils were analysed for gravimetric soil water content and inorganic N at field

moisture content. Soils collected during the period when the soil is field dry (i.e. on 19 th

Nov 2010, 1st Dec 2010 and 2nd May 2011) were first wet-up to 45% water holding

capacity, then pre-incubated for 7 d at 25 °C before analysis for microbial biomass C

(MBC), dissolved organic C (DOC) and potentially mineralisable N (PMN). On the

other sampling dates there was no need to first wet-up the soil.

4.3.3. Microbial biomass C and dissolved organic C

Microbial biomass C was measured using the chloroform fumigation–extraction method

(Brookes et al., 1985). Fresh soil (10 g) was fumigated with chloroform (containing low

levels of the stabilising agent amylene) under vacuum in the dark for 24 h at 25 °C.

Fumigated samples, with replicate non-fumigated samples, were then extracted by

shaking for 1 hr with 40 mL of 0.5 M potassium sulphate (K2SO4). Filtered extracts

(Whatman No. 42) were frozen until further analysis. Fumigated and non-fumigated

extracts were analysed using an OI Analytical Aurora 1030 Wet Oxidation TOC

Analyser (College Station, TX, USA) for non-purgeable organic C. Dissolved organic C

values were determined using the results from non-fumigated extracts. Microbial


95

biomass C was calculated from the difference between fumigated and non-fumigated

organic C, divided by a kEC factor of 0.45 (Wu et al., 1990).

4.3.4. Inorganic N analysis and potentially mineralisable N

Inorganic N was extracted from 20 g soil by shaking for 1 h with 80 mL of 0.5 M

K2SO4. Filtered extract solutions (Whatman No. 42) were kept frozen until colorimetric

analysis on a Skalar San Plus auto-analyser (Breda, The Netherlands), using the

modified Berthelot reaction for NH4+ (Krom, 1980) and the hydrazinium reduction

method for nitrate (NO3-; Kamphake et al., 1967).

Potentially mineralisable N was determined by anaerobic incubation (Keeney and

Bremner, 1966; Waring and Bremner, 1964). Fresh soil (20 g) was incubated in 80 mL

of deionised water for 7 d at 40 °C. Potassium sulphate was added (6.97 g) to adjust the

soil solution to 0.5 M K2SO4, then samples were shaken for 1 h. Replicate 20 g samples

of non-incubated soil in 80 mL of 0.5 M K2SO4 were also shaken. Incubated and non-

incubated samples were filtered (Whatman No. 42) then frozen until further analysis.

Samples were analysed for NH4+ on an autoanalyser, as described above.

4.3.5. Nucleic acid extraction and qPCR

DNA was extracted from 700 mg subsamples of field-moist soil (Griffiths et al., 2000)

and stored at -40 °C until further analysis. The functional genes encoding bacterial and

archaeal ammonia monooxygenase (amoA) were determined by quantitative real-time

polymerase chain reaction (qPCR). Bacterial amoA primers were amoA-1F, with primer

sequence of 5ʹ-GGGGTTTCTACTGGTGGT-3ʹ, and amoA-2R, with primer sequence

of 5ʹ-CCCCTCKGSAAAGCCTTCTTC-3ʹ (K = G or T, S = G or C), amplifying a

fragment length of 491 bp (Rotthauwe et al., 1997). Bacterial amoA gene abundance


96

was quantified using an ABI 7500 Fast qPCR machine (Applied Biosystems, Carlsbad,

CA, USA). Each 20 µL qPCR reaction contained 10 µL of Power SYBR® Green PCR

Master Mix (Applied Biosystems, Warrington, UK), 0.2 µL each of the specific forward

and reverse primers at 10 µM, 2 µL of bovine serum albumin at 5 mg mL-1 (Ambion®

UltraPure BSA, Carlsbad, CA, USA), 2 µL of template DNA and 5.6 µL of water.

Thermocycling conditions were: 95 °C for 10 min; then 40 cycles of 94 °C for 60 s, 56

°C for 60 s, 72 °C for 60 s and 78 °C for 60 s; followed by a melt curve. Fluorescence

data was collected at the 78 °C stage.

Archaeal amoA primers were Arch-amoAF (5ʹ-STAATGGTCTGGCTTAGACG-3ʹ)

and Arch-amoAR (5ʹ-GCGGCCATCCATCTGTATGT-3ʹ) amplifying a fragment

length of 635 bp (Francis et al., 2005). Archaeal amoA gene abundance was quantified

using an Applied Biosystems ViiA™ 7 (Carlsbad, CA, USA). Each 10 µL qPCR

reaction contained 5 µL of Power SYBR® Green PCR Master Mix (Applied

Biosystems, Warrington, UK), 0.1 µL each of the specific forward and reverse primers

at 10 µM, 1 µL of BSA, 1 µL of template DNA and 2.8 µL of water. Thermocycling

conditions were: 94°C for 10 min; then 40 cycles of 94°C for 1 min, 52°C for 1 min,

72°C for 1 min and 78°C for 1 min; followed by a melt curve. Fluorescence data was

collected at the 78°C stage.

Standard templates used to determine gene copy numbers in the qPCR reactions were

cloned plasmids, as described in Barton et al. (2013a). Samples were tested over a series

of dilutions to determine if there was inhibition, and further analysis was completed

using the dilution that produced the highest copy number. Samples were replicated

three times in each qPCR run. Standard curves generated in each reaction were linear

over four orders of magnitude for AOB (103–106 gene copies) and six orders of


97

magnitude for AOA (103–108 gene copies) with r2 values greater than 0.98.

Amplification efficiencies ranged from 79–111%.

4.3.6. Statistical analysis

Statistical differences between OM treatments for the basic soil properties were

determined using analysis of variance (ANOVA) with associated TukeyHSD post hoc

tests in R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria).

Bacterial amoA gene abundance was initially log transformed before all statistical

analyses. Statistical significances of OM treatment with time for each soil property were

evaluated using PROC MIXED in SAS version 9.3 (SAS Institute Inc., Cary, NC,

USA), as replication was at the highest level (i.e. field plot). For this analysis, NH4+,

PMN, MBC and DOC were also log transformed, while total inorganic N, NO3- and

AOB abundance were square root transformed to ensure homoscedasticity.

Backwards multiple linear regression was carried out also using PROC MIXED, for

response of logged AOB abundance to: cumulative rainfall and mean daily maximum

and minimum soil temperatures at 5 cm depth in the 30 days prior to collection;

gravimetric soil water content at collection; logged NH4+, PMN, MBC and DOC values;

and square-root total inorganic N and NO3- values. Random effects of field block and

field block interacting with OM treatment were included in the model. The best

regression model was evaluated by minimising the Akaike Information Criteria

(Akaike, 1981).

Principal component analysis (PCA) was carried out using prcomp in R version 3.1.0

with scaled and centred data of logged AOB abundance, cumulative rainfall and mean

daily minimum and maximum soil temperature at 5 cm depth in the 30 days prior to soil


98

collection, soil water content at collection, MBC, DOC, total inorganic N, NH4+, NO3

-

and PMN.

4.4. Results

4.4.1. Environmental conditions

Daily rainfall over the study period ranged from 0–25.2 mm, while daily soil

temperature at 5 cm depth ranged from 6.6–42.7 °C (Fig. 4.1a). Growing season rainfall

(April–October) was greater in 2011 (283 mm) than in 2010 (144 mm). Daily minimum

soil temperature at 5 cm depth ranged from 6.6 °C in July 2011 to 30.4 °C in January

2011 (Fig. 4.1a). Daily maximum soil temperature at 5 cm depth ranged from 9.2 °C in

August 2010 to 41.7 °C in January 2011 (Fig. 4.1a).

Soil water content at time of soil collection followed a similar pattern to rainfall,

increasing during the winter growing season (May–November) and at a minimum

during summer (mean <0.01 g g-1; October 2010–May 2011; Fig. 4.1b). Mean

maximum soil water content at sampling was greater in 2011 (0.09 g g-1 in August) than

in 2010 (0.05 g g-1 in July–August; P<0.0001). Tilled+OM soil had greater soil water

content than the other OM treatments on two occasions in winter (July 2010, August

2011; P<0.01), and greater soil water content than the No Till, Burnt and Tilled soils on

one occasion in late spring (November 2011; P<0.05; Fig. 4.1b).

4.4.2. Microbial biomass C and dissolved organic C

Mean MBC ranged from 46–489 µg C g-1 (Fig. 4.2a). Microbial biomass C followed

similar patterns with time in all OM treatments, but the Tilled+OM soil had

significantly greater MBC than the other OM treatments (P<0.05), except in June 2011


99

(winter). Microbial biomass C was at a maximum during the winter in 2010 and at a

minimum in summer (i.e. December 2010).

Figure 4.2. Change in (a) microbial biomass carbon; and (b) dissolved organic

carbon through time. Error bars are ±SEM (n = 3). Legend is the same for both panels.

Legend abbreviations: OM: organic matter; RD: Run-Down.

Mean DOC ranged from 60–217 µg C g-1 (Fig. 4.2b). Dissolved organic C in No Till,

Burnt and Tilled soils were not different (P>0.05). Dissolved organic C in Tilled+OM

soil however was greater than No Till, Burnt and Tilled soils at all sampling times

(P<0.01), and was greater than Tilled+OM Run-Down from July–December in 2010

(mid-winter to mid-summer), and from September–December in 2011 (spring to mid-


100

summer; P<0.01). Dissolved organic C was at a maximum in December 2010 (summer)

and at a minimum in August 2011 (winter, mid-growing season).

4.4.3. Inorganic N and potentially mineralisable N

Mean NH4+ concentrations ranged from <1–13 µg N g-1 (Fig. 4.3b), while mean NO3

-

concentrations were greater than NH4+, and ranged from <1–44 µg N g-1 (Fig. 4.3c).

Mean NH4+ in No Till, Burnt and Tilled soils peaked in winter (July 2010 and 2011),

coinciding with N fertiliser inputs. Surprisingly, NH4+ concentrations in the Tilled+OM

soil did respond to N fertiliser inputs in July 2011. However, NH4+ concentrations in

Tilled+OM soil were greater than the other OM treatments during summer (October

2010–May 2011; P<0.05). Nitrate increased over summer (October 2010–May 2011;

P<0.0001) in response to a series of rainfall events, peaking at the start of each growing

season, and then declining to a minimum by the end of winter (around September;

P<0.0001). Mean NO3- concentrations over time in Tilled+OM soil and Tilled+OM Run

Down were greater than the other OM treatments (P<0.001), and patterns in NO3- over

time were the same irrespective of treatment. Total inorganic N (NH4+ + NO3

-) ranged

from means of 2–48 µg N g-1, and was dominated by NO3- (mean of 69 %; Fig. 4.3a).

Mean PMN ranged from 10–98 µg N g-1 (Fig. 4.3d). Potentially mineralisable N in

Tilled+OM soil peaked in August 2010 (late winter), approximately three months after

OM additions to this treatment, and remained greater than the other OM treatments until

December 2010 (summer; P<0.05). Mean PMN in No Till, Burnt, Tilled and

Tilled+OM Run-Down soils did not change from October 2010–November 2011

(P>0.05).


101

Figure 4.3. Change in (a) total inorganic nitrogen; (b) NH4+-N; (c) NO3

--N; and (d)

potentially mineralisable nitrogen through time. Arrows indicate dates of N fertiliser

application. Error bars are ±SEM (n = 3). Legend is the same for all panels. Legend

abbreviations: OM: organic matter; RD: Run-Down. Note difference in y-axis scale

between panels.


102

Figure 4.4. Change in bacterial amoA gene abundance (AOB) through time. Error

bars are ±SEM (n = 3). Note log scale of y-axis. Legend abbreviations: OM: organic

matter; RD: Run-Down.

4.4.4. Ammonia oxidiser gene abundance

Archaeal amoA gene abundance was below detection limits for 96% of samples (144

out of 150 samples; data not shown). Detected archaeal amoA gene abundances ranged

from 1.16 x 105–3.25 x 107 gene copies g-1 dry soil.

Bacterial amoA gene abundance ranged from 2.35 x 107–1.11 x 109 gene copies g-1 dry

soil (Fig. 4.4), and was detected in all samples. Bacterial amoA gene abundance

generally decreased from the middle to the end of each growing season and was at a

minimum in spring to early summer. Bacterial amoA gene abundance increased over

summer to a maximum at the start of the growing season (May 2011). Tilled+OM soil

had greater bacterial amoA gene abundance than the other OM treatments (P<0.05), and

Tilled+OM Run-Down had greater bacterial amoA gene abundance than Burnt and

Tilled soils (P<0.05), but was not different from No Till soil (P>0.05). Patterns in

bacterial amoA gene abundance due to OM treatments were independent of sampling

date (OM treatment by sampling date interaction P>0.05).


103

4.4.5. Relationships between bacterial amoA gene abundance and other variables

Linear regressions between bacterial amoA gene abundance and each variable

separately provided models with the lowest Akaike Information Criteria. Bacterial

amoA gene abundance had significant positive relationships with NO3-, MBC and DOC

(P<0.01), but no relationship with cumulative rainfall, mean daily maximum or

minimum soil temperature in the 30 days prior to sampling, soil water content at

collection, NH4+ or PMN (P>0.05; Table 4.2; Fig. 4.5). The potential relationship

between AOB and each significantly correlated variable was calculated by multiplying

the coefficient by the maximum value measured for that variable. Variables with linear

regression coefficients in order of greatest to least correlation to AOB were DOC,

MBC, total inorganic N and NO3-. Maximum measured DOC (216.7 µg C g-1) however

would only be related to an increase in bacterial amoA gene abundance of 538 ± 8 gene

copies g-1, while maximum measured NO3- (43.8 µg N g-1) would be related to an

increase in bacterial amoA gene abundance of 28 ± 2 gene copies g-1.

Principal component analysis required six principal components to explain greater than

90% of the variance in soil biochemical and environmental properties (Table 4.3).

Principal component 1 (PC1) explained 36.4% of total variance in the data (Table 4.3),

and separated most strongly cumulative 30-day rainfall and soil water content at

collection (positive loadings) from 30-day mean minimum and maximum soil

temperatures (most strongly negative loadings; Fig. 4.6; Table 4.4). Otherwise, PC1

separated MBC, NH4+, NO3

-, PMN and logged AOB gene abundance (with loadings

near zero) from DOC, which had a slightly negative loading. Principal component 2

(PC2) explained 22.3% of total variance in the data (Table 4.3), and separated 30-day

mean minimum and maximum soil temperatures, cumulative 30-day rainfall and soil

water content at collection (positive or near zero loadings) from MBC, DOC, NO3-,


104

PMN and logged AOB gene abundance (most strongly negative loadings; Fig. 4.6;

Table 4.4).

Samples from the same sample collection date were grouped across the biplot of PC1

and PC2, roughly in line with PC1 as rainfall, soil water content and soil temperature

varied (Fig. 4.6). Samples from the Tilled+OM soil, and Tilled+OM Run-Down to a

lesser extent, had greater scatter across the biplot in line with PC2, compared to the No

Till, Burnt and Tilled soils, which were grouped towards positive scores of PC2.

Table 4.2. Linear regression results for response of logged bacterial amoA gene

abundance to each soil and environmental variable separately. Regression

coefficients are only reported for significant relationships. Abbreviations: sqrt: square

root transformed; log: log transformed; DOC: dissolved organic carbon; MBC:

microbial biomass carbon; Rainfall: cumulative rainfall of 30 days prior to sampling;

Max. Temp: mean daily maximum soil temperature at 5 cm depth during 30 days prior

to sampling; Min Temp: mean daily minimum soil temperature at 5 cm depth during 30

days prior to sampling; Soil Water: soil water content at time of collection; PMN:

potentially mineralisable nitrogen.

Predictor

Significance

level Coefficient

Standard

error of

coefficient Intercept

Standard

error of

intercept

log DOC P=0.0029 1.1690 0.3858 5.8534 0.7836

log MBC P=0.0012 0.7440 0.2256 6.6650 0.4751

sqrt NO3- P<0.0001 0.2180 0.0266 7.5275 0.0959

Rainfall P=0.9690

Max. Temp. P=0.5436

Min. Temp. P=0.8774

Soil Water P=0.1417

log NH4+ P=0.3633

log PMN P=0.7610


105

Figure 4.5. Significant linear regression relationships between logged bacterial

amoA gene abundance (logAOB) and (a) logged dissolved organic carbon

(logDOC); (b) logged microbial biomass carbon (logMBC); and (c) square root

transformed nitrate concentration (sqrtNO3-).


106

Figure 4.6. Principal component analysis biplot of principal components 1 (PC1)

and 2 (PC2). Abbreviations: logAOB: logged bacteria amoA gene abundance; Rf:

rainfall; Tmax and Tmin: mean daily maximum and minimum soil temperature at 5 cm

depth respectively, Wa: soil water content at collection, MBC: microbial biomass

carbon, DOC: dissolved organic carbon, NH4: ammonium, NO3: nitrate, PMN:

potentially mineralisable nitrogen, OM: organic matter; RD: Run-Down. Symbol

colours represent field organic matter treatments and symbol shapes represent sampling

date.

Tabl

e 4.

3. E

igen

vect

or lo

adin

gs o

f pri

ncip

al c

ompo

nent

s 1–6

.

PC

1 PC

2 PC

3 PC

4 PC

5 PC

6

Stan

dard

dev

iatio

n 1.

9086

1.

4933

1.

1831

0.

9747

0.

8023

0.

6193

Var

ianc

e (e

igen

valu

e)

3.64

28

2.22

98

1.39

98

0.95

00

0.64

37

0.44

73

Prop

ortio

n of

var

ianc

e 0.

3643

0.

2230

0.

1400

0.

0950

0.

0644

0.

0447

Cum

ulat

ive

prop

ortio

n of

var

ianc

e 0.

3643

0.

5873

0.

7272

0.

8223

0.

8866

0.

9313


108

Table 4.4. Loadings matrix (eigenvectors) for principal components 1–6.

Abbreviations: Rainfall: cumulative rainfall of 30 days prior to soil collection; Water:

soil water content at collection; Min. Temp. and Max. Temp: mean daily minimum and

maximum soil temperature at 5 cm depth of 30 days prior to soil collection; MBC:

microbial biomass carbon; DOC: dissolved organic carbon; PMN: potentially

mineralisable nitrogen; log AOB: logged bacterial amoA gene abundance. PC1 PC2 PC3 PC4 PC5 PC6

Rainfall 0.4113 0.0203 0.1119 -0.3201 -0.3437 -0.2311

Water 0.4882 -0.0813 0.0337 0.0731 -0.0796 -0.2072

Min. Temp. -0.4778 0.1972 -0.0120 -0.0306 -0.2070 -0.0803

Max. Temp. -0.4709 0.2078 0.0693 -0.0335 -0.1947 -0.0429

MBC -0.0409 -0.4864 0.3002 -0.0846 0.5695 0.2874

DOC -0.3398 -0.4062 0.2305 0.0899 -0.0609 -0.2257

NH4+ 0.0529 -0.2250 -0.4530 0.7184 -0.2123 0.2411

NO3- -0.1359 -0.4051 -0.4754 -0.1341 0.1372 -0.6772

PMN 0.0025 -0.4055 0.5129 0.1785 -0.5219 -0.0455

log AOB -0.0647 -0.3607 -0.3792 -0.5559 -0.3611 0.4929

4.5. Discussion

Ammonia-oxidising bacteria rather than AOA appear to regulate nitrification processes

in the surface layer of this semi-arid agricultural soil across both the hot and dry

summer fallow and the cool and wet winter cropped seasons. This agrees with other

studies from this region (Appendix A; Gleeson et al., 2010; O’Sullivan et al., 2013) but

is in contrast to other semi-arid environments and agricultural soils, which have found

that AOA are often dominant to AOB (Adair and Schwartz, 2008; Leininger et al.,

2006; O’Sullivan et al., 2013; Zhang et al., 2012). Here, we speculate that AOA were

not detectable in the topsoil as a consequence of soil properties that have been modified

by agricultural practices, particularly NH4+ availability. Archaeal ammonia oxidisers

were present in the subsoil of the same soil as the present study (mean maximum 2.1 x

105 gene copies g-1 dry soil in 60–90 cm layer; Appendix A), and also in the surface


109

layer of the adjacent native bushland (2.60 x 104 gene copies g-1 dry soil using the same

archaeal amoA gene primers; L. Maccarone, pers. comm.). This agricultural soil has

annual additions of inorganic N fertiliser, which increases NH4+ availability in the

surface layers and may have inhibited AOA, as has been attributed in other soils (Di et

al., 2010; Di et al., 2009; Pratscher et al., 2011; Shen et al., 2011). Archaeal ammonia

oxidisers have greater substrate affinity for ammonia than AOB so may prefer low

ammonia environments, and AOA are more sensitive to high concentrations of

ammonia than AOB (Di et al., 2010; Martens-Habbena et al., 2009). Bacterial ammonia

oxidisers therefore may dominate in soils as in the present study that are regularly

fertilised (Prosser and Nicol, 2012). Other factors regulating AOA and AOB

abundances however cannot be ruled out. Collaborative work that examined ammonia

oxidiser gene abundances with depth in the same soil as the present study found AOB

abundance was positively and AOA abundance was negatively correlated with soil pH

and soil OM (Appendix A). Archaeal ammonia oxidisers tend to dominate over AOB in

soils with low (acidic) pH (He et al., 2012; Hu et al., 2013), but the surface soil

examined in the present study has a pH close to neutral (Table 4.1). Agricultural

modification of soil pH by additions of lime may therefore have combined with N

fertiliser and crop residue additions to increase soil pH and substrate availability,

creating conditions that have allowed AOB to compete more successfully in the surface

soil.

The poor relationship between bacterial amoA gene abundance and rainfall, soil

temperature, soil water content at collection was unexpected. Seasonal variation in AOB

abundance instead was related to DOC, MBC and NO3- concentrations, but as

hypothesised, not to NH4+ concentrations. Other authors have observed that climate

effects such as rainfall, soil water content and temperature are more important than


110

agricultural management effects on soil and microbial processes in semi-arid soils

(Hoyle and Murphy, 2011; Noy-Meir, 1973). A positive correlation has also been

reported between AOB and soil water content (adjusted R2 of multiple regression model

including OM and NO3- = 0.53; Sher et al., 2013). The lack of a relationship between

AOB abundance and environmental conditions in the present study might be explained

by the disconnect between longer-term population dynamics and shorter-term

environmental events. Although directly influenced by environmental conditions such

as soil water and temperature, abundances of functional genes are less dynamic than

many environmental and edaphic characteristics, so functional gene abundances likely

reflect the longer-term variation of those characteristics (Petersen et al., 2012).

Although there was no linear correlation between AOB and climate characteristics, the

PCA showed that variation in all the data was partly explained by rainfall, soil water

content and temperature and clearly grouped by sampling date (Fig. 4.6; PC1 in Table

4.4). The absence of a correlation between AOB abundance and NH4+ concentration

was expected because soil NH4+ concentrations are not a good indication of NH4

+

substrate availability or activity of ammonia oxidisers (Prosser and Nicol, 2012). Nitrate

dominates inorganic N pools in semi-arid soil from this region, indicating active

nitrification and rapid depletion of NH4+ pools (Fig. 4.3; Barton et al., 2008; Barton et

al., 2013b; Cookson et al., 2006b). Alternatively, heterotrophic nitrifiers may be

important in this soil, producing NO3- from organic N sources, which may also explain

the correlation between DOC and AOB abundance. Modelling of gross N

transformation rates in another Australian semi-arid soil suggested that greater than

50% of nitrification could be explained by heterotrophic nitrification (Cookson et al.,

2006b). From culture studies there is evidence for mixotrophic and heterotrophic

growth in AOB, including the ability to take up low molecular weight organic

compounds, the presence of genes encoding organotrophic metabolic pathways and


111

anaerobic growth on organic compounds (Sayavedra-Soto and Arp, 2011; Schmidt,

2009; Walker et al., 2010). These findings suggest that seasonal dynamics of AOB

populations are not yet well understood, but are due to many interacting factors that will

consequently influence risk of N loss through the production of NO3-.

Crop residue inputs may have positively but indirectly influenced bacterial amoA gene

abundance (irrespective of the time of year) due to the enhanced supply of either C or N

substrates. The effect of soil OM content on AOB in other semi-arid soils varies from

these observations. For example, bacterial amoA gene abundance in summer and winter

over six years in a semi-arid shrubland soil was negatively correlated with soil OM

(Sher et al., 2013). This negative correlation would be expected to occur if soil OM

stimulates heterotrophic microorganisms to compete more successfully for NH4+,

decreasing N substrate availability and thus growth of autotrophic ammonia oxidisers.

The positive effect of increased soil OM pools on AOB abundance in the present study

could be attributed to the associated increases in N supply to autotrophic ammonia

oxidisers, or greater DOC pools (and presumably dissolved organic N), which would

also directly stimulate heterotrophic ammonia oxidisers. Previous work on the same soil

showed additional crop residue inputs increased gross N mineralisation, immobilisation

as well as nitrification rates (Chapter 3), suggesting that increased soil OM stimulates

both heterotrophic and autotrophic microbial populations.

Bacterial ammonia oxidiser growth during summer fallow is likely an important factor

determining increases in soil NO3- pools and consequently risk of N loss in this semi-

arid soil. Soil NO3- pools are a balance of inputs and outputs. Nitrate inputs into the

surface soil (0–10cm) appear to continue as summer progresses (Fig. 4.3), as also

observed in similar semi-arid soils in response to summer rainfall events, which


112

stimulate N mineralisation (Barton et al., 2008; Barton et al., 2013b; Murphy et al.,

1998b). In contrast to inputs, outputs of NO3- in this region are likely to be low during

summer fallow, with plant uptake minimal or non-existent due to the absence of plants,

and it is not until the start of the growing season rains that any significant deep drainage

and NO3- leaching occurs (Anderson et al., 1998). Correlation of bacterial amoA gene

abundance and NO3- pool size suggests that increases in NO3

- during summer are not

due merely to N mineralisation flushes and lack of inorganic N uptake, but also due to

growth of the AOB population, strongly coupled to activity of nitrite-oxidising bacteria.

Bacterial amoA gene abundance was less in summer than in winter in another semi-arid

soil from Israel, which was attributed to the sensitivity of AOB to high temperatures, at

that site ranging from 23–35 °C in summer (Sher et al., 2013). By contrast, bacterial

amoA gene abundance in the present study continued to increase over summer, even

though soil temperatures exceeded 40 °C (Fig. 4.1a). This may be because AOB were

sampled more often, so finer scale population dynamics could be observed in this soil.

An alternative explanation is that AOB in the present study region have adapted to

periods of high temperature and low water availability, allowing them to survive then

continue to grow when conditions become favourable, even if only briefly due to

transient water pulses from summer rainfall events.

Predicted changes in climate for this region are likely to exacerbate the effects of AOB

population dynamics and NO3- production, particularly during summer fallow. Since the

1970s, mean annual rainfall in Southern Hemisphere semi-arid regions has been

decreasing, especially during autumn and winter at the start of the growing season (Cai

et al., 2012; Nicholls, 2010). Summer rainfall events however are increasing (Alexander

et al., 2007), which will likely enhance inorganic N supply through OM decomposition

when there is no plant N uptake from fallow soil (Austin et al., 2004; Murphy et al.,


113

1998b). This inorganic N is then at risk of loss if it is in excess of microbial demand and

is nitrified. With continuing climate change in this semi-arid region, it is likely to

become increasingly challenging to manage undesirable losses of N from soil.

4.6. Conclusions

Seasonal variation of bacterial amoA gene abundance in this soil is likely a factor

contributing to NO3- production and risk of N loss, especially as summer fallow

progresses. Archaea however are unlikely to be influential drivers of ammonia

oxidation as archaeal amoA gene abundances were predominantly below detection

limits in the surface layers, possibly due to N fertilisers enhancing soil N substrate

supply. Increased soil OM levels by additional crop residue inputs positively influenced

bacterial amoA gene abundances, but did not modify seasonal variation in ammonia

oxidiser abundance. As expected, bacterial amoA gene abundance was not related to

NH4+ concentration, but surprisingly was also not related to soil water content, 30-day

rainfall or mean soil temperature. Bacterial amoA gene abundance was related to DOC,

MBC, and NO3- suggesting that ammonia oxidiser populations are either regulated by

longer-term changes in climate and substrate supply, or that heterotrophic nitrification

may be important in this semi-arid soil.


114

115

Chapter 5.

Nitrapyrin decreased nitrification of nitrogen

released from soil organic matter but not amoA

gene abundance at high soil temperature

5.1. Abstract

Water pulses have a significant impact on nitrogen (N) cycling, making management of

N challenging in agricultural soils that are exposed to episodic rainfall. In hot, dry

environments, wetting of dry soil during summer fallow causes a rapid flush of organic

matter mineralisation and subsequent nitrification, which may lead to N loss via nitrous

oxide emission and nitrate leaching. Here we examined the potential for the nitrification

inhibitor nitrapyrin to decrease gross nitrification at elevated temperature in soils with

contrasting soil organic matter contents, and the consequent effects on ammonia

oxidiser populations. Soil was collected during summer fallow while dry (water content

0.01 g g-1 soil) from a research site with two management treatments (tilled soil and

tilled soil with long-term additional crop residues) by three field replicates. The field

dry soil (0–10 cm) was wet with or without nitrapyrin, and incubated (20 or 40 °C) at

either constant soil water content or allowed to dry (to simulate summer drying after a

rainfall event). Gross N transformation rates and inorganic N pools sizes were

determined on six occasions during the 14 day incubation. Bacterial and archaeal amoA

Ch. 5: Nitrapyrin at High Temperature

116

gene abundance was determined on days 0, 1, 7 and 14. Nitrapyrin increased

ammonium retention and decreased gross nitrification rates even with soil drying at 40

°C. Nitrification was likely driven by bacterial ammonia oxidisers, as the archaeal amoA

gene was below detection in the surface soil layer. Bacterial ammonia oxidiser gene

abundances were not affected by nitrapyrin, despite the decrease in nitrifier activity.

Increased soil organic matter from long-term additional crop residues diminished the

effectiveness of nitrapyrin. The present study highlights the potential for nitrapyrin to

decrease nitrification and the risk of N loss due to mineralisation of soil organic matter

under summer fallow conditions.


117

5.2. Introduction

Regions where water-limited soils occur at high temperatures include those with semi-

arid, arid and Mediterranean-type climates, which are widely used for agricultural

production. Most agricultural mitigation strategies for nitrogen (N) loss are targeted

towards increasing N fertiliser use efficiency, through for example, matching spatial and

temporal N supply to crop N demand during the growing season (Meisinger and

Delgado, 2002; Murphy et al., 2004). A large proportion of N losses in water-limited

soils however can be in response to biochemical processes that occur during the dry,

non-growing season (Anderson et al., 1998; Austin et al., 2004; Barton et al., 2008;

Mummey et al., 1997). Management of these losses are challenging, as they occur in

response to episodic rainfall events, rather than agricultural management practices.

After summer rainfall in semi-arid environments, microorganisms rapidly become

active, resulting in a flush of soil organic matter (OM) mineralisation that increases

inorganic N availability (Austin et al., 2004; Murphy et al., 1998b). Production of

inorganic N is particularly detrimental in fallow soil, as plant uptake is non-existent and

nitrate (NO3-) is at risk of loss by leaching during subsequent rainfall and drainage

events (Anderson et al., 1998; Arregui and Quemada, 2006). In addition, up to half of

annual emissions of the greenhouse gases nitric oxide (NO) and nitrous oxide (N2O) can

occur when hot, dry soil is wetted (Barton et al., 2008; Barton et al., 2013b; Galbally et

al., 2008).

One strategy that decreases the potential for N loss is the use of a nitrification inhibitor.

These chemicals control nitrification, the key pathway for N loss, often by binding or

otherwise deactivating one of the enzymes involved (Slangen and Kerkhoff, 1984).

Generally, the effectiveness of nitrification inhibitors decreases with increasing

temperature, due to increasing microbial degradation, stimulation of microbial activity


118

and loss of volatile chemicals (Slangen and Kerkhoff, 1984). Nitrapyrin (2-chloro-6-

(trichloromethyl)-pyridine) has been successfully used for many years to decrease

nitrification and N loss from applied fertiliser where N inputs are high (45–338 kg N ha-

1; Wolt, 2004), and has been effective at soil temperatures as high as 25–35 °C (Ali et

al., 2008; Chen et al., 2010). These studies have investigated the effectiveness of

nitrapyrin in the presence of N fertiliser, however it is not clear if nitrapyrin inhibits

nitrification due to N released from soil OM mineralisation.

The effectiveness of nitrapyrin at decreasing nitrification in soil depends on a number of

interacting factors besides soil temperature (Slangen and Kerkhoff, 1984). Soil OM both

absorbs nitrapyrin and provides an energy source for the microorganisms which degrade

nitrapyrin, decreasing the ability of nitrapyrin to inhibit nitrification (Goring, 1962;

Lewis and Stefanson, 1975). Semi-arid soils often have low OM contents due to the

seasonally dry climate, low plant productivity and continual soil loss by erosion

(Archibold, 1995; Ryan, 2011). We expected that nitrapyrin would more effectively

inhibit nitrification at elevated temperatures in a low OM soil compared to where

additional crop residue inputs have increased soil OM. There is a paucity of research

about the effect of soil wetting and drying events on the effectiveness of nitrapyrin,

however some research suggests that nitrification inhibitors can be more effective when

either low or high water content limits the activity of nitrifying organisms (Keeney,

1986). We therefore hypothesised that nitrapyrin would be more effective as soil dried

and nitrification activity decreased.

The mechanism by which nitrapyrin inhibits nitrification is thought to involve ammonia

monooxygenase (AMO), the main enzyme involved in ammonia oxidation (Vannelli

and Hooper, 1992). Nitrapyrin is a substrate for AMO, producing 6-chloropicolinic acid


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which then binds indiscriminately to other membrane proteins, the suggested

mechanism for inactivation of ammonia oxidation (Vannelli and Hooper, 1992).

Nitrapyrin does not inhibit hydroxylamine oxidation or nitrite oxidation (the steps in

nitrification following ammonia oxidation) except at extremely high concentrations

(80–175 ppm; Campbell and Aleem, 1965a, b). The subunit of AMO contains the

enzyme’s active site and is encoded by the amoA gene, which has homologous gene

sequences in both ammonia-oxidising bacteria (AOB) and archaea (AOA; Nicol and

Schleper, 2006). We therefore hypothesised that inhibition of AMO by nitrapyrin would

diminish the ability of ammonia-oxidising microorganisms to obtain energy and to

grow, thus decreasing amoA gene abundance. Few studies have examined the effect of

nitrapyrin on ammonia oxidiser populations, and findings have been contradictory (Cui

et al., 2013; Lehtovirta-Morley et al., 2013; Shen et al., 2013). Further research is

necessary to unravel the many interacting factors that control effectiveness of nitrapyrin

at inhibiting ammonia-oxidising microorganisms and nitrification.

Consequently, we examined the potential of the nitrification inhibitor nitrapyrin to

control nitrification at elevated soil temperature in response to a simulated rainfall

wetting and drying event. Specifically, we determined (i) whether nitrapyrin decreased

gross nitrification rates without altering other N transformation rates at 20 and 40 °C;

(ii) whether increased soil OM content diminishes the ability of nitrapyrin to inhibit

nitrification at elevated temperature; (iii) whether decreasing water content with time

(as occurs when soil dries after a summer rainfall event) increases the ability of

nitrapyrin to inhibit nitrification compared to when soil water content is optimal; and

(iv) if populations of AOA or AOB are consequently affected.


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5.3. Methods

5.3.1. Soil and soil collection

Soil was collected from the Liebe Group’s Soil Biology Trial (30.00° S, 116.33° E), in

the northern wheatbelt of Western Australia, approximately 221 km north-northeast of

Perth. This research site was established in 2003 with a three year lupin-wheat-wheat

rotation and a range of field management treatments to create a range of soil OM

contents. Each treatment has three field replicate plots that are 80 m long and 10 m

wide. Two treatments with contrasting OM contents were selected for the present study:

tilled soil (‘Tilled’), and tilled soil loaded with additional OM (‘Tilled+OM’). The

Tilled soil was tilled to 10–15 cm depth annually using offset discs before seeding, and

seeded with knife point tines to 15 cm depth. Tilled+OM soil had 20 t ha-1 barley,

canola, oat and oat chaff tilled into the soil in 2003, 2006, 2010 and 2012 respectively,

using the same tillage method described for the Tilled soil. This represented an

additional 36 t C ha-1, of which 7.0 t C ha-1 was retained as extra soil organic carbon

(SOC) in the Tilled+OM soil nine years after trial establishment (i.e. 64% more SOC in

Tilled+OM than in Tilled soil; Table 5.1).

The region has a semi-arid climate, with hot, dry summers and cool, wet winters (when

cropping occurs). Based on 15 years of climate data (1997–2014) the area has a mean

annual rainfall of 284.9 mm, mean monthly temperatures ranging from 5.8–35.3 °C and

actual temperatures ranging from -1.0–46.9 °C (Commonwealth of Australia Bureau of

Meteorology, http://www.bom.gov.au/climate/data). At the research site, soil

temperatures (5 cm depth) ranged from 6.2–45.6 °C (2008–2012). Soil at the site is a

deep sand (92% sand, 2% silt, 6% clay) and classified as a Basic Regolithic Yellow-

Orthic Tenosol (Australian soil classification; Isbell, 2002), or a Haplic Arenosol


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(World Reference Base for Soil Resources; IUSS Working Group WRB, 2007).

Selected soil chemical and biological properties are listed in Table 5.1.

Soil was sampled in late summer (27 March 2012), prior to the first rains of the

autumn/winter growing season. At this time, the soil was fallow and naturally air-dry

(field soil water content of 0.01 g g-1 soil). Rain had last fallen in mid-summer (3

February 2012), 52 days prior to soil sampling, and over those 52 days, daily maximum

soil temperatures at 5 cm depth ranged from 26–39 °C. A composite sample (40 cores,

each 7 cm diameter by 10 cm depth) were taken from each replicate field plot in a

zigzag sampling pattern. Samples were sieved (<2 mm) and stored without further

drying at room temperature until further analysis.

Table 5.1. Properties of field soils (0–10 cm depth), collected nine years after soil

organic matter (OM) treatments were imposed. Values are means ±SEM (n = 3). Tilled soil Tilled+OM soil

Soil pHCaCl2 # 6.15 ± 0.18 6.23 ± 0.13

Total carbon (%) § 0.78 ± 0.03 1.31 ± 0.07**

Total carbon (t ha-1) 10.83 ± 0.55 17.78 ± 1.10**

Total nitrogen (%) § 0.06 ± 0.00 0.10 ± 0.01**

Soil C:N ratio 12.9 ± 0.2 12.9 ± 0.2

Ammonium-N (µg g-1) 2.25 ± 0.45 1.76 ± 0.57

Nitrate-N (µg g-1) 20.47 ± 1.05 31.83 ± 2.49

Bacterial amoA (gene copies g-1) 2.08 x 107 ± 4.22 x 106 2.01 x 107 ± 6.65 x 106

Archaeal amoA (gene copies g-1) <1 x 103 <1 x 103

**Tilled+OM soil is significantly different from Tilled soil at P<0.01.

#Soil pH measured in 0.01 M CaCl2 with a 1:5 soil:extract ratio

§Total C, and total N determined by dry combustion of finely ground soil using an Elementar Vario

MACRO CNS elemental analyser (Hanau, Germany).

5.3.2. Laboratory experimental design

The laboratory experimental design was two nitrification inhibitor treatments (with or

without nitrapyrin), two incubation temperatures (20 and 40 °C), two soils of differing


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OM contents (Tilled and Tilled+OM soil) by three field replicates, and two soil water

regimes (OWC and DRY; explained below). The nitrification inhibitor used was

nitrapyrin (2-chloro-6-(trichloromethyl)-pyridine) at 9 µg active ingredient g-1 dry soil.

The soil water regimes were: simulated rainfall event to optimum soil water content that

was held at optimum over the course of the experiment (OWC); and a simulated rainfall

event to optimum soil water content with subsequent drying (DRY). The optimal soil

water content chosen was 45% water-filled pore space (WFPS), as (i) this is the WFPS

that occurs following a common summer rainfall event for this region of 15 mm; (ii)

measured field soil water content in this region rarely exceeds 45% WFPS (Barton et

al., 2011; Barton et al., 2008; Barton et al., 2013b); and (iii) because at this WFPS

neither mineralisation nor nitrification are constrained in this soil type (Gleeson et al.,

2010).

5.3.3. Gross N transformation rates and inorganic N analysis

15N isotopic pool dilution was used to calculate gross N cycling transformation rates.

Paired treatments of 15N were either 15N enriched (60 atom%) ammonium sulphate

[(NH4)2SO4] + potassium nitrate (KNO3) at natural abundance, or (NH4)2SO4 at natural

abundance + 15N enriched (60 atom%) KNO3. Both (NH4)2SO4 and KNO3 were applied

at 5 µg N g-1. In total, four solutions were prepared for application to the samples:

(15NH4)2SO4 + KNO3; (NH4)2SO4 + K15NO3; (15NH4)2SO4 + KNO3 + nitrapyrin; and

(NH4)2SO4 + K15NO3 + nitrapyrin. Each of the four 15N solutions were then added to

separate subsamples of soil, which were mixed well and then packed into 120 mL vials

to 10 cm depth and to the bulk density at the research site (1.4 g cm-3 bulk density).

Optimal water content vials were sealed inside 500 mL glass jars with 5 mL of water at

the bottom, to minimise evaporation. These jars were aerated every 24 hours to prevent


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anaerobic conditions developing. DRY vials were incubated without lids. Samples were

incubated at either 20 or 40 °C for up to 14 days.

Soils were extracted on six occasions during the incubation: 2–4 hours after 15N

addition, and at 1, 3, 7, 10 and 14 days. At each of these sampling times, soil was mixed

and then a subsample (ca. 20 g) was snap-frozen in liquid N, and then stored at -80 °C

for subsequent DNA analysis (see below). Another subsample of soil (ca. 30 g) was

used to determine gravimetric soil water content. Water-filled pore space was calculated

by dividing volumetric water content by total porosity, where volumetric water content

is gravimetric water content multiplied by bulk density (1.4 g cm-3), and total porosity is

[1 – (bulk density / particle density)] × 100, using measured particle density for each

field soil replicate (Linn and Doran, 1984).

A further subsample (20.0 g) was extracted with 80 mL of 0.5 M potassium sulphate

(K2SO4) for 30 minutes in an end-over-end shaker, then filtered through Whatman No.

42 filter paper. The extracts were kept frozen at -20 °C until further analysis for

inorganic N. Using Buchner funnels under vacuum, the inorganic N remaining in soil

solution was removed by a second extraction with 80 mL of 0.5 M K2SO4 followed by

two extractions with 80 mL of MilliQ water. The remaining washed soil was dried at 70

°C and ground to a fine powder, then analysed for 15N atom% and total N using a

continuous flow system, consisting of a SERCON 20-22 Stable Isotope Ratio Mass

Spectrometer (IRMS) connected with an Automated Nitrogen Carbon analyser (Sercon,

Crewe, UK).

Inorganic N concentrations of the extracts were determined by colorimetric analysis on

a Skalar San Plus auto-analyser (Skalar Inc., Breda, The Netherlands), using the


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modified Berthelot reaction for ammonium-N (NH4+-N; Krom, 1980) and the

hydrazinium reduction method for NO3--N (Kamphake et al., 1967). The extracts from

each sampling time were prepared for IRMS 15N/14N isotope ratio analysis using a

modified diffusion method (Brooks et al., 1989; Sørensen and Jensen, 1991). The

extract NH4+ and NO3

- was trapped on separate acidified diffusion disc, and the discs

were analysed by IRMS as described above.

5.3.4. Calculation of gross N transformation rates

The analytical equations of Kirkham and Bartholomew (1954) were used to calculate

gross N mineralisation and nitrification rates between each sampling time point (i.e.

days 1–3, days 3–7, days 7–10, and days 10–14).

5.3.5. Nucleic acid extraction and qPCR

DNA was extracted from 800 mg sub-samples of soil immediately after wet-up and

from days 1, 7 and 14 of the incubation. The DNA PowerSoil® Kit (MoBio) was used

following the manufacturer’s instructions with one exception: the DNA was eluted in 50

µL of the final solution. DNA was stored at -40 °C prior to further analysis.

Bacterial and archaeal amoA genes were quantified by quantitative real-time

polymerase chain reaction (qPCR; Applied Biosystems ViiA™ 7) using GoTaq® qPCR

System (Promega Corp.). For bacterial amoA gene quantification, primers used were

amoA-1F (5ʹ-GGGGTTTCTACTGGTGGT-3ʹ) and amoA-2R (5ʹ-CCCCTCKGSAAA

GCCTTCTTC-3ʹ) with fragment length of 491 bp (Rotthauwe et al., 1997). Each 20 µL

qPCR reaction contained 10 µL of SYBR Green GoTaq® qPCR 2 × Master Mix

(Promega Corp.), 0.2 µL of each forward and reverse primer at a concentration of 10

µM, 2 µL bovine serum albumin (Ambion® UltraPure™ BSA, 5mg mL-1), 2 µL of


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template DNA and 5.6 µL of water. Cycling conditions were: 94 °C for 10 min, then 40

cycles of: 94 °C for 30 sec, 56 °C for 30 sec, 72 °C for 30 sec and 78 °C for 30 sec,

followed by a melt curve. Fluorescence data was collected at the 78 °C stage.

For archaeal amoA gene quantification, primers used were Arch-amoAF (5ʹ-

STAATGGTCTGGCTTAGACG-3ʹ) and Arch-amoAR (5ʹ-GCGGCCATCCATCT

GTATGT-3ʹ) with fragment length of 635 bp (Francis et al., 2005). Each 10 µL qPCR

reaction contained 5 µL of SYBR Green GoTaq® qPCR 2 × Master Mix (Promega

Corp.), 0.1 µL of each forward and reverse primer at a concentration of 10 µM, 1 µL

BSA, 1 µL of template DNA and 2.8 µL of water. Cycling conditions were: 94 °C for

10 min, then 40 cycles of: 94 °C for 1 min, 52 °C for 1 min, 72 °C for 1 min and 78 °C

for 1 min, followed by a melt curve. Fluorescence data was collected at the 78 °C stage.

Each standard and sample was replicated three times during each qPCR run. Templates

for determining gene copy numbers in the qPCR reactions were cloned plasmids as

described in Barton et al. (2013a). The standard curves generated were linear over four

orders of magnitude (103–106 gene copies) for AOB and over six orders of magnitude

for AOA (103–108 gene copies) with r2 values greater than 0.98. Amplification

efficiencies ranged from 83–98% and a dilution series determined if there was any

inhibition in the samples. The lower detection limit was 103 gene copies in 1 µL

template.

5.3.6. Statistical analysis

Statistical differences between the field soil properties of the soil OM management

treatments were examined using analysis of variance (ANOVA) with associated

TukeyHSD post hoc tests in R version 3.1.0 (R Foundation for Statistical Computing,


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Vienna, Austria). Statistical significances of nitrapyrin, soil OM management,

temperature, and water regime treatments on WFPS, labelled NH4+ and NO3

-, gross N

transformation rates and amoA gene abundance with time were evaluated using a mixed

model, PROC MIXED in SAS version 9.3 (SAS Institute Inc., Cary, NC, USA).

Ammonia oxidiser amoA gene abundance data was log10 transformed before all

analyses. Time was a significant factor for all variables, so each time point was

analysed separately to better clarify statistical relationships between treatments. R

version 3.1.0 was used to run linear regressions of gross N transformation rates, against

ammonia oxidiser amoA gene abundance at the end of the period over which each rate

was calculated.

5.4. Results

5.4.1. Recovery of 15N

Two hours after 15NH4+ application, mean recovery of 15N was 33% at 20 °C and 93%

at 40 °C (combined inhibitor, soil and water regime treatments are shown in Fig. 5.1a).

Recovery of applied 15NH4+ at 20 °C increased to day 7, after which mean 15N recovery

was stable at 74%. The low recovery of 15N two hours after 15NH4+ application at 20 °C

was not due to nitrification, as there was no appearance of 15NO3-. Due to this low

recovery of 15NH4+ at two hours, gross N mineralisation rates from 2 hr–day 1 were not

calculated. Mean recovery of applied 15NO3- at both temperatures and all time points

was approximately constant at 80% (combined inhibitor, soil and water regime

treatments are shown in Fig. 5.1b). In all treatments where 15NO3- was applied, no

remineralisation through to 15NH4+ was detected.


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5.4.2. Water-filled pore space

Water-filled pore space in the OWC samples was maintained between 41–45%

throughout the incubation (combined inhibitor treatments are shown in Fig. 5.2). Water-

filled pore space in DRY samples decreased with time, from a mean of 43% to a

minimum on day 14. Minimum mean WFPS in DRY samples was less in Tilled soil

than in Tilled+OM soil [10 and 15% respectively at 20 °C (Fig. 5.2a); and 1 and 5% at

40 °C respectively (Fig. 5.2b); P<0.0001].

5.4.3. Labelled ammonium and nitrate-N

After 15NH4+ application, nitrapyrin maintained labelled NH4

+ pools approximately

constant from day 3 at 40 °C, in contrast to labelled NH4+ pools without nitrapyrin

which generally decreased with time, and were less than in the presence of nitrapyrin

from day 3 until day 14 (P<0.0001; data not shown). Nitrapyrin was more effective at

retaining labelled NH4+ in soil without additional OM at 40 °C, and in soil held at

OWC. Labelled NH4+ in Tilled+OM soil with nitrapyrin was less than in Tilled soil with

nitrapyrin on all days of incubation (P<0.05), and labelled NH4+ in DRY soil with

nitrapyrin was less than in OWC soil with nitrapyrin from day 1 onwards (P<0.0001).

Labelled NH4+ at 20 °C followed the same general pattern as at 40 °C but on average

was 27% of 40 °C labelled NH4+ (data not shown).


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Figure 5.1. Change in total recovery of 15N (% of applied 15N) through time from

soil applied with (a) 15N-labelled NH4+ and (b) 15N-labelled NO3

-. Error bars are

±SEM (n = 24) and may be smaller than the symbols. Legend is the same for both

panels.

Figure 5.2. Change in water-filled pore space (% WFPS) through time (a) at 20 °C;

and (b) at 40 °C. Error bars are ±SEM (n = 12) and are smaller than the symbols.

Legend is the same for both panels. Legend abbreviations: OWC: samples held at

optimal water content (45% WFPS); DRY: samples wet-up to 45% WFPS then allowed

to dry; OM: organic matter.


129

Figure 5.3. Change in 15N-labelled nitrate (NO3-) above natural abundance through

time with added 15(NH4)2SO4. (a) at 20 °C in soil held at optimal water content (45%

WFPS); (b) at 20 °C in soil wet-up to 45% WFPS then allowed to dry; (c) at 40 °C in

soil held at optimal water content; and (d) at 40 °C in soil wet-up then allowed to dry.

Error bars are ±SEM (n = 3). Legend is the same for all panels. Legend abbreviation:

OM: organic matter.

After 15NH4+ application, nitrapyrin kept labelled NO3

- pools constant at 0.1 µg 15N g-1

from days 1–14 at 40 °C, in contrast to labelled NO3- pools without nitrapyrin, which

generally increased with time (Fig. 5.3c–d). There was no difference in labelled NO3-

pools between Tilled and Tilled+OM soils or between OWC and DRY samples in the

presence of nitrapyrin at 40 °C (P<0.05; Fig. 5.3c–d). Without nitrapyrin however,

labelled NO3- pools in Tilled soil were greater than in Tilled+OM soil from days 1–14

at 40 °C (P<0.0001; Fig. 5.3c–d). Labelled NO3- at 20 °C generally followed a similar

pattern and magnitude as at 40 °C (Fig. 5.3a–b).


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5.4.4. Unlabelled inorganic N

Unlabelled NH4+ pools at 40 °C were greater in the presence of nitrapyrin than without

from days 7–14 (P<0.0001; Fig. 5.4a–b). Ammonium pools increased to a greater extent

in the presence of nitrapyrin in Tilled+OM soil than in Tilled soil at 40 °C. Nitrapyrin

retained more NH4+ in OWC samples than in DRY samples at 40 °C (P<0.001).

Nitrapyrin had a similar effect on NH4+ in soil incubated at 20 °C, but NH4

+ pools were

on average 40% of NH4+ pools at 40 °C (Fig. 5.5a–b).

Nitrapyrin kept unlabelled NO3- pools at 40 °C approximately stable in DRY soil, while

NO3- pools decreased with time at OWC (Fig. 5.4c–d). Without nitrapyrin, NO3

- pools

generally increased with time to levels greater than in soil with nitrapyrin on days 10–

14 at OWC (P<0.0001) and on days 7–14 in DRY soil (P<0.01). Tilled+OM soil had

greater NO3- than Tilled soil in DRY samples from days 1–10 (P<0.05), but at OWC,

there was no difference in NO3- between OM treatments after 2 hr (P<0.05). Nitrate

changes with time at 20 °C were more related to OM treatment than nitrapyrin (Fig.

5.5c–d). At OWC and 20 °C, NO3- pools in Tilled+OM soil were greater than Tilled soil

until day 10, and nitrapyrin decreased the NO3- pool size in both OM treatments on days

10–14 (P<0.05; Fig. 5.5c). Nitrate in DRY soil at 20 °C remained generally constant in

all treatments from days 3–14 (Fig. 5.5d).

Total inorganic N at 40 °C generally increased with time, and nitrapyrin had no effect in

DRY soil (P>0.05; Fig. 5.4e–f). At OWC however, total inorganic N was greater in the

presence of nitrapyrin in Tilled+OM soil (P<0.05) but not in Tilled soil at 40 °C

(P>0.05; Fig. 5.4e). Total inorganic N in Tilled+OM soil was generally greater than

Tilled soil at 40 °C. Total inorganic N at 20 °C was not affected by nitrapyrin, and


131

followed similar patterns with time and OM treatment as that observed in the NO3-

traces at 40 °C (Fig. 5.5e–f).

Figure 5.4. Change in unlabelled inorganic N through time at 40 °C. (a) NH4+-N in

soil held at optimal water content (45% WFPS); (b) NH4+-N in soil wet-up (to 45%

WFPS) then allowed to dry; (c) NO3--N in soil held at optimal water content; (d) NO3

--

N in soil wet-up then allowed to dry; (e) total inorganic N in soil held at optimal water

content; and (f) total inorganic N in soil wet-up then allowed to dry. Error bars are

±SEM (n = 6). Legend is the same for all panels. Legend abbreviation: OM: organic

matter.


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Figure 5.5. Change in unlabelled inorganic N through time at 20 °C. (a) NH4+-N in

soil held at optimal water content (45% WFPS); (b) NH4+-N in soil wet-up (to 45%

WFPS) then allowed to dry; (c) NO3--N in soil held at optimal water content; (d) NO3

--

N in soil wet-up then allowed to dry; (e) total inorganic N in soil held at optimal water

content; and (f) total inorganic N in soil wet-up then allowed to dry. Error bars are

±SEM (n = 6). Legend is the same for all panels. Legend abbreviation: OM: organic

matter.


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5.4.5. Gross N transformation rates

Mean gross nitrification rates at 40 °C ranged from 0–3.3 µg N g-1 d-1 (Fig. 5.6g–h).

Addition of nitrapyrin decreased gross nitrification at 40 °C in DRY samples between

days 1–7, and in OWC samples between days 1–3, and again between days 7–10

(P<0.05). During these time periods at 40 °C, nitrapyrin inhibited nitrification by a

mean of 86%. At 20 °C between days 1–3, nitrapyrin decreased gross nitrification in

DRY Tilled soil and OWC Tilled+OM soil (P<0.05; Fig. 5.6e–f). Furthermore, addition

of nitrapyrin decreased gross nitrification at 20 °C in DRY samples between days 3–7

(P<0.0001) and in OWC samples between days 7–14 (P<0.001; Fig. 5.6e–f). For these

samples at 20 °C, nitrapyrin inhibited nitrification by a mean of 62%. There was no

consistent effect of OM treatment, experimental water regime, or incubation

temperature on gross nitrification rates.

Gross N mineralisation rates at 40 °C generally decreased with time, and means ranged

from 0.1–7.1 µg N g-1 d-1 (Fig. 5.6c–d). Nitrapyrin had no consistent effect on gross N

mineralisation rates at either 20 or 40 °C (P>0.05). Tilled+OM soil had greater gross N

mineralisation rates than Tilled soil between 2 h and day 3 at 40 °C (P<0.05). Optimal

water content samples had greater gross N mineralisation than DRY samples between

days 3–14 at 40 °C (P<0.05). In general, gross N mineralisation at 20 °C was half of

gross N mineralisation at 40 °C, and followed a similar pattern over the incubation

period (Fig. 5.6a–b).


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Figure 5.6. Change in gross N mineralisation and nitrification rates through time.

Gross N mineralisation (a) at 20 °C in soil held at optimal water content (45% WFPS);

(b) at 20 °C in soil wet-up (to 45% WFPS) then allowed to dry; (c) at 40 °C in soil held

at optimal water content; and (d) at 40 °C in soil wet-up then allowed to dry. Gross

nitrification (e) at 20 °C in soil held at optimal water content; (f) at 20 °C in soil wet-up

then allowed to dry; (g) at 40 °C in soil held at optimal water content; and (h) at 40 °C

in soil wet-up then allowed to dry. Points shown are at the middle of the time period

over which the rates were calculated (1–3 days; 3–7 days; 7–10 days; and 10–14 days).

Error bars are ±SEM (n = 3). Legend is the same for all panels. Legend abbreviation:

OM: organic matter. Note the different y-axis scales of (c) and (d).


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5.4.6. Bacterial and archaeal amoA gene abundance

Archaeal amoA gene abundance was below detection limits in the majority of samples,

and followed no pattern when detected (mean across all treatments: 3.30 x 104 gene

copies g-1 dry soil). Mean bacterial amoA gene abundance at 40 °C ranged from below

detection limits to 3.71 x 107 gene copies g-1 dry soil (Fig. 5.7c–d). Wetting of dry soil

at 40 °C immediately decreased bacterial amoA gene abundance between time zero and

day 1 in all 40 °C samples (P<0.0001; Fig. 5.7c–d) but gene abundance recovered to

similar levels as the original soil by day 14. Addition of nitrapyrin at 40 °C decreased

AOB gene abundance only in the DRY Tilled soil on day 7 (P<0.05; Fig. 5.7d).

Mean AOB gene abundance at 20 °C ranged from 4.32 x 105–7.28 x 107 gene copies g-1

dry soil (Fig. 5.7a–b). Wetting of dry soil at 20 °C decreased bacterial amoA gene

abundance between time zero and day 1 in DRY samples (P<0.0001) but not in OWC

samples (P>0.05; Fig. 5.7a–b). Addition of nitrapyrin at 20 °C decreased AOB gene

abundance only on day 7, in the Tilled soil for both OWC and DRY water regimes, and

in the DRY Tilled+OM soil (P<0.05; Fig. 5.7a–b). Otherwise there was no consistent

effect of experimental water regime or OM treatment on AOB gene abundance at 20 or

40 °C.

Bacterial amoA gene abundance had no relationship with gross nitrification (P>0.05),

but had a statistically significant negative relationship with gross N mineralisation

(P<0.05; data not shown). Adjusted R-squared values for this relationship however was

only 0.11, and the coefficient estimate was -0.052.


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Figure 5.7. Change in bacterial amoA gene abundance (AOB) through time. (a) at

20 °C in soil held at optimal water content (45% WFPS); (b) at 20 °C in soil wet-up (to

45% WFPS) then allowed to dry; (c) at 40 °C in soil held at optimal water content; and

(d) at 40 °C in soil wet-up then allowed to dry. Error bars are ±SEM (n = 3). Legend is

the same for both panels. Legend abbreviation: OM: organic matter.

5.5. Discussion

Nitrapyrin has potential to decrease nitrification and thus the risk of N loss under

elevated soil temperatures. Despite the fact that nitrapyrin is reported to become less

effective at inhibiting nitrification with increasing temperature, the present study

indicates that nitrapyrin was still able to inhibit nitrification at elevated temperatures in

this semi-arid soil, without affecting other N transformation rates. Other studies have

found that nitrapyrin can decrease nitrification at temperatures from 25–35 °C (Ali et

al., 2008; Chen et al., 2010). Most research to date has focussed on effectiveness of


137

nitrapyrin at decreasing nitrification of NH4+-based fertilisers when applied during the

cropping season (for example Chen et al., 1994; Wolt, 2004). When nitrapyrin is

applied with a N source (such as N fertiliser), there is a more noticeable retention of

applied inorganic N due to the higher NH4+ concentration (for example Tu, 1973). Our

findings however extend the use of nitrapyrin to control nitrification of OM mineralised

outside the cropping season during summer fallow, with soil temperatures up to 40 °C.

Ammonia oxidiser gene abundance did not change in response to nitrapyrin, despite

decreased gross nitrification rates and therefore ammonia oxidiser function. Function

and population size were also disconnected as the AOB gene abundance had no

correlation to N transformation rates. This is in contrast to our expectations that

nitrapyrin would decrease ammonia oxidiser gene abundance, by diminishing energy

production and potential for growth. Few studies have examined the effect of nitrapyrin

on ammonia oxidiser gene abundance, and there is no clear evidence whether nitrapyrin

affects AOA or AOB to a greater extent. Nitrapyrin decreased both growth and activity

of the AOA Nitrosotalea devanaterra in liquid culture and soil (Lehtovirta-Morley et

al., 2013), while nitrapyrin had weak inhibitory effects on nitrification and AOB but not

AOA gene abundance in three Chinese soils (Cui et al., 2013). Nitrapyrin inhibited

production of nitrite by the AOA Ca. Nitrososphaera viennensis but had only a weak

inhibitory effect on production of nitrite by the AOB Nitrosospira multiformis in culture

(Shen et al., 2013). Evidently, different strains and communities of ammonia oxidisers

are influenced by nitrapyrin to differing degrees, likely also depending on

environmental and experimental conditions. Here we attributed nitrification to AOB, as

we were unable to detect AOA in the surface soil layer. Although AOB gene abundance

was not affected by nitrapyrin, an effect on gross nitrification was still observed. Our


138

results illustrate the need for further study to understand the complexities of ammonia

oxidiser sensitivities to nitrapyrin.

Organic matter additions to this soil decreased the effectiveness of nitrapyrin, observed

as a diminished retention of labelled NH4+. This was as expected, as nitrapyrin adsorbs

onto OM, decreasing its ability to inhibit ammonia oxidation (Goring, 1962). Organic

matter also increases soil microbial activity and provides carbon and N substrates for

microorganisms which degrade nitrapyrin (Goring, 1962). Recently there has been

much interest in building soil OM particularly for the purpose of sequestering C to

decrease atmospheric carbon dioxide levels and mitigate climate change (Powlson et al.,

2011; Viscarra Rossel et al., 2014). Our results suggest that although nitrapyrin could be

effective under summer conditions, these responses are likely to be greatest in low OM

soils. Increasing soil OM, for example through crop residue additions as was done here,

will have complex consequences on N cycling and our ability to manage N losses by the

use of nitrapyrin.

Bacterial amoA gene abundance notably declined due to initial wet-up of dry soil, but

was not affected by whether soil was subsequently held at optimal water content or

allowed to dry. Rapid increases in soil water potential, as occur when rain falls on dry

soil, place soil microorganisms under greater stress than they experience as soil dries

(Schimel et al., 2007). If microorganisms are unable to adjust to the increasing water

potential, they may release intracellular solutes, lyse and die (Halverson et al., 2000;

Kieft et al., 1987). Recent evidence from in situ microbial communities suggests that

soil microorganisms do not accumulate osmolytes as they dry (which might allow them

to remain active; Boot et al., 2013), but instead the best strategy for survival is drought

avoidance by dormancy until reactivation by a wetting event (Manzoni et al., 2014).


139

Although we expected that microbial communities in this soil would be adapted to and

able to cope with the climate (i.e. sporadic wetting events during the summer when soil

is dry), a proportion of the AOB population appears not to be able to adjust rapidly

enough to the increased water potential on soil rewetting, causing lysis and death. This

is in contrast to the heterotrophic N mineralisers and immobilisers, which showed

maximum activity during the first 24 h after wet-up. By day 14 however, bacterial

amoA gene abundance in all treatments had recovered to the similar levels as in pre-wet

soils. This follows a similar pattern to that observed in another semi-arid soil, where

bacterial amoA gene abundance 72 h after wetting was the same or less than in pre-wet

soil (Placella and Firestone, 2013).

Low recovery of 15NH4+ within two hours of application at 20 °C was attributed to rapid

bacterial uptake. Uptake was followed by slow release of 15NH4+ back into the soil

environment presumably once cells were saturated with N. This effect has been

previously observed by Jones et al. (2013) using high-resolution nano-scale secondary

ion mass spectrometry (NanoSIMS) stable isotope imaging: metabolically active

bacterial cells in the rhizosphere of wheat plants accumulated and became saturated

with 15NH4+ within 30 minutes of application of low levels of 15NH4

+ (3 mM). In the

present study we were not able to measure this bacterial 15NH4+ uptake due to the

relatively enormous size of the organic N pool (307–1048 µg N g-1) compared to the

amount of applied 15N (5 µg N g-1 at 60 atom%), and thus detected it as diminished 15N

recovery. Rapid bacterial uptake of applied 15NH4+ was not observed at 40 °C, which

we attribute to limitation of immobilisation at elevated temperatures: in a similar semi-

arid soil, Hoyle et al. (2006) noted that N immobilisation was restricted at temperatures

greater than 30 °C, likely due to C substrate limitation. Our results imply that 15N

isotopic pool dilution may not be a useful tool to measure short-term rates (i.e. over the


140

initial 24 hours) of N transformations in N-limited soils, as these measurements appear

to be confounded by rapid immediate bacterial uptake and release of 15NH4+

independent of soil OM mineralisation.

5.6. Conclusions

It is difficult to manage N losses during summer fallow when dry soils can experience

elevated temperatures and wetting events. The nitrification inhibitor nitrapyrin has the

potential to retain mineralised NH4+ that is released from these wetting events in this

semi-arid soil. However increasing soil OM through long-term crop residue additions

may make nitrapyrin less effective. Although ammonia oxidiser activity was diminished

by nitrapyrin, and ammonia oxidation was attributed to AOB, bacterial amoA gene

abundances were not affected by the inhibitor. Instead, AOB populations were most

affected by wet-up of dry soil, suggesting that these microbial communities are driven

mainly by changes in environmental conditions rather than management practices per

se. Further research needs to be conducted to evaluate whether nitrapyrin can be

effective under field conditions, and whether this is an economic solution to potential N

losses over summer fallow.

141

Chapter 6.

General Discussion

6.1. Introduction

The objective of this thesis was to gain a better understanding of how to manage

microbially-mediated nitrogen (N) cycling in order to prevent N loss from semi-arid

rainfed cropped soils. The objective was achieved in two ways: by investigating factors

contributing to variability in risk of N loss; and by investigating possible management

solutions to decrease the risk of N loss. This general discussion will bring together the

main findings from the three preceding research chapters, and will also make reference

to my collaborative research that is contained in Appendix A. The main findings of this

thesis will be critically assessed and put in context with what is already known about

semi-arid rainfed agricultural soils, and finally opportunities for future research will be

suggested.

Ch. 6: Discussion

142

6.2. Contributing Factors to Variation in Risk of Nitrogen Loss from

Semi-Arid Rainfed Agricultural Soils

6.2.1. Variation in rainfall and temperature

Seasonal variation in rainfall and temperature are of primary importance determining

patterns of risk of N loss from semi-arid rainfed agricultural soils. This is due to several

interacting factors: microbial production of inorganic and gaseous N in response to

rainfall and elevated temperatures, seasonality of N removal and carbon (C) inputs to

soil by annual crop plants, and the effect of timing and size of rainfall events on deep

drainage and potential nitrate (NO3-) leaching.

Production of inorganic N during summer fallow in response to rainfall and elevated

temperatures in this semi-arid soil is a key factor determining risk of N loss. Increasing

inorganic N pools appear to be partly a consequence of net microbial N mineralisation

at soil temperatures above 30 °C, which was associated with low microbial C use

efficiency (Chapter 3). This suggests that decomposing microorganisms are less

efficient at converting C from low molecular weight organic matter (LMWOM)

substrates into biomass, and respire more at elevated temperatures. Decreased microbial

C use efficiency also decreases demand for N, promoting N mineralisation when the N

content of LMWOM substrates is high enough to meet microbial requirements (Austin

et al., 2004). The mechanism for lowered microbial C use efficiency is likely linked to

the increased physiological costs of maintaining respiration at elevated temperatures

and inducing heat avoidance and survival mechanisms (Schimel et al., 2007). Increased

net N mineralisation and accumulation of inorganic N at elevated soil temperature has

also been observed in other semi-arid agricultural soils (Hoyle et al., 2006; Luxhøi et

al., 2008) and in annual grassland ecosystems (Jackson et al., 1988; Parker and Schimel,

2011). This accumulated inorganic N will be at risk of loss by NO3- leaching depending

Ch. 6: Discussion

143

on the timing and size of rainfall events, and particularly at the start of the winter

growing season when deep drainage below the crop rooting zone begins (Anderson et

al., 1998; Arregui and Quemada, 2006). Risk of N loss is also exacerbated when

seasonality of plant N uptake and available C inputs (almost exclusively occurring

during the winter growing season in this annual cropping system) does not match

seasonality of N supply from organic matter (OM) decomposition (taking place year-

round; Knops et al., 2002).

Nitrification of this mineralised N was also active during summer fallow, as shown by

low soil ammonium (NH4+) concentrations and the accumulation of NO3

- in response to

rainfall as the season progressed (Chapter 4). Increasing abundance of bacterial

ammonia oxidisers (AOB) during summer fallow was also linked to increasing soil

NO3- pools. Communities of N cycling microorganisms in this semi-arid soil are

therefore likely to be acclimated to drought and wetting events, which they generally

experience every year during summer. In fact, amoA transcripts have been observed in

semi-arid soils even when soil is dry (1–5% gravimetric water content), and AOB can

increase amoA transcription within one hour of soil wetting (Placella and Firestone,

2013). Potential nitrification rates in semi-arid soils can also be greater in dry seasons

than in wet seasons (Parker and Schimel, 2011; Sullivan et al., 2012). Adaptation of

microbial communities to drought and rewetting stress has been observed in other

environments (de Vries et al., 2012; Evans and Wallenstein, 2012; Peralta et al., 2013).

For example, potential nitrification rates in humid continental upland soils were not

affected by drought, while potential nitrification rates in adjacent non-acclimated

wetland soils decreased (Peralta et al., 2013). All of these lines of evidence suggest that

dry seasons in semi-arid soils are critical periods for understanding annual variation in

N cycling and risk of N loss.

Ch. 6: Discussion

144

6.2.2. Root exudate carbon inputs

Actively growing plant roots are highly important for decreasing risk of N loss, by

supplying available C to retain N in the microbial N loop and prevent production of

excess inorganic N. Both crop residue OM and root exudate inputs were expected to

increase C availability in soil, allowing heterotrophic microorganisms to compete more

effectively for NH4+, thus decreasing activity of nitrifying microorganisms and

decreasing the risk of N loss. The findings supported this hypothesis only in the case of

root exudate inputs (Chapter 3). Crop residue inputs did not change the risk of N loss, as

gross nitrification rates were enhanced along with N immobilisation rates (Fig. 3.5).

This occurred despite crop residue inputs resulting in significantly greater levels of light

fraction organic matter C (LFOM-C) and dissolved organic C (DOC), which have been

used as indicators of available C (Haynes, 2005; Janzen et al., 1992). In contrast, root

exudates increased heterotrophic microbial N immobilisation relative to nitrification, so

overall the risk of N loss decreased.

These findings fit with plant detritus decomposition theory. Current understanding of

decomposition is that litter or residue quality is less important for soil C and N

availability than soil characteristics such as microbial community composition

(Delgado-Baquerizo et al., 2015). The majority of N from plant litter (in this case crop

residues) becomes incorporated into soil OM during decomposition, which represents a

bottleneck between plant N and the soil N cycle. Release of N from soil OM is

subsequently regulated by microbial mineralisers, the majority of N being incorporated

into microbial biomass and returned to the soil OM pool upon death (Knops et al.,

2002). This is termed the microbial N loop, and is evidenced by much greater gross than

net N mineralisation rates, in this semi-arid soil at soil temperatures between 10 and 30

°C (Fig. 3.4). In fact, gross nitrification rates can also be much greater than net

Ch. 6: Discussion

145

nitrification rates, and in natural ecosystems are linked to high NH4+ and NO3

-

consumption rates, indicating tight internal soil N cycling (Stark and Hart, 1997;

Verchot et al., 2001).

The microbial N loop is regulated by recent inputs of microbially available C supplied

by plant rhizodeposits, such as root exudates and root turnover, which stimulate

microbial N immobilisation. This was clearly observed in the present study (Chapter 3),

where addition of synthetic root exudates resulted in gross N immobilisation rates

greater than nitrification rates, and an N:I ratio (gross nitrification to N immobilisation

ratio; an indicator of risk of N loss) less than one (Fig. 3.5). Stimulation of N

immobilisation by available C has also been observed in nutrient-deficient Arctic soils

with additions of glucose (Schmidt et al., 1997). In terms of the effect of available C on

actual N losses, one study in a Spanish semi-arid soil showed that available C in the

form of glucose decreased nitrous oxide (N2O) emissions by 23% and nitric oxide (NO)

emissions by 71% at 40% water-filled pore space (WFPS), when high rates of fertiliser

N were also added (200 kg N ha-1) but not low rates of N (50 kg N ha-1; Sánchez-Martín

et al., 2008). The authors hypothesised that the reductions were due to labile C

favouring complete denitrification over nitrifier denitrification, but equally the

reductions in gaseous N loss may have been due to increased microbial N

immobilisation (Sánchez-Martín et al., 2008). The authors did not consider that their

NH4+ and NO3

--N measurements indicated decreased net N mineralisation and

nitrification rates (and therefore increased immobilisation) in the presence of glucose

compared to without glucose for both rates of N fertiliser addition at 40% WFPS

(Sánchez-Martín et al., 2008). These findings suggest that risk of N loss in semi-arid

agricultural soils might be managed by increasing the time of influence of root growth

and root exudate C inputs, to enhance N retention in soil.

Ch. 6: Discussion

146

The effects of C inputs from plant roots will vary spatially over small scales

(centimetres to micrometres) and interact with N availability and hydrological

connectivity to influence risk of N loss. This study was conducted in the laboratory, so

root exudates could be applied homogenously to the samples, allowing the

quantification of gross N transformation rates from the soil as a whole. In the field

however, inputs of available C from root exudates will not be evenly distributed

throughout the bulk soil, and microsites will vary in both C and N availability. Carbon

flow from crop roots is highly complex and varies spatially and temporally along the

plant root. Rates of exudation are generally greater at the tips of roots than in mature

roots, though passive diffusion occurs along the entire length of the root (Hoffland et

al., 1989; McDougall and Rovira, 1970). The composition and quantity of rhizodeposits

also are influenced by a range of other factors such as plant species, developmental and

nutrient status, and environmental conditions (Jones et al., 2004a). Release of

rhizodeposits gives rise to zones of stimulated microbial activity that have different

characteristics to bulk soil, on the surface of roots (the rhizoplane), within roots (the

endorhizosphere) or outside roots (the ectorhizosphere; Lynch and Whipps, 1990).

Microscale variation in C availability will also interact with microscale variation in N

availability to produce sites where N is mineralised and sites where N is immobilised

(Schimel and Bennett, 2004). In addition, the laboratory incubation (Chapter 3) was

carried out at optimal water content, so microsites of differing C and N availabilities in

the samples would have been well connected. As soil water content varies in the field

with wetting and drying events, the hydrological connections between microsites will

also vary. Diffusion of substrates from sites of production to sites of consumption is

limited when microsites are not well connected, so those substrates can accumulate

(Parker and Schimel, 2011). The hydrological connections between spatially

Ch. 6: Discussion

147

heterogeneous microsites needs to be taken into account when inferring the effect of

varying management on accumulation of inorganic N and subsequent risk of N loss.

6.2.3. The importance of surface soil layers

The surface 10 cm of this semi-arid agricultural soil is where N transformation rates are

greatest, where most AOB are found, and where most N that is at risk of loss is

produced, particularly N2O and NO3-. Gross nitrification rates (actual and potential)

decrease with depth, and AOB amoA gene abundances that are correlated with

nitrification rates are also highest in the surface soil (Appendix A). Other researchers

have additionally shown that 70–88% of gross N mineralisation, 46–57% of NH4+

consumption and 55% of microbial biomass in the 50 cm soil profile occurs in the top

10 cm (Murphy et al., 1998a). The surface layer of soil in this water-limited

environment is where rainfall first increases soil water availability, where crop residue

and OM inputs are greatest, and where temperature fluctuations are most extreme.

Minimal or no-tillage are common agricultural practices in south-west Australian semi-

arid soils, so soils are generally highly stratified, without the deep mixing of tillage or

mouldboard ploughing (Roper et al., 2010). Subsoil layers are buffered by the overlying

soil from extremes in temperature, drying by evaporation and rewetting by rainfall.

Bacterial ammonia oxidisers dominate over archaeal ammonia oxidisers (AOA) in the

surface layer of this semi-arid soil, controlling nitrification and production of NO3- that

is at risk of loss. It is important to distinguish between nitrification carried out by AOB

or AOA because the differing physiologies and ecological functions of these ammonia

oxidisers likely influence how they respond to management practices, such as addition

of nitrification inhibitors, and their roles in production of environmentally detrimental

N2O, NO3- (Di et al., 2009; Prosser and Nicol, 2008). Both AOA and AOB were

Ch. 6: Discussion

148

expected to have greatest abundance in the surface soil compared to subsoil of this

semi-arid soil, because this is where most N mineralisation occurs and therefore where

most substrate for ammonia oxidisers would be found. This hypothesis was supported in

the case of AOB (Chapters 4 and 5, Appendix A), but not for AOA. Ammonia-

oxidising archaea were instead greater in the subsoil (10–90 cm) compared to the

surface layer (Appendix A). Bacterial ammonia oxidisers may dominate over AOA in

the surface layers due to the long-term effects of agricultural practices such as N

fertiliser application on these agricultural soils: in adjacent native bushland to the

present study site, AOA abundances in the surface soil were measured as approximately

an order of magnitude higher than in the agricultural soil. In some cases, AOB appear to

dominate in abundance or in activity over AOA when soil NH4+ levels are high, while

AOA prefer low nutrient conditions and can be inhibited by high NH4+ fertilisation (Di

et al., 2010; Di et al., 2009; Jia and Conrad, 2009; Pratscher et al., 2011; Shen et al.,

2011). Archaeal ammonia oxidisers may compete more effectively over AOB when soil

NH4+ levels are low due to their greater affinity for NH4

+ and their greater sensitivity to

inhibition by high NH4+ (Martens-Habbena et al., 2009; Prosser and Nicol, 2012). These

studies suggest that AOB dominate in this semi-arid agricultural soil as a consequence

of N fertiliser application and increased ammonia substrate availability, so it is more

important to understand AOB regulation of nitrification than that of AOA in order to

manage the risk of N loss in this environment.

Archaea do not appear to be important regulators of the risk of N loss through ammonia

oxidation, even in the subsoil where AOA were detected in greatest numbers. Total

archaeal populations that were detected in the surface layers predominantly appear not

to possess the ability to oxidise ammonia (Appendix A). Archaeal ammonia oxidisers

that were detected in the subsoil were negatively correlated to gross nitrification rates

Ch. 6: Discussion

149

(Appendix A). Therefore, in spite of possessing the amoA gene, these AOA appear to be

either not transcribing amoA, not producing the ammonia monooxygenase enzyme

(AMO) or not utilising ammonia oxidation for energy production and metabolism.

Instead these AOA may be using some form of heterotrophic or mixotrophic

metabolism. Other studies have provided evidence of heterotrophic and mixotrophic

growth by AOA, due to the presence of genes encoding transporters for organic C

compounds, and by enhanced growth rates when organic C compounds are added to

pure culture (Tourna et al., 2011; Walker et al., 2010). In addition, Thaumarchaeotes in

waste water treatment plants appear not to be restricted to chemolithoautotrophic

metabolism despite expressing the amoA gene (Mußmann et al., 2011). Mußmann et al.

(2011) suggested that these amoA-encoding Thaumarchaeota instead are heterotrophs,

gaining energy and C from an unknown organic compound. It is possible that instead of

carrying out chemolithoautotrophic ammonia oxidation, AOA detected in the subsoil

layers of the semi-arid soil in the present study are using heterotrophic or mixotrophic

metabolism, and therefore are not important producers of NO3- and do not need to be

taken into account when considering management of risk of N loss.

Although AOB were found to dominate over AOA in the surface of this semi-arid soil,

this may not be applicable to other semi-arid soils. Niche specialisation and

differentiation between AOA and AOB has not yet been adequately described, with the

possible exception of a consistent dominance of AOA at very low soil pH (pH <5.5;

Prosser and Nicol, 2012). In addition, communities of ammonia-oxidising

microorganisms vary between biogeographical regions, possibly due to differences in

current and historical environmental conditions and dispersal limitations (Fierer et al.,

2009; Pester et al., 2012). Western Australia has been geologically isolated and stable

for millions of years (McKenzie et al., 2004), so it is likely that unique microbial

Ch. 6: Discussion

150

communities have evolved here. The relative importance of AOA and AOB to

nitrification and risk of N loss varies between different soil types, environments and

regions (for example Adair and Schwartz, 2008; Di et al., 2009; Gubry-Rangin et al.,

2010; Jia and Conrad, 2009; Taylor et al., 2010), and the findings of AOB dominance in

this semi-arid agricultural soil should be applied to other semi-arid soils with caution.

6.3. Management of Semi-Arid Soils to Decrease Risk of Nitrogen Loss

Outside the Growing Season

The greatest risk of N loss in semi-arid rainfed agricultural soils with winter-dominant

rainfall is in response to inorganic N production during infrequent summer rainfall

events when soil is fallow, and during the first rains of the growing season before crop

establishment (see section 2.4.2 and 2.5 in Chapter 2). Management solutions therefore

are required for these periods of the year, to decrease the risk of N loss and improve the

synchrony of N supply to crops. This research evaluated two approaches to achieve

these objectives, through increasing soil OM by crop residue additions, or by

application of the nitrification inhibitor nitrapyrin.

6.3.1. Crop residue inputs and increased soil organic matter

Increased soil OM from additional crop residue inputs was not an effective management

tool to decrease risk of N loss and improve synchrony of N supply to crops. Greater soil

OM was hypothesised to increase C availability to heterotrophic N immobilising

microorganisms and thus limit autotrophic nitrifiers through competition for NH4+

substrate. This was expected because soil with OM inputs had measured increases in

LFOM-C and DOC, which are microbial substrates with rapid turnover rates and have

been used as indicators of soil available C (Haynes, 2005; Janzen et al., 1992). This

Ch. 6: Discussion

151

hypothesis was not supported by the findings, because increased soil OM stimulated

gross N mineralisation and nitrification as well as immobilisation, so the balance

between N retention and N loss pathways was unchanged (Chapter 3, Fig. 3.5). In fact,

it is likely that with increased total amounts of N cycling through soil, more N will be

available for loss processes such as N2O emissions and NO3- leaching. Nitrate

concentrations in soil with crop residue inputs were greater across all seasons than in

soil without these additional OM inputs, but particularly at the end of summer fallow

when there is greatest risk of loss by leaching (Fig. 4.3). In the short term, the high C:N

ratio of crop residues compared to soil OM tends to cause net N immobilisation and

decreased inorganic N concentrations (for example Geisseler et al., 2012; Janzen and

Kucey, 1988). Over the long term however, increased nutrient cycling and C and N

contents are similarly observed in other studies where crop residue inputs have been

used to increase soil OM (reviewed in Kumar and Goh, 2000). Manipulating total soil

OM pools in this semi-arid agricultural soil therefore is not an effective method to

manage the risk of N loss.

6.3.2. Nitrapyrin

Nitrapyrin has potential to decrease risk of N loss and improve synchrony of N supply

to crops by retaining mineralised N in soil as NH4+ during summer fallow. Nitrapyrin

typically performs better in cooler climates and becomes less effective as soil

temperature increases, due to increased volatilisation losses, microbial activity and more

rapid microbial degradation (Goring, 1962; Slangen and Kerkhoff, 1984; Wolt, 2004).

Nonetheless, in this semi-arid soil nitrapyrin was able to inhibit nitrification at 20 and

40 °C. Other researchers have found nitrapyrin to be effective at inhibiting nitrification

at temperatures between 25 and 35 °C (Ali et al., 2008; Bundy and Bremner, 1973;

Ch. 6: Discussion

152

Chen et al., 2010), though Ali et al. (2008) questioned the economic viability of

applying nitrapyrin at rates high enough to have beneficial effects on crop yields.

Nitrapyrin may have been effective at elevated temperatures in this semi-arid soil

because of the low soil OM content (0.8–1.3% total C). Soil OM is reported to decrease

the effectiveness of nitrapyrin by providing substrates for and stimulating the activity of

microorganisms that degrade nitrapyrin, and by absorbing nitrapyrin (Goring, 1962;

Lewis and Stefanson, 1975). Increased OM content in this semi-arid soil due to crop

residue inputs also decreased the observed effectiveness of nitrapyrin (Chapter 5).

However, some of the previous studies that reported decreased effectiveness at elevated

temperatures were carried out on soils with even lower OM contents to the present

study (0.8% OM, Goring, 1962; 1.7% OM, Tu, 1973). Other interacting soil and

environmental factors therefore may be involved, and further work is needed to better

understand these factors and whether nitrapyrin would be effective in other soil types in

the grainbelt of south-western Australia.

The findings of the present study showed for the first time that the ability of nitrapyrin

to inhibit nitrification at elevated temperatures was combined with the ability to control

nitrification of NH4+ released from OM mineralisation. Previous research has been

focussed on using nitrapyrin to prevent loss of applied NH4+-based fertiliser during the

growing season, and the effectiveness of nitrapyrin at retaining inorganic N is more

noticeable when the inhibitor is applied with a large N source (for example Tu, 1973).

Most laboratory based studies have applied nitrapyrin with N sources [for example

Chen et al. (2010) applied nitrapyrin with 715 µg urea-N g-1, similar to fertiliser

applications of 80 kg N ha-1]. In semi-arid rainfed cropping systems with winter-

dominant rainfall, a significant proportion of N at risk of loss is a consequence of OM

Ch. 6: Discussion

153

mineralisation during summer fallow, instead of N fertiliser applied during the growing

season. The present study shows that nitrapyrin may also be able to control

transformations of this mineralised N during summer.

6.4. Future Research

6.4.1. Further unravelling the interactions between ammonia oxidisers and N loss

This thesis has highlighted several key areas where further research is needed.

Development of clone libraries and design of primers specifically for AOA present in

south-west Australian soils will make us more confident that lack of detection is a real

effect and that we are not missing novel AOA species. In Chapters 4 and 5, AOA were

not detected in the surface soils of the sandy agricultural site, and in collaborative

research (Appendix A) they were detected in very low abundances in the 0–10 cm layer

(means ranging from 0–1.8 x 103 gene copies g-1 dry soil). These low abundances and

lack of detection could be because either the primers did not detect AOA that were

present, or AOA were simply not there. From the best evidence available (detection of

AOA with the same primers in subsoil and an adjacent native bushland soil), it was

concluded that AOA were not present, so were unimportant for nitrification (Chapter 4,

Appendix A). In order to be sure of this however, primers need to be specifically

designed for this region. The primers used here, amoA-1F and amoA-1R have been

extensively used to measure archaeal amoA gene abundance in acidic agricultural soils

and in semi-arid environments elsewhere (Adair and Schwartz, 2008; Delgado-

Baquerizo et al., 2013; O’Sullivan et al., 2013; Sher et al., 2013; Zhang et al., 2012), but

were designed from archaeal amoA gene sequences from the Sargasso Sea and soil from

Germany (Francis et al., 2005). Diverse biogeographical regions have communities of

ammonia-oxidising microorganisms that are distinctly different (Fierer et al., 2009;

Ch. 6: Discussion

154

Pester et al., 2012), and Western Australia is a unique environment that has been

geographically isolated, deeply weathered and stable for millions of years (McKenzie et

al., 2004). Therefore the possibility that AOA in this region are genetically divergent

from those in other parts of the world cannot be dismissed. Characterisation of the

genetic diversity of native AOA populations and design of appropriate amoA primers

will facilitate a better understanding of ammonia oxidiser regulation of nitrification and

N loss in this region.

The interactions between ammonia oxidisers and N loss in the semi-arid agricultural

soils of south-western Australia may be further unravelled by applying other methods

and technologies besides detection of amoA gene abundances. These methods include

detecting ribonucleic acid (RNA) transcripts, metagenomics and metatranscriptomics,

and the use of radioactive tracers, stable isotope probes and fluorescent markers.

Detection of amoA messenger RNA (mRNA) transcripts allows tracking of genes that

are being actively expressed in response to short-term treatment effects (for example

Gubry-Rangin et al., 2010; Placella and Firestone, 2013). This is in contrast to detection

of amoA gene abundance (i.e. population growth and decline), changes of which take

longer to occur. In addition, presence of amoA genes in the microbial population is also

not enough to determine whether those ammonia oxidisers are in fact active regulators

of nitrification: the genes may not be expressed, or the gene transcripts or enzymes

might be inactivated (Di et al., 2009; Mußmann et al., 2011).

Metagenomics characterises all genes present in a sample, providing information about

the potential functions of the microbial community, while metatranscriptomics

characterise all transcripts of mRNA in order to describe what the microbial community

is actually doing (Carvalhais et al., 2012; Prosser, 2015). These techniques are best used

Ch. 6: Discussion

155

for investigating spatial and temporal dynamics of the whole microbial community in

response to environmental changes (Prosser, 2015). In this soil the use of metagenomics

and metatranscriptomics would give a clearer idea of the functional groups or

phylotypes that are driving N loss processes of N2O and NO3- production, in response to

triggers such as wetting and drying events due to summer and autumn rainfall that may

resuscitate different N cycling microorganisms from dormancy at different speeds.

These techniques may also identify processes that have not previously been associated

with N loss processes in this environment, such as nitrate ammonification or

heterotrophic nitrification.

Two methods that use radioactive tracers are fluorescence in situ hybridisation (FISH)

combined with microautoradiography (Lee et al., 1999) and the isotope array

(Adamczyk et al., 2003). Combining FISH with microautoradiography allows

identification of fluorescent and radioactive cells that are metabolising a radiolabelled

substrate (Lee et al., 1999). An example of how FISH-microautoradiography has been

used is to show that AOB in a waste water treatment plant are active autotrophic

ammonia oxidisers, but archaea able to encode AMO did not have autotrophic

metabolism, despite having the genes to carry out ammonia oxidation (Mußmann et al.,

2011). In order to target mRNA that is expressed in low amounts, catalysed reporter

deposition can also be combined with FISH (CARD-FISH) to increase the signal

intensities of fluorescence labels (Pernthaler and Amann, 2004; Speel et al., 1999).

Pratscher et al. (2011) used CARD-FISH to visually investigate expression of AOA

amoA mRNA simultaneously with archaeal 16S rRNA, to show a high abundance of

AOA relative to the total archaeal population in an agricultural soil. The isotope array

similarly tracks the incorporation of a radiolabelled substrate into rRNA, and allows

Ch. 6: Discussion

156

high-throughput screening and the ability to apply many probes in parallel (Adamczyk

et al., 2003).

Stable isotopes may be used for more direct measurement of ammonia oxidisers by

nano-scale stable isotope mass spectrometry (NanoSIMS) and stable isotope probing

(SIP). NanoSIMS allows high resolution (submicron length scales) imaging of stable

isotopic ratios and elemental mapping, so isotopic tracers can be used to follow

individual cells of microorganisms that have assimilated stable isotopic tracers (Clode et

al., 2009; Lechene et al., 2007; Wagner, 2009). Stable isotope probing allows the

identification of cellular components, such as DNA or RNA, of microorganisms which

have assimilated a substrate labelled with a stable isotope such as 13C, and are

subsequently using the labelled substrate for growth or transcription (Dumont and

Murrell, 2005). Stable isotope probing can be used to demonstrate ammonia oxidation,

assuming it is coupled to autotrophic carbon dioxide (CO2) fixation, and has been used

to show the importance of AOA over AOB for ammonia oxidation in agricultural soils

(Pratscher et al., 2011; Zhang et al., 2012).

Applying these technologies to south-west Australian semi-arid soils, in combination

with specific AOA primers for this region, may answer outstanding questions about

factors regulating ammonia-oxidising populations and activity. These questions include:

whether agricultural practices have changed the relative dominance of AOA and AOB;

what the response of AOB activity is to changing management practices; and how AOB

populations and activity can be managed to decrease growth and production of NO3-

during summer fallow.

Ch. 6: Discussion

157

The possibility of heterotrophic nitrification needs further investigation as an important

pathway of NO3- production and risk of N loss in this semi-arid agricultural soil. The

findings in Chapter 4 raised this possibility, either through heterotrophic or mixotrophic

growth of AOB, or heterotrophic nitrification by other microorganisms. This was due to

a lack of correlation between AOB amoA gene abundance and soil NH4+ content, but a

correlation between AOB and DOC (Table 4.2). Modelling of N cycling in a similar

Western Australian semi-arid soil suggested that approximately 50% of nitrification

could be explained by heterotrophic nitrification, in that case production of NO3-

directly from organic N (Cookson et al., 2006b). There is also evidence from cultures

that AOB are able to assimilate LMWOM compounds and have genes coding for

organotrophic metabolisms (Sayavedra-Soto and Arp, 2011; Schmidt, 2009; Walker et

al., 2010).

Specific investigations need to be carried out in this semi-arid soil to quantify the

relative importance of heterotrophic nitrification compared to autotrophic nitrification.

Quantifying heterotrophic nitrification may be difficult by enumeration using specific

metabolites or molecular methods, due to the diverse metabolic activities that can be

coupled to nitrification and the polyphyletic nature of heterotrophic bacteria (De Boer

and Kowalchuk, 2001). Molecular targets developed for functional genes encoding

enzymes of specific groups of heterotrophic nitrifiers also may not fully reveal the role

of those detected nitrifiers in soil N transformations, as those enzymes can catalyse a

variety of other reactions (De Boer and Kowalchuk, 2001). However, a combination of

methods may allow investigation of heterotrophic nitrification compared to autotrophic

nitrification, including selective inhibition of autotrophic nitrifiers (for example by

acetylene; Persson and Wirén, 1995), use of 15N tracers or isotopic pool dilution (for

example Islam et al., 2007; Pedersen et al., 1999), removal of CO2 from soil atmosphere

Ch. 6: Discussion

158

(since autotrophs are reliant on CO2 for C fixation while heterotrophs incorporate C

from organic compounds) (for example De Boer et al., 1991) and measurement of

fixation of labelled CO2 into cell biomass (for example Kreitinger et al., 1985).

Management techniques that are targeted at decreasing autotrophic nitrification, or

stimulating heterotrophic activity at the expense of autotrophs, may not be effective at

decreasing the risk of N loss if heterotrophic nitrification accounts for a significant

proportion of total nitrification.

6.4.2. Field application of nitrapyrin

Nitrapyrin has potential to decrease risk of N loss during summer fallow in semi-arid

soils by minimising conversion of mineralised NH4+ to NO3

-, but needs to be tested

under field conditions. Several issues are still unclear, as follows.

How to target nitrapyrin application to prevent nitrification in response to

sporadic summer rainfall events (the time when significant production of

inorganic N can occur).

What rate, method and timing of nitrapyrin application should be used.

If nitrapyrin is equally effective in other soil types besides the sandy soil used in

the present study, particularly with differing OM contents.

If manipulating inorganic N forms (i.e. retaining NH4+ and preventing NO3

-

production) influences crop growth and yields in the following growing season.

If nitrapyrin is economically viable for land managers to use, considering the

extra cost and labour required against any potential yield and environmental

benefits.

Ch. 6: Discussion

159

Effectiveness of nitrapyrin in the field will depend on a variety of interacting factors.

The present study was conducted under laboratory conditions, which allow homogenous

application of nitrapyrin to soil samples, and losses by volatilisation are easy to control

in closed incubation vessels. In the field, the rate, method, and timing of application all

affect how persistent and effective nitrapyrin is at inhibiting nitrification, by influencing

loss rates of nitrapyrin (by volatilisation and leaching) and through the interaction

between nitrapyrin and environmental and soil variables such as nitrifying microbial

populations, substrates for nitrification, and soil OM content (Keeney, 1980). Under

certain conditions, for example with alkaline soils and fertiliser broadcast onto the soil

surface, nitrification inhibitors may in fact increase N losses by enhancing ammonia

volatilisation from NH4+-based fertilisers (Arregui and Quemada, 2006).

The costs and effort of nitrapyrin application for land managers also need to be

balanced with the potential benefits for farm profitability (through increased crop yields

or decreased N fertiliser application), and environmental benefits (through decreased

N2O emissions and NO3- leaching). Nitrification inhibitors, particularly in the Midwest

Cornbelt of the USA are often used as an insurance policy against N loss and thus

making yield responses to applied fertiliser more probable, although annual variability

in crop, environment and management factors creates uncertainty in the realisation of

these economic benefits from year to year (Nelson and Huber, 1980; Wolt, 2004). In the

rainfed annual cropping systems of semi-arid south-western Australia, annual yields are

particularly reliant on the timing and amount of rainfall, and N fertiliser additions are

generally low and targeted to expected growing season rainfall. In this region, economic

benefits of applying nitrapyrin may therefore be difficult to quantify at the farm scale,

although environmental benefits of decreased N loss to the environment may be seen

over broader scales of time and space.

Ch. 6: Discussion

160

6.4.3. Other methods to manage risk of nitrogen loss

Other effective approaches need to be found to manage the risk of N loss and

asynchrony of N supply to crops. Due to the dominating influence on N cycling of

seasonal changes in soil water availability and temperature, the nature of annual rainfed

agriculture, and since most inorganic N at risk of loss is produced outside the cropping

period in these regions, finding options for managing risk of N loss will likely prove

difficult. Methods that may have potential to decrease risk of N loss include

management of OM mineralisation by understanding and manipulating the

mineralisation gene cascades, and increasing the duration and extent of the active

rhizosphere to provide available C to microbial N immobilisers and recapture any

mineralised N.

Managing OM mineralisation during summer fallow may be possible by disconnecting

the flow from breakdown of high molecular weight OM to NH4+ production. This

mineralisation cascade can be monitored by a series of genes that encode fungal and

bacterial enzymes for OM and cellulose decomposition and soil organic N release (Bach

et al., 2001; Edwards et al., 2008; Kellner et al., 2007). Evidence suggests that sandy

soils cannot stabilise extracellular proteolytic enzymes, so breakdown of proteins is

reliant on continued transcription of the genes encoding these enzymes (Fuka et al.,

2008). A significant proportion of semi-arid south-western Australia has coarse-textured

surface soils (McArthur, 2004; Schoknecht, 2002), so if production of extracellular

proteolytic enzymes could be inhibited, then OM mineralisation in these soils might be

controlled. Chemicals that have been found to inhibit protease and peptidase activities,

and may have potential for OM mineralisation control include flavonoids, biflavones in

root exudates, plant hormones and tannins (Vranova et al., 2013). Besides inhibition of

these enzymes, investigation of the influence of agricultural management and soil

Ch. 6: Discussion

161

factors (such as timing and method of tillage) on N decomposition and mineralisation

functional gene abundances may highlight ways to manage mineralisation, especially

during summer fallow.

The importance of an active rhizosphere to supply easily available C to microbial N

immobilisers, as emphasised in the present study (Chapter 3), suggests that increasing

the extent and duration of plant root growth may decrease the risk of N loss. Annual

farming systems in rainfed, winter rainfall dominant semi-arid regions have limited

potential for increasing root growth, due to the dependence of plant growth on natural

patterns of water availability. Selection for crop varieties with traits such as early

growth vigour and increased root branching may increase rhizodeposition in the early

growing season, as will management of soil constraints that restrict root growth, such as

compaction and subsoil acidity (Dunbabin et al., 2003; Hoad et al., 2001; Lynch, 1995).

The asynchrony of N supply and crop N demand, exacerbated by asynchrony of

available C supply from plant roots, might also be mitigated by planting summer crops

or by integration of perennial species into the annual cropping system. Short duration

drought tolerant summer crops such as millet (a C4 grass) and cowpea (a tropical

legume) are currently being trialled by the Western Australian No-Tillage Farmers

Association (WANTFA, 2014). These are sown late in winter and may be used as a

cover crop or harvested if there has been enough summer rainfall. Besides decreasing N

losses, hypothesised benefits of these summer crops include decreasing summer weeds

(and the cost of spraying), and improving non-wetting soils by increasing water

infiltration pathways through root growth. There are concerns though about whether

these summer crops might use water and nutrients that normally accumulate over

summer, to the detriment of the following winter crop (WANTFA, 2014).

Ch. 6: Discussion

162

There are several ways that perennials might be integrated into the annual cropping

system. These include alley cropping, where annual crops are planted between rows of

perennial shrubs or trees; by intercropping or companion cropping, where cereals are

oversown into perennial pastures such as lucerne (alfalfa); or by perennial cropping, for

example utilising nut or seed producing trees (Abdel Magid et al., 1991; Crews and

Peoples, 2004; Crews and Peoples, 2005). Development of perennial grain crops has

also been suggested, but will require long-term investment in breeding programmes

before these are feasible (Cox et al., 2002). The balance between perennial and annual

crops however needs to be carefully managed to avoid competition for water and

nutrients between annuals and perennials which can lead to decreased yields, especially

in dry years (Angus et al., 2000).

6.5. Conclusions

This thesis makes several contributions to the current understanding of N cycling and

loss in semi-arid rainfed agricultural soils, particularly those with winter-dominant

rainfall. Variation in the risk of N loss from these soils is predominantly related to

seasonal variation in rainfall and temperature, and supply of microbially available C

inputs from root exudates. Ammonia-oxidising bacteria regulate nitrification in the

surface of the semi-arid soils examined in this study, and production of NO3- during

summer fallow is linked to increases in AOB abundance, likely in response to rainfall

events. Although AOA were detected in the subsoil, they were negatively correlated to

gross nitrification rates, suggesting that these microorganisms are not dependent on

autotrophic ammonia oxidation, and are not important regulators of risk of N loss in this

environment.

Ch. 6: Discussion

163

Management of the risk of N loss is not likely to be effective by building soil OM

content through crop residue inputs, as increased C and N substrates supplied by

residues tended to stimulate N transformation rates overall. However, the nitrification

inhibitor, nitrapyrin, has potential to retain mineralised N in soil at elevated soil

temperatures during summer fallow, although this needs to be evaluated under field

conditions along with other methods of controlling the risk of N loss.

This thesis set out to gain a better understanding of microbially-mediated N cycling, in

order to manage risk of N loss from semi-arid rainfed agricultural soils (specific

questions, hypotheses and their answers are set out in Table 6.1). Taken together, the

findings of this thesis suggest that annual cropping systems in semi-arid regions restrict

options for management of N loss, particularly during summer fallow. This is due to the

prevailing influence of variation in rainfall and temperature on risk of N loss, and

production of inorganic N outside the cropping period. Management to decrease risk of

N loss therefore might need to involve changes in the annual cropping system, such as

including more perennials or crops during summer to decrease accumulation of

inorganic N at this time.

Tabl

e 6.

1. S

peci

fic th

esis

ques

tions

as s

et o

ut in

Cha

pter

1, t

he c

hapt

er in

whi

ch e

ach

ques

tion

was

ans

wer

ed, r

elat

ed h

ypot

hese

s and

ans

wer

s.

The

sis Q

uest

ion

Cha

pter

H

ypot

hese

s A

nsw

er to

Que

stio

n

Wha

t env

ironm

enta

l and

bio

chem

ical

fact

ors c

ontri

bute

to te

mpo

ral v

aria

tion

in

risk

of N

loss

?

Cha

pter

s 3

and

4

Wat

er a

vaila

bilit

y (r

ainf

all a

nd so

il w

ater

cont

ent),

soil

tem

pera

ture

, C

avai

labi

lity

(tota

l soi

l C a

nd ro

ot e

xuda

te C

).

Rain

fall,

soil

tem

pera

ture

, roo

t exu

date

C.

How

do

tota

l soi

l C a

nd ro

ot e

xuda

te C

affe

ct N

cyc

ling

and

risk

of lo

ss?

Cha

pter

3

Incr

easin

g to

tal s

oil C

and

root

exu

date

C

will

dec

reas

e ris

k of

N lo

ss b

y in

crea

sing

N im

mob

ilisa

tion.

Tota

l soi

l C in

crea

ses N

cyc

ling

over

all b

ut d

oes n

ot c

hang

e th

e ris

k of

N

loss

. Roo

t exu

date

C st

imul

ates

N im

mob

ilisa

tion

over

nitr

ifica

tion

so

decr

ease

s ris

k of

N lo

ss.

How

do

amm

onia

-oxi

disin

g po

pula

tions

vary

with

dep

th, s

easo

n an

d ag

ricul

tura

l

man

agem

ent?

Cha

pter

s 4

and

5,

App

endi

x A

AO

A a

nd A

OB

will

dec

reas

e w

ith d

epth

as N

subs

trate

s dim

inish

.

Bot

h A

OA

and

AO

B w

ill b

e gr

eate

r

durin

g th

e w

inte

r gro

win

g se

ason

than

durin

g su

mm

er fa

llow

.

AO

A a

nd A

OB

will

be

enha

nced

by

crop

resi

due

inpu

ts a

nd n

o til

lage

, but

will

be

decr

ease

d by

tilla

ge a

nd st

ubbl

e bu

rnin

g.

AO

A a

re lo

w to

non

-exi

sten

t in

surf

ace

soils

(0–1

0 cm

) but

incr

ease

with

dept

h.

AO

B ar

e m

ore

abun

dant

than

AO

A in

surf

ace

soils

, and

dec

reas

e w

ith

dept

h.

AO

B in

crea

se in

abu

ndan

ce in

surf

ace

soils

ove

r sum

mer

fallo

w, a

nd

decr

ease

ove

r the

win

ter g

row

ing

seas

on.

AO

B ab

unda

nce

is g

reat

er in

tille

d so

il w

ith a

dditi

onal

cro

p re

sidu

e

inpu

ts th

an in

soil

with

no

tilla

ge, b

urnt

stub

ble

or ti

llage

with

out c

rop

resi

due

inpu

ts.

Tabl

e 6.

1. c

ontin

ued

on n

ext p

age.

Tabl

e 6.

1. S

peci

fic th

esis

ques

tions

as s

et o

ut in

Cha

pter

1, t

he c

hapt

er in

whi

ch e

ach

ques

tion

was

ans

wer

ed, r

elat

ed h

ypot

hese

s and

ans

wer

s

(con

tinue

d).

The

sis Q

uest

ion

Cha

pter

H

ypot

hese

s A

nsw

er to

Que

stio

n

How

are

am

mon

ia-o

xidi

sing

popu

latio

ns

rela

ted

to o

ther

soil

envi

ronm

enta

l and

bioc

hem

ical

fact

ors?

Cha

pter

4,

App

endi

x A

AO

A a

nd A

OB

abun

danc

e w

ill b

e

posi

tivel

y re

late

d to

rain

fall,

soil

wat

er

cont

ent,

MBC

and

NO

3- con

cent

ratio

ns;

nega

tivel

y re

late

d to

soil

tem

pera

ture

,

DO

C a

nd to

tal s

oil C

;

but n

ot re

late

d to

NH

4+ con

cent

ratio

ns.

AO

B ab

unda

nce

in su

rfac

e so

il w

as p

ositi

vely

rela

ted

to so

il N

O3-

conc

entra

tions

, mic

robi

al b

iom

ass C

and

DO

C.

AO

B ab

unda

nce

in su

rfac

e so

il w

as n

ot re

late

d to

soil

NH

4+

conc

entra

tions

, pot

entia

lly m

iner

alis

able

N, w

ater

con

tent

at t

ime

of

colle

ctio

n, so

il te

mpe

ratu

re o

r rai

nfal

l ove

r the

pre

viou

s 30

days

bef

ore

colle

ctio

n.

AO

B ab

unda

nce

with

dep

th w

as p

ositi

vely

rela

ted

to to

tal s

oil C

.

AO

A a

bund

ance

with

dep

th w

as n

egat

ivel

y re

late

d to

tota

l soi

l C.

Doe

s inc

reas

ing

soil

tota

l C th

roug

h

addi

tiona

l cro

p re

sidu

e in

puts

decr

ease

the

risk

of N

loss

?

Cha

pter

3

Yes

. N

o.

Doe

s the

nitr

ifica

tion

inhi

bito

r nitr

apyr

in

decr

ease

risk

of l

oss o

f NH

4+ pro

duce

d by

OM

min

eral

isat

ion

unde

r tem

pera

ture

and

wat

er a

vaila

bilit

y co

nditi

ons t

hat m

ay o

ccur

durin

g su

mm

er?

Cha

pter

5

Yes

. Y

es.

Ch. 6: Discussion

166

167

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201

Appendix A.

Ammonia-oxidising bacteria not archaea dominate

nitrification activity in semi-arid agricultural soil

(With Supplementary Information)

App. A: Ammonia Oxidisers with Depth

202

Ammonia-oxidising bacteria not archaea dominate nitrification

activity in semi-arid agricultural soil

Natasha C. Banning, Linda D. Maccarone, Louise M. Fisk and Daniel V. Murphy*

Soil Biology and Molecular Ecology Group, School of Earth and Environment, Institute

of Agriculture, The University of Western Australia, Crawley, WA 6009, Australia.

* Corresponding author: [email protected]

Ammonia-oxidising archaea (AOA) and bacteria (AOB) are responsible for the rate

limiting step in nitrification; a key nitrogen (N) loss pathway in agricultural systems.

Dominance of AOA relative to AOB in the amoA soil gene pool has been reported in

many ecosystems globally, although their relative contributions to nitrification act ivity

are less clear. Here we examined the distribution of AOA and AOB with depth in semi-

arid agricultural soils in which soil organic matter content or pH had been altered, and

related their distribution to gross nitrification rates. Soil depth had a significant effect on

gene abundances, irrespective of management history. Contrary to reports of AOA

dominance in soils elsewhere, AOA gene copy numbers were four-fold lower than AOB

in the surface (0–10 cm). AOA gene abundance increased with depth while AOB

decreased, and sub-soil abundances were approximately equal (10–90 cm). The depth

profile of total archaea did not mirror that of AOA, indicating the likely presence of

archaea without nitrification capacity in the surface. Gross nitrification rates declined

significantly with depth and were positively correlated to AOB but negatively correlated

to AOA gene abundances. We conclude that AOB are the dominant population

regulating nitrification in these semi-arid soils.


203

Introduction

Nitrification, the microbially-mediated process which converts ammonia to nitrate

(through nitrite), is the major pathway by which nitrogen (N) can be lost from terrestrial

ecosystems. The autotrophic ammonia-oxidising bacteria (AOB), which produce the

key functional enzyme ammonia monooxygenase (AMO), have historically been

thought solely responsible for most ammonia oxidation, the rate limiting step in

nitrification, in terrestrial and aquatic ecosystems. This changed following the discovery

of intact amoA genes in mesophilic Crenarchaeota, members of the domain Archaea1.

Ammonia-oxidising archaea (AOA) have since been shown to numerically dominate

AOB in several European agricultural and pristine soils2 and subsequently elsewhere3,4.

More recent phylogenetic analyses have placed the AOA in the new phylum

Thaumarchaeota5.

In soils globally, the availability of inorganic nitrogen (ammonium and nitrate)

is important to plant nutrition and can regulate net primary productivity. In agricultural

soils, nitrogen loss (facilitated by nitrification) can be substantial, decreasing the

efficiency of nitrogen fertilizer use at huge economic cost6. Furthermore, nitrogen loss

through nitrate leaching contributes to groundwater pollution and the conversion of

nitrate to nitrous oxide (N2O) via denitrification pathways contributes to soil greenhouse

gas emissions7. One major question yet to be answered is to what extent AOA are

important in the nitrification process.

The presence of an amoA gene, transcript or protein is not sufficient to infer in

situ ammonia oxidization activity5. Consequently, there is considerable uncertainty

regarding the relative contributions of AOB and AOA to soil nitrification. Recently,

there has been evidence that AOA functionally dominate in acidic soils (with pH <

5.5)8. Elsewhere, AOB have been shown to dominate nitrification activity, even where

they were numerically less dominant than AOA9-11.


204

The relative importance of AOA vs. AOB to nitrification in semi-arid

agricultural soils also remains unclear. Semi-arid and arid lands constitute one-third of

the global land area and are widely used for agricultural production7. In the semi-arid

south western Australian grain-belt AOA have been found to be either similar to or less

abundant than AOB, in surface soil horizons12,13. The region typically has acidic sandy

soils with low organic matter content, where inorganic nitrogen fertilizers are required

for crop production. As two of the main drivers thought to provide a competitive niche

for AOA over AOB are a low soil pH14,15 and low substrate (i.e. ammonia)

availability16, the low abundance of AOA in earlier studies was unexpected17. However,

it has also been hypothesized that nitrogen supply through inorganic fertilizer

application, as opposed to an organic nitrogen supply pathway, may favour AOB

activity in agricultural soils5,18.

The impact of nitrogen fertilizer application as well as other agricultural

practices (e.g. liming, organic matter amendment, tillage) is predominantly in the soil

surface horizon and it has not been investigated whether the higher relative abundance

of AOB persists below the surface soil layer or if the surface dominance of AOB is

management induced. Soils exhibit strong environmental gradients with depth and little

is known more widely about the distribution of AOA and AOB abundance and function

down the soil profile. In many terrestrial ecosystems, microbial biomass and activity

declines with increasing depth19,20. However, studies have found AOA abundance either

stays relatively constant with soil depth or even increases with soil depth (this was

observed in an analysis of total archaeal abundance in which most of the population was

identified as Thaumarchaeota)20, while AOB abundance generally declines2,10.

As such, the aims of this study were to i) quantify the distribution of AOA and

AOB in the profile of semi-arid soils, ii) examine the relationship between gross


205

nitrification rates and amoA gene abundance of AOA and AOB, and iii) determine the

influence of soil pH and soil organic matter on AOA and AOB populations.

Results and Discussion

Irrespective of agricultural management, there were distinct depth profiles of both AOA

and AOB populations. AOB populations were significantly higher (P<0.001) in the

surface layer (0–10 cm) compared to the sub-soil (10–90cm; Fig. 1a). This was

consistent with the depth distribution of total bacterial population (Fig. 1b) and was

expected given depth gradients in soil organic matter, nutrients and aeration. In contrast

the AOA population was low in the surface layer but of similar magnitude to AOB

below 10 cm (Fig. 1a). The low AOA population in the surface was not mirrored by the

total archaeal population which varied little with depth (Fig. 1b). This suggests that in

the surface soil horizon at all sites there is either (i) a population of AOA that is not

detected with the primers used in this study or (ii) a large population of non-ammonia-

oxidising archaea, such as the Euryarchaeota (which includes methanogens) or group

1.1c Thaumarchaea which have been found in many acidic soils but have no known link

to amoA phylogeny8,21.


206

Fig. 1. Abundance of bacterial and archaeal amoA genes (a) and bacterial and archaeal

16S rRNA genes (b) in Western Australian semi-arid agricultural soils. Data points

represent means of three soil cores (per soil layer) collected from trial sites where soil

pH (-lime versus +lime) or soil organic matter (-OM versus +OM) had been historically

altered. Error bars represent ± 1SE. The mean of all treatments combined at each depth

are shown by the dashed line (archaea) or solid line (bacteria). Note: gene copy numbers

are plotted on a log10 scale.

The primers used in this study, developed by Francis et al.22 for use in the

marine environment, have been widely used for qPCR determination of AOA

abundance in acidic agricultural soils12,13,15,23 and elsewhere24,25. The primers amplify a

near full-length amoA gene product (635 bp) and thus in silico primer analysis is limited

by the availability of full-length amoA sequences. Nonetheless, a limited in silico

analysis of all available archaeal amoA sequences (n = 15; as published previously26)

covering the primer target regions (with the exception of the first three bases of the

0

10

20

30

40

50

60

70

80

90

1E+2 1E+4 1E+6 1E+8 1E+10

De

pth

mid

-po

int (

cm)

amoA gene copies g dry soil -1

AOA

AOB

0

10

20

30

40

50

60

70

80

90

1E+2 1E+4 1E+6 1E+8 1E+10

16S rRNA gene copies g dry soil -1

Archaea

Bacteria

102 104 106 108 1010 102 104 106 108 1010

(a) (b)


207

forward primer which was only covered by the soil fosmid clone 54d91), revealed

between 0 and 2 mismatches with the forward primer and between 0 and 3 mismatches

with the reverse primer. However, the majority of the mismatches were with AOA

associated with, or isolated from, non-soil environments (marine, estuarine, hot spring

or the sponge symbiont Cenarchaeum symbiosum). There were no mismatches with

either primer for the sandy soil fosmid clone 54d91 or the two Nitrososphaera

sequences (belonging to “group 1.1b” which have been shown to be the dominant AOA

in many soils21,26. Furthermore, none of the mismatches that were present were in the

five bases at the 3’ end of the forward primer or the 5’ end of the reverse primer, the

regions where target-primer matches are the most important for successful PCR

amplification27. Thus, there was no evidence of AOA amplification being restricted by

primer coverage limitations. However, geographic location on the continental scale has

been purported to effect soil AOA population structure26 and it cannot be ruled out that

these deeply weathered, ancient and geographically isolated Western Australian soils28

harbor novel AOA not detected by the current primers.

Nonetheless, we hypothesize that annual applications of inorganic nitrogen (20–

100 kg N ha-1) have favoured AOB over AOA. Previous studies have indicated that

AOA may have a competitive advantage at low ammonia concentrations due to their

higher substrate affinity29 or possibly due to higher sensitivity to growth inhibition at

high ammonia concentrations16. Activity of soil AOA is generally detected below 15 µg

NH4+-N per g soil5, although this is not always the case30. The measured ammonium

concentrations in this study were all low (< 1 µg NH4+-N per g soil) at the time of

sampling, and declined with depth. However, soil was collected in summer prior to

cropping and this does not reflect the historical annual applications of urea and

inorganic ammonium-based fertilizers, which may have contributed to the numerical

dominance of AOB.


208

A subsequent investigation of archaeal amoA gene abundance in the surface 0–

10 cm of native remnant bushland adjacent to each of the agricultural trial sites and on

the same soil type measured mean archaeal amoA gene abundances of 2.6x104 and

1.4x104 gene copies per g of soil at Buntine and Wongan Hills, respectively. This is

approximately an order of magnitude higher than AOA abundance in the 0–10 cm

depths of the agricultural soils, irrespective of treatment, potentially indicating a decline

in AOA with agricultural management. A study of Scottish soils has previously

provided evidence of a land use relationship with ammonia oxidiser communities with

an increase in abundance of AOB in the agricultural ecosystems compared to the natural

ecosystems surveyed, although AOA were always numerically more dominant18.

Examination of the relationship between changing AOB and AOA abundance

with depth and two purported drivers of niche specialization between ammonia

oxidisers, soil pH and substrate availability (as indicated by soil organic matter content),

revealed significant positive correlations between these factors and AOB abundance and

negative correlations with AOA abundance (Fig. 2b,d). This suggests that AOA are

better competitors in the more acidic, organic matter depleted soil conditions at depth

which is in agreement with trends observed elsewhere8 and in physiological studies with

the limited number of AOA in cultivation to date16. However, other variables such as

water, oxygen and temperature, will also exhibit gradients with soil depth and may also

play a role in regulating population abundances to depth.

In addition to the trends with depth this study also examined the effect of direct

manipulation of soil pH through liming and indirect manipulation of substrate

availability through organic amendment on ammonia oxidiser population abundance.

The depth profiles demonstrate that, as expected, the influence of these agricultural

practices was predominantly in the surface 10 cm (Fig. 2a,c). Increases in soil pH

through liming (to near-neutral pH) did not alter total bacterial or archaeal abundances


209

(16S rRNA gene copies), nor did it influence the uniformly low AOA abundance.

However, the increased pH had a positive effect on AOB abundance in the surface 0–20

cm layer (Supplementary Fig. S1). This is in agreement with a study of acidic tea

orchard soils in China which reported evidence of pH exerting a greater influence on

AOB abundance than AOA abundance31.

Increases in total organic carbon in response to the addition of extra plant

residues (+OM treatment) were mirrored by increases in total nitrogen and nitrate and

did not alter the soil pH. This suggests that, although the ammonium pool size remained

low (< 1 µg NH4+-N per g soil), the assumption that organic amendment increases

substrate (ammonia) availability holds. The abundance of 16S rRNA and amoA genes

from both bacteria and archaea was found to decrease in the surface layer in the +OM

treatment (Supplementary Fig. S1). This was surprising as the addition of extra organic

carbon and nutrients was expected to increase the size of the prokaryotic community in

general. However, gross nitrification rates were still higher in the surface soils of the

+OM treatment (Fig. 3a), suggesting a more active ammonia-oxidising community.

Although total carbon levels were the same in the sub-soil in the OM trial, all measured

populations were generally more abundant below 15 cm in the +OM treatment. It is

possible this was due to downward movement of dissolved and particulate organic

matter. This pool is known to provide a major energy source for microorganisms and

has been quantified to contribute as much as 42–49% of gross nitrification activity in a

similar agricultural soil32.


210

Fig. 2. Depth profiles (0–90 cm) of soil carbon (a) and pH (c) in Western Australian

agricultural soils collected from trial sites where soil pH (-lime versus +lime) or soil

organic matter (-OM versus +OM) had been historically altered and the correlation of

soil carbon and pH with bacterial and archaeal amoA gene abundance (b and d). Error

bars represent ± 1SE. Trendlines show a log-linear fit (all regressions significant at P <

0.001). Soil pH was determined in a 1:5 (w/w) soil suspension in 0.01 M CaCl2. Note:

gene copy numbers are plotted on a log10 scale.

R² = 0.46

R² = 0.621E+2

1E+3

1E+4

1E+5

1E+6

1E+7

1E+8

0.0 0.5 1.0 1.5 2.0 2.5

Log 1

0a

mo

Age

ne

co

pie

s g

dry

so

il -1

Soil C (%)

AOB

AOA

R² = 0.28

R² = 0.19

1E+2

1E+3

1E+4

1E+5

1E+6

1E+7

1E+8

3.0 4.0 5.0 6.0 7.0 8.0

Log 1

0a

mo

Age

ne

co

pie

s g

dry

so

il -1

Soil pH (CaCl2)

AOB

AOA

0

10

20

30

40

50

60

70

80

90

0 0.5 1 1.5 2 2.5

De

pth

mid

-po

int (

cm)

Soil C (%)

+lime

-lime

-OM

+OM

0

10

20

30

40

50

60

70

80

90

3 4 5 6 7 8

De

pth

mid

-po

int (

cm)

Soil pH (CaCl2)

+lime

-lime

-OM

+OM

(a) (b)

(c) (d)

102

103

104

105

106

107

108

102

103

104

105

106

107

108


211

Fig. 3. Depth profiles (0–30 cm) of actual (a) and potential (c) gross nitrification rates in

Western Australian agricultural soils collected from trial sites where soil pH (-lime

versus +lime) or soil organic matter (-OM versus +OM) had been historically altered

and the correlation of nitrification rates with bacterial and archaeal amoA gene

abundance (b and d). Error bars represent ± 1SE. Trendlines show a log-linear fit (all

regressions were significant at P <0.001 for all except actual nitrification-logAOB

where P = 0.003). Note: gene copy numbers are plotted on a log10 scale.

R² = 0.33 R² = 0.14

0

1

2

3

4

5

1E+2 1E+3 1E+4 1E+5 1E+6 1E+7 1E+8

Act

ual

gro

ss n

itri

fica

tio

n (

mg

N k

g-1d

-1)

Log amoA gene copies g dry soil-1

AOAAOB

0

5

10

15

20

25

30

0 1 2 3 4 5

De

pth

mid

-po

int (

cm)

Actual gross nitrification (mg N kg-1 d-1)

+lime

-lime

-OM

+OM

0

5

10

15

20

25

30

0 1 2 3 4 5

De

pth

mid

-po

int (

cm)

Potential gross nitrification (mg N kg-1 d-1)

+lime

-lime

-OM

+OM

R² = 0.44 R² = 0.28

0

1

2

3

4

5

1E+2 1E+3 1E+4 1E+5 1E+6 1E+7 1E+8

Po

ten

tial

gro

ss n

itri

fica

tio

n (

mg

N k

g-1d

-1)

Log amoA gene copies g dry soil-1

AOAAOB

(a) (b)

(c) (d)

102 104 106 108103 105 107

102 104 106 108103 105 107


212

In this study, 15N isotopic pool dilution was used to determine actual (no

ammonium addition) and potential (with ammonium addition) nitrification rates at in

situ pH values. Correlations between the abundance of ammonia oxidiser populations

and potential nitrification rates have been observed previously31,33. However, analyses

using potential nitrification assays34 are limited by the need to add substrate

(ammonium) and the use of incubation conditions adjusted to a neutral pH which may

inhibit species intolerant of those conditions16. The abundance of AOB was found to

positively correlate to both actual (P=0.003) and potential (P<0.001) gross nitrification

rates while AOA abundance was negatively correlated (P<0.001; Fig 3b, d). Gross

nitrification rates declined significantly with depth (P<0.001) at both sites (Fig 3a,c)

and declined significantly with liming treatment (P<0.001; actual nitrification rate and

P<0.05; potential nitrification rate). The addition of organic matter had no significant

effect on gross nitrification rates (P=0.109; actual nitrification rate and P=0.365;

potential nitrification rate). Our findings indicate that AOB are most likely responsible

for soil nitrification and is supported by previous studies on similar soil types within

this region13,17.

We conclude that AOB is the primary driver of nitrification in these semi-arid

agricultural soils. This is supported by (i) the niche separation of AOA and AOB

populations in these semi-arid agricultural soils with AOB populations dominant in the

surface, (ii) AOB abundance was positively correlated with gross nitrification rates

while AOA was negatively correlated and (iii) indication of a large proportion of the

archaeal population in the surface layer not having amoA genes, while this is where the

majority of nitrification occurs.


213

Methods

Soil collection. Soil was collected from two field trials on semi-arid agricultural soil

within the central grain growing region of Western Australia: an organic matter trial at

Buntine (30o 55’ S, 116o 21’ E) and a liming trial at Wongan Hills (30° 51’ S, 116° 44’

E). The region has a semi-arid climate, with hot, dry summers and cool, wet winters

(when cropping occurs).

At Buntine mean annual rainfall is 285 mm, mean monthly temperatures range

from 5.8–35.3 °C and actual temperatures range from -1.0–46.9 °C (calculated from 15

years of data, 1997–2014, Australian Bureau of Meteorology35). At the research site,

soil temperatures (5 cm depth) ranged from 6–46 °C (2008–2012). Soil at the site is a

deep sand (92% sand, 2% silt, 6% clay) and classified as a Basic Regolithic Yellow-

Orthic Tenosol (Australian soil classification36), or a Haplic Arenosol (World Reference

Base classification37). Soil organic matter (OM) treatments at Buntine were sampled

from plots (10 m × 18 m) that were either tilled only (-OM) or tilled with the addition of

extra plant residues (+OM) that had been surface applied at rate of 20 t ha-1 in 2003,

2006, 2010 and 2012 (12 days prior to sampling). This represented an additional 36 t ha-

1, of which 7.0 t of C ha-1 was retained as extra soil organic carbon in the +OM

treatment nine years after trial establishment (i.e. 64% more soil organic carbon in the

+OM treatments compared to the -OM treatments). Tillage was by means of offset disks

to 10 cm depth prior to seeding. Lime was applied to both treatments to maintain a

surface pH > 5.5 to prevent sub-soil acidification in accordance with regional

guidelines38.

At Wongan Hills, mean annual rainfall is 374 mm, with mean daily temperatures

ranging from 11.7 °C–25.3 °C (calculated from 30 years of data, 1981–2010, Australian

Bureau of Meteorology35). The soil at the experimental site is also a free-draining sand

classified as an Acidic Ferric Yellow-Orthic Tenosol (Australian soil classification36).


214

Soil pH treatments at Wongan Hills were sampled from plots (1 m × 4 m) that had

either been limed (+lime; 3.5 t ha-1 in March 2009) or not limed (-lime). Three years

after trial establishment the soil pH (CaCl2; 0–10 cm) was 4.4 in -lime and 5.5 in +lime

treatments.

Soil was collected in March (summer) of 2012, prior to crop establishment.

Three soil cores were collected from each replicate plot (n = 3) of each treatment. Soil

from the three soil cores were combined to produce one sample per field plot at each of

the following depth intervals (in cm): 0–2.5, 2.5–5, 5–7.5, 7.5–10, 10–20, 20–30, 30–60

and 60–90. Sub-samples for DNA extraction were frozen immediately upon collection

in a portable freezer and transferred to -20 °C within 1 h.

Nucleic acid extraction and qPCR. For each soil sample, DNA was extracted from

duplicate 800 mg sub-samples using UltraClean™ DNA Isolation Kit (MoBio

Laboratories Inc., Carlsbad, CA, USA). Cell lysis was performed using a Mini Bead

beater (BioSpec products, Inc., USA) at 2500 rpm for 2 minutes. Duplicate DNA

extractions were combined to give a total extract volume of 100 µl.

Functional genes, archaeal and bacterial amoA as well as archaeal and bacterial

16S rRNA genes were quantified using a 7500FAST qPCR machine (Applied

Biosystems, Life Technologies, USA). Each 20 µl qPCR reaction contained 10 µl of

Power SYBR® Green PCR Master Mix (Applied Biosystems), 0.2 µl of the specific

forward and reverse primer at a concentration of 10 µM, 2 µL BSA (Ambion Ultrapure

BSA; 5 mg ml -1), 2 µl of template DNA (8–115 ng) and 5.6 µL of water. DNA extracts

were tested over a series of dilutions to determine if there was inhibition and the

dilution which produced the highest copy number was used for further analysis. Primers

and thermal cycling conditions for both bacterial (primers amoA-1F and amoA-2R) and

archaeal (primers Arch-amoAF and Arch-amoAR) amoA genes were as described


215

previously13. Archaeal 16S rRNA gene primers Parch519F

(CAGCMGCCGCGGTAA39) and Arch915R ( GTGCTCCCCCGCCAATTCCT40)

were used with the following thermal cycling conditions: 94 °C for 5 min then 40 cycles

of 94 °C for 30 sec, 63 °C for 40 sec and 72 °C for 40 sec. Bacterial 16S rRNA gene

primers Eub338 (ACTCCTACGGGAGGCAGCAG41) and Eub518 (

ATTACCGCGGCTGCTGG42) were used with the following thermal cycling

conditions: 94 °C for 5 min then 40 cycles of 95 °C for 60 sec, 53 °C for 60 sec and 72

°C for 90 sec.

Melting curves were generated for each qPCR run and fluorescence data was

collected at temperatures above the Tm of the primers but below that of the target (78

°C for both amoA genes, 72 °C for archaeal and 75 °C for bacterial 16S rRNA genes) to

verify product specificity. Each qPCR reaction was run in triplicate. Standard curves

were generated using dilutions of linearized cloned plasmids. Template amplified with

each primer pair described above, was cloned with the P-GEM T-easy system

(Promega, USA), plasmid DNA extracted and inserts sequenced using Big Dye

Terminator chemistry (Australian Genome Research Facility, Western Australia) to

confirm correct length and identity. The standard curve gene sequences were as

described previously13. Standard curves generated in each reaction were linear over four

orders of magnitude (104 to 107 gene copies) with r2 values greater than 0.99.

Efficiencies for all quantification reactions were 80–100 %.

Gross nitrification. Gross nitrification rates were determined by 15N isotopic pool

dilution (see Murphy et al.43 for theory and methodological considerations) in soil

adjusted to 45 % water filled pore space and incubated at 25 °C. Subsamples of soil (20

g dry weight equivalent) were packed into 120 ml vials at a bulk density of 1.4 g cm-3.

To determine actual nitrification rates, 1 ml of 15N enriched (60 atom % excess) KNO3


216

was applied as multiple droplets to the vials to obtain a concentration of 5 µg N g -1 soil.

Potential nitrification rates were determined in separate vials by adding 1 ml of 15N

enriched (60 atom % excess) KNO3 (5 µg N g-1 soil) and (NH4)2SO4 at natural

abundance (5 µg N g-1 soil). The vials were incubated with the lids closed to avoid

water loss and aerated every 24 h. Extractions occurred 2 h and 96 h after 15N addition

with 80 ml of 0.5 M K2SO4 for 1 h on an end-over-end shaker, allowed to settle for 30

min and then filtered through Whatman No. 42 filter paper. Soil extracts were prepared

for 15N/14N isotope ratio analysis using a modified diffusion method 44,45 with

subsequent isotope ratio analysis (SERCON 20-22 mass spectrometer connected with

an Automated Nitrogen Carbon Analyzer; Sercon, UK). Gross nitrification was

calculated using the equation by Kirkham and Bartholomew46.

Statistical analyses. All data were statistically analyzed using mixed model Restricted

Maximum Likelihood (REML) repeated measures analysis using GenStat v14.047.

Skewed data was corrected by transforming to the natural logarithm prior to analysis.

AOB data could not be normalized by natural log transformation so was transformed by

log10 prior to analysis. A significance level of 5 % was used for all analysis and the

Power model (City block metric) was used allowing for variance heterogeneity.

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22 Francis, C. A., Roberts, K. J., Beman, J. M., Santoro, A. E. & Oakley, B. B. Ubiquity

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25 Szukics, U., Hackl, E., Zechmeister-Boltenstern, S. & Sessitsch, A. Rapid and

dissimilar response of ammonia oxidizing archaea and bacteria to nitrogen and water

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27 Banning, N. et al. Investigation of the methanogen population structure and activity in a

brackish lake sediment. Environ. Microbiol. 7, 947-960 (2005).

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30 Stopnisek, N. et al. Thaumarchaeal ammonia oxidation in an acidic forest peat soil is

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and Nitrification Potential in Acidic Soils. Appl. Environ. Microbiol. 77, 4618-4625

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Acknowledgements

This research was funded by the Australian Research Council (ARC), the Australian

Grains Research and Development Corporation’s Soil Biology Initiative II

(UWA00139), and The University of Western Australia. D.V.M. is the recipient of an

ARC Future Fellowship (FT110100246). L.M.F. is supported by an Australian

Postgraduate Award and University of Western Australia Safety Net Top-Up Award.

We are grateful to Hazel Gaza for laboratory assistance and to Deirdre Gleeson and Lori


221

Phillips for useful discussions. We thank the Liebe Group and Department of

Agriculture and Food Western Australia for maintaining field trials.

Author contributions

D.V.M. and N.C.B conceived the research and obtained funding. L.D.M., L.M.F. and

D.V.M designed the experiment. L.D.M and L.M.F. carried out the experiment. N.C.B

and D.V.M led the manuscript preparation with substantial inputs on data interpretation

from L.D.M. and L.M.F. All authors have reviewed the manuscript before submission.

Competing financial interests

The authors declare no competing financial interests.

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225

Appendix B.

Data not shown in Chapter 3

Figure B.1. Influence of temperature over 7 d of incubation on net nitrification

rates (a) without root exudates; and (b) with root exudates. Error bars are ±SEM (n

= 3), and may be smaller than the symbols. Legend is the same for both panels. Legend


App. B: Data Not Shown Ch. 3

226

Figure B.2. Total recovery of applied 15N from the NH4+, NO3

- and residual soil

pools, calculated by FLUAZ as percentage of 15N at T0 (4–6 h) that was recovered

at T1 (24 h), (a) without root exudates; and (b) with root exudates. Legend is the

same for both panels. Legend abbreviation: OM: organic matter.

227

Appendix C.

Data not shown in Chapter 4

Table C.1. Archaeal amoA gene abundance (gene copies g-1 dry soil). Actual values

are reported. Numbers in parentheses indicate number of replicates in which archaeal

amoA genes were detected. Abbreviations: ND: not detected; OM: organic matter.

Date No Till

No Till

Burnt Stubble Tilled Tilled+OM

Tilled+OM

Run-Down

18 May 2010 ND ND ND ND ND

21 Jul 2010 6.58 x 105 (1) ND ND ND ND

23 Aug 2010 1.16 x 105 (1) ND ND ND ND

19 Oct 2010 ND ND ND ND ND

1 Dec 2010 2.09 x 105 (1) ND ND ND ND

2 May 2011 ND ND ND ND ND

20 Jun 2011 2.95 x 107 (1) ND ND ND 3.25 x 107 (1)

1 Aug 2011 ND ND ND ND ND

19 Sep 2011 ND ND ND ND ND

15 Nov 2011 2.39 x 107 (1) ND ND ND ND

App. C: Data Not Shown Ch. 4

228

229

Appendix D.

Data Not Shown in Chapter 5

Figure D.1. Change in 15N-labelled ammonium (NH4+) above natural abundance

through time with added 15(NH4)2SO4. (a) at 20 °C in soil held at optimal water

content (45% WFPS); (b) at 20 °C in soil wet-up to 45% WFPS then allowed to dry; (c)

at 40 °C in soil held at optimal water content; and (d) at 40 °C in soil wet-up then

allowed to dry. Error bars are ±SEM (n = 3). Legend is the same for all panels. Legend


App. D: Data Not Shown Ch. 5

230

Table D.1. Linear regression results for response of logged bacterial amoA gene

abundance to gross N transformation rates. Regression coefficients are only reported

for the significant relationship. Abbreviations: Min: gross N mineralisation; Nitr: gross

nitrification.

Predictor

Significance

level Coefficient

Standard

error of

coefficient Intercept

Standard

error of

intercept

Adjusted

R2

Min. P<0.0001 -0.0524 0.0128 6.7409 0.0765 0.1111

Nitr. P=0.3435

231

Appendix E.

Publications arising from this thesis

Peer-Reviewed Journal Articles

Fisk, L.M., Barton, L., Jones, D.L., Glanville, H.C. & Murphy, D.V., 2015. Root

exudate carbon mitigates nitrogen loss in a semi-arid soil. Soil Biology & Biochemistry,

88, 380-389. doi:10.1016/j.soilbio.2015.06.011


Fisk, L.M., Maccarone, L.D., Barton, L. & Murphy, D.V., 2015. Nitrapyrin decreased

nitrification of nitrogen released from soil organic matter but not amoA gene abundance

at high soil temperature. Soil Biology & Biochemistry, 88, 214-223.

doi:10.1016/j.soilbio.2015.05.029


Banning, N.C., Maccarone, L.D., Fisk, L.M. & Murphy, D.V, 2015. Ammonia-

oxidising bacteria not archaea dominate nitrification activity in semi-arid agricultural

soil. Scientific Reports, 5. doi:10.1038/srep11146

Appears as Appendix A.

App. E: Publications Arising

232

Fisk, L.M., Barton, L., Maccarone, L.D. & Murphy, D.V. Seasonal dynamics of

ammonia-oxidising bacteria but not archaea influence risk of nitrogen loss in a semi-

arid agricultural soil. Manuscript in preparation.


Conference papers

Fisk, L.M., Murphy, D.V. & Barton, L., 2012. The effect of temperature and organic

carbon availability on the relative rates of microbial nitrogen immobilisation and

nitrification in a semi-arid soil, In: Burkitt, L.L., Sparrow, L.A. (Eds.), 5th Joint

Australian and New Zealand Soil Science Conference: Soil solutions for diverse

landscapes. Australian Society of Soil Science Inc., Hobart. (Oral presentation)

*awarded best oral presentation by a researcher under 35 years old

Maccarone, L., Fisk, L., Barton, L., Gleeson, D. & Murphy, D., 2012. Is there niche

separation of archaeal and bacterial nitrifying populations in semi-arid soil? In: Burkitt,

L.L., Sparrow, L.A. (Eds.), 5th Joint Australian and New Zealand Soil Science

Conference: Soil solutions for diverse landscapes. Australian Society of Soil Science

Inc., Hobart. (Poster presentation).

Fisk, L., Maccarone, L., Barton, L. & Murphy, D., 2013. Effectiveness of the

nitrification inhibitor nitrapyrin at reducing the risk of nitrogen loss in semi-arid soil.

Earth and Environment Postgraduate Symposium, The University of Western Australia,

Fremantle, Western Australia, 7th November, 2013. (Oral Presentation).

Fisk, L. M., Barton, L., Jones, D. L., Glanville, H. C. & Murphy, D. V., 2014. Root or

residues: Do carbon additions decrease the risk of nitrogen loss in semi-arid cropping

App. E: Publications Arising

233

soils? In: Crop Nutrition Symposium, Murdoch University, Murdoch, Western

Australia, 9th June 2014, p. 7. (Oral presentation)

Fisk, L., 2014. Roots or residues: Do carbon additions decrease the risk of nitrogen loss

in semi-arid cropping soils? The UWA Institute of Agriculture Postgraduate Showcase

2014: Frontiers in Agriculture, Crawley, Western Australia, 5th June, 2014. (Oral

Presentation)

Reports

Maccarone, L., Fisk, L., Sawada, Y., Barton, L., Gleeson, D. & Murphy, D., 2012.

Determining nitrogen cycling dynamics in semi-arid soil. Liebe Grower Group Trial

Report.

Murphy, D.V., Fisk, L., Kaiser, C., Maccarone, L., Jones, D., Kilburn, M., Clode, P.,

Banning, N., Gleeson, D., Phillips, L., Stockdale, E. & Barton, L., 2014. Harnessing the

nitrogen cycle through novel solutions, UWA00139. Final report for Soil Biology

Initiative II, The University of Western Australia and Grains Research & Development

Corporation.

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